def post(self):
"""Classifies given data using specified classifier."""
# Retrieve specified classifier from database.
classifier_id = self.request.POST['classifier_id']
conn = ds.create_sqlite_connection()
db_entry = ds.fetch_classifier(conn, classifier_id)
classifier = db_entry[3]
# Transform given MRS data for classifier input.
file_name = self.request.POST['myfile'].filename
raw_data = self.request.POST['myfile'].file.read()
d = dataparser.get_xy_data(raw_data)
fftd = fourier_transformer.get_fft(d)
# Classify the transformed MRS data.
test_input = np.array([fftd])
classification = classifier.predict(test_input)
# Show classification results.
template = JINJA_ENVIRONMENT.get_template('classificationresults.html')
self.response.write(template.render(
classification=classification, file_name=file_name))
def load_specified_classifier(self):
"""Loads the user-specified classifier.
Returns:
Tuple containing (classifier, classifier_name, classifier_type).
If the user did not specify a classifier, then classifier and
classifier_name will be None.
"""
classifier = None
classifier_name = None
classifier_type = self.request.POST['classifier_type']
# Load a saved classifier if specified by the user.
if self.request.POST['load_classifier'] == 'true':
# Query database for classifier with specified ID.
classifier_id = self.request.POST['classifier_id']
db_entry = ds.fetch_classifier(
ds.create_sqlite_connection(), classifier_id)
classifier = db_entry[3]
classifier_type = db_entry[2]
classifier_name = db_entry[1]
return (classifier, classifier_name, classifier_type)
