wip
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Félix Dorn
parent
62296e1b69
commit
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37 changed files with 1413 additions and 2433 deletions
225
pipeline/classification.py
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225
pipeline/classification.py
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from pathlib import Path
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import pandas as pd
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from .logger import logger
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from .utils import enrich
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import json
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ALLOWED_UNITS = [
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"minute",
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"hour",
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"day",
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"week",
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"month",
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"trimester",
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"semester",
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"year",
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]
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ESTIMABLE_CLASSIFICATION_VERSION = "old_version"
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TIME_ESTIMATES_GENERATION_VERSION = "old_version"
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def classify_tasks_as_estimable(cache_dir: Path, df_to_process: pd.DataFrame, bust: bool = False) -> pd.DataFrame:
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CACHE_PATH = cache_dir / f"task_estimability.{ESTIMABLE_CLASSIFICATION_VERSION}.parquet"
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if CACHE_PATH.exists() and not bust:
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logger.info(f"Loading cached task estimability from {CACHE_PATH}")
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return pd.read_parquet(CACHE_PATH)
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logger.info("Enriching tasks with estimability classification.")
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df_unique_tasks = df_to_process.drop_duplicates(subset=['task']).copy()
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logger.info(f"Found {len(df_unique_tasks)} unique remote tasks to classify.")
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if df_unique_tasks.empty:
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raise ValueError("No unique tasks to classify.")
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results = enrich(
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model="gpt-4.1-mini",
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rpm=5000,
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messages_to_process=[
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[
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{"role": "system", "content": """
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Classify the provided O*NET task into one of these categories:
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- ATOMIC (schedulable): A single, clearly-bounded activity, typically lasting minutes, hours, or a few days.
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- ONGOING-CONSTRAINT (background role/ethical rule): A continuous responsibility or behavioural norm with no schedulable duration (e.g., “follow confidentiality rules,” “serve as department head”).
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""".strip()},
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{"role": "user", "content": f"Task: {row.task}"},
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]
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for row in df_unique_tasks.itertuples()
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],
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schema={
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"name": "estimability_classification",
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"schema": {
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"type": "object",
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"properties": {"task_category": {"type": "string", "enum": ["ATOMIC", "ONGOING-CONSTRAINT"]}},
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"required": ["task_category"],
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"additionalProperties": False
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}
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},
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chunk_size=300,
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)
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if not results or len(results) != len(df_unique_tasks):
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raise ValueError(f"Task estimability classification failed or returned mismatched number of results. Expected {len(df_unique_tasks)}, got {len(results) if results else 0}.")
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classifications = []
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for index, response in enumerate(results):
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task_label = df_unique_tasks.iloc[index]['task']
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task_category_flag = None
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if response is None:
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logger.warning(f"API call failed for task (enrich returned None): '{task_label}'")
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else:
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try:
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content_str = response.choices[0].message.content
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if not content_str:
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raise ValueError("No content found in the response message")
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data = json.loads(content_str)
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if 'task_category' in data and isinstance(data['task_category'], str):
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task_category_flag = data['task_category']
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else:
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logger.warning(f"Invalid or missing 'task_category' payload for task '{task_label}'. Data: '{data}'")
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except (json.JSONDecodeError, AttributeError, KeyError, IndexError, ValueError) as e:
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logger.warning(f"Could not parse response for task '{task_label}'. Error: {e}. Response: {response}")
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classifications.append({
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'task': task_label,
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'estimable': task_category_flag == 'ATOMIC'
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})
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classification_df = pd.DataFrame(classifications)
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logger.info(f"Finished classification. Got {classification_df['estimable'].notna().sum()} successful classifications out of {len(df_unique_tasks)} unique tasks.")
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logger.info(f"Saving task estimability classifications to {CACHE_PATH}")
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classification_df.to_parquet(CACHE_PATH)
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return classification_df
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def generate_time_estimates_for_tasks(cache_dir: Path, df_to_process: pd.DataFrame, bust: bool = False) -> pd.DataFrame:
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CACHE_PATH = cache_dir / f"task_estimates.{TIME_ESTIMATES_GENERATION_VERSION}.parquet"
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if CACHE_PATH.exists() and not bust:
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logger.info(f"Loading cached task estimates from {CACHE_PATH}")
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return pd.read_parquet(CACHE_PATH)
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logger.info("Enriching tasks with time estimates.")
