def test_pipeline_same_results(self):
X, y, Z = self.make_classification(2, 10000, 2000)
loc_clf = LogisticRegression()
loc_filter = VarianceThreshold()
loc_pipe = Pipeline([
('threshold', loc_filter),
('logistic', loc_clf)
])
dist_clf = SparkLogisticRegression()
dist_filter = SparkVarianceThreshold()
dist_pipe = SparkPipeline([
('threshold', dist_filter),
('logistic', dist_clf)
])
dist_filter.fit(Z)
loc_pipe.fit(X, y)
dist_pipe.fit(Z, logistic__classes=np.unique(y))
assert_true(np.mean(np.abs(
loc_pipe.predict(X) -
np.concatenate(dist_pipe.predict(Z[:, 'X']).collect())
)) < 0.1)
def test_pipeline_same_results(self):
X, y, Z = self.make_classification(2, 10000, 2000)
loc_clf = LogisticRegression()
loc_filter = VarianceThreshold()
loc_pipe = Pipeline([
('threshold', loc_filter),
('logistic', loc_clf)
])
dist_clf = SparkLogisticRegression()
dist_filter = SparkVarianceThreshold()
dist_pipe = SparkPipeline([
('threshold', dist_filter),
('logistic', dist_clf)
])
dist_filter.fit(Z)
loc_pipe.fit(X, y)
dist_pipe.fit(Z, logistic__classes=np.unique(y))
assert_true(np.mean(np.abs(
loc_pipe.predict(X) -
np.concatenate(dist_pipe.predict(Z[:, 'X']).collect())
)) < 0.1)
def test_same_variances(self):
local = VarianceThreshold()
dist = SparkVarianceThreshold()
shapes = [((10, 5), None),
((1e3, 20), None),
((1e3, 20), 100),
((1e4, 100), None),
((1e4, 100), 600)]
for shape, block_size in shapes:
X, X_rdd = self.generate_dataset(shape, block_size)
local.fit(X)
dist.fit(X_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
X, X_rdd = self.generate_sparse_dataset()
local.fit(X)
dist.fit(X_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
def test_same_variances(self):
local = VarianceThreshold()
dist = SparkVarianceThreshold()
shapes = [((10, 5), None),
((1e3, 20), None),
((1e3, 20), 100),
((1e4, 100), None),
((1e4, 100), 600)]
for shape, block_size in shapes:
X_dense, X_dense_rdd = self.make_dense_rdd()
X_sparse, X_sparse_rdd = self.make_sparse_rdd()
Z = DictRDD([X_sparse_rdd, X_dense_rdd], columns=('X', 'Y'))
local.fit(X_dense)
dist.fit(X_dense_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
local.fit(X_sparse)
dist.fit(X_sparse_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
dist.fit(Z)
assert_array_almost_equal(local.variances_, dist.variances_)
def test_same_variances(self):
local = VarianceThreshold()
dist = SparkVarianceThreshold()
shapes = [((10, 5), None), ((1e3, 20), None), ((1e3, 20), 100),
((1e4, 100), None), ((1e4, 100), 600)]
for shape, block_size in shapes:
X_dense, X_dense_rdd = self.make_dense_rdd()
X_sparse, X_sparse_rdd = self.make_sparse_rdd()
Z = DictRDD([X_sparse_rdd, X_dense_rdd], columns=('X', 'Y'))
local.fit(X_dense)
dist.fit(X_dense_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
local.fit(X_sparse)
dist.fit(X_sparse_rdd)
assert_array_almost_equal(local.variances_, dist.variances_)
dist.fit(Z)
assert_array_almost_equal(local.variances_, dist.variances_)
