Migrate Track 1 fit layer into renunney
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Codex
parent
aefd4e4ccb
commit
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@ -28,6 +28,7 @@ plane and the Track 1 runner/API boundary are now local to `renunney`.
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- a local Track 1 report generator,
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- a local Track 1 extinction-model data layer,
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- a local Track 1 dataset generator,
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- a local Track 1 fit layer,
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- a Makefile for common tasks,
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- migration notes for pulling code into this repo in stages.
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@ -94,9 +95,7 @@ The current state is split:
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- Track 1 report generator: local to `renunney`
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- Track 1 extinction-model data layer: local to `renunney`
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- Track 1 dataset generator: local to `renunney`
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- Track 1 fit helper: still imported
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from the older `cost_of_substitution` directory through the local
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compatibility layer
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- Track 1 fit layer: local to `renunney`
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This repo is now the clean operational entry point while the simulation code is
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migrated in later stages.
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@ -39,10 +39,11 @@ Operational code still lives in:
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- `src/renunney/track1_extinction.py`
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9. Track 1 dataset generator has been migrated locally:
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- `src/renunney/track1_dataset.py`
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10. Migrate the fit module next:
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- `python/track1_fit.py`
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11. Reduce or remove the remaining compatibility-layer imports after those modules are local.
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12. Migrate docs and example configs last, after path references are updated.
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10. Track 1 fit layer has been migrated locally:
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- `src/renunney/track1_fit.py`
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11. Track 1 runtime path is now fully local to `renunney`.
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12. Reduce or remove any remaining compatibility-layer imports outside the Track 1 runtime path.
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13. Migrate docs and example configs last, after path references are updated.
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## Constraint
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@ -49,8 +49,6 @@ make status
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The Makefile now drives the local orchestration code in `renunney`, while the
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Track 1 runner/API boundary, analysis layer, threshold/search layer, and
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simulation kernel, report generator, and extinction-model data layer are also
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local to `renunney`, and the dataset generator is now local as well. The
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remaining Track 1 fit helper is still imported from the legacy
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`cost_of_substitution` directory through the compatibility layer in
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`src/renunney/legacy.py`. The paper-scale Figure 1 configs used for submission
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are now local to `renunney/config`.
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local to `renunney`, and the dataset generator and fit layer are now local as
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well. The paper-scale Figure 1 configs used for submission are now local to
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`renunney/config`.
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@ -36,6 +36,22 @@ from .track1_extinction import (
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build_extinction_run_row,
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save_jsonl,
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)
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from .track1_fit import (
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DEFAULT_RUN_FEATURES,
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ExtinctionClassBalance,
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ExtinctionFitSummary,
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ExtinctionLogitModel,
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class_balance,
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design_matrix_from_run_rows,
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fit_extinction_run_model,
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fit_extinction_run_model_from_jsonl,
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fit_logistic_regression,
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fit_payload_from_jsonl,
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load_jsonl,
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predict_probabilities,
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standardize_design_matrix,
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summarize_fit,
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)
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from .track1_reference import (
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GenerationSummary,
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PopulationState,
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@ -110,16 +126,26 @@ __all__ = [
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"Track1RunConfig",
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"allele_tracking_metrics",
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"approximate_ne",
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"class_balance",
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"config_from_mapping",
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"DEFAULT_RUN_FEATURES",
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"design_matrix_from_run_rows",
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"expected_female_productivity",
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"expected_mutations_for_population",
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"evaluate_threshold_candidate",
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"generate_extinction_dataset",
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"ExtinctionClassBalance",
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"ExtinctionFitSummary",
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"ExtinctionGenerationRow",
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"ExtinctionLogitModel",
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"ExtinctionRunRow",
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"female_fecundity",
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"female_fraction",
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"fit_extinction_run_model",
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"fit_extinction_run_model_from_jsonl",
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"fit_linear_cost_by_loci",
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"fit_logistic_regression",
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"fit_payload_from_jsonl",
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"generation_metrics",
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"genotype_fitness",
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"generate_report_bundle",
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@ -129,9 +155,11 @@ __all__ = [
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"build_extinction_generation_rows",
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"build_extinction_run_row",
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"load_config",
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"load_jsonl",
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"nunney_threshold_accepts",
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"paper_mutation_supply_M",
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"published_threshold_accepts",
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"predict_probabilities",
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"plot_mean_allele_vs_target",
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"plot_series",
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"plot_series_with_reference",
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@ -144,8 +172,10 @@ __all__ = [
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"save_payload",
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"save_jsonl",
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"search_threshold_over_candidates",
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"standardize_design_matrix",
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"summarize_tracking",
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"summarize_generation",
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"summarize_fit",
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"sweep_number_of_loci",
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"aggregate_derived_series",
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"aggregate_series",
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@ -17,14 +17,13 @@ from typing import Any, Optional
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from .legacy import ensure_legacy_python_path
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from .track1_analysis import summarize_tracking, sweep_number_of_loci
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from .track1_dataset import generate_extinction_dataset
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from .track1_fit import class_balance, fit_payload_from_jsonl, load_jsonl
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from .track1_reference import Track1Parameters, simulate_run
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from .track1_report import generate_report_bundle
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from .track1_threshold import evaluate_threshold_candidate, search_threshold_over_candidates
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ensure_legacy_python_path()
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from track1_fit import class_balance, fit_payload_from_jsonl, load_jsonl
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@dataclass(frozen=True, init=False)
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class Track1RunConfig:
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@ -0,0 +1,250 @@
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"""
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track1_fit.py
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Simple dependency-free baseline fitting for extinction-risk models using
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run-level Track 1 extinction datasets.
