Visualization

Martanto edited this page Mar 26, 2026 · 9 revisions

Visualization

All plots use colorblind-safe palettes and high-DPI output suitable for publications and presentations.

Tremor Time-Series Plots

`plot_tremor()`

Visualize RSAM, DSAR, and Shannon Entropy as multi-panel time-series plots.

from eruption_forecast.plots.tremor_plots import plot_tremor
import pandas as pd
df = pd.read_csv("output/VG.OJN.00.EHZ/tremor/tremor.csv", index_col=0, parse_dates=True)
# Basic plot — all columns
plot_tremor(df=df, figure_dir="output/figures", dpi=150)
# Custom interval and selected columns
plot_tremor(
 df=df,
 interval=6,
 interval_unit="hours",
 selected_columns=["rsam_f2", "rsam_f3", "dsar_f2-f3"],
 figure_dir="output/figures",
 filename="tremor_selected",
 dpi=300,
)

`replot_tremor()` — Batch Replot

Regenerate plots for all daily tremor CSV files (sequential or parallel).

from eruption_forecast.plots.tremor_plots import replot_tremor
results = replot_tremor(
 daily_dir="output/VG.OJN.00.EHZ/tremor/daily",
 output_dir="output/VG.OJN.00.EHZ/tremor/figures",
 n_jobs=4,
 dpi=300,
 overwrite=False,
)
print(f"Created: {results['created']}, Skipped: {results['skipped']}, Failed: {results['failed']}")

Feature Importance Plots

`plot_significant_features()`

Horizontal bar chart of feature scores. The score column is always "score" regardless of the feature selection method used ("tsfresh", "random_forest", or "combined").

from eruption_forecast.plots.feature_plots import plot_significant_features
plot_significant_features(
 features="path/to/features.csv", # or a DataFrame
 number_of_features=50,
 top_features=20, # Highlighted with darker colour
 values_column="score",
 output_dir="output/figures",
 filename="feature_importance",
 dpi=150,
)

`replot_significant_features()` — Batch Replot

from eruption_forecast.plots.feature_plots import replot_significant_features
results = replot_significant_features(
 all_features_dir="output/trainings/features/all_features",
 output_dir="output/trainings/features/figures/significant",
 n_jobs=4,
 number_of_features=50,
 top_features=20,
 dpi=300,
 overwrite=False,
)

Model Evaluation Plots

Single-Seed (`ModelEvaluator`)

plot_all() generates all 7–8 plots at once. Individual methods:

from eruption_forecast import ModelEvaluator
evaluator = ModelEvaluator.from_files(
 model_path="output/.../models/00042.pkl",
 X_test="output/.../tests/00042_X_test.csv",
 y_test="output/.../tests/00042_y_test.csv",
 model_name="xgb_seed_42",
 output_dir="output/eval",
)
evaluator.plot_all() # All plots
evaluator.plot_confusion_matrix()
evaluator.plot_roc_curve()
evaluator.plot_precision_recall_curve()
evaluator.plot_threshold_analysis()
evaluator.plot_feature_importance()
evaluator.plot_calibration()
evaluator.plot_prediction_distribution()
evaluator.plot_shap_summary(max_display=20) # Requires shap>=0.46

Aggregate (`MultiModelEvaluator`)

Requires evaluate() output — loads test data per seed and aggregates.

from eruption_forecast import MultiModelEvaluator
base = "output/trainings/evaluations/xgb-classifier/stratified-shuffle-split"
ev = MultiModelEvaluator(
 trained_model_csv=f"{base}/trained_model_XGBClassifier-StratifiedShuffleSplit_rs-0_ts-500_top-20.csv"
)
figs = ev.plot_all(dpi=150, show_individual=True)
# Keys: roc_curve, pr_curve, calibration, prediction_distribution,
# confusion_matrix, threshold_analysis, feature_importance,
# shap_summary, seed_stability, frequency_band_contribution,
# learning_curve

Aggregation Strategy

Plot	How seeds are combined
ROC Curve	Each seed's TPR interpolated onto shared FPR grid → mean ± std band
Precision-Recall	Precision interpolated onto shared recall grid → mean ± std band
Threshold Analysis	Metrics computed per threshold per seed → mean ± std bands
Calibration	Fraction of positives interpolated → mean ± std band
Prediction Distribution	Predicted probabilities pooled across seeds → single KDE per class
Confusion Matrix	Raw confusion matrices summed across all seeds
Feature Importance	Importances stacked → mean bar + std error bar

Classifier Comparison Plots (`ClassifierComparator`)

ClassifierComparator provides four ready-made comparison plots across multiple classifiers.

