def main():
ridge_estimator = Ridge(alpha=0.1)
ridge_estimator.fit(X_train, y_train)
print(ridge_estimator.intercept_, ridge_estimator.coef_)
lasso_estimator = Lasso(alpha=0.01)
lasso_estimator.fit(X_train, y_train)
print(lasso_estimator.intercept_, lasso_estimator.coef_)
sklin_estimator = LinearRegression()
sklin_estimator.fit(X_train, y_train)
print(sklin_estimator.intercept_, sklin_estimator.coef_)
my_estimator = MyLinearRegression(n_epochs=500)
my_estimator.fit(X_train, y_train)
print(my_estimator.w)
myl2_estimator = MyLinearRegression(n_epochs=500, l2=0.01)
myl2_estimator.fit(X_train, y_train)
print(myl2_estimator.w)
print("train scores")
print(r2_score(y_train, ridge_estimator.predict(X_train)))
print(r2_score(y_train, lasso_estimator.predict(X_train)))
print(r2_score(y_train, sklin_estimator.predict(X_train)))
print(r2_score(y_train, my_estimator.predict(X_train)))
print(r2_score(y_train, myl2_estimator.predict(X_train)))
print("test scores")
print(r2_score(y_test, ridge_estimator.predict(X_test)))
print(r2_score(y_test, lasso_estimator.predict(X_test)))
print(r2_score(y_test, sklin_estimator.predict(X_test)))
print(r2_score(y_test, my_estimator.predict(X_test)))
print(r2_score(y_test, myl2_estimator.predict(X_test)))
def test_holdout_shuffle(self):
seed(34)
estimator = MyLinearRegression(holdout_size=0.5, shuffle=True)
v = numpy.asarray([1, 2, 3, 4, 5, 6, 7, 8])
estimator.fit(v.reshape(8, 1), v)
self.assertTrue(
(estimator.validation[1] != numpy.asarray([1, 2, 3, 4])).all())
def test_standardize_y(self):
X = numpy.asarray([[1.63295], [-1.63295], [0.]])
y = numpy.asarray([2., -2., 0.])
estimator = MyLinearRegression(standardize=True)
estimator.fit(X, y)
assert_array_almost_equal(estimator.w, [0., 1.], decimal=3)
assert_array_almost_equal(estimator.predict(X), [2., -2., 0.],
decimal=3)
def test_example_two_features(self):
X = numpy.asarray([[2., 2.], [0., 2.], [2., 0.], [0., 0.]])
y = numpy.asarray([1., -1., 3., 1.])
estimator = MyLinearRegression(learning_rate=0.2)
estimator.fit(X, y)
self.assertAlmostEqual(estimator.w[0], 1, places=1)
self.assertAlmostEqual(estimator.w[1], 1, places=1)
self.assertAlmostEqual(estimator.w[2], -1, places=1)
y_pred = estimator.predict(X)
self.assertAlmostEqual(y_pred[0], 1., places=1)
def test_standardization_is_used(self):
X = numpy.asarray([[2., 3.], [0., 1.], [2., 0.], [0., 0.]])
y = numpy.asarray([1., 5., 3., 1.])
estimator = MyLinearRegression(standardize=True)
estimator.fit(X, y)
assert_array_equal(estimator.standard_scaler_x.mean_, [1., 1.])
y_pred = estimator.predict(X)
estimator.standard_scaler_x.mean_ = [23., 23.]
y_pred_unscaled = estimator.predict(X)
self.assertNotEqual(list(y_pred), list(y_pred_unscaled))
0.5211079298372449, 0.7610907666606702, 0.7650513166070351,
0.5430171847213586, 0.6838572594876159, 0.5257790543690498,
0.749720166273106, 0.7417230143992304, 0.7170392536658252,
0.6054873002426322, 0.6494912378903008, 0.7836036247312703,
0.5660181206880279, 0.6611925537901934, 0.677418202867866,
0.6212332444014119, 0.755211481708266, 0.6574209206631763,
0.7961329669363469, 0.6780963740303935, 0.5777549618568788,
0.6029694914018995, 0.7055898454746773, 0.7615936253567677,
0.6509820273225375, 0.6921296101111231, 0.4965640242465189,
0.5969105590529076, 0.7922304500021877
]
Xs = numpy.asarray(xs)
Xs = Xs.reshape((len(xs), 1))
Ys = numpy.asarray(ys)
my_estimator.fit(Xs, ys)
my_PYs = my_estimator.predict(Xs)
sk_estimator.fit(Xs, ys)
sk_PYs = sk_estimator.predict(Xs)
my_squared_error = mean_squared_error(Ys, my_PYs)
print("my squared errors", my_squared_error)
scikit_quared_error = mean_squared_error(Ys, sk_PYs)
print("scikit squarred errors", scikit_quared_error)
assert my_squared_error < 0.006
from matplotlib import pyplot as plt
plt.subplot(3, 1, 1)
plt.hist(Ys)
plt.xlim((0.3, 0.9))
return y lin_fun = get_y_fun(a, b, c) n = 1000 range_points = 1 range_plot = 1.1 sigma = 0.05 X = range_points * 2 * (np.random.rand(n, 2) - 0.5) y = np.asfarray([lin_rule(x, sigma * np.random.normal()) for x in X]) print(X[:10]) print(y[:10]) clf = MyLinearRegression() clf.fit(X, y) y_pred = clf.predict(X) from matplotlib import pyplot pyplot.hist(y) pyplot.hist(y_pred) pyplot.draw() pyplot.show()
print(lr1.cost_elem_(lr1.predict(x), y)) # Output: # array([[77.72116511], # [49.33699664], # [72.38621816], # [37.29223426], # [78.28360514]]) # Example 0.2: print(lr1.cost_(lr1.predict(x), y)) # Output: # 315.0202193084312 # Example 1.0: lr2 = MyLR([0, 0]) lr2.fit(x, y, alpha=5e-8, n_cycle=1500000) print(lr2.theta) # Output: # array([[1.40709365], # [1.1150909 ]]) # Example 1.1: print(lr2.predict(x)) # Output: # array([[15.3408728 ], # [25.38243697], # [36.59126492], # [55.95130097], # [65.53471499]]) # Example 1.2:
def test_holdout(self):
estimator = MyLinearRegression(holdout_size=0.5)
v = numpy.asarray([1, 2, 3, 4, 5, 6, 7, 8])
estimator.fit(v.reshape(8, 1), v)
assert_array_equal(estimator.validation[1], [1, 2, 3, 4])
def test_zero_epochs(self):
estimator = MyLinearRegression(n_epochs=0)
X = numpy.asarray([[1., 2.], [3., 4.]])
y = numpy.asarray([1., 3.])
estimator.fit(X, y)
assert_array_equal(estimator.w, [0., 0., 0.])
def test_two_y(self):
estimator = MyLinearRegression()
with self.assertRaises(ValueError):
estimator.fit(numpy.asarray([[1]]), numpy.asarray([[1, 1]]))