diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_fit.json
index fd34ca2eeb..3d68e7557b 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_predict.json
index 10ce7638cd..ff89bd812a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_transform.json
index a9749f4e99..e6d9990343 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.peakmem_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_fit.json
index 13af34e2e9..7ad26ce514 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_predict.json
index 7738746057..a07b0759a8 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_transform.json
index effe296911..f217eba66c 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.time_transform.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_test_score.json
index 520d95c401..6b17301f89 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_test_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_train_score.json
index 2b55ac07b3..699cb9f732 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.KMeansBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json
index 676a5383b4..07c46aee45 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json
index 18575e3385..16492ae5bb 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json
index 4934721339..e9b5f14ff3 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_fit.json
index b6df97b955..a20753c892 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_predict.json
index 702d74dfbe..439786c112 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_transform.json
index f398b1e1ad..d7b6c422cd 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.time_transform.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_test_score.json
index 93b4d8035d..b76f47fb49 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_train_score.json
index ccdfebb8d8..1cba3e0b41 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/cluster.MiniBatchKMeansBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_fit.json
index 478bc1926b..99a825f380 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_transform.json
index 908ac050a6..ed5e4da4d3 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.peakmem_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_fit.json
index 09370ed262..a1d78e031a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_transform.json
index 9cee1374c5..8d30d08b3a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.time_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_test_score.json
index 086096f2ba..3f3c63cce8 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_train_score.json
index 0bb21dc45e..90caec689a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.DictionaryLearningBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json
index 1638fbb61b..e4d419e201 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json
index f3691a04bc..804b6f6996 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json
index 166b92b067..84150f61c5 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json	
@@ -1 +1 @@
-[[34113, [10.278902363999805, 17.81125016099986, 3.0049109685000985, 20.003798924999955]], [34115, [10.942894361000072, 20.521345728999677, 3.0408156544999656, 18.662708792000103]], [34120, [11.643599353999889, 21.848660600999665, 3.0803943260002598, 19.35254050200001]], [34126, [11.759634247000122, 21.117197495000255, 3.1037892630001807, 17.82164057099999]], [34139, [11.192596227000195, 19.055224494999948, 3.21538228899999, 19.679745584000102]], [34140, [10.678223337999952, 22.562457605000418, 3.048900023999977, 18.712739478999993]], [34141, [10.985714807000022, 21.288947729999563, 3.21961655899986, 20.471603603999938]], [34155, [10.603525870999874, 20.457598145000247, 3.0303118039998935, 17.674937408999995]], [34158, [10.3805415060001, 18.30381863499997, 3.1605699839997214, 20.248392609999883]], [34160, [10.544752687000255, 17.782455906999985, 3.030441095499782, 16.70915287700018]], [34162, [10.524889747000088, 18.625950976999775, 3.080503712499876, 17.60100341799989]], [34164, [10.731920985000215, 16.360008843000287, 3.0739739484999973, 19.51018649400021]], [34171, [10.716432478000115, 17.573173880000013, 3.004551551500299, 19.57249609199971]], [34177, [10.792533574000117, 20.87933765700018, 3.0722606389999783, 17.69906954399994]], [34183, [10.620193265000125, 20.10287982600039, 3.0735366245000932, 17.705882797999948]], [34188, [10.711243747000026, 26.634482886000114, 3.035264006000034, 19.77415537700017]], [34193, [10.254140663000271, 20.938151218999792, 3.0799822779999886, 17.303038153000216]], [34195, [10.634846467999978, 20.704528466000284, 3.0798956329999783, 19.91058943200005]], [34198, [10.636959912999828, 21.034076790999734, 3.1197387219999655, 18.791250005000165]], [34203, [10.402349043999948, 17.437165875999654, 3.1232165920000625, 20.1019666279999]], [34208, [10.281380518999867, 20.719067910000376, 3.00754586700009, 18.241283597000347]], [34211, [10.445560277999903, 21.121308266000142, 3.1379394250002406, 19.373780414000066]], [34213, [10.293221671000083, 18.90790639800025, 3.093987855000023, 19.10639995199972]], [34214, [10.522459111999979, 18.588397272000293, 3.037916057999837, 19.375284252000256]], [34215, [10.478691973999958, 20.188706837000154, 3.115213242000209, 16.992905138000424]], [34221, [10.690706216999843, 18.19631265499993, 3.135576882999885, 19.205396246999953]], [34227, [10.392044552000243, 19.328087219999816, 3.050231382999982, 17.762425900999915]], [34231, [10.803799885999979, 22.588036815999658, 3.1810097709999354, 18.201082686000063]], [34237, [10.566293997999765, 20.296683241999744, 2.932920467000258, 18.778829846000008]], [34244, [10.895322763999957, 18.91327576499998, 3.0792955850001817, 17.722523704000196]], [34247, [10.51444189499989, 20.27770893500019, 3.162087918999987, 19.23869314800004]], [34256, [10.701794176000021, 18.500481514000057, 3.079476859999886, 19.112648647999777]], [34259, [10.323855380000168, 17.539677261999714, 3.061894214500171, 19.37390574200026]], [34264, [10.689378533999843, 20.92764428700002, 3.1304091890001473, 18.127274439999837]], [34268, [11.70918572499977, 21.61601879700038, 3.118412534000072, 15.901785910999934]], [34273, [1.2091781345000072, 1.7601530300003105, 0.38343184950008435, 1.952422211000112]]]
\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json
index f8507fe391..27e105694e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score.json
index ed8bf9af31..068d382d8d 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score.json
index d636e297ec..9c199f1a07 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_fit.json
index a013c5e1da..22e2a35a0b 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_transform.json
index 3bcf7891c1..f46699fe18 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.peakmem_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_fit.json
index 6570ecfa47..49e00b5a1c 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_transform.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_transform.json
index 9da589aac9..1702e0b0f6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_transform.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.time_transform.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_test_score.json
index a9c60143d8..46551c887c 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_train_score.json
index b3e94a15bc..848e12231e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/decomposition.PCABenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json
index 72606a257b..a11a21761f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json	
@@ -1 +1 @@
-[[34113, [92717056.0, 116801536.0]], [34115, [92385280.0, 116486144.0]], [34120, [92549120.0, 116740096.0]], [34126, [92704768.0, 116854784.0]], [34139, [92753920.0, 116846592.0]], [34140, [92364800.0, 116436992.0]], [34141, [92782592.0, 116924416.0]], [34155, [92762112.0, 116838400.0]], [34158, [92569600.0, 116572160.0]], [34160, [91000832.0, 116596736.0]], [34162, [91279360.0, 116699136.0]], [34164, [92676096.0, 116559872.0]], [34171, [90968064.0, 116576256.0]], [34177, [91443200.0, 116948992.0]], [34183, [91611136.0, 116723712.0]], [34188, [91561984.0, 116690944.0]], [34193, [92237824.0, 116428800.0]], [34195, [92487680.0, 116764672.0]], [34198, [91574272.0, 116727808.0]], [34203, [91774976.0, 116944896.0]], [34208, [91607040.0, 116772864.0]], [34211, [91688960.0, 117362688.0]], [34213, [91484160.0, 116658176.0]], [34214, [92561408.0, 117145600.0]], [34215, [91492352.0, 117047296.0]], [34221, [92434432.0, 117043200.0]], [34227, [91693056.0, 117178368.0]], [34231, [92573696.0, 116604928.0]], [34237, [91607040.0, 116740096.0]], [34244, [91688960.0, 117301248.0]], [34247, [91717632.0, 117231616.0]], [34256, [91721728.0, 116813824.0]], [34259, [91705344.0, 116846592.0]], [34264, [92893184.0, 117489664.0]], [34268, [92094464.0, 117694464.0]], [34273, [91860992.0, 117391360.0]]]
\ No newline at end of file
+[[34113, [92717056.0, 116801536.0]], [34115, [92385280.0, 116486144.0]], [34120, [92549120.0, 116740096.0]], [34126, [92704768.0, 116854784.0]], [34139, [92753920.0, 116846592.0]], [34140, [92364800.0, 116436992.0]], [34141, [92782592.0, 116924416.0]], [34155, [92762112.0, 116838400.0]], [34158, [92569600.0, 116572160.0]], [34160, [91000832.0, 116596736.0]], [34162, [91279360.0, 116699136.0]], [34164, [92676096.0, 116559872.0]], [34171, [90968064.0, 116576256.0]], [34177, [91443200.0, 116948992.0]], [34183, [91611136.0, 116723712.0]], [34188, [91561984.0, 116690944.0]], [34193, [92237824.0, 116428800.0]], [34195, [92487680.0, 116764672.0]], [34198, [91574272.0, 116727808.0]], [34203, [91774976.0, 116944896.0]], [34208, [91607040.0, 116772864.0]], [34211, [91688960.0, 117362688.0]], [34213, [91484160.0, 116658176.0]], [34214, [92561408.0, 117145600.0]], [34215, [91492352.0, 117047296.0]], [34221, [92434432.0, 117043200.0]], [34227, [91693056.0, 117178368.0]], [34231, [92573696.0, 116604928.0]], [34237, [91607040.0, 116740096.0]], [34244, [91688960.0, 117301248.0]], [34247, [91717632.0, 117231616.0]], [34256, [91721728.0, 116813824.0]], [34259, [91705344.0, 116846592.0]], [34264, [92893184.0, 117489664.0]], [34268, [92094464.0, 117694464.0]], [34273, [91860992.0, 117391360.0]], [34274, [92028928.0, 117080064.0]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json
index a2d9fc5acd..fa83283918 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json	
@@ -1 +1 @@
-[[34113, [88731648.0, 98676736.0]], [34115, [88768512.0, 98406400.0]], [34120, [88788992.0, 98451456.0]], [34126, [89047040.0, 98693120.0]], [34139, [88825856.0, 98451456.0]], [34140, [88547328.0, 98181120.0]], [34141, [89116672.0, 98762752.0]], [34155, [88870912.0, 98504704.0]], [34158, [88735744.0, 98369536.0]], [34160, [88735744.0, 98365440.0]], [34162, [88780800.0, 98435072.0]], [34164, [88694784.0, 98353152.0]], [34171, [88686592.0, 98332672.0]], [34177, [88760320.0, 98369536.0]], [34183, [88891392.0, 98537472.0]], [34188, [88813568.0, 98455552.0]], [34193, [88580096.0, 98213888.0]], [34195, [88760320.0, 98422784.0]], [34198, [88887296.0, 98529280.0]], [34203, [89157632.0, 98799616.0]], [34208, [88911872.0, 98557952.0]], [34211, [88764416.0, 98402304.0]], [34213, [88666112.0, 98308096.0]], [34214, [88551424.0, 98447360.0]], [34215, [88825856.0, 98463744.0]], [34221, [88829952.0, 98488320.0]], [34227, [88887296.0, 98529280.0]], [34231, [88760320.0, 98390016.0]], [34237, [88903680.0, 98553856.0]], [34244, [88866816.0, 98488320.0]], [34247, [89014272.0, 98660352.0]], [34256, [89047040.0, 98693120.0]], [34259, [88977408.0, 98623488.0]], [34264, [88813568.0, 98672640.0]], [34268, [89452544.0, 99106816.0]], [34273, [89051136.0, 98680832.0]]]
\ No newline at end of file
+[[34113, [88731648.0, 98676736.0]], [34115, [88768512.0, 98406400.0]], [34120, [88788992.0, 98451456.0]], [34126, [89047040.0, 98693120.0]], [34139, [88825856.0, 98451456.0]], [34140, [88547328.0, 98181120.0]], [34141, [89116672.0, 98762752.0]], [34155, [88870912.0, 98504704.0]], [34158, [88735744.0, 98369536.0]], [34160, [88735744.0, 98365440.0]], [34162, [88780800.0, 98435072.0]], [34164, [88694784.0, 98353152.0]], [34171, [88686592.0, 98332672.0]], [34177, [88760320.0, 98369536.0]], [34183, [88891392.0, 98537472.0]], [34188, [88813568.0, 98455552.0]], [34193, [88580096.0, 98213888.0]], [34195, [88760320.0, 98422784.0]], [34198, [88887296.0, 98529280.0]], [34203, [89157632.0, 98799616.0]], [34208, [88911872.0, 98557952.0]], [34211, [88764416.0, 98402304.0]], [34213, [88666112.0, 98308096.0]], [34214, [88551424.0, 98447360.0]], [34215, [88825856.0, 98463744.0]], [34221, [88829952.0, 98488320.0]], [34227, [88887296.0, 98529280.0]], [34231, [88760320.0, 98390016.0]], [34237, [88903680.0, 98553856.0]], [34244, [88866816.0, 98488320.0]], [34247, [89014272.0, 98660352.0]], [34256, [89047040.0, 98693120.0]], [34259, [88977408.0, 98623488.0]], [34264, [88813568.0, 98672640.0]], [34268, [89452544.0, 99106816.0]], [34273, [89051136.0, 98680832.0]], [34274, [89329664.0, 98955264.0]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_fit.json
index 01fe7a0fc5..124f9750ca 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_fit.json	
@@ -1 +1 @@
-[[34113, [2.7723530729999766, 2.4561092534997897]], [34115, [2.7972221055001683, 2.236694880000414]], [34120, [2.8581781809998574, 2.3060040135001145]], [34126, [2.781807273500135, 2.318331097499822]], [34139, [3.094916280000234, 2.2614375369998925]], [34140, [2.7866730430000644, 2.477774012500049]], [34141, [2.7623300440000094, 2.3173976569999013]], [34155, [2.8655262935001247, 2.2923448010001266]], [34158, [2.8014932509997834, 2.2305168039997625]], [34160, [2.7702543259997583, 2.463514964000069]], [34162, [3.1671906420001505, 2.3063645810000253]], [34164, [2.8864417795000463, 2.2212748059996557]], [34171, [2.967877140999917, 2.3334756730000663]], [34177, [2.7641332299999704, 2.274792547999823]], [34183, [3.1306484050001018, 2.224202508000417]], [34188, [3.1125958159996117, 2.2802612810000937]], [34193, [2.7626577420001013, 2.267879730000004]], [34195, [2.742485775499972, 2.146857962500235]], [34198, [2.830489475500144, 2.330648640500158]], [34203, [2.818939851499863, 2.26459094799975]], [34208, [2.8460305814999174, 2.1727343170000495]], [34211, [3.1087648489997264, 2.1452170930001557]], [34213, [2.740711030000057, 2.257183977000068]], [34214, [3.1278946389998055, 2.121612645999903]], [34215, [2.7983447799999794, 2.1778554610000356]], [34221, [3.0593913289999364, 2.490432514500071]], [34227, [2.7655225559997234, 2.2845047340001656]], [34231, [2.7970530529998996, 2.279299568999704]], [34237, [2.771364156499885, 2.2882777395000176]], [34244, [2.7472341590000724, 2.221111230000133]], [34247, [2.770983518499861, 2.155194407000181]], [34256, [3.183733889999985, 2.2544607340000766]], [34259, [2.864238255499913, 2.308355051000035]], [34264, [3.154771690999951, 2.463326098500147]], [34268, [3.13484637800002, 2.480104294000057]], [34273, [2.7469681604998186, 2.314808054499963]]]
\ No newline at end of file
+[[34113, [2.7723530729999766, 2.4561092534997897]], [34115, [2.7972221055001683, 2.236694880000414]], [34120, [2.8581781809998574, 2.3060040135001145]], [34126, [2.781807273500135, 2.318331097499822]], [34139, [3.094916280000234, 2.2614375369998925]], [34140, [2.7866730430000644, 2.477774012500049]], [34141, [2.7623300440000094, 2.3173976569999013]], [34155, [2.8655262935001247, 2.2923448010001266]], [34158, [2.8014932509997834, 2.2305168039997625]], [34160, [2.7702543259997583, 2.463514964000069]], [34162, [3.1671906420001505, 2.3063645810000253]], [34164, [2.8864417795000463, 2.2212748059996557]], [34171, [2.967877140999917, 2.3334756730000663]], [34177, [2.7641332299999704, 2.274792547999823]], [34183, [3.1306484050001018, 2.224202508000417]], [34188, [3.1125958159996117, 2.2802612810000937]], [34193, [2.7626577420001013, 2.267879730000004]], [34195, [2.742485775499972, 2.146857962500235]], [34198, [2.830489475500144, 2.330648640500158]], [34203, [2.818939851499863, 2.26459094799975]], [34208, [2.8460305814999174, 2.1727343170000495]], [34211, [3.1087648489997264, 2.1452170930001557]], [34213, [2.740711030000057, 2.257183977000068]], [34214, [3.1278946389998055, 2.121612645999903]], [34215, [2.7983447799999794, 2.1778554610000356]], [34221, [3.0593913289999364, 2.490432514500071]], [34227, [2.7655225559997234, 2.2845047340001656]], [34231, [2.7970530529998996, 2.279299568999704]], [34237, [2.771364156499885, 2.2882777395000176]], [34244, [2.7472341590000724, 2.221111230000133]], [34247, [2.770983518499861, 2.155194407000181]], [34256, [3.183733889999985, 2.2544607340000766]], [34259, [2.864238255499913, 2.308355051000035]], [34264, [3.154771690999951, 2.463326098500147]], [34268, [3.13484637800002, 2.480104294000057]], [34273, [2.7469681604998186, 2.314808054499963]], [34274, [2.8839598180002213, 2.353539504999844]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_predict.json
index 3c2e87a0f1..d3c434341e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json
index d88c5636fc..fc2db6ee8d 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json
index dbd70d971e..622acd1c7c 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json
index 903bbf0e5b..0471afab52 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
+[[34113, 103030784.0], [34115, 102924288.0], [34120, 102899712.0], [34126, 103399424.0], [34139, 102875136.0], [34140, 102612992.0], [34141, 102993920.0], [34155, 102854656.0], [34158, 102883328.0], [34160, 102764544.0], [34162, 103010304.0], [34164, 103116800.0], [34171, 102883328.0], [34177, 102727680.0], [34183, 103075840.0], [34188, 102989824.0], [34193, 103112704.0], [34195, 103002112.0], [34198, 103157760.0], [34203, 103305216.0], [34208, 102907904.0], [34211, 103002112.0], [34213, 102666240.0], [34214, 102666240.0], [34215, 103051264.0], [34221, 103018496.0], [34227, 103563264.0], [34231, 103608320.0], [34237, 103612416.0], [34244, 103559168.0], [34247, 103923712.0], [34256, 103415808.0], [34259, 103563264.0], [34264, 103542784.0], [34268, 103854080.0], [34273, 103456768.0], [34274, 104267776.0]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json
index d3f553f05b..f354b230d6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json	
@@ -1 +1 @@
-[[34113, 91521024.0], [34115, 90980352.0], [34120, 90525696.0], [34126, 91549696.0], [34139, 91324416.0], [34140, 90341376.0], [34141, 91381760.0], [34155, 91303936.0], [34158, 91103232.0], [34160, 91013120.0], [34162, 91062272.0], [34164, 90468352.0], [34171, 90963968.0], [34177, 90656768.0], [34183, 90890240.0], [34188, 91365376.0], [34193, 91107328.0], [34195, 91230208.0], [34198, 91348992.0], [34203, 91672576.0], [34208, 91357184.0], [34211, 91238400.0], [34213, 91086848.0], [34214, 91234304.0], [34215, 91340800.0], [34221, 91041792.0], [34227, 91807744.0], [34231, 91992064.0], [34237, 92110848.0], [34244, 92176384.0], [34247, 91791360.0], [34256, 91963392.0], [34259, 92151808.0], [34264, 92094464.0], [34268, 92647424.0], [34273, 92372992.0]]
\ No newline at end of file
+[[34113, 91521024.0], [34115, 90980352.0], [34120, 90525696.0], [34126, 91549696.0], [34139, 91324416.0], [34140, 90341376.0], [34141, 91381760.0], [34155, 91303936.0], [34158, 91103232.0], [34160, 91013120.0], [34162, 91062272.0], [34164, 90468352.0], [34171, 90963968.0], [34177, 90656768.0], [34183, 90890240.0], [34188, 91365376.0], [34193, 91107328.0], [34195, 91230208.0], [34198, 91348992.0], [34203, 91672576.0], [34208, 91357184.0], [34211, 91238400.0], [34213, 91086848.0], [34214, 91234304.0], [34215, 91340800.0], [34221, 91041792.0], [34227, 91807744.0], [34231, 91992064.0], [34237, 92110848.0], [34244, 92176384.0], [34247, 91791360.0], [34256, 91963392.0], [34259, 92151808.0], [34264, 92094464.0], [34268, 92647424.0], [34273, 92372992.0], [34274, 92491776.0]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json
index 07ccc59e21..6ace9afe1f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json	
@@ -1 +1 @@
-[[34113, 2.457242207499803], [34115, 2.4975013709999985], [34120, 2.3545510505000493], [34126, 2.401516767999965], [34139, 2.44353313900001], [34140, 2.4544404060000033], [34141, 2.3755439290000595], [34155, 2.4368824595001115], [34158, 2.343000346500048], [34160, 2.3359379720000106], [34162, 2.4711404590000257], [34164, 2.4861634539997794], [34171, 2.4326271159998214], [34177, 2.4348378520001006], [34183, 2.33110123150027], [34188, 2.3954222630002278], [34193, 2.358247436000056], [34195, 2.37962545649998], [34198, 2.4356284049999886], [34203, 2.319047470500209], [34208, 2.486922175000018], [34211, 2.450088184999913], [34213, 2.303163244499956], [34214, 2.401709257499988], [34215, 2.408535246000156], [34221, 2.350196761499774], [34227, 2.3858730239999204], [34231, 2.416047189999972], [34237, 2.378309756999897], [34244, 2.4044203259998085], [34247, 2.4226255369999308], [34256, 2.445090383999741], [34259, 2.454271130000052], [34264, 2.4214168729999983], [34268, 2.427930182000182], [34273, 2.3247326890000295]]
\ No newline at end of file
+[[34113, 2.457242207499803], [34115, 2.4975013709999985], [34120, 2.3545510505000493], [34126, 2.401516767999965], [34139, 2.44353313900001], [34140, 2.4544404060000033], [34141, 2.3755439290000595], [34155, 2.4368824595001115], [34158, 2.343000346500048], [34160, 2.3359379720000106], [34162, 2.4711404590000257], [34164, 2.4861634539997794], [34171, 2.4326271159998214], [34177, 2.4348378520001006], [34183, 2.33110123150027], [34188, 2.3954222630002278], [34193, 2.358247436000056], [34195, 2.37962545649998], [34198, 2.4356284049999886], [34203, 2.319047470500209], [34208, 2.486922175000018], [34211, 2.450088184999913], [34213, 2.303163244499956], [34214, 2.401709257499988], [34215, 2.408535246000156], [34221, 2.350196761499774], [34227, 2.3858730239999204], [34231, 2.416047189999972], [34237, 2.378309756999897], [34244, 2.4044203259998085], [34247, 2.4226255369999308], [34256, 2.445090383999741], [34259, 2.454271130000052], [34264, 2.4214168729999983], [34268, 2.427930182000182], [34273, 2.3247326890000295], [34274, 2.406329114000073]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json
index ece72679e1..017264e4dd 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json	
@@ -1 +1 @@
-[[34113, 0.08456578100003753], [34115, 0.08634114549977312], [34120, 0.09060399500003768], [34126, 0.08663618550008323], [34139, 0.08268839199990907], [34140, 0.08462493000001814], [34141, 0.0855075530000704], [34155, 0.08763014299984206], [34158, 0.08412637700007508], [34160, 0.08247170899994671], [34162, 0.08514108499980466], [34164, 0.08508909750003113], [34171, 0.08956946249986686], [34177, 0.0847139005002191], [34183, 0.08262063649999618], [34188, 0.08481937299984565], [34193, 0.0839297129998613], [34195, 0.08517073300004085], [34198, 0.08458181900005002], [34203, 0.08283724300008544], [34208, 0.08395285949973186], [34211, 0.08758140349982568], [34213, 0.08258996499989735], [34214, 0.08795029950010758], [34215, 0.09062863799999832], [34221, 0.08348468799999864], [34227, 0.08956336699998246], [34231, 0.08402433650007879], [34237, 0.08575656549987798], [34244, 0.08232960450004612], [34247, 0.08480311549988073], [34256, 0.08581835650011271], [34259, 0.08407290050013216], [34264, 0.0829609364998305], [34268, 0.08254960349995599], [34273, 0.08887526600005913]]
\ No newline at end of file
