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from asreview.models.base import BaseModel
[docs]class BaseTrainClassifier(BaseModel):
"""
Base model, abstract class to be implemented by derived ones.
All the non-abstract methods are okay if they are not implemented.
All functions dealing with hyperparameters can be ignore if you don't
use hyperopt for hyperparameter tuning.
There is a distinction between model parameters, which are needed during
model creation and fit parameters, which are used during the fitting
process. Fit parameters can be distinct from fit_kwargs (which are passed
to the fit function).
"""
name = "base-train"
def __init__(self):
self._model = None
[docs] def fit(self, X, y):
"""Fit the model to the data.
Arguments
---------
X: numpy.ndarray
Feature matrix to fit.
y: numpy.ndarray
Labels for supervised learning.
"""
return self._model.fit(X, y)
[docs] def predict_proba(self, X):
"""Get the inclusion probability for each sample.
Arguments
---------
X: numpy.ndarray
Feature matrix to predict.
Returns
-------
numpy.ndarray
Array with the probabilities for each class, with two
columns (class 0, and class 1) and the number of samples rows.
"""
return self._model.predict_proba(X)
[docs] def full_hyper_space(self):
"""Get a hyperparameter space to use with hyperopt.
Returns
-------
dict, dict
Parameter space. Parameter choices; in case of hyperparameters
with a list of choices, store the choices there.
"""
return {}, {}