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
412 lines
15 KiB
Python
412 lines
15 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, _py2java, _java2py, inherit_doc
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from pyspark.mllib.linalg import SparseVector, _convert_to_vector
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from pyspark.mllib.util import Saveable, Loader
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__all__ = ['LabeledPoint', 'LinearModel',
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'LinearRegressionModel', 'LinearRegressionWithSGD',
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'RidgeRegressionModel', 'RidgeRegressionWithSGD',
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'LassoModel', 'LassoWithSGD']
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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,
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list, pyspark.mllib.linalg.SparseVector, or scipy.sparse
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column matrix)
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Note: 'label' and 'features' are accessible as class attributes.
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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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@inherit_doc
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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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@inherit_doc
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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), iterations=10,
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... 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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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> lrm.save(sc, path)
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>>> sameModel = LinearRegressionModel.load(sc, path)
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>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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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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>>> 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), iterations=10,
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... 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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>>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
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... miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2",
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... intercept=True, validateData=True)
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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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def save(self, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel(
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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.regression.LinearRegressionModel.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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model = LinearRegressionModel(weights, intercept)
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return model
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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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from pyspark.mllib.classification import LogisticRegressionModel
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first = data.first()
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if not isinstance(first, LabeledPoint):
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raise TypeError("data should be an RDD of LabeledPoint, but got %s" % type(first))
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if initial_weights is None:
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initial_weights = [0.0] * len(data.first().features)
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if (modelClass == LogisticRegressionModel):
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weights, intercept, numFeatures, numClasses = train_func(
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data, _convert_to_vector(initial_weights))
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return modelClass(weights, intercept, numFeatures, numClasses)
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else:
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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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validateData=True):
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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). (default: False)
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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("trainLinearRegressionModelWithSGD", rdd, int(iterations),
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float(step), float(miniBatchFraction), i, float(regParam),
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regType, bool(intercept), bool(validateData))
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return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
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@inherit_doc
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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), iterations=10, 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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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> lrm.save(sc, path)
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>>> sameModel = LassoModel.load(sc, path)
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>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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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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>>> 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), iterations=10,
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... 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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>>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
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... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
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... validateData=True)
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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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def save(self, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel(
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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.regression.LassoModel.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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model = LassoModel(weights, intercept)
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return model
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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, intercept=False,
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validateData=True):
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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, bool(intercept),
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bool(validateData))
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return _regression_train_wrapper(train, LassoModel, data, initialWeights)
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@inherit_doc
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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), iterations=10,
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... 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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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> lrm.save(sc, path)
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>>> sameModel = RidgeRegressionModel.load(sc, path)
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>>> abs(sameModel.predict(np.array([0.0])) - 0) < 0.5
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True
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>>> abs(sameModel.predict(np.array([1.0])) - 1) < 0.5
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True
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>>> abs(sameModel.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5
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True
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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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>>> 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), iterations=10,
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... 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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>>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0,
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... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True,
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... validateData=True)
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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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def save(self, sc, path):
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java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(
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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.regression.RidgeRegressionModel.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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model = RidgeRegressionModel(weights, intercept)
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return model
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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, intercept=False,
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validateData=True):
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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, bool(intercept),
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bool(validateData))
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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[2]', '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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