def message_loop():
source_dir = config.options.data_dir / "source"
last_image = latest_image()
last_status = predict(last_image)
last_change = image_date(last_image)
print(f"Current status: {last_status} at {last_change:%Y-%m-%d %H:%M}")
while True:
try:
get_ftp_files(**config.ftp, source_dir=source_dir)
last_image = latest_image()
current_status = predict(latest_image())
if (last_status == "OPEN"
and (datetime.datetime.now() - last_change).seconds >
config.open_warning_seconds):
print(
f"Warning: Door has been open since {last_change:%Y-%m-%d %H:%M}"
)
send_warning_message(last_change)
last_change = datetime.datetime.now()
if current_status != last_status:
last_change = image_date(last_image)
print(
f"Status changed to {current_status} at {datetime.datetime.now():%Y-%m-%d %H:%M}"
)
send_message(current_status)
last_status = current_status
time.sleep(293)
except Exception as ex:
traceback.print_exc()
def main_with_class_explore():
class_uri = 'http://dbpedia.org/ontology/Person'
properties = easysparql.get_numerical_properties_for_class_tbox(endpoint=RAW_ENDPOINT, class_uri=class_uri)
if properties is None:
return
class_property_combinations = zip((len(properties) * [class_uri]), properties)
# print class_property_combinations
data, meta_data = data_extraction.data_and_meta_from_class_property_uris(
class_property_uris=class_property_combinations)
# data_extraction.save_data_and_meta_to_files(data=data, meta_data=meta_data)
model = learning.train_with_data_and_meta(data=data, meta_data=meta_data)
meta_with_clusters = learning.get_cluster_for_meta(training_meta=meta_data, testing_meta=meta_data)
learning.test_with_data_and_meta(model=model, data=data, meta_data=meta_with_clusters)
# now testing some files
test_data, test_meta_data = data_extraction.data_and_meta_from_files(['person_waist.csv','person_hipsize.csv',
'person_bustsize.csv'])
learning.predict(model, data=test_data, meta_data=test_meta_data)
def main():
class_property_combinations = [
('http://xmlns.com/foaf/0.1/Person', 'http://dbpedia.org/ontology/numberOfMatches'),
# ('http://schema.org/Place', 'http://dbpedia.org/property/longew'),
# ('http://schema.org/Place', 'http://dbpedia.org/property/latns'),
('http://schema.org/Place', 'http://www.georss.org/georss/point'),
# ('http://schema.org/Place', 'http://dbpedia.org/property/latm'),
# ('http://schema.org/Place', 'http://dbpedia.org/property/longm'),
('http://schema.org/Place', 'http://dbpedia.org/property/latd'),
('http://schema.org/Place', 'http://dbpedia.org/property/longd'),
]
class_property_combinations_test = [
# ('http://schema.org/Place', 'http://dbpedia.org/property/latm'),
# ('http://schema.org/Place', 'http://dbpedia.org/property/longm'),
('http://schema.org/Place', 'http://dbpedia.org/property/latd'),
('http://schema.org/Place', 'http://dbpedia.org/property/longd'),
]
data1, meta_data1 = data_extraction.data_and_meta_from_class_property_uris(class_property_combinations)
data2, meta_data2 = data_extraction.data_and_meta_from_files(['novHighC.csv'])
data, meta_data = data_manipulation.merge_data_and_meta_naive(data1=data1, meta_data1=meta_data1, data2=data2,
meta_data2=meta_data2)
for clus, md in enumerate(meta_data):
print "cluster %d => type: %s" % (clus, md["type"])
model = learning.train_with_data_and_meta(data=data, meta_data=meta_data)
test_data1, test_meta_data1 = data_extraction.data_and_meta_from_class_property_uris(
class_property_combinations_test)
test_data2, test_meta_data2 = data_extraction.data_and_meta_from_files(['mayHighC.csv'])
# merge the two data sets
test_data, test_meta_data = data_manipulation.merge_data_and_meta_naive(
data1=test_data1, meta_data1=test_meta_data1, data2=test_data2, meta_data2=test_meta_data2)
# test_meta_data_with_clusters = learning.get_cluster_for_meta(training_meta=meta_data, testing_meta=test_meta_data)
# learning.test_with_data_and_meta(model=model, data=test_data, meta_data=test_meta_data_with_clusters)
learning.predict(model=model, data=test_data, meta_data=test_meta_data)
def predict_files(predictionrun_id=None,
model_dir=None,
files=[],
original_uploaded_filenames=[],
has_header=False):
"""
:param predictionrun_id:
:param model_dir: the dir of the FCM model csv file abs dir
:param files: list of files to be predicted
:return:
"""
if predictionrun_id is None:
print "predict_files> predictionrun_id should not be None"
return
if model_dir is None:
print "predict_files> model_dir should not be None"
return
if len(files) != len(original_uploaded_filenames):
print "predict_files> number of files (%d) does not equal original_uploaded_filenames (%d)" % \
