PUBDEV-8547 - eif - test scoring with large data in python

h2o-py/tests/testdir_algos/isoforextended/pyunit_isoforextended_metrics_large.py

@@ -0,0 +1,31 @@
+from __future__ import print_function
+import sys, os
+sys.path.insert(1, os.path.join("..","..",".."))
+import h2o
+from tests import pyunit_utils, assert_equals
+from h2o.estimators.extended_isolation_forest import H2OExtendedIsolationForestEstimator
+
+
+def extended_isolation_forest_metrics_large():
+    print("Extended Isolation Forest Anomaly Metrics Test On Large Data")
+
+    train = h2o.import_file(pyunit_utils.locate("bigdata/laptop/creditcardfraud/creditcardfraud.csv"))
+
+    eif_model = H2OExtendedIsolationForestEstimator(ntrees=100, seed=0xBEEF, sample_size=256, extension_level=1)
+    eif_model.train(training_frame=train)
+    metrics_by_python = eif_model.predict(train).mean()
+    average_mean_length = metrics_by_python[0, 1]
+    average_anomaly_score = metrics_by_python[0, 0]
+
+    print(metrics_by_python)
+    print(eif_model)
+
+    perf = eif_model.model_performance()
+    assert_equals(perf.mean_score(), average_mean_length, "Mean score metric is not correct", 1e-3)
+    assert_equals(perf.mean_normalized_score(), average_anomaly_score, "Anomaly score metric is not correct", 1e-3)
+
+
+if __name__ == "__main__":
+    pyunit_utils.standalone_test(extended_isolation_forest_metrics_large)
+else:
+    extended_isolation_forest_metrics_large()