逆变换预测结果

问题描述

我有一个包含三列的训练数据CSV(两列用于数据，第三列用于目标)，并且我成功地预测了测试CSV的目标列.问题是我需要将结果逆变换回字符串以进行进一步分析.下面是代码和错误.

I have a training data CSV with three columns (two for data and a third for targets) and I successfully predicted the target column for my test CSV. The problem is I need to inverse transform the results back to strings for further analysis. Below is the code and error.

from sklearn import datasets
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelEncoder

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import defaultdict

df_train = pd.read_csv('/Users/justinchristensen/Documents/Python_Education/SKLearn/Path_Training_Data.csv')
df_test = pd.read_csv('/Users/justinchristensen/Documents/Python_Education/SKLearn/Path_Test_Data.csv')

#Separate columns in training data set
x_train = df_train.iloc[:,:-1]
y_train = df_train.iloc[:,-1:]

#Separate columns in test data set
x_test = df_test.iloc[:,:-1]

#Initiate classifier
clf = svm.SVC(gamma=0.001, C=100)
le = LabelEncoder()

#Transform strings into integers
x_train_encoded = x_train.apply(LabelEncoder().fit_transform)
y_train_encoded = y_train.apply(LabelEncoder().fit_transform)
x_test_encoded = x_test.apply(LabelEncoder().fit_transform)

#Fit the model into the classifier
clf.fit(x_train_encoded,y_train_encoded)

#Predict test values
y_pred = clf.predict(x_test_encoded)

错误

NotFittedError
Traceback (most recent call last)
<ipython-input-38-09840b0071d5> in <module>()
      1
----> 2 y_pred_inverse = le.inverse_transform(y_pred)

~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/label.py in inverse_transform(self, y)
    146         y : numpy array of shape [n_samples]
    147         """
--> 148         check_is_fitted(self, 'classes_')
    149
    150         diff = np.setdiff1d(y, np.arange(len(self.classes_)))

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_is_fitted(estimator, attributes, msg, all_or_any)
    766
    767     if not all_or_any([hasattr(estimator, attr) for attr in attributes]):
--> 768         raise NotFittedError(msg % {'name': type(estimator).__name__})
    769
    770

NotFittedError: This LabelEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

推荐答案

您需要使用与转换目标时使用的标签对象相同的标签对象才能将其取回.每次使用Label Enocder时，都会实例化一个新对象.使用相同的对象.

You need to use the same label object which you used for transforming your targets to get them back. Each time you use the Label Enocder you instantiated a new object. Use the same object.

更改以下行

y_train_encoded = y_train.apply(le().fit_transform)
y_test_encoded = y_test.apply(le().fit_transform)

然后使用相同的对象反转转换.您可以在文档中的第一个示例中查看供参考.

Then use the same object to reverse the transformation. You can check the first example here in the documentation for reference as well.

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