opendataval.model

Prediction models to be trained, predict, and evaluated.

Models

Model is an ABC used to take an existing model and make it compatible with the DataEvaluator and other related objects.

API

Model()	Abstract class of Models.
GradientModel()	Provides access to gradients of a Model
ModelFactory(model_name[, fetcher, device])	Factory to create prediction models from specified presets

Torch Mixins

TorchClassMixin(*args, **kwargs)	Classifier Mixin for Torch Neural Networks.
TorchRegressMixin(*args, **kwargs)	Regressor Mixin for Torch Neural Networks.
TorchPredictMixin(*args, **kwargs)	Torch .predict() method mixin for Torch Neural Networks.
TorchGradMixin(*args, **kwargs)	Gradient Mixin for Torch Neural Networks.

Sci-kit learn wrappers

ClassifierSkLearnWrapper(base_model, ...)	Wrapper for sk-learn classifiers that can have weighted fit methods.
ClassifierUnweightedSkLearnWrapper(...)	Wrapper for sk-learn classifiers that can don't have weighted fit methods.
RegressionSkLearnWrapper(base_model, *args, ...)	Wrapper for sk-learn regression models.

Default Hyperparameters

\[ \begin{align}\begin{aligned}\newcommand\T{\Rule{0pt}{1em}{.3em}}\\\begin{split}\begin{array}{llll} \hline \textbf{Algorithm} & \textbf{Hyperparameter} & \textbf{Default Value} & \textbf{Key word argument} \\ \hline \mbox{Logistic Regression} & \mbox{epochs} & 1 & \mbox{yes} \\ & \mbox{batch size} & 32 & \mbox{yes} \\ & \mbox{learning rate} & 0.01 & \mbox{yes} \\ & \mbox{optimizer} & \mbox{ADAM} & \mbox{no} \\ & \mbox{loss function} & \mbox{Cross Entropy} & \mbox{no} \\ \hline \mbox{MLP Classification} & \mbox{epochs} & 1 & \mbox{yes} \\ & \mbox{batch size} & 32 & \mbox{yes} \\ & \mbox{learning rate} & 0.01 & \mbox{yes} \\ & \mbox{optimizer} & \mbox{ADAM} & \mbox{no} \\ & \mbox{loss function} & \mbox{Cross Entropy} & \mbox{no} \\ \hline \mbox{BERT Classification} & \mbox{epochs} & 1 & \mbox{yes} \\ & \mbox{batch size} & 32 & \mbox{yes} \\ & \mbox{learning rate} & 0.001 & \mbox{yes} \\ & \mbox{optimizer} & \mbox{ADAMW} & \mbox{no} \\ & \mbox{loss function} & \mbox{Cross Entropy} & \mbox{no} \\ \hline \mbox{LeNet-5 Classification} & \mbox{epochs} & 1 & \mbox{yes} \\ & \mbox{batch size} & 32 & \mbox{yes} \\ & \mbox{learning rate} & 0.01 & \mbox{yes} \\ & \mbox{optimizer} & \mbox{ADAM} & \mbox{no} \\ & \mbox{loss function} & \mbox{Cross Entropy} & \mbox{no} \\ \hline \mbox{MLP Regression} & \mbox{epochs} & 1 & \mbox{yes} \\ & \mbox{batch size} & 32 & \mbox{yes} \\ & \mbox{learning rate} & 0.01 & \mbox{yes} \\ & \mbox{optimizer} & \mbox{ADAM} & \mbox{no} \\ & \mbox{loss function} & \mbox{Mean Square Error} & \mbox{no} \\ \hline \end{array}\end{split}\end{aligned}\end{align} \]

Functions

ModelFactory(model_name[, fetcher, device])

Factory to create prediction models from specified presets