| Name | time-split-app JSON |
| Version |
0.7.2
JSON |
| download |
| home_page | None |
| Summary | Companion app for the `time-split` library. |
| upload_time | 2025-07-17 10:17:37 |
| maintainer | None |
| docs_url | None |
| author | Richard Sundqvist |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Time Split <!-- omit in toc -->
Time-based k-fold validation splits for heterogeneous data.
-----------------
[](https://pypi.python.org/pypi/time-split)
[](https://pypi.python.org/pypi/time-split)
[](https://github.com/rsundqvist/time-split/actions?workflow=tests)
[](https://codecov.io/gh/rsundqvist/time-split)
[](https://time-split.readthedocs.io/)
[](https://pypi.python.org/pypi/time-split)
[](https://hub.docker.com/r/rsundqvist/time-split/)
<div align="center">
<img alt="Plotted folds on a two-by-two grid."
title="Examples" height="300" width="1200"
src="https://raw.githubusercontent.com/rsundqvist/time-split/master/docs/2x2-examples.jpg"><br>
</div>
Folds plotted on a two-by-two grid. See the
[examples](https://time-split.readthedocs.io/en/stable/auto_examples/index.html) page for more.
# About this image
The **Time Split** application
(available [here](https://time-split.streamlit.app/?data=1554942900-1557610200&schedule=0+0+%2A+%2A+MON%2CFRI&n_splits=2&step=2&show_removed=True))
is designed to help evaluate the effects of different
[parameters](https://time-split.readthedocs.io/en/stable/#parameter-overview).
To start it locally, run
```sh
docker run -p 8501:8501 rsundqvist/time-split
```
or
```bash
pip install time-split[app]
python -m time_split app start
```
in the terminal. You may use
[`create_explorer_link()`](https://time-split.readthedocs.io/en/stable/api/time_split.app.html#time_split.app.create_explorer_link)
to build application URLs with preselected splitting parameters.
# Documentation
Click [here](https://time-split.readthedocs.io/en/stable/api/time_split.app.reexport.html) 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:
```Dockerfile
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.
# Custom datasets
To bundle datasets, mount a configuration file (determined by
[`DATASETS_CONFIG_PATH='/home/streamlit/datasets.toml'`](https://time-split.readthedocs.io/en/stable/generated/time_split.streamlit.config.html#time_split.streamlit.config.DATASETS_CONFIG_PATH)
). The `DatasetConfig` struct has 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 `đ 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. |
[pandas.to_datetime()]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html
The read function is chosen automatically based on the path.
> âšī¸ Additional dependencies are required for remote filesystems.
> You may use `EXTRA_PIP_PACKAGES=s3fs` to install dependencies for the S3 paths used below.
```toml
[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.
## Mounted datasets
A convenient way to keep datasets up-to-date without relying on network storage is to mount a dataset folder on a local
machine, using e.g. a CRON job to update the data. To start the image with datasets mounted, run:
```bash
docker run \
-p 8501:8501 \
-v ./data:/home/streamlit/data:ro \
-v ./datasets.toml:/home/streamlit/datasets.toml:ro \
-e REQUIRE_DATASETS=true \
rsundqvist/time-split
```
in the terminal. The [tomli-w](https://pypi.org/project/tomli-w/) package may be used to emit TOML files if using Python.
* 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](src/time_split_app/config.py) for configurable values. Use `true|false` for boolean variables.
Documentation for the underlying framework (Streamlit) is available
[here](https://docs.streamlit.io/develop/concepts/configuration/options/).
## User choice
Users may *lower* some configured values by using the Performance tweaker widget in the `â About tab` of application. To
set a lower default, add a `DEFAULT_`-prefix to the regular name.
```bash
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.
