def test_true_divide(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A / B
assert_array_equal(
A_rdd.true_divide(B).toarray(), np_res
)
def test_unblocking_rdd(self):
data = np.arange(400)
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
X_unblocked = X.unblock()
assert_is_instance(X_unblocked, RDD)
assert_array_equal(X_unblocked.take(12), np.arange(12).tolist())
def test_unblocking_rdd(self):
data = np.arange(400)
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
X_unblocked = X.unblock()
assert_is_instance(X_unblocked, RDD)
assert_array_equal(X_unblocked.take(12), np.arange(12).tolist())
def test_remainder(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = np.remainder(A, B)
assert_array_equal(
A_rdd.remainder(B).toarray(), np_res
)
def test_same_classes(self):
Y, Y_rdd = self.make_dense_randint_rdd(low=0, high=10, shape=(1000, ))
local = LabelEncoder().fit(Y)
dist = SparkLabelEncoder().fit(Y_rdd)
assert_array_equal(local.classes_, dist.classes_)
def test_same_fit_transform(self):
Y, Y_rdd = self.make_dense_randint_rdd(low=0, high=10, shape=(1000,))
local = LabelEncoder()
dist = SparkLabelEncoder()
assert_array_equal(local.fit_transform(Y), dist.fit_transform(Y_rdd).toarray())
def test_same_classes(self):
Y, Y_rdd = self.make_dense_randint_rdd(low=0, high=10, shape=(1000,))
local = LabelEncoder().fit(Y)
dist = SparkLabelEncoder().fit(Y_rdd)
assert_array_equal(local.classes_, dist.classes_)
def test_limit_features(self):
X, X_rdd = self.make_text_rdd()
params = [{
'min_df': .5
}, {
'min_df': 2,
'max_df': .9
}, {
'min_df': 1,
'max_df': .6
}, {
'min_df': 2,
'max_features': 3
}]
for paramset in params:
local = CountVectorizer(**paramset)
dist = SparkCountVectorizer(**paramset)
result_local = local.fit_transform(X).toarray()
result_dist = dist.fit_transform(X_rdd).toarray()
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local, result_dist)
result_dist = dist.transform(X_rdd).toarray()
assert_array_equal(result_local, result_dist)
def test_fmod(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = np.fmod(A, B)
assert_array_equal(
A_rdd.fmod(B).toarray(), np_res
)
def test_same_fit_transform(self):
Y, Y_rdd = self.make_dense_randint_rdd(low=0, high=10, shape=(1000, ))
local = LabelEncoder()
dist = SparkLabelEncoder()
assert_array_equal(local.fit_transform(Y),
dist.fit_transform(Y_rdd).toarray())
def test_transform(self):
X, X_rdd = self.make_dense_rdd((100, 4))
fn = lambda x: x**2
X1 = list(map(fn, X_rdd.collect()))
X2 = X_rdd.transform(fn).collect()
assert_array_equal(X1, X2)
def test_same_output(self):
X, X_rdd = self.make_text_rdd()
local = HashingVectorizer()
dist = SparkHashingVectorizer()
result_local = local.transform(X)
result_dist = sp.vstack(dist.transform(X_rdd).collect())
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_transform(self):
X, X_rdd = self.make_dense_rdd((100, 4))
fn = lambda x: x ** 2
X1 = list(map(fn, X_rdd.collect()))
X2 = X_rdd.transform(fn).collect()
assert_array_equal(X1, X2)
def test_same_output(self):
X, X_rdd = self.make_text_rdd()
local = HashingVectorizer()
dist = SparkHashingVectorizer()
result_local = local.transform(X).toarray()
result_dist = dist.transform(X_rdd).toarray()
assert_array_equal(result_local, result_dist)
def test_same_output(self):
X, X_rdd = self.make_text_rdd()
local = CountVectorizer()
dist = SparkCountVectorizer()
result_local = local.fit_transform(X).toarray()
result_dist = dist.fit_transform(X_rdd).toarray()
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local, result_dist)
def test_transform(self):
data = np.arange(400).reshape((100, 4))
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
fn = lambda x: x ** 2
X1 = map(fn, X.collect())
