About The Package
The package is a wrapper of tensorflow data validation for our specific needs. It can analyze training data and serving data to compute desscriptive statistics, infer a schema, and detect anomalies.
Dependencies
Installation
pip install data-drift-detector
Usage
Initialize a Harvest client:
# The Dataset, TrainDataset, ServeDataset can be initialized with different methods.
train = TrainDataset.from_GCS()
train = TrainDataset.from_bigquery()
train = TrainDataset.from_dataframe()
train = TrainDataset.from_stats_file()
Populate the class variables and submit.
# Get training dataset schema
schema = train.schema_dict()
1 maintainer
Release files for data-drift-detector-mightyhive 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| data-drift-detector-mightyhive-0.0.4.tar.gz | 8.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| data_drift_detector_mightyhive-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 17.4 kB
Release files / data-drift-detector-mightyhive-0.0.4.tar.gz
| Download URL | data-drift-detector-mightyhive-0.0.4.tar.gz |
|---|---|
| Size | 8.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / data_drift_detector_mightyhive-0.0.4-py3-none-any.whl
| Download URL | data_drift_detector_mightyhive-0.0.4-py3-none-any.whl |
|---|---|
| Size | 9.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12
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