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46
machine_learning/ridge_regression/test_ridge_regression.py
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@ -14,24 +14,26 @@ To run these tests, use the following command:
import numpy as np import numpy as np
from ridge_regression import RidgeRegression from ridge_regression import RidgeRegression
def test_feature_scaling(): def test_feature_scaling():
""" """
Tests the feature_scaling function of RidgeRegression. Tests the feature_scaling function of RidgeRegression.
-------- --------
>>> model = RidgeRegression() >>> model = RidgeRegression()
>>> X = np.array([[1, 2], [2, 3], [3, 4]]) >>> X = np.array([[1, 2], [2, 3], [3, 4]])
>>> X_scaled, mean, std = model.feature_scaling(X) >>> X_scaled, mean, std = model.feature_scaling(X)
>>> np.round(X_scaled, 2) >>> np.round(X_scaled, 2)
array([[-1.22, -1.22], array([[-1.22, -1.22],
[ 0. , 0. ], [ 0. , 0. ],
[ 1.22, 1.22]]) [ 1.22, 1.22]])
>>> np.round(mean, 2) >>> np.round(mean, 2)
array([2., 3.]) array([2., 3.])
>>> np.round(std, 2) >>> np.round(std, 2)
array([0.82, 0.82]) array([0.82, 0.82])
""" """
pass pass
def test_fit(): def test_fit():
""" """
Tests the fit function of RidgeRegression Tests the fit function of RidgeRegression
@ -39,19 +41,20 @@ def test_fit():
>>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000) >>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
>>> X = np.array([[1], [2], [3]]) >>> X = np.array([[1], [2], [3]])
>>> y = np.array([2, 3, 4]) >>> y = np.array([2, 3, 4])
# Adding a bias term # Adding a bias term
>>> X = np.c_[np.ones(X.shape[0]), X] >>> X = np.c_[np.ones(X.shape[0]), X]
# Fit the model # Fit the model
>>> model.fit(X, y) >>> model.fit(X, y)
# Check if the weights have been updated # Check if the weights have been updated
>>> np.round(model.theta, decimals=2) >>> np.round(model.theta, decimals=2)
array([0. , 0.79]) array([0. , 0.79])
""" """
pass pass
def test_predict(): def test_predict():
""" """
Tests the predict function of RidgeRegression Tests the predict function of RidgeRegression
@ -59,13 +62,13 @@ def test_predict():
>>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000) >>> model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
>>> X = np.array([[1], [2], [3]]) >>> X = np.array([[1], [2], [3]])
>>> y = np.array([2, 3, 4]) >>> y = np.array([2, 3, 4])
# Adding a bias term # Adding a bias term
>>> X = np.c_[np.ones(X.shape[0]), X] >>> X = np.c_[np.ones(X.shape[0]), X]
# Fit the model # Fit the model
>>> model.fit(X, y) >>> model.fit(X, y)
# Predict with the model # Predict with the model
>>> predictions = model.predict(X) >>> predictions = model.predict(X)
>>> np.round(predictions, decimals=2) >>> np.round(predictions, decimals=2)
@ -73,6 +76,7 @@ def test_predict():
""" """
pass pass
def test_mean_absolute_error(): def test_mean_absolute_error():
""" """
Tests the mean_absolute_error function of RidgeRegression Tests the mean_absolute_error function of RidgeRegression
@ -86,6 +90,8 @@ def test_mean_absolute_error():
""" """
pass pass
if __name__ == "__main__": if __name__ == "__main__":
import doctest import doctest
doctest.testmod()
doctest.testmod()