Merge pull request #125 from EpistasisLab/dev

Dev

setup.py

@@ -33,7 +33,7 @@ def calculate_version():
                       'update_checker>=0.16',
                       'tqdm>=4.36.1',
                       'stopit>=1.1.1',
-                      'pandas>=1.5.3,<2.0.0',
+                      'pandas>=2.2.0',
                       'joblib>=1.1.1',
                       'xgboost>=1.7.0',
                       'matplotlib>=3.6.2',

tpot2/_version.py

@@ -1 +1 @@
-__version__ = '0.1.5-alpha'
+__version__ = '0.1.6-alpha'

tpot2/builtin_modules/column_one_hot_encoder.py

@@ -11,16 +11,13 @@
 
 
 
-def auto_select_categorical_features(X):
+def auto_select_categorical_features(X, min_unique=10,):
 
-    if not isinstance(X, pd.DataFrame):
-        return []
-
-    feature_mask = []
-    for column in X.columns:
-        feature_mask.append(not is_numeric_dtype(X[column]))
+    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]
 
-    return feature_mask
 
 
 def _X_selected(X, selected):
@@ -41,6 +38,21 @@ class ColumnOneHotEncoder(BaseEstimator, TransformerMixin):
 
 
     def __init__(self, columns='auto', drop=None, handle_unknown='error', sparse_output=False, min_frequency=None,max_categories=None):
+        '''
+        
+        Parameters
+        ----------
+
+        columns : str, list, default='auto'
+            - 'auto' : Automatically select categorical features based on columns with less than 10 unique values
+            - 'categorical' : Automatically select categorical features
+            - 'numeric' : Automatically select numeric features
+            - 'all' : Select all features
+            - list : A list of columns to select
+        
+        drop, handle_unknown, sparse_output, min_frequency, max_categories : see sklearn.preprocessing.OneHotEncoder
+
+        '''
 
         self.columns = columns
         self.drop = drop
@@ -73,6 +85,8 @@ def fit(self, X, y=None):
             self.columns_ = list(X.select_dtypes(exclude='number').columns)
         elif self.columns == "numeric":
             self.columns_ =  [col for col in X.columns if is_numeric_dtype(X[col])]
+        elif self.columns == "auto":
+            self.columns_ = auto_select_categorical_features(X)
         elif self.columns == "all":
             if isinstance(X, pd.DataFrame):
                 self.columns_ = X.columns

tpot2/config/transformers.py

@@ -1,7 +1,7 @@
 from functools import partial
 import numpy as np
 
-from tpot2.builtin_modules import ZeroCount, OneHotEncoder
+from tpot2.builtin_modules import ZeroCount, OneHotEncoder, ColumnOneHotEncoder
 from sklearn.preprocessing import Binarizer
 from sklearn.decomposition import FastICA
 from sklearn.cluster import FeatureAgglomeration
@@ -99,5 +99,5 @@ def make_transformer_config_dictionary(random_state=None, n_features=10):
                 RobustScaler: {},
                 StandardScaler: {},
                 ZeroCount: params_tpot_builtins_ZeroCount,
-                OneHotEncoder: params_tpot_builtins_OneHotEncoder,
+                ColumnOneHotEncoder: params_tpot_builtins_OneHotEncoder,
             }

tpot2/evolvers/base_evolver.py

@@ -483,9 +483,10 @@ def optimize(self, generations=None):
         except KeyboardInterrupt:
             if self.verbose >= 3:
                 print("KeyboardInterrupt")
-
             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")
 
 
 
@@ -623,17 +624,17 @@ 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 = 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, **self.objective_kwargs)
 
         self.population.update_column(individuals_to_evaluate, column_names=self.objective_names, data=scores)
         if budget is not None:
             self.population.update_column(individuals_to_evaluate, column_names="Budget", data=budget)
 
         self.population.update_column(individuals_to_evaluate, column_names="Submitted Timestamp", data=start_times)
         self.population.update_column(individuals_to_evaluate, column_names="Completed Timestamp", data=end_times)
-        self.population.remove_invalid_from_population(column_names=self.objective_names)
-        self.population.remove_invalid_from_population(column_names=self.objective_names, invalid_value="TIMEOUT")
+        self.population.update_column(individuals_to_evaluate, column_names="Eval Error", data=eval_errors)
+        self.population.remove_invalid_from_population(column_names="Eval Error")
+        self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")
 
     def get_unevaluated_individuals(self, column_names, budget=None, individual_list=None):
         if individual_list is not None:
@@ -695,7 +696,7 @@ def evaluate_population_selection_early_stop(self,survival_counts, thresholds=No
             if parallel_timeout < 0:
                 parallel_timeout = 10
 
