ht/spacing update

h2oai · Jan 18, 2024 · 195bc71 · 195bc71
1 parent 9f1e7f5
commit 195bc71
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Showing 2 changed files with 74 additions and 20 deletions.
diff --git a/h2o-bindings/bin/custom/python/gen_modelselection.py b/h2o-bindings/bin/custom/python/gen_modelselection.py
@@ -51,7 +51,9 @@ def coef_norm(self, predictor_size=None):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -119,7 +121,9 @@ def coef(self, predictor_size=None):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -265,7 +269,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", build_glm_model=False)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        build_glm_model=False)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -279,7 +286,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", influence="dfbetas")
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        influence="dfbetas")
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -293,7 +303,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", multinode_mode=False)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        multinode_mode=False)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -307,7 +320,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", nparallelism=0)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        nparallelism=0)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -321,7 +337,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", p_values_threshold=0.0)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        p_values_threshold=0.0)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -335,7 +354,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", early_stopping=False)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        early_stopping=False)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -349,7 +371,10 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", obj_reg=-1.0)
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr",
+...                                        obj_reg=-1.0)
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)
@@ -363,7 +388,9 @@ def get_best_model_predictors(self):
 >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
 >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
 >>> response = "GLEASON"
->>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+>>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+...                                        seed=12345,
+...                                        mode="maxr")
 >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
 >>> results = maxrModel.result()
 >>> print(results)

diff --git a/h2o-py/h2o/estimators/model_selection.py b/h2o-py/h2o/estimators/model_selection.py
@@ -1007,7 +1007,10 @@ def obj_reg(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", obj_reg=-1.0)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        obj_reg=-1.0)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1154,7 +1157,10 @@ def custom_metric_func(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", early_stopping=False)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        early_stopping=False)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1183,7 +1189,10 @@ def nparallelism(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", nparallelism=0)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        nparallelism=0)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1244,7 +1253,9 @@ def mode(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1276,7 +1287,10 @@ def build_glm_model(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", build_glm_model=False)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        build_glm_model=False)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1306,7 +1320,10 @@ def p_values_threshold(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", p_values_threshold=0.0)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        p_values_threshold=0.0)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1335,7 +1352,10 @@ def influence(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", influence="dfbetas")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        influence="dfbetas")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1365,7 +1385,10 @@ def multinode_mode(self):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr", multinode_mode=False)
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr",
+        ...                                        multinode_mode=False)
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1432,7 +1455,9 @@ def coef_norm(self, predictor_size=None):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)
@@ -1500,7 +1525,9 @@ def coef(self, predictor_size=None):
         >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/logreg/prostate.csv")
         >>> predictors = ["AGE", "RACE", "CAPSULE", "DCAPS", "PSA", "VOL", "DPROS"]
         >>> response = "GLEASON"
-        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7, seed=12345, mode="maxr")
+        >>> maxrModel = H2OModelSelectionEstimator(max_predictor_number=7,
+        ...                                        seed=12345,
+        ...                                        mode="maxr")
         >>> maxrModel.train(x=predictors, y=response, training_frame=prostate)
         >>> results = maxrModel.result()
         >>> print(results)