PyPI Stats
google-research

spade-anomaly-detection

Semi-supervised Pseudo Labeler Anomaly Detection with Ensembling (SPADE) is a semi-supervised anomaly detection method that uses an ensemble of one class classifiers as the pseudo-labelers and supervised classifiers to achieve state of the art results especially on datasets with distribution mismatch between labeled and unlabeled samples.
PyPI GitHub
Downloads
Day
424
Week
544
Month
890
Stars
52
Forks
3
Issues
0
Version
0.4.0

Package Insights

What's this?
Score breakdown - each component scores 0–100, weighted by percentage
Maintenance
60
Popularity
30
Adoption
29
Community
10
How the maintenance status was determined
Last commit
65 days ago
Monthly downloads
890
Active
Activity within 30 days
• Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
890
Stars
52
Stars per 1k downloads
58.43
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
• Unknown
Fewer than 1,000 monthly downloads, or no star data
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
78
Month daily avg
30
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
Python version support tiers
requires_python
>=3.8
Cutting Edge
Minimum supported version is 3.12 or newer
Modern
Minimum supported version is 3.9, 3.10, or 3.11
• Legacy
Supports 3.8 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
Apache Software License
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
468 days ago
Total releases
9
Releases/year
4.5
Rapid
Last release within 365 days and > 12 releases/year
Regular
Last release within 365 days
• Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
3
Monthly downloads
890
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
25
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
100.0%
contrib / forks
Stale Release
402d
 
PR Velocity
Active
 

Weekly Downloads

GitHub Stars

Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

No OS data available

Python Versions

No Python version data available

Top Countries

1. CA CA
1

Dependencies

17
4 optional dependencies

Used By

0
Not used by any tracked packages

Package Information

License
Apache Software License
First Release
May 4, 2024
Last Release
1 year ago
Last Commit
2 months ago
Latest Version
0.4.0
Total Releases
9
Contributors
3
Open PRs
2
Archived
No
Wheels
py3
Keywords
anomaly detection ensemble model one class classifiers
Topics
anomaly-detection-algorithm one-class-classification semi-supervised ensemble-model
Tracking Since
January 21, 2026
Data Updated
May 3, 2026
Data Freshness
Fresh

Links