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	307862713369513384122340672343784091509	lr-regularization-1.0e-07	2026-04-29T16:09:43.595961+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682846	0.984122	0.084666	0.052542	None	None	None	None	None
1	72300269779018014322538223969888188142	lr-regularization-2.9e-07	2026-04-29T16:09:43.890022+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682710	0.984122	0.084336	0.051357	None	None	None	None	None
2	317267696953940985493885183284663009509	lr-regularization-8.6e-07	2026-04-29T16:09:44.187441+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.682313	0.984122	0.087762	0.055632	None	None	None	None	None
3	89434007882337542663992991099321321875	lr-regularization-2.5e-06	2026-04-29T16:09:44.490951+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.681153	0.984122	0.086014	0.053638	None	None	None	None	None
4	169240593754696919966411725727622801751	lr-regularization-7.4e-06	2026-04-29T16:09:44.789697+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.677790	0.984122	0.086058	0.053008	None	None	None	None	None
5	115062531627001553764758614174165613951	lr-regularization-2.2e-05	2026-04-29T16:09:45.084767+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.668210	0.984122	0.085970	0.053650	None	None	None	None	None
6	282372804303398372513142487516799519458	lr-regularization-6.3e-05	2026-04-29T16:09:45.382858+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.642328	0.985075	0.086744	0.054688	None	None	None	None	None
7	222957049904136159831665466512698294301	lr-regularization-1.8e-04	2026-04-29T16:09:45.682922+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.581579	0.985392	0.086600	0.054060	None	None	None	None	None
8	12429702799072986260156164812757418341	lr-regularization-5.4e-04	2026-04-29T16:09:45.983501+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.476411	0.985710	0.086738	0.063812	None	None	None	None	None
9	172771347977912884423250045581195051750	lr-regularization-1.6e-03	2026-04-29T16:09:46.335878+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.355309	0.987615	0.104764	0.061421	None	None	None	None	None
10	47478329694633176300697091612767366729	lr-regularization-4.6e-03	2026-04-29T16:09:46.642744+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.252513	0.990156	0.077792	0.049887	None	None	None	None	None
11	67454727598363893156640957522416290359	lr-regularization-1.4e-02	2026-04-29T16:09:46.862699+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.178052	0.992379	0.055372	0.033499	None	None	None	None	None
12	152554943822159127437400371378631139767	lr-regularization-4.0e-02	2026-04-29T16:09:47.062879+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.128754	0.995872	0.055497	0.033662	None	None	None	None	None
13	20758909240931351899264256229843972439	lr-regularization-1.2e-01	2026-04-29T16:09:47.279634+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.098897	0.996189	0.080498	0.034187	None	None	None	None	None
14	177253022518991813990160134833781605440	lr-regularization-3.4e-01	2026-04-29T16:09:47.475020+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.083941	0.995872	0.057596	0.038265	None	None	None	None	None
15	121754851089765931132411348294051522607	lr-regularization-1.0e+00	2026-04-29T16:09:47.746135+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.080457	0.995554	0.126074	0.035837	None	None	None	None	None
16	224976678747127048765556762363666044620	lr-regularization-2.9e+00	2026-04-29T16:09:47.949530+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.089466	0.994601	0.058603	0.034421	None	None	None	None	None
17	2507230742836466766336310476342430538	lr-regularization-8.6e+00	2026-04-29T16:09:48.144224+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.104241	0.993649	0.056640	0.034372	None	None	None	None	None
18	133980706858289172307339372537655710626	lr-regularization-2.5e+01	2026-04-29T16:09:48.338923+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.121671	0.992379	0.057423	0.034004	None	None	None	None	None
19	252623136459884386978309948730034969924	lr-regularization-7.4e+01	2026-04-29T16:09:48.535936+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.142282	0.991426	0.060576	0.035210	None	None	None	None	None
20	112423665264228725084358503434888329487	lr-regularization-2.2e+02	2026-04-29T16:09:48.751024+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.162450	0.991426	0.064800	0.053385	None	None	None	None	None
21	237100718933020661116656362033009607060	lr-regularization-6.3e+02	2026-04-29T16:09:49.063828+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.204586	0.991108	0.098506	0.055777	None	None	None	None	None
22	267623986587846697893502992369688874180	lr-regularization-1.8e+03	2026-04-29T16:09:49.392839+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.291953	0.990473	0.101991	0.054837	None	None	None	None	None
23	271646794980916538122290848300007899563	lr-regularization-5.4e+03	2026-04-29T16:09:49.713779+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.471382	0.989838	0.103231	0.056033	None	None	None	None	None
24	325394858534058471977026928761016439320	lr-regularization-1.6e+04	2026-04-29T16:09:50.046681+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	0.796526	0.983169	0.114313	0.055179	None	None	None	None	None
25	32163775526283497612584165209502114930	lr-regularization-4.6e+04	2026-04-29T16:09:50.381404+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.133167	0.984757	0.118067	0.056244	None	None	None	None	None
26	25023344105609492707148849540188730748	lr-regularization-1.4e+05	2026-04-29T16:09:50.722451+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.229179	0.985551	0.119369	0.056330	None	None	None	None	None
27	33555221866672967420353558588594504875	lr-regularization-4.0e+05	2026-04-29T16:09:51.063756+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.276041	0.985869	0.117273	0.052361	None	None	None	None	None
28	242212987094220930269024975725612032276	lr-regularization-1.2e+06	2026-04-29T16:09:51.383095+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.447319	0.975865	0.112137	0.053127	None	None	None	None	None
29	138238230049298706784819690412196226769	lr-regularization-3.4e+06	2026-04-29T16:09:51.675630+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.448698	0.976500	0.068272	0.035832	None	None	None	None	None
30	300877653742059185753650029830094924136	lr-regularization-1.0e+07	2026-04-29T16:09:51.896858+00:00	LogisticRegression	binary-classification	estimator	9f4622fc73c9ccd9bc3725923827edeb	None	1.897034	0.976183	0.075861	0.039563	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, '307862713369513384122340672343784091509') to (30, '300877653742059185753650029830094924136')
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)	0.080498	0.057596	0.126074	0.058603
Predict time (s)	0.035041	0.033670	0.034234	0.036047

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

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