# Copyright 2019-2022 The ASReview Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["BaseTrainClassifier"]
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.
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)