dabl.AnyClassifier

class dabl.AnyClassifier(n_jobs=None, min_resources='exhaust', verbose=0, type_hints=None, portfolio='baseline')

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')

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None, *, target_col=None)

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)

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)

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)

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.