* chore(quickadapter): bump strategy and regressor version 3.11.8 → 3.11.9
* feat(weights): add compose_sample_weights helper with mean=1 multiplicative composition
AFML §4.10 / mlfinpy canonical: per-label mean=1 normalization, multiplicative composition with temporal decay, geometric-mean aggregation for multi-label, NaN/inf handling, all-zero degenerate fallback. Validated locally with pytest (evidence: .omo/evidence/task-5-{red,green}.txt).
* fix(weights): persist label weights into <label>_weight column instead of rescaling target
Removes statistically incorrect target rescaling (label = direction × weight). Persists raw direction labels and a separate <label>_weight column for downstream sample_weight composition. Validated locally with pytest (evidence: .omo/evidence/task-6-{red,green}.txt).
* feat(weights): add _strip_label_weight_columns helper for find_labels collision avoidance
* feat(weights): compose per-label weights with temporal decay before model.fit
* feat(weights): integrate sample_weight composition into both train() data split paths
Add _train_default() mirroring BaseRegressionModel.train() with _compose_train_weights inserted between make_train_test_datasets and _apply_pipelines. Routes train_test_split path through _train_default instead of super().train(). Inserts _compose_train_weights before _apply_pipelines in timeseries_split path. Calls _strip_label_weight_columns(dk) at top of train() for both branches. Validated locally with pytest + structural AST checks (evidence: .omo/evidence/task-9-{pytest,structural}.txt).
* refactor(weights): align _train with BaseRegressionModel.train
Rename _train_default to _train to match the upstream method name (with
underscore prefix to mark it as the internal mirror, since the public
train() method routes between data split paths).
Mirror BaseRegressionModel.train line-for-line with _compose_train_weights
as the single intentional insertion between make_train_test_datasets and
the pipeline application:
- Drop ensure_datetime_series wrapper around unfiltered_df['date']:
upstream calls .iloc[].strftime() directly.
- Drop **kwargs from self.fit(dd, dk) to match upstream signature.
- Use dk.data_dictionary['train_features'].columns for feature count log,
matching upstream source of truth.
- Apply the same cosmetic alignment to the timeseries_split path for
consistency between both train code paths.
- Add docstring documenting the mirror relationship and the single
functional difference.
* refactor(weights): drop dead apply_label_weighting wrapper
Now that QuickAdapterV3.set_freqai_targets persists raw label direction
into the label column and weights into <label>_weight (consumed by
sample_weight downstream), the apply_label_weighting wrapper that
multiplied label values by their weights is no longer used.
- Drop Utils.apply_label_weighting (returned (weighted_label, weights)).
- Drop Utils._apply_label_weights (the values × weights helper).
- Switch QuickAdapterV3.set_freqai_targets to call compute_label_weights
directly (already used internally by the removed wrapper).
* refactor(weights): simplify MAXIMA/MINIMA plot columns for binary direction
Now that the label column holds raw direction in {-1, 0, +1}, the MAXIMA
and MINIMA plot columns reduce to a where(direction>0, 0.0) /
where(direction<0, 0.0) projection.
The previous magnitude-aware logic (plot_eps padding, mask of zero values
on positive direction, etc.) was tailored to the weighted label
amplitudes and is now dead code:
- extrema.abs().where(extrema.ne(0.0)).min() always evaluates to 1.0,
so plot_eps is always max(0.5, _PLOT_EXTREMA_MIN_EPS) = 0.5.
- direction.gt(0) & extrema.eq(0.0) is always False because direction>0
implies extrema = +1, never 0. The .mask() branches never trigger.
Drop the now-unused _PLOT_EXTREMA_MIN_EPS class constant.
* refactor(weights): rename smooth_label to smooth for genericity
The function applies generic smoothing kernels (gaussian, kaiser, triang,
smm, sma, savgol, gaussian_filter1d) to any pd.Series. The 'label'
suffix narrowed it to label-specific use, but it now also smooths the
<label>_weight column (next commit). Drop the suffix; the smoothing
config dict is still named label_smoothing because the per-column
config map remains label-keyed.
* feat(weights): smooth <label>_weight column with the same kernel as label
Per-label weights are pointwise: only pivot indices carry the
metric-derived weight, while non-pivot indices are filled with the
median weight (see compute_label_weights / _build_weights_array).
The label column is smoothed with smooth() to spread pivot signals
over neighbouring candles. Without smoothing the weight column, the
smoothed label values around a pivot keep the constant median weight,
so the model treats high-amplitude pivot neighbours and the pivot
itself as equally important during training.
Apply smooth() to the <label>_weight column with the exact same
per-column smoothing config as the label, so the weight profile
follows the label profile candle-for-candle.
The smooth() positional argument list was redundant with the
col_smoothing_config dict keys; collapse both calls to **kwargs
unpacking. _SMOOTHING_SPECS keys exactly match smooth() parameter
names, so the unpack is type-safe.
compose_sample_weights already replaces non-finite or non-positive
values with 1.0, which absorbs any sign overshoot from kernels like
savgol at series edges.
* feat(weights): add direction and weight subplots showing raw + smoothed signals
Replace the MAXIMA/MINIMA bar visualization with two new subplots that
show both the raw and the smoothed direction/weight curves:
- Subplot 'direction' overlays raw direction (extrema_direction) and
smoothed direction (smoothed_extrema).
- Subplot 'weight' overlays raw weight (extrema_weight) and smoothed
weight (smoothed_extrema_weight).
Each visualization column is captured at the right point in
set_freqai_targets:
- Raw columns (EXTREMA_DIRECTION_COLUMN, EXTREMA_WEIGHT_COLUMN) are
written before the smooth() call.
- Smoothed columns (SMOOTHED_EXTREMA_COLUMN, SMOOTHED_EXTREMA_WEIGHT_COLUMN)
are written after the smooth() call.
Drop MAXIMA_COLUMN and MINIMA_COLUMN constants — they were only used by
the old min_max bar subplot. The new subplots convey the same direction
information plus the per-pivot weight magnitude that the legacy
weighted_label visualization showed (before the sample-weight refactor).
All four visualization column names lack the '&' prefix, so FreqAI's
find_labels auto-detection ignores them; they cannot leak into model
targets.
* refactor(weights): align visualization column names on extrema_<axis>[_smoothed]
Three-agent audit (explore + librarian + oracle) found the previous viz
column names suffered from three inconsistencies:
- 'extrema_direction' (raw) carries the axis word, but 'smoothed_extrema'
drops it, breaking the 2x2 grid (axis × stage).
- Stage qualifier appears as PREFIX ('smoothed_extrema') for one column
and as SUFFIX ('smoothed_extrema_weight') for another.
- 'smoothed_extrema_weight' mixes prefix stage word with suffix axis word.
Production codebases (statsmodels Kalman, bukosabino/ta MACD, mlflow
NPMI, FreqAI's own _mean/_std) overwhelmingly use suffix-decorated
processed forms with the raw form as the plain base. FreqAI's internal
pattern is suffix (&s-extrema_weight, &s-extrema_mean, &s-extrema_std);
align with it.
Rename:
- SMOOTHED_EXTREMA_COLUMN ('smoothed_extrema')
-> EXTREMA_DIRECTION_SMOOTHED_COLUMN ('extrema_direction_smoothed')
- SMOOTHED_EXTREMA_WEIGHT_COLUMN ('smoothed_extrema_weight')
-> EXTREMA_WEIGHT_SMOOTHED_COLUMN ('extrema_weight_smoothed')
Result: every viz column follows extrema_<axis>[_smoothed]. The 2x2
grid is uniform, sort-order groups raw and smoothed pairs together,
and the pattern is internally consistent with FreqAI's existing
suffix-based derivations.
* fix(weights): address PR #72 review comments
Three-agent cross-validation (explore + librarian + oracle) of Copilot
review comments produced these verdicts:
C1 (Utils.py:compose_sample_weights) — REAL BUG. Replacing 0-valued
weights with 1.0 silently undoes sklearn / AFML §4.10's canonical 'drop
this sample' semantic. sklearn's _check_sample_weight_equivalence,
DecisionTree _splitter.pyx, HistGBM docs, LightGBM #5553/#905, XGBoost
#3787 and mlfinlab time-decay all converge on the same contract:
sample_weight=0 means 'this sample contributes nothing'. Preserve zeros
via a drop_mask that is OR'd across labels (any label saying 'drop'
wins), then re-applied after the geometric-mean composition. Non-zero
non-finite or negative values still collapse to 1.0 (geometric mean's
neutral element) since they represent undefined weights, not exclusions.