+[[34113, 0.08456578100003753], [34115, 0.08634114549977312], [34120, 0.09060399500003768], [34126, 0.08663618550008323], [34139, 0.08268839199990907], [34140, 0.08462493000001814], [34141, 0.0855075530000704], [34155, 0.08763014299984206], [34158, 0.08412637700007508], [34160, 0.08247170899994671], [34162, 0.08514108499980466], [34164, 0.08508909750003113], [34171, 0.08956946249986686], [34177, 0.0847139005002191], [34183, 0.08262063649999618], [34188, 0.08481937299984565], [34193, 0.0839297129998613], [34195, 0.08517073300004085], [34198, 0.08458181900005002], [34203, 0.08283724300008544], [34208, 0.08395285949973186], [34211, 0.08758140349982568], [34213, 0.08258996499989735], [34214, 0.08795029950010758], [34215, 0.09062863799999832], [34221, 0.08348468799999864], [34227, 0.08956336699998246], [34231, 0.08402433650007879], [34237, 0.08575656549987798], [34244, 0.08232960450004612], [34247, 0.08480311549988073], [34256, 0.08581835650011271], [34259, 0.08407290050013216], [34264, 0.0829609364998305], [34268, 0.08254960349995599], [34273, 0.08887526600005913], [34274, 0.08449520599992866]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_test_score.json
index e6705e4f8a..2611f2a263 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_test_score.json	
@@ -1 +1 @@
-[[34113, 0.7230709112942986], [34115, 0.7230709112942986], [34120, 0.7230709112942986], [34126, 0.7230709112942986], [34139, 0.7230709112942986], [34140, 0.7230709112942986], [34141, 0.7230709112942986], [34155, 0.7230709112942986], [34158, 0.7230709112942986], [34160, 0.7230709112942986], [34162, 0.7230709112942986], [34164, 0.7230709112942986], [34171, 0.7230709112942986], [34177, 0.7230709112942986], [34183, 0.7230709112942986], [34188, 0.7230709112942986], [34193, 0.7230709112942986], [34195, 0.7230709112942986], [34198, 0.7230709112942986], [34203, 0.7230709112942986], [34208, 0.7230709112942986], [34211, 0.7230709112942986], [34213, 0.7230709112942986], [34214, 0.7230709112942986], [34215, 0.7230709112942986], [34221, 0.7230709112942986], [34227, 0.7230709112942986], [34231, 0.7230709112942986], [34237, 0.7230709112942986], [34244, 0.7230709112942986], [34247, 0.7230709112942986], [34256, 0.7230709112942986], [34259, 0.7230709112942986], [34264, 0.7230709112942986], [34268, 0.7230709112942986], [34273, 0.7230709112942986]]
\ No newline at end of file
+[[34113, 0.7230709112942986], [34115, 0.7230709112942986], [34120, 0.7230709112942986], [34126, 0.7230709112942986], [34139, 0.7230709112942986], [34140, 0.7230709112942986], [34141, 0.7230709112942986], [34155, 0.7230709112942986], [34158, 0.7230709112942986], [34160, 0.7230709112942986], [34162, 0.7230709112942986], [34164, 0.7230709112942986], [34171, 0.7230709112942986], [34177, 0.7230709112942986], [34183, 0.7230709112942986], [34188, 0.7230709112942986], [34193, 0.7230709112942986], [34195, 0.7230709112942986], [34198, 0.7230709112942986], [34203, 0.7230709112942986], [34208, 0.7230709112942986], [34211, 0.7230709112942986], [34213, 0.7230709112942986], [34214, 0.7230709112942986], [34215, 0.7230709112942986], [34221, 0.7230709112942986], [34227, 0.7230709112942986], [34231, 0.7230709112942986], [34237, 0.7230709112942986], [34244, 0.7230709112942986], [34247, 0.7230709112942986], [34256, 0.7230709112942986], [34259, 0.7230709112942986], [34264, 0.7230709112942986], [34268, 0.7230709112942986], [34273, 0.7230709112942986], [34274, 0.7230709112942986]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_train_score.json
index 9178a20d0e..d58c40f3bc 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.HistGradientBoostingClassifierBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json
index 1fb9b1dee4..0c191d5ccb 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json
index 6afabe474b..5babff1d3f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_fit.json
index 59082d8f26..4d56a1d9d1 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_predict.json
index 182c390f6f..c95f482bd7 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_test_score.json
index 98d3cd3061..b8fa9af38e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_train_score.json
index 23e6384cdd..98d988a8ef 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.RandomForestClassifierBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_fit.json
index 0a402dee37..b130e3e5ca 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_predict.json
index 4e7987120d..ef3c9d3b62 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_fit.json
index cc3808a2bb..9aabf6aaed 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_predict.json
index d97b22f773..51cca663f1 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.time_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_test_score.json
index a385050705..c31d00e6db 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_test_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_train_score.json
index dbe622ec88..40e527825e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.ElasticNetBenchmark.track_train_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_fit.json
index da7d57714b..929f1ca575 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_predict.json
index 3a532e1899..b412e68697 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_fit.json
index 7266651d80..d09469af65 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_predict.json
index 57c5b54aad..c4ecd088aa 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_test_score.json
index 5b7202d6ff..dba11e251b 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_test_score.json	
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_train_score.json
index 592aa3a197..fc0370f7f9 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.track_train_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_fit.json
index 88d4fd3c92..406cac72be 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_predict.json
index f193a33446..7c221df06e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_fit.json
index e22485081f..7bae081f73 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_predict.json
index aa86141a75..2798691eac 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_test_score.json
index 0d6398b1ed..79cab036de 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_train_score.json
index d1011cf9f3..d79cfbf946 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LinearRegressionBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_fit.json
index 3b8d7ad3ba..e2b9e2c9dd 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_predict.json
index 88f9f5c3bf..2061dfbc67 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_fit.json
index 240ee4c5ab..3211811602 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_predict.json
index 48732481db..f63c49e7c8 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_test_score.json
index 825b775a0c..584b129ecb 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_test_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_train_score.json
index 498bb3e7f1..059dfedbd9 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.track_train_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_fit.json
index 14c66b9687..162c407a04 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_predict.json
index 0a6058fbde..0bb2a794d8 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_fit.json
index 8d24528fc5..f58737a6b6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_predict.json
index 151e646378..a7d993eaf0 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.time_predict.json	
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_test_score.json
index 8a0a03d178..147accb110 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_test_score.json	
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_train_score.json
index 720f95b7c1..358f5b6380 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.RidgeBenchmark.track_train_score.json	
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_fit.json
index eed057cef5..540d53ee3d 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_predict.json
index dae09b7dac..bac2acf148 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_fit.json
index dbf5e01f73..7a6be27634 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_fit.json	
@@ -1 +1 @@
-[[34113, [5.785732121000365, 4.727221952500258]], [34115, [5.555846737999673, 4.399997176999932]], [34120, [5.7577098060000935, 4.371343242499734]], [34126, [5.407501024999874, 4.410115528999995]], [34139, [5.657023298999775, 4.390607579499829]], [34140, [5.715017427999555, 4.749195399499968]], [34141, [5.278649515999859, 4.383912037999835]], [34155, [5.705368566000288, 4.270766128000105]], [34158, [5.7074692440000945, 4.394337704500231]], [34160, [5.750299745999655, 4.412599603999297]], [34162, [5.435246809000091, 4.2374637789998815]], [34164, [5.4982449179997275, 4.379062000500198]], [34171, [5.708359613000539, 4.81143199600001]], [34177, [5.535188415000448, 4.352305365499888]], [34183, [5.6964613319996715, 4.841501867500028]], [34188, [5.793738303999817, 4.347009990999595]], [34193, [5.497034244000133, 4.409927930499634]], [34195, [5.457862690999718, 4.70989491850014]], [34198, [5.437502525999662, 4.313086909000049]], [34203, [5.688016611000421, 4.7213909559995955]], [34208, [5.357632862000173, 4.284272539500307]], [34211, [5.383357591999811, 4.373021779000737]], [34213, [5.344874788999732, 4.255516944000192]], [34214, [5.873698248999972, 4.847322674999759]], [34215, [5.804459752000184, 4.846558266499869]], [34221, [5.845827733000078, 4.856785335500263]], [34227, [5.3330052599994815, 4.7796187210001335]], [34231, [5.752222498999799, 4.88731430200005]], [34237, [5.395126636999521, 4.812423290500192]], [34244, [5.644468477999908, 4.267252050000025]], [34247, [5.357492635000199, 4.849625557500531]], [34256, [5.725010292000661, 4.69288808049987]], [34259, [5.309087836999424, 4.329174489000252]], [34264, [5.3912693709999076, 4.267919068999618]], [34268, [5.739890166000805, 4.818498998500218]], [34273, [5.7465759609995075, 4.35909683600039]]]
\ No newline at end of file
+[[34113, [5.785732121000365, 4.727221952500258]], [34115, [5.555846737999673, 4.399997176999932]], [34120, [5.7577098060000935, 4.371343242499734]], [34126, [5.407501024999874, 4.410115528999995]], [34139, [5.657023298999775, 4.390607579499829]], [34140, [5.715017427999555, 4.749195399499968]], [34141, [5.278649515999859, 4.383912037999835]], [34155, [5.705368566000288, 4.270766128000105]], [34158, [5.7074692440000945, 4.394337704500231]], [34160, [5.750299745999655, 4.412599603999297]], [34162, [5.435246809000091, 4.2374637789998815]], [34164, [5.4982449179997275, 4.379062000500198]], [34171, [5.708359613000539, 4.81143199600001]], [34177, [5.535188415000448, 4.352305365499888]], [34183, [5.6964613319996715, 4.841501867500028]], [34188, [5.793738303999817, 4.347009990999595]], [34193, [5.497034244000133, 4.409927930499634]], [34195, [5.457862690999718, 4.70989491850014]], [34198, [5.437502525999662, 4.313086909000049]], [34203, [5.688016611000421, 4.7213909559995955]], [34208, [5.357632862000173, 4.284272539500307]], [34211, [5.383357591999811, 4.373021779000737]], [34213, [5.344874788999732, 4.255516944000192]], [34214, [5.873698248999972, 4.847322674999759]], [34215, [5.804459752000184, 4.846558266499869]], [34221, [5.845827733000078, 4.856785335500263]], [34227, [5.3330052599994815, 4.7796187210001335]], [34231, [5.752222498999799, 4.88731430200005]], [34237, [5.395126636999521, 4.812423290500192]], [34244, [5.644468477999908, 4.267252050000025]], [34247, [5.357492635000199, 4.849625557500531]], [34256, [5.725010292000661, 4.69288808049987]], [34259, [5.309087836999424, 4.329174489000252]], [34264, [5.3912693709999076, 4.267919068999618]], [34268, [5.739890166000805, 4.818498998500218]], [34273, [5.7465759609995075, 4.35909683600039]], [34274, [5.8807758169996305, 4.817005938499733]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_predict.json
index 1bc447e80e..9d4146efca 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_test_score.json
index ef362ae4e3..ca1e12e705 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_train_score.json
index fb78be4a8b..a76ef147b7 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.SGDRegressorBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.peakmem_fit.json
index 380888791b..6275b8aa2d 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.peakmem_fit.json	
@@ -1 +1 @@
-[[34113, [89276416.0, 96989184.0]], [34115, [89120768.0, 97112064.0]], [34120, [88748032.0, 96579584.0]], [34126, [88784896.0, 96530432.0]], [34139, [88875008.0, 96407552.0]], [34140, [88952832.0, 97062912.0]], [34141, [89088000.0, 97079296.0]], [34155, [88965120.0, 96464896.0]], [34158, [89001984.0, 96133120.0]], [34160, [89018368.0, 96497664.0]], [34162, [89264128.0, 96927744.0]], [34164, [89579520.0, 96636928.0]], [34171, [88555520.0, 95784960.0]], [34177, [88977408.0, 96665600.0]], [34183, [89210880.0, 97128448.0]], [34188, [89206784.0, 96661504.0]], [34193, [89382912.0, 96907264.0]], [34195, [88846336.0, 96952320.0]], [34198, [89395200.0, 97550336.0]], [34203, [89411584.0, 96915456.0]], [34208, [89092096.0, 96829440.0]], [34211, [89214976.0, 97329152.0]], [34213, [88711168.0, 96243712.0]], [34214, [88780800.0, 97079296.0]], [34215, [89014272.0, 97366016.0]], [34221, [89165824.0, 97476608.0]], [34227, [88698880.0, 96813056.0]], [34231, [89600000.0, 96796672.0]], [34237, [89305088.0, 96931840.0]], [34244, [89624576.0, 97013760.0]], [34247, [89731072.0, 97751040.0]], [34256, [89935872.0, 96997376.0]], [34259, [89669632.0, 97210368.0]], [34264, [89542656.0, 97619968.0]], [34268, [89423872.0, 96927744.0]], [34273, [89784320.0, 97128448.0]]]
\ No newline at end of file
+[[34113, [89276416.0, 96989184.0]], [34115, [89120768.0, 97112064.0]], [34120, [88748032.0, 96579584.0]], [34126, [88784896.0, 96530432.0]], [34139, [88875008.0, 96407552.0]], [34140, [88952832.0, 97062912.0]], [34141, [89088000.0, 97079296.0]], [34155, [88965120.0, 96464896.0]], [34158, [89001984.0, 96133120.0]], [34160, [89018368.0, 96497664.0]], [34162, [89264128.0, 96927744.0]], [34164, [89579520.0, 96636928.0]], [34171, [88555520.0, 95784960.0]], [34177, [88977408.0, 96665600.0]], [34183, [89210880.0, 97128448.0]], [34188, [89206784.0, 96661504.0]], [34193, [89382912.0, 96907264.0]], [34195, [88846336.0, 96952320.0]], [34198, [89395200.0, 97550336.0]], [34203, [89411584.0, 96915456.0]], [34208, [89092096.0, 96829440.0]], [34211, [89214976.0, 97329152.0]], [34213, [88711168.0, 96243712.0]], [34214, [88780800.0, 97079296.0]], [34215, [89014272.0, 97366016.0]], [34221, [89165824.0, 97476608.0]], [34227, [88698880.0, 96813056.0]], [34231, [89600000.0, 96796672.0]], [34237, [89305088.0, 96931840.0]], [34244, [89624576.0, 97013760.0]], [34247, [89731072.0, 97751040.0]], [34256, [89935872.0, 96997376.0]], [34259, [89669632.0, 97210368.0]], [34264, [89542656.0, 97619968.0]], [34268, [89423872.0, 96927744.0]], [34273, [89784320.0, 97128448.0]], [34274, [89665536.0, 97976320.0]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.time_fit.json
index 3d8bc2dc6d..bf8dc845f1 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
+[[34113, [6.8866077249995215, 3.287346090000028]], [34115, [6.398079502499968, 3.1800816280001527]], [34120, [6.352371390999906, 3.1843909919998623]], [34126, [6.823952748499778, 3.3756803279998167]], [34139, [7.348718904499947, 3.0746666800005187]], [34140, [6.126922135999848, 3.107337338999969]], [34141, [6.458831746500437, 3.1526409349999085]], [34155, [6.369614407999961, 3.2864330430002155]], [34158, [6.618332401999851, 3.2927622309998696]], [34160, [6.664801721499316, 3.145763741999872]], [34162, [6.222238675499739, 3.508111628000279]], [34164, [6.4386592949999795, 3.1488580360000924]], [34171, [6.513327174000096, 3.195959571000458]], [34177, [6.2968963179996535, 3.389134446499611]], [34183, [7.07459296900015, 3.249107423499936]], [34188, [6.840836724999917, 3.499674485500236]], [34193, [6.758159777000401, 3.230746172000181]], [34195, [6.437817268500112, 3.3718600434999644]], [34198, [6.307441455499884, 3.4317276070000844]], [34203, [6.17936598749975, 3.0934886790000746]], [34208, [6.238244423999731, 3.249405113499961]], [34211, [7.6592556960004, 3.3023895589994936]], [34213, [6.217503082500116, 3.4885182235002503]], [34214, [6.575579937499697, 3.11406241900022]], [34215, [6.741219062500477, 3.0865530410001156]], [34221, [6.2879355929994745, 3.229787297999792]], [34227, [6.392852451000181, 3.233718272999795]], [34231, [6.899113899500662, 3.1805316830004813]], [34237, [6.354838637499597, 3.327746351999849]], [34244, [6.673549338000157, 3.273921669500396]], [34247, [6.872860266499629, 3.1885374060002505]], [34256, [6.218824106499596, 3.607650438499604]], [34259, [6.448930051499701, 3.048743289000413]], [34264, [6.233385336000083, 3.1866464459999406]], [34268, [6.832901050000146, 3.011682051999742]], [34273, [6.477365873499821, 3.2178239109998685]], [34274, [6.401197722500456, 3.190089538000393]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_test_score.json
index 5513af9df4..16a48d31e6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_train_score.json
index 5513af9df4..16a48d31e6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/manifold.TSNEBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json
index b70ae6022e..b4db39304a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json	
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json
index 628604e783..b755a7ce02 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.peakmem_crossval.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.peakmem_crossval.json
index 6e30d0c81d..36b4838bb2 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.peakmem_crossval.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.peakmem_crossval.json	
@@ -1 +1 @@
-[[34113, [218046464.0, 118632448.0]], [34115, [217903104.0, 118136832.0]], [34120, [217542656.0, 118091776.0]], [34126, [217755648.0, 118341632.0]], [34139, [218038272.0, 118169600.0]], [34140, [217804800.0, 118128640.0]], [34141, [218091520.0, 118374400.0]], [34155, [217985024.0, 118468608.0]], [34158, [217710592.0, 117874688.0]], [34160, [217960448.0, 118276096.0]], [34162, [218128384.0, 118403072.0]], [34164, [217829376.0, 118136832.0]], [34171, [217464832.0, 117559296.0]], [34177, [217931776.0, 118120448.0]], [34183, [218087424.0, 118296576.0]], [34188, [218046464.0, 118292480.0]], [34193, [217866240.0, 117989376.0]], [34195, [217956352.0, 118108160.0]], [34198, [218058752.0, 118579200.0]], [34203, [218206208.0, 118669312.0]], [34208, [217907200.0, 118116352.0]], [34211, [217927680.0, 118226944.0]], [34213, [217698304.0, 118116352.0]], [34214, [217976832.0, 118194176.0]], [34215, [217894912.0, 118149120.0]], [34221, [217989120.0, 118267904.0]], [34227, [217694208.0, 117907456.0]], [34231, [217989120.0, 118210560.0]], [34237, [218083328.0, 118497280.0]], [34244, [218177536.0, 118386688.0]], [34247, [218140672.0, 118534144.0]], [34256, [218345472.0, 118394880.0]], [34259, [218394624.0, 118304768.0]], [34264, [218415104.0, 118444032.0]], [34268, [218234880.0, 118534144.0]], [34273, [218320896.0, 118493184.0]]]
\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.time_crossval.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.time_crossval.json
index ed4af1b32e..bb14fdfeeb 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.time_crossval.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.time_crossval.json	
@@ -1 +1 @@
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\ No newline at end of file
+[[34113, [64.2886444120004, 17.09262771000067]], [34115, [64.21327091200055, 17.30094022999947]], [34120, [64.08283332500014, 17.195363630999964]], [34126, [64.27279506600007, 17.233828422999977]], [34139, [57.47834499600049, 17.196938581999348]], [34140, [55.77678843800004, 17.397581309999623]], [34141, [63.216229812000165, 17.070054533000075]], [34155, [64.20923325100011, 16.2959160850005]], [34158, [64.43946394399973, 17.885456244000125]], [34160, [62.73974995699973, 17.364847056000144]], [34162, [57.223468524000054, 17.245325756000057]], [34164, [64.02981140800057, 16.772217910999643]], [34171, [64.6412345939998, 18.099187195999548]], [34177, [64.29516267799954, 17.53282753199983]], [34183, [59.504080497999894, 17.26126307300001]], [34188, [60.966023678000056, 16.9694065270005]], [34193, [55.99273642800017, 16.959901495999475]], [34195, [56.669159051999486, 16.63096666199999]], [34198, [55.967404554000495, 17.139156560999254]], [34203, [64.64616338299948, 17.467856518999724]], [34208, [61.81115140400016, 17.52370854199944]], [34211, [56.287977936999596, 17.017800421000175]], [34213, [56.427444668000135, 17.21439545999965]], [34214, [64.37759390500014, 17.182535195999662]], [34215, [58.11640588099999, 16.64085829800024]], [34221, [55.779868249000174, 16.938923742000043]], [34227, [56.11092738100069, 17.283331284999804]], [34231, [65.1537342620004, 17.536035268999512]], [34237, [55.93196910300048, 16.74072721399989]], [34244, [56.30199261300004, 16.65362520600047]], [34247, [55.94012472099985, 16.716247923000083]], [34256, [56.65132466199975, 17.353657597000165]], [34259, [56.679368623999835, 17.203371352999966]], [34264, [59.85790516299949, 17.184281992999786]], [34268, [62.80518005700014, 17.22322009800064]], [34273, [55.920381191999695, 16.68484950400034]], [34274, [56.17177899499984, 16.89725634700062]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.track_crossval.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.track_crossval.json
index ed443df4a8..7e603a4569 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.track_crossval.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.CrossValidationBenchmark.track_crossval.json	
@@ -1 +1 @@
-[[34113, [0.9001555555555555, 0.9001555555555555]], [34115, [0.9001555555555555, 0.9001555555555555]], [34120, [0.9001555555555555, 0.9001555555555555]], [34126, [0.9001555555555555, 0.9001555555555555]], [34139, [0.9001555555555555, 0.9001555555555555]], [34140, [0.9001555555555555, 0.9001555555555555]], [34141, [0.9001555555555555, 0.9001555555555555]], [34155, [0.9001555555555555, 0.9001555555555555]], [34158, [0.9001555555555555, 0.9001555555555555]], [34160, [0.9001555555555555, 0.9001555555555555]], [34162, [0.9001555555555555, 0.9001555555555555]], [34164, [0.9001555555555555, 0.9001555555555555]], [34171, [0.9001555555555555, 0.9001555555555555]], [34177, [0.9001555555555555, 0.9001555555555555]], [34183, [0.9001555555555555, 0.9001555555555555]], [34188, [0.9001555555555555, 0.9001555555555555]], [34193, [0.9001555555555555, 0.9001555555555555]], [34195, [0.9001555555555555, 0.9001555555555555]], [34198, [0.9001555555555555, 0.9001555555555555]], [34203, [0.9001555555555555, 0.9001555555555555]], [34208, [0.9001555555555555, 0.9001555555555555]], [34211, [0.9001555555555555, 0.9001555555555555]], [34213, [0.9001555555555555, 0.9001555555555555]], [34214, [0.9001555555555555, 0.9001555555555555]], [34215, [0.9001555555555555, 0.9001555555555555]], [34221, [0.9001555555555555, 0.9001555555555555]], [34227, [0.9001555555555555, 0.9001555555555555]], [34231, [0.9001555555555555, 0.9001555555555555]], [34237, [0.9001555555555555, 0.9001555555555555]], [34244, [0.9001555555555555, 0.9001555555555555]], [34247, [0.9001555555555555, 0.9001555555555555]], [34256, [0.9001555555555555, 0.9001555555555555]], [34259, [0.9001555555555555, 0.9001555555555555]], [34264, [0.9001555555555555, 0.9001555555555555]], [34268, [0.9001555555555555, 0.9001555555555555]], [34273, [0.9001555555555555, 0.9001555555555555]]]
