Update placeholder_test.py

tests/placeholder_test.py

@@ -1,143 +1,76 @@
+import pytest
 import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import re
+import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import (
-    accuracy_score,
-    classification_report,
-    confusion_matrix,
-    roc_curve,
-    auc,
-    precision_recall_curve,
-)
+from sklearn.metrics import accuracy_score
+
+# Mock data for testing
+@pytest.fixture
+def sample_data():
+    return pd.DataFrame({
+        'text': [
+            "This is a test sentence.",
+            "Another example text from an AI model.",
+            "Humans write text like this.",
+            "AI can generate sentences too.",
+            "What makes AI different is its patterns."
+        ],
+        'generated': [0, 1, 0, 1, 1]  # 0 = Human, 1 = AI
+    })
+
+
+# Test for data preprocessing
+def test_preprocessing(sample_data):
+    # Example preprocessing function
+    def clean_text(text):
+        return text.lower().replace(".", "").replace(",", "")
+
+    sample_data['cleaned_text'] = sample_data['text'].apply(clean_text)
+
+    assert 'cleaned_text' in sample_data.columns
+    assert sample_data['cleaned_text'][0] == "this is a test sentence"
+
+
+# Test for TF-IDF vectorization
+def test_tfidf_vectorization(sample_data):
+    vectorizer = TfidfVectorizer(max_features=500)
+    X = vectorizer.fit_transform(sample_data['text'])
+
+    assert X.shape[0] == len(sample_data)  # Check rows
+    assert X.shape[1] <= 500  # Check feature limit
+
+
+# Test for Logistic Regression training
+def test_logistic_regression_training(sample_data):
+    # Vectorize text
+    vectorizer = TfidfVectorizer(max_features=500)
+    X = vectorizer.fit_transform(sample_data['text'])
+    y = sample_data['generated']
+
+    # Train model
+    model = LogisticRegression(max_iter=1000, solver='liblinear')
+    model.fit(X, y)
+
+    # Ensure the model has been trained
+    assert len(model.coef_[0]) == X.shape[1]
+
+
+# Test for accuracy calculation
+def test_model_accuracy(sample_data):
+    # Vectorize text
+    vectorizer = TfidfVectorizer(max_features=500)
+    X = vectorizer.fit_transform(sample_data['text'])
+    y = sample_data['generated']
+
+    # Train model
+    model = LogisticRegression(max_iter=1000, solver='liblinear')
+    model.fit(X, y)
+
+    # Predict on the same data
+    y_pred = model.predict(X)
+
+    # Calculate accuracy
+    accuracy = accuracy_score(y, y_pred)
+    assert accuracy > 0.5  # Ensure accuracy is reasonable
 
-# Create mock data
-mock_data = {
-    'text': [
-        "This is an example of human-written text.",
-        "The AI generated this piece of text for testing.",
-        "Humans write text like this for documentation purposes.",
-        "AI can create very convincing human-like text examples.",
-        "This is another piece of human-written content.",
-        "AI has been used to generate this sample text."
-    ],
-    'generated': [0, 1, 0, 1, 0, 1]  # 0 for human, 1 for AI
-}
-
-# Convert mock data to a DataFrame
-df = pd.DataFrame(mock_data)
-
-# Total entries
-print("Total entries:", df.count().sum())
-
-# A brief overview
-print(df.describe())
-
-# Class distribution in 'generated' column
-class_distribution = df['generated'].value_counts()
-print("\nClass Distribution:\n", class_distribution)
-
-# Function to clean text without NLTK
-def clean_text_no_nltk(text):
-    text = text.lower()
-    text = re.sub(r'[^a-z\s]', '', text)
-    stop_words = {'the', 'and', 'is', 'in', 'to', 'of', 'for', 'it', 'on', 'this', 'that', 'with', 'a', 'as'}
-    tokens = text.split()
-    tokens = [word for word in tokens if word not in stop_words]
-    return ' '.join(tokens)
-
-
-# Apply the cleaning function to the 'text' column
-df['cleaned_text'] = df['text'].apply(clean_text_no_nltk)
-
-# Display the first few rows to verify cleaning
-print(df[['text', 'cleaned_text']].head())
-
-# Vectorize text data
-vectorizer = TfidfVectorizer(max_features=5000)
-X = vectorizer.fit_transform(df['cleaned_text'])
-y = df['generated']
-
-# Split data
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
-
-# Logistic Regression
-log_model = LogisticRegression(max_iter=1000, solver='liblinear')
-log_model.fit(X_train, y_train)
-
-# Predictions and evaluation
-y_pred = log_model.predict(X_test)
-y_score = log_model.decision_function(X_test)
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Logistic Regression Accuracy: {accuracy}")
-print("Classification Report:")
-print(classification_report(y_test, y_pred, zero_division=0))
-
-# Visualization: Confusion Matrix
-cm = confusion_matrix(y_test, y_pred)
-plt.figure(figsize=(6, 4))
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Human', 'AI'], yticklabels=['Human', 'AI'])
-plt.title('Confusion Matrix')
-plt.xlabel('Predicted Label')
-plt.ylabel('True Label')
-plt.show()
-
-# Visualization: ROC Curve
-fpr, tpr, _ = roc_curve(y_test, y_score)
-roc_auc = auc(fpr, tpr)
-plt.figure(figsize=(6, 4))
-plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')
-plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
-plt.title('ROC Curve')
-plt.xlabel('False Positive Rate')
-plt.ylabel('True Positive Rate')
-plt.legend(loc="lower right")
-plt.show()
-
-# Visualization: Precision-Recall Curve
-precision, recall, _ = precision_recall_curve(y_test, y_score)
-plt.figure(figsize=(6, 4))
-plt.plot(recall, precision, marker='.', label='Precision-Recall')
-plt.fill_between(recall, precision, alpha=0.3, color='blue')
-plt.title('Precision-Recall Curve')
-plt.xlabel('Recall')
-plt.ylabel('Precision')
-plt.legend()
-plt.show()
-
-# Top Positive and Negative Features
-feature_names = vectorizer.get_feature_names_out()
-coefficients = log_model.coef_[0]
-
-coef_df = pd.DataFrame({'Feature': feature_names, 'Coefficient': coefficients})
-top_positive = coef_df.nlargest(10, 'Coefficient')
-top_negative = coef_df.nsmallest(10, 'Coefficient')
-
-# Plot top positive coefficients
-plt.figure(figsize=(8, 5))
-sns.barplot(x='Coefficient', y='Feature', data=top_positive, palette='Greens', hue='Feature', legend=False)
-plt.title('Top 10 Positive Features (AI Indicating Words)')
-plt.xlabel('Coefficient Value')
-plt.ylabel('Feature')
-plt.show()
-
-# Plot top negative coefficients
-plt.figure(figsize=(8, 5))
-sns.barplot(x='Coefficient', y='Feature', data=top_negative, palette='Reds', hue='Feature', legend=False)
-plt.title('Top 10 Negative Features (Human Indicating Words)')
-plt.xlabel('Coefficient Value')
-plt.ylabel('Feature')
-plt.show()
-
-# Visualization: Histogram of Predictions
-plt.figure(figsize=(6, 4))
-sns.histplot(y_score, kde=True, bins=30, color='purple')
-plt.axvline(0, color='red', linestyle='--', label='Decision Boundary')
-plt.title('Histogram of Decision Function Scores')
-plt.xlabel('Decision Score')
-plt.ylabel('Frequency')
-plt.legend()
-plt.show()