Publish angle_extended (lan) from run ce3cb0db4dc64c8192444b5d025f1c91

angle_extended.onnx

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lan/angle_extended/README.md

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+---
+tags:
+- lan
+- ssm
+- hssm
+- angle
+- collapsing-bound
+library_name: onnx
+license: bsd-2-clause
+---
+
+# angle_extended (LAN)
+
+Likelihood approximation network for the angle_extended sequential sampling model: the standard angle model -- constant drift v with a linearly collapsing decision bound whose collapse angle is theta -- with the drift bounds widened from (-3, 3) to (-6, 6) for designs that produce strong evidence. Parameters are v, a, z, t, theta, in that order, and the network was trained on the full ssm-simulators bounds for the model.
+Recovery verdict: PASS. Judged alone on a 240-fit sweep (12 design cells x 20 paired datasets, 81 family-corrected tests) this network records zero coverage failures, zero bias failures, and zero cells the design could not identify, with 84% of parameter records converged (r-hat <= 1.01, bulk ESS >= 400). The extended drift range is exercised, not merely declared: density checks and recovery truths reach |v| near 4.5 and beyond. The sweep contains no analytical or blackbox reference arm -- none exists for a collapsing- bound model -- so, as with every LAN in this family, coverage is judged against nominal rather than against an exact-likelihood ceiling.
+Density: the worst Hellinger distance to a 20k-trial simulation is 0.054, which is BELOW the Monte Carlo sampling floor at that sample size (ratio 0.92 against a gate bar of 3.0) -- the network is closer to the simulator than the simulator is to itself at 20k trials. Total probability mass stays within 0.4% of 1.0 on every draw.
+A minority of truth draws produce genuinely hard posteriors -- long sampler trajectories, occasional non-convergence; wall time and convergence correlate at -0.50 across the sweep. This is posterior geometry at specific truths, not a defect localised to the network, and no design cell fell below the judgement threshold because of it.
+
+## Architecture
+
+- **Network Type:** lan
+- **Layer Sizes:** [128, 128, 128, 128, 1]
+- **Activations:** ['tanh', 'tanh', 'tanh', 'tanh', 'linear']
+
+## Training
+
+- **Epochs:** 20
+- **Optimizer:** adam
+- **Learning Rate:** 0.001
+- **Loss:** huber
+
+## Usage with HSSM
+
+```python
+import hssm
+
+# p_outlier=0.0 is deliberate. HSSM attaches a 5% lapse by default, but the
+# training data contains no lapses, so the default misspecifies every fit --
+# the bias stays fixed while the posterior sd shrinks as 1/sqrt(n), so
+# coverage gets WORSE as the dataset grows. Set a lapse only if your data
+# actually has one.
+model = hssm.HSSM(data=data, model="angle_extended", p_outlier=0.0)
+
+# On an HSSM release that predates the built-in config, pass the network
+# and the box explicitly. Keep the parameter order exactly as written: it
+# is the ONNX input column order, and a wrong order does not raise -- it
+# silently evaluates the wrong likelihood.
+model = hssm.HSSM(
+    data=data,
+    model="angle_extended",
+    loglik="angle_extended.onnx",
+    loglik_kind="approx_differentiable",
+    p_outlier=0.0,
+    model_config={
+        "list_params": ["v", "a", "z", "t", "theta"],
+        "backend": "jax",
+        "bounds": {
+            "v": (-6.0, 6.0), "a": (0.3, 3.0), "z": (0.1, 0.9),
+            "t": (0.001, 2.0), "theta": (-0.1, 1.3),
+        },
+    },
+)
+```
+
+## Citation
+
+If you use this model, please cite:
+
+- [LANfactory](https://github.com/lnccbrown/LANfactory)
+- [HSSM](https://github.com/lnccbrown/HSSM)

lan/angle_extended/angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_data_details.pickle

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lan/angle_extended/angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_model.onnx

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lan/angle_extended/angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_train_config.pickle

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lan/angle_extended/angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_train_state_dict.pt

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lan/angle_extended/angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_training_history.csv

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lan/angle_extended/e0a375cea73d11f1abc5cfbfc94f3670_lan_angle_extended_network_config.pickle

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lan/angle_extended/model_card.yaml

