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	284093228217490968778865723131983556241	lr-regularization-1.0e-07	2026-05-27T07:59:17.122115+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682846	0.984122	0.129016	0.087332	None	None	None	None	None
1	187124531107489554712585294680137485213	lr-regularization-2.9e-07	2026-05-27T07:59:17.578104+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682710	0.984122	0.137199	0.077172	None	None	None	None	None
2	255572861750338550086951979958917350907	lr-regularization-8.6e-07	2026-05-27T07:59:17.998726+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682313	0.984122	0.113589	0.064240	None	None	None	None	None
3	118137799761384911433456339563806335964	lr-regularization-2.5e-06	2026-05-27T07:59:18.397637+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.681153	0.984122	0.126430	0.073544	None	None	None	None	None
4	269873250232530704378680498425703122099	lr-regularization-7.4e-06	2026-05-27T07:59:18.774880+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.677790	0.984122	0.095717	0.057962	None	None	None	None	None
5	108456935244834542735306799514453316528	lr-regularization-2.2e-05	2026-05-27T07:59:19.125381+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.668210	0.984122	0.107499	0.065768	None	None	None	None	None
6	23516806759272617501250838066184165185	lr-regularization-6.3e-05	2026-05-27T07:59:19.490057+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.642328	0.985075	0.109727	0.073067	None	None	None	None	None
7	226416952655437036927258701929029645846	lr-regularization-1.8e-04	2026-05-27T07:59:19.883623+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.581579	0.985392	0.117158	0.073161	None	None	None	None	None
8	101205377183891174150497585505854257324	lr-regularization-5.4e-04	2026-05-27T07:59:20.280599+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.476411	0.985710	0.120897	0.074223	None	None	None	None	None
9	20756074311773863042752298315277459239	lr-regularization-1.6e-03	2026-05-27T07:59:20.687135+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.355309	0.987615	0.120795	0.062921	None	None	None	None	None
10	76103546144602923813113170563604190915	lr-regularization-4.6e-03	2026-05-27T07:59:21.060111+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.252513	0.990156	0.127296	0.077641	None	None	None	None	None
11	329856455932364756479133574012727079906	lr-regularization-1.4e-02	2026-05-27T07:59:21.474669+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.178052	0.992379	0.120312	0.073737	None	None	None	None	None
12	12790112837799415491224872989446528369	lr-regularization-4.0e-02	2026-05-27T07:59:21.874487+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.128754	0.995872	0.120977	0.066205	None	None	None	None	None
13	256457119736808341817496702573720782744	lr-regularization-1.2e-01	2026-05-27T07:59:22.280348+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.098897	0.996189	0.136284	0.074866	None	None	None	None	None
14	10600963640072191759637782152006052600	lr-regularization-3.4e-01	2026-05-27T07:59:22.691288+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.083941	0.995872	0.124531	0.069589	None	None	None	None	None
15	188512052453481463715206434928113039338	lr-regularization-1.0e+00	2026-05-27T07:59:23.097988+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.080457	0.995554	0.126883	0.071788	None	None	None	None	None
16	313280988523725479988958801166690741906	lr-regularization-2.9e+00	2026-05-27T07:59:23.483237+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.089466	0.994601	0.116288	0.071801	None	None	None	None	None
17	139797029602366530085144210029418639913	lr-regularization-8.6e+00	2026-05-27T07:59:23.871149+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.104241	0.993649	0.119199	0.070794	None	None	None	None	None
18	324806226343292816414284855810191245469	lr-regularization-2.5e+01	2026-05-27T07:59:24.257902+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.121671	0.992379	0.119365	0.071102	None	None	None	None	None
19	306456581851611774278301759695704667115	lr-regularization-7.4e+01	2026-05-27T07:59:24.646868+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.142282	0.991426	0.119881	0.070795	None	None	None	None	None
20	245146307623671139999261302766137455442	lr-regularization-2.2e+02	2026-05-27T07:59:24.917254+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.162450	0.991426	0.077111	0.042141	None	None	None	None	None
21	282027601573083288322832643196977963132	lr-regularization-6.3e+02	2026-05-27T07:59:25.159756+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.204586	0.991108	0.078119	0.041098	None	None	None	None	None
22	138037448102193612206679638083219062629	lr-regularization-1.8e+03	2026-05-27T07:59:25.400235+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.291953	0.990473	0.079603	0.042939	None	None	None	None	None
23	121677214771911742771517337895787610609	lr-regularization-5.4e+03	2026-05-27T07:59:25.652044+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.471382	0.989838	0.084534	0.043277	None	None	None	None	None
24	154457705516942024331015364969730581866	lr-regularization-1.6e+04	2026-05-27T07:59:25.920765+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.796526	0.983169	0.094053	0.042422	None	None	None	None	None
25	144546556981004345707470729603841290914	lr-regularization-4.6e+04	2026-05-27T07:59:26.184830+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.133167	0.984757	0.096843	0.043191	None	None	None	None	None
26	196497950024616241038880694831102142712	lr-regularization-1.4e+05	2026-05-27T07:59:26.450619+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.229179	0.985551	0.096811	0.041906	None	None	None	None	None
27	109328361929574213298851959484156363177	lr-regularization-4.0e+05	2026-05-27T07:59:26.714024+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.276041	0.985869	0.095933	0.042468	None	None	None	None	None
28	330108745408851579067801943053500206367	lr-regularization-1.2e+06	2026-05-27T07:59:26.973371+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.447319	0.975865	0.093343	0.042523	None	None	None	None	None
29	205153643696139539283671099743102182057	lr-regularization-3.4e+06	2026-05-27T07:59:27.230801+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.448698	0.976500	0.090737	0.042536	None	None	None	None	None
30	264755494987473349840243633281080082399	lr-regularization-1.0e+07	2026-05-27T07:59:27.488791+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.897034	0.976183	0.090789	0.043725	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, '284093228217490968778865723131983556241') to (30, '264755494987473349840243633281080082399')
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)	0.136284	0.124531	0.126883	0.116288
Predict time (s)	0.041241	0.042053	0.043687	0.042517

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

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