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Martanto edited this page Jul 19, 2026 · 15 revisions

API Reference

Parameter tables and method signatures for every public class exported from eruption_forecast. Imports throughout this page:

from eruption_forecast import (
    ForecastModel,
    TrainingModel,
    PredictionModel,
    EvaluationModel,
    ExplanationModel,
    CalculateTremor,
    LabelBuilder,
    DynamicLabelBuilder,
    FeaturesBuilder,
    TremorMatrixBuilder,
    TremorData,
    LabelData,
    enable_logging,
    disable_logging,
    notify,
    timer,
    TelegramNotification,
)
from eruption_forecast.ensemble import SeedEnsemble, ClassifierEnsemble
from eruption_forecast.ensemble.base_ensemble import BaseEnsemble
from eruption_forecast.ensemble.metrics_ensemble import MetricsEnsemble
from eruption_forecast.ensemble.explainer_ensemble import ExplainerEnsemble
from eruption_forecast.dataclass import (
    SeedExplanation,
    ClassifierExplanation,
)
from eruption_forecast.model.classifier_comparator import ClassifierComparator
from eruption_forecast.features.feature_selector import FeatureSelector
# ⚠ Experimental — see the Feature Count Sweep wiki page:
from eruption_forecast.features import FeatureCountSweep, sweep_feature_count

ForecastModel

Top-level pipeline orchestrator. See Pipeline Walkthrough for full examples.

Constructor

ForecastModel(
    station: str,
    channel: str,
    network: str,
    location: str = "",
    day_to_forecast: int = 2,
    output_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    verbose: bool = False,
)
Param Type Default Notes
station str - Station code (uppercased)
channel str - Channel code (uppercased)
network str - FDSN network code
location str "" FDSN location code
day_to_forecast int 2 Look-ahead window in days; threaded into TrainingModel/PredictionModel as window_size
output_dir str | None None Defaults to {cwd}/output (or {root_dir}/output when root_dir set)
root_dir str | None None Anchor for relative output_dir
overwrite bool False Default for stage methods
n_jobs int 1 Default for stage methods; clamped to cpu_count - 2
verbose bool False Default for stage methods

calculate(...)

fm.calculate(
    start_date: str | datetime,
    end_date: str | datetime,
    source: Literal["sds", "fdsn"] = "sds",
    methods: str | list[str] | None = None,
    remove_outlier_method: Literal["all", "maximum"] = "maximum",
    remove_tremor_anomalies: bool = False,
    interpolate: bool = True,
    value_multiplier: float | None = None,
    cleanup_daily_dir: bool = False,
    plot_daily: bool = False,
    save_plot: bool = False,
    plot_overwrite: bool = False,
    sds_dir: str | None = None,
    client_url: str = "https://service.iris.edu",
    minimum_completion_ratio: float = 0.3,
    overwrite: bool | None = None,
    n_jobs: int | None = None,
    verbose: bool | None = None,
) -> Self

start_date is internally pushed back by day_to_forecast days for full lead-in coverage. sds_dir is required when source="sds". None for overwrite/n_jobs/verbose means "inherit from constructor". Sets self.CalculateTremor, self.tremor_df, self.tremor_start_date, self.tremor_end_date.

train(...)

fm.train(
    start_date: str | datetime,
    end_date: str | datetime,
    eruption_dates: list[str],
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
    label_builder: Literal["standard", "dynamic"] = "standard",
    days_before_eruption: int | None = None,
    classifiers: str | list[str] = "rf",
    cv_strategy: Literal["shuffle", "stratified", "shuffle-stratified"] = "shuffle-stratified",
    cv_splits: int = 5,
    scoring: str = "balanced_accuracy",
    top_n_features: int = 20,
    include_eruption_date: bool = True,
    select_tremor_columns: list[str] | None = None,
    save_tremor_matrix_per_method: bool = True,
    exclude_features: list[str] | None = None,
    select_features: str | list[str] | None = None,
    minimum_completion: float = 1.0,
    seeds: int = 10,
    resample_method: Literal["under", "over", "auto"] | None = "auto",
    minority_threshold: float = 0.15,
    sampling_strategy: str | float = 0.75,
    plot_features: bool = True,
    output_dir: str | None = None,
    overwrite: bool | None = None,
    n_jobs: int | None = None,
    n_grids: int = 1,
    use_cache: bool = True,
    verbose: bool | None = None,
) -> Self

Requires calculate() to have populated tremor data first. label_builder="dynamic" requires days_before_eruption. use_cache=True short-circuits via TrainingModel.load(training_dir, identity) when the cache identity matches. Sets self.TrainingModel, self.ClassifierEnsemble, and self._training_cache_hash.

predict(...)

fm.predict(
    start_date: str | datetime,
    end_date: str | datetime,
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
    save_seed_result: bool = True,
    plot_threshold: float = 0.5,
    plot_title: str | None = None,
    plot_pdf: bool = True,
    use_features_from: Literal["all", "files", "training"] = "all",
    features_matrix_path: str | None = None,
    label_features_csv: str | None = None,
    enable_segments_plot: bool = False,
    output_dir: str | None = None,
    overwrite: bool | None = None,
    n_jobs: int | None = None,
    use_cache: bool = True,
    verbose: bool | None = None,
    **plot_kwargs: Any,
) -> Self

