3134c3fe49
This PR try to speed up some python tests: ``` tests.py 144s -> 103s -41s mllib/classification.py 24s -> 17s -7s mllib/regression.py 27s -> 15s -12s mllib/tree.py 27s -> 13s -14s mllib/tests.py 64s -> 31s -33s streaming/tests.py 185s -> 84s -101s ``` Considering python3, the total saving will be 558s (almost 10 minutes) (core, and streaming run three times, mllib runs twice). During testing, it will show used time for each test file: ``` Run core tests ... Running test: pyspark/rdd.py ... ok (22s) Running test: pyspark/context.py ... ok (16s) Running test: pyspark/conf.py ... ok (4s) Running test: pyspark/broadcast.py ... ok (4s) Running test: pyspark/accumulators.py ... ok (4s) Running test: pyspark/serializers.py ... ok (6s) Running test: pyspark/profiler.py ... ok (5s) Running test: pyspark/shuffle.py ... ok (1s) Running test: pyspark/tests.py ... ok (103s) 144s ``` Author: Reynold Xin <rxin@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes #5605 from rxin/python-tests-speed and squashes the following commits: d08542d [Reynold Xin] Merge pull request #14 from mengxr/SPARK-6953 89321ee [Xiangrui Meng] fix seed in tests 3ad2387 [Reynold Xin] Merge pull request #5427 from davies/python_tests
557 lines
21 KiB
Python
557 lines
21 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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from math import exp
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import numpy
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from numpy import array
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from pyspark import RDD
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from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
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from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector
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from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper
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from pyspark.mllib.util import Saveable, Loader, inherit_doc
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__all__ = ['LogisticRegressionModel', 'LogisticRegressionWithSGD', 'LogisticRegressionWithLBFGS',
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'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes']
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class LinearClassificationModel(LinearModel):
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"""
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A private abstract class representing a multiclass classification model.
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The categories are represented by int values: 0, 1, 2, etc.
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"""
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def __init__(self, weights, intercept):
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super(LinearClassificationModel, self).__init__(weights, intercept)
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self._threshold = None
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def setThreshold(self, value):
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"""
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.. note:: Experimental
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Sets the threshold that separates positive predictions from negative
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predictions. An example with prediction score greater than or equal
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to this threshold is identified as an positive, and negative otherwise.
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It is used for binary classification only.
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"""
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self._threshold = value
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@property
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def threshold(self):
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"""
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.. note:: Experimental
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Returns the threshold (if any) used for converting raw prediction scores
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into 0/1 predictions. It is used for binary classification only.
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"""
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return self._threshold
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def clearThreshold(self):
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"""
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.. note:: Experimental
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Clears the threshold so that `predict` will output raw prediction scores.
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It is used for binary classification only.
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"""
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self._threshold = None
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def predict(self, test):
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"""
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Predict values for a single data point or an RDD of points using
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the model trained.
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"""
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raise NotImplementedError
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class LogisticRegressionModel(LinearClassificationModel):
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"""A linear binary classification model derived from logistic regression.
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>>> data = [
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... LabeledPoint(0.0, [0.0, 1.0]),
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... LabeledPoint(1.0, [1.0, 0.0]),
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... ]
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>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10)
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>>> lrm.predict([1.0, 0.0])
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1
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>>> lrm.predict([0.0, 1.0])
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0
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>>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
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[1, 0]
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>>> lrm.clearThreshold()
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>>> lrm.predict([0.0, 1.0])
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0.279...
