tool updates

Deep-SAD-PyTorch/src/onnx_export.py

@@ -1,11 +1,15 @@
+from pathlib import Path
+
 import torch
 import torch.onnx
-from networks.mnist_LeNet import MNIST_LeNet_Autoencoder
+
+from networks.subter_LeNet import SubTer_LeNet_Autoencoder
+from networks.subter_LeNet_rf import SubTer_Efficient_AE
 
 
-def export_model_to_onnx(model, filepath, input_shape=(1, 1, 28, 28)):
+def export_model_to_onnx(model, filepath):
     model.eval()  # Set the model to evaluation mode
-    dummy_input = torch.randn(input_shape)  # Create a dummy input tensor
+    dummy_input = torch.randn(model.input_dim)  # Create a dummy input tensor
     torch.onnx.export(
         model,  # model being run
         dummy_input,  # model input (or a tuple for multiple inputs)
@@ -23,13 +27,17 @@ def export_model_to_onnx(model, filepath, input_shape=(1, 1, 28, 28)):
 
 
 if __name__ == "__main__":
-    # Initialize the autoencoder model
+    output_folder_path = Path("./onnx_models")
-    autoencoder = MNIST_LeNet_Autoencoder(rep_dim=32)
+    output_folder_path.mkdir(parents=True, exist_ok=True)
 
-    # Define the file path where the ONNX model will be saved
+    models_to_visualize = [
-    onnx_file_path = "mnist_lenet_autoencoder.onnx"
+        (
+            SubTer_LeNet_Autoencoder(rep_dim=32),
+            output_folder_path / "subter_lenet_ae.onnx",
+        ),
+        (SubTer_Efficient_AE(rep_dim=32), output_folder_path / "subter_ef_ae.onnx"),
+    ]
 
-    # Export the model
+    for model, output_path in models_to_visualize:
-    export_model_to_onnx(autoencoder, onnx_file_path)
+        export_model_to_onnx(model, output_path)
-
+        print(f"Model has been exported to {output_path}")
-    print(f"Model has been exported to {onnx_file_path}")

tools/.gitignore

@@ -7,4 +7,7 @@ tmp
 .envrc
 .vscode
 test
+*.jpg
+*.jpeg
+*.png
 

tools/evaluate_roc.py

@@ -1,13 +1,18 @@
+import json
 import pickle
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
+from rich.progress import track
 from scipy.stats import sem, t
-from sklearn.metrics import auc
+
+models = ["deepsad", "isoforest", "ocsvm"]
+evaluation_types = ["exp_based", "manual_based"]
+parent_results_path = Path("/home/fedex/mt/results/done")
+base_output_path = Path("/home/fedex/mt/results/tmp_plots")
 
 
-# Confidence interval function
 def confidence_interval(data, confidence=0.95):
     n = len(data)
     mean = np.mean(data)
@@ -16,67 +21,178 @@ def confidence_interval(data, confidence=0.95):
     return mean, h
 
 
-# Load ROC and AUC values from pickle files
+def load_results_data(folder):
-roc_data = []
+    experiment_data = {}
-auc_scores = []
-isoforest_roc_data = []
-isoforest_auc_scores = []
 
-results_path = Path(
+    json_config_path = folder / "config.json"
-    "/home/fedex/mt/projects/thesis-kowalczyk-jan/Deep-SAD-PyTorch/log/DeepSAD/subter_kfold_0_0"
+    with json_config_path.open("r") as f:
-)
+        config = json.load(f)
+    try:
+        net = config["net_name"]
+        num_known_normal, num_known_anomalous = (
+            config["num_known_normal"],
+            config["num_known_outlier"],
+        )
+        semi_known_nums = (num_known_normal, num_known_anomalous)
+        latent_dim = config["latent_space_dim"]
 