Raw data
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"description": "# Time Split <!-- omit in toc -->\nTime-based k-fold validation splits for heterogeneous data.\n\n-----------------\n[](https://pypi.python.org/pypi/time-split)\n[](https://pypi.python.org/pypi/time-split)\n[](https://github.com/rsundqvist/time-split/actions?workflow=tests)\n[](https://codecov.io/gh/rsundqvist/time-split)\n[](https://time-split.readthedocs.io/)\n[](https://pypi.python.org/pypi/time-split)\n[](https://hub.docker.com/r/rsundqvist/time-split/)\n\n<div align=\"center\">\n <img alt=\"Plotted folds on a two-by-two grid.\" \n title=\"Examples\" height=\"300\" width=\"1200\" \n src=\"https://raw.githubusercontent.com/rsundqvist/time-split/master/docs/2x2-examples.jpg\"><br>\n</div>\n\nFolds plotted on a two-by-two grid. See the\n[examples](https://time-split.readthedocs.io/en/stable/auto_examples/index.html) page for more.\n\n# About this image\n\nThe **Time Split** application\n(available [here](https://time-split.streamlit.app/?data=1554942900-1557610200&schedule=0+0+%2A+%2A+MON%2CFRI&n_splits=2&step=2&show_removed=True))\nis designed to help evaluate the effects of different\n[parameters](https://time-split.readthedocs.io/en/stable/#parameter-overview).\nTo start it locally, run\n```sh\ndocker run -p 8501:8501 rsundqvist/time-split\n```\nor \n```bash\npip install time-split[app]\npython -m time_split app start\n```\nin the terminal. You may use\n[`create_explorer_link()`](https://time-split.readthedocs.io/en/stable/api/time_split.app.html#time_split.app.create_explorer_link)\nto build application URLs with preselected splitting parameters.\n\n# Documentation\nClick [here](https://time-split.readthedocs.io/en/stable/api/time_split.app.reexport.html) for documentation of the most\nimportant types, functions and classes used by the application.\n\n# Custom dataset loaders\nDataset loaders are a flexible way to load or create datasets that requires user input. The existing images (`>=0.7.0`)\ncan be extended to use custom loaders:\n\n```Dockerfile\nFROM python:3.13\n\nRUN pip install --no-cache --compile time-split[app]\nRUN pip install --no-cache --compile your-dependencies\n\nENV DATASET_LOADER=custom_dataset_loader:CustomDatasetLoader\nCOPY custom_dataset_loader.py .\n\n# Entrypoint etc.\n```\n\nLoaders must implement the `DataLoaderWidget` interface.\n\n# Custom datasets\nTo bundle datasets, mount a configuration file (determined by \n[`DATASETS_CONFIG_PATH='/home/streamlit/datasets.toml'`](https://time-split.readthedocs.io/en/stable/generated/time_split.streamlit.config.html#time_split.streamlit.config.DATASETS_CONFIG_PATH)\n). The `DatasetConfig` struct has the following keys:\n\n| Key | Type | Required | Description |\n|------------------------|------------------|----------|-------------------------------------------------------------------------------|\n| `label` | `string` | | Name shown in the UI. Defaults to section header (i.e. *\"my-dataset\"* below). |\n| `path` | `string` | Required | First argument to the `pandas` read function. |\n| `index` | `string` | Required | Datetime-like column. Will be converted using [pandas.to_datetime()]. |\n| `aggregations` | `dict[str, str]` | | Determines function to use in the `\ud83d\udcc8 Aggregations per fold` tab. |\n| `description` | `string` | | Markdown. The first line will be used as the summary in the UI. |\n| `read_function_kwargs` | `dict[str, Any]` | | Keyword arguments for the `pandas` read function used. |\n\n[pandas.to_datetime()]: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html\n\nThe read function is chosen automatically based on the path.\n\n> \u2139\ufe0f Additional dependencies are required for remote filesystems.\n> You may use `EXTRA_PIP_PACKAGES=s3fs` to install dependencies for the S3 paths used below.\n\n```toml\n[my-dataset]\nlabel = \"Dataset name\"\npath = \"s3://my-bucket/data/title_basics.csv\"\nindex = \"from\"\naggregations = { runtimeMinutes = \"min\", isAdult = \"mean\" }\ndescription = \"\"\"This is the summary.\n\nSimplified version of the\n[Title basics](https://developer.imdb.com/non-commercial-datasets/#titlebasicstsvgz) IMDB\ndataset. The description supports Markdown syntax.\n\nLast updated: `2019-05-11T20:30:00+00:00'\n\"\"\"\n[my-dataset.read_function_kwargs]\n# Valid options depend on the read function used (pandas.read_csv, in this case).\n```\n\nMultiple datasets may be configured in their own top-level sections. Labels must be unique.\n\n## Mounted datasets\nA convenient way to keep datasets up-to-date without relying on network storage is to mount a dataset folder on a local\nmachine, using e.g. a CRON job to update the data. To start the image with datasets mounted, run:\n```bash\ndocker run \\\n -p 8501:8501 \\\n -v ./data:/home/streamlit/data:ro \\\n -v ./datasets.toml:/home/streamlit/datasets.toml:ro \\\n -e REQUIRE_DATASETS=true \\\n rsundqvist/time-split\n```\nin the terminal. The [tomli-w](https://pypi.org/project/tomli-w/) package may be used to emit TOML files if using Python.\n\n* The dataframes returned by the dataset loader are cached for `config.DATASET_CACHE_TTL` seconds (default = 12 hours).\n* The dataset configuration file is read every `config.DATASET_CONFIG_CACHE_TTL` seconds (default = 30 seconds).\n\nAll datasets are reloaded immediately if the configuration changes, ignoring comments and formatting.\n\n# Environment variables\nSee [config.py](src/time_split_app/config.py) for configurable values. Use `true|false` for boolean variables. \nDocumentation for the underlying framework (Streamlit) is available \n[here](https://docs.streamlit.io/develop/concepts/configuration/options/).\n\n## User choice\nUsers may *lower* some configured values by using the Performance tweaker widget in the `\u2754 About tab` of application. To \nset a lower default, add a `DEFAULT_`-prefix to the regular name.\n```bash\nPLOT_AGGREGATIONS_PER_FOLD=true\nDEFAULT_PLOT_AGGREGATIONS_PER_FOLD=false\n```\nThis will disable the (expensive) per-column fold aggregation figures, but users who need them can turn them back on.\n\n",
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