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10)
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>>> lrm.predict(array([0.0, 1.0]))
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1
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>>> lrm.predict(array([1.0, 0.0]))
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0
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>>> lrm.predict(SparseVector(2, {1: 1.0}))
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1
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>>> lrm.predict(SparseVector(2, {0: 1.0}))
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0
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> lrm.save(sc, path)
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>>> sameModel = LogisticRegressionModel.load(sc, path)
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>>> sameModel.predict(array([0.0, 1.0]))
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1
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>>> sameModel.predict(SparseVector(2, {0: 1.0}))
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0
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>>> try:
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... os.removedirs(path)
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... except:
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... pass
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>>> multi_class_data = [
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... LabeledPoint(0.0, [0.0, 1.0, 0.0]),
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... LabeledPoint(1.0, [1.0, 0.0, 0.0]),
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... LabeledPoint(2.0, [0.0, 0.0, 1.0])
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... ]
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>>> data = sc.parallelize(multi_class_data)
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>>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3)
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>>> mcm.predict([0.0, 0.5, 0.0])
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0
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>>> mcm.predict([0.8, 0.0, 0.0])
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1
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>>> mcm.predict([0.0, 0.0, 0.3])
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2
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"""
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def __init__(self, weights, intercept, numFeatures, numClasses):
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super(LogisticRegressionModel, self).__init__(weights, intercept)
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self._numFeatures = int(numFeatures)
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self._numClasses = int(numClasses)
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self._threshold = 0.5
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if self._numClasses == 2:
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self._dataWithBiasSize = None
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self._weightsMatrix = None
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else:
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self._dataWithBiasSize = self._coeff.size / (self._numClasses - 1)
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self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1,
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self._dataWithBiasSize)
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@property
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def numFeatures(self):
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return self._numFeatures
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@property
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def numClasses(self):
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return self._numClasses
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def predict(self, x):
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"""
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Predict values for a single data point or an RDD of points using
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the model trained.
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"""
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if isinstance(x, RDD):
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return x.map(lambda v: self.predict(v))
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x = _convert_to_vector(x)
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if self.numClasses == 2:
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margin = self.weights.dot(x) + self._intercept
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if margin > 0:
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prob = 1 / (1 + exp(-margin))
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else:
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exp_margin = exp(margin)
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prob = exp_margin / (1 + exp_margin)
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if self._threshold is None:
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return prob
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else:
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return 1 if prob > self._threshold else 0
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else:
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best_class = 0
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max_margin = 0.0
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if x.size + 1 == self._dataWithBiasSize:
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for i in range(0, self._numClasses - 1):
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margin = x.dot(self._weightsMatrix[i][0:x.size]) + \
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self._weightsMatrix[i][x.size]
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if margin > max_margin:
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max_margin = margin
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best_class = i + 1
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else:
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for i in range(0, self._numClasses - 1):
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margin = x.dot(self._weightsMatrix[i])
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if margin > max_margin:
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max_margin = margin
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best_class = i + 1
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return best_class
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def save(self, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel(
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_py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses)
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java_model.save(sc._jsc.sc(), path)
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@classmethod
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def load(cls, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel.load(
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sc._jsc.sc(), path)
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weights = _java2py(sc, java_model.weights())
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intercept = java_model.intercept()
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numFeatures = java_model.numFeatures()
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numClasses = java_model.numClasses()
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threshold = java_model.getThreshold().get()
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model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses)
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model.setThreshold(threshold)
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return model
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class LogisticRegressionWithSGD(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=0.01, regType="l2", intercept=False,
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validateData=True):
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"""
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Train a logistic regression model on the given data.
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:param data: The training data, an RDD of LabeledPoint.
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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: 0.01).
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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
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- "l2" for using L2 regularization
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- None for no regularization
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(default: "l2")
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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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:param validateData: Boolean parameter which indicates if the
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algorithm should validate data before training.
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(default: True)
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"""
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def train(rdd, i):
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return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
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float(step), float(miniBatchFraction), i, float(regParam), regType,
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bool(intercept), bool(validateData))
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return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
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class LogisticRegressionWithLBFGS(object):
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@classmethod
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def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2",
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intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2):
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"""
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Train a logistic regression model on the given data.
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:param data: The training data, an RDD of LabeledPoint.
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:param iterations: The number of iterations (default: 100).
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:param initialWeights: The initial weights (default: None).
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:param regParam: The regularizer parameter (default: 0.01).
