def __init__(self, database):
self._web_handlers = {}
for c in get_all_classes(["web_handlers.py"], WebHandler):
obj = c(database, cache)
self._web_handlers[obj.url] = obj
self._web_server = Flask("web_server")
self._register(database)
def __init__(self, database):
self._web_handlers = {}
for c in get_all_classes(["web_handlers.py"], WebHandler):
obj = c(database)
self._web_handlers[obj.url] = obj
self._web_server = Flask("web_server")
self._register(database)
def __init__(self, database):
self._database = database
self._articles = database.get_collection("article")
self._database_writers = {}
for c in get_all_classes(["database_writers.py"], DatabaseWriter):
obj = c(database)
self._database_writers[obj.flag] = obj
self._file_path = ""
def __init__(self):
self._markdown_parser = MarkdownParser()
self._meta_parsers = {}
for c in get_all_classes(["meta_parsers.py"], MetaDataParser):
obj = c()
self._meta_parsers[obj.flag] = obj
self._file_path = ""
def __init__(self):
self._markdown_parser = MarkdownParser()
self._meta_parsers = {}
for c in get_all_classes(["meta_parsers.py"], MetaDataParser):
obj = c()
self._meta_parsers[obj.flag] = obj
self._file_path = ""
def train_model(train_file, classifier, batch_size):
all_classes = get_all_classes(train_file)
col_label, col_description = get_column_position(form=1)
with open(train_file, "r") as input_file:
first_line = input_file.readline().strip()
entry = literal_eval(first_line)
vector_size = len(entry[col_description])
input_file.seek(0) # reset the file pointer after reading first line
y_train = []
X_train = np.full(shape=(batch_size, vector_size),
fill_value=0,
dtype=float)
line_count = 0
idx = 0
start_time = time.time()
for line in input_file:
line_count += 1
entry = literal_eval(line)
y_train.append(entry[col_label])
X_train[idx, :] = entry[col_description]
if line_count % batch_size == 0:
update_model(classifier, X_train, y_train, all_classes)
print "training model for lines = ", line_count, 'time=', int(
time.time() - start_time), 's'
print "precision score", classifier.score(X_train, y_train)
del y_train, X_train
y_train = []
X_train = np.full(shape=(batch_size, vector_size),
fill_value=0,
dtype=float)
idx = -1
idx += 1
if line_count % batch_size > 1:
X_train = X_train[:idx, :]
update_model(classifier, X_train, y_train, all_classes)
print "training model for lines = ", line_count, 'time=', int(
time.time() - start_time), 's'
print "precision score", classifier.score(X_train, y_train)
def train_model(train_file, classifier, batch_size):
all_classes = get_all_classes(train_file)
col_label, col_description = get_column_position(form=1)
with open(train_file, "r") as input_file:
first_line = input_file.readline().strip()
entry = literal_eval(first_line)
vector_size = len(entry[col_description])
input_file.seek(0) # reset the file pointer after reading first line
y_train = []
X_train = np.full(shape=(batch_size, vector_size), fill_value=0, dtype=float)
line_count = 0
idx = 0
start_time = time.time()
for line in input_file:
line_count += 1
entry = literal_eval(line)
y_train.append(entry[col_label])
X_train[idx, :] = entry[col_description]
if line_count % batch_size == 0:
update_model(classifier, X_train, y_train, all_classes)
print "training model for lines = ", line_count, 'time=', int(time.time() - start_time), 's'
print "precision score", classifier.score(X_train, y_train)
del y_train, X_train
y_train = []
X_train = np.full(shape=(batch_size, vector_size), fill_value=0, dtype=float)
idx = -1
idx += 1
if line_count % batch_size > 1:
X_train = X_train[:idx, :]
update_model(classifier, X_train, y_train, all_classes)
print "training model for lines = ", line_count, 'time=', int(time.time() - start_time), 's'
print "precision score", classifier.score(X_train, y_train)
def __init__(self):
self._web_caches = {}
for c in get_all_classes(["web_caches.py"], WebCache):
obj = c()
self._web_caches[obj.flag] = obj
def __init__(self):
self._slug_wrappers = {}
for c in get_all_classes(["slug_wrappers.py"], SlugWrapper):
obj = c()
self._slug_wrappers[obj.flag] = obj
def __init__(self):
self._slug_wrappers = {}
for c in get_all_classes(["slug_wrappers.py"], SlugWrapper):
obj = c()
self._slug_wrappers[obj.flag] = obj
