def build_iforest_housing_anomaly(iforest, name):
mapper = DataFrameMapper([(housing_X.columns.values, ContinuousDomain())])
pipeline = PMMLPipeline([("mapper", mapper), ("estimator", iforest)])
pipeline.fit(housing_X)
store_pkl(pipeline, name + ".pkl")
decisionFunction = DataFrame(pipeline.decision_function(housing_X),
columns=["decisionFunction"])
outlier = DataFrame(pipeline.predict(housing_X) == -1,
columns=["outlier"
]).replace(True,
"true").replace(False, "false")
store_csv(pandas.concat([decisionFunction, outlier], axis=1),
name + ".csv")
def build_svm_housing_anomaly(svm, name):
mapper = DataFrameMapper([(housing_columns[:-1], ContinuousDomain())])
pipeline = PMMLPipeline([("mapper", mapper),
("estimator",
Pipeline([("first", MaxAbsScaler()),
("second", svm)]))])
pipeline.fit(housing_X)
store_pkl(pipeline, name + ".pkl")
decisionFunction = DataFrame(pipeline.decision_function(housing_X),
columns=["decisionFunction"])
outlier = DataFrame(pipeline.predict(housing_X) <= 0,
columns=["outlier"
]).replace(True,
"true").replace(False, "false")
store_csv(pandas.concat([decisionFunction, outlier], axis=1),
name + ".csv")