X2 = X.transform(fn).collect()
assert_array_equal(X1, X2)
def test_same_output(self):
X, X_rdd = self.make_dict_dataset()
local = DictVectorizer()
dist = SparkDictVectorizer()
result_local = local.fit_transform(X)
result_dist = sp.vstack(dist.fit_transform(X_rdd).collect())
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_same_inverse_transform(self):
Y, Y_rdd = self.make_dense_randint_rdd((1000,), low_high=(0, 10))
local = LabelEncoder().fit(Y)
dist = SparkLabelEncoder().fit(Y_rdd)
assert_array_equal(
local.inverse_transform(Y),
dist.inverse_transform(Y_rdd).toarray()
)
def test_same_output(self):
X, X_rdd = self.make_text_rdd()
local = CountVectorizer()
dist = SparkCountVectorizer()
result_local = local.fit_transform(X).toarray()
result_dist = dist.fit_transform(X_rdd).toarray()
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local, result_dist)
def test_same_output_sparse(self):
X, X_rdd = self.make_dict_dataset()
local = DictVectorizer(sparse=True)
dist = SparkDictVectorizer(sparse=True)
result_local = local.fit_transform(X)
result_dist = dist.fit_transform(X_rdd)
assert_true(check_rdd_dtype(result_dist, (sp.spmatrix,)))
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_same_output_sparse(self):
X, X_rdd = self.make_dict_dataset()
local = DictVectorizer(sparse=True)
dist = SparkDictVectorizer(sparse=True)
result_local = local.fit_transform(X)
result_dist = dist.fit_transform(X_rdd)
assert_true(check_rdd_dtype(result_dist, (sp.spmatrix, )))
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_convert_toarray(self):
data = np.arange(400)
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
X_array = X.toarray()
assert_array_equal(X_array, data)
data = [2, 3, 5, 1, 6, 7, 9, 9]
rdd = self.sc.parallelize(data, 2)
X = ArrayRDD(rdd)
X_array = X.toarray()
assert_array_equal(X_array, np.array(data))
def test_convert_toarray(self):
data = np.arange(400)
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
X_array = X.toarray()
assert_array_equal(X_array, data)
data = [2, 3, 5, 1, 6, 7, 9, 9]
rdd = self.sc.parallelize(data, 2)
X = ArrayRDD(rdd)
X_array = X.toarray()
assert_array_equal(X_array, np.array(data))
def test_sum(self):
data = np.arange(400).reshape((100, 4))
rdd = self.sc.parallelize(data)
assert_equal(ArrayRDD(rdd).sum(), data.sum())
assert_array_equal(ArrayRDD(rdd).sum(axis=0), data.sum(axis=0))
assert_array_equal(ArrayRDD(rdd).sum(axis=1), data.sum(axis=1))
data = np.arange(600).reshape((100, 3, 2))
rdd = self.sc.parallelize(data)
assert_equal(ArrayRDD(rdd).sum(), data.sum())
assert_array_equal(ArrayRDD(rdd).sum(axis=0), data.sum(axis=0))
assert_array_equal(ArrayRDD(rdd).sum(axis=1), data.sum(axis=1))
assert_array_equal(ArrayRDD(rdd).sum(axis=2), data.sum(axis=2))
def test_dummy_analyzer(self):
X, X_rdd = self.make_text_rdd()
def splitter(x):
return x.split()
X = map(splitter, X)
X_rdd = X_rdd.map(lambda x: map(splitter, x))
local = HashingVectorizer(analyzer=lambda x: x)
dist = SparkHashingVectorizer(analyzer=lambda x: x)
result_local = local.transform(X)
result_dist = sp.vstack(dist.transform(X_rdd).collect())
assert_array_equal(result_local.toarray(), result_dist.toarray())
result_local = local.fit_transform(X)
result_dist = sp.vstack(dist.fit_transform(X_rdd).collect())
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_same_fit_transforms(self):
X, X_rdd = self.make_dense_rdd((1e3, 12))
n_components = 4
random_state = 42
tol = 1e-7
local = TruncatedSVD(n_components, n_iter=5, tol=tol,
random_state=random_state)
dist = SparkTruncatedSVD(n_components, n_iter=50, tol=tol,
random_state=random_state)
Z_local = local.fit_transform(X)
Z_dist = dist.fit_transform(X_rdd).toarray()
tol = 1e-1
assert_array_equal(Z_local.shape, Z_dist.shape)
