期权收益的 TabM：检查点选择与验证纪律

# 08_tabular_dl.py


```py
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# %% [markdown]
# # S&P 500 Options: Tabular Deep Learning
#
# Gradient boosting has been the default answer for tabular data for a decade, and neural networks
# have repeatedly failed to beat it there. TabM is one of the architectures built to close that
# gap, and it gets an ensemble's benefit without an ensemble's cost.
#
# Every member shares one two-layer network, so the features are transformed once. What separates
# the members is applied after that: each owns a vector, the same length as the shared layer's
# output, that multiplies those activations element by element, and its own final linear layer
# mapping the scaled activations to a prediction. The vectors are randomly initialised and trained,
# so each member reads a differently emphasised view of the same representation and reaches a
# different answer; the model's prediction is their mean. Disagreement between members is where an
# ensemble's benefit comes from, and here it is bought with one vector and one output layer per
# member rather than a whole additional network.
#
# This notebook fits the three declared TabM sizes on the short-straddle return and publishes a
# prediction set for every training epoch the configuration checkpoints at. The epoch is part of
# the model's identity, exactly as the boosting iteration is in
# [`07_gbm`](07_gbm.ipynb): a network at epoch 50 and the same network at epoch 200 are two
# candidates, not one candidate observed twice.
#
# **Learning objectives**
#
# - Fit an ensembling neural architecture on the same panel the linear and boosting notebooks used,
#   so that the comparison is between model families and not between inputs.
# - Publish every checkpoint as its own candidate rather than keeping the epoch that scored best,
#   and say why keeping one would be a selection.
# - Pin the compute device the population was fitted on, because it is part of what the training
#   identity is computed from.
#
# **Book reference**: Chapter 12 (Deep Learning for Trading).
#
# **Prerequisites**: [`03_financial_features`](03_financial_features.ipynb) and
# [`04_model_based_features`](04_model_based_features.ipynb) have written the feature matrices, and
# [`05_evaluation`](05_evaluation.ipynb) has established the walk-forward folds.
#
# **What it writes**: one training run per configuration and one complete validation prediction set
# per checkpoint, grouped under a named population that
# [`11_model_analysis`](11_model_analysis.ipynb) and [`12_backtest`](12_backtest.ipynb) resolve by
# name. Selection happens in `12_backtest`, on validation backtest Sharpe. The information
# coefficient shown here is a diagnostic and decides nothing.

# %%
"""Fit the declared S&P 500 options tabular deep-learning population."""

import plotly.graph_objects as go
import polars as pl

from case_studies.research import (
    declared_labels,
    load_model_configs,
    model_requests,
    narrows_declared_catalog,
    open_study,
    resolved_model_plan,
    run_model_population,
    supersedes_for_run,
)
from case_studies.sp500_options.research_workflow import (
    declared_dl_device,
    published_dl_device,
)
from utils.style import COLORS, show_plotly_with_alt

# %% tags=["parameters"]
LABELS: list[str] = []
EXECUTION_TIER = "canonical"
WORKSPACE: str = ""
PREVIEW_REDUCTIONS: dict = {}
CONFIG_NAMES: list[str] = []
POPULATION_NAME = ""
SUPERSEDES_POPULATION: str = ""
DEVICE: str = ""

# %%
study = open_study("sp500_options", execution_tier=EXECUTION_TIER, workspace=WORKSPACE or None)

# %% [markdown]
# ## 1. Which labels, and which models
#
# The label set comes from the study's own declaration rather than from a constant in this
# notebook. `config/setup.yaml` says which labels the sweep fits, and each label's menu at
# `config/training/{label}.yaml` says what to fit for it; `declared_labels` is the intersection.
# That distinction matters here more than anywhere else in the repository: this case study keeps
# full training menus for four fixed-horizon labels that `02_labels` writes for other notebooks to
# read and that the sweep dropped, and each of those four declares the same three TabM sizes. A
# notebook holding its own copy of the label list would agree with the declaration until someone
# changed one of them, and then fit a different population without saying so.

# %%
labels = tuple(LABELS) or declared_labels(study, "tabular_dl")
configs = load_model_configs(
    study,
    "tabular_dl",
    labels=labels,
    config_names=CONFIG_NAMES or None,
)
# `model_class` is empty for every row: the TabM presets under `case_studies/config/tabm/`
# declare their architecture in `params` and carry no `model_class` key, the way the LightGBM
# presets do not either. Showing the column would put a blank field in front of the reader.
configs.drop("model_class")

