model/civil-comments #1
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WalkthroughA new Jupyter Notebook has been added that implements a machine learning pipeline for the "Civil Comments" dataset. It loads the dataset into a pandas DataFrame, applies a TF-IDF transformation to the text, and trains a Linear Regression model using an 80-20 train-test split. Model performance is evaluated using mean squared error and R² metrics. Additionally, the notebook defines a Changes
Sequence Diagram(s)sequenceDiagram
participant U as User
participant N as Notebook
participant V as TfidfVectorizer
participant LR as Linear Regression Model
participant EV as Evaluation Module
U->>N: Run notebook
N->>N: Load dataset into DataFrame
N->>V: Transform text data to features
N->>LR: Train model on training set
N->>EV: Evaluate model (MSE, R²)
U->>N: Call get_comment_rating(comment)
N->>V: Transform input comment
N->>LR: Predict toxicity score
LR-->>N: Return prediction
N-->>U: Display toxicity score
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Actionable comments posted: 1
🧹 Nitpick comments (6)
overview_of_machine_learning/ml_training/binary_classification_rain_tomorrow.ipynb (2)
1073-1081: Language inconsistency detected in task titleThe task title is in Polish ("Zadanie 6") while the rest of the notebook uses English. This creates inconsistency in the documentation.
-## Zadanie 6: Wydzielenie zbioru treningowego i testowego, uczenie klasyfikatorów +## Task 6: Training and Test Set Splitting, Training Classifiers
1083-1095: Data splitting implementation looks good, but lacks subsequent classifier trainingThe train_test_split implementation uses appropriate parameters including stratification to maintain class distribution. However, the title mentions "uczenie klasyfikatorów" (training classifiers) but the actual implementation of classifier training is missing.
Consider completing this task by adding classifier implementation code such as:
# Example of implementing a classifier (after the train_test_split) from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, accuracy_score # Initialize and train a classifier clf = RandomForestClassifier(random_state=42) clf.fit(X_train, y_train) # Make predictions and evaluate y_pred = clf.predict(X_test) print(f"Accuracy: {accuracy_score(y_test, y_pred)}") print(classification_report(y_test, y_pred))overview_of_machine_learning/ml_training/civil_comments.ipynb (4)
4-7: Missing documentation in markdown cellThe notebook lacks an introduction or explanation of its purpose. Adding documentation would improve readability and understanding.
Consider adding an introduction in the empty markdown cell:
# Civil Comments Toxicity Analysis This notebook implements a machine learning pipeline for analyzing toxicity in comments from the "Civil Comments" dataset. We'll: 1. Load and prepare the dataset 2. Transform text data using TF-IDF vectorization 3. Train a regression model to predict toxicity scores 4. Evaluate the model's performance 5. Create a function to predict toxicity of new comments
623-627: Use classification metrics for evaluationMean squared error and R² score are regression metrics. For toxicity classification, classification metrics would be more informative.
Add classification metrics to better evaluate the model:
y_pred = model.predict(X_test_tfidf) print(f"mean_squared_error: {mean_squared_error(y_test, y_pred)}") print(f"R^2 score: {r2_score(y_test, y_pred)}") + +# Add classification metrics +from sklearn.metrics import classification_report, accuracy_score + +# Convert predictions to binary using a threshold (e.g., 0.5) +y_pred_binary = (y_pred > 0.5).astype(int) +y_test_binary = (y_test > 0.5).astype(int) + +# Calculate and print metrics for each toxicity label +for i, label in enumerate(labels): + print(f"\nMetrics for {label}:") + print(f"Accuracy: {accuracy_score(y_test_binary.iloc[:, i], y_pred_binary[:, i])}") + print(classification_report(y_test_binary.iloc[:, i], y_pred_binary[:, i]))
645-650: Improve the comment rating function with error handling and thresholdsThe
get_comment_ratingfunction lacks error handling and doesn't provide a clear interpretation of whether a comment is toxic based on a threshold.Enhance the function with error handling and threshold-based interpretation:
-def get_comment_rating(comment): - - comment_tfidf = vectorizer.transform([comment]) - prediction = model.predict(comment_tfidf) - return prediction[0] +def get_comment_rating(comment, threshold=0.5): + """ + Predict toxicity ratings for a comment. + + Args: + comment (str): The text comment to analyze + threshold (float): Threshold for considering a comment toxic (default: 0.5) + + Returns: + dict: Dictionary with toxicity scores and binary classification + """ + try: + if not comment or not isinstance(comment, str): + raise ValueError("Comment must be a non-empty string") + + comment_tfidf = vectorizer.transform([comment]) + prediction = model.predict(comment_tfidf)[0] + + # Create a dictionary of results + results = { + "scores": {label: score for label, score in zip(labels, prediction)}, + "is_toxic": any(score > threshold for score in prediction) + } + + return results + except Exception as e: + print(f"Error analyzing comment: {e}") + return None
674-677: Update the output presentation for better interpretationThe current output doesn't clearly indicate whether a comment is considered toxic or not - it prints "Toxic comment detected:" for all comments.
