dotmesh-io / dotscience Goto Github PK

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)