New search space def

tpot2/builtin_modules/__init__.py

@@ -5,4 +5,5 @@
 from .arithmetictransformer import AddTransformer, mul_neg_1_Transformer, MulTransformer, SafeReciprocalTransformer, EQTransformer, NETransformer, GETransformer, GTTransformer, LETransformer, LTTransformer, MinTransformer, MaxTransformer, ZeroTransformer, OneTransformer, NTransformer
 from .passthrough import Passthrough
 from .imputer import ColumnSimpleImputer
-from .estimatortransformer import EstimatorTransformer
+from .estimatortransformer import EstimatorTransformer
+from .passkbinsdiscretizer import PassKBinsDiscretizer

tpot2/builtin_modules/column_one_hot_encoder.py

@@ -37,7 +37,7 @@ def _X_selected(X, selected):
 class ColumnOneHotEncoder(BaseEstimator, TransformerMixin):
 
 
-    def __init__(self, columns='auto', drop=None, handle_unknown='error', sparse_output=False, min_frequency=None,max_categories=None):
+    def __init__(self, columns='auto', drop=None, handle_unknown='infrequent_if_exist', sparse_output=False, min_frequency=None,max_categories=None):
         '''
 
         Parameters

tpot2/builtin_modules/passkbinsdiscretizer.py

@@ -0,0 +1,43 @@
+import pandas as pd
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import KBinsDiscretizer
+import numpy as np
+
+def select_features(X, min_unique=10,):
+
+    if isinstance(X, pd.DataFrame):
+        return [col for col in X.columns if len(X[col].unique()) > min_unique]
+    else:
+        return [i for i in range(X.shape[1]) if len(np.unique(X[:, i])) > min_unique]
+
+class PassKBinsDiscretizer(BaseEstimator, TransformerMixin):
+    """
+    Same as sklearn.preprocessing.KBinsDiscretizer, but passes through columns that are not discretized due to having fewer than n_bins unique values instead of ignoring them.
+    """
+    def __init__(self, n_bins=5,  encode='onehot-dense', strategy='quantile', subsample='warn', random_state=None):
+        self.n_bins = n_bins
+        self.encode = encode
+        self.strategy = strategy
+        self.subsample = subsample
+        self.random_state = random_state
+
+    def fit(self, X, y=None):
+        # Identify columns with more than n unique values
+                # Create a ColumnTransformer to select and discretize the chosen columns
+        self.selected_columns_ = select_features(X, min_unique=10)
+        if isinstance(X, pd.DataFrame):
+            self.not_selected_columns_ = [col for col in X.columns if col not in self.selected_columns_]
+        else:
+            self.not_selected_columns_ = [i for i in range(X.shape[1]) if i not in self.selected_columns_]
+
+        enc = KBinsDiscretizer(n_bins=self.n_bins, encode=self.encode, strategy=self.strategy, subsample=self.subsample, random_state=self.random_state)
+        self.transformer = ColumnTransformer([
+            ('discretizer', enc, self.selected_columns_),
+            ('passthrough', 'passthrough', self.not_selected_columns_)
+        ])
+        self.transformer.fit(X)
+        return self
+
+    def transform(self, X):
+        return self.transformer.transform(X)

tpot2/config/classifiers.py

@@ -386,10 +386,18 @@ def GradientBoostingClassifier_hyperparameter_parser(params):
         final_params['n_iter_no_change'] = None
         final_params['validation_fraction'] = None
     elif params['early_stop'] == 'valid':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
-        final_params['validation_fraction'] = params['validation_fraction']
+        #this is required because in crossover, its possible that n_iter_no_change is not in the params
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']    
+        if 'validation_fraction' not in params:
+            final_params['validation_fraction'] = 0.1
+        else:
+            final_params['validation_fraction'] = params['validation_fraction']
     elif params['early_stop'] == 'train':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
         final_params['validation_fraction'] = None
 
 
@@ -445,16 +453,32 @@ def HistGradientBoostingClassifier_hyperparameter_parser(params):
         final_params['random_state'] = params['random_state']
 
 
+
     if params['early_stop'] == 'off':
         # final_params['n_iter_no_change'] = 0
         final_params['validation_fraction'] = None
         final_params['early_stopping'] = False
     elif params['early_stop'] == 'valid':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
-        final_params['validation_fraction'] = params['validation_fraction']
+
+        #this is required because in crossover, its possible that n_iter_no_change is not in the params
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']    
+        if 'validation_fraction' not in params:
+            final_params['validation_fraction'] = 0.1
+        else:
+            final_params['validation_fraction'] = params['validation_fraction']
+
         final_params['early_stopping'] = True
     elif params['early_stop'] == 'train':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
+
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']
+
+
         final_params['validation_fraction'] = None
         final_params['early_stopping'] = True
 

