def test_same_prediction(self):
X, y, Z = self.make_classification(4, 100000, nonnegative=True)
local = MultinomialNB()
dist = SparkMultinomialNB()
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z, classes=np.unique(y)).predict(Z[:, 'X'])
assert_array_almost_equal(y_local, np.concatenate(y_dist.collect()))
def test_same_coefs(self):
X, y, Z = self.make_classification(2, 10000)
local = LogisticRegression(tol=1e-4, C=10)
dist = SparkLogisticRegression(tol=1e-4, C=10)
local.fit(X, y)
dist.fit(Z, classes=np.unique(y))
assert_array_almost_equal(local.coef_, dist.coef_, decimal=1)
def test_same_coefs(self):
X, y, Z = self.make_classification(2, 100000)
local = LinearSVC()
dist = SparkLinearSVC()
local.fit(X, y)
dist.fit(Z, classes=np.unique(y))
assert_array_almost_equal(local.coef_, dist.coef_, decimal=3)
def test_same_coefs(self):
X, y, Z = self.make_regression(1, 100000)
local = LinearRegression()
dist = SparkLinearRegression()
local.fit(X, y)
dist.fit(Z)
assert_array_almost_equal(local.coef_, dist.coef_)
assert_array_almost_equal(local.intercept_, dist.intercept_)
def test_same_prediction(self):
X, y, Z = self.make_classification(4, 100000, nonnegative=True)
local = MultinomialNB()
dist = SparkMultinomialNB()
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z, classes=np.unique(y)).predict(Z[:, 'X'])
assert_true(check_rdd_dtype(y_dist, (np.ndarray,)))
assert_array_almost_equal(y_local, y_dist.toarray())
def test_same_coefs(self):
X, y, Z = self.make_regression(1, 100000)
local = LinearRegression()
dist = SparkLinearRegression()
local.fit(X, y)
dist.fit(Z)
assert_array_almost_equal(local.coef_, dist.coef_)
assert_array_almost_equal(local.intercept_, dist.intercept_)
def test_same_prediction(self):
X, y, Z = self.make_regression(1, 100000)
local = LinearRegression()
dist = SparkLinearRegression()
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z).predict(Z[:, 'X'])
assert_true(check_rdd_dtype(y_dist, (np.ndarray, )))
assert_array_almost_equal(y_local, y_dist.toarray())
def test_same_prediction(self):
X, y, Z = self.make_regression(1, 100000)
local = LinearRegression()
dist = SparkLinearRegression()
y_local = local.fit(X, y).predict(X)
y_dist = dist.fit(Z).predict(Z[:, 'X'])
assert_true(check_rdd_dtype(y_dist, (np.ndarray,)))
assert_array_almost_equal(y_local, y_dist.toarray())
def test_sparse_matrix(self):
n_partitions = 10
n_samples = 100
sparse_row = sp.csr_matrix([[0, 0, 1, 0, 1]])
data = self.sc.parallelize([sparse_row for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_true(sp.issparse(blocked_data.first()))
expected_block = sp.vstack([sparse_row] * 10)
assert_array_almost_equal(expected_block.toarray(),
blocked_data.first().toarray())
def test_same_prediction(self):
X, y, Z = self.make_classification(2, 800000, nonnegative=True)
local = GaussianNB()
dist = SparkGaussianNB()
local_model = local.fit(X, y)
dist_model = dist.fit(Z, classes=np.unique(y))
