Getting ValueError: y contains new labels when using scikit learn's LabelEncoder

you can try solution from "sklearn.LabelEncoder with never seen before values" https://stackoverflow.com/a/48169252/9043549 The thing is to create dictionary with classes, than map column and fill new classes with some "known value"

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
suf="_le"
col="a"
df[col+suf] = le.fit_transform(df[col])
dic = dict(zip(le.classes_, le.transform(le.classes_)))
col='b'
df[col+suf]=df[col].map(dic).fillna(dic["c"]).astype(int) 

If your data are pd.DataFrame I suggest you this simple solution...

I build a custom transformer that integer maps each categorical feature. When fitted you can transform all the data you want. You can compute also simple label encoding or onehot encoding.

If new unseen categories or NaNs are present in new data:

1] For label encoding, 0 is a special token reserved for mapping these cases.

2] For onehot encoding, all the onehot columns are zeros in these cases.

class FeatureTransformer:
    
    def __init__(self, categorical_features):
        self.categorical_features = categorical_features
        
    def fit(self, X):

        if not isinstance(X, pd.DataFrame):
            raise ValueError("Pass a pandas.DataFrame")
            
        if not isinstance(self.categorical_features, list):
            raise ValueError(
                "Pass categorical_features as a list of column names")
                    
        self.encoding = {}
        for c in self.categorical_features:

            _, int_id = X[c].factorize()
            self.encoding[c] = dict(zip(list(int_id), range(1,len(int_id)+1)))
            
        return self

    def transform(self, X, onehot=True):

        if not isinstance(X, pd.DataFrame):
            raise ValueError("Pass a pandas.DataFrame")

        if not hasattr(self, 'encoding'):
            raise AttributeError("FeatureTransformer must be fitted")
            
        df = X.drop(self.categorical_features, axis=1)
        
        if onehot:  # one-hot encoding
            for c in sorted(self.categorical_features):            
                categories = X[c].map(self.encoding[c]).values
                for val in self.encoding[c].values():
                    df["{}_{}".format(c,val)] = (categories == val).astype('int16')
        else:       # label encoding
            for c in sorted(self.categorical_features):
                df[c] = X[c].map(self.encoding[c]).fillna(0)
            
        return df

Usage:

X_train = pd.DataFrame(np.random.randint(10,20, (100,10)))
X_test = pd.DataFrame(np.random.randint(20,30, (100,10)))

ft = FeatureTransformer(categorical_features=[0,1,3])
ft.fit(X_train)

ft.transform(X_test, onehot=False).shape

I'm able to mentally process operations better when dealing in DataFrames. The approach below fits and transforms the LabelEncoder() using the training data, then uses a series of pd.merge joins to map the trained fit/transform encoder values to the test data. When there is a test data value not seen in the training data, the code defaults to the max trained encoder value + 1.

# encode class values as integers
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoder.fit(y_train)
encoded_y_train = encoder.transform(y_train)

# make a dataframe of the unique train values and their corresponding encoded integers
y_map = pd.DataFrame({'y_train': y_train, 'encoded_y_train': encoded_y_train})
y_map = y_map.drop_duplicates()

# map the unique test values to the trained encoded integers
y_test_df = pd.DataFrame({'y_test': y_test})
y_test_unique = y_test_df.drop_duplicates()
y_join = pd.merge(y_test_unique, y_map, 
                  left_on = 'y_test', right_on = 'y_train', 
                  how = 'left')

# if the test category is not found in the training category group, then make the 
# value the maximum value of the training group + 1                  
y_join['encoded_y_test'] = np.where(y_join['encoded_y_train'].isnull(), 
                                    y_map.shape[0] + 1, 
                                    y_join['encoded_y_train']).astype('int')

encoded_y_test = pd.merge(y_test_df, y_join, on = 'y_test', how = 'left') \
    .encoded_y_test.values

I hope this helps someone as it's more recent.

sklearn uses the fit_transform to perform the fit function and transform function directing on label encoding. To solve the problem for Y label throwing error for unseen values, use:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()     
le.fit_transform(Col) 

This solves it!

I found an easy hack around this issue.

Assuming X is the dataframe of features,

1. First, we need to create a list of dicts which would have the key as the iterable starting from 0 and the corresponding value pair would be the categorical column name. We easily accomplish this using enum.

   cat_cols_enum = list(enumerate(X.select_dtypes(include = ['O']).columns))

2. Then the idea is to create a list of label encoders whose dimension is equal to the number of qualitative(categorical) columns present in the dataframe X.

   le = [LabelEncoder() for i in range(len(cat_cols_enum))]

3. Next and the last part would be fitting each of the label encoders present in the list of encoders with the unique values of each of the categorical columns present in the list of dicts respectively.

   for i in cat_cols_enum: le[i[0]].fit(X[i[1]].value_counts().index)

Now, we can transform the labels to their respective encodings using

for i in cat_cols_enum:
X[i[1]] = le[i[0]].transform(X[i[1]])

This error comes when transform function getting any new value for which LabelEncoder try to encode and because in training samples, when you are using fit_transform, that specific value did not present in the corpus. So there is a hack, whether use all the unique values with fit_transform function if you are sure that no new value will come further, or try some different encoding method which suits on the problem statement like HashingEncoder.

Here is the example if no further new values will come in testing

le_id.fit_transform(list(set(df['ID'].unique()).union(set(new_df['ID'].unique())))) 
new_df['ID'] = le_id.transform(new_df.ID)

I used

       le.fit_transform(Col) 

and I was able to resolve the issue. It does fit and transform both. we dont need to worry about unknown values in the test split