如何在不同的数组上使用numpy return\u逆表?

我有一个使用-

lookupTable, data_training_panda_y_indexed = np.unique(data_training_panda_y, return_inverse=True)

lookupTable 在另一个数组上 data_cross_validation_panda_y

data_training_panda_y 是一个字符串列表,可以是这些值-传入、传出、中性。

所以, lookUpTable ndArray ('Incoming' 'Outgoing 'Neutral')

目前为止的代码-

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from numpy import dtype
from _codecs import lookup

#Load data
data = np.genfromtxt('../Data/bezdekIris.csv',delimiter=',',usecols=[0,1,2,3,4],dtype=None)
labels = np.genfromtxt('../Data/bezdekIris.csv',delimiter=',',usecols=[4],dtype=None)
#Shuffle the rows
np.random.shuffle(data)

#Cut the data into 3 parts
data_rows = np.size(data, 0)
training_rows = int(round(0.6*data_rows))
cross_validation_rows = int(round(0.2*data_rows))
testing_rows = data_rows - training_rows - cross_validation_rows

data_training_panda = pd.DataFrame(data[:training_rows])
data_training_panda_X = data_training_panda.iloc[:,0:4]
data_training_panda_y = data_training_panda.iloc[:,4]

data_cross_validation_panda = pd.DataFrame(data[training_rows:training_rows+cross_validation_rows])
data_cross_validation_panda_X = data_cross_validation_panda.iloc[:,0:4]
data_cross_validation_panda_y = data_cross_validation_panda.iloc[:,4]

data_testing_panda = pd.DataFrame(data[training_rows+cross_validation_rows:])
data_testing_panda_X = data_testing_panda.iloc[:,0:4]
data_testing_panda_y = data_testing_panda.iloc[:,4]

#Take out the labels from the 3 parts
lookupTable, data_training_panda_y_indexed = np.unique(data_training_panda_y, return_inverse=True)

#Label the CV and Testing 
data_cross_validation_panda_y_indexed = np.array([])
data_testing_panda_y_indexed = np.array([])

bezdekIris.csv示例数据-

5.1,3.5,1.4,0.2,Incoming
4.9,3.0,1.4,0.2,Outgoing
4.7,3.2,1.3,0.2,Netural