def record_experment_data(fs, fnus):
# print(fnus[0].Neq)
m2 = 800000
threshold = 400000
temperature, Humidity = dataset.import_data(fs)
number = []
decomposition = []
extension_rate = []
litter = []
record_x = []
record_y = []
# training
for i in range(5000):
for j in range(50):
fnus[j].T_real = temperature[i]
fnus[j].W_real = Humidity[i]
total_number, total_decomposition_rate, m2, d_number, flag, threshold = Q1.update_real_number(
fnus, m2, threshold)
number.append(total_number)
decomposition.append(total_decomposition_rate)
extension_rate.append(d_number)
litter.append(m2)
if flag == 1:
record_x.append(i)
record_y.append(threshold * 2)
# plt.plot()
# plt.show()
return extension_rate, number, fnus, decomposition, litter, record_x, record_y, temperature, Humidity
def train_rf_squad():
dataset = import_data(None, True)
x_train, y, feature_names = feature_engineering(dataset, 'squad-fpp', True)
model = rf_model_squad(x_train, y)
print("Model training completed successfully")
print(model)
def ramdon_forest_duo(file):
dataset = import_data(file, False)
df, y, feature_names = feature_engineering(dataset, 'duo-fpp', False)
ans = rf_duo(df)
return ans
def model_training():
dataset = import_data(None, True)
x_train, y = feature_engineering(dataset, True)
model = rf_model_solo(x_train, y)
print(model)
def ramdon_forest_solo(file):
dataset = import_data(file, False)
df, _ = feature_engineering(dataset, False)
ans = random_forest(df)
return ans
import tornado.ioloop
import tornado.web
from transformers import BertForTokenClassification
from dataset import import_data, split_data, convert_dataframe_to_data, tags_and_tag_to_idx, tokenizer
from model import one_sentence_prediction_bert
path_to_dataset = '/home/andrei/Documents/ML/ner.csv'
path_to_model = '/home/andrei/Documents/ML/bert_uncased/'
data = import_data(path_to_dataset)
training, testing = split_data(data)
train_data = convert_dataframe_to_data(training)
test_data = convert_dataframe_to_data(testing)
_, tag_to_idx = tags_and_tag_to_idx(train_data, test_data)
tag_values = list(tag_to_idx.keys())
bert_model_loaded = BertForTokenClassification.from_pretrained(path_to_model)
class Ner(tornado.web.RequestHandler):
def get(self):
form = """<form method="post">
<input type="text" name="sentence"/>
<input type="submit"/>
</form>"""
self.write(form)
def post(self):
sentence = self.get_argument('sentence')
prediction = one_sentence_prediction_bert(sentence, bert_model_loaded,