SimpleArgmaxAverage¶
- class proglearn.SimpleArgmaxAverage(classes=[])[source]¶
A class for a decider that uses the average vote for classification.
- Parameters
classes : list, default=[]
List of final output classification labels of type obj.
Attributes
transformer_id_to_transformers_
(dict) A dictionary with keys of type obj corresponding to transformer ids and values of type obj corresponding to a transformer. This dictionary maps transformers to a particular transformer id.
transformer_id_to_voters_
(dict) A dictionary with keys of type obj corresponding to transformer ids and values of type obj corresponding to a voter class. This dictionary maps voter classes to a particular transformer id.
Methods Summary
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Function for fitting. |
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Get parameters for this estimator. |
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Predicts the most likely class per input example. |
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Predicts posterior probabilities per input example. |
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Return the mean accuracy on the given test data and labels. |
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Set the parameters of this estimator. |
- SimpleArgmaxAverage.fit(X, y, transformer_id_to_transformers, transformer_id_to_voters)[source]¶
Function for fitting. Stores attributes (classes, transformer_id_to_transformers, and transformer_id_to_voters) of a ClassificationDecider.
- Returns
self : SimpleArgmaxAverage
The object itself.
- Raises
ValueError
When the labels have not been provided and the classes are empty.
- SimpleArgmaxAverage.get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep : bool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params : dict
Parameter names mapped to their values.
- SimpleArgmaxAverage.predict(X, transformer_ids=None)[source]¶
Predicts the most likely class per input example.
Uses the predict_proba method to get the mean vote per id. Returns the class with the highest vote.
- Parameters
X : ndarray
Input data matrix.
transformer_ids : list, default=None
A list with all transformer ids. Defaults to None if no transformer ids are given.
- Returns
y_hat : ndarray of shape [n_samples]
predicted class label per example
- Raises
NotFittedError
When the model is not fitted.
- SimpleArgmaxAverage.predict_proba(X, transformer_ids=None)[source]¶
Predicts posterior probabilities per input example.
Loops through each transformer and bag of transformers. Performs a transformation of the input data with the transformer. Gets a voter to map the transformed input data into a posterior distribution. Gets the mean vote per bagging component and append it to a vote per transformer id. Returns the aggregate average vote.
- Parameters
X : ndarray
Input data matrix.
transformer_ids : list, default=None
A list with specific transformer ids that will be used for inference. Defaults to using all transformers if no transformer ids are given.
- Returns
y_proba_hat : ndarray of shape [n_samples, n_classes]
posteriors per example
- Raises
NotFittedError
When the model is not fitted.
- SimpleArgmaxAverage.score(X, y, sample_weight=None)¶
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters
X : array-like of shape (n_samples, n_features)
Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
- Returns
score : float
Mean accuracy of
self.predict(X)wrt. y.
- SimpleArgmaxAverage.set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it's possible to update each component of a nested object.- Parameters
**params : dict
Estimator parameters.
- Returns
self : estimator instance
Estimator instance.