def test():
clf = RandomForest
X_train, y_train = data.split(data.train)
X_test = data.extract_features(data.test)
clf.fit(X_train, y_train)
labels = clf.predict(X_test)
pd.DataFrame({
"PassengerId": np.array(data.test["PassengerId"]),
"Survived": labels
}).to_csv("submit.csv", index=False)
def test():
clf = RandomForest
X_train, y_train = data.split(data.train)
X_test = data.extract_features(data.test)
clf.fit(X_train, y_train)
labels = clf.predict(X_test)
pd.DataFrame({
"PassengerId": np.array(data.test["PassengerId"]),
"Survived": labels
}).to_csv("submit.csv", index=False)
def main():
cfg = keys.cfg_keys()
tweet_list = guardian.get_guardian_summary(1)
sentiment = data.classifier.classify(
data.extract_features(tweet_list[0][0].split()))
if sentiment == 'negative':
icon = emoji.emojize(':neutral_face:')
elif sentiment == 'positive':
icon = emoji.emojize(':thinking_face:')
else:
icon = ''
api = get_api(cfg)
tweet = tweet_list[0][0] + ' ' + icon + ' ' + ' #' + tweet_list[
1] + ' #' + tweet_list[2] + ' #' + tweet_list[3]
status = api.update_status(status=tweet)
def predict(tweet, theta):
x = extract_features(tweet)
pred = forward(x, theta)
return ('Positive' if pred > 0.5 else 'Negative')
taxonomies = {'gillet': gillet, 'super': super_category, 'basic': basic_level, 'sub': sub_category}
classifiers = ['knn', 'svm', 'bin_svm', 'bin_knn']
message = """Usage: %s taxonomy classifier features
taxonomy: 'gillet', 'super', 'basic', 'sub'
classifier: 'knn', 'svm', 'bin_svm' or 'bin_knn'
features: 'all', 'reduced', 'auto' (works only with binary classifiers)"""
try:
_, tax, clf, feature_set = sys.argv
taxonomy = taxonomies[tax]
if clf not in classifiers:
raise Exception
except:
sys.exit(message % sys.argv[0])
features_, labels_ = extract_features(feature_set)
features = []
labels = []
# Convert an instrument label into the corresponding taxonomy category
def get_category(label):
for category_, labels in taxonomy.iteritems():
if label in labels:
return category_
return None
# Convert a list of instrument labels into a list of taxonomy categories
def get_categories(labels):
categories = set([])
for label in labels:
category_ = get_category(label)
if category_ is None:
}
classifiers = ['knn', 'svm', 'bin_svm', 'bin_knn']
message = """Usage: %s taxonomy classifier features
taxonomy: 'gillet', 'super', 'basic', 'sub'
classifier: 'knn', 'svm', 'bin_svm' or 'bin_knn'
features: 'all', 'reduced', 'auto' (works only with binary classifiers)"""
try:
_, tax, clf, feature_set = sys.argv
taxonomy = taxonomies[tax]
if clf not in classifiers:
raise Exception
except:
sys.exit(message % sys.argv[0])
features_, labels_ = extract_features(feature_set)
features = []
labels = []
# Convert an instrument label into the corresponding taxonomy category
def get_category(label):
for category_, labels in taxonomy.iteritems():
if label in labels:
return category_
return None
# Convert a list of instrument labels into a list of taxonomy categories
def get_categories(labels):
categories = set([])