from eruption_forecast.model import ClassifierComparator
# From a dict
base = "output/trainings/evaluations"
comparator = ClassifierComparator(
 classifiers={
 "rf": f"{base}/rf/stratified/trained_model_rf_...csv",
 "xgb": f"{base}/xgb/stratified/trained_model_xgb_...csv",
 "gb": f"{base}/gb/stratified/trained_model_gb_...csv",
 },
 output_dir="output/comparison",
 metrics=["f1_score", "roc_auc", "recall"], # or None for all DEFAULT_METRICS
)
# From a JSON file {"ClassifierName": "/path/to/trained_model_*.csv", ...}
comparator = ClassifierComparator.from_json(
 "output/VG.OJN.00.EHZ/evaluations_trained_models.json",
 output_dir="output/comparison",
 metrics=["f1_score", "roc_auc", "recall"],
)
# Bar chart per metric (mean ± std) — plus a combined "all" overview figure
figs_bar = comparator.plot_metric_bar()
# {"f1_score": Figure, "roc_auc": Figure, "recall": Figure, "all": Figure}
# Violin + strip of per-seed distributions per metric — plus combined "all" figure
figs_violin = comparator.plot_seed_stability()
# {"f1_score": Figure, "roc_auc": Figure, "recall": Figure, "all": Figure}
# Grid of subplots: rows = classifiers, columns = metrics
fig_grid = comparator.plot_comparison_grid()
# Overlaid mean ROC curves with ± std bands
fig_roc = comparator.plot_roc()
# All plots + ranking in one call
results = comparator.plot_all()
# results["metric_bar"] → dict[str, Figure]
# results["seed_stability"] → dict[str, Figure]
# results["comparison_grid"] → Figure
# results["roc"] → Figure
# results["ranking"] → DataFrame (ranked by recall)

Output files are written to output/comparison/figures/ and the ranking CSV to output/comparison/metrics/ranking_recall.csv.

Plot method	Saved files
`plot_metric_bar()`	`metric_bar_{metric}.png` per metric + `metric_bar_all.png`
`plot_seed_stability()`	`seed_stability_{metric}.png` per metric + `seed_stability_all.png`
`plot_comparison_grid()`	`comparison_grid.png`
`plot_roc()`	`comparison_roc.png`

SHAP Explainability

Understand which features drive predictions and in which direction.

from eruption_forecast import ModelEvaluator, MultiModelEvaluator
# Single-seed beeswarm
evaluator = ModelEvaluator.from_files(
 model_path="output/.../models/00042.pkl",
 X_test="output/.../tests/00042_X_test.csv",
 y_test="output/.../tests/00042_y_test.csv",
 model_name="xgb_seed_42",
)
fig = evaluator.plot_shap_summary(max_display=20)
# Aggregate SHAP beeswarm across seeds
ev = MultiModelEvaluator(trained_model_csv="output/.../trained_model_registry.csv")
fig = ev.plot_shap_summary(max_display=20)
# Plot directly from a saved SHAP Explanation pickle
from eruption_forecast.plots.shap_plots import plot_shap_from_file
fig, explanation = plot_shap_from_file(
 "output/.../shap_values.pkl",
 max_display=20,
 title="SHAP Summary",
)

Requires shap >= 0.46.

Seed Stability Plot

Visualize metric variability across random seeds using a violin + strip plot.

from eruption_forecast import MultiModelEvaluator
from eruption_forecast.plots import plot_seed_stability
# Single classifier
ev = MultiModelEvaluator(
 metrics_dir="output/.../classifiers/xgb-classifier/stratified-shuffle-split/metrics"
)
fig = ev.plot_seed_stability(metric="f1_score")
# Compare multiple classifiers side-by-side
metrics_by_clf = {}
for clf in ["xgb", "rf", "gb", "svm"]:
 ev = MultiModelEvaluator(metrics_dir=f"output/.../classifiers/{clf}-classifier/stratified-shuffle-split/metrics")
 metrics_by_clf[clf] = ev.get_metrics_list()
fig, df = plot_seed_stability(
 metrics_by_classifier=metrics_by_clf,
 metric="balanced_accuracy",
 dpi=150,
)

Frequency Band Contribution

Shows which seismic frequency bands dominate the selected features. RSAM bands are blue; DSAR bands are orange.

from eruption_forecast import MultiModelEvaluator
# Multi-seed, mean ± std per band
ev = MultiModelEvaluator(trained_model_csv="output/.../trained_model_registry.csv")
fig = ev.plot_frequency_band_contribution()
# Single-seed standalone
from eruption_forecast.plots.feature_plots import plot_frequency_band_contribution
import pandas as pd
features = pd.read_csv("output/.../significant_features.csv", index_col=0).index.tolist()
fig, df = plot_frequency_band_contribution(feature_names=features)
# df columns: band, count

Learning Curve Plots

Visualize how model performance scales with training-set size. Useful for diagnosing underfitting / overfitting and confirming that more data is beneficial.

