9a0dfdf868
- Added a Python wrapper for Naive Bayes - Updated the Scala Naive Bayes to match the style of our other algorithms better and in particular make it easier to call from Java (added builder pattern, removed default value in train method) - Updated Python MLlib functions to not require a SparkContext; we can get that from the RDD the user gives - Added a toString method in LabeledPoint - Made the Python MLlib tests run as part of run-tests as well (before they could only be run individually through each file)
114 lines
4.7 KiB
Python
114 lines
4.7 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
from numpy import array, dot
|
|
from pyspark import SparkContext
|
|
from pyspark.mllib._common import \
|
|
_get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
|
|
_serialize_double_matrix, _deserialize_double_matrix, \
|
|
_serialize_double_vector, _deserialize_double_vector, \
|
|
_get_initial_weights, _serialize_rating, _regression_train_wrapper, \
|
|
_linear_predictor_typecheck
|
|
|
|
class LinearModel(object):
|
|
"""Something that has a vector of coefficients and an intercept."""
|
|
def __init__(self, coeff, intercept):
|
|
self._coeff = coeff
|
|
self._intercept = intercept
|
|
|
|
class LinearRegressionModelBase(LinearModel):
|
|
"""A linear regression model.
|
|
|
|
>>> lrmb = LinearRegressionModelBase(array([1.0, 2.0]), 0.1)
|
|
>>> abs(lrmb.predict(array([-1.03, 7.777])) - 14.624) < 1e-6
|
|
True
|
|
"""
|
|
def predict(self, x):
|
|
"""Predict the value of the dependent variable given a vector x"""
|
|
"""containing values for the independent variables."""
|
|
_linear_predictor_typecheck(x, self._coeff)
|
|
return dot(self._coeff, x) + self._intercept
|
|
|
|
class LinearRegressionModel(LinearRegressionModelBase):
|
|
"""A linear regression model derived from a least-squares fit.
|
|
|
|
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
|
|
>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
|
|
"""
|
|
|
|
class LinearRegressionWithSGD(object):
|
|
@classmethod
|
|
def train(cls, data, iterations=100, step=1.0,
|
|
mini_batch_fraction=1.0, initial_weights=None):
|
|
"""Train a linear regression model on the given data."""
|
|
sc = data.context
|
|
return _regression_train_wrapper(sc, lambda d, i:
|
|
sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
|
|
d._jrdd, iterations, step, mini_batch_fraction, i),
|
|
LinearRegressionModel, data, initial_weights)
|
|
|
|
class LassoModel(LinearRegressionModelBase):
|
|
"""A linear regression model derived from a least-squares fit with an
|
|
l_1 penalty term.
|
|
|
|
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
|
|
>>> lrm = LassoWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
|
|
"""
|
|
|
|
class LassoWithSGD(object):
|
|
@classmethod
|
|
def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
|
|
mini_batch_fraction=1.0, initial_weights=None):
|
|
"""Train a Lasso regression model on the given data."""
|
|
sc = data.context
|
|
return _regression_train_wrapper(sc, lambda d, i:
|
|
sc._jvm.PythonMLLibAPI().trainLassoModelWithSGD(d._jrdd,
|
|
iterations, step, reg_param, mini_batch_fraction, i),
|
|
LassoModel, data, initial_weights)
|
|
|
|
class RidgeRegressionModel(LinearRegressionModelBase):
|
|
"""A linear regression model derived from a least-squares fit with an
|
|
l_2 penalty term.
|
|
|
|
>>> data = array([0.0, 0.0, 1.0, 1.0, 3.0, 2.0, 2.0, 3.0]).reshape(4,2)
|
|
>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initial_weights=array([1.0]))
|
|
"""
|
|
|
|
class RidgeRegressionWithSGD(object):
|
|
@classmethod
|
|
def train(cls, data, iterations=100, step=1.0, reg_param=1.0,
|
|
mini_batch_fraction=1.0, initial_weights=None):
|
|
"""Train a ridge regression model on the given data."""
|
|
sc = data.context
|
|
return _regression_train_wrapper(sc, lambda d, i:
|
|
sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(d._jrdd,
|
|
iterations, step, reg_param, mini_batch_fraction, i),
|
|
RidgeRegressionModel, data, initial_weights)
|
|
|
|
def _test():
|
|
import doctest
|
|
globs = globals().copy()
|
|
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
|
|
(failure_count, test_count) = doctest.testmod(globs=globs,
|
|
optionflags=doctest.ELLIPSIS)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|