Merge pull request #15945 from h2oai/sy/#15798

GH-15798: Craft GAM Examples

h2o-bindings/bin/custom/python/gen_gam.py

@@ -40,7 +40,7 @@ def scoring_history(self):
 
     def get_knot_locations(self, gam_column=None):
         """
-        Retrieve gam columns knot locations if store_knot_location parameter is enabled.  If a gam column name is 
+        Retrieve gam columns knot locations if store_knot_locations parameter is enabled.  If a gam column name is 
         specified, the know loations corresponding to that gam column is returned.  Otherwise, all knot locations are
         returned for all gam columns.  The order of the gam columns are specified in gam_knot_column_names of the 
         model output.
@@ -61,8 +61,8 @@ def get_knot_locations(self, gam_column=None):
 
     def get_gam_knot_column_names(self):
         """
-        Retrieve gam column names corresponding to the knot locations that will be returned if store_knot_location
-        parameter is enabled.  
+        Retrieve gam column names corresponding to the knot locations that will be returned if store_knot_locations
+        parameter is enabled.
      
         :return: gam column names whose knot locations are stored in the knot_locations.
         """
@@ -117,3 +117,172 @@ def get_gam_knot_column_names(self):
 MSE, AUC (for logistic regression), degrees of freedom, and confusion matrices.
 """
 )
+examples = dict(
+    bs="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             gam_columns=["C6","C7","C8"],
+...                                             bs=[0,1,3])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef() # note the spline type in the names of gam column coefficients
+""",
+    gam_columns="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             gam_columns=["C6","C7","C8"])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef()
+""",
+    get_gam_knot_column_names="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             store_knot_locations=True,
+...                                             gam_columns=["C6","C7","C8"])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.get_gam_knot_column_names()
+""",
+    get_knot_locations="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             store_knot_locations=True,
+...                                             gam_columns=["C6","C7","C8"])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.get_knot_locations()
+""",
+    keep_gam_cols="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> train, test = h2o_data.split_frame(ratios = [.8])
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             keep_gam_cols=True,
+...                                             gam_columns=["C6","C7","C8"])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o.get_frame(h2o_model._model_json["output"] ["gam_transformed_center_key"])
+""",
+    knot_ids="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> knots1 = [-1.99905699, -0.98143075, 0.02599159, 1.00770987, 1.99942290]
+>>> frameKnots1 = h2o.H2OFrame(python_obj=knots1)
+>>> knots2 = [-1.999821861, -1.005257990, -0.006716042, 1.002197392, 1.999073589]
+>>> frameKnots2 = h2o.H2OFrame(python_obj=knots2)
+>>> knots3 = [-1.999675688, -0.979893796, 0.007573327, 1.011437347, 1.999611676]
+>>> frameKnots3 = h2o.H2OFrame(python_obj=knots3)
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")()
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> train, test = h2o_data.split_frame(ratios = [.8])
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             gam_columns=["C6","C7","C8"],
+...                                             store_knot_locations=True,
+...                                             knot_ids=[frameKnots1.key, frameKnots2.key, frameKnots3.key])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.get_knot_locations()
+""",
+    num_knots="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> train, test = h2o_data.split_frame(ratios = [.8])
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             store_knot_locations=True,
+...                                             gam_columns=["C6","C7","C8"],
+...                                             num_knots=[3,4,5])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.get_knot_locations()
+""",
+    scale_tp_penalty_mat="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.cs
+>>> h2o_data["C11"] = h2o_data["C11"].asfactor()
+>>> y = "C11"
+>>> x = ["C9","C10"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='multinomial',
+...                                             scale_tp_penalty_mat=True,
+...                                             gam_columns=["C6","C7","C8"],
+...                                             bs=[1,1,1])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef()
+""",
+    splines_non_negative="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/binomial_20_cols_10KRows.csv")
+>>> y = "C21"
+>>> x = ["C19","C20"]
+>>> numKnots = [5,5,5]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='gaussian',
+...                                             gam_columns=["C16","C17","C18"],
+...                                             bs=[2,2,2],
+...                                             splines_non_negative=[True, True, True])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef()
+""",
+    spline_orders="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/binomial_20_cols_10KRows.csv")
+>>> y = "C21"
+>>> x = ["C19","C20"]
+>>> numKnots = [5,5,5]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='gaussian',
+...                                             gam_columns=["C16","C17","C18"],
+...                                             bs=[2,2,2],
+...                                             spline_orders=[3,4,5])
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef()
+""",
+    standardize_tp_gam_cols="""
+>>> import h2o
+>>> from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
+>>> h2o.init()
+>>> h2o_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/binomial_20_cols_10KRows.csv")
+>>> y = "C21"
+>>> x = ["C19","C20"]
+>>> h2o_model = H2OGeneralizedAdditiveEstimator(family='gaussian',
+...                                             gam_columns=["C16","C17","C18"],
+...                                             bs=[1,1,1],
+...                                             standardize_tp_gam_cols=True)
+>>> h2o_model.train(x=x, y=y, training_frame=h2o_data)
+>>> h2o_model.coef()
+""",
+)