def test_fewer_distinct_points_than_cluster(self):
input = self.t_env.from_data_stream(
self.env.from_collection(
[
(Vectors.dense([0.0, 0.1]), ),
(Vectors.dense([0.0, 0.1]), ),
(Vectors.dense([0.0, 0.1]), ),
],
type_info=Types.ROW_NAMED(['features'],
[DenseVectorTypeInfo()])))
kmeans = KMeans().set_k(2)
model = kmeans.fit(input)
output = model.transform(input)[0]
results = [
result for result in self.t_env.to_data_stream(
output).execute_and_collect()
]
field_names = output.get_schema().get_field_names()
actual_groups = group_features_by_prediction(
results, field_names.index(kmeans.features_col),
field_names.index(kmeans.prediction_col))
expected_groups = [{DenseVector([0.0, 0.1])}]
self.assertEqual(actual_groups, expected_groups)
def setUp(self):
super(OneHotEncoderTest, self).setUp()
self.train_data = self.t_env.from_data_stream(
self.env.from_collection([
(0.0,),
(1.0,),
(2.0,),
(0.0,),
],
type_info=Types.ROW_NAMED(
['input'],
[Types.DOUBLE()])))
self.predict_data = self.t_env.from_data_stream(
self.env.from_collection([
(0.0,),
(1.0,),
(2.0,),
],
type_info=Types.ROW_NAMED(
['input'],
[Types.DOUBLE()])))
self.expected_data = {
0.0: Vectors.sparse(2, [0], [1.0]),
1.0: Vectors.sparse(2, [1], [1.0]),
2.0: Vectors.sparse(2, [], [])
}
self.estimator = OneHotEncoder().set_input_cols('input').set_output_cols('output')
def test_drop_last(self):
self.estimator.set_drop_last(False)
expected_data = {
0.0: Vectors.sparse(3, [0], [1.0]),
1.0: Vectors.sparse(3, [1], [1.0]),
2.0: Vectors.sparse(3, [2], [1.0])
}
model = self.estimator.fit(self.train_data) # type: OneHotEncoderModel
output_table = model.transform(self.predict_data)[0]
self.verify_output_result(
output_table,
model.input_cols,
model.output_cols,
output_table.get_schema().get_field_names(),
expected_data)
def setUp(self):
super(LogisticRegressionTest, self).setUp()
self.binomial_data_table = self.t_env.from_data_stream(
self.env.from_collection(
[
(Vectors.dense([1, 2, 3, 4]), 0., 1.),
(Vectors.dense([2, 2, 3, 4]), 0., 2.),
(Vectors.dense([3, 2, 3, 4]), 0., 3.),
(Vectors.dense([4, 2, 3, 4]), 0., 4.),
(Vectors.dense([5, 2, 3, 4]), 0., 5.),
(Vectors.dense([11, 2, 3, 4]), 1., 1.),
(Vectors.dense([12, 2, 3, 4]), 1., 2.),
(Vectors.dense([13, 2, 3, 4]), 1., 3.),
(Vectors.dense([14, 2, 3, 4]), 1., 4.),
(Vectors.dense([15, 2, 3, 4]), 1., 5.),
],
type_info=Types.ROW_NAMED(
['features', 'label', 'weight'],
[DenseVectorTypeInfo(),
Types.DOUBLE(),
Types.DOUBLE()])))
def test_max_value_equas_min_value_but_predict_value_not_equals(self):
train_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([40.0, 80.0]), ),
],
type_info=Types.ROW_NAMED(
['input'], [DenseVectorTypeInfo()])))
predict_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([30.0, 50.0]), ),
],
type_info=Types.ROW_NAMED(
['input'], [DenseVectorTypeInfo()])))
min_max_scalar = MinMaxScaler() \
.set_min(0.0) \
.set_max(10.0)
model = min_max_scalar.fit(train_data)
result = model.transform(predict_data)[0]
self.verify_output_result(result, min_max_scalar.get_output_col(),
result.get_schema().get_field_names(),
[Vectors.dense(5.0, 5.0)])
def setUp(self):
super(VectorAssemblerTest, self).setUp()
self.input_data_table = self.t_env.from_data_stream(
self.env.from_collection([
(0, Vectors.dense(2.1, 3.1), 1.0, Vectors.sparse(
5, [3], [1.0])),
(1, Vectors.dense(2.1, 3.1), 1.0,
Vectors.sparse(5, [1, 2, 3, 4], [1.0, 2.0, 3.0, 4.0])),
(2, None, None, None),
],
type_info=Types.ROW_NAMED(