\ No newline at end of file
+[[34113, [0.9001555555555555, 0.9001555555555555]], [34115, [0.9001555555555555, 0.9001555555555555]], [34120, [0.9001555555555555, 0.9001555555555555]], [34126, [0.9001555555555555, 0.9001555555555555]], [34139, [0.9001555555555555, 0.9001555555555555]], [34140, [0.9001555555555555, 0.9001555555555555]], [34141, [0.9001555555555555, 0.9001555555555555]], [34155, [0.9001555555555555, 0.9001555555555555]], [34158, [0.9001555555555555, 0.9001555555555555]], [34160, [0.9001555555555555, 0.9001555555555555]], [34162, [0.9001555555555555, 0.9001555555555555]], [34164, [0.9001555555555555, 0.9001555555555555]], [34171, [0.9001555555555555, 0.9001555555555555]], [34177, [0.9001555555555555, 0.9001555555555555]], [34183, [0.9001555555555555, 0.9001555555555555]], [34188, [0.9001555555555555, 0.9001555555555555]], [34193, [0.9001555555555555, 0.9001555555555555]], [34195, [0.9001555555555555, 0.9001555555555555]], [34198, [0.9001555555555555, 0.9001555555555555]], [34203, [0.9001555555555555, 0.9001555555555555]], [34208, [0.9001555555555555, 0.9001555555555555]], [34211, [0.9001555555555555, 0.9001555555555555]], [34213, [0.9001555555555555, 0.9001555555555555]], [34214, [0.9001555555555555, 0.9001555555555555]], [34215, [0.9001555555555555, 0.9001555555555555]], [34221, [0.9001555555555555, 0.9001555555555555]], [34227, [0.9001555555555555, 0.9001555555555555]], [34231, [0.9001555555555555, 0.9001555555555555]], [34237, [0.9001555555555555, 0.9001555555555555]], [34244, [0.9001555555555555, 0.9001555555555555]], [34247, [0.9001555555555555, 0.9001555555555555]], [34256, [0.9001555555555555, 0.9001555555555555]], [34259, [0.9001555555555555, 0.9001555555555555]], [34264, [0.9001555555555555, 0.9001555555555555]], [34268, [0.9001555555555555, 0.9001555555555555]], [34273, [0.9001555555555555, 0.9001555555555555]], [34274, [0.9001555555555555, 0.9001555555555555]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_fit.json
index d20476a25e..55f166ba40 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_fit.json	
@@ -1 +1 @@
-[[34113, [95596544.0, 93147136.0]], [34115, [95379456.0, 92909568.0]], [34120, [94920704.0, 92553216.0]], [34126, [95195136.0, 92930048.0]], [34139, [95379456.0, 92917760.0]], [34140, [95145984.0, 92479488.0]], [34141, [95662080.0, 93159424.0]], [34155, [95436800.0, 92954624.0]], [34158, [95027200.0, 92598272.0]], [34160, [95338496.0, 92966912.0]], [34162, [95539200.0, 93138944.0]], [34164, [95387648.0, 92856320.0]], [34171, [94801920.0, 92332032.0]], [34177, [95469568.0, 92884992.0]], [34183, [95535104.0, 93032448.0]], [34188, [95592448.0, 93102080.0]], [34193, [95244288.0, 92758016.0]], [34195, [95309824.0, 92889088.0]], [34198, [95612928.0, 93089792.0]], [34203, [95756288.0, 93458432.0]], [34208, [95432704.0, 93048832.0]], [34211, [95301632.0, 92897280.0]], [34213, [95059968.0, 92770304.0]], [34214, [95313920.0, 92966912.0]], [34215, [95440896.0, 93028352.0]], [34221, [95502336.0, 92975104.0]], [34227, [95354880.0, 92688384.0]], [34231, [95535104.0, 92987392.0]], [34237, [95784960.0, 93196288.0]], [34244, [95838208.0, 93208576.0]], [34247, [95776768.0, 93310976.0]], [34256, [95805440.0, 93171712.0]], [34259, [95907840.0, 93126656.0]], [34264, [95739904.0, 93208576.0]], [34268, [95895552.0, 93368320.0]], [34273, [95907840.0, 93265920.0]]]
\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_predict.json
index 8b32a708b4..a0d906f973 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.peakmem_predict.json	
@@ -1 +1 @@
-[[34113, [87896064.0, 87896064.0]], [34115, [87900160.0, 87900160.0]], [34120, [87318528.0, 87318528.0]], [34126, [87814144.0, 87683072.0]], [34139, [87703552.0, 87703552.0]], [34140, [87461888.0, 87461888.0]], [34141, [88137728.0, 88141824.0]], [34155, [87703552.0, 87703552.0]], [34158, [87498752.0, 87494656.0]], [34160, [87748608.0, 87744512.0]], [34162, [87937024.0, 87937024.0]], [34164, [87658496.0, 87666688.0]], [34171, [87248896.0, 87248896.0]], [34177, [87666688.0, 87646208.0]], [34183, [87826432.0, 87810048.0]], [34188, [87855104.0, 87851008.0]], [34193, [87638016.0, 87625728.0]], [34195, [87797760.0, 87789568.0]], [34198, [87961600.0, 87887872.0]], [34203, [87871488.0, 87904256.0]], [34208, [87916544.0, 87916544.0]], [34211, [87805952.0, 87805952.0]], [34213, [87609344.0, 87609344.0]], [34214, [87793664.0, 87797760.0]], [34215, [87838720.0, 87834624.0]], [34221, [87818240.0, 87822336.0]], [34227, [87506944.0, 87498752.0]], [34231, [87773184.0, 87773184.0]], [34237, [88043520.0, 88051712.0]], [34244, [87965696.0, 87957504.0]], [34247, [87969792.0, 87928832.0]], [34256, [87912448.0, 87912448.0]], [34259, [87920640.0, 87928832.0]], [34264, [88096768.0, 88096768.0]], [34268, [88109056.0, 88137728.0]], [34273, [88313856.0, 88182784.0]]]
\ No newline at end of file
+[[34113, [87896064.0, 87896064.0]], [34115, [87900160.0, 87900160.0]], [34120, [87318528.0, 87318528.0]], [34126, [87814144.0, 87683072.0]], [34139, [87703552.0, 87703552.0]], [34140, [87461888.0, 87461888.0]], [34141, [88137728.0, 88141824.0]], [34155, [87703552.0, 87703552.0]], [34158, [87498752.0, 87494656.0]], [34160, [87748608.0, 87744512.0]], [34162, [87937024.0, 87937024.0]], [34164, [87658496.0, 87666688.0]], [34171, [87248896.0, 87248896.0]], [34177, [87666688.0, 87646208.0]], [34183, [87826432.0, 87810048.0]], [34188, [87855104.0, 87851008.0]], [34193, [87638016.0, 87625728.0]], [34195, [87797760.0, 87789568.0]], [34198, [87961600.0, 87887872.0]], [34203, [87871488.0, 87904256.0]], [34208, [87916544.0, 87916544.0]], [34211, [87805952.0, 87805952.0]], [34213, [87609344.0, 87609344.0]], [34214, [87793664.0, 87797760.0]], [34215, [87838720.0, 87834624.0]], [34221, [87818240.0, 87822336.0]], [34227, [87506944.0, 87498752.0]], [34231, [87773184.0, 87773184.0]], [34237, [88043520.0, 88051712.0]], [34244, [87965696.0, 87957504.0]], [34247, [87969792.0, 87928832.0]], [34256, [87912448.0, 87912448.0]], [34259, [87920640.0, 87928832.0]], [34264, [88096768.0, 88096768.0]], [34268, [88109056.0, 88137728.0]], [34273, [88313856.0, 88182784.0]], [34274, [88375296.0, 88371200.0]]]
\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_fit.json
index cf8a874916..ec227763ae 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_fit.json	
@@ -1 +1 @@
-[[34113, [374.4642131640012, 102.40908155500074]], [34115, [372.1445956989992, 102.37117459599904]], [34120, [344.9167734419998, 103.44403336799951]], [34126, [375.1419901519994, 101.97054501500133]], [34139, [346.896038814999, 104.49649032300113]], [34140, [386.8189775109995, 101.11131621200002]], [34141, [373.46130339499905, 102.00770049400126]], [34155, [349.34945297299964, 101.55457300200032]], [34158, [372.74220539200087, 102.56052423499932]], [34160, [350.47552635600096, 102.00490205000096]], [34162, [346.82019891100026, 103.23968204899938]], [34164, [385.6497539159991, 101.87242891000096]], [34171, [357.9379666370005, 104.97397072500098]], [34177, [346.47980497200115, 103.57321951199992]], [34183, [344.9002866810006, 102.61696911000035]], [34188, [340.22195801399994, 100.4870204430008]], [34193, [367.6441950090002, 102.7783582560005]], [34195, [346.03912104899973, 102.69718619800005]], [34198, [387.70751673700033, 101.56524041299963]], [34203, [350.3979098680011, 103.08051910499853]], [34208, [344.79128504099936, 102.2679372420007]], [34211, [362.72670218299936, 102.01802122700065]], [34213, [382.4193636579994, 101.86862705299973]], [34214, [355.3118401239999, 103.38609545600048]], [34215, [376.0761402850003, 102.22401397199974]], [34221, [387.3723472539996, 101.61043335999966]], [34227, [338.0149075669997, 102.51642351100054]], [34231, [341.67261474100087, 101.76191665500119]], [34237, [386.88970322000023, 101.50147140499939]], [34244, [355.4039165079994, 102.08534838500054]], [34247, [343.78846589100067, 103.55952622900077]], [34256, [346.2039192049988, 101.61019282099915]], [34259, [379.175653753, 103.15988348999963]], [34264, [340.03958358099953, 104.53320344300118]], [34268, [355.3234839409997, 102.38100179899993]], [34273, [379.9832993980008, 102.58037772300122]]]
\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_predict.json
index 43a4fae0f4..6852d7c09d 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_test_score.json
index c2c94488c7..adf8b8ebee 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_train_score.json
index 29b72f25aa..6ef870ab56 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/model_selection.GridSearchBenchmark.track_train_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json
index 7fbc4c2db7..5b311f4404 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json	
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json
index 9563dede57..e4d02fc402 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_fit.json
index 0f00f595ee..b27a5e8b9b 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_fit.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_predict.json
index dbff634314..7b6491532f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_test_score.json
index 436a422e50..5de2c2767f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_train_score.json
index 36e8b3d07d..2364be874f 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.track_train_score.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/summary.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/summary.json
index 959f121b0b..44fa781066 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/summary.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/summary.json	
@@ -1 +1 @@
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diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_fit.json
index d7c9d32acb..f2535b85e7 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_fit.json	
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_predict.json
index 803dc391c2..65b537c36c 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.peakmem_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_fit.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_fit.json
index a0a56d0ea4..88b94050b6 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_fit.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_fit.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_predict.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_predict.json
index 6f6f71c1f0..9d49d4062e 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_predict.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.time_predict.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_test_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_test_score.json
index 3a62e2bc78..980186da6a 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_test_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_test_score.json	
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_train_score.json b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_train_score.json
index 8ac36663fe..59e6891edb 100644
--- a/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_train_score.json	
+++ b/graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.3/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/svm.SVCBenchmark.track_train_score.json	
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/cluster.KMeansBenchmark.peakmem_fit.json b/graphs/summary/cluster.KMeansBenchmark.peakmem_fit.json
index b2de6482ee..664a68ee12 100644
--- a/graphs/summary/cluster.KMeansBenchmark.peakmem_fit.json
+++ b/graphs/summary/cluster.KMeansBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.KMeansBenchmark.peakmem_predict.json b/graphs/summary/cluster.KMeansBenchmark.peakmem_predict.json
index b6b1ddebbf..502d319da7 100644
--- a/graphs/summary/cluster.KMeansBenchmark.peakmem_predict.json
+++ b/graphs/summary/cluster.KMeansBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.KMeansBenchmark.peakmem_transform.json b/graphs/summary/cluster.KMeansBenchmark.peakmem_transform.json
index b137a3b117..b4757be3db 100644
--- a/graphs/summary/cluster.KMeansBenchmark.peakmem_transform.json
+++ b/graphs/summary/cluster.KMeansBenchmark.peakmem_transform.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.KMeansBenchmark.time_fit.json b/graphs/summary/cluster.KMeansBenchmark.time_fit.json
index ae9f683601..809a50560c 100644
--- a/graphs/summary/cluster.KMeansBenchmark.time_fit.json
+++ b/graphs/summary/cluster.KMeansBenchmark.time_fit.json
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\ No newline at end of file
diff --git a/graphs/summary/cluster.KMeansBenchmark.time_predict.json b/graphs/summary/cluster.KMeansBenchmark.time_predict.json
index 711f972b54..94071ca3b5 100644
--- a/graphs/summary/cluster.KMeansBenchmark.time_predict.json
+++ b/graphs/summary/cluster.KMeansBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.KMeansBenchmark.time_transform.json b/graphs/summary/cluster.KMeansBenchmark.time_transform.json
index 969c33f5e0..e16fb4f9be 100644
--- a/graphs/summary/cluster.KMeansBenchmark.time_transform.json
+++ b/graphs/summary/cluster.KMeansBenchmark.time_transform.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/cluster.KMeansBenchmark.track_test_score.json b/graphs/summary/cluster.KMeansBenchmark.track_test_score.json
index 6c44460e00..bf732f3e5d 100644
--- a/graphs/summary/cluster.KMeansBenchmark.track_test_score.json
+++ b/graphs/summary/cluster.KMeansBenchmark.track_test_score.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/cluster.KMeansBenchmark.track_train_score.json b/graphs/summary/cluster.KMeansBenchmark.track_train_score.json
index 78fdf32c35..dc3d45ead4 100644
--- a/graphs/summary/cluster.KMeansBenchmark.track_train_score.json
+++ b/graphs/summary/cluster.KMeansBenchmark.track_train_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json
index 041f3926b3..555b88c2fc 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json
index c793bb55d0..d11bbaeb82 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_predict.json
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json
index 9aee3f85db..40902c5608 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.peakmem_transform.json
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_fit.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_fit.json
index 880511c17b..4dfbb474e0 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_fit.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_fit.json
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_predict.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_predict.json
index 9aaf52e36b..175fe5f3bd 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_predict.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_transform.json b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_transform.json
index d946832b30..cc17337029 100644
--- a/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_transform.json
+++ b/graphs/summary/cluster.MiniBatchKMeansBenchmark.time_transform.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_fit.json b/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_fit.json
index d003c7bf90..e7bafea364 100644
--- a/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_fit.json
+++ b/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_transform.json b/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_transform.json
index 6978f3e602..85c63174c3 100644
--- a/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_transform.json
+++ b/graphs/summary/decomposition.DictionaryLearningBenchmark.peakmem_transform.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.DictionaryLearningBenchmark.time_fit.json b/graphs/summary/decomposition.DictionaryLearningBenchmark.time_fit.json
index dc7fcc33e1..680e8b78d2 100644
--- a/graphs/summary/decomposition.DictionaryLearningBenchmark.time_fit.json
+++ b/graphs/summary/decomposition.DictionaryLearningBenchmark.time_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.DictionaryLearningBenchmark.time_transform.json b/graphs/summary/decomposition.DictionaryLearningBenchmark.time_transform.json
index f6f87a68a7..f68bbfcf6a 100644
--- a/graphs/summary/decomposition.DictionaryLearningBenchmark.time_transform.json
+++ b/graphs/summary/decomposition.DictionaryLearningBenchmark.time_transform.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json
index 033c646725..ae208e204c 100644
--- a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json
+++ b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json
index 30f42f9f9e..12eced55d9 100644
--- a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json
+++ b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform.json
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diff --git a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json
index 7ea70db2e0..28a3cd41cd 100644
--- a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json
+++ b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json
index b8de57388e..9a5d76f0f6 100644
--- a/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json
+++ b/graphs/summary/decomposition.MiniBatchDictionaryLearningBenchmark.time_transform.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/decomposition.PCABenchmark.peakmem_fit.json b/graphs/summary/decomposition.PCABenchmark.peakmem_fit.json
index 240c2a894f..a06ce41a6f 100644
--- a/graphs/summary/decomposition.PCABenchmark.peakmem_fit.json
+++ b/graphs/summary/decomposition.PCABenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/decomposition.PCABenchmark.peakmem_transform.json b/graphs/summary/decomposition.PCABenchmark.peakmem_transform.json
index 6b7664bd93..5ebba39359 100644
--- a/graphs/summary/decomposition.PCABenchmark.peakmem_transform.json
+++ b/graphs/summary/decomposition.PCABenchmark.peakmem_transform.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/decomposition.PCABenchmark.time_fit.json b/graphs/summary/decomposition.PCABenchmark.time_fit.json
index 98678d890e..363785d7c4 100644
--- a/graphs/summary/decomposition.PCABenchmark.time_fit.json
+++ b/graphs/summary/decomposition.PCABenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/decomposition.PCABenchmark.time_transform.json b/graphs/summary/decomposition.PCABenchmark.time_transform.json
index af856082cc..e9f90140b7 100644
--- a/graphs/summary/decomposition.PCABenchmark.time_transform.json
+++ b/graphs/summary/decomposition.PCABenchmark.time_transform.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json
index 2716ef9962..035bd2e48a 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json
index d28aa1d158..6398629e2c 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_fit.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_fit.json
index 8dd6d1a527..8748267bb8 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_fit.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_predict.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_predict.json
index 6972454cd9..d132f4d4a7 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_predict.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.time_predict.json
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\ No newline at end of file
diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json
index a56da93636..8cb391e962 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_test_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json
index 7ea79ed47f..8ccd6a936b 100644
--- a/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json
+++ b/graphs/summary/ensemble.GradientBoostingClassifierBenchmark.track_train_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json
index 90a0d58500..a1753d4363 100644
--- a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json
+++ b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json
index d6d669260a..9df4b1bc1a 100644
--- a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json
+++ b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json
index 1a07c7137a..bb64b2e276 100644
--- a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json
+++ b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json
index 65829cbf57..46f6ee50ff 100644
--- a/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json
+++ b/graphs/summary/ensemble.HistGradientBoostingClassifierBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json
index 0a0c785937..4d4d3b2cf0 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_fit.json
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json
index 534d75cb19..6813fd21b5 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json
+++ b/graphs/summary/ensemble.RandomForestClassifierBenchmark.peakmem_predict.json
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_fit.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_fit.json
index da563a7040..37b002ed21 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_fit.json
+++ b/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_fit.json
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_predict.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_predict.json
index fb651603aa..67a751e1f1 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_predict.json
+++ b/graphs/summary/ensemble.RandomForestClassifierBenchmark.time_predict.json
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_test_score.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_test_score.json
index b4de277c00..fc35d4c13b 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_test_score.json
+++ b/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_test_score.json
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diff --git a/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_train_score.json b/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_train_score.json
index eb0af8f0b8..0bc232b105 100644
--- a/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_train_score.json
+++ b/graphs/summary/ensemble.RandomForestClassifierBenchmark.track_train_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_fit.json b/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_fit.json
index 43b1ba32c9..6bc606f372 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_predict.json b/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_predict.json
index 32826c5c1c..38e9908065 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.time_fit.json b/graphs/summary/linear_model.ElasticNetBenchmark.time_fit.json
index 17e0ad30d9..57b3bb3cd9 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.time_predict.json b/graphs/summary/linear_model.ElasticNetBenchmark.time_predict.json
index 8e149559aa..e30570aff7 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.track_test_score.json b/graphs/summary/linear_model.ElasticNetBenchmark.track_test_score.json
index a2ee5e010d..c67b166dd2 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.track_test_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.ElasticNetBenchmark.track_train_score.json b/graphs/summary/linear_model.ElasticNetBenchmark.track_train_score.json
index e5fc48b845..b29e2b1b44 100644