Requires train() first. **plot_kwargs is forwarded to plot_forecast (see Visualization for keys) and is not captured in ForecastConfig because matplotlib objects do not round-trip through YAML. Sets self.PredictionModel, self.results.

use_features_from switches feature scoping between three modes — see Prediction Workflow → Feature Scoping for the full mode table:

Mode Behaviour Path kwargs
"all" (default) Extract every tsfresh feature (select_features=None) Ignored
"files" Skip tsfresh and load features_matrix_path + label_features_csv via PredictionModel.load_features(...). Both paths are required and must exist (raises ValueError / FileNotFoundError otherwise). use_cache is forced to False because load_features() bypasses the extract-features kwargs the cache identity depends on. Both required
"training" Narrow tsfresh to the union of features any seed picked during train() — pulled from self.TrainingModel.features_selected_df.index; falls back to None when the frame is empty. Ignored

enable_segments_plot=True forwards the training and prediction date ranges to plot_forecast so it renders the top Training → Gap → Prediction segment strip above the forecast panels. When the prediction start is on or before the training end, the strip helper snaps the prediction start forward to training_end + 1 day and logs a warning; when False (default) the strip is omitted.

evaluate(...)

fm.evaluate(
    model: Literal["training", "prediction"] = "prediction",
    eruption_dates: list[str] | None = None,
    plot_per_seed: bool = False,
    plot_aggregate: bool = True,
    output_dir: str | None = None,
    overwrite: bool | None = None,
    n_jobs: int | None = None,
    use_cache: bool = True,
    verbose: bool | None = None,
) -> Self

eruption_dates=None falls back to the dates captured during train(). use_cache=True (default) consults {evaluation_dir}/{hash}.EvaluationModel.pkl before running the per-classifier predict_proba pass; pass use_cache=False to force a fresh evaluation even when a cached pickle exists on disk. use_cache is threaded down to EvaluationModel.evaluate(..., use_cache=...) so it gates both the internal load and the write. Independent of overwrite, which additionally controls plot regeneration. Always auto-calls save_config() at the end. Sets self.EvaluationModel, self.evaluation_results.

explain(...)

fm.explain(
    model: Literal["training", "prediction"] = "prediction",
    eruption_dates: list[str] | None = None,
    save_per_seed: bool = True,
    plot_per_seed: bool = True,
    figsize: tuple[float, float] | None = None,
    max_display: int = 20,
    group_remaining_features: bool = False,
    dpi: int = 150,
    check_additivity: bool = False,
    overwrite_classifier_explanation: bool = False,
    output_dir: str | None = None,
    overwrite: bool | None = None,
    n_jobs: int | None = None,
    use_cache: bool = True,
    verbose: bool | None = None,
) -> Self

Requires the upstream TrainingModel or PredictionModel to exist on self (run train() and, for model="prediction", predict() first). eruption_dates=None falls back to the dates captured during train(). use_cache=True (default) consults {explanation_dir}/{hash}.ExplanationModel.pkl before re-running SHAP; pass use_cache=False to force a fresh explanation even when a cached pickle exists on disk. use_cache is threaded down to ExplanationModel.explain(..., use_cache=...) so it gates both the internal load and the write. Independent of overwrite, which additionally controls per-classifier artefact regeneration. Internally constructs an ExplanationModel, runs explain() then plot(), and sets self.ExplanationModel. See Explanation Workflow for the TreeExplainer constraint (RF / lite-rf / GB / XGB only — other classifiers are skipped with a warning).

Config round-trip

Method Returns Notes
fm.save_config(path=None, fmt="yaml") str Defaults to {station_dir}/forecast.config.{yaml,json}
ForecastModel.from_config(path) ForecastModel Classmethod; restores the captured ForecastConfig
fm.run() Self Idempotent replay of every captured non-None stage

TrainingModel

BaseModel subclass with content-addressable cache participation. Use standalone when running outside ForecastModel; otherwise fm.train(...) constructs it for you.

Constructor

TrainingModel(
    tremor_data: str | pd.DataFrame,
    start_date: str | datetime,
    end_date: str | datetime,
    classifiers: str | list[str],
    eruption_dates: list[str],
    window_size: int = 2,
    cv_strategy: Literal["shuffle", "stratified", "shuffle-stratified"] = "shuffle-stratified",
    cv_splits: int = 5,
    top_n_features: int = 20,
    include_eruption_date: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    n_grids: int = 1,
    verbose: bool = False,
)

Pipeline methods (all return Self)

tm.build_label(
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
    builder: Literal["standard", "dynamic"] = "standard",
    days_before_eruption: int | None = None,
    verbose: bool | None = None,
)

tm.extract_features(
    select_tremor_columns: list[str] | None = None,
    save_tremor_matrix_per_method: bool = False,
    exclude_features: list[str] | None = None,
    select_features: str | list[str] | None = None,
    save_tremor_matrix_per_id: bool = False,
    minimum_completion: float = 1.0,
    overwrite: bool = False,
    n_jobs: int | None = None,
    verbose: bool | None = None,
)

tm.fit(
    seeds: int = 25,
    resample_method: Literal["under", "over", "auto"] | None = "auto",
    minority_threshold: float = 0.15,
    sampling_strategy: str | float = 0.75,
    plot_features: bool = False,
    scoring: str = "balanced_accuracy",
    compute_learning_curve: bool = False,
)