(len(files), len(original_uploaded_filenames))
return
print "original uploaded files:"
print original_uploaded_filenames
update_func = partial(update_predictionrun_progress_for_partial,
predictionrun_id)
update_predictionrun_state(predictionrun_id=predictionrun_id,
new_progress=0,
new_state=PredictionRun.RUNNING)
model, types = learning.load_model(model_dir)
num_of_files = len(files)
for idx, fname in enumerate(files):
update_predictionrun_state(predictionrun_id=predictionrun_id,
new_notes='predicting columns in file: ' +
fname.split('/')[-1].strip()[:-4])
data, meta_data = data_extraction.data_and_meta_from_a_mixed_file(
file_name=fname,
has_header=has_header,
original_file_name=original_uploaded_filenames[idx])
print "predict_files> extracted data shape is %s " % str(data.shape)
u = learning.predict(model=model,
data=data,
meta_data=meta_data,
update_func=update_func)
predictionrun = PredictionRun.objects.filter(id=predictionrun_id)
if len(predictionrun) == 1:
predictionrun = predictionrun[0]
file_column_list = [{
"file_name": fc["type"].split(' , ')[0],
"column_no": fc["type"].split(' , ')[1]
} for fc in meta_data]
predictionrun.add_memberships(u, file_column_list)
else:
update_predictionrun_state(
predictionrun_id=predictionrun_id,
new_notes="predictionrun_id is not longer exists",
new_state=PredictionRun.STOPPED)
return
predictionrun = PredictionRun.objects.filter(id=predictionrun_id)
if len(predictionrun) == 1:
predictionrun = predictionrun[0]
predictionrun.set_types(types)
print "setting types"
print types
else:
update_predictionrun_state(
predictionrun_id=predictionrun_id,
new_notes="predictionrun_id is not longer exists",
new_state=PredictionRun.STOPPED)
return
update_predictionrun_state(predictionrun_id=predictionrun_id,
new_progress=100,
new_state=PredictionRun.COMPLETE,
new_notes='')
# first parameter is empty (in fact it's an id), ignore it
data_columns = list(test_df.columns.values)[1:]
data_frame_training_data = data_frame.loc[:, data_columns].values
data_frame_target = data_frame.loc[:, "res_name"].tolist()
data_frame_2_target = data_frame_2.loc[:, "res_name_group"].tolist()
data_frame_training_data = lrn.clean_data_values(data_frame_training_data)
# verbose output: UserWarning: Deleting features without observed values:
# [690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707
# 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725
# 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743
# 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758]
data_columns_exclude = [690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707,
708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725,
726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743,
744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 0]
simple_model = lrn.model_rfc(data_frame_training_data, data_frame_target, 50, 50, "data/tmp/simple_model.pkl")
grouped_model = lrn.model_rfc(data_frame_training_data, data_frame_2_target, 50, 50, "data/tmp/grouped_model.pkl")
test_df.drop(test_df.columns[data_columns_exclude], axis=1, inplace=True)
test_data = test_df.values
test_data = lrn.clean_data_values(test_data)
simple_prediction = lrn.predict(simple_model, test_data)
grouped_prediction = lrn.predict(grouped_model, test_data)
pickle.dump(simple_prediction, open("data/tmp/simple_prediction.p", "wb"))
pickle.dump(grouped_prediction, open("data/tmp/grouped_prediction.p", "wb"))
parser.add_argument("checkpoint_path", type=str, help="path to the checkpoint")
parser.add_argument("--mapping_path",
type=str,
default="",
help="path to the checkpoint (Default: No_Mapping)")
parser.add_argument("--top_k",
type=int,
default="3",
help="path to the checkpoint (Default: 3)")
parser.add_argument('--gpu',
action='store_true',
help="Use GPU for training (Default: False)")
args = parser.parse_args()
#load name mapping from json file
cat_to_name = utility.load_mapping(
args.mapping_path) if args.mapping_path != "" else {}
#load model from checkpoint
model = learning.load_model(args.checkpoint_path)
#predict classes and propabilities
ps, preds = learning.predict(args.img_path, model, args.top_k, args.gpu)
#map classes to names
preds = [cat_to_name[pred] for pred in preds] if cat_to_name else preds
#print results
for name, prob in zip(preds, ps[-1, :]):
print("Class: {}, Propability: {}%".format(name, prob * 100))
def answer_per_question(Question, model, tokenizer, all_events,
all_event_keys):
answer, method = find(Question, model, tokenizer, all_events,
all_event_keys)
answer_sentence = ""
try:
answer["type"] = str(predict(Question))
except Exception:
raise ValueError("Type Predict Error!")