# %% [markdown]
# The three configurations differ on one axis, capacity, in two places at once: the width of each
# hidden layer and the number of ensemble members sharing the backbone. `tabm_s` is 64 units wide
# with 4 members, `tabm_m` doubles both, and `tabm_l` doubles them again. Dropout, batch size,
# training length and the checkpoint interval are identical across the three, so what separates
# them is how much the model can represent and how many disagreeing views it averages, and not how
# long or how hard it was trained.

# %% [markdown]
# `LABELS` and `CONFIG_NAMES` both narrow what is fitted, and a narrowed run declares a different
# set of members than the canonical population does. A population is immutable once written, so
# such a run must publish under its own name. The comparison is over `(label, config_name)` pairs
# rather than over a count, because the four out-of-sweep menus each declare exactly these three
# configurations - so a narrowed run can match the canonical population on size while sharing none
# of its members.
#
# The device is checked in the same cell. A network trained on a GPU and the same network trained
# on a CPU accumulate their sums in different orders and reach different weights, so the device is
# part of what the fitted model is and is recorded inside the computation's identity rather than
# beside it. The device this population was fitted on is declared once, in `modeling.dl.device` in
# `config/setup.yaml`, and read from there by all four deep-learning notebooks rather than retyped
# in each. On a machine with no NVIDIA card the run therefore stops at the next cell rather than
# quietly training something else: set `DEVICE="cpu"` and pass a `POPULATION_NAME` to fit the same
# grid there, under its own name.

# %%
published_device = published_dl_device()
device = declared_dl_device(DEVICE)
print(f"training device: {device} (declared: {published_device})")

if (
    narrows_declared_catalog(study, "tabular_dl", configs) or device != published_device
) and not POPULATION_NAME:
    raise ValueError(
        f"this run declares {configs.height} label-configuration pairs on device {device!r}, "
        f"which is not the complete declared catalog on {published_device!r}, so it cannot "
        f"publish the canonical population; pass POPULATION_NAME to give it its own"
    )

# %% [markdown]
# ## 2. Binding the declarations to the data
#
# Resolving reads the label and feature files, computes the fold boundaries, works out the exact
# rows each fit must predict, and fixes the checkpoint schedule. Nothing is fitted yet, so the plan
# can be inspected first. Four things to check:
#
# - **`feature_count`, `eligible_entities` and `eligible_rows` agree across every row**, so the
#   three sizes are being measured on the same sample as each other and as the linear and boosting
#   notebooks.
# - **`folds` is the same everywhere** and equals the number of walk-forward splits `05_evaluation`
#   established.
# - **`validation_start` and `validation_end` bracket the development sample**, with none of the
#   held-out tail visible.
# - **`checkpoints` is the number of training states each configuration will publish predictions
#   for.** Multiply it by the number of rows to get how many candidate models this notebook is
#   about to create.

# %%
requests = model_requests(
    study,
    configs,
    execution_tier=EXECUTION_TIER,
    overrides={"device": device},
    preview_reductions=PREVIEW_REDUCTIONS,
)
resolved = tuple(request.resolve() for request in requests)

plan = resolved_model_plan(resolved)
plan.select(
    "config_name",
    "feature_count",
    "eligible_entities",
    "eligible_rows",
    "folds",
    "validation_start",
    "validation_end",
    "checkpoints",
)

# %% [markdown]
# ## 3. Fitting the population
#
# `run_model_population` fits every resolved request. For one request it walks the folds, and on
# each one imputes and standardizes the features on the training rows alone, trains the network for
# the declared number of epochs, and at each checkpoint epoch predicts that fold's validation rows
# with the weights as they stand. The per-fold predictions are concatenated into one series
# covering the whole validation period, one series per checkpoint.
#
# **Every checkpoint becomes its own registered prediction set.** Keeping only the epoch whose
# information coefficient came out highest would be a choice made on validation data and then
# reported as if the training procedure had produced it, which is the same error as reporting the
# best of a hyperparameter search as a single experiment. The stopping point is a free parameter
# like any other, so it is carried into the comparison and paid for there.
#
# `SUPERSEDES_POPULATION` names an earlier snapshot this run replaces. It is empty because this
# population has no predecessor; a re-fit under a corrected parameter would set it, and the value
# is part of what the population is hashed over.

# %%
population_name = POPULATION_NAME or "sp500-options-tabular-dl-validation-v1"
execution, population = run_model_population(
    study,
    resolved,
    population_name=population_name,
    supersedes=supersedes_for_run(
        study,
        population_name=population_name,
        declared=SUPERSEDES_POPULATION or None,
        execution_tier=EXECUTION_TIER,
    ),
)

print(f"{len(execution.runs)} configurations fitted")
print(f"population {population.name}: {len(population.members)} prediction sets")