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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
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- "l2" for using L2 regularization
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- None for no regularization
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(default: "l2")
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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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:param corrections: The number of corrections used in the LBFGS
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update (default: 10).
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:param tolerance: The convergence tolerance of iterations for
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L-BFGS (default: 1e-4).
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:param validateData: Boolean parameter which indicates if the
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algorithm should validate data before training.
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(default: True)
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:param numClasses: The number of classes (i.e., outcomes) a label can take
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in Multinomial Logistic Regression (default: 2).
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>>> data = [
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... LabeledPoint(0.0, [0.0, 1.0]),
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... LabeledPoint(1.0, [1.0, 0.0]),
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... ]
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>>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10)
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>>> lrm.predict([1.0, 0.0])
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1
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>>> lrm.predict([0.0, 1.0])
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0
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"""
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def train(rdd, i):
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return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i,
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float(regParam), regType, bool(intercept), int(corrections),
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float(tolerance), bool(validateData), int(numClasses))
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if initialWeights is None:
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if numClasses == 2:
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initialWeights = [0.0] * len(data.first().features)
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else:
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if intercept:
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initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1)
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else:
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initialWeights = [0.0] * len(data.first().features) * (numClasses - 1)
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return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
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class SVMModel(LinearClassificationModel):
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"""A support vector machine.
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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(1.0, [2.0]),
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... LabeledPoint(1.0, [3.0])
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... ]
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>>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10)
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>>> svm.predict([1.0])
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1
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>>> svm.predict(sc.parallelize([[1.0]])).collect()
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[1]
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>>> svm.clearThreshold()
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>>> svm.predict(array([1.0]))
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1.44...
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: -1.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10)
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>>> svm.predict(SparseVector(2, {1: 1.0}))
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1
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>>> svm.predict(SparseVector(2, {0: -1.0}))
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0
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> svm.save(sc, path)
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>>> sameModel = SVMModel.load(sc, path)
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>>> sameModel.predict(SparseVector(2, {1: 1.0}))
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1
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>>> sameModel.predict(SparseVector(2, {0: -1.0}))
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0
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>>> try:
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... os.removedirs(path)
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... except:
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... pass
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"""
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def __init__(self, weights, intercept):
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super(SVMModel, self).__init__(weights, intercept)
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self._threshold = 0.0
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def predict(self, x):
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"""
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Predict values for a single data point or an RDD of points using
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the model trained.
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"""
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if isinstance(x, RDD):
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return x.map(lambda v: self.predict(v))
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x = _convert_to_vector(x)
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margin = self.weights.dot(x) + self.intercept
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if self._threshold is None:
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return margin
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else:
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return 1 if margin > self._threshold else 0
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def save(self, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel(
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_py2java(sc, self._coeff), self.intercept)
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java_model.save(sc._jsc.sc(), path)
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@classmethod
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def load(cls, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.classification.SVMModel.load(
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sc._jsc.sc(), path)
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weights = _java2py(sc, java_model.weights())
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intercept = java_model.intercept()
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threshold = java_model.getThreshold().get()
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model = SVMModel(weights, intercept)
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model.setThreshold(threshold)
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return model
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class SVMWithSGD(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, regType="l2",
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intercept=False, validateData=True):
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"""
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Train a support vector machine on the given data.
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:param data: The training data, an RDD of LabeledPoint.
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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 regParam: The regularizer parameter (default: 0.01).
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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 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
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- "l2" for using L2 regularization
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- None for no regularization
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(default: "l2")
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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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:param validateData: Boolean parameter which indicates if the
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algorithm should validate data before training.
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(default: True)
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"""
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def train(rdd, i):
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return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
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float(regParam), float(miniBatchFraction), i, regType,
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bool(intercept), bool(validateData))
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return _regression_train_wrapper(train, SVMModel, data, initialWeights)
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@inherit_doc
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class NaiveBayesModel(Saveable, Loader):
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"""
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Model for Naive Bayes classifiers.
