In [1]:

    

%logstart
%matplotlib inline

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tspsolver.tsp_generator import TSPGenerator
from tspsolver.ga.simulator import Simulator
from tspsolver.tuning import GeneticAlgorithmParameterEstimation

OUTPUT_TABLE_DIR = "../report/tables/"
OUTPUT_FIG_DIR = "../report/figures/"


    

    




Activating auto-logging. Current session state plus future input saved.
Filename       : ipython_log.py
Mode           : rotate
Output logging : False
Raw input log  : False
Timestamping   : False
State          : active

In [2]:

    

NUM_DATASETS = 5 # number of datasets to take the mean fitness over 
NUM_POINTS = 50  # number of points to use in each dataset
tuner = GeneticAlgorithmParameterEstimation(NUM_DATASETS, NUM_POINTS)


    

In [14]:

    

params = {
    "num_epochs": [1000],
    "num_elites": [0, 1, 2],
    "generator": ["SimplePopulationGenerator"],
    "generator_population_size": [40],
    "selector": ["TournamentSelection"],
    "selector_tournament_size": [10],
    "crossover": ["OrderCrossover"],
    "crossover_pcross": [0.9],
    "mutator": ["InversionMutation"],
    "mutator_pmutate": [0.2]
}


    

In [15]:

    

elite_results = tuner.perform_grid_search(params)


    

In [16]:

    

elite_results


    

    
Out[16]:






  

    

      

      
crossover
      
crossover_pcross
      
generator
      
generator_population_size
      
mutator
      
mutator_pmutate
      
num_elites
      
num_epochs
      
selector
      
selector_tournament_size
      
fitness
    
  
  

    

      
0
      
OrderCrossover
      
0.9
      
SimplePopulationGenerator
      
40
      
InversionMutation
      
0.2
      
0
      
1000
      
TournamentSelection
      
10
      
74.018956
    
    

      
1
      
OrderCrossover
      
0.9
      
SimplePopulationGenerator
      
40
      
InversionMutation
      
0.2
      
1
      
1000
      
TournamentSelection
      
10
      
73.651241
    
    

      
2
      
OrderCrossover
      
0.9
      
SimplePopulationGenerator
      
40
      
InversionMutation
      
0.2
      
2
      
1000
      
TournamentSelection
      
10
      
74.376653
    
  

In [17]:

    

elite_results[['num_elites', 'fitness']].to_latex(OUTPUT_TABLE_DIR + "elite_fitness.tex")


    

In [7]:

    

gen = TSPGenerator(NUM_POINTS)
data = gen.generate()

all_fitness = []
for i, row in elite_results.iterrows():
    params = row.to_dict()
    sim = Simulator(**params)
    sim.evolve(data)
    all_fitness.append(sim.get_min_fitness()[::10])
    
df = pd.DataFrame(np.array(all_fitness))


    

In [11]:

    

ax = df.T.plot()
ax.set_xlabel("Epoch")
ax.set_ylabel("Fitness")
plt.savefig(OUTPUT_FIG_DIR + 'elite_convergence.png', bbox_inches='tight')


    

In [ ]:

    

params = {
    "num_epochs": [1000],
    "num_elites": [1],
    "generator": ["SimplePopulationGenerator", "KNNPopulationGenerator"],
    "generator_population_size": [40],
    "selector": ["TournamentSelection"],
    "selector_tournament_size": [10],
    "crossover": ["OrderCrossover"],
    "crossover_pcross": [0.9],
    "mutator": ["InversionMutation"],
    "mutator_pmutate": [0.2]
}