C3/C4 (QuickAdapterRegressorV3.py:_train, timeseries_split) — REAL
REGRESSION. Commit
9953f0c removed ensure_datetime_series with the
rationale 'mirror BaseRegressionModel.train exactly'. But
ensure_datetime_series was introduced in commit
ce843f9 specifically
as a workaround for freqtrade issue #13107 (int64 epoch-ms date
columns from feather/parquet handlers). Mirror the algorithm, retain
project-specific safety patches. Restore ensure_datetime_series in
both train paths.
C2/C7 (_label_weight_column_name unused) — DRY violation. Both call
sites in _compose_train_weights now use the helper instead of inline
f-strings.
C8 (train() docstring) — Inaccurate. The default path was claimed to
'Delegate to BaseRegressionModel.train()' but actually routes to
self._train() (a mirror with weight composition). Fix docstring to
reflect actual control flow.
C5/C6 (**kwargs forwarding) — FALSE POSITIVE. freqai_interface never
passes kwargs to model.train(); upstream BaseRegressionModel.train
also calls self.fit(dd, dk) without kwargs. The current code matches
upstream and the call chain is dead in practice.
* refactor(weights): factor train paths and relocate methods for structural coherence
Three-agent structural audit (explore + librarian + oracle) identified
five issues; fixes that don't fight existing conventions:
1. _train and the timeseries_split inline branch in train() shared
~30 lines of identical scaffolding (filter_features, dates logging,
fit_labels guard, weight composition, pipeline application, fit,
timing logs). Extract _train_common(unfiltered_df, pair, dk, split_fn)
that owns the full mirror; _train_default and _train_timeseries_split
become 4-line dispatchers passing the split callback. train() routing
collapses to a clean two-line if/elif.
2. _label_weight_column_name, _strip_label_weight_columns and
_compose_train_weights were inserted into the middle of the class
constants block (between _TEST_SIZE and _SQRT_2), interrupting the
constant-block coherence. Move them to the private instance method
zone, immediately after _apply_pipelines (their natural neighbour).
3. _compose_train_weights duplicated the train/test weight extraction
loop verbatim. Factor into a static _extract_split_weights helper
that takes a split index and returns the per-label weight map; both
train and test call sites become single expressions.
4. The four visualization column constants (EXTREMA_DIRECTION_COLUMN,
EXTREMA_DIRECTION_SMOOTHED_COLUMN, EXTREMA_WEIGHT_COLUMN,
EXTREMA_WEIGHT_SMOOTHED_COLUMN) were 75 lines below EXTREMA_COLUMN /
LABEL_COLUMNS, separated by the LabelData dataclass and label
generator registry. Move them next to EXTREMA_COLUMN where they
logically belong.
5. Revert the train() docstring bullet to its upstream form. The
modification introduced RST cross-reference syntax inconsistent with
the surrounding plain-text docstring style.
* refactor(weights): centralize LABEL_WEIGHT_SUFFIX in Utils
The "_weight" suffix was duplicated as a class constant in
QuickAdapterRegressorV3 and as 5 hardcoded f-strings in QuickAdapterV3.
Three-agent audit (explore + librarian + oracle) converged on moving
this column-naming convention to Utils.py:
- PEP 8 default for constants is module-level; class-level is the
exception for class-private semantics.
- Both consumers already import column-naming constants from Utils.py
(LABEL_COLUMNS, EXTREMA_COLUMN, EXTREMA_WEIGHT_COLUMN, etc.). The
suffix belongs with them.
- Production precedents (sklearn UNUSED/WARN/UNCHANGED, mlflow
_SAMPLE_WEIGHT/_TRAINING_PREFIX, lightgbm _DatasetNames, pandas
LOCAL_TAG) all place cross-module string tokens at module level.
- The constant describes a dataframe schema contract (column names),
not model behaviour. Schema concerns belong in the schema module.
Add LABEL_WEIGHT_SUFFIX to Utils.py next to LABEL_COLUMNS. Remove the
class-level _LABEL_WEIGHT_SUFFIX in QuickAdapterRegressorV3 and import
the module-level constant. Replace 5 hardcoded f-strings in
QuickAdapterV3 with a local label_weight_col binding using the
imported constant.
The private _label_weight_column_name helper is kept in the regressor
since it is used twice (in _strip_label_weight_columns and
_compose_train_weights) and still adds a thin DRY layer over the
suffix synthesis.
* style: apply ruff formatting
* feat(weights): add label_weight_column helper with regex prefix strip and collision assertion
* fix(weights): preserve drop_mask and prevent NaN in compose_sample_weights fallback
* refactor(weights): adopt label_weight_column helper for canonical training column
* feat(weights): add _build_per_row_weights helper for pre-split weight composition
* feat(weights): add _make_default_split_datasets mirror with sklearn-key whitelist
* refactor(weights): make timeseries split helper accept external weights parameter
* refactor(weights): refactor _train_common chain and delete obsolete weight helpers
* refactor(weights): replace train() if/elif with dispatch dict and add weight-column uniqueness check
* fix(weights): harden compose_sample_weights for degenerate inputs
Address audit findings A0-1, A0-2, A0-3, A0-4, P2 #6, P2 #9, P2 #11
on branch feat/per-label-sample-weights.
- Extract _sanitize_and_renormalize private helper with four-guard chain
(positive sum, finite sum, finite ratio, finite scaled) and uniform
fallback. Used at empty-map fast path and at final fallback site.
- Empty label_weights_map now sanitizes raw temporal (A0-3).
- Subnormal temporal no longer overflows: ratio + scaled both checked
for finiteness before returning (A0-2).
- Drop predicate unified as 'arr <= 0 or non-finite' instead of exact
zero, eliminating the discontinuity at zero from smoothing artifacts
(A0-4); negatives now drop, no longer rescued to 1.0 (subsumes A1-10).
- Surviving positive values floored at np.finfo(float).tiny to prevent
subnormal arithmetic in the geo-mean log step.
- drop_mask covering all rows now raises ValueError instead of silently
returning all-zero weights that crash XGBoost / sklearn HGBR (A0-1).
- Up-front per-label shape validation raises a precise error instead of
letting numpy broadcasting fail mid-computation (P2 #11).
* docs(weights): document compose_sample_weights contract
Address audit findings A1-2, P2 #12, A1-12 on branch
feat/per-label-sample-weights.
Add 11-line docstring covering: output invariant (mean=1), per-label
sanitization predicate, aggregation operator, drop semantics, error
conditions, and the bounded full-series-median leakage in
compute_label_weights.
* refactor(weights): inline data-split dispatch with match/case
Address audit findings A1-1, A1-7, A1-6 (obsolete), P2 #14 on branch
feat/per-label-sample-weights. Conflict C1: A1-7 wins over A1-3
(YAGNI on subclass extension; LSP traceability preserved).
- Delete _DATA_SPLIT_DISPATCH class attribute (4 LOC).
- Delete _data_split_methods_set lru_cached helper (4 LOC dead code).
- Delete _train_default and _train_timeseries_split wrappers (~30 LOC
pure boilerplate dispatching to _train_common).
- Inline match/case on method name in train(); pick the right
_make_*_split_datasets via a local split_builder; nested split_fn
closes over dk. Net -37 LOC; full LSP traceability.
- Add SplitFn module-level type alias used in _train_common signature.
* fix(weights): reject bool config values in numeric validators
Address audit findings A1-4, P2 #5, P2 #7 on branch
feat/per-label-sample-weights.
Python's `bool` is an `int` subclass, so `isinstance(True, int)` is
true and config values of `true`/`false` silently passed through as
`1`/`0` in the validators for n_splits, gap, max_train_size,
test_size and weight_factor. This commit closes that footgun:
- Add static helpers _coerce_int (always returns int, raises on bool
or non-int) and _coerce_optional_int (returns Optional[int]) to
centralize the validation; both echo the offending raw value via
`{value!r}` so the diagnostic shows True/False rather than 1/0.
- Apply _coerce_int to n_splits and gap, _coerce_optional_int to
max_train_size in _make_timeseries_split_datasets.
- Add explicit bool guard for test_size in both default-split and
timeseries-split paths; previously test_size=true would slip past
isinstance(_, int) and silently train on 1 sample.
- Add explicit bool guard for weight_factor before the >0 comparison.