Improve the output presentation to clearly indicate toxicity classification:
-new_comment = "This is a very nice comment! thank you" -print(new_comment) -print(f"Toxic comment detected: {get_comment_rating(new_comment)}") +new_comment = "This is a very nice comment! thank you" +print(f"Comment: {new_comment}") + +result = get_comment_rating(new_comment) +if result["is_toxic"]: + print("⚠️ TOXIC COMMENT DETECTED") +else: + print("✓ Non-toxic comment") + +print("\nToxicity scores:") +for label, score in result["scores"].items(): + print(f"- {label}: {score:.4f}")
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overview_of_machine_learning/ml_training/binary_classification_rain_tomorrow.ipynb(1 hunks)overview_of_machine_learning/ml_training/civil_comments.ipynb(1 hunks)
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Actionable comments posted: 2
♻️ Duplicate comments (1)
overview_of_machine_learning/ml_training/civil_comments.ipynb (1)
605-607:⚠️ Potential issueUse classification models instead of Linear Regression
Linear Regression is inappropriate for a classification task. This can lead to predictions outside the expected [0,1] range, as seen in your results that include negative values.
This issue was previously flagged and marked as addressed in commit 31899b7, but the current code still uses LinearRegression.
-model = LinearRegression() -model.fit(X_train_tfidf, y_train) +from sklearn.linear_model import LogisticRegression +from sklearn.multioutput import MultiOutputClassifier + +# Using LogisticRegression with MultiOutputClassifier for multiple labels +base_model = LogisticRegression(max_iter=1000) +model = MultiOutputClassifier(base_model) +model.fit(X_train_tfidf, y_train)
🧹 Nitpick comments (8)
overview_of_machine_learning/ml_training/civil_comments.ipynb (8)
4-6: Add descriptive content to the empty markdown cellThe notebook begins with an empty markdown cell. Consider adding a title and description of the notebook's purpose, the dataset used, and the approach taken for toxicity classification.
+# Civil Comments Toxicity Classification + +This notebook implements a machine learning model to detect toxic comments using the Google Civil Comments dataset. It demonstrates how to: + +1. Load and prepare the dataset +2. Transform text data using TF-IDF vectorization +3. Train a classification model +4. Evaluate model performance +5. Make predictions on new comments
126-137: Consider adding dataset exploration and preprocessing stepsThe code loads the dataset and displays the head, but lacks exploratory data analysis and preprocessing steps that would improve model performance.
Consider adding:
- Basic statistics about the dataset size and class distribution
- Text preprocessing (lowercasing, removing special characters, stemming/lemmatization)
- Handling of missing values if any
- Visualization of label distributions
# Add after displaying the dataframe head print(f"Dataset shape: {df.shape}") print("\nLabel distribution:") for label in labels: print(f"{label}: {df[label].mean():.4f}") # Basic text preprocessing import re import nltk from nltk.corpus import stopwords nltk.download('stopwords') stop_words = set(stopwords.words('english')) def preprocess_text(text): text = text.lower() text = re.sub(r'[^\w\s]', '', text) tokens = text.split() tokens = [word for word in tokens if word not in stop_words] return ' '.join(tokens) df['processed_text'] = df['text'].apply(preprocess_text)
145-147: Include all available toxicity labels for comprehensive analysisThe code omits the 'sexual_explicit' label shown in the dataset preview.
-labels = ['toxicity', 'severe_toxicity', 'obscene', 'threat', 'insult', 'identity_attack'] +labels = ['toxicity', 'severe_toxicity', 'obscene', 'threat', 'insult', 'identity_attack', 'sexual_explicit'] X = df['text'] y = df[labels]
156-156: Consider using stratified sampling for imbalanced classificationThe current train_test_split doesn't account for potential class imbalance in toxicity labels.
