def _fit(self):
"""
Run the hyper-parameter optimization process
Returns
-------
tested_parameters_ : ndarray, the parameters tested during the process
cv_scores_ : if score_format == 'cv', list of all the CV results of the
parameters tested; if score_format == 'avg', array of the
mean CV results of the parameters tested
"""
n_tested_parameters = 0
tested_parameters = np.zeros((self.n_iter, self.n_parameters))
cv_scores = []
if (self.detailed_res == 2):
search_path = np.zeros((self.n_iter, self.n_parameters))
### Initialize with random candidates ###
init_candidates = utils.sample_candidates(self.n_init,
self.param_bounds,
self.param_isInt)
self.n_init = init_candidates.shape[0]
if (self.verbose):
print('Start random init')
for i in range(self.n_init):
dict_candidate = self.vector_to_dict(init_candidates[i, :])
cv_score = self.score(dict_candidate)
if (self.verbose):
print('Step ' + str(i) + ' - Hyperparameter ' +
str(dict_candidate) + ' ' + str(np.mean(cv_score)))
is_in, idx = utils.is_in_ndarray(
init_candidates[i, :],
tested_parameters[:n_tested_parameters, :])
if not is_in:
tested_parameters[n_tested_parameters, :] = init_candidates[
i, :]
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if (self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if (self.detailed_res == 2):
search_path[i, :] = init_candidates[i, :]
### Smart Search ###
if (self.verbose):
print('Start smart search')
i_mod_10 = 0
for i in range(self.n_iter - self.n_init - self.n_final_iter):
if (i == 20 and cluster_evol == 'step'):
self.GCP_args[1] = n_clusters
if (i / 10 > (i_mod_10 + 2) and self.cluster_evol == 'variable'):
self.GCP_args[0] = self.GCP_args[0]
self.GCP_args[1] = min(self.GCP_args[1] + 1,
self.n_clusters_max)
i_mod_10 += 3
# Sample candidates and predict their corresponding acquisition values
candidates = utils.sample_candidates(self.n_candidates,
self.param_bounds,
self.param_isInt)
# Model and retrieve the candidate that maximezes the acquisiton function
best_candidate = utils.find_best_candidate(
self.model, tested_parameters[:n_tested_parameters, :],
cv_scores, self.GCP_args, candidates, self.verbose,
self.acquisition_function)
dict_candidate = self.vector_to_dict(best_candidate)
cv_score = self.score(dict_candidate)
if (self.verbose):
print('Step ' + str(i + self.n_init) + ' - Hyperparameter ' +
str(dict_candidate) + ' ' + str(np.mean(cv_score)))
is_in, idx = utils.is_in_ndarray(
best_candidate, tested_parameters[:n_tested_parameters, :])
if not is_in:
tested_parameters[n_tested_parameters, :] = best_candidate
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if (self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if (self.detailed_res == 2):
search_path[i + self.n_init, :] = best_candidate
### Final steps ###
self.acquisition_function = 'Simple'
for i in range(self.n_final_iter):
# Sample candidates and predict their corresponding acquisition values
candidates = utils.sample_candidates(self.n_candidates,
self.param_bounds,
self.param_isInt)
# Model and retrieve the candidate that maximezes the acquisiton function
best_candidate = utils.find_best_candidate(
self.model, tested_parameters[:n_tested_parameters, :],
cv_scores, self.GCP_args, candidates, self.verbose,
self.acquisition_function)
dict_candidate = self.vector_to_dict(best_candidate)
cv_score = self.score(dict_candidate)
if (self.verbose):
print('Step ' + str(i + self.n_iter - self.n_final_iter) +
' - Hyperparameter ' + str(dict_candidate) + ' ' +
str(np.mean(cv_score)))
is_in, idx = utils.is_in_ndarray(
best_candidate, tested_parameters[:n_tested_parameters, :])
if not is_in:
tested_parameters[n_tested_parameters, :] = best_candidate
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if (self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if (self.detailed_res == 2):
search_path[i + self.n_iter -