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if df_to_process.empty:
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raise ValueError("No tasks to process for estimates.")
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results = enrich(
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model="gpt-4.1-mini",
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rpm=5000,
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messages_to_process=[
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[
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{
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"role": "system",
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"content": """
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You are an expert assistant evaluating the time required for job tasks. Your goal is to estimate the 'effective time' range needed for a skilled human to complete the following job task **remotely**, without supervision
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'Effective time' is the active, focused work duration required to complete the task. Crucially, **exclude all waiting periods, delays, or time spent on other unrelated activities**. Think of it as the continuous, productive time investment needed if the worker could pause and resume instantly without cost.
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Provide a lower and upper bound estimate for the 'effective time'. These bounds should capture the time within which approximately 80% of instances of performing this specific task are typically completed by a qualified individual.
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Base your estimate on the provided task and the associated occupation and occupation description. Your estimate must be in one the allowed units: minute, hour, day, week, month, trimester, semester, year.""".strip()
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},
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{
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"role": "user",
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"content": f"{row.task} done by {row.occupation_title} ({row.occupation_description})"
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}
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]
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for row in df_to_process.itertuples()
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],
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schema= {
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"name": "estimate_time",
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"strict": True,
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"schema": {
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"type": "object",
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"properties": {
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"lower_bound_estimate": {
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"type": "object",
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"properties": {
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"quantity": {
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"type": "number",
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"description": "The numerical value for the lower bound of the estimate.",
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},
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"unit": {
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"type": "string",
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"enum": ALLOWED_UNITS,
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"description": "The unit of time for the lower bound.",
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},
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},
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"required": ["quantity", "unit"],
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"additionalProperties": False,
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},
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"upper_bound_estimate": {
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"type": "object",
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"properties": {
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"quantity": {
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"type": "number",
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"description": "The numerical value for the upper bound of the estimate.",
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},
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"unit": {
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"type": "string",
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"enum": ALLOWED_UNITS,
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"description": "The unit of time for the upper bound.",
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},
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},
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"required": ["quantity", "unit"],
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"additionalProperties": False,
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},
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},
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"required": ["lower_bound_estimate", "upper_bound_estimate"],
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"additionalProperties": False,
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},
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},
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chunk_size=200,
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)
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if not results or len(results) != len(df_to_process):
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raise ValueError(f"API call for task estimates failed or returned mismatched number of results. "
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f"Expected {len(df_to_process)}, got {len(results) if results else 0}.")
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estimates = []
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for index, response in enumerate(results):
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row = df_to_process.iloc[index]
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task_info = f"O*NET: {row.onetsoc_code}, Task ID: {row.task_id}"
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lb_qty, lb_unit, ub_qty, ub_unit = None, None, None, None
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if response is None:
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logger.warning(f"API call failed for task (enrich returned None): {task_info}")
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else:
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try:
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content_str = response.choices[0].message.content
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if not content_str:
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raise ValueError("No content found in the response message")
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data = json.loads(content_str)
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lb_qty = data['lower_bound_estimate']['quantity']
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lb_unit = data['lower_bound_estimate']['unit']
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ub_qty = data['upper_bound_estimate']['quantity']
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ub_unit = data['upper_bound_estimate']['unit']
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except Exception as e:
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logger.warning(f"Could not parse valid estimate for task {task_info}. Error: {e}. Response: {response}")
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lb_qty, lb_unit, ub_qty, ub_unit = None, None, None, None # Reset on failure
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estimates.append({
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'onetsoc_code': row.onetsoc_code,
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'task_id': row.task_id,
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'lb_estimate_qty': lb_qty,
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'lb_estimate_unit': lb_unit,
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'ub_estimate_qty': ub_qty,
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'ub_estimate_unit': ub_unit
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})
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estimates_df = pd.DataFrame(estimates)
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logger.info(f"Finished estimates. Got {estimates_df['lb_estimate_qty'].notna().sum()} successful estimates out of {len(df_to_process)} tasks.")
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logger.info(f"Saving task estimates to {CACHE_PATH}")
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estimates_df.to_parquet(CACHE_PATH)
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return estimates_df
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