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import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import confusion_matrix
import torch
def extract_hidden_state(input_text, tokenizer, language_model):
tokens = tokenizer(input_text, padding=True, return_tensors="pt")
with torch.no_grad():
outputs = language_model(**tokens)
return outputs.last_hidden_state[:,0].numpy()
def get_metrics(y_true, y_preds):
accuracy = accuracy_score(y_true, y_preds)
f1_macro = f1_score(y_true, y_preds, average="macro")
f1_weighted = f1_score(y_true, y_preds, average="weighted")
print(f"Accuracy: {accuracy}")
print(f"F1 macro average: {f1_macro}")
print(f"F1 weighted average: {f1_weighted}")
def evaluate_predictions(model:str, train_preds, y_train, test_preds, y_test):
print(model)
print("\nTrain set:")
get_metrics(y_train, train_preds)
print("-"*50)
print("Test set:")
get_metrics(y_test, test_preds)
def plot_confusion_matrix(y_true, y_preds):
labels = sorted(set(y_true.tolist() + y_preds.tolist()))
cm = confusion_matrix(y_true, y_preds)
plt.figure(figsize=(12, 10))
sns.heatmap(cm, annot=True, cmap="Blues",
xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('Confusion Matrix')
plt.show() |