# Copyright 2024 Sergio Nava Muñoz and Mario Graff Guerrero
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pandas as pd
from CompStats.bootstrap import StatisticSamples
[docs]
def CI(samples: np.ndarray, alpha=0.05):
"""Compute the Confidence Interval of a statistic using bootstrap.
:param samples: Bootstrap samples
:type samples: np.ndarray
:param alpha: :math:`[\\frac{\\alpha}{2}, 1 - \\frac{\\alpha}{2}]`.
:type alpha: float
>>> from CompStats import StatisticSamples, CI
>>> from sklearn.metrics import accuracy_score
>>> import numpy as np
>>> labels = np.r_[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]
>>> pred = np.r_[[0, 0, 1, 0, 0, 1, 1, 1, 0, 1]]
>>> bootstrap = StatisticSamples(statistic=accuracy_score)
>>> samples = bootstrap(labels, pred)
>>> CI(samples)
(0.6, 1.0)
"""
if isinstance(samples, StatisticSamples):
return {k: CI(v, alpha=alpha) for k, v in samples.calls.items()}
alpha = alpha / 2
return (np.percentile(samples, alpha * 100, axis=0),
np.percentile(samples, (1 - alpha) * 100, axis=0))
[docs]
def SE(samples: np.ndarray):
"""Compute the Standard Error of a statistic using bootstrap.
>>> from CompStats import StatisticSamples, SE
>>> from sklearn.metrics import accuracy_score
>>> import numpy as np
>>> labels = np.r_[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]
>>> pred = np.r_[[0, 0, 1, 0, 0, 1, 1, 1, 0, 1]]
>>> bootstrap = StatisticSamples(statistic=accuracy_score)
>>> samples = bootstrap(labels, pred)
>>> SE(samples)
"""
if isinstance(samples, StatisticSamples):
return {k: SE(v) for k, v in samples.calls.items()}
return np.std(samples, axis=0)
[docs]
def difference_p_value(samples: np.ndarray, BiB: bool = True):
"""Compute the difference p-value"""
if isinstance(samples, StatisticSamples):
if samples.BiB:
return {k: (v > 2 * np.mean(v)).mean() for k, v in samples.calls.items()}
else:
return {k: (v < 2 * np.mean(v)).mean() for k, v in samples.calls.items()}
else:
if BiB:
return np.mean(samples > 2 * np.mean(samples, axis=0), axis=0)
else:
return np.mean(samples < 2 * np.mean(samples, axis=0), axis=0)
