col_name_categories = 'categories'

new_col_name_cat_vec = 'categories vector'

col_name_class_out = 'classification output'

col_name_result = 'result'

ext_out_vecs = '_category_vectors'

ext_categorized = '_categorized'

threshold_key = 'classification_interpreter_output_threshold'

# expects list of strings

# returns a category vector, based on the stored category list

@staticmethod

def get_cat_vec(str_list):

category_list = CategoryListHandler.read_categories()

fv = np.zeros(len(category_list))

idx = 0

for cat in category_list:

if cat in str_list:

fv[idx] = 1

idx = idx+1

return fv

# expects a pandas data frame, containing a category column

# creates an output vector for training from each entry in the category column from the data frame and stores it in a new column

# returns pandas data frame with added column for added output vectors

@staticmethod

def create_out_vectors(data_frame, col_name=col_name_categories, new_col_name=new_col_name_cat_vec, storage_level=0, storage_name=''):

data_frame[new_col_name] = data_frame.apply(lambda x: ClassificationInterpreterCustom2.get_cat_vec(x[col_name]), axis=1)

log_text = 'Category vectors for classifier training have been created (' + str(len(data_frame.index)) + ' entries).'

if storage_level >= 1 and storage_name != '':

storage_name = storage_name + ClassificationInterpreterCustom2.ext_out_vecs

Storage.store_pd_frame(data_frame, storage_name)

log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'

SessionLogger.log(log_text)

return data_frame

# expects a classification output vector, a category list and an output threshold

# returns a list with corresponding categories, based on the stored category list

@staticmethod

def get_categories_from_vec(vec, category_list, threshold):

output_categories = list()

idx = 0

while idx < len(vec) and idx < len(category_list):

if vec[idx] > threshold:

output_categories.append(category_list[idx])

idx = idx+1

return output_categories

# expects a pandas data frame, containing a column with classification output vectors

# creates a list of categories from each classification output vector and stores it in a new column

# returns pandas data frame with added column for added category lists

@staticmethod

def interpret_output(data_frame, col_name=col_name_class_out, new_col_name=col_name_result, storage_level=0, storage_name='', log=1):

category_list = CategoryListHandler.read_categories()

threshold = SessionConfigReader.read_value(ClassificationInterpreterCustom2.threshold_key)

data_frame[new_col_name] = data_frame.apply(lambda x: ClassificationInterpreterCustom2.get_categories_from_vec(x[col_name], category_list, threshold), axis=1)

log_text = 'Categories have been determined (' + str(len(data_frame.index)) + ' entries).'

if storage_level >= 1 and storage_name != '':

storage_name = storage_name + ClassificationInterpreterCustom2.ext_categorized

Storage.store_pd_frame(data_frame, storage_name)

log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'

if log:

SessionLogger.log(log_text)

return data_frame

# expects a pandas data frame, containing a column with categories and a column with interpreted classification outputs

# compares content from both columns and makes a summarizing statement about accuracy

# returns accuracy in percent

@staticmethod

def evaluate_output(data_frame, col_name_categories=col_name_categories, col_name_outputs=col_name_result):

idx = 0

matches = 0

for index, row in data_frame.iterrows():

categories = row[col_name_categories]

if len(categories) > 0:

category = categories[0]

else:

category = ''

outputs = row[col_name_outputs]

if len(outputs) > 0:

output = outputs[0]

else:

output = ''

if category == output and category != '':

matches = matches+1

idx = idx+1