tpot2/config/get_configspace.py

@@ -26,7 +26,7 @@
 from tpot2.builtin_modules import genetic_encoders, feature_encoding_frequency_selector
 from tpot2.builtin_modules import AddTransformer, mul_neg_1_Transformer, MulTransformer, SafeReciprocalTransformer, EQTransformer, NETransformer, GETransformer, GTTransformer, LETransformer, LTTransformer, MinTransformer, MaxTransformer, ZeroTransformer, OneTransformer, NTransformer
 from tpot2.builtin_modules.genetic_encoders import DominantEncoder, RecessiveEncoder, HeterosisEncoder, UnderDominanceEncoder, OverDominanceEncoder 
-from tpot2.builtin_modules import ZeroCount, ColumnOneHotEncoder
+from tpot2.builtin_modules import ZeroCount, ColumnOneHotEncoder, PassKBinsDiscretizer
 from tpot2.builtin_modules import Passthrough
 from sklearn.linear_model import SGDClassifier, LogisticRegression, SGDRegressor, Ridge, Lasso, ElasticNet, Lars, LassoLars, LassoLarsCV, RidgeCV, ElasticNetCV, PassiveAggressiveClassifier, ARDRegression
 from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, ExtraTreesRegressor, ExtraTreesClassifier, AdaBoostRegressor, AdaBoostClassifier, GradientBoostingRegressor,RandomForestRegressor, BaggingRegressor, ExtraTreesRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor
@@ -55,6 +55,7 @@
                DominantEncoder, RecessiveEncoder, HeterosisEncoder, UnderDominanceEncoder, OverDominanceEncoder,
                GaussianProcessClassifier, BaggingClassifier,LGBMRegressor,
                Passthrough,
+               PassKBinsDiscretizer,
                ]
 
 
@@ -117,7 +118,7 @@
         "regressors" : ["LGBMRegressor", 'AdaBoostRegressor', "ARDRegression", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor',  'LinearSVR', "MLPRegressor", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'],
 
 
-        "transformers":  ["Binarizer", "PCA", "ZeroCount", "ColumnOneHotEncoder", "FastICA", "FeatureAgglomeration", "Nystroem", "RBFSampler", "QuantileTransformer", "PowerTransformer"],
+        "transformers":  ["PassKBinsDiscretizer", "Binarizer", "PCA", "ZeroCount", "ColumnOneHotEncoder", "FastICA", "FeatureAgglomeration", "Nystroem", "RBFSampler", "QuantileTransformer", "PowerTransformer"],
         "scalers": ["MinMaxScaler", "RobustScaler", "StandardScaler", "MaxAbsScaler", "Normalizer", ],
         "all_transformers" : ["transformers", "scalers"],
 
@@ -291,6 +292,8 @@ def get_configspace(name, n_classes=3, n_samples=1000, n_features=100, random_st
             return transformers.PolynomialFeatures_configspace
         case "StandardScaler":
             return {}
+        case "PassKBinsDiscretizer":
+            return transformers.get_passkbinsdiscretizer_configspace(random_state=random_state)
 
         #selectors.py
         case "SelectFwe":

tpot2/config/regressors.py

@@ -436,10 +436,20 @@ def GradientBoostingRegressor_hyperparameter_parser(params):
         final_params['n_iter_no_change'] = None
         final_params['validation_fraction'] = None
     elif params['early_stop'] == 'valid':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
-        final_params['validation_fraction'] = params['validation_fraction']
+        #this is required because in crossover, its possible that n_iter_no_change is not in the params
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']    
+        if 'validation_fraction' not in params:
+            final_params['validation_fraction'] = 0.1
+        else:
+            final_params['validation_fraction'] = params['validation_fraction']
     elif params['early_stop'] == 'train':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']  
         final_params['validation_fraction'] = None
 
 
@@ -498,11 +508,20 @@ def HistGradientBoostingRegressor_hyperparameter_parser(params):
         # final_params['validation_fraction'] = None
         final_params['early_stopping'] = False
     elif params['early_stop'] == 'valid':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
-        final_params['validation_fraction'] = params['validation_fraction']
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change']    
+        if 'validation_fraction' not in params:
+            final_params['validation_fraction'] = 0.1
+        else:
+            final_params['validation_fraction'] = params['validation_fraction']
         final_params['early_stopping'] = True
     elif params['early_stop'] == 'train':
-        final_params['n_iter_no_change'] = params['n_iter_no_change']
+        if 'n_iter_no_change' not in params:
+            final_params['n_iter_no_change'] = 10
+        else:
+            final_params['n_iter_no_change'] = params['n_iter_no_change'] 
         final_params['validation_fraction'] = None
         final_params['early_stopping'] = True
 

tpot2/config/transformers.py

@@ -124,6 +124,22 @@ def get_QuantileTransformer_configspace(random_state=None):
     )
 
 
+def get_passkbinsdiscretizer_configspace(random_state=None):
+    space = {
+        'n_bins': Integer('n_bins', bounds=(3, 100)),
+        'encode': 'onehot-dense',
+        'strategy': Categorical('strategy', ['uniform', 'quantile', 'kmeans']),
+        # 'subsample': Categorical('subsample', ['auto', 'warn', 'ignore']),
+    }
+
+    if random_state is not None: #This is required because configspace doesn't allow None as a value
+        space['random_state'] = random_state
+
+    return ConfigurationSpace(
+        space = space
+
+    )
+
 