-            scores, start_times, end_times = tpot2.utils.eval_utils.parallel_eval_objective_list2(individual_list=unevaluated_individuals_this_step,
+            scores, start_times, end_times, eval_errors = tpot2.utils.eval_utils.parallel_eval_objective_list2(individual_list=unevaluated_individuals_this_step,
                                     objective_list=self.objective_functions,
                                     verbose=self.verbose,
                                     max_eval_time_seconds=self.max_eval_time_seconds,
@@ -706,14 +707,14 @@ def evaluate_population_selection_early_stop(self,survival_counts, thresholds=No
                                     client=self._client,
                                     **self.objective_kwargs,
                                     )
-
+            
             self.population.update_column(unevaluated_individuals_this_step, column_names=this_step_names, data=scores)
             self.population.update_column(unevaluated_individuals_this_step, column_names="Submitted Timestamp", data=start_times)
             self.population.update_column(unevaluated_individuals_this_step, column_names="Completed Timestamp", data=end_times)
+            self.population.update_column(unevaluated_individuals_this_step, column_names="Eval Error", data=eval_errors)
 
-
-            self.population.remove_invalid_from_population(column_names=this_step_names)
-            self.population.remove_invalid_from_population(column_names=this_step_names, invalid_value="TIMEOUT")
+            self.population.remove_invalid_from_population(column_names="Eval Error")
+            self.population.remove_invalid_from_population(column_names="Eval Error", invalid_value="TIMEOUT")
 
             #remove invalids:
             invalids = []

tpot2/evolvers/steady_state_evolver.py

@@ -22,6 +22,15 @@
 import dask
 import warnings
 
+
+def ind_mutate(ind, rng_):
+    rng = np.random.default_rng(rng_)
+    return ind.mutate(rng_=rng)
+
+def ind_crossover(ind1, ind2, rng_):
+    rng = np.random.default_rng(rng_)
+    return ind1.crossover(ind2, rng_=rng)
+
 class SteadyStateEvolver():
     def __init__(   self,
                     individual_generator ,
@@ -241,6 +250,8 @@ def optimize(self):
 
             done = False
             start_time = time.time()
+
+            enough_parents_evaluated=False
             while not done:
 
                 ###############################
@@ -257,20 +268,31 @@ def optimize(self):
 
                 #Loop through all futures, collect completed and timeout futures.
                 for completed_future in list(submitted_futures.keys()):
-
+                    eval_error = None
                     #get scores and update
                     if completed_future.done(): #if future is done
                         #If the future is done but threw and error, record the error
                         if completed_future.exception() or completed_future.status == "error": #if the future is done and threw an error
                             print("Exception in future")
                             print(completed_future.exception())
-                            scores = ["INVALID" for _ in range(len(self.objective_names))]
+                            scores = [np.nan for _ in range(len(self.objective_names))]
+                            eval_error = "INVALID"
                         elif completed_future.cancelled(): #if the future is done and was cancelled
                             print("Cancelled future (likely memory related)")
-                            scores = ["INVALID" for _ in range(len(self.objective_names))]
+                            scores = [np.nan for _ in range(len(self.objective_names))]
+                            eval_error = "INVALID"
                         else: #if the future is done and did not throw an error, get the scores
                             try:
                                 scores = completed_future.result()
+
+                                #check if scores contain "INVALID" or "TIMEOUT"
+                                if "INVALID" in scores:
+                                    eval_error = "INVALID"
+                                    scores = [np.nan]
+                                elif "TIMEOUT" in scores:
+                                    eval_error = "TIMEOUT"
+                                    scores = [np.nan]
+
                             except Exception as e:
                                 print("Exception in future, but not caught by dask")
                                 print(e)
@@ -279,7 +301,8 @@ def optimize(self):
                                 print("status", completed_future.status)
                                 print("done", completed_future.done())
                                 print("cancelld ", completed_future.cancelled())
-                                scores = ["INVALID" for _ in range(len(self.objective_names))]
+                                scores = [np.nan for _ in range(len(self.objective_names))]
+                                eval_error = "INVALID"
                     else: #if future is not done
 
                         #check if the future has been running for too long, cancel the future
@@ -289,7 +312,8 @@ def optimize(self):
                             if self.verbose >= 4:
                                 print(f'WARNING AN INDIVIDUAL TIMED OUT (Fallback): \n {submitted_futures[completed_future]} \n')
 
-                            scores = ["TIMEOUT" for _ in range(len(self.objective_names))]
+                            scores = [np.nan for _ in range(len(self.objective_names))]
+                            eval_error = "TIMEOUT"
                         else:
                             continue #otherwise, continue to next future
 
@@ -304,6 +328,7 @@ def optimize(self):
                         scores = [scores[0] for _ in range(len(self.objective_names))]
                     self.population.update_column(this_individual, column_names=self.objective_names, data=scores)
                     self.population.update_column(this_individual, column_names="Completed Timestamp", data=time.time())
+                    self.population.update_column(this_individual, column_names="Eval Error", data=eval_error)
                     if budget is not None:
                         self.population.update_column(this_individual, column_names="Budget", data=this_budget)
 
@@ -314,9 +339,8 @@ def optimize(self):
 
                 #now we have a list of completed futures
 
-
-                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")
 
