def call_yoci(config=None, ci=None, req_type=None, name=None, verbose=False):
_set_global_verbosity_level(verbose)
config = config if config else DEFAULT_CONFIG_FILE
if not ci:
ci = 'Travis'
if not req_type:
req_type = 'repos'
ci_config = import_config(config)
if ci.lower() == 'Travis'.lower():
if hasattr(travis, req_type.title()):
getattr(travis, req_type.title())(
lgr, ci_config[ci.title()], id=name)
# getattr(ci_instance, req_type)
else:
raise RuntimeError('type not found')
else:
raise RuntimeError('ci {} not supported'.format(ci))
def call_yoci(config=None, ci=None, req_type=None, name=None, verbose=False):
_set_global_verbosity_level(verbose)
config = config if config else DEFAULT_CONFIG_FILE
if not ci:
ci = 'Travis'
if not req_type:
req_type = 'repos'
ci_config = import_config(config)
if ci.lower() == 'Travis'.lower():
if hasattr(travis, req_type.title()):
getattr(travis, req_type.title())(lgr,
ci_config[ci.title()],
id=name)
# getattr(ci_instance, req_type)
else:
raise RuntimeError('type not found')
else:
raise RuntimeError('ci {} not supported'.format(ci))
def __init__(self, input_file, input_ent, output_weka, output_nn, limit):
"""
"""
self.input_file = input_file
self.input_ent = input_ent
self.out_weka = output_weka
self.out_nn = output_nn
self.limit = limit
self.cfg_feat = import_config('features')
self.gazzetters = self.import_gazetters()
self.DictEntities = self.import_entities_annotated()
self.tagger = PosChunkTagger()
self.POSTags = set()
self.ChunkTags = set()
self.out_tokens = []
def __init__(self, input_file, schedule_file, limit, work_tsl, contr_tsl,
time_tsl):
"""
"""
self.cfg_match = import_config('matcher')
self.input_file = input_file
self.schedule_file = schedule_file
self.limit = limit
self.work_tsl = work_tsl
self.contr_tsl = contr_tsl
self.time_tsl = time_tsl
self.DictTweets = self.import_ugc_tweets()
self.DictSched = self.import_schedule()
self.stopwords = self.import_stopwords()
# Initialize counters
(self.tp_count, self.fp_count, self.tn_count, self.fn_count,
self.tp_c_count, self.fp_c_count, self.tn_c_count,
self.fn_c_count) = (0, ) * 8
self.outfile = "../results/schedule_matcher_%s_%s_%s.txt" % (
work_tsl, contr_tsl, time_tsl)
import datetime
import copy
import os
import tensorflow as tf
from dataclass import data, reader, mnist_data
from argparse import Namespace
import utils
import argparse
CONFIG = utils.import_config()
def evaluate(classifier,
params,
eval_dir=None,
result_dir=None,
wrong_fn=None,
save_all=False):
"""
Get the predictions based on the features given by the input_fn.
The goal here was to make something that would make a folder to showall the instances where
the model went wrong. Unfortunatly this does not work with the current version of tensorflow.
"""
assert result_dir is not None or wrong_fn is not None
if eval_dir is None:
eval_dir = CONFIG["MNIST"]["test"]["csv"]
json_dict = {}
def __init__(self, inpath):
"""
"""
self.cfg_tw_api = import_config('twitter_api')
self.inpath = inpath