ce95bd8e13
The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step. This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster. Author: Davies Liu <davies@databricks.com> Closes #3397 from davies/cache and squashes the following commits: 7f6e6ce [Davies Liu] Update -> Updater 4b52edd [Davies Liu] using named argument 63b984e [Davies Liu] fix 7da0332 [Davies Liu] add unpersist() dff33e1 [Davies Liu] address comments c2bdfc2 [Davies Liu] refactor d572f00 [Davies Liu] Merge branch 'master' into cache f1063e1 [Davies Liu] cache serialized java object
280 lines
10 KiB
Python
280 lines
10 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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from numpy import array
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from pyspark.mllib.common import callMLlibFunc
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from pyspark.mllib.linalg import SparseVector, _convert_to_vector
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__all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'RidgeRegressionModel',
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'LinearRegressionWithSGD', 'LassoWithSGD', 'RidgeRegressionWithSGD']
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class LabeledPoint(object):
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"""
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The features and labels of a data point.
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:param label: Label for this data point.
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:param features: Vector of features for this point (NumPy array, list,
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pyspark.mllib.linalg.SparseVector, or scipy.sparse column matrix)
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"""
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def __init__(self, label, features):
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self.label = float(label)
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self.features = _convert_to_vector(features)
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def __reduce__(self):
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return (LabeledPoint, (self.label, self.features))
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def __str__(self):
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return "(" + ",".join((str(self.label), str(self.features))) + ")"
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def __repr__(self):
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return "LabeledPoint(%s, %s)" % (self.label, self.features)
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class LinearModel(object):
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"""A linear model that has a vector of coefficients and an intercept."""
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def __init__(self, weights, intercept):
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self._coeff = _convert_to_vector(weights)
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self._intercept = float(intercept)
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@property
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def weights(self):
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return self._coeff
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@property
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def intercept(self):
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return self._intercept
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def __repr__(self):
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return "(weights=%s, intercept=%r)" % (self._coeff, self._intercept)
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class LinearRegressionModelBase(LinearModel):
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"""A linear regression model.
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>>> lrmb = LinearRegressionModelBase(np.array([1.0, 2.0]), 0.1)
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>>> abs(lrmb.predict(np.array([-1.03, 7.777])) - 14.624) < 1e-6
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True
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>>> abs(lrmb.predict(SparseVector(2, {0: -1.03, 1: 7.777})) - 14.624) < 1e-6
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True
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"""
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def predict(self, x):
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"""
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Predict the value of the dependent variable given a vector x
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containing values for the independent variables.
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"""
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x = _convert_to_vector(x)
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return self.weights.dot(x) + self.intercept
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class LinearRegressionModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit.
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(3.0, [2.0]),
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... LabeledPoint(2.0, [3.0])
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... ]
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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=np.array([1.0]))
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>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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>>> data = [
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... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
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... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
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... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
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... ]
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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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>>> abs(lrm.predict(array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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"""
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# train_func should take two parameters, namely data and initial_weights, and
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# return the result of a call to the appropriate JVM stub.
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# _regression_train_wrapper is responsible for setup and error checking.
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def _regression_train_wrapper(train_func, modelClass, data, initial_weights):
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first = data.first()
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if not isinstance(first, LabeledPoint):
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raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first)
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initial_weights = initial_weights or [0.0] * len(data.first().features)
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weights, intercept = train_func(data, _convert_to_vector(initial_weights))
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return modelClass(weights, intercept)
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class LinearRegressionWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
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initialWeights=None, regParam=0.0, regType=None, intercept=False):
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"""
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Train a linear regression model on the given data.
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:param data: The training data.
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:param iterations: The number of iterations (default: 100).
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:param step: The step parameter used in SGD
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(default: 1.0).
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:param miniBatchFraction: Fraction of data to be used for each SGD
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iteration.
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:param initialWeights: The initial weights (default: None).
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:param regParam: The regularizer parameter (default: 0.0).
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:param regType: The type of regularizer used for training
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our model.
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:Allowed values:
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- "l1" for using L1 regularization (lasso),
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- "l2" for using L2 regularization (ridge),
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- None for no regularization
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(default: None)
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@param intercept: Boolean parameter which indicates the use
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or not of the augmented representation for
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training data (i.e. whether bias features
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are activated or not).
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"""
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def train(rdd, i):
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return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
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float(step), float(miniBatchFraction), i, float(regParam),
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regType, bool(intercept))
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return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
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class LassoModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit with an
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l_1 penalty term.
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(3.0, [2.0]),
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... LabeledPoint(2.0, [3.0])
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... ]
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>>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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>>> data = [
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... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
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... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
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... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
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... ]
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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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"""
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class LassoWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0, regParam=0.01,
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miniBatchFraction=1.0, initialWeights=None):
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"""Train a Lasso regression model on the given data."""
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def train(rdd, i):
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return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
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float(regParam), float(miniBatchFraction), i)
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return _regression_train_wrapper(train, LassoModel, data, initialWeights)
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class RidgeRegressionModel(LinearRegressionModelBase):
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"""A linear regression model derived from a least-squares fit with an
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l_2 penalty term.
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(3.0, [2.0]),
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... LabeledPoint(2.0, [3.0])
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... ]
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>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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>>> data = [
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... LabeledPoint(0.0, SparseVector(1, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(1, {0: 1.0})),
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... LabeledPoint(3.0, SparseVector(1, {0: 2.0})),
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... LabeledPoint(2.0, SparseVector(1, {0: 3.0}))
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... ]
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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0]))
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>>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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"""
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class RidgeRegressionWithSGD(object):
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@classmethod
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def train(cls, data, iterations=100, step=1.0, regParam=0.01,
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miniBatchFraction=1.0, initialWeights=None):
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"""Train a ridge regression model on the given data."""
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def train(rdd, i):
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return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
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float(regParam), float(miniBatchFraction), i)
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return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
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def _test():
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import doctest
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from pyspark import SparkContext
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import pyspark.mllib.regression
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globs = pyspark.mllib.regression.__dict__.copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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