--- a/graphs/summary/linear_model.ElasticNetBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.ElasticNetBenchmark.track_train_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LassoBenchmark.peakmem_fit.json b/graphs/summary/linear_model.LassoBenchmark.peakmem_fit.json
index ca918f5512..b790870215 100644
--- a/graphs/summary/linear_model.LassoBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.LassoBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LassoBenchmark.peakmem_predict.json b/graphs/summary/linear_model.LassoBenchmark.peakmem_predict.json
index 628326d580..29179dd077 100644
--- a/graphs/summary/linear_model.LassoBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.LassoBenchmark.peakmem_predict.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LassoBenchmark.time_fit.json b/graphs/summary/linear_model.LassoBenchmark.time_fit.json
index f4146de068..1480ddc2fb 100644
--- a/graphs/summary/linear_model.LassoBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.LassoBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LassoBenchmark.time_predict.json b/graphs/summary/linear_model.LassoBenchmark.time_predict.json
index 85ac40af41..98894ada53 100644
--- a/graphs/summary/linear_model.LassoBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.LassoBenchmark.time_predict.json
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LassoBenchmark.track_test_score.json b/graphs/summary/linear_model.LassoBenchmark.track_test_score.json
index 9160876309..251408a822 100644
--- a/graphs/summary/linear_model.LassoBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.LassoBenchmark.track_test_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LassoBenchmark.track_train_score.json b/graphs/summary/linear_model.LassoBenchmark.track_train_score.json
index ac66da4e10..81b6cfdc70 100644
--- a/graphs/summary/linear_model.LassoBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.LassoBenchmark.track_train_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_fit.json b/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_fit.json
index b3064c063d..2b999fce48 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_fit.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_predict.json b/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_predict.json
index 6cb387176b..002c5bee5d 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.time_fit.json b/graphs/summary/linear_model.LinearRegressionBenchmark.time_fit.json
index 220e671ef2..237d36912b 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.time_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.time_predict.json b/graphs/summary/linear_model.LinearRegressionBenchmark.time_predict.json
index 7e9fe0c207..cefd578cad 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.time_predict.json
@@ -1 +1 @@
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\ No newline at end of file
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diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.track_test_score.json b/graphs/summary/linear_model.LinearRegressionBenchmark.track_test_score.json
index 76b890c328..9ee7b3f3e7 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.track_test_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LinearRegressionBenchmark.track_train_score.json b/graphs/summary/linear_model.LinearRegressionBenchmark.track_train_score.json
index 7e4b859038..6bf485f459 100644
--- a/graphs/summary/linear_model.LinearRegressionBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.LinearRegressionBenchmark.track_train_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_fit.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_fit.json
index bdfa66bc4f..7fb4f83ac2 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_predict.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_predict.json
index 69ea4b82c0..b6fc090254 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.peakmem_predict.json
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diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.time_fit.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.time_fit.json
index e3e01cde9d..237b2b7511 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.time_predict.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.time_predict.json
index 13b321ee27..24f112067e 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.track_test_score.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.track_test_score.json
index 699ddf4ee2..2b91b44df4 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.track_test_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.LogisticRegressionBenchmark.track_train_score.json b/graphs/summary/linear_model.LogisticRegressionBenchmark.track_train_score.json
index 28d8bef3ae..a250d558d1 100644
--- a/graphs/summary/linear_model.LogisticRegressionBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.LogisticRegressionBenchmark.track_train_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.RidgeBenchmark.peakmem_fit.json b/graphs/summary/linear_model.RidgeBenchmark.peakmem_fit.json
index 6f37765e95..ba519d76f9 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.RidgeBenchmark.peakmem_predict.json b/graphs/summary/linear_model.RidgeBenchmark.peakmem_predict.json
index a00522d714..309a024898 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.peakmem_predict.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.RidgeBenchmark.time_fit.json b/graphs/summary/linear_model.RidgeBenchmark.time_fit.json
index 0bd5e364a8..eb1ccbb9de 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.time_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.RidgeBenchmark.time_predict.json b/graphs/summary/linear_model.RidgeBenchmark.time_predict.json
index a03dcc7382..8f852f33aa 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.time_predict.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.RidgeBenchmark.track_test_score.json b/graphs/summary/linear_model.RidgeBenchmark.track_test_score.json
index 73fd9228f4..ab5b8a5e9c 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.track_test_score.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.RidgeBenchmark.track_train_score.json b/graphs/summary/linear_model.RidgeBenchmark.track_train_score.json
index 1deca5a2a0..bb9877e7ed 100644
--- a/graphs/summary/linear_model.RidgeBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.RidgeBenchmark.track_train_score.json
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diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_fit.json b/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_fit.json
index 8869cf5621..b4d9a9a689 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_fit.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_predict.json b/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_predict.json
index ce92a078db..88ddad2080 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_predict.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.peakmem_predict.json
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diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.time_fit.json b/graphs/summary/linear_model.SGDRegressorBenchmark.time_fit.json
index 9dcebe80ac..f1b3e2ea47 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.time_fit.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.time_fit.json
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.time_predict.json b/graphs/summary/linear_model.SGDRegressorBenchmark.time_predict.json
index 59791c9a86..12d0c8395f 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.time_predict.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.time_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.track_test_score.json b/graphs/summary/linear_model.SGDRegressorBenchmark.track_test_score.json
index c97cb2951b..901d961994 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.track_test_score.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.track_test_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/linear_model.SGDRegressorBenchmark.track_train_score.json b/graphs/summary/linear_model.SGDRegressorBenchmark.track_train_score.json
index 19b1591164..4660a01996 100644
--- a/graphs/summary/linear_model.SGDRegressorBenchmark.track_train_score.json
+++ b/graphs/summary/linear_model.SGDRegressorBenchmark.track_train_score.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/manifold.TSNEBenchmark.peakmem_fit.json b/graphs/summary/manifold.TSNEBenchmark.peakmem_fit.json
index ed787a9baa..908667c144 100644
--- a/graphs/summary/manifold.TSNEBenchmark.peakmem_fit.json
+++ b/graphs/summary/manifold.TSNEBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/manifold.TSNEBenchmark.time_fit.json b/graphs/summary/manifold.TSNEBenchmark.time_fit.json
index c4eeb361fe..57569cfce8 100644
--- a/graphs/summary/manifold.TSNEBenchmark.time_fit.json
+++ b/graphs/summary/manifold.TSNEBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json b/graphs/summary/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json
index 43932efb83..1d49290cae 100644
--- a/graphs/summary/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json
+++ b/graphs/summary/metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json b/graphs/summary/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json
index 82aaeb948d..f8f24c39ec 100644
--- a/graphs/summary/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json
+++ b/graphs/summary/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/model_selection.CrossValidationBenchmark.peakmem_crossval.json b/graphs/summary/model_selection.CrossValidationBenchmark.peakmem_crossval.json
index d174700d7f..fd8208dce8 100644
--- a/graphs/summary/model_selection.CrossValidationBenchmark.peakmem_crossval.json
+++ b/graphs/summary/model_selection.CrossValidationBenchmark.peakmem_crossval.json
@@ -1 +1 @@
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diff --git a/graphs/summary/model_selection.CrossValidationBenchmark.time_crossval.json b/graphs/summary/model_selection.CrossValidationBenchmark.time_crossval.json
index c8861e0479..a52ae8bc53 100644
--- a/graphs/summary/model_selection.CrossValidationBenchmark.time_crossval.json
+++ b/graphs/summary/model_selection.CrossValidationBenchmark.time_crossval.json
@@ -1 +1 @@
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diff --git a/graphs/summary/model_selection.GridSearchBenchmark.peakmem_fit.json b/graphs/summary/model_selection.GridSearchBenchmark.peakmem_fit.json
index 5d69201193..bd93c2f07d 100644
--- a/graphs/summary/model_selection.GridSearchBenchmark.peakmem_fit.json
+++ b/graphs/summary/model_selection.GridSearchBenchmark.peakmem_fit.json
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diff --git a/graphs/summary/model_selection.GridSearchBenchmark.peakmem_predict.json b/graphs/summary/model_selection.GridSearchBenchmark.peakmem_predict.json
index a07df53334..1044915b18 100644
--- a/graphs/summary/model_selection.GridSearchBenchmark.peakmem_predict.json
+++ b/graphs/summary/model_selection.GridSearchBenchmark.peakmem_predict.json
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\ No newline at end of file
diff --git a/graphs/summary/model_selection.GridSearchBenchmark.time_fit.json b/graphs/summary/model_selection.GridSearchBenchmark.time_fit.json
index ade3ecc60a..de3cd5603d 100644
--- a/graphs/summary/model_selection.GridSearchBenchmark.time_fit.json
+++ b/graphs/summary/model_selection.GridSearchBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/model_selection.GridSearchBenchmark.time_predict.json b/graphs/summary/model_selection.GridSearchBenchmark.time_predict.json
index 416163f1f9..d86b6a3575 100644
--- a/graphs/summary/model_selection.GridSearchBenchmark.time_predict.json
+++ b/graphs/summary/model_selection.GridSearchBenchmark.time_predict.json
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\ No newline at end of file
diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json
index c21bb0735f..1f3249b60a 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json
index c1f72e3894..0a774245a0 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.peakmem_predict.json
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diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_fit.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_fit.json
index 92515a2f79..10843a5b6f 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_fit.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_fit.json
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\ No newline at end of file
diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_predict.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_predict.json
index bd7f64b3e7..41152a38f4 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_predict.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.time_predict.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_test_score.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_test_score.json
index f09fdaa236..f39d4820b6 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_test_score.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_test_score.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_train_score.json b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_train_score.json
index 67ad205938..d2bcf53c51 100644
--- a/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_train_score.json
+++ b/graphs/summary/neighbors.KNeighborsClassifierBenchmark.track_train_score.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/graphs/summary/svm.SVCBenchmark.peakmem_fit.json b/graphs/summary/svm.SVCBenchmark.peakmem_fit.json
index 77c9c883b3..27dfd70597 100644
--- a/graphs/summary/svm.SVCBenchmark.peakmem_fit.json
+++ b/graphs/summary/svm.SVCBenchmark.peakmem_fit.json
@@ -1 +1 @@
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diff --git a/graphs/summary/svm.SVCBenchmark.peakmem_predict.json b/graphs/summary/svm.SVCBenchmark.peakmem_predict.json
index 6a84a76fbd..fdcd45170e 100644
--- a/graphs/summary/svm.SVCBenchmark.peakmem_predict.json
+++ b/graphs/summary/svm.SVCBenchmark.peakmem_predict.json
@@ -1 +1 @@
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diff --git a/graphs/summary/svm.SVCBenchmark.time_fit.json b/graphs/summary/svm.SVCBenchmark.time_fit.json
index bae49ab0f2..eb64ea5ef2 100644
--- a/graphs/summary/svm.SVCBenchmark.time_fit.json
+++ b/graphs/summary/svm.SVCBenchmark.time_fit.json
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/graphs/summary/svm.SVCBenchmark.time_predict.json b/graphs/summary/svm.SVCBenchmark.time_predict.json
index 5f1d3492ef..ffe0c30936 100644
--- a/graphs/summary/svm.SVCBenchmark.time_predict.json
+++ b/graphs/summary/svm.SVCBenchmark.time_predict.json
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/index.json b/index.json
index 71eea4167c..48a3863a76 100644
--- a/index.json
+++ b/index.json
@@ -1 +1 @@
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1694116492000, "33734": 1694366006000, "33756": 1694455478000, "33766": 1694542477000, "33802": 1694634394000, "33808": 1694689715000, "33813": 1694807049000, "33818": 1694874439000, "33820": 1694956547000, "33826": 1695059234000, "33833": 1695143438000, "34030": 1695204060000, "34031": 1695224738000, "34034": 1695308063000, "34046": 1695653805000, "34049": 1695740821000, "34052": 1695830869000, "34058": 1695899976000, "34065": 1696007297000, "34068": 1696024508000, "34075": 1696265713000, "34079": 1696357529000, "34113": 1697533319000, "34115": 1697561306000, "34120": 1697643038000, "34126": 1697716358000, "34139": 1697824316000, "34140": 1697879383000, "34141": 1697997241000, "34152": 1698055850000, "34155": 1698081283000, "34158": 1698155592000, "34160": 1698235049000, "34162": 1698326139000, "34164": 1698408361000, "34171": 1698662860000, "34177": 1698769375000, "34183": 1698872459000, "34188": 1698948475000, "34193": 1699045091000, "34195": 1699118804000, "34198": 1699282282000, "34203": 1699368806000, "34208": 1699457308000, "34211": 1699548326000, "34213": 1699610845000, "34214": 1699660269000, "34215": 1699792428000, "34221": 1699883326000, "34227": 1699986361000, "34231": 1700066735000, "34237": 1700145316000, "34244": 1700248028000, "34247": 1700327418000, "34256": 1700488628000, "34259": 1700588722000, "34264": 1700675885000, "34268": 1700771717000, "34273": 1700850422000}, "params": {"arch": ["x86_64"], "cpu": ["Intel Core Processor (Haswell, no TSX)"], "machine": ["sklearn-benchmark"], "num_cpu": ["8"], "os": ["Linux 4.15.0-20-generic"], "ram": ["16424684"], "python": ["3.11", "3.8"], "numpy": ["", "1.25.2"], "scipy": ["", "1.11.2"], "cython": ["", "0.29.36", "3.0.2", "3.0.3"], "joblib": ["", "1.3.2"], "threadpoolctl": ["", "3.2.0"], "pandas": ["", "2.1.0", null], "branch": ["main"]}, "graph_param_list": [{"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.8", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "pandas": "", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.8", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "branch": "main", "pandas": null}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "pandas": "", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.3", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "0.29.36", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.2", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}], "benchmarks": {"cluster.KMeansBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_fit", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "a9d893de2d92e56e4dbeab4d7b7b4d5e00add3f4e993b164664b7ebbdd036dc7"}, "cluster.KMeansBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_predict", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "0f7d945338d774baae43a82dff7fe9f0db16ff6d1481b5afd437048b544396fc"}, "cluster.KMeansBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_transform", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "576d76cb6e3aa82315e3d3dc6a2e8b7e58cb38bdd510df6801ab645465f4eb70"}, "cluster.KMeansBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_fit", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "a8dbf56b4b6365cb1e3c23b71c7df95ae011632c95124496f8fd1c30430154ec", "warmup_time": 1}, "cluster.KMeansBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_predict", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "611589f0c08131355c37720106ea132a2653a100660cd34884455259325b9c83", "warmup_time": 1}, "cluster.KMeansBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_transform", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "8a02da09ca430e84b11161256032a2d4c08df103e4626718278e8b69b90acb0c", "warmup_time": 1}, "cluster.KMeansBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.track_test_score", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "track", "unit": "unit", "version": "67ff12752edbc7e4055ec0bddc293f22d59aec0eaa675c166f502a008bc22c59"}, "cluster.KMeansBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.track_train_score", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "track", "unit": "unit", "version": "a5e07140ef3dd16920358a4c08c2f9f81d0e21f40bf5d8990b8a37b6a07c64e9"}, "cluster.MiniBatchKMeansBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_fit", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "42f5ccf79079b60ff9fa8b0220a6b1fb951f46d27574d343f6e6b4b69dd8f1f0"}, "cluster.MiniBatchKMeansBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_predict", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "03ee5b059682250c4b3d0793edf5a4978efb06c760148550f068e82dcb71e75f"}, "cluster.MiniBatchKMeansBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_transform", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "f844a68fb0292b177c459487189f905d1afb89d79ab35a0539f9f4217b445ff2"}, "cluster.MiniBatchKMeansBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_fit", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "40617ec6fdd94e3a650dba98f8204c9779e63ae7803343b23e7368f7b068f26e", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_predict", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "561b80fa4780c86ee698a0efd05c5d6c1bce24014006c5ec9096c2f3a960b9fe", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_transform", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "2cf7a764c9fa1511ff1e1dee921060dc33eb0608e1cee96b782dc7b5d3f91f89", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.track_test_score", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "track", "unit": "unit", "version": "7aeb8a20f66080e18e15f92e245fd1dd8de235e2f53d355596424dc7c30ae295"}, "cluster.MiniBatchKMeansBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.track_train_score", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "track", "unit": "unit", "version": "b0c324767051682ea0bfc17c991f3de50e4876d683b1167c11ae8d4f2349b9c9"}, "decomposition.DictionaryLearningBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.peakmem_fit", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "8c3205268eade479bfda221734bf6129ab2a7b2bf4c59a8e6e855469f519672a"}, "decomposition.DictionaryLearningBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.peakmem_transform", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "b2c047a9e4d9d9cc6dd2d41a3738c21c183facbf5d609e8bfb354499f0b38ee4"}, "decomposition.DictionaryLearningBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.DictionaryLearningBenchmark.time_fit", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:41", "timeout": 500, "type": "time", "unit": "seconds", "version": "9f6c61216a35b8b5a205dbce3e4f541613c2e1df4cda9f85b16a7a41a02a9e02", "warmup_time": 1}, "decomposition.DictionaryLearningBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.DictionaryLearningBenchmark.time_transform", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:41", "timeout": 500, "type": "time", "unit": "seconds", "version": "a9786d7edfa47d44fb49632ba092edb63068a33298968990f071b58ad312e45a", "warmup_time": 1}, "decomposition.DictionaryLearningBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.track_test_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "track", "unit": "unit", "version": "022cecb50e5ba330d0728d08afbca34a130f0e74529bce6c8c93aca80a4bcae5"}, "decomposition.DictionaryLearningBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.track_train_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "track", "unit": "unit", "version": "6ed8ae7b04c1c5b4be0a5847795321be4c50195958c72642e1ca9367d838649d"}, "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "cf54cbec149e048235b388e434462754421fc5f8593697b431e17bd01f79cbe1"}, "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "c9e62449ea399590080c790df85e63df815e88ceaa029196f9ea925d3acfecd7"}, "decomposition.MiniBatchDictionaryLearningBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.MiniBatchDictionaryLearningBenchmark.time_fit", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:75", "timeout": 500, "type": "time", "unit": "seconds", "version": "48d290a4fd2b0a9ecdd1a7add3b7e7b231d8df63e32889758ec0bbd8c1cb37ef", "warmup_time": 1}, "decomposition.MiniBatchDictionaryLearningBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.MiniBatchDictionaryLearningBenchmark.time_transform", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:75", "timeout": 500, "type": "time", "unit": "seconds", "version": "3ac7bfb8060c67e6dd13c37f1901d3f4274b8b0edfaf3d6a575d4e20ecbaf45e", "warmup_time": 