def train():
clf = request.form['train']
if allowed_classifier(clf):
string = str('train')
hist_n = string + "hist.jpeg"
cnmt_n = string + "cnmt.jpeg"
pkl_hnd = store(app.config['static_path'], app.root_path)
# Feature extraction
data = utils.file_parser(
os.path.join(app.config['upload_path'], "data.txt"))
features = utils.feature_extractor(data['text'], 5000).todense()
sh = data.shape
# Preprocessing features and labels
data_x = utils.preprocess_features(features, 2500)
data_y, enc = utils.label_encoder(data['label'], False, None)
pkl_hnd.dump(enc, 'enc') # storing encoder
# Splitting data into training set and validation set
train_x, train_y, valid_x, valid_y = utils.train_valid(
data_x, data_y, 0.2)
#Balancing data with SMOTE
text, label = utils.balance_data(train_x, train_y)
# Selecting model and tuning hyperparameters
tr = model(clf, text[:sh[0], :], label[:sh[0]], valid_x, valid_y)
comb_mod = tr.model_selection()
# Fitting model and predicting
mod = tr.build_model(comb_mod)
pkl_hnd.dump(mod, 'model') # storing the model
pr = predict_model(valid_x)
pred = pr.predict_model(mod)
#Training Statistics
st = stats(pred, valid_y)
acc, f1 = st.train_stats()
#Plotting histogram and confusion matrix
pkl_hnd.plot_hist(data['label'], hist_n)
n_labels = np.unique(np.asarray(data['label']))
pkl_hnd.dump(n_labels, 'n_labels') # storing labels
cnf_matrix = st.cnf_mtx()
pkl_hnd.plot_confusion_matrix(
cnf_matrix,
n_labels,
cnmt_n,
normalize=True,
title='Confusion matrix',
cmap=plt.cm.Blues,
)
return render_template("train_result.html",
accuracy=acc,
img_hist=url_for(app.config['static_path'],
filename=hist_n),
img_cfmt=url_for(app.config['static_path'],
filename=cnmt_n),
f1=f1)
else:
flash('Please enter a valid classifier')
return redirect(url_for('index'))
def test_xml_parse_to_dict(self):
testxml = 'testxml.xml'
xmltodict = file_parser('xml', testxml)
self.assertEqual(type(xmltodict), type({}))
self.assertTrue(xmltodict['shapes'][0]['type'] == 'circle')
def test_json_parse_to_dict(self):
testjson = 'testjson.json'
jsontodict = file_parser('json', testjson)
self.assertEqual(type(jsontodict), type({}))
self.assertTrue(jsontodict['shapes'][0]['type'] == 'circle')
from Rectangle import Rectangle
from Triangle import Triangle
from Ellipse import Ellipse
from utils import file_parser, area_organizer, file_exporter
filetype = raw_input("What file type (json/xml)? ")
filename = raw_input("filename: ")
data = file_parser(filetype, filename)
shape_list = []
if(data):
for shape in data['shapes']:
if shape['type'] == 'circle' or shape['type'] == 'non-circle':
shape_list.append(Ellipse(shape['type'], int(shape['height']), int(shape['width'])))
elif shape['type'] == 'square'or shape['type'] == 'rectangle':
shape_list.append(Rectangle(shape['type'], int(shape['height']), int(shape['width'])))
elif shape['type'] in ['scalene','isosceles','equillateral']:
shape_list.append(Triangle(shape['type'], shape['angles'], int(shape['base']), int(shape['height'])))
area_dict = area_organizer(shape_list)
output = raw_input("output to (f)ile or (s)creen ")
if 's' in output:
file_exporter('screen', area_dict)
elif 'f' in output:
file_exporter('file', area_dict)
else:
print 'invalid destination'
