dabl.SimpleClassifier

class dabl.SimpleClassifier(refit=True, random_state=None, verbose=1, type_hints=None, shuffle=True)

Automagic anytime classifier.

Parameters

refitboolean, True

Whether to refit the model on the full dataset.

random_staterandom state, int or None (default=None)

Random state or seed.

verboseinteger, default=1

Verbosity (higher is 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.

shuffleboolean, default=True

Whether to shuffle the training set in cross-validation.

Attributes

est_sklearn estimator

Best estimator found.

__init__(refit=True, random_state=None, verbose=1, type_hints=None, shuffle=True)

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

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

Fit classifier.

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 class labels. 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.