https://github.com/royalhaskoningdhv/sam

Python package for time series analysis and machine learning

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (18.1%) to scientific vocabulary

Keywords

anomaly-detection asset-management data-science forecasting machine-learning python time-series

Last synced: 5 months ago · JSON representation

Repository

Python package for time series analysis and machine learning

Basic Info

Host: GitHub
Owner: RoyalHaskoningDHV
License: mit
Language: Python
Default Branch: main
Homepage:
Size: 12.1 MB

Statistics

Stars: 26
Watchers: 4
Forks: 6
Open Issues: 25
Releases: 0

Topics

anomaly-detection asset-management data-science forecasting machine-learning python time-series

Created about 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Codeowners

SAM

SAM (Smart Asset Management) is a Python package for timeseries analysis, anomaly detection and forecasting.

The documentation is available on ReadTheDocs.

Author: Royal HaskoningDHV

Email: ruud.kassing@rhdhv.com

Getting started

Installation

The easiest way to install is package is using pip: pip install sam

There are different optional dependencies for SAM, if you are unsure use pip install 'sam[all]' other options include plotting (just use the plotting functionality), data_science (all dependencies needed for a data scientist) and data_engineering (dependencies for data engineer).

Keep in mind that the sam package is updated frequently, and after a while, your local version may be out of date with the online documentation. To be sure, run the pip install -U sam command to install the latest version.

Simple example

Below you can find a simple example on how to use one of our timeseries models. For more examples, check our example notebooks

```python from sam.datasets import loadrainbowbeach from sam.models import MLPTimeseriesRegressor from sam.feature_engineering import SimpleFeatureEngineer

data = loadrainbowbeach() X, y = data, data["water_temperature"]

Easily create rolling and time features to be used by the model

simplefeatures = SimpleFeatureEngineer( rollingfeatures=[ ("waveheight", "mean", 24), ("waveheight", "mean", 12), ], timefeatures=[ ("hourofday", "cyclical"), ], keeporiginal=False, )

Define your model, see the docs for all parameters

model = MLPTimeseriesRegressor( predictahead=(1, 2, 3), # Multiple predict aheads are possible quantiles=(0.025, 0.975), # Predict quantile bounds for anomaly detection featureengineer=simple_features, epochs=20, ) model.fit(X, y) ```

Configuration

A configuration file can be created as .config and should be located in your working directory. This configuration file only stores api credentials for now, but more options may be added in the future. The configuration file is parsed using the Python3 configparser, and an example configuration is shown below:

```ini [regenradar] url=https://rhdhv.lizard.net/api/v3/raster-aggregates/? user=user.name password=secret

[openweathermap] apikey=secret ```

Issue tracking and Feature Requests

Anyone can create feature requests or bug reports! You can browse and create new issues on GitHub: https://github.com/RoyalHaskoningDHV/sam/issues

Owner

Name: Royal HaskoningDHV
Login: RoyalHaskoningDHV
Kind: organization

Website: www.rhdhv.com
Repositories: 5
Profile: https://github.com/RoyalHaskoningDHV

Enhancing Society Together

GitHub Events

Total

Watch event: 2
Delete event: 3
Push event: 19
Pull request review event: 11
Pull request review comment event: 5
Pull request event: 5
Create event: 2

Last Year

Watch event: 2
Delete event: 3
Push event: 19
Pull request review event: 11
Pull request review comment event: 5
Pull request event: 5
Create event: 2

Committers

Last synced: about 2 years ago

All Time

Total Commits: 641
Total Committers: 28
Avg Commits per committer: 22.893
Development Distribution Score (DDS): 0.637

Past Year

Commits: 12
Committers: 4
Avg Commits per committer: 3.0
Development Distribution Score (DDS): 0.5