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"""
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from __future__ import annotations
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from dataclasses import asdict, dataclass
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import json
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from math import log
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from pathlib import Path
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from typing import Iterable
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import numpy as np
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DEFAULT_RUN_FEATURES = (
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"log_M",
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"inv_T",
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"n",
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"log_K",
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"log_N0_over_K",
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"mean_abs_tracking_gap",
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"fraction_generations_below_replacement",
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"longest_zero_mutation_streak",
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"cumulative_mutation_shortfall_per_generation",
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)
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@dataclass(frozen=True)
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class ExtinctionLogitModel:
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"""Baseline run-level logistic regression model."""
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feature_names: list[str]
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feature_means: list[float]
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feature_scales: list[float]
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coefficients: list[float]
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intercept: float
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iterations: int
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converged: bool
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@dataclass(frozen=True)
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class ExtinctionFitSummary:
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"""Training-set summary for the baseline extinction fit."""
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sample_count: int
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extinction_count: int
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non_extinction_count: int
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brier_score: float
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log_loss: float
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mean_predicted_probability: float
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@dataclass(frozen=True)
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class ExtinctionClassBalance:
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"""Outcome-class counts for a run-level extinction dataset."""
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sample_count: int
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extinction_count: int
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non_extinction_count: int
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def load_jsonl(path: str | Path) -> list[dict]:
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rows: list[dict] = []
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with Path(path).open("r", encoding="utf-8") as handle:
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for line in handle:
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text = line.strip()
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if text:
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rows.append(json.loads(text))
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return rows
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def class_balance(rows: Iterable[dict]) -> ExtinctionClassBalance:
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row_list = list(rows)
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extinction_count = sum(1 for row in row_list if bool(row["extinction_occurred"]))
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sample_count = len(row_list)
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return ExtinctionClassBalance(
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sample_count=sample_count,
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extinction_count=extinction_count,
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non_extinction_count=sample_count - extinction_count,
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)
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def _safe_log(value: float, eps: float = 1.0e-12) -> float:
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return float(log(max(value, eps)))
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def feature_value(row: dict, feature_name: str) -> float:
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if feature_name == "log_M":
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return _safe_log(float(row["M"]))
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if feature_name == "inv_T":
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return 1.0 / float(row["T"])
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if feature_name == "n":
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return float(row["n"])
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if feature_name == "log_K":
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return _safe_log(float(row["K"]))
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if feature_name == "log_N0_over_K":
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return _safe_log(float(row["N0"]) / float(row["K"]))
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if feature_name == "mean_abs_tracking_gap":
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return float(row["mean_abs_tracking_gap"])
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if feature_name == "fraction_generations_below_replacement":
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return float(row["fraction_generations_below_replacement"])
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if feature_name == "longest_zero_mutation_streak":
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return float(row["longest_zero_mutation_streak"])
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if feature_name == "cumulative_mutation_shortfall_per_generation":
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generations = max(1.0, float(row["generations_recorded"]))
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return float(row["cumulative_mutation_shortfall"]) / generations
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raise ValueError(f"Unsupported extinction-fit feature: {feature_name}")
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def design_matrix_from_run_rows(
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rows: Iterable[dict],
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feature_names: Iterable[str] = DEFAULT_RUN_FEATURES,
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) -> tuple[np.ndarray, np.ndarray, list[str]]:
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feature_list = list(feature_names)
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row_list = list(rows)
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x = np.array(
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[[feature_value(row, name) for name in feature_list] for row in row_list],
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dtype=float,
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)
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y = np.array([1.0 if bool(row["extinction_occurred"]) else 0.0 for row in row_list], dtype=float)
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return x, y, feature_list
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def standardize_design_matrix(x: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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means = np.mean(x, axis=0)
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scales = np.std(x, axis=0, ddof=0)
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scales = np.where(scales <= 1.0e-12, 1.0, scales)
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x_std = (x - means) / scales
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return x_std, means, scales
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def _sigmoid(z: np.ndarray) -> np.ndarray:
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clipped = np.clip(z, -30.0, 30.0)
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return 1.0 / (1.0 + np.exp(-clipped))
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def fit_logistic_regression(
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x: np.ndarray,
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y: np.ndarray,
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l2_penalty: float = 1.0e-6,
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max_iter: int = 200,
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tol: float = 1.0e-8,
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) -> tuple[np.ndarray, int, bool]:
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if x.ndim != 2:
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raise ValueError("x must be a 2D array.")
