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"rule": "reportArgumentType",
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"rule": "reportArgumentType",
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from Utils import (
_CATBOOST_TASK_TYPES,
- _FORMAT_STYLES,
+ _FORMAT_STYLE_DICT,
+ _FORMAT_STYLE_PARAMS,
_OPTUNA_LABEL_SELECTION_SCHEMA_VERSION,
_OPTUNA_NAMESPACES,
_REGRESSOR_SPECS,
optuna.study.StudyDirection.MAXIMIZE,
) * _OPTUNA_LABEL_N_OBJECTIVES
_OPTUNA_STORAGE_BACKENDS: Final[tuple[str, ...]] = ("file", "sqlite")
- _STORAGE_FILE: Final[str] = _OPTUNA_STORAGE_BACKENDS[0]
- _STORAGE_SQLITE: Final[str] = _OPTUNA_STORAGE_BACKENDS[1]
+ _STORAGE_FILE: Final[str] = _OPTUNA_STORAGE_BACKENDS[0] # "file"
+ _STORAGE_SQLITE: Final[str] = _OPTUNA_STORAGE_BACKENDS[1] # "sqlite"
_OPTUNA_JOURNAL_QUARANTINE_TAG: Final[str] = "corrupt"
_OPTUNA_JOURNAL_RECOVERABLE_ERRORS: Final[tuple[type[Exception], ...]] = (
"robust",
)
SCALER_DEFAULT: Final[ScalerType] = _SCALER_TYPES[0] # "minmax"
- _SCALER_MAXABS: Final[str] = _SCALER_TYPES[1]
- _SCALER_STANDARD: Final[str] = _SCALER_TYPES[2]
- _SCALER_ROBUST: Final[str] = _SCALER_TYPES[3]
+ _SCALER_MAXABS: Final[str] = _SCALER_TYPES[1] # "maxabs"
+ _SCALER_STANDARD: Final[str] = _SCALER_TYPES[2] # "standard"
+ _SCALER_ROBUST: Final[str] = _SCALER_TYPES[3] # "robust"
_SCALER_TYPES_SET: Final[frozenset[ScalerType]] = frozenset(_SCALER_TYPES)
RANGE_DEFAULT: Final[tuple[float, float]] = (-1.0, 1.0)
"compromise_programming",
"topsis",
)
- _METHOD_COMPROMISE_PROGRAMMING: Final[str] = _DISTANCE_METHODS[0]
- _METHOD_TOPSIS: Final[str] = _DISTANCE_METHODS[1]
+ _METHOD_COMPROMISE_PROGRAMMING: Final[str] = _DISTANCE_METHODS[0] # "compromise_programming"
+ _METHOD_TOPSIS: Final[str] = _DISTANCE_METHODS[1] # "topsis"
_DISTANCE_METHODS_SET: Final[frozenset[DistanceMethod]] = frozenset(_DISTANCE_METHODS)
_CLUSTER_METHODS: Final[tuple[ClusterMethod, ...]] = (
"kmeans",
"kmeans2",
"kmedoids",
)
- _CLUSTER_KMEANS: Final[str] = _CLUSTER_METHODS[0]
- _CLUSTER_KMEANS2: Final[str] = _CLUSTER_METHODS[1]
- _CLUSTER_KMEDOIDS: Final[str] = _CLUSTER_METHODS[2]
+ _CLUSTER_KMEANS: Final[str] = _CLUSTER_METHODS[0] # "kmeans"
+ _CLUSTER_KMEANS2: Final[str] = _CLUSTER_METHODS[1] # "kmeans2"
+ _CLUSTER_KMEDOIDS: Final[str] = _CLUSTER_METHODS[2] # "kmedoids"
_DENSITY_METHODS: Final[tuple[DensityMethod, ...]] = ("knn", "medoid")
- _DENSITY_KNN: Final[str] = _DENSITY_METHODS[0]
- _DENSITY_MEDOID: Final[str] = _DENSITY_METHODS[1]
