:py:mod:`python.dgbpy.dgbscikit` ================================ .. py:module:: python.dgbpy.dgbscikit Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: python.dgbpy.dgbscikit.hasXGBoost python.dgbpy.dgbscikit.getMLPlatform python.dgbpy.dgbscikit.getUIMLPlatform python.dgbpy.dgbscikit.getUiModelTypes python.dgbpy.dgbscikit.getUiLinearTypes python.dgbpy.dgbscikit.getUiLogTypes python.dgbpy.dgbscikit.getUiClusterTypes python.dgbpy.dgbscikit.getUiClusterMethods python.dgbpy.dgbscikit.getUiEnsembleTypes python.dgbpy.dgbscikit.getUiNNTypes python.dgbpy.dgbscikit.getUiSVMTypes python.dgbpy.dgbscikit.getUiSolverTypes python.dgbpy.dgbscikit.getUiNNKernelTypes python.dgbpy.dgbscikit.getDefaultSolver python.dgbpy.dgbscikit.getDefaultNNKernel python.dgbpy.dgbscikit.getClusterParsKMeans python.dgbpy.dgbscikit.getClusterParsMeanShift python.dgbpy.dgbscikit.getClusterParsSpectral python.dgbpy.dgbscikit.getLinearPars python.dgbpy.dgbscikit.getLogPars python.dgbpy.dgbscikit.getEnsembleParsXGDT python.dgbpy.dgbscikit.getEnsembleParsXGRF python.dgbpy.dgbscikit.getEnsembleParsRF python.dgbpy.dgbscikit.getEnsembleParsGB python.dgbpy.dgbscikit.getEnsembleParsAda python.dgbpy.dgbscikit.getNNPars python.dgbpy.dgbscikit.getSVMPars python.dgbpy.dgbscikit.getNewScaler python.dgbpy.dgbscikit.getScaler python.dgbpy.dgbscikit.transform python.dgbpy.dgbscikit.transformBack python.dgbpy.dgbscikit.scale python.dgbpy.dgbscikit.unscale python.dgbpy.dgbscikit.getDefaultModel python.dgbpy.dgbscikit.train python.dgbpy.dgbscikit.assessQuality python.dgbpy.dgbscikit.onnx_from_sklearn python.dgbpy.dgbscikit.save python.dgbpy.dgbscikit.load python.dgbpy.dgbscikit.apply Attributes ~~~~~~~~~~ .. autoapisummary:: python.dgbpy.dgbscikit.tot_cpu python.dgbpy.dgbscikit.n_cpu python.dgbpy.dgbscikit.platform python.dgbpy.dgbscikit.regmltypes python.dgbpy.dgbscikit.classmltypes python.dgbpy.dgbscikit.lineartypes python.dgbpy.dgbscikit.logistictypes python.dgbpy.dgbscikit.clustertypes python.dgbpy.dgbscikit.ensembletypes python.dgbpy.dgbscikit.nntypes python.dgbpy.dgbscikit.svmtypes python.dgbpy.dgbscikit.clustermethods python.dgbpy.dgbscikit.solvertypes python.dgbpy.dgbscikit.linkernel python.dgbpy.dgbscikit.kerneltypes python.dgbpy.dgbscikit.savetypes python.dgbpy.dgbscikit.defsavetype python.dgbpy.dgbscikit.xgboostjson python.dgbpy.dgbscikit.scikit_dict python.dgbpy.dgbscikit.defdtregressor .. py:data:: tot_cpu .. py:data:: n_cpu .. py:function:: hasXGBoost() .. py:data:: platform .. py:data:: regmltypes :annotation: = [['linear', 'Linear'], ['ensemble', 'Ensemble'], ['neuralnet', 'Neural Network'], ['svm', 'SVM']] .. py:data:: classmltypes :annotation: = [['logistic', 'Logistic'], ['ensemble', 'Ensemble'], ['neuralnet', 'Neural Network'], ['svm', 'SVM']] .. py:data:: lineartypes :annotation: = [['oslq', 'Ordinary Least Squares']] .. py:data:: logistictypes :annotation: = [['log', 'Logistic Regression Classifier']] .. py:data:: clustertypes :annotation: = [['cluster', 'Clustering']] .. py:data:: ensembletypes :annotation: = [] .. py:data:: nntypes :annotation: = [['mlp', 'Multi-Layer Perceptron']] .. py:data:: svmtypes :annotation: = [['svm', 'Support Vector Machine']] .. py:data:: clustermethods :annotation: = [['kmeans', 'K-Means'], ['meanshift', 'Mean Shift'], ['spec', 'Spectral Clustering']] .. py:data:: solvertypes :annotation: = [['newton-cg', 'Newton-CG'], ['lbfgs', 'Lbfgs'], ['liblinear', 'Liblinear'], ['sag', 'Sag'],... .. py:data:: linkernel :annotation: = linear .. py:data:: kerneltypes :annotation: = [None, ['poly', 'Polynomial'], ['rbf', 'Radial Basis Function'], ['sigmoid', 