1}, "decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "track", "unit": "unit", "version": "a0774d6e8f922842e4b916076deab0ed00f152a84632f732cec174d5d619192f"}, "decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "track", "unit": "unit", "version": "13436574eaa40a2dd7b58fbef029cd2171941b963cec765dff95a1889f6a157a"}, "decomposition.PCABenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.peakmem_fit", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "1dc803fc882e472d9bc70274b900eb03304281cd2dff33ec3e3c039fffbb74a4"}, "decomposition.PCABenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.peakmem_transform", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "2b31c7c4510aca17a679729f995ba0b7d6548de119b989f414876912bc7851ff"}, "decomposition.PCABenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.PCABenchmark.time_fit", "number": 0, "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "4a753451bb0c872008db51c421599cf8badfcb691aab39aaa7822dad561241dc", "warmup_time": 1}, "decomposition.PCABenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.PCABenchmark.time_transform", "number": 0, "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "1f73bed6685d54963e63f4de9713d472d0ab242d57c6a0cfa54fac7d1e1e83ef", "warmup_time": 1}, "decomposition.PCABenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.track_test_score", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "track", "unit": "unit", "version": "2bf9b85b93e81f43bf6e2e900a8ef13fb83b9b9298c32d0808bda654eb57c0c3"}, "decomposition.PCABenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.track_train_score", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "track", "unit": "unit", "version": "16881c7fae4abaa9923123fc258e6542b0b3aa0c232d2db206548668bced23bb"}, "ensemble.GradientBoostingClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "0bed7eba4859779600e1b1cb03539724eddad1419ad1332e897185f93bd3d6df"}, "ensemble.GradientBoostingClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "5f65eed561196ec366996fc83d2258f891bbe9608480b00948108fa794a3c81e"}, "ensemble.GradientBoostingClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.GradientBoostingClassifierBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:64", "timeout": 500, "type": "time", "unit": "seconds", "version": "5702f67868717f5884d82ed22b8940f7ff2b626a2b46e2a4b6e1173c83db484f", "warmup_time": 1}, "ensemble.GradientBoostingClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.GradientBoostingClassifierBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:64", "timeout": 500, "type": "time", "unit": "seconds", "version": "17e3a4098ef2254174162a060b1286418984a4be5438aefd71301bf6a8f4c13d", "warmup_time": 1}, "ensemble.GradientBoostingClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "track", "unit": "unit", "version": "55092be1f965aa51752829bda24b944bdb1222a6b8e5fdf0d4026c9580953dea"}, "ensemble.GradientBoostingClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "track", "unit": "unit", "version": "cd78924c15421dcfc8b45a7d26a8b035a1e441c45612ee6ae169b9155e5a631b"}, "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "733ac3d039d12bcb9c7f00c745fad3c3723c29f21f67e444aabac276cbc3a916"}, "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "ef8fbc52879ef0add38e7cec975a52bd207c7003e688e265dcc4839b0bcaa3a8"}, "ensemble.HistGradientBoostingClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.HistGradientBoostingClassifierBenchmark.time_fit", "number": 0, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:103", "timeout": 500, "type": "time", "unit": "seconds", "version": "6840b9adeccbde7fc736aae2aa37f9b118d3fa9a78f2e0b2f0a4f09503e1a48d", "warmup_time": 1}, "ensemble.HistGradientBoostingClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.HistGradientBoostingClassifierBenchmark.time_predict", "number": 0, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:103", "timeout": 500, "type": "time", "unit": "seconds", "version": "ce65e5d6c62eebe1e73935285f56bc6c7ecebde97fb73fb4b9370c9b5979288a", "warmup_time": 1}, "ensemble.HistGradientBoostingClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.track_test_score", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "track", "unit": "unit", "version": "8c9a915f4dd669a61ceb38728602e43f84a976ebb06628f8026837eb7a5d30f6"}, "ensemble.HistGradientBoostingClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.track_train_score", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "track", "unit": "unit", "version": "43f6c225874e5193533ad56bdd398f471c7caa0f0f8c9a8ebb5486e10a2bec7f"}, "ensemble.RandomForestClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.peakmem_fit", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "14ca8ab81b54b4f2775c94fd794a13b049f48b9927979b964b90acc22ebf08a8"}, "ensemble.RandomForestClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.peakmem_predict", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "3febfd0d8977731921e7d366e6a585c8e405c0520f9a94415b96c6c334ed1c2f"}, "ensemble.RandomForestClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.RandomForestClassifierBenchmark.time_fit", "number": 0, "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:24", "timeout": 500, "type": "time", "unit": "seconds", "version": "7f77f2ea22b57af3ff27a86843c654c08e6aa926896f2c421666fd31d95ce65f", "warmup_time": 1}, "ensemble.RandomForestClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.RandomForestClassifierBenchmark.time_predict", "number": 0, "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:24", "timeout": 500, "type": "time", "unit": "seconds", "version": "6a93ee4dfb1d21e994eb8d7568e75ee99e0f416c9711bb2b45d1c022a27f0746", "warmup_time": 1}, "ensemble.RandomForestClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.track_test_score", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "track", "unit": "unit", "version": "a3b01d5f39647a3a6d6bfac31a005a5fdc82ad531a1b71f249ae2f4e98dc44c9"}, "ensemble.RandomForestClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.track_train_score", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "track", "unit": "unit", "version": "42daa18fec647c7415a78ff56b84cf07b2253222c89f24baf5f12238463b9400"}, "linear_model.ElasticNetBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.peakmem_fit", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "aeb53df9e9543a762cf297b5697780eafb3cfbf2a7e750ad0f079ea50ff3d051"}, "linear_model.ElasticNetBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.peakmem_predict", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "27f5ef6022a3a4cdd5a0854b9428349a5e461bf638a76337760251639c9d7f02"}, "linear_model.ElasticNetBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.ElasticNetBenchmark.time_fit", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:187", "timeout": 500, "type": "time", "unit": "seconds", "version": "02947c3221cd53cf645dab6e61c787646637d66474795a722ec537ff85976c8c", "warmup_time": 1}, "linear_model.ElasticNetBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.ElasticNetBenchmark.time_predict", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:187", "timeout": 500, "type": "time", "unit": "seconds", "version": "4140a00f540d4dea5268a3813730d6fbb270e40b74285dbf046b3e26069f1287", "warmup_time": 1}, "linear_model.ElasticNetBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.track_test_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "track", "unit": "unit", "version": "1c0c2170cd23de65b973498cfc8dff584d9983a7513d2ead27d49594b092a280"}, "linear_model.ElasticNetBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.track_train_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "track", "unit": "unit", "version": "6cff911c1163f327c86117f3dc306d62cdba87d9ea8068cc68977b61d151da78"}, "linear_model.LassoBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.peakmem_fit", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "8fa5824a898ff683065f60683a1805ae55dfdde209f22eb520e3dddac3b02688"}, "linear_model.LassoBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.peakmem_predict", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "2fd8dcd052ba7edddc85213c847b8fd149f36b0b9c0b058b88adae232e1fe0c6"}, "linear_model.LassoBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LassoBenchmark.time_fit", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:228", "timeout": 500, "type": "time", "unit": "seconds", "version": "d0f2ce5dce5564f0fc096b1e4ee47c583962a62baf6a13edf150b4edaf353e05", "warmup_time": 1}, "linear_model.LassoBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LassoBenchmark.time_predict", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:228", "timeout": 500, "type": "time", "unit": "seconds", "version": "9adfa14dab87a3a8a4de49d179ab5891f08045e1b81194ae39643622cb6487c9", "warmup_time": 1}, "linear_model.LassoBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.track_test_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "track", "unit": "unit", "version": "b56564a7b1756eeff8bbe981fa28bfb58f992c09069c2672ae87e637ba91f565"}, "linear_model.LassoBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.track_train_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "track", "unit": "unit", "version": "97c0e5ecdddd2ee7c31046ec01b6d29119975ffd6774712a778a26f8499350c4"}, "linear_model.LinearRegressionBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "a905fac8878f96b0ccc05296ca1cbb10654937aa04579453c79a4255ec7865f1"}, "linear_model.LinearRegressionBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "759535a8913b60467ef240fed4181399ff149efa33bea0eb69eed231df3f8876"}, "linear_model.LinearRegressionBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LinearRegressionBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:119", "timeout": 500, "type": "time", "unit": "seconds", "version": "e5dbbebbcad57689afbefadad2fb87cb71161a32a9d6caa162975a5b419f0f97", "warmup_time": 1}, "linear_model.LinearRegressionBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LinearRegressionBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:119", "timeout": 500, "type": "time", "unit": "seconds", "version": "ba279c28dffebbb04b7b173dee9e976b48b05ffbada322dd53d9a2de22ff70f6", "warmup_time": 1}, "linear_model.LinearRegressionBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "track", "unit": "unit", "version": "0641fe07389de5e5b9811f51f186e4a4a96a856eb50c8810c62734a593513594"}, "linear_model.LinearRegressionBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "track", "unit": "unit", "version": "5853e8435e7eba58f2264852c35444debdedaa05b29cccd9fef895ce848a5f06"}, "linear_model.LogisticRegressionBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.peakmem_fit", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "3b211361ab927e53d5697c6b9e81013a7575a9ce103d21adeb68544ea7a4e5dc"}, "linear_model.LogisticRegressionBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.peakmem_predict", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "be6101aa74d4f94ec846334788b859094ed4b1d36ca92bf7d693b3043a8d39da"}, "linear_model.LogisticRegressionBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LogisticRegressionBenchmark.time_fit", "number": 0, "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:28", "timeout": 500, "type": "time", "unit": "seconds", "version": "7cd42b119dd9bf6c8bf33ee70890f7ebaf0b159e0c0c8cd5b299c3f632725f21", "warmup_time": 1}, "linear_model.LogisticRegressionBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LogisticRegressionBenchmark.time_predict", "number": 0, "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:28", "timeout": 500, "type": "time", "unit": "seconds", "version": "b9dd34b6a7b894f6afbd6e27f4d0cfe829c1b0e4af4f7203e56269b873ee049b", "warmup_time": 1}, "linear_model.LogisticRegressionBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.track_test_score", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "track", "unit": "unit", "version": "37e537f82f54da1d89003018c8370da6ac290d672b6343a457ee49dd444e3c9f"}, "linear_model.LogisticRegressionBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.track_train_score", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "track", "unit": "unit", "version": "5647f2df8db098461a973d60cb900515f403287ed3e109a99151c94309e568f1"}, "linear_model.RidgeBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.peakmem_fit", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "d06a93be5cf39a0df663d696943713376c4ba4abb2568ebc0bc97fd2a2fd5173"}, "linear_model.RidgeBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.peakmem_predict", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "24e4dc55ce8e6b9311d26ca2936364cad3ddb0b8827b656a81886db508e69866"}, "linear_model.RidgeBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.RidgeBenchmark.time_fit", "number": 0, "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:78", "timeout": 500, "type": "time", "unit": "seconds", "version": "6ebbadd5cfeeb543e69ca73d777ab88bff49391965e0c0e3270868f1fa6bb385", "warmup_time": 1}, "linear_model.RidgeBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.RidgeBenchmark.time_predict", "number": 0, "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:78", "timeout": 500, "type": "time", "unit": "seconds", "version": "f41e9813a5f8239fbb0ef5e81982549e297ff18f751b7e4b42982972851b7cd0", "warmup_time": 1}, "linear_model.RidgeBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.track_test_score", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "track", "unit": "unit", "version": "663c821271fbdb18d0252832535191137787f37ad6d6397624c92edd96dc6f3f"}, "linear_model.RidgeBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.track_train_score", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "track", "unit": "unit", "version": "92bfe13d8da10b255d1c72982a3a88f70985b864b39584e51a184ea88116f35d"}, "linear_model.SGDRegressorBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "d81ea5fe5efd3e1ec085f0a2c66d712cfec7e2c4e03b19b7a50ab79dfd661f77"}, "linear_model.SGDRegressorBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "cf12939ffeb2f97f86ada7e454920a994d254a98a02eac5e34ea687f8ec11ad0"}, "linear_model.SGDRegressorBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.SGDRegressorBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:151", "timeout": 500, "type": "time", "unit": "seconds", "version": "cc477ee1fcfc2e853f5113072b49c7dc80acf8f2e19f83518368e1efe8dd5374", "warmup_time": 1}, "linear_model.SGDRegressorBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.SGDRegressorBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:151", "timeout": 500, "type": "time", "unit": "seconds", "version": "fcd01e3604428cf2063cbbfe4e708974618533d55ec0784a787b8bb838a2ece9", "warmup_time": 1}, "linear_model.SGDRegressorBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "track", "unit": "unit", "version": "5c184c0b957ec177b911999a0cc2cd3996407b726efe5d7adeaeddea222a16d0"}, "linear_model.SGDRegressorBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "track", "unit": "unit", "version": "84040f29c546fcb4fb3fed1db35c198a88607a920f0d1aad43ae542738470257"}, "manifold.TSNEBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.peakmem_fit", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "5b07ad9c3bf4af00ff4022361ced135ee0591fe9a613dddb484f0142685497c4"}, "manifold.TSNEBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "manifold.TSNEBenchmark.time_fit", "number": 0, "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "manifold:15", "timeout": 500, "type": "time", "unit": "seconds", "version": "d7906ea0d1e8bee6103afdb924f0c5471c1c3e392feb7dbcc411b9e3f3535e6a", "warmup_time": 1}, "manifold.TSNEBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.track_test_score", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "track", "unit": "unit", "version": "7c865f9e53f16c8492cb36db3b656646d6585227ebfbf097102fda1e83f5e953"}, "manifold.TSNEBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.track_train_score", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "track", "unit": "unit", "version": "92a5fc587f972e63dad98657097b1bcd64a743dfce5f07b3e1847ed8b720d46d"}, "metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances": {"code": "class PairwiseDistancesBenchmark:\n    def peakmem_pairwise_distances(self, *args):\n        pairwise_distances(self.X, **self.pdist_params)\n\n    def setup(self, *params):\n        representation, metric, n_jobs = params\n    \n        if representation == \"sparse\" and metric == \"correlation\":\n            raise NotImplementedError\n    \n        if Benchmark.data_size == \"large\":\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 8000\n            else:\n                n_samples = 24000\n        else:\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 4000\n            else:\n                n_samples = 12000\n    \n        data = _random_dataset(n_samples=n_samples, representation=representation)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.pdist_params = {\"metric\": metric, \"n_jobs\": n_jobs}", "name": "metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances", "param_names": ["representation", "metric", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'cosine'", "'euclidean'", "'manhattan'", "'correlation'"], ["1", "4"]], "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "f982061fc53163362f863b2455a1f2f660861c4a4357be712dcacab0682833db"}, "metrics.PairwiseDistancesBenchmark.time_pairwise_distances": {"code": "class PairwiseDistancesBenchmark:\n    def time_pairwise_distances(self, *args):\n        pairwise_distances(self.X, **self.pdist_params)\n\n    def setup(self, *params):\n        representation, metric, n_jobs = params\n    \n        if representation == \"sparse\" and metric == \"correlation\":\n            raise NotImplementedError\n    \n        if Benchmark.data_size == \"large\":\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 8000\n            else:\n                n_samples = 24000\n        else:\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 4000\n            else:\n                n_samples = 12000\n    \n        data = _random_dataset(n_samples=n_samples, representation=representation)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.pdist_params = {\"metric\": metric, \"n_jobs\": n_jobs}", "min_run_count": 2, "name": "metrics.PairwiseDistancesBenchmark.time_pairwise_distances", "number": 0, "param_names": ["representation", "metric", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'cosine'", "'euclidean'", "'manhattan'", "'correlation'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "timeout": 500, "type": "time", "unit": "seconds", "version": "968c4646f4f823e76e062ccbbe50249793691ae13e472124d1ebd2642c86b5e0", "warmup_time": 1}, "model_selection.CrossValidationBenchmark.peakmem_crossval": {"code": "class CrossValidationBenchmark:\n    def peakmem_crossval(self, *args):\n        cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "name": "model_selection.CrossValidationBenchmark.peakmem_crossval", "param_names": ["n_jobs"], "params": [["1", "4"]], "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "4b6fc20c55d0bbbf2b9b56117a6725d0351eec991f183d04b136f1ce4552e0c7"}, "model_selection.CrossValidationBenchmark.time_crossval": {"code": "class CrossValidationBenchmark:\n    def time_crossval(self, *args):\n        cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "min_run_count": 2, "name": "model_selection.CrossValidationBenchmark.time_crossval", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "timeout": 20000, "type": "time", "unit": "seconds", "version": "9959454181a0ddb22a0ce5727c352955d47bf0ce97d2189ff73d567981c64587", "warmup_time": 1}, "model_selection.CrossValidationBenchmark.track_crossval": {"code": "class CrossValidationBenchmark:\n    def track_crossval(self, *args):\n        return float(cross_val_score(self.clf, self.X, self.y, **self.cv_params).mean())\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "name": "model_selection.CrossValidationBenchmark.track_crossval", "param_names": ["n_jobs"], "params": [["1", "4"]], "timeout": 20000, "type": "track", "unit": "unit", "version": "5e7e6b0e0116bbc68953414ce4e0eec203cc51c80a71d897dd599172b82aeedd"}, "model_selection.GridSearchBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.peakmem_fit", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "95b48fd99a9327cc7b96fbaf3fb68de0488e277098c5f71aa31f5bcc0af0aa35"}, "model_selection.GridSearchBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.peakmem_predict", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "c892cd859a96073eba53d60e549a73d8202c9d0e97f5469f5e0333d949ce8dce"}, "model_selection.GridSearchBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "model_selection.GridSearchBenchmark.time_fit", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "time", "unit": "seconds", "version": "ce048ba169bd01fc98abe0f363fcaafc41dfc6d88ae4658795602e37fd0bd003", "warmup_time": 1}, "model_selection.GridSearchBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "model_selection.GridSearchBenchmark.time_predict", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "time", "unit": "seconds", "version": "acfeae20f02fb4775c69f3a23452a495e43a2abb0e684954a33824913f37e3eb", "warmup_time": 1}, "model_selection.GridSearchBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.track_test_score", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "track", "unit": "unit", "version": "ec63718f94ff0beda5889deafa3b1f1f3ff7750bf6254fce002cadda537bee4b"}, "model_selection.GridSearchBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.track_train_score", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "track", "unit": "unit", "version": "51575cdf87b95c5096592cecfc1f386cff2b52230b19a7bd6b1afee2fb55910c"}, "neighbors.KNeighborsClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.peakmem_fit", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "66acb7f2ea52ea49f9a5239c437782b0ac7cd030f150166dff686727e06ae1f5"}, "neighbors.KNeighborsClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.peakmem_predict", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "e7fb64f5f61911736101d3ca625806937a6bb4fc75b64a8ed0c9be1f37e0f10f"}, "neighbors.KNeighborsClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "neighbors.KNeighborsClassifierBenchmark.time_fit", "number": 0, "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "neighbors:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "455e6017e69db76a36673f135b8300f5288867804ae588240ef238340de7bc82", "warmup_time": 1}, "neighbors.KNeighborsClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "neighbors.KNeighborsClassifierBenchmark.time_predict", "number": 0, "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "neighbors:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "6611e4000bd5b1d8935eb7407c10355bf476dfef925640b9a8445912b72c8183", "warmup_time": 