Scikit-learn API

class falcon.sklapi.FalconTabularClassifier(config: Union[str, Dict] = 'SuperLearner', make_eval_set: bool = False)

Falcon sklearn wrapper to be used for tabular classification tasks. Alternatively, FalconClassifier can be used as an alias.

__init__(config: Union[str, Dict] = 'SuperLearner', make_eval_set: bool = False) → None

Parameters

config (Union[str, Dict], optional) – configuration to be used, by default “SuperLearner”
make_eval_set (bool, optional) – determines if an evaluation set should be created, by default False

fit(X: Union[DataFrame, ndarray[Any, dtype[ScalarType]]], y: Union[DataFrame, ndarray[Any, dtype[ScalarType]]]) → _FalconBaseEstimator

Fits the classifier

Parameters

X (Union[pd.DataFrame, npt.NDArray]) – data
y (Union[pd.DataFrame, npt.NDArray]) – labels

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 – Parameter names mapped to their values.
Return type: dict

save_model(filename: Optional[str]) → None

Saves model in onnx format

Parameters: filename (str, optional) – filename of the saved model

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 – Mean accuracy of self.predict(X) wrt. y.

Return type

float

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.
Return type: estimator instance

class falcon.sklapi.FalconTabularRegressor(config: Union[str, Dict] = 'SuperLearner', make_eval_set: bool = False)

Falcon sklearn wrapper to be used for tabular regression tasks. Alternatively, FalconRegressor can be used as an alias.

__init__(config: Union[str, Dict] = 'SuperLearner', make_eval_set: bool = False) → None

Parameters

config (Union[str, Dict], optional) – configuration to be used, by default “SuperLearner”
make_eval_set (bool, optional) – determines if an evaluation set should be created, by default False

fit(X: Union[DataFrame, ndarray[Any, dtype[ScalarType]]], y: Union[DataFrame, ndarray[Any, dtype[ScalarType]]]) → _FalconBaseEstimator

Fits the regressor

Parameters

X (Union[pd.DataFrame, npt.NDArray]) – data
y (Union[pd.DataFrame, npt.NDArray]) – labels

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 – Parameter names mapped to their values.
Return type: dict

save_model(filename: Optional[str]) → None

Saves model in onnx format

Parameters: filename (str, optional) – filename of the saved model

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.
Return type: estimator instance