def main():
wine_file = 'data/wine.data'
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'
try:
data = pd.read_csv(wine_file, header=None)
except IOError:
print ('No {0} file found, downloading it from {1}'
.format(wine_file, url))
wine_reques = requests.get(url).content
data = pd.read_csv(io.StringIO(wine_reques.decode('utf-8')),
header=None)
X = data.iloc[:, 1:].values
X_scaled = scale(X)
y = data.iloc[:, 0].values
kf = KFold(len(data.index), n_folds=5, shuffle=True, random_state=42)
neighbors_range = [x for x in range(1, 51)]
write_submission(
classifier_choice_cv(
X, y, KNeighborsClassifier, 'n_neighbors', neighbors_range, kf)[0],
'31')
write_submission(
classifier_choice_cv(
X, y, KNeighborsClassifier, 'n_neighbors', neighbors_range, kf)[1],
'32')
write_submission(
classifier_choice_cv(
X_scaled, y, KNeighborsClassifier, 'n_neighbors', neighbors_range, kf)[0],
'33')
write_submission(
classifier_choice_cv(
X_scaled, y, KNeighborsClassifier, 'n_neighbors', neighbors_range, kf)[1],
'34')
def main():
boston = load_boston()
X = scale(boston.data)
y = boston.target
classifier = KNeighborsRegressor(n_neighbors=5, weights='distance')
kf = KFold(len(X), n_folds=5, shuffle=True, random_state=42)
neighbors_range = np.linspace(1.0, 10.0, num=200)
write_submission(
int(classifier_choice_cv(
X, y, classifier, 'p', neighbors_range, kf,
scoring='mean_squared_error')[0]),
'41')