 ### ROBUST SCALER
 
@@ -133,4 +149,7 @@ def get_QuantileTransformer_configspace(random_state=None):
         })
 
 def robust_scaler_hyperparameter_parser(params):
-    return {"quantile_range": (params["q_min"], params["q_max"])}
+    return {"quantile_range": (params["q_min"], params["q_max"])}
+
+
+

tpot2/evolvers/base_evolver.py

@@ -498,7 +498,7 @@ def optimize(self, generations=None):
             self._client.close()
             self._cluster.close()
 
-        tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights, invalid_values=["TIMEOUT","INVALID"])
+        tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights)
 
     def step(self,):
         if self.population_size_list is not None:
@@ -624,7 +624,7 @@ def evaluate_population_full(self, budget=None):
             parallel_timeout = 10
 
         #scores = tpot2.utils.eval_utils.parallel_eval_objective_list(individuals_to_evaluate, self.objective_functions, self.n_jobs, verbose=self.verbose, timeout=self.max_eval_time_seconds, budget=budget, n_expected_columns=len(self.objective_names), client=self._client, parallel_timeout=parallel_timeout, **self.objective_kwargs)
-        scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list2(individuals_to_evaluate, self.objective_functions, verbose=self.verbose, max_eval_time_seconds=self.max_eval_time_seconds, budget=budget, n_expected_columns=len(self.objective_names), client=self._client, **self.objective_kwargs)
+        scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list2(individuals_to_evaluate, self.objective_functions, verbose=self.verbose, max_eval_time_seconds=self.max_eval_time_seconds, budget=budget, n_expected_columns=len(self.objective_names), client=self._client, scheduled_timeout_time=self.scheduled_timeout_time, **self.objective_kwargs)
 
         self.population.update_column(individuals_to_evaluate, column_names=self.objective_names, data=scores)
         if budget is not None:
@@ -705,6 +705,7 @@ def evaluate_population_selection_early_stop(self,survival_counts, thresholds=No
                                     generation = self.generation,
                                     n_expected_columns=len(self.objective_names),
                                     client=self._client,
+                                    scheduled_timeout_time=self.scheduled_timeout_time,
                                     **self.objective_kwargs,
                                     )
 

tpot2/evolvers/steady_state_evolver.py

@@ -342,7 +342,7 @@ def optimize(self):
                 ###############################
                 if self.verbose >= 3:
                     sign = np.sign(self.objective_function_weights)
-                    valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
+                    valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                     cur_best_scores = valid_df.max(axis=0)*sign
                     cur_best_scores = cur_best_scores.to_numpy()
                     for i, obj in enumerate(self.objective_names):
@@ -353,7 +353,7 @@ def optimize(self):
                         #get sign of objective_function_weights
                         sign = np.sign(self.objective_function_weights)
                         #get best score for each objective
-                        valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
+                        valid_df = self.population.evaluated_individuals[~self.population.evaluated_individuals[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)][self.objective_names]*sign
                         cur_best_scores = valid_df.max(axis=0)
                         cur_best_scores = cur_best_scores.to_numpy()
                         #cur_best_scores =  self.population.get_column(self.population.population, column_names=self.objective_names).max(axis=0)*sign #TODO this assumes the current population is the best
@@ -499,7 +499,7 @@ def optimize(self):
                     elif len(submitted_futures) < self.max_queue_size:
 
                         initial_population = self.population.evaluated_individuals.iloc[:self.initial_population_size*3]
-                        invalid_initial_population = initial_population[initial_population[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)]
+                        invalid_initial_population = initial_population[initial_population[["Eval Error"]].isin(["TIMEOUT","INVALID"]).any(axis=1)]
                         if len(invalid_initial_population) >= self.initial_population_size*3: #if all individuals in the 3*initial population are invalid
                             raise Exception("No individuals could be evaluated in the initial population. This may indicate a bug in the configuration, included models, or objective functions. Set verbose>=4 to see the errors that caused individuals to fail.")
 
@@ -540,8 +540,8 @@ def optimize(self):
         # Step 7: Cleanup
         ###############################
 
-        self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="INVALID")
-        self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="TIMEOUT")
+        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="INVALID")
+        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")
 
 
         #done, cleanup futures
@@ -556,7 +556,7 @@ def optimize(self):
             self._client.close()
             self._cluster.close()
 
-        tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights, invalid_values=["TIMEOUT","INVALID"])
+        tpot2.utils.get_pareto_frontier(self.population.evaluated_individuals, column_names=self.objective_names, weights=self.objective_function_weights)
 
 
 

tpot2/search_spaces/nodes/genetic_feature_selection.py

@@ -50,6 +50,11 @@ def __init__(   self,
         else:
             self.mask = mask
 
+        # check if there are no features selected, if so select one
+        if sum(self.mask) == 0:
+            index = rng.choice(len(self.mask))
+            self.mask[index] = True
+
         self.mutation_list = [self._mutate_add, self._mutate_remove]
         self.crossover_list = [self._crossover_swap]