* fix(weights): preserve mean=1 invariant across pipeline stages
Address audit finding A1-5 on branch feat/per-label-sample-weights.
compose_sample_weights guarantees sum(w)==N (mean(w)==1) over the full
training series, but this invariant breaks twice downstream: (1) the
train/test split partitions weights into disjoint subsets whose means
no longer equal 1, and (2) feature_pipeline.fit_transform may drop
rows via SVM/DBSCAN, drifting the means further. XGBoost
min_child_weight, LightGBM min_sum_hessian_in_leaf and L2
regularization are all sensitive to absolute weight scale.
- Add static helper _renormalize_to_unit_mean with the same four-guard
chain as _sanitize_and_renormalize (positive sum, finite sum, finite
ratio, finite scaled, uniform fallback).
- Apply at four sites: before dk.build_data_dictionary in both
_make_default_split_datasets and _make_timeseries_split_datasets,
and after feature_pipeline.fit_transform / .transform in
_apply_pipelines (train and test sides).
* feat(weights): make label-weights aggregation configurable
Address audit finding A1-11 on branch feat/per-label-sample-weights.
Conflict C3: switch default to arithmetic_mean (matching
_compute_combined_label_weights), expose all 6 aggregations via
existing _aggregate_metrics infrastructure.
The hardcoded geometric mean over per-label normalized arrays was
mathematically conservative (one weak label dominates) and inconsistent
with the project's _compute_combined_label_weights default
(arithmetic_mean). For PR #44's correlated multi-target labels
(amplitude, time_to_pivot, efficiency, natr all derived from zigzag),
geomean over-counts redundant evidence and silently degrades to ~0
when any single factor is small. AFML \xc2\xa74.4 recommends arithmetic-mean
equivalents for correlated meta-labels.
- Add aggregation parameter to compose_sample_weights with default
COMBINED_AGGREGATIONS[0] ("arithmetic_mean"); also expose
softmax_temperature.
- Delegate the row-wise aggregation step to _aggregate_metrics, reusing
the existing 6-operator infrastructure with uniform unit coefficients.
- Read both knobs from feature_parameters in _build_per_row_weights:
label_weights_aggregation and label_weights_softmax_temperature.
* refactor(weights): align naming on compose/sigil/base_weights
Address audit findings A1-9, A1-14, A1-15, A1-16 on branch
feat/per-label-sample-weights.
- Rename _LABEL_WEIGHT_PREFIX_PATTERN to _FREQAI_LABEL_SIGIL_PATTERN:
the regex strips the freqtrade-native '&' sigil, not a label-weight
prefix; the new name describes what is matched (Utils.py).
- Rename compose_sample_weights parameter `temporal` to `base_weights`:
the parameter accepts any base vector (recency weights or uniform
ones), not exclusively temporal data (Utils.py).
- Rename _build_per_row_weights to _compose_per_row_weights:
standardize on the 'compose' verb to mirror compose_sample_weights;
this helper is the orchestrator that calls the kernel
(QuickAdapterRegressorV3.py).
- Rename _build_weights_array to _scatter_weights: the function
scatters sparse pivot weights into a dense default-filled array,
not a generic 'build' (Utils.py).
- Rename eval_set_and_weights to make_test_set_and_weights: aligns
with FreqAI's 'test_*' data_dictionary vocabulary while avoiding
the 'test_' prefix that pytest auto-discovers (the verb 'make_'
also clarifies it as a constructor, not a test) (Utils.py + caller).
* refactor(weights): extract _shuffle_in_unison helper
Address audit findings A1-8 and A1-13 on branch
feat/per-label-sample-weights.
- Extract the train/test shuffle pattern into a static
_shuffle_in_unison helper. Each call shuffles features, labels and
weights with the same random seed in lockstep. The shuffle block in
_make_default_split_datasets shrinks from ~28 lines (two duplicated
5-line idioms x train+test) to two helper invocations.
- Fix the dk.data_dictionary vs dd inconsistency at the feature-count
log line: read from local dd (the pipeline's return value) rather
than dk.data_dictionary (a side-effect set by _apply_pipelines).
* chore(weights): polish naming, validators, caching
Address audit P2 polish items #2, #4, #8, #13, #15 on branch
feat/per-label-sample-weights. P2 #21 and #22 (logger telemetry)
deliberately skipped per the no-new-comments/no-new-infrastructure
constraint; the existing fail-fast ValueError on degenerate inputs
already surfaces the most critical failure modes loudly.
- Counter-based duplicate-label diagnostic now names the offending
weight columns instead of merely raising on a length mismatch
(P2 #2).
- Widen shuffle seed space from random.randint(0, 100) (101 distinct
seeds, birthday collisions at sqrt(101) ~ 10) to randint(0, 2**31-1)
at both _shuffle_in_unison call sites (P2 #4).
- dsp = dict(self.config['freqai']['data_split_parameters']) replaced
with self.data_split_parameters (the safe pre-populated FreqAI
attribute used everywhere else in the file) (P2 #8).
- Cache label_weight_column with @lru_cache(maxsize=16): the helper
is pure on its single string argument and called in tight loops at
training; matches the file's existing convention for similar helpers
(P2 #13).
- Rename loop variable w to label_values in compose_sample_weights;
the outer scope spans ~20 lines and prior single-letter w obscured
the role (P2 #15).
* docs(weights): align train() and helper docstrings with current behavior
- Rewrite train() docstring to describe match-based dispatch and the per-row
weight composition flow through _train_common; remove stale delegation claim.
- Sync _compose_per_row_weights docstring: aggregation default is
arithmetic_mean, not geometric_mean.
- Fix AFML citation in compose_sample_weights from section 7.4 to chapter 4.
- Document _aggregate_metrics softmax branch as a per-column convex
combination with explicit T->0 and T->+inf limits.
* refactor(weights): consolidate sample weight renormalization helper
- Promote Utils._sanitize_and_renormalize to public sanitize_and_renormalize.
- Drop QuickAdapterRegressorV3._renormalize_to_unit_mean (cross-file
duplication of the mean=1 invariant); replace 6 call sites with the
unified helper. Sites that previously skipped per-element sanitization
now also reject non-finite or non-positive entries.
- Collapse triple-guard ladder in sanitize_and_renormalize to a single
finite-positive total check; surviving 'safe' is provably finite-nonneg
so intermediate isfinite checks were dead code.
- Tighten _shuffle_in_unison signature from Any to concrete pd.DataFrame
and NDArray types.
- Drop empty-fold sentinel in _make_timeseries_split_datasets and raise
ValueError on degenerate generator output instead of silently producing
empty index arrays.
* feat(weights): validate label_weights tunables via label_weighting block
- _compose_per_row_weights now consumes get_label_weighting_config (which
validates aggregation against COMBINED_AGGREGATIONS and enforces
softmax_temperature > 0 via _WEIGHTING_SPECS) instead of reading raw
feature_parameters.label_weights_*.
- Add CONFIG_MIGRATIONS entries auto-migrating
freqai.feature_parameters.label_weights_aggregation and
freqai.feature_parameters.label_weights_softmax_temperature to the
freqai.label_weighting block; users get one warning per key.
- Add module-level _logger to Utils.py and warn on
compose_sample_weights silent fallback so collapsed-aggregation paths
are observable.
- _make_timeseries_split_datasets honors reverse_train_test_order for
parity with _make_default_split_datasets and raises ValueError on
shuffle_after_split=True (chronological + shuffle is incoherent and
would leak future data into training).
* fix(weights): seed shuffle deterministically from data_split_parameters.random_state
Replace global random.randint() with a random.Random instance derived from
data_split_parameters.random_state. When the user provides a random_state
(whitelisted in _SKLEARN_TRAIN_TEST_SPLIT_KEYS), train and test shuffles
become reproducible end-to-end; when absent, behavior remains
non-deterministic. The single parent RNG draws two independent sub-seeds
so train and test shuffles stay decorrelated.
* refactor(weights): rename for naming coherence
- Rename _make_default_split_datasets to _make_train_test_split_datasets
to restore the case-key/method-name grep-line in train()'s match
dispatch.
- Rename label_weight_column to label_weight_column_name across Utils.py,
QuickAdapterV3.py and QuickAdapterRegressorV3.py: the helper returns
a column-name string, not a column accessor; the new name matches the
*_COLUMN constant convention used elsewhere in Utils.py.
- Drop redundant 'dk.data_dictionary = dd' in _apply_pipelines:
build_data_dictionary already self-assigns the dict on dk and dd is
the same object reference.
