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_zscore.py
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_zscore.py
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# Copyright 2019 The FastEstimator 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 typing import TypeVar
import numpy as np
import tensorflow as tf
import torch
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)
def zscore(data: Tensor, epsilon: float = 1e-7) -> Tensor:
"""Apply Zscore processing to a given tensor or array.
This method can be used with Numpy data:
```python
n = np.array([[0,1],[2,3]])
b = fe.backend.zscore(n) # [[-1.34164079, -0.4472136 ],[0.4472136 , 1.34164079]]
```
This method can be used with TensorFlow tensors:
```python
t = tf.constant([[0,1],[2,3]])
b = fe.backend.zscore(t) # [[-1.34164079, -0.4472136 ],[0.4472136 , 1.34164079]]
```
This method can be used with PyTorch tensors:
```python
p = torch.tensor([[0,1],[2,3]])
b = fe.backend.zscore(p) # [[-1.34164079, -0.4472136 ],[0.4472136 , 1.34164079]]
```
Args:
data: The input tensor or array.
epsilon: A numerical stability constant.
Returns:
Data after subtracting mean and divided by standard deviation.
Raises:
ValueError: If `tensor` is an unacceptable data type.
"""
if tf.is_tensor(data):
data = tf.cast(data, tf.float32)
mean = tf.reduce_mean(data)
std = tf.keras.backend.std(data)
return (data - mean) / tf.maximum(std, epsilon)
elif isinstance(data, torch.Tensor):
data = data.type(torch.float32)
mean = torch.mean(data)
std = torch.std(data, unbiased=False)
return (data - mean) / torch.max(std, torch.tensor(epsilon))
elif isinstance(data, np.ndarray):
mean = np.mean(data)
std = np.std(data)
return (data - mean) / max(std, epsilon)
else:
raise ValueError("Unrecognized data type {}".format(type(data)))