+ _DENSITY_KNN: Final[str] = _DENSITY_METHODS[0] # "knn"
+ _DENSITY_MEDOID: Final[str] = _DENSITY_METHODS[1] # "medoid"
_SELECTION_CATEGORIES: Final[dict[str, tuple[SelectionMethod, ...]]] = {
"distance": _DISTANCE_METHODS,
"density": _DENSITY_METHODS,
}
_CATEGORY_DISTANCE, _CATEGORY_CLUSTER, _CATEGORY_DENSITY = _SELECTION_CATEGORIES
- _SUPPORT_POLICY_FALLBACK: Final[Literal["fallback"]] = LABEL_WEIGHT_SUPPORT_POLICIES[0]
- _SUPPORT_POLICY_RAISE: Final[Literal["raise"]] = LABEL_WEIGHT_SUPPORT_POLICIES[1]
+ _SUPPORT_POLICY_FALLBACK: Final[Literal["fallback"]] = LABEL_WEIGHT_SUPPORT_POLICIES[
+ 0
+ ] # "fallback"
+ _SUPPORT_POLICY_RAISE: Final[Literal["raise"]] = LABEL_WEIGHT_SUPPORT_POLICIES[1] # "raise"
_SELECTION_METHODS: Final[tuple[SelectionMethod, ...]] = (
*_DISTANCE_METHODS,
*_CLUSTER_METHODS,
*_DENSITY_METHODS,
)
- _SELECTION_KMEANS: Final[str] = _SELECTION_METHODS[2]
- _SELECTION_KMEANS2: Final[str] = _SELECTION_METHODS[3]
- _SELECTION_KMEDOIDS: Final[str] = _SELECTION_METHODS[4]
- _SELECTION_KNN: Final[str] = _SELECTION_METHODS[5]
- _SELECTION_MEDOID: Final[str] = _SELECTION_METHODS[6]
+ _SELECTION_KMEANS: Final[str] = _SELECTION_METHODS[2] # "kmeans"
+ _SELECTION_KMEANS2: Final[str] = _SELECTION_METHODS[3] # "kmeans2"
+ _SELECTION_KMEDOIDS: Final[str] = _SELECTION_METHODS[4] # "kmedoids"
+ _SELECTION_KNN: Final[str] = _SELECTION_METHODS[5] # "knn"
+ _SELECTION_MEDOID: Final[str] = _SELECTION_METHODS[6] # "medoid"
_SELECTION_METHODS_SET: Final[frozenset[SelectionMethod]] = frozenset(_SELECTION_METHODS)
_DISTANCE_METRICS: Final[tuple[str, ...]] = (
"power_mean",
"weighted_sum",
)
- _METRIC_EUCLIDEAN: Final[str] = _DISTANCE_METRICS[0]
- _METRIC_MINKOWSKI: Final[str] = _DISTANCE_METRICS[1]
- _METRIC_HELLINGER: Final[str] = _DISTANCE_METRICS[8]
- _METRIC_SHELLINGER: Final[str] = _DISTANCE_METRICS[9]
- _METRIC_POWER_MEAN: Final[str] = _DISTANCE_METRICS[15]
- _METRIC_WEIGHTED_SUM: Final[str] = _DISTANCE_METRICS[16]
+ _METRIC_EUCLIDEAN: Final[str] = _DISTANCE_METRICS[0] # "euclidean"
+ _METRIC_MINKOWSKI: Final[str] = _DISTANCE_METRICS[1] # "minkowski"
+ _METRIC_HELLINGER: Final[str] = _DISTANCE_METRICS[8] # "hellinger"
+ _METRIC_SHELLINGER: Final[str] = _DISTANCE_METRICS[9] # "shellinger"
+ _METRIC_POWER_MEAN: Final[str] = _DISTANCE_METRICS[15] # "power_mean"
+ _METRIC_WEIGHTED_SUM: Final[str] = _DISTANCE_METRICS[16] # "weighted_sum"
_DISTANCE_METRICS_SET: Final[frozenset[str]] = frozenset(_DISTANCE_METRICS)
