dabl
.AnyClassifier¶
-
class
dabl.
AnyClassifier
(n_jobs=None, min_resources='exhaust', verbose=0, type_hints=None, portfolio='baseline')[source]¶ Classifier with automatic model selection.
This model uses successive halving on a portfolio of complex models (HistGradientBoosting, RandomForest, SVC, LogisticRegression) to pick the best model family and hyper-parameters.
AnyClassifier internally applies EasyPreprocessor, so no preprocessing is necessary.
- Parameters
- n_jobsint, default=None
Number of processes to spawn for parallelizing the search.
- min_resources{‘exhaust’, ‘smallest’} or int, default=’exhaust’
The minimum amount of resource that any candidate is allowed to use for a given iteration. Equivalently, this defines the amount of resources r0 that are allocated for each candidate at the first iteration. See the documentation of HalvingGridSearchCV for more information.
- verboseinteger, default=0
Verbosity. Higher means more output.
- type_hintsdict or None
If dict, provide type information for columns. Keys are column names, values are types as provided by detect_types.
- portfoliostr, default=’baseline’
Lets you choose a portfolio. Choose ‘baseline’ for multiple classifiers with default parameters, ‘hgb’ for high-performing HistGradientBoostingClassifiers, ‘svc’ for high-performing support vector classifiers, ‘rf’ for high-performing random forest classifiers, ‘lr’ for high-performing logistic regression classifiers, ‘mixed’ for a portfolio of different high-performing classifiers.
- Attributes
- search_HalvingGridSearchCV instance
Fitted HalvingGridSearchCV instance for inspection.
- est_sklearn estimator
Best estimator (pipeline) found during search.
-
__init__
(n_jobs=None, min_resources='exhaust', verbose=0, type_hints=None, portfolio='baseline')[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, y=None, *, target_col=None)[source]¶ Fit estimator.
Requires to either specify the target as separate 1d array or Series y (in scikit-learn fashion) or as column of the dataframe X specified by target_col. If y is specified, X is assumed not to contain the target.
- Parameters
- XDataFrame
Input features. If target_col is specified, X also includes the target.
- ySeries or numpy array, optional.
Target. You need to specify either y or target_col.
- target_colstring or int, optional
Column name of target if included in X.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
-
score
(X, y, sample_weight=None)[source]¶ 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
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
Mean accuracy of
self.predict(X)
wrt. y.
-
set_params
(**params)[source]¶ 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
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.