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splitter.py
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from random import shuffle
import numpy as np
import pydgn
import torch
from numpy import random
from pydgn.data.splitter import Splitter, OuterFold, InnerFold
class DebugSplitter(Splitter):
def split(
self,
dataset: pydgn.data.dataset.DatasetInterface,
targets: np.ndarray = None,
):
r"""
Computes the splits and stores them in the list fields
``self.outer_folds`` and ``self.inner_folds``.
IMPORTANT: calling split() sets the seed of numpy, torch, and
random for reproducibility.
Args:
dataset (:class:`~pydgn.data.dataset.DatasetInterface`):
the Dataset object
targets (np.ndarray]): targets used for stratification.
Default is ``None``
"""
np.random.seed(self.seed)
torch.manual_seed(self.seed)
torch.cuda.manual_seed(self.seed)
random.seed(self.seed)
idxs = range(len(dataset))
stratified = self.stratify
outer_idxs = np.array(idxs)
outer_splitter = self._get_splitter(
n_splits=self.n_outer_folds,
stratified=stratified,
eval_ratio=self.test_ratio,
) # This is the true test (outer test)
for train_idxs, test_idxs in outer_splitter.split(
outer_idxs, y=targets if stratified else None
):
assert set(train_idxs) == set(outer_idxs[train_idxs])
assert set(test_idxs) == set(outer_idxs[test_idxs])
inner_fold_splits = []
inner_idxs = outer_idxs[
train_idxs
] # equals train_idxs because outer_idxs was ordered
inner_targets = (
targets[train_idxs] if targets is not None else None
)
inner_splitter = self._get_splitter(
n_splits=self.n_inner_folds,
stratified=stratified,
eval_ratio=self.inner_val_ratio,
) # The inner "test" is, instead, the validation set
for inner_train_idxs, inner_val_idxs in inner_splitter.split(
inner_idxs, y=inner_targets if stratified else None
):
if self.inner_val_ratio == 0.0:
inner_fold = InnerFold(
train_idxs=inner_idxs[inner_train_idxs].tolist(),
val_idxs=inner_idxs[inner_train_idxs[:2]].tolist(),
)
else:
inner_fold = InnerFold(
train_idxs=inner_idxs[inner_train_idxs].tolist(),
val_idxs=inner_idxs[inner_val_idxs].tolist(),
)
inner_fold_splits.append(inner_fold)
# False if empty
assert not bool(
set(inner_train_idxs)
& set(inner_val_idxs)
& set(test_idxs)
)
assert not bool(
set(inner_idxs[inner_train_idxs])
& set(inner_idxs[inner_val_idxs])
& set(test_idxs)
)
self.inner_folds.append(inner_fold_splits)
# Obtain outer val from outer train in an holdout fashion
outer_val_splitter = self._get_splitter(
n_splits=1,
stratified=stratified,
eval_ratio=self.outer_val_ratio,
)
outer_train_idxs, outer_val_idxs = list(
outer_val_splitter.split(inner_idxs, y=inner_targets)
)[0]
# False if empty
assert not bool(
set(outer_train_idxs) & set(outer_val_idxs) & set(test_idxs)
)
assert not bool(
set(outer_train_idxs) & set(outer_val_idxs) & set(test_idxs)
)
assert not bool(
set(inner_idxs[outer_train_idxs])
& set(inner_idxs[outer_val_idxs])
& set(test_idxs)
)
np.random.shuffle(outer_train_idxs)
np.random.shuffle(outer_val_idxs)
np.random.shuffle(test_idxs)
if self.outer_val_ratio == 0.0:
outer_fold = OuterFold(
train_idxs=inner_idxs[outer_train_idxs].tolist(),
val_idxs=inner_idxs[outer_train_idxs[:2]].tolist(),
test_idxs=outer_idxs[test_idxs].tolist(),
)
else:
outer_fold = OuterFold(
train_idxs=inner_idxs[outer_train_idxs].tolist(),
val_idxs=inner_idxs[outer_val_idxs].tolist(),
test_idxs=outer_idxs[test_idxs].tolist(),
)
self.outer_folds.append(outer_fold)
class GraphPropPredSplitter(Splitter):
def split(
self,
dataset: pydgn.data.dataset.DatasetInterface,
targets: np.ndarray = None,
):
r"""
Computes the splits and stores them in the list fields
``self.outer_folds`` and ``self.inner_folds``.
IMPORTANT: calling split() sets the seed of numpy, torch, and
random for reproducibility.
Args:
dataset (:class:`~pydgn.data.dataset.DatasetInterface`):
the Dataset object
targets (np.ndarray]): targets used for stratification.
Default is ``None``
"""
assert len(dataset) == 5120+640+1280
assert self.n_inner_folds == 1
self.n_outer_folds == 1
train_idxs = torch.arange(0,5120)
val_idxs = torch.arange(5120, 5120+640)
test_idxs = torch.arange(5120+640, 5120+640+1280)
inner_fold_splits = []
inner_fold = InnerFold(
train_idxs=train_idxs.tolist(),
val_idxs=val_idxs.tolist(),
)
inner_fold_splits.append(inner_fold)
self.inner_folds.append(inner_fold_splits)
outer_fold = OuterFold(
train_idxs=train_idxs.tolist(),
val_idxs=val_idxs.tolist(),
test_idxs=test_idxs.tolist(),
)
self.outer_folds.append(outer_fold)
class PeptidesSplitter(Splitter):
# PyG version
def split(
self,
dataset: pydgn.data.dataset.DatasetInterface,
targets: np.ndarray = None,
):
r"""
Computes the splits and stores them in the list fields
``self.outer_folds`` and ``self.inner_folds``.
IMPORTANT: calling split() sets the seed of numpy, torch, and
random for reproducibility.
Args:
dataset (:class:`~pydgn.data.dataset.DatasetInterface`):
the Dataset object
targets (np.ndarray]): targets used for stratification.
Default is ``None``
"""
np.random.seed(self.seed)
torch.manual_seed(self.seed)
torch.cuda.manual_seed(self.seed)
random.seed(self.seed)
idxs = list(range(len(dataset)))
assert len(dataset) == 10873+2331+2331
assert self.n_inner_folds == 1
self.n_outer_folds == 1
train_idxs = idxs[:10873]
val_idxs = idxs[10873:10873+2331]
test_idxs = idxs[10873+2331:10873+2331+2331]
assert len(train_idxs) == 10873
assert len(val_idxs) == 2331
assert len(test_idxs) == 2331
assert len(train_idxs) + len(val_idxs) + len(test_idxs) == len(dataset)
assert set(train_idxs).isdisjoint(set(val_idxs)), "Sets overlap"
assert set(train_idxs).isdisjoint(set(test_idxs)), "Sets overlap"
assert set(val_idxs).isdisjoint(set(test_idxs)), "Sets overlap"
inner_fold_splits = []
inner_fold = InnerFold(
train_idxs=train_idxs,
val_idxs=val_idxs,
)
inner_fold_splits.append(inner_fold)
self.inner_folds.append(inner_fold_splits)
outer_fold = OuterFold(
train_idxs=train_idxs,
val_idxs=val_idxs,
test_idxs=test_idxs,
)
self.outer_folds.append(outer_fold)