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if y.ndim != 1 or y.shape[0] != x.shape[0]:
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raise ValueError("y must be a 1D array aligned with x.")
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if x.shape[0] == 0:
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raise ValueError("Cannot fit logistic regression with zero rows.")
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if np.all(y == y[0]):
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raise ValueError("Extinction fit requires both extinct and non-extinct runs.")
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x1 = np.column_stack([np.ones(x.shape[0], dtype=float), x])
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beta = np.zeros(x1.shape[1], dtype=float)
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converged = False
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for iteration in range(1, max_iter + 1):
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eta = x1 @ beta
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p = _sigmoid(eta)
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w = np.clip(p * (1.0 - p), 1.0e-8, None)
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z = eta + (y - p) / w
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wx = x1 * w[:, None]
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xtwx = x1.T @ wx
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penalty = np.eye(x1.shape[1], dtype=float) * l2_penalty
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penalty[0, 0] = 0.0
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xtwz = x1.T @ (w * z)
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beta_new = np.linalg.solve(xtwx + penalty, xtwz)
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if np.max(np.abs(beta_new - beta)) < tol:
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beta = beta_new
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converged = True
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return beta, iteration, converged
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beta = beta_new
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return beta, max_iter, converged
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def predict_probabilities(model: ExtinctionLogitModel, rows: Iterable[dict]) -> np.ndarray:
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row_list = list(rows)
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if not row_list:
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return np.array([], dtype=float)
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x = np.array(
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[[feature_value(row, name) for name in model.feature_names] for row in row_list],
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dtype=float,
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)
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means = np.array(model.feature_means, dtype=float)
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scales = np.array(model.feature_scales, dtype=float)
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x_std = (x - means) / scales
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eta = model.intercept + x_std @ np.array(model.coefficients, dtype=float)
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return _sigmoid(eta)
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def summarize_fit(y: np.ndarray, p: np.ndarray) -> ExtinctionFitSummary:
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p_clip = np.clip(p, 1.0e-12, 1.0 - 1.0e-12)
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brier = float(np.mean((p - y) ** 2))
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log_loss = float(-np.mean(y * np.log(p_clip) + (1.0 - y) * np.log(1.0 - p_clip)))
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extinction_count = int(np.sum(y))
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sample_count = int(y.shape[0])
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return ExtinctionFitSummary(
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sample_count=sample_count,
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extinction_count=extinction_count,
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non_extinction_count=sample_count - extinction_count,
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brier_score=brier,
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log_loss=log_loss,
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mean_predicted_probability=float(np.mean(p)),
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)
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def fit_extinction_run_model(
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run_rows: Iterable[dict],
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feature_names: Iterable[str] = DEFAULT_RUN_FEATURES,
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) -> tuple[ExtinctionLogitModel, ExtinctionFitSummary]:
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row_list = list(run_rows)
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x, y, feature_list = design_matrix_from_run_rows(row_list, feature_names=feature_names)
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x_std, means, scales = standardize_design_matrix(x)
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beta, iterations, converged = fit_logistic_regression(x_std, y)