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Contains two parameters:
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- pi: vector of logs of class priors (dimension C)
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- theta: matrix of logs of class conditional probabilities (CxD)
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>>> data = [
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... LabeledPoint(0.0, [0.0, 0.0]),
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... LabeledPoint(0.0, [0.0, 1.0]),
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... LabeledPoint(1.0, [1.0, 0.0]),
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... ]
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>>> model = NaiveBayes.train(sc.parallelize(data))
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>>> model.predict(array([0.0, 1.0]))
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0.0
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>>> model.predict(array([1.0, 0.0]))
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1.0
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>>> model.predict(sc.parallelize([[1.0, 0.0]])).collect()
|
|
[1.0]
|
|
>>> sparse_data = [
|
|
... LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
|
|
... LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
|
|
... LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
|
|
... ]
|
|
>>> model = NaiveBayes.train(sc.parallelize(sparse_data))
|
|
>>> model.predict(SparseVector(2, {1: 1.0}))
|
|
0.0
|
|
>>> model.predict(SparseVector(2, {0: 1.0}))
|
|
1.0
|
|
>>> import os, tempfile
|
|
>>> path = tempfile.mkdtemp()
|
|
>>> model.save(sc, path)
|
|
>>> sameModel = NaiveBayesModel.load(sc, path)
|
|
>>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
|
|
True
|
|
>>> try:
|
|
... os.removedirs(path)
|
|
... except OSError:
|
|
... pass
|
|
"""
|
|
|
|
def __init__(self, labels, pi, theta):
|
|
self.labels = labels
|
|
self.pi = pi
|
|
self.theta = theta
|
|
|
|
def predict(self, x):
|
|
"""Return the most likely class for a data vector or an RDD of vectors"""
|
|
if isinstance(x, RDD):
|
|
return x.map(lambda v: self.predict(v))
|
|
x = _convert_to_vector(x)
|
|
return self.labels[numpy.argmax(self.pi + x.dot(self.theta.transpose()))]
|
|
|
|
def save(self, sc, path):
|
|
java_labels = _py2java(sc, self.labels.tolist())
|
|
java_pi = _py2java(sc, self.pi.tolist())
|
|
java_theta = _py2java(sc, self.theta.tolist())
|
|
java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel(
|
|
java_labels, java_pi, java_theta)
|
|
java_model.save(sc._jsc.sc(), path)
|
|
|
|
@classmethod
|
|
def load(cls, sc, path):
|
|
java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel.load(
|
|
sc._jsc.sc(), path)
|
|
# Can not unpickle array.array from Pyrolite in Python3 with "bytes"
|
|
py_labels = _java2py(sc, java_model.labels(), "latin1")
|
|
py_pi = _java2py(sc, java_model.pi(), "latin1")
|
|
py_theta = _java2py(sc, java_model.theta(), "latin1")
|
|
return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta))
|
|
|
|
|
|
class NaiveBayes(object):
|
|
|
|
@classmethod
|
|
def train(cls, data, lambda_=1.0):
|
|
"""
|
|
Train a Naive Bayes model given an RDD of (label, features) vectors.
|
|
|
|
This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which can
|
|
handle all kinds of discrete data. For example, by converting
|
|
documents into TF-IDF vectors, it can be used for document
|
|
classification. By making every vector a 0-1 vector, it can also be
|
|
used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
|
|
|
|
:param data: RDD of LabeledPoint.
|
|
:param lambda_: The smoothing parameter
|
|
"""
|
|
first = data.first()
|
|
if not isinstance(first, LabeledPoint):
|
|
raise ValueError("`data` should be an RDD of LabeledPoint")
|
|
labels, pi, theta = callMLlibFunc("trainNaiveBayes", data, lambda_)
|
|
return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta))
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
from pyspark import SparkContext
|
|
import pyspark.mllib.classification
|
|
globs = pyspark.mllib.classification.__dict__.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()
|