multiregression: Start of example

Author: jonnor

examples/multiregression_trees/multiregression_train.py

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+import pandas as pd
+import numpy as np
+from pathlib import Path
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.pipeline import Pipeline
+from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_percentage_error
+import urllib.request
+import zipfile
+import os.path
+
+from sklearn.multioutput import MultiOutputRegressor
+
+
+def airquality_download(data_dir='data'):
+    """
+    UCI Air Quality dataset
+    https://archive.ics.uci.edu/dataset/360/air+quality
+    """
+
+    data_path = Path(data_dir)
+    data_path.mkdir(exist_ok=True)
+    
+    csv_file = data_path / 'AirQualityUCI.csv'
+    
+    if not csv_file.exists():
+        url = "https://archive.ics.uci.edu/ml/machine-learning-databases/00360/AirQualityUCI.zip"
+        zip_path = data_path / 'AirQualityUCI.zip'
+        
+        urllib.request.urlretrieve(url, zip_path)
+        
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(data_path)
+        
+        zip_path.unlink()
+    
+    return csv_file
+
+
+def airquality_load(csv_file):
+    df = pd.read_csv(csv_file, sep=';', decimal=',')
+    
+    # Remove missing values
+    df = df.replace(-200, np.nan)
+    df = df.dropna(axis=1, how='all').dropna()
+    
+    df['datetime'] = pd.to_datetime(df['Date'] + ' ' + df['Time'], format='%d/%m/%Y %H.%M.%S')
+    df = df.drop(['Date', 'Time'], axis=1)
+
+    target_cols = ['CO(GT)', 'NOx(GT)', 'NO2(GT)', 'NMHC(GT)', 'C6H6(GT)']
+    exclude_cols = target_cols + ['datetime', 'Unnamed: 15', 'Unnamed: 16']
+    feature_cols = [col for col in df.columns if col not in exclude_cols]    
+
+    X = df[feature_cols]
+    y = df[target_cols]
+    
+    return X, y
+
+
+from emlearn.preprocessing import Quantizer
+import emlearn
+
+def convert_multiregressor(multi, out_dir, format=None, prefix='regressor', **kwargs):
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(exist_ok=True)
+
+    if format is not None:
+        kwargs['format'] = format
+
+    for i, estimator in enumerate(multi.estimators_):
+        ext = '.h' if format is None else '.'+format
+        p = out_dir / (f'{prefix}{i}' + ext)
+        converted = emlearn.convert(estimator)
+        converted.save(file=p, **kwargs)
+
+
+
+def main():
+
+    print('Load dataset...')
+    csv_file = airquality_download()
+    X, y = airquality_load(csv_file)
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    print('Training...')
+    rf = RandomForestRegressor(n_estimators=10, random_state=42, n_jobs=-1)
+    regressor = MultiOutputRegressor(estimator=rf)
+
+    pipeline = Pipeline([
+        ('scaler', Quantizer()), # convert data to int16 range
+        ('regressor', regressor),
+    ])
+    pipeline.fit(X_train, y_train)
+
+    model_dir = 'models/'
+    convert_multiregressor(pipeline.named_steps['regressor'], out_dir=model_dir, format='csv')
+    print('Models exported to:', model_dir)
+
+
+    print("Performance Metrics:")
+    print("-" * 60)
+    y_pred = pd.DataFrame(pipeline.predict(X_test), columns=y_train.columns)
+    for i, target in enumerate(y.columns):
+        rmse = np.sqrt(mean_squared_error(y_test[target], y_pred[target]))
+        r2 = r2_score(y_test[target], y_pred[target])
+        mape = mean_absolute_percentage_error(y_test[target], y_pred[target]) * 100
+        
+        print(f"{target:12} | RMSE: {rmse:8.3f} | MAPE: {mape:6.2f}% | R²: {r2:6.3f}")
+
+if __name__ == '__main__':
+    main()
+
+