From b5b698bde5921916225717100f16969dc23250da Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 11:52:04 +0000 Subject: [PATCH 01/34] Created outline for new estimator + file for manual tests --- TestRAD.py | 3 +++ river/anomaly/__init__.py | 2 ++ river/anomaly/rad.py | 20 ++++++++++++++++++++ 3 files changed, 25 insertions(+) create mode 100644 TestRAD.py create mode 100644 river/anomaly/rad.py diff --git a/TestRAD.py b/TestRAD.py new file mode 100644 index 0000000000..8ecb645aee --- /dev/null +++ b/TestRAD.py @@ -0,0 +1,3 @@ +from river import anomaly + +model = anomaly.ReconstructionAnomalyDetecion() \ No newline at end of file diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index ca5ffd0888..3de442fa37 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -20,6 +20,7 @@ from .lof import LocalOutlierFactor from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM +from .rad import ReconstructionAnomalyDetecion __all__ = [ "base", @@ -31,4 +32,5 @@ "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", + "ReconstructionAnomalyDetecion" ] diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py new file mode 100644 index 0000000000..4ed84469fa --- /dev/null +++ b/river/anomaly/rad.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +from river import anomaly + +__all__ = ["ReconstructionAnomalyDetecion"] + + +class ReconstructionAnomalyDetecion(anomaly.base.AnomalyDetector): + """Reconstruction Anomaly-Detection (RAD). + This is the place for documentation + """ + + def __init__(self): + print("Success") + + def learn_one(self, x): + return self + + def score_one(self, x): + return 1 From a4ca4f9d2dc089b3cd701f1d3252372100a5d488 Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 11:56:28 +0000 Subject: [PATCH 02/34] fixed import order --- TestRAD.py | 2 +- river/anomaly/__init__.py | 5 +++-- river/anomaly/rad.py | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/TestRAD.py b/TestRAD.py index 8ecb645aee..3915fb39da 100644 --- a/TestRAD.py +++ b/TestRAD.py @@ -1,3 +1,3 @@ from river import anomaly -model = anomaly.ReconstructionAnomalyDetecion() \ No newline at end of file +model = anomaly.ReconstructionAnomalyDetecion() diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index 3de442fa37..380f9e58b6 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -18,9 +18,10 @@ from .gaussian import GaussianScorer from .hst import HalfSpaceTrees from .lof import LocalOutlierFactor +from .rad import ReconstructionAnomalyDetecion from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM -from .rad import ReconstructionAnomalyDetecion + __all__ = [ "base", @@ -29,8 +30,8 @@ "HalfSpaceTrees", "OneClassSVM", "QuantileFilter", + "ReconstructionAnomalyDetecion", "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", - "ReconstructionAnomalyDetecion" ] diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 4ed84469fa..8aa25be1ba 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -7,7 +7,7 @@ class ReconstructionAnomalyDetecion(anomaly.base.AnomalyDetector): """Reconstruction Anomaly-Detection (RAD). - This is the place for documentation + This is the place for documentation """ def __init__(self): From b48bced979f82400932cbddc3759a15c7766a11b Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 11:59:36 +0000 Subject: [PATCH 03/34] fixed import order again --- river/anomaly/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index 380f9e58b6..ebf8242916 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -22,7 +22,6 @@ from .sad import StandardAbsoluteDeviation from .svm import OneClassSVM - __all__ = [ "base", "AnomalyDetector", @@ -30,8 +29,8 @@ "HalfSpaceTrees", "OneClassSVM", "QuantileFilter", - "ReconstructionAnomalyDetecion", "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", + "ReconstructionAnomalyDetecion" ] From 00c9babfc3128bcc26a917b69c83d09e3b267a4c Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 12:00:34 +0000 Subject: [PATCH 04/34] fixed import order again --- river/anomaly/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/anomaly/__init__.py b/river/anomaly/__init__.py index ebf8242916..cb5944f78d 100644 --- a/river/anomaly/__init__.py +++ b/river/anomaly/__init__.py @@ -32,5 +32,5 @@ "StandardAbsoluteDeviation", "ThresholdFilter", "LocalOutlierFactor", - "ReconstructionAnomalyDetecion" + "ReconstructionAnomalyDetecion", ] From 