def test_reduce_1():
reductions = ['sum', 'mean', 'std', 'var', 'prod']
for red in reductions:
schema = Schema()
schema.add_string_column('name')
schema.add_double_column('amount')
schema.add_integer_column('hours')
tp = TransformProcess(schema)
tp.reduce('name', red)
tp.to_java()
def test_reduce_1():
reductions = ['sum', 'mean', 'std', 'var', 'prod']
for red in reductions:
schema = Schema()
schema.add_string_column('name')
schema.add_double_column('amount')
schema.add_integer_column('hours')
tp = TransformProcess(schema)
tp.reduce('name', red)
tp.to_java()
def test_schema():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
schema.add_integer_column('int1')
schema.add_integer_column('int2')
schema.add_double_column('dbl1')
schema.add_double_column('dbl2')
schema.add_float_column('flt1')
schema.add_float_column('flt2')
schema.add_categorical_column('cat1', ['A', 'B', 'C'])
schema.add_categorical_column('cat2', ['A', 'B', 'C'])
schema.to_java()
def test_schema():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
schema.add_integer_column('int1')
schema.add_integer_column('int2')
schema.add_double_column('dbl1')
schema.add_double_column('dbl2')
schema.add_float_column('flt1')
schema.add_float_column('flt2')
schema.add_categorical_column('cat1', ['A', 'B', 'C'])
schema.add_categorical_column('cat2', ['A', 'B', 'C'])
schema.to_java()
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
if not os.path.isfile(filename):
if os.path.isfile(temp_filename):
os.remove(temp_filename)
download_file(url, temp_filename)
os.rename(temp_filename, filename)
# We use pyspark to filter empty lines
sc = pyspark.SparkContext(master='local[*]', appName='iris')
data = sc.textFile('iris.data')
filtered_data = data.filter(lambda d: len(d) > 0)
# Define Input Schema
input_schema = Schema()
input_schema.add_double_column('Sepal length')
input_schema.add_double_column('Sepal width')
input_schema.add_double_column('Petal length')
input_schema.add_double_column('Petal width')
input_schema.add_categorical_column(
"Species", ["Iris-setosa", "Iris-versicolor", "Iris-virginica"])
# Define Transform Process
tp = TransformProcess(input_schema)
tp.one_hot("Species")
# Do the transformation on spark and convert to numpy
output = tp(filtered_data)
np_array = np.array([[float(i) for i in x.split(',')] for x in output])
x = np_array[:, :-3]
y = np_array[:, -3:]
input_schema = Schema()
input_schema.add_string_column("DateTimeString")
input_schema.add_string_column("CustomerID")
input_schema.add_string_column("MerchantID")
input_schema.add_integer_column("NumItemsInTransaction")
input_schema.add_categorical_column("MerchantCountryCode",
["USA", "CAN", "FR", "MX"])
# Some columns have restrictions on the allowable values, that we consider valid:
input_schema.add_double_column(
"TransactionAmountUSD", 0.0, None, False,
False) # $0.0 or more, no maximum limit, no NaN and no Infinite values
input_schema.add_categorical_column("FraudLabel", ["Fraud", "Legit"])
# Lets define some operations to execute on the data...
# We do this by defining a TransformProcess
# At each step, we identify column by the name we gave them in the input data schema, above
tp = TransformProcess(input_schema)
# Let's remove some column we don't need
tp.remove_column("CustomerID")
tp.remove_column("MerchantID")