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) +from sklearn.model_selection import StratifiedShuffleSplit + +# Use one of the labels for stratification (typically the main toxicity label) +stratifier = df['toxicity'] > 0.5 # Convert to binary for stratification +split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) + +for train_idx, test_idx in split.split(X, stratifier): + X_train, X_test = X.iloc[train_idx], X.iloc[test_idx] + y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
165-167: Enhance TF-IDF vectorization with additional parametersThe current TF-IDF implementation uses only max_features without other parameters that could improve performance.
-vectorizer = TfidfVectorizer(max_features=5000) +vectorizer = TfidfVectorizer( + max_features=5000, + min_df=5, # Minimum document frequency + max_df=0.8, # Maximum document frequency (ignore terms that appear in >80% of documents) + ngram_range=(1,2), # Include both unigrams and bigrams + stop_words='english' # Remove English stop words +) X_train_tfidf = vectorizer.fit_transform(X_train) X_test_tfidf = vectorizer.transform(X_test)
645-649: Enhance the comment rating function with input validation and better formattingThe current function lacks input validation and doesn't provide well-formatted output.
-def get_comment_rating(comment): - - comment_tfidf = vectorizer.transform([comment]) - prediction = model.predict(comment_tfidf) - return prediction[0] +def get_comment_rating(comment): + """ + Predicts toxicity ratings for a given comment. + + Args: + comment (str): The text comment to analyze + + Returns: + dict: Dictionary with toxicity scores for each category + """ + if not isinstance(comment, str): + raise TypeError("Comment must be a string") + + if not comment.strip(): + raise ValueError("Comment cannot be empty") + + comment_tfidf = vectorizer.transform([comment]) + prediction = model.predict(comment_tfidf) + + # Create a dictionary of label-prediction pairs + results = {label: max(0, float(score)) for label, score in zip(labels, prediction[0])} + return results
652-656: Improve the prediction output displayThe current output formatting for predictions is not user-friendly and doesn't clearly show which toxicity category corresponds to each score.
-print(labels) - -new_comment = "This is a terrible comment!" -print(new_comment) -print(f"Toxic comment detected: {get_comment_rating(new_comment)}") +new_comment = "This is a terrible comment!" +print(f"Analyzing comment: '{new_comment}'\n") + +results = get_comment_rating(new_comment) + +print("Toxicity analysis:") +for label, score in results.items(): + # Format the score as a percentage and add a visual indicator + percentage = score * 100 + bar_length = int(percentage / 5) # 20 chars = 100% + bar = '█' * bar_length + '░' * (20 - bar_length) + + print(f"{label.replace('_', ' ').title():20}: {percentage:5.1f}% {bar}")
1-701: Add model persistence functionalityThe notebook trains a model but doesn't include functionality to save and reload it for future use.
Add a new code cell at the end of the notebook:
import joblib import os # Create a directory for models if it doesn't exist os.makedirs('models', exist_ok=True) # Save the model and vectorizer joblib.dump(model, 'models/civil_comments_toxicity_model.pkl') joblib.dump(vectorizer, 'models/civil_comments_vectorizer.pkl') print("Model and vectorizer saved to 'models/' directory") # Example of how to load the model and vectorizer loaded_model = joblib.load('models/civil_comments_toxicity_model.pkl') loaded_vectorizer = joblib.load('models/civil_comments_vectorizer.pkl') # Test the loaded model test_comment = "This is a test comment" test_comment_tfidf = loaded_vectorizer.transform([test_comment]) test_prediction = loaded_model.predict(test_comment_tfidf) print(f"Test prediction with loaded model: {test_prediction[0]}")
| "new_comment = \"This is a very nice comment! thank you\"\n", | ||
| "print(new_comment)\n", | ||
| "print(f\"Toxic comment detected: {get_comment_rating(new_comment)}\")" |
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Fix negative prediction values and improve result display
The model is producing negative toxicity scores, which is semantically incorrect for probabilities and highlights the issue with using linear regression for this task.