1}, "neighbors.KNeighborsClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.track_test_score", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "track", "unit": "unit", "version": "95d25638754fb5c021b379d6f4f51868a7303bedb4868e52cac2a036eb0bc248"}, "neighbors.KNeighborsClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.track_train_score", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "track", "unit": "unit", "version": "1e1b6891a1563cf0c8ae446c751cfd6dec48cc2fcc4d0157f30a7189f05e938a"}, "svm.SVCBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.peakmem_fit", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "b5099e172031e9a0f7b7706ec386c5bf36f9bd427f7d47274c66c6c39f5bf730"}, "svm.SVCBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.peakmem_predict", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "4a6611919a2e5a6cbd1f6c211b23857b3912da5e1484da897bf70155d62b7980"}, "svm.SVCBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "svm.SVCBenchmark.time_fit", "number": 0, "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "svm:14", "timeout": 500, "type": "time", "unit": "seconds", "version": "60f69bb46f97b00f249209ee47574d1508b9e81a8ba3ec0a8a3a430eb6aa719d", "warmup_time": 1}, "svm.SVCBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "svm.SVCBenchmark.time_predict", "number": 0, "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "svm:14", "timeout": 500, "type": "time", "unit": "seconds", "version": "ab64f47e62b03803ca9ee0734d6a0734af8278b39121b069d9abdf86a8e79285", "warmup_time": 1}, "svm.SVCBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.track_test_score", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "track", "unit": "unit", "version": "f04be6a642a0b71d7bb27a3b184c53cf61aa01dead2ec4bbc68bf22ea78b68b3"}, "svm.SVCBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.track_train_score", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "track", "unit": "unit", "version": "30dfcaa0d1c48f402d28bf241f4c7c013ac533fbb528d470c427a34c237993f7"}}, "machines": {"sklearn-benchmark": {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "version": 1}}, "tags": {"0.10": 7904, "0.10-branching": 7872, "0.11": 9357, "0.11-beta": 9331, "0.11-branching": 9349, "0.12": 10436, "0.12-branching": 10413, "0.12.1": 10778, "0.13": 12373, "0.13-branching": 12368, "0.13.1": 12748, "0.14.1": 14725, "0.14a1": 14515, "0.15-branching": 17074, "0.15.0b1": 16940, "0.15.1": 17645, "0.16-branching": 19198, "0.16.0": 19375, "0.16.1": 19504, "0.16b1": 19199, "0.17-branching": 20502, "0.17.1": 21601, "0.17.1-1": 21602, "0.17b1": 20509, "0.18": 22393, "0.18.1": 22702, "0.18.2": 23197, "0.18rc": 22265, "0.18rc1": 22268, "0.18rc2": 22283, "0.19-branching": 23280, "0.19.1": 23768, "0.19.2": 24417, "0.20.0": 24809, "0.20.1": 25159, "0.20.2": 25261, "0.20.3": 25563, "0.20.4": 26284, "0.20rc1": 24644, "0.21.0": 25841, "0.21.1": 25874, "0.21.2": 25910, "0.21.3": 26278, "0.21b2": 25766, "0.21rc1": 25758, "0.21rc2": 25767, "0.22.1": 27063, "0.3": 745, "0.5": 1969, "0.5.rc": 1915, "0.5.rc2": 1917, "0.5.rc3": 1919, "0.6-rc": 2701, "0.6.0": 2711, "0.7": 3151, "0.7-branching": 3102, "0.7.1": 3212, "0.8": 4037, "0.8-branching": 3905, "0.8.1": 4684, "0.9": 6225, "0.9-branching": 6177, "1.0.1": 29997, "1.1.1": 31297, "1.3.1": 34030, "1.3.2": 34152, "sprint01": 1207, "0.2": 590, "0.1": 395, "0.2-beta": 587, "0.1-beta": 382, "debian/0.2+svn625-1": 646, "debian/0.3-1": 755, "debian/0.4-3": 1288, "debian/0.3-2": 811, "debian/0.3-3": 813, "debian/0.4-2": 1131, "debian/0.4-1": 1016, "debian/0.3-4": 826, "0.4": 998, "debian/0.5-1": 1977, "debian/0.6.0.dfsg-1": 2743, "debian/0.7.1.dfsg-1": 3289, "debian/0.7.1.dfsg-3": 3741, "debian/0.8.0.dfsg-1": 4054, "debian/0.8.1.dfsg-1": 4696, "debian/0.9.0.dfsg-1": 6511, "debian/0.10.0-1": 7919, "debian/0.11.0-1": 9369, "debian/0.11.0-2": 9799, "debian/0.12.0-1": 10457, "0.14": 14698, "0.15.0b2": 17075, "0.15.0": 17279, "0.15.2": 17934, "debian/0.16.1-2": 19920, "debian/0.17.0_b1-1": 20952, "debian/0.17.0_b1+git14-g4e6829c-1": 20955, "0.17": 21113, "debian/0.17.0-1": 21126, "debian/0.17.0-3": 21322, "debian/0.17.0-4": 21323, "0.19b1": 23283, "0.19b2": 23307, "0.19.0": 23480, "0.22rc1": 26820, "0.22rc2": 26881, "0.22rc2.post1": 26890, "0.22rc3": 26944, "0.22": 26953, "0.22.2": 27300, "0.22.2.post1": 27325, "0.23.0rc1": 27571, "0.23.0": 27604, "0.23.1": 27674, "0.23.2": 28086, "0.24.0rc1": 28501, "0.24.0": 28541, "0.24.1": 28625, "0.24.2": 29084, "1.0.rc1": 29586, "1.0.rc2": 29636, "1.0": 29733, "1.0.2": 30500, "1.1.0rc1": 31120, "1.1.0": 31184, "1.1.2": 31927, "1.1.3": 32291, "1.2.0rc1": 32454, "1.2.0": 32499, "1.2.1": 32770, "1.2.2": 32984, "1.3.0rc1": 33376, "1.3.0": 33504}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]}
\ No newline at end of file
+{"project": "scikit-learn", "project_url": "scikit-learn.org/", "show_commit_url": "https://github.com/scikit-learn/scikit-learn/commit/", "hash_length": 8, "revision_to_hash": {"382": "e6989efd71a2adddd03979d1fe7a2e82e37ea51f", "395": "8ff9fc895bd6032636e3716f02773fdcd9cdd3d3", "587": "0e1faafec9871df73e875a0aadfcb67ec578c0e5", "590": "a40d325cec40da6cbcff8193a4ab4890823dfc76", "646": "ddc6d8f80dcf0a6cdd606efdefb89211a4dc7e9d", "745": "8a4bc2f03733e530591d6641f266a60670a373f1", "755": "8216797c4b1abca9dafd8de9d65472d32450b389", "811": "c7208c1a43335179ccddafc7748c1d7224e904fc", "813": "47890ac823314f1a9e2920dff7575850af56c273", "826": "b573fc0dcbfc2528807b5f0f8c0bc719c25d36f4", "998": "65d06f830ec6604b44d1a0510255868a8f762e3a", "1016": "9072aa593d76262fe445cf492ffac77e853501ea", "1131": "959e267898090e3c68ee118d5048afad124ff61d", "1207": "c83447b72c4f48ceb8249ea394ebf042618b8a2a", "1288": "f13dba15e3d56455c58867685ec554755a346c32", "1915": "d6b4444bbcc54a241cc955a5ceea80be15e7db2b", 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1693915368000, "33722": 1694012791000, "33733": 1694116492000, "33734": 1694366006000, "33756": 1694455478000, "33766": 1694542477000, "33802": 1694634394000, "33808": 1694689715000, "33813": 1694807049000, "33818": 1694874439000, "33820": 1694956547000, "33826": 1695059234000, "33833": 1695143438000, "34030": 1695204060000, "34031": 1695224738000, "34034": 1695308063000, "34046": 1695653805000, "34049": 1695740821000, "34052": 1695830869000, "34058": 1695899976000, "34065": 1696007297000, "34068": 1696024508000, "34075": 1696265713000, "34079": 1696357529000, "34113": 1697533319000, "34115": 1697561306000, "34120": 1697643038000, "34126": 1697716358000, "34139": 1697824316000, "34140": 1697879383000, "34141": 1697997241000, "34152": 1698055850000, "34155": 1698081283000, "34158": 1698155592000, "34160": 1698235049000, "34162": 1698326139000, "34164": 1698408361000, "34171": 1698662860000, "34177": 1698769375000, "34183": 1698872459000, "34188": 1698948475000, "34193": 1699045091000, "34195": 1699118804000, "34198": 1699282282000, "34203": 1699368806000, "34208": 1699457308000, "34211": 1699548326000, "34213": 1699610845000, "34214": 1699660269000, "34215": 1699792428000, "34221": 1699883326000, "34227": 1699986361000, "34231": 1700066735000, "34237": 1700145316000, "34244": 1700248028000, "34247": 1700327418000, "34256": 1700488628000, "34259": 1700588722000, "34264": 1700675885000, "34268": 1700771717000, "34273": 1700850422000, "34274": 1700921198000}, "params": {"arch": ["x86_64"], "cpu": ["Intel Core Processor (Haswell, no TSX)"], "machine": ["sklearn-benchmark"], "num_cpu": ["8"], "os": ["Linux 4.15.0-20-generic"], "ram": ["16424684"], "python": ["3.11", "3.8"], "numpy": ["", "1.25.2"], "scipy": ["", "1.11.2"], "cython": ["", "0.29.36", "3.0.2", "3.0.3"], "joblib": ["", "1.3.2"], "threadpoolctl": ["", "3.2.0"], "pandas": ["", "2.1.0", null], "branch": ["main"]}, "graph_param_list": [{"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.8", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "pandas": "", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.8", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "branch": "main", "pandas": null}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "", "scipy": "", "cython": "", "joblib": "", "threadpoolctl": "", "pandas": "", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.3", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "0.29.36", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}, {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "python": "3.11", "numpy": "1.25.2", "scipy": "1.11.2", "cython": "3.0.2", "joblib": "1.3.2", "threadpoolctl": "3.2.0", "pandas": "2.1.0", "branch": "main"}], "benchmarks": {"cluster.KMeansBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_fit", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "a9d893de2d92e56e4dbeab4d7b7b4d5e00add3f4e993b164664b7ebbdd036dc7"}, "cluster.KMeansBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_predict", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "0f7d945338d774baae43a82dff7fe9f0db16ff6d1481b5afd437048b544396fc"}, "cluster.KMeansBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.peakmem_transform", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "576d76cb6e3aa82315e3d3dc6a2e8b7e58cb38bdd510df6801ab645465f4eb70"}, "cluster.KMeansBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_fit", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "a8dbf56b4b6365cb1e3c23b71c7df95ae011632c95124496f8fd1c30430154ec", "warmup_time": 1}, "cluster.KMeansBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_predict", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "611589f0c08131355c37720106ea132a2653a100660cd34884455259325b9c83", "warmup_time": 1}, "cluster.KMeansBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.KMeansBenchmark.time_transform", "number": 0, "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "8a02da09ca430e84b11161256032a2d4c08df103e4626718278e8b69b90acb0c", "warmup_time": 1}, "cluster.KMeansBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.track_test_score", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "track", "unit": "unit", "version": "67ff12752edbc7e4055ec0bddc293f22d59aec0eaa675c166f502a008bc22c59"}, "cluster.KMeansBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.KMeansBenchmark.track_train_score", "param_names": ["representation", "algorithm", "init"], "params": [["'dense'", "'sparse'"], ["'lloyd'", "'elkan'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:16", "timeout": 500, "type": "track", "unit": "unit", "version": "a5e07140ef3dd16920358a4c08c2f9f81d0e21f40bf5d8990b8a37b6a07c64e9"}, "cluster.MiniBatchKMeansBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_fit", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "42f5ccf79079b60ff9fa8b0220a6b1fb951f46d27574d343f6e6b4b69dd8f1f0"}, "cluster.MiniBatchKMeansBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_predict", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "03ee5b059682250c4b3d0793edf5a4978efb06c760148550f068e82dcb71e75f"}, "cluster.MiniBatchKMeansBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.peakmem_transform", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "f844a68fb0292b177c459487189f905d1afb89d79ab35a0539f9f4217b445ff2"}, "cluster.MiniBatchKMeansBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_fit", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "40617ec6fdd94e3a650dba98f8204c9779e63ae7803343b23e7368f7b068f26e", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_predict", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "561b80fa4780c86ee698a0efd05c5d6c1bce24014006c5ec9096c2f3a960b9fe", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "cluster.MiniBatchKMeansBenchmark.time_transform", "number": 0, "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "cluster:65", "timeout": 500, "type": "time", "unit": "seconds", "version": "2cf7a764c9fa1511ff1e1dee921060dc33eb0608e1cee96b782dc7b5d3f91f89", "warmup_time": 1}, "cluster.MiniBatchKMeansBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.track_test_score", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "track", "unit": "unit", "version": "7aeb8a20f66080e18e15f92e245fd1dd8de235e2f53d355596424dc7c30ae295"}, "cluster.MiniBatchKMeansBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchKMeansBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "cluster.MiniBatchKMeansBenchmark.track_train_score", "param_names": ["representation", "init"], "params": [["'dense'", "'sparse'"], ["'random'", "'k-means++'"]], "setup_cache_key": "cluster:65", "timeout": 500, "type": "track", "unit": "unit", "version": "b0c324767051682ea0bfc17c991f3de50e4876d683b1167c11ae8d4f2349b9c9"}, "decomposition.DictionaryLearningBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.peakmem_fit", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "8c3205268eade479bfda221734bf6129ab2a7b2bf4c59a8e6e855469f519672a"}, "decomposition.DictionaryLearningBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.peakmem_transform", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "b2c047a9e4d9d9cc6dd2d41a3738c21c183facbf5d609e8bfb354499f0b38ee4"}, "decomposition.DictionaryLearningBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.DictionaryLearningBenchmark.time_fit", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:41", "timeout": 500, "type": "time", "unit": "seconds", "version": "9f6c61216a35b8b5a205dbce3e4f541613c2e1df4cda9f85b16a7a41a02a9e02", "warmup_time": 1}, "decomposition.DictionaryLearningBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.DictionaryLearningBenchmark.time_transform", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:41", "timeout": 500, "type": "time", "unit": "seconds", "version": "a9786d7edfa47d44fb49632ba092edb63068a33298968990f071b58ad312e45a", "warmup_time": 1}, "decomposition.DictionaryLearningBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.track_test_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "track", "unit": "unit", "version": "022cecb50e5ba330d0728d08afbca34a130f0e74529bce6c8c93aca80a4bcae5"}, "decomposition.DictionaryLearningBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass DictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.DictionaryLearningBenchmark.track_train_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:41", "timeout": 500, "type": "track", "unit": "unit", "version": "6ed8ae7b04c1c5b4be0a5847795321be4c50195958c72642e1ca9367d838649d"}, "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_fit", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "cf54cbec149e048235b388e434462754421fc5f8593697b431e17bd01f79cbe1"}, "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.peakmem_transform", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "c9e62449ea399590080c790df85e63df815e88ceaa029196f9ea925d3acfecd7"}, "decomposition.MiniBatchDictionaryLearningBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.MiniBatchDictionaryLearningBenchmark.time_fit", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:75", "timeout": 500, "type": "time", "unit": "seconds", "version": "48d290a4fd2b0a9ecdd1a7add3b7e7b231d8df63e32889758ec0bbd8c1cb37ef", "warmup_time": 1}, "decomposition.MiniBatchDictionaryLearningBenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.MiniBatchDictionaryLearningBenchmark.time_transform", "number": 0, "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:75", "timeout": 500, "type": "time", "unit": "seconds", "version": "3ac7bfb8060c67e6dd13c37f1901d3f4274b8b0edfaf3d6a575d4e20ecbaf45e", "warmup_time": 1}, "decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.track_test_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "track", "unit": "unit", "version": "a0774d6e8f922842e4b916076deab0ed00f152a84632f732cec174d5d619192f"}, "decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass MiniBatchDictionaryLearningBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.MiniBatchDictionaryLearningBenchmark.track_train_score", "param_names": ["fit_algorithm", "n_jobs"], "params": [["'lars'", "'cd'"], ["1", "4"]], "setup_cache_key": "decomposition:75", "timeout": 500, "type": "track", "unit": "unit", "version": "13436574eaa40a2dd7b58fbef029cd2171941b963cec765dff95a1889f6a157a"}, "decomposition.PCABenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.peakmem_fit", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "1dc803fc882e472d9bc70274b900eb03304281cd2dff33ec3e3c039fffbb74a4"}, "decomposition.PCABenchmark.peakmem_transform": {"code": "class Transformer:\n    def peakmem_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.peakmem_transform", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "2b31c7c4510aca17a679729f995ba0b7d6548de119b989f414876912bc7851ff"}, "decomposition.PCABenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.PCABenchmark.time_fit", "number": 0, "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "4a753451bb0c872008db51c421599cf8badfcb691aab39aaa7822dad561241dc", "warmup_time": 1}, "decomposition.PCABenchmark.time_transform": {"code": "class Transformer:\n    def time_transform(self, *args):\n        self.estimator.transform(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "decomposition.PCABenchmark.time_transform", "number": 0, "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "decomposition:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "1f73bed6685d54963e63f4de9713d472d0ab242d57c6a0cfa54fac7d1e1e83ef", "warmup_time": 1}, "decomposition.PCABenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.track_test_score", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "track", "unit": "unit", "version": "2bf9b85b93e81f43bf6e2e900a8ef13fb83b9b9298c32d0808bda654eb57c0c3"}, "decomposition.PCABenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass PCABenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "decomposition.PCABenchmark.track_train_score", "param_names": ["svd_solver"], "params": [["'full'", "'arpack'", "'randomized'"]], "setup_cache_key": "decomposition:16", "timeout": 500, "type": "track", "unit": "unit", "version": "16881c7fae4abaa9923123fc258e6542b0b3aa0c232d2db206548668bced23bb"}, "ensemble.GradientBoostingClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "0bed7eba4859779600e1b1cb03539724eddad1419ad1332e897185f93bd3d6df"}, "ensemble.GradientBoostingClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "5f65eed561196ec366996fc83d2258f891bbe9608480b00948108fa794a3c81e"}, "ensemble.GradientBoostingClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.GradientBoostingClassifierBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:64", "timeout": 500, "type": "time", "unit": "seconds", "version": "5702f67868717f5884d82ed22b8940f7ff2b626a2b46e2a4b6e1173c83db484f", "warmup_time": 1}, "ensemble.GradientBoostingClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.GradientBoostingClassifierBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:64", "timeout": 500, "type": "time", "unit": "seconds", "version": "17e3a4098ef2254174162a060b1286418984a4be5438aefd71301bf6a8f4c13d", "warmup_time": 1}, "ensemble.GradientBoostingClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "track", "unit": "unit", "version": "55092be1f965aa51752829bda24b944bdb1222a6b8e5fdf0d4026c9580953dea"}, "ensemble.GradientBoostingClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.GradientBoostingClassifierBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "ensemble:64", "timeout": 500, "type": "track", "unit": "unit", "version": "cd78924c15421dcfc8b45a7d26a8b035a1e441c45612ee6ae169b9155e5a631b"}, "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_fit", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "733ac3d039d12bcb9c7f00c745fad3c3723c29f21f67e444aabac276cbc3a916"}, "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.peakmem_predict", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "ef8fbc52879ef0add38e7cec975a52bd207c7003e688e265dcc4839b0bcaa3a8"}, "ensemble.HistGradientBoostingClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.HistGradientBoostingClassifierBenchmark.time_fit", "number": 0, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:103", "timeout": 500, "type": "time", "unit": "seconds", "version": "6840b9adeccbde7fc736aae2aa37f9b118d3fa9a78f2e0b2f0a4f09503e1a48d", "warmup_time": 1}, "ensemble.HistGradientBoostingClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.HistGradientBoostingClassifierBenchmark.time_predict", "number": 0, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:103", "timeout": 500, "type": "time", "unit": "seconds", "version": "ce65e5d6c62eebe1e73935285f56bc6c7ecebde97fb73fb4b9370c9b5979288a", "warmup_time": 1}, "ensemble.HistGradientBoostingClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.track_test_score", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "track", "unit": "unit", "version": "8c9a915f4dd669a61ceb38728602e43f84a976ebb06628f8026837eb7a5d30f6"}, "ensemble.HistGradientBoostingClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass HistGradientBoostingClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.HistGradientBoostingClassifierBenchmark.track_train_score", "param_names": [], "params": [], "setup_cache_key": "ensemble:103", "timeout": 500, "type": "track", "unit": "unit", "version": "43f6c225874e5193533ad56bdd398f471c7caa0f0f8c9a8ebb5486e10a2bec7f"}, "ensemble.RandomForestClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.peakmem_fit", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "14ca8ab81b54b4f2775c94fd794a13b049f48b9927979b964b90acc22ebf08a8"}, "ensemble.RandomForestClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.peakmem_predict", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "3febfd0d8977731921e7d366e6a585c8e405c0520f9a94415b96c6c334ed1c2f"}, "ensemble.RandomForestClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.RandomForestClassifierBenchmark.time_fit", "number": 0, "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:24", "timeout": 500, "type": "time", "unit": "seconds", "version": "7f77f2ea22b57af3ff27a86843c654c08e6aa926896f2c421666fd31d95ce65f", "warmup_time": 1}, "ensemble.RandomForestClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "ensemble.RandomForestClassifierBenchmark.time_predict", "number": 0, "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "ensemble:24", "timeout": 500, "type": "time", "unit": "seconds", "version": "6a93ee4dfb1d21e994eb8d7568e75ee99e0f416c9711bb2b45d1c022a27f0746", "warmup_time": 1}, "ensemble.RandomForestClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.track_test_score", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "track", "unit": "unit", "version": "a3b01d5f39647a3a6d6bfac31a005a5fdc82ad531a1b71f249ae2f4e98dc44c9"}, "ensemble.RandomForestClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RandomForestClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "ensemble.RandomForestClassifierBenchmark.track_train_score", "param_names": ["representation", "n_jobs"], "params": [["'dense'", "'sparse'"], ["1", "4"]], "setup_cache_key": "ensemble:24", "timeout": 500, "type": "track", "unit": "unit", "version": "42daa18fec647c7415a78ff56b84cf07b2253222c89f24baf5f12238463b9400"}, "linear_model.ElasticNetBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.peakmem_fit", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "aeb53df9e9543a762cf297b5697780eafb3cfbf2a7e750ad0f079ea50ff3d051"}, "linear_model.ElasticNetBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.peakmem_predict", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "27f5ef6022a3a4cdd5a0854b9428349a5e461bf638a76337760251639c9d7f02"}, "linear_model.ElasticNetBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.ElasticNetBenchmark.time_fit", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:187", "timeout": 