* style(weights): group EXTREMA_* constants and separate LABEL_* declarations
* docs(weights): rewrite _make_train_test_split_datasets docstring without history narration
Replace the deviation list and PR-history reference with a concise
description of what the function IS (sklearn-key whitelist, honored
tunables, weight propagation contract).
* fix(weights): resolve KeyError in label_weighting config consumption
_compose_per_row_weights passed self.config (root) to
get_label_weighting_config and accessed weighting_config['aggregation']
directly; the helper expects freqai.label_weighting and returns
{default, columns}. Fix consumes self.freqai_info['label_weighting']
and reads ['default']['aggregation'] / ['default']['softmax_temperature'].
* fix(weights): honor drop_mask in sanitize_and_renormalize fallback
When total <= 0 or non-finite, the helper returned np.ones_like(arr)
ignoring drop_mask, resurrecting dropped rows with weight=1. Fallback
now zeros drop_mask rows before returning.
* fix(config): rename reverse_test_train_order to reverse_train_test_order
Match the canonical key name from upstream freqtrade and from the code
in QuickAdapterRegressorV3._make_train_test_split_datasets and
_make_timeseries_split_datasets. The previous template key was silently
ignored.
* fix(strategy): clip smoothed weight column to non-negative finite
Some smoothing methods (savgol, filtfilt) can ring negative on positive
input. Clip the smoothed weight series to >= 0 and replace non-finite
with 0 before assigning to the dataframe so compose_sample_weights does
not silently drop rows that were positive before smoothing.
* fix(weights): apply project _TEST_SIZE default when data_split_parameters omits test_size
The whitelist comprehension that builds sklearn_kwargs only preserved keys
present in data_split_parameters; the local test_size variable computed via
dsp.get(..., _TEST_SIZE) was never injected back. Configs without an explicit
test_size silently fell through to sklearn's stock 0.25 default instead of
the project's 0.1.
Replace bare 'shuffle' insertion with setdefault for both shuffle and
test_size so sklearn_kwargs always carries the project defaults.
Update _make_train_test_split_datasets docstring to reflect the actual
default behavior.
* refactor(strategy): rename smoothed_weights to smoothed_label_weights
Aligns naming with surrounding label_weights variable, EXTREMA_WEIGHT_SMOOTHED_COLUMN
constant and compute_label_weights helper.
* feat(weights): split per-row aggregation into freqai.sample_weighting block
Cross-metric (per-pivot) and cross-label (per-row) compositions are
distinct distributions. Decouple their tunables:
- freqai.label_weighting.{aggregation,softmax_temperature} stay for
cross-metric aggregation in compute_label_weights (combined strategy).
- New freqai.sample_weighting.{aggregation,softmax_temperature} for
cross-label composition in
QuickAdapterRegressorV3._compose_per_row_weights.
Add _SAMPLE_WEIGHTING_SPECS, DEFAULTS_SAMPLE_WEIGHTING and
get_sample_weighting_config helper routed through _get_label_config so
the returned shape ({default, columns}) matches the get_label_*_config
family.
* refactor(weights): pass logger as parameter and log per-label weight column status
Drop the module-level _logger introduced in Utils.py; compose_sample_weights
now takes a keyword-only logger argument, matching the caller-passes-logger
pattern used by every other helper in the file.
In _compose_per_row_weights, log per-label weight column resolution at
debug when columns are present (static across retrains) and at warning
when none are found (unexpected configuration; falls back to temporal
weights only).
* fix(weights): use shape-consistent empty containers for test_size=0 sentinels
Replace np.zeros(2)/pd.DataFrame() sentinels with iloc[:0] / weights[:0]
slices so test_features, test_labels and test_weights all have 0 rows
with preserved column names and dtype. Behavior unchanged because
_apply_pipelines skips test-side processing when test_size == 0, but
shape-consistent containers respect the declared types and avoid
surprising downstream consumers.
// }
// }
},
+ "sample_weighting": {
+ "aggregation": "arithmetic_mean",
+ "softmax_temperature": 1.0
+ },
"label_smoothing": {
"method": "kaiser",
"window_candles": 5,
"indicator_periods_candles": [8, 16, 32],
"inlier_metric_window": 0,
"noise_standard_deviation": 0.02,
- "reverse_test_train_order": false,
+ "reverse_train_test_order": false,
"plot_feature_importances": 0,
"buffer_train_data_candles": 100
},
import random
import time
import warnings
+from collections import Counter
from functools import lru_cache
from pathlib import Path
from typing import AbstractSet, Any, Callable, Final, Literal, Optional, Union, cast
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from numpy.typing import NDArray
from optuna.study.study import ObjectiveFuncType
-from sklearn.model_selection import TimeSeriesSplit
+from sklearn.model_selection import TimeSeriesSplit, train_test_split
from sklearn.preprocessing import (
MaxAbsScaler,
MinMaxScaler,
LABEL_COLUMNS,
REGRESSORS,
Regressor,
+ compose_sample_weights,
ensure_datetime_series,
- eval_set_and_weights,
+ make_test_set_and_weights,
fit_regressor,
format_dict,
format_number,
get_label_defaults,
get_label_pipeline_config,
get_label_prediction_config,
+ get_sample_weighting_config,
get_min_max_label_period_candles,
get_optuna_study_model_parameters,
+ label_weight_column_name,
migrate_config,
optuna_load_best_params,
optuna_save_best_params,
+ sanitize_and_renormalize,
soft_extremum,
zigzag,
)
DensityMethod = Literal["knn", "medoid"]
SelectionMethod = Union[DistanceMethod, ClusterMethod, DensityMethod]
ValidationMode = Literal["warn", "raise", "none"]
+SplitFn = Callable[[pd.DataFrame, pd.DataFrame, NDArray[np.floating]], dict[str, Any]]
warnings.simplefilter(action="ignore", category=FutureWarning)
logger = logging.getLogger(__name__)
https://github.com/sponsors/robcaulk
"""
- version = "3.11.8"
+ version = "3.11.9"
_TEST_SIZE: Final[float] = 0.1
+ _SKLEARN_TRAIN_TEST_SPLIT_KEYS: Final[frozenset[str]] = frozenset(
+ {"test_size", "train_size", "random_state", "shuffle", "stratify"}
+ )
+
_SQRT_2: Final[float] = np.sqrt(2.0)
_OPTUNA_LABEL_N_OBJECTIVES: Final[int] = 7
return set(QuickAdapterRegressorV3._POWER_MEAN_MAP.keys())
@staticmethod
- @lru_cache(maxsize=None)
- def _data_split_methods_set() -> set[str]:
- return set(QuickAdapterRegressorV3._DATA_SPLIT_METHODS)
+ def _shuffle_in_unison(
+ features: pd.DataFrame,
+ labels: pd.DataFrame,
+ weights: NDArray[np.floating],
+ seed: int,
+ ) -> tuple[pd.DataFrame, pd.DataFrame, NDArray[np.floating]]:
+ features = features.sample(frac=1, random_state=seed).reset_index(drop=True)
+ labels = labels.sample(frac=1, random_state=seed).reset_index(drop=True)
+ weights = (
+ pd.DataFrame(weights)
+ .sample(frac=1, random_state=seed)
+ .reset_index(drop=True)
+ .to_numpy()[:, 0]
+ )
+ return features, labels, weights
+
+ @staticmethod
+ def _coerce_int(value: Any, name: str, *, minimum: int) -> int:
+ if isinstance(value, bool) or not isinstance(value, int) or value < minimum:
+ raise ValueError(
+ f"Invalid data_split_parameters.{name} value {value!r}: "
+ f"must be int >= {minimum}"
+ )
+ return value
+
+ @staticmethod
+ def _coerce_optional_int(value: Any, name: str, *, minimum: int) -> Optional[int]:
+ if value is None:
+ return None
+ return QuickAdapterRegressorV3._coerce_int(value, name, minimum=minimum)
@staticmethod
def _get_selection_category(method: str) -> Optional[str]:
def train(
self, unfiltered_df: pd.DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
- """
- Filter the training data and train a model to it.
-
- Supports two data split methods:
- - 'train_test_split' (default): Delegates to BaseRegressionModel.train()
- - 'timeseries_split': Chronological split with configurable gap. Uses the final
- fold from sklearn's TimeSeriesSplit.
-
- :param unfiltered_df: Full dataframe for the current training period
- :param pair: Trading pair being trained
- :param dk: FreqaiDataKitchen object containing configuration
- :return: Trained model
+ """Train a model with per-row sample weights.