Cache + persistence

Method Notes
TrainingModel.build_identity(**kwargs) Classmethod; returns canonical identity dict for hashing. Called both by ForecastModel.train() (before the instance exists, for cache lookup) and inside fit() (with kwargs pulled from self, for the save).
tm.save(identity) Cache mode: writes {training_dir}/{hash}.TrainingModel.pkl + .params.json sidecar
tm.save() Legacy mode (no identity): {output_dir}/TrainingModel_{basename}.pkl joblib dump
TrainingModel.load(stage_dir, identity) Classmethod; returns the instance on cache hit or None
tm.save_config(path=None, fmt="yaml") {training_dir}/training.config.{yaml,json} — auto-called at end of fit()

Populated attributes after fit(): tm.results (per-classifier trained-model JSON registry paths written by save_model_json), tm.ClassifierEnsemble, tm.classifier_ensemble_path, tm.features_df, tm.labels.

PredictionModel

BaseModel subclass with content-addressable cache participation.

Constructor

PredictionModel(
    model: str | ClassifierEnsemble | SeedEnsemble,
    tremor_data: str | pd.DataFrame,
    start_date: str | datetime,
    end_date: str | datetime,
    window_size: int = 2,
    overwrite: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
)

model accepts a live ClassifierEnsemble / SeedEnsemble, a ClassifierEnsemble.json / .pkl, a SeedEnsemble_*.pkl, or a trained-model registry .csv - resolved via ClassifierEnsemble.from_any(...).

Pipeline methods

pm.build_label(
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
) -> Self

pm.extract_features(
    select_tremor_columns: list[str] | None = None,
    save_tremor_matrix_per_method: bool = False,
    exclude_features: list[str] | None = None,
    overwrite: bool = False,
    n_jobs: int | None = None,
    verbose: bool | None = None,
) -> Self

pm.forecast(
    save_seed_result: bool = True,
    plot_threshold: float = 0.5,
    plot_title: str | None = None,
    plot_pdf: bool = True,
    **plot_kwargs,
) -> pd.DataFrame

forecast() returns the results DataFrame indexed by datetime with one column per {classifier}_{eruption_probability|uncertainty|confidence|prediction} plus the four consensus_* columns. Also sets pm.results and pm.forecast_plot_path.

Cache + persistence

Same surface as TrainingModel: build_identity(**kwargs), save(identity), load(stage_dir, identity). The cache identity embeds the upstream training_hash (constructor param).

Method Notes
pm.save_config(path=None, fmt="yaml") {prediction_dir}/prediction.config.{yaml,json} — auto-called at end of forecast()

PredictionConfig captures the user-supplied model and tremor_data as string handles (null when a live in-memory object was passed).

EvaluationModel

BaseModel subclass (no cache).

Constructor

EvaluationModel(
    model: TrainingModel | PredictionModel,
    eruption_dates: list[str] | None = None,
    overwrite: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
)

Raises ValueError if model is a PredictionModel and eruption_dates is None, or if model.ClassifierEnsemble is None. Output is namespaced to evaluation/{model.kind}/.

Methods

em.evaluate(
    plot_aggregate: bool = True,
    plot_per_seed: bool = False,
    plot_shap: bool = False,
    compare_classifiers: bool = True,
) -> dict[str, pd.DataFrame]

em.compare(
    metrics: str | list[str] | None = None,
) -> ClassifierComparator
EvaluationModel.from_file(
    filepath: str,
    eruption_dates: list[str] | None = None,
    overwrite: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
) -> EvaluationModel

Classmethod. Loads a .pkl produced by TrainingModel.save() or PredictionModel.save() and dispatches on kind.

plot_shap=True is reserved on this surface and emits a warning — SHAP rendering is produced by the dedicated Explanation Workflow via ExplanationModel.explain(). plot_per_seed=True is plumbed through to MetricsEnsemble.plot_seed() for the metric plots (ROC, PR, confusion, etc.) — it does not render SHAP.

Method Notes
em.save_config(path=None, fmt="yaml") {evaluation_dir}/evaluation.config.{yaml,json} — auto-called at end of evaluate()

EvaluationConfig omits the upstream model parameter (live model instances are not serializable). Captured fields: eruption_dates, overwrite, output_dir, root_dir, n_jobs, verbose.

ExplanationModel

BaseModel subclass with content-addressable cache participation. Per-seed SHAP explanations over a fitted ClassifierEnsemble — never re-fits. See Explanation Workflow.

Constructor

ExplanationModel(
    model: TrainingModel | PredictionModel,
    eruption_dates: list[str] | None = None,
    overwrite: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
)

Output is namespaced to explanation/{model.kind}/. The constructor sets self.kind="explanation", self.model_kind (mirrored from the upstream TrainingModel.kind / PredictionModel.kind), self.ClassifierEnsemble, self.features_df, self.explanation_dir, self.classifiers_dir, and self.ExplainerEnsemble.