500, "type": "time", "unit": "seconds", "version": "02947c3221cd53cf645dab6e61c787646637d66474795a722ec537ff85976c8c", "warmup_time": 1}, "linear_model.ElasticNetBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.ElasticNetBenchmark.time_predict", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:187", "timeout": 500, "type": "time", "unit": "seconds", "version": "4140a00f540d4dea5268a3813730d6fbb270e40b74285dbf046b3e26069f1287", "warmup_time": 1}, "linear_model.ElasticNetBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.track_test_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "track", "unit": "unit", "version": "1c0c2170cd23de65b973498cfc8dff584d9983a7513d2ead27d49594b092a280"}, "linear_model.ElasticNetBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass ElasticNetBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.ElasticNetBenchmark.track_train_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:187", "timeout": 500, "type": "track", "unit": "unit", "version": "6cff911c1163f327c86117f3dc306d62cdba87d9ea8068cc68977b61d151da78"}, "linear_model.LassoBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.peakmem_fit", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "8fa5824a898ff683065f60683a1805ae55dfdde209f22eb520e3dddac3b02688"}, "linear_model.LassoBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.peakmem_predict", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "2fd8dcd052ba7edddc85213c847b8fd149f36b0b9c0b058b88adae232e1fe0c6"}, "linear_model.LassoBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LassoBenchmark.time_fit", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:228", "timeout": 500, "type": "time", "unit": "seconds", "version": "d0f2ce5dce5564f0fc096b1e4ee47c583962a62baf6a13edf150b4edaf353e05", "warmup_time": 1}, "linear_model.LassoBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LassoBenchmark.time_predict", "number": 0, "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:228", "timeout": 500, "type": "time", "unit": "seconds", "version": "9adfa14dab87a3a8a4de49d179ab5891f08045e1b81194ae39643622cb6487c9", "warmup_time": 1}, "linear_model.LassoBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.track_test_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "track", "unit": "unit", "version": "b56564a7b1756eeff8bbe981fa28bfb58f992c09069c2672ae87e637ba91f565"}, "linear_model.LassoBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LassoBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LassoBenchmark.track_train_score", "param_names": ["representation", "precompute"], "params": [["'dense'", "'sparse'"], ["True", "False"]], "setup_cache_key": "linear_model:228", "timeout": 500, "type": "track", "unit": "unit", "version": "97c0e5ecdddd2ee7c31046ec01b6d29119975ffd6774712a778a26f8499350c4"}, "linear_model.LinearRegressionBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "a905fac8878f96b0ccc05296ca1cbb10654937aa04579453c79a4255ec7865f1"}, "linear_model.LinearRegressionBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "759535a8913b60467ef240fed4181399ff149efa33bea0eb69eed231df3f8876"}, "linear_model.LinearRegressionBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LinearRegressionBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:119", "timeout": 500, "type": "time", "unit": "seconds", "version": "e5dbbebbcad57689afbefadad2fb87cb71161a32a9d6caa162975a5b419f0f97", "warmup_time": 1}, "linear_model.LinearRegressionBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LinearRegressionBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:119", "timeout": 500, "type": "time", "unit": "seconds", "version": "ba279c28dffebbb04b7b173dee9e976b48b05ffbada322dd53d9a2de22ff70f6", "warmup_time": 1}, "linear_model.LinearRegressionBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "track", "unit": "unit", "version": "0641fe07389de5e5b9811f51f186e4a4a96a856eb50c8810c62734a593513594"}, "linear_model.LinearRegressionBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LinearRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LinearRegressionBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:119", "timeout": 500, "type": "track", "unit": "unit", "version": "5853e8435e7eba58f2264852c35444debdedaa05b29cccd9fef895ce848a5f06"}, "linear_model.LogisticRegressionBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.peakmem_fit", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "3b211361ab927e53d5697c6b9e81013a7575a9ce103d21adeb68544ea7a4e5dc"}, "linear_model.LogisticRegressionBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.peakmem_predict", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "be6101aa74d4f94ec846334788b859094ed4b1d36ca92bf7d693b3043a8d39da"}, "linear_model.LogisticRegressionBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LogisticRegressionBenchmark.time_fit", "number": 0, "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:28", "timeout": 500, "type": "time", "unit": "seconds", "version": "7cd42b119dd9bf6c8bf33ee70890f7ebaf0b159e0c0c8cd5b299c3f632725f21", "warmup_time": 1}, "linear_model.LogisticRegressionBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.LogisticRegressionBenchmark.time_predict", "number": 0, "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:28", "timeout": 500, "type": "time", "unit": "seconds", "version": "b9dd34b6a7b894f6afbd6e27f4d0cfe829c1b0e4af4f7203e56269b873ee049b", "warmup_time": 1}, "linear_model.LogisticRegressionBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.track_test_score", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "track", "unit": "unit", "version": "37e537f82f54da1d89003018c8370da6ac290d672b6343a457ee49dd444e3c9f"}, "linear_model.LogisticRegressionBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass LogisticRegressionBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.LogisticRegressionBenchmark.track_train_score", "param_names": ["representation", "solver", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'lbfgs'", "'saga'"], ["1", "4"]], "setup_cache_key": "linear_model:28", "timeout": 500, "type": "track", "unit": "unit", "version": "5647f2df8db098461a973d60cb900515f403287ed3e109a99151c94309e568f1"}, "linear_model.RidgeBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.peakmem_fit", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "d06a93be5cf39a0df663d696943713376c4ba4abb2568ebc0bc97fd2a2fd5173"}, "linear_model.RidgeBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.peakmem_predict", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "24e4dc55ce8e6b9311d26ca2936364cad3ddb0b8827b656a81886db508e69866"}, "linear_model.RidgeBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.RidgeBenchmark.time_fit", "number": 0, "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:78", "timeout": 500, "type": "time", "unit": "seconds", "version": "6ebbadd5cfeeb543e69ca73d777ab88bff49391965e0c0e3270868f1fa6bb385", "warmup_time": 1}, "linear_model.RidgeBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.RidgeBenchmark.time_predict", "number": 0, "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:78", "timeout": 500, "type": "time", "unit": "seconds", "version": "f41e9813a5f8239fbb0ef5e81982549e297ff18f751b7e4b42982972851b7cd0", "warmup_time": 1}, "linear_model.RidgeBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.track_test_score", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "track", "unit": "unit", "version": "663c821271fbdb18d0252832535191137787f37ad6d6397624c92edd96dc6f3f"}, "linear_model.RidgeBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass RidgeBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.RidgeBenchmark.track_train_score", "param_names": ["representation", "solver"], "params": [["'dense'", "'sparse'"], ["'auto'", "'svd'", "'cholesky'", "'lsqr'", "'sparse_cg'", "'sag'", "'saga'"]], "setup_cache_key": "linear_model:78", "timeout": 500, "type": "track", "unit": "unit", "version": "92bfe13d8da10b255d1c72982a3a88f70985b864b39584e51a184ea88116f35d"}, "linear_model.SGDRegressorBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.peakmem_fit", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "d81ea5fe5efd3e1ec085f0a2c66d712cfec7e2c4e03b19b7a50ab79dfd661f77"}, "linear_model.SGDRegressorBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.peakmem_predict", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "cf12939ffeb2f97f86ada7e454920a994d254a98a02eac5e34ea687f8ec11ad0"}, "linear_model.SGDRegressorBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.SGDRegressorBenchmark.time_fit", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:151", "timeout": 500, "type": "time", "unit": "seconds", "version": "cc477ee1fcfc2e853f5113072b49c7dc80acf8f2e19f83518368e1efe8dd5374", "warmup_time": 1}, "linear_model.SGDRegressorBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "linear_model.SGDRegressorBenchmark.time_predict", "number": 0, "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "linear_model:151", "timeout": 500, "type": "time", "unit": "seconds", "version": "fcd01e3604428cf2063cbbfe4e708974618533d55ec0784a787b8bb838a2ece9", "warmup_time": 1}, "linear_model.SGDRegressorBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.track_test_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "track", "unit": "unit", "version": "5c184c0b957ec177b911999a0cc2cd3996407b726efe5d7adeaeddea222a16d0"}, "linear_model.SGDRegressorBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SGDRegressorBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "linear_model.SGDRegressorBenchmark.track_train_score", "param_names": ["representation"], "params": [["'dense'", "'sparse'"]], "setup_cache_key": "linear_model:151", "timeout": 500, "type": "track", "unit": "unit", "version": "84040f29c546fcb4fb3fed1db35c198a88607a920f0d1aad43ae542738470257"}, "manifold.TSNEBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.peakmem_fit", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "5b07ad9c3bf4af00ff4022361ced135ee0591fe9a613dddb484f0142685497c4"}, "manifold.TSNEBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "manifold.TSNEBenchmark.time_fit", "number": 0, "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "manifold:15", "timeout": 500, "type": "time", "unit": "seconds", "version": "d7906ea0d1e8bee6103afdb924f0c5471c1c3e392feb7dbcc411b9e3f3535e6a", "warmup_time": 1}, "manifold.TSNEBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.track_test_score", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "track", "unit": "unit", "version": "7c865f9e53f16c8492cb36db3b656646d6585227ebfbf097102fda1e83f5e953"}, "manifold.TSNEBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass TSNEBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "manifold.TSNEBenchmark.track_train_score", "param_names": ["method"], "params": [["'exact'", "'barnes_hut'"]], "setup_cache_key": "manifold:15", "timeout": 500, "type": "track", "unit": "unit", "version": "92a5fc587f972e63dad98657097b1bcd64a743dfce5f07b3e1847ed8b720d46d"}, "metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances": {"code": "class PairwiseDistancesBenchmark:\n    def peakmem_pairwise_distances(self, *args):\n        pairwise_distances(self.X, **self.pdist_params)\n\n    def setup(self, *params):\n        representation, metric, n_jobs = params\n    \n        if representation == \"sparse\" and metric == \"correlation\":\n            raise NotImplementedError\n    \n        if Benchmark.data_size == \"large\":\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 8000\n            else:\n                n_samples = 24000\n        else:\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 4000\n            else:\n                n_samples = 12000\n    \n        data = _random_dataset(n_samples=n_samples, representation=representation)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.pdist_params = {\"metric\": metric, \"n_jobs\": n_jobs}", "name": "metrics.PairwiseDistancesBenchmark.peakmem_pairwise_distances", "param_names": ["representation", "metric", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'cosine'", "'euclidean'", "'manhattan'", "'correlation'"], ["1", "4"]], "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "f982061fc53163362f863b2455a1f2f660861c4a4357be712dcacab0682833db"}, "metrics.PairwiseDistancesBenchmark.time_pairwise_distances": {"code": "class PairwiseDistancesBenchmark:\n    def time_pairwise_distances(self, *args):\n        pairwise_distances(self.X, **self.pdist_params)\n\n    def setup(self, *params):\n        representation, metric, n_jobs = params\n    \n        if representation == \"sparse\" and metric == \"correlation\":\n            raise NotImplementedError\n    \n        if Benchmark.data_size == \"large\":\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 8000\n            else:\n                n_samples = 24000\n        else:\n            if metric in (\"manhattan\", \"correlation\"):\n                n_samples = 4000\n            else:\n                n_samples = 12000\n    \n        data = _random_dataset(n_samples=n_samples, representation=representation)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.pdist_params = {\"metric\": metric, \"n_jobs\": n_jobs}", "min_run_count": 2, "name": "metrics.PairwiseDistancesBenchmark.time_pairwise_distances", "number": 0, "param_names": ["representation", "metric", "n_jobs"], "params": [["'dense'", "'sparse'"], ["'cosine'", "'euclidean'", "'manhattan'", "'correlation'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "timeout": 500, "type": "time", "unit": "seconds", "version": "968c4646f4f823e76e062ccbbe50249793691ae13e472124d1ebd2642c86b5e0", "warmup_time": 1}, "model_selection.CrossValidationBenchmark.peakmem_crossval": {"code": "class CrossValidationBenchmark:\n    def peakmem_crossval(self, *args):\n        cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "name": "model_selection.CrossValidationBenchmark.peakmem_crossval", "param_names": ["n_jobs"], "params": [["1", "4"]], "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "4b6fc20c55d0bbbf2b9b56117a6725d0351eec991f183d04b136f1ce4552e0c7"}, "model_selection.CrossValidationBenchmark.time_crossval": {"code": "class CrossValidationBenchmark:\n    def time_crossval(self, *args):\n        cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "min_run_count": 2, "name": "model_selection.CrossValidationBenchmark.time_crossval", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "timeout": 20000, "type": "time", "unit": "seconds", "version": "9959454181a0ddb22a0ce5727c352955d47bf0ce97d2189ff73d567981c64587", "warmup_time": 1}, "model_selection.CrossValidationBenchmark.track_crossval": {"code": "class CrossValidationBenchmark:\n    def track_crossval(self, *args):\n        return float(cross_val_score(self.clf, self.X, self.y, **self.cv_params).mean())\n\n    def setup(self, *params):\n        (n_jobs,) = params\n    \n        data = _synth_classification_dataset(n_samples=50000, n_features=100)\n        self.X, self.X_val, self.y, self.y_val = data\n    \n        self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n    \n        cv = 16 if Benchmark.data_size == \"large\" else 4\n    \n        self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}", "name": "model_selection.CrossValidationBenchmark.track_crossval", "param_names": ["n_jobs"], "params": [["1", "4"]], "timeout": 20000, "type": "track", "unit": "unit", "version": "5e7e6b0e0116bbc68953414ce4e0eec203cc51c80a71d897dd599172b82aeedd"}, "model_selection.GridSearchBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.peakmem_fit", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "95b48fd99a9327cc7b96fbaf3fb68de0488e277098c5f71aa31f5bcc0af0aa35"}, "model_selection.GridSearchBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.peakmem_predict", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "peakmemory", "unit": "bytes", "version": "c892cd859a96073eba53d60e549a73d8202c9d0e97f5469f5e0333d949ce8dce"}, "model_selection.GridSearchBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "model_selection.GridSearchBenchmark.time_fit", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "time", "unit": "seconds", "version": "ce048ba169bd01fc98abe0f363fcaafc41dfc6d88ae4658795602e37fd0bd003", "warmup_time": 1}, "model_selection.GridSearchBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "model_selection.GridSearchBenchmark.time_predict", "number": 0, "param_names": ["n_jobs"], "params": [["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "time", "unit": "seconds", "version": "acfeae20f02fb4775c69f3a23452a495e43a2abb0e684954a33824913f37e3eb", "warmup_time": 1}, "model_selection.GridSearchBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.track_test_score", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "track", "unit": "unit", "version": "ec63718f94ff0beda5889deafa3b1f1f3ff7750bf6254fce002cadda537bee4b"}, "model_selection.GridSearchBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass GridSearchBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "model_selection.GridSearchBenchmark.track_train_score", "param_names": ["n_jobs"], "params": [["1", "4"]], "setup_cache_key": "model_selection:51", "timeout": 20000, "type": "track", "unit": "unit", "version": "51575cdf87b95c5096592cecfc1f386cff2b52230b19a7bd6b1afee2fb55910c"}, "neighbors.KNeighborsClassifierBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.peakmem_fit", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "66acb7f2ea52ea49f9a5239c437782b0ac7cd030f150166dff686727e06ae1f5"}, "neighbors.KNeighborsClassifierBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.peakmem_predict", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "e7fb64f5f61911736101d3ca625806937a6bb4fc75b64a8ed0c9be1f37e0f10f"}, "neighbors.KNeighborsClassifierBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "neighbors.KNeighborsClassifierBenchmark.time_fit", "number": 0, "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "neighbors:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "455e6017e69db76a36673f135b8300f5288867804ae588240ef238340de7bc82", "warmup_time": 1}, "neighbors.KNeighborsClassifierBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "neighbors.KNeighborsClassifierBenchmark.time_predict", "number": 0, "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "neighbors:16", "timeout": 500, "type": "time", "unit": "seconds", "version": "6611e4000bd5b1d8935eb7407c10355bf476dfef925640b9a8445912b72c8183", "warmup_time": 1}, "neighbors.KNeighborsClassifierBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.track_test_score", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "track", "unit": "unit", "version": "95d25638754fb5c021b379d6f4f51868a7303bedb4868e52cac2a036eb0bc248"}, "neighbors.KNeighborsClassifierBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass KNeighborsClassifierBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "neighbors.KNeighborsClassifierBenchmark.track_train_score", "param_names": ["algorithm", "dimension", "n_jobs"], "params": [["'brute'", "'kd_tree'", "'ball_tree'"], ["'low'", "'high'"], ["1", "4"]], "setup_cache_key": "neighbors:16", "timeout": 500, "type": "track", "unit": "unit", "version": "1e1b6891a1563cf0c8ae446c751cfd6dec48cc2fcc4d0157f30a7189f05e938a"}, "svm.SVCBenchmark.peakmem_fit": {"code": "class Estimator:\n    def peakmem_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.peakmem_fit", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "b5099e172031e9a0f7b7706ec386c5bf36f9bd427f7d47274c66c6c39f5bf730"}, "svm.SVCBenchmark.peakmem_predict": {"code": "class Predictor:\n    def peakmem_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.peakmem_predict", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "peakmemory", "unit": "bytes", "version": "4a6611919a2e5a6cbd1f6c211b23857b3912da5e1484da897bf70155d62b7980"}, "svm.SVCBenchmark.time_fit": {"code": "class Estimator:\n    def time_fit(self, *args):\n        self.estimator.fit(self.X, self.y)\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "svm.SVCBenchmark.time_fit", "number": 0, "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "svm:14", "timeout": 500, "type": "time", "unit": "seconds", "version": "60f69bb46f97b00f249209ee47574d1508b9e81a8ba3ec0a8a3a430eb6aa719d", "warmup_time": 1}, "svm.SVCBenchmark.time_predict": {"code": "class Predictor:\n    def time_predict(self, *args):\n        self.estimator.predict(self.X)\n\nclass Estimator:\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "min_run_count": 2, "name": "svm.SVCBenchmark.time_predict", "number": 0, "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "svm:14", "timeout": 500, "type": "time", "unit": "seconds", "version": "ab64f47e62b03803ca9ee0734d6a0734af8278b39121b069d9abdf86a8e79285", "warmup_time": 1}, "svm.SVCBenchmark.track_test_score": {"code": "class Estimator:\n    def track_test_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_val_pred = self.estimator.predict(self.X_val)\n        else:\n            y_val_pred = None\n        return float(self.test_scorer(self.y_val, y_val_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.track_test_score", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "track", "unit": "unit", "version": "f04be6a642a0b71d7bb27a3b184c53cf61aa01dead2ec4bbc68bf22ea78b68b3"}, "svm.SVCBenchmark.track_train_score": {"code": "class Estimator:\n    def track_train_score(self, *args):\n        if hasattr(self.estimator, \"predict\"):\n            y_pred = self.estimator.predict(self.X)\n        else:\n            y_pred = None\n        return float(self.train_scorer(self.y, y_pred))\n\n    def setup(self, *params):\n        \"\"\"Generate dataset and load the fitted estimator\"\"\"\n        # This is run once per combination of parameters and per repeat so we\n        # need to avoid doing expensive operations there.\n    \n        if self.skip(params):\n            raise NotImplementedError\n    \n        self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n    \n        est_path = get_estimator_path(\n            self, Benchmark.save_dir, params, Benchmark.save_estimators\n        )\n        with est_path.open(mode=\"rb\") as f:\n            self.estimator = pickle.load(f)\n    \n        self.make_scorers()\n\nclass SVCBenchmark:\n    def setup_cache(self):\n        super().setup_cache()", "name": "svm.SVCBenchmark.track_train_score", "param_names": ["kernel"], "params": [["'linear'", "'poly'", "'rbf'", "'sigmoid'"]], "setup_cache_key": "svm:14", "timeout": 500, "type": "track", "unit": "unit", "version": "30dfcaa0d1c48f402d28bf241f4c7c013ac533fbb528d470c427a34c237993f7"}}, "machines": {"sklearn-benchmark": {"arch": "x86_64", "cpu": "Intel Core Processor (Haswell, no TSX)", "machine": "sklearn-benchmark", "num_cpu": "8", "os": "Linux 4.15.0-20-generic", "ram": "16424684", "version": 1}}, "tags": {"0.10": 7904, "0.10-branching": 7872, "0.11": 9357, "0.11-beta": 9331, "0.11-branching": 9349, "0.12": 10436, "0.12-branching": 10413, "0.12.1": 10778, "0.13": 12373, "0.13-branching": 12368, "0.13.1": 12748, "0.14.1": 14725, "0.14a1": 14515, "0.15-branching": 17074, "0.15.0b1": 16940, "0.15.1": 17645, "0.16-branching": 19198, "0.16.0": 19375, "0.16.1": 19504, "0.16b1": 19199, "0.17-branching": 20502, "0.17.1": 21601, "0.17.1-1": 21602, "0.17b1": 20509, "0.18": 22393, "0.18.1": 22702, "0.18.2": 23197, "0.18rc": 22265, "0.18rc1": 22268, "0.18rc2": 22283, "0.19-branching": 23280, "0.19.1": 23768, "0.19.2": 24417, "0.20.0": 24809, "0.20.1": 25159, "0.20.2": 25261, "0.20.3": 25563, "0.20.4": 26284, "0.20rc1": 24644, "0.21.0": 25841, "0.21.1": 25874, "0.21.2": 25910, "0.21.3": 26278, "0.21b2": 25766, "0.21rc1": 25758, "0.21rc2": 25767, "0.22.1": 27063, "0.3": 745, "0.5": 1969, "0.5.rc": 1915, "0.5.rc2": 1917, "0.5.rc3": 1919, "0.6-rc": 2701, "0.6.0": 2711, "0.7": 3151, "0.7-branching": 3102, "0.7.1": 3212, "0.8": 4037, "0.8-branching": 3905, "0.8.1": 4684, "0.9": 6225, "0.9-branching": 6177, "1.0.1": 29997, "1.1.1": 31297, "1.3.1": 34030, "1.3.2": 34152, "sprint01": 1207, "0.2": 590, "0.1": 395, "0.2-beta": 587, "0.1-beta": 382, "debian/0.2+svn625-1": 646, "debian/0.3-1": 755, "debian/0.4-3": 1288, "debian/0.3-2": 811, "debian/0.3-3": 813, "debian/0.4-2": 1131, "debian/0.4-1": 1016, "debian/0.3-4": 826, "0.4": 998, "debian/0.5-1": 1977, "debian/0.6.0.dfsg-1": 2743, "debian/0.7.1.dfsg-1": 3289, "debian/0.7.1.dfsg-3": 3741, "debian/0.8.0.dfsg-1": 4054, "debian/0.8.1.dfsg-1": 4696, "debian/0.9.0.dfsg-1": 6511, "debian/0.10.0-1": 7919, "debian/0.11.0-1": 9369, "debian/0.11.0-2": 9799, "debian/0.12.0-1": 10457, "0.14": 14698, "0.15.0b2": 17075, "0.15.0": 17279, "0.15.2": 17934, "debian/0.16.1-2": 19920, "debian/0.17.0_b1-1": 20952, "debian/0.17.0_b1+git14-g4e6829c-1": 20955, "0.17": 21113, "debian/0.17.0-1": 21126, "debian/0.17.0-3": 21322, "debian/0.17.0-4": 21323, "0.19b1": 23283, "0.19b2": 23307, "0.19.0": 23480, "0.22rc1": 26820, "0.22rc2": 26881, "0.22rc2.post1": 26890, "0.22rc3": 26944, "0.22": 26953, "0.22.2": 27300, "0.22.2.post1": 27325, "0.23.0rc1": 27571, "0.23.0": 27604, "0.23.1": 27674, "0.23.2": 28086, "0.24.0rc1": 28501, "0.24.0": 28541, "0.24.1": 28625, "0.24.2": 29084, "1.0.rc1": 29586, "1.0.rc2": 29636, "1.0": 29733, "1.0.2": 30500, "1.1.0rc1": 31120, "1.1.0": 31184, "1.1.2": 31927, "1.1.3": 32291, "1.2.0rc1": 32454, "1.2.0": 32499, "1.2.1": 32770, "1.2.2": 32984, "1.3.0rc1": 33376, "1.3.0": 33504}, "pages": [["", "Grid view", "Display as a agrid"], ["summarylist", "List view", "Display as a list"], ["regressions", "Show regressions", "Display information about recent regressions"]]}
