Add dataset_generator.py

dataset_generator.py

@@ -0,0 +1,512 @@
+"""
+Dataset Generation Pipeline for TinyBert-CNN Intent Classifier.
+Generates (student_input, session_context, label) triples for 5-class classification.
+"""
+
+import random
+import pandas as pd
+import os
+import re
+
+# ─────────────────────────────────────────────────────────────────────
+# CONSTANTS & METADATA
+# ─────────────────────────────────────────────────────────────────────
+
+PYTHON_TOPICS = [
+    "Variables and Data Types",
+    "Strings and Formatting",
+    "Arithmetic Operators",
+    "Boolean Logic",
+    "If/Else Conditionals",
+    "For Loops",
+    "While Loops",
+    "Lists and Tuples",
+    "Dictionaries",
+    "Sets",
+    "Functions and Scope",
+    "Lambda Functions",
+    "Error Handling (Try/Except)",
+    "Classes and OOP",
+    "File Handling"
+]
+
+LABEL_MAP = {
+    'On-Topic Question': 0,
+    'Off-Topic Question': 1,
+    'Emotional-State': 2,
+    'Pace-Related': 3,
+    'Repeat/clarification': 4
+}
+
+EMOTIONS = ["neutral", "engaged", "focused", "frustrated", "confused", "bored", "tired", "anxious", "excited", "overwhelmed"]
+PACES = ["normal", "fast", "slow", "rushed", "dragging", "moderate", "steady"]
+
+# ─────────────────────────────────────────────────────────────────────
+# CONTEXT GENERATION (Compact key-value format)
+# ─────────────────────────────────────────────────────────────────────
+
+def generate_session_context(current_topic_idx):
+    """Generates a compact session context string."""
+    current_topic = PYTHON_TOPICS[current_topic_idx]
+
+    if current_topic_idx > 0:
+        prev_count = min(3, current_topic_idx)
+        prev_topics = PYTHON_TOPICS[current_topic_idx - prev_count : current_topic_idx]
+    else:
+        prev_topics = []
+
+    # Ability scores for previous topics
+    abilities = []
+    for pt in prev_topics:
+        short_name = pt.split("(")[0].strip().replace(" and ", "&")
+        score = random.randint(30, 100)
+        abilities.append(f"{short_name}:{score}%")
+
+    ability_str = ",".join(abilities) if abilities else "N/A"
+    prev_str = ",".join([t.split("(")[0].strip() for t in prev_topics]) if prev_topics else "None"
+    emotion = random.choice(EMOTIONS)
+    pace = random.choice(PACES)
+    slide = random.randint(5, 60)
+
+    context = (
+        f"topic:{current_topic} | "
+        f"prev:{prev_str} | "
+        f"ability:{ability_str} | "
+        f"emotion:{emotion} | "
+        f"pace:{pace} | "
+        f"slides:{slide-1},{slide},{slide+1}"
+    )
+    return context, current_topic, prev_topics
+
+
+# ─────────────────────────────────────────────────────────────────────
+# EXPANDED TEMPLATE BANKS (40+ per class)
+# ─────────────────────────────────────────────────────────────────────
+
+ON_TOPIC_TEMPLATES = [
+    # Direct questions
+    "How do I use {topic} in my code?",
+    "Can you explain {topic} again?",
+    "What are the best practices for {topic}?",
+    "Can you show me an example of {topic}?",
+    "Why is {topic} giving me a syntax error?",
+    "Is there a different way to write {topic}?",
+    "I don't get the part about {topic}.",
+    "Can we do another exercise for {topic}?",
+    "What happens if I forget to close the bracket in {topic}?",
+    "How is {topic} different from the previous topic?",
+    # Conceptual questions
+    "Why do we need {topic}?",
+    "When should I use {topic} vs the other approach?",
+    "What's the point of {topic}?",
+    "Is {topic} used a lot in real projects?",
+    "Can you give me a real-world example of {topic}?",
+    "Does {topic} work the same way in other languages?",
+    # Problem-solving
+    "I'm stuck on this challenge about {topic}.",
+    "My code for {topic} isn't working, can you help?",
+    "I keep getting an error with {topic}.",
+    "Why does my {topic} code print the wrong output?",
+    "What am I doing wrong with {topic}?",
+    "Can you debug this {topic} example with me?",
+    # Clarification on current material
+    "What did you mean when you said {topic} works like that?",
+    "Can you go deeper into {topic}?",
+    "Is there more to know about {topic}?",
+    "How does {topic} connect to what we learned before?",
+    "What's the difference between the two approaches you showed for {topic}?",