-for i in range(5):
+        exp_title = f"{net} - {num_known_normal} normal, {num_known_anomalous} anomalous, latent dim {latent_dim}"
-    with (results_path / f"results_{i}.pkl").open("rb") as f:
-        data = pickle.load(f)
-        roc_data.append(data["test_roc"])
-        auc_scores.append(data["test_auc"])
-    with (results_path / f"results.isoforest_{i}.pkl").open("rb") as f:
-        data = pickle.load(f)
-        isoforest_roc_data.append(data["test_roc"])
-        isoforest_auc_scores.append(data["test_auc"])
 
-# Calculate mean and confidence interval for AUC scores
+        if not config["k_fold"]:
-mean_auc, auc_ci = confidence_interval(auc_scores)
+            raise ValueError(f"{folder.name} was not trained as k-fold. Exiting...")
 
-# Combine ROC curves
+        k_fold_num = config["k_fold_num"]
-mean_fpr = np.linspace(0, 1, 100)
+    except KeyError as e:
-tprs = []
+        print(f"Missing key in config.json for experiment folder {folder.name}: {e}")
+        raise
 
-for fpr, tpr, _ in roc_data:
+    experiment_data["exp_title"] = exp_title
-    interp_tpr = np.interp(mean_fpr, fpr, tpr)
+    experiment_data["k_fold_num"] = k_fold_num
-    interp_tpr[0] = 0.0
+    experiment_data["semi_known_nums"] = semi_known_nums
-    tprs.append(interp_tpr)
+    experiment_data["folder"] = folder
+    experiment_data["net"] = net
+    experiment_data["latent_dim"] = latent_dim
 
-mean_tpr = np.mean(tprs, axis=0)
+    roc_data = {}
-mean_tpr[-1] = 1.0
+    roc_auc_data = {}
-std_tpr = np.std(tprs, axis=0)
+    prc_data = {}
 
-# Plot ROC curves with confidence margins
+    for model in models:
-plt.figure()
+        # You can adjust the number of folds if needed
-plt.plot(
+        for fold_idx in range(k_fold_num):
-    mean_fpr,
+            results_file = folder / f"results_{model}_{fold_idx}.pkl"
-    mean_tpr,
+            if not results_file.exists():
-    color="b",
+                print(
-    label=f"Mean ROC (AUC = {mean_auc:.2f} ± {auc_ci:.2f})",
+                    f"Expected results file {results_file.name} does not exist. Skipping..."
-)
+                )
-plt.fill_between(
+            with results_file.open("rb") as f:
-    mean_fpr,
+                data = pickle.load(f)
-    mean_tpr - std_tpr,
+                try:
-    mean_tpr + std_tpr,
+                    if model == "deepsad":
-    color="b",
+                        test_results = data["test"]
-    alpha=0.2,
+                        for evaluation_type in evaluation_types:
-    label="± 1 std. dev.",
+                            eval_type_results = test_results[evaluation_type]
-)
+                            roc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = eval_type_results["roc"]
+                            roc_auc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = eval_type_results["auc"]
+                            prc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = eval_type_results["prc"]
+                    elif model in ["isoforest", "ocsvm"]:
+                        for evaluation_type in evaluation_types:
+                            roc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = data[f"test_roc_{evaluation_type}"]
+                            roc_auc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = data[f"test_auc_{evaluation_type}"]
+                            prc_data.setdefault(model, {}).setdefault(
+                                evaluation_type, {}
+                            )[fold_idx] = data[f"test_prc_{evaluation_type}"]
 
-# Plot each fold's ROC curve (optional)
+                except KeyError as e:
- for i, (fpr, tpr, _) in enumerate(roc_data):
+                    print(f"Missing key in results file {results_file.name}: {e}")
-     plt.plot(fpr, tpr, lw=1, alpha=0.3, label=f"Fold {i + 1} ROC")
+                    raise
 
