add additional plots

azoz01 · Aug 20, 2024 · ed643d5 · ed643d5
1 parent 9265af2
commit ed643d5
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Showing 2 changed files with 67 additions and 6 deletions.
diff --git a/bin/analysis/analyze_distance_correlation.py b/bin/analysis/analyze_distance_correlation.py
@@ -47,6 +47,17 @@ def main():
                 )[0]
             ),
         ),
+        (
+            "Dataset2Vec reconstruction (representations)",
+            Dataset2VecForLandmarkerReconstruction.load_from_checkpoint(
+                list(
+                    (
+                        paths_provider.encoders_results_path
+                        / "d2v_reconstruction"
+                    ).rglob("*.ckpt")
+                )[0]
+            ),
+        ),
     ]
     logger.info("Analyzing")
     results = dict()
@@ -102,7 +113,7 @@ def main():
         correlations.append(
             spearmanr(landmarkers_distances, datasets_distances).statistic
         )
-    results["Dataset2Vec reconstruction"] = {
+    results["Dataset2Vec reconstruction (to landmarkers)"] = {
         "mean": np.mean(correlations),
         "std": np.std(correlations),
     }

diff --git a/bin/analysis/analyze_warmstart.py b/bin/analysis/analyze_warmstart.py
@@ -20,10 +20,19 @@ def main():
     aggregates.columns = ["dataset", "min_value", "max_value"]
 
     plot_data = df.merge(aggregates, "left", "dataset")
-    plot_data = plot_data.sort_values(["dataset", "warmstart", "number"])
+    plot_data = plot_data.sort_values(
+        ["dataset", "warmstart", "number", "value"]
+    )
     plot_data["cumulative_max_value"] = plot_data.groupby(
         ["dataset", "warmstart"]
     )["value"].cummax()
+    plot_data["neg_value"] = -plot_data["value"]
+    plot_data = plot_data.sort_values(
+        ["dataset", "warmstart", "number", "neg_value"]
+    )
+    plot_data["rank"] = plot_data.groupby(["dataset", "number"])[
+        "neg_value"
+    ].rank()
 
     plot_data["distance"] = (
         plot_data["max_value"] - plot_data["cumulative_max_value"]
@@ -34,18 +43,59 @@ def main():
     plot_data["scaled_value"] = (df["value"] - plot_data["min_value"]) / (
         plot_data["max_value"] - plot_data["min_value"]
     )
+    plot_data["number"] = plot_data["number"] + 1
+
+    logger.info("Generating plot of ranks over time")
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.lineplot(plot_data, x="number", y="rank", hue="warmstart", ax=ax)
+    ax.vlines(
+        x=5,
+        ymin=1,
+        ymax=7,
+        colors="black",
+        label="End of warm-start phase",
+        linestyles="dotted",
+    )
+    ax.set_ylabel("Rank")
+    ax.set_xlabel("Number of iteration")
+    ax.set_title("Rank of the negative ROC AUC over time")
+    plt.savefig(paths_provider.results_analysis_path / "rank_over_time.png")
+    plt.clf()
 
     logger.info("Generating plot for raw values")
-    fig, ax = plt.subplots(figsize=(10, 10))
+    fig, ax = plt.subplots(figsize=(10, 5))
     sns.lineplot(
         plot_data, x="number", y="scaled_value", hue="warmstart", ax=ax
     )
+    ax.vlines(
+        x=5,
+        ymin=0,
+        ymax=1,
+        colors="black",
+        label="End of warm-start phase",
+        linestyles="dotted",
+    )
+    ax.set_ylabel("Scaled ROC AUC")
+    ax.set_xlabel("Number of iteration")
+    ax.set_title("Scaled value of the ROC AUC over time")
     plt.savefig(paths_provider.results_analysis_path / "raw_values.png")
     plt.clf()
 
     logger.info("Generating ADTM plot")
     fig, ax = plt.subplots(figsize=(10, 5))
-    sns.lineplot(data=plot_data, x="number", y="distance", hue="warmstart")
+    sns.lineplot(
+        data=plot_data, x="number", y="distance", hue="warmstart", ax=ax
+    )
+    ax.vlines(
+        x=5,
+        ymin=0,
+        ymax=0.25,
+        colors="black",
+        label="End of warm-start phase",
+        linestyles="dotted",
+    )
+    ax.set_xlabel("Number of iteration")
+    ax.set_ylabel("Scaled distance")
     plt.savefig(paths_provider.results_analysis_path / "adtm.png")
     plt.clf()
 
@@ -55,7 +105,7 @@ def main():
     plot_data["dataset_name"] = plot_data["dataset"]
     draw_cd_diagram(
         df_perf=plot_data[["classifier_name", "dataset_name", "accuracy"]].loc[
-            plot_data.number == 4
+            plot_data.number == 5
         ]
     )
     plt.savefig(
@@ -65,7 +115,7 @@ def main():
 
     draw_cd_diagram(
         df_perf=plot_data[["classifier_name", "dataset_name", "accuracy"]].loc[
-            plot_data.number == 19
+            plot_data.number == 20
         ]
     )
     plt.savefig(