"""Training Validation Split class."""
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection._split import BaseCrossValidator
[docs]class TrainingValidationSplit(BaseCrossValidator):
"""Split the training data into training and validation sets.
Args:
test_size (float): What percentage of data points should be included in the validation
set. Defalts to the complement of `train_size` if `train_size` is set, and 0.25 otherwise.
train_size (float): What percentage of data points should be included in the training set.
Defaults to the complement of `test_size`
shuffle (boolean): Whether to shuffle the data before splitting. Defaults to False.
stratify (list): Splits the data in a stratified fashion, using this argument as class labels.
Defaults to None.
random_seed (int): The seed to use for random sampling. Defaults to 0.
Examples:
>>> import numpy as np
>>> import pandas as pd
...
>>> X = pd.DataFrame([i for i in range(10)], columns=["First"])
>>> y = pd.Series([i for i in range(10)])
...
>>> tv_split = TrainingValidationSplit()
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([0, 1, 2, 3, 4, 5, 6])).all()
>>> assert (split_[1] == np.array([7, 8, 9])).all()
...
...
>>> tv_split = TrainingValidationSplit(test_size=0.5)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([0, 1, 2, 3, 4])).all()
>>> assert (split_[1] == np.array([5, 6, 7, 8, 9])).all()
...
...
>>> tv_split = TrainingValidationSplit(shuffle=True)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([9, 1, 6, 7, 3, 0, 5])).all()
>>> assert (split_[1] == np.array([2, 8, 4])).all()
...
...
>>> y = pd.Series([i % 3 for i in range(10)])
>>> tv_split = TrainingValidationSplit(shuffle=True, stratify=y)
>>> split_ = next(tv_split.split(X, y))
>>> assert (split_[0] == np.array([1, 9, 3, 2, 8, 6, 7])).all()
>>> assert (split_[1] == np.array([0, 4, 5])).all()
"""
def __init__(
self,
test_size=None,
train_size=None,
shuffle=False,
stratify=None,
random_seed=0,
):
self.test_size = test_size
self.train_size = train_size
self.shuffle = shuffle
self.stratify = stratify
self.random_seed = random_seed
[docs] @staticmethod
def get_n_splits():
"""Return the number of splits of this object.
Returns:
int: Always returns 1.
"""
return 1
@property
def is_cv(self):
"""Returns whether or not the data splitter is a cross-validation data splitter.
Returns:
bool: If the splitter is a cross-validation data splitter
"""
return False
[docs] def split(self, X, y=None):
"""Divide the data into training and testing sets.
Args:
X (pd.DataFrame): Dataframe of points to split
y (pd.Series): Series of points to split
Returns:
list: Indices to split data into training and test set
"""
train, test = train_test_split(
np.arange(X.shape[0]),
test_size=self.test_size,
train_size=self.train_size,
shuffle=self.shuffle,
stratify=self.stratify,
random_state=self.random_seed,
)
return iter([(train, test)])