if answer["type"] == '1':
# HANDLED BY ARGUMENTS
# method = "temp"
time_len = len(answer["time"])
if answer["religious_time"]:
time_len = 1
time, answer["api_url"] = find_time_from_religious(answer)
answer["time"].extend(time)
en_datetime = dateparser.parse('امروز', settings={'TIMEZONE': '+0330'})
naive_dt = JalaliDate(en_datetime)
d1 = datetime.datetime.strptime(answer['date'][0], "%Y-%m-%d")
difference_days = d1.day - naive_dt.day
if not answer["time"]:
hour = datetime.datetime.now().hour
if hour < 12 or difference_days > 0:
answer["time"] = ["12:00"]
else:
if (hour + 1) == 24:
hour = 0
time = str(
str(hour + 1).zfill(2) + ":" +
str(datetime.datetime.now().minute).zfill(2))
answer["time"] = [time]
try:
if "اختلاف" in Question or "تفاوت" in Question:
temps, urls, logic1, logic2 = weather_difference(
Question, answer, ' و ')
temp = round(abs(temps[1] - temps[0]), 2)
answer_mode = 1
elif "سردتر " in Question or "سرد تر " in Question:
temps, urls, logic1, logic2 = weather_difference(
Question, answer, ' یا ')
if temps[1] < temps[0]:
answer_number = 1
temp = find_fit_word(answer, True)
else:
answer_number = 0
temp = find_fit_word(answer, False)
answer_mode = 2
elif "گرمتر " in Question or "گرمتر " in Question or "گرم تر " in Question:
temps, urls, logic1, logic2 = weather_difference(
Question, answer, ' یا ')
if temps[1] > temps[0]:
answer_number = 1
temp = find_fit_word(answer, True)
else:
answer_number = 0
temp = find_fit_word(answer, False)
answer_mode = 3
else:
greg_date = convert_date(answer["date"][0], "shamsi",
"greg") + " " + answer["time"][0]
temp, cond, url, logic1 = find_weather_from_city_date(
Question, answer["city"][0], greg_date)
urls = [url]
answer_mode = 4
answer["api_url"].extend(urls)
if method == "temp":
answer["result"] = str(temp)
elif method == "cond":
answer["result"] = cond
if time_len == 0:
answer["time"] = []
else:
answer["time"] = answer["time"][:time_len]
if answer_mode == 0:
pass
elif answer_mode == 1:
answer_sentence = weather_logical_sentence(
answer, logic1, logic2, 'اختلاف')
elif answer_mode == 2:
answer_sentence = weather_logical_sentence(
answer, logic1, logic2, 'سردتر', answer_number)
elif answer_mode == 3:
answer_sentence = weather_logical_sentence(
answer, logic1, logic2, 'گرمتر', answer_number)
elif answer_mode == 4:
answer_sentence = weather_sentence(answer, logic1)
if answer["religious_time"]:
answer["time"] = []
except Exception:
# raise ValueError("Type 1 Error!")
pass
elif answer["type"] == '2':
try:
result, answer["api_url"] = find_time_from_religious(answer)
answer["result"] = result[0]
answer_sentence = religion_sentence(answer)
except Exception:
# raise ValueError("Type 2 Error!")
pass
elif answer["type"] == '3':
try:
t = Time(answer["city"][0])
res = t.send_request()
answer["result"] = res
answer["api_url"] = [t.url]
answer["date"] = []
answer["time"] = []
answer_sentence = time_sentence(answer)
except Exception:
# raise ValueError("Type 3 Error!")
pass
elif answer["type"] == '4':
answer["city"] = []
try:
answer["api_url"] = ["https://www.time.ir/"]
if 'مناسبت' in Question:
answer["result"] = answer["event"][0]
answer["event"] = []
else:
if answer["calendar_type"] and answer["date"]:
target = answer["calendar_type"][0]
if target == "شمسی":
answer["result"] = convert_date(
answer["date"][0], "shamsi", "shamsi")
elif target == "قمری" or target == "هجری":
answer["result"] = convert_date(
answer["date"][0], "shamsi", "hijri")
elif target == "میلادی":
answer["result"] = convert_date(
answer["date"][0], "shamsi", "greg")
elif answer["date"]:
answer["result"] = answer["date"][0]
answer_sentence = date_sentence(answer)
except Exception:
# raise ValueError("Type 4 Error!")
pass
elif answer["type"] == '-1':
answer = {
'type': '-1',
'city': [],
'date': [],
'time': [],
'religious_time': [],
'calendar_type': [],
'event': [],
'api_url': [],
'result': ''
}
answer_sentence = unknown_sentence()
return answer, answer_sentence
def predict_image(image):
test = []
array = convert_to_ndarray(image, [56, 56])
test.append(array)
predict(test)