Methods

em.explain(
    save_per_seed: bool = True,
    check_additivity: bool = False,
    overwrite_classifier_explanation: bool = False,
) -> Self

em.plot(
    figsize: tuple[float, float] | None = None,
    max_display: int = 20,
    group_remaining_features: bool = False,
    dpi: int = 150,
    plot_per_seed: bool = True,
)

explain() delegates to ExplainerEnsemble.explain() and caches the result via BaseModel.save(identity). On a cache hit the stored self.explanations is restored without re-running SHAP. plot() renders the per-eruption waterfall (only when eruption_dates is available) and, optionally, per-seed bar + beeswarm plots.

ExplanationModel.from_file(
    filepath: str,
    eruption_dates: list[str] | None = None,
    overwrite: bool = False,
    output_dir: str | None = None,
    root_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
) -> ExplanationModel

Classmethod. Loads a .pkl from TrainingModel.save() or PredictionModel.save() and constructs an ExplanationModel against it. Raises TypeError if the pickle holds anything else.

Populated attributes after explain(): em.explanations: list[ClassifierExplanation].

Method Notes
em.save_config(path=None, fmt="yaml") {explanation_dir}/explanation.config.{yaml,json} — auto-called at end of explain()

ExplanationConfig omits the upstream model parameter (live model instances are not serializable). Captured fields: eruption_dates, overwrite, output_dir, root_dir, n_jobs, verbose.

ExplainerEnsemble

ExplainerEnsemble(
    classifier_ensemble: ClassifierEnsemble,
    features_df: pd.DataFrame,
    kind: Literal["training", "prediction"] = "prediction",
    output_dir: str | None = None,
    explanation_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    verbose: bool = False,
)

Per-seed SHAP engine driven by shap.TreeExplainer. Non-tree classifiers (svm, lr, nn, dt, knn, nb, voting) are skipped at the per-classifier loop with a warning. explanation_dir is the sibling-of-classifiers/ root used for per-eruption waterfall plots; when omitted it falls back to dirname(output_dir).

Methods

ee.explain(
    save_per_seed: bool = True,
    check_additivity: bool = False,
    overwrite_classifier_explanation: bool = False,
) -> Self

ee.plot_seed(
    max_display: int = 20,
    group_remaining_features: bool = False,
    dpi: int = 150,
)   # per-classifier bar + beeswarm under classifiers/{ClfName}/figures/

ee.plot_waterfall(
    labels: pd.Series | pd.DataFrame,
    eruption_dates: list[str],
    figsize: tuple[float, float] | None = None,
    max_display: int = 20,
    dpi: int = 150,
)   # per-eruption waterfall under {explanation_dir}/eruptions/{date}/

Static helpers

ExplainerEnsemble.explain_seed(
    seed: dict,
    features_df: pd.DataFrame,
    save_per_seed: bool = False,
    check_additivity: bool = False,
    seed_explanation_filepath: str | None = None,
) -> shap.Explanation

ExplainerEnsemble.explain_classifier(
    seed_ensemble: SeedEnsemble,
    features_df: pd.DataFrame,
    save_per_seed: bool = False,
    kind: Literal["training", "prediction"] = "prediction",
    check_additivity: bool = False,
    output_dir: str | None = None,
    overwrite: bool = False,
    verbose: bool = False,
) -> ClassifierExplanation

ExplainerEnsemble.normalise_shap_values(
    explanation: shap.Explanation,
) -> tuple[np.ndarray, np.ndarray]

Imported from eruption_forecast.ensemble.explainer_ensemble (intentionally not re-exported from ensemble/__init__.py to keep that subpackage cycle-free).

SeedExplanation / ClassifierExplanation

@dataclass(frozen=True, slots=True)
class SeedExplanation:
    random_state: int
    shap_values: shap.Explanation

@dataclass(slots=True)
class ClassifierExplanation:
    classifier_name: str
    seeds: list[SeedExplanation] = field(default_factory=list)

Both re-exported from eruption_forecast.dataclass. SeedExplanation is frozen; ClassifierExplanation is mutable so ExplainerEnsemble.explain_classifier() can append seeds incrementally. Produced by the explanation stage and consumed by every plot helper in plots/explanation_plots.py.

CalculateTremor

Constructor

CalculateTremor(
    start_date: str | datetime,
    end_date: str | datetime,
    station: str,
    channel: str,
    network: str,
    location: str | None = None,
    channel_type: str = "D",
    methods: list[str] | None = None,
    output_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    remove_outlier_method: Literal["all", "maximum"] = "maximum",
    remove_tremor_anomalies: bool = False,
    interpolate: bool = False,
    value_multiplier: float | None = None,
    cleanup_daily_dir: bool = False,
    plot_daily: bool = False,
    save_plot: bool = False,
    plot_overwrite: bool = False,
    filename_prefix: str | None = None,
    minimum_completion_ratio: float = 0.3,
    n_jobs: int = 1,
    verbose: bool = False,
    debug: bool = False,
)

Source binding + execution (all return Self)

ct.from_sds(sds_dir: str)
ct.from_fdsn(client_url: str | None = None)
ct.change_freq_bands(freq_bands: list[tuple[float, float]])
ct.run()

After run(): ct.df (the tremor DataFrame), ct.csv (path to the merged file), ct.daily_files, ct.daily_dir.

