├── 01 Continuous Deployment ├── images │ ├── AgileDevOps1.png │ ├── AgileLowersRisk.png │ ├── AgileScrumFramework.png │ ├── ContinuousIntegrationDeliveryDeployment_01.png │ ├── ContinuousIntegrationDeliveryDeployment_02.png │ ├── ContinuousIntegrationDeliveryDeployment_03.png │ ├── ContinuousIntegrationDeliveryDeployment_04.png │ ├── ContinuousIntegrationDeliveryDeployment_05.png │ ├── DevOps.png │ ├── SCRUMCheatSheet.jpg │ ├── SCRUMTaskBoard.png │ ├── UnbreakableCloudNativePipelines.png │ └── UnitTestingMotivation.png └── readme.md ├── 02 Recommender Systems ├── 5 Common Recommendation Engines in 10 lines.ipynb ├── images │ ├── CollaborativeContent.png │ ├── CollaborativeFiltering.png │ ├── CollaborativeFilteringExample.png │ ├── CollaborativeFilteringModel.png │ ├── Customer360.png │ ├── EverythingisaRecommendation.png │ ├── Hybrid.png │ ├── HybridEnsemble.png │ ├── LongTail.png │ ├── MatrixFactorization.png │ ├── MicrosoftRecommendationEngines.png │ ├── Personalization.png │ ├── RatingFrequency.png │ ├── RecommendationEngineExample_01.png │ ├── RecommendationEngineExample_02.png │ ├── RecommendationEngineExample_03.png │ ├── RecommenderSystemApproach_01.png │ ├── RecommenderSystemApproach_02.png │ ├── SimilarityMeasures.png │ ├── SimilarityMeasuresCosineEuclidean.png │ ├── TheGoalOfRecommenderSystems.png │ ├── TheParadoxOfChoice.png │ ├── TypicalRecommendationSystem.png │ └── TypicalRecommenderPipeline.png ├── readme.md ├── recommendation_bank.csv ├── recommendation_cars.csv ├── recommendation_cuisine.csv └── recommendation_movies.csv ├── 03 Language Processing ├── 5 Common Natural Language Processing Engines in 10 lines.ipynb ├── images │ ├── AnatomyOfaChatbot.png │ ├── BotEvolution.png │ ├── CosineSimilarityBetweenWords.png │ ├── GrammerDependencyTrees.png │ ├── LDA_01.png │ ├── LDA_02.png │ ├── NLPTaskWorkflow.png │ ├── NLPvsNLUvsASR.png │ ├── NLUTasks.png │ ├── SyntacticAnalysisTechniques.png │ ├── TF-IDF.png │ ├── TextCleaningPipeline.png │ ├── TopSixPythonNLPLibraries.png │ ├── TopicModelling.png │ ├── TopicModellingNamedEntityRecognition.png │ ├── VectorSpaceModel.png │ └── WordVectors.png └── readme.md ├── 04 Self-Healing Systems ├── images │ ├── Auto-remediation.png │ ├── AutoTuneServiceCatalogArtifacts.png │ ├── PerformanceSignature_01.png │ ├── PerformanceSignature_02.png │ └── TypesOfMetrics.png └── readme.md ├── 05 Project Management ├── images │ ├── 5ReasonsToHaveFixed-LengthSprints.png │ ├── AcceptanceCriteriaChecklist.jpeg │ ├── AgileProductRoadmap.png │ ├── BuildTrainDeploy.png │ ├── DataScienceLifecycle.png │ ├── DevelopmentTeam.png │ ├── HighvsLowPriority.png │ ├── InitiativeEpicStory.png │ ├── KanbanBoard.png │ ├── OKR_01.png │ ├── OKR_02.png │ ├── OKR_03.png │ ├── PortfolioForJIRA.png │ ├── ProductOwner.png │ ├── ProgrammingLanguages.png │ ├── ProgressiveWebAppProjectManagementTemplate.png │ ├── ProjectManagementTriangle.png │ ├── QAAutomationWithAWSCodePipeline.png │ ├── SCRUMUX.png │ ├── SampleBurndownChart.png │ ├── ScrumMaster.png │ ├── SmallCodebase.png │ ├── ThePainCurve.jpg │ ├── TypicalMachineLearningProjectWorkflow.png │ ├── UserStoriesvsTasks.jpeg │ ├── UserStoryChecklist.jpeg │ └── VelocityChart.png └── readme.md ├── 06 Statistical Methods ├── 10 Common Time Series Forecasting Methods in 10 lines.ipynb ├── 10 Common Use Cases for Business Analytics.ipynb ├── 14 Statistical Tests for Machine Learning.ipynb ├── 5 Panel Linear Models.ipynb ├── 8 GLM Families with H2O.ipynb ├── Linear Mixed Effect Models.ipynb ├── data │ ├── browsing.csv │ ├── churn.csv │ ├── international-airline-passengers.csv │ └── timeseries.csv ├── images │ ├── AllModelsAreWrong_01.png │ ├── AllModelsAreWrong_02.png │ ├── BayesRule.png │ ├── BayesTheorem.png │ ├── Bias.png │ ├── CLT.png │ ├── ConditionalProbability.png │ ├── CustomerJourney.png │ ├── FrequentistBayesian.png │ ├── GLMs_01.png │ ├── GLMs_02.png │ ├── IndependentEvents.png │ ├── LinearRegression.png │ ├── MeanAbsoluteError.png │ ├── MeanSquaredError.png │ ├── MultipleLinearRegression.png │ ├── NormalCurve.png │ ├── PolynomialRegression.png │ ├── ProbabilityDistributionsFlowchart.png │ ├── SimpleLinearRegression.png │ ├── SkewnessAndKurtosis.png │ ├── Stationary_Covariance.png │ ├── Stationary_Mean.png │ ├── Stationary_Variance.png │ ├── StatisticalPower.png │ ├── Statistics.png │ ├── StatisticsAndHypothesis.png │ ├── TimeSeriesDifferencing.png │ ├── TimeSeriesExample.png │ ├── TimeSeriesLinearNonlinear.png │ ├── TimeSeriesModels.png │ ├── TimeSeriesMostKnown.png │ ├── TimeSeriesPastPresentFuture.png │ ├── TimeSeriesPatterns.png │ ├── TimeSeriesProcess.png │ ├── TimeSeriesSeasonalNonseasonal.png │ ├── TimeSeriesSlidingWindow.png │ ├── TimeSeriesUnivariateMultivariate.png │ ├── TimeSeriesUseCases.png │ ├── TwoBranchesOfStatistics.png │ ├── TypeOneTypeTwo.png │ ├── ValidityAndReliability.png │ ├── p-value_01.png │ └── p-value_02.png └── readme.md ├── 07 Machine Learning ├── 10 Common Dimension Reduction Techniques in 10 lines.ipynb ├── 10 Common Feature Engineering Techniques in 10 lines.ipynb ├── 10 Common Loss Functions in 10 lines.ipynb ├── 10 Common Machine Learning Algorithms in 10 lines.ipynb ├── 10 Common Ways To Evaluate a Model in 10 lines.ipynb ├── 10 Common Ways To Prevent Overfitting in 10 lines.ipynb ├── 2 Common Hyperparameter Tuning Strategies in 10 lines.ipynb ├── AWSBatchInference │ ├── AWSBatchInference.jpeg │ ├── Dockerfile │ ├── inference_coach.py │ ├── inference_coach_policy.json │ ├── inference_runner.py │ ├── inference_runner_policy.json │ ├── object_counter.py │ ├── object_maker.py │ └── object_remover.py ├── CreditCardFraud │ ├── Dockerfile │ ├── KNN Fraud Detection (no feature engineering).ipynb │ ├── Logistic Regression Fraud Detection (no feature engineering).ipynb │ ├── Naive Bayes Classifier Fraud Detection (no feature engineering).ipynb │ ├── PySpark GBTClassifier Fraud Detection (no feature engineering).ipynb │ ├── PySpark LinearSVC Fraud Detection (no feature engineering).ipynb │ ├── PySpark LogisticRegression Fraud Detection (no feature engineering).ipynb │ ├── PySpark RandomForestClassifier Fraud Detection (no feature engineering).ipynb │ ├── Random Forest Classifier Fraud Detection (no feature engineering).ipynb │ ├── Support Vector Classifier Fraud Detection (no feature engineering).ipynb │ ├── XGBoost Fraud Detection (no feature engineering).ipynb │ ├── awsbatch_inference.py │ └── fetch_and_run.sh ├── SparkML │ ├── readme.md │ ├── sparkML_BASIC_correlation.py │ ├── sparkML_BASIC_hypothesistesting.py │ ├── sparkML_BASIC_summarizer.py │ ├── sparkML_CF_als.py │ ├── sparkML_CL_decisiontreeclassification.py │ ├── sparkML_CL_gradientboostedtreeclassifier.py │ ├── sparkML_CL_linearsvc.py │ ├── sparkML_CL_logisticregressionsummary.py │ ├── sparkML_CL_logisticregressionwithelasticnet.py │ ├── sparkML_CL_multiclasslogisticregressionwithelasticnet.py │ ├── sparkML_CL_multilayerperceptronclassification.py │ ├── sparkML_CL_naivebayes.py │ ├── sparkML_CL_onevsrest.py │ ├── sparkML_CL_randomforestclassifier.py │ ├── sparkML_CU_bisectingkmeans.py │ ├── sparkML_CU_gaussianmixture.py │ ├── sparkML_CU_kmeans.py │ ├── sparkML_CU_lda.py │ ├── sparkML_FE_countvectorizer.py │ ├── sparkML_FE_featurehasher.py │ ├── sparkML_FE_tfidf.py │ ├── sparkML_FE_word2vec.py │ ├── sparkML_FP_fpgrowth.py │ ├── sparkML_FS_bucketedrandomprojectionlsh.py │ ├── sparkML_FS_chisqselector.py │ ├── sparkML_FS_minhashlsh.py │ ├── sparkML_FS_rformula.py │ ├── sparkML_FS_vectorslicer.py │ ├── sparkML_FT_binarizer.py │ ├── sparkML_FT_bucketizer.py │ ├── sparkML_FT_dct.py │ ├── sparkML_FT_elementwiseproduct.py │ ├── sparkML_FT_imputer.py │ ├── sparkML_FT_indextostring.py │ ├── sparkML_FT_maxabsscaler.py │ ├── sparkML_FT_minmaxscaler.py │ ├── sparkML_FT_ngram.py │ ├── sparkML_FT_normalizer.py │ ├── sparkML_FT_onehotencoderestimator.py │ ├── sparkML_FT_pca.py │ ├── sparkML_FT_polynomialexpansion.py │ ├── sparkML_FT_quantilediscretizer.py │ ├── sparkML_FT_sqltransformer.py │ ├── sparkML_FT_standardscaler.py │ ├── sparkML_FT_stopwords.py │ ├── sparkML_FT_stringindexer.py │ ├── sparkML_FT_tokenizer.py │ ├── sparkML_FT_vectorassembler.py │ ├── sparkML_FT_vectorindexer.py │ ├── sparkML_FT_vectorsizehint.py │ ├── sparkML_PIPE_estimatortransformerparameter.py │ ├── sparkML_PIPE_pipeline.py │ ├── sparkML_RG_aftsuvival.py │ ├── sparkML_RG_decisiontree.py │ ├── sparkML_RG_generalizedlinear.py │ ├── sparkML_RG_gradientboostedtree.py │ ├── sparkML_RG_isotonic.py │ ├── sparkML_RG_linearwithelasticnet.py │ ├── sparkML_RG_randomforest.py │ ├── sparkML_TU_crossvalidator.py │ └── sparkML_TU_trainvalidationsplit.py ├── The Importance of Feature Scaling.ipynb ├── images │ ├── 100pageMLBook.png │ ├── AUCScores.png │ ├── AWSAlogorithms.png │ ├── AWSBuiltInAlgorithms.png │ ├── AWSMLStack.png │ ├── AWSMLStack2020.png │ ├── Accuracy.png │ ├── Bagging.png │ ├── BiasVariance.png │ ├── BuildingBlocksSupervisedLearning.png │ ├── Classification-Machine-Learning-Algorithm.png │ ├── ClassificationAndRegressionLossFunctions.png │ ├── CommonEvaluationMetrics.png │ ├── CommonMachineLearningAlgorithms.jpg │ ├── ComparingMachineLearningModels.png │ ├── ComprehensiveConfusionMatrix.png │ ├── Confusion.png │ ├── CurseOfDimensionality.png │ ├── DataSetSize.png │ ├── DeployAlgorithmsToProduction.png │ ├── DimensionalityReductionTechniques.png │ ├── EvolutionOfXGBoostAlgorithmFromDecisionTrees.png │ ├── FlexibilityInterpretabilityTradeOff.png │ ├── HPOApproaches.png │ ├── HoldoutValidation.png │ ├── HyperParameterTuning_01.png │ ├── HyperParameterTuning_02.png │ ├── ImbalancedClasses.png │ ├── KaggleWinners.png │ ├── L1L2Regularization_01.png │ ├── L1L2Regularization_02.png │ ├── LDAPCA.png │ ├── LackOfData_01.png │ ├── LackOfData_02.png │ ├── LackOfData_03.png │ ├── MachineLearningAlgorithmsCheatSheet.png │ ├── MachineLearningAlgorithms_01.jpg │ ├── MachineLearningAlgorithms_02.png │ ├── MachineLearningAlgorithms_03.png │ ├── MachineLearningChecklist.png │ ├── MatrixFactorization.png │ ├── MicrosoftAzureMachineLearningAlgorithmCheatSheet.png │ ├── NeighbourGraphs.png │ ├── OnlineOfflineEvaluation.png │ ├── OptimalModel.png │ ├── OverallAccuraciesForDifferentlyReducedDataSets.png │ ├── Overfitting.png │ ├── PrecisionRecallF1.png │ ├── SVM.png │ ├── SVMApplication.png │ ├── SVMHyperPlane.png │ ├── SVMKernelTrick.png │ ├── SagemakerAutopilotUseCases.png │ ├── SagemakerEndpoints.png │ ├── SagemakerLifecycle.png │ ├── ShuffleDataToBalanceData.png │ ├── SupervisedvsUnsupervised.png │ ├── TheMachineLearningProcess.png │ ├── TrainingValidationTesting.png │ ├── ValidationCurve_01.png │ ├── ValidationCurve_02.png │ ├── ValidationCurve_03.png │ ├── ValidationCurve_04.png │ ├── WeightedVoter.png │ ├── WhiteboardMLWorkFlow.png │ └── azure-machine-learning-algorithm-cheat-sheet-nov2019.png └── readme.md ├── 08 Network Analysis ├── images │ ├── ComponentsOfANetwork.png │ ├── GraphTypes.png │ └── NetworkExamples.png └── readme.md ├── 09 Data Engineering ├── .DS_Store ├── databases │ ├── images │ │ ├── ABriefHistoryDatabase.png │ │ ├── CloudServiceProviderDatabaseOfferings.png │ │ ├── DatabaseArchitectureBeforeAndAfterMicroservices.png │ │ ├── DatabaseAsAService.png │ │ ├── DatabaseMarketCompetitiveLandscape.png │ │ ├── DeveloperDatabaseUsageByType.png │ │ ├── DynamicDataManagementSystemsVendorShare2017.png │ │ ├── MultimodelDatabase.png │ │ ├── NonrelationalDatabaseManagementSystemsVendorShare2017.png │ │ ├── NonrelationalDatabaseTypes.png │ │ ├── NonrelationalDatabaseVendorsAndProductsByType.png │ │ ├── Open-SourceLicensingModels.png │ │ ├── OpenSourceBusinessModels.png │ │ ├── OperationalDatabaseCompetitiveLandscape.png │ │ ├── PopularityBrokenDownByDatabaseModel2019.png │ │ ├── PopularityOfOpen-SourceDBMSVersusCommercialDBMS.png │ │ ├── PrimaryDatabaseTypesAndUseCases.png │ │ ├── RelationalDatabaseVendorShare2017.png │ │ ├── StrategicImportanceDatabase.png │ │ ├── Top10MostDesiredDatabasesByDevelopers.png │ │ └── Top10MostLovedDatabasesByDevelopers.png │ └── readme.md ├── images │ ├── AWSLakeFormation_01.png │ ├── AWSLakeFormation_02.png │ ├── AWSLakeFormation_03.png │ ├── AWSNetworkSegmentation.png │ ├── AWSSagemakerEndpoints.png │ ├── CloudComputingPatterns.png │ ├── CloudComputingVariants.png │ ├── CloudInfrastructureMarketShare.png │ ├── CloudServices.png │ ├── Containerization.png │ ├── DataEngineering.png │ ├── DataManagement.jpeg │ ├── DataProcessingEvolution.png │ ├── Graphana_Kafka.png │ ├── HighLevel_Pipeline.png │ ├── Layering_Kafka.png │ ├── MapReduce.png │ ├── PandasDaskPySpark.png │ ├── SpotifyDiscoverWeekly.png │ ├── Streaming_Netflix.png │ └── Streaming_Spark.png ├── parquet │ └── python_parquet.py ├── python │ ├── images │ │ └── DataStructures.png │ ├── python_LineByLine.py │ └── readme.md ├── pywren │ └── python_pywren.py ├── readme.md └── spark │ ├── images │ ├── ClusteredComputing.png │ ├── SparkOverview.png │ └── SparkRDD.png │ ├── readme.md │ └── spark-dynamodb.py ├── 10 Computer Vision ├── images │ ├── ElephantMask.png │ ├── ObjectRecognitionTasks.png │ ├── RegionProposals.png │ └── YOLOPredictions.png └── readme.md ├── 11 Deep Learning ├── 5 Step Life-Cycle for Neural Network Models in Keras.ipynb ├── The Math Behind Neural Networks.ipynb ├── images │ ├── ActivationFunctions_01.png │ ├── ActivationFunctions_02.png │ ├── ArtificialNeuralNetwork.png │ ├── AugmentedCat.png │ ├── Autoencoder_01.png │ ├── Autoencoder_02.png │ ├── BasicCNN.png │ ├── BatchNormalization.png │ ├── CGAN.png │ ├── ConvolutionalNeuralNetwork_01.png │ ├── ConvolutionalNeuralNetwork_02.png │ ├── CostFunctionManyParameters.png │ ├── CostFunctionOneParameter.png │ ├── Dropout.png │ ├── EarlyStopping.png │ ├── Entropy.jpg │ ├── ForwardAndBackwardPropagation.png │ ├── GAN.png │ ├── GANStructure.png │ ├── GeneralLearningAlgorithm.png │ ├── GlobalvsLocalMinimum.png │ ├── GradientClipping.png │ ├── GradientDescent.png │ ├── HumanNeuralNetwork.png │ ├── Keras_Lifecycle.png │ ├── LearningRate.png │ ├── LearningRateDecay.png │ ├── LinearFunction.png │ ├── LongShortTermMemory.png │ ├── NeuralNetworkLayers_01.png │ ├── NeuralNetworkLayers_02.png │ ├── NeuralNetworkLayers_03.png │ ├── NeuralNetworkLayers_04.png │ ├── NeuralNetworkLayers_05.png │ ├── NeuralNetworkLayers_06.png │ ├── NeuralNetworkLayers_07.png │ ├── NeuralNetworkLayers_08.png │ ├── NeuralNetworkWeightsActivation.png │ ├── NeuralNetworkWeightsBias_01.png │ ├── NeuralNetworkWeightsBias_02.png │ ├── NeuralNetworkWeightsBias_03.png │ ├── NeuralNetworkWeightsBias_04.png │ ├── NeuralNetworkWeightsBias_05.png │ ├── NeuralNetwork_01.png │ ├── NeuralNetwork_02.png │ ├── Overfitting.png │ ├── ParsimonyWins.png │ ├── Perceptron.png │ ├── ReLUFunction.png │ ├── RecurrentNeuralNetwork.png │ ├── ReinforcementLearning_01.png │ ├── ReinforcementLearning_02.png │ ├── SigmoidFunction.png │ ├── SoftmaxFunction_01.png │ ├── SoftmaxFunction_02.png │ ├── StepFunction.png │ ├── TanhFunction.png │ ├── TaxonomyOfDeepGenerativeModels.png │ ├── TransferLearningIdea.png │ ├── TransferLearning_01.png │ ├── TransferLearning_02.png │ ├── TransferLearning_03.png │ ├── TransferLearning_04.png │ ├── TransferLearning_Basic.png │ ├── VGG16.png │ └── Weights.png ├── pima-indians-diabetes.csv ├── readme.md └── titanic-train.csv ├── 12 Visualizations ├── 10 Common Data Visualization Techniques for EDA in 10 lines.ipynb ├── images │ ├── ChoosingTheCorrectDataVisualization.jpeg │ ├── GreatDataVisualization.jpg │ ├── PythonVisualizationLandscape.png │ ├── Visualizations_01.png │ ├── Visualizations_02.png │ ├── Visualizations_03.png │ └── Visualizations_04.png └── readme.md ├── 13 Serverless ├── images │ ├── 5CostCategoriesOfServerless.png │ ├── AWSGreenGrass.png │ ├── AWSLanguageComparison.png │ ├── AWSServerlessPortfolio.png │ ├── BasicServerlessArchitecture.png │ ├── CloudEdge.png │ ├── CloudFogEdge_01.png │ ├── CloudFogEdge_02.png │ ├── ConnectedCows.png │ ├── DeploymentAbstractions.png │ ├── DeviceEdge.png │ ├── EdgeEcosystem.png │ ├── EffectiveCostByMemorySize.png │ ├── EndOfCloudComputing.png │ ├── FaaS.png │ ├── HiddenCostOfServerless.png │ ├── HybridAppDeployment.png │ ├── JavascriptLinesOfCode.png │ ├── LambdaColdStarts.png │ ├── NativeAppDeployment.png │ ├── PythonvsJava.png │ ├── SOAPvsREST_01.png │ ├── SOAPvsREST_02.png │ ├── ServerlessCarAnalogy.png │ ├── ServerlessChallenges.jpeg │ ├── ServerlessCostComparison.png │ ├── ServerlessFreeTierComparison.png │ ├── ServerlessProsCons.png │ ├── ServerlessUsage.png │ ├── ServerlessUseCases.png │ ├── ServerlessWebApplicationwithAPIGateway_01.png │ ├── ServerlessWebApplicationwithAPIGateway_02.png │ ├── ServerlessWebApplicationwithAPIGateway_03.png │ └── ServerlessWebApplicationwithAPIGateway_04.png └── readme.md ├── 14 React ├── images │ ├── JSRuntimeEnvironment.png │ ├── LanguageClassification.png │ ├── NodeJSSystem_01.png │ ├── NodeJSSystem_02.png │ ├── ReactProsCons.png │ └── WebBrowserMarketShare.png ├── readme.md └── robofriends │ ├── .gitignore │ ├── README.md │ ├── package-lock.json │ ├── package.json │ ├── public │ ├── favicon.ico │ ├── index.html │ ├── logo192.png │ ├── logo512.png │ ├── manifest.json │ └── robots.txt │ └── src │ ├── App.css │ ├── App.js │ ├── App.test.js │ ├── index.css │ ├── index.js │ ├── logo.svg │ ├── serviceWorker.js │ └── setupTests.js ├── AlternativeDatainFinancialServices.pdf ├── Data Science FAQs.ipynb ├── DetectingDuplicateQuestionPairswithQuoraPaper.pdf ├── DetectingDuplicateQuestionPairswithQuoraPresentation.pdf ├── GeoffreyLinkResume.pdf ├── images ├── 10-vs-of-big-data.png ├── 3DimensionalVectorSpace.png ├── 4-Vs-of-big-data.jpg ├── 6Rs.png ├── 7GuidingPrinciplesForMatureCloudStrategy.png ├── AIProjectCanvas.png ├── AIUseCases.png ├── AlgorithmsInDecisionMaking.png ├── AmazonMLStack.png ├── AmountOfData.png ├── DataScienceApproach.png ├── DataScienceEcosystem.jpg ├── DataScienceHistory.png ├── 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"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1 8 | "Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4 9 | "Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2 10 | "Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2 11 | "Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4 12 | "Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4 13 | "Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3 14 | "Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3 15 | "Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3 16 | "Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4 17 | "Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4 18 | "Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4 19 | "Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1 20 | "Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2 21 | "Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1 22 | "Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1 23 | "Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2 24 | "AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2 25 | "Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4 26 | "Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2 27 | "Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1 28 | "Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2 29 | "Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2 30 | "Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4 31 | "Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6 32 | "Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8 33 | "Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2 -------------------------------------------------------------------------------- /03 Language Processing/images/AnatomyOfaChatbot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/03 Language Processing/images/AnatomyOfaChatbot.png 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-------------------------------------------------------------------------------- /04 Self-Healing Systems/readme.md: -------------------------------------------------------------------------------- 1 | ## Reference 2 | * [Chaos Monkey](https://github.com/Netflix/chaosmonkey) 3 | * [Site Reliability Engineering](https://en.wikipedia.org/wiki/Site_Reliability_Engineering) 4 | * [Principles of Chaos Engineering](http://principlesofchaos.org) 5 | * [How to Approach Self-Healing Systems](https://technologyconversations.com/2016/01/26/self-healing-systems/) 6 | * [AWS re:Invent 2018: Shift-Left SRE: Self-Healing with AWS Lambda Functions (DEV313-S)](https://youtu.be/PsI4pc0NtoI) 7 | * [Building Self-Healing Infrastructure-as-Code with Dynatrace, AWS Lambda, and AWS Service Catalog](https://aws.amazon.com/blogs/apn/building-self-healing-infrastructure-as-code-with-dynatrace-aws-lambda-and-aws-service-catalog/) 8 | 9 | ## Metrics to Monitor 10 | ![](https://github.com/geoffreylink/Projects/blob/master/04%20Self-Healing%20Systems/images/TypesOfMetrics.png) 11 | 12 | ## Performance Signature is Paramount 13 | ![](https://github.com/geoffreylink/Projects/blob/master/04%20Self-Healing%20Systems/images/PerformanceSignature_01.png) 14 | ![](https://github.com/geoffreylink/Projects/blob/master/04%20Self-Healing%20Systems/images/PerformanceSignature_02.png) 15 | 16 | ## Auto-Remediation 17 | ![](https://github.com/geoffreylink/Projects/blob/master/04%20Self-Healing%20Systems/images/Auto-remediation.png) 18 | 19 | ## Auto-Tune Service Catalog Artifacts 20 | ![](https://github.com/geoffreylink/Projects/blob/master/04%20Self-Healing%20Systems/images/AutoTuneServiceCatalogArtifacts.png) 21 | -------------------------------------------------------------------------------- /05 Project Management/images/5ReasonsToHaveFixed-LengthSprints.