# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# 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.
# ============================================================================
from math import ceil
from .base_method import BaseMethod
from ..utils.common import safe_indexing
[docs]
class RatioSplit(BaseMethod):
"""Splitting data into training, validation, and test sets based on provided sizes.
Data is always shuffled before split.
Parameters
----------
data: array-like, required
Raw preference data in the triplet format [(user_id, item_id, rating_value)].
test_size: float, optional, default: 0.2
The proportion of the test set, \
if > 1 then it is treated as the size of the test set.
val_size: float, optional, default: 0.0
The proportion of the validation set, \
if > 1 then it is treated as the size of the validation set.
rating_threshold: float, optional, default: 1.0
Threshold used to binarize rating values into positive or negative feedback for
model evaluation using ranking metrics (rating metrics are not affected).
seed: int, optional, default: None
Random seed for reproducibility.
exclude_unknowns: bool, optional, default: True
If `True`, unknown users and items will be ignored during model evaluation.
verbose: bool, optional, default: False
Output running log.
"""
def __init__(
self,
data,
test_size=0.2,
val_size=0.0,
rating_threshold=1.0,
seed=None,
exclude_unknowns=True,
verbose=False,
**kwargs,
):
super().__init__(
data=data,
rating_threshold=rating_threshold,
seed=seed,
exclude_unknowns=exclude_unknowns,
verbose=verbose,
**kwargs,
)
self.train_size, self.val_size, self.test_size = self.validate_size(
val_size=val_size,
test_size=test_size,
data_size=kwargs.get("data_size", len(data)),
)
self._split()
@staticmethod
def validate_size(val_size, test_size, data_size):
if val_size is None:
val_size = 0.0
elif val_size < 0:
raise ValueError("val_size={} should be greater than zero".format(val_size))
elif val_size >= data_size:
raise ValueError(
f"val_size={val_size} should be smaller than data_size={data_size}"
)
if test_size is None:
test_size = 0.0
elif test_size < 0:
raise ValueError(f"test_size={test_size} should be greater than zero")
elif test_size >= data_size:
raise ValueError(
f"test_size={test_size} should be smaller than data_size={data_size}"
)
if val_size < 1:
val_size = ceil(val_size * data_size)
if test_size < 1:
test_size = ceil(test_size * data_size)
val_test_size = val_size + test_size
if val_test_size >= data_size:
raise ValueError(
f"val_size + test_size ({val_test_size}) should be smaller than data_size={data_size}"
)
train_size = data_size - (val_size + test_size)
return int(train_size), int(val_size), int(test_size)
def _split(self):
data_idx = self.rng.permutation(len(self.data))
train_idx = data_idx[: self.train_size]
test_idx = data_idx[-self.test_size :]
val_idx = data_idx[self.train_size : -self.test_size]
train_data = safe_indexing(self.data, train_idx)
test_data = safe_indexing(self.data, test_idx)
val_data = safe_indexing(self.data, val_idx) if len(val_idx) > 0 else None
self.build(train_data=train_data, test_data=test_data, val_data=val_data)
