def main():
### Set parameters ###
parameters = { 'x' : ['float',[0,15]],
'y' : ['float',[0,15]] }
nugget = 1.e-10
n_clusters = 1
cluster_evol ='constant'
corr_kernel = 'squared_exponential'
mapWithNoise= False
model_noise = None
sampling_model = 'GCP'
n_candidates= 300
n_random_init= 15
n_iter = 100
nb_iter_final = 0
acquisition_function = 'UCB'
search = SmartSearch(parameters,
estimator=scoring_function,
corr_kernel = corr_kernel,
acquisition_function = acquisition_function,
GCP_mapWithNoise=mapWithNoise,
model_noise = model_noise,
model = sampling_model,
n_candidates=n_candidates,
n_iter = n_iter,
n_init = n_random_init,
n_final_iter=nb_iter_final,
n_clusters=n_clusters,
cluster_evol = cluster_evol,
verbose=2,
detailed_res = 0)
search._fit()
def main():
### Set parameters ###
parameters = {'x': ['float', [0, 15]], 'y': ['float', [0, 15]]}
nugget = 1.e-10
n_clusters = 1
cluster_evol = 'constant'
corr_kernel = 'squared_exponential'
mapWithNoise = False
model_noise = None
sampling_model = 'GCP'
n_candidates = 300
n_random_init = 15
n_iter = 100
nb_iter_final = 0
acquisition_function = 'UCB'
search = SmartSearch(parameters,
estimator=scoring_function,
corr_kernel=corr_kernel,
acquisition_function=acquisition_function,
GCP_mapWithNoise=mapWithNoise,
model_noise=model_noise,
model=sampling_model,
n_candidates=n_candidates,
n_iter=n_iter,
n_init=n_random_init,
n_final_iter=nb_iter_final,
n_clusters=n_clusters,
cluster_evol=cluster_evol,
verbose=2,
detailed_res=0)
search._fit()
def hpo_custom_function():
parameters = {'kernel': ['cat', ['rbf', 'poly']],
'd': ['int', [1, 3]],
'C': ['float', [1, 10]]}
def scoring_function(x):
return [0.5]
search = SmartSearch(parameters, model='GP', estimator=scoring_function, n_iter=20)
search._fit()
def hpo_sklearn_pipeline():
iris = load_digits()
X, y = iris.data, iris.target
clf = RandomForestClassifier(n_estimators=20)
# specify parameters and distributions to sample from
parameters = {"max_depth": ['int', [3, 3]],
"max_features": ['int', [1, 11]],
"min_samples_split": ['int', [1, 11]],
"min_samples_leaf": ['int', [1, 11]],
"bootstrap": ['cat', [True, False]],
"criterion": ['cat', ["gini", "entropy"]]}
search = SmartSearch(parameters, estimator=clf, X=X, y=y, n_iter=20)
search._fit()
if __name__ == '__main__':
n_test = int(sys.argv[1])
sub_size = int(sys.argv[2])
print 'Running', n_test, 'tests with a subsample size of', sub_size
data, clean_reviews = get_data()
def scoring(parameters):
return scoring_function(parameters,
data=clean_reviews,
Y=data["sentiment"],
sub_size_=sub_size)
search = SmartSearch(estimator=scoring,
parameters=parameters,
model='rand',
n_init=n_test,
n_iter=n_test,
n_final_iter=0,
detailed_res=1)
tested_params, outputs = search._fit()
f = open(("scoring_function/" + str(sub_size) + "_output.csv"), 'w')
for line in outputs:
print >> f, line
np.savetxt("scoring_function/" + str(sub_size) + "_params.csv",
tested_params,
delimiter=",")
def runExperiment(first_exp,
n_exp,
dir_,
parameters,
subsize,
model = 'GCP',
n_random_init = 10,
n_total_iter = 30,
n_candidates = 500,
corr_kernel = 'squared_exponential',
acquisition_function = 'UCB',
n_clusters = 1,
cluster_evol = 'constant',
GCP_mapWithNoise = False,
GCP_useAllNoisyY = False,
model_noise = None):
last_exp = first_exp + n_exp
print 'Run experiment', model, first_exp, 'to', last_exp
# Load data
output = []
f =open(( dir_ + 'scoring_function/' + str(subsize) + '_output.csv'),'r')
for l in f:
l = l[1:-3]
string_l = l.split(',')
output.append( [ float(i) for i in string_l] )
f.close()
print 'Loaded output file,', len(output),'rows'
params = np.genfromtxt((dir_ + 'scoring_function/' + str(subsize) + '_params.csv'),
delimiter=',')
print 'Loaded parameters file, shape :', params.shape
KNN = NearestNeighbors()
KNN.fit(params)
# KNN.kneighbors(p,1,return_distance=False)[0]
keys = sorted(parameters.keys())
# function that retrieves a performance evaluation from the stored results
def get_cv_res(p_dict):
p = np.zeros(len(parameters))
# convert dict parameter into array of numbers
for k in xrange(len(keys)):
if(parameters[keys[k]][0] != 'cat'):
p[k] = p_dict[keys[k]]
else:
p[k] = parameters[keys[k]][1].index(p_dict[keys[k]])
idx = KNN.kneighbors(p, 1, return_distance=False)[0]
all_o = output[idx]
r = 0
if(len(all_o) > 5):
r = np.random.randint(len(all_o)/5)
return all_o[(5*r):(5*r+5)]
model_dir = model + '/' + str(subsize)
### Run experiment ###
if not os.path.exists(dir_ + 'exp_results'):
os.mkdir(dir_ + 'exp_results')
if not os.path.exists(dir_ + 'exp_results/' + model):
os.mkdir(dir_ + 'exp_results/' + model)
if not os.path.exists(dir_ + 'exp_results/' + model_dir):
os.mkdir(dir_ + 'exp_results/' + model_dir)
for n_exp in range(first_exp,last_exp):
print ' **** Run exp',n_exp,' ****'
### set directory
if not os.path.exists(dir_ + 'exp_results/' + model_dir + '/exp' + str(n_exp)):
os.mkdir(dir_ + 'exp_results/'+ model_dir + '/exp' + str(n_exp))
else:
print('Warning : directory already exists')
search = SmartSearch(parameters,
estimator = get_cv_res,
corr_kernel = corr_kernel ,
GCP_mapWithNoise=GCP_mapWithNoise,
GCP_useAllNoisyY=GCP_useAllNoisyY,
model_noise = model_noise,
model = model,
n_candidates = n_candidates,
n_iter = n_total_iter,
n_init = n_random_init,
n_clusters = n_clusters,
cluster_evol = cluster_evol,
verbose = 2,
acquisition_function = acquisition_function,
detailed_res = 2)
all_parameters, all_search_path, all_raw_outputs, all_mean_outputs = search._fit()
## save experiment's data
f =open((dir_ + 'exp_results/'+ model_dir + '/exp'+str(n_exp)+'/output.csv'),'w')
for line in all_raw_outputs:
print>>f,line
f.close()
np.savetxt((dir_ + 'exp_results/'+ model_dir + '/exp' + str(n_exp) +'/param.csv'),
all_parameters,
delimiter=',')
np.savetxt((dir_ + 'exp_results/'+ model_dir + '/exp' + str(n_exp) +'/param_path.csv'),
all_search_path,
delimiter=',')
print ' **** End experiment', n_exp, ' ****\n'
return cv_results
if __name__ == "__main__":
n_test = int(sys.argv[1])
sub_size = int(sys.argv[2])
print "Running", n_test, "tests with a subsample size of", sub_size
data, clean_reviews = get_data()
def scoring(parameters):
return scoring_function(parameters, data=clean_reviews, Y=data["sentiment"], sub_size_=sub_size)
search = SmartSearch(
estimator=scoring,
parameters=parameters,
model="rand",
n_init=n_test,
n_iter=n_test,
n_final_iter=0,
detailed_res=1,
)
