def test_classification_toy(): # Check classification on a toy dataset, including sparse versions. clf = NearestCentroid() clf.fit(X, y) assert_array_equal(clf.predict(T), true_result) # Same test, but with a sparse matrix to fit and test. clf = NearestCentroid() clf.fit(X_csr, y) assert_array_equal(clf.predict(T_csr), true_result) # Fit with sparse, test with non-sparse clf = NearestCentroid() clf.fit(X_csr, y) assert_array_equal(clf.predict(T), true_result) # Fit with non-sparse, test with sparse clf = NearestCentroid() clf.fit(X, y) assert_array_equal(clf.predict(T_csr), true_result) # Fit and predict with non-CSR sparse matrices clf = NearestCentroid() clf.fit(X_csr.tocoo(), y) assert_array_equal(clf.predict(T_csr.tolil()), true_result)
def test_predict_translated_data(): # Test that NearestCentroid gives same results on translated data rng = np.random.RandomState(0) X = rng.rand(50, 50) y = rng.randint(0, 3, 50) noise = rng.rand(50) clf = NearestCentroid(shrink_threshold=0.1) clf.fit(X, y) y_init = clf.predict(X) clf = NearestCentroid(shrink_threshold=0.1) X_noise = X + noise clf.fit(X_noise, y) y_translate = clf.predict(X_noise) assert_array_equal(y_init, y_translate)
def test_iris_shrinkage(): # Check consistency on dataset iris, when using shrinkage. for metric in ('euclidean', 'cosine'): for shrink_threshold in [None, 0.1, 0.5]: clf = NearestCentroid(metric=metric, shrink_threshold=shrink_threshold) clf = clf.fit(iris.data, iris.target) score = np.mean(clf.predict(iris.data) == iris.target) assert score > 0.8, "Failed with score = " + str(score)
# we only take the first two features. We could avoid this ugly # slicing by using a two-dim dataset X = iris.data[:, :2] y = iris.target h = .02 # step size in the mesh # Create color maps cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue']) cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue']) for shrinkage in [None, .2]: # we create an instance of Neighbours Classifier and fit the data. clf = NearestCentroid(shrink_threshold=shrinkage) clf.fit(X, y) y_pred = clf.predict(X) print(shrinkage, np.mean(y == y_pred)) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light) # Plot also the training points
def test_iris(): # Check consistency on dataset iris. for metric in ('euclidean', 'cosine'): clf = NearestCentroid(metric=metric).fit(iris.data, iris.target) score = np.mean(clf.predict(iris.data) == iris.target) assert score > 0.9, "Failed with score = " + str(score)