# SciPy-compatible distance metrics: the first 8 entries of
# ``_DISTANCE_METRICS`` route to ``scipy.spatial.distance.cdist``.
)
_PROBABILITY_DISTANCE_METRICS: Final[tuple[str, ...]] = (
- _DISTANCE_METRICS[7],
+ _DISTANCE_METRICS[7], # "jensenshannon"
_METRIC_HELLINGER,
_METRIC_SHELLINGER,
)
"min",
"max",
)
- _DENSITY_AGG_POWER_MEAN: Final[str] = _DENSITY_AGGREGATIONS[0]
- _DENSITY_AGG_QUANTILE: Final[str] = _DENSITY_AGGREGATIONS[1]
- _DENSITY_AGG_MIN: Final[str] = _DENSITY_AGGREGATIONS[2]
- _DENSITY_AGG_MAX: Final[str] = _DENSITY_AGGREGATIONS[3]
+ _DENSITY_AGG_POWER_MEAN: Final[str] = _DENSITY_AGGREGATIONS[0] # "power_mean"
+ _DENSITY_AGG_QUANTILE: Final[str] = _DENSITY_AGGREGATIONS[1] # "quantile"
+ _DENSITY_AGG_MIN: Final[str] = _DENSITY_AGGREGATIONS[2] # "min"
+ _DENSITY_AGG_MAX: Final[str] = _DENSITY_AGGREGATIONS[3] # "max"
_DENSITY_AGGREGATIONS_SET: Final[frozenset[DensityAggregation]] = frozenset(
_DENSITY_AGGREGATIONS
)
_POWER_MEAN_MAP: Final[dict[str, float]] = {
- _DISTANCE_METRICS[10]: -1.0,
- _DISTANCE_METRICS[11]: 0.0,
- _DISTANCE_METRICS[12]: 1.0,
- _DISTANCE_METRICS[13]: 2.0,
- _DISTANCE_METRICS[14]: 3.0,
+ _DISTANCE_METRICS[10]: -1.0, # "harmonic_mean"
+ _DISTANCE_METRICS[11]: 0.0, # "geometric_mean"
+ _DISTANCE_METRICS[12]: 1.0, # "arithmetic_mean"
+ _DISTANCE_METRICS[13]: 2.0, # "quadratic_mean"
+ _DISTANCE_METRICS[14]: 3.0, # "cubic_mean"
}
_POWER_MEAN_METRICS_SET: Final[frozenset[str]] = frozenset(_POWER_MEAN_MAP)
"train_test_split",
"timeseries_split",
)
- DATA_SPLIT_METHOD_DEFAULT: Final[str] = _DATA_SPLIT_METHODS[0]
- _DATA_SPLIT_TIMESERIES: Final[str] = _DATA_SPLIT_METHODS[1]
+ DATA_SPLIT_METHOD_DEFAULT: Final[str] = _DATA_SPLIT_METHODS[0] # "train_test_split"
+ _DATA_SPLIT_TIMESERIES: Final[str] = _DATA_SPLIT_METHODS[1] # "timeseries_split"
TIMESERIES_N_SPLITS_DEFAULT: Final[int] = 5
TIMESERIES_GAP_DEFAULT: Final[int] = 0
TIMESERIES_MAX_TRAIN_SIZE_DEFAULT: Final[int | None] = None
label_weights,
logger=logger,
context=context,
- on_collapse=LABEL_WEIGHT_SUPPORT_POLICIES[0],
+ on_collapse=LABEL_WEIGHT_SUPPORT_POLICIES[0], # "fallback"
)
except LabelWeightSupportError as exc:
logger.warning(
# governs the contract -- ``raise`` raises, ``fallback``
# warns. A direct return to base weights would bypass the
# policy silently.
- strategy = label_weighting_config.get("strategy", WEIGHT_STRATEGIES[0])
+ strategy = label_weighting_config.get("strategy", WEIGHT_STRATEGIES[0]) # "none"
if strategy != WEIGHT_STRATEGIES[0]: # "none"
return QuickAdapterRegressorV3._apply_support_policy(
base_weights,
) -> float | None:
if value is None:
return None
- if mode == _VALIDATION_MODES[2]:
+ if mode == _VALIDATION_MODES[2]: # "none"
return (
float(value)
if (np.isfinite(value) and (predicate is None or predicate(value)))
if not np.isfinite(value):
msg = f"Invalid {ctx} value {value!r}: must be finite"
- if mode == _VALIDATION_MODES[1]:
+ if mode == _VALIDATION_MODES[1]: # "raise"
raise ValueError(msg)
logger.warning(f"{msg}, using default")
return None
if predicate is not None and not predicate(value):
msg = f"Invalid {ctx} value {value!r}: {constraint}"
- if mode == _VALIDATION_MODES[1]:
+ if mode == _VALIDATION_MODES[1]: # "raise"
raise ValueError(msg)
logger.warning(f"{msg}, using default")
return None
@staticmethod
def _validate_minkowski_p(
- p: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
+ p: float | None,
+ *,
+ ctx: str,
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(
p, ctx=ctx, mode=mode, predicate=lambda v: v > 0, constraint="must be > 0"
distance_metric: str,
weights: NDArray[np.floating] | None = None,
p: float | None = None,
- mode: ValidationMode = _VALIDATION_MODES[2],
+ mode: ValidationMode = _VALIDATION_MODES[2], # "none"
metric_ctx: str = "distance_metric",
p_ctx: str = "p",
*,
@staticmethod
def _validate_quantile_q(
- q: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
+ q: float | None,
+ *,
+ ctx: str,
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(
q,
@staticmethod
def _validate_power_mean_p(
