Add catOnly test for coxPH

Author: Mathanraj-Sharma

h2o-genmodel/src/test/java/hex/genmodel/algos/coxph/CoxPHMojoModelTest.java

@@ -39,11 +39,15 @@ public void testForOneCategory() {
         assertEquals(0.6, mojo.forOneCategory(row, 1, 0), 0);
     }
 
+    /*
+        Test backward compatibility of CoxPH mojo model trained in 3.32.1.3.
+        Only using all features (both categorical and numeric). 
+    */
     @Test
-    public void testCoxPHBackwardCompatibility() throws Exception {
+    public void testCoxPHBackwardCompatibilityAll332() throws Exception {
         String mojofile = String.valueOf(
             Paths.get(
-                CoxPHMojoModelTest.class.getClassLoader().getResource("hex/genmodel/algos/coxph/CoxPH_bc.zip").toURI()
+                CoxPHMojoModelTest.class.getClassLoader().getResource("hex/genmodel/algos/coxph/CoxPH_bc_all_3_32.zip").toURI()
             ).toFile()
         );
 
@@ -81,4 +85,117 @@ public void testCoxPHBackwardCompatibility() throws Exception {
         assertArrayEquals(expected, preds, 0.000001);
     }
 
+    /*
+        Test backward compatibility of CoxPH mojo model trained in 3.42.0.4.
+        Only using all features (both categorical and numeric). 
+    */
+    @Test
+    public void testCoxPHBackwardCompatibilityAll342() throws Exception {
+        String mojofile = String.valueOf(
+            Paths.get(
+                CoxPHMojoModelTest.class.getClassLoader().getResource("hex/genmodel/algos/coxph/CoxPH_bc_all_3_42.zip").toURI()
+            ).toFile()
+        );
+
+        EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config()
+                .setModel(MojoModel.load(mojofile));
+        EasyPredictModelWrapper model = new EasyPredictModelWrapper(config);
+
+        String [][] inputs = new String[][] {
+            {"0", "50", "1", "-17.1553730321697", "0.123203285420945","0",
+                    "0", "1", "-2.3004572363122033", "-23.840464872142775", "c0.l2", "c1.l2"},
+            {"0", "6", "1", "3.83572895277207", "0.254620123203285", "0",
+                    "0", "2", "33.01605679101838", "-12.944002705270874" ,"c0.l1","c1.l2"},
+            {"0", "1", "0", "6.29705681040383", "0.265571526351814", "0",
+                    "0", "3", "-22.54601829241052", "77.61631885563669", "c0.l2", "c1.l0"}
+        };
+        double [] expected = {0.0902431, 0.422815, 0.663896};
+        double[] preds = new double[inputs.length];
+        for (int i = 0; i < inputs.length; i++) {
+            RowData row = new RowData();
+            row.put("start", inputs[i][0]);
+            row.put("stop", inputs[i][1]);
+            row.put("event", inputs[i][2]);
+            row.put("age", inputs[i][3]);
+            row.put("year", inputs[i][4]);
+            row.put("surgery", inputs[i][5]);
+            row.put("transplant", inputs[i][6]);
+            row.put("id", inputs[i][7]);
+            row.put("C1", inputs[i][8]);
+            row.put("C2", inputs[i][9]);
+            row.put("C3", inputs[i][10]);
+            row.put("C4", inputs[i][11]);
+            preds[i] = model.predictCoxPH(row).value;
+        }
+
+        assertArrayEquals(expected, preds, 0.000001);
+    }
+
+    /*
+        Test backward compatibility of CoxPH mojo model trained in 3.32.1.3.
+        Only using categorical features. 
+    */
+    @Test
+    public void testCoxPHBackwardCompatibilityCatOnly332() throws Exception {
+        String mojofile = String.valueOf(
+            Paths.get(
+                CoxPHMojoModelTest.class.getClassLoader().getResource("hex/genmodel/algos/coxph/CoxPH_bc_catOnly_3_32.zip").toURI()
+            ).toFile()
+        );
+
+        EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config()
+                .setModel(MojoModel.load(mojofile));
+        EasyPredictModelWrapper model = new EasyPredictModelWrapper(config);
+
+        String [][] inputs = new String[][] {
+            {"0", "0"},
+            {"0", "1"},
+            {"1", "0"},
+            {"1", "1"},
+        };
+        double [] expected = {0.0628001, 0.221134, -0.686394, -0.528061};
+        double[] preds = new double[inputs.length];
+        for (int i = 0; i < inputs.length; i++) {
+            RowData row = new RowData();
+            row.put("surgery", inputs[i][0]);
+            row.put("transplant", inputs[i][1]);
+            preds[i] = model.predictCoxPH(row).value;
+        }
+
+        assertArrayEquals(expected, preds, 0.000001);
+    }
+    
+    /*
+        Test backward compatibility of CoxPH mojo model trained in 3.42.0.4.
+        Only using categorical features. 
+    */
+    @Test
+    public void testCoxPHBackwardCompatibilityCatOnly342() throws Exception {
+        String mojofile = String.valueOf(
+            Paths.get(
+                CoxPHMojoModelTest.class.getClassLoader().getResource("hex/genmodel/algos/coxph/CoxPH_bc_catOnly_3_42.zip").toURI()
+            ).toFile()
+        );
+
+        EasyPredictModelWrapper.Config config = new EasyPredictModelWrapper.Config()
+                .setModel(MojoModel.load(mojofile));
+        EasyPredictModelWrapper model = new EasyPredictModelWrapper(config);
+
+        String [][] inputs = new String[][] {
+            {"0", "0"},
+            {"0", "1"},
+            {"1", "0"},
+            {"1", "1"},
+        };
+        double [] expected = {0.0628001, 0.221134, -0.686394, -0.528061};
+        double[] preds = new double[inputs.length];
+        for (int i = 0; i < inputs.length; i++) {
+            RowData row = new RowData();
+            row.put("surgery", inputs[i][0]);
+            row.put("transplant", inputs[i][1]);
+            preds[i] = model.predictCoxPH(row).value;
+        }
+
+        assertArrayEquals(expected, preds, 0.000001);
+    }
 }