.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/integrations/plot_skore_hub_project.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_integrations_plot_skore_hub_project.py: .. _example_skore_hub_project: ======================================= Store and retrieve reports on Skore Hub ======================================= This example shows how to use :class:`~skore.Project` in **hub** mode: store reports remotely and inspect them. A key point is that :meth:`~skore.Project.summarize` returns a :class:`~skore.Summary` object that holds the metadata and metrics of every report. In Jupyter it renders as an interactive table with different views where you can filter and pick rows to build a query string; outside of Jupyter you can work with the underlying :class:`pandas.DataFrame` via its :meth:`~skore.Summary.frame` method. Examples -------- To run this example and push in your own Skore Hub workspace and project, you can run this example with the following command: .. code-block:: bash WORKSPACE= PROJECT= python plot_skore_hub_project.py In this gallery, we are going to push the different reports into a public workspace. .. GENERATED FROM PYTHON SOURCE LINES 31-36 `skore` can communicate with Skore Hub which serves two main purposes: storing and retrieving any reports that you created and a user-friendly interface for you to explore and compare models. First, we need to login to Skore Hub such that later we can push our reports to it. .. GENERATED FROM PYTHON SOURCE LINES 37-44 .. code-block:: Python from skore import login login(mode="hub") .. rst-class:: sphx-glr-script-out .. code-block:: none ╭───────────────────────────────── Login to Skore Hub ─────────────────────────────────╮ │ │ │ Successfully logged in, using API key. │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 79-81 To illustrate the integration with Skore Hub, we use a binary classification task where the goal is to predict whether a patient has a tumor or not. .. GENERATED FROM PYTHON SOURCE LINES 82-91 .. code-block:: Python import numpy as np import skrub from sklearn.datasets import load_breast_cancer X, y = load_breast_cancer(return_X_y=True, as_frame=True) labels = np.array(["no tumor", "tumor"], dtype=object) y = labels[y] skrub.TableReport(X) .. raw:: html

Column	Column name	dtype	Is sorted	Unique values	Mean	Std	Min	Median	Max
0	mean radius	Float64DType	False	456 (80.1%)	14.1	3.52	6.98	13.4	28.1
1	mean texture	Float64DType	False	479 (84.2%)	19.3	4.30	9.71	18.8	39.3
2	mean perimeter	Float64DType	False	522 (91.7%)	92.0	24.3	43.8	86.2	188.
3	mean area	Float64DType	False	539 (94.7%)	655.	352.	144.	551.	2.50e+03
4	mean smoothness	Float64DType	False	474 (83.3%)	0.0964	0.0141	0.0526	0.0959	0.163
5	mean compactness	Float64DType	False	537 (94.4%)	0.104	0.0528	0.0194	0.0926	0.345
6	mean concavity	Float64DType	False	537 (94.4%)	0.0888	0.0797	0.00	0.0615	0.427
7	mean concave points	Float64DType	False	542 (95.3%)	0.0489	0.0388	0.00	0.0335	0.201
8	mean symmetry	Float64DType	False	432 (75.9%)	0.181	0.0274	0.106	0.179	0.304
9	mean fractal dimension	Float64DType	False	499 (87.7%)	0.0628	0.00706	0.0500	0.0615	0.0974
10	radius error	Float64DType	False	540 (94.9%)	0.405	0.277	0.112	0.324	2.87
11	texture error	Float64DType	False	519 (91.2%)	1.22	0.552	0.360	1.11	4.88
12	perimeter error	Float64DType	False	533 (93.7%)	2.87	2.02	0.757	2.29	22.0
13	area error	Float64DType	False	528 (92.8%)	40.3	45.5	6.80	24.5	542.
14	smoothness error	Float64DType	False	547 (96.1%)	0.00704	0.00300	0.00171	0.00638	0.0311
15	compactness error	Float64DType	False	541 (95.1%)	0.0255	0.0179	0.00225	0.0204	0.135
16	concavity error	Float64DType	False	533 (93.7%)	0.0319	0.0302	0.00	0.0259	0.396
17	concave points error	Float64DType	False	507 (89.1%)	0.0118	0.00617	0.00	0.0109	0.0528
18	symmetry error	Float64DType	False	498 (87.5%)	0.0205	0.00827	0.00788	0.0187	0.0790
19	fractal dimension error	Float64DType	False	545 (95.8%)	0.00379	0.00265	0.000895	0.00319	0.0298
20	worst radius	Float64DType	False	457 (80.3%)	16.3	4.83	7.93	15.0	36.0
21	worst texture	Float64DType	False	511 (89.8%)	25.7	6.15	12.0	25.4	49.5
22	worst perimeter	Float64DType	False	514 (90.3%)	107.	33.6	50.4	97.7	251.
23	worst area	Float64DType	False	544 (95.6%)	881.	569.	185.	686.	4.25e+03
24	worst smoothness	Float64DType	False	411 (72.2%)	0.132	0.0228	0.0712	0.131	0.223
25	worst compactness	Float64DType	False	529 (93.0%)	0.254	0.157	0.0273	0.212	1.06
26	worst concavity	Float64DType	False	539 (94.7%)	0.272	0.209	0.00	0.227	1.25
27	worst concave points	Float64DType	False	492 (86.5%)	0.115	0.0657	0.00	0.0999	0.291
28	worst symmetry	Float64DType	False	500 (87.9%)	0.290	0.0619	0.157	0.282	0.664
29	worst fractal dimension	Float64DType	False	535 (94.0%)	0.0839	0.0181	0.0550	0.0800	0.207