LabelBuilder / DynamicLabelBuilder

LabelBuilder(
    start_date: str | datetime,
    end_date: str | datetime,
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
    day_to_forecast: int,
    eruption_dates: list[str] | list[datetime],
    volcano_id: str | None = None,
    include_eruption_date: bool = True,
    output_dir: str | None = None,
    root_dir: str | None = None,
    verbose: bool = False,
    debug: bool = False,
)

DynamicLabelBuilder(
    days_before_eruption: int,
    window_step: int,
    window_step_unit: Literal["minutes", "hours"],
    day_to_forecast: int,
    eruption_dates: list[str],
    volcano_id: str | None = None,
    output_dir: str | None = None,
    root_dir: str | None = None,
    prefix_filename: str | None = None,
    verbose: bool = False,
    debug: bool = False,
)

Both expose .build() -> Self. After build(): lb.df (DateTime-indexed id/is_erupted frame), lb.csv (path to the label CSV).

Note that within TrainingModel, include_eruption_date defaults to False (training pipeline behaviour); the LabelBuilder constructor's own default is True. The difference is intentional - see Training Workflow.

FeaturesBuilder

FeaturesBuilder(
    tremor_matrix_df: pd.DataFrame,
    output_dir: str | None = None,
    label_df: pd.DataFrame | None = None,
    select_features: list[str] | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    verbose: bool = False,
)

fb.extract_features(
    select_tremor_columns: list[str] | None = None,
    exclude_features: list[str] | None = None,
) -> pd.DataFrame

label_df=None switches the builder to prediction mode (no tsfresh relevance filtering). select_features pre-filters tsfresh to the supplied fully-qualified feature names.

TremorMatrixBuilder

TremorMatrixBuilder(
    tremor_df: pd.DataFrame,
    label_df: pd.DataFrame,
    output_dir: str | None = None,
    window_size: int = 1,
    root_dir: str | None = None,
    minimum_completion: float = 1.0,
    overwrite: bool = False,
    verbose: bool = False,
)

tmb.build(
    select_tremor_columns: list[str] | None = None,
    save_tremor_matrix_per_method: bool = False,
    save_tremor_matrix_per_id: bool = False,
) -> Self

After build(): tmb.df is the matrix with id, datetime, and tremor columns.

FeatureSelector

FeatureSelector(
    method: Literal["tsfresh", "random_forest"] = "tsfresh",
    random_state: int = 42,
    output_dir: str | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
)

Used internally by TrainingModel.fit() per-seed (hardcoded method="tsfresh"); surfaced publicly for ad-hoc selection experiments. Pick method="tsfresh" for FDR-controlled p-value filtering (fast, model-agnostic) or method="random_forest" for permutation importance from a RandomForest probe. Populated after fit(X, y): selected_features_, p_values_, importance_scores_, n_features_tsfresh, n_features_rf, n_features, feature_names_.

FeatureCountSweep (Experimental)

⚠ Experimental — not production-ready. Signature, defaults, and on-disk layout may change without deprecation. See Feature Count Sweep for the full contract, examples, and caveats.

Post-hoc top_n_features recommender. Consumes per-seed FDR rankings, resampled ids, and tuned best_model.pkl files already written by TrainingModel.fit(), then re-scores each seed at multiple candidate N values.

Constructor

FeatureCountSweep(
    estimator: BaseEstimator | None = None,
    *,
    strategy: Literal["shared", "per-seed"] = "per-seed",
    n_candidates: list[int] | None = None,
    cv: BaseCrossValidator | int = 5,
    scoring: str = "average_precision",
    parsimony: bool = True,
    parsimony_tolerance: float | None = None,
    resample_method: Literal["under", "over", "auto"] | None = None,
    minority_threshold: float = 0.15,
    estimator_mode: Literal["default", "tuned"] = "default",
    mode: Literal["cv", "forecast"] = "forecast",
    n_jobs: int = 1,
    random_state: int = 42,
    verbose: bool = False,
)
Param Notes
mode "forecast" (default) — score against a held-out prediction window; "cv" — RFECV-analog CV inside the trainer's resampled subset
estimator_mode "default" (recommended) drops the tuned hyperparameters via estimator.__class__(); "tuned" keeps them and biases the curve toward the trained N
parsimony_tolerance None → adaptive 1-SE rule; float in (0, 1) → fractional tolerance peak × (1 − t)
n_jobs Applied over CV folds when mode="cv"; unused in mode="forecast" (one fit per (seed, N))

.fit(...)

sweep.fit(
    X: pd.DataFrame,
    y: pd.Series,
    *,
    per_seed_inputs: dict[int, dict[str, Any]] | None = None,
    shared_ranking: pd.Series | pd.Index | list[str] | None = None,
    per_seed_rankings: dict[int, ...] | None = None,
    X_test: pd.DataFrame | None = None,
    y_test: pd.Series | None = None,
) -> Self

per_seed_inputs / shared_ranking / per_seed_rankings are mutually exclusive input modes. X_test / y_test are required in mode="forecast"; missing per-seed features in X_test raise KeyError (never silently intersected).