50c8528f8a7c4d5dd37aea22bb1849f3b74f0988 Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 12:04:17 +0000 Subject: [PATCH 05/34] testing local credential helper --- river/anomaly/rad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 8aa25be1ba..46d4676c43 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -17,4 +17,4 @@ def learn_one(self, x): return self def score_one(self, x): - return 1 + return 2 From 14da4c424b66718545e1a6cabb60da408009ba48 Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 12:09:47 +0000 Subject: [PATCH 06/34] changed class to base off of supervised AD --- TestRAD.py | 3 +++ river/anomaly/rad.py | 37 ++++++++++++++++++++++++++++++++----- 2 files changed, 35 insertions(+), 5 deletions(-) diff --git a/TestRAD.py b/TestRAD.py index 3915fb39da..4ce2a71de9 100644 --- a/TestRAD.py +++ b/TestRAD.py @@ -1,3 +1,6 @@ from river import anomaly model = anomaly.ReconstructionAnomalyDetecion() + +print(type(model.learn_one({"x": 0}, 1))) +print(type(model.score_one({"x": 0}, 1))) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 46d4676c43..29e795bbac 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -1,11 +1,11 @@ from __future__ import annotations -from river import anomaly +from river import anomaly, base __all__ = ["ReconstructionAnomalyDetecion"] -class ReconstructionAnomalyDetecion(anomaly.base.AnomalyDetector): +class ReconstructionAnomalyDetecion(anomaly.base.SupervisedAnomalyDetector): """Reconstruction Anomaly-Detection (RAD). This is the place for documentation """ @@ -13,8 +13,35 @@ class ReconstructionAnomalyDetecion(anomaly.base.AnomalyDetector): def __init__(self): print("Success") - def learn_one(self, x): + def learn_one(self, x: dict, y: base.typing.Target): + """Update the model. + + Parameters + ---------- + x + A dictionary of features. + + Returns + ------- + self + + """ return self - def score_one(self, x): - return 2 + def score_one(self, x: dict, y: base.typing.Target): + """Return an outlier score. + + A high score is indicative of an anomaly. A low score corresponds a normal observation. + + Parameters + ---------- + x + A dictionary of features. + + Returns + ------- + An anomaly score. A high score is indicative of an anomaly. A low score corresponds a + normal observation. + + """ + return 1.0 From 94dd5111d229628f39afd8a4784fa42d5716318b Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 13:42:03 +0000 Subject: [PATCH 07/34] basic RAD functionality but yet without docs --- river/anomaly/rad.py | 68 ++++++++++++++++++++++++++++---------------- 1 file changed, 43 insertions(+), 25 deletions(-) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 29e795bbac..1ccfd891c7 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -1,6 +1,8 @@ from __future__ import annotations -from river import anomaly, base +import math + +from river import anomaly, base, linear_model, preprocessing, stats, time_series __all__ = ["ReconstructionAnomalyDetecion"] @@ -10,38 +12,54 @@ class ReconstructionAnomalyDetecion(anomaly.base.SupervisedAnomalyDetector): This is the place for documentation """ - def __init__(self): - print("Success") + def __init__( + self, + reconstruction: base.Estimator | None = None, + horizon: int | None = None, + n_std: int | None = None, + ): + self.reconstruction = ( + reconstruction + if reconstruction is not None + else preprocessing.MinMaxScaler() | linear_model.LinearRegression() + ) - def learn_one(self, x: dict, y: base.typing.Target): - """Update the model. + self.n_std = n_std if n_std is not None else 3 - Parameters - ---------- - x - A dictionary of features. + self.horizon = horizon if horizon is not None else 1 - Returns - ------- - self + self.dynamic_mean_squared_error = stats.Mean() + self.dynamic_squared_error_variance = stats.Var() + self.predictions = [] + self.squared_errors = [] + self.thresholds = [] - """ + def learn_one(self, x: dict, y: base.typing.Target): + self.reconstruction.learn_one(x, y) return self def score_one(self, x: dict, y: base.typing.Target): - """Return an outlier score. + if isinstance(self.reconstruction, time_series.base.Forecaster): + y_pred = self.reconstruction.forecast(self.horizon)[0] + else: + y_pred = self.reconstruction.predict_one(x) + self.predictions.append(y_pred) + + abs_error = abs(y_pred - y) + squared_error = abs_error**2 + self.squared_errors.append(squared_error) - A high score is indicative of an anomaly. A low score corresponds a normal observation. + threshold = self.dynamic_mean_squared_error.get() + ( + self.n_std * math.sqrt(self.dynamic_squared_error_variance.get()) + ) + self.thresholds.append(threshold) - Parameters - ---------- - x - A dictionary of features. + self.dynamic_mean_squared_error.update(squared_error) + self.dynamic_squared_error_variance.update(squared_error) - Returns - ------- - An anomaly score. A high score is indicative of an anomaly. A low score corresponds a - normal observation. + self.reconstruction.learn_one(x, y) - """ - return 1.0 + if squared_error >= threshold: + return 1.0 + else: + return squared_error / threshold From 884aa6e8b068ea3a03dbb8f6aa8b8dee3ac77bae Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 8 Nov 2023 13:45:44 +0000 Subject: [PATCH 08/34] fixed missing type annotation --- river/anomaly/rad.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 1ccfd891c7..519b5356c5 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -30,9 +30,9 @@ def __init__( self.dynamic_mean_squared_error = stats.Mean() self.dynamic_squared_error_variance = stats.Var() - self.predictions = [] - self.squared_errors = [] - self.thresholds = [] + self.predictions: list[float] = [] + self.squared_errors: list[float] = [] + self.thresholds: list[float] = [] def learn_one(self, x: dict, y: base.typing.Target): self.reconstruction.learn_one(x, y) From 35318a6fdedb1a1c4549a32c9a1de4e4b3f158f6 Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Thu, 9 Nov 2023 09:59:50 +0000 Subject: [PATCH 09/34] tested and fixed for ARIMA, added warmup --- river/anomaly/rad.py | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 519b5356c5..34deaae2fd 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -16,7 +16,8 @@ def __init__( self, reconstruction: base.Estimator | None = None, horizon: int | None = None, - n_std: int | None = None, + n_std: float | None = None, + warmup_period: int | None = None, ): self.reconstruction = ( reconstruction @@ -25,20 +26,31 @@ def __init__( ) self.n_std = n_std if n_std is not None else 3 - self.horizon = horizon if horizon is not None else 1 + self.warmup_period = warmup_period if warmup_period is not None else 0 self.dynamic_mean_squared_error = stats.Mean() self.dynamic_squared_error_variance = stats.Var() self.predictions: list[float] = [] self.squared_errors: list[float] = [] self.thresholds: list[float] = [] + self.iterations: int = 0 + self.warmed_up: bool = self.iterations >= self.warmup_period + + def learn_one(self, x: dict | None, y: float): + self.iterations += 1 + self.warmed_up = self.iterations >= self.warmup_period - def learn_one(self, x: dict, y: base.typing.Target): - self.reconstruction.learn_one(x, y) + if x is None: + self.reconstruction.learn_one(y) + else: + self.reconstruction.learn_one(x, y) return self def score_one(self, x: dict, y: base.typing.Target): + self.iterations += 1 + self.warmed_up = self.iterations >= self.warmup_period + if isinstance(self.reconstruction, time_series.base.Forecaster): y_pred = self.reconstruction.forecast(self.horizon)[0] else: @@ -57,7 +69,8 @@ def score_one(self, x: dict, y: base.typing.Target): self.dynamic_mean_squared_error.update(squared_error) self.dynamic_squared_error_variance.update(squared_error) - self.reconstruction.learn_one(x, y) + if not self.warmed_up: + return 0.0 if squared_error >= threshold: return 1.0 From 1d2af9785bf7fd45a47d43a2359051b79583b11c Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Thu, 9 Nov 2023 10:01:01 +0000 Subject: [PATCH 10/34] changed typing of learn_one2 --- river/anomaly/rad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 34deaae2fd..01fa677a94 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -37,7 +37,7 @@ def __init__( self.iterations: int = 0 self.warmed_up: bool = self.iterations >= self.warmup_period - def learn_one(self, x: dict | None, y: float): + def learn_one(self, x: dict | None, y: base.typing.Target): self.iterations += 1 self.warmed_up = self.iterations >= self.warmup_period From c09fafea757d0ed3d974a45e0267e90807ead56f Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Wed, 15 Nov 2023 11:15:49 +0000 Subject: [PATCH 11/34] added comments for doc purposes --- river/anomaly/rad.