-# Labels and legend
+    experiment_data["roc_data"] = roc_data
-plt.plot([0, 1], [0, 1], "k--", label="Chance")
+    experiment_data["roc_auc_data"] = roc_auc_data
-plt.xlabel("False Positive Rate")
+    experiment_data["prc_data"] = prc_data
-plt.ylabel("True Positive Rate")
+    return experiment_data
-plt.title("ROC Curve with 5-Fold Cross-Validation")
+
-plt.legend(loc="lower right")
+
-plt.savefig("roc_curve_0_0.png")
+def plot_roc_curve(experiment_data, output_path):
+    try:
+        k_fold_num = experiment_data["k_fold_num"]
+        roc_data = experiment_data["roc_data"]
+        roc_auc_data = experiment_data["roc_auc_data"]
+        folder = experiment_data["folder"]
+        exp_title = experiment_data["exp_title"]
+    except KeyError as e:
+        print(f"Missing key in experiment data: {e}")
+        raise
+    for evaluation_type in evaluation_types:
+        plt.figure(figsize=(8, 6))
+        for model in models:
+            # Gather all folds' ROC data for this model and evaluation_type
+            fold_rocs = []
+            auc_scores = []
+            for fold_idx in range(k_fold_num):
+                try:
+                    fpr, tpr, thresholds = roc_data[model][evaluation_type][fold_idx]
+                    fold_rocs.append((fpr, tpr))
+                    auc_scores.append(roc_auc_data[model][evaluation_type][fold_idx])
+                except KeyError:
+                    continue
+
+            if not fold_rocs:
+                print(
+                    f"No ROC data for model {model}, evaluation {evaluation_type} in {folder.name}"
+                )
+                continue
+
+            # Interpolate TPRs to a common FPR grid
+            mean_fpr = np.linspace(0, 1, 100)
+            interp_tprs = []
+            for fpr, tpr in fold_rocs:
+                interp_tpr = np.interp(mean_fpr, fpr, tpr)
+                interp_tpr[0] = 0.0
+                interp_tprs.append(interp_tpr)
+            mean_tpr = np.mean(interp_tprs, axis=0)
+            std_tpr = np.std(interp_tprs, axis=0)
+            mean_tpr[-1] = 1.0
+
+            # Mean and CI for AUC
+            mean_auc, auc_ci = confidence_interval(auc_scores)
+
+            # Plot mean ROC and std band
+            plt.plot(
+                mean_fpr,
+                mean_tpr,
+                label=f"{model} (AUC={mean_auc:.2f}±{auc_ci:.2f})",
+            )
+            plt.fill_between(
+                mean_fpr,
+                mean_tpr - std_tpr,
+                mean_tpr + std_tpr,
+                alpha=0.15,
+            )
+
+        plt.plot([0, 1], [0, 1], "k--", label="Chance")
+        plt.xlabel("False Positive Rate")
+        plt.ylabel("True Positive Rate")
+        plt.title(f"ROC Curve ({exp_title} - {evaluation_type})")
+        plt.legend(loc="lower right")
+        plt.tight_layout()
+        plt.savefig(
+            (output_path / f"roc_curve_{folder.name}_{evaluation_type}.png").as_posix()
+        )
+        plt.close()
+
+
+def main():
+    base_output_path.mkdir(exist_ok=True, parents=True)
+    # Find all subfolders (skip files)
+    subfolders = [f for f in parent_results_path.iterdir() if f.is_dir()]
+    print(f"Found {len(subfolders)} subfolders in {parent_results_path}")
+    all_experiments_data = []
+    for folder in track(
+        subfolders, description="[cyan]Loading data...", total=len(subfolders)
+    ):
+        all_experiments_data.append(load_results_data(folder))
+
+    print("Data loading complete. Plotting ROC curves...")
+    roc_curves_output_path = base_output_path / "roc_curves"
+    roc_curves_output_path.mkdir(exist_ok=True, parents=True)
+    for experiment_data in track(
+        all_experiments_data,
+        description="[green]Plotting ROC curves...",
+        total=len(all_experiments_data),
+    ):
+        plot_roc_curve(experiment_data, roc_curves_output_path)
+
+
+if __name__ == "__main__":
+    main()