Populated attributes: cv_scores_ (aggregated N × [mean, std, n_seeds]), cv_scores_raw_ (full N × seed matrix), n_features_ (recommended N*), seed_argmax_ (per-seed argmax N), support_ (per-seed top-N* feature lists).

sweep_feature_count(...)

sweep_feature_count(
    source: str | os.PathLike | TrainingModel,
    *,
    mode: Literal["cv", "forecast"] = "forecast",
    evaluation_source: EvaluationModel | None = None,
    classifier_name: str | None = None,
    n_candidates: list[int] | None = None,
    cv: BaseCrossValidator | int = 5,
    scoring: str = "average_precision",
    parsimony: bool = True,
    parsimony_tolerance: float | None = None,
    resample_method: Literal["under", "over", "auto"] | None = None,
    minority_threshold: float = 0.15,
    estimator_mode: Literal["default", "tuned"] = "default",
    n_jobs: int = 1,
    random_state: int = 42,
    output_dir: str | None = None,
    save: bool = True,
    verbose: bool = False,
) -> FeatureCountSweep | dict[str, FeatureCountSweep]

Convenience wrapper that harvests per-seed inputs from an existing training/ directory (or live TrainingModel). In mode="forecast", evaluation_source must be an EvaluationModel in prediction-reuse mode with a populated y_true (call em.build_label(...) or em.evaluate() first). Returns a single sweep when classifier_name is given, otherwise a dict keyed by discovered classifier names.

Persistence

Method Notes
sweep.save(path) joblib-dump the full instance
FeatureCountSweep.load(path) Classmethod; restore a saved sweep

sweep_feature_count(save=True) also writes cv_scores.csv, cv_scores_raw.csv, seed_argmax_hist.csv, support.json, and curve.png alongside the pickle under {training_dir}/features/{cv-slug}/sweep/{mode}/{classifier}/.

SeedEnsemble

class SeedEnsemble(BaseEnsemble, BaseEstimator, ClassifierMixin):
    classifier_name: str
    seeds: list[dict]      # per-seed records: {random_state, model, feature_names}
    features: list[str]    # sorted union of per-seed feature_names (set of features
                           # used by at least one seed in this ensemble); populated
                           # by every factory, empty on the bare constructor

Construction

SeedEnsemble(classifier_name: str)

# Recommended: dispatch on file extension (.json or .csv).
SeedEnsemble.from_any(
    trained_model_path: str,
    classifier_name: str | None = None,
    verbose: bool = False,
) -> SeedEnsemble

# New JSON trained-model registry written by utils.ml.save_model_json.
SeedEnsemble.from_json(
    trained_model_json: str,
    classifier_name: str | None = None,
    verbose: bool = False,
) -> SeedEnsemble

# Legacy CSV registry loader (kept for backwards compatibility).
SeedEnsemble.from_registry(
    registry_csv: str,
    classifier_name: str | None = None,
    verbose: bool = False,
) -> SeedEnsemble

Inference

se.predict_proba(X: pd.DataFrame) -> np.ndarray   # (n_samples, 2)

se.predict_with_uncertainty(
    X: pd.DataFrame,
    threshold: float = 0.5,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]
# returns (mean_proba, std_proba, confidence, prediction)

Inspection

se.features             # list[str]: sorted union of per-seed feature_names
se.seeds                # list[dict]: per-seed records
se[i]                   # seed record at index i (dict)
len(se)                 # number of seeds

save(path) / load(path) inherited from BaseEnsemble.

ClassifierEnsemble

class ClassifierEnsemble(BaseEnsemble, BaseEstimator, ClassifierMixin):
    ensembles: dict[str, SeedEnsemble]   # classifier name → its SeedEnsemble
    features: list[str]                  # sorted union of SeedEnsemble.features
                                         # across every registered classifier;
                                         # populated by every factory, empty on
                                         # the bare constructor

Construction (classmethods)

Factory Accepts
from_any(source, verbose=False) ClassifierEnsemble.{json,pkl}, SeedEnsemble_*.pkl, trained-model registry .json (list) or .csv, or a live SeedEnsemble
from_seed_ensembles(seed_ensembles) Pre-built SeedEnsemble instances
from_dict(trained_model_paths, verbose=False) Dict mapping classifier name → trained-model registry path (.json or .csv); each value is dispatched through SeedEnsemble.from_any
from_json(json_path, verbose=False) Top-level results map (ClassifierEnsemble_{cv}.json) written by TrainingModel.fit()

Inference

ce.predict_proba(X: pd.DataFrame) -> np.ndarray             # consensus, (n_samples, 2)
ce.predict_with_uncertainty(X: pd.DataFrame, threshold: float = 0.5)
# → (mean, std, confidence, prediction, per_classifier_dict)

Inspection

ce.classifiers          # list[str]: classifier class names in registration order
ce.features             # list[str]: sorted union of SeedEnsemble.features across
                        #            every registered classifier
ce[name]                # SeedEnsemble for the named classifier
len(ce)                 # number of classifiers

save(path) / load(path) inherited from BaseEnsemble.