Column 1	Column 2	Cramér's V	Pearson's Correlation
mean radius	mean perimeter	0.846	0.998
mean radius	mean area	0.800	0.987
radius error	perimeter error	0.768	0.973
worst radius	worst perimeter	0.757	0.994
mean perimeter	mean area	0.754	0.987
radius error	area error	0.732	0.952
area error	worst area	0.699	0.811
perimeter error	area error	0.687	0.938
worst radius	worst area	0.676	0.984
worst perimeter	worst area	0.662	0.978
mean area	area error	0.652	0.800
mean perimeter	worst radius	0.643	0.969
mean area	worst radius	0.640	0.963
mean radius	worst radius	0.635	0.970
concavity error	concave points error	0.628	0.772
mean radius	area error	0.626	0.736
concavity error	fractal dimension error	0.610	0.727
worst compactness	worst concavity	0.599	0.892
mean area	worst perimeter	0.596	0.959
mean perimeter	area error	0.594	0.745
mean perimeter	worst perimeter	0.592	0.970
radius error	worst area	0.574	0.752
area error	worst perimeter	0.573	0.761
mean radius	worst perimeter	0.570	0.965
worst compactness	worst fractal dimension	0.568	0.810
mean radius	worst area	0.567	0.941
compactness error	fractal dimension error	0.565	0.803
area error	worst radius	0.561	0.757
mean area	worst area	0.559	0.959
perimeter error	worst area	0.558	0.731
mean texture	worst texture	0.554	0.912
mean perimeter	worst area	0.553	0.942
mean concave points	area error	0.551	0.690
mean concavity	mean concave points	0.549	0.921
compactness error	concavity error	0.538	0.801
mean area	radius error	0.514	0.733
concave points error	fractal dimension error	0.511	0.611
mean concave points	worst concave points	0.507	0.910
mean radius	radius error	0.494	0.679
worst concavity	worst fractal dimension	0.487	0.687
radius error	worst perimeter	0.480	0.720
mean perimeter	mean concave points	0.476	0.851
perimeter error	worst perimeter	0.472	0.721
mean compactness	worst compactness	0.467	0.866
mean area	perimeter error	0.466	0.727
mean fractal dimension	worst fractal dimension	0.465	0.767
mean perimeter	radius error	0.465	0.692
mean concave points	worst perimeter	0.463	0.856
mean area	mean concave points	0.463	0.823
worst symmetry	worst fractal dimension	0.461	0.538
mean smoothness	worst smoothness	0.458	0.805
mean radius	perimeter error	0.457	0.674
mean compactness	mean concavity	0.456	0.883
mean radius	mean concave points	0.454	0.823
mean concavity	worst concave points	0.453	0.861
mean concave points	radius error	0.446	0.698
radius error	worst radius	0.445	0.715
mean concave points	perimeter error	0.444	0.711
compactness error	concave points error	0.443	0.744
mean perimeter	perimeter error	0.439	0.693
mean fractal dimension	fractal dimension error	0.439	0.688
concavity error	worst concavity	0.437	0.663
mean concavity	worst concavity	0.433	0.884
perimeter error	worst radius	0.426	0.697
mean compactness	worst concavity	0.423	0.816
mean compactness	worst concave points	0.423	0.816
mean concavity	concavity error	0.421	0.691
mean concave points	worst area	0.419	0.810
mean concavity	area error	0.416	0.617
mean concave points	worst radius	0.412	0.830
mean compactness	mean concave points	0.412	0.831
worst perimeter	worst concave points	0.408	0.816
symmetry error	worst symmetry	0.406	0.389
worst concavity	worst concave points	0.404	0.855
perimeter error	concave points error	0.401	0.556
worst compactness	worst concave points	0.394	0.801
mean concavity	concave points error	0.394	0.683
mean concavity	radius error	0.394	0.632
mean concavity	worst perimeter	0.393	0.730
mean symmetry	worst symmetry	0.393	0.700
mean compactness	compactness error	0.385	0.739
worst radius	worst concave points	0.385	0.787
mean perimeter	mean concavity	0.384	0.716
mean concavity	perimeter error	0.383	0.660
mean perimeter	worst concave points	0.378	0.771
compactness error	worst compactness	0.377	0.679
mean compactness	worst fractal dimension	0.377	0.687
worst smoothness	worst fractal dimension	0.374	0.618
mean area	mean concavity	0.370	0.686
worst area	worst concave points	0.364	0.747
mean symmetry	symmetry error	0.362	0.449
mean compactness	concave points error	0.362	0.642
mean fractal dimension	concavity error	0.362	0.447
area error	worst concave points	0.360	0.538
mean concavity	worst compactness	0.360	0.755
mean compactness	mean symmetry	0.360	0.603
mean concavity	compactness error	0.359	0.670
mean compactness	perimeter error	0.356	0.549
mean concavity	worst area	0.354	0.676
mean radius	mean concavity	0.352	0.677
mean area	worst concave points	0.351	0.722
mean radius	worst concave points	0.351	0.744
mean concavity	worst radius	0.350	0.688
mean concave points	worst concavity	0.346	0.752
worst compactness	worst symmetry	0.345	0.614
compactness error	worst concavity	0.339	0.639
concave points error	worst concave points	0.338	0.602
mean concave points	concave points error	0.337	0.616
mean smoothness	mean compactness	0.331	0.659
mean smoothness	worst fractal dimension	0.331	0.499
mean compactness	worst symmetry	0.328	0.510
mean fractal dimension	worst smoothness	0.325	0.505
worst smoothness	worst compactness	0.321	0.568
compactness error	worst fractal dimension	0.319	0.591
smoothness error	concave points error	0.318	0.328
mean concavity	fractal dimension error	0.317	0.449
mean concave points	worst compactness	0.316	0.667
mean smoothness	mean fractal dimension	0.316	0.585
smoothness error	concavity error	0.312	0.269
mean compactness	symmetry error	0.312	0.230
texture error	perimeter error	0.311	0.223
compactness error	worst symmetry	0.307	0.278
compactness error	symmetry error	0.307	0.395
radius error	worst concave points	0.301	0.531
perimeter error	worst concave points	0.301	0.555
mean fractal dimension	compactness error	0.300	0.560
concave points error	worst concavity	0.294	0.550
worst smoothness	worst symmetry	0.292	0.494
fractal dimension error	worst fractal dimension	0.290	0.591
mean fractal dimension	worst compactness	0.286	0.459
worst concavity	worst symmetry	0.285	0.533
worst perimeter	worst concavity	0.285	0.618
mean compactness	mean fractal dimension	0.283	0.565
perimeter error	symmetry error	0.283	0.266
mean compactness	concavity error	0.282	0.571
mean compactness	area error	0.280	0.456
mean smoothness	mean symmetry	0.279	0.558
perimeter error	compactness error	0.279	0.416
mean smoothness	worst symmetry	0.278	0.394
mean fractal dimension	worst concavity	0.277	0.346
mean compactness	worst perimeter	0.275	0.590
mean concavity	mean symmetry	0.275	0.501
texture error	concave points error	0.275	0.230
radius error	concave points error	0.274	0.513
worst texture	worst area	0.272	0.346
mean concave points	mean symmetry	0.272	0.462
mean fractal dimension	concave points error	0.271	0.341
mean compactness	radius error	0.271	0.497
mean compactness	worst smoothness	0.269	0.566
concavity error	symmetry error	0.268	0.309
mean compactness	fractal dimension error	0.267	0.507
worst radius	worst concavity	0.266	0.574
smoothness error	fractal dimension error	0.266	0.427
mean perimeter	mean compactness	0.265	0.557
worst perimeter	worst compactness	0.265	0.529
mean concavity	worst fractal dimension	0.262	0.515
mean radius	smoothness error	0.261	-0.223
mean compactness	worst area	0.259	0.510
worst smoothness	worst concave points	0.259	0.548
mean compactness	worst radius	0.257	0.535
mean perimeter	worst concavity	0.256	0.564
mean smoothness	mean concavity	0.255	0.522
smoothness error	compactness error	0.254	0.337
mean smoothness	mean concave points	0.254	0.554
mean perimeter	smoothness error	0.250	-0.203
mean fractal dimension	smoothness error	0.249	0.402
mean symmetry	concave points error	0.248	0.393
worst area	worst concavity	0.248	0.543
mean area	mean compactness	0.247	0.499
texture error	smoothness error	0.246	0.397
mean concave points	concavity error	0.246	0.439
worst concave points	worst fractal dimension	0.245	0.511
mean symmetry	compactness error	0.244	0.422
area error	compactness error	0.244	0.285
smoothness error	symmetry error	0.244	0.414
mean symmetry	radius error	0.244	0.303
area error	worst concavity	0.244	0.385
radius error	compactness error	0.242	0.356
mean smoothness	worst compactness	0.242	0.472
mean concave points	compactness error	0.241	0.490
mean fractal dimension	worst symmetry	0.240	0.334
area error	concave points error	0.240	0.416
mean radius	worst concavity	0.239	0.527
mean radius	mean compactness	0.239	0.506
mean area	worst concavity	0.238	0.513
mean symmetry	worst compactness	0.238	0.473
compactness error	worst concave points	0.237	0.483
mean concave points	worst symmetry	0.237	0.376
mean symmetry	worst concave points	0.236	0.430
concave points error	worst compactness	0.236	0.453
texture error	worst texture	0.234	0.409
concave points error	symmetry error	0.233	0.313
radius error	smoothness error	0.232	0.165
radius error	symmetry error	0.231	0.241
concave points error	worst perimeter	0.231	0.395
mean concave points	symmetry error	0.231	0.0954
symmetry error	fractal dimension error	0.231	0.369
mean concavity	worst symmetry	0.230	0.409
mean perimeter	concave points error	0.230	0.407
mean symmetry	worst concavity	0.230	0.434
worst radius	worst compactness	0.229	0.476
worst concave points	worst symmetry	0.229	0.503
fractal dimension error	worst concavity	0.229	0.380
mean texture	worst area	0.229	0.344
texture error	symmetry error	0.229	0.412
area error	smoothness error	0.228	0.0752
perimeter error	concavity error	0.228	0.362
mean area	smoothness error	0.227	-0.167
worst smoothness	worst concavity	0.226	0.519
mean perimeter	worst compactness	0.225	0.456
mean symmetry	area error	0.225	0.224
worst texture	worst concavity	0.225	0.368
mean smoothness	radius error	0.225	0.301
texture error	fractal dimension error	0.223	0.280
area error	worst texture	0.223	0.196
mean fractal dimension	symmetry error	0.223	0.345
concavity error	worst fractal dimension	0.223	0.439