['id', 'vec', 'num', 'sparse_vec'], [
Types.INT(),
DenseVectorTypeInfo(),
Types.DOUBLE(),
SparseVectorTypeInfo()
])))
self.expected_output_data_1 = Vectors.sparse(8, [0, 1, 2, 6],
[2.1, 3.1, 1.0, 1.0])
self.expected_output_data_2 = Vectors.dense(2.1, 3.1, 1.0, 0.0, 1.0,
2.0, 3.0, 4.0)
def setUp(self):
super(KMeansTest, self).setUp()
self.data_table = self.t_env.from_data_stream(
self.env.from_collection(
[
(Vectors.dense([0.0, 0.0]), ),
(Vectors.dense([0.0, 0.3]), ),
(Vectors.dense([0.3, 3.0]), ),
(Vectors.dense([9.0, 0.0]), ),
(Vectors.dense([9.0, 0.6]), ),
(Vectors.dense([9.6, 0.0]), ),
],
type_info=Types.ROW_NAMED(['features'],
[DenseVectorTypeInfo()])))
self.expected_groups = [{
DenseVector([0.0, 0.3]),
DenseVector([0.3, 3.0]),
DenseVector([0.0, 0.0])
},
{
DenseVector([9.6, 0.0]),
DenseVector([9.0, 0.0]),
DenseVector([9.0, 0.6])
}]
def setUp(self):
super(BinaryClassificationEvaluatorTest, self).setUp()
self.input_data_table = self.t_env.from_data_stream(
self.env.from_collection([
(1.0, Vectors.dense(0.1, 0.9)),
(1.0, Vectors.dense(0.2, 0.8)),
(1.0, Vectors.dense(0.3, 0.7)),
(0.0, Vectors.dense(0.25, 0.75)),
(0.0, Vectors.dense(0.4, 0.6)),
(1.0, Vectors.dense(0.35, 0.65)),
(1.0, Vectors.dense(0.45, 0.55)),
(0.0, Vectors.dense(0.6, 0.4)),
(0.0, Vectors.dense(0.7, 0.3)),
(1.0, Vectors.dense(0.65, 0.35)),
(0.0, Vectors.dense(0.8, 0.2)),
(1.0, Vectors.dense(0.9, 0.1))
],
type_info=Types.ROW_NAMED(
['label', 'rawPrediction'],
[Types.DOUBLE(), DenseVectorTypeInfo()]))
)
self.input_data_table_score = self.t_env.from_data_stream(
self.env.from_collection([
(1, 0.9),
(1, 0.8),
(1, 0.7),
(0, 0.75),
(0, 0.6),
(1, 0.65),
(1, 0.55),
(0, 0.4),
(0, 0.3),
(1, 0.35),
(0, 0.2),
(1, 0.1)
],
type_info=Types.ROW_NAMED(
['label', 'rawPrediction'],
[Types.INT(), Types.DOUBLE()]))
)
self.input_data_table_with_multi_score = self.t_env.from_data_stream(
self.env.from_collection([
(1.0, Vectors.dense(0.1, 0.9)),
(1.0, Vectors.dense(0.1, 0.9)),
(1.0, Vectors.dense(0.1, 0.9)),
(0.0, Vectors.dense(0.25, 0.75)),
(0.0, Vectors.dense(0.4, 0.6)),
(1.0, Vectors.dense(0.1, 0.9)),
(1.0, Vectors.dense(0.1, 0.9)),
(0.0, Vectors.dense(0.6, 0.4)),
(0.0, Vectors.dense(0.7, 0.3)),
(1.0, Vectors.dense(0.1, 0.9)),
(0.0, Vectors.dense(0.8, 0.2)),
(1.0, Vectors.dense(0.9, 0.1))
],
type_info=Types.ROW_NAMED(
['label', 'rawPrediction'],
[Types.DOUBLE(), DenseVectorTypeInfo()]))
)
self.input_data_table_with_weight = self.t_env.from_data_stream(
self.env.from_collection([
(1.0, Vectors.dense(0.1, 0.9), 0.8),
(1.0, Vectors.dense(0.1, 0.9), 0.7),
(1.0, Vectors.dense(0.1, 0.9), 0.5),
(0.0, Vectors.dense(0.25, 0.75), 1.2),
(0.0, Vectors.dense(0.4, 0.6), 1.3),
(1.0, Vectors.dense(0.1, 0.9), 1.5),
(1.0, Vectors.dense(0.1, 0.9), 1.4),
(0.0, Vectors.dense(0.6, 0.4), 0.3),
(0.0, Vectors.dense(0.7, 0.3), 0.5),
(1.0, Vectors.dense(0.1, 0.9), 1.9),
(0.0, Vectors.dense(0.8, 0.2), 1.2),
(1.0, Vectors.dense(0.9, 0.1), 1.0)
],
type_info=Types.ROW_NAMED(
['label', 'rawPrediction', 'weight'],
[Types.DOUBLE(), DenseVectorTypeInfo(), Types.DOUBLE()]))
)
self.expected_data = [0.7691481137909708, 0.3714285714285714, 0.6571428571428571]
self.expected_data_m = [0.8571428571428571, 0.9377705627705628,
0.8571428571428571, 0.6488095238095237]
self.expected_data_w = 0.8911680911680911
self.eps = 1e-5
def setUp(self):
super(MinMaxScalerTest, self).setUp()
self.train_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([0.0, 3.0]), ),
(Vectors.dense([2.1, 0.0]), ),
(Vectors.dense([4.1, 5.1]), ),
