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Wed, Feb 5, 20:12

testing.py
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"""
EE-311
======
Lab 3: logistic regression and gradient descent
----------------------------------------
created by Francois Marelli on 05.03.2020
"""
import unittest
import numpy as np
import homework
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import importlib
importlib.reload(homework)
class Test(unittest.TestCase):
def test_model_prediction(self):
data_X, data_y = load_iris(return_X_y=True)
mask = (data_y == 0) | (data_y == 1)
data_X = data_X[mask]
data_y = data_y[mask]
classifier = LogisticRegression().fit(data_X, data_y)
beta = np.concatenate((classifier.intercept_, classifier.coef_[0, :])).squeeze()
p = homework.model_prediction(data_X.copy(), beta.copy())
p2 = classifier.predict(data_X)
np.testing.assert_array_equal(p, p2)
sample_X = np.zeros_like(data_X[0])
sample_X[0] = (0.25 - beta[0]) / beta[1]
np.testing.assert_array_equal(
homework.model_prediction(sample_X[None, :], beta), 1
)
def test_gradient(self):
data_X, data_y = load_iris(return_X_y=True)
mask = (data_y == 0) | (data_y == 1)
data_X = data_X[mask]
data_y = data_y[mask]
data_X = data_X[:-5]
data_y = data_y[:-5]
grad_ref = np.load("test_data.npy")
beta_0 = np.zeros_like(grad_ref)
grad_out = homework.logistic_loss_derivative(
data_X.copy(), data_y.copy(), beta_0
)
np.testing.assert_allclose(grad_out, grad_ref, atol=1e-9, rtol=1e-5)
def test_gradient_descent(self):
data_X, data_y = load_iris(return_X_y=True)
mask = (data_y == 0) | (data_y == 1)
data_X = data_X[mask]
data_y = data_y[mask]
classifier = LogisticRegression().fit(data_X, data_y)
beta = np.concatenate((classifier.intercept_, classifier.coef_[0, :]))
beta_0 = np.zeros_like(beta)
beta_out = homework.gradient_descent(
data_X.copy(), data_y.copy(), beta_0, 1e-1, 5
)
p = homework.model_prediction(data_X.copy(), beta_out)
p2 = classifier.predict(data_X)
np.testing.assert_array_equal(p, p2)
if __name__ == "__main__":
unittest.main()

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