mean symmetry	mean fractal dimension	0.222	0.480
mean smoothness	worst concavity	0.222	0.435
perimeter error	worst concavity	0.221	0.419
mean smoothness	worst concave points	0.221	0.503
smoothness error	worst smoothness	0.221	0.314
mean concavity	mean fractal dimension	0.220	0.337
smoothness error	worst radius	0.219	-0.231
mean radius	concave points error	0.219	0.376
mean texture	texture error	0.218	0.386
concavity error	worst compactness	0.218	0.485
mean smoothness	area error	0.217	0.247
radius error	worst concavity	0.217	0.381
mean smoothness	symmetry error	0.217	0.201
mean concavity	symmetry error	0.217	0.178
fractal dimension error	worst compactness	0.217	0.390
symmetry error	worst fractal dimension	0.216	0.0781
mean area	concave points error	0.216	0.372
mean symmetry	concavity error	0.216	0.343
symmetry error	worst compactness	0.214	0.0603
smoothness error	worst perimeter	0.214	-0.217
worst area	worst compactness	0.213	0.438
mean radius	mean fractal dimension	0.213	-0.312
mean area	worst compactness	0.213	0.390
mean smoothness	smoothness error	0.212	0.332
concave points error	worst area	0.212	0.342
worst texture	worst compactness	0.212	0.361
mean symmetry	texture error	0.212	0.128
mean symmetry	worst fractal dimension	0.210	0.438
mean symmetry	fractal dimension error	0.208	0.332
mean symmetry	perimeter error	0.207	0.314
mean smoothness	compactness error	0.207	0.319
mean concavity	worst smoothness	0.206	0.449
mean texture	area error	0.206	0.260
concave points error	worst radius	0.205	0.358
mean smoothness	concave points error	0.205	0.381
perimeter error	worst compactness	0.204	0.342
concavity error	worst symmetry	0.203	0.198
mean perimeter	symmetry error	0.203	-0.0816
mean radius	worst compactness	0.202	0.413
texture error	concavity error	0.202	0.195
mean concave points	worst smoothness	0.202	0.453
area error	symmetry error	0.201	0.134
mean concave points	worst fractal dimension	0.200	0.369
area error	worst compactness	0.199	0.283
mean symmetry	smoothness error	0.198	0.187
perimeter error	fractal dimension error	0.198	0.244
mean radius	symmetry error	0.197	-0.104
concavity error	worst concave points	0.196	0.440
mean symmetry	worst smoothness	0.196	0.427
mean texture	worst concavity	0.195	0.301
mean perimeter	concavity error	0.194	0.228
mean concavity	smoothness error	0.193	0.0986
symmetry error	worst concavity	0.192	0.0371
mean texture	mean concavity	0.188	0.302
mean perimeter	mean fractal dimension	0.188	-0.261
mean texture	worst concave points	0.187	0.295
worst radius	worst texture	0.187	0.360
mean texture	worst radius	0.185	0.353
mean smoothness	fractal dimension error	0.183	0.284
texture error	compactness error	0.183	0.232
mean perimeter	compactness error	0.182	0.251
worst texture	worst perimeter	0.182	0.365
radius error	concavity error	0.182	0.332
mean texture	worst perimeter	0.182	0.358
mean fractal dimension	worst radius	0.181	-0.254
mean compactness	texture error	0.181	0.0462
mean area	symmetry error	0.181	-0.0725
concave points error	worst fractal dimension	0.180	0.311
symmetry error	worst perimeter	0.179	-0.104
mean radius	concavity error	0.179	0.194
compactness error	worst perimeter	0.178	0.261
mean texture	radius error	0.178	0.276
mean perimeter	mean symmetry	0.178	0.183
mean perimeter	mean smoothness	0.177	0.207
concavity error	worst perimeter	0.177	0.227
radius error	worst compactness	0.177	0.287
texture error	worst smoothness	0.175	-0.0737
worst perimeter	worst symmetry	0.175	0.269
mean texture	perimeter error	0.175	0.282
mean texture	mean compactness	0.173	0.237
symmetry error	worst radius	0.173	-0.128
mean fractal dimension	worst perimeter	0.173	-0.205
worst texture	worst concave points	0.173	0.360
mean radius	mean texture	0.172	0.324
mean radius	compactness error	0.172	0.206
radius error	texture error	0.172	0.213
mean texture	mean perimeter	0.171	0.330
compactness error	worst smoothness	0.170	0.227
mean concavity	worst texture	0.170	0.300
worst texture	worst fractal dimension	0.170	0.219
mean radius	mean smoothness	0.169	0.171
mean area	compactness error	0.169	0.213
mean radius	worst texture	0.168	0.297
mean fractal dimension	perimeter error	0.168	0.0398
mean area	mean smoothness	0.168	0.177
mean symmetry	worst perimeter	0.167	0.219
mean texture	mean area	0.166	0.321
mean texture	worst compactness	0.165	0.278
mean texture	mean concave points	0.165	0.293
mean radius	mean symmetry	0.165	0.148
mean area	mean fractal dimension	0.165	-0.283
mean smoothness	perimeter error	0.164	0.296
symmetry error	worst smoothness	0.164	-0.0127
symmetry error	worst concave points	0.163	-0.0304
mean compactness	smoothness error	0.162	0.135
mean area	mean symmetry	0.162	0.151
worst texture	worst smoothness	0.161	0.225
mean symmetry	worst radius	0.160	0.186
compactness error	worst radius	0.160	0.205
mean area	worst texture	0.160	0.287
mean perimeter	worst texture	0.160	0.303
smoothness error	worst area	0.160	-0.182
mean compactness	worst texture	0.159	0.248
compactness error	worst texture	0.159	0.143
mean concave points	fractal dimension error	0.159	0.258
fractal dimension error	worst symmetry	0.157	0.111
worst radius	worst symmetry	0.156	0.244
area error	concavity error	0.156	0.271
mean smoothness	worst perimeter	0.155	0.239
mean smoothness	concavity error	0.155	0.248
radius error	worst texture	0.155	0.195
concavity error	worst radius	0.155	0.187
mean concave points	worst texture	0.155	0.293
mean concave points	smoothness error	0.154	0.0277
worst texture	worst symmetry	0.153	0.233
symmetry error	worst area	0.153	-0.110
worst perimeter	worst smoothness	0.153	0.237
perimeter error	worst symmetry	0.153	0.110
fractal dimension error	worst smoothness	0.152	0.171
worst perimeter	worst fractal dimension	0.152	0.139
mean perimeter	texture error	0.152	-0.0868
concave points error	worst smoothness	0.151	0.215
texture error	area error	0.150	0.112
radius error	fractal dimension error	0.150	0.228
mean smoothness	texture error	0.150	0.0684
mean concavity	texture error	0.149	0.0762
mean perimeter	worst symmetry	0.149	0.189
compactness error	worst area	0.148	0.199
mean concave points	mean fractal dimension	0.148	0.167
mean texture	concave points error	0.147	0.164
worst radius	worst smoothness	0.146	0.217
fractal dimension error	worst concave points	0.145	0.215
mean area	concavity error	0.145	0.208
mean area	texture error	0.144	-0.0663
radius error	worst symmetry	0.144	0.0945
worst area	worst symmetry	0.143	0.209
perimeter error	smoothness error	0.142	0.151
mean texture	compactness error	0.142	0.192
texture error	worst perimeter	0.141	-0.102
mean smoothness	worst radius	0.139	0.213
texture error	worst symmetry	0.139	-0.128
mean fractal dimension	worst texture	0.139	-0.0513
texture error	worst concave points	0.138	-0.120
worst radius	worst fractal dimension	0.138	0.0935
concave points error	worst symmetry	0.138	0.143
mean fractal dimension	worst area	0.137	-0.232
mean symmetry	worst area	0.136	0.177
mean perimeter	worst smoothness	0.136	0.151
mean radius	texture error	0.135	-0.0973
mean radius	worst symmetry	0.134	0.164
mean radius	worst smoothness	0.134	0.120
area error	fractal dimension error	0.133	0.127
texture error	worst radius	0.133	-0.112
mean concave points	texture error	0.133	0.0215
mean texture	concavity error	0.132	0.143
mean area	worst symmetry	0.132	0.144
fractal dimension error	worst radius	0.131	-0.0375
area error	worst symmetry	0.130	0.0741
perimeter error	worst texture	0.130	0.200
worst area	worst smoothness	0.130	0.209
mean symmetry	worst texture	0.129	0.0907
mean fractal dimension	worst concave points	0.129	0.175
mean area	worst smoothness	0.129	0.124
worst area	worst fractal dimension	0.129	0.0796
mean fractal dimension	texture error	0.129	0.164
mean radius	fractal dimension error	0.127	-0.0426
mean fractal dimension	radius error	0.126	0.000111
mean perimeter	fractal dimension error	0.126	-0.00552
mean smoothness	worst area	0.125	0.207
radius error	worst smoothness	0.125	0.142
fractal dimension error	worst perimeter	0.125	-0.00100
perimeter error	worst smoothness	0.124	0.130
mean texture	worst smoothness	0.124	0.0775
concavity error	worst area	0.123	0.188
smoothness error	worst concave points	0.122	-0.102
mean texture	worst fractal dimension	0.122	0.119
mean radius	worst fractal dimension	0.121	0.00707
concave points error	worst texture	0.120	0.0867
area error	worst smoothness	0.118	0.125
mean fractal dimension	area error	0.117	-0.0902
mean texture	worst symmetry	0.117	0.105
concavity error	worst smoothness	0.116	0.168
smoothness error	worst fractal dimension	0.116	0.101
mean perimeter	worst fractal dimension	0.116	0.0510
mean area	fractal dimension error	0.116	-0.0199
mean texture	fractal dimension error	0.113	0.0545
smoothness error	worst concavity	0.112	-0.0583
mean texture	mean symmetry	0.110	0.0714
mean texture	mean fractal dimension	0.109	-0.0764
mean texture	mean smoothness	0.108	-0.0234
smoothness error	worst symmetry	0.108	-0.107
fractal dimension error	worst texture	0.107	-0.00320
texture error	worst compactness	0.106	-0.0924
mean area	worst fractal dimension	0.105	0.00374
symmetry error	worst texture	0.104	-0.0775
perimeter error	worst fractal dimension	0.103	0.0854
mean smoothness	worst texture	0.103	0.0361
texture error	worst area	0.101	-0.0832
area error	worst fractal dimension	0.0978	0.0175
concavity error	worst texture	0.0976	0.100
mean texture	symmetry error	0.0971	0.00913
texture error	worst concavity	0.0966	-0.0690
fractal dimension error	worst area	0.0927	-0.0227
texture error	worst fractal dimension	0.0927	-0.0457
radius error	worst fractal dimension	0.0914	0.0496
mean texture	smoothness error	0.0905	0.00661
smoothness error	worst compactness	0.0896	-0.0556
smoothness error	worst texture	0.0894	-0.0747