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM amazonlinux:latest 2 | RUN yum -y update 3 | RUN yum -y install python-pip 4 | RUN pip install boto3 5 | ADD inference_coach.py /usr/local/bin/inference_coach.py 6 | ENTRYPOINT ["/usr/local/bin/inference_coach.py"] 7 | -------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/inference_coach_policy.json: -------------------------------------------------------------------------------- 1 | { 2 | "Version": "2012-10-17", 3 | "Statement": [ 4 | { 5 | "Sid": "VisualEditor0", 6 | "Effect": "Allow", 7 | "Action": [ 8 | "cloudwatch:PutMetricAlarm", 9 | "secretsmanager:GetSecretValue", 10 | "cloudwatch:DeleteAlarms", 11 | "cloudwatch:DescribeAlarms", 12 | "s3:ListBucket" 13 | ], 14 | "Resource": [ 15 | "arn:aws:s3:::project1-landing", 16 | "arn:aws:secretsmanager:us-east-1:888888888888:secret:rds-db-credentials/cluster-888888888888/postgres-888888", 17 | "arn:aws:cloudwatch:*:888888888888:alarm:*" 18 | ] 19 | }, 20 | { 21 | "Sid": "VisualEditor1", 22 | "Effect": "Allow", 23 | "Action": [ 24 | "sagemaker:CreateEndpoint", 25 | "application-autoscaling:RegisterScalableTarget", 26 | "sagemaker:DescribeEndpointConfig", 27 | "batch:SubmitJob", 28 | "sagemaker:DeleteEndpoint", 29 | "rds-data:*", 30 | "application-autoscaling:PutScalingPolicy", 31 | "batch:ListJobs", 32 | "sagemaker:DescribeEndpoint", 33 | "sagemaker:UpdateEndpointWeightsAndCapacities", 34 | "application-autoscaling:DeregisterScalableTarget" 35 | ], 36 | "Resource": "*" 37 | } 38 | ] 39 | } -------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/inference_runner_policy.json: -------------------------------------------------------------------------------- 1 | { 2 | "Version": "2012-10-17", 3 | "Statement": [ 4 | { 5 | "Sid": "VisualEditor0", 6 | "Effect": "Allow", 7 | "Action": [ 8 | "s3:GetAccessPoint", 9 | "s3:PutAccountPublicAccessBlock", 10 | "s3:GetAccountPublicAccessBlock", 11 | "s3:ListAllMyBuckets", 12 | "s3:ListAccessPoints", 13 | "s3:ListJobs", 14 | "rds-data:*", 15 | "s3:CreateJob", 16 | "s3:HeadBucket" 17 | ], 18 | "Resource": "*" 19 | }, 20 | { 21 | "Sid": "VisualEditor1", 22 | "Effect": "Allow", 23 | "Action": [ 24 | "s3:PutObject", 25 | "secretsmanager:GetSecretValue", 26 | "sagemaker:InvokeEndpoint" 27 | ], 28 | "Resource": [ 29 | "arn:aws:sagemaker:*:888888888888:endpoint/*", 30 | "arn:aws:s3:::project1-processed/*", 31 | "arn:aws:secretsmanager:us-east-1:888888888888:secret:rds-db-credentials/cluster-888888888888/postgres-888" 32 | ] 33 | }, 34 | { 35 | "Sid": "VisualEditor2", 36 | "Effect": "Allow", 37 | "Action": [ 38 | "s3:GetObject", 39 | "s3:DeleteObject" 40 | ], 41 | "Resource": "arn:aws:s3:::project1-landing/*" 42 | } 43 | ] 44 | } 45 | -------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/object_counter.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import boto3 4 | 5 | count=0 6 | for objectsummary in boto3.resource('s3').Bucket('project1-processed').objects.all(): 7 | count+=1 8 | print(count) 9 | -------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/object_maker.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import os 4 | import boto3 5 | 6 | count=1 7 | while count < 2: 8 | filename = f'{count:07d}'+'.txt' 9 | f=open(filename,'w+') 10 | f.close() 11 | boto3.client('s3').upload_file(filename, 'project1-landing', filename) 12 | os.remove(filename) 13 | count+=1 14 | -------------------------------------------------------------------------------- /07 Machine Learning/AWSBatchInference/object_remover.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import boto3 4 | 5 | for objectsummary in boto3.resource('s3').Bucket('project1-processed').objects.all(): 6 | boto3.resource('s3').Object('project1-processed', objectsummary.key).delete() 7 | -------------------------------------------------------------------------------- /07 Machine Learning/CreditCardFraud/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM amazonlinux:latest 2 | RUN yum -y install which unzip aws-cli 3 | RUN yum install -y python python-dev python-pip 4 | RUN pip install sagemaker 5 | ADD fetch_and_run.sh /usr/local/bin/fetch_and_run.sh 6 | WORKDIR /tmp 7 | USER nobody 8 | ENTRYPOINT ["/usr/local/bin/fetch_and_run.sh"] 9 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_BASIC_correlation.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.linalg import Vectors 2 | from pyspark.ml.stat import Correlation 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = [ 9 | (Vectors.dense([21.0 , 110 , 3.90]),), 10 | (Vectors.dense([22.8 , 93 , 3.85]),), 11 | (Vectors.dense([18.1 , 105 , 2.76]),) 12 | ] 13 | 14 | df = spark.createDataFrame(data, ['features']) 15 | r1 = Correlation.corr(df, 'features', 'pearson').head() 16 | r2 = Correlation.corr(df, 'features', 'spearman').head() 17 | 18 | print 'Data:' 19 | print df.show() 20 | 21 | print 'Pearson Correlation:' 22 | print str(r1[0]) 23 | 24 | print 'Spearman Correlation:' 25 | print str(r2[0]) 26 | 27 | spark.stop() 28 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_BASIC_hypothesistesting.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.ml.stat import ChiSquareTest 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = [ 9 | (0.0, Vectors.dense(0.5, 10.0)), 10 | (0.0, Vectors.dense(1.5, 20.0)), 11 | (1.0, Vectors.dense(1.5, 30.0)), 12 | (0.0, Vectors.dense(3.5, 30.0)), 13 | (0.0, Vectors.dense(3.5, 40.0)), 14 | (1.0, Vectors.dense(3.5, 40.0)) 15 | ] 16 | 17 | df = spark.createDataFrame(data, ["label", "features"]) 18 | r = ChiSquareTest.test(df, "features", "label").head() 19 | 20 | df.show() 21 | print("pValues: " + str(r.pValues)) 22 | print("degreesOfFreedom: " + str(r.degreesOfFreedom)) 23 | print("statistics: " + str(r.statistics)) 24 | 25 | spark.stop() 26 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_BASIC_summarizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.stat import Summarizer 3 | from pyspark.sql import Row 4 | from pyspark.ml.linalg import Vectors 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | sc = spark.sparkContext 10 | 11 | df = sc.parallelize([ 12 | Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)), 13 | Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0)) 14 | ]).toDF() 15 | 16 | summarizer = Summarizer.metrics("mean", "count") 17 | 18 | df.show() 19 | df.select(summarizer.summary(df.features, df.weight)).show(truncate=False) 20 | df.select(summarizer.summary(df.features)).show(truncate=False) 21 | df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False) 22 | df.select(Summarizer.mean(df.features)).show(truncate=False) 23 | 24 | spark.stop() 25 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CF_als.py: -------------------------------------------------------------------------------- 1 | import sys 2 | if sys.version >= '3': 3 | long = int 4 | from pyspark.sql import SparkSession 5 | from pyspark.ml.evaluation import RegressionEvaluator 6 | from pyspark.ml.recommendation import ALS 7 | from pyspark.sql import Row 8 | 9 | spark = SparkSession.builder.getOrCreate() 10 | spark.sparkContext.setLogLevel("ERROR") 11 | 12 | lines = spark.read.text("file:///usr/lib/spark/data/mllib/als/sample_movielens_ratings.txt").rdd 13 | parts = lines.map(lambda row: row.value.split("::")) 14 | ratingsRDD = parts.map(lambda p: Row(userId=int(p[0]), movieId=int(p[1]),rating=float(p[2]), timestamp=long(p[3]))) 15 | ratings = spark.createDataFrame(ratingsRDD) 16 | 17 | (training, test) = ratings.randomSplit([0.8, 0.2]) 18 | 19 | # Note we set cold start strategy to 'drop' to ensure we don't get NaN evaluation metrics 20 | als = ALS(maxIter=5, regParam=0.01, userCol="userId", itemCol="movieId", ratingCol="rating",coldStartStrategy="drop") 21 | model = als.fit(training) 22 | 23 | predictions = model.transform(test) 24 | evaluator = RegressionEvaluator(metricName="rmse", labelCol="rating",predictionCol="prediction") 25 | rmse = evaluator.evaluate(predictions) 26 | print("Root-mean-square error = " + str(rmse)) 27 | 28 | userRecs = model.recommendForAllUsers(10) 29 | movieRecs = model.recommendForAllItems(10) 30 | 31 | users = ratings.select(als.getUserCol()).distinct().limit(3) 32 | userSubsetRecs = model.recommendForUserSubset(users, 10) 33 | 34 | movies = ratings.select(als.getItemCol()).distinct().limit(3) 35 | movieSubSetRecs = model.recommendForItemSubset(movies, 10) 36 | 37 | userRecs.show() 38 | movieRecs.show() 39 | userSubsetRecs.show() 40 | movieSubSetRecs.show() 41 | 42 | spark.stop() 43 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_decisiontreeclassification.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.classification import DecisionTreeClassifier 3 | from pyspark.ml.feature import StringIndexer, VectorIndexer 4 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) 13 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 14 | 15 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 16 | 17 | dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures") 18 | 19 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt]) 20 | model = pipeline.fit(trainingData) 21 | 22 | predictions = model.transform(testData) 23 | predictions.select("prediction", "indexedLabel", "features").show(5) 24 | 25 | evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") 26 | accuracy = evaluator.evaluate(predictions) 27 | print("Test Error = %g " % (1.0 - accuracy)) 28 | 29 | treeModel = model.stages[2] 30 | print(treeModel) 31 | 32 | spark.stop() 33 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_gradientboostedtreeclassifier.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.classification import GBTClassifier 3 | from pyspark.ml.feature import StringIndexer, VectorIndexer 4 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) 13 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 14 | 15 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 16 | 17 | gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10) 18 | 19 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt]) 20 | model = pipeline.fit(trainingData) 21 | 22 | predictions = model.transform(testData) 23 | predictions.select("prediction", "indexedLabel", "features").show(5) 24 | 25 | evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") 26 | accuracy = evaluator.evaluate(predictions) 27 | print("Test Error = %g" % (1.0 - accuracy)) 28 | 29 | gbtModel = model.stages[2] 30 | print(gbtModel) 31 | 32 | spark.stop() 33 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_linearsvc.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import LinearSVC 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | training = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 8 | 9 | lsvc = LinearSVC(maxIter=10, regParam=0.1) 10 | lsvcModel = lsvc.fit(training) 11 | 12 | print("Coefficients: " + str(lsvcModel.coefficients)) 13 | print("Intercept: " + str(lsvcModel.intercept)) 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_logisticregressionsummary.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import LogisticRegression 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | training = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 8 | 9 | lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) 10 | lrModel = lr.fit(training) 11 | trainingSummary = lrModel.summary 12 | 13 | objectiveHistory = trainingSummary.objectiveHistory 14 | print("objectiveHistory:") 15 | for objective in objectiveHistory: 16 | print(objective) 17 | 18 | trainingSummary.roc.show() 19 | print("areaUnderROC: " + str(trainingSummary.areaUnderROC)) 20 | 21 | fMeasure = trainingSummary.fMeasureByThreshold 22 | maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head() 23 | bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']).select('threshold').head()['threshold'] 24 | lr.setThreshold(bestThreshold) 25 | 26 | spark.stop() 27 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_logisticregressionwithelasticnet.