MetricsEnsemble

MetricsEnsemble(
    classifier_ensemble: ClassifierEnsemble,
    features_df: pd.DataFrame,
    y_true: pd.Series | np.ndarray,
    kind: Literal["prediction", "training"] = "prediction",
    output_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    verbose: bool = False,
)

MetricsEnsemble.from_file(
    model_filepath: str,
    features_path: str,
    features_label_csv: str,
    eruption_dates: list[str] | None = None,
    kind: Literal["prediction", "training"] = "prediction",
    output_dir: str | None = None,
    root_dir: str | None = None,
    overwrite: bool = False,
    n_jobs: int = 1,
    verbose: bool = False,
) -> MetricsEnsemble
Method Notes
me.compute() -> Self Per-seed metric loop. Writes only the (n_samples, n_seeds) y_proba.csv / y_pred.csv matrices under classifiers/{ClfName}/predictions/. metrics, y_probas, y_preds stay in memory; no per-seed JSON is produced. Idempotent once y_probas is populated.
me.plot_aggregate(include_plots=None, exclude_plots=None) -> list[str] Aggregate plots per classifier — ROC, PR, threshold analysis, g-mean curve, MCC curve. Writes figures/aggregate/{plot_name}.{png,csv} per classifier.
me.plot_seed(include_plots=None, exclude_plots=None) -> list[str] Per-seed plots — same dispatcher catalogue. Writes figures/{plot_name}/{seed:05d}.png per classifier in parallel via joblib.
me.metrics dict[str, pd.DataFrame] — populated after compute()
me.save(path=None) / MetricsEnsemble.load(path) joblib round-trip of the full instance to MetricsEnsemble.pkl.

Imported from eruption_forecast.ensemble.metrics_ensemble (intentionally not in ensemble/__init__.py to avoid an import cycle).

BaseEnsemble

class BaseEnsemble:
    def save(self, path: str) -> None
    @classmethod
    def load(cls, path: str) -> Self

Joblib save/load mixin inherited by SeedEnsemble and ClassifierEnsemble. Imported from eruption_forecast.ensemble.base_ensemble.

ClassifierComparator

ClassifierComparator(
    metrics_ensemble: MetricsEnsemble,
    metrics: str | list[str] | None = None,
    output_dir: str | None = None,
)

ClassifierComparator.from_classifier_ensemble(
    classifier_ensemble: ClassifierEnsemble,
    features_df: pd.DataFrame,
    y_true: pd.Series | np.ndarray,
    kind: Literal["training", "prediction"] = "training",
    output_dir: str | None = None,
    root_dir: str | None = None,
    metrics: str | list[str] | None = None,
    n_jobs: int = 1,
    verbose: bool = False,
) -> ClassifierComparator
Method Returns
cc.get_ranking() pd.DataFrame - cross-classifier ranking
cc.plot_all() None - writes ranking plots under {output_dir}/comparison/figures/

Imported from eruption_forecast.model.classifier_comparator. Usually instantiated indirectly via em.compare() or fm.EvaluationModel.compare().

TremorData

Thin wrapper around a tremor CSV. Imported as TremorData from the package root.

TremorData(df: pd.DataFrame)                  # wrap an in-memory frame
TremorData.from_csv(path: str) -> TremorData  # classmethod

@cached_property accessors: df, start_date, end_date, filename, basename, filetype.

LabelData

Thin wrapper around a label CSV. Imported as LabelData from the package root.

LabelData(df: pd.DataFrame)
LabelData.from_csv(path: str) -> LabelData

@cached_property accessors: df, parameters (dict parsed from filename - window_size, window_step, window_step_unit, day_to_forecast), filename, basename.

Feature alias utilities

Aggregation across seeds ranks every significant tsfresh feature, and each row of the ranked CSV is tagged with a short display alias (ft_1, ft_2, …) plus a plain-English description. The alias is deterministic — it follows the rank position — so regenerating the CSV with the same inputs produces identical aliases. The utilities below let callers write, read, and translate that mapping.

Writers — alias + description columns

from eruption_forecast.utils.dataframe import (
    concat_significant_features,
    find_common_features,
)

concat_significant_features(
    features_csvs: list[str],
    features_dir: str,
    number_of_features: int | None = None,
    freq_bands: dict[str, tuple[float, float]] | None = None,
) -> pd.DataFrame

find_common_features(
    top_features_csv: list[str],
    output_dir: str | None = None,
    freq_bands: dict[str, tuple[float, float]] | None = None,
) -> pd.DataFrame

Both functions decorate the ranked frame with alias (ft_1..ft_N by rank) and description (plain-English via humanize_feature_name). concat_significant_features writes top_features.csv and top_{N}_features.csv under features_dir when number_of_features > 0; find_common_features writes common_top_features.csv under output_dir (or os.getcwd()).

freq_bands is optional. When omitted, the humanizer uses the default edges from DEFAULT_FREQUENCY_BANDS (config/constants.py), which matches CalculateTremor's defaults. Pass a matching dict — same shape as {"f0": (low, high), ...} — when the tremor was calculated with CalculateTremor.change_freq_bands(...) so descriptions in the CSVs reflect the actual Hz values.

Reader — load_feature_aliases 

from eruption_forecast.utils.dataframe import load_feature_aliases

load_feature_aliases(
    source: str | pd.DataFrame,
    reverse: bool = True,
) -> dict[str, str]
  • source accepts either a CSV path (top_features.csv, top_{N}_features.csv, common_top_features.csv) OR the DataFrame those functions return directly — the DataFrame path skips the disk round-trip when the frame is still in memory (natural right after concat_significant_features(...) in the same session).
  • reverse=True (default) → {alias: canonical_name} so callers can translate a plot label back to the tsfresh name (aliases["ft_5"] -> 'dsar_f3-f4__fft_coefficient__attr_"abs"__coeff_91').
  • reverse=False → {canonical_name: alias}, useful for features_df.rename(columns=mapping).

