Solve the problem that LabelEncoder cannot recognize previously 'seen' tags

This article aims to resolve the "y contains previously unseen labels" error encountered when encoding data using LabelEncoder. This error usually occurs when there are different category tags in the training set and the test set (or validation set). This article will explain the causes of the error in detail and provide the correct encoding method to ensure that the model can handle all categories correctly.

When encoding category data using LabelEncoder, you often encounter a common error: "ValueError: y contains previously unseen labels". This error usually occurs in the following scenario: you fit the LabelEncoder using the training set, and then try to transform the dataset (for example, a test set or a validation set) that contains the category labels that do not appear in the training set.

Analysis of the cause of error

LabelEncoder works by assigning a unique integer to each unique category label. When you use the fit method, LabelEncoder learns all the unique category labels in the training set and creates a mapping from labels to integers. When you use the transform method, LabelEncoder looks for the corresponding integer for each label. If the transform method encounters a label that has not been seen in the fit phase, it cannot find the corresponding integer, thus throwing a "unseen labels" error.

Error code example

The following code demonstrates common practices that cause this error:

 import pandas as pd
from sklearn.preprocessing import LabelEncoder

# Assume that tr_df is the training set DataFrame, and cv_df is the verification set DataFrame
encodable_columns = ['Education', 'EmploymentType', 'MaritalStatus',
                     'HasMortgage', 'HasDependents', 'LoanPurpose', 'HasCoSigner']

le = LabelEncoder()

# Wrong practice: Apply fit_transform to each column of DataFrame
encoded_df = cv_df[encodable_columns].apply(le.fit_transform)
cv_df.drop(columns=encodable_columns, axis=1, inplace=True)
cv_df = pd.concat([tr_df, encoded_df], axis=1) # This line of code may be incorrect. Please check whether you need to connect tr_df and cv_df

encoded_df = tr_df[encodable_columns].apply(le.transform)
tr_df.drop(columns=encodable_columns, axis=1, inplace=True)
tr_df = pd.concat([tr_df, encoded_df], axis=1) # This line of code may be incorrect. Please check whether you need to connect tr_df and cv_df

The error with the above code is that you try to apply le.fit_transform to each column of the DataFrame using the apply method. Doing so will cause LabelEncoder to fit individually on each column instead of using a global view of all category labels in all datasets.

The correct solution

The correct way is to create an independent LabelEncoder instance for each column, first use the training set fit each LabelEncoder, and then use the training set fit good LabelEncoder to transform the training set and validation set.

 import pandas as pd
from sklearn.preprocessing import LabelEncoder

# Assume that tr_df is the training set DataFrame, and cv_df is the verification set DataFrame
encodable_columns = ['Education', 'EmploymentType', 'MaritalStatus',
                     'HasMortgage', 'HasDependents', 'LoanPurpose', 'HasCoSigner']

# Create a dictionary to store the LabelEncoder for each column
label_encoders = {}

# Looping for col in encodable_columns:
    # Create a LabelEncoder instance for the current column label_encoders[col] = LabelEncoder()

    # Fit LabelEncoder using training set
    tr_df[col] = label_encoders[col].fit_transform(tr_df[col])

    # Use the LabelEncoder fitted with the training set to convert the verification set cv_df[col] = label_encoders[col].transform(cv_df[col])


# If necessary, you can delete the original category column # tr_df.drop(columns=encodable_columns, axis=1, inplace=True)
# cv_df.drop(columns=encodable_columns, axis=1, inplace=True)

# Print the converted DataFrame (optional)
print("Training Data:")
print(tr_df.head())
print("\nValidation Data:")
print(cv_df.head())

Code explanation

1. Create a LabelEncoder dictionary: label_encoders = {} Create a dictionary that stores the LabelEncoder instance for each column.
2. Looping to process each column: for col in encodable_columns: Looping through each column that needs to be encoded.
3. Create a LabelEncoder instance: label_encoders[col] = LabelEncoder() Creates a new LabelEncoder instance for the current column and stores it in the label_encoders dictionary.
4. Use the training set fit and transform: tr_df[col] = label_encoders[col].fit_transform(tr_df[col]) First use the training set fit LabelEncoder, and then use the same LabelEncoder to transform the training set.
5. Use the training set fit to convert the validation set to the good LabelEncoder: cv_df[col] = label_encoders[col].transform(cv_df[col]) Use the previous training set to the good LabelEncoder to convert the validation set. Note: Only transform is used here, and fit is no longer used. This is the key to make sure that the validation set uses the same encoding rules as the training set.

Things to note

• Data consistency: Ensure that the category labels of the training set and the test set (or validation set) are semantically consistent. For example, if the "High School" in the training set is encoded as 0, the "High School" in the test set should also be encoded as 0.
• Unknown tag handling: LabelEncoder still throws an error if the test set contains tags that do not appear in the training set. In this case, you need to consider using other encoding methods, such as One-Hot Encoding, or manually adding an "unknown" category to the training set and encoding it as a specific integer.
• Other encoding methods: LabelEncoder is suitable for situations where there is no intrinsic order relationship between category labels. If there is an sequential relationship between category tags (e.g., "Low", "Medium", "High"), then the OrdinalEncoder should be used. For more complex situations, consider One-Hot Encoding.

Summarize

LabelEncoder is a convenient category data encoding tool, but must be used correctly to avoid "unseen labels" errors. The correct way is to create an independent LabelEncoder instance for each column, first use the training set fit each LabelEncoder, and then use the training set fit good LabelEncoder to transform the training set and validation set. At the same time, it is necessary to pay attention to data consistency and consider how to deal with unknown tags.

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