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import LogisticRegression 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | training = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 8 | 9 | lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) 10 | lrModel = lr.fit(training) 11 | print("Coefficients: " + str(lrModel.coefficients)) 12 | print("Intercept: " + str(lrModel.intercept)) 13 | 14 | mlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8, family="multinomial") 15 | mlrModel = mlr.fit(training) 16 | print("Multinomial coefficients: " + str(mlrModel.coefficientMatrix)) 17 | print("Multinomial intercepts: " + str(mlrModel.interceptVector)) 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_multiclasslogisticregressionwithelasticnet.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import LogisticRegression 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | training = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_multiclass_classification_data.txt") 8 | 9 | lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) 10 | lrModel = lr.fit(training) 11 | print("Coefficients: \n" + str(lrModel.coefficientMatrix)) 12 | print("Intercept: " + str(lrModel.interceptVector)) 13 | 14 | trainingSummary = lrModel.summary 15 | objectiveHistory = trainingSummary.objectiveHistory 16 | print("objectiveHistory:") 17 | for objective in objectiveHistory: 18 | print(objective) 19 | 20 | print("False positive rate by label:") 21 | for i, rate in enumerate(trainingSummary.falsePositiveRateByLabel): 22 | print("label %d: %s" % (i, rate)) 23 | 24 | print("True positive rate by label:") 25 | for i, rate in enumerate(trainingSummary.truePositiveRateByLabel): 26 | print("label %d: %s" % (i, rate)) 27 | 28 | print("Precision by label:") 29 | for i, prec in enumerate(trainingSummary.precisionByLabel): 30 | print("label %d: %s" % (i, prec)) 31 | 32 | print("Recall by label:") 33 | for i, rec in enumerate(trainingSummary.recallByLabel): 34 | print("label %d: %s" % (i, rec)) 35 | 36 | print("F-measure by label:") 37 | for i, f in enumerate(trainingSummary.fMeasureByLabel()): 38 | print("label %d: %s" % (i, f)) 39 | 40 | accuracy = trainingSummary.accuracy 41 | falsePositiveRate = trainingSummary.weightedFalsePositiveRate 42 | truePositiveRate = trainingSummary.weightedTruePositiveRate 43 | fMeasure = trainingSummary.weightedFMeasure() 44 | precision = trainingSummary.weightedPrecision 45 | recall = trainingSummary.weightedRecall 46 | print("Accuracy: %s\nFPR: %s\nTPR: %s\nF-measure: %s\nPrecision: %s\nRecall: %s" % (accuracy, falsePositiveRate, truePositiveRate, fMeasure, precision, recall)) 47 | 48 | spark.stop() 49 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_multilayerperceptronclassification.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import MultilayerPerceptronClassifier 2 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_multiclass_classification_data.txt") 9 | 10 | splits = data.randomSplit([0.6, 0.4], 1234) 11 | train = splits[0] 12 | test = splits[1] 13 | 14 | layers = [4, 5, 4, 3] 15 | trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234) 16 | model = trainer.fit(train) 17 | result = model.transform(test) 18 | 19 | predictionAndLabels = result.select("prediction", "label") 20 | evaluator = MulticlassClassificationEvaluator(metricName="accuracy") 21 | print("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels))) 22 | 23 | spark.stop() 24 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_naivebayes.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import NaiveBayes 2 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 9 | 10 | splits = data.randomSplit([0.6, 0.4], 1234) 11 | train = splits[0] 12 | test = splits[1] 13 | 14 | nb = NaiveBayes(smoothing=1.0, modelType="multinomial") 15 | model = nb.fit(train) 16 | predictions = model.transform(test) 17 | predictions.show() 18 | 19 | evaluator = MulticlassClassificationEvaluator(labelCol="label", predictionCol="prediction",metricName="accuracy") 20 | accuracy = evaluator.evaluate(predictions) 21 | print("Test set accuracy = " + str(accuracy)) 22 | 23 | spark.stop() 24 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_onevsrest.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.classification import LogisticRegression, OneVsRest 2 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | inputData = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_multiclass_classification_data.txt") 9 | 10 | (train, test) = inputData.randomSplit([0.8, 0.2]) 11 | 12 | lr = LogisticRegression(maxIter=10, tol=1E-6, fitIntercept=True) 13 | ovr = OneVsRest(classifier=lr) 14 | ovrModel = ovr.fit(train) 15 | 16 | predictions = ovrModel.transform(test) 17 | evaluator = MulticlassClassificationEvaluator(metricName="accuracy") 18 | accuracy = evaluator.evaluate(predictions) 19 | print("Test Error = %g" % (1.0 - accuracy)) 20 | 21 | spark.stop() 22 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CL_randomforestclassifier.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.classification import RandomForestClassifier 3 | from pyspark.ml.feature import IndexToString, StringIndexer, VectorIndexer 4 | from pyspark.ml.evaluation import MulticlassClassificationEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data) 13 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 14 | 15 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 16 | 17 | rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", numTrees=10) 18 | 19 | labelConverter = IndexToString(inputCol="prediction", outputCol="predictedLabel",labels=labelIndexer.labels) 20 | 21 | pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf, labelConverter]) 22 | model = pipeline.fit(trainingData) 23 | 24 | predictions = model.transform(testData) 25 | predictions.select("predictedLabel", "label", "features").show(5) 26 | 27 | evaluator = MulticlassClassificationEvaluator(labelCol="indexedLabel", predictionCol="prediction", metricName="accuracy") 28 | accuracy = evaluator.evaluate(predictions) 29 | print("Test Error = %g" % (1.0 - accuracy)) 30 | 31 | rfModel = model.stages[2] 32 | print(rfModel) 33 | 34 | spark.stop() 35 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CU_bisectingkmeans.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.clustering import BisectingKMeans 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_kmeans_data.txt") 8 | 9 | bkm = BisectingKMeans().setK(2).setSeed(1) 10 | model = bkm.fit(dataset) 11 | 12 | cost = model.computeCost(dataset) 13 | print("Within Set Sum of Squared Errors = " + str(cost)) 14 | 15 | print("Cluster Centers: ") 16 | centers = model.clusterCenters() 17 | for center in centers: 18 | print(center) 19 | 20 | spark.stop() 21 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CU_gaussianmixture.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.clustering import GaussianMixture 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_kmeans_data.txt") 8 | 9 | gmm = GaussianMixture().setK(2).setSeed(538009335) 10 | model = gmm.fit(dataset) 11 | 12 | print("Gaussians shown as a DataFrame: ") 13 | model.gaussiansDF.show(truncate=False) 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CU_kmeans.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.clustering import KMeans 2 | from pyspark.ml.evaluation import ClusteringEvaluator 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataset = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_kmeans_data.txt") 9 | 10 | kmeans = KMeans().setK(2).setSeed(1) 11 | model = kmeans.fit(dataset) 12 | 13 | predictions = model.transform(dataset) 14 | evaluator = ClusteringEvaluator() 15 | 16 | silhouette = evaluator.evaluate(predictions) 17 | print("Silhouette with squared euclidean distance = " + str(silhouette)) 18 | 19 | centers = model.clusterCenters() 20 | print("Cluster Centers: ") 21 | for center in centers: 22 | print(center) 23 | 24 | spark.stop() 25 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_CU_lda.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.clustering import LDA 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_lda_libsvm_data.txt") 8 | 9 | lda = LDA(k=10, maxIter=10) 10 | model = lda.fit(dataset) 11 | 12 | ll = model.logLikelihood(dataset) 13 | lp = model.logPerplexity(dataset) 14 | print("The lower bound on the log likelihood of the entire corpus: " + str(ll)) 15 | print("The upper bound on perplexity: " + str(lp)) 16 | 17 | topics = model.describeTopics(3) 18 | print("The topics described by their top-weighted terms:") 19 | topics.show(truncate=False) 20 | 21 | transformed = model.transform(dataset) 22 | transformed.show(truncate=False) 23 | 24 | spark.stop() 25 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FE_countvectorizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.feature import CountVectorizer 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame([ 8 | (0, "a b c".split(" ")), 9 | (1, "a b b c a".split(" ")) 10 | ], ["id", "words"]) 11 | 12 | cv = CountVectorizer(inputCol="words", outputCol="features", vocabSize=3, minDF=2.0) 13 | model = cv.fit(df) 14 | 15 | result = model.transform(df) 16 | result.show(truncate=False) 17 | 18 | spark.stop() 19 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FE_featurehasher.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.feature import FeatureHasher 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.createDataFrame([ 8 | (2.2, True, "1", "foo"), 9 | (3.3, False, "2", "bar"), 10 | (4.4, False, "3", "baz"), 11 | (5.5, False, "4", "foo") 12 | ], ["real", "bool", "stringNum", "string"]) 13 | 14 | hasher = FeatureHasher(inputCols=["real", "bool", "stringNum", "string"],outputCol="features") 15 | 16 | featurized = hasher.transform(dataset) 17 | featurized.show(truncate=False) 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FE_tfidf.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import HashingTF, IDF, Tokenizer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | sentenceData = spark.createDataFrame([ 8 | (0.0, "a b b a a a b"), 9 | (0.0, "a b a b b a a"), 10 | (1.0, "b a b bb aa b") 11 | ], ["label", "sentence"]) 12 | 13 | # TF 14 | tokenizer = Tokenizer(inputCol="sentence", outputCol="words") 15 | wordsData = tokenizer.transform(sentenceData) 16 | hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) 17 | featurizedData = hashingTF.transform(wordsData) 18 | # alternatively, CountVectorizer can also be used to get term frequency vectors 19 | 20 | # IDF 21 | idf = IDF(inputCol="rawFeatures", outputCol="features") 22 | idfModel = idf.fit(featurizedData) 23 | rescaledData = idfModel.transform(featurizedData) 24 | 25 | rescaledData.show(20, False) 26 | 27 | spark.stop() 28 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FE_word2vec.