def test_model(self):
print "Initialize KMeansModel object"
k = ta.KMeansModel()
print "Initialize LogisticRegressionModel object"
l = ta.LogisticRegressionModel()
print "Initialize NaiveBayesModel object"
n = ta.NaiveBayesModel()
def test_model(self):
print "Initialize KMeansModel object with name"
k1 = ta.KMeansModel(name='smoke_kmeans_model')
name = k1.name
print "Initialize KMeansModel object"
k2 = ta.KMeansModel()
print "Initialize LogisticRegressionModel object with name"
l1 = ta.LogisticRegressionModel(name='myLogisticRegressionModel1')
print "Initialize LogisticRegressionModel object"
l2 = ta.LogisticRegressionModel()
print "Initialize NaiveBayesModel object"
n = ta.NaiveBayesModel()
def test_naive_bayes(self):
print "define csv file"
schema = [("Class", ta.int32), ("Dim_1", ta.int32),
("Dim_2", ta.int32), ("Dim_3", ta.int32)]
train_file = ta.CsvFile("/datasets/naivebayes_spark_data.csv",
schema=schema)
print "creating the frame"
train_frame = ta.Frame(train_file)
print "initializing the naivebayes model"
n = ta.NaiveBayesModel()
print "training the model on the frame"
n.train(train_frame, 'Class', ['Dim_1', 'Dim_2', 'Dim_3'])
print "predicting the class using the model and the frame"
output = n.predict(train_frame)
self.assertEqual(
output.column_names,
['Class', 'Dim_1', 'Dim_2', 'Dim_3', 'predicted_class'])