Source code for tensortrade.features.scalers.min_max_normalizer
# Copyright 2019 The TensorTrade Authors.
#
# 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 pandas as pd
import numpy as np
from gym import Space
from copy import copy
from typing import Union, List, Tuple
from tensortrade.features.feature_transformer import FeatureTransformer
[docs]class MinMaxNormalizer(FeatureTransformer):
"""A transformer for normalizing values within a feature pipeline by the column-wise extrema."""
[docs] def __init__(self, columns: Union[List[str], str, None] = None, feature_min=0, feature_max=1, inplace=True):
"""
Arguments:
columns (optional): A list of column names to normalize.
feature_min (optional): The minimum value in the range to scale to.
feature_max (optional): The maximum value in the range to scale to.
inplace (optional): If `False`, a new column will be added to the output for each input column.
"""
self._feature_min = feature_min
self._feature_max = feature_max
self._inplace = inplace
self.columns = columns
self._history = {}
[docs] def transform_space(self, input_space: Space) -> Space:
if self._inplace:
return input_space
output_space = copy(input_space)
shape_x, *shape_y = input_space.shape
columns = self.columns or range(len(shape_x))
output_space.shape = (shape_x + len(columns), *shape_y)
for _ in columns:
output_space.low = np.append(output_space.low, self._feature_min)
output_space.high = np.append(output_space.high, self._feature_max)
return output_space
[docs] def transform(self, X: pd.DataFrame) -> pd.DataFrame:
if self.columns is None:
self.columns = list(X.columns)
for column in self.columns:
prev_extrema = self._history.get(column, {'min': np.inf, 'max': -np.inf})
curr_min = min(X[column].min(), prev_extrema['min'])
curr_max = max(X[column].max(), prev_extrema['max'])
self._history[column] = {'min': curr_min, 'max': curr_max}
scale = (self._feature_max - self._feature_min) + self._feature_min
normalized_column = (X[column] - curr_min) / (curr_max - curr_min + 1E-9) * scale
if self._inplace:
X[column] = normalized_column
else:
X[f'{column}_minmax_{self._feature_min}_{self._feature_max}'] = normalized_column
return X