Local skore Project

This example shows how to use Project in local mode: store reports on your machine 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.

Create a local project and store reports

We use a temporary directory as the workspace so the example is self-contained. In practice you can omit workspace to use the default (e.g. a skore/ directory in your user cache).

from pathlib import Path
from tempfile import TemporaryDirectory

from skore import Project

tmp_dir = TemporaryDirectory()
tmp_path = Path(tmp_dir.name)
project = Project("example-project", workspace=tmp_path)

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))

import numpy as np
from sklearn.base import clone
from skore import evaluate

for regularization in np.logspace(-7, 7, 31):
    report = evaluate(
        clone(estimator).set_params(logisticregression__C=regularization),
        X,
        y,
        splitter=0.2,
        pos_label=1,
    )
    project.put(f"lr-regularization-{regularization:.1e}", report)

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	271692610522960083605784088307679298913	lr-regularization-1.0e-07	2026-04-30T11:50:59.624156+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682846	0.984122	0.098884	0.062001	None	None	None	None	None
1	105740856719690226206709377693101769809	lr-regularization-2.9e-07	2026-04-30T11:50:59.961037+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682710	0.984122	0.098925	0.054140	None	None	None	None	None
2	285539499092396579611819982605296449893	lr-regularization-8.6e-07	2026-04-30T11:51:00.270092+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682313	0.984122	0.094620	0.063088	None	None	None	None	None
3	116400262161192563184757582935042231911	lr-regularization-2.5e-06	2026-04-30T11:51:00.588825+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.681153	0.984122	0.094470	0.047832	None	None	None	None	None
4	247105970899819443446608087143117765503	lr-regularization-7.4e-06	2026-04-30T11:51:00.865624+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.677790	0.984122	0.082088	0.051483	None	None	None	None	None
5	328528035354860533273963343093411284751	lr-regularization-2.2e-05	2026-04-30T11:51:01.151953+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.668210	0.984122	0.081639	0.054003	None	None	None	None	None
6	203698431914299296253700257371296331731	lr-regularization-6.3e-05	2026-04-30T11:51:01.453884+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.642328	0.985075	0.086901	0.054173	None	None	None	None	None
7	52829089585668783290735274114279097771	lr-regularization-1.8e-04	2026-04-30T11:51:01.754324+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.581579	0.985392	0.086713	0.053941	None	None	None	None	None
8	275506235325590191315915558605893073560	lr-regularization-5.4e-04	2026-04-30T11:51:02.051800+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.476411	0.985710	0.086497	0.054455	None	None	None	None	None
9	90793713195048709728929236423049124409	lr-regularization-1.6e-03	2026-04-30T11:51:02.350613+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.355309	0.987615	0.087495	0.049176	None	None	None	None	None
10	70349256814426157559213596790926558477	lr-regularization-4.6e-03	2026-04-30T11:51:02.652920+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.252513	0.990156	0.096686	0.057630	None	None	None	None	None
11	175117142157411499657580235140924725297	lr-regularization-1.4e-02	2026-04-30T11:51:02.970030+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.178052	0.992379	0.088074	0.053589	None	None	None	None	None
12	41739432927435735900995269862148007421	lr-regularization-4.0e-02	2026-04-30T11:51:03.268913+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.128754	0.995872	0.088216	0.053649	None	None	None	None	None
13	29068764257991844949874656259110196677	lr-regularization-1.2e-01	2026-04-30T11:51:03.601674+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.098897	0.996189	0.124105	0.066333	None	None	None	None	None
14	198062656510429879827773196163157776088	lr-regularization-3.4e-01	2026-04-30T11:51:03.918295+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.083941	0.995872	0.090259	0.059797	None	None	None	None	None
15	174714875495834445635179677317963597058	lr-regularization-1.0e+00	2026-04-30T11:51:04.328604+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.080457	0.995554	0.186593	0.053436	None	None	None	None	None
16	185328608747926540323439092007237714644	lr-regularization-2.9e+00	2026-04-30T11:51:04.621626+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.089466	0.994601	0.086560	0.052533	None	None	None	None	None
