Update TPOT2 in docstring

tpot2/evolvers/steady_state_evolver.py

@@ -58,7 +58,7 @@ def ind_mutate(ind, rng):
 
     Parameters
     ----------
-    ind : tpot2.BaseIndividual
+    ind : tpot.BaseIndividual
         The individual to mutate
     rng : int or numpy.random.Generator
         A numpy random generator to use for reproducibility
@@ -71,8 +71,8 @@ def ind_crossover(ind1, ind2, rng):
     Calls the ind1.crossover(ind2, rng=rng)
     Parameters
     ----------
-    ind1 : tpot2.BaseIndividual
-    ind2 : tpot2.BaseIndividual
+    ind1 : tpot.BaseIndividual
+    ind2 : tpot.BaseIndividual
     rng : int or numpy.random.Generator
         A numpy random generator to use for reproducibility
     """
@@ -219,7 +219,7 @@ def __init__(   self,
         periodic_checkpoint_folder : str, default=None
             Folder to save the population to periodically. If None, no periodic saving will be done.
             If provided, training will resume from this checkpoint.
-        callback : tpot2.CallBackInterface, default=None
+        callback : tpot.CallBackInterface, default=None
             Callback object. Not implemented
         rng : Numpy.Random.Generator, None, default=None
             An object for reproducability of experiments. This value will be passed to numpy.random.default_rng() to create an instnce of the genrator to pass to other classes
@@ -231,7 +231,7 @@ def __init__(   self,
         
         Attributes
         ----------
-        population : tpot2.Population
+        population : tpot.Population
             The population of individuals.
             Use population.population to access the individuals in the current population.
             Use population.evaluated_individuals to access a data frame of all individuals that have been explored.

tpot2/search_spaces/base.py

@@ -76,7 +76,7 @@ def unique_id(self):
         """
         return self
 
-    #TODO currently TPOT2 population class manually uses the unique_id to generate the index for the population data frame.
+    #TODO currently TPOT population class manually uses the unique_id to generate the index for the population data frame.
     #alternatively, the index could be the individual itself, with the __eq__ and __hash__ methods implemented.
 
     # Though this breaks the graphpipeline. When a mutation is called, it changes the __eq__ and __hash__ outputs.

tpot2/search_spaces/tuple_index.py

@@ -36,7 +36,7 @@
 
 class TupleIndex():
     """
-    TPOT2 uses tuples to create a unique id for some pipeline search spaces. However, tuples sometimes don't interact correctly with pandas indexes.
+    TPOT uses tuples to create a unique id for some pipeline search spaces. However, tuples sometimes don't interact correctly with pandas indexes.
     This class is a wrapper around a tuple that allows it to be used as a key in a dictionary, without it being an itereable.
 
     An alternative could be to make unique id return a string, but this would not work with graphpipelines, which require a special object.

tpot2/tpot_estimator/estimator.py

@@ -142,7 +142,7 @@ def __init__(self,
 
         Parameters
         ----------
-        search_space : (String, tpot2.search_spaces.SearchSpace)
+        search_space : (String, tpot.search_spaces.SearchSpace)
             - String : The default search space to use for the optimization.
             | String     | Description      |
             | :---        |    :----:   |
@@ -157,7 +157,7 @@ def __init__(self,
             
             - SearchSpace : The search space to use for the optimization. This should be an instance of a SearchSpace.
                 The search space to use for the optimization. This should be an instance of a SearchSpace.
-                TPOT2 has groups of search spaces found in the following folders, tpot2.search_spaces.nodes for the nodes in the pipeline and tpot2.search_spaces.pipelines for the pipeline structure.
+                TPOT has groups of search spaces found in the following folders, tpot.search_spaces.nodes for the nodes in the pipeline and tpot.search_spaces.pipelines for the pipeline structure.
         
         scorers : (list, scorer)
             A scorer or list of scorers to be used in the cross-validation process.
@@ -205,7 +205,7 @@ def __init__(self,
 
         categorical_features: list or None
             Categorical columns to inpute and/or one hot encode during the preprocessing step. Used only if preprocessing is not False.
-            - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing.
+            - None : If None, TPOT will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing.
             - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices
 
         preprocessing : bool or BaseEstimator/Pipeline,
@@ -236,7 +236,7 @@ def __init__(self,
             Maximum time to evaluate a single individual. If none or inf, there will be no time limit per evaluation.
 
         validation_strategy : str, default='none'
-            EXPERIMENTAL The validation strategy to use for selecting the final pipeline from the population. TPOT2 may overfit the cross validation score. A second validation set can be used to select the final pipeline.
+            EXPERIMENTAL The validation strategy to use for selecting the final pipeline from the population. TPOT may overfit the cross validation score. A second validation set can be used to select the final pipeline.
             - 'auto' : Automatically determine the validation strategy based on the dataset shape.
             - 'reshuffled' : Use the same data for cross validation and final validation, but with different splits for the folds. This is the default for small datasets.
             - 'split' : Use a separate validation set for final validation. Data will be split according to validation_fraction. This is the default for medium datasets.
@@ -246,7 +246,7 @@ def __init__(self,
           EXPERIMENTAL The fraction of the dataset to use for the validation set when validation_strategy is 'split'. Must be between 0 and 1.
 
         disable_label_encoder : bool, default=False
-            If True, TPOT will check if the target needs to be relabeled to be sequential ints from 0 to N. This is necessary for XGBoost compatibility. If the labels need to be encoded, TPOT2 will use sklearn.preprocessing.LabelEncoder to encode the labels. The encoder can be accessed via the self.label_encoder_ attribute.
+            If True, TPOT will check if the target needs to be relabeled to be sequential ints from 0 to N. This is necessary for XGBoost compatibility. If the labels need to be encoded, TPOT will use sklearn.preprocessing.LabelEncoder to encode the labels. The encoder can be accessed via the self.label_encoder_ attribute.
             If False, no additional label encoders will be used.
 
         early_stop : int, default=None
@@ -340,7 +340,7 @@ def __init__(self,
             Folder to save the population to periodically. If None, no periodic saving will be done.
             If provided, training will resume from this checkpoint.
 
