Add remove offset effect workaround

h2o-py/tests/testdir_algos/glm/pyunit_remove_offset_glm.py

@@ -0,0 +1,47 @@
+from builtins import range
+import sys
+sys.path.insert(1,"../../../")
+import h2o
+from tests import pyunit_utils
+from h2o.estimators.glm import H2OGeneralizedLinearEstimator
+
+
+def remove_offset_glm():
+
+    cars = h2o.upload_file(pyunit_utils.locate("smalldata/junit/cars_20mpg.csv"))
+    cars = cars[cars["economy_20mpg"].isna() == 0]
+    cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()
+
+    offset_col = "offset"
+    offset = h2o.H2OFrame([[.5]]*398)
+    offset.set_names([offset_col])
+    cars = cars.cbind(offset)
+
+    # offset_column passed in the train method
+    glm_model = H2OGeneralizedLinearEstimator(family="binomial")
+    glm_model.train(x=list(range(2,8)),y="economy_20mpg", training_frame=cars, offset_column=offset_col)
+
+    # predict with offset
+    predictions_train = glm_model.predict(cars).as_data_frame()
+    print(predictions_train)
+
+    # metrics with offset
+    perf = glm_model.model_performance(cars)
+    print(perf)
+
+    # setup offset column to zero to remove its effect
+    cars[offset_col] = 0
+
+    # predict with offset effect removed
+    predictions_train_remove_offset = glm_model.predict(cars).as_data_frame()
+    print(predictions_train_remove_offset)
+
+    # metrics with offset effect removed
+    perf = glm_model.model_performance(cars)
+    print(perf)
+
+
+if __name__ == "__main__":
+    pyunit_utils.standalone_test(remove_offset_glm)
+else:
+    remove_offset_glm()