Project#

class skore.Project(name, **kwargs)[source]#

API to manage a collection of key-report pairs.

Its constructor initializes a project by creating a new project or by loading an existing one.

The class main methods are put(), summarize() and get(), respectively to insert a key-report pair into the project, to obtain the metadata/metrics of the inserted reports and to get a specific report by its id.

Two mutually exclusive modes are available and can be configured using the name parameter of the constructor:

Hub mode

If the name takes the form of the URI hub://<tenant>/<name>, the project is configured to the hub mode to communicate with the skore hub.

A tenant is a skore hub concept that must be configured on the skore hub interface. It represents an isolated entity managing users, projects, and resources. It can be a company, organization, or team that operates independently within the system.

In this mode, you must have an account to the skore hub and must be authorized to the specified tenant. You must also be authenticated beforehand, using the skore-hub-login CLI.

Local mode

Otherwise, the project is configured to the local mode to be persisted on the user machine in a directory called workspace.

The workspace can be shared between all the projects.
The workspace can be set using kwargs or the environment variable
SKORE_WORKSPACE.
If not, it will be by default set to a skore/ directory in the USER
cache directory:

on Windows, usually C:\Users\%USER%\AppData\Local\skore,
on Linux, usually ${HOME}/.cache/skore,
on macOS, usually ${HOME}/Library/Caches/skore.

Refer to the Storing data science artifacts section of the user guide for more details.

Parameters:

namestr

The name of the project:

if the name takes the form of the URI hub://<tenant>/<name>, the project is configured to communicate with the skore hub,
otherwise, the project is configured to communicate with a local storage, on the user machine.

**kwargsdict

Extra keyword arguments passed to the project, depending on its mode.

workspacePath, mode:local only.

The directory where the local project is persisted.

The workspace can be shared between all the projects.
The workspace can be set using kwargs or the environment variable
SKORE_WORKSPACE.
If not, it will be by default set to a skore/ directory in the USER
cache directory:

on Windows, usually C:\Users\%USER%\AppData\Local\skore,
on Linux, usually ${HOME}/.cache/skore,
on macOS, usually ${HOME}/Library/Caches/skore.

Attributes:

namestr: The name of the project.
modestr: The mode of the project.

See also

Metadata: DataFrame designed to investigate persisted reports’ metadata/metrics.

Examples

Construct reports.

>>> from sklearn.datasets import make_classification, make_regression
>>> from sklearn.linear_model import LinearRegression, LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from skore.sklearn import EstimatorReport
>>>
>>> X, y = make_classification(random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> classifier = LogisticRegression(max_iter=10)
>>> classifier_report = EstimatorReport(
>>>     classifier,
>>>     X_train=X_train,
>>>     y_train=y_train,
>>>     X_test=X_test,
>>>     y_test=y_test,
>>> )
>>>
>>> X, y = make_regression(random_state=42)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
>>> regressor = LinearRegression()
>>> regressor_report = EstimatorReport(
>>>     regressor,
>>>     X_train=X_train,
>>>     y_train=y_train,
>>>     X_test=X_test,
>>>     y_test=y_test,
>>> )

Construct the project in local mode, persisted in a temporary directory.

>>> from pathlib import Path
>>> from tempfile import TemporaryDirectory
>>> from skore import Project
>>>
>>> tmpdir = TemporaryDirectory().name
>>> local_project = Project("my-xp", workspace=Path(tmpdir))

Put reports in the project.

>>> local_project.put("my-simple-classification", classifier_report)
>>> local_project.put("my-simple-regression", regressor_report)

Investigate metadata/metrics to filter the best reports.

>>> summary = local_project.summarize()
>>> summary = summary.query("ml_task.str.contains('regression') and (rmse < 67)")
>>> reports = summary.reports()

static delete(name, **kwargs)[source]#

Delete a project.

Parameters:

namestr

The name of the project:

if the name takes the form of the URI hub://<tenant>/<name>, the project is configured to communicate with the skore hub,
otherwise, the project is configured to communicate with a local storage, on the user machine.

**kwargsdict

Extra keyword arguments passed to the project, depending on its mode.

workspacePath, mode:local only.

The directory where the local project is persisted.

The workspace can be shared between all the projects.
The workspace can be set using kwargs or the environment variable
SKORE_WORKSPACE.
If not, it will be by default set to a skore/ directory in the
USER cache directory:

on Windows, usually C:\Users\%USER%\AppData\Local\skore,
on Linux, usually ${HOME}/.cache/skore,
on macOS, usually ${HOME}/Library/Caches/skore.

get(id)[source]#: Get a persisted report by its id.

property mode#: The mode of the project.

property name#: The name of the project.

put(key, report)[source]#

Put a key-report pair to the project.

If the key already exists, its last report is modified to point to this new report, while keeping track of the report history.

Parameters:

keystr: The key to associate with report in the project.
reportskore.EstimatorReport: The report to associate with key in the project.

Raises:

TypeError: If the combination of parameters are not valid.

summarize()[source]#: Obtain metadata/metrics for all persisted reports.

Gallery examples#

Quick start

Skore: getting started

Project#

Gallery examples#

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