From 4e51f8a5426374d95d36d8b51078951a6f5ad2a7 Mon Sep 17 00:00:00 2001
From: perib <pedroribeiroh@gmail.com>
Date: Wed, 5 Jul 2023 11:24:13 -0700
Subject: [PATCH] edit api

---
 tpot2/evolvers/steady_state_evolver.py         |  4 ++--
 tpot2/tpot_estimator/steady_state_estimator.py | 14 +++++---------
 2 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/tpot2/evolvers/steady_state_evolver.py b/tpot2/evolvers/steady_state_evolver.py
index 8de25a60..5ac6da00 100644
--- a/tpot2/evolvers/steady_state_evolver.py
+++ b/tpot2/evolvers/steady_state_evolver.py
@@ -47,7 +47,7 @@ def __init__(   self,
                     memory_limit="4GB",
                     client=None,
                     
-                    survival_percentage = 1,
+
                     crossover_probability=.2,
                     mutate_probability=.7,
                     mutate_then_crossover_probability=.05,
@@ -113,7 +113,7 @@ def __init__(   self,
 
         self.survival_selector=survival_selector
         self.parent_selector=parent_selector
-        self.survival_percentage = survival_percentage
+
         
         total_var_p = crossover_probability + mutate_probability + mutate_then_crossover_probability + crossover_then_mutate_probability
         self.crossover_probability = crossover_probability / total_var_p
diff --git a/tpot2/tpot_estimator/steady_state_estimator.py b/tpot2/tpot_estimator/steady_state_estimator.py
index d0ac6633..884ba191 100644
--- a/tpot2/tpot_estimator/steady_state_estimator.py
+++ b/tpot2/tpot_estimator/steady_state_estimator.py
@@ -63,7 +63,7 @@ def __init__(self,  scorers= [],
                         n_jobs=1,
                         memory_limit = "4GB",
                         client = None,
-                        survival_percentage = 1,
+
                         crossover_probability=.2,
                         mutate_probability=.7,
                         mutate_then_crossover_probability=.05,
@@ -287,11 +287,7 @@ def __init__(self,  scorers= [],
         
         client : dask.distributed.Client, default=None
             A dask client to use for parallelization. If not None, this will override the n_jobs and memory_limit parameters. If None, will create a new client with num_workers=n_jobs and memory_limit=memory_limit. 
-        
-        survival_percentage : float, default=1
-            Percentage of the population size to utilize for mutation and crossover at the beginning of the generation. The rest are discarded. Individuals are selected with the selector passed into survival_selector. The value of this parameter must be between 0 and 1, inclusive. 
-            For example, if the population size is 100 and the survival percentage is .5, 50 individuals will be selected with NSGA2 from the existing population. These will be used for mutation and crossover to generate the next 100 individuals for the next generation. The remainder are discarded from the live population. In the next generation, there will now be the 50 parents + the 100 individuals for a total of 150. Surivival percentage is based of the population size parameter and not the existing population size. Therefore, in the next generation we will still select 50 individuals from the currently existing 150.
-        
+
         crossover_probability : float, default=.2
             Probability of generating a new individual by crossover between two individuals.
         
@@ -309,7 +305,7 @@ def __init__(self,  scorers= [],
         
         survival_selector : function, default=survival_select_NSGA2
             Function to use to select individuals for survival. Must take a matrix of scores and return selected indexes.
-            Used to selected population_size * survival_percentage individuals at the start of each generation to use for mutation and crossover.
+            Used to selected population_size individuals at the start of each generation to use for mutation and crossover.
         
         parent_selector : function, default=parent_select_NSGA2
             Function to use to select pairs parents for crossover and individuals for mutation. Must take a matrix of scores and return selected indexes.
@@ -447,7 +443,7 @@ def __init__(self,  scorers= [],
         self.n_jobs= n_jobs
         self.memory_limit = memory_limit
         self.client = client
-        self.survival_percentage = survival_percentage
+
         self.crossover_probability = crossover_probability
         self.mutate_probability = mutate_probability
         self.mutate_then_crossover_probability= mutate_then_crossover_probability
@@ -719,7 +715,7 @@ def objective_function(pipeline_individual,
                                             objective_kwargs = {"X": X_future, "y": y_future},
                                             survival_selector=self.survival_selector,
                                             parent_selector=self.parent_selector,
-                                            survival_percentage = self.survival_percentage,
+
                                             crossover_probability = self.crossover_probability,
                                             mutate_probability = self.mutate_probability,
                                             mutate_then_crossover_probability= self.mutate_then_crossover_probability,