- p: float | None, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[1]
+ p: float | None,
+ *,
+ ctx: str,
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
) -> float | None:
return QuickAdapterRegressorV3._validate_scalar(p, ctx=ctx, mode=mode)
@staticmethod
def _validate_metric_weights_support(
- metric: str, *, ctx: str, mode: ValidationMode = _VALIDATION_MODES[0]
+ metric: str,
+ *,
+ ctx: str,
+ mode: ValidationMode = _VALIDATION_MODES[0], # "warn"
) -> str | None:
if metric not in QuickAdapterRegressorV3._UNSUPPORTED_WEIGHTS_METRICS_SET:
return metric
- if mode == _VALIDATION_MODES[2]:
+ if mode == _VALIDATION_MODES[2]: # "none"
return None
msg = f"Invalid {ctx} value {metric!r}: does not support custom weights"
- if mode == _VALIDATION_MODES[1]:
+ if mode == _VALIDATION_MODES[1]: # "raise"
raise ValueError(msg)
logger.warning(f"{msg}, using uniform weights")
return None
n_objectives: int,
*,
ctx: str,
- mode: ValidationMode = _VALIDATION_MODES[1],
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
) -> NDArray[np.floating]:
if weights is None:
return np.full(n_objectives, 1.0 / n_objectives)
return normalized
msg = f"Invalid {ctx} value: sum is zero"
- if mode == _VALIDATION_MODES[1]:
+ if mode == _VALIDATION_MODES[1]: # "raise"
raise ValueError(msg)
- if mode == _VALIDATION_MODES[0]:
+ if mode == _VALIDATION_MODES[0]: # "warn"
logger.warning(f"{msg}, using uniform weights")
return np.full(n_objectives, 1.0 / n_objectives)
valid_options: tuple[str, ...],
*,
ctx: str,
- mode: ValidationMode = _VALIDATION_MODES[1],
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
default: str | None = None,
) -> str | None:
if isinstance(value, str) and value in valid_set:
return value
- if mode == _VALIDATION_MODES[2]:
+ if mode == _VALIDATION_MODES[2]: # "none"
return default
msg = enum_error_message(ctx, value, valid_options)
- if mode == _VALIDATION_MODES[1]:
+ if mode == _VALIDATION_MODES[1]: # "raise"
raise ValueError(msg)
logger.warning(f"{msg}, using {default!r}")
return default
ctx: str,
default: str,
aggregate_allowed: bool,
- mode: ValidationMode = _VALIDATION_MODES[0],
+ mode: ValidationMode = _VALIDATION_MODES[0], # "warn"
) -> str:
if aggregate_allowed:
valid_metrics = QuickAdapterRegressorV3._LABEL_SELECTION_DISTANCE_METRICS_SET
label_p_order: float | None,
*,
ctx: str,
- mode: ValidationMode = _VALIDATION_MODES[1],
+ mode: ValidationMode = _VALIDATION_MODES[1], # "raise"
) -> float | None:
p = (
label_p_order
QuickAdapterRegressorV3._SELECTION_METHODS_SET,
QuickAdapterRegressorV3._SELECTION_METHODS,
ctx="label_method",
- mode=_VALIDATION_MODES[1],
+ mode=_VALIDATION_MODES[1], # "raise"
)
category = QuickAdapterRegressorV3._get_selection_category(label_method)
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_distance_metric",
- mode=_VALIDATION_MODES[0],
+ mode=_VALIDATION_MODES[0], # "warn"
default=QuickAdapterRegressorV3.LABEL_DISTANCE_METRIC_DEFAULT,
aggregate_allowed=True,
)
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_cluster_metric",
- mode=_VALIDATION_MODES[0],
+ mode=_VALIDATION_MODES[0], # "warn"
default=QuickAdapterRegressorV3.LABEL_CLUSTER_METRIC_DEFAULT,
aggregate_allowed=False,
)
distance_metric = QuickAdapterRegressorV3._validate_label_selection_metric(
distance_metric,
ctx="label_density_metric",
- mode=_VALIDATION_MODES[0],
+ mode=_VALIDATION_MODES[0], # "warn"
default=density_metric_default,
aggregate_allowed=False,
)
QuickAdapterRegressorV3._SCALER_TYPES_SET,
QuickAdapterRegressorV3._SCALER_TYPES,
ctx="scaler",
- mode=_VALIDATION_MODES[1],
+ mode=_VALIDATION_MODES[1], # "raise"
)
feature_range = self.ft_params.get("range", QuickAdapterRegressorV3.RANGE_DEFAULT)
"unfiltered_df.index (filter_features should preserve original "
"row labels)"
)
- if LABEL_COLUMNS[0] not in dk.label_list:
+ label_column = LABEL_COLUMNS[0] # "&s-extrema"
+ if label_column not in dk.label_list:
raise ValueError(
- f"LABEL_COLUMNS[0]={LABEL_COLUMNS[0]!r} is not in "
+ f"LABEL_COLUMNS[0]={label_column!r} is not in "
