GLM Model | BYOM | Teradata Vantage - GLM Model - Teradata Vantage

This Python pipeline creates a PMML Generalized Linear Model (GLM). Scikit-learn APIs fetch the data from Teradata Vantage. Therefore, this pipeline must specify the connection, predictors, and variables.

from sklearn2pmml import sklearn2pmml
from sklearn2pmml.pipeline import PMMLPipeline
from sklearn.linear_model import LogisticRegression
import os
import time
from teradataml import *

display.print_sqlmr_query = True
passwd = "alice"
uid = "alice"

con = create_context(host="server123@mydomain.com", username=uid, password=passwd)
con

train_df = DataFrame.from_query("select * from iris_train")
traid_pd = train_df.to_pandas()
traid_pd
type(traid_pd)

X = traid_pd[['sepal_length','sepal_width','petal_length', 'petal_width' ]]
y=traid_pd[['species']]

#pipeline
pipeline = PMMLPipeline([
     ("classifier", LogisticRegression(random_state=0))
     ])

pipeline.fit(X,y)
sklearn2pmml(pipeline,"../sql/iris_db_glm_model.pmml",with_repr=True)