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import Word2Vec 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | documentDF = spark.createDataFrame([ 8 | ("Hi I heard about Spark".split(" "),), 9 | ("I wish Java could use case classes".split(" "),), 10 | ("Logistic regression models are neat".split(" "),) 11 | ], ["text"]) 12 | 13 | word2Vec = Word2Vec(vectorSize=3, minCount=0, inputCol="text", outputCol="result") 14 | model = word2Vec.fit(documentDF) 15 | result = model.transform(documentDF) 16 | 17 | for row in result.collect(): 18 | text, vector = row 19 | print("Text: [%s] => \nVector: %s\n" % (", ".join(text), str(vector))) 20 | 21 | spark.stop() 22 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FP_fpgrowth.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.fpm import FPGrowth 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame([ 8 | (0, [1, 2, 5]), 9 | (1, [1, 2, 3, 5]), 10 | (2, [1, 2]) 11 | ], ["id", "items"]) 12 | 13 | fpGrowth = FPGrowth(itemsCol="items", minSupport=0.5, minConfidence=0.6) 14 | model = fpGrowth.fit(df) 15 | 16 | model.freqItemsets.show() 17 | 18 | model.associationRules.show() 19 | 20 | model.transform(df).show() 21 | 22 | spark.stop() 23 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FS_bucketedrandomprojectionlsh.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import BucketedRandomProjectionLSH 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql.functions import col 4 | from pyspark.sql import SparkSession 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | dataA = [(0, Vectors.dense([1.0, 1.0]),), 10 | (1, Vectors.dense([1.0, -1.0]),), 11 | (2, Vectors.dense([-1.0, -1.0]),), 12 | (3, Vectors.dense([-1.0, 1.0]),)] 13 | 14 | dfA = spark.createDataFrame(dataA, ["id", "features"]) 15 | 16 | dataB = [(4, Vectors.dense([1.0, 0.0]),), 17 | (5, Vectors.dense([-1.0, 0.0]),), 18 | (6, Vectors.dense([0.0, 1.0]),), 19 | (7, Vectors.dense([0.0, -1.0]),)] 20 | 21 | dfB = spark.createDataFrame(dataB, ["id", "features"]) 22 | 23 | key = Vectors.dense([1.0, 0.0]) 24 | 25 | brp = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", bucketLength=2.0,numHashTables=3) 26 | 27 | model = brp.fit(dfA) 28 | 29 | print("The hashed dataset where hashed values are stored in the column 'hashes':") 30 | model.transform(dfA).show() 31 | 32 | print("Approximately joining dfA and dfB on Euclidean distance smaller than 1.5:") 33 | model.approxSimilarityJoin(dfA, dfB, 1.5, distCol="EuclideanDistance").select(col("datasetA.id").alias("idA"),col("datasetB.id").alias("idB"),col("EuclideanDistance")).show() 34 | 35 | print("Approximately searching dfA for 2 nearest neighbors of the key:") 36 | model.approxNearestNeighbors(dfA, key, 2).show() 37 | 38 | spark.stop() 39 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FS_chisqselector.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.feature import ChiSqSelector 3 | from pyspark.ml.linalg import Vectors 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | df = spark.createDataFrame([ 9 | (7, Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0,), 10 | (8, Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0,), 11 | (9, Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0,) 12 | ], ["id", "features", "clicked"]) 13 | 14 | selector = ChiSqSelector(numTopFeatures=1, featuresCol="features",outputCol="selectedFeatures", labelCol="clicked") 15 | result = selector.fit(df).transform(df) 16 | 17 | print("ChiSqSelector output with top %d features selected" % selector.getNumTopFeatures()) 18 | result.show() 19 | 20 | spark.stop() 21 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FS_minhashlsh.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import MinHashLSH 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql.functions import col 4 | from pyspark.sql import SparkSession 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | dataA = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),), 10 | (1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),), 11 | (2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)] 12 | 13 | dfA = spark.createDataFrame(dataA, ["id", "features"]) 14 | 15 | dataB = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),), 16 | (4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),), 17 | (5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)] 18 | 19 | dfB = spark.createDataFrame(dataB, ["id", "features"]) 20 | 21 | key = Vectors.sparse(6, [1, 3], [1.0, 1.0]) 22 | 23 | mh = MinHashLSH(inputCol="features", outputCol="hashes", numHashTables=5) 24 | 25 | model = mh.fit(dfA) 26 | 27 | print("The hashed dataset where hashed values are stored in the column 'hashes':") 28 | model.transform(dfA).show() 29 | 30 | print("Approximately joining dfA and dfB on distance smaller than 0.6:") 31 | model.approxSimilarityJoin(dfA, dfB, 0.6, distCol="JaccardDistance").select(col("datasetA.id").alias("idA"),col("datasetB.id").alias("idB"),col("JaccardDistance")).show() 32 | 33 | # May return less than 2 rows when not enough approximate near-neighbor candidates are found. 34 | print("Approximately searching dfA for 2 nearest neighbors of the key:") 35 | model.approxNearestNeighbors(dfA, key, 2).show() 36 | 37 | spark.stop() 38 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FS_rformula.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import RFormula 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.createDataFrame([ 8 | (7, "US", 18, 1.0), 9 | (8, "CA", 12, 0.0), 10 | (9, "NZ", 15, 0.0) 11 | ],["id", "country", "hour", "clicked"]) 12 | 13 | formula = RFormula( 14 | formula="clicked ~ country + hour", 15 | featuresCol="features", 16 | labelCol="label" 17 | ) 18 | 19 | output = formula.fit(dataset).transform(dataset) 20 | output.select("features", "label").show() 21 | 22 | spark.stop() 23 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FS_vectorslicer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import VectorSlicer 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql.types import Row 4 | 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | df = spark.createDataFrame([ 11 | Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3})), 12 | Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0])) 13 | ]) 14 | 15 | slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1]) 16 | output = slicer.transform(df) 17 | output.select("userFeatures", "features").show() 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_binarizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.feature import Binarizer 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | continuousDataFrame = spark.createDataFrame([ 8 | (0, 0.1), 9 | (1, 0.8), 10 | (2, 0.2) 11 | ], ["id", "feature"]) 12 | 13 | binarizer = Binarizer(threshold=0.5, inputCol="feature", outputCol="binarized_feature") 14 | binarizedDataFrame = binarizer.transform(continuousDataFrame) 15 | 16 | print("Binarizer output with Threshold = %f" % binarizer.getThreshold()) 17 | binarizedDataFrame.show() 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_bucketizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.feature import Bucketizer 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")] 8 | data = [(-999.9,), (-0.5,), (-0.3,), (0.0,), (0.2,), (999.9,)] 9 | dataFrame = spark.createDataFrame(data, ["features"]) 10 | 11 | bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures") 12 | bucketedData = bucketizer.transform(dataFrame) 13 | 14 | print("Bucketizer output with %d buckets" % (len(bucketizer.getSplits())-1)) 15 | bucketedData.show() 16 | 17 | spark.stop() 18 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_dct.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import DCT 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | df = spark.createDataFrame([ 9 | (Vectors.dense([0.0, 1.0, -2.0, 3.0]),), 10 | (Vectors.dense([-1.0, 2.0, 4.0, -7.0]),), 11 | (Vectors.dense([14.0, -2.0, -5.0, 1.0]),) 12 | ], ["features"]) 13 | 14 | dct = DCT(inverse=False, inputCol="features", outputCol="featuresDCT") 15 | dctDf = dct.transform(df) 16 | dctDf.select("featuresDCT").show(truncate=False) 17 | 18 | spark.stop() 19 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_elementwiseproduct.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import ElementwiseProduct 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = [ 9 | (Vectors.dense([1.0, 2.0, 3.0]),), 10 | (Vectors.dense([4.0, 5.0, 6.0]),) 11 | ] 12 | 13 | df = spark.createDataFrame(data, ["vector"]) 14 | 15 | transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]),inputCol="vector", outputCol="transformedVector") 16 | transformer.transform(df).show() 17 | 18 | spark.stop() 19 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_imputer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import Imputer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame([ 8 | (1.0, float("nan")), 9 | (2.0, float("nan")), 10 | (float("nan"), 3.0), 11 | (4.0, 4.0), 12 | (5.0, 5.0) 13 | ], ["a", "b"]) 14 | 15 | imputer = Imputer(inputCols=["a", "b"], outputCols=["out_a", "out_b"]) 16 | model = imputer.fit(df) 17 | model.transform(df).show() 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_indextostring.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import IndexToString, StringIndexer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame( 8 | [ 9 | (0, "a"), 10 | (1, "b"), 11 | (2, "c"), 12 | (3, "a"), 13 | (4, "a"), 14 | (5, "c") 15 | ], 16 | ["id", "category"] 17 | ) 18 | 19 | indexer = StringIndexer(inputCol="category", outputCol="categoryIndex") 20 | model = indexer.fit(df) 21 | indexed = model.transform(df) 22 | print("Transformed string column '%s' to indexed column '%s'" % (indexer.getInputCol(), indexer.getOutputCol())) 23 | indexed.show() 24 | 25 | converter = IndexToString(inputCol="categoryIndex", outputCol="originalCategory") 26 | converted = converter.transform(indexed) 27 | print("Transformed indexed column '%s' back to original string column '%s' using labels in metadata" % (converter.getInputCol(), converter.getOutputCol())) 28 | converted.select("id", "categoryIndex", "originalCategory").show() 29 | 30 | spark.stop() 31 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_maxabsscaler.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import MaxAbsScaler 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataFrame = spark.createDataFrame([ 9 | (0, Vectors.dense([1.0, 0.1, -8.0]),), 10 | (1, Vectors.dense([2.0, 1.0, -4.0]),), 11 | (2, Vectors.dense([4.0, 10.0, 8.0]),) 12 | ], ["id", "features"]) 13 | 14 | scaler = MaxAbsScaler(inputCol="features", outputCol="scaledFeatures") 15 | scalerModel = scaler.fit(dataFrame) 16 | scaledData = scalerModel.transform(dataFrame) 17 | scaledData.select("features", "scaledFeatures").show() 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_minmaxscaler.