Keras Tokenizer仅对CSV文件首行执行字符级分词问题求助

Hey there! Let's figure out why your Tokenizer is only processing what looks like the first line—it's actually not the first row of your CSV, but the column name of your DataFrame, and here's how to fix it:

The Root Cause #

When you pass test_dataset (a pandas DataFrame with one column) to fit_on_texts(), Keras Tokenizer iterates over the columns of the DataFrame instead of the rows in your host column. That means it's only processing the string 'host' (the column label) instead of all the actual host values in your CSV.

Fixed Code #

import pandas as pd
from keras.preprocessing.text import Tokenizer

# Read the CSV and extract the 'host' column as a Series
fields = ['host']
test_dataset = pd.read_csv('dga_data.csv', usecols=fields)
host_texts = test_dataset['host']  # This is the key—grab the actual column data

# Initialize Tokenizer (char_level=True handles character-level tokenization automatically)
test_dataset_tok = Tokenizer(char_level=True, oov_token=True)

# Fit on the actual host texts, not the DataFrame
test_dataset_tok.fit_on_texts(host_texts)

# Convert all host texts to sequences
test_dataset_sequences = test_dataset_tok.texts_to_sequences(host_texts)

# Verify your results
print(test_dataset_tok)
print(test_dataset_sequences)
print(test_dataset_tok.word_index)

Quick Notes #

• host_texts is a pandas Series containing every value from your host column—this is the iterable of strings that fit_on_texts() expects.
• Since you're using char_level=True, the split parameter is ignored (Tokenizer splits every character automatically), so I removed it to avoid confusion.
• oov_token=True ensures any unseen characters later get a dedicated token, which is perfect for your DGA detection use case.

内容的提问来源于stack exchange，提问作者Rholmes