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/river/anomaly/rad.py b/river/anomaly/rad.py index 01fa677a94..19563a78b9 100644 --- a/river/anomaly/rad.py +++ b/river/anomaly/rad.py @@ -19,48 +19,61 @@ def __init__( n_std: float | None = None, warmup_period: int | None = None, ): + # Setting the reconstruction model that learns how normal data behaves self.reconstruction = ( reconstruction if reconstruction is not None else preprocessing.MinMaxScaler() | linear_model.LinearRegression() ) + # Standard values for hyperparameters self.n_std = n_std if n_std is not None else 3 self.horizon = horizon if horizon is not None else 1 self.warmup_period = warmup_period if warmup_period is not None else 0 + # Initialize necessary stats self.dynamic_mean_squared_error = stats.Mean() self.dynamic_squared_error_variance = stats.Var() + + # Initialize necessary lists for tracking values self.predictions: list[float] = [] self.squared_errors: list[float] = [] self.thresholds: list[float] = [] + + # Initialize necessary values for warm-up procedure self.iterations: int = 0 self.warmed_up: bool = self.iterations >= self.warmup_period + # This method is called to make the reconstruction model learn one example def learn_one(self, x: dict | None, y: base.typing.Target): self.iterations += 1 self.warmed_up = self.iterations >= self.warmup_period + # Checking if the features should be passed to model if x is None: self.reconstruction.learn_one(y) else: self.reconstruction.learn_one(x, y) return self + # This method is calles to calculate an anomaly score for one example def score_one(self, x: dict, y: base.typing.Target): self.iterations += 1 self.warmed_up = self.iterations >= self.warmup_period + # Check if model is a time-series forecasting model if isinstance(self.reconstruction, time_series.base.Forecaster): y_pred = self.reconstruction.forecast(self.horizon)[0] else: y_pred = self.reconstruction.predict_one(x) self.predictions.append(y_pred) + # Calculate the errors necessary for thresholding abs_error = abs(y_pred - y) squared_error = abs_error**2 self.squared_errors.append(squared_error) + # Based on the errors and hyperparameters, calculate threshold threshold = self.dynamic_mean_squared_error.get() + ( self.n_std * math.sqrt(self.dynamic_squared_error_variance.get()) ) @@ -69,9 +82,12 @@ def score_one(self, x: dict, y: base.typing.Target): self.dynamic_mean_squared_error.update(squared_error) self.dynamic_squared_error_variance.update(squared_error) + # When warmup hyperparam is used, only return score if warmed up if not self.warmed_up: return 0.0 + # Every error above threshold is scored with 100% or 1.0 + # Everything below is distributed linearly from 0.0 - 0.999... if squared_error >= threshold: return 1.0 else: From ce80c93d4fcecc7d4bf06e01f4a6e1bdf30b8229 Mon Sep 17 00:00:00 2001 From: sebastianwette Date: Thu, 23 Nov 2023 13:47:23 +0000 Subject: [PATCH 12/34] added docstring --- TestRAD.py | 6 - .../benchmarks/Binary classification/index.md | 16 +- .../Multiclass classification/index.md | 16 +- docs/benchmarks/Regression/index.md | 16 +- .../on-hoeffding-trees_12_0.svg | 2 +- .../on-hoeffding-trees_21_0.png | Bin 190653 -> 190655 bytes .../on-hoeffding-trees_23_0.png | Bin 190161 -> 191052 bytes .../on-hoeffding-trees_25_0.png | Bin 207243 -> 207140 bytes .../on-hoeffding-trees_27_0.png | Bin 211985 -> 212956 bytes river/anomaly/__init__.py | 4 +- river/anomaly/pad.py | 200 ++++++++++++++++++ river/anomaly/rad.py | 94 -------- 12 files changed, 227 insertions(+), 127 deletions(-) delete mode 100644 TestRAD.py create mode 100644 river/anomaly/pad.py delete mode 100644 river/anomaly/rad.py diff --git a/TestRAD.py b/TestRAD.py deleted file mode 100644 index 4ce2a71de9..0000000000 --- a/TestRAD.py +++ /dev/null @@ -1,6 +0,0 @@ -from river import anomaly - -model = anomaly.ReconstructionAnomalyDetecion() - -print(type(model.learn_one({"x": 0}, 1))) -print(type(model.score_one({"x": 0}, 1))) diff --git a/docs/benchmarks/Binary classification/index.md b/docs/benchmarks/Binary classification/index.md index 3d8c8e14c0..da0e5b20ec 100644 --- a/docs/benchmarks/Binary classification/index.md +++ b/docs/benchmarks/Binary classification/index.md @@ -37930,21 +37930,21 @@ ## Environment
Python implementation: CPython
-Python version       : 3.10.13
+Python version       : 3.10.12
 IPython version      : 8.16.1
 
