时间序列的蒙特卡罗交叉验证

 from typing import List, Generator
 
 import numpy as np
 
 from sklearn.model_selection._split import _BaseKFold
 from sklearn.utils.validation import indexable, _num_samples
 
 
 class MonteCarloCV(_BaseKFold):
 
     def __init__(self,
                  n_splits: int,
                  train_size: float,
                  test_size: float,
                  gap: int = 0):
         """
         Monte Carlo Cross-Validation
 
         Holdout applied in multiple testing periods
         Testing origin (time-step where testing begins) is randomly chosen according to a monte carlo simulation
 
         :param n_splits: (int) Number of monte carlo repetitions in the procedure
         :param train_size: (float) Train size, in terms of ratio of the total length of the series
         :param test_size: (float) Test size, in terms of ratio of the total length of the series
         :param gap: (int) Number of samples to exclude from the end of each train set before the test set.
         """
 
         self.n_splits = n_splits
         self.n_samples = -1
         self.gap = gap
         self.train_size = train_size
         self.test_size = test_size
         self.train_n_samples = 0
         self.test_n_samples = 0
 
         self.mc_origins = []
 
     def split(self, X, y=None, groups=None) -> Generator:
         """Generate indices to split data into training and test set.
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
             Training data, where `n_samples` is the number of samples
             and `n_features` is the number of features.
         y : array-like of shape (n_samples,)
             Always ignored, exists for compatibility.
         groups : array-like of shape (n_samples,)
             Always ignored, exists for compatibility.
         Yields
         ------
         train : ndarray
             The training set indices for that split.
         test : ndarray
             The testing set indices for that split.
         """
 
         X, y, groups = indexable(X, y, groups)
         self.n_samples = _num_samples(X)
 
         self.train_n_samples = int(self.n_samples * self.train_size) - 1
         self.test_n_samples = int(self.n_samples * self.test_size) - 1
 
         # Make sure we have enough samples for the given split parameters
         if self.n_splits > self.n_samples:
             raise ValueError(
                 f'Cannot have number of folds={self.n_splits} greater'
                 f' than the number of samples={self.n_samples}.'
             )
         if self.train_n_samples - self.gap <= 0:
             raise ValueError(
                 f'The gap={self.gap} is too big for number of training samples'
                 f'={self.train_n_samples} with testing samples={self.test_n_samples} and gap={self.gap}.'
             )
 
         indices = np.arange(self.n_samples)
 
         selection_range = np.arange(self.train_n_samples + 1, self.n_samples - self.test_n_samples - 1)
 
         self.mc_origins = \
             np.random.choice(a=selection_range,
                              size=self.n_splits,
                              replace=True)
 
         for origin in self.mc_origins:
             if self.gap > 0:
                 train_end = origin - self.gap + 1
             else:
                 train_end = origin - self.gap
             train_start = origin - self.train_n_samples - 1
 
             test_end = origin + self.test_n_samples
 
             yield (
                 indices[train_start:train_end],
                 indices[origin:test_end],
             )
 
     def get_origins(self) -> List[int]:
         return self.mc_origins

 from sklearn.datasets import make_regression
 from src.mccv import MonteCarloCV
 
 X, y = make_regression(n_samples=120)
 
 mccv = MonteCarloCV(n_splits=5, 
                     train_size=0.6, 
                     test_size=0.1, 
                     gap=0)
 
 for train_index, test_index in mccv.split(X):
     print("TRAIN:", train_index, "TEST:", test_index)
     X_train, X_test = X[train_index], X[test_index]
     y_train, y_test = y[train_index], y[test_index]

 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import GridSearchCV
 
 model = RandomForestRegressor()
 param_search = {'n_estimators': [10, 100]}
 
 gsearch = GridSearchCV(estimator=model, cv=mccv, param_grid=param_search)
 gsearch.fit(X, y)