API Reference

Martanto edited this page Jun 18, 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,
 send_telegram_notification,
)
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

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,
 overwrite_plot: 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_from_cache 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,
 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.

`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,
 verbose: bool | None = None,
) -> Self

eruption_dates=None falls back to the dates captured during train(). 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,
 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(). 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 + CacheModel subclass. 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_cache_identity(**kwargs)`	Classmethod; returns canonical identity dict for hashing
`tm.save_to_cache(identity)`	Writes `{output_dir}/cache/TrainingModel/{hash}.pkl` (+ `.params.json`)
`TrainingModel.load_from_cache(output_dir, identity)`	Classmethod; returns the instance or `None`
`tm.save(path=None)`	`{output_dir}/TrainingModel_{basename}.pkl`
`TrainingModel.load(path)`	Classmethod
`tm.save_config(path=None)`	Standalone `training.config.yaml`

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 + CacheModel subclass.

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_cache_identity, save_to_cache, load_from_cache, save, load. The cache identity embeds the upstream training_hash.

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.

ExplanationModel

BaseModel + CacheModel subclass. 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 CacheModel. 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].

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,
 overwrite_plot: 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_.

SeedEnsemble

class SeedEnsemble(BaseEnsemble, BaseEstimator, ClassifierMixin):
 classifier_name: str
 seeds: list[dict]

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)

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

ClassifierEnsemble

class ClassifierEnsemble(BaseEnsemble, BaseEstimator, ClassifierMixin)

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[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_csv: 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.

Logger helpers

from eruption_forecast import enable_logging, disable_logging
from eruption_forecast.logger import set_log_level, set_log_directory
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)

Telegram helpers

from eruption_forecast import notify, send_telegram_notification
@notify(label: str)
def my_func(): ...
send_telegram_notification(
 message: str,
 files: list[str] | None = None,
 file_caption: str | None = None,
 send_as_document: bool = False,
)

Credentials are read from environment (TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID). Both helpers degrade gracefully when the env vars are absent - they emit a warning and skip the network call instead of raising.

Cross-references

High-level pipeline overview: Architecture
End-to-end example: Usage
Caching internals: Architecture and Training Workflow
Output paths for every artefact: Output Structure

API Reference

API Reference

ForecastModel

Constructor

calculate(...)

train(...)

predict(...)

evaluate(...)

explain(...)

Config round-trip

TrainingModel

Constructor

Pipeline methods (all return Self)

Cache + persistence

PredictionModel

Constructor

Pipeline methods

Cache + persistence

EvaluationModel

Constructor

Methods

ExplanationModel

Constructor

Methods

ExplainerEnsemble

Methods

Static helpers

SeedExplanation / ClassifierExplanation

CalculateTremor

Constructor

Source binding + execution (all return Self)

LabelBuilder / DynamicLabelBuilder

FeaturesBuilder

TremorMatrixBuilder

FeatureSelector

SeedEnsemble

Construction

Inference

ClassifierEnsemble

Construction (classmethods)

Inference

Inspection

MetricsEnsemble

BaseEnsemble

ClassifierComparator

TremorData

LabelData

Logger helpers

Telegram helpers

Cross-references

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`calculate(...)`

`train(...)`

`predict(...)`

`evaluate(...)`

`explain(...)`

Pipeline methods (all return `Self`)

Source binding + execution (all return `Self`)