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+tags:
+  - lan
+  - ssm
+  - hssm
+  - angle
+  - collapsing-bound
+library_name: onnx
+license: bsd-2-clause
+title: angle_extended (LAN)
+
+# `architecture` and `training` are deliberately absent: LANfactory fills them
+# from *_network_config.pickle and *_train_config.pickle, which are the
+# authoritative record of what was trained. Restating them here would only
+# create something that can drift -- and supplying one in the wrong shape
+# silently bypasses that fallback.
+
+description: >-
+  Likelihood approximation network for the angle_extended sequential sampling
+  model: the standard angle model -- constant drift v with a linearly
+  collapsing decision bound whose collapse angle is theta -- with the drift
+  bounds widened from (-3, 3) to (-6, 6) for designs that produce strong
+  evidence. Parameters are v, a, z, t, theta, in that order, and the network
+  was trained on the full ssm-simulators bounds for the model.
+
+  Recovery verdict: PASS. Judged alone on a 240-fit sweep (12 design cells x
+  20 paired datasets, 81 family-corrected tests) this network records zero
+  coverage failures, zero bias failures, and zero cells the design could not
+  identify, with 84% of parameter records converged (r-hat <= 1.01, bulk ESS
+  >= 400). The extended drift range is exercised, not merely declared: density
+  checks and recovery truths reach |v| near 4.5 and beyond. The sweep contains
+  no analytical or blackbox reference arm -- none exists for a collapsing-
+  bound model -- so, as with every LAN in this family, coverage is judged
+  against nominal rather than against an exact-likelihood ceiling.
+
+  Density: the worst Hellinger distance to a 20k-trial simulation is 0.054,
+  which is BELOW the Monte Carlo sampling floor at that sample size (ratio
+  0.92 against a gate bar of 3.0) -- the network is closer to the simulator
+  than the simulator is to itself at 20k trials. Total probability mass stays
+  within 0.4% of 1.0 on every draw.
+
+  A minority of truth draws produce genuinely hard posteriors -- long sampler
+  trajectories, occasional non-convergence; wall time and convergence
+  correlate at -0.50 across the sweep. This is posterior geometry at specific
+  truths, not a defect localised to the network, and no design cell fell
+  below the judgement threshold because of it.
+
+selection_note: >-
+  Selected over a [128]x5 sibling trained on identical data, with datasets and
+  seeds shared across arms so the comparison is paired. The rejected arm has
+  the BETTER validation loss (0.002341 against 0.002398) and the better
+  density ratio (0.895 against 0.919), and fails recovery: five cells short of
+  the ten-converged-fits bar, against zero here. No coverage or bias failure
+  on either arm. This is the second model family in a row where validation
+  loss ranked candidates in reverse order of recovery quality, and the first
+  where the density gate did too -- neither should be used alone to choose
+  between candidates of this family. The rejected arm's artifacts are
+  retained (run_uuid e2733e98a73d11f1960d3517857ccb84) with its own failing
+  recovery report; it is rejected for unproven convergence behaviour, not for
+  demonstrated miscalibration.
+
+model: angle_extended
+network_type: lan
+n_epochs: 20
+input_dim: 7                  # 5 parameters + rt + choice
+parameters: [v, a, z, t, theta]
+bounds:
+  v:     [-6.0, 6.0]
+  a:     [0.3, 3.0]
+  z:     [0.1, 0.9]
+  t:     [0.001, 2.0]
+  theta: [-0.1, 1.3]
+choices: [-1, 1]
+
+training_data:
+  n_parameter_sets_per_file: 5000
+  n_files: 300                # 294 train / 6 validation
+  n_samples_per_parameter_set: 2000
+  simulator_samples: 200000
+  delta_t: 0.001
+
+usage_example: |
+  import hssm
+
+  # p_outlier=0.0 is deliberate. HSSM attaches a 5% lapse by default, but the
+  # training data contains no lapses, so the default misspecifies every fit --
+  # the bias stays fixed while the posterior sd shrinks as 1/sqrt(n), so
+  # coverage gets WORSE as the dataset grows. Set a lapse only if your data
+  # actually has one.
+  model = hssm.HSSM(data=data, model="angle_extended", p_outlier=0.0)
+
+  # On an HSSM release that predates the built-in config, pass the network
+  # and the box explicitly. Keep the parameter order exactly as written: it
+  # is the ONNX input column order, and a wrong order does not raise -- it
+  # silently evaluates the wrong likelihood.
+  model = hssm.HSSM(
+      data=data,
+      model="angle_extended",
+      loglik="angle_extended.onnx",
+      loglik_kind="approx_differentiable",
+      p_outlier=0.0,
+      model_config={
+          "list_params": ["v", "a", "z", "t", "theta"],
+          "backend": "jax",
+          "bounds": {
+              "v": (-6.0, 6.0), "a": (0.3, 3.0), "z": (0.1, 0.9),
+              "t": (0.001, 2.0), "theta": (-0.1, 1.3),
+          },
+      },
+  )

lan/angle_extended/recovery_report.json

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+{
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+  "n_shards": 240,
+  "nominal_coverage": 0.94,
+  "thresholds": {
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+    "min_ess": 400.0,
+    "min_converged_fits": 10,
+    "max_divergence_rate": 0.05,
+    "min_choice_share": 0.02,
+    "max_abs_z": 2.0,
+    "unreferenced_max_bias_rate": 0.25,
+    "max_contraction": 0.95,
+    "max_contraction_ratio": 1.5,
+    "family_alpha": 0.05
+  },
+  "passed": true,
+  "failures": [],
+  "unidentified": [],
+  "onnx_files": [
+    "angle_extended_lan_e0a375cea73d11f1abc5cfbfc94f3670_model.onnx"
+  ],
+  "cells": {
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+}

lan/angle_extended/validation_report.json

@@ -0,0 +1,117 @@
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+}

manifest.json

@@ -1,6 +1,24 @@
 {
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