b5bd75d90a
Python API parity check for classification and multiclass classification support, major disparities need to be added for Python:
```scala
LogisticRegressionWithLBFGS
setNumClasses
setValidateData
LogisticRegressionModel
getThreshold
numClasses
numFeatures
SVMWithSGD
setValidateData
SVMModel
getThreshold
```
For users the greatest benefit in this PR is multiclass classification was supported by Python API.
Users can train multiclass classification model and use it to predict in pyspark.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #5137 from yanboliang/spark-6255 and squashes the following commits:
0bd531e [Yanbo Liang] address comments
444d5e2 [Yanbo Liang] LogisticRegressionModel.predict() optimization
fc7990b [Yanbo Liang] address comments
b0d9c63 [Yanbo Liang] Support Mulinomial LR model predict in Python API
ded847c [Yanbo Liang] Python API parity check for classification (support multiclass classification)
407 lines
15 KiB
Python
407 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), 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), 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=100, 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 ValueError("data should be an RDD of LabeledPoint, but got %s" % 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), 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), 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=100, 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), 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), 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=100, 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[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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