# Copyright 2019 IBM Corporation
#
# 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
try:
from torch.utils.data import Dataset
except ModuleNotFoundError as exc:
raise ModuleNotFoundError(
"""Your Python environment does not have torch installed. You can install it with
pip install torch
or with
pip install 'lale[full]'"""
) from exc
[docs]class NumpyTorchDataset(Dataset):
"""Pytorch Dataset subclass that takes a numpy array and an optional label array."""
def __init__(self, X, y=None):
"""X and y are the dataset and labels respectively.
Parameters
----------
X : numpy array
Two dimensional dataset of input features.
y : numpy array
Labels
"""
self.X = X
self.y = y
def __len__(self):
return self.X.shape[0]
def __getitem__(self, idx):
if self.y is not None:
return self.X[idx], self.y[idx]
else:
return self.X[idx]
[docs] def get_data(self):
if self.y is None:
return self.X
else:
return self.X, self.y
[docs]def numpy_collate_fn(batch):
return_X = None
return_y = None
for item in batch:
if isinstance(item, tuple):
if return_X is None:
return_X = item[0]
else:
return_X = np.vstack((return_X, item[0]))
if return_y is None:
return_y = item[1]
else:
return_y = np.vstack((return_y, item[1])) # type: ignore
else:
if return_X is None:
return_X = item
else:
return_X = np.vstack((return_X, item)) # type: ignore
if return_y is not None:
if len(return_y.shape) > 1 and return_y.shape[1] == 1:
return_y = np.reshape(return_y, (len(return_y),))
return return_X, return_y
else:
return return_X