+    "Can you break down {topic} step by step?",
+    # Practical application
+    "How would I use {topic} in a project?",
+    "Can I combine {topic} with what we learned earlier?",
+    "Is {topic} something I'll use every day?",
+    "Where does {topic} fit in a larger program?",
+    "Can you show me a more advanced use of {topic}?",
+    # Short/informal
+    "Tell me more about {topic}",
+    "What's {topic} again?",
+    "{topic} is confusing",
+    "Help me with {topic}",
+    "I need help understanding {topic}",
+    "So how does {topic} actually work?",
+    "Wait, explain {topic} one more time",
+]
+
+# Context-aware on-topic templates (reference ability scores, prev topics)
+ON_TOPIC_CONTEXT_TEMPLATES = [
+    "You said I scored low on {prev_topic}, does that affect how I should approach {topic}?",
+    "Since I did well on {prev_topic}, is {topic} going to be similar?",
+    "How does {prev_topic} relate to {topic}?",
+    "I understood {prev_topic} but {topic} feels completely different, why?",
+    "Can we review {prev_topic} briefly before diving deeper into {topic}?",
+    "My score on {prev_topic} was not great, will I need it for {topic}?",
+]
+
+OFF_TOPIC_GENERAL = [
+    "What's the weather like today?",
+    "How do I cook pasta?",
+    "Who won the soccer match last night?",
+    "Can you recommend a good movie to watch?",
+    "What is the capital of France?",
+    "How much does a new car cost?",
+    "Do you like listening to music?",
+    "Tell me a joke.",
+    "I'm feeling hungry, should I order pizza?",
+    "What is your favorite color?",
+    "What time is it?",
+    "Do you know any good restaurants nearby?",
+    "Who is the president of the United States?",
+    "What's the best phone to buy right now?",
+    "Can you help me with my math homework?",
+    "How tall is the Eiffel Tower?",
+    "What should I eat for dinner?",
+    "Do you watch Netflix?",
+    "What's the meaning of life?",
+    "How do I fix my car?",
+]
+
+OFF_TOPIC_FUTURE_TOPIC_TEMPLATES = [
+    "Are we going to learn about {topic} soon?",
+    "What is {topic} exactly?",
+    "I heard about {topic}, can you explain it to me?",
+    "How does {topic} work in Python?",
+    "Can we skip ahead to {topic}?",
+    "Is {topic} hard to learn?",
+    "I saw someone using {topic}, what does it do?",
+    "Do we need to know about {topic}?",
+    "When will we cover {topic}?",
+    "My friend told me {topic} is important, is that true?",
+    "Will {topic} be on the exam?",
+    "Can you give me a sneak peek of {topic}?",
+    "I already know a bit about {topic}, can we jump to it?",
+    "How long until we get to {topic}?",
+    "Is {topic} related to what we are doing now?",
+]
+
+EMOTIONAL_TEMPLATES = [
+    # Frustration
+    "I am so frustrated right now.",
+    "This is making me really angry.",
+    "I can't take this anymore.",
+    "I feel like giving up.",
+    "Nothing makes sense to me.",
+    "I'm losing my patience.",
+    "Why is this so hard?",
+    "I feel stupid for not getting this.",
+    # Positive
+    "This is really starting to make sense!",
+    "I love coding, this is fun!",
+    "Wow, I finally understand it!",
+    "I am ready to tackle the next challenge!",
+    "This is getting exciting!",
+    "I feel so good about this now.",
+    "I'm having a great time learning this.",
+    "That was actually easier than I thought.",
+    # Confusion
+    "I feel completely stuck and confused.",
+    "I have no idea what's going on.",
+    "My brain is fried.",
+    "I'm lost.",
+    "I don't understand anything.",
+    "This is so confusing it hurts.",
+    # Boredom / tiredness
+    "This is getting boring.",
+    "I'm feeling super tired today.",
+    "My head hurts from all this information.",
+    "I feel like I'm not making any progress.",
+    "Can we do something more interesting?",
+    "I'm so sleepy right now.",
+    "This is not engaging at all.",
+    "My eyes are glazing over.",
+    # Anxiety
+    "I'm nervous about the upcoming test.",
+    "What if I fail?",
+    "I feel anxious about falling behind.",
+    "Everyone else seems to get it except me.",
+    "I'm stressed out.",
+    # Mixed / ambiguous (touches emotional + other intents)
+    "I'm confused, I feel so dumb right now.",
+    "I'm excited but also scared I'll mess up.",