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import MinMaxScaler 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataFrame = spark.createDataFrame([ 9 | (0, Vectors.dense([1.0, 0.1, -1.0]),), 10 | (1, Vectors.dense([2.0, 1.1, 1.0]),), 11 | (2, Vectors.dense([3.0, 10.1, 3.0]),) 12 | ], ["id", "features"]) 13 | 14 | scaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures") 15 | scalerModel = scaler.fit(dataFrame) 16 | scaledData = scalerModel.transform(dataFrame) 17 | 18 | print("Features scaled to range: [%f, %f]" % (scaler.getMin(), scaler.getMax())) 19 | scaledData.select("features", "scaledFeatures").show() 20 | 21 | spark.stop() 22 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_ngram.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import NGram 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | wordDataFrame = spark.createDataFrame([ 8 | (0, ["Hi", "I", "heard", "about", "Spark"]), 9 | (1, ["I", "wish", "Java", "could", "use", "case", "classes"]), 10 | (2, ["Logistic", "regression", "models", "are", "neat"]) 11 | ], ["id", "words"]) 12 | 13 | ngram = NGram(n=2, inputCol="words", outputCol="ngrams") 14 | ngramDataFrame = ngram.transform(wordDataFrame) 15 | ngramDataFrame.select("ngrams").show(truncate=False) 16 | 17 | spark.stop() 18 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_normalizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import Normalizer 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataFrame = spark.createDataFrame([ 9 | (0, Vectors.dense([1.0, 0.5, -1.0]),), 10 | (1, Vectors.dense([2.0, 1.0, 1.0]),), 11 | (2, Vectors.dense([4.0, 10.0, 2.0]),) 12 | ], ["id", "features"]) 13 | 14 | normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0) 15 | l1NormData = normalizer.transform(dataFrame) 16 | print("Normalized using L^1 norm") 17 | l1NormData.show() 18 | 19 | lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")}) 20 | print("Normalized using L^inf norm") 21 | lInfNormData.show() 22 | 23 | spark.stop() 24 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_onehotencoderestimator.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import OneHotEncoderEstimator 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame([ 8 | (0.0, 1.0), 9 | (1.0, 0.0), 10 | (2.0, 1.0), 11 | (0.0, 2.0), 12 | (0.0, 1.0), 13 | (2.0, 0.0) 14 | ], ["categoryIndex1", "categoryIndex2"]) 15 | 16 | encoder = OneHotEncoderEstimator( 17 | inputCols=["categoryIndex1", "categoryIndex2"], 18 | outputCols=["categoryVec1", "categoryVec2"] 19 | ) 20 | 21 | model = encoder.fit(df) 22 | encoded = model.transform(df) 23 | encoded.show() 24 | 25 | spark.stop() 26 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_pca.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import PCA 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | data = [ 9 | (Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),), 10 | (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),), 11 | (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),) 12 | ] 13 | 14 | df = spark.createDataFrame(data, ["features"]) 15 | 16 | pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures") 17 | model = pca.fit(df) 18 | 19 | result = model.transform(df).select("pcaFeatures") 20 | result.show(truncate=False) 21 | 22 | spark.stop() 23 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_polynomialexpansion.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import PolynomialExpansion 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | df = spark.createDataFrame([ 9 | (Vectors.dense([2.0, 1.0]),), 10 | (Vectors.dense([0.0, 0.0]),), 11 | (Vectors.dense([3.0, -1.0]),) 12 | ], ["features"]) 13 | 14 | polyExpansion = PolynomialExpansion(degree=3, inputCol="features", outputCol="polyFeatures") 15 | polyDF = polyExpansion.transform(df) 16 | 17 | polyDF.show(truncate=False) 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_quantilediscretizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import QuantileDiscretizer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2)] 8 | df = spark.createDataFrame(data, ["id", "hour"]) 9 | 10 | # Output of QuantileDiscretizer for such small datasets can depend on the number of 11 | # partitions. Here we force a single partition to ensure consistent results. 12 | # Note this is not necessary for normal use cases 13 | df = df.repartition(1) 14 | 15 | discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result") 16 | result = discretizer.fit(df).transform(df) 17 | result.show() 18 | 19 | spark.stop() 20 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_sqltransformer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import SQLTransformer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame([ 8 | (0, 1.0, 3.0), 9 | (2, 2.0, 5.0) 10 | ], ["id", "v1", "v2"]) 11 | 12 | sqlTrans = SQLTransformer(statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__") 13 | sqlTrans.transform(df).show() 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_standardscaler.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import StandardScaler 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataFrame = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 8 | 9 | scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",withStd=True, withMean=False) 10 | scalerModel = scaler.fit(dataFrame) 11 | 12 | scaledData = scalerModel.transform(dataFrame) 13 | scaledData.show() 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_stopwords.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import StopWordsRemover 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | sentenceData = spark.createDataFrame([ 8 | (0, ["I", "saw", "the", "red", "balloon"]), 9 | (1, ["Mary", "had", "a", "little", "lamb"]) 10 | ], ["id", "raw"]) 11 | 12 | remover = StopWordsRemover(inputCol="raw", outputCol="filtered") 13 | remover.transform(sentenceData).show(truncate=False) 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_stringindexer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import StringIndexer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | df = spark.createDataFrame( 8 | [ 9 | (0, "a"), 10 | (1, "b"), 11 | (2, "c"), 12 | (3, "a"), 13 | (4, "a"), 14 | (5, "c") 15 | ], 16 | ["id", "category"] 17 | ) 18 | 19 | indexer = StringIndexer(inputCol="category", outputCol="categoryIndex") 20 | indexed = indexer.fit(df).transform(df) 21 | indexed.show() 22 | 23 | spark.stop() 24 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_tokenizer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import Tokenizer, RegexTokenizer 2 | from pyspark.sql.functions import col, udf 3 | from pyspark.sql.types import IntegerType 4 | from pyspark.sql import SparkSession 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | countTokens = udf(lambda words: len(words), IntegerType()) 10 | 11 | sentenceDataFrame = spark.createDataFrame([ 12 | (0, "Hi I heard about spark Spark"), 13 | (1, "I wish Java could use case classes"), 14 | (2, "Logistic,regression,models,are,neat") 15 | ], ["id", "sentence"]) 16 | 17 | 18 | tokenizer = Tokenizer(inputCol="sentence", outputCol="words") 19 | tokenized = tokenizer.transform(sentenceDataFrame) 20 | tokenized.select("sentence", "words").withColumn("tokens", countTokens(col("words"))).show(truncate=False) 21 | 22 | regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W") # alternatively: pattern="\\w+", gaps(False) 23 | regexTokenized = regexTokenizer.transform(sentenceDataFrame) 24 | regexTokenized.select("sentence", "words").withColumn("tokens", countTokens(col("words"))).show(truncate=False) 25 | 26 | spark.stop() 27 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_vectorassembler.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.linalg import Vectors 2 | from pyspark.ml.feature import VectorAssembler 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataset = spark.createDataFrame( 9 | [(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0)], 10 | ["id", "hour", "mobile", "userFeatures", "clicked"] 11 | ) 12 | 13 | assembler = VectorAssembler( 14 | inputCols=["hour", "mobile", "userFeatures"], 15 | outputCol="features" 16 | ) 17 | 18 | output = assembler.transform(dataset) 19 | print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'") 20 | output.select("features", "clicked").show(truncate=False) 21 | 22 | spark.stop() 23 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_vectorindexer.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.feature import VectorIndexer 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 8 | indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10) 9 | indexerModel = indexer.fit(data) 10 | categoricalFeatures = indexerModel.categoryMaps 11 | 12 | print("Chose %d categorical features: %s" % (len(categoricalFeatures), ", ".join(str(k) for k in categoricalFeatures.keys()))) 13 | indexedData = indexerModel.transform(data) 14 | indexedData.show() 15 | 16 | spark.stop() 17 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_FT_vectorsizehint.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.linalg import Vectors 2 | from pyspark.ml.feature import (VectorSizeHint, VectorAssembler) 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | dataset = spark.createDataFrame([ 9 | (0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0), 10 | (0, 18, 1.0, Vectors.dense([0.0, 10.0]), 0.0) 11 | ], 12 | ["id", "hour", "mobile", "userFeatures", "clicked"]) 13 | 14 | sizeHint = VectorSizeHint( 15 | inputCol="userFeatures", 16 | handleInvalid="skip", 17 | size=3 18 | ) 19 | 20 | datasetWithSize = sizeHint.transform(dataset) 21 | print("Rows where 'userFeatures' is not the right size are filtered out") 22 | datasetWithSize.show(truncate=False) 23 | 24 | assembler = VectorAssembler( 25 | inputCols=["hour", "mobile", "userFeatures"], 26 | outputCol="features" 27 | ) 28 | output = assembler.transform(datasetWithSize) 29 | 30 | print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'") 31 | output.select("features", "clicked").show(truncate=False) 32 | 33 | spark.stop() 34 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_PIPE_estimatortransformerparameter.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.linalg import Vectors 2 | from pyspark.ml.classification import LogisticRegression 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | training = spark.createDataFrame([ 9 | (1.0, Vectors.dense([0.0, 1.1, 0.1])), 10 | (0.0, Vectors.dense([2.0, 1.0, -1.0])), 11 | (0.0, Vectors.dense([2.0, 1.3, 1.0])), 12 | (1.0, Vectors.dense([0.0, 1.2, -0.5])) 13 | ], ["label", "features"]) 14 | 15 | # Estimator 16 | lr = LogisticRegression(maxIter=10, regParam=0.01) 17 | print("LogisticRegression parameters:\n" + lr.explainParams() + "\n") 18 | 19 | # Transformer 20 | model1 = lr.fit(training) 21 | print("Model 1 was fit using parameters: ") 22 | print(model1.extractParamMap()) 23 | 24 | # Parameters 25 | paramMap = {lr.maxIter: 20} 26 | paramMap[lr.maxIter] = 30 27 | paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) 28 | 29 | paramMap2 = {lr.probabilityCol: "myProbability"} 30 | 31 | paramMapCombined = paramMap.copy() 32 | paramMapCombined.update(paramMap2) 33 | 34 | # Transformer 35 | model2 = lr.fit(training, paramMapCombined) 36 | print("Model 2 was fit using parameters: ") 37 | print(model2.extractParamMap()) 38 | 39 | test = spark.createDataFrame([ 40 | (1.0, Vectors.dense([-1.0, 1.5, 1.3])), 41 | (0.0, Vectors.dense([3.0, 2.0, -0.1])), 42 | (1.0, Vectors.dense([0.0, 2.2, -1.5])) 43 | ], ["label", "features"]) 44 | 45 | prediction = model2.transform(test) 46 | result = prediction.select("features", "label", "myProbability", "prediction").collect() 47 | 48 | for row in result: 49 | print("features=%s, label=%s -> prob=%s, prediction=%s" % (row.features, row.label, row.myProbability, row.prediction)) 50 | 51 | spark.stop() 52 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_PIPE_pipeline.