python - Inverse_transform方法(LabelEncoder)

Question

您可以在下面找到我在互联网上找到的用于构建简单神经网络的代码。一切正常。我对 y 标签进行了编码，这些是我得到的预测:

2 0 1 2 1 2 2 0 2 1 0 0 0 1 1 1 1 1 1 1 2 1 2 1 0 1 0 1 0 2

所以现在我需要将其转换回原来的 Iris 类(Iris-Virginica、Setosa、Versicolor)。我需要使用 inverse_transform 方法。你能帮忙吗？

    import pandas as pd
    from sklearn import preprocessing
    from sklearn.model_selection import train_test_split
    from sklearn.neural_network import MLPClassifier
    from sklearn.metrics import classification_report, confusion_matrix 
    
    
    # Location of dataset
    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
    
    # Assign colum names to the dataset
    names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']
    
    # Read dataset to pandas dataframe
    irisdata = pd.read_csv(url, names=names)  
    
    irisdata.head()
    #head_tableau=irisdata.head()
    #print(head_tableau)
    
    # Assign data from first four columns to X variable
    X = irisdata.iloc[:, 0:4]
    
    # Assign data from first fifth columns to y variable
    y = irisdata.select_dtypes(include=[object])  
    
    y.head()
    #afficher_y=y.head()
    #print(afficher_y)
    
    y.Class.unique()
    #affiche=y.Class.unique()
    #print(affiche)
    
    le = preprocessing.LabelEncoder()
    
    y = y.apply(le.fit_transform)  
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)
    
    mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)  
    mlp.fit(X_train, y_train.values.ravel())
    
    predictions = mlp.predict(X_test)
    print(predictions)

Answer 1

你走在正确的道路上:

In [7]: le.inverse_transform(predictions[:5])
Out[7]: 
array(['Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica'], dtype=object)