LSTMのための正規化例（sklearn.preprocessing.MinMaxScaler)

概要

LSTMのためのデータ正規化の例。
sklearn.preprocessing.MinMaxScaler
環境
- Google Colaboratory
- Keras
参考
- https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/

MinMaxScalerインポート、データ定義

from pandas import Series
from sklearn.preprocessing import MinMaxScaler

## define contrived series
data = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
series = Series(data)
print(series)

# 0     10.0
# 1     20.0
# 2     30.0
# 3     40.0
# 4     50.0
# 5     60.0
# 6     70.0
# 7     80.0
# 8     90.0
# 9    100.0
# dtype: float64

正規化の定義 : fit

## prepare data for normalization
values = series.values
values = values.reshape((len(values), 1))

## train the normalization
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(values)
print('Min: %f, Max: %f' % (scaler.data_min_, scaler.data_max_))

# Min: 10.000000, Max: 100.000000

データセットの正規化 : transform

データを0から１の間に変換する

## normalize the dataset and print
normalized = scaler.transform(values)
print(normalized)

# [[0.        ]
#  [0.11111111]
#  [0.22222222]
#  [0.33333333]
#  [0.44444444]
#  [0.55555556]
#  [0.66666667]
#  [0.77777778]
#  [0.88888889]
#  [1.        ]]

正規化したデータセットをもとに戻す：inverse_transform

## inverse transform and print
inversed = scaler.inverse_transform(normalized)
print(inversed)
# [[ 10.]
#  [ 20.]
#  [ 30.]
#  [ 40.]
#  [ 50.]
#  [ 60.]
#  [ 70.]
#  [ 80.]
#  [ 90.]
#  [100.]]