[ADD] n_subsample to the support for distribution

evaluate/_utils_compute.py

@@ -71,16 +71,16 @@ def merge_preds(df, tolerence, tensor_length,frame_shift):
     result = df.groupby("group").agg({"Starttime": "min", "Endtime": "max"})
     return result
 
-def reshape_support(support_samples, tensor_length=128):
+def reshape_support(support_samples, tensor_length=128, n_subsample=1):
     new_input = []
     for x in support_samples:
-        #for _ in range(n_subsample):
-        if x.shape[1] > tensor_length:
-            rand_start = torch.randint(0, x.shape[1] - tensor_length, (1,))
-            new_x = torch.tensor(x[:, rand_start : rand_start + tensor_length])
-            new_input.append(new_x.unsqueeze(0))
-        else:
-            new_input.append(torch.tensor(x))
+        for _ in range(n_subsample):
+            if x.shape[1] > tensor_length:
+                rand_start = torch.randint(0, x.shape[1] - tensor_length, (1,))
+                new_x = torch.tensor(x[:, rand_start : rand_start + tensor_length])
+                new_input.append(new_x.unsqueeze(0))
+            else:
+                new_input.append(torch.tensor(x))
     all_supports = torch.cat([x for x in new_input])
     return(all_supports)
 

evaluate/_utils_writing.py

@@ -134,4 +134,55 @@ def write_wav(
     result_wav = np.vstack(
         (arr, gt_labels, merged_pred, pred_labels , distances_to_pos / 10, z_scores_pos)
     )
-    wavfile.write(output, target_fs, result_wav.T)
+    wavfile.write(output, target_fs, result_wav.T)
+
+def plot_2_d_representation():
+        import numpy as np
+        import matplotlib.pyplot as plt
+        from sklearn.manifold import TSNE
+
+        # Assuming `prototypes`, `z_pos_supports`, `z_neg_supports`, `q_embeddings`, and `labels` are already defined
+        # Convert tensors to numpy arrays if they are in tensor format
+        # e.g., z_pos_supports = z_pos_supports.detach().numpy()
+
+        # Create a labels array for all points
+        # Label for prototypes, positive supports, negative supports, and query embeddings respectively
+        prototypes_labels = np.array([2] * prototypes.shape[0])  # Assuming 2 is not used in `gt_labels`
+        pos_supports_labels = np.array([3] * z_pos_supports.shape[0])  # Assuming 3 is not used in `gt_labels`
+        neg_supports_labels = np.array([4] * z_neg_supports.shape[0])  # Assuming 4 is not used in `gt_labels`
+        q_embeddings = q_embeddings.detach().numpy()
+        gt_labels = labels.detach().numpy()
+
+        # Concatenate everything into one dataset
+        feat = np.concatenate([prototypes, z_pos_supports, z_neg_supports, q_embeddings])
+        all_labels = np.concatenate([prototypes_labels, pos_supports_labels, neg_supports_labels, gt_labels])
+
+        # Run t-SNE
+        tsne = TSNE(n_components=2, perplexity=30)
+        features_2d = tsne.fit_transform(feat)
+
+        # Plot
+        plt.figure(figsize=(10, 8))
+        # Define marker for each type of point
+        markers = {2: "P", 3: "o", 4: "X"}  # P for prototypes, o for supports, X for negative supports
+
+        for label in np.unique(all_labels):
+            # Plot each class with its own color and marker
+            idx = np.where(all_labels == label)
+            if label in markers:  # Prototypes or supports
+                plt.scatter(features_2d[idx, 0], features_2d[idx, 1], label=label, alpha=1.0, marker=markers[label], s=100)  # Larger size
+            else:  # Query embeddings
+                plt.scatter(features_2d[idx, 0], features_2d[idx, 1], label=label, alpha=0.5, s=50)  # Smaller size, more transparent
+
+        plt.legend()
+        plt.title('t-SNE visualization of embeddings, prototypes, and supports')
+        plt.xlabel('Dimension 1')
+        plt.ylabel('Dimension 2')
+        plt.grid(True)
+
+        fig_name = os.path.basename(support_spectrograms).split("data_")[1].split(".")[0] + ".png"
+        output = os.path.join(target_path, fig_name)
+
+        # Save the figure
+        plt.savefig(output, bbox_inches="tight")
+        plt.show()

evaluate/evaluateDCASE.py

@@ -100,7 +100,9 @@ def compute(
     # GET EMBEDDINGS FOR THE NEG SAMPLES #
     ######################################
     support_samples_neg = df_support[df_support["category"] == "NEG"]["feature"].to_numpy()
-    support_samples_neg = reshape_support(support_samples_neg, tensor_length=cfg["data"]["tensor_length"])
+    support_samples_neg = reshape_support(support_samples_neg, 
+                                          tensor_length=cfg["data"]["tensor_length"], 
+                                          n_subsample=cfg["predict"]["n_subsample"])
     z_neg_supports, _ = model.get_embeddings(support_samples_neg, padding_mask=None)
 
     ### Get the query dataset ###
@@ -200,28 +202,6 @@ def compute(
     ################################################
     if cfg["plot"]["tsne"]:
 
-        from sklearn.manifold import TSNE
-        import seaborn as sns
-
-        prototypes=prototypes.detach().numpy()
-        z_pos_supports = z_pos_supports.detach().numpy()
-        z_neg_supports = z_neg_supports.detach().numpy()
-        q_embeddings = q_embeddings.detach().numpy()
-        gt_labels = labels
-        other_labels = np.concatenate(([0,1], np.repeat(1, z_pos_supports.shape(0)), np.repeat(0, z_neg_supports.shape(0))), axis=None)
-
-        feat = np.concatenate([q_embeddings, prototypes, z_pos_supports, z_neg_supports])
-        tsne = TSNE(n_components=2, perplexity=5)
-        features_2d = tsne.fit_transform(feat)
-
-        # Do the figure!
-        fig = sns.scatterplot(x=features_2d[:, 0], y=features_2d[:, 1], hue=labels)
-        sns.move_legend(fig, "upper left", bbox_to_anchor=(1, 1))
-
-        fig_name = os.path.basename(support_spectrograms).split("data_")[1].split(".")[0] + ".png"
-        output = os.path.join(target_path, fig_name)
-        fig.get_figure().savefig(output, bbox_inches="tight")
-
         import numpy as np
         import matplotlib.pyplot as plt
         from sklearn.manifold import TSNE
@@ -235,6 +215,8 @@ def compute(
         prototypes_labels = np.array([2] * prototypes.shape[0])  # Assuming 2 is not used in `gt_labels`
         pos_supports_labels = np.array([3] * z_pos_supports.shape[0])  # Assuming 3 is not used in `gt_labels`
         neg_supports_labels = np.array([4] * z_neg_supports.shape[0])  # Assuming 4 is not used in `gt_labels`
+        q_embeddings = q_embeddings.detach().numpy()
+        gt_labels = labels.detach().numpy()
 
         # Concatenate everything into one dataset
         feat = np.concatenate([prototypes, z_pos_supports, z_neg_supports, q_embeddings])
@@ -263,6 +245,9 @@ def compute(
         plt.ylabel('Dimension 2')
         plt.grid(True)
 
+        fig_name = os.path.basename(support_spectrograms).split("data_")[1].split(".")[0] + ".png"
+        output = os.path.join(target_path, fig_name)
+
         # Save the figure
         plt.savefig(output, bbox_inches="tight")
         plt.show()