-        callback : tpot2.CallBackInterface, default=None
+        callback : tpot.CallBackInterface, default=None
             Callback object. Not implemented
 
         verbose : int, default=1

tpot2/tpot_estimator/estimator_utils.py

@@ -66,11 +66,11 @@ def convert_parents_tuples_to_integers(row, object_to_int):
 #TODO add kwargs
 def apply_make_pipeline(ind, preprocessing_pipeline=None, export_graphpipeline=False, **pipeline_kwargs):
     """
-    Helper function to create a column of sklearn pipelines from the tpot2 individual class.
+    Helper function to create a column of sklearn pipelines from the tpot individual class.
 
     Parameters
     ----------
-    ind: tpot2.SklearnIndividual
+    ind: tpot.SklearnIndividual
         The individual to convert to a pipeline.
     preprocessing_pipeline: sklearn.pipeline.Pipeline, optional
         The preprocessing pipeline to include before the individual's pipeline.
@@ -109,7 +109,7 @@ def objective_function_generator(pipeline, x,y, scorers, cv, other_objective_fun
 
     Parameters
     ----------
-    pipeline: tpot2.SklearnIndividual
+    pipeline: tpot.SklearnIndividual
         The individual to evaluate.
     x: np.ndarray
         The feature matrix.
@@ -179,7 +179,7 @@ def val_objective_function_generator(pipeline, X_train, y_train, X_test, y_test,
     Parameters
     ----------
 
-    pipeline: tpot2.SklearnIndividual
+    pipeline: tpot.SklearnIndividual
         The individual to evaluate.
     X_train: np.ndarray
         The feature matrix of the training set.

tpot2/tpot_estimator/steady_state_estimator.py

@@ -67,7 +67,7 @@ def __init__(self,
                         scorers_weights = [],
                         classification = False,
                         cv = 10,
-                        other_objective_functions=[], #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
+                        other_objective_functions=[], #tpot.objectives.estimator_objective_functions.number_of_nodes_objective],
                         other_objective_functions_weights = [],
                         objective_function_names = None,
                         bigger_is_better = True,
@@ -227,7 +227,7 @@ def __init__(self,
 
         categorical_features: list or None
             Categorical columns to inpute and/or one hot encode during the preprocessing step. Used only if preprocessing is not False.
-            - None : If None, TPOT2 will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing.
+            - None : If None, TPOT will automatically use object columns in pandas dataframes as objects for one hot encoding in preprocessing.
             - List of categorical features. If X is a dataframe, this should be a list of column names. If X is a numpy array, this should be a list of column indices
 
 
@@ -254,7 +254,7 @@ def __init__(self,
             - Pipeline : If an instance of a pipeline is given, will use that pipeline as the preprocessing pipeline.
 
         validation_strategy : str, default='none'
-            EXPERIMENTAL The validation strategy to use for selecting the final pipeline from the population. TPOT2 may overfit the cross validation score. A second validation set can be used to select the final pipeline.
+            EXPERIMENTAL The validation strategy to use for selecting the final pipeline from the population. TPOT may overfit the cross validation score. A second validation set can be used to select the final pipeline.
             - 'auto' : Automatically determine the validation strategy based on the dataset shape.
             - 'reshuffled' : Use the same data for cross validation and final validation, but with different splits for the folds. This is the default for small datasets.
             - 'split' : Use a separate validation set for final validation. Data will be split according to validation_fraction. This is the default for medium datasets.
@@ -264,7 +264,7 @@ def __init__(self,
           EXPERIMENTAL The fraction of the dataset to use for the validation set when validation_strategy is 'split'. Must be between 0 and 1.
 
         disable_label_encoder : bool, default=False
-            If True, TPOT will check if the target needs to be relabeled to be sequential ints from 0 to N. This is necessary for XGBoost compatibility. If the labels need to be encoded, TPOT2 will use sklearn.preprocessing.LabelEncoder to encode the labels. The encoder can be accessed via the self.label_encoder_ attribute.
+            If True, TPOT will check if the target needs to be relabeled to be sequential ints from 0 to N. This is necessary for XGBoost compatibility. If the labels need to be encoded, TPOT will use sklearn.preprocessing.LabelEncoder to encode the labels. The encoder can be accessed via the self.label_encoder_ attribute.
             If False, no additional label encoders will be used.
 
         population_size : int, default=50
@@ -404,7 +404,7 @@ def __init__(self,
             Folder to save the population to periodically. If None, no periodic saving will be done.
             If provided, training will resume from this checkpoint.
 
-        callback : tpot2.CallBackInterface, default=None
+        callback : tpot.CallBackInterface, default=None
             Callback object. Not implemented
 
         processes : bool, default=True