17	52859448295347070239286976641836523388	lr-regularization-8.6e+00	2026-04-30T11:51:04.916909+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.104241	0.993649	0.088163	0.052383	None	None	None	None	None
18	240439142197468231477410497826314273685	lr-regularization-2.5e+01	2026-04-30T11:51:05.212190+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.121671	0.992379	0.090069	0.053581	None	None	None	None	None
19	290221280591060176092957306010626532438	lr-regularization-7.4e+01	2026-04-30T11:51:05.508597+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.142282	0.991426	0.089197	0.052080	None	None	None	None	None
20	196885528799010525714935012120480242445	lr-regularization-2.2e+02	2026-04-30T11:51:05.805994+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.162450	0.991426	0.092885	0.052772	None	None	None	None	None
21	269910979035768389334005588226437745197	lr-regularization-6.3e+02	2026-04-30T11:51:06.106342+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.204586	0.991108	0.094156	0.054360	None	None	None	None	None
22	273168639879024955043767331892334650573	lr-regularization-1.8e+03	2026-04-30T11:51:06.369388+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.291953	0.990473	0.060293	0.032807	None	None	None	None	None
23	131835965717469418215829981175592444102	lr-regularization-5.4e+03	2026-04-30T11:51:06.563450+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.471382	0.989838	0.061014	0.033623	None	None	None	None	None
24	21622120584003305274687599784922941185	lr-regularization-1.6e+04	2026-04-30T11:51:06.771660+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.796526	0.983169	0.071383	0.033822	None	None	None	None	None
25	167304291692937273996782691424872820620	lr-regularization-4.6e+04	2026-04-30T11:51:06.978591+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.133167	0.984757	0.070677	0.034186	None	None	None	None	None
26	99948939942351731052632123465912405360	lr-regularization-1.4e+05	2026-04-30T11:51:07.186596+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.229179	0.985551	0.071505	0.034650	None	None	None	None	None
27	190157760102987964298052411177663895154	lr-regularization-4.0e+05	2026-04-30T11:51:07.402861+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.276041	0.985869	0.070951	0.033951	None	None	None	None	None
28	224319153825701072417328029336398599538	lr-regularization-1.2e+06	2026-04-30T11:51:07.606723+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.447319	0.975865	0.069598	0.033093	None	None	None	None	None
29	205956125384632278570878575496712013105	lr-regularization-3.4e+06	2026-04-30T11:51:07.830822+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.448698	0.976500	0.067941	0.033771	None	None	None	None	None
30	6177555830103179008770208277906796433	lr-regularization-1.0e+07	2026-04-30T11:51:08.038749+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.897034	0.976183	0.070984	0.036970	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: 31 entries, (0, '271692610522960083605784088307679298913') to (30, '6177555830103179008770208277906796433')
Data columns (total 16 columns):
 #   Column             Non-Null Count  Dtype
---  ------             --------------  -----
 0   key                31 non-null     object
 1   date               31 non-null     object
 2   learner            31 non-null     category
 3   ml_task            31 non-null     object
 4   report_type        31 non-null     object
 5   dataset            31 non-null     object
 6   rmse               0 non-null      object
 7   log_loss           31 non-null     float64
 8   roc_auc            31 non-null     float64
 9   fit_time           31 non-null     float64
 10  predict_time       31 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: 5.3+ 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 < 0.1")["key"].tolist()

['lr-regularization-1.2e-01', 'lr-regularization-3.4e-01', 'lr-regularization-1.0e+00', 'lr-regularization-2.9e+00']

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

reports = summary.query("log_loss < 0.1").reports(return_as="comparison")
len(reports.reports_)

4

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	LogisticRegression_4
Metric
Accuracy	0.947368	0.964912	0.964912	0.964912
Precision	0.942029	0.970149	0.970149	0.970149
Recall	0.970149	0.970149	0.970149	0.970149
ROC AUC	0.996189	0.995872	0.995554	0.994601
Log loss	0.098897	0.083941	0.080457	0.089466
Brier score	0.027157	0.024990	0.025149	0.026218
Fit time (s)	0.124105	0.090259	0.186593	0.086560
Predict time (s)	0.034160	0.033081	0.033231	0.033419

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

project.delete("example-project", workspace=tmp_path)
tmp_dir.cleanup()