Luigi-Based Data Processing with Model Training and Prediction

This code defines a function named `process_data` that uses the Luigi framework along with pandas and scikit-learn libraries to process data. It first defines a class `ProcessDataTask` that inherits from `luigi.Task`. This class reads input data, splits it into training and test sets, then uses a hypothetical model for training and prediction, and finally saves the predicted results to the output path.

Technology Stack : Luigi, pandas, scikit-learn

Code Type : Function

Code Difficulty : Advanced

                
                    
def process_data(input_path, output_path):
    import luigi
    import pandas as pd
    from sklearn.model_selection import train_test_split

    class ProcessDataTask(luigi.Task):
        def requires(self):
            return ReadData(input_path)

        def output(self):
            return luigi.LocalTarget(output_path)

        def run(self):
            input_data = self.input().open('r')
            df = pd.read_csv(input_data)
            X, y = df.drop('target', axis=1), df['target']
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
            model = SomeModel()  # Dummy model class
            model.fit(X_train, y_train)
            predictions = model.predict(X_test)
            output_data = self.output().open('w')
            pd.DataFrame(predictions).to_csv(output_data, index=False)

    class ReadData(luigi.Task):
        def requires(self):
            return luigi.LocalTarget(input_path)

        def output(self):
            return luigi.LocalTarget(input_path)

        def run(self):
            pass

    luigi.run()