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| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.metrics import classification_report, confusion_matrix | |
| from imblearn.over_sampling import SMOTE | |
| from imblearn.pipeline import Pipeline as imbpipeline | |
| import mlflow | |
| import dagshub | |
| # Initialize DagsHub and MLflow for tracking | |
| dagshub.init(repo_owner='alaa.hussein401', repo_name='Winemakers-Dilemma', mlflow=True) | |
| mlflow.set_tracking_uri('https://dagshub.com/alaa.hussein401/Winemakers-Dilemma.mlflow') | |
| # Start an MLflow run | |
| with mlflow.start_run(): | |
| # Load the dataset | |
| df = pd.read_csv('vineyard_weather_1948-2017.csv') | |
| # Preprocess and feature engineering | |
| df['DATE'] = pd.to_datetime(df['DATE'], format='%Y-%m-%d') | |
| df['year'] = df['DATE'].dt.year | |
| df['week_of_year'] = df['DATE'].dt.isocalendar().week | |
| # Filter data for weeks 35 to 40 | |
| df_filtered = df[(df['week_of_year'] >= 35) & (df['week_of_year'] <= 40)] | |
| # Aggregate data by year and week number | |
| weekly_data = df_filtered.groupby(['year', 'week_of_year']).agg({ | |
| 'PRCP': 'sum', | |
| 'TMAX': 'mean', | |
| 'TMIN': 'mean', | |
| }).reset_index() | |
| # Feature engineering | |
| weekly_data['PRCP_lag1'] = weekly_data['PRCP'].shift(1) | |
| weekly_data['PRCP_lag2'] = weekly_data['PRCP'].shift(2) | |
| weekly_data['PRCP_MA3'] = weekly_data['PRCP'].rolling(window=3).mean() | |
| # Drop rows with NaN values that were created by shifts and rolling means | |
| weekly_data.dropna(inplace=True) | |
| # Select features for training | |
| features = ['TMAX', 'TMIN', 'PRCP_lag1', 'PRCP_lag2', 'PRCP_MA3'] | |
| X = weekly_data[features] | |
| y = (weekly_data['PRCP'] >= 0.35).astype(int) | |
| # Split into train and test sets | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Create a pipeline that first standardizes the data, then applies SMOTE, and finally fits the model | |
| pipeline = imbpipeline([ | |
| ('scaler', StandardScaler()), | |
| ('smote', SMOTE(random_state=42)), | |
| ('classifier', RandomForestClassifier(n_estimators=200, random_state=42)) | |
| ]) | |
| # Fit the pipeline | |
| pipeline.fit(X_train, y_train) | |
| # Make predictions | |
| y_pred = pipeline.predict(X_test) | |
| # Log parameters, metrics, and artifacts | |
| mlflow.log_params(pipeline.named_steps['classifier'].get_params()) | |
| mlflow.log_metric('accuracy', pipeline.score(X_test, y_test)) | |
| # Generate and log classification report | |
| report = classification_report(y_test, y_pred) | |
| mlflow.log_text(report, 'classification_report_3.txt') | |
| # Generate and log confusion matrix | |
| conf_matrix = confusion_matrix(y_test, y_pred) | |
| mlflow.log_text(str(conf_matrix), 'confusion_matrix_3.txt') | |
| # Print the classification report and confusion matrix | |
| print(report) | |
| print(conf_matrix) | |