diff --git a/analysis/validation.ipynb b/analysis/validation.ipynb index 23f8e4e..ca85a71 100644 --- a/analysis/validation.ipynb +++ b/analysis/validation.ipynb @@ -1,5 +1,20 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "f65c3ad9", + "metadata": {}, + "source": [ + "# GIANT validation notebook\n", + "\n", + "Diagnostics for a trained checkpoint's sample quality, run against `giant predict --coord local` output (`pred_*`/`true_*` columns, denormalized but still local-frame/log-scaled — see `giant.analysis`'s module docstring). Four tiers, each building on the last:\n", + "\n", + "1. **stratified marginals** — per-dimension real-vs-generated, sliced by pdg/material/energy\n", + "2. **joint structure** — correlation matrices, physically-coupled pairwise plots, direction alignment\n", + "3. **physical constraints** — unit-norm directions, non-negative step_length/delta_e/edep\n", + "4. **event-level (shower) observables** — total energy, longitudinal/transverse profiles, shower-max depth, in world-frame physical units (mm, MeV)\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -9,9 +24,21 @@ "source": [ "from giant.analysis import load_predicted_local, plot_kl_bars_pl\n", "\n", + "# Predict parquet produced by `giant predict --coord local --checkpoint ...`,\n", + "# carrying both pred_*/true_* columns so real vs. generated can be compared.\n", "FILE = \"/home/lars/Programming/giant/pbwo4_10k_9_predicted_local.parquet\"\n" ] }, + { + "cell_type": "markdown", + "id": "82142e6b", + "metadata": {}, + "source": [ + "## Tier 1: stratified marginals\n", + "\n", + "KL(real || generated) per target dimension, computed lazily straight from the parquet over every row in the file." + ] + }, { "cell_type": "code", "execution_count": 2, @@ -22,13 +49,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_97078/906935110.py:3: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/tmp/ipykernel_338481/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n", - "/tmp/ipykernel_97078/906935110.py:3: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/tmp/ipykernel_338481/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n", - "/tmp/ipykernel_97078/906935110.py:3: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/tmp/ipykernel_338481/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n", - "/tmp/ipykernel_97078/906935110.py:3: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + "/tmp/ipykernel_338481/3415847466.py:7: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, @@ -74,11 +101,25 @@ } ], "source": [ + "# KL(real || generated) per target dimension, read lazily straight from the\n", + "# parquet (no SampleCollection, no row subsampling) so this covers every row\n", + "# in the file regardless of size. \"energy\"/\"pdg\"/\"material\" stratify the\n", + "# aggregate check so a failure hidden by the overall KL doesn't go unnoticed.\n", "for grouping in [None, \"energy\", \"pdg\", \"material\"]:\n", " fig = plot_kl_bars_pl(FILE, group_by=grouping)\n", " fig.show()" ] }, + { + "cell_type": "markdown", + "id": "22c67dc4", + "metadata": {}, + "source": [ + "## Detailed marginals, correlation & constraints (Tiers 1–3, in-memory sample)\n", + "\n", + "The richer per-row diagnostics below (overlaid histograms, correlation matrices, pairwise scatter, direction alignment, constraint violations) need `real_raw`/`gen_raw` materialized as numpy arrays, so they run on a `SampleCollection` built from a 50% row sample rather than the lazy, full-file path used above." + ] + }, { "cell_type": "code", "execution_count": 3, @@ -86,13 +127,16 @@ "metadata": {}, "outputs": [], "source": [ + "# Tier 2/3 plots need real_raw/gen_raw arrays in memory (per-row scatter,\n", + "# correlation matrices, ...), not just lazy aggregates, so they're built on\n", + "# a SampleCollection rather than the FILE path directly (see the next cell).\n", "from giant.analysis import plot_marginals, plot_correlation_matrices, plot_pairwise\n", - "from giant.analysis import plot_direction_alignment, plot_constraint_violations" + "from giant.analysis import plot_direction_alignment, plot_constraint_violations\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "id": "6d292c9c", "metadata": {}, "outputs": [ @@ -108,13 +152,16 @@ } ], "source": [ + "# sample_frac=0.5 keeps this a manageable in-memory size — fine for these\n", + "# per-row diagnostics, unlike the event-level checks further down, which\n", + "# need every row of an event present to sum correctly.\n", "samples = load_predicted_local(FILE, sample_frac=0.5)\n", "_ = plot_marginals(samples)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "79e066b1", "metadata": {}, "outputs": [ @@ -135,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "id": "d5a0bdb6", "metadata": {}, "outputs": [ @@ -154,9 +201,17 @@ "_ = plot_marginals(samples, group_by=\"pdg\")" ] }, + { + "cell_type": "markdown", + "id": "c3585ead", + "metadata": {}, + "source": [ + "## Tier 2: joint structure" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "id": "8cdb9c2a", "metadata": {}, "outputs": [ @@ -172,12 +227,15 @@ } ], "source": [ + "# Real vs. generated Pearson correlation matrices (+ their difference) over\n", + "# the 9 raw target dims — catches a model that decorrelates targets that are\n", + "# physically coupled even when every individual marginal looks clean.