All functions
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ConditionalDensityEvaluator
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ConditionalDensityEvaluator |
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ConstrainedGlm.fit()
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Constrained logistic regression
In this function we create a regression for which the predicted
probabilities are contstrained. That is, they can not be less than a minimum
of delta, or a maxiumum of 1 - delta. |
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ConstrainedGlm.fit_new_glm()
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Fit a new GLM
In this function we create a new instance a (constrained)
glm fit. |
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ConstrainedGlm.predict()
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Predict using a constrained glm
In this function we predict usng an instance of a (constrained)
glm fit. |
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ConstrainedGlm.update_glm()
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Update Constrained logistic regression
In this function we update a previously trained instance of a (constrained)
glm fit. |
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CrossValidationRiskCalculator
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CrossValidationRiskCalculator |
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Data.Base
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Data.Base |
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Data.Static
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Data.Static |
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Data.Stream
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Data.Stream |
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Data.Stream.Simulator
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Data.Stream.Simulator |
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DataCache
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DataCache |
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DataSplitter
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DataSplitter |
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DensityEstimation
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DensityEstimation |
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DensityEstimation.are_all_estimators_online()
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Static method to check whether all included estimators are in fact specified
as being online estimators. If this is not the case, we should keep a cache
of all data somewhere. |
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Evaluation.accuracy()
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Evaluation.Accuracy |
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Evaluation.get_evaluation_function()
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Evaluation |
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Evaluation.log_likelihood_loss()
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Evaluation.log_likelihood_loss |
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Evaluation.log_loss()
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Evaluation.log_loss |
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Evaluation.mean_squared_error()
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Evaluation.mean_squared_error |
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Evaluation.mse_loss()
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Evaluation.mse_loss |
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Evaluation.root_mean_squared_error()
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Evaluation.root_mean_squared_error |
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H2O.Available()
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H2O.Available |
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H2O.Initializer()
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H2O.Initializer |
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H2O.Interactor
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H2O.Interactor |
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InterventionEffectCalculator
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InterventionEffectCalculator |
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InterventionParser.first_intervention()
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InterventionParser.first_intervention |
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InterventionParser.generate_intervention()
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InterventionParser.generate_intervention |
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InterventionParser.is_current_node_treatment()
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InterventionParser.is_current_node_treatment |
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InterventionParser.parse_intervention()
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InterventionParser.parse_intervention |
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InterventionParser.valid_intervention()
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InterventionParser.valid_intervention |
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LibraryFactory
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LibraryFactory |
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ML.Base
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ML.Base |
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ML.GLMnet
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ML.GLMnet |
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ML.H2O
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ML.H2O |
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ML.H2O.gbm
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ML.H2O.gbm |
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ML.H2O.glm
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ML.H2O.glm |
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ML.H2O.randomForest
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ML.H2O.randomForest |
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ML.Local.lm
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ML.Local.lm |
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ML.NeuralNet
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ML.NeuralNet |
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ML.SVM
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ML.SVM |
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ML.SpeedGLMSGD
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ML.SpeedGLMSGD |
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ML.XGBoost
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ML.XGBoost |
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ML.randomForest
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ML.randomForest |
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OneStepEstimator
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OneStepEstimator |
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OnlineSuperLearner.Predict
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OnlineSuperLearner.Predict |
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OnlineSuperLearner
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OnlineSuperLearner |
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OnlineSuperLearner.SampleIteratively
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OnlineSuperLearner.SampleIteratively |
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OutputPlotGenerator.create_convergence_plot()
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OutputPlotGenerator.create_convergence_plot |
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OutputPlotGenerator.create_density_plot()
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OutputPlotGenerator.create_density_plot |
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OutputPlotGenerator.create_risk_plot()
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OutputPlotGenerator.create_risk_plot |
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OutputPlotGenerator.create_training_curve()
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OutputPlotGenerator.create_training_curve |
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OutputPlotGenerator.export_key_value()
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OutputPlotGenerator.export_key_value |
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OutputPlotGenerator.get_colors()
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OutputPlotGenerator.get_colors |
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OutputPlotGenerator.get_simple_colors()
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OutputPlotGenerator.get_simple_colors |
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OutputPlotGenerator.store_oos_osl_difference()
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OutputPlotGenerator.store_oos_osl_difference |
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PreProcessor
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PreProcessor |
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PreProcessor.generate_bounds()
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Static function |
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RelevantVariable
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RelevantVariable |
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RelevantVariable.find_ordering()
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Algorithm to find a possible ordering of the functions.
The worst case run time of this algorithm is pretty bad, and can it
probably done more efficiently |
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SMG.Base
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SMG.Base |
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SMG.Lag
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SMG.Lag |
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SMG.Latest.Entry
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SMG.Latest.Entry |
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SMG.Mean
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SMG.Mean |
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SMG.Mock
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SMG.Mock |
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SMG.Transformation
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SMG.Transformation |
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SMGFactory
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SMGFactory |
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Simulator.GAD
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Simulator.GAD |
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Simulator.RunningExample
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Simulator.RunningExample |
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Simulator.Simple
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SimpleSimulator |
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Simulator.Slow
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Simulator.Slow |
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SummaryMeasureGenerator
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SummaryMeasureGenerator |
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WCC.CG
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WCC.CG
Constrained descent optimizer |
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WCC.NMBFGS
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WCC.NMBFGS |
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WCC.NNLS
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WCC.NNLS |
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WCC.SGD
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WCC.SGD
Stochastic gradient descent optimizer, based on the R sgd package |
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WCC.SGD.Simplex
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WCC.SGD.Simplex
This is the SGD computer used in the Online SL package by David Benkeser;
https://github.com/benkeser/onlinesl/. It performs a gradient descent update
(or a number of gradient descent updates) using the estimates of the
separate estimators. |
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WeightedCombinationComputer
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WeightedCombinationComputer |
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fit.OnlineSuperLearner()
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fit.OnlineSuperLearner |
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generalImports
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General packages used by all of the other classes |
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get_file_location()
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Returns the file location for both windows and linux |
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get_looping_function()
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Returns the looping function to use |
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is.a()
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Checks whether an object is an instance of the provided class |
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predict(<OnlineSuperLearner>)
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predict.OnlineSuperLearner |
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sampledata(<OnlineSuperLearner>)
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sampledata.OnlineSuperLearner |
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summary.OnlineSuperLearner()
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summary.OnlineSuperLearner |