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