ht/spacing update

h2oai · Jan 18, 2024 · 74556cd · 74556cd
1 parent 9ac6105
commit 74556cd
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Showing 2 changed files with 24 additions and 6 deletions.
diff --git a/h2o-bindings/bin/custom/python/gen_rulefit.py b/h2o-bindings/bin/custom/python/gen_rulefit.py
@@ -64,7 +64,10 @@ def predict_rules(self, frame, rule_ids):
 >>> train, test = df.split_frame(ratios=[0.8], seed=1)
 >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
 >>> y = "survived"
->>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, algorithm="auto", seed=1)
+>>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+...                            max_num_rules=100,
+...                            algorithm="auto",
+...                            seed=1)
 >>> rfit.train(training_frame=train, x=x, y=y)
 >>> print(rfit.rule_importance())
 >>> rfit.predict(test)
@@ -79,7 +82,10 @@ def predict_rules(self, frame, rule_ids):
 >>> train, test = df.split_frame(ratios=[0.8], seed=1)
 >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
 >>> y = "survived"
->>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, max_categorical_levels=10, seed=1)
+>>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+...                            max_num_rules=100,
+...                            max_categorical_levels=10,
+...                            seed=1)
 >>> rfit.train(training_frame=train, x=x, y=y)
 >>> print(rfit.rule_importance())
 >>> rfit.predict(test)
@@ -93,7 +99,10 @@ def predict_rules(self, frame, rule_ids):
 >>> train, test = df.split_frame(ratios=[0.8], seed=1)
 >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
 >>> y = "survived"
->>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, max_num_rules=-1, seed=1)
+>>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+...                            max_num_rules=100,
+...                            max_num_rules=-1,
+...                            seed=1)
 >>> rfit.train(training_frame=train, x=x, y=y)
 >>> print(rfit.rule_importance())
 >>> rfit.predict(test)

diff --git a/h2o-py/h2o/estimators/rulefit.py b/h2o-py/h2o/estimators/rulefit.py
@@ -217,7 +217,10 @@ def algorithm(self):
         >>> train, test = df.split_frame(ratios=[0.8], seed=1)
         >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
         >>> y = "survived"
-        >>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, algorithm="auto", seed=1)
+        >>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+        ...                            max_num_rules=100,
+        ...                            algorithm="auto",
+        ...                            seed=1)
         >>> rfit.train(training_frame=train, x=x, y=y)
         >>> print(rfit.rule_importance())
         >>> rfit.predict(test)
@@ -275,7 +278,10 @@ def max_num_rules(self):
         >>> train, test = df.split_frame(ratios=[0.8], seed=1)
         >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
         >>> y = "survived"
-        >>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, max_num_rules=-1, seed=1)
+        >>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+        ...                            max_num_rules=100,
+        ...                            max_num_rules=-1,
+        ...                            seed=1)
         >>> rfit.train(training_frame=train, x=x, y=y)
         >>> print(rfit.rule_importance())
         >>> rfit.predict(test)
@@ -411,7 +417,10 @@ def max_categorical_levels(self):
         >>> train, test = df.split_frame(ratios=[0.8], seed=1)
         >>> x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
         >>> y = "survived"
-        >>> rfit = H2ORuleFitEstimator(max_rule_length=10, max_num_rules=100, max_categorical_levels=10, seed=1)
+        >>> rfit = H2ORuleFitEstimator(max_rule_length=10,
+        ...                            max_num_rules=100,
+        ...                            max_categorical_levels=10,
+        ...                            seed=1)
         >>> rfit.train(training_frame=train, x=x, y=y)
         >>> print(rfit.rule_importance())
         >>> rfit.predict(test)