def crossval(clf=None, X=None, y=None, folds=10, n=5):
if X is None or y is None:
X, y = get_train_data()
clf = clf or get_model()
tot = 0
print("KFold folds={}, running {} times".format(folds, n))
for i in range(n):
res = cross_val_score(clf, X, y, cv=folds).mean()
tot += res
print("{}/{}: {}".format(i + 1, n, res))
print("-------- total --------")
print(tot / n)
return tot / n
def predict_test_data(input_file_path, model_path=None, device=""):
lp = get_model(model_path)
predictions = []
labels = []
with open(input_file_path) as f:
for line in f:
data_sample = json.loads(line)
predictions.append(lp.predict(data_sample)[0])
labels.append(
input_file_path.replace(
't.txt', '').replace('.txt', '').replace(
'/home/pi/.whereami/test_data/test_', '').replace(
'C:\\Users\\Daniel\\.whereami\\test_data\\test_',
''))
return predictions, labels
def crossval(clf=None, X=None, y=None, folds=10, n=5, path=None):
if X is None or y is None:
X, y = get_train_data(path)
if len(X) < folds:
raise ValueError('There are not enough samples ({}). Need at least {}.'.format(len(X), folds))
clf = clf or get_model(path)
tot = 0
print("KFold folds={}, running {} times".format(folds, n))
for i in range(n):
res = cross_val_score(clf, X, y, cv=folds).mean()
tot += res
print("{}/{}: {}".format(i + 1, n, res))
print("-------- total --------")
print(tot / n)
return tot / n
def refresh(self):
self.clf = get_model(self.model)
self.wifi_scanner = get_scanner(self.device)
def __init__(self, model=None, device=""):
self.model = model
self.device = device
self.clf = get_model(model)
self.wifi_scanner = get_scanner(device)
self.predicted_value = None
def predictprint(input_path=None, model_path=None, device=""):
lp = get_model(model_path)
data_sample = sample(
device) if input_path is None else get_external_sample(input_path)
return json.dumps(dict(zip(lp.classes_, lp.predict_proba(data_sample)[0])))
def predict(input_path=None, model_path=None, device=""):
lp = get_model(model_path)
data_sample = sample(
device) if input_path is None else get_external_sample(input_path)
return lp.predict(data_sample)[0]
def predict_proba():
lp = get_model()
print({x: y for x, y in zip(lp.classes_, lp.predict_proba(sample())[0])})
def predict():
lp = get_model()
return lp.predict(sample())[0]
def predict():
lp = get_model()
print(lp.predict(sample())[0])
def crossval():
X, y = get_train_data()
lp = get_model()
print(cross_validate_model(lp, X, y))
