Slide 27
Slide 27 text
spawns
Python interface for Embedded Python Execution
Example of parallel partitioned data flow using third party package
# user-defined function using sklearn
def score_mod(dat):
import pandas as pd
import oml
obj_dict = oml.ds.load(name="ds_regr",to_globals=False)
regr = obj_dict["regr"]
pred = regr.predict(dat[['SEPAL_LENGTH]])
return
pd.concat([dat[['SPECIES','PETAL_WIDTH']],
pd.DataFrame(pred,
columns=['Pred_PETAL_WIDTH'])],
axis=1)
# invoke function in parallel on IRIS table
pred = oml.row_apply(IRIS,
score_mod,
rows=10,
parallel=True,
func_value=pd.DataFrame([('a', 1, 1)],
columns=['SPECIES', 'PETAL_LENGTH','PRED_PETAL_LENGTH']))
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