# TODO: investigate the variance further!
assert_array_almost_equal(local_model.sigma_, dist_model.sigma_, 2)
assert_array_almost_equal(local_model.theta_, dist_model.theta_, 6)
def test_block_rdd_dict(self):
n_partitions = 3
n_samples = 57
dicts = [{'a': i, 'b': float(i)**2} for i in range(n_samples)]
data = self.sc.parallelize(dicts, n_partitions)
block_data_5 = block(data, bsize=5)
blocks = block_data_5.collect()
assert_true(all(len(b) <= 5 for b in blocks))
assert_array_almost_equal(blocks[0][0], np.arange(5))
assert_array_almost_equal(blocks[0][1],
np.arange(5, dtype=np.float)**2)
def _test_func_on_axis(self, func, toarray=True):
X, X_rdd = self.make_sparse_rdd(block_size=100)
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1):
if toarray:
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes).toarray(),
getattr(X, func)(axis=axes).toarray())
else:
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
def _test_func_on_axis(self, func, toarray=True):
X, X_rdd = self.make_sparse_rdd(block_size=100)
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1):
if toarray:
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes).toarray(),
getattr(X, func)(axis=axes).toarray())
else:
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
def test_block_rdd_dict(self):
n_partitions = 3
n_samples = 57
dicts = [{'a': i, 'b': float(i) ** 2} for i in range(n_samples)]
data = self.sc.parallelize(dicts, n_partitions)
block_data_5 = block(data, bsize=5)
blocks = block_data_5.collect()
assert_true(all(len(b) <= 5 for b in blocks))
assert_array_almost_equal(blocks[0][0], np.arange(5))
assert_array_almost_equal(blocks[0][1],
np.arange(5, dtype=np.float) ** 2)
def test_sparse_matrix(self):
n_partitions = 10
n_samples = 100
sparse_row = sp.csr_matrix([[0, 0, 1, 0, 1]])
data = self.sc.parallelize([sparse_row for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_true(sp.issparse(blocked_data.first()))
expected_block = sp.vstack([sparse_row] * 10)
assert_array_almost_equal(expected_block.toarray(),
blocked_data.first().toarray())
def _test_func_on_axis(self, func):
X, X_rdd = self.make_dense_rdd(block_size=100)
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1):
assert_array_almost_equal(getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
X, X_rdd = self.make_dense_rdd((100, 3, 2))
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1, 2):
assert_array_almost_equal(getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
def test_same_transform_result(self):
X, y, Z_rdd = self.make_classification(4, 1000, -1)
X_rdd = Z_rdd[:, 'X']
local = TfidfTransformer()
dist = SparkTfidfTransformer()
Z_local = local.fit_transform(X)
Z_dist = dist.fit_transform(X_rdd)
assert_true(check_rdd_dtype(Z_dist, sp.spmatrix))
assert_array_almost_equal(Z_local.toarray(), Z_dist.toarray())
def test_same_transform_result(self):
X, y, Z_rdd = self.make_classification(4, 1000, None)
X_rdd = Z_rdd[:, 'X']
local = TfidfTransformer()
dist = SparkTfidfTransformer()
Z_local = local.fit_transform(X)
Z_dist = sp.vstack(dist.fit_transform(X_rdd).collect())
assert_array_almost_equal(Z_local.toarray(),
Z_dist.toarray())
def test_same_centroids(self):
X, y, X_rdd = self.make_blobs(centers=4, n_samples=200000)
local = KMeans(n_clusters=4, init='k-means++', random_state=42)
dist = SparkKMeans(n_clusters=4, init='k-means++', random_state=42)
local.fit(X)
dist.fit(X_rdd)
local_centers = np.sort(local.cluster_centers_, axis=0)
dist_centers = np.sort(dist.cluster_centers_, axis=0)
assert_array_almost_equal(local_centers, dist_centers, decimal=4)
def test_same_centroids(self):
X, y, X_rdd = self.make_blobs(centers=4, n_samples=200000)
local = KMeans(n_clusters=4, init='k-means++', random_state=42)
dist = SparkKMeans(n_clusters=4, init='k-means++', random_state=42)
local.fit(X)
dist.fit(X_rdd)
local_centers = np.sort(local.cluster_centers_, axis=0)
dist_centers = np.sort(dist.cluster_centers_, axis=0)
assert_array_almost_equal(local_centers, dist_centers, decimal=4)
def test_same_transform_result(self):
X, y, Z_rdd = self.make_classification(4, 1000, -1)
X_rdd = Z_rdd[:, 'X']
local = TfidfTransformer()
dist = SparkTfidfTransformer()