(Vectors.dense([6.1, 8.1]), ),
(Vectors.dense([200., 400.]), ),
],
type_info=Types.ROW_NAMED(
['input'], [DenseVectorTypeInfo()])))
self.predict_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([150.0, 90.0]), ),
(Vectors.dense([50.0, 40.0]), ),
(Vectors.dense([100.0, 50.0]), ),
],
type_info=Types.ROW_NAMED(
['input'], [DenseVectorTypeInfo()])))
self.expected_data = [
Vectors.dense(0.25, 0.1),
Vectors.dense(0.5, 0.125),
Vectors.dense(0.75, 0.225)
]
def setUp(self):
super(StandardScalerTest, self).setUp()
self.dense_input = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense(-2.5, 9.0, 1.0), ),
(Vectors.dense(1.4, -5.0, 1.0), ),
(Vectors.dense(2.0, -1.0, -2.0), ),
],
type_info=Types.ROW_NAMED(
['input'], [DenseVectorTypeInfo()])))
self.expected_res_with_mean = [
Vectors.dense(-2.8, 8.0, 1.0),
Vectors.dense(1.1, -6.0, 1.0),
Vectors.dense(1.7, -2.0, -2.0)
]
self.expected_res_with_std = [
Vectors.dense(-1.0231819, 1.2480754, 0.5773502),
Vectors.dense(0.5729819, -0.6933752, 0.5773503),
Vectors.dense(0.8185455, -0.1386750, -1.1547005)
]
self.expected_res_with_mean_and_std = [
Vectors.dense(-1.1459637, 1.1094004, 0.5773503),
Vectors.dense(0.45020003, -0.8320503, 0.5773503),
Vectors.dense(0.69576368, -0.2773501, -1.1547005)
]
self.expected_mean = [0.3, 1.0, 0.0]
self.expected_std = [2.4433583, 7.2111026, 1.7320508]
def setUp(self):
super(KNNTest, self).setUp()
self.train_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([2.0, 3.0]), 1.0),
(Vectors.dense([2.1, 3.1]), 1.0),
(Vectors.dense([200.1, 300.1]), 2.0),
(Vectors.dense([200.2, 300.2]), 2.0),
(Vectors.dense([200.3, 300.3]), 2.0),
(Vectors.dense([200.4, 300.4]), 2.0),
(Vectors.dense([200.4, 300.4]), 2.0),
(Vectors.dense([200.6, 300.6]), 2.0),
(Vectors.dense([2.1, 3.1]), 1.0),
(Vectors.dense([2.1, 3.1]), 1.0),
(Vectors.dense([2.1, 3.1]), 1.0),
(Vectors.dense([2.1, 3.1]), 1.0),
(Vectors.dense([2.3, 3.2]), 1.0),
(Vectors.dense([2.3, 3.2]), 1.0),
(Vectors.dense([2.8, 3.2]), 3.0),
(Vectors.dense([300., 3.2]), 4.0),
(Vectors.dense([2.2, 3.2]), 1.0),
(Vectors.dense([2.4, 3.2]), 5.0),
(Vectors.dense([2.5, 3.2]), 5.0),
(Vectors.dense([2.5, 3.2]), 5.0),
(Vectors.dense([2.1, 3.1]), 1.0)
],
type_info=Types.ROW_NAMED(
['features', 'label'],
[DenseVectorTypeInfo(), Types.DOUBLE()])))
self.predict_data = self.t_env.from_data_stream(
self.env.from_collection([
(Vectors.dense([4.0, 4.1]), 5.0),
(Vectors.dense([300, 42]), 2.0),
],
type_info=Types.ROW_NAMED(
['features', 'label'],
[DenseVectorTypeInfo(), Types.DOUBLE()])))
def setUp(self):
super(NaiveBayesTest, self).setUp()
self.env.set_parallelism(1)
self.train_data = self.t_env.from_data_stream(
self.env.from_collection(
[
(Vectors.dense([0, 0.]), 11.),
(Vectors.dense([1, 0]), 10.),
(Vectors.dense([1, 1.]), 10.),
],
type_info=Types.ROW_NAMED(
['features', 'label'],
[DenseVectorTypeInfo(),
Types.DOUBLE()])))
self.predict_data = self.t_env.from_data_stream(
self.env.from_collection(
[
(Vectors.dense([0, 1.]), ),
(Vectors.dense([0, 0.]), ),
(Vectors.dense([1, 0]), ),
(Vectors.dense([1, 1.]), ),
],
type_info=Types.ROW_NAMED(['features'],
[DenseVectorTypeInfo()])))
self.expected_output = {
Vectors.dense([0, 1.]): 11.,
Vectors.dense([0, 0.]): 11.,
Vectors.dense([1, 0.]): 10.,
Vectors.dense([1, 1.]): 10.,
}
self.estimator = NaiveBayes() \
.set_smoothing(1.0) \
.set_features_col('features') \
.set_label_col('label') \
.set_prediction_col('prediction') \
.set_model_type('multinomial') # type: NaiveBayes