+
+ Dispatches on ``data_split_parameters.method``:
+ - ``train_test_split``: random sklearn split.
+ - ``timeseries_split``: chronological final-fold split.
+ Both paths compose per-row weights via ``_compose_per_row_weights``
+ before splitting and feed them to ``model.fit(sample_weight=...)``
+ through ``_train_common``. Train and test weights are renormalized
+ to mean=1 after ``feature_pipeline.fit_transform`` to preserve the
+ invariant despite pipeline-level row drops.
"""
method = self.data_split_parameters.get(
"method", QuickAdapterRegressorV3.DATA_SPLIT_METHOD_DEFAULT
)
- if method not in QuickAdapterRegressorV3._data_split_methods_set():
+ match method:
+ case "train_test_split":
+ split_builder = self._make_train_test_split_datasets
+ case "timeseries_split":
+ split_builder = self._make_timeseries_split_datasets
+ case _:
+ raise ValueError(
+ f"Invalid data_split_parameters.method value {method!r}: "
+ f"supported values are "
+ f"{', '.join(QuickAdapterRegressorV3._DATA_SPLIT_METHODS)}"
+ )
+
+ def split_fn(
+ features: pd.DataFrame,
+ labels: pd.DataFrame,
+ weights: NDArray[np.floating],
+ ) -> dict[str, Any]:
+ return split_builder(features, labels, weights, dk)
+
+ weight_col_counts = Counter(
+ label_weight_column_name(label) for label in dk.label_list
+ )
+ duplicates = {col: n for col, n in weight_col_counts.items() if n > 1}
+ if duplicates:
raise ValueError(
- f"Invalid data_split_parameters.method value {method!r}: "
- f"supported values are {', '.join(QuickAdapterRegressorV3._DATA_SPLIT_METHODS)}"
+ f"Duplicate weight column names {duplicates!r} from labels "
+ f"{dk.label_list}: each label must produce a unique weight_column_name"
)
logger.info(f"Using data split method: {method}")
+ return self._train_common(unfiltered_df, pair, dk, split_fn, **kwargs)
- if method == QuickAdapterRegressorV3.DATA_SPLIT_METHOD_DEFAULT:
- return super().train(unfiltered_df, pair, dk, **kwargs)
-
- elif (
- method == QuickAdapterRegressorV3._DATA_SPLIT_METHODS[1]
- ): # timeseries_split
- logger.info(
- f"-------------------- Starting training {pair} --------------------"
+ def _make_train_test_split_datasets(
+ self,
+ features: pd.DataFrame,
+ labels: pd.DataFrame,
+ weights: NDArray[np.floating],
+ dk: FreqaiDataKitchen,
+ ) -> dict[str, Any]:
+ """Train/test split via sklearn's ``train_test_split``.
+
+ Routes ``data_split_parameters`` to sklearn through a whitelist of
+ sklearn-recognized keys; project-custom keys (``method``,
+ ``n_splits``, ``gap``, ``max_train_size``) are filtered out.
+ ``shuffle`` and ``test_size`` default to ``False`` and ``_TEST_SIZE``
+ respectively when absent from ``data_split_parameters``. Honors
+ ``feature_parameters.shuffle_after_split`` (deterministic when
+ ``random_state`` is set) and ``feature_parameters.reverse_train_test_order``.
+ Per-row sample weights are sliced positionally and propagate to both
+ train and test sets.
+ """
+ feat_dict = self.freqai_info.get("feature_parameters", {})
+ dsp = dict(self.data_split_parameters)
+ dsp.setdefault("shuffle", False)
+ dsp.setdefault("test_size", QuickAdapterRegressorV3._TEST_SIZE)
+ sklearn_kwargs = {
+ k: v
+ for k, v in dsp.items()
+ if k in QuickAdapterRegressorV3._SKLEARN_TRAIN_TEST_SPLIT_KEYS
+ }
+ test_size = dsp["test_size"]
+ if isinstance(test_size, bool) or not isinstance(test_size, (int, float)):
+ raise ValueError(
+ f"Invalid data_split_parameters.test_size value {test_size!r}: "
+ f"must be int or float"
+ )
+
+ if test_size != 0:
+ (
+ train_features,
+ test_features,
+ train_labels,
+ test_labels,
+ train_weights,
+ test_weights,
+ ) = train_test_split(features, labels, weights, **sklearn_kwargs)
+ else:
+ train_features = features
+ train_labels = labels
+ train_weights = weights
+ test_features = features.iloc[:0]
+ test_labels = labels.iloc[:0]
+ test_weights = weights[:0]
+
+ if feat_dict.get("shuffle_after_split", False):
+ parent_seed = sklearn_kwargs.get("random_state")
+ shuffle_rng = (
+ random.Random(parent_seed)
+ if parent_seed is not None
+ else random.Random()
+ )
+ train_features, train_labels, train_weights = (
+ QuickAdapterRegressorV3._shuffle_in_unison(
+ train_features,
+ train_labels,
+ train_weights,
+ shuffle_rng.randint(0, 2**31 - 1),
+ )
)
+ if test_size != 0:
+ test_features, test_labels, test_weights = (
+ QuickAdapterRegressorV3._shuffle_in_unison(
+ test_features,
+ test_labels,
+ test_weights,
+ shuffle_rng.randint(0, 2**31 - 1),
+ )
+ )
- start_time = time.time()
+ train_weights = sanitize_and_renormalize(train_weights)
+ if test_size != 0:
+ test_weights = sanitize_and_renormalize(test_weights)
- features_filtered, labels_filtered = dk.filter_features(
- unfiltered_df,
- dk.training_features_list,
- dk.label_list,
- training_filter=True,
+ if feat_dict.get("reverse_train_test_order", False):
+ return dk.build_data_dictionary(
+ test_features,
+ train_features,
+ test_labels,
+ train_labels,
+ test_weights,
+ train_weights,
)
+ return dk.build_data_dictionary(
+ train_features,
+ test_features,
+ train_labels,
+ test_labels,
+ train_weights,
+ test_weights,
+ )
- dates = ensure_datetime_series(unfiltered_df["date"])
- start_date = dates.iloc[0].strftime("%Y-%m-%d")
- end_date = dates.iloc[-1].strftime("%Y-%m-%d")
- logger.info(
- f"-------------------- Training on data from {start_date} to "
- f"{end_date} --------------------"
+ def _compose_per_row_weights(
+ self,
+ features_filtered: pd.DataFrame,
+ unfiltered_df: pd.DataFrame,
+ dk: FreqaiDataKitchen,
+ ) -> NDArray[np.floating]:
+ """Build a per-row sample weight vector aligned to features_filtered.index.
+
+ Composes freqtrade's temporal recency weight with the configured
+ per-label aggregation (default ``arithmetic_mean``) of every
+ per-target weight column present on ``unfiltered_df``. Alignment
+ runs before any shuffle/split on ``features_filtered.index``
+ (a subset of ``unfiltered_df.index``) to avoid post-hoc reindex
+ against shuffled data. Iterates ``dk.label_list`` and only includes
+ labels whose ``label_weight_column_name(label)`` exists on
+ ``unfiltered_df``.
