Snippet of mini-batch using SGDRegressor in sklearn

minibatch_sgd_sklearn.py

def _get_chunk(X, y, chunkrows):
    X_chunk, y_chunk = X[chunkrows], y[chunkrows]
    return X_chunk, y_chunk


def _iter_minibatch(X, y, chunk_size):
    '''
    Construct minibatch generator
    '''
    _start = 0
    _total_observation = X.shape[0]
    while _start < _total_observation:
        chunkrows = range(_start, _start + chunk_size)
        X_chunk, y_chunk = _get_chunk(X, y, chunkrows)
        yield X_chunk, y_chunk
        _start += chunk_size
        

mini_batch_generator = _iter_minibatch(X=X, y=y, chunk_size=100)
SGD_model = SGDRegressor(fit_intercept=True, random_state=SEED, eta0=learning_rate,
                         learning_rate='constant', max_iter=n_epochs)
for X_chunk, y_chunk in mini_batch_generator:
    SGD_model.partial_fit(X_chunk, y_chunk)
    
y_pred = SGD_model.predict(X)
print(SGD_model)
print(f'Intercept: {SGD_model.intercept_}, weights: {SGD_model.coef_}')
_ = print_regress_metric(y, y_pred)