def test_print_in_file_only_error_or_higher_levels(self):
log = LogHelper()
log.debug(self.DEBUG_MESSAGE)
log.info(self.INFO_MESSAGE)
log.warning(self.WARNING_MESSAGE)
log.error(self.ERROR_MESSAGE)
log.fatal(self.FATAL_MESSAGE)
log_name = 'test_print_in_file_only_error_or_higher_levels.log'
self.assertTrue(log.logs.__len__() == 5)
self.assertEqual(log.save_logs(log_name, log_level=LogHelper.ERROR),
True)
with open(f'./{log_name}') as file:
data = file.read()
self.assertFalse(self.DEBUG_MESSAGE in data)
self.assertFalse(self.INFO_MESSAGE in data)
self.assertFalse(self.WARNING_MESSAGE in data)
self.assertTrue(self.ERROR_MESSAGE in data)
self.assertTrue(self.FATAL_MESSAGE in data)
def add_log(self, log):
"""Add a log to InfluxDB"""
mapped_log = LogHelper().prepare_influx_insert_query(log)
self.influx_client.write_points(mapped_log)
def __init__(self):
LogHelper.log("created")
def __init__(self, module_dir, user='******'):
BaseConfig.__init__(self)
print("module dir: " + module_dir)
self.test_config = BaseConfig()
self.test_config.batch_size = 1
self.wv = self.test_config.wv
# tf.flags.DEFINE_integer("embedding_dim_cn", 300, "Dimensionality of character embedding (default: 128)")
# tf.flags.DEFINE_integer("batch_size_classify", 1, "Batch Size (default: 64)")
#
# self.FLAGS = tf.flags.FLAGS
# self.FLAGS._parse_flags()
# print("\nParameters:")
# for attr, value in sorted(self.FLAGS.__dict__['__flags'].items()):
# print("{}={}".format(attr.upper(), value))
# print("")
checkpoint_dir = os.path.join(module_dir, "cn", "checkpoints")
classes_file = codecs.open(os.path.join(module_dir, "cn", "classes"),
"r", "utf-8")
self.classes = list(line.strip() for line in classes_file.readlines())
classes_file.close()
print("\nEvaluating...\n")
# Evaluation
# ==================================================
checkpoint_file = tf.train.latest_checkpoint(checkpoint_dir)
graph = tf.Graph()
with graph.as_default():
with tf.device("/cpu:0"):
session_conf = tf.ConfigProto(
allow_soft_placement=self.test_config.allow_soft_placement,
log_device_placement=self.test_config.log_device_placement)
session_conf.gpu_options.allow_growth = True
self.sess = tf.Session(config=session_conf)
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
saver.restore(self.sess, checkpoint_file)
# Get the placeholders from the graph by name
self.embedded_chars = graph.get_operation_by_name(
"embedded_chars").outputs[0]
# input_y = graph.get_operation_by_name("input_y").outputs[0]
self.dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# Tensors we want to evaluate
self.scores = graph.get_operation_by_name(
"output/scores").outputs[0]
self.probabilities = graph.get_operation_by_name(
"output/probabilities").outputs[0]
this_file = inspect.getfile(inspect.currentframe())
dir_name = os.path.abspath(os.path.dirname(this_file))
self.chat_log_path = os.path.join(dir_name, '..',
'log/module/cnn_classify')
if not os.path.exists(os.path.join(self.chat_log_path, user)):
if not os.path.exists(self.chat_log_path):
os.makedirs(self.chat_log_path)
f = open(self.chat_log_path + '/' + user, 'w', encoding='utf-8')
f.close()
if not self.lh:
self.lh = LogHelper(user, self.chat_log_path)