+ """
+ if not unfiltered_df.index.is_unique:
+ raise ValueError(
+ "unfiltered_df.index must be unique for label-based weight "
+ "alignment; received non-unique index"
)
-
- dd = self._make_timeseries_split_datasets(
- features_filtered, labels_filtered, dk
+ if not features_filtered.index.isin(unfiltered_df.index).all():
+ raise ValueError(
+ "features_filtered.index must be a subset of "
+ "unfiltered_df.index (filter_features should preserve original "
+ "row labels)"
)
-
- if (
- not self.freqai_info.get("fit_live_predictions_candles", 0)
- or not self.live
- ):
- dk.fit_labels()
-
- dd = self._apply_pipelines(dd, dk, pair)
-
- logger.info(
- f"Training model on {len(dd['train_features'].columns)} features"
+ n_rows = len(features_filtered)
+ feat_dict = self.freqai_info.get("feature_parameters", {})
+ weight_factor = feat_dict.get("weight_factor", 0)
+ if (
+ not isinstance(weight_factor, bool)
+ and isinstance(weight_factor, (int, float))
+ and weight_factor > 0
+ ):
+ temporal = np.asarray(dk.set_weights_higher_recent(n_rows), dtype=float)
+ else:
+ temporal = np.ones(n_rows, dtype=float)
+
+ per_label: dict[str, NDArray[np.floating]] = {}
+ missing: list[str] = []
+ for label in dk.label_list:
+ col = label_weight_column_name(label)
+ if col in unfiltered_df.columns:
+ per_label[label] = unfiltered_df.loc[
+ features_filtered.index, col
+ ].to_numpy(dtype=float)
+ else:
+ missing.append(col)
+ if per_label:
+ logger.debug(
+ f"per-label weight columns active: {sorted(per_label)}"
+ + (f" (no weight column for: {sorted(missing)})" if missing else "")
)
- logger.info(f"Training model on {len(dd['train_features'])} data points")
-
- model = self.fit(dd, dk, **kwargs)
-
- end_time = time.time()
-
- logger.info(
- f"-------------------- Done training {pair} "
- f"({end_time - start_time:.2f} secs) --------------------"
+ else:
+ logger.warning(
+ f"no per-label weight columns found (expected: {sorted(missing)}); "
+ f"falling back to temporal weights only"
)
+ sample_weighting = get_sample_weighting_config(
+ self.freqai_info.get("sample_weighting", {}), logger
+ )
+ sample_weighting_default = sample_weighting["default"]
+ return compose_sample_weights(
+ temporal,
+ per_label,
+ logger=logger,
+ aggregation=sample_weighting_default["aggregation"],
+ softmax_temperature=sample_weighting_default["softmax_temperature"],
+ )
- return model
+ def _train_common(
+ self,
+ unfiltered_df: pd.DataFrame,
+ pair: str,
+ dk: FreqaiDataKitchen,
+ split_fn: SplitFn,
+ **kwargs,
+ ) -> Any:
+ logger.info(
+ f"-------------------- Starting training {pair} --------------------"
+ )
+ start_time = time.time()
+ features_filtered, labels_filtered = dk.filter_features(
+ unfiltered_df,
+ dk.training_features_list,
+ dk.label_list,
+ training_filter=True,
+ )
+ weights = self._compose_per_row_weights(features_filtered, unfiltered_df, dk)
+ dates = ensure_datetime_series(unfiltered_df["date"])
+ start_date = dates.iloc[0].strftime("%Y-%m-%d")
+ end_date = dates.iloc[-1].strftime("%Y-%m-%d")
+ logger.info(
+ f"-------------------- Training on data from {start_date} to "
+ f"{end_date} --------------------"
+ )
+ dd = split_fn(features_filtered, labels_filtered, weights)
+ if not self.freqai_info.get("fit_live_predictions_candles", 0) or not self.live:
+ dk.fit_labels()
+ dd = self._apply_pipelines(dd, dk, pair)
+ if len(dd["train_features"]) != len(dd["train_weights"]):
+ raise RuntimeError(
+ f"Pipeline broke shape invariant: "
+ f"len(train_features)={len(dd['train_features'])} != "
+ f"len(train_weights)={len(dd['train_weights'])}"
+ )
+ logger.info(f"Training model on {len(dd['train_features'].columns)} features")
+ logger.info(f"Training model on {len(dd['train_features'])} data points")
+ model = self.fit(dd, dk, **kwargs)
+ end_time = time.time()
+ logger.info(
+ f"-------------------- Done training {pair} "
+ f"({end_time - start_time:.2f} secs) --------------------"
+ )
+ return model
def _apply_pipelines(
self,
dd["train_features"], dd["train_labels"], dd["train_weights"]
)
)
+ dd["train_weights"] = sanitize_and_renormalize(dd["train_weights"])
dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
dd["test_features"], dd["test_labels"], dd["test_weights"]
)
)
+ dd["test_weights"] = sanitize_and_renormalize(dd["test_weights"])
dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
- dk.data_dictionary = dd
-
return dd
def _make_timeseries_split_datasets(
self,
filtered_dataframe: pd.DataFrame,
labels: pd.DataFrame,
+ weights: NDArray[np.floating],
dk: FreqaiDataKitchen,
) -> dict:
"""
:param filtered_dataframe: Feature data to split
:param labels: Label data to split
- :param dk: FreqaiDataKitchen instance for weight calculation and data building
+ :param weights: Pre-computed per-row sample weights aligned to
+ filtered_dataframe rows by position; sliced via
+ ``weights[train_idx]`` / ``weights[test_idx]``.
+ :param dk: FreqaiDataKitchen instance for data building
:return: data_dictionary with train/test features/labels/weights
"""
- n_splits = int(
+ feat_dict = self.freqai_info.get("feature_parameters", {})
+ if feat_dict.get("shuffle_after_split", False):
+ raise ValueError(
+ "feature_parameters.shuffle_after_split=True is incompatible "
+ "with data_split_parameters.method='timeseries_split': "
+ "chronological split must preserve temporal ordering"
+ )
+ n_splits = QuickAdapterRegressorV3._coerce_int(
self.data_split_parameters.get(
"n_splits", QuickAdapterRegressorV3.TIMESERIES_N_SPLITS_DEFAULT
- )
+ ),
+ "n_splits",
+ minimum=2,
)
- gap = int(
+ gap = QuickAdapterRegressorV3._coerce_int(
self.data_split_parameters.get(
"gap", QuickAdapterRegressorV3.TIMESERIES_GAP_DEFAULT
- )
+ ),
+ "gap",
+ minimum=0,
)
- max_train_size = self.data_split_parameters.get(
- "max_train_size", QuickAdapterRegressorV3.TIMESERIES_MAX_TRAIN_SIZE_DEFAULT
+ max_train_size = QuickAdapterRegressorV3._coerce_optional_int(
+ self.data_split_parameters.get(
+ "max_train_size",
+ QuickAdapterRegressorV3.TIMESERIES_MAX_TRAIN_SIZE_DEFAULT,
+ ),
+ "max_train_size",
+ minimum=1,
)
- max_train_size = int(max_train_size) if max_train_size is not None else None
-
- if n_splits < 2:
- raise ValueError(
- f"Invalid data_split_parameters.n_splits value {n_splits!r}: must be >= 2"
- )
- if gap < 0:
- raise ValueError(
- f"Invalid data_split_parameters.gap value {gap!r}: must be >= 0"
- )
- if max_train_size is not None and max_train_size < 1:
- raise ValueError(
- f"Invalid data_split_parameters.max_train_size value {max_train_size!r}: "
- f"must be >= 1 or None"
- )
test_size = self.data_split_parameters.get("test_size", None)
if test_size is not None:
- if isinstance(test_size, float) and 0 < test_size < 1:
+ if (
+ not isinstance(test_size, bool)
+ and isinstance(test_size, float)
+ and 0 < test_size < 1
+ ):
test_size = int(len(filtered_dataframe) * test_size)
- elif isinstance(test_size, int) and test_size >= 1:
+ elif (
+ not isinstance(test_size, bool)
+ and isinstance(test_size, int)
+ and test_size >= 1
+ ):
pass
else:
raise ValueError(
max_train_size=max_train_size,
test_size=test_size,
)
- train_idx: np.ndarray = np.array([])
- test_idx: np.ndarray = np.array([])
- for train_idx, test_idx in tscv.split(filtered_dataframe):
- pass
+ folds = list(tscv.split(filtered_dataframe))
+ if not folds:
+ raise ValueError(