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model = ExtinctionLogitModel(
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feature_names=feature_list,
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feature_means=[float(value) for value in means],
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feature_scales=[float(value) for value in scales],
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intercept=float(beta[0]),
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coefficients=[float(value) for value in beta[1:]],
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iterations=iterations,
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converged=converged,
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)
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p = predict_probabilities(model, row_list)
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return model, summarize_fit(y, p)
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def fit_extinction_run_model_from_jsonl(
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run_rows_path: str | Path,
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feature_names: Iterable[str] = DEFAULT_RUN_FEATURES,
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) -> tuple[ExtinctionLogitModel, ExtinctionFitSummary]:
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rows = load_jsonl(run_rows_path)
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return fit_extinction_run_model(rows, feature_names=feature_names)
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def fit_payload_from_jsonl(
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run_rows_path: str | Path,
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feature_names: Iterable[str] = DEFAULT_RUN_FEATURES,
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) -> dict:
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model, summary = fit_extinction_run_model_from_jsonl(run_rows_path, feature_names=feature_names)
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return {
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"run_rows_path": str(Path(run_rows_path)),
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"model": asdict(model),
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"summary": asdict(summary),
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}
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@ -0,0 +1,200 @@
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import json
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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SRC_DIR = ROOT / "src"
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if str(SRC_DIR) not in sys.path:
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sys.path.insert(0, str(SRC_DIR))
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import renunney.track1_api as api
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import renunney.track1_fit as fit
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def _synthetic_run_rows():
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return [
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{
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"seed": 1,
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"K": 500,
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"N0": 500,
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"n": 1,
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"u": 0.005,
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"M": 5.0,
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"R": 10.0,
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"T": 20,
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"epochs": 2,
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"p": 0.5,
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"generations_recorded": 25,
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"extinction_occurred": False,
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"first_extinction_t": None,
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"final_extinct": False,
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"final_N": 300,
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"min_N": 200,
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"max_N": 520,
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"mean_N": 360.0,
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"final_mean_allele_value": 1.8,
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"final_target_value": 1.9,
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"final_tracking_gap": -0.1,
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"mean_abs_tracking_gap": 0.2,
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"max_abs_tracking_gap": 0.4,
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"first_nonzero_allele_t": -3,
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"last_nonzero_allele_t": 19,
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"stayed_zero_after_initialization": False,
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"first_productivity_below_replacement_t": None,
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"fraction_generations_below_replacement": 0.1,
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"longest_zero_mutation_streak": 0,
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"cumulative_expected_mutations": 90.0,
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"cumulative_realized_mutations": 110,