Raises FileNotFoundError for missing paths and ValueError when the frame lacks the alias column (typical mistake: pointing at significant_features.csv, the raw per-seed dump that carries no rank).

Backfill — update_top_features_csv 

from eruption_forecast.utils.dataframe import update_top_features_csv

update_top_features_csv(
    csv_path: str,
    output_path: str | None = None,
    freq_bands: dict[str, tuple[float, float]] | None = None,
    overwrite: bool = False,
) -> pd.DataFrame

Adds alias and description columns to a legacy top_features.csv, top_{N}_features.csv, or common_top_features.csv that predates the alias rollout (or was hand-prepared). Row order is trusted as-is — aliases follow the existing rank, no re-sorting — so a CSV that's already sorted by frequency desc / mean_score asc lines up with the same ft_N values concat_significant_features would produce today.

Also handles the score → frequency column rename: when the loaded CSV still carries the legacy score column (which was actually a per-seed count, not a score), it's renamed on disk to frequency so downstream readers pick up the current schema without further action.

  • output_path=None (default) rewrites the file in place; pass a different path to keep the original for comparison.
  • freq_bands forwards to humanize_feature_name so descriptions can reflect custom bands used for that scenario.
  • overwrite=False (default) is idempotent — when frequency, alias, and description are all already present, the CSV is not touched. Pass overwrite=True to drop and regenerate the alias / description columns, useful after changing freq_bands or after the calculator humanization table is extended.

Raises FileNotFoundError if csv_path does not exist.

Label formatters — shorten_feature_name / humanize_feature_name 

from eruption_forecast.utils.formatting import (
    shorten_feature_name,
    humanize_feature_name,
)

shorten_feature_name(name: str) -> str
humanize_feature_name(
    name: str,
    freq_bands: dict[str, tuple[float, float]] | None = None,
) -> str
  • shorten_feature_name renders a compact axis-tick label (dsar_f3-f4 | fft_coef(abs, 91)), used by ExplainerEnsemble at ingest and by the two cross-scenario heatmaps in label_style="short" mode.
  • humanize_feature_name renders a plain-English phrase (Fourier coefficient (attr=abs, coeff=91) of DSAR ratio 4.5/8 Hz), used to populate the description column of the ranked CSVs. The freq_bands kwarg has the same meaning as on concat_significant_features / find_common_features above.

Both return the input unchanged when it does not match the column__calculator[__key_value]* shape.

Heatmap label style

plot_common_features_heatmap(
    top_features_csv: dict[str, str],
    output_path: str | None = None,
    cmap: str = "viridis",
    label_style: Literal["short", "alias"] = "short",
) -> plt.Axes

plot_common_features_correlation(
    top_features_csv: dict[str, str],
    output_path: str | None = None,
    cmap: str = "RdBu_r",
    label_style: Literal["short", "alias"] = "short",
) -> plt.Axes

Both cross-scenario heatmaps accept label_style. "short" (default) preserves the existing shorten_feature_name labels; "alias" uses the ft_1..ft_N aliases from the merged ranking returned by find_common_features — useful when the shortened names still crowd the axis.

Logger helpers

from eruption_forecast import enable_logging, disable_logging
from eruption_forecast.logger import (
    get_category_logger,
    register_error_category,
    set_log_directory,
    set_log_level,
)

enable_logging()              # restore console + file handlers
disable_logging()             # remove every loguru handler
set_log_level(level: str)     # "DEBUG" | "INFO" | "WARNING" | "ERROR" | "CRITICAL"
set_log_directory(dir: str)   # move the log file to a new directory (created if absent)

# Per-category error log files.
register_error_category(
    name: str,
    level: str = "WARNING",
    retention: str = "90 days",
) -> None                     # create/update logs/{name}_YYYY-MM-DD.log
get_category_logger(category: str)   # returns logger.bind(category=category)

The default installation registers a "telegram" category so warnings from TelegramNotification land in logs/telegram_YYYY-MM-DD.log and are excluded from forecast_*.log / errors_*.log. Re-registering an existing category is idempotent — no duplicate sinks are created.

Telegram helpers

from eruption_forecast import notify, timer, TelegramNotification

@notify(
    task: str,
    message: str | None = None,
    to: Literal["telegram", "email"] = "telegram",
    on_success: bool = True,
    on_error: bool = True,
    timeout: float = 3.0,
    verbose: bool = False,
)
def my_func(): ...

@timer(name: str | None = None, send_to: Literal["telegram"] | None = None)
def my_func(): ...

tn = TelegramNotification(
    token: str | None = None,
    chat_id: str | int | None = None,
    verbose: bool = False,
)
tn.send_message(message: str, timeout: float = 3.0) -> Self
tn.send_document(file: str, timeout: float = 30.0, **kwargs) -> Self
tn.send_photo(file: str, timeout: float = 30.0, **kwargs) -> Self
tn.send_media_group(
    files: str | list[str],
    kind: Literal["photo", "document"] = "photo",
    caption: str | None = None,
    timeout: float = 30.0,
    disable_notification: bool = False,
) -> Self

Credentials are read from constructor arguments or the TELEGRAM_BOT_TOKEN / TELEGRAM_CHAT_ID environment variables (.env supported via python-dotenv). Every send method returns self for fluent chaining, and network failures are logged and swallowed — a dead network never blocks the caller.

Cross-references

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