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.. GENERATED FROM PYTHON SOURCE LINES 92-100 Store reports on Skore Hub ========================== On this problem, we use a logistic regression classifier with skrub's :func:`~skrub.tabular_pipeline` to preprocess the data if needed. To send several reports to Skore Hub, we send models with different regularization parameters. .. GENERATED FROM PYTHON SOURCE LINES 101-119 .. code-block:: Python from numpy import logspace from sklearn.linear_model import LogisticRegression from skore import Project, evaluate project = Project(name=PROJECT, mode="hub", workspace=WORKSPACE) for regularization in logspace(-3, 3, 5): project.put( f"lr-regularization-{regularization:.1e}", evaluate( skrub.tabular_pipeline(LogisticRegression(C=regularization)), X, y, splitter=0.2, pos_label="tumor", ), ) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/runner/work/skore/skore/skore/venv/lib/python3.14/site-packages/skore/_plugins/hub/artifact/environment.py:48: UserWarning: Package matplotlib_skore_scraper seems to be an editable or local install (loaded from outside site-packages). It will not be recorded in the inferred requirements. for requirement in infer() Putting lr-regularization-1.0e-03 0:00:16 Consult your report at https://skore.probabl.ai/skore/example-skore-hub-project-pull-3233/estimators/38954 /home/runner/work/skore/skore/skore/venv/lib/python3.14/site-packages/skore/_plugins/hub/artifact/environment.py:48: UserWarning: Package matplotlib_skore_scraper seems to be an editable or local install (loaded from outside site-packages). It will not be recorded in the inferred requirements. for requirement in infer() Putting lr-regularization-3.2e-02 0:00:14 Consult your report at https://skore.probabl.ai/skore/example-skore-hub-project-pull-3233/estimators/38955 /home/runner/work/skore/skore/skore/venv/lib/python3.14/site-packages/skore/_plugins/hub/artifact/environment.py:48: UserWarning: Package matplotlib_skore_scraper seems to be an editable or local install (loaded from outside site-packages). It will not be recorded in the inferred requirements. for requirement in infer() Putting lr-regularization-1.0e+00 0:00:14 Consult your report at https://skore.probabl.ai/skore/example-skore-hub-project-pull-3233/estimators/38956 /home/runner/work/skore/skore/skore/venv/lib/python3.14/site-packages/skore/_plugins/hub/artifact/environment.py:48: UserWarning: Package matplotlib_skore_scraper seems to be an editable or local install (loaded from outside site-packages). It will not be recorded in the inferred requirements. for requirement in infer() Putting lr-regularization-3.2e+01 0:00:14 Consult your report at https://skore.probabl.ai/skore/example-skore-hub-project-pull-3233/estimators/38957 /home/runner/work/skore/skore/skore/venv/lib/python3.14/site-packages/skore/_plugins/hub/artifact/environment.py:48: UserWarning: Package matplotlib_skore_scraper seems to be an editable or local install (loaded from outside site-packages). It will not be recorded in the inferred requirements. for requirement in infer() Putting lr-regularization-1.0e+03 0:00:14 Consult your report at https://skore.probabl.ai/skore/example-skore-hub-project-pull-3233/estimators/38958 .. GENERATED FROM PYTHON SOURCE LINES 120-129 Retrieve report stored on Skore Hub =================================== Retrieving a report on Skore Hub is similar to retrieving a report in local mode. :meth:`~skore.Project.summarize` returns a :class:`~skore.Summary` object. In a Jupyter environment it renders as an interactive table where you can filter rows and pick reports across the different views; the selection produces a query string ready to pass to :meth:`~skore.Summary.query`. .. GENERATED FROM PYTHON SOURCE LINES 129-132 .. code-block:: Python summary = project.summarize() summary .. raw:: html

