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Metrics for Machine Learning

Project description

ML Metrics

A simple and flexible API to log metrics. Currently a metrics logger for logging to SQLite is implemented. Other backends can be implemented as needed.


Write Logs

from mlmetrics.sqlitemetrics.sqlite_metric import SqliteMetric

db = './metrics.db'
metric = SqliteMetric(db, name='fuel_gauge', labels={'model', 'trip'})
metric.log(model='toyota', trip='short', value=1.2)

Query Logs

from mlmetrics.sqlitemetrics.sqlite_metric import SqliteMetric

db = './metrics.db'
logs = metric.logs(start=1550554038.80172, end=1550554038.80265)
for row in logs:
    for fld in row.keys():
        print(fld, row[fld])

For more details see the Homepage

Project details

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Filename, size & hash SHA256 hash help File type Python version Upload date
avilabs-ml-metrics-1.1.0.tar.gz (4.1 kB) Copy SHA256 hash SHA256 Source None

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