python.dgbpy.dgbscikit¶
Module Contents¶
Functions¶
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Gets new scaler object |
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Attributes¶
- python.dgbpy.dgbscikit.tot_cpu¶
- python.dgbpy.dgbscikit.n_cpu¶
- python.dgbpy.dgbscikit.hasXGBoost()¶
- python.dgbpy.dgbscikit.platform¶
- python.dgbpy.dgbscikit.regmltypes = [['linear', 'Linear'], ['ensemble', 'Ensemble'], ['neuralnet', 'Neural Network'], ['svm', 'SVM']]¶
- python.dgbpy.dgbscikit.classmltypes = [['logistic', 'Logistic'], ['ensemble', 'Ensemble'], ['neuralnet', 'Neural Network'], ['svm', 'SVM']]¶
- python.dgbpy.dgbscikit.lineartypes = [['oslq', 'Ordinary Least Squares']]¶
- python.dgbpy.dgbscikit.logistictypes = [['log', 'Logistic Regression Classifier']]¶
- python.dgbpy.dgbscikit.clustertypes = [['cluster', 'Clustering']]¶
- python.dgbpy.dgbscikit.ensembletypes = []¶
- python.dgbpy.dgbscikit.nntypes = [['mlp', 'Multi-Layer Perceptron']]¶
- python.dgbpy.dgbscikit.svmtypes = [['svm', 'Support Vector Machine']]¶
- python.dgbpy.dgbscikit.clustermethods = [['kmeans', 'K-Means'], ['meanshift', 'Mean Shift'], ['spec', 'Spectral Clustering']]¶
- python.dgbpy.dgbscikit.solvertypes = [['newton-cg', 'Newton-CG'], ['lbfgs', 'Lbfgs'], ['liblinear', 'Liblinear'], ['sag', 'Sag'],...¶
- python.dgbpy.dgbscikit.linkernel = linear¶
- python.dgbpy.dgbscikit.kerneltypes = [None, ['poly', 'Polynomial'], ['rbf', 'Radial Basis Function'], ['sigmoid', 'Sigmoid']]¶
- python.dgbpy.dgbscikit.savetypes = ['onnx', 'joblib', 'pickle']¶
- python.dgbpy.dgbscikit.defsavetype¶
- python.dgbpy.dgbscikit.xgboostjson = xgboostjson¶
- python.dgbpy.dgbscikit.getMLPlatform()¶
- python.dgbpy.dgbscikit.getUIMLPlatform()¶
- python.dgbpy.dgbscikit.getUiModelTypes(learntype, isclassification, ismultiregression)¶
- python.dgbpy.dgbscikit.getUiLinearTypes()¶
- python.dgbpy.dgbscikit.getUiLogTypes()¶
- python.dgbpy.dgbscikit.getUiClusterTypes()¶
- python.dgbpy.dgbscikit.getUiClusterMethods()¶
- python.dgbpy.dgbscikit.getUiEnsembleTypes(ismultiregression)¶
- python.dgbpy.dgbscikit.getUiNNTypes()¶
- python.dgbpy.dgbscikit.getUiSVMTypes()¶
- python.dgbpy.dgbscikit.getUiSolverTypes()¶
- python.dgbpy.dgbscikit.getUiNNKernelTypes()¶
- python.dgbpy.dgbscikit.getDefaultSolver(uiname=True)¶
- python.dgbpy.dgbscikit.getDefaultNNKernel(isclass, uiname=True)¶
- python.dgbpy.dgbscikit.scikit_dict¶
- python.dgbpy.dgbscikit.defdtregressor¶
- python.dgbpy.dgbscikit.getClusterParsKMeans(methodname, nclust, ninit, maxiter)¶
- python.dgbpy.dgbscikit.getClusterParsMeanShift(methodname, maxiter)¶
- python.dgbpy.dgbscikit.getClusterParsSpectral(methodname, nclust, ninit)¶
- python.dgbpy.dgbscikit.getLinearPars(modelname='Ordinary Least Squares')¶
- python.dgbpy.dgbscikit.getLogPars(modelname='Logistic Regression Classifier', solver=None)¶
- python.dgbpy.dgbscikit.getEnsembleParsXGDT(modelname='XGBoost: (Decision Tree)', maxdep=scikit_dict['ensemblepars']['xgdt']['maxdep'], est=scikit_dict['ensemblepars']['xgdt']['est'], lr=scikit_dict['ensemblepars']['xgdt']['lr'])¶
- python.dgbpy.dgbscikit.getEnsembleParsXGRF(modelname='XGBoost: (Random Forests)', maxdep=scikit_dict['ensemblepars']['xgrf']['maxdep'], est=scikit_dict['ensemblepars']['xgrf']['est'], lr=scikit_dict['ensemblepars']['xgrf']['lr'])¶
- python.dgbpy.dgbscikit.getEnsembleParsRF(modelname='Random Forests', maxdep=scikit_dict['ensemblepars']['rf']['maxdep'], est=scikit_dict['ensemblepars']['rf']['est'])¶
- python.dgbpy.dgbscikit.getEnsembleParsGB(modelname='Gradient Boosting', maxdep=scikit_dict['ensemblepars']['gb']['maxdep'], est=scikit_dict['ensemblepars']['gb']['est'], lr=scikit_dict['ensemblepars']['gb']['lr'])¶
- python.dgbpy.dgbscikit.getEnsembleParsAda(modelname='Adaboost', est=scikit_dict['ensemblepars']['ada']['est'], lr=scikit_dict['ensemblepars']['ada']['lr'])¶
- python.dgbpy.dgbscikit.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'])¶
- python.dgbpy.dgbscikit.getSVMPars(modelname='Support Vector Machine', kernel=scikit_dict['svmpars']['kernel'], degree=scikit_dict['svmpars']['degree'])¶
- python.dgbpy.dgbscikit.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())
- python.dgbpy.dgbscikit.getScaler(x_train, byattrib)¶
- python.dgbpy.dgbscikit.transform(samples, mean, stddev)¶
- python.dgbpy.dgbscikit.transformBack(samples, mean, stddev)¶
- python.dgbpy.dgbscikit.scale(samples, scaler)¶
- python.dgbpy.dgbscikit.unscale(samples, scaler)¶
- python.dgbpy.dgbscikit.getDefaultModel(setup, params=scikit_dict)¶
- python.dgbpy.dgbscikit.train(model, trainingdp)¶
- python.dgbpy.dgbscikit.assessQuality(model, trainingdp)¶
- python.dgbpy.dgbscikit.onnx_from_sklearn(model)¶
- python.dgbpy.dgbscikit.save(model, outfnm, save_type=defsavetype)¶
- python.dgbpy.dgbscikit.load(modelfnm)¶
- python.dgbpy.dgbscikit.apply(model, samples, scaler, isclassification, withpred, withprobs, withconfidence, doprobabilities)¶