Code¶
Submodules¶
variable_dropout.variable_dropout¶
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class
variable_dropout.variable_dropout.
DropoutType
¶ Method of variable dropout loss representation. One of the following:
RAW - raw value of variable dropout loss,
RATIO - ratio of loss of variable dropout to loss for unperturbed model,
DIFFERENCE - difference between variable dropout loss and unperturbed model loss
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variable_dropout.variable_dropout.
variable_dropout
(estimator: Any, X: pandas.core.frame.DataFrame, y: Iterable[Any], loss_function: Callable[[Iterable[Any], Iterable[Any]], float] = <function mean_squared_error>, dropout_type: variable_dropout.variable_dropout.DropoutType = <DropoutType.RAW: (<function DropoutType.<lambda>>,)>, n_sample: int = 1000, n_iters: int = 100, random_state: Union[int, mtrand.RandomState, NoneType] = None, label=None) → pandas.core.frame.DataFrame¶ Determines importance of variables in the model. Model trained on all variables is used to predict result variable for data with one variable randomly shuffled. The worse the result with a particular variable shuffled is, the more important the variable is.
Parameters: - estimator – any fitted classification or regression model with predict method.
- X – samples.
- y – result variable for samples.
- loss_function – a function taking vectors of real and predicted results. The better the prediction, the smaller the returned value.
- dropout_type – method of loss representation. One of values specified in DropoutType enumeration.
- n_sample – number of samples to predict for. Given number of samples. is randomly chosen from X with replacement.
- n_iters – number of iterations. Final result is mean of the results of iterations.
- random_state – ensures deterministic results if run twice with the same value.
Returns: series of variable dropout loss sorted descending.
variable_dropout.plot_variable_dropout¶
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variable_dropout.plot_variable_dropout.
plot_variable_dropout
(*args, max_vars: Union[int, NoneType] = 10, include_baseline_and_full: bool = True) → None¶ Plots the results of variable_dropout.
Parameters: - args – any number of variable_dropout results.
- max_vars – maximum number of variables to plot per classifier, or None to plot all of them.
- include_baseline_and_full – whether to include _baseline_ and _full_model_ in the plot.