assert(np.allclose(+Z_dist[:, 0], Z_local[:, 0], atol=tol) |
np.allclose(-Z_dist[:, 0], Z_local[:, 0], atol=tol))
def test_get_multiple_items(self):
x, y = np.arange(80).reshape((40, 2)), np.arange(40)
x_rdd = self.sc.parallelize(x, 2)
y_rdd = self.sc.parallelize(y, 2)
z_rdd = x_rdd.zip(y_rdd)
z = DictRDD(z_rdd, bsize=5)
expected = [(np.arange(0, 10).reshape((5, 2)), np.arange(0, 5)),
(np.arange(10, 20).reshape((5, 2)), np.arange(5, 10))]
assert_array_equal(z[:2, 1].collect(),
[expected[0][1], expected[1][1]])
assert_array_equal(z[[0, 1], 0].collect(),
[expected[0][0], expected[1][0]])
assert_multiple_tuples_equal(z[[0, 1], [1]].collect(),
[(expected[0][1], ), (expected[1][1], )])
assert_multiple_tuples_equal(z[[0, 1], -1:].collect(),
[(expected[0][1], ), (expected[1][1], )])
assert_multiple_tuples_equal(z[[1, 0], [1, 0]].collect(),
[expected[1][::-1], expected[0][::-1]])
def test_dummy_analyzer(self):
X, X_rdd = self.make_text_rdd()
def splitter(x):
return x.split()
X = list(map(splitter, X))
X_rdd = X_rdd.map(lambda x: list(map(splitter, x)))
local = HashingVectorizer(analyzer=lambda x: x)
dist = SparkHashingVectorizer(analyzer=lambda x: x)
result_local = local.transform(X).toarray()
result_dist = dist.transform(X_rdd).toarray()
assert_array_equal(result_local, result_dist)
result_local = local.fit_transform(X).toarray()
result_dist = dist.fit_transform(X_rdd).toarray()
assert_array_equal(result_local, result_dist)
def test_limit_features(self):
X, X_rdd = self.make_text_rdd()
params = [{'min_df': .5},
{'min_df': 2, 'max_df': .9},
{'min_df': 1, 'max_df': .6},
{'min_df': 2, 'max_features': 3}]
for paramset in params:
local = CountVectorizer(**paramset)
dist = SparkCountVectorizer(**paramset)
result_local = local.fit_transform(X)
result_dist = sp.vstack(dist.fit_transform(X_rdd).collect())
assert_equal(local.vocabulary_, dist.vocabulary_)
assert_array_equal(result_local.toarray(), result_dist.toarray())
result_dist = sp.vstack(dist.transform(X_rdd).collect())
assert_array_equal(result_local.toarray(), result_dist.toarray())
def test_same_fit_transforms(self):
X, X_rdd = self.make_dense_rdd((1e3, 12))
n_components = 4
random_state = 42
tol = 1e-7
local = TruncatedSVD(n_components, n_iter=5, tol=tol,
random_state=random_state)
dist = SparkTruncatedSVD(n_components, n_iter=50, tol=tol,
random_state=random_state)
Z_local = local.fit_transform(X)
Z_dist = dist.fit_transform(X_rdd)
Z_collected = Z_dist.toarray()
assert_true(check_rdd_dtype(Z_dist, (np.ndarray,)))
tol = 1e-1
assert_array_equal(Z_local.shape, Z_collected.shape)
assert(np.allclose(+Z_collected[:, 0], Z_local[:, 0], atol=tol) |
np.allclose(-Z_collected[:, 0], Z_local[:, 0], atol=tol))
def test_get_multiple_items(self):
x, y = np.arange(80).reshape((40, 2)), np.arange(40)
x_rdd = self.sc.parallelize(x, 2)
y_rdd = self.sc.parallelize(y, 2)
z_rdd = x_rdd.zip(y_rdd)
z = DictRDD(z_rdd, bsize=5)
expected = [(np.arange(0, 10).reshape((5, 2)), np.arange(0, 5)),
(np.arange(10, 20).reshape((5, 2)), np.arange(5, 10))]
assert_array_equal(z[:2, 1].collect(),
[expected[0][1], expected[1][1]])
assert_array_equal(z[[0, 1], 0].collect(),
[expected[0][0], expected[1][0]])
assert_multiple_tuples_equal(z[[0, 1], [1]].collect(),
[(expected[0][1],),
(expected[1][1],)])
assert_multiple_tuples_equal(z[[0, 1], -1:].collect(),
[(expected[0][1],),
(expected[1][1],)])
assert_multiple_tuples_equal(z[[1, 0], [1, 0]].collect(),
[expected[1][::-1], expected[0][::-1]])
def test_subtract(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A - B
assert_array_equal(A_rdd.subtract(B).toarray(), np_res)
assert_array_equal((A_rdd - B).toarray(), np_res)
