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July 17, 2017 13:09
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| #!/usr/bin/env python | |
| from __future__ import print_function | |
| from pyspark import SparkContext | |
| from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating | |
| from pyspark.mllib.evaluation import RegressionMetrics | |
| import math | |
| if __name__ == "__main__": | |
| sc = SparkContext(appName="PythonCollaborativeFilteringExample") | |
| # $example on$ | |
| # Load and parse the data | |
| data = sc.textFile("preference_not0.csv") | |
| f = open("outputRecord.txt", "w") | |
| # Splitting the data | |
| ratings = data.map(lambda l: l.split(','))\ | |
| .map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))) | |
| train, validation, test, adjustment = ratings.randomSplit([6, 2, 2, 0]) # splitting data into testing data, validation data and training data | |
| train.cache() # caching data for quick optimization | |
| validation.cache() | |
| test.cache() | |
| validationForPredict = validation.map(lambda x: (x[0], x[1])) | |
| actualReformatted = validation.map(lambda x: ((x[0], x[1]), x[2])) | |
| # Build the recommendation model using Alternating Least Squares | |
| #rank = 10 | |
| #numIterations = 10 | |
| #model = ALS.train(train, rank, numIterations) | |
| iterations = [5, 7, 10] | |
| regularizationParameter = 0.1 | |
| ranks = [10, 12, 15] | |
| RMSEs = [0, 0, 0, 0, 0, 0, 0, 0, 0] | |
| err = 0 | |
| tolerance = 0.03 | |
| minRMSE = float('inf') | |
| bestIteration = -1 | |
| bestRank = -1 | |
| ptr1 = "output \n" | |
| #validating hyper-parameters | |
| for rank in ranks: | |
| for iteration in iterations: | |
| model = ALS.trainImplicit(train, rank, iteration, lambda_=regularizationParameter) | |
| predictedRatings = model.predictAll(validationForPredict) | |
| predictedReformatted = predictedRatings.map(lambda x: ((x[0], x[1]), x[2])) | |
| predictionAndObservations = (predictedReformatted.join(actualReformatted).map(lambda x: x[1])) | |
| metrics = RegressionMetrics(predictionAndObservations) | |
| RMSE = metrics.rootMeanSquaredError | |
| RMSEs[err] = RMSE | |
| err += 1 | |
| #print ("For rank %s and iteration %s, the RMSE is %s") % (rank, iteration, RMSE) | |
| ptr1 = ptr1 + "For rank " + str(rank) + " and iterations " + str(iteration) + " the RMSE is " + str(RMSE) + " \n" | |
| if RMSE < minRMSE: | |
| minRMSE = RMSE | |
| bestIteration = iteration | |
| bestRank = rank | |
| #print ("The best model was trained with rank %s and iteration %s") % (bestRank, bestIteration) | |
| ptr2 = "The best model was trained with rank " + str(bestRank) + " and iteration " + str(bestIteration) + " \n" | |
| bestModel = ALS.trainImplicit(train, bestRank, iterations=bestIteration, lambda_=regularizationParameter) | |
| testForPredicting = test.map(lambda x: (x[0], x[1])) | |
| testReformatted = test.map(lambda x: ((x[0], x[1]), x[2])) | |
| predictedTest = bestModel.predictAll(testForPredicting) | |
| predictedTestReformatted = predictedTest.map(lambda x: ((x[0], x[1]), x[2])) | |
| predictionAndObservationTest = (predictedTestReformatted.join(testReformatted). map(lambda x: x[1])) | |
| metrics = RegressionMetrics(predictionAndObservationTest) | |
| testRMSE = metrics.rootMeanSquaredError | |
| print ("The Model had a RMSE on the test set of " + str(testRMSE)) | |
| ptr3 = "The Model had a RMSE on the test set of " + str(testRMSE) | |
| f.write(ptr1 + ptr2 + ptr3) | |
| # Evaluate the model on training data | |
| #testdata = test.map(lambda p: (p[0], p[1])) | |
| #predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) | |
| # Keep the it if there is already a data, if not put in the data camputed by ALS | |
| #ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) | |
| #RMSE = math.sqrt(ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()) | |
| #print("Root Mean Squared Error = " + str(RMSE)) | |
| # Save and load model | |
| bestModel.save(sc, "target/tmp/myCollaborativeFilter") | |
| sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter") | |
| # $example off$ | |
| f.close() |
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