+    "I'm frustrated because this used to make sense.",
+    "I feel overwhelmed by all this new stuff.",
+    "I just feel really down today.",
+]
+
+PACE_TEMPLATES = [
+    # Slow down
+    "Can we slow down a bit?",
+    "You are going way too fast.",
+    "Wait, can you slow down the explanation?",
+    "I need more time to process this.",
+    "Can you wait a second before moving to the next slide?",
+    "Hold on, I'm still writing notes.",
+    "Please slow down, I can't keep up.",
+    "You're moving too quickly for me.",
+    "I need a moment to think about this.",
+    "Can we pause for a minute?",
+    "Don't rush through this please.",
+    "Slow down, I'm still on the last example.",
+    "Give me a sec, I'm still processing.",
+    # Speed up
+    "Let's move on to the next topic.",
+    "Can we skip this?",
+    "I think I got this, let's speed up.",
+    "Can we go through the next part faster?",
+    "Let's speed up the pace, I'm bored.",
+    "I already know this, can we move on?",
+    "This part is easy, let's go faster.",
+    "Skip ahead please.",
+    "Next topic please.",
+    "We're spending too long on this.",
+    "Can we pick up the pace?",
+    # Break / timing
+    "Can we take a break?",
+    "How much time do we have left?",
+    "When does this session end?",
+    "I need a 5 minute break.",
+    "Let's take a quick breather.",
+    # General pacing
+    "The pace feels about right.",
+    "Can you adjust the speed a bit?",
+    "I think the pacing is off.",
+    "Are we on schedule?",
+    "How many more slides do we have?",
+]
+
+REPEAT_TEMPLATES = [
+    "Can you repeat that last part?",
+    "What did you say about the slide right before this one?",
+    "Could you clarify what you meant?",
+    "I didn't catch that, can you say it again?",
+    "Say that again?",
+    "Can you go back to the previous slide for a second?",
+    "I missed the first step, can you re-explain?",
+    "Can you repeat the rule for that?",
+    "Could you run through the explanation one more time?",
+    "Can you clarify the difference between the two examples?",
+    "Wait, what was that?",
+    "Huh? Can you repeat?",
+    "I didn't understand, please say it again.",
+    "Sorry, I zoned out. What did you just say?",
+    "Come again?",
+    "Can you show that example one more time?",
+    "Go back to that last point please.",
+    "I need you to repeat the definition.",
+    "What was the syntax you just showed?",
+    "Can you re-explain how that works?",
+    "I lost you there, can you start over on that point?",
+    "Please repeat the steps.",
+    "Sorry, can you go over that again from the beginning?",
+    "What was the output of that code again?",
+    "Can you re-run that example?",
+    "I missed it, one more time please.",
+    "I need to hear that explanation again.",
+    "Can you walk me through that once more?",
+    "Let me see that slide again.",
+    "I need a recap of what you just said.",
+    "Can you summarize what you just explained?",
+    "What were the key points of that last section?",
+]
+
+
+# ─────────────────────────────────────────────────────────────────────
+# AUGMENTATION STRATEGIES
+# ─────────────────────────────────────────────────────────────────────
+
+SYNONYM_MAP = {
+    "explain": ["describe", "clarify", "elaborate on", "break down", "walk me through"],
+    "show": ["demonstrate", "present", "display", "give me"],
+    "help": ["assist", "support", "aid"],
+    "use": ["utilize", "apply", "work with"],
+    "understand": ["get", "grasp", "comprehend", "follow"],
+    "repeat": ["say again", "go over again", "redo", "recap"],
+    "confused": ["lost", "puzzled", "unsure", "baffled"],
+    "stuck": ["blocked", "stalled", "unable to proceed"],
+    "slow down": ["take it easy", "go slower", "ease up"],
+    "speed up": ["go faster", "pick up the pace", "hurry up"],
+    "example": ["demo", "sample", "illustration", "instance"],
+    "error": ["bug", "mistake", "issue", "problem"],
+    "different": ["alternative", "another", "other"],
+    "code": ["program", "script", "snippet"],
+}
+
+FILLERS = ["umm", "so", "like", "hey", "well", "basically", "honestly", "actually", "ok so", "right"]
+
+def augment_synonym(text):
+    """Replace one random word with a synonym."""