f"dk.label_list={dk.label_list!r}: project label constant "
f"diverged from freqtrade's runtime label list"
)
label_weighting = self.label_weighting
label_weighting_config = get_label_column_config(
- LABEL_COLUMNS[0], label_weighting["default"], label_weighting["columns"]
+ label_column,
+ label_weighting["default"],
+ label_weighting["columns"],
)
- weight_col = label_weight_column_name(LABEL_COLUMNS[0])
+ weight_col = label_weight_column_name(label_column)
if weight_col in unfiltered_df.columns:
label_weights = unfiltered_df.loc[features_filtered.index, weight_col].to_numpy(
dtype=float
model_class = LGBMRegressor
elif self.regressor == _REGRESSOR_SPECS.catboost.name:
if (
- self.model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0])
- == _CATBOOST_TASK_TYPES[1]
+ self.model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0]) # "CPU"
+ == _CATBOOST_TASK_TYPES[1] # "GPU"
):
return None
from catboost import CatBoostRegressor
*,
weights: NDArray[np.floating] | None = None,
p: float | None = None,
- mode: ValidationMode = _VALIDATION_MODES[2],
+ mode: ValidationMode = _VALIDATION_MODES[2], # "none"
p_ctx: str = "p",
) -> NDArray[np.floating]:
if distance_metric in QuickAdapterRegressorV3._POWER_MEAN_METRICS_SET:
distance_metric,
weights=weights,
p=p,
- mode=_VALIDATION_MODES[0],
+ mode=_VALIDATION_MODES[0], # "warn"
p_ctx="label_distance_p",
)
return np.abs(distances) if apply_abs else distances
label_weights_array,
original_n_objectives,
ctx="label_weights",
- mode=_VALIDATION_MODES[1],
+ mode=_VALIDATION_MODES[1], # "raise"
)
sliced_weights = label_weights_array[objective_indices]
if np.all(sliced_weights == 0.0):
label_weights,
n_objectives,
ctx="label_weights",
- mode=_VALIDATION_MODES[1],
+ mode=_VALIDATION_MODES[1], # "raise"
)
if category == QuickAdapterRegressorV3._CATEGORY_CLUSTER:
positive_weight = weights > 0.0
distance_metric,
label_p_order,
ctx=f"label_p_order for {method}",
- mode=_VALIDATION_MODES[2],
+ mode=_VALIDATION_MODES[2], # "none"
)
distance_kwargs = (
QuickAdapterRegressorV3._prepare_distance_kwargs(
distance_metric,
weights=weights,
p=p,
- mode=_VALIDATION_MODES[0],
+ mode=_VALIDATION_MODES[0], # "warn"
metric_ctx=f"label_{category}_metric",
reference_matrix=normalized_matrix,
)
label_config = self._resolve_label_method_config(
self.ft_params.get("label_method", QuickAdapterRegressorV3.LABEL_METHOD_DEFAULT)
)
- metric_log_msg = f" ({format_dict(label_config, style=_FORMAT_STYLES[1])})"
+ metric_log_msg = f" ({format_dict(label_config, style=_FORMAT_STYLE_PARAMS)})"
logger.info(
f"[{pair}] Optuna {namespace} {objective_type} objective hyperopt completed"
f"{metric_log_msg} ({time_spent:.2f} secs)"
)
if study_best_results:
logger.info(
- f"[{pair}] Optuna {namespace} {objective_type} objective hyperopt best params: {format_dict(study_best_results, style=_FORMAT_STYLES[0])}"
+ f"[{pair}] Optuna {namespace} {objective_type} objective hyperopt best params: {format_dict(study_best_results, style=_FORMAT_STYLE_DICT)}"
)
if not self.optuna_validate_params(pair, namespace, study):
logger.warning(
series.loc[result.indices] = result.directions
metrics: dict[str, list[float]] = {
- COMBINED_METRICS[0]: result.amplitudes,
- COMBINED_METRICS[1]: result.amplitude_threshold_ratios,
- COMBINED_METRICS[2]: result.volume_rates,
- COMBINED_METRICS[3]: result.speeds,
- COMBINED_METRICS[4]: result.efficiency_ratios,
- COMBINED_METRICS[5]: result.volume_weighted_efficiency_ratios,
+ COMBINED_METRICS[0]: result.amplitudes, # "amplitude"
+ COMBINED_METRICS[1]: result.amplitude_threshold_ratios, # "amplitude_threshold_ratio"
+ COMBINED_METRICS[2]: result.volume_rates, # "volume_rate"
+ COMBINED_METRICS[3]: result.speeds, # "speed"
+ COMBINED_METRICS[4]: result.efficiency_ratios, # "efficiency_ratio"
+ COMBINED_METRICS[
+ 5
+ ]: result.volume_weighted_efficiency_ratios, # "volume_weighted_efficiency_ratio"
}
known_at_lookahead = pd.Series(
(``smooth()`` top-level no-op), and for the filtfilt/savgol routes
when their downstream short-series guards make smoothing a no-op.
