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A data drift detection and schema validation package

Project description

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()

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data-drift-detector-mightyhive-0.0.4.tar.gz (8.0 kB view hashes)

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