 river       : 0.19.0
-numpy       : 1.25.2
+numpy       : 1.26.1
 scikit-learn: 1.3.1
 pandas      : 2.1.1
 scipy       : 1.11.3
 
-Compiler    : Clang 14.0.6 
-OS          : Darwin
-Release     : 22.6.0
-Machine     : arm64
-Processor   : arm
-CPU cores   : 8
+Compiler    : GCC 11.4.0
+OS          : Linux
+Release     : 5.15.0-1046-kvm
+Machine     : x86_64
+Processor   : x86_64
+CPU cores   : 2
 Architecture: 64bit
 
diff --git a/docs/benchmarks/Multiclass classification/index.md b/docs/benchmarks/Multiclass classification/index.md index de3b47ecae..c38bf5ad82 100644 --- a/docs/benchmarks/Multiclass classification/index.md +++ b/docs/benchmarks/Multiclass classification/index.md @@ -24888,21 +24888,21 @@ ## Environment
Python implementation: CPython
-Python version       : 3.10.13
+Python version       : 3.10.12
 IPython version      : 8.16.1
 
 river       : 0.19.0
-numpy       : 1.25.2
+numpy       : 1.26.1
 scikit-learn: 1.3.1
 pandas      : 2.1.1
 scipy       : 1.11.3
 
-Compiler    : Clang 14.0.6 
-OS          : Darwin
-Release     : 22.6.0
-Machine     : arm64
-Processor   : arm
-CPU cores   : 8
+Compiler    : GCC 11.4.0
+OS          : Linux
+Release     : 5.15.0-1046-kvm
+Machine     : x86_64
+Processor   : x86_64
+CPU cores   : 2
 Architecture: 64bit
 
diff --git a/docs/benchmarks/Regression/index.md b/docs/benchmarks/Regression/index.md index ac378dd8ac..6cbcce41db 100644 --- a/docs/benchmarks/Regression/index.md +++ b/docs/benchmarks/Regression/index.md @@ -20644,21 +20644,21 @@ ## Environment
Python implementation: CPython
-Python version       : 3.10.13
+Python version       : 3.10.12
 IPython version      : 8.16.1
 
 river       : 0.19.0
-numpy       : 1.25.2
+numpy       : 1.26.1
 scikit-learn: 1.3.1
 pandas      : 2.1.1
 scipy       : 1.11.3
 
-Compiler    : Clang 14.0.6 
-OS          : Darwin
-Release     : 22.6.0
-Machine     : arm64
-Processor   : arm
-CPU cores   : 8
+Compiler    : GCC 11.4.0
+OS          : Linux
+Release     : 5.15.0-1046-kvm
+Machine     : x86_64
+Processor   : x86_64
+CPU cores   : 2
 Architecture: 64bit
 
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