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Create test_ovr_decision_function.py #7810

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63 changes: 63 additions & 0 deletions sklearn/svm/tests/test_ovr_decision_function.py
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import numpy as np

from sklearn import svm, multiclass

from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raise_message

def test_ovr_decision_function():
train_base_points = np.array([[1, 2], [2, 1]])

# For all the quadrants (classes)
x = np.vstack((
train_base_points * [1, 1], # Q1
train_base_points * [-1, 1], # Q2
train_base_points * [-1, -1], # Q3
train_base_points * [1, -1] # Q4
))

y = [0] * 2 + [1] * 2 + [2] * 2 + [3] * 2

# First point is closer to the decision boundaries than the second point
test_base_points = np.array([[5, 5], [10, 10]])

# For all the quadrants (classes)
x_test = np.vstack((
test_base_points * [1, 1], # Q1
test_base_points * [-1, 1], # Q2
test_base_points * [-1, -1], # Q3
test_base_points * [1, -1], # Q4
))

y_test = [0] * 2 + [1] * 2 + [2] * 2 + [3] * 2

svc = svm.SVC(kernel='linear', decision_function_shape='ovr')
svc.fit(x, y)

y_pred = svc.predict(x_test)

# Test if the prediction is the same as y
assert_array_equal(y_pred, y_test)

deci_vals = svc.decision_function(x_test)

# Assert that the predicted class has the maximum value
assert_array_equal(np.argmax(deci_vals, axis=1), y_pred)

# Get decision value at test points for the predicted class
pred_class_deci_vals = deci_vals[range(8), y_pred].reshape((4, 2))

# Assert pred_class_deci_vals > 0 here
assert_greater(np.min(pred_class_deci_vals), 0.0)

# Test if the first point has lower decision value on every quadrant
# compared to the second point
assert_true(np.all(pred_class_deci_vals[:, 0] < pred_class_deci_vals[:, 1]))