def test_reduce_4():
reductions = ['first', 'last', 'append', 'prepend', 'count', 'count_unique']
for red in reductions:
schema = Schema()
schema.add_string_column('col1')
schema.add_string_column('col2')
tp = TransformProcess(schema)
tp.reduce('col1', 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_derive_col_from_time():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
tp = TransformProcess(schema)
tp.string_to_time('str1')
tp.derive_column_from_time('str1', 'hour', 'hour_of_day')
assert 'hour' in tp.final_schema.columns
tp.to_java()
def test_replace_empty():
schema = Schema()
schema.add_string_column('str1')
tp = TransformProcess(schema)
tp.replace_empty_string('str1', 'xx')
tp.to_java()
def test_remove_white_spaces():
schema = Schema()
schema.add_string_column('str1')
tp = TransformProcess(schema)
tp.remove_white_spaces('str1')
tp.to_java()
def test_lower():
schema = Schema()
schema.add_string_column('str1')
tp = TransformProcess(schema)
tp.lower('str1')
tp.to_java()
def test_append_string():
schema = Schema()
schema.add_string_column('str1')
tp = TransformProcess(schema)
tp.append_string('str1', 'xxx')
tp.to_java()
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_rename():
schema = Schema()
schema.add_string_column('str1')
tp = TransformProcess(schema)
tp.rename_column('str1', 'str2')
assert 'str1' not in tp.final_schema.columns
assert 'str2' in tp.final_schema.columns
tp.to_java()
def test_remove():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
tp = TransformProcess(schema)
tp.remove_column('str1')
assert tp.final_schema.columns.keys() == ['str2']
tp.to_java()
def test_concat():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
tp = TransformProcess(schema)
tp.concat(['str1', 'str2'], 'str3')
assert 'str3' in tp.final_schema.columns
tp.to_java()
def test_str_to_time():
schema = Schema()
schema.add_string_column('str1')
schema.add_string_column('str2')
tp = TransformProcess(schema)
tp.string_to_time('str1')
assert tp.final_schema.get_column_type('str1') == 'DateTime'
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_reduce_4():
reductions = ['first', 'last', 'append',
'prepend', 'count', 'count_unique']
for red in reductions:
schema = Schema()
schema.add_string_column('col1')
schema.add_string_column('col2')
tp = TransformProcess(schema)
tp.reduce('col1', red)
tp.to_java()
# 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'))
model.add(Dense(3, activation='softmax', name='output'))
optimizer = Adam(lr=0.001)
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')
# For each person, reduce all columns using `sum` op, except for entry_time; reduce it using `max` op :
tp.reduce('person', 'sum', {'entry_time': 'max'})
# Rename column
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")
# Now, suppose we only want to analyze transactions involving merchants in USA or Canada. Let's filter out
# everything except for those countries.
# Here, we are applying a conditional filter. We remove all of the examples that match the condition
# The condition is "MerchantCountryCode" isn't one of {"USA", "CAN"}
tp.filter(NotInSet("MerchantCountryCode", ["USA", "CAN"]))
# Let's suppose our data source isn't perfect, and we have some invalid data: negative dollar amounts that we want to replace with 0.0
# For positive dollar amounts, we don't want to modify those values
temp_filename = filename + '_temp'
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 x: len(x) > 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.categorical_to_integer("Species")
# Do the transformation on spark
output = tp(filtered_data)
print(list(output))