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.classification import LogisticRegression 3 | from pyspark.ml.feature import HashingTF, Tokenizer 4 | from pyspark.sql import SparkSession 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | training = spark.createDataFrame([ 10 | (0, "a b c d e spark", 1.0), 11 | (1, "b d", 0.0), 12 | (2, "spark f g h", 1.0), 13 | (3, "hadoop mapreduce", 0.0) 14 | ], ["id", "text", "label"]) 15 | 16 | # Pipeline: (1)tokenizer, (2)hashingTF, (3)lr 17 | tokenizer = Tokenizer(inputCol="text", outputCol="words") 18 | hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") 19 | lr = LogisticRegression(maxIter=10, regParam=0.001) 20 | pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) 21 | 22 | model = pipeline.fit(training) 23 | 24 | test = spark.createDataFrame([ 25 | (4, "spark i j k"), 26 | (5, "l m n"), 27 | (6, "spark hadoop spark"), 28 | (7, "apache hadoop") 29 | ], ["id", "text"]) 30 | 31 | prediction = model.transform(test) 32 | selected = prediction.select("id", "text", "probability", "prediction") 33 | for row in selected.collect(): 34 | rid, text, prob, prediction = row 35 | print("(%d, %s) --> prob=%s, prediction=%f" % (rid, text, str(prob), prediction)) 36 | 37 | spark.stop() 38 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_aftsuvival.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.regression import AFTSurvivalRegression 2 | from pyspark.ml.linalg import Vectors 3 | from pyspark.sql import SparkSession 4 | 5 | spark = SparkSession.builder.getOrCreate() 6 | spark.sparkContext.setLogLevel("ERROR") 7 | 8 | training = spark.createDataFrame([ 9 | (1.218, 1.0, Vectors.dense(1.560, -0.605)), 10 | (2.949, 0.0, Vectors.dense(0.346, 2.158)), 11 | (3.627, 0.0, Vectors.dense(1.380, 0.231)), 12 | (0.273, 1.0, Vectors.dense(0.520, 1.151)), 13 | (4.199, 0.0, Vectors.dense(0.795, -0.226)) 14 | ], ["label", "censor", "features"]) 15 | 16 | quantileProbabilities = [0.3, 0.6] 17 | 18 | aft = AFTSurvivalRegression(quantileProbabilities=quantileProbabilities,quantilesCol="quantiles") 19 | model = aft.fit(training) 20 | 21 | print("Coefficients: " + str(model.coefficients)) 22 | print("Intercept: " + str(model.intercept)) 23 | print("Scale: " + str(model.scale)) 24 | model.transform(training).show(truncate=False) 25 | 26 | spark.stop() 27 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_decisiontree.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.regression import DecisionTreeRegressor 3 | from pyspark.ml.feature import VectorIndexer 4 | from pyspark.ml.evaluation import RegressionEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 13 | 14 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 15 | 16 | dt = DecisionTreeRegressor(featuresCol="indexedFeatures") 17 | 18 | pipeline = Pipeline(stages=[featureIndexer, dt]) 19 | model = pipeline.fit(trainingData) 20 | 21 | predictions = model.transform(testData) 22 | predictions.select("prediction", "label", "features").show(5) 23 | 24 | evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") 25 | rmse = evaluator.evaluate(predictions) 26 | print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) 27 | 28 | treeModel = model.stages[1] 29 | print(treeModel) 30 | 31 | spark.stop() 32 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_generalizedlinear.py: -------------------------------------------------------------------------------- 1 | from pyspark.sql import SparkSession 2 | from pyspark.ml.regression import GeneralizedLinearRegression 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_linear_regression_data.txt") 8 | 9 | glr = GeneralizedLinearRegression(family="gaussian", link="identity", maxIter=10, regParam=0.3) 10 | model = glr.fit(dataset) 11 | print("Coefficients: " + str(model.coefficients)) 12 | print("Intercept: " + str(model.intercept)) 13 | 14 | summary = model.summary 15 | print("Coefficient Standard Errors: " + str(summary.coefficientStandardErrors)) 16 | print("T Values: " + str(summary.tValues)) 17 | print("P Values: " + str(summary.pValues)) 18 | print("Dispersion: " + str(summary.dispersion)) 19 | print("Null Deviance: " + str(summary.nullDeviance)) 20 | print("Residual Degree Of Freedom Null: " + str(summary.residualDegreeOfFreedomNull)) 21 | print("Deviance: " + str(summary.deviance)) 22 | print("Residual Degree Of Freedom: " + str(summary.residualDegreeOfFreedom)) 23 | print("AIC: " + str(summary.aic)) 24 | print("Deviance Residuals: ") 25 | 26 | summary.residuals().show() 27 | 28 | spark.stop() 29 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_gradientboostedtree.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.regression import GBTRegressor 3 | from pyspark.ml.feature import VectorIndexer 4 | from pyspark.ml.evaluation import RegressionEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 13 | 14 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 15 | 16 | gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10) 17 | 18 | pipeline = Pipeline(stages=[featureIndexer, gbt]) 19 | model = pipeline.fit(trainingData) 20 | 21 | predictions = model.transform(testData) 22 | predictions.select("prediction", "label", "features").show(5) 23 | 24 | evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") 25 | rmse = evaluator.evaluate(predictions) 26 | print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) 27 | 28 | gbtModel = model.stages[1] 29 | print(gbtModel) 30 | 31 | spark.stop() 32 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_isotonic.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.regression import IsotonicRegression 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | dataset = spark.read.format("libsvm").load("file:////usr/lib/spark/data/mllib/sample_isotonic_regression_libsvm_data.txt") 8 | 9 | model = IsotonicRegression().fit(dataset) 10 | print("Boundaries in increasing order: %s\n" % str(model.boundaries)) 11 | print("Predictions associated with the boundaries: %s\n" % str(model.predictions)) 12 | 13 | model.transform(dataset).show() 14 | 15 | spark.stop() 16 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_linearwithelasticnet.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.regression import LinearRegression 2 | from pyspark.sql import SparkSession 3 | 4 | spark = SparkSession.builder.getOrCreate() 5 | spark.sparkContext.setLogLevel("ERROR") 6 | 7 | training = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_linear_regression_data.txt") 8 | 9 | lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) 10 | lrModel = lr.fit(training) 11 | 12 | print("Coefficients: %s" % str(lrModel.coefficients)) 13 | print("Intercept: %s" % str(lrModel.intercept)) 14 | 15 | trainingSummary = lrModel.summary 16 | print("numIterations: %d" % trainingSummary.totalIterations) 17 | print("objectiveHistory: %s" % str(trainingSummary.objectiveHistory)) 18 | 19 | trainingSummary.residuals.show() 20 | print("RMSE: %f" % trainingSummary.rootMeanSquaredError) 21 | print("r2: %f" % trainingSummary.r2) 22 | 23 | spark.stop() 24 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_RG_randomforest.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml import Pipeline 2 | from pyspark.ml.regression import RandomForestRegressor 3 | from pyspark.ml.feature import VectorIndexer 4 | from pyspark.ml.evaluation import RegressionEvaluator 5 | from pyspark.sql import SparkSession 6 | 7 | spark = SparkSession.builder.getOrCreate() 8 | spark.sparkContext.setLogLevel("ERROR") 9 | 10 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_libsvm_data.txt") 11 | 12 | featureIndexer = VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data) 13 | 14 | (trainingData, testData) = data.randomSplit([0.7, 0.3]) 15 | 16 | rf = RandomForestRegressor(featuresCol="indexedFeatures") 17 | 18 | pipeline = Pipeline(stages=[featureIndexer, rf]) 19 | model = pipeline.fit(trainingData) 20 | 21 | predictions = model.transform(testData) 22 | predictions.select("prediction", "label", "features").show(5) 23 | 24 | evaluator = RegressionEvaluator(labelCol="label", predictionCol="prediction", metricName="rmse") 25 | rmse = evaluator.evaluate(predictions) 26 | print("Root Mean Squared Error (RMSE) on test data = %g" % rmse) 27 | 28 | rfModel = model.stages[1] 29 | print(rfModel) 30 | 31 | spark.stop() 32 | -------------------------------------------------------------------------------- /07 Machine Learning/SparkML/sparkML_TU_trainvalidationsplit.py: -------------------------------------------------------------------------------- 1 | from pyspark.ml.evaluation import RegressionEvaluator 2 | from pyspark.ml.regression import LinearRegression 3 | from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit 4 | from pyspark.sql import SparkSession 5 | 6 | spark = SparkSession.builder.getOrCreate() 7 | spark.sparkContext.setLogLevel("ERROR") 8 | 9 | data = spark.read.format("libsvm").load("file:///usr/lib/spark/data/mllib/sample_linear_regression_data.txt") 10 | train, test = data.randomSplit([0.9, 0.1], seed=12345) 11 | 12 | lr = LinearRegression(maxIter=10) 13 | 14 | paramGrid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01]).addGrid(lr.fitIntercept, [False, True]).addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0]).build() 15 | 16 | tvs = TrainValidationSplit(estimator = lr, 17 | estimatorParamMaps = paramGrid, 18 | evaluator = RegressionEvaluator(), 19 | trainRatio = 0.8) 20 | 21 | model = tvs.fit(train) 22 | 23 | model.transform(test).select("features", "label", "prediction").show() 24 | 25 | spark.stop() 26 | -------------------------------------------------------------------------------- /07 Machine Learning/images/100pageMLBook.png: 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Engineering/images/Streaming_Spark.