+ f"TimeSeriesSplit yielded no folds for {len(filtered_dataframe)} "
+ f"samples (n_splits={n_splits}, gap={gap}, "
+ f"max_train_size={max_train_size}, test_size={test_size})"
+ )
+ train_idx, test_idx = folds[-1]
train_features = filtered_dataframe.iloc[train_idx]
test_features = filtered_dataframe.iloc[test_idx]
train_labels = labels.iloc[train_idx]
test_labels = labels.iloc[test_idx]
-
- feature_parameters = self.freqai_info.get("feature_parameters", {})
- if feature_parameters.get("weight_factor", 0) > 0:
- total_weights = dk.set_weights_higher_recent(len(train_idx) + len(test_idx))
- train_weights = total_weights[: len(train_idx)]
- test_weights = total_weights[len(train_idx) :]
- else:
- train_weights = np.ones(len(train_idx))
- test_weights = np.ones(len(test_idx))
-
+ train_weights = sanitize_and_renormalize(weights[train_idx])
+ test_weights = sanitize_and_renormalize(weights[test_idx])
+
+ if feat_dict.get("reverse_train_test_order", False):
+ return dk.build_data_dictionary(
+ test_features,
+ train_features,
+ test_labels,
+ train_labels,
+ test_weights,
+ train_weights,
+ )
return dk.build_data_dictionary(
train_features,
test_features,
**optuna_hp_params,
}
- eval_set, eval_weights = eval_set_and_weights(
+ eval_set, eval_weights = make_test_set_and_weights(
X_test,
y_test,
test_weights,
)
model_training_parameters = {**model_training_parameters, **study_model_parameters}
- eval_set, eval_weights = eval_set_and_weights(
+ eval_set, eval_weights = make_test_set_and_weights(
X_test, y_test, test_weights, test_size
)
"softmax_temperature": 1.0,
}
+DEFAULTS_SAMPLE_WEIGHTING: Final[dict[str, Any]] = {
+ "aggregation": COMBINED_AGGREGATIONS[0], # "arithmetic_mean"
+ "softmax_temperature": 1.0,
+}
+
DEFAULTS_LABEL_PIPELINE: Final[dict[str, Any]] = {
"standardization": STANDARDIZATION_TYPES[0], # "none"
"robust_quantiles": (0.25, 0.75),
from Utils import (
DEFAULT_FIT_LIVE_PREDICTIONS_CANDLES,
EXTREMA_COLUMN,
+ EXTREMA_DIRECTION_COLUMN,
+ EXTREMA_DIRECTION_SMOOTHED_COLUMN,
+ EXTREMA_WEIGHT_COLUMN,
+ EXTREMA_WEIGHT_SMOOTHED_COLUMN,
LABEL_COLUMNS,
- MAXIMA_COLUMN,
- MINIMA_COLUMN,
- SMOOTHED_EXTREMA_COLUMN,
TRADE_PRICE_TARGETS,
alligator,
- apply_label_weighting,
bottom_log_return,
calculate_quantile,
+ compute_label_weights,
ensure_datetime_series,
ewo,
format_dict,
get_label_smoothing_config,
get_label_weighting_config,
get_zl_ma_fn,
+ label_weight_column_name,
migrate_config,
nan_average,
non_zero_diff,
optuna_load_best_params,
price_retracement_percent,
- smooth_label,
+ smooth,
top_log_return,
validate_range,
vwapb,
_ANNOTATION_LINE_OFFSET_CANDLES: Final[int] = 10
- _PLOT_EXTREMA_MIN_EPS: Final[float] = 0.01
-
def version(self) -> str:
- return "3.11.8"
+ return "3.11.9"
timeframe = "5m"
timeframe_minutes = timeframe_to_minutes(timeframe)
},
EXTREMA_COLUMN: {"color": "orange", "type": "line"},
},
- "min_max": {
- SMOOTHED_EXTREMA_COLUMN: {"color": "wheat", "type": "line"},
- MAXIMA_COLUMN: {"color": "red", "type": "bar"},
- MINIMA_COLUMN: {"color": "green", "type": "bar"},
+ "direction": {
+ EXTREMA_DIRECTION_COLUMN: {"color": "wheat", "type": "line"},
+ EXTREMA_DIRECTION_SMOOTHED_COLUMN: {
+ "color": "orange",
+ "type": "line",
+ },
+ },
+ "weight": {
+ EXTREMA_WEIGHT_COLUMN: {"color": "wheat", "type": "line"},
+ EXTREMA_WEIGHT_SMOOTHED_COLUMN: {
+ "color": "orange",
+ "type": "line",
+ },
},
},
}
label_col, label_weighting["default"], label_weighting["columns"]
)
- weighted_label, _ = apply_label_weighting(
- label=label_data.series,
+ label_weights = compute_label_weights(
+ n_values=len(label_data.series),
indices=label_data.indices,
metrics=label_data.metrics,
weighting_config=col_weighting_config,
)
- dataframe[label_col] = weighted_label
+ label_weight_col = label_weight_column_name(label_col)
+
+ dataframe[label_col] = label_data.series
+ dataframe[label_weight_col] = label_weights
if label_col == EXTREMA_COLUMN:
- extrema = dataframe[label_col]
- extrema_direction = label_data.series
- plot_eps = extrema.abs().where(extrema.ne(0.0)).min()
- if not np.isfinite(plot_eps):
- plot_eps = 0.0
- plot_eps = max(
- float(plot_eps) * 0.5, QuickAdapterV3._PLOT_EXTREMA_MIN_EPS
- )
- dataframe[MAXIMA_COLUMN] = (
- extrema.where(extrema_direction.gt(0), 0.0)
- .clip(lower=0.0)
- .mask(
- extrema_direction.gt(0) & extrema.eq(0.0),
- plot_eps,
- )
- )
- dataframe[MINIMA_COLUMN] = (
- extrema.where(extrema_direction.lt(0), 0.0)
- .clip(upper=0.0)
- .mask(
- extrema_direction.lt(0) & extrema.eq(0.0),
- -plot_eps,
- )
- )
+ dataframe[EXTREMA_DIRECTION_COLUMN] = dataframe[label_col]
+ dataframe[EXTREMA_WEIGHT_COLUMN] = dataframe[label_weight_col]
col_smoothing_config = get_label_column_config(
label_col, label_smoothing["default"], label_smoothing["columns"]
)
- dataframe[label_col] = smooth_label(
- dataframe[label_col],
- col_smoothing_config["method"],
- col_smoothing_config["window_candles"],
- col_smoothing_config["beta"],
- col_smoothing_config["polyorder"],
- col_smoothing_config["mode"],
- col_smoothing_config["sigma"],
+ dataframe[label_col] = smooth(dataframe[label_col], **col_smoothing_config)
+ smoothed_label_weights = smooth(
+ dataframe[label_weight_col], **col_smoothing_config
+ )
+ dataframe[label_weight_col] = smoothed_label_weights.where(
+ smoothed_label_weights.gt(0) & smoothed_label_weights.notna(), 0.0
)
if label_col == EXTREMA_COLUMN:
- dataframe[SMOOTHED_EXTREMA_COLUMN] = dataframe[label_col]
+ dataframe[EXTREMA_DIRECTION_SMOOTHED_COLUMN] = dataframe[label_col]
+ dataframe[EXTREMA_WEIGHT_SMOOTHED_COLUMN] = dataframe[label_weight_col]
return dataframe
import hashlib
import json
import math
+import re
from dataclasses import dataclass
from enum import IntEnum
from functools import lru_cache, singledispatch
DEFAULTS_LABEL_PREDICTION,
DEFAULTS_LABEL_SMOOTHING,
DEFAULTS_LABEL_WEIGHTING,
+ DEFAULTS_SAMPLE_WEIGHTING,
EXTREMA_SELECTION_METHODS,
NORMALIZATION_TYPES,
PREDICTION_METHODS,
),
}
+_SAMPLE_WEIGHTING_SPECS: Final[dict[str, _ParamSpec]] = {
+ "aggregation": _ParamSpec(_EnumValidator(COMBINED_AGGREGATIONS)),
+ "softmax_temperature": _ParamSpec(
+ _NumericValidator(min_value=0, min_exclusive=True)
+ ),
+}
+
_PREDICTION_SPECS: Final[dict[str, _ParamSpec]] = {
"method": _ParamSpec(_EnumValidator(PREDICTION_METHODS)),
"selection_method": _ParamSpec(_EnumValidator(EXTREMA_SELECTION_METHODS)),
EXTREMA_COLUMN: Final = "&s-extrema"
+EXTREMA_DIRECTION_COLUMN: Final = "extrema_direction"
+EXTREMA_DIRECTION_SMOOTHED_COLUMN: Final = "extrema_direction_smoothed"
+EXTREMA_WEIGHT_COLUMN: Final = "extrema_weight"
+EXTREMA_WEIGHT_SMOOTHED_COLUMN: Final = "extrema_weight_smoothed"
+
+LABEL_WEIGHT_SUFFIX: Final[str] = "_weight"
+
LABEL_COLUMNS: Final[tuple[str, ...]] = (EXTREMA_COLUMN,)
+_FREQAI_LABEL_SIGIL_PATTERN: Final = re.compile(r"^&-?")
+
+
+@lru_cache(maxsize=16)
+def label_weight_column_name(label_col: str) -> str:
+ """Return the weight column name for a label column.
+
+ Strips the freqtrade label sigil (``&`` and its optional immediate ``-``
+ separator) so the resulting column does NOT collide with
+ ``FreqaiDataKitchen.find_labels`` (which selects columns containing ``&``)
+ nor with ``find_features`` (which selects columns containing ``%``).
+ Preserves the project convention where a leading ``s`` denotes a smoothed
+ target series (e.g. ``&s-extrema``); no ``s`` denotes a raw target.