+    for word, synonyms in SYNONYM_MAP.items():
+        if word in text.lower() and random.random() < 0.35:
+            pattern = re.compile(re.escape(word), re.IGNORECASE)
+            text = pattern.sub(random.choice(synonyms), text, count=1)
+            break
+    return text
+
+def augment_case(text):
+    """Randomly change casing."""
+    r = random.random()
+    if r < 0.3:
+        return text.lower()
+    if r < 0.38:
+        return text.upper()
+    return text
+
+def augment_punctuation(text):
+    """Randomly alter punctuation."""
+    r = random.random()
+    if r < 0.25:
+        return text.rstrip("?!.") + "?"
+    if r < 0.4:
+        return text.rstrip("?!.")
+    if r < 0.48:
+        return text.rstrip("?!.") + "!!"
+    return text
+
+def augment_filler(text):
+    """Randomly prepend a filler word."""
+    if random.random() < 0.2:
+        return random.choice(FILLERS) + " " + text
+    return text
+
+def augment_typo(text, prob=0.08):
+    """Inject character-level typos."""
+    if random.random() > 0.35:
+        return text
+    chars = list(text)
+    for i in range(len(chars)):
+        if random.random() < prob and chars[i].isalpha():
+            op = random.choice(["swap", "delete", "duplicate"])
+            if op == "swap" and i < len(chars) - 1:
+                chars[i], chars[i+1] = chars[i+1], chars[i]
+            elif op == "delete":
+                chars[i] = ""
+            elif op == "duplicate":
+                chars[i] = chars[i] * 2
+    return "".join(chars)
+
+def augment_word_swap(text):
+    """Swap two adjacent words."""
+    words = text.split()
+    if len(words) <= 2 or random.random() > 0.15:
+        return text
+    idx = random.randint(0, len(words) - 2)
+    words[idx], words[idx+1] = words[idx+1], words[idx]
+    return " ".join(words)
+
+def augment_word_delete(text):
+    """Delete a random non-essential word."""
+    words = text.split()
+    if len(words) <= 3 or random.random() > 0.12:
+        return text
+    idx = random.randint(1, len(words) - 2)
+    words.pop(idx)
+    return " ".join(words)
+
+def augment_text(text):
+    """Apply a random combination of augmentation strategies."""