A_rdd -= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_multiply(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A * B
assert_array_equal(A_rdd.multiply(B).toarray(), np_res)
assert_array_equal((A_rdd * B).toarray(), np_res)
A_rdd *= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_power(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A**B
assert_array_equal(A_rdd.power(B).toarray(), np_res)
assert_array_equal((A_rdd**B).toarray(), np_res)
A_rdd **= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_floor_divide(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A // B
assert_array_equal(A_rdd.floor_divide(B).toarray(), np_res)
assert_array_equal((A_rdd // B).toarray(), np_res)
A_rdd //= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_add(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A + B
assert_array_equal(A_rdd.add(B).toarray(), np_res)
assert_array_equal((A_rdd + B).toarray(), np_res)
A_rdd += B
assert_array_equal(A_rdd.toarray(), np_res)
def test_mod(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A % B
assert_array_equal(A_rdd.mod(B).toarray(), np_res)
assert_array_equal((A_rdd % B).toarray(), np_res)
A_rdd %= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_floor_divide(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A // B
assert_array_equal(
A_rdd.floor_divide(B).toarray(), np_res
)
assert_array_equal((A_rdd // B).toarray(), np_res)
A_rdd //= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_add(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A + B
assert_array_equal(
A_rdd.add(B).toarray(), np_res
)
assert_array_equal((A_rdd + B).toarray(), np_res)
A_rdd += B
assert_array_equal(A_rdd.toarray(), np_res)
def test_multiply(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A * B
assert_array_equal(
A_rdd.multiply(B).toarray(), np_res
)
assert_array_equal((A_rdd * B).toarray(), np_res)
A_rdd *= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_subtract(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A - B
assert_array_equal(
A_rdd.subtract(B).toarray(), np_res
)
assert_array_equal((A_rdd - B).toarray(), np_res)
A_rdd -= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_power(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A ** B
assert_array_equal(
A_rdd.power(B).toarray(), np_res
)
assert_array_equal((A_rdd ** B).toarray(), np_res)
A_rdd **= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_mod(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A % B
assert_array_equal(
A_rdd.mod(B).toarray(), np_res
)
assert_array_equal((A_rdd % B).toarray(), np_res)
A_rdd %= B
assert_array_equal(A_rdd.toarray(), np_res)
def test_dtype(self):
n_partitions = 10
n_samples = 100
data = self.sc.parallelize(["lorem" for i in range(n_samples)],
n_partitions)
blocked_data = block(data, dtype=list)
assert_array_equal(["lorem"] * 10, blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_array_equal(["lorem"] * 10, blocks[-1])
assert_equal(sum(len(b) for b in blocks), n_samples)
n_partitions = 17
data = self.sc.parallelize([1 for i in range(n_samples)], n_partitions)
blocked_data = block(data, dtype=tuple)
assert_array_equal(tuple([1] * (n_samples // n_partitions)),
blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_equal(sum(len(b) for b in blocks), n_samples)
def test_dtype(self):
n_partitions = 10
n_samples = 100
data = self.sc.parallelize(["lorem" for i in range(n_samples)],
n_partitions)
blocked_data = block(data, dtype=list)
assert_array_equal(["lorem"] * 10, blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_array_equal(["lorem"] * 10, blocks[-1])
assert_equal(sum(len(b) for b in blocks), n_samples)
n_partitions = 17
data = self.sc.parallelize([1 for i in range(n_samples)],
n_partitions)
