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 setUp(self): super(StringIndexerTest, self).setUp() self.train_table = self.t_env.from_data_stream( self.env.from_collection([ ('a', 1.0), ('b', 1.0), ('b', 2.0), ('c', 0.0), ('d', 2.0), ('a', 2.0), ('b', 2.0), ('b', -1.0), ('a', -1.0), ('c', -1.0), ], type_info=Types.ROW_NAMED( ['input_col1', 'input_col2'], [Types.STRING(), Types.DOUBLE()]))) self.predict_table = self.t_env.from_data_stream( self.env.from_collection([ ('a', 2.0), ('b', 1.0), ('e', 2.0), ], type_info=Types.ROW_NAMED( ['input_col1', 'input_col2'], [Types.STRING(), Types.DOUBLE()]))) self.expected_alphabetic_asc_predict_data = [ Row('a', 2.0, 0, 3), Row('b', 1.0, 1, 2), Row('e', 2.0, 4, 3) ]
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 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(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(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
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