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"cumulative_mutation_shortfall": 6.0,
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},
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{
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"seed": 2,
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||||
"K": 500,
|
||||
"N0": 500,
|
||||
"n": 1,
|
||||
"u": 0.001,
|
||||
"M": 1.0,
|
||||
"R": 10.0,
|
||||
"T": 10,
|
||||
"epochs": 2,
|
||||
"p": 0.5,
|
||||
"generations_recorded": 25,
|
||||
"extinction_occurred": True,
|
||||
"first_extinction_t": 15,
|
||||
"final_extinct": True,
|
||||
"final_N": 0,
|
||||
"min_N": 0,
|
||||
"max_N": 500,
|
||||
"mean_N": 140.0,
|
||||
"final_mean_allele_value": 0.2,
|
||||
"final_target_value": 1.9,
|
||||
"final_tracking_gap": -1.7,
|
||||
"mean_abs_tracking_gap": 1.1,
|
||||
"max_abs_tracking_gap": 1.8,
|
||||
"first_nonzero_allele_t": 2,
|
||||
"last_nonzero_allele_t": 8,
|
||||
"stayed_zero_after_initialization": False,
|
||||
"first_productivity_below_replacement_t": -1,
|
||||
"fraction_generations_below_replacement": 0.9,
|
||||
"longest_zero_mutation_streak": 7,
|
||||
"cumulative_expected_mutations": 12.0,
|
||||
"cumulative_realized_mutations": 3,
|
||||
"cumulative_mutation_shortfall": 9.0,
|
||||
},
|
||||
{
|
||||
"seed": 3,
|
||||
"K": 500,
|
||||
"N0": 20,
|
||||
"n": 1,
|
||||
"u": 0.001,
|
||||
"M": 1.0,
|
||||
"R": 10.0,
|
||||
"T": 10,
|
||||
"epochs": 2,
|
||||
"p": 0.5,
|
||||
"generations_recorded": 25,
|
||||
"extinction_occurred": True,
|
||||
"first_extinction_t": 12,
|
||||
"final_extinct": True,
|
||||
"final_N": 0,
|
||||
"min_N": 0,
|
||||
"max_N": 200,
|
||||
"mean_N": 70.0,
|
||||
"final_mean_allele_value": 0.0,
|
||||
"final_target_value": 1.9,
|
||||
"final_tracking_gap": -1.9,
|
||||
"mean_abs_tracking_gap": 1.3,
|
||||
"max_abs_tracking_gap": 2.0,
|
||||
"first_nonzero_allele_t": None,
|
||||
"last_nonzero_allele_t": None,
|
||||
"stayed_zero_after_initialization": True,
|
||||
"first_productivity_below_replacement_t": -3,
|
||||
"fraction_generations_below_replacement": 1.0,
|
||||
"longest_zero_mutation_streak": 12,
|
||||
"cumulative_expected_mutations": 8.0,
|
||||
"cumulative_realized_mutations": 1,
|
||||
"cumulative_mutation_shortfall": 7.0,
|
||||
},
|
||||
{
|
||||
"seed": 4,
|
||||
"K": 500,
|
||||
"N0": 500,
|
||||
"n": 1,
|
||||
"u": 0.005,
|
||||
"M": 5.0,
|
||||
"R": 10.0,
|
||||
"T": 10,
|
||||
"epochs": 2,
|
||||
"p": 0.5,
|
||||
"generations_recorded": 25,
|
||||
"extinction_occurred": False,
|
||||
"first_extinction_t": None,
|
||||
"final_extinct": False,
|
||||
"final_N": 380,
|
||||
"min_N": 180,
|
||||
"max_N": 520,
|
||||
"mean_N": 350.0,
|
||||
"final_mean_allele_value": 1.7,
|
||||
"final_target_value": 1.9,
|
||||
"final_tracking_gap": -0.2,
|
||||
"mean_abs_tracking_gap": 0.25,
|
||||
"max_abs_tracking_gap": 0.6,
|
||||
"first_nonzero_allele_t": -2,
|
||||
"last_nonzero_allele_t": 19,
|
||||
"stayed_zero_after_initialization": False,
|
||||
"first_productivity_below_replacement_t": 1,
|
||||
"fraction_generations_below_replacement": 0.2,
|
||||
"longest_zero_mutation_streak": 1,
|
||||
"cumulative_expected_mutations": 80.0,
|
||||
"cumulative_realized_mutations": 90,
|
||||
"cumulative_mutation_shortfall": 10.0,
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def test_fit_extinction_run_model_returns_model_and_summary():
|
||||
model, summary = fit.fit_extinction_run_model(_synthetic_run_rows())
|
||||
assert model.converged is True
|
||||
assert model.feature_names == list(fit.DEFAULT_RUN_FEATURES)
|
||||
assert len(model.feature_means) == len(model.feature_names)
|
||||
assert len(model.feature_scales) == len(model.feature_names)
|
||||
assert len(model.coefficients) == len(model.feature_names)
|
||||
assert summary.sample_count == 4
|
||||
assert summary.extinction_count == 2
|
||||
assert 0.0 <= summary.brier_score <= 1.0
|
||||
|
||||
|
||||
def test_fit_payload_from_jsonl_and_api_mode(tmp_path: Path):
|
||||
path = tmp_path / "run_rows.jsonl"
|
||||
with path.open("w", encoding="utf-8") as handle:
|
||||
for row in _synthetic_run_rows():
|
||||
handle.write(json.dumps(row, sort_keys=True) + "\n")
|
||||
|
||||
payload = fit.fit_payload_from_jsonl(path)
|
||||
assert payload["summary"]["sample_count"] == 4
|
||||
assert payload["model"]["converged"] is True
|
||||
|
||||
config = api.Track1RunConfig(mode="extinction_fit", run_rows_path=str(path))
|
||||
api_payload = api.run_config(config)
|
||||
assert api_payload["mode"] == "extinction_fit"
|
||||
assert api_payload["fit_status"] == "ok"
|
||||
assert api_payload["summary"]["extinction_count"] == 2
|
||||
|
||||
|
||||
def test_api_extinction_fit_reports_insufficient_outcome_variation(tmp_path: Path):
|
||||
path = tmp_path / "run_rows.jsonl"
|
||||
only_survivors = _synthetic_run_rows()[:1] + [_synthetic_run_rows()[3]]
|
||||
with path.open("w", encoding="utf-8") as handle:
|
||||
for row in only_survivors:
|
||||
row = dict(row)
|
||||
row["extinction_occurred"] = False
|
||||
row["final_extinct"] = False
|
||||
row["first_extinction_t"] = None
|
||||
handle.write(json.dumps(row, sort_keys=True) + "\n")
|
||||
|
||||
config = api.Track1RunConfig(mode="extinction_fit", run_rows_path=str(path))
|
||||
payload = api.run_config(config)
|
||||
assert payload["mode"] == "extinction_fit"
|
||||
assert payload["fit_status"] == "insufficient_outcome_variation"
|
||||
assert payload["model"] is None
|
||||
assert payload["summary"]["sample_count"] == 2
|
||||
Loading…
Reference in New Issue