train.py

import numpy as np
import pickle
from sklearn.datasets import fetch_california_housing
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge


def model():
    """Simple example of scikit-learn pipeline"""
    p = Pipeline([
        ('preprocess', StandardScaler()),
        ('model', Ridge(alpha=10.)),
    ])
    return p


def train():
    # induce some randomness to illustrate model refreshing
    data = fetch_california_housing()
    X = data.data
    X = X[np.random.choice(range(X.shape[0]), size=X.shape[0], replace=True), :]
    pipeline = model()
    pipeline.fit(X=X, y=data.target)
    with open('model.pkl', 'wb') as f:
        pickle.dump(pipeline, f)


if __name__ == '__main__':
    train()