\ No newline at end of file
diff --git a/info.json b/info.json
index d231c756f4..0085abb28a 100644
--- a/info.json
+++ b/info.json
@@ -1,4 +1,4 @@
 {
     "asv-version": "0.6.1",
-    "timestamp": 1700869775614
+    "timestamp": 1700956026554
 }
\ No newline at end of file
diff --git a/regressions.json b/regressions.json
index 303e98870b..e1efa1e5a7 100644
--- a/regressions.json
+++ b/regressions.json
@@ -1 +1 @@
-{"regressions": [["linear_model.LassoBenchmark.time_fit('dense', True)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython/joblib/machine-sklearn-benchmark/num_cpu-8/numpy/os-Linux 4.15.0-20-generic/pandas/python-3.8/ram-16424684/scipy/threadpoolctl/linear_model.LassoBenchmark.time_fit.json", {"python": "3.8", "cython": ""}, 0, 1.4256798984999932, 1.2750800849998996, [[32651, 32838, 1.2750800849998996, 1.4256798984999932]]], ["linear_model.LassoBenchmark.time_fit('dense', False)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython/joblib/machine-sklearn-benchmark/num_cpu-8/numpy/os-Linux 4.15.0-20-generic/pandas/python-3.8/ram-16424684/scipy/threadpoolctl/linear_model.LassoBenchmark.time_fit.json", {"python": "3.8", "cython": ""}, 1, 1.6213949935001892, 1.4736898109999856, [[32651, 32838, 1.4736898109999856, 1.6213949935001892]]], ["model_selection.GridSearchBenchmark.peakmem_predict(1)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython/joblib/machine-sklearn-benchmark/num_cpu-8/numpy/os-Linux 4.15.0-20-generic/pandas/python-3.8/ram-16424684/scipy/threadpoolctl/model_selection.GridSearchBenchmark.peakmem_predict.json", {"python": "3.8", "cython": ""}, 0, 98850816.0, 91230208.0, [[32294, 32303, 94040064.0, 97390592.0]]], ["model_selection.GridSearchBenchmark.peakmem_predict(4)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython/joblib/machine-sklearn-benchmark/num_cpu-8/numpy/os-Linux 4.15.0-20-generic/pandas/python-3.8/ram-16424684/scipy/threadpoolctl/model_selection.GridSearchBenchmark.peakmem_predict.json", {"python": "3.8", "cython": ""}, 1, 98838528.0, 91246592.0, [[32294, 32303, 94040064.0, 97390592.0]]], ["cluster.KMeansBenchmark.track_test_score('dense', 'lloyd', 'random')", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no 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"graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.2/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/ensemble.GradientBoostingClassifierBenchmark.time_fit.json", {"python": "3.11", "cython": "3.0.2"}, 1, 2.462212150500136, 2.108637685500298, [[34046, 34049, 2.108637685500298, 2.462212150500136]]], ["neighbors.KNeighborsClassifierBenchmark.time_predict('kd_tree', 'high', 4)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.2/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/neighbors.KNeighborsClassifierBenchmark.time_predict.json", {"python": "3.11", "cython": "3.0.2"}, 7, 123.64697125900057, 117.0689821349988, [[34058, 34065, 117.0689821349988, 123.64697125900057]]], 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18.536215199000253, 21.906948195999576]]], ["metrics.PairwiseDistancesBenchmark.time_pairwise_distances('sparse', 'euclidean', 4)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.2/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/metrics.PairwiseDistancesBenchmark.time_pairwise_distances.json", {"python": "3.11", "cython": "3.0.2"}, 11, 2.0897179319995303, 1.865396010999575, [[34031, 34034, 1.865396010999575, 2.0897179319995303]]], ["linear_model.LassoBenchmark.time_fit('sparse', True)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.2/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LassoBenchmark.time_fit.json", {"python": "3.11", "cython": "3.0.2"}, 2, 2.7477894640005616, 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"3.0.2"}, 3, -3.0780560970306396, -3.0780563354492188, [[34031, 34034, -3.0780563354492188, -3.0780563354492188]]], ["linear_model.LogisticRegressionBenchmark.time_predict('sparse', 'saga', 4)", "graphs/arch-x86_64/branch-main/cpu-Intel Core Processor (Haswell, no TSX)/cython-3.0.2/joblib-1.3.2/machine-sklearn-benchmark/num_cpu-8/numpy-1.25.2/os-Linux 4.15.0-20-generic/pandas-2.1.0/python-3.11/ram-16424684/scipy-1.11.2/threadpoolctl-3.2.0/linear_model.LogisticRegressionBenchmark.time_predict.json", {"python": "3.11", "cython": "3.0.2"}, 7, 0.006289843499871495, 0.00497264433336871, [[34065, 34068, 0.00497264433336871, 0.006289843499871495]]]]}
\ No newline at end of file
diff --git a/regressions.xml b/regressions.xml
index 5b786765a0..9049717311 100644
--- a/regressions.xml
+++ b/regressions.xml
@@ -1,23 +1,29 @@
 <?xml version='1.0' encoding='utf-8'?>
-<feed xmlns="http://www.w3.org/2005/Atom"><id>tag:scikit-learn.asv,1970-01-01:/c56a199a51c70d74ba80d9fe43aea5de364a127d27912e716301f6ed0100464b</id><author><name>Airspeed Velocity</name></author><title xml:lang="en">scikit-learn performance regressions</title><updated>2023-11-20T22:19:34Z</updated><entry><id>tag:scikit-learn.asv,2023-11-20:/4374488d7ef4063be31bce0a7a9c0ad3fe086bc7a35c842cc21c2ab52f41632c</id><title xml:lang="en">35.68% linear_model.LogisticRegressionBenchmark.time_predict('sparse', 'saga', 1)</title><updated>2023-11-20T22:19:34Z</updated><link href="index.html#linear_model.LogisticRegressionBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;p-n_jobs=1&amp;commits=5c4288dba42cb67d954cb56c2cebfbf25c05ef89-78249441d89a74ad3bee053bb6fce267d8b0393e" /><content xml:lang="en" type="html">&lt;a href="index.html#linear_model.LogisticRegressionBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;p-n_jobs=1&amp;commits=5c4288dba42cb67d954cb56c2cebfbf25c05ef89-78249441d89a74ad3bee053bb6fce267d8b0393e"&gt;35.68% regression&lt;/a&gt; on 2023-11-20 13:57:08 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/5c4288dba42cb67d954cb56c2cebfbf25c05ef89...78249441d89a74ad3bee053bb6fce267d8b0393e"&gt;5c4288db...78249441&lt;/a&gt;.&lt;br&gt;
-                New value: 6.36ms, old value: 4.69ms.&lt;br&gt;
-                Latest value: 6.36ms (35.68% worse
-                than best value 4.69ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-18:/947365df4c7eb728e5f0f65b38286e92205cf35771e168610c08910cca4fea94</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_test_score('dense', 'elkan', 'random')</title><updated>2023-11-19T21:32:30Z</updated><link href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1-5c4288dba42cb67d954cb56c2cebfbf25c05ef89" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1-5c4288dba42cb67d954cb56c2cebfbf25c05ef89"&gt;-0.00% regression&lt;/a&gt; on 2023-11-18 17:10:18 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1...5c4288dba42cb67d954cb56c2cebfbf25c05ef89"&gt;4cf13d2b...5c4288db&lt;/a&gt;.&lt;br&gt;
+<feed xmlns="http://www.w3.org/2005/Atom"><id>tag:scikit-learn.asv,1970-01-01:/c56a199a51c70d74ba80d9fe43aea5de364a127d27912e716301f6ed0100464b</id><author><name>Airspeed Velocity</name></author><title xml:lang="en">scikit-learn performance regressions</title><updated>2023-11-23T21:30:59Z</updated><entry><id>tag:scikit-learn.asv,2023-11-23:/b10ec20f23a0dc2ef7f2a97d413d2b54cefa107aa5f5f8e9af35f702b38349aa</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_test_score('dense', 'lloyd', 'random')</title><updated>2023-11-23T21:30:59Z</updated><link href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27random%27&amp;commits=e84a4c2935e9531c574e61cfce840e53866f6546-0816e0012ce6446f28ffbb5430e4afad2fa44125" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27random%27&amp;commits=e84a4c2935e9531c574e61cfce840e53866f6546-0816e0012ce6446f28ffbb5430e4afad2fa44125"&gt;-0.00% regression&lt;/a&gt; on 2023-11-23 20:35:17 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/e84a4c2935e9531c574e61cfce840e53866f6546...0816e0012ce6446f28ffbb5430e4afad2fa44125"&gt;e84a4c29...0816e001&lt;/a&gt;.&lt;br&gt;
                 New value: -4.109886169433594, old value: -4.109886169433594.&lt;br&gt;
-                Latest value: -4.109885215759277 (-0.00% worse
-                than best value -4.109886169433594).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-14:/07a1f3aca4b108ce46483a0e1601f83458103554539ceff0e91afbe891a5018f</id><title xml:lang="en">14.30% cluster.KMeansBenchmark.time_transform('dense', 'lloyd', 'k-means++')</title><updated>2023-11-14T21:28:50Z</updated><link href="index.html#cluster.KMeansBenchmark.time_transform?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=2137ee0335a12d6544121ce94f0c89ba84940d25-fc11deaf7f565352e6db278464bbd0b1769b6d83" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.time_transform?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=2137ee0335a12d6544121ce94f0c89ba84940d25-fc11deaf7f565352e6db278464bbd0b1769b6d83"&gt;14.30% regression&lt;/a&gt; on 2023-11-14 18:26:01 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/2137ee0335a12d6544121ce94f0c89ba84940d25...fc11deaf7f565352e6db278464bbd0b1769b6d83"&gt;2137ee03...fc11deaf&lt;/a&gt;.&lt;br&gt;
+                Latest value: -4.1098856925964355 (-0.00% worse
+                than best value -4.109886169433594).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-18:/947365df4c7eb728e5f0f65b38286e92205cf35771e168610c08910cca4fea94</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_test_score('dense', 'elkan', 'random')</title><updated>2023-11-19T21:32:30Z</updated><link href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1-5c4288dba42cb67d954cb56c2cebfbf25c05ef89" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1-5c4288dba42cb67d954cb56c2cebfbf25c05ef89"&gt;-0.00% regression&lt;/a&gt; on 2023-11-18 17:10:18 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/4cf13d2b5fcf68a656cfdf273ac636d7dd6038f1...5c4288dba42cb67d954cb56c2cebfbf25c05ef89"&gt;4cf13d2b...5c4288db&lt;/a&gt;.&lt;br&gt;
+                New value: -4.109886169433594, old value: -4.109886169433594.&lt;br&gt;
+                Latest value: -4.1098856925964355 (-0.00% worse
+                than best value -4.109886169433594).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-15:/73e9891e92305701928c53e734700715f6cc97546a16bffc692b4d16cba66579</id><title xml:lang="en">9.59% cluster.KMeansBenchmark.time_fit('dense', 'lloyd', 'k-means++')</title><updated>2023-11-15T21:25:12Z</updated><link href="index.html#cluster.KMeansBenchmark.time_fit?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=fc11deaf7f565352e6db278464bbd0b1769b6d83-5c09163d776c3609ed4cfc68254fabf49f48d7cf" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.time_fit?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=fc11deaf7f565352e6db278464bbd0b1769b6d83-5c09163d776c3609ed4cfc68254fabf49f48d7cf"&gt;9.59% regression&lt;/a&gt; on 2023-11-15 16:45:35 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/fc11deaf7f565352e6db278464bbd0b1769b6d83...5c09163d776c3609ed4cfc68254fabf49f48d7cf"&gt;fc11deaf...5c09163d&lt;/a&gt;.&lt;br&gt;
+                New value: 1.28s, old value: 1.17s.&lt;br&gt;
+                Latest value: 1.28s (9.59% worse
+                than best value 1.17s).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-14:/07a1f3aca4b108ce46483a0e1601f83458103554539ceff0e91afbe891a5018f</id><title xml:lang="en">14.30% cluster.KMeansBenchmark.time_transform('dense', 'lloyd', 'k-means++')</title><updated>2023-11-14T21:28:50Z</updated><link href="index.html#cluster.KMeansBenchmark.time_transform?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=2137ee0335a12d6544121ce94f0c89ba84940d25-fc11deaf7f565352e6db278464bbd0b1769b6d83" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.time_transform?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=2137ee0335a12d6544121ce94f0c89ba84940d25-fc11deaf7f565352e6db278464bbd0b1769b6d83"&gt;14.30% regression&lt;/a&gt; on 2023-11-14 18:26:01 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/2137ee0335a12d6544121ce94f0c89ba84940d25...fc11deaf7f565352e6db278464bbd0b1769b6d83"&gt;2137ee03...fc11deaf&lt;/a&gt;.&lt;br&gt;
                 New value: 97.9ms, old value: 85.6ms.&lt;br&gt;
                 Latest value: 97.9ms (14.30% worse
-                than best value 85.6ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-10:/e7f3069114006be440470dd14cfa932c1a76a9178617ab63872262770e5ef014</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_train_score('dense', 'elkan', 'k-means++')</title><updated>2023-11-10T21:30:48Z</updated><link href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;-0.00% regression&lt;/a&gt; on 2023-11-10 10:07:25 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/8a8842fc4569c6158741511af65322dc5a00e5e5...2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;8a8842fc...2c1472d3&lt;/a&gt;.&lt;br&gt;
-                New value: -3.0780563354492188, old value: -3.0780563354492188.&lt;br&gt;
-                Latest value: -3.0780560970306396 (-0.00% worse
-                than best value -3.0780563354492188).</content></entry><entry><id>tag:scikit-learn.asv,2023-10-30:/eee60317499821d9092d43abf888c5372d92315f82d200abf119811eef8d90a6</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_train_score('dense', 'lloyd', 'k-means++')</title><updated>2023-10-30T21:31:13Z</updated><link href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=5fc67aeb092d636895b599921283221a68c7a2ad-38d499b50752339795f077f40e7846c0c86b0026" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27lloyd%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=5fc67aeb092d636895b599921283221a68c7a2ad-38d499b50752339795f077f40e7846c0c86b0026"&gt;-0.00% regression&lt;/a&gt; on 2023-10-30 10:47:40 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/5fc67aeb092d636895b599921283221a68c7a2ad...38d499b50752339795f077f40e7846c0c86b0026"&gt;5fc67aeb...38d499b5&lt;/a&gt;.&lt;br&gt;
+                than best value 85.6ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-10:/ae39ca98f324977a38f669752739713b68987650010713ee5d5a93858e16598d</id><title xml:lang="en">10.47% linear_model.RidgeBenchmark.time_predict('sparse', 'saga')</title><updated>2023-11-10T22:28:15Z</updated><link href="index.html#linear_model.RidgeBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e" /><content xml:lang="en" type="html">&lt;a href="index.html#linear_model.RidgeBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;10.47% regression&lt;/a&gt; on 2023-11-10 10:07:25 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/8a8842fc4569c6158741511af65322dc5a00e5e5...2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;8a8842fc...2c1472d3&lt;/a&gt;.&lt;br&gt;
+                New value: 7.75ms, old value: 7.02ms.&lt;br&gt;
+                Latest value: 7.75ms (10.47% worse
+                than best value 7.02ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-11-10:/e7f3069114006be440470dd14cfa932c1a76a9178617ab63872262770e5ef014</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_train_score('dense', 'elkan', 'k-means++')</title><updated>2023-11-10T21:30:48Z</updated><link href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=8a8842fc4569c6158741511af65322dc5a00e5e5-2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;-0.00% regression&lt;/a&gt; on 2023-11-10 10:07:25 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/8a8842fc4569c6158741511af65322dc5a00e5e5...2c1472d3970bff63319bed58ee92f850b1bfff8e"&gt;8a8842fc...2c1472d3&lt;/a&gt;.&lt;br&gt;
                 New value: -3.0780563354492188, old value: -3.0780563354492188.&lt;br&gt;
                 Latest value: -3.0780560970306396 (-0.00% worse
                 than best value -3.0780563354492188).</content></entry><entry><id>tag:scikit-learn.asv,2023-10-26:/20b99063bf2182269bea709c203046acaaaba0e6b9266d704934a77d308abf51</id><title xml:lang="en">8.38% svm.SVCBenchmark.time_predict('poly')</title><updated>2023-10-26T22:45:16Z</updated><link href="index.html#svm.SVCBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-kernel=%27poly%27&amp;commits=5c85b581b858c5c99b9f90c6a5fd049f0ad85a4b-dcebbc4c5c97f886a57b0684a5e943f94db1bd58" /><content xml:lang="en" type="html">&lt;a href="index.html#svm.SVCBenchmark.time_predict?python=3.11&amp;cython=3.0.3&amp;p-kernel=%27poly%27&amp;commits=5c85b581b858c5c99b9f90c6a5fd049f0ad85a4b-dcebbc4c5c97f886a57b0684a5e943f94db1bd58"&gt;8.38% regression&lt;/a&gt; on 2023-10-26 13:15:39 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/5c85b581b858c5c99b9f90c6a5fd049f0ad85a4b...dcebbc4c5c97f886a57b0684a5e943f94db1bd58"&gt;5c85b581...dcebbc4c&lt;/a&gt;.&lt;br&gt;
                 New value: 686ms, old value: 633ms.&lt;br&gt;
                 Latest value: 686ms (8.38% worse
-                than best value 633ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-10-03:/3c6abb18e3ca86676132cc21759d2e8f9ee0874e2a796531ab716e8ba9ccacd1</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_test_score('dense', 'elkan', 'random')</title><updated>2023-10-03T20:30:49Z</updated><link href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.2&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=04e39db499afab852e4e2603807384a402a871a9-286f0c9d17019e52f532d63b5ace9f8e1beb5fe5" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.2&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=04e39db499afab852e4e2603807384a402a871a9-286f0c9d17019e52f532d63b5ace9f8e1beb5fe5"&gt;-0.00% regression&lt;/a&gt; on 2023-10-03 18:25:29 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/04e39db499afab852e4e2603807384a402a871a9...286f0c9d17019e52f532d63b5ace9f8e1beb5fe5"&gt;04e39db4...286f0c9d&lt;/a&gt;.&lt;br&gt;
+                than best value 633ms).</content></entry><entry><id>tag:scikit-learn.asv,2023-10-17:/05b8882ebaad0525848b369363fe215cd3e5806e446ab232b2a6ccb2167dd880</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_train_score('sparse', 'elkan', 'k-means++')</title><updated>2023-10-17T20:30:10Z</updated><link href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=ebe4c7ea999a32bafc9044ebe75b4901f92037a0-b470ba1bd5c5b4955016f509a6baeb06ffbbafe9" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_train_score?python=3.11&amp;cython=3.0.3&amp;p-representation=%27sparse%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27k-means%2B%2B%27&amp;commits=ebe4c7ea999a32bafc9044ebe75b4901f92037a0-b470ba1bd5c5b4955016f509a6baeb06ffbbafe9"&gt;-0.00% regression&lt;/a&gt; on 2023-10-17 16:48:26 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/ebe4c7ea999a32bafc9044ebe75b4901f92037a0...b470ba1bd5c5b4955016f509a6baeb06ffbbafe9"&gt;ebe4c7ea...b470ba1b&lt;/a&gt;.&lt;br&gt;
+                New value: -0.9221000671386719, old value: -0.9221000671386719.&lt;br&gt;
+                Latest value: -0.9221000075340271 (-0.00% worse
+                than best value -0.9221000671386719).</content></entry><entry><id>tag:scikit-learn.asv,2023-10-03:/3c6abb18e3ca86676132cc21759d2e8f9ee0874e2a796531ab716e8ba9ccacd1</id><title xml:lang="en">-0.00% cluster.KMeansBenchmark.track_test_score('dense', 'elkan', 'random')</title><updated>2023-10-03T20:30:49Z</updated><link href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.2&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=04e39db499afab852e4e2603807384a402a871a9-286f0c9d17019e52f532d63b5ace9f8e1beb5fe5" /><content xml:lang="en" type="html">&lt;a href="index.html#cluster.KMeansBenchmark.track_test_score?python=3.11&amp;cython=3.0.2&amp;p-representation=%27dense%27&amp;p-algorithm=%27elkan%27&amp;p-init=%27random%27&amp;commits=04e39db499afab852e4e2603807384a402a871a9-286f0c9d17019e52f532d63b5ace9f8e1beb5fe5"&gt;-0.00% regression&lt;/a&gt; on 2023-10-03 18:25:29 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/04e39db499afab852e4e2603807384a402a871a9...286f0c9d17019e52f532d63b5ace9f8e1beb5fe5"&gt;04e39db4...286f0c9d&lt;/a&gt;.&lt;br&gt;
                 New value: -4.109885215759277, old value: -4.1098856925964355.&lt;br&gt;
                 Latest value: -4.109885215759277 (-0.00% worse
                 than best value -4.109886169433594).</content></entry><entry><id>tag:scikit-learn.asv,2023-09-29:/4fa4af95385b8fa19bd5d462646ad461a154a73ea3a7048c5f01085e0a1858c1</id><title xml:lang="en">26.49% linear_model.LogisticRegressionBenchmark.time_predict('sparse', 'saga', 4)</title><updated>2023-10-01T21:42:55Z</updated><link href="index.html#linear_model.LogisticRegressionBenchmark.time_predict?python=3.11&amp;cython=3.0.2&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;p-n_jobs=4&amp;commits=bdf66d048c2113e94397b11ff17b7b5c03938ab7-9f6592f714cf1b0fb6f2d8fb636d9e458fad41d5" /><content xml:lang="en" type="html">&lt;a href="index.html#linear_model.LogisticRegressionBenchmark.time_predict?python=3.11&amp;cython=3.0.2&amp;p-representation=%27sparse%27&amp;p-solver=%27saga%27&amp;p-n_jobs=4&amp;commits=bdf66d048c2113e94397b11ff17b7b5c03938ab7-9f6592f714cf1b0fb6f2d8fb636d9e458fad41d5"&gt;26.49% regression&lt;/a&gt; on 2023-09-29 21:55:08 in commits &lt;a href="https://github.com/scikit-learn/scikit-learn/commit/../compare/bdf66d048c2113e94397b11ff17b7b5c03938ab7...9f6592f714cf1b0fb6f2d8fb636d9e458fad41d5"&gt;bdf66d04...9f6592f7&lt;/a&gt;.&lt;br&gt;