"""
- method = config.get("method", SMOOTHING_METHODS[0])
+ method = config.get("method", SMOOTHING_METHODS[0]) # "none"
if method == SMOOTHING_METHODS[0]: # "none"
return 0
raw_window = max(
return int(4.0 * sigma + 0.5)
if method == SMOOTHING_METHODS[7]: # "savgol"
polyorder = max(int(config.get("polyorder", DEFAULTS_LABEL_SMOOTHING["polyorder"])), 0)
- effective_window, _, _ = get_savgol_params(raw_window, polyorder, SMOOTHING_MODES[0])
+ effective_window, _, _ = get_savgol_params(
+ raw_window, polyorder, SMOOTHING_MODES[0]
+ ) # "mirror"
elif method == SMOOTHING_METHODS[3]: # "kaiser_bessel_derived"
effective_window = get_even_window(raw_window)
else:
if series_length <= _filtfilt_default_padlen(effective_window, 1):
return 0
return effective_window - 1
- if method == SMOOTHING_METHODS[7] and series_length < effective_window:
+ if method == SMOOTHING_METHODS[7] and series_length < effective_window: # "savgol"
return 0
return effective_window // 2
center=True,
min_periods=1,
).max()
- if method == SMOOTHING_METHODS[7] and mode == SMOOTHING_MODES[4]:
+ if (
+ method == SMOOTHING_METHODS[7] and mode == SMOOTHING_MODES[4] # "interp"
+ ): # "savgol"; SMOOTHING_MODES[4]='interp'
smoothed_known_at_positions.iloc[:kernel_half_width] = known_at_positions.iloc[
: 2 * kernel_half_width + 1
].max()
*,
logger: Logger,
context: str,
- on_collapse: Literal["raise", "fallback"] = LABEL_WEIGHT_SUPPORT_POLICIES[1],
+ on_collapse: Literal["raise", "fallback"] = LABEL_WEIGHT_SUPPORT_POLICIES[1], # "raise"
) -> NDArray[np.floating]:
"""Combine base sample weights with the label importance weights.
pivot_weights: NDArray[np.floating],
sigma_candles: float,
*,
- bandwidth: str = FILL_BANDWIDTHS[0],
+ bandwidth: str = FILL_BANDWIDTHS[0], # "fixed"
bandwidth_neighbors: int = 1,
bandwidth_alpha: float = 1.0,
sigma_min_candles: float = 0.5,
< float(label_weighting["fill_sigma_candles"])
)
uniform_gaussian = (
- label_weighting["strategy"] == WEIGHT_STRATEGIES[1]
+ label_weighting["strategy"] == WEIGHT_STRATEGIES[1] # "uniform"
and label_weighting["fill_method"] == FILL_METHODS[2] # "gaussian"
)
band_weight_availability = (
_FORMAT_STYLES: Final[tuple[Literal["dict", "params"], ...]] = ("dict", "params")
+_FORMAT_STYLE_DICT: Final[Literal["dict"]] = _FORMAT_STYLES[0] # "dict"
+_FORMAT_STYLE_PARAMS: Final[Literal["params"]] = _FORMAT_STYLES[1] # "params"
def format_dict(
d: dict[str, Any],
- style: Literal["dict", "params"] = _FORMAT_STYLES[0],
+ style: Literal["dict", "params"] = _FORMAT_STYLE_DICT,
significant_digits: int = 5,
) -> str:
if not d:
- return "{}" if style == _FORMAT_STYLES[0] else ""
+ return "{}" if style == _FORMAT_STYLE_DICT else ""
- ctx = _FormatContext(quote_strings=(style == _FORMAT_STYLES[0]), sig_digits=significant_digits)
- sep = ": " if style == _FORMAT_STYLES[0] else "="
+ ctx = _FormatContext(quote_strings=(style == _FORMAT_STYLE_DICT), sig_digits=significant_digits)
+ sep = ": " if style == _FORMAT_STYLE_DICT else "="
items = [f"{k}{sep}{_format_value(v, ctx, 0)}" for k, v in d.items()]
joined = ", ".join(items)
- return f"{{{joined}}}" if style == _FORMAT_STYLES[0] else joined
+ return f"{{{joined}}}" if style == _FORMAT_STYLE_DICT else joined
@lru_cache(maxsize=_CACHE_MAXSIZE_LARGE)
str,
Callable[[pd.Series | NDArray[np.floating], int], pd.Series | NDArray[np.floating]],
] = {