# %% [markdown]
# ## 4. What came out
#
# One row per configuration and checkpoint. `ic_mean` is the **information coefficient**: on each
# validation date, rank the positions by the model's prediction, rank them by the return they went
# on to earn, correlate the two rankings, and average that daily correlation over the validation
# period. It describes the predictions; it selects nothing, here or downstream.

# %% tags=["results"]
catalog = execution.catalog_rows.select(
    "config_name",
    "label",
    "complete",
    "checkpoint_value",
    "ic_mean",
    "ic_std",
    "ic_n_days",
    "n_folds",
    "training_hash",
    "prediction_hash",
).sort("config_name", "checkpoint_value")
if catalog.filter(~pl.col("complete")).height:
    raise RuntimeError("tabular execution returned a partial checkpoint")
catalog = catalog.with_columns(
    full_coverage=pl.col("ic_n_days") == pl.col("ic_n_days").max().over("label")
)
if catalog.get_column("label").n_unique() > 1:
    raise NotImplementedError(
        "this notebook charts one label; facet the figure before adding a sweep variant"
    )
print(f"{catalog.height} candidate models: {catalog.n_unique('config_name')} configurations")
print(f"at {catalog.n_unique('checkpoint_value')} checkpoints each")
catalog.select("config_name", "checkpoint_value", "ic_mean", "ic_std", "ic_n_days", "full_coverage")

# %% [markdown]
# ### What more training does
#
# Each line traces one configuration's out-of-sample information coefficient as training proceeds.
# This is the figure the checkpoint dimension exists to produce, and it separates two things that a
# single end-of-training number cannot.
#
# A line that rises and then falls has an interior optimum: the network was still learning, then
# began fitting the training window at the expense of the validation folds. A line that wanders
# around zero without trend never had anything to learn, and its highest point is wherever the
# noise happened to peak. Both produce a respectable-looking maximum, which is why the maximum is
# not what gets carried forward.

# %%
curves = catalog.filter("full_coverage").sort("config_name", "checkpoint_value")
sizes = {"tabm_s": COLORS["blue"], "tabm_m": COLORS["amber"], "tabm_l": COLORS["copper"]}
fig_curves = go.Figure()
for config_name in curves.get_column("config_name").unique(maintain_order=True):
    series = curves.filter(pl.col("config_name") == config_name)
    fig_curves.add_trace(
        go.Scatter(
            x=series.get_column("checkpoint_value").to_list(),
            y=series.get_column("ic_mean").to_list(),
            mode="lines+markers",
            name=config_name,
            line=dict(color=sizes.get(config_name, COLORS["neutral"]), width=1.8),
        )
    )
fig_curves.add_hline(y=0, line_width=1, line_dash="dash", line_color=COLORS["neutral"])
fig_curves.update_layout(
    title="Validation information coefficient against training epoch, by ensemble size",
    height=520,
    width=1000,
    margin=dict(t=70),
    legend=dict(font=dict(size=10)),
)
fig_curves.update_xaxes(title_text="Training epochs completed")
fig_curves.update_yaxes(title_text="Mean cross-sectional IC (validation)")
show_plotly_with_alt(
    fig_curves,
    "Line chart of mean cross-sectional validation information coefficient against training "
    "epoch, one line per declared TabM ensemble size.",
)

# %% [markdown]
# ### Running configurations of your own
#
# The published run log is read-only. To add runs, open the study against a workspace, which holds
# its own registry and artifacts and reads the same labels and features:
#
# ```python
# study = open_study("sp500_options", workspace="~/ml4t-experiments")
# configs = load_model_configs(study, "tabular_dl", config_names=["tabm_s"])
# requests = model_requests(study, configs, overrides={"device": "cuda"})
# resolved = tuple(request.resolve() for request in requests)
# execution, population = run_model_population(study, resolved, population_name="my-tabm-v1")
# ```
#
# To fit something new, add a preset at `case_studies/config/tabm/tabm_xl.yaml` and list `tabm_xl`
# under `tabular_dl:` in the label's menu. Editing an existing preset changes that configuration's
# identity, so its result registers as a new row beside the old one rather than replacing it.
#
# ## Key takeaways
#
# - A checkpoint is part of a model's identity. Publishing every one and letting the downstream
#   backtest choose keeps the cost of that choice inside the comparison, where it can be accounted
#   for, instead of hiding it in a number reported as a single experiment.
# - Anything that enters the identity is pinned rather than discovered. The compute device is the
#   example here: reading it off the hardware present would give the same notebook different
#   identities on different machines.
# - The label set has one home. A notebook that restates it agrees with the declaration until the
#   declaration moves, and then fits a different population silently.
#
# **Known limitations**: three sizes on one axis is a demonstration of how capacity is varied, not
# a search over TabM's hyperparameters, and dropout, learning rate and depth are held at the
# preset's values throughout. The information coefficient shown is pooled across folds and carries
# no interval here; `11_model_analysis` is where the predictions are compared with uncertainty.

```