👋 Welcome to skore

With skore, data scientists can:

1. Track and visualize their ML/DS results.
2. Get assistance when developing their ML/DS projects.
   • Scikit-learn compatible skore.cross_validate() and skore.train_test_split() provide insights and checks on cross-validation and train-test-split.

These are only the first features: skore is a work in progress and aims to be an end-to-end library for data scientists. Stay tuned! Feedbacks are welcome: please feel free to join our Discord.

⚙️ Installation

First of all, we recommend using a virtual environment (venv). You need python>=3.9.

Then, you can install skore by using pip:

pip install -U skore

🚀 Quick start

Note: For more information on how and why to use skore, see our documentation.

Manipulating the skore UI

1. From your Python code, create and load a skore project, here named my_project:

import skore
my_project = skore.create("my_project")

This will create a skore project directory named my_project.skore in your current working directory.

2. Start storing some items, for example you can store an integer:

my_project.put("my_int", 3)

or the result of a scikit-learn grid search:

import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV

diabetes = load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]

gs_cv = GridSearchCV(
    Ridge(),
    param_grid={"alpha": np.logspace(-3, 5, 50)},
    scoring="neg_root_mean_squared_error",
)
gs_cv.fit(X, y)

my_project.put("my_gs_cv", gs_cv)

3. Finally, from your shell (in the same directory), start the UI locally:

skore launch "my_project"

This will automatically open a browser at the UI's location:

1. On the top left, by default, you can observe that you are in a View called default. You can rename this view or create another one.
2. From the Items section on the bottom left, you can add stored items to this view, either by clicking on + or by dragging an item to the right.
3. In the skore UI on the right, you can drag-and-drop items to re-order them, remove items, etc.

Get assistance when developing your ML/DS projects

By using skore.cross_validate():

import skore
my_project = skore.create("my_project")

from sklearn.datasets import load_iris
from sklearn.svm import SVC

X, y = load_iris(return_X_y=True)
clf = SVC(kernel="linear", C=1, random_state=0)

cv_results = skore.cross_validate(clf, X, y, cv=5, project=my_project)

You will automatically be able to visualize some key metrics (although you might have forgotten to specify all of them):

There is also a train-test split function that enhances scikit-learn. See more in our documentation.

🔨 Contributing

Thank you for your interest! See CONTRIBUTING.rst.

💬 Where to ask questions

Type	Platforms
🐛 Bug reports	GitHub Issue Tracker
✨ Feature requests and ideas	GitHub Issue Tracker & Discord
💬 Usage questions, discussions, contributions, etc	Discord

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