Initial commit: PastPaper Master full stack
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Zhao
268
backend/split_comp2211_2023_spring_midterm.py
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268
backend/split_comp2211_2023_spring_midterm.py
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"""Split COMP2211 Spring 2023 midterm into subquestions."""
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from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from app.services.supabase_client import get_supabase
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EXAM_KEY = "COMP2211-2023-spring-midterm"
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PROBLEM_SEED_PATH = (
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Path(__file__).resolve().parent.parent
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/ "pastpaper-scraper"
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/ "reviews"
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/ "COMP2211"
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/ "problem_seed.json"
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)
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TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}]
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@dataclass(frozen=True)
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class ChildSpec:
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question_number: str
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parent_question: str
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top_level_number: str
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path: tuple[str, ...]
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score: float
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question_type: str
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question_format: str | None = None
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analytics_topic: str | None = None
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topic_primary: str | None = None
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topic_tags: tuple[str, ...] | None = None
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skill_tags: tuple[str, ...] | None = None
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options: tuple[tuple[str, str], ...] | None = None
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correct_option: str | None = None
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correct_answer: str | None = None
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page_number: int = 1
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def short_answer(
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question_number: str,
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parent_question: str,
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top_level_number: str,
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path: tuple[str, ...],
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score: float,
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*,
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analytics_topic: str | None = None,
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topic_primary: str | None = None,
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topic_tags: tuple[str, ...] | None = None,
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skill_tags: tuple[str, ...] | None = None,
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correct_answer: str | None = None,
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page_number: int,
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) -> ChildSpec:
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return ChildSpec(
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question_number=question_number,
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parent_question=parent_question,
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top_level_number=top_level_number,
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path=path,
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score=score,
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question_type="long_question",
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question_format="short_answer",
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analytics_topic=analytics_topic,
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topic_primary=topic_primary,
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topic_tags=topic_tags,
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skill_tags=skill_tags,
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correct_answer=correct_answer,
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page_number=page_number,
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)
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def mc(
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question_number: str,
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parent_question: str,
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top_level_number: str,
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path: tuple[str, ...],
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score: float,
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*,
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options: tuple[tuple[str, str], ...],
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correct_option: str,
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analytics_topic: str,
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skill_tags: tuple[str, ...],
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page_number: int,
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) -> ChildSpec:
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return ChildSpec(
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question_number=question_number,
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parent_question=parent_question,
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top_level_number=top_level_number,
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path=path,
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score=score,
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question_type="mc",
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question_format="mc",
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analytics_topic=analytics_topic,
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topic_primary=analytics_topic,
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topic_tags=(analytics_topic,),
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skill_tags=skill_tags,
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options=options,
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correct_option=correct_option,
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page_number=page_number,
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)
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ABCDE = (("A", "A"), ("B", "B"), ("C", "C"), ("D", "D"), ("E", "E"))
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CHILDREN: list[ChildSpec] = [
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ChildSpec("1a", "1", "1", ("a",), 1, "true_false", "true_false", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("concept_check", "classification_decision"), page_number=3),
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ChildSpec("1b", "1", "1", ("b",), 1, "true_false", "true_false", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("concept_check", "classification_decision"), page_number=3),
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ChildSpec("1c", "1", "1", ("c",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3),
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ChildSpec("1d", "1", "1", ("d",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "distance_reasoning"), page_number=3),
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ChildSpec("1e", "1", "1", ("e",), 1, "true_false", "true_false", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("concept_check", "validation_reasoning"), page_number=3),
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ChildSpec("1f", "1", "1", ("f",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3),
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ChildSpec("1g", "1", "1", ("g",), 1, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "robustness_reasoning"), page_number=3),
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ChildSpec("1h", "1", "1", ("h",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "decision_boundary"), page_number=3),
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ChildSpec("1i", "1", "1", ("i",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "optimization_reasoning"), page_number=3),
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ChildSpec("1j", "1", "1", ("j",), 1, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "expressiveness_reasoning"), page_number=3),
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short_answer("2a_i", "2a", "2", ("a", "i"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
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short_answer("2a_ii", "2a", "2", ("a", "ii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
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short_answer("2a_iii", "2a", "2", ("a", "iii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
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short_answer("2a_iv", "2a", "2", ("a", "iv"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
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short_answer("2a_v", "2a", "2", ("a", "v"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("indexing", "code_tracing"), page_number=4),
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short_answer("2a_vi", "2a", "2", ("a", "vi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("indexing", "error_reasoning"), page_number=5),
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short_answer("2a_vii", "2a", "2", ("a", "vii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("masking", "code_tracing"), page_number=5),
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short_answer("2a_viii", "2a", "2", ("a", "viii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("aggregation", "code_tracing"), page_number=5),
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short_answer("2a_ix", "2a", "2", ("a", "ix"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("transpose", "code_tracing"), page_number=5),
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short_answer("2b_i", "2b", "2", ("b", "i"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "code_tracing"), page_number=6),
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short_answer("2b_ii", "2b", "2", ("b", "ii"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "error_reasoning"), page_number=6),
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short_answer("2b_iii", "2b", "2", ("b", "iii"), 2, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting", "code_tracing"), page_number=6),
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ChildSpec("2c", "2", "2", ("c",), 6, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "vectorization", "geometry_reasoning"), page_number=7),
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short_answer("3", "3", "3", (), 8, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("concept_explanation", "missing_data_reasoning"), page_number=9),
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ChildSpec("4a", "4", "4", ("a",), 8, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "classification_decision"), page_number=10),
