time-split-app 1.0.0

Companion app for the `time-split` library.

Time Split

Time-based k-fold validation splits for heterogeneous data.

---

Folds plotted on a two-by-two grid. See the examples page for more.

About this image

The Time Split application (available here) is designed to help evaluate the effects of different parameters. To start it locally, run

docker run -p 8501:8501 rsundqvist/time-split

or

pip install time-split[app]
python -m time_split app start

in the terminal. You may use create_explorer_link() to build application URLs with preselected splitting parameters.

Documentation

Click here for documentation of the most important types, functions and classes used by the application.

Custom dataset loaders

Dataset loaders are a flexible way to load or create datasets that requires user input. The existing images (>=0.7.0) can be extended to use custom loaders:

FROM python:3.13

RUN pip install --no-cache --compile time-split[app]
RUN pip install --no-cache --compile your-dependencies

ENV DATASET_LOADER=custom_dataset_loader:CustomDatasetLoader
COPY custom_dataset_loader.py .

# Entrypoint etc.

Loaders must implement the DataLoaderWidget interface. You may use

python -m time_split app new

to create a template project to get you started.

Custom datasets

To bundle datasets, specify a configuration file (e.g. DATASETS_CONFIG_PATH='s3://my-bucket/data/datasets.toml') with the following keys:

Key	Type	Required	Description
label	string		Name shown in the UI. Defaults to section header (i.e. "my-dataset" below).
path	string	Required	First argument to the pandas read function.
index	string	Required	Datetime-like column. Will be converted using pandas.to_datetime().
aggregations	dict[str, str]		Determines function to use in the 📈 Aggregations per fold tab.
description	string		Markdown. The first line will be used as the summary in the UI.
read_function_kwargs	dict[str, Any]		Keyword arguments for the pandas read function used.

ℹ️ The read function is chosen automatically based on the path.

ℹ️ Additional dependencies are required for remote filesystems. You may use EXTRA_PIP_PACKAGES=s3fs to install dependencies for the S3 paths used below.

See the DatasetConfig class for internal representation.

[my-dataset]
label = "Dataset name"
path = "s3://my-bucket/data/title_basics.csv"
index = "from"
aggregations = { runtimeMinutes = "min", isAdult = "mean" }
description = """This is the summary.

Simplified version of the
[Title basics](https://developer.imdb.com/non-commercial-datasets/#titlebasicstsvgz) IMDB
dataset. The description supports Markdown syntax.

Last updated: `2019-05-11T20:30:00+00:00'
"""
[my-dataset.read_function_kwargs]
# Valid options depend on the read function used (pandas.read_csv, in this case).

Multiple datasets may be configured in their own top-level sections. Labels must be unique.

Updating datasets

Datasets may be updated while the app is running. This is best done by changing the datasets config TOML file (e.g. by) writing a timestamp, as above.

Default timings:

• The dataframes returned by the dataset loader are cached for config.DATASET_CACHE_TTL seconds (default = 12 hours).
• The dataset configuration file is read every config.DATASET_CONFIG_CACHE_TTL seconds (default = 30 seconds).

All datasets are reloaded immediately if the configuration changes, ignoring comments and formatting.

Environment variables

See config.py for configurable values.

User choice

Users may lower some configured values by using the Performance tweaker widget in the ❔ About tab of application. To set a lower default, add a DEFAULT_-prefix to the regular name.

PLOT_AGGREGATIONS_PER_FOLD=true
DEFAULT_PLOT_AGGREGATIONS_PER_FOLD=false

This will disable the (expensive) per-column fold aggregation figures, but users who need them can turn them back on.