self.n_final_iter, :] = best_candidate
# compute the averages of CV results
mean_scores = []
for o in cv_scores:
mean_scores.append(np.mean(o))
# find the max
best_idx = np.argmax(mean_scores)
vector_best_param = tested_parameters[best_idx]
best_parameter = self.vector_to_dict(vector_best_param)
# store
self.best_parameter_ = best_parameter
self.tested_parameters_ = tested_parameters[:n_tested_parameters, :]
if (self.verbose):
print('\nTested ' + str(n_tested_parameters) + ' parameters')
print('Max cv score ' + str(mean_scores[best_idx]))
print('Best parameter ' + str(tested_parameters[best_idx]))
print(best_parameter)
if (self.detailed_res == 1):
self.cv_scores_ = list(cv_scores)
return tested_parameters[:n_tested_parameters, :], cv_scores
elif (self.detailed_res == 2):
self.cv_scores_ = list(cv_scores)
return tested_parameters[:
n_tested_parameters, :], search_path, cv_scores, mean_scores
else:
self.cv_scores_ = mean_scores
return tested_parameters[:n_tested_parameters, :], mean_scores
def _fit(self):
"""
Run the hyper-parameter optimization process
Returns
-------
`tested_parameters_` : ndarray, the parameters tested during the process
`cv_scores_` : if score_format == 'cv', list of all the CV results of the
parameters tested; if score_format == 'avg', array of the
mean CV results of the parameters tested
"""
n_tested_parameters = 0
tested_parameters = np.zeros((self.n_iter,self.n_parameters))
cv_scores = []
if(self.detailed_res ==2):
search_path = np.zeros((self.n_iter,self.n_parameters))
### Initialize with random candidates ###
init_candidates = utils.sample_candidates(self.n_init,self.param_bounds,self.param_isInt)
self.n_init = init_candidates.shape[0]
if(self.verbose):
print('Start random init')
for i in range(self.n_init):
dict_candidate = self.vector_to_dict(init_candidates[i,:])
cv_score = self.score(dict_candidate)
if(self.verbose):
print ('Step ' + str(i) + ' - Hyperparameter ' + str(dict_candidate) + ' ' + str(np.mean(cv_score)))
is_in,idx = utils.is_in_ndarray(init_candidates[i,:],tested_parameters[:n_tested_parameters,:])
if not is_in:
tested_parameters[n_tested_parameters,:] = init_candidates[i,:]
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if(self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if(self.detailed_res ==2):
search_path[i,:] = init_candidates[i,:]
### Smart Search ###
if(self.verbose):
print('Start smart search')
i_mod_10 = 0
for i in range(self.n_iter - self.n_init - self.n_final_iter):
if(i==20 and self.cluster_evol=='step'):
self.GCP_args[1] = n_clusters
if(i/10 > (i_mod_10+2) and self.cluster_evol=='variable'):
self.GCP_args[0] = self.GCP_args[0]
self.GCP_args[1] = min(self.GCP_args[1]+1,self.n_clusters_max)
i_mod_10 += 3
# Sample candidates and predict their corresponding acquisition values
candidates = utils.sample_candidates(self.n_candidates,self.param_bounds,self.param_isInt)
# Model and retrieve the candidate that maximezes the acquisiton function
best_candidate = utils.find_best_candidate(self.model,
tested_parameters[:n_tested_parameters,:],
cv_scores,
self.GCP_args,
candidates,
self.verbose,
self.acquisition_function)
dict_candidate = self.vector_to_dict(best_candidate)
cv_score = self.score(dict_candidate)
if(self.verbose):
print ('Step ' + str(i+self.n_init) + ' - Hyperparameter ' + str(dict_candidate) + ' ' + str(np.mean(cv_score)))
is_in,idx = utils.is_in_ndarray(best_candidate,tested_parameters[:n_tested_parameters,:])
if not is_in:
tested_parameters[n_tested_parameters,:] = best_candidate
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if(self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if(self.detailed_res ==2):
search_path[i + self.n_init,:] = best_candidate
### Final steps ###
self.acquisition_function = 'Simple'
for i in range(self.n_final_iter):
# Sample candidates and predict their corresponding acquisition values
candidates = utils.sample_candidates(self.n_candidates,self.param_bounds,self.param_isInt)
# Model and retrieve the candidate that maximezes the acquisiton function
best_candidate = utils.find_best_candidate(self.model,
tested_parameters[:n_tested_parameters,:],
cv_scores,
self.GCP_args,
candidates,
self.verbose,
self.acquisition_function)
dict_candidate = self.vector_to_dict(best_candidate)
cv_score = self.score(dict_candidate)
if(self.verbose):
print ('Step ' + str(i+self.n_iter - self.n_final_iter) + ' - Hyperparameter ' + str(dict_candidate) + ' ' + str(np.mean(cv_score)))
is_in,idx = utils.is_in_ndarray(best_candidate,tested_parameters[:n_tested_parameters,:])
if not is_in:
tested_parameters[n_tested_parameters,:] = best_candidate
cv_scores.append(cv_score)
n_tested_parameters += 1
else:
if(self.verbose):
print('Hyperparameter already tesed')
cv_scores[idx] += cv_score
if(self.detailed_res ==2):
search_path[i + self.n_iter - self.n_final_iter,:] = best_candidate
# compute the averages of CV results
mean_scores = []
for o in cv_scores:
mean_scores.append(np.mean(o))
# find the max
best_idx = np.argmax(mean_scores)
vector_best_param = tested_parameters[best_idx]
best_parameter = self.vector_to_dict(vector_best_param)
# store
self.best_parameter_ = best_parameter
self.tested_parameters_ = tested_parameters[:n_tested_parameters,:]
if(self.verbose):
print ('\nTested ' + str(n_tested_parameters) + ' parameters')
print ('Max cv score ' + str(mean_scores[best_idx]))
print ('Best parameter ' + str(tested_parameters[best_idx]))
print(best_parameter)
if(self.detailed_res == 1):
self.cv_scores_ = list(cv_scores)
return tested_parameters[:n_tested_parameters,:], cv_scores
elif(self.detailed_res == 2):
self.cv_scores_ = list(cv_scores)
return tested_parameters[:n_tested_parameters,:], search_path, cv_scores, mean_scores
else:
self.cv_scores_ = mean_scores
return tested_parameters[:n_tested_parameters,:], mean_scores
fig = plt.figure()
abs = range(0, 400)
f_plot = [scoring_function(i) for i in abs]
n_rows = nb_GCP_steps / 3
if not (nb_GCP_steps % 3 == 0):
n_rows += 1
if (save_plots):
save_data = np.asarray([np.asarray(abs), np.asarray(f_plot)[:, 0]]).T
np.savetxt('data_plots/data_plot.csv', save_data, delimiter=',')
#-------------------- Random initialization --------------------#
# sample n_random_init random parameters to initialize the process
init_rand_candidates = utils.sample_candidates(n_random_init, parameter_bounds,
isInt)
for i in range(init_rand_candidates.shape[0]):
print i
rand_candidate = init_rand_candidates[i]
new_output = scoring_function(rand_candidate)
if (verbose):
print('Random try ' + str(rand_candidate) + ', score : ' +
str(np.mean(new_output)))
if (parameters is None):
parameters = np.asarray([rand_candidate])
raw_outputs = [new_output]
mean_outputs = [np.mean(new_output)]
std_outputs = [np.std(new_output)]
else:
fig = plt.figure()
abs = range(0,400)
f_plot = [scoring_function(i) for i in abs]
n_rows = nb_GCP_steps/3
if not(nb_GCP_steps% 3 == 0):
n_rows += 1
if(save_plots):
save_data = np.asarray([np.asarray(abs),np.asarray(f_plot)[:,0]]).T
np.savetxt('data_plots/data_plot.csv',save_data,delimiter=',')
#-------------------- Random initialization --------------------#
# sample n_random_init random parameters to initialize the process
init_rand_candidates = utils.sample_candidates(n_random_init,parameter_bounds,isInt)
for i in range(init_rand_candidates.shape[0]):
print i
rand_candidate = init_rand_candidates[i]
new_output = scoring_function(rand_candidate)
if(verbose):
print('Random try '+str(rand_candidate)+', score : '+str(np.mean(new_output)))
if(parameters is None):
parameters = np.asarray([rand_candidate])
raw_outputs = [new_output]
mean_outputs = [np.mean(new_output)]
std_outputs = [np.std(new_output)]
else:
parameters,raw_outputs,mean_outputs,std_outputs = \