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short_answer("4b", "4", "4", ("b",), 6, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("distance_reasoning", "comparison"), page_number=11),
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ChildSpec("5a", "5", "5", ("a",), 7, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "algorithm_tracing"), page_number=12),
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ChildSpec("5b", "5", "5", ("b",), 7, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("centroid_update", "algorithm_tracing"), page_number=12),
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short_answer("5c", "5", "5", ("c",), 5, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("concept_explanation", "model_selection"), page_number=14),
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short_answer("6a", "6", "6", ("a",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("convergence_reasoning",), page_number=15),
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mc("6b", "6", "6", ("b",), 2, options=ABCDE, correct_option="D", analytics_topic="Perceptron and MLP", skill_tags=("generalization_reasoning",), page_number=15),
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short_answer("6c", "6", "6", ("c",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("activation_reasoning",), page_number=16),
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ChildSpec("6d", "6", "6", ("d",), 6, "long_question", "coding", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("debugging", "implementation", "weight_update"), page_number=16),
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short_answer("7a", "7", "7", ("a",), 4, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("decision_boundary", "linearity_reasoning"), page_number=18),
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short_answer("7b", "7", "7", ("b",), 2, analytics_topic="Perceptron and MLP", topic_primary="Perceptron and MLP", topic_tags=("Perceptron and MLP",), skill_tags=("decision_boundary", "linearity_reasoning"), page_number=18),
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ChildSpec("7c", "7", "7", ("c",), 10, "long_question", "long_answer", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("architecture_reasoning", "parameter_design"), page_number=19),
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]
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MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*")
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def split_sections(text: str) -> tuple[str, dict[str, str]]:
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matches = list(MARKER_RE.finditer(text))
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if not matches:
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return text.strip(), {}
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intro = text[: matches[0].start()].strip()
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sections: dict[str, str] = {}
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for idx, match in enumerate(matches):
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marker = match.group(1)
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end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
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sections[marker] = text[match.start() : end].strip()
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return intro, sections
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def extract_segment(text: str, path: tuple[str, ...]) -> str:
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current = text.strip()
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carried_intro: list[str] = []
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for depth, marker in enumerate(path):
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intro, sections = split_sections(current)
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if depth == 0 and intro:
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carried_intro.append(intro)
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current = sections.get(marker, current)
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return "\n".join(part for part in [*carried_intro, current] if part).strip()
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def extract_true_false_answers(answer_text: str) -> dict[str, str]:
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answers: dict[str, str] = {}
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matches = list(re.finditer(r"(?m)^\(([a-j])\)\s*\n?T\s*F", answer_text))
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if matches:
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return answers
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for match in re.finditer(r"(?m)^\(([a-j])\)\s*\n?([TF])\b", answer_text):
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answers[match.group(1)] = match.group(2)
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if answers:
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return answers
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lines = [line.strip() for line in answer_text.splitlines() if line.strip()]
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current = None
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for line in lines:
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m = re.fullmatch(r"\(([a-j])\)", line)
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if m:
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current = m.group(1)
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continue
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if current and line in {"T", "F"}:
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answers[current] = line
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current = None
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return answers
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def load_seed_rows() -> dict[str, dict]:
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data = json.loads(PROBLEM_SEED_PATH.read_text())
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return {row["question_number"]: row for row in data if row["source_exam_key"] == EXAM_KEY}
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def main() -> None:
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sb = get_supabase()
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paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0]
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paper_id = paper["id"]
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current_rows = (
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sb.table("paper_questions")
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.select("*")
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.eq("paper_id", paper_id)
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.order("display_order")
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.execute()
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.data
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)
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existing_by_number = {row["question_number"]: row for row in current_rows}
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parent_rows = load_seed_rows()
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tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "")
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inserts = []
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for display_order, child in enumerate(CHILDREN, start=1):
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parent = parent_rows[child.top_level_number]
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existing = existing_by_number.get(child.question_number, {})
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question_text = extract_segment(parent["question_text"] or "", child.path)
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raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) if child.path else (parent["raw_answer_text"] or "")
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options = None
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correct_option = child.correct_option
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if child.options:
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options = [{"label": label, "text": text} for label, text in child.options]
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if child.question_type == "true_false":
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options = TRUE_FALSE_OPTIONS
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correct_option = tf_answers.get(child.path[0])
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inserts.append(
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{
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"paper_id": paper_id,
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"question_number": child.question_number,
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"parent_question": child.parent_question,
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"display_order": display_order,
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"question_type": child.question_type,
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"question_format": child.question_format,
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"question_text": question_text,
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"score": child.score,
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"page_number": child.page_number,
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"page_y_ratio": existing.get("page_y_ratio"),
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"options": options,
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"correct_option": correct_option,
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"correct_answer": child.correct_answer,
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"raw_answer_text": raw_answer_text,
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"topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")),
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"topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"),
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"analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"),
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"topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")),
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"skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")),
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"difficulty": existing.get("difficulty") or parent.get("difficulty"),
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"knowledge_reminder": existing.get("knowledge_reminder", ""),
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"ai_hint": existing.get("ai_hint", ""),
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"solution": existing.get("solution", ""),
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}
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)
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sb.table("paper_questions").delete().eq("paper_id", paper_id).execute()
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sb.table("paper_questions").insert(inserts).execute()
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sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute()
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print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user