Got it, let's break down how to solve this problem exactly as you need it. You want to pull character-level 2-gram TF (term frequency) features from your name samples, convert them into an array for classification, and use TfIdfVectorizer to do the heavy lifting. Here's a step-by-step solution with code examples:

Step 1: Set Up Dependencies and Data #

First, import the necessary libraries and define your text dataset:

from sklearn.feature_extraction.text import TfIdfVectorizer
import numpy as np

# Your original name dataset
text = ['James Jackson Jammy', 'Steve Smith Something', 'Chamak Chalo Chanta', 'Polo Rolo Colo']

Step 2: Configure the TF Vectorizer #

We need to tweak TfIdfVectorizer to work at the character level instead of the default word level, and focus only on TF values (not TF-IDF):

# Configure vectorizer for character 2-grams, TF-only (no IDF or normalization)
tf_vectorizer = TfIdfVectorizer(
    analyzer='char',          # Process text at the character level
    ngram_range=(2, 2),       # Extract only 2-character sequences (ja, am, etc.)
    use_idf=False,            # Disable IDF calculation to get pure TF values
    norm=None                 # Skip normalization so values are raw term counts
)

Step 3: Generate TF Features and Convert to Array #

Fit the vectorizer to your dataset and transform the text into a numerical feature array:

# Fit to the corpus and convert text to TF feature matrix
tf_features_matrix = tf_vectorizer.fit_transform(text)

# Convert sparse matrix to a dense numpy array for classification use
tf_features_array = tf_features_matrix.toarray()

Step 4: Inspect the Results (Optional) #

To verify what features were extracted and their values, you can print the feature names and corresponding TF values:

# Get all unique character 2-gram features
feature_names = tf_vectorizer.get_feature_names_out()
print("Extracted Character 2-gram Features:")
print(feature_names)

# Print TF values for each sample
print("\nTF Feature Array:")
print(tf_features_array)

Bonus: Extract TF Features for Individual Words (Name, Middle Name, Last Name) #

If you need to process each name component separately (instead of the full name string), split the text into individual words first:

# Split all names into individual words (first, middle, last)
all_individual_words = [word for full_name in text for word in full_name.split()]

# Reuse the vectorizer (or create a new one) to process individual words
tf_word_vectorizer = TfIdfVectorizer(analyzer='char', ngram_range=(2,2), use_idf=False, norm=None)
tf_word_features = tf_word_vectorizer.fit_transform(all_individual_words)
tf_word_features_array = tf_word_features.toarray()

# Print results for each word
print("\nTF Features for Individual Words:")
for word, features in zip(all_individual_words, tf_word_features_array):
    print(f"\nWord: {word}")
    # Only show 2-grams that actually appear in the word
    active_features = {feature_names[i]: features[i] for i in range(len(features)) if features[i] > 0}
    print("Character 2-gram TF Values:", active_features)

Save the Feature Array #

To save the array for later use in classification:

# Save full name features
np.save('full_name_tf_features.npy', tf_features_array)

# Save individual word features (if needed)
np.save('individual_word_tf_features.npy', tf_word_features_array)

Key Notes: #

• analyzer='char' is critical here—it tells the vectorizer to look at characters instead of whole words.
• ngram_range=(2,2) restricts us to exactly 2-character sequences. Adjust this if you want longer/shorter character groups (e.g., (1,3) for 1, 2, and 3-character n-grams).
• Disabling use_idf and norm ensures you get raw term counts (TF values) instead of scaled TF-IDF scores.

内容的提问来源于stack exchange，提问作者Raady