sklearndf.wrapper.numpy.ClassifierNPDF#

class sklearndf.wrapper.numpy.ClassifierNPDF(delegate, column_names=None)[source]#

Adapter class that delegates to ClassifierDF and accepts numpy arrays in addition to pandas data frames and series.

Converts all numpy arrays to pandas series or data frames before deferring to the delegate estimator, and passes through pandas objects unchanged.

For use in meta-estimators that internally hand numpy arrays on to sub-estimators.

Bases

SupervisedLearnerNPDF [~T_DelegateClassifierDF], ClassifierDF

Generic types

~T_DelegateClassifierDF(bound= ClassifierDF)

Metaclasses

ABCMeta

Parameters

delegate (EstimatorNPDF) – the sklearndf estimator to invoke after transforming the incoming numpy arrays to pandas data frames or series
column_names (Union[Sequence[str], Callable[[], Sequence[str]], None]) – optional column names to use for the pandas data frame derived from the features numpy array; passed either as a sequence of strings, or as a function that dynamically provides the column names

Method summary

`clone`	Make an unfitted clone of this estimator.
`decision_function`	Compute the decision function for the given inputs.
`fit`	Fit this estimator using the given inputs.
`get_params`	Get the parameters for this estimator.
`predict`	Predict outputs for the given inputs.
`predict_log_proba`	Predict class log-probabilities for the given inputs.
`predict_proba`	Predict class probabilities for the given inputs.
`score`	Score this learner using the given inputs and outputs.
`set_params`	Set the parameters of this estimator.
`to_expression`	Render this object as an expression.

Attribute summary

`COL_FEATURE`	Name assigned to an `Index` or a `Series` with the names of the features used to fit a `EstimatorDF`.
`classes_`	Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems
`feature_names_in_`	The pandas column index with the names of the features used to fit this estimator.
`is_fitted`	`True` if this object is fitted, `False` otherwise.
`n_features_in_`	The number of features used to fit this estimator.
`n_outputs_`	The number of outputs used to fit this estimator.
`native_estimator`	The native estimator underlying this estimator.
`output_names_`	The name(s) of the output(s) this supervised learner was fitted to.
`delegate`	The sklearndf estimator to invoke after transforming the incoming numpy arrays to pandas data frames or series.
`column_names`	Column names to use for the pandas data frame derived from the features numpy array.

Definitions

clone()#

Make an unfitted clone of this estimator.

Return type: ClassifierNPDF
Returns: the unfitted clone

decision_function(X, **predict_params)[source]#

Compute the decision function for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[Series, DataFrame]

Returns

a data frame with observations as rows and classes as columns, and values as the raw values predicted per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

fit(X, y=None, **fit_params)#

Fit this estimator using the given inputs.

Parameters

X (Union[ndarray[Any, dtype[Any]], spmatrix, Series, DataFrame]) – input data frame with observations as rows and features as columns
y (Union[ndarray[Any, dtype[Any]], Series, DataFrame, None]) – an optional series or data frame with one or more outputs
fit_params (Any) – additional keyword parameters as required by specific estimator implementations

Return type

ClassifierNPDF

Returns

self

get_params(deep=True)#

Get the parameters for this estimator.

Parameters: deep (bool) – if True, return the parameters for this estimator, and for any sub-estimators contained in this estimator
Return type: Mapping[str, Any]
Returns: a mapping of parameter names to their values

predict(X, **predict_params)#

Predict outputs for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[Series, DataFrame]

Returns

predictions per observation as a series, or as a data frame in case of multiple outputs

predict_log_proba(X, **predict_params)[source]#

Predict class log-probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as log-probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

predict_proba(X, **predict_params)[source]#

Predict class probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

score(X, y, sample_weight=None)#

Score this learner using the given inputs and outputs.

Parameters

X (Union[ndarray[Any, dtype[Any]], DataFrame]) – data frame with observations as rows and features as columns
y (Union[ndarray[Any, dtype[Any]], Series]) – a series or data frame with the true outputs per observation
sample_weight (Optional[Series]) – optional series of scalar weights, for calculating the resulting score as the weighted mean of the scores for the individual predictions

Return type

float

Returns

the score

set_params(**params)#

Set the parameters of this estimator.

Valid parameter keys can be obtained by calling get_params().

Parameters: params (Any) – the estimator parameters to set
Return type: ClassifierNPDF
Returns: self

to_expression()#

Render this object as an expression.

Return type: Expression
Returns: the expression representing this object

property classes_: Union[numpy.ndarray[Any, numpy.dtype[Any]], List[numpy.ndarray[Any, numpy.dtype[Any]]]]#

Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems

Raises: AttributeError – this classifier is not fitted
Return type: Union[ndarray[Any, dtype[Any]], List[ndarray[Any, dtype[Any]]]]

property feature_names_in_: pandas.Index#

The pandas column index with the names of the features used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: Index

property is_fitted: bool#

True if this object is fitted, False otherwise.

Return type: bool

property n_features_in_: int#

The number of features used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: int

property n_outputs_: int#

The number of outputs used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: int

property native_estimator: sklearn.base.BaseEstimator#

The native estimator underlying this estimator.

This can be another estimator that this estimator delegates to, otherwise the native estimator is self.

Return type: BaseEstimator

property output_names_: List[str]#

The name(s) of the output(s) this supervised learner was fitted to.

Raises: sklearn.exceptions.NotFittedError – this estimator is not fitted
Return type: List[str]

sklearndf.wrapper.numpy

sklearndf.wrapper.numpy.EstimatorNPDF