-new_comment = "This is a very nice comment! thank you"
-print(new_comment)
-print(f"Toxic comment detected: {get_comment_rating(new_comment)}")
+new_comment = "This is a very nice comment! thank you"
+print(f"Analyzing comment: '{new_comment}'\n")
+
+results = get_comment_rating(new_comment)
+
+print("Toxicity analysis:")
+for label, score in results.items():
+ # Format the score as a percentage and ensure non-negative values
+ percentage = max(0, score) * 100
+ bar_length = int(percentage / 5) # 20 chars = 100%
+ bar = '█' * bar_length + '░' * (20 - bar_length)
+
+ print(f"{label.replace('_', ' ').title():20}: {percentage:5.1f}% {bar}")
+
+# Overall toxicity assessment
+overall_toxicity = sum(results.values()) / len(results)
+print(f"\nOverall assessment: {'Potentially toxic' if overall_toxicity > 0.2 else 'Non-toxic'}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "new_comment = \"This is a very nice comment! thank you\"\n", | |
| "print(new_comment)\n", | |
| "print(f\"Toxic comment detected: {get_comment_rating(new_comment)}\")" | |
| new_comment = "This is a very nice comment! thank you" | |
| print(f"Analyzing comment: '{new_comment}'\n") | |
| results = get_comment_rating(new_comment) | |
| print("Toxicity analysis:") | |
| for label, score in results.items(): | |
| # Format the score as a percentage and ensure non-negative values | |
| percentage = max(0, score) * 100 | |
| bar_length = int(percentage / 5) # 20 chars = 100% | |
| bar = '█' * bar_length + '░' * (20 - bar_length) | |
| print(f"{label.replace('_', ' ').title():20}: {percentage:5.1f}% {bar}") | |
| # Overall toxicity assessment | |
| overall_toxicity = sum(results.values()) / len(results) | |
| print(f"\nOverall assessment: {'Potentially toxic' if overall_toxicity > 0.2 else 'Non-toxic'}") |
| "y_pred = model.predict(X_test_tfidf)\n", | ||
| "print(f\"mean_squared_error: {mean_squared_error(y_test, y_pred)}\")\n", | ||
| "print(f\"R^2 score: {r2_score(y_test, y_pred)}\")" |
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🛠️ Refactor suggestion
Use appropriate evaluation metrics for classification tasks
MSE and R² are regression metrics. For classification tasks, especially with multiple labels, different metrics should be used.
-y_pred = model.predict(X_test_tfidf)
-print(f"mean_squared_error: {mean_squared_error(y_test, y_pred)}")
-print(f"R^2 score: {r2_score(y_test, y_pred)}")
+from sklearn.metrics import classification_report, roc_auc_score
+
+# For binary classification per label (assuming threshold of 0.5)
+y_pred = model.predict(X_test_tfidf)
+
+# For probability scores (AUC-ROC)
+y_pred_proba = model.predict_proba(X_test_tfidf)
+
+# Evaluate each label separately
+for i, label in enumerate(labels):
+ print(f"\n--- {label} ---")
+ # Convert continuous values to binary using 0.5 threshold for evaluation
+ y_test_binary = (y_test[label] >= 0.5).astype(int)
+ y_pred_binary = (y_pred[:, i] >= 0.5).astype(int)
+
+ print(classification_report(y_test_binary, y_pred_binary))
+
+ # AUC-ROC score (works with probabilities)
+ if hasattr(model, "predict_proba"):
+ try:
+ auc = roc_auc_score(y_test_binary, y_pred_proba[i][:, 1])
+ print(f"AUC-ROC: {auc:.4f}")
+ except:
+ print("Could not calculate AUC-ROC")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "y_pred = model.predict(X_test_tfidf)\n", | |
| "print(f\"mean_squared_error: {mean_squared_error(y_test, y_pred)}\")\n", | |
| "print(f\"R^2 score: {r2_score(y_test, y_pred)}\")" | |
| from sklearn.metrics import classification_report, roc_auc_score | |
| # For binary classification per label (assuming threshold of 0.5) | |
| y_pred = model.predict(X_test_tfidf) | |
| # For probability scores (AUC-ROC) | |
| y_pred_proba = model.predict_proba(X_test_tfidf) | |
| # Evaluate each label separately | |
| for i, label in enumerate(labels): | |
| print(f"\n--- {label} ---") | |
| # Convert continuous values to binary using 0.5 threshold for evaluation | |
| y_test_binary = (y_test[label] >= 0.5).astype(int) | |
| y_pred_binary = (y_pred[:, i] >= 0.5).astype(int) | |
| print(classification_report(y_test_binary, y_pred_binary)) | |
| # AUC-ROC score (works with probabilities) | |
| if hasattr(model, "predict_proba"): | |
| try: | |
| auc = roc_auc_score(y_test_binary, y_pred_proba[i][:, 1]) | |
| print(f"AUC-ROC: {auc:.4f}") | |
| except: | |
| print("Could not calculate AUC-ROC") |
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