def test_shuffle():
X = np.array([[1], [2], [3]])
y = np.array([1, 2, 3])
est = _BaseEstimator(print_progress=0, random_seed=1)
X_sh, y_sh = est._shuffle_arrays(arrays=[X, np.array(y)])
np.testing.assert_equal(X_sh, np.array([[1], [3], [2]]))
np.testing.assert_equal(y_sh, np.array([1, 3, 2]))
def test_check_array_1():
X = np.array([1, 2, 3])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(ValueError,
'X must be a 2D array. Try X[:, numpy.newaxis]',
est._check_arrays,
X)
def test_shuffle():
X = np.array([[1], [2], [3]])
y = np.array([1, 2, 3])
est = _BaseEstimator(print_progress=0, random_seed=1)
X_sh, y_sh = est._shuffle_arrays(arrays=[X, np.array(y)])
np.testing.assert_equal(X_sh, np.array([[1], [3], [2]]))
np.testing.assert_equal(y_sh, np.array([1, 3, 2]))
def test_check_array_1():
X = np.array([1, 2, 3])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(ValueError,
'X must be a 2D array. Try X[:, numpy.newaxis]',
est._check_arrays,
X)
def test_minibatch_allsample():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=1,
data_ary=ary,
shuffle=False)
arys = list(gen_arys)[0]
assert (arys == np.array([0, 1, 2, 3, 4, 5, 6])).all()
def test_check_array_2():
X = list([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(ValueError,
'X must be a numpy array',
est._check_arrays,
X)
def test_minibatch_1sample():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=7, data_ary=ary)
arys = list(gen_arys)
assert len(arys) == 7
assert arys[0] == np.array([6])
def test_minibatches_divisible():
ary = np.array([1, 2, 3, 4, 5, 6, 7, 8])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=2, data_ary=ary)
arys = list(gen_arys)
assert (arys[0] == np.array([7, 2, 1, 6])).all()
assert (arys[1] == np.array([0, 4, 3, 5])).all()
def test_predict_1():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(AttributeError,
'Model is not fitted, yet.',
est.predict,
X)
def test_predict_1():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(AttributeError,
'Model is not fitted, yet.',
est.predict,
X)
def test_check_array_2():
X = list([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
assert_raises(ValueError,
'X must be a numpy array',
est._check_arrays,
X)
def test_minibatch_allsample():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=1,
data_ary=ary,
shuffle=False)
arys = list(gen_arys)[0]
assert (arys == np.array([0, 1, 2, 3, 4, 5, 6])).all()
def test_minibatches_divisible():
ary = np.array([1, 2, 3, 4, 5, 6, 7, 8])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=2, data_ary=ary)
arys = list(gen_arys)
assert (arys[0] == np.array([7, 2, 1, 6])).all()
assert (arys[1] == np.array([0, 4, 3, 5])).all()
def test_minibatch_1sample():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=7, data_ary=ary)
arys = list(gen_arys)
assert len(arys) == 7
assert arys[0] == np.array([6])
def test_minibatches_remainder():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=2, data_ary=ary)
arys = list(gen_arys)
assert len(arys) == 2
assert (arys[0] == np.array([6, 2, 1])).all()
assert (arys[1] == np.array([0, 4, 3, 5])).all()
def test_minibatches_remainder():
ary = np.array([1, 2, 3, 4, 5, 6, 7])
est = _BaseEstimator(print_progress=0, random_seed=1)
gen_arys = est._yield_minibatches_idx(n_batches=2, data_ary=ary)
arys = list(gen_arys)
assert len(arys) == 2
assert (arys[0] == np.array([6, 2, 1])).all()
assert (arys[1] == np.array([0, 4, 3, 5])).all()
def test_init_params():
est = _BaseEstimator(print_progress=0, random_seed=1)
b, w = est._init_params(weights_shape=(3, 3),
bias_shape=(1, ),
random_seed=0)
assert b == np.array([0.0]), b
expect_w = np.array([[0.016, -0.006, -0.005], [-0.011, 0.009, -0.023],
[0.017, -0.008, 0.003]])
np.testing.assert_almost_equal(w, expect_w, decimal=3)
def test_init_params():
est = _BaseEstimator(print_progress=0, random_seed=1)
b, w = est._init_params(weights_shape=(3, 3),
bias_shape=(1,),
random_seed=0)
assert b == np.array([0.0]), b
expect_w = np.array([[0.016, -0.006, -0.005],
[-0.011, 0.009, -0.023],
[0.017, -0.008, 0.003]])
np.testing.assert_almost_equal(w, expect_w, decimal=3)
def test_init():
est = _BaseEstimator(print_progress=0, random_seed=1)
assert hasattr(est, 'print_progress')
assert hasattr(est, 'random_seed')
def test_check_array_3():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
est._check_arrays(X)
def test_predict_2():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
est.fit(X)
est.predict(X)
def test_predict_2():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
est.fit(X)
est.predict(X)
def test_check_array_3():
X = np.array([[1], [2], [3]])
est = _BaseEstimator(print_progress=0, random_seed=1)
est._check_arrays(X)
def test_init():
est = _BaseEstimator(print_progress=0, random_seed=1)
assert hasattr(est, 'print_progress')
assert hasattr(est, 'random_seed')
