Building a Predictive Text Model Pipeline

This challenge focuses on creating a robust machine learning pipeline in Python to predict the next word in a sequence. This is a fundamental task in Natural Language Processing (NLP) and has wide applications, from autocorrect and predictive text on your phone to more advanced language generation systems. You will implement a pipeline that preprocesses text, engineers features, trains a model, and evaluates its performance.

Problem Description

Your task is to build a complete machine learning pipeline for a next-word prediction task. The pipeline should take raw text as input, process it, train a model to predict the next word given a sequence of preceding words, and then evaluate the model's accuracy.

Key Requirements:

1. Data Preprocessing:
   • Tokenize the input text into individual words.
   • Convert all tokens to lowercase.
   • Remove punctuation.
   • Handle potential empty tokens resulting from punctuation removal.
2. Feature Engineering:
   • Create sequences of input words (n-grams) and their corresponding target next word. For example, if n=3, a sequence like "the quick brown fox" would generate pairs:
     • (["the", "quick"], "brown")
     • (["quick", "brown"], "fox")
   • Convert these word sequences into numerical representations suitable for machine learning models (e.g., using a vocabulary and integer encoding).
3. Model Training:
   • Train a suitable model for sequence prediction. A simple approach like a Naive Bayes classifier or a Logistic Regression on TF-IDF features of the input sequence could be a starting point. More advanced models like Recurrent Neural Networks (RNNs) are also an option but might be beyond the scope of a basic pipeline challenge. For this challenge, we'll focus on a simpler, classical ML approach.
4. Model Evaluation:
   • Split the data into training and testing sets.
   • Evaluate the model's accuracy on the test set. Accuracy can be defined as the percentage of times the model correctly predicts the next word.
5. Pipeline Structure:
   • Encapsulate the entire process within a Python class or a set of functions that can be easily reused.

Expected Behavior:

The pipeline should accept a string of text, process it, train a model, and then be able to take a sequence of words and predict the most likely next word.

Edge Cases to Consider:

• Empty Input Text: What happens if the input text is empty?
• Short Input Text: What if the input text is too short to form the required n-grams?
• New Words (Out-of-Vocabulary): How will the model handle words it hasn't seen during training? (For a simpler model, we can assume all training words will be in the vocabulary, or we can map unknown words to a special token).
• Punctuation Handling: Ensure punctuation is removed consistently.

Examples

Example 1: Basic Text Processing and Prediction

Input Text: "The quick brown fox jumps over the lazy dog. The lazy dog barks."
N-gram size (for input sequence): 2 (meaning we use 2 preceding words to predict the next)

Initial Preprocessing Steps (simplified for illustration): Tokens: ["the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog", "the", "lazy", "dog", "barks"]

Generated Sequences (n=2): (["the", "quick"], "brown") (["quick", "brown"], "fox") (["brown", "fox"], "jumps") (["fox", "jumps"], "over") (["jumps", "over"], "the") (["over", "the"], "lazy") (["the", "lazy"], "dog") (["lazy", "dog"], "the") (["dog", "the"], "lazy") (["the", "lazy"], "dog") (["lazy", "dog"], "barks")

Training Data (after numerical encoding): Imagine the=1, quick=2, brown=3, fox=4, jumps=5, over=6, lazy=7, dog=8, barks=9 ([1, 2], 3) ([2, 3], 4) ... ([7, 8], 9)

Model Training: A classifier is trained on these pairs.

Prediction Example: Input sequence to predict: ["the", "lazy"] Expected Output: "dog" (or possibly "barks" if the model learns the second occurrence of "lazy dog")

Example 2: Handling Punctuation and Case

Input Text: "Hello, world! This is a test. Is this a good test?"
N-gram size: 3

Preprocessing: Lowercase, remove punctuation: "hello world this is a test is this a good test" Tokens: ["hello", "world", "this", "is", "a", "test", "is", "this", "a", "good", "test"]

Generated Sequences (n=3): (["hello", "world", "this"], "is") (["world", "this", "is"], "a") (["this", "is", "a"], "test") ...

Prediction Example: Input sequence to predict: ["is", "this", "a"] Expected Output: "test"

Example 3: Edge Case - Short Text

Input Text: "One two."
N-gram size: 3

Explanation: The input text "One two." will be processed to ["one", "two"]. With an n-gram size of 3, it's impossible to form an input sequence of length 3, and thus no training examples can be generated. The pipeline should handle this gracefully, perhaps by returning an error or indicating that no model could be trained.

Constraints

• The input text will be a single string.
• The N-gram size (for input sequences) will be an integer greater than or equal to 1.
• The model should be trainable and offer predictions within a reasonable time for moderate-sized text datasets (e.g., a few paragraphs to a few pages). Avoid extremely computationally intensive models if performance is a concern.
• The final prediction should be a single word (string).

Notes

• Consider using libraries like nltk for tokenization and punctuation removal, and sklearn for model selection, training, and evaluation.
• For feature representation, you might want to explore techniques like Count Vectorization or TF-IDF on the input sequences.
• The definition of "accuracy" in next-word prediction can be nuanced. For this challenge, a simple accuracy metric (percentage of correctly predicted next words on the test set) will suffice.
• Think about how to handle the case where multiple words have the same probability of being the next word. For simplicity, you can return any one of them.
• The goal is to build a functional pipeline. Feel free to start with simpler models and then consider optimizations or more advanced techniques.