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nkak pushed a commit to branch madlib2-master
in repository https://gitbox.apache.org/repos/asf/madlib.git
commit 0c1cd4ff9387eb563fecacb0a5d4bb2fdc96ce16
Author: Nikhil Kak <n...@vmware.com>
AuthorDate: Thu Feb 22 16:05:43 2024 -0800
PMML: Add tests for intercept acting as a predictor
JIRA: MADLIB-1517
A previous commit 0cd28f9733927d63beaefc9488db7f8bfdb3bd80 made changes to
the
pmml code so that the intercept won't be used as a predictor. But it's still
possible that this assumption may not be true in some scenarios and the
intercept might still be treated as a predictor in the pmml.
For e.g. consider this scenario:
While using any of the regression algorithms, user passes the independent
variable as "ARRAY[x1,1,x2] or ARRAY[x1,x2,1]" instead of "ARRAY[1,x1,x2]"
In this scenario, the pmml code will assume that there isn't a intercept in
this expression and will treat "1" as a predictor.
When predicting using this pmml, users will need to create a column/field
named
"1" which has the value 1 for each data row. The test added in this commit
mimics this scenario
---
.../pmml/test/pmml_intercept_as_predictor.sql_in | 84 ++++++++++++++++++++++
1 file changed, 84 insertions(+)
diff --git
a/src/ports/postgres/modules/pmml/test/pmml_intercept_as_predictor.sql_in
b/src/ports/postgres/modules/pmml/test/pmml_intercept_as_predictor.sql_in
new file mode 100644
index 00000000..dc0e28bd
--- /dev/null
+++ b/src/ports/postgres/modules/pmml/test/pmml_intercept_as_predictor.sql_in
@@ -0,0 +1,84 @@
+/* -----------------------------------------------------------------------
*//**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ *
+ *//* -----------------------------------------------------------------------
*/
+
+ \i m4_regexp(MADLIB_LIBRARY_PATH, `\(.*\)/lib',
+ `\1/../modules/pmml/test/pmml.setup.sql_in'
+)
+
+m4_changequote(`<!'', `!>'')
+
+------------------ This file will test scenarios when the intercept might
still be treated as a predictor in the pmml --------------------
+
+-------------- intercept expression "1" not being the first value in the array
-------------------
+
+-- logistic
+DROP TABLE IF EXISTS logregr_model, logregr_model_summary;
+SELECT logregr_train(
+ '"Patients"',
+ 'logregr_model',
+ '"Second_attack"',
+ 'ARRAY["Treatment", 1, trait_anxiety]',
+ NULL,
+ 20,
+ 'irls'
+);
+CREATE TABLE patients_with_1 as SELECT 1 as "1", * from "Patients";
+
+DROP TABLE IF EXISTS logregr_predict_output; CREATE TABLE
logregr_predict_output as SELECT id, logregr_predict(coef, ARRAY["Treatment",
1, trait_anxiety])
+FROM logregr_model, "Patients";
+SELECT test_pmml_output('patients_with_1', 'logregr_model',
'logregr_predict_output', 'id', 'logregr_predict',
'predicted_Second_attack_pmml_prediction');
+
+DROP TABLE IF EXISTS logregr_predict_output; CREATE TABLE
logregr_predict_output as SELECT id, logregr_predict_prob(coef,
ARRAY["Treatment", 1, trait_anxiety])
+FROM logregr_model, "Patients";
+SELECT test_pmml_output('patients_with_1', 'logregr_model',
'logregr_predict_output', 'id', 'logregr_predict_prob', 'probability_true');
+
+DROP TABLE IF EXISTS logregr_model, logregr_model_summary;
+SELECT logregr_train(
+ '"Patients"',
+ 'logregr_model',
+ '"Second_attack"',
+ 'ARRAY["Treatment", trait_anxiety, 1]',
+ NULL,
+ 20,
+ 'irls'
+);
+
+DROP TABLE IF EXISTS logregr_predict_output; CREATE TABLE
logregr_predict_output as SELECT id, logregr_predict(coef, ARRAY["Treatment",
trait_anxiety, 1])
+FROM logregr_model, "Patients";
+SELECT test_pmml_output('patients_with_1', 'logregr_model',
'logregr_predict_output', 'id', 'logregr_predict',
'predicted_Second_attack_pmml_prediction');
+
+DROP TABLE IF EXISTS logregr_predict_output; CREATE TABLE
logregr_predict_output as SELECT id, logregr_predict_prob(coef,
ARRAY["Treatment", trait_anxiety, 1])
+FROM logregr_model, "Patients";
+SELECT test_pmml_output('patients_with_1', 'logregr_model',
'logregr_predict_output', 'id', 'logregr_predict_prob', 'probability_true');
+
+---- glm -----
+DROP TABLE IF EXISTS glm_model, glm_model_summary;
+SELECT glm(
+ 'abalone',
+ 'glm_model',
+ 'rings',
+ 'ARRAY[1.0, length, diameter, height, whole, shucked, viscera, shell]',
+ 'family=gaussian, link=identity', NULL, 'max_iter=1000, tolerance=1e-16'
+);
+CREATE TABLE abalone_with_1 AS SELECT 1, * FROM abalone;
+DROP TABLE IF EXISTS glm_predict_out; CREATE TABLE glm_predict_out as SELECT
id, glm_predict(coef, ARRAY[1, length, diameter, height, whole, shucked,
viscera, shell], 'identity')
+FROM glm_model, abalone_with_1;
+SELECT test_pmml_output('abalone_with_1', 'glm_model', 'glm_predict_out',
'id', 'glm_predict', 'predicted_rings_pmml_prediction');
