Delete _multiclass_confusion_matrix.py

_multiclass_confusion_matrix.py

@@ -1,161 +0,0 @@
-import pandas as pd
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from sklearn.metrics import confusion_matrix
-import seaborn as sns
-import matplotlib
-matplotlib.use('Qt5Agg')
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-import numpy as np
-import os
-os.environ['QT_QPA_PLATFORM'] = 'xcb'
-
-# Define label mappings
-label_map = {0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}
-reverse_label_map = {v: k for k, v in label_map.items()}  # Reverse mapping for converting labels to integers
-
-# Load the dataset
-df = pd.read_csv('./dataset/emotions.csv')
-
-# Ensure the 'label' column exists
-if 'label' not in df.columns:
-    print("Error: 'label' column is missing from the dataset.")
-    exit(1)
-
-# Convert text labels to numeric if they're not already numeric
-if df['label'].dtype == 'object':
-    df['label'] = df['label'].map(reverse_label_map)
-
-# Verify label conversion
-if df['label'].isnull().any():
-    print("Error: Some labels could not be mapped properly.")
-    exit(1)
-
-# Sample a smaller subset for faster debugging
-sample_size = 20000  # Adjust sample size as needed
-df_sampled = df.sample(n=sample_size, random_state=42)
-
-
-# Split the sampled dataset
-train_texts, val_texts, train_labels, val_labels = train_test_split(
-    df_sampled['text'].tolist(),
-    df_sampled['label'].tolist(),
-    test_size=0.2,
-    random_state=42
-)
-
-model_6_path = "./models/stardust_6"
-tokenizer = AutoTokenizer.from_pretrained(model_6_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_6_path, num_labels=6)
-model.eval()  # Set model to evaluation mode
-
-# Define a function for tokenization and encoding
-def tokenize_and_encode(texts, labels):
-    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
-    inputs['labels'] = torch.tensor(labels)
-    return inputs
-
-# Create datasets with labels
-train_dataset = tokenize_and_encode(train_texts, train_labels)
-val_dataset = tokenize_and_encode(val_texts, val_labels)
-
-# Move model to GPU if available
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Move validation inputs to the device
-val_inputs = {k: v.to(device) for k, v in val_dataset.items() if k != 'labels'}
-val_labels = val_dataset['labels'].to(device)
-
-def plot_classification_analysis(val_labels, val_inputs, model, label_map):
-    # Convert labels if they're one-hot encoded
-    true_labels = val_labels.argmax(dim=-1).cpu().numpy() if len(val_labels.shape) > 1 else val_labels.cpu().numpy()
-    
-    with torch.no_grad():
-        # Get the raw logits from the model
-        outputs = model(**val_inputs)
-        logits = outputs.logits.cpu().numpy()
-        
-        # Calculate softmax probabilities
-        probabilities = np.exp(logits) / np.exp(logits).sum(axis=1, keepdims=True)
-        predictions_softmax = np.argmax(probabilities, axis=-1)
-    
-    # Convert label_map to list for plotting
-    label_map_list = list(label_map.values())
-    
-    # Create figure with two subplots
-    fig, axes = plt.subplots(1, 2, figsize=(20, 8))
-    
-    # First subplot: Confusion Matrix
-    cm_softmax = confusion_matrix(true_labels, predictions_softmax)
-    sns.heatmap(
-        cm_softmax,
-        annot=True,
-        fmt="d",
-        cmap="Oranges",
-        xticklabels=label_map_list,
-        yticklabels=label_map_list,
-        ax=axes[0],
-        square=True
-    )
-    axes[0].set_xlabel("Prediction")
-    axes[0].set_ylabel("Truth")
-    axes[0].set_title(f"Softmax [{sample_size}]")
-    
-    # Rotate x-axis labels for better readability
-    axes[0].set_xticklabels(axes[0].get_xticklabels(), rotation=45, ha='right')
-    axes[0].set_yticklabels(axes[0].get_yticklabels(), rotation=0)
-    
-    # Second subplot: Raw Logits Heatmap
-    sample_size_r = min(sample_size, logits.shape[0])  # Show up to 50 samples
-    logits_subset = logits[:sample_size_r]
-    
-    sns.heatmap(
-        logits_subset,
-        annot=False,
-        cmap="Oranges",
-        cbar=True,
-        xticklabels=label_map_list,
-        yticklabels=False,
-        ax=axes[1]
-    )
-    axes[1].set_xlabel("Classes")
-    axes[1].set_ylabel("Samples")
-    axes[1].set_title(f"Logits Distribution [{sample_size}]")
-
-    # Rotate x-axis labels for better readability
-    axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=45, ha='right')
-    
-    # Add color bar labels
-    for im, title in zip(axes, ['Number of Samples', 'Logit Value']):
-        cbar = im.collections[0].colorbar
-        cbar.set_label(title)
-    
-    plt.tight_layout()
-    
-    # Calculate and return additional metrics
-    metrics = {
-        'confusion_matrix': cm_softmax,
-        'raw_logits_stats': {
-            'mean': np.mean(logits, axis=0),
-            'std': np.std(logits, axis=0),
-            'min': np.min(logits, axis=0),
-            'max': np.max(logits, axis=0)
-        }
-    }
-    
-    return fig, metrics
-
-fig, metrics = plot_classification_analysis(
-    val_labels=val_labels,
-    val_inputs=val_inputs,
-    model=model,
-    label_map=label_map
-)
-
-plt.show()
-
-
-
-