blocked_data = block(data, dtype=tuple)
assert_array_equal(tuple([1] * (n_samples / n_partitions)),
blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_equal(sum(len(b) for b in blocks), n_samples)
def test_array(self):
n_partitions = 10
n_samples = 100
data = self.sc.parallelize([np.array([1]) for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_array_equal(np.ones((10, 1)), blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_array_equal(np.ones((10, 1)), blocks[-1])
assert_equal(sum(len(b) for b in blocks), n_samples)
n_partitions = 17
data = self.sc.parallelize([np.array([1]) for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_array_equal(np.ones((n_samples / n_partitions, 1)),
blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_equal(sum(len(b) for b in blocks), n_samples)
def test_array(self):
n_partitions = 10
n_samples = 100
data = self.sc.parallelize([np.array([1]) for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_array_equal(np.ones((10, 1)), blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_array_equal(np.ones((10, 1)), blocks[-1])
assert_equal(sum(len(b) for b in blocks), n_samples)
n_partitions = 17
data = self.sc.parallelize([np.array([1]) for i in range(n_samples)],
n_partitions)
blocked_data = block(data)
assert_array_equal(np.ones((n_samples // n_partitions, 1)),
blocked_data.first())
blocks = blocked_data.collect()
assert_equal(len(blocks), n_partitions)
assert_equal(sum(len(b) for b in blocks), n_samples)
def test_get_multiple_item(self):
X, X_rdd = self.make_dense_range_rdd((100, 4), block_size=5)
exp0th = np.arange(0, 20).reshape((5, 4))
exp1st = np.arange(20, 40).reshape((5, 4))
exp2nd = np.arange(40, 60).reshape((5, 4))
exp7th = np.arange(140, 160).reshape((5, 4))
exp18th = np.arange(360, 380).reshape((5, 4))
exp19th = np.arange(380, 400).reshape((5, 4))
assert_array_equal(X_rdd[[0, 1]].collect(), [exp0th, exp1st])
assert_array_equal(X_rdd[[0, 2]].collect(), [exp0th, exp2nd])
assert_array_equal(X_rdd[[0, -1]].collect(), [exp0th, exp19th])
assert_array_equal(X_rdd[[0, -2]].collect(), [exp0th, exp18th])
assert_array_equal(X_rdd[[1, -2]].collect(), [exp1st, exp18th])
assert_array_equal(X_rdd[[7, 0]].collect(), [exp7th, exp0th])
assert_array_equal(X_rdd[[1, 2, 7, 19]].collect(),
[exp1st, exp2nd, exp7th, exp19th])
def test_get_single_item(self):
x, y = np.arange(80).reshape((40, 2)), np.arange(40)
x_rdd = self.sc.parallelize(x, 2)
y_rdd = self.sc.parallelize(y, 2)
z_rdd = x_rdd.zip(y_rdd)
z = DictRDD(z_rdd, bsize=5)
assert_array_equal(z[0, 0].first(), np.arange(0, 10).reshape((5, 2)))
assert_array_equal(z[0, 1].first(), np.arange(5))
assert_array_equal(z[3, 0].first(), np.arange(30, 40).reshape((5, 2)))
assert_array_equal(z[3, 1].first(), np.arange(15, 20))
# assert_array_equal(z[3, -1].first(), np.arange(15, 20))
assert_array_equal(z[7, 0].first(), np.arange(70, 80).reshape((5, 2)))
assert_array_equal(z[-1, 0].first(), np.arange(70, 80).reshape((5, 2)))
assert_array_equal(z[7, 1].first(), np.arange(35, 40))
def test_flatten(self):
X, X_rdd = self.make_dense_rdd((100, 3, 2))
X = X.flatten()
X_rdd = X_rdd.flatten()
assert_array_equal(X_rdd.toarray(), X)
def test_remainder(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = np.remainder(A, B)
assert_array_equal(A_rdd.remainder(B).toarray(), np_res)
def test_fmod(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = np.fmod(A, B)
assert_array_equal(A_rdd.fmod(B).toarray(), np_res)
def test_array_slice_syntax(self):
data = np.arange(400).reshape((100, 4))
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
exp0th = np.arange(0, 20).reshape((5, 4))
exp1st = np.arange(20, 40).reshape((5, 4))
exp7th = np.arange(140, 160).reshape((5, 4))