- MA_MODES[0]: ta.SMA,
- MA_MODES[1]: ta.EMA,
- MA_MODES[2]: ta.WMA,
- MA_MODES[3]: ta.DEMA,
- MA_MODES[4]: ta.TEMA,
- MA_MODES[5]: ta.TRIMA,
- MA_MODES[6]: ta.KAMA,
- MA_MODES[7]: ta.T3,
+ MA_MODES[0]: ta.SMA, # "sma"
+ MA_MODES[1]: ta.EMA, # "ema"
+ MA_MODES[2]: ta.WMA, # "wma"
+ MA_MODES[3]: ta.DEMA, # "dema"
+ MA_MODES[4]: ta.TEMA, # "tema"
+ MA_MODES[5]: ta.TRIMA, # "trima"
+ MA_MODES[6]: ta.KAMA, # "kama"
+ MA_MODES[7]: ta.T3, # "t3"
}
- return mamodes.get(mamode, mamodes[MA_MODES[0]])
+ return mamodes.get(mamode, mamodes[MA_MODES[0]]) # "sma"
@lru_cache(maxsize=_CACHE_MAXSIZE_SMALL)
@lru_cache(maxsize=_CACHE_MAXSIZE_SMALL)
def get_price_fn(pricemode: str) -> Callable[[pd.DataFrame], pd.Series]:
pricemodes = {
- PRICE_MODES[0]: ta.AVGPRICE,
- PRICE_MODES[1]: ta.MEDPRICE,
- PRICE_MODES[2]: ta.TYPPRICE,
- PRICE_MODES[3]: ta.WCLPRICE,
- PRICE_MODES[4]: lambda df: df.get("close"),
+ PRICE_MODES[0]: ta.AVGPRICE, # "average"
+ PRICE_MODES[1]: ta.MEDPRICE, # "median"
+ PRICE_MODES[2]: ta.TYPPRICE, # "typical"
+ PRICE_MODES[3]: ta.WCLPRICE, # "weighted-close"
+ PRICE_MODES[4]: lambda df: df.get("close"), # "close"
}
- return pricemodes.get(pricemode, pricemodes[PRICE_MODES[4]])
+ return pricemodes.get(pricemode, pricemodes[PRICE_MODES[4]]) # "close"
def ewo(
dataframe: pd.DataFrame,
ma1_length: int = 5,
ma2_length: int = 34,
- pricemode: str = PRICE_MODES[4],
- mamode: str = MA_MODES[0],
+ pricemode: str = PRICE_MODES[4], # "close"
+ mamode: str = MA_MODES[0], # "sma"
zero_lag: bool = False,
normalize: bool = False,
*,
prices = get_price_fn(pricemode)(dataframe)
if zero_lag:
- if mamode == MA_MODES[1]:
+ if mamode == MA_MODES[1]: # "ema"
def ma_fn(series, timeperiod):
return zlema(series, period=timeperiod)
jaw_shift: int = 8,
teeth_shift: int = 5,
lips_shift: int = 3,
- pricemode: str = PRICE_MODES[1],
+ pricemode: str = PRICE_MODES[1], # "median"
zero_lag: bool = False,
) -> tuple[pd.Series, pd.Series, pd.Series]:
"""
from ngboost.distns import Exponential, Laplace, LogNormal, Normal, T
dist_map = {
- _NGBOOST_DISTRIBUTIONS[0]: Normal,
- _NGBOOST_DISTRIBUTIONS[1]: LogNormal,
- _NGBOOST_DISTRIBUTIONS[2]: Exponential,
- _NGBOOST_DISTRIBUTIONS[3]: Laplace,
- _NGBOOST_DISTRIBUTIONS[4]: T,
+ _NGBOOST_DISTRIBUTIONS[0]: Normal, # "normal"
+ _NGBOOST_DISTRIBUTIONS[1]: LogNormal, # "lognormal"
+ _NGBOOST_DISTRIBUTIONS[2]: Exponential, # "exponential"
+ _NGBOOST_DISTRIBUTIONS[3]: Laplace, # "laplace"
+ _NGBOOST_DISTRIBUTIONS[4]: T, # "t"
}
if dist_name not in dist_map:
early_stopping_rounds = _pop_early_stopping_rounds(model_training_parameters, has_eval_set)
- dist = model_training_parameters.pop("dist", _NGBOOST_DISTRIBUTIONS[0])
- if dist == _NGBOOST_DISTRIBUTIONS[1]:
+ dist = model_training_parameters.pop("dist", _NGBOOST_DISTRIBUTIONS[0]) # "normal"
+ if dist == _NGBOOST_DISTRIBUTIONS[1]: # "lognormal"
label_sets = [y] + ([labels for _, labels in eval_set] if eval_set else [])
if any(
not np.all(np.isfinite(values) & (values > 0))
else:
model_training_parameters["train_dir"] = str(model_path / "catboost_info")
- task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0])
+ task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0]) # "CPU"
loss_function = model_training_parameters.get("loss_function", _CATBOOST_DEFAULT_LOSS)
- if task_type == _CATBOOST_TASK_TYPES[1]:
+ if task_type == _CATBOOST_TASK_TYPES[1]: # "GPU"
