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# with X_train, X_test, Y_train, Y_test | |
import numpy as np | |
from sklearn_pandas import DataFrameMapper | |
from sklearn2pmml import PMMLPipeline, sklearn2pmml | |
from sklearn.linear_model import LogisticRegression | |
from sklearn.preprocessing import FunctionTransformer | |
clf = PMMLPipeline([ | |
("mapper", DataFrameMapper([ | |
(['mass'], FunctionTransformer(np.log1p)), |
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# with X_train, X_test, Y_train, Y_test | |
from sklearn.linear_model import LogisticRegression | |
clf = LogisticRegression() | |
clf.fit(X_train, Y_train) | |
print(clf.score(X_test, Y_test)) | |
import json | |
with open('logreg_coefs', 'w') as f: | |
json.dump(clf.coef_.tolist(), f) |
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from pandas import read_csv | |
from sklearn.model_selection import train_test_split | |
url = "https://raw.githubusercontent.com/baatout/ml-in-prod/master/pima-indians-diabetes.csv" | |
features = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age'] | |
label = 'label' | |
dataframe = read_csv(url, names=features + [label]) | |
X = dataframe[features] | |
Y = dataframe[label] | |
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42) |
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