Assignment #8 (demo). Implementation of online regressor. Solution# – Open Machine Learning Course

Author: Yury Kashnitsky. Translated by Sergey Oreshkov. This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

Same assignment as a Kaggle Notebook + solution.

Here we’ll implement a regressor trained with stochastic gradient descent (SGD). Fill in the missing code. If you do everything right, you’ll pass a simple embedded test.

Linear regression and Stochastic Gradient Descent#

import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.metrics import log_loss, mean_squared_error, roc_auc_score
from sklearn.model_selection import train_test_split
from tqdm import tqdm

from matplotlib import pyplot as plt
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
from sklearn.preprocessing import StandardScaler

Implement class SGDRegressor. Specification:

  • class is inherited from sklearn.base.BaseEstimator

  • constructor takes parameters eta – gradient step (\(10^{-3}\) by default) and n_epochs – dataset pass count (3 by default)

  • constructor also creates mse_ and weights_ lists in order to track mean squared error and weight vector during gradient descent iterations

  • Class has fit and predict methods

  • The fit method takes matrix X and vector y (numpy.array objects) as parameters, appends column of ones to X on the left side, initializes weight vector w with zeros and then makes n_epochs iterations of weight updates (you may refer to this article for details), and for every iteration logs mean squared error and weight vector w in corresponding lists we created in the constructor.

  • Additionally the fit method will create w_ variable to store weights which produce minimal mean squared error

  • The fit method returns current instance of the SGDRegressor class, i.e. self

  • The predict method takes X matrix, adds column of ones to the left side and returns prediction vector, using weight vector w_, created by the fit method.

class SGDRegressor(BaseEstimator):
    def __init__(self, eta=1e-3, n_epochs=3):
        self.eta = eta
        self.n_epochs = n_epochs
        self.mse_ = []
        self.weights_ = []

    def fit(self, X, y):
        X = np.hstack([np.ones([X.shape[0], 1]), X])

        w = np.zeros(X.shape[1])

        for it in tqdm(range(self.n_epochs)):
            for i in range(X.shape[0]):

                new_w = w.copy()
                new_w[0] += self.eta * (y[i] -[i, :]))
                for j in range(1, X.shape[1]):
                    new_w[j] += self.eta * (y[i] -[i, :])) * X[i, j]
                w = new_w.copy()


        self.w_ = self.weights_[np.argmin(self.mse_)]

        return self

    def predict(self, X):
        X = np.hstack([np.ones([X.shape[0], 1]), X])


Let’s test out the algorithm on height/weight data. We will predict heights (in inches) based on weights (in lbs).

# for Jupyter-book, we copy data from GitHub, locally, to save Internet traffic,
# you can specify the data/ folder from the root of your cloned
# repo, to save Internet traffic
data_demo = pd.read_csv(DATA_PATH + "weights_heights.csv")
plt.scatter(data_demo["Weight"], data_demo["Height"])
plt.xlabel("Weight (lbs)")
plt.ylabel("Height (Inch)")
X, y = data_demo["Weight"].values, data_demo["Height"].values

Perform train/test split and scale data.

X_train, X_valid, y_train, y_valid = train_test_split(
    X, y, test_size=0.3, random_state=17
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train.reshape([-1, 1]))
X_valid_scaled = scaler.transform(X_valid.reshape([-1, 1]))

Train created SGDRegressor with (X_train_scaled, y_train) data. Leave default parameter values for now.

# you code here
sgd_reg = SGDRegressor(), y_train)
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In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Draw a chart with training process – dependency of mean squared error from the i-th SGD iteration number.

# you code here
plt.plot(range(len(sgd_reg.mse_)), sgd_reg.mse_)

Print the minimal value of mean squared error and the best weights vector.

# you code here
np.min(sgd_reg.mse_), sgd_reg.w_
(2.7151352406643623, array([67.9898497 ,  0.94447605]))

Draw chart of model weights (\(w_0\) and \(w_1\)) behavior during training.

# you code here
plt.plot(range(len(sgd_reg.weights_)), [w[0] for w in sgd_reg.weights_])
plt.plot(range(len(sgd_reg.weights_)), [w[1] for w in sgd_reg.weights_]);

Make a prediction for hold-out set (X_valid_scaled, y_valid) and check MSE value.

# you code here
sgd_holdout_mse = mean_squared_error(y_valid, sgd_reg.predict(X_valid_scaled))

Do the same thing for LinearRegression class from sklearn.linear_model. Evaluate MSE for hold-out set.

# you code here
from sklearn.linear_model import LinearRegression

lm = LinearRegression().fit(X_train_scaled, y_train)
print(lm.coef_, lm.intercept_)
linreg_holdout_mse = mean_squared_error(y_valid, lm.predict(X_valid_scaled))
[0.94537278] 67.98930834742858
    assert (sgd_holdout_mse - linreg_holdout_mse) < 1e-4
except AssertionError:
        "Something's not good.\n Linreg's holdout MSE: {}"
        "\n SGD's holdout MSE: {}".format(linreg_holdout_mse, sgd_holdout_mse)