Merge pull request #40 from perib/dev

Dev
EpistasisLab · Aug 16, 2023 · b041ea8 · b041ea8
2 parents e6a2647 + 2e0d6e1
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diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md
@@ -18,7 +18,7 @@
 
 ## What are the relevant issues?
 
-[you can link directly to issues by entering # then the number of the issue, for example, #3 links to issue 3]
+[you can link directly to issues by entering # then the number of the issue]
 
 ## Screenshots (if appropriate)
 

diff --git a/README.md b/README.md
@@ -17,6 +17,11 @@ If you are interested in using the current stable release of TPOT, you can do th
 Please see the [repository license](https://github.com/EpistasisLab/tpot2/blob/main/LICENSE) for the licensing and usage information for TPOT2.
 Generally, we have licensed TPOT2 to make it as widely usable as possible.
 
+## Documentation
+
+[The documentation webpage can be found here.](epistasislab.github.io/tpot2/)
+
+We also recommend looking at the Tutorials folder for jupyter notebooks with examples and guides.
 
 ## Installation
 

diff --git a/Tutorial/3_Genetic_Feature_Set_Selectors.ipynb b/Tutorial/3_Genetic_Feature_Set_Selectors.ipynb
diff --git a/Tutorial/4_Symbolic_Regression_and_Classification.ipynb b/Tutorial/4_Symbolic_Regression_and_Classification.ipynb
diff --git a/tpot2/evolvers/base_evolver.py b/tpot2/evolvers/base_evolver.py
@@ -390,7 +390,7 @@ def optimize(self, generations=None):
                         self.evaluate_population()
 
                     if len(self.population.population) == 0:
-                        raise Exception("No individuals could be evaluated in the initial population")
+                        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.")
 
                     self.generation += 1
                 # Generation 1 is the first generation after the initial population

diff --git a/tpot2/evolvers/steady_state_evolver.py b/tpot2/evolvers/steady_state_evolver.py
@@ -20,6 +20,7 @@
 import math
 from tpot2.utils.utils import get_thresholds, beta_interpolation, remove_items, equalize_list
 import dask
+import warnings
 
 class SteadyStateEvolver():
     def __init__(   self, 
@@ -451,10 +452,18 @@ def optimize(self):
                     # 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:
+
+                        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)]
+                        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.")
+
                         n_individuals_to_create = self.max_queue_size - len(submitted_futures)
                         initial_population = [next(self.individual_generator) for _ in range(n_individuals_to_create)]
                         self.population.add_to_population(initial_population)
 
+
+
 
                 ###############################
                 # Step 6: Add Unevaluated Individuals Generated by Variation

diff --git a/tpot2/objectives/__init__.py b/tpot2/objectives/__init__.py
@@ -1,15 +1,16 @@
-from .average_path_length_objective import average_path_length_objective
-from .number_of_nodes_objective import number_of_nodes_objective
-from .number_of_leaves_scorer import number_of_leaves_scorer, number_of_leaves_objective
-from .complexity_objective import complexity_scorer
+from .average_path_length import average_path_length_objective
+from .number_of_nodes import number_of_nodes_objective
+from .number_of_leaves import number_of_leaves_scorer, number_of_leaves_objective
+from .complexity import complexity_scorer
 
 
 #these scorers are calculated per fold of CV on the fitted pipeline for that fold
-SCORERS =       {   "number_of_leaves": number_of_leaves_scorer,
-                    "complexity": complexity_scorer
+SCORERS =       {   
+                    "complexity_scorer": complexity_scorer
                 }
 
 #these objectives are calculated once on unfitted models as secondary objectives
-OBJECTIVES =    {  "average_path_length": average_path_length_objective,
-                    "number_of_nodes": number_of_nodes_objective,
+OBJECTIVES =    {  "average_path_length_objective": average_path_length_objective,
+                    "number_of_nodes_objective": number_of_nodes_objective,
+                    "number_of_leaves_objective": number_of_leaves_objective
                 }
diff --git a/...jectives/average_path_length_objective.py → tpot2/objectives/average_path_length.py b/...jectives/average_path_length_objective.py → tpot2/objectives/average_path_length.py
diff --git a/tpot2/objectives/complexity_objective.py → tpot2/objectives/complexity.py b/tpot2/objectives/complexity_objective.py → tpot2/objectives/complexity.py
diff --git a/tpot2/objectives/number_of_leaves_scorer.py → tpot2/objectives/number_of_leaves.py b/tpot2/objectives/number_of_leaves_scorer.py → tpot2/objectives/number_of_leaves.py
diff --git a/...2/objectives/number_of_nodes_objective.py → tpot2/objectives/number_of_nodes.py b/...2/objectives/number_of_nodes_objective.py → tpot2/objectives/number_of_nodes.py
diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py
@@ -374,7 +374,7 @@ def __init__(self,  scorers= [],
             3. best individual
             4. warnings
             >=5. full warnings trace
-            6. evaluations progress bar. (Temporary: This used to be 2. Currently, using evaluation progress bar may prevent some instances were we terminate a generation early due to it reaching max_time_seconds in the middle of a generation OR a pipeline failed to be terminated normally and we need to manually terminate it.)
+           
         
         periodic_checkpoint_folder : str, default=None
             Folder to save the population to periodically. If None, no periodic saving will be done.