+    strategies = [augment_synonym, augment_case, augment_punctuation,
+                  augment_filler, augment_typo, augment_word_swap, augment_word_delete]
+    # Apply 1-3 random strategies
+    chosen = random.sample(strategies, k=random.randint(1, 3))
+    for fn in chosen:
+        text = fn(text)
+    return text.strip()
+
+
+# ─────────────────────────────────────────────────────────────────────
+# INTENT GENERATORS
+# ─────────────────────────────────────────────────────────────────────
+
+def get_on_topic_question(current_topic, prev_topics):
+    # 20% chance of context-aware template if prev_topics exist
+    if prev_topics and random.random() < 0.2:
+        prev_topic = random.choice(prev_topics)
+        template = random.choice(ON_TOPIC_CONTEXT_TEMPLATES)
+        return template.replace("{topic}", current_topic).replace("{prev_topic}", prev_topic)
+    template = random.choice(ON_TOPIC_TEMPLATES)
+    return template.replace("{topic}", current_topic)
+
+def get_off_topic_question(current_topic_idx):
+    if current_topic_idx < len(PYTHON_TOPICS) - 1 and random.random() < 0.5:
+        future_topic = random.choice(PYTHON_TOPICS[current_topic_idx + 1:])
+        template = random.choice(OFF_TOPIC_FUTURE_TOPIC_TEMPLATES)
+        return template.replace("{topic}", future_topic)
+    return random.choice(OFF_TOPIC_GENERAL)
+
+def get_emotional_state():
+    return random.choice(EMOTIONAL_TEMPLATES)
+
+def get_pace_related():
+    return random.choice(PACE_TEMPLATES)
+
+def get_repeat_clarification():
+    return random.choice(REPEAT_TEMPLATES)
+
+
+# ─────────────────────────────────────────────────────────────────────
+# PIPELINE GENERATION (3-way split: train/val/test)
+# ─────────────────────────────────────────────────────────────────────
+
+def build_dataset(num_samples_per_class=2000, train_ratio=0.70, val_ratio=0.15, test_ratio=0.15):
+    print(f"Starting Dataset Generation ({num_samples_per_class} per class)...")
+
+    dataset = []
+
+    for intent, label_id in LABEL_MAP.items():
+        for _ in range(num_samples_per_class):
+            topic_idx = random.randint(0, len(PYTHON_TOPICS) - 1)
+            context_str, current_topic, prev_topics = generate_session_context(topic_idx)
+
+            if intent == 'On-Topic Question':
+                student_input = get_on_topic_question(current_topic, prev_topics)
+            elif intent == 'Off-Topic Question':
+                student_input = get_off_topic_question(topic_idx)
+            elif intent == 'Emotional-State':
+                student_input = get_emotional_state()
+            elif intent == 'Pace-Related':
+                student_input = get_pace_related()
+            elif intent == 'Repeat/clarification':
+                student_input = get_repeat_clarification()
+            else:
+                student_input = get_off_topic_question(topic_idx)
+
+            student_input = augment_text(student_input)
+
+            dataset.append({
+                'student_input': student_input,
+                'session_context': context_str,
+                'label': label_id,
+                'intent_name': intent
+            })
+
+    df = pd.DataFrame(dataset)
+    df = df.sample(frac=1, random_state=42).reset_index(drop=True)
+
+    # Stratified 3-way split
+    train_dfs, val_dfs, test_dfs = [], [], []
+    for label_id in sorted(df['label'].unique()):
+        label_df = df[df['label'] == label_id].reset_index(drop=True)
+        n = len(label_df)
+        t1 = int(n * train_ratio)
+        t2 = int(n * (train_ratio + val_ratio))
+        train_dfs.append(label_df.iloc[:t1])
+        val_dfs.append(label_df.iloc[t1:t2])
+        test_dfs.append(label_df.iloc[t2:])
+
+    train_df = pd.concat(train_dfs).sample(frac=1, random_state=42).reset_index(drop=True)
+    val_df = pd.concat(val_dfs).sample(frac=1, random_state=42).reset_index(drop=True)
+    test_df = pd.concat(test_dfs).sample(frac=1, random_state=42).reset_index(drop=True)
+
+    output_dir = 'data'
+    os.makedirs(output_dir, exist_ok=True)
+
+    train_df.to_csv(os.path.join(output_dir, 'train.csv'), index=False)
+    val_df.to_csv(os.path.join(output_dir, 'val.csv'), index=False)
+    test_df.to_csv(os.path.join(output_dir, 'test.csv'), index=False)
+
+    print("[+] Data Generation Complete!")
+    print(f"Total: {len(df)} | Train: {len(train_df)} | Val: {len(val_df)} | Test: {len(test_df)}")
+    print(f"Train distribution:\n{train_df['label'].value_counts().sort_index().to_string()}")
+
+
+if __name__ == '__main__':
+    build_dataset(num_samples_per_class=2000)