 
                 ###############################
@@ -429,33 +453,56 @@ def optimize(self):
                 ###############################
                 n_individuals_to_submit = self.max_queue_size - len(submitted_futures)
                 if n_individuals_to_submit > 0:
-                    parents_df = self.population.get_column(self.population.population, column_names=self.objective_names+ ["Individual"], to_numpy=False)
-                    parents_df = parents_df[~parents_df[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)]
-                    parents_df = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]
-
-                    cur_evaluated_population = parents_df["Individual"].to_numpy()
-                    if len(cur_evaluated_population) > 0:
-                        scores = parents_df[self.objective_names].to_numpy()
-                        weighted_scores = scores * self.objective_function_weights
-                        #number of crossover pairs and mutation only parent to generate
-
-                        if len(parents_df) < 2:
-                            var_ops = ["mutate" for _ in range(n_individuals_to_submit)]
-                        else:
-                            var_ops = [self.rng.choice(["crossover","mutate_then_crossover","crossover_then_mutate",'mutate'],p=[self.crossover_probability,self.mutate_then_crossover_probability, self.crossover_then_mutate_probability,self.mutate_probability]) for _ in range(n_individuals_to_submit)]
-
-                        parents = []
-                        for op in var_ops:
+                    #count non-nan values in the objective columns
+                    if not enough_parents_evaluated:
+                        parents_df = self.population.get_column(self.population.population, column_names=self.objective_names, to_numpy=False)
+                        scores = parents_df[self.objective_names[0]].to_numpy()
+                        #count non-nan values in the objective columns
+                        n_evaluated = np.count_nonzero(~np.isnan(scores))
+                        if n_evaluated >0 :
+                            enough_parents_evaluated=True
+
+                    # parents_df = self.population.get_column(self.population.population, column_names=self.objective_names+ ["Individual"], to_numpy=False)
+                    # parents_df = parents_df[~parents_df[self.objective_names].isin(["TIMEOUT","INVALID"]).any(axis=1)]
+                    # parents_df = parents_df[~parents_df[self.objective_names].isna().any(axis=1)]
+
+                    # cur_evaluated_population = parents_df["Individual"].to_numpy()
+                    # if len(cur_evaluated_population) > 0:
+                    #     scores = parents_df[self.objective_names].to_numpy()
+                    #     weighted_scores = scores * self.objective_function_weights
+                    #     #number of crossover pairs and mutation only parent to generate
+
+                    #     if len(parents_df) < 2:
+                    #         var_ops = ["mutate" for _ in range(n_individuals_to_submit)]
+                    #     else:
+                    #         var_ops = [self.rng.choice(["crossover","mutate_then_crossover","crossover_then_mutate",'mutate'],p=[self.crossover_probability,self.mutate_then_crossover_probability, self.crossover_then_mutate_probability,self.mutate_probability]) for _ in range(n_individuals_to_submit)]
+
+                    #     parents = []
+                    #     for op in var_ops:
+                    #         if op == "mutate":
+                    #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=1, rng_=self.rng)])
+                    #         else:
+                    #             parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=2, rng_=self.rng)])
+
+                    #     #_offspring = self.population.create_offspring2(parents, var_ops, rng_=self.rng, add_to_population=True)
+                    #     offspring = self.population.create_offspring2(parents, var_ops, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng_=self.rng)
+
+                    if enough_parents_evaluated:
+
+                        parents = self.population.parent_select(selector=self.parent_selector, weights=self.objective_function_weights, columns_names=self.objective_names, k=n_individuals_to_submit, n_parents=2, rng_=self.rng)
+                        p = np.array([self.crossover_probability, self.mutate_then_crossover_probability, self.crossover_then_mutate_probability, self.mutate_probability])
+                        p = p / p.sum()
+                        var_op_list = self.rng.choice(["crossover", "mutate_then_crossover", "crossover_then_mutate", "mutate"], size=n_individuals_to_submit, p=p)
+
+                        for i, op in enumerate(var_op_list):
                             if op == "mutate":
-                                parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=1, rng_=self.rng)])
-                            else:
-                                parents.extend(np.array(cur_evaluated_population)[self.parent_selector(weighted_scores, k=1, n_parents=2, rng_=self.rng)])
+                                parents[i] = parents[i][0] #mutations take a single individual
 
-                        _offspring = self.population.create_offspring(parents, var_ops, rng_=self.rng, n_jobs=1, add_to_population=True)
+                        offspring = self.population.create_offspring2(parents, var_op_list, [ind_mutate], None, [ind_crossover], None, add_to_population=True, keep_repeats=False, mutate_until_unique=True, rng_=self.rng)
 
                     # If we don't have enough evaluated individuals to use as parents for variation, we create new individuals randomly
                     # This can happen if the individuals in the initial population are invalid
-                    if len(cur_evaluated_population) == 0 and len(submitted_futures) < self.max_queue_size:
+                    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)]

tpot2/individual_representations/graph_pipeline_individual/individual.py

@@ -1137,7 +1137,10 @@ def _cached_transform(cache_nunber=0):
         pass
 
     def __str__(self):
-        return self.export_pipeline().__str__()
+        try:
+            return f"<GraphIdnividual {0}".format(self.export_pipeline().__str__())
+        except:
+            return "<Invalid GraphIdnividual>"
 
     def unique_id(self) -> GraphKey:
         if self.key is None: