def setup_smart_search() -> None:
"""Create an instance to test against.
Returns:
SmartSearch: Instantiated smart search class
"""
scopes = ["api_listings_read", "api_agencies_read"]
searcher = SmartSearch(
domain_client_id=os.getenv("CLIENT_ID"),
domain_client_secret=os.getenv("CLIENT_SECRET"),
domain_scopes=scopes,
google_maps_key=os.getenv("GOOGLE_MAPS_KEY"),
walkscore_api_key=os.getenv("WSAPIKEY"),
)
return searcher
def test3():
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print("Loading 20 newsgroups dataset for categories:")
print(categories)
data = fetch_20newsgroups(subset='train', categories=categories)
print("%d documents" % len(data.filenames))
print("%d categories" % len(data.target_names))
print()
# define a pipeline combining a text feature extractor with a simple
# classifier
pipeline = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier()),
])
# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
parameters = {
'vect__max_df': ['float', [0.5, 1.]],
#'vect__max_features': (None, 5000, 10000, 50000),
'vect__ngram_range': ['cat', [(1, 1), (1, 2)]], # unigrams or bigrams
#'tfidf__use_idf': (True, False),
'tfidf__norm': ['cat', ('l1', 'l2')],
'clf__alpha': ['float', [0.000001, 0.0001]],
'clf__penalty': ['cat', ['l2', 'elasticnet']]
#'clf__n_iter': (10, 50, 80),
}
search = SmartSearch(parameters,
estimator=pipeline,
X=data.data,
y=data.target,
n_iter=30)
search._fit()
def test2():
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=15,
n_init=10,
n_final_iter=3)
search._fit()
def test1():
iris = load_digits()
X, y = iris.data, iris.target
clf = RandomForestClassifier(n_estimators=20)
# specify parameters
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()
from response_parser import clean_response
from smart_search import SmartSearch
from utils import json_serial
# Instantiate Logger as per config
LOGGER = configure_logger()
# Instantiate searcher
scopes = ["api_listings_read", "api_agencies_read"]
searcher = SmartSearch(
domain_client_id=os.getenv("CLIENT_ID"),
domain_client_secret=os.getenv("CLIENT_SECRET"),
domain_scopes=scopes,
google_maps_key=os.getenv("GOOGLE_MAPS_KEY"),
walkscore_api_key=os.getenv("WSAPIKEY"),
)
def search(event: dict, context: object) -> dict:
"""Entry point function for the API to manage & handle requests.
Args:
event (dict): API request including gateway information
context (object): Methods and properties that provide information about the invocation,
function, and execution environment
Returns:
dict: Filtered properties based on search criteria
def runExperiment(first_exp,
n_exp,
parameters,
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',first_exp,'to',last_exp
# Load data
output = []
f =open(("scoring_function/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(("scoring_function/params.csv"),delimiter=',')
print 'Loaded parameters file, shape :',params.shape
KNN = NearestNeighbors()
KNN.fit(params)
# KNN.kneighbors(p,1,return_distance=False)[0]
# function that retrieves a performance evaluation from the stored results
def get_cv_res(p_dict):
p = np.zeros(len(parameters))
for k in p_dict.keys():
p[int(k)] = p_dict[k]
idx = KNN.kneighbors(p,1,return_distance=False)[0]
all_o = output[idx]
r = np.random.randint(len(all_o)/5)
return all_o[(5*r):(5*r+5)]
### Run experiment ###
for n_exp in range(first_exp,last_exp):
print ' **** Run exp',n_exp,' ****'
### set directory
if not os.path.exists("exp_results/exp"+str(n_exp)):
os.mkdir("exp_results/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
for i in range(len(all_raw_outputs)):
f =open(("exp_results/exp"+str(n_exp)+"/output_"+str(i)+".csv"),'w')
for line in all_raw_outputs[i]:
print>>f,line
f.close()
np.savetxt(("exp_results/exp"+str(n_exp)+"/param_"+str(i)+".csv"),all_parameters[i], delimiter=",")
np.savetxt(("exp_results/exp"+str(n_exp)+"/param_path_"+str(i)+".csv"),all_search_path[i], delimiter=",")
print ' **** End experiment',n_exp,' ****\n'
print ' **** Run exp', n_exp, ' ****'
### set directory
if not os.path.exists("exp_results/exp" + str(n_exp)):
os.mkdir("exp_results/exp" + str(n_exp))
else:
print('Warning : directory already exists')
all_parameters,all_raw_outputs,all_mean_outputs, all_std_outputs, all_param_path = \