tested_params, outputs = search._fit()
f = open(("scoring_function/" + str(sub_size) + "_output.csv"), "w")
for line in outputs:
print >> f, line
np.savetxt("scoring_function/" + str(sub_size) + "_params.csv", tested_params, delimiter=",")
def runExperiment(first_exp,
n_exp,
dir_,
parameters,
subsize,
model='GCP',
n_random_init=10,
n_total_iter=30,
n_candidates=500,
corr_kernel='squared_exponential',
acquisition_function='UCB',
n_clusters=1,
cluster_evol='constant',
GCP_mapWithNoise=False,
GCP_useAllNoisyY=False,
model_noise=None):
last_exp = first_exp + n_exp
print 'Run experiment', model, first_exp, 'to', last_exp
# Load data
output = []
f = open((dir_ + 'scoring_function/' + str(subsize) + '_output.csv'), 'r')
for l in f:
l = l[1:-3]
string_l = l.split(',')
output.append([float(i) for i in string_l])
f.close()
print 'Loaded output file,', len(output), 'rows'
params = np.genfromtxt(
(dir_ + 'scoring_function/' + str(subsize) + '_params.csv'),
delimiter=',')
print 'Loaded parameters file, shape :', params.shape
KNN = NearestNeighbors()
KNN.fit(params)
# KNN.kneighbors(p,1,return_distance=False)[0]
keys = sorted(parameters.keys())
# function that retrieves a performance evaluation from the stored results
def get_cv_res(p_dict):
p = np.zeros(len(parameters))
# convert dict parameter into array of numbers
for k in xrange(len(keys)):
if (parameters[keys[k]][0] != 'cat'):
p[k] = p_dict[keys[k]]
else:
p[k] = parameters[keys[k]][1].index(p_dict[keys[k]])
idx = KNN.kneighbors(p, 1, return_distance=False)[0]
all_o = output[idx]
r = 0
if (len(all_o) > 5):
r = np.random.randint(len(all_o) / 5)
return all_o[(5 * r):(5 * r + 5)]
model_dir = model + '/' + str(subsize)
### Run experiment ###
if not os.path.exists(dir_ + 'exp_results'):
os.mkdir(dir_ + 'exp_results')
if not os.path.exists(dir_ + 'exp_results/' + model):
os.mkdir(dir_ + 'exp_results/' + model)
if not os.path.exists(dir_ + 'exp_results/' + model_dir):
os.mkdir(dir_ + 'exp_results/' + model_dir)
for n_exp in range(first_exp, last_exp):
print ' **** Run exp', n_exp, ' ****'
### set directory
if not os.path.exists(dir_ + 'exp_results/' + model_dir + '/exp' +
str(n_exp)):
os.mkdir(dir_ + 'exp_results/' + model_dir + '/exp' + str(n_exp))
else:
print('Warning : directory already exists')
search = SmartSearch(parameters,
estimator=get_cv_res,
corr_kernel=corr_kernel,
GCP_mapWithNoise=GCP_mapWithNoise,
GCP_useAllNoisyY=GCP_useAllNoisyY,
model_noise=model_noise,
model=model,
n_candidates=n_candidates,
n_iter=n_total_iter,
n_init=n_random_init,
n_clusters=n_clusters,
cluster_evol=cluster_evol,
verbose=2,
acquisition_function=acquisition_function,
detailed_res=2)
all_parameters, all_search_path, all_raw_outputs, all_mean_outputs = search._fit(
)
## save experiment's data
f = open((dir_ + 'exp_results/' + model_dir + '/exp' + str(n_exp) +
'/output.csv'), 'w')
for line in all_raw_outputs:
print >> f, line
f.close()
np.savetxt((dir_ + 'exp_results/' + model_dir + '/exp' + str(n_exp) +
'/param.csv'),
all_parameters,
delimiter=',')
np.savetxt((dir_ + 'exp_results/' + model_dir + '/exp' + str(n_exp) +
'/param_path.csv'),
all_search_path,
delimiter=',')
print ' **** End experiment', n_exp, ' ****\n'