ID	Skore report ID	Key	Date	Learner	Dataset	Report type
skore:report:estimator:38954	01a039bc-1d80-7299-9ba8-5ad6f8e2701d	lr-regularization-1.0e-03	2026-08-25T16:24:02.264997+00:00	LogisticRegression	7887e234e3f622242e475e3da0cb5837	estimator
skore:report:estimator:38955	01a039bc-62dc-76e9-96aa-b0998850161b	lr-regularization-3.2e-02	2026-08-25T16:24:16.929400+00:00	LogisticRegression	7887e234e3f622242e475e3da0cb5837	estimator
skore:report:estimator:38956	01a039bc-9c1d-7525-8cb7-7628ceb266c4	lr-regularization-1.0e+00	2026-08-25T16:24:31.499361+00:00	LogisticRegression	7887e234e3f622242e475e3da0cb5837	estimator
skore:report:estimator:38957	01a039bc-d4da-73f9-ae48-e7c0b57cac51	lr-regularization-3.2e+01	2026-08-25T16:24:46.248244+00:00	LogisticRegression	7887e234e3f622242e475e3da0cb5837	estimator
skore:report:estimator:38958	01a039bd-0f8e-71a5-a4be-e5620270cbdb	lr-regularization-1.0e+03	2026-08-25T16:25:00.782039+00:00	LogisticRegression	7887e234e3f622242e475e3da0cb5837	estimator

.. GENERATED FROM PYTHON SOURCE LINES 133-135 To work with the underlying table (e.g. in scripts or when you prefer a :class:`pandas.DataFrame`), use the :meth:`~skore.Summary.frame` method: .. GENERATED FROM PYTHON SOURCE LINES 136-138 .. code-block:: Python summary.frame() .. raw:: html

		report_id	key	date	learner	report_type	dataset	log_loss	roc_auc	fit_time	predict_time
	id
0	skore:report:estimator:38954	01a039bc-1d80-7299-9ba8-5ad6f8e2701d	lr-regularization-1.0e-03	2026-08-25 16:24:02.264997+00:00	LogisticRegression	estimator	7887e234e3f622242e475e3da0cb5837	0.406397	0.987298	0.095063	0.042820
1	skore:report:estimator:38955	01a039bc-62dc-76e9-96aa-b0998850161b	lr-regularization-3.2e-02	2026-08-25 16:24:16.929400+00:00	LogisticRegression	estimator	7887e234e3f622242e475e3da0cb5837	0.137499	0.995237	0.081301	0.042117
2	skore:report:estimator:38956	01a039bc-9c1d-7525-8cb7-7628ceb266c4	lr-regularization-1.0e+00	2026-08-25 16:24:31.499361+00:00	LogisticRegression	estimator	7887e234e3f622242e475e3da0cb5837	0.080457	0.995554	0.081879	0.041815
3	skore:report:estimator:38957	01a039bc-d4da-73f9-ae48-e7c0b57cac51	lr-regularization-3.2e+01	2026-08-25 16:24:46.248244+00:00	LogisticRegression	estimator	7887e234e3f622242e475e3da0cb5837	0.127250	0.992061	0.084524	0.042444
4	skore:report:estimator:38958	01a039bd-0f8e-71a5-a4be-e5620270cbdb	lr-regularization-1.0e+03	2026-08-25 16:25:00.782039+00:00	LogisticRegression	estimator	7887e234e3f622242e475e3da0cb5837	0.245632	0.990314	0.090729	0.041758