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/09 Data Engineering/images/Streaming_Spark.png -------------------------------------------------------------------------------- /09 Data Engineering/parquet/python_parquet.py: -------------------------------------------------------------------------------- 1 | # m5.4xlarge, 120Gb gp2 volume 2 | 3 | import os 4 | import boto3 5 | import pandas as pd 6 | import pyarrow as pa 7 | import pyarrow.parquet as pq 8 | 9 | s3 = boto3.resource('s3') 10 | bucket = s3.Bucket('amazon-reviews-pds') 11 | 12 | for obj in bucket.objects.all(): 13 | 14 | if obj.key[:3] == 'tsv' and len(obj.key) > 4: 15 | 16 | cmd = 'aws s3 cp s3://amazon-reviews-pds/' + obj.key + ' /home/ec2-user/' 17 | os.system(cmd) 18 | 19 | cmd = 'gunzip /home/ec2-user/*.gz' 20 | os.system(cmd) 21 | 22 | for root, dirs, files in os.walk('/home/ec2-user/'): 23 | 24 | for filename in files: 25 | 26 | if filename[-3:] == 'tsv': 27 | 28 | df = pd.read_csv(filename, sep='\t', error_bad_lines=False, dtype={'marketplace': str,'customer_id': str,'review_id': str,'product_id': str,'product_parent': str,'product_title': str,'product_category': str,'star_rating': str,'helpful_votes': str,'total_votes': str,'vine': str,'verified_purchase': str,'review_headline': str,'review_date': str,'review_body': str}) 29 | 30 | table = pa.Table.from_pandas(df) 31 | 32 | pq.write_to_dataset(table, '/home/ec2-user/', partition_cols=['product_category','review_date'], compression='gzip') 33 | -------------------------------------------------------------------------------- /09 Data Engineering/python/images/DataStructures.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/09 Data Engineering/python/images/DataStructures.png -------------------------------------------------------------------------------- /09 Data Engineering/python/python_LineByLine.py: -------------------------------------------------------------------------------- 1 | # t2.nano, 165Gb gp2 volume, ~380Mb/minute 2 | 3 | import os 4 | import time 5 | import boto3 6 | 7 | s3 = boto3.resource('s3') 8 | bucket = s3.Bucket('amazon-reviews-pds') 9 | 10 | for obj in bucket.objects.all(): 11 | if obj.key[:3] == 'tsv' and len(obj.key) > 4: 12 | cmd = 'aws s3 cp s3://amazon-reviews-pds/' + obj.key + ' /home/ec2-user/' 13 | os.system(cmd) 14 | 15 | cmd = 'gunzip /home/ec2-user/*.gz' 16 | os.system(cmd) 17 | 18 | start = time.time() 19 | 20 | def line_manipulation(line_to_manipulate): 21 | 22 | line_to_manipulate = 'prefix: ' + line_to_manipulate 23 | 24 | return line_to_manipulate 25 | 26 | line_count = 0 27 | file_count = 0 28 | file_number = "%05d" % file_count 29 | file_to_write = open('/home/ec2-user/text_' + file_number + '.txt', 'a') 30 | 31 | for root, dirs, files in os.walk('/home/ec2-user/'): 32 | 33 | for filename in files: 34 | 35 | if filename[-3:] == 'tsv': 36 | 37 | file_to_read = open('/home/ec2-user/' + filename, 'r') 38 | line_to_read = file_to_read.readline() 39 | 40 | while line_to_read: 41 | 42 | while line_count < 100000: 43 | 44 | file_to_write.write(line_manipulation(line_to_read)) 45 | line_count += 1 46 | line_to_read = file_to_read.readline() 47 | 48 | if line_to_read: 49 | 50 | line_count = 0 51 | file_count += 1 52 | file_number = "%05d" % file_count 53 | file_to_write.close() 54 | file_to_write = open('/home/ec2-user/text_' + file_number + '.txt', 'a') 55 | 56 | file_to_write.close() 57 | file_to_read.close() 58 | 59 | end = time.time() 60 | print(end - start)/60 61 | -------------------------------------------------------------------------------- /09 Data Engineering/python/readme.md: -------------------------------------------------------------------------------- 1 | ## Data Structures 2 | ![](https://github.com/geoffreylink/Projects/blob/master/08%20Data%20Engineering/python/images/DataStructures.png) 3 | -------------------------------------------------------------------------------- /09 Data Engineering/spark/images/ClusteredComputing.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/09 Data Engineering/spark/images/ClusteredComputing.png -------------------------------------------------------------------------------- /09 Data Engineering/spark/images/SparkOverview.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/09 Data Engineering/spark/images/SparkOverview.png 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![](https://github.com/geoffreylink/Projects/blob/master/08%20Data%20Engineering/spark/images/SparkOverview.png) 10 | ![](https://github.com/geoffreylink/Projects/blob/master/08%20Data%20Engineering/spark/images/SparkRDD.png) 11 | -------------------------------------------------------------------------------- /10 Computer Vision/images/ElephantMask.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/10 Computer Vision/images/ElephantMask.png -------------------------------------------------------------------------------- /10 Computer Vision/images/ObjectRecognitionTasks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/10 Computer Vision/images/ObjectRecognitionTasks.png 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-------------------------------------------------------------------------------- /14 React/robofriends/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "robofriends", 3 | "version": "0.1.0", 4 | "private": true, 5 | "dependencies": { 6 | "@testing-library/jest-dom": "^4.2.4", 7 | "@testing-library/react": "^9.5.0", 8 | "@testing-library/user-event": "^7.2.1", 9 | "react": "^16.14.0", 10 | "react-dom": "^16.14.0", 11 | "react-scripts": "3.4.3" 12 | }, 13 | "scripts": { 14 | "start": "react-scripts start", 15 | "build": "react-scripts build", 16 | "test": "react-scripts test", 17 | "eject": "react-scripts eject" 18 | }, 19 | "eslintConfig": { 20 | "extends": "react-app" 21 | }, 22 | "browserslist": { 23 | "production": [ 24 | ">0.2%", 25 | "not dead", 26 | "not op_mini all" 27 | ], 28 | "development": [ 29 | "last 1 chrome version", 30 | "last 1 firefox version", 31 | "last 1 safari version" 32 | ] 33 | } 34 | } 35 | -------------------------------------------------------------------------------- /14 React/robofriends/public/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/14 React/robofriends/public/favicon.ico -------------------------------------------------------------------------------- /14 React/robofriends/public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 12 | 13 | 17 | 18 | 27 | React App 28 | 29 | 30 | 31 |
32 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /14 React/robofriends/public/logo192.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/14 React/robofriends/public/logo192.png -------------------------------------------------------------------------------- /14 React/robofriends/public/logo512.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/14 React/robofriends/public/logo512.png -------------------------------------------------------------------------------- /14 React/robofriends/public/manifest.json: -------------------------------------------------------------------------------- 1 | { 2 | "short_name": "React App", 3 | "name": "Create React App Sample", 4 | "icons": [ 5 | { 6 | "src": "favicon.ico", 7 | "sizes": "64x64 32x32 24x24 16x16", 8 | "type": "image/x-icon" 9 | }, 10 | { 11 | "src": "logo192.png", 12 | "type": "image/png", 13 | "sizes": "192x192" 14 | }, 15 | { 16 | "src": "logo512.png", 17 | "type": "image/png", 18 | "sizes": "512x512" 19 | } 20 | ], 21 | "start_url": ".", 22 | "display": "standalone", 23 | "theme_color": "#000000", 24 | "background_color": "#ffffff" 25 | } 26 | -------------------------------------------------------------------------------- /14 React/robofriends/public/robots.txt: -------------------------------------------------------------------------------- 1 | # https://www.robotstxt.org/robotstxt.html 2 | User-agent: * 3 | Disallow: 4 | -------------------------------------------------------------------------------- /14 React/robofriends/src/App.css: -------------------------------------------------------------------------------- 1 | .App { 2 | text-align: center; 3 | } 4 | 5 | .App-logo { 6 | height: 40vmin; 7 | pointer-events: none; 8 | } 9 | 10 | @media (prefers-reduced-motion: no-preference) { 11 | .App-logo { 12 | animation: App-logo-spin infinite 20s linear; 13 | } 14 | } 15 | 16 | .App-header { 17 | background-color: #282c34; 18 | min-height: 100vh; 19 | display: flex; 20 | flex-direction: column; 21 | align-items: center; 22 | justify-content: center; 23 | font-size: calc(10px + 2vmin); 24 | color: white; 25 | } 26 | 27 | .App-link { 28 | color: #61dafb; 29 | } 30 | 31 | @keyframes App-logo-spin { 32 | from { 33 | transform: rotate(0deg); 34 | } 35 | to { 36 | transform: rotate(360deg); 37 | } 38 | } 39 | -------------------------------------------------------------------------------- /14 React/robofriends/src/App.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import logo from './logo.svg'; 3 | import './App.css'; 4 | 5 | function App() { 6 | return ( 7 |
8 |
9 | logo 10 |

11 | Edit src/App.js and save to reload. 12 |

13 | 19 | Learn React 20 | 21 |
22 |
23 | ); 24 | } 25 | 26 | export default App; 27 | -------------------------------------------------------------------------------- /14 React/robofriends/src/App.test.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import { render } from '@testing-library/react'; 3 | import App from './App'; 4 | 5 | test('renders learn react link', () => { 6 | const { getByText } = render(); 7 | const linkElement = getByText(/learn react/i); 8 | expect(linkElement).toBeInTheDocument(); 9 | }); 10 | -------------------------------------------------------------------------------- /14 React/robofriends/src/index.css: -------------------------------------------------------------------------------- 1 | body { 2 | margin: 0; 3 | font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 4 | 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', 5 | sans-serif; 6 | -webkit-font-smoothing: antialiased; 7 | -moz-osx-font-smoothing: grayscale; 8 | } 9 | 10 | code { 11 | font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', 12 | monospace; 13 | } 14 | -------------------------------------------------------------------------------- /14 React/robofriends/src/index.js: -------------------------------------------------------------------------------- 1 | import React from 'react'; 2 | import ReactDOM from 'react-dom'; 3 | import './index.css'; 4 | import App from './App'; 5 | import * as serviceWorker from './serviceWorker'; 6 | 7 | ReactDOM.render( 8 | 9 | 10 | , 11 | document.getElementById('root') 12 | ); 13 | 14 | // If you want your app to work offline and load faster, you can change 15 | // unregister() to register() below. Note this comes with some pitfalls. 16 | // Learn more about service workers: https://bit.ly/CRA-PWA 17 | serviceWorker.unregister(); 18 | -------------------------------------------------------------------------------- /14 React/robofriends/src/setupTests.js: -------------------------------------------------------------------------------- 1 | // jest-dom adds custom jest matchers for asserting on DOM nodes. 2 | // allows you to do things like: 3 | // expect(element).toHaveTextContent(/react/i) 4 | // learn more: https://github.com/testing-library/jest-dom 5 | import '@testing-library/jest-dom/extend-expect'; 6 | -------------------------------------------------------------------------------- /AlternativeDatainFinancialServices.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/geoffreylink/Projects/bd4a67fb3f0d6b67c2600dac1725cb964002212a/AlternativeDatainFinancialServices.pdf 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username = 'python-community' , 15 | password = 'Umivipawe179' 16 | ).head()) --------------------------------------------------------------------------------