106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
import pandas as pd
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from sklearn.preprocessing import OneHotEncoder
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from typing import Tuple
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import os
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
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def process_data(train_path: str, test_path: str) -> Tuple[pd.DataFrame, pd.Series, pd.DataFrame, pd.Series]:
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columns: list[str] = [
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'age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status',
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'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss',
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'hours-per-week', 'native-country', 'income'
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]
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df_train: pd.DataFrame = pd.read_csv(train_path, header=None, names=columns, sep=r',\s*', engine='python', na_values='?')
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df_test: pd.DataFrame = pd.read_csv(test_path, header=None, names=columns, sep=r',\s*', engine='python', na_values='?', skiprows=1)
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df_train.dropna(inplace=True)
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df_test.dropna(inplace=True)
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continuous_attributes: list[str] = [
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'age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week'
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]
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X_train: pd.DataFrame = df_train.drop(columns=['income'])
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y_train: pd.Series = df_train['income'].str.replace('.', '', regex=False)
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X_test: pd.DataFrame = df_test.drop(columns=['income'])
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y_test: pd.Series = df_test['income'].str.replace('.', '', regex=False)
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X_train = X_train.drop(columns=continuous_attributes)
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X_test = X_test.drop(columns=continuous_attributes)
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categorical_attributes: list[str] = X_train.columns.tolist()
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encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)
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X_train_encoded: pd.DataFrame = pd.DataFrame(encoder.fit_transform(X_train[categorical_attributes]), columns=encoder.get_feature_names_out(categorical_attributes))
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X_test_encoded: pd.DataFrame = pd.DataFrame(encoder.transform(X_test[categorical_attributes]), columns=encoder.get_feature_names_out(categorical_attributes))
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return X_train_encoded, y_train, X_test_encoded, y_test
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def evaluate_model(X_train: pd.DataFrame, y_train: pd.Series, X_test: pd.DataFrame, y_test: pd.Series, model, model_name: str):
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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report = classification_report(y_test, y_pred, output_dict=True)
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cm = confusion_matrix(y_test, y_pred, labels=model.classes_)
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print(f"--- {model_name} Evaluation ---")
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print(f"Overall Accuracy: {accuracy_score(y_test, y_pred):.4f}\n")
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for label in model.classes_:
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class_idx = list(model.classes_).index(label)
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tp = cm[class_idx, class_idx]
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fp = cm[:, class_idx].sum() - tp
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fn = cm[class_idx, :].sum() - tp
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tn = cm.sum() - (tp + fp + fn)
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tp_rate = tp / (tp + fn) if (tp + fn) > 0 else 0 # Same as recall
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fp_rate = fp / (fp + tn) if (fp + tn) > 0 else 0
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print(f"Class: {label}")
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print(f" TP Rate (Recall): {tp_rate:.4f}")
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print(f" FP Rate : {fp_rate:.4f}")
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print(f" Precision : {report[label]['precision']:.4f}")
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print(f" F1-Score : {report[label]['f1-score']:.4f}")
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print("-" * 20)
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if __name__ == '__main__':
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train_file = 'adult/adult.data'
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test_file = 'adult/adult.test'
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output_dir = 'adult-clean'
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os.makedirs(output_dir, exist_ok=True)
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X_train, y_train, X_test, y_test = process_data(train_file, test_file)
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y_train = y_train.reset_index(drop=True)
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X_train = X_train.reset_index(drop=True)
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y_test = y_test.reset_index(drop=True)
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X_test = X_test.reset_index(drop=True)
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train_cleaned = pd.concat([X_train, y_train], axis=1)
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test_cleaned = pd.concat([X_test, y_test], axis=1)
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train_cleaned.to_csv(os.path.join(output_dir, 'train_clean.csv'), index=False)
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test_cleaned.to_csv(os.path.join(output_dir, 'test_clean.csv'), index=False)
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print(f"Preprocessed data saved to '{output_dir}' directory.")
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print(f"Training data shape: {train_cleaned.shape}")
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print(f"Test data shape: {test_cleaned.shape}\n")
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# Decision Tree Classifier
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dt_classifier = DecisionTreeClassifier(random_state=42)
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evaluate_model(X_train, y_train, X_test, y_test, dt_classifier, "Decision Tree Classifier")
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# Naive Bayesian Classifier
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nb_classifier = BernoulliNB()
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evaluate_model(X_train, y_train, X_test, y_test, nb_classifier, "Naïve Bayesian Classifier")
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