\n", "_ = plot_correlation_matrices(samples)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "id": "e88c8bd9", "metadata": {}, "outputs": [ @@ -193,12 +251,14 @@ } ], "source": [ + "# Scatter for physically-coupled pairs (step_length/delta_e/edep) — the\n", + "# joint-structure check correlation matrices alone can't fully capture.\n", "_ = plot_pairwise(samples, n_sample=len(samples.gen_raw))" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "8c6e5d81", "metadata": {}, "outputs": [ @@ -214,12 +274,22 @@ } ], "source": [ + "# cos(angle) between post_dir and travel_dir — coupled through the\n", + "# scattering physics, so this is another joint-structure check.\n", "_ = plot_direction_alignment(samples)" ] }, + { + "cell_type": "markdown", + "id": "6aed1234", + "metadata": {}, + "source": [ + "## Tier 3: physical constraints" + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "id": "2fb6c511", "metadata": {}, "outputs": [ @@ -235,9 +305,165 @@ } ], "source": [ + "# Unit-norm direction vectors, non-negative step_length/delta_e/edep — the\n", + "# unconstrained MLP has nothing enforcing these, so any violation here is a\n", + "# pure generation artifact rather than a real-data property.\n", "_ = plot_constraint_violations(samples)" ] }, + { + "cell_type": "markdown", + "id": "0a0446d6", + "metadata": {}, + "source": [ + "## Tier 4: event-level (shower) observables\n", + "\n", + "Everything above is a **step-level** check: one row in, one row out, compared in the local frame (`pre_dir = ẑ`). This section aggregates those same rows **per `event_id`**, reconstructed into world-frame physical units (mm, MeV), to check the shower-level quantities that actually matter physically: total deposited energy, longitudinal/transverse shower profiles, and shower-max depth (see `diffusion-model-tutorial.md` §7.2).\n", + "\n", + "**Caveat:** this re-aggregates one-step-ahead generations — each row is generated conditioned on the *real* preceding state, then grouped by event — not a full autoregressive shower rollout. It won't surface covariate-shift failures that only appear under true rollout, only how well one-step generation reconstructs aggregate shower structure when fed real conditioning throughout.\n", + "\n", + "`compute_event_observables_pl` streams the full file directly (two polars passes, no `SampleCollection`) rather than reusing `samples` above — per-event sums would be silently corrupted by `sample_frac`-style row subsampling, since a partially-sampled event no longer sums to the true per-event total." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d9a6c47b", + "metadata": {}, + "outputs": [], + "source": [ + "from giant.analysis import compute_event_observables_pl\n", + "from giant.analysis import plot_total_energy, plot_longitudinal_profile\n", + "from giant.analysis import plot_transverse_profile, plot_shower_max_depth\n", + "\n", + "# Full file, not `samples` — see the markdown cell above for why.\n", + "obs = compute_event_observables_pl(FILE)" + ] + }, + { + "cell_type": "markdown", + "id": "078d4844", + "metadata": {}, + "source": [ + "### Total deposited energy per event\n", + "\n", + "`sum(edep)` grouped by `event_id`, real vs. generated, with the resolution (σ/μ) for each annotated in the legend." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0e5d3f74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_total_energy(obs)" + ] + }, + { + "cell_type": "markdown", + "id": "cf3ea615", + "metadata": {}, + "source": [ + "### Longitudinal profile\n", + "\n", + "Mean deposited energy per event, binned by depth along the shower axis (the `pre_dir` of each event's highest-`pre_E` row), with the event-to-event RMS as error bars — the classic `E_dep(depth)` profile." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a94c6034", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_longitudinal_profile(obs)" + ] + }, + { + "cell_type": "markdown", + "id": "adbbd0ef", + "metadata": {}, + "source": [ + "### Transverse profile\n", + "\n", + "Same idea, binned by perpendicular distance from the shower axis instead of depth — a Molière-radius-style lateral containment check." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f3652b29", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_transverse_profile(obs)" + ] + }, + { + "cell_type": "markdown", + "id": "de4713d9", + "metadata": {}, + "source": [ + "### Shower-maximum depth\n", + "\n", + "Per event, the depth bin where that event's longitudinal profile peaks — compares the real vs. generated distribution of shower-max depth across events, rather than the pooled profile above." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "78bf19d5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = plot_shower_max_depth(obs)" + ] + }, { "cell_type": "code", "execution_count": null,