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dotscience Python library
We want to make it easier for people to emit the DOTSCIENCE_
annotations, and the print
statements have the following problems:
json.dumps
isn't obvious to a data scientist.Develop a trivial Python library and publish it on PyPI which solves the above problems. Usage should be like:
import dotscience as ds
# The following two methods throws exceptions if agent1/2 or model isn't a mountpoint
ds.input("agent1", "agent2")
ds.output("model")
# The following methods can either copy the values at call time, or keep the
# reference for completion - probably a copy is better as the user will probably
# expect the value _right now_ to be captured.
ds.metric("f-score", f_score)
ds.parameter("batch-size", batch_size)
ds.label("frobrinator", "off")
# They also return the result for handy use like this:
tensorflow.setBatchSize(ds.parameter("batch-size", 0.3))
# Multiple stats, params or labels can be passed as long as there are 2*x params
ds.metric("f-score", f_score, "batch_size", batch_size)
# Alternate calling style with **kwargs
ds.metric(a=1, b=2)
ds.parameter(c=3, d=4)
# Preview the metrics in human-readable form without publishing them to
# dotscience even if the notebook is saved
ds.debug()
# Report the metrics
ds.report()
# Report data changes, but no metrics (summary stats)
ds.report(plot=False)
The final method will print:
---
DOTSCIENCE_INPUTS=["agent1", "agent2"]
DOTSCIENCE_OUTPUTS=["model"]
DOTSCIENCE_SUMMARY={"f-score": 0.1, "batch-size": 0.9}
DOTSCIENCE_PARAMETERS={"c": 3, "d": 4}
---
Note to Jupyter users: don't forget to save your notebook in order to publish
these results to dotscience.
metric
or summary
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