Merge pull request #2 from neurodata/staging

Staging updates

proglearn/forest.py

@@ -15,8 +15,9 @@ class LifelongClassificationForest(ClassificationProgressiveLearner):
 
     Parameters
     ----------
-    n_estimators : int, default=100
-        The number of estimators used in the Lifelong Classification Forest
+    default_n_estimators : int, default=100
+        The number of trees used in the Lifelong Classification Forest
+        used if 'n_estimators' is not fed to add_{task, transformer}.
 
     default_tree_construction_proportion : int, default=0.67
         The proportions of the input data set aside to train each decision
@@ -40,12 +41,12 @@ class LifelongClassificationForest(ClassificationProgressiveLearner):
 
     def __init__(
         self,
-        n_estimators=100,
+        default_n_estimators=100,
         default_tree_construction_proportion=0.67,
         default_finite_sample_correction=False,
         default_max_depth=30,
     ):
-        self.n_estimators = n_estimators
+        self.default_n_estimators = default_n_estimators
         self.default_tree_construction_proportion = default_tree_construction_proportion
         self.default_finite_sample_correction = default_finite_sample_correction
         self.default_max_depth = default_max_depth
@@ -65,9 +66,10 @@ def add_task(
         X,
         y,
         task_id=None,
-        tree_construction_proportion=None,
-        finite_sample_correction=None,
-        max_depth=None,
+        n_estimators="default",
+        tree_construction_proportion="default",
+        finite_sample_correction="default",
+        max_depth="default",
     ):
         """
         adds a task with id task_id, max tree depth max_depth, given input data matrix X
@@ -87,29 +89,34 @@ def add_task(
         task_id : obj, default=None
             The id corresponding to the task being added.
 
-        tree_construction_proportion : int, default=None
+        n_estimators : int or str, default='default'
+            The number of trees used for the given task.
+
+        tree_construction_proportion : int or str, default='default'
             The proportions of the input data set aside to train each decision
             tree. The remainder of the data is used to fill in voting posteriors.
-            The default is used if 'None' is provided.
+            The default is used if 'default' is provided.
 
-        finite_sample_correction : bool, default=False
+        finite_sample_correction : bool or str, default='default'
             Boolean indicating whether this learner will have finite sample correction.
-            The default is used if 'None' is provided.
+            The default is used if 'default' is provided.
 
-        max_depth : int, default=30
+        max_depth : int or str, default='default'
             The maximum depth of a tree in the Lifelong Classification Forest.
-            The default is used if 'None' is provided.
+            The default is used if 'default' is provided.
 
         Returns
         -------
         self : LifelongClassificationForest
             The object itself.
         """
-        if tree_construction_proportion is None:
+        if n_estimators == "default":
+            n_estimators = self.default_n_estimators
+        if tree_construction_proportion == "default":
             tree_construction_proportion = self.default_tree_construction_proportion
-        if finite_sample_correction is None:
+        if finite_sample_correction == "default":
             finite_sample_correction = self.default_finite_sample_correction
-        if max_depth is None:
+        if max_depth == "default":
             max_depth = self.default_max_depth
 
         self.pl_.add_task(
@@ -121,7 +128,7 @@ def add_task(
                 1 - tree_construction_proportion,
                 0,
             ],
-            num_transformers=self.n_estimators,
+            num_transformers=n_estimators,
             transformer_kwargs={"kwargs": {"max_depth": max_depth}},
             voter_kwargs={
                 "classes": np.unique(y),
@@ -131,7 +138,14 @@ def add_task(
         )
         return self
 
-    def add_transformer(self, X, y, transformer_id=None, max_depth=None):
+    def add_transformer(
+        self,
+        X,
+        y,
+        transformer_id=None,
+        n_estimators="default",
+        max_depth="default",
+    ):
         """
         adds a transformer with id transformer_id and max tree depth max_depth, trained on
         given input data matrix, X, and output data matrix, y, to the Lifelong Classification Forest.
@@ -149,24 +163,29 @@ def add_transformer(self, X, y, transformer_id=None, max_depth=None):
         transformer_id : obj, default=None
             The id corresponding to the transformer being added.
 
-        max_depth : int, default=30
+        n_estimators : int or str, default='default'
+            The number of trees used for the given task.
+
+        max_depth : int or str, default='default'
             The maximum depth of a tree in the UncertaintyForest.
-            The default is used if 'None' is provided.
+            The default is used if 'default' is provided.
 
         Returns
         -------
         self : LifelongClassificationForest
             The object itself.
         """
-        if max_depth is None:
+        if n_estimators == "default":
+            n_estimators = self.default_n_estimators
+        if max_depth == "default":
             max_depth = self.default_max_depth
 
         self.pl_.add_transformer(
             X,
             y,
             transformer_kwargs={"kwargs": {"max_depth": max_depth}},
             transformer_id=transformer_id,
-            num_transformers=self.n_estimators,
+            num_transformers=n_estimators,
         )
 
         return self
@@ -257,7 +276,7 @@ def fit(self, X, y):
             The object itself.
         """
         self.lf_ = LifelongClassificationForest(
-            n_estimators=self.n_estimators,
+            default_n_estimators=self.n_estimators,
             default_finite_sample_correction=self.finite_sample_correction,
             default_max_depth=self.max_depth,
             default_tree_construction_proportion=self.tree_construction_proportion,

proglearn/network.py

@@ -95,7 +95,7 @@ def __init__(
             default_decider_kwargs={},
         )
 
-    def add_task(self, X, y, task_id=None, transformer_voter_decider_split=None):
+    def add_task(self, X, y, task_id=None, transformer_voter_decider_split="default"):
         """
         adds a task with id task_id, given input data matrix X
         and output data matrix y, to the Lifelong Classification Network
@@ -111,18 +111,18 @@ def add_task(self, X, y, task_id=None, transformer_voter_decider_split=None):
         task_id: obj
             The id corresponding to the task being added.
 
-        transformer_voter_decider_split: ndarray, default=None
+        transformer_voter_decider_split: ndarray or str, default='default'
             1D array of length 3 corresponding to the proportions of data used to train the
             transformer(s) corresponding to the task_id, to train the voter(s) from the
             transformer(s) to the task_id, and to train the decider for task_id, respectively.
-            The default is used if 'None' is provided.
+            The default is used if 'default' is provided.
 
         Returns
         -------
         self : LifelongClassificationNetwork
             The object itself.
         """
-        if transformer_voter_decider_split is None:
+        if transformer_voter_decider_split == "default":
             transformer_voter_decider_split = (
                 self.default_transformer_voter_decider_split
             )

proglearn/tests/test_forest.py

@@ -42,7 +42,7 @@ def test_correct_default_kwargs(self):
 
     def test_correct_default_n_estimators(self):
         l2f = LifelongClassificationForest()
-        assert l2f.n_estimators == 100
+        assert l2f.default_n_estimators == 100
 
     def test_correct_true_initilization_finite_sample_correction(self):
         l2f = LifelongClassificationForest(default_finite_sample_correction=True)