.. GENERATED FROM PYTHON SOURCE LINES 139-141 Basically, our summary contains metadata related to various information that we need to quickly help filtering the reports. .. GENERATED FROM PYTHON SOURCE LINES 142-144 .. code-block:: Python summary.frame().info() .. rst-class:: sphx-glr-script-out .. code-block:: none MultiIndex: 5 entries, (0, 'skore:report:estimator:38954') to (4, 'skore:report:estimator:38958') Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 report_id 5 non-null object 1 key 5 non-null string 2 date 5 non-null datetime64[ns, UTC] 3 learner 5 non-null category 4 report_type 5 non-null string 5 dataset 5 non-null string 6 log_loss 5 non-null float64 7 roc_auc 5 non-null float64 8 fit_time 5 non-null float64 9 predict_time 5 non-null float64 dtypes: category(1), datetime64[ns, UTC](1), float64(4), object(1), string(3) memory usage: 894.0+ bytes .. GENERATED FROM PYTHON SOURCE LINES 145-147 Filter reports by metric (e.g. keep only those above a given accuracy) and work with the result as a table. .. GENERATED FROM PYTHON SOURCE LINES 148-150 .. code-block:: Python summary.query("log_loss < 0.2").frame()["key"].tolist() .. rst-class:: sphx-glr-script-out .. code-block:: none ['lr-regularization-3.2e-02', 'lr-regularization-1.0e+00', 'lr-regularization-3.2e+01'] .. GENERATED FROM PYTHON SOURCE LINES 151-154 Use :meth:`~skore.Summary.compare` to load the corresponding reports from the project (optionally after filtering the summary). Passing ``return_as="report"`` returns a :class:`~skore.ComparisonReport` built from those reports. .. GENERATED FROM PYTHON SOURCE LINES 155-158 .. code-block:: Python reports = summary.query("log_loss < 0.2").compare(return_as="report") len(reports.reports_) .. rst-class:: sphx-glr-script-out .. code-block:: none 3 .. GENERATED FROM PYTHON SOURCE LINES 159-161 Since we got a :class:`~skore.ComparisonReport`, we can use the metrics accessor to summarize the metrics across the reports. .. GENERATED FROM PYTHON SOURCE LINES 162-164 .. code-block:: Python reports.metrics.summarize().frame() .. raw:: html