Z_local = local.fit_transform(X)
Z_dist = dist.fit_transform(X_rdd)
assert_true(check_rdd_dtype(Z_dist, sp.spmatrix))
assert_array_almost_equal(Z_local.toarray(),
Z_dist.toarray())
def _test_func_on_axis(self, func):
X, X_rdd = self.make_dense_rdd(block_size=100)
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1):
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
X, X_rdd = self.make_dense_rdd((100, 3, 2))
assert_almost_equal(getattr(X_rdd, func)(), getattr(X, func)())
for axes in (0, 1, 2):
assert_array_almost_equal(
getattr(X_rdd, func)(axis=axes),
getattr(X, func)(axis=axes))
def test_svd(self):
X, X_rdd = self.make_dense_rdd()
u, s, v = svd(X_rdd, 1)
u = np.squeeze(np.concatenate(np.array(u.collect()))).T
u_true, s_true, v_true = ln.svd(X)
assert_array_almost_equal(v[0], match_sign(v[0], v_true[0, :]))
assert_array_almost_equal(s[0], s_true[0])
assert_array_almost_equal(u, match_sign(u, u_true[:, 0]))
def test_svd(self):
X, X_rdd = self.make_dense_rdd()
u, s, v = svd(X_rdd, 1)
u = np.squeeze(np.concatenate(np.array(u.collect()))).T
u_true, s_true, v_true = ln.svd(X)
assert_array_almost_equal(v[0], match_sign(v[0], v_true[0, :]))
assert_array_almost_equal(s[0], s_true[0])
assert_array_almost_equal(u, match_sign(u, u_true[:, 0]))
def test_block_rdd_tuple(self):
n_partitions = 10
n_samples = 100
sparse_row = sp.csr_matrix([[0, 0, 1, 0, 1]])
data = self.sc.parallelize([(np.array([1., 2.]), 0, sparse_row)
for i in range(n_samples)], n_partitions)
blocked_data = block(data)
expected_first_block = np.array([[1., 2.]] * 10)
expected_second_block = np.zeros(10, dtype=np.int)
expected_third_block = sp.vstack([sparse_row] * 10)
first_block_tuple = blocked_data.first()
assert_array_almost_equal(expected_first_block, first_block_tuple[0])
assert_array_almost_equal(expected_second_block, first_block_tuple[1])
assert_array_almost_equal(expected_third_block.toarray(),
first_block_tuple[2].toarray())
tuple_blocks = blocked_data.collect()
assert_equal(len(tuple_blocks), n_partitions)
assert_equal(sum(len(b[0]) for b in tuple_blocks), n_samples)
assert_equal(sum(len(b[1]) for b in tuple_blocks), n_samples)
def test_block_rdd_tuple(self):
n_partitions = 10
n_samples = 100
sparse_row = sp.csr_matrix([[0, 0, 1, 0, 1]])
data = self.sc.parallelize(
[(np.array([1., 2.]), 0, sparse_row) for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
expected_first_block = np.array([[1., 2.]] * 10)
expected_second_block = np.zeros(10, dtype=np.int)
expected_third_block = sp.vstack([sparse_row] * 10)
first_block_tuple = blocked_data.first()
assert_array_almost_equal(expected_first_block, first_block_tuple[0])
assert_array_almost_equal(expected_second_block, first_block_tuple[1])
assert_array_almost_equal(expected_third_block.toarray(),
first_block_tuple[2].toarray())
tuple_blocks = blocked_data.collect()
assert_equal(len(tuple_blocks), n_partitions)
assert_equal(sum(len(b[0]) for b in tuple_blocks), n_samples)
assert_equal(sum(len(b[1]) for b in tuple_blocks), n_samples)
def test_dot(self):
A, A_rdd = self.make_sparse_rdd((20, 10))
B, B_rdd = self.make_sparse_rdd((10, 20))
assert_array_almost_equal(A_rdd.dot(B).toarray(), A.dot(B).toarray())
assert_array_almost_equal(B_rdd.dot(A).toarray(), B.dot(A).toarray())
def test_dot(self):
A, A_rdd = self.make_sparse_rdd((20, 10))
B, B_rdd = self.make_sparse_rdd((10, 20))
assert_array_almost_equal(A_rdd.dot(B).toarray(), A.dot(B).toarray())
assert_array_almost_equal(B_rdd.dot(A).toarray(), B.dot(A).toarray())
def test_dot(self):
a = np.arange(200).reshape(20, 10)
b = np.arange(200).reshape(10, 20)
a_rdd = ArrayRDD(self.sc.parallelize(a))
assert_array_almost_equal(unpack(a_rdd.dot(b)), a.dot(b))
def test_dot_sparse(self):
a, a_rdd = self.generate_sparse_dataset(shape=(10, 20))
b = sp.rand(20, 10, random_state=2, density=0.1)
assert_array_almost_equal(unpack(a_rdd.dot(b)).toarray(),
a.dot(b).toarray())
def test_mean_sparse(self):
data, rdd = self.generate_sparse_dataset()
assert_almost_equal(ArrayRDD(rdd).mean(), data.mean())
assert_array_almost_equal(ArrayRDD(rdd).mean(axis=0), data.mean(axis=0))
assert_array_almost_equal(ArrayRDD(rdd).mean(axis=1), data.mean(axis=1))