Each seed's learning curve is stored as a JSON file with one key per scoring metric:

{
 "balanced_accuracy": {
 "train_sizes": [50, 100, 200, 400],
 "train_scores": [[0.72, 0.73], [0.78, 0.79], ...],
 "test_scores": [[0.65, 0.66], [0.70, 0.71], ...]
 },
 "f1_weighted": { ... }
}

The old flat single-metric format is still accepted for backward compatibility.

Single-seed (`ModelEvaluator`)

from eruption_forecast import ModelEvaluator
evaluator = ModelEvaluator.from_files(
 model_path="output/.../models/00042.pkl",
 X_test="output/.../tests/00042_X_test.csv",
 y_test="output/.../tests/00042_y_test.csv",
 learning_curve_path="output/.../learning_curves/00042_lc.json",
 model_name="xgb_seed_42",
 output_dir="output/eval",
)
# One subplot per scoring metric
fig = evaluator.plot_learning_curve(dpi=150)

Aggregate across seeds (`MultiModelEvaluator`)

from eruption_forecast import MultiModelEvaluator
ev = MultiModelEvaluator(
 trained_model_csv="output/.../trained_model_registry.csv"
)
# Bold mean line + ±1 std band per metric
fig = ev.plot_learning_curve(dpi=150)

Standalone (`plot_learning_curve_grid`)

from eruption_forecast.plots.evaluation_plots import plot_learning_curve_grid
fig = plot_learning_curve_grid(
 json_filepath="output/.../learning_curve_seed042.json",
 output_dir="output/figures",
 scorings=["balanced_accuracy", "f1_weighted"],
 dpi=150,
)

Forecast Visualization

ModelPredictor.predict_proba(plot=True) automatically generates an eruption probability time-series plot saved to figures/eruption_forecast.png.

Per-classifier lines (dashed)
Consensus line (solid black)
Uncertainty band (shaded ± std)
Eruption event markers (if labels provided)

Common Parameters

Parameter	Type	Default	Description
`dpi`	`int`	`150`	Resolution (use 300 for publication)
`overwrite`	`bool`	`True`	Replace existing plots
`n_jobs`	`int`	`1`	Parallel workers for batch utilities
`output_dir` / `figure_dir`	`str`	varies	Directory for saved plots
`verbose`	`bool`	`False`	Log plot generation
`filename`	`str`	auto	Custom filename stem (extension added automatically)

Import Reference

from eruption_forecast.plots.tremor_plots import plot_tremor, replot_tremor
from eruption_forecast.plots.feature_plots import (
 plot_significant_features,
 replot_significant_features,
 plot_frequency_band_contribution,
)
from eruption_forecast.plots.shap_plots import plot_shap_summary, plot_aggregate_shap_summary, plot_shap_from_file
from eruption_forecast.plots import plot_classifier_comparison, plot_seed_stability
from eruption_forecast.plots.evaluation_plots import (
 plot_learning_curve,
 plot_aggregate_learning_curve,
 plot_learning_curve_grid,
)
# Top-level shortcuts
from eruption_forecast import ModelEvaluator, MultiModelEvaluator

Navigation Menu

Visualization

Visualization

Tremor Time-Series Plots

`plot_tremor()`

`replot_tremor()` — Batch Replot

Feature Importance Plots

`plot_significant_features()`

`replot_significant_features()` — Batch Replot

Model Evaluation Plots

Single-Seed (`ModelEvaluator`)

Aggregate (`MultiModelEvaluator`)

Aggregation Strategy

Classifier Comparison Plots (`ClassifierComparator`)

SHAP Explainability

Seed Stability Plot

Frequency Band Contribution

Learning Curve Plots

Single-seed (`ModelEvaluator`)

Aggregate across seeds (`MultiModelEvaluator`)

Standalone (`plot_learning_curve_grid`)

Forecast Visualization

Common Parameters

Import Reference

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Visualization

Visualization

Tremor Time-Series Plots

plot_tremor()

replot_tremor() — Batch Replot

Feature Importance Plots

plot_significant_features()

replot_significant_features() — Batch Replot

Model Evaluation Plots

Single-Seed (ModelEvaluator)

Aggregate (MultiModelEvaluator)

Aggregation Strategy

Classifier Comparison Plots (ClassifierComparator)

SHAP Explainability

Seed Stability Plot

Frequency Band Contribution

Learning Curve Plots

Single-seed (ModelEvaluator)

Aggregate across seeds (MultiModelEvaluator)

Standalone (plot_learning_curve_grid)

Forecast Visualization

Common Parameters

Import Reference

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`plot_tremor()`

`replot_tremor()` — Batch Replot

`plot_significant_features()`

`replot_significant_features()` — Batch Replot

Single-Seed (`ModelEvaluator`)

Aggregate (`MultiModelEvaluator`)

Classifier Comparison Plots (`ClassifierComparator`)

Single-seed (`ModelEvaluator`)

Aggregate across seeds (`MultiModelEvaluator`)

Standalone (`plot_learning_curve_grid`)