'Sigmoid']] .. py:data:: savetypes :annotation: = ['onnx', 'joblib', 'pickle'] .. py:data:: defsavetype .. py:data:: xgboostjson :annotation: = xgboostjson .. py:function:: getMLPlatform() .. py:function:: getUIMLPlatform() .. py:function:: getUiModelTypes(learntype, isclassification, ismultiregression) .. py:function:: getUiLinearTypes() .. py:function:: getUiLogTypes() .. py:function:: getUiClusterTypes() .. py:function:: getUiClusterMethods() .. py:function:: getUiEnsembleTypes(ismultiregression) .. py:function:: getUiNNTypes() .. py:function:: getUiSVMTypes() .. py:function:: getUiSolverTypes() .. py:function:: getUiNNKernelTypes() .. py:function:: getDefaultSolver(uiname=True) .. py:function:: getDefaultNNKernel(isclass, uiname=True) .. py:data:: scikit_dict .. py:data:: defdtregressor .. py:function:: getClusterParsKMeans(methodname, nclust, ninit, maxiter) .. py:function:: getClusterParsMeanShift(methodname, maxiter) .. py:function:: getClusterParsSpectral(methodname, nclust, ninit) .. py:function:: getLinearPars(modelname='Ordinary Least Squares') .. py:function:: getLogPars(modelname='Logistic Regression Classifier', solver=None) .. py:function:: getEnsembleParsXGDT(modelname='XGBoost: (Decision Tree)', maxdep=scikit_dict['ensemblepars']['xgdt']['maxdep'], est=scikit_dict['ensemblepars']['xgdt']['est'], lr=scikit_dict['ensemblepars']['xgdt']['lr']) .. py:function:: getEnsembleParsXGRF(modelname='XGBoost: (Random Forests)', maxdep=scikit_dict['ensemblepars']['xgrf']['maxdep'], est=scikit_dict['ensemblepars']['xgrf']['est'], lr=scikit_dict['ensemblepars']['xgrf']['lr']) .. py:function:: getEnsembleParsRF(modelname='Random Forests', maxdep=scikit_dict['ensemblepars']['rf']['maxdep'], est=scikit_dict['ensemblepars']['rf']['est']) .. py:function:: getEnsembleParsGB(modelname='Gradient Boosting', maxdep=scikit_dict['ensemblepars']['gb']['maxdep'], est=scikit_dict['ensemblepars']['gb']['est'], lr=scikit_dict['ensemblepars']['gb']['lr']) .. py:function:: getEnsembleParsAda(modelname='Adaboost', est=scikit_dict['ensemblepars']['ada']['est'], lr=scikit_dict['ensemblepars']['ada']['lr']) .. py:function:: getNNPars(modelname='Multi-Layer Perceptron', maxitr=scikit_dict['nnpars']['maxitr'], lr=scikit_dict['nnpars']['lr'], lay1=scikit_dict['nnpars']['lay1'], lay2=scikit_dict['nnpars']['lay2'], lay3=scikit_dict['nnpars']['lay3'], lay4=scikit_dict['nnpars']['lay4'], lay5=scikit_dict['nnpars']['lay5'], nb=scikit_dict['nnpars']['nb']) .. py:function:: getSVMPars(modelname='Support Vector Machine', kernel=scikit_dict['svmpars']['kernel'], degree=scikit_dict['svmpars']['degree']) .. py:function:: getNewScaler(mean, scale) Gets new scaler object Parameters: * mean (ndarray of shape (n_features,) or None): mean value to be used for scaling * scale ndarray of shape (n_features,) or None: Per feature relative scaling of the data to achieve zero mean and unit variance (fromm sklearn docs) Returns: * object: scaler (an instance of sklearn.preprocessing..StandardScaler()) .. py:function:: getScaler(x_train, byattrib) .. py:function:: transform(samples, mean, stddev) .. py:function:: transformBack(samples, mean, stddev) .. py:function:: scale(samples, scaler) .. py:function:: unscale(samples, scaler) .. py:function:: getDefaultModel(setup, params=scikit_dict) .. py:function:: train(model, trainingdp) .. py:function:: assessQuality(model, trainingdp) .. py:function:: onnx_from_sklearn(model) .. py:function:: save(model, outfnm, save_type=defsavetype) .. py:function:: load(modelfnm) .. py:function:: apply(model, samples, scaler, isclassification, withpred, withprobs, withconfidence, doprobabilities)