[MRG+1] replaced some assert_true(np.allclose(x, y)) with assert_almost_equal (scikit-learn#7742)

* replaced some assert_true(np.allclose(x, y)) with assert_almost_equal for better error messages.

also some pep8.

* typo fixes

Author: amueller

sklearn/decomposition/tests/test_nmf.py

@@ -68,7 +68,7 @@ def test_initialize_variants():
                                    random_state=0)
 
     for ref, evl in ((W0, Wa), (W0, War), (H0, Ha), (H0, Har)):
-        assert_true(np.allclose(evl[ref != 0], ref[ref != 0]))
+        assert_almost_equal(evl[ref != 0], ref[ref != 0])
 
 
 @ignore_warnings
@@ -128,9 +128,10 @@ def test_nmf_transform_custom_init():
     H_init = np.abs(avg * random_state.randn(n_components, 5))
     W_init = np.abs(avg * random_state.randn(6, n_components))
 
-    m = NMF(solver='cd', n_components=n_components, init='custom', random_state=0)
-    ft = m.fit_transform(A, W=W_init, H=H_init)
-    t = m.transform(A)
+    m = NMF(solver='cd', n_components=n_components, init='custom',
+            random_state=0)
+    m.fit_transform(A, W=W_init, H=H_init)
+    m.transform(A)
 
 
 @ignore_warnings
@@ -140,7 +141,7 @@ def test_nmf_inverse_transform():
     A = np.abs(random_state.randn(6, 4))
     for solver in ('pg', 'cd'):
         m = NMF(solver=solver, n_components=4, init='random', random_state=0)
-        ft = m.fit_transform(A)
+        m.fit_transform(A)
         t = m.transform(A)
         A_new = m.inverse_transform(t)
         assert_array_almost_equal(A, A_new, decimal=2)
@@ -235,9 +236,11 @@ def test_non_negative_factorization_checking():
     # Test parameters checking is public function
     nnmf = non_negative_factorization
     assert_no_warnings(nnmf, A, A, A, np.int64(1))
-    msg = "Number of components must be a positive integer; got (n_components=1.5)"
+    msg = ("Number of components must be a positive integer; "
+           "got (n_components=1.5)")
     assert_raise_message(ValueError, msg, nnmf, A, A, A, 1.5)
-    msg = "Number of components must be a positive integer; got (n_components='2')"
+    msg = ("Number of components must be a positive integer; "
+           "got (n_components='2')")
     assert_raise_message(ValueError, msg, nnmf, A, A, A, '2')
     msg = "Negative values in data passed to NMF (input H)"
     assert_raise_message(ValueError, msg, nnmf, A, A, -A, 2, 'custom')

sklearn/gaussian_process/tests/test_gpr.py

@@ -41,8 +41,8 @@ def test_gpr_interpolation():
         gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
         y_pred, y_cov = gpr.predict(X, return_cov=True)
 
-        assert_true(np.allclose(y_pred, y))
-        assert_true(np.allclose(np.diag(y_cov), 0.))
+        assert_almost_equal(y_pred, y)
+        assert_almost_equal(np.diag(y_cov), 0.)
 
 
 def test_lml_improving():

sklearn/preprocessing/tests/test_imputation.py

@@ -6,14 +6,13 @@
 from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_false
-from sklearn.utils.testing import assert_true
 
 from sklearn.preprocessing.imputation import Imputer
 from sklearn.pipeline import Pipeline
 from sklearn.model_selection import GridSearchCV
 from sklearn import tree
 from sklearn.random_projection import sparse_random_matrix
- 
+
 
 def _check_statistics(X, X_true,
                       strategy, statistics, missing_values):
@@ -92,16 +91,16 @@ def test_imputation_mean_median_only_zero():
     # Test imputation using the mean and median strategies, when
     # missing_values == 0.
     X = np.array([
-        [np.nan, 0, 0,  0,  5],
-        [np.nan, 1, 0,  np.nan,  3],
-        [np.nan, 2, 0,  0, 0],
-        [np.nan, 6, 0,  5,  13],
+        [np.nan, 0, 0, 0, 5],
+        [np.nan, 1, 0, np.nan, 3],
+        [np.nan, 2, 0, 0, 0],
+        [np.nan, 6, 0, 5, 13],
     ])
 
     X_imputed_mean = np.array([
-        [3,  5],
-        [1,  3],
-        [2,  7],
+        [3, 5],
+        [1, 3],
+        [2, 7],
         [6, 13],
     ])
     statistics_mean = [np.nan, 3, np.nan, np.nan, 7]
@@ -144,7 +143,7 @@ def test_imputation_mean_median():
     shape = (dim * dim, dim + dec)
 
     zeros = np.zeros(shape[0])
-    values = np.arange(1, shape[0]+1)
+    values = np.arange(1, shape[0] + 1)
     values[4::2] = - values[4::2]
 
     tests = [("mean", "NaN", lambda z, v, p: safe_mean(np.hstack((z, v)))),
@@ -236,17 +235,17 @@ def test_imputation_median_special_cases():
 def test_imputation_most_frequent():
     # Test imputation using the most-frequent strategy.
     X = np.array([
-        [-1, -1,  0,  5],
-        [-1,  2, -1,  3],
-        [-1,  1,  3, -1],
-        [-1,  2,  3,  7],
+        [-1, -1, 0, 5],
+        [-1, 2, -1, 3],
+        [-1, 1, 3, -1],
+        [-1, 2, 3, 7],
     ])
 
     X_true = np.array([
-        [2,  0,  5],
-        [2,  3,  3],
-        [1,  3,  3],
-        [2,  3,  7],
+        [2, 0, 5],
+        [2, 3, 3],
+        [1, 3, 3],
+        [2, 3, 7],
     ])
 
     # scipy.stats.mode, used in Imputer, doesn't return the first most
@@ -315,23 +314,23 @@ def test_imputation_copy():
     imputer = Imputer(missing_values=0, strategy="mean", copy=False)
     Xt = imputer.fit(X).transform(X)
     Xt[0, 0] = -1
-    assert_true(np.all(X == Xt))
+    assert_array_equal(X, Xt)
 
     # copy=False, sparse csr, axis=1 => no copy
     X = X_orig.copy()
     imputer = Imputer(missing_values=X.data[0], strategy="mean",
                       copy=False, axis=1)
     Xt = imputer.fit(X).transform(X)
     Xt.data[0] = -1
-    assert_true(np.all(X.data == Xt.data))
+    assert_array_equal(X.data, Xt.data)
 
     # copy=False, sparse csc, axis=0 => no copy
     X = X_orig.copy().tocsc()
     imputer = Imputer(missing_values=X.data[0], strategy="mean",
                       copy=False, axis=0)
     Xt = imputer.fit(X).transform(X)
     Xt.data[0] = -1
-    assert_true(np.all(X.data == Xt.data))
+    assert_array_equal(X.data, Xt.data)
 
     # copy=False, sparse csr, axis=0 => copy
     X = X_orig.copy()

sklearn/utils/tests/test_extmath.py

@@ -17,7 +17,6 @@
 from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import assert_greater
 from sklearn.utils.testing import assert_raises
-from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_warns
 from sklearn.utils.testing import skip_if_32bit
 from sklearn.utils.testing import SkipTest
@@ -65,8 +64,8 @@ def test_uniform_weights():
         mode, score = stats.mode(x, axis)
         mode2, score2 = weighted_mode(x, weights, axis)
 
-        assert_true(np.all(mode == mode2))
-        assert_true(np.all(score == score2))
+        assert_array_equal(mode, mode2)
+        assert_array_equal(score, score2)
 
 
 def test_random_weights():