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	0.127250
brier_score	0.035253	0.025149	0.029948
fit_time	0.081301	0.081879	0.084524
predict_time	0.041698	0.041700	0.042028

.. GENERATED FROM PYTHON SOURCE LINES 165-167 .. code-block:: Python _ = reports.metrics.roc().plot(subplot_by=None) .. image-sg:: /auto_examples/integrations/images/sphx_glr_plot_skore_hub_project_001.png :alt: ROC Curve Positive label: tumor Data source: Test set :srcset: /auto_examples/integrations/images/sphx_glr_plot_skore_hub_project_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 168-173 Conclusion ========== Skore Hub provides a user-friendly interface for you to explore and compare models. You can easily store reports created using Skore. .. rst-class:: sphx-glr-timing **Total running time of the script:** (1 minutes 26.866 seconds) .. _sphx_glr_download_auto_examples_integrations_plot_skore_hub_project.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_skore_hub_project.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_skore_hub_project.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_skore_hub_project.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	radius error	texture error	perimeter error	area error	smoothness error	compactness error	concavity error	concave points error	symmetry error	fractal dimension error	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	radius error	texture error	perimeter error	area error	smoothness error	compactness error	concavity error	concave points error	symmetry error	fractal dimension error	worst radius	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	18.0	10.4	123.	1.00e+03	0.118	0.278	0.300	0.147	0.242	0.0787	1.09	0.905	8.59	153.	0.00640	0.0490	0.0537	0.0159	0.0300	0.00619	25.4	17.3	185.	2.02e+03	0.162	0.666	0.712	0.265	0.460	0.119
1	20.6	17.8	133.	1.33e+03	0.0847	0.0786	0.0869	0.0702	0.181	0.0567	0.543	0.734	3.40	74.1	0.00522	0.0131	0.0186	0.0134	0.0139	0.00353	25.0	23.4	159.	1.96e+03	0.124	0.187	0.242	0.186	0.275	0.0890
2	19.7	21.2	130.	1.20e+03	0.110	0.160	0.197	0.128	0.207	0.0600	0.746	0.787	4.58	94.0	0.00615	0.0401	0.0383	0.0206	0.0225	0.00457	23.6	25.5	152.	1.71e+03	0.144	0.424	0.450	0.243	0.361	0.0876
3	11.4	20.4	77.6	386.	0.142	0.284	0.241	0.105	0.260	0.0974	0.496	1.16	3.44	27.2	0.00911	0.0746	0.0566	0.0187	0.0596	0.00921	14.9	26.5	98.9	568.	0.210	0.866	0.687	0.258	0.664	0.173
4	20.3	14.3	135.	1.30e+03	0.100	0.133	0.198	0.104	0.181	0.0588	0.757	0.781	5.44	94.4	0.0115	0.0246	0.0569	0.0188	0.0176	0.00511	22.5	16.7	152.	1.58e+03	0.137	0.205	0.400	0.163	0.236	0.0768

564	21.6	22.4	142.	1.48e+03	0.111	0.116	0.244	0.139	0.173	0.0562	1.18	1.26	7.67	159.	0.0103	0.0289	0.0520	0.0245	0.0111	0.00424	25.4	26.4	166.	2.03e+03	0.141	0.211	0.411	0.222	0.206	0.0712
565	20.1	28.2	131.	1.26e+03	0.0978	0.103	0.144	0.0979	0.175	0.0553	0.765	2.46	5.20	99.0	0.00577	0.0242	0.0395	0.0168	0.0190	0.00250	23.7	38.2	155.	1.73e+03	0.117	0.192	0.322	0.163	0.257	0.0664
566	16.6	28.1	108.	858.	0.0846	0.102	0.0925	0.0530	0.159	0.0565	0.456	1.07	3.42	48.5	0.00590	0.0373	0.0473	0.0156	0.0132	0.00389	19.0	34.1	127.	1.12e+03	0.114	0.309	0.340	0.142	0.222	0.0782
567	20.6	29.3	140.	1.26e+03	0.118	0.277	0.351	0.152	0.240	0.0702	0.726	1.59	5.77	86.2	0.00652	0.0616	0.0712	0.0166	0.0232	0.00619	25.7	39.4	185.	1.82e+03	0.165	0.868	0.939	0.265	0.409	0.124
568	7.76	24.5	47.9	181.	0.0526	0.0436	0.00	0.00	0.159	0.0588	0.386	1.43	2.55	19.1	0.00719	0.00466	0.00	0.00	0.0268	0.00278	9.46	30.4	59.2	269.	0.0900	0.0644	0.00	0.00	0.287	0.0704

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mean area

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mean concavity

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mean symmetry

mean fractal dimension

radius error

texture error

perimeter error

area error

smoothness error

compactness error

concavity error

concave points error

symmetry error

fractal dimension error

worst radius

worst texture

worst perimeter

worst area

worst smoothness

worst compactness

worst concavity

worst concave points

worst symmetry

worst fractal dimension