def test_cat_to_int():
schema = Schema()
schema.add_categorical_column('cat', ['A', 'B', 'C'])
tp = TransformProcess(schema)
tp.categorical_to_integer('cat')
assert tp.final_schema.get_column_type('cat') == 'integer'
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()
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:]
# Build the Keras model
model = Sequential()
model.add(Dense(10, input_shape=(4,), activation='relu', name='fc1'))
model.add(Dense(10, activation='relu', name='fc2'))
from pydatavec import Schema, TransformProcess
from pydatavec import NotInSet, LessThan
# Let's define the schema of the data that we want to import
# The order in which columns are defined here should match the order in which they appear in the input data
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)
#
# SPDX-License-Identifier: Apache-2.0
################################################################################
'''
In this simple example: We'll show how to combine multiple independent records by key.
Specifically, assume we have data like "person,country_visited,entry_time" and we want to know how many times
each person has entered each country.
'''
from pydatavec import Schema, TransformProcess
# Define the input schema
schema = Schema()
schema.add_string_column('person')
schema.add_categorical_column('country_visited',
['USA', 'Japan', 'China', 'India'])
schema.add_string_column('entry_time')
# Define the operations we want to do
tp = TransformProcess(schema)
# Parse date-time
# Format for parsing times is as per http://www.joda.org/joda-time/apidocs/org/joda/time/format/DateTimeFormat.html
tp.string_to_time('entry_time', 'YYYY/MM/dd')
# Take the "country_visited" column and expand it to a one-hot representation
# So, "USA" becomes [1,0,0,0], "Japan" becomes [0,1,0,0], "China" becomes [0,0,1,0] etc
tp.one_hot('country_visited')