Lesson 23. Learn the question type from examples

Why this matters

A pupil notices that time cards often contain “қашан” while place cards contain “қайда.” Let the program count such patterns. This small classifier chooses a known question type. It has no club facts and does not generate free text.

Before the program

Use the step 23 examples.json: it contains 13 open training and tuning cards; the test envelope remains sealed. A feature is an observable part of a question. For each word we use both the full word and its first four letters. word[:4] takes positions from the start up to, but not including, position 4. A short word can contribute the same feature twice; this exercise keeps both counts. weights stores how often each feature occurred for each training label. feature not in weights means a record must be created.

Copy examples.json beside the program. Its 13 cards use the format explained in lesson 22.

From the project root, enter the step folder and run the program:

cd course/kazakh-ai/step-23
python3 classifier.py

On Windows, use py classifier.py.

import json

with open("examples.json", encoding="utf-8") as file:
    rows = json.load(file)
weights = {}
for row in rows:
    if row["split"] == "train":
        words = row["text"].lower().replace("?", "").replace(",", "").split()
        for word in words:
            for feature in [word, word[:4]]:
                if feature not in weights:
                    weights[feature] = {"уақыт": 0, "орын": 0}
                weights[feature][row["label"]] += 1
for row in rows:
    if row["split"] == "tune":
        scores = {"уақыт": 0, "орын": 0}
        words = row["text"].lower().replace("?", "").replace(",", "").split()
        for word in words:
            for feature in [word, word[:4]]:
                if feature in weights:
                    scores["уақыт"] += weights[feature]["уақыт"]
                    scores["орын"] += weights[feature]["орын"]
        if scores["уақыт"] > scores["орын"]:
            prediction = "уақыт"
        elif scores["орын"] > scores["уақыт"]:
            prediction = "орын"
        else:
            prediction = "білмеймін"
        print(row["text"], "→", prediction)

Step files.

How the code works

The code learns only from train. weights[feature][row["label"]] += 1 increments the feature count for the human label. For each tune question, familiar counts are added into scores. The higher score wins; a tie yields білмеймін. These are counts, not probabilities or calibrated confidence. For Шахмат қашан және қайда өтеді? Уақыты қандай?, the classifier incorrectly picks time because two time cues outweigh one place cue. The next lesson adds a stop rule.

Support map

train → words and first 4 letters → label counts → tune → compare two scores → type or refusal.

Recall and check

Hide the code and explain why Шахмат нешеде? gets a refusal: нешеде was absent from training and the club name appeared equally often with both labels. Expected predictions for the five tune questions are уақыт, орын, білмеймін, білмеймін, уақыт. Hint: count the қашан, қайда, and уақыты features. Common mistake: treating a high count as proof that the question has only one meaning.