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import numpy as np
from . import check_consistent_length, check_matplotlib_support
from ._response import _get_response_values_binary
from .multiclass import type_of_target
from .validation import _check_pos_label_consistency
class _BinaryClassifierCurveDisplayMixin:
"""Mixin class to be used in Displays requiring a binary classifier.
The aim of this class is to centralize some validations regarding the estimator and
the target and gather the response of the estimator.
"""
def _validate_plot_params(self, *, ax=None, name=None):
check_matplotlib_support(f"{self.__class__.__name__}.plot")
import matplotlib.pyplot as plt
if ax is None:
_, ax = plt.subplots()
name = self.estimator_name if name is None else name
return ax, ax.figure, name
@classmethod
def _validate_and_get_response_values(
cls, estimator, X, y, *, response_method="auto", pos_label=None, name=None
):
check_matplotlib_support(f"{cls.__name__}.from_estimator")
name = estimator.__class__.__name__ if name is None else name
y_pred, pos_label = _get_response_values_binary(
estimator,
X,
response_method=response_method,
pos_label=pos_label,
)
return y_pred, pos_label, name
@classmethod
def _validate_from_predictions_params(
cls, y_true, y_pred, *, sample_weight=None, pos_label=None, name=None
):
check_matplotlib_support(f"{cls.__name__}.from_predictions")
if type_of_target(y_true) != "binary":
raise ValueError(
f"The target y is not binary. Got {type_of_target(y_true)} type of"
" target."
)
check_consistent_length(y_true, y_pred, sample_weight)
pos_label = _check_pos_label_consistency(pos_label, y_true)
name = name if name is not None else "Classifier"
return pos_label, name
def _validate_score_name(score_name, scoring, negate_score):
"""Validate the `score_name` parameter.
If `score_name` is provided, we just return it as-is.
If `score_name` is `None`, we use `Score` if `negate_score` is `False` and
`Negative score` otherwise.
If `score_name` is a string or a callable, we infer the name. We replace `_` by
spaces and capitalize the first letter. We remove `neg_` and replace it by
`"Negative"` if `negate_score` is `False` or just remove it otherwise.
"""
if score_name is not None:
return score_name
elif scoring is None:
return "Negative score" if negate_score else "Score"
else:
score_name = scoring.__name__ if callable(scoring) else scoring
if negate_score:
if score_name.startswith("neg_"):
score_name = score_name[4:]
else:
score_name = f"Negative {score_name}"
elif score_name.startswith("neg_"):
score_name = f"Negative {score_name[4:]}"
score_name = score_name.replace("_", " ")
return score_name.capitalize()
def _interval_max_min_ratio(data):
"""Compute the ratio between the largest and smallest inter-point distances.
A value larger than 5 typically indicates that the parameter range would
better be displayed with a log scale while a linear scale would be more
suitable otherwise.
"""
diff = np.diff(np.sort(data))
return diff.max() / diff.min()
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