Package: amlr
AMLR- Auto Machine Learning ReportCreate a bealtifull Machine Learning Report withOne-Line-CodeMain Features:Exploratory Data AnalisysDataset ConfigurationShapeDetect number of classes (Bernoulli or binary for while)Automatically Duplicate Observations droppedYou can drop  Duplicate Observations  manually as wellExclude automatically features with highest frequencies (Names, IDs, FW keys etc)Regression AnalysisAutomatic Balance ClassesCorrelation AnalysisDetecting Multicollinearity with VIFResidual AnalisysGrid - Hyperparameter optimizationPartial dependence plot (PDP)Individual Conditional Expectation (ICE)Variable Importance by ModelAML - Partial DependenceEnsemble - (ICE) Individual Condition ExpectationCorrelation Heatmap by ModelModel PerformanceAnalytical Performance ModelingComparative Metrics Table with:Overall ACCKappaOverallRACCSOA1(Landis & Koch)SOA1(Fleiss)SOA1(Altman)SOA1(Cicchetti)SOA1(Cramer)SOA1(Matthews)TNR MacroTPR MacroFPR MacroFNR MacroPPV MacroACC MacroF1 MacroTNR MicroFPR MicroTPR MicroFNR MicroPPV MicroF1 MicroScott PIGwet AC1Bennett SKappa Standard ErrorKappa 1% CIChi-SquaredPhi-SquaredCramer VChi-Squared DF1% CIStandard ErrorResponse EntropyReference EntropyCross EntropyJoint EntropyConditional EntropyKL DivergenceLambda BLambda AKappa UnbiasedOverall RACCUKappa No PrevalenceMutual InformationOverall JHamming LossZero-one LossNIRP-ValueOverall CENOverall MCENOverall MCCRRCBAAUNUAUNPRCIPearson CCSIARIBangdiwala BKrippendorffAlphaThe Best Algorithms TableAutomatically chooses the best model based on the metrics aboveConfusion Matrix for all ModelsFeature Importance for all modelsSave all Models into a Pickle fileHow to Installsudoapt-getinstalldefault-jre
pipinstallamlrHow to usesintax:fromamlrimportamlrasrpimportwebbrowserrp=rp.report()rp.create_report(dataset='data/titanic-passengers.csv',target='Survived',max_runtime_secs=1)webbrowser.open('report/index.html')Another option is to load your own data set withpandasand switch, or parse, to theAMLRreport command, but you cannot use both methods. The code will be:df=pd.read_csv('data/titanic-passengers.csv',sep=';')rp.create_report(data_frame=df,target='Survived',max_runtime_secs=1)Parametersdataset: File to read by AMLRdata_frame: Pandas DataFrametarget: The target columnduplicated: DefaultTrueLooking for duplicated linessep: Default;if file is a csv, you must explicity the column sepatator characterexclude: DefaultTruea list with the columns to exclude to the processmax_runtime_secs: Default1time limit to run deep learnig modelsmax_run_timeWhen building a model, this option specifes the maximum runtime in seconds that you want to allot in order to complete the model. If this maximum runtime is exceeded before the model build is completed, then the model will fail.Specifying max_runtime_secs=1 disables this option for production enviroment, thus allowing for an unlimited amount of runtime. If you just want to do a test, regardless of the results, use 1 seconds or a maximum of 61 seconds.We tested with the following DatasetClassic dataset onTitanicdisasterBernoulli Distribution Target or Binary ClassificationDownload here:TitanicOutputSee the outputhereThis is an example of the test made with the Titanic Dataset;enjoi!