+ Raises ``ValueError`` if the result still contains ``&`` or ``%``.
+
+ Examples:
+ ``"&s-extrema"`` -> ``"s-extrema_weight"`` (smoothed marker preserved)
+ ``"&-amplitude"`` -> ``"amplitude_weight"`` (raw target)
+ ``"&-time_to_pivot"`` -> ``"time_to_pivot_weight"`` (raw target)
+ ``"&-natr"`` -> ``"natr_weight"`` (raw target)
+ """
+ stripped = _FREQAI_LABEL_SIGIL_PATTERN.sub("", label_col, count=1)
+ result = f"{stripped}{LABEL_WEIGHT_SUFFIX}"
+ if "&" in result or "%" in result:
+ raise ValueError(
+ f"label_weight_column_name produced collision-prone name {result!r} "
+ f"from {label_col!r}; weight columns must not contain '&' or '%'"
+ )
+ return result
+
@dataclass
class LabelData:
return generator(dataframe, params)
-MAXIMA_COLUMN: Final = "maxima"
-MINIMA_COLUMN: Final = "minima"
-SMOOTHED_EXTREMA_COLUMN: Final = "smoothed_extrema"
-
SmoothingKernel = Literal["gaussian", "kaiser", "triang"]
SMOOTHING_KERNELS: Final[tuple[SmoothingKernel, ...]] = (
"gaussian",
)
+def _validate_sample_weighting_params(
+ config: dict[str, Any],
+ logger: Logger,
+ config_name: str = "sample_weighting",
+) -> dict[str, Any]:
+ return _validate_params(
+ config, logger, config_name, _SAMPLE_WEIGHTING_SPECS, DEFAULTS_SAMPLE_WEIGHTING
+ )
+
+
+def get_sample_weighting_config(
+ config: dict[str, Any],
+ logger: Logger,
+) -> dict[str, Any]:
+ return _get_label_config(
+ config,
+ logger,
+ "sample_weighting",
+ _validate_sample_weighting_params,
+ DEFAULTS_SAMPLE_WEIGHTING,
+ )
+
+
def _validate_prediction_params(
config: dict[str, Any],
logger: Logger,
return (value1 + value2) / 2
+def sanitize_and_renormalize(
+ arr: NDArray[np.floating],
+ drop_mask: NDArray[np.bool_] | None = None,
+) -> NDArray[np.floating]:
+ arr = np.asarray(arr, dtype=float)
+ if arr.size == 0:
+ return arr
+ safe = np.where(np.isfinite(arr) & (arr > 0), arr, 0.0)
+ if drop_mask is not None:
+ safe = safe.copy()
+ safe[drop_mask] = 0.0
+ total = safe.sum()
+ if total > 0 and np.isfinite(total):
+ return safe * (len(safe) / total)
+ fallback = np.ones_like(arr)
+ if drop_mask is not None:
+ fallback[drop_mask] = 0.0
+ return fallback
+
+
+def compose_sample_weights(
+ base_weights: NDArray[np.floating],
+ label_weights_map: dict[str, NDArray[np.floating]],
+ *,
+ logger: Logger,
+ aggregation: CombinedAggregation = COMBINED_AGGREGATIONS[0],
+ softmax_temperature: float = 1.0,
+) -> NDArray[np.floating]:
+ """Combine base sample weights with per-label importance weights.
+
+ Returns w in R+^N with mean(w) == 1. Per-label arrays are sanitized
+ (non-finite or <= 0 -> row dropped), individually mean-normalized,
+ aggregated row-wise via ``aggregation`` (default arithmetic_mean),
+ multiplied with base_weights, zeroed on dropped rows, and renormalized
+ to mean=1.
+
+ Raises ValueError on shape mismatch or when every row is dropped.
+ Default-weight imputation in compute_label_weights uses full-series
+ median (bounded leakage; see AFML chapter 4).
+ """
+ base_weights = np.asarray(base_weights, dtype=float)
+ if not label_weights_map:
+ return sanitize_and_renormalize(base_weights)
+ n = len(base_weights)
+ for label, label_values in label_weights_map.items():
+ arr = np.asarray(label_values, dtype=float)
+ if arr.shape != (n,):
+ raise ValueError(
+ f"compose_sample_weights: label {label!r} has shape {arr.shape}, "
+ f"expected ({n},)"
+ )
+ normalized_per_label: list[NDArray[np.floating]] = []
+ drop_mask = np.zeros(n, dtype=bool)
+ for label_values in label_weights_map.values():
+ arr = np.asarray(label_values, dtype=float)
+ invalid = ~np.isfinite(arr) | (arr <= 0.0)
+ drop_mask |= invalid
+ arr = np.where(invalid, 1.0, np.maximum(arr, np.finfo(float).tiny))
+ normalized_per_label.append(sanitize_and_renormalize(arr))
+ if drop_mask.all():
+ raise ValueError(
+ f"compose_sample_weights: all rows dropped by per-label zero weights "
+ f"(labels={list(label_weights_map)}); no surviving training samples"
+ )
+ stacked = np.vstack(normalized_per_label)
+ agg = _aggregate_metrics(
+ stacked_metrics=stacked,
+ coefficients=np.ones(stacked.shape[0], dtype=float),
+ aggregation=aggregation,
+ softmax_temperature=softmax_temperature,
+ )
+ combined = base_weights * agg
+ combined[drop_mask] = 0.0
+ combined_sum = combined.sum()
+ if combined_sum > 0 and np.isfinite(combined_sum):
+ ratio = n / combined_sum
+ if np.isfinite(ratio):
+ scaled = combined * ratio
+ if np.all(np.isfinite(scaled)):
+ return scaled
+ logger.warning(
+ "compose_sample_weights: aggregated weights collapsed (labels=%s, "
+ "aggregation=%s, combined_sum=%r); falling back to base weights",
+ list(label_weights_map),
+ aggregation,
+ combined_sum,
+ )
+ return sanitize_and_renormalize(base_weights, drop_mask=drop_mask)
+
+
def nan_average(
values: NDArray[np.floating],
weights: NDArray[np.floating] | None = None,
return pd.Series(filtered_values, index=series.index)
-def smooth_label(
+def smooth(
series: pd.Series,
method: SmoothingMethod = DEFAULTS_LABEL_SMOOTHING["method"],
window_candles: int = DEFAULTS_LABEL_SMOOTHING["window_candles"],
return weights
-def _build_weights_array(
+def _scatter_weights(
n_values: int,
indices: list[int],
weights: NDArray[np.floating],
]
)
elif aggregation == COMBINED_AGGREGATIONS[5]: # "softmax"
+ # Per-column softmax-weighted convex combination of stacked rows.
+ # T -> 0 collapses to argmax row; T -> +inf collapses to coefficient-weighted mean.
scaled_metrics = stacked_metrics / softmax_temperature
softmax_weights = sp.special.softmax(scaled_metrics, axis=0)
combined_weights = softmax_weights * coefficients[:, np.newaxis]
weights=weights,
)
- return _build_weights_array(
+ return _scatter_weights(
n_values=n_values,
indices=indices,
weights=weights,
)
-def _apply_label_weights(
- values: NDArray[np.floating], weights: NDArray[np.floating]
-) -> NDArray[np.floating]:
- if weights.size == 0:
- return values
-
- if not np.isfinite(weights).all():
- return values
-
- if np.allclose(weights, weights[0]):
- return values
-
- if np.allclose(weights, DEFAULT_LABEL_WEIGHT):
- return values
-
- return values * weights
-
-
-def apply_label_weighting(
- label: pd.Series,
- indices: list[int],
- metrics: dict[str, list[float]],
- weighting_config: dict[str, Any],
-) -> tuple[pd.Series, pd.Series]:
- label_values = label.to_numpy(dtype=float)
- label_index = label.index
- n_values = label_values.size
-
- weights = compute_label_weights(
- n_values=n_values,
- indices=indices,
- metrics=metrics,
- weighting_config=weighting_config,
- )
-
- return pd.Series(
- _apply_label_weights(label_values, weights), index=label_index
- ), pd.Series(weights, index=label_index)
-
-
def get_callable_sha256(fn: Callable[..., Any]) -> str:
if not callable(fn):
raise ValueError(f"Invalid fn value {type(fn).__name__!r}: must be callable")
return model
-def eval_set_and_weights(
+def make_test_set_and_weights(
X_test: pd.DataFrame,
y_test: pd.DataFrame,
test_weights: NDArray[np.floating],
repositories / freqai-strategies.git / commitdiff
