c24aeb6a31
Adds rank, recommendUsers and RecommendProducts to MatrixFactorizationModel in PySpark. Author: MechCoder <manojkumarsivaraj334@gmail.com> Closes #5807 from MechCoder/spark-6257 and squashes the following commits: 09629c6 [MechCoder] doc 953b326 [MechCoder] [SPARK-6257] MLlib API missing items in Recommendation
220 lines
7.5 KiB
Python
220 lines
7.5 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 array
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from collections import namedtuple
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from pyspark import SparkContext
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from pyspark.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
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from pyspark.mllib.util import JavaLoader, JavaSaveable
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from pyspark.sql import DataFrame
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__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
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class Rating(namedtuple("Rating", ["user", "product", "rating"])):
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"""
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Represents a (user, product, rating) tuple.
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>>> r = Rating(1, 2, 5.0)
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>>> (r.user, r.product, r.rating)
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(1, 2, 5.0)
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>>> (r[0], r[1], r[2])
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(1, 2, 5.0)
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"""
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def __reduce__(self):
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return Rating, (int(self.user), int(self.product), float(self.rating))
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@inherit_doc
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class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
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"""A matrix factorisation model trained by regularized alternating
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least-squares.
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>>> r1 = (1, 1, 1.0)
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>>> r2 = (1, 2, 2.0)
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>>> r3 = (2, 1, 2.0)
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>>> ratings = sc.parallelize([r1, r2, r3])
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>>> model = ALS.trainImplicit(ratings, 1, seed=10)
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>>> model.predict(2, 2)
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0.4...
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>>> testset = sc.parallelize([(1, 2), (1, 1)])
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>>> model = ALS.train(ratings, 2, seed=0)
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>>> model.predictAll(testset).collect()
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[Rating(user=1, product=1, rating=1.0...), Rating(user=1, product=2, rating=1.9...)]
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>>> model = ALS.train(ratings, 4, seed=10)
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>>> model.userFeatures().collect()
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[(1, array('d', [...])), (2, array('d', [...]))]
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>>> model.recommendUsers(1, 2)
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[Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
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>>> model.recommendProducts(1, 2)
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[Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
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>>> model.rank
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4
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>>> first_user = model.userFeatures().take(1)[0]
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>>> latents = first_user[1]
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>>> len(latents) == 4
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True
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>>> model.productFeatures().collect()
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[(1, array('d', [...])), (2, array('d', [...]))]
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>>> first_product = model.productFeatures().take(1)[0]
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>>> latents = first_product[1]
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>>> len(latents) == 4
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True
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>>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
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>>> model.predict(2, 2)
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3.8...
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>>> df = sqlContext.createDataFrame([Rating(1, 1, 1.0), Rating(1, 2, 2.0), Rating(2, 1, 2.0)])
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>>> model = ALS.train(df, 1, nonnegative=True, seed=10)
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>>> model.predict(2, 2)
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3.8...
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>>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
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>>> model.predict(2, 2)
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0.4...
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>>> import os, tempfile
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>>> path = tempfile.mkdtemp()
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>>> model.save(sc, path)
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>>> sameModel = MatrixFactorizationModel.load(sc, path)
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>>> sameModel.predict(2, 2)
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0.4...
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>>> sameModel.predictAll(testset).collect()
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[Rating(...
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>>> try:
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... os.removedirs(path)
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... except OSError:
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... pass
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"""
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def predict(self, user, product):
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"""
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Predicts rating for the given user and product.
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"""
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return self._java_model.predict(int(user), int(product))
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def predictAll(self, user_product):
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"""
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Returns a list of predicted ratings for input user and product pairs.
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"""
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assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
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first = user_product.first()
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assert len(first) == 2, "user_product should be RDD of (user, product)"
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user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1])))
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return self.call("predict", user_product)
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def userFeatures(self):
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"""
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Returns a paired RDD, where the first element is the user and the
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second is an array of features corresponding to that user.
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"""
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return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v))
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def productFeatures(self):
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"""
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Returns a paired RDD, where the first element is the product and the
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second is an array of features corresponding to that product.
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"""
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return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v))
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def recommendUsers(self, product, num):
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"""
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Recommends the top "num" number of users for a given product and returns a list
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of Rating objects sorted by the predicted rating in descending order.
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"""
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return list(self.call("recommendUsers", product, num))
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def recommendProducts(self, user, num):
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"""
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Recommends the top "num" number of products for a given user and returns a list
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of Rating objects sorted by the predicted rating in descending order.
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"""
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return list(self.call("recommendProducts", user, num))
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@property
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def rank(self):
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return self.call("rank")
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@classmethod
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def load(cls, sc, path):
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model = cls._load_java(sc, path)
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wrapper = sc._jvm.MatrixFactorizationModelWrapper(model)
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return MatrixFactorizationModel(wrapper)
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class ALS(object):
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@classmethod
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def _prepare(cls, ratings):
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if isinstance(ratings, RDD):
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pass
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elif isinstance(ratings, DataFrame):
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ratings = ratings.rdd
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else:
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raise TypeError("Ratings should be represented by either an RDD or a DataFrame, "
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"but got %s." % type(ratings))
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first = ratings.first()
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if isinstance(first, Rating):
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pass
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elif isinstance(first, (tuple, list)):
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ratings = ratings.map(lambda x: Rating(*x))
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else:
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raise TypeError("Expect a Rating or a tuple/list, but got %s." % type(first))
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return ratings
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@classmethod
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def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
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seed=None):
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model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations,
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lambda_, blocks, nonnegative, seed)
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return MatrixFactorizationModel(model)
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@classmethod
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def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01,
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nonnegative=False, seed=None):
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model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank,
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iterations, lambda_, blocks, alpha, nonnegative, seed)
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return MatrixFactorizationModel(model)
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def _test():
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import doctest
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import pyspark.mllib.recommendation
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from pyspark.sql import SQLContext
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globs = pyspark.mllib.recommendation.__dict__.copy()
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sc = SparkContext('local[4]', 'PythonTest')
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globs['sc'] = sc
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globs['sqlContext'] = SQLContext(sc)
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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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