exp8th = np.arange(160, 180).reshape((5, 4))
exp9th = np.arange(180, 200).reshape((5, 4))
exp18th = np.arange(360, 380).reshape((5, 4))
exp19th = np.arange(380, 400).reshape((5, 4))
assert_array_equal(X[:1].collect(), [exp0th])
assert_array_equal(X[:2].collect(), [exp0th, exp1st])
assert_array_equal(X[18:].collect(), [exp18th, exp19th])
assert_array_equal(X[-1:].collect(), [exp19th])
assert_array_equal(X[-2:].collect(), [exp18th, exp19th])
assert_array_equal(X[7:10].collect(), [exp7th, exp8th, exp9th])
assert_array_equal(X[7:10:2].collect(), [exp7th, exp9th])
assert_array_equal(X[::9].collect(), [exp0th, exp9th, exp18th])
assert_array_equal(X[::-10].collect(), [exp19th, exp9th])
assert_array_equal(X[-1:1].collect(), [])
def test_true_divide(self):
A, A_rdd = self.make_dense_rdd((8, 3))
B, B_rdd = self.make_dense_rdd((1, 3))
np_res = A / B
assert_array_equal(A_rdd.true_divide(B).toarray(), np_res)
def test_mean(self):
data = np.arange(600).reshape((100, 3, 2))
rdd = self.sc.parallelize(data)
assert_equal(ArrayRDD(rdd).mean(), data.mean())
assert_array_equal(ArrayRDD(rdd).mean(axis=0), data.mean(axis=0))
assert_array_equal(ArrayRDD(rdd).mean(axis=1), data.mean(axis=1))
def test_get_single_item(self):
x, y = np.arange(80).reshape((40, 2)), np.arange(40)
x_rdd = self.sc.parallelize(x, 2)
y_rdd = self.sc.parallelize(y, 2)
z_rdd = x_rdd.zip(y_rdd)
z = DictRDD(z_rdd, bsize=5)
assert_array_equal(z[0, 0].first(), np.arange(0, 10).reshape((5, 2)))
assert_array_equal(z[0, 1].first(), np.arange(5))
assert_array_equal(z[3, 0].first(), np.arange(30, 40).reshape((5, 2)))
assert_array_equal(z[3, 1].first(), np.arange(15, 20))
# assert_array_equal(z[3, -1].first(), np.arange(15, 20))
assert_array_equal(z[7, 0].first(), np.arange(70, 80).reshape((5, 2)))
assert_array_equal(z[-1, 0].first(), np.arange(70, 80).reshape((5, 2)))
assert_array_equal(z[7, 1].first(), np.arange(35, 40))
def test_get_single_item(self):
data = np.arange(400).reshape((100, 4))
rdd = self.sc.parallelize(data, 4)
X = ArrayRDD(rdd, 5)
expected = np.arange(0, 20).reshape((5, 4))
assert_array_equal(X.first(), expected)
assert_array_equal(X[0].first(), expected)
expected = np.arange(20, 40).reshape((5, 4))
assert_array_equal(X[1].first(), expected)
expected = np.arange(380, 400).reshape((5, 4))
assert_array_equal(X[19].first(), expected)
assert_array_equal(X[-1].first(), expected)
expected = np.arange(340, 360).reshape((5, 4))
assert_array_equal(X[17].first(), expected)
assert_array_equal(X[-3].first(), expected)
def test_dot(self):
A, A_rdd = self.make_dense_rdd((20, 10))
B, B_rdd = self.make_dense_rdd((10, 20))
assert_array_equal(A_rdd.dot(B).toarray(), A.dot(B))
assert_array_equal(B_rdd.dot(A).toarray(), B.dot(A))
def test_array_slice_syntax(self):
X, X_rdd = self.make_dense_range_rdd((100, 4), block_size=5)
exp0th = np.arange(0, 20).reshape((5, 4))
exp1st = np.arange(20, 40).reshape((5, 4))
exp7th = np.arange(140, 160).reshape((5, 4))
exp8th = np.arange(160, 180).reshape((5, 4))
exp9th = np.arange(180, 200).reshape((5, 4))
exp18th = np.arange(360, 380).reshape((5, 4))
exp19th = np.arange(380, 400).reshape((5, 4))
assert_array_equal(X_rdd[:1].collect(), [exp0th])
assert_array_equal(X_rdd[:2].collect(), [exp0th, exp1st])
assert_array_equal(X_rdd[18:].collect(), [exp18th, exp19th])
assert_array_equal(X_rdd[-1:].collect(), [exp19th])
assert_array_equal(X_rdd[-2:].collect(), [exp18th, exp19th])
assert_array_equal(X_rdd[7:10].collect(), [exp7th, exp8th, exp9th])
assert_array_equal(X_rdd[7:10:2].collect(), [exp7th, exp9th])
assert_array_equal(X_rdd[::9].collect(), [exp0th, exp9th, exp18th])
assert_array_equal(X_rdd[::-10].collect(), [exp19th, exp9th])
assert_array_equal(X_rdd[-1:1].collect(), [])