model_training_parameters.pop("gpu_vram_gb", None)
model_training_parameters.pop("n_jobs", None)
model_training_parameters.setdefault("max_ctr_complexity", 4)
_apply_verbosity_alias(model_training_parameters)
pruning_callback = None
- if trial is not None and has_eval_set and task_type != _CATBOOST_TASK_TYPES[1]:
+ if trial is not None and has_eval_set and task_type != _CATBOOST_TASK_TYPES[1]: # "GPU"
pruning_callback = optuna.integration.CatBoostPruningCallback(
trial, _CATBOOST_DEFAULT_LOSS
)
resolved = params.copy()
if (
regressor == _REGRESSOR_SPECS.xgboost.name
- and resolved.get("grow_policy") == _XGBOOST_GROW_POLICIES[1]
+ and resolved.get("grow_policy") == _XGBOOST_GROW_POLICIES[1] # "lossguide"
):
resolved["max_depth"] = 0
elif regressor == _REGRESSOR_SPECS.histgradientboostingregressor.name and resolved.pop(
trial, "max_leaves", ranges["max_leaves"], min_val=2, log=True
),
}
- if grow_policy == _XGBOOST_GROW_POLICIES[1]
+ if grow_policy == _XGBOOST_GROW_POLICIES[1] # "lossguide"
else {
"max_depth": _optuna_suggest_int_from_range(
trial, "max_depth", ranges["max_depth"], min_val=1
),
}
- if booster == _XGBOOST_BOOSTERS[1]:
+ if booster == _XGBOOST_BOOSTERS[1]: # "dart"
params["sample_type"] = trial.suggest_categorical(
"sample_type", ["uniform", "weighted"]
)
),
}
- if boosting_type == _LIGHTGBM_BOOSTING_TYPES[1]:
+ if boosting_type == _LIGHTGBM_BOOSTING_TYPES[1]: # "dart"
params["xgboost_dart_mode"] = trial.suggest_categorical(
"xgboost_dart_mode", [False, True]
)
elif regressor == _REGRESSOR_SPECS.catboost.name:
# Parameter order: boosting -> tree structure -> regularization -> sampling
- task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0])
+ task_type = model_training_parameters.get("task_type", _CATBOOST_TASK_TYPES[0]) # "CPU"
loss_function = model_training_parameters.get("loss_function", _CATBOOST_DEFAULT_LOSS)
- if task_type == _CATBOOST_TASK_TYPES[1]:
+ if task_type == _CATBOOST_TASK_TYPES[1]: # "GPU"
gpu_vram_gb = model_training_parameters.get("gpu_vram_gb", _CATBOOST_GPU_VRAM_DEFAULT)
matched_vram_gb = max(
(v for v in _CATBOOST_GPU_VRAM_PARAM_RANGES if v <= gpu_vram_gb),
bootstrap_type = trial.suggest_categorical("bootstrap_type", bootstrap_options)
grow_policy = trial.suggest_categorical("grow_policy", _CATBOOST_GROW_POLICIES)
if (
- boosting_type == _CATBOOST_BOOSTING_TYPES[1]
- and grow_policy != _CATBOOST_GROW_POLICIES[0]
+ boosting_type == _CATBOOST_BOOSTING_TYPES[1] # "Ordered"
+ and grow_policy != _CATBOOST_GROW_POLICIES[0] # "SymmetricTree"
):
raise optuna.TrialPruned("Ordered boosting is not supported for nonsymmetric trees")
}
if (
- task_type == _CATBOOST_TASK_TYPES[0]
+ task_type == _CATBOOST_TASK_TYPES[0] # "CPU"
or loss_function in _CATBOOST_GPU_RSM_LOSS_FUNCTIONS
):
params["rsm"] = trial.suggest_float(
ranges["rsm"][1],
)
- if bootstrap_type == _CATBOOST_BOOTSTRAP_TYPES[0]:
+ if bootstrap_type == _CATBOOST_BOOTSTRAP_TYPES[0]: # "Bayesian"
params["bagging_temperature"] = trial.suggest_float(
"bagging_temperature",
ranges["bagging_temperature"][0],
ranges["subsample"][1],
)
- if task_type == _CATBOOST_TASK_TYPES[1]:
+ if task_type == _CATBOOST_TASK_TYPES[1]: # "GPU"
params["border_count"] = _optuna_suggest_int_from_range(
trial, "border_count", ranges["border_count"], min_val=1
)
repositories / freqai-strategies.git / commitdiff