Top Committers

Name	Email	Commits
Arjan Bontsema	a**a@r**m	233
fenno	f**o@y**l	105
Arjan Bontsema	a**n@y**l	85
Ruben Peters	r**s@r**m	46
Daan van Es	d**s@y**l	46
Rutger Kerkhoff	r**r@y**l	24
Steffen Buergers	s**s@r**m	16
Daan van Es	d**s@r**m	15
Ruud Kassing	r**g@r**m	10
Sebastiaan Grasdijk	s**n@M**l	10
Tim Paauw	t**m@y**l	7
Ruben Peters	r**n@y**l	7
Arjan Bontsema	3****a	4
Tim Paauw	t**m@y**l	4
Murşit Sezen	m**n@r**m	4
Bart van der Lugt	B**t@h**l	3
Your Name	y**u@e**m	3
Loes Knoben	l**s@y**l	3
Philip Roeleveld	p**p@l**l	3
Arjan Bontsema	A**a@h**l	3
Miguel Hernandez	m**z@r**m	2
mi2354	3****4	2
Rutger Kerkhoff	r**f@r**m	1
Daan van Es	d**s@g**m	1
Steffen Bürgers	3****s	1
Daan van Es	v**s@M**e	1
miguel	m**r@g**m	1
Ruben Peters	r**1@p**m	1

Committer Domains (Top 20 + Academic)

rhdhv.com: 8 ynformed.nl: 7 mbp-van-daan.home: 1 hiemstraendevries.nl: 1 live.nl: 1 hendev.nl: 1 ynfsrv01.hendev.nl: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 39
Total pull requests: 55
Average time to close issues: 18 days
Average time to close pull requests: 5 days
Total issue authors: 4
Total pull request authors: 6
Average comments per issue: 0.49
Average comments per pull request: 0.29
Merged pull requests: 45
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 10
Average time to close issues: N/A
Average time to close pull requests: 12 days
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

sbuergers (14)
abontsema (13)
rubenpeters91 (11)
philiproeleveld (1)

Pull Request Authors

abontsema (19)
rubenpeters91 (12)
amobular (11)
sbuergers (6)
philiproeleveld (3)
mi2354 (3)

Top Labels

Issue Labels

Priority: Low (25) Type: Improvement (13) Type: Feature (9) Priority: Medium (9) Good First Issue (8) Type: Maintenance (6) Priority: High (4) Type: Bug (3) Type: Question (3) Type: Documentation (2)

Pull Request Labels

Priority: Low (8) Type: Documentation (7) Type: Improvement (7) Type: Feature (6) Priority: High (6) Priority: Medium (5) Type: Bug (5) Type: Maintenance (4)

Packages

Total packages: 1
Total downloads:
- pypi 1,584 last-month

Total dependent packages: 1
Total dependent repositories: 7
Total versions: 32
Total maintainers: 5

pypi.org: sam

Time series anomaly detection and forecasting

Documentation: https://sam.readthedocs.io/
License: MIT
Latest release: 5.1.0
published 6 months ago

Versions: 32
Dependent Packages: 1
Dependent Repositories: 7
Downloads: 1,584 Last month

Rankings

Dependent repos count: 5.6%

Downloads: 5.8%

Average: 7.1%

Dependent packages count: 10.0%

Maintainers (5)

rkassing rubenpeters91 sbuergers plucarellirhdhv MigPer

Last synced: 6 months ago

Dependencies

.github/workflows/linting.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite

.github/workflows/python-publish.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite
pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite

.github/workflows/unittest.yml actions

actions/checkout v3 composite
actions/setup-python v3 composite
codecov/codecov-action v3 composite

pyproject.toml pypi

numpy >=1.22,<1.24
pandas ~=1.3
scikit-learn ~=1.1,<1.3

https://github.com/royalhaskoningdhv/sam

Science Score: 26.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

SAM

Getting started

Installation

Simple example

Easily create rolling and time features to be used by the model

Define your model, see the docs for all parameters

Configuration

Issue tracking and Feature Requests

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: sam

Rankings

Maintainers (5)

Dependencies