Hub skore Project

This example shows how to use Project in hub mode: store reports remotely and inspect them. A key point is that summarize() returns a Summary, which is a pandas.DataFrame. In Jupyter you get an interactive widget, but you can always inspect and filter the summary as a DataFrame if you prefer.

Examples

To run this example and push in your own Skore Hub workspace and project, you can run this example with the following command:

WORKSPACE=<workspace> PROJECT=<project> python plot_skore_hub_project.py

In this gallery, we are going to push the different reports into a public workspace.

from skore import login

login()

╭───────────────────────────────── Login to Skore Hub ─────────────────────────────────╮
│                                                                                      │
│                        Successfully logged in, using API key.                        │
│                                                                                      │
╰──────────────────────────────────────────────────────────────────────────────────────╯

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from skrub import tabular_pipeline

X, y = load_breast_cancer(return_X_y=True, as_frame=True)
estimator = tabular_pipeline(LogisticRegression(max_iter=1_000))

from numpy import logspace
from sklearn.base import clone
from skore import Project, evaluate

project = Project(f"{WORKSPACE}/{PROJECT}", mode="hub")

for regularization in logspace(-3, 3, 5):
    project.put(
        f"lr-regularization-{regularization:.1e}",
        evaluate(
            clone(estimator).set_params(logisticregression__C=regularization),
            X,
            y,
            splitter=0.2,
            pos_label=1,
        ),
    )

  Putting lr-regularization-1.0e-03 0:00:39
Consult your report at
https://skore.probabl.ai/skore/example-skore-hub-project-0.16/estimators/6648



  Putting lr-regularization-3.2e-02 0:00:38
Consult your report at
https://skore.probabl.ai/skore/example-skore-hub-project-0.16/estimators/6649



  Putting lr-regularization-1.0e+00 0:00:38
Consult your report at
https://skore.probabl.ai/skore/example-skore-hub-project-0.16/estimators/6650



  Putting lr-regularization-3.2e+01 0:00:36
Consult your report at
https://skore.probabl.ai/skore/example-skore-hub-project-0.16/estimators/6651



  Putting lr-regularization-1.0e+03 0:00:37
Consult your report at
https://skore.probabl.ai/skore/example-skore-hub-project-0.16/estimators/6652

Summarize: you get a DataFrame

summarize() returns a Summary, which subclasses pandas.DataFrame. In a Jupyter environment it renders an interactive parallel-coordinates widget by default.

summary = project.summarize()

To see the normal DataFrame table instead of the widget (e.g. in scripts or when you prefer the table), wrap the summary in pandas.DataFrame:

import pandas as pd

pandas_summary = pd.DataFrame(summary)
pandas_summary

		key	date	learner	ml_task	report_type	dataset	rmse	log_loss	roc_auc	fit_time	predict_time	rmse_mean	log_loss_mean	roc_auc_mean	fit_time_mean	predict_time_mean
	id
0	skore:report:estimator:6648	lr-regularization-1.0e-03	2026-04-20T17:53:09.801928+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.406397	0.987298	0.099669	0.062750	None	None	None	None	None
1	skore:report:estimator:6649	lr-regularization-3.2e-02	2026-04-20T17:53:48.113932+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.137499	0.995237	0.067136	0.041440	None	None	None	None	None
2	skore:report:estimator:6650	lr-regularization-1.0e+00	2026-04-20T17:54:27.094152+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.080457	0.995554	0.069633	0.033966	None	None	None	None	None
3	skore:report:estimator:6651	lr-regularization-3.2e+01	2026-04-20T17:55:04.017459+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.127249	0.992061	0.057548	0.214782	None	None	None	None	None
4	skore:report:estimator:6652	lr-regularization-1.0e+03	2026-04-20T17:55:41.275130+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.249753	0.990156	0.061281	0.032864	None	None	None	None	None

Basically, our summary contains metadata related to various information that we need to quickly help filtering the reports.

summary.info()

<class 'skore._project._summary.Summary'>
MultiIndex: 5 entries, (0, 'skore:report:estimator:6648') to (4, 'skore:report:estimator:6652')
Data columns (total 16 columns):
 #   Column             Non-Null Count  Dtype
---  ------             --------------  -----
 0   key                5 non-null      object
 1   date               5 non-null      object
 2   learner            5 non-null      category
 3   ml_task            5 non-null      object
 4   report_type        5 non-null      object
 5   dataset            5 non-null      object
 6   rmse               0 non-null      object
 7   log_loss           5 non-null      float64
 8   roc_auc            5 non-null      float64
 9   fit_time           5 non-null      float64
 10  predict_time       5 non-null      float64
 11  rmse_mean          0 non-null      object
 12  log_loss_mean      0 non-null      object
 13  roc_auc_mean       0 non-null      object
 14  fit_time_mean      0 non-null      object
 15  predict_time_mean  0 non-null      object
dtypes: category(1), float64(4), object(11)
memory usage: 1.1+ KB

Filter reports by metric (e.g. keep only those above a given accuracy) and work with the result as a table.

summary.query("log_loss < 0.2")["key"].tolist()

['lr-regularization-3.2e-02', 'lr-regularization-1.0e+00', 'lr-regularization-3.2e+01']

Use reports() to load the corresponding reports from the project (optionally after filtering the summary).

reports = summary.query("log_loss < 0.2").reports(return_as="comparison")
len(reports.reports_)

3

Since we got a ComparisonReport, we can use the metrics accessor to summarize the metrics across the reports.

reports.metrics.summarize().frame()

Estimator	LogisticRegression_1	LogisticRegression_2	LogisticRegression_3
Metric
Accuracy	0.956140	0.964912	0.947368
Precision	0.930556	0.970149	0.955224
Recall	1.000000	0.970149	0.955224
ROC AUC	0.995237	0.995554	0.992061
Log loss	0.137499	0.080457	0.127249
Brier score	0.035253	0.025149	0.029948
Fit time (s)	0.067136	0.069633	0.057548
Predict time (s)	0.034069	0.033424	0.033357

reports.metrics.roc().plot(subplot_by=None)

<Figure size 600x800 with 1 Axes>