Delete intent_graphs.py

intent_graphs.py

@@ -1,88 +0,0 @@
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve
-from sklearn.preprocessing import label_binarize
-from transformers import BertTokenizer, BertForSequenceClassification
-from datasets import load_dataset
-
-# Check for CUDA
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load dataset
-dataset = load_dataset("clinc_oos", "plus")
-label_names = dataset["train"].features["intent"].names  # Ensure correct order
-
-# Load model
-num_labels = len(label_names)
-model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=num_labels)
-model.load_state_dict(torch.load("intent_classifier.pth", map_location=device))
-model.to(device)
-model.eval()
-
-# Load tokenizer
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-
-# Prepare data
-true_labels = []
-pred_labels = []
-all_probs = []
-
-for example in dataset["test"]:
-    sentence = example["text"]
-    true_label = example["intent"]
-
-    # Tokenize
-    inputs = tokenizer(sentence, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
-    inputs = {key: val.to(device) for key, val in inputs.items()}
-
-    # Predict
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=1).cpu().numpy()[0]
-        predicted_class = np.argmax(probs)
-
-    # Store results
-    true_labels.append(true_label)
-    pred_labels.append(predicted_class)
-    all_probs.append(probs)
-
-# Convert to numpy arrays
-true_labels = np.array(true_labels)
-pred_labels = np.array(pred_labels)
-all_probs = np.array(all_probs)
-
-# Compute confusion matrix
-conf_matrix = confusion_matrix(true_labels, pred_labels)
-
-# Plot confusion matrix
-plt.figure(figsize=(12, 10))
-sns.heatmap(conf_matrix, annot=False, fmt="d", cmap="Blues")
-plt.xlabel("Predicted Label")
-plt.ylabel("True Label")
-plt.title("Confusion Matrix for Intent Classification")
-plt.savefig("confusion_matrix.png", dpi=300, bbox_inches="tight")
-plt.close()
-
-print("Confusion matrix saved as confusion_matrix.png")
-
-# --- Multi-Class Precision-Recall Curve ---
-# Binarize true labels for multi-class PR calculation
-true_labels_bin = label_binarize(true_labels, classes=np.arange(num_labels))
-
-# Plot Precision-Recall Curve for multiple classes
-plt.figure(figsize=(10, 8))
-for i in range(num_labels):
-    precision, recall, _ = precision_recall_curve(true_labels_bin[:, i], all_probs[:, i])
-    plt.plot(recall, precision, lw=1, alpha=0.7, label=f"Class {i}: {label_names[i]}")
-
-plt.xlabel("Recall")
-plt.ylabel("Precision")
-plt.title("Multi-Class Precision-Recall Curve")
-plt.legend(loc="best", fontsize=6, ncol=2, frameon=True)
-plt.grid(True)
-plt.savefig("precision_recall_curve.png", dpi=300, bbox_inches="tight")
-plt.close()
-
-print("Precision-Recall curve saved as precision_recall_curve.png")