bayesOpt file added

Author: maurock

bayesOpt.py

@@ -0,0 +1,83 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 15 21:10:29 2020
+
+@author: mauro
+"""
+from snakeClass import run
+from GPyOpt.methods import BayesianOptimization
+import datetime
+
+################################################
+#   Set parameters for Bayesian Optimization   #
+################################################
+
+class BayesianOptimizer():
+    def __init__(self, params):
+        self.params = params
+
+    def optimize_RL(self):
+        def optimize(inputs):
+            print("INPUT", inputs)
+            inputs = inputs[0]
+
+            # Variables to optimize
+            self.params["learning_rate"] = inputs[0]
+            lr_string = '{:.8f}'.format(self.params["learning_rate"])[2:]
+            self.params["first_layer_size"] = int(inputs[1])
+            self.params["second_layer_size"] = int(inputs[2])
+            self.params["third_layer_size"] = int(inputs[3])
+            self.params["epsilon_decay_linear"] = int(inputs[4])
+
+            self.params['name_scenario'] = 'snake_lr{}_struct{}_{}_{}_eps{}'.format(lr_string,
+                                                                               self.params['first_layer_size'],
+                                                                               self.params['second_layer_size'],
+                                                                               self.params['third_layer_size'],
+                                                                               self.params['epsilon_decay_linear'])
+
+            self.params['weights_path'] = 'weights/weights_' + self.params['name_scenario'] + '.h5'
+            self.params['load_weights'] = False
+            self.params['train'] = True
+            print(self.params)
+            score, mean, stdev = run(self.params)
+            print('Total score: {}   Mean: {}   Std dev:   {}'.format(score, mean, stdev))
+            with open(self.params['log_path'], 'a') as f: 
+                f.write(str(self.params['name_scenario']) + '\n')
+                f.write('Params: ' + str(self.params) + '\n')
+            return score
+
+        optim_params = [
+            {"name": "learning_rate", "type": "continuous", "domain": (0.00005, 0.001)},
+            {"name": "first_layer_size", "type": "discrete", "domain": (20,50,100,200)},
+            {"name": "second_layer_size", "type": "discrete", "domain": (20,50,100,200)},
+            {"name": "third_layer_size", "type": "discrete", "domain": (20,50,100,200)},
+            {"name":'epsilon_decay_linear', "type": "discrete", "domain": (self.params['episodes']*0.2,
+                                                                           self.params['episodes']*0.4,
+                                                                           self.params['episodes']*0.6,
+                                                                           self.params['episodes']*0.8,
+                                                                           self.params['episodes']*1)}
+        ]
+
+        bayes_optimizer = BayesianOptimization(f=optimize,
+                                               domain=optim_params,
+                                               initial_design_numdata=6,
+                                               acquisition_type="EI",
+                                               exact_feval=True,
+                                               maximize=True)
+
+        bayes_optimizer.run_optimization(max_iter=20)
+        print('Optimized learning rate: ', bayes_optimizer.x_opt[0])
+        print('Optimized first layer: ', bayes_optimizer.x_opt[1])
+        print('Optimized second layer: ', bayes_optimizer.x_opt[2])
+        print('Optimized third layer: ', bayes_optimizer.x_opt[3])
+        print('Optimized epsilon linear decay: ', bayes_optimizer.x_opt[4])
+        return self.params
+
+
+##################
+#      Main      #
+##################
+if __name__ == '__main__':
+    # Define optimizer
+    bayesOpt = BayesianOptimizer(params)
+    bayesOpt.optimize_RL()