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 = 2)
all_parameters, all_search_path, all_raw_outputs, all_mean_outputs = search._fit(
)
## save experiment's data
for i in range(len(all_raw_outputs)):
f = open(
("exp_results/exp" + str(n_exp) + "/output_" + str(i) + ".csv"),
'w')
def gp_vs_random_search(test_name, n_tests, search_lenght, save_data=False):
"""
Compare GP-based search vs a simple random one
Choose test_name in {'iris','text'}
"""
n_iter_search = search_lenght
if (test_name == 'iris'):
iris = load_digits()
X, y = iris.data, iris.target
pipeline = RandomForestClassifier()
# 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"]]
}
elif (test_name == 'text'):
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
# Load some categories from the training set
categories = [
'alt.atheism',
'talk.religion.misc',
]
# Uncomment the following to do the analysis on all the categories
#categories = None
print("Loading 20 newsgroups dataset for categories:")
print(categories)
data = fetch_20newsgroups(subset='train', categories=categories)
print("%d documents" % len(data.filenames))
print("%d categories" % len(data.target_names))
X = data.data
y = data.target
# define a pipeline combining a text feature extractor with a simple
# classifier
pipeline = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier()),
])
# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
parameters = {
'vect__max_df': ['float', [0.5, 1.]],
#'vect__max_features': (None, 5000, 10000, 50000),
'vect__ngram_range': ['cat', [(1, 1),
(1, 2)]], # unigrams or bigrams
#'tfidf__use_idf': (True, False),
#'tfidf__norm': ('l1', 'l2'),
'clf__alpha': ['float', [0.000001, 0.00001]],
'clf__penalty': ['cat', ['l2', 'elasticnet']]
#'clf__n_iter': (10, 50, 80),
}
else:
print('Dataset not available for test')
# GP UCB search
all_gp_ucb_results = []
print 'GP_ucb search'
for i in range(n_tests):
ucb_search = SmartSearch(parameters,
estimator=pipeline,
X=X,
y=y,
acquisition_function='UCB',
n_iter=n_iter_search,
n_init=20,
verbose=False)
_, scores = ucb_search._fit()
max_scores = [scores[0]]
print 'Test', i, '-', len(scores), 'parameters tested'
for j in range(1, len(scores)):
max_scores.append(max(max_scores[j - 1], scores[j]))
all_gp_ucb_results.append(extend_result(n_iter_search, max_scores))
all_gp_ucb_results = np.asarray(all_gp_ucb_results)
print all_gp_ucb_results.shape
if (save_data):
np.savetxt('gp_ucb_scores.csv', all_gp_ucb_results, delimiter=',')
# # GP EI search
# all_gp_ei_results = []
# print 'GP_ei search'
# for i in range(n_tests):
# ei_search = SmartSearch(parameters,estimator=pipeline,X=X,y=y,
# acquisition_function='EI',
# n_iter=n_iter_search, n_init=20, verbose=False)
# _,scores = ei_search._fit()
# max_scores = [scores[0]]
# print 'Test',i,'-',len(scores),'parameters tested'
# for j in range(1,len(scores)):
# max_scores.append(max(max_scores[j-1],scores[j]))
# all_gp_ei_results.append(extend_result(n_iter_search,max_scores))
# all_gp_ei_results = np.asarray(all_gp_ei_results)
# print all_gp_ei_results.shape
# if(save_data):
# np.savetxt('gp_ei_scores.csv',all_gp_ei_results,delimiter=',')
# Randomized search
print 'Random search'
all_random_results = []
for i in range(n_tests):
random_search = SmartSearch(parameters,
estimator=pipeline,
X=X,
y=y,
n_iter=n_iter_search,
n_init=n_iter_search,
verbose=False)
_, scores = random_search._fit()
max_scores = [scores[0]]
print 'Test', i, '-', len(scores), 'parameters tested'
for j in range(1, len(scores)):
max_scores.append(max(max_scores[j - 1], scores[j]))
all_random_results.append(extend_result(n_iter_search, max_scores))
all_random_results = np.asarray(all_random_results)
if (save_data):
np.savetxt('rand_scores.csv', all_random_results, delimiter=',')
plt.figure()
# plt.plot(range(n_iter_search),np.mean(all_gp_ei_results,axis=0),'r',label='GP-EI')
plt.plot(range(n_iter_search),
np.mean(all_gp_ucb_results, axis=0),
'b',
label='GP-UCB')
plt.plot(range(n_iter_search),
np.mean(all_random_results, axis=0),
'g',
label='Random')
plt.legend(loc=4)
plt.title('Test GP vs Random on ' + test_name + ' dataset - Average on ' +
str(n_tests) + ' trials')
plt.xlabel('Iterations')
plt.ylabel('Max CV performance')
plt.show()