async def beanbtn(conv, SENDER, msg):
'''定义bean脚本按钮'''
from lib import get_data, show_data
try:
markup = [Button.inline(cntr, data=cntr) for cntr in containers]
markup.append(Button.inline('取消', data='cancel'))
markup = split_list(markup, 3)
msg = await client.edit_message(msg, '请选择容器：', buttons=markup)
date = await conv.wait_event(press_event(SENDER))
res = bytes.decode(date.data)
if res == 'cancel':
msg = await client.edit_message(msg, '对话已取消')
conv.cancel()
return None, None
else:
text = show_data(get_data(containers[res]))
# msg = await conv.send_message(text)
msg = await client.edit_message(msg, text)
conv.cancel()
return None, None
except exceptions.TimeoutError:
msg = await client.edit_message(msg, '选择已超时，对话已停止')
return None, None
except Exception as e:
msg = await client.edit_message(
msg, 'something wrong,I\'m sorry\n' + str(e))
logger.error('something wrong,I\'m sorry\n' + str(e))
return None, None
def main(data_name):
if (data_name == "fer2013"):
name = "fer2013"
data_file_path = "../../../data/fer2013/fer2013.csv"
else:
name = "icv_mefed"
train_path = "../../../data/icv_mefed/training/"
test_path = "../../../data/icv_mefed/testing/"
# Obtain the data from the path provided
# data = get_data(name=name, data_file_path=data_file_path)
# Obtain the data from the path provided
train_data = get_data(name=name,
data_file_path=train_path + 'training.txt')
training_data, training_labels = make_sets(train_data,
train_path,
extract_landmarks=False)
test_data = get_data(name=name, data_file_path=test_path + 'testing.txt')
testing_data, testing_labels = make_sets(test_data,
test_path,
extract_landmarks=False)
# Generate training and test sets from the data
# X_train, Y_train, X_test, Y_test = generate_data_split(data=data, num_of_classes=7, name=name)
# Turn the training set into a numpy array for the classifier
X_train = np.array(training_data)
Y_train = np.array(training_labels)
X_test = np.array(testing_data)
Y_test = np.array(testing_labels)
# Pre-process the image data
X_train, X_test = normalize_data(X_train, X_test)
# Generate or load trained model
model = generate_model(X_train, Y_train)
# Evaluate model
evaluate_model(model, X_train, Y_train, X_test, Y_test)
# Save model to disk
save_model(name="cnn", model=model)
def main(data_name):
#clf = SVC(kernel='linear', probability=True, tol=1e-3)
# , verbose = True) #Set the classifier as a support vector machines with polynomial kernel
if (data_name == "fer2013"):
name = "fer2013"
data_file_path = "../../../data/fer2013/fer2013.csv"
else:
name = "icv_mefed"
train_path = '../../../data/icv_mefed/training/'
test_path = '../../../data/icv_mefed/testing/'
# Obtain the data from the path provided
train_data = get_data(name=name,
data_file_path=train_path + 'training.txt')
training_data, training_labels = make_sets(train_data, train_path)
test_data = get_data(name=name, data_file_path=test_path + 'testing.txt')
testing_data, testing_labels = make_sets(test_data, test_path)
# Turn the training set into a numpy array for the classifier
X_train = np.array(training_data)
Y_train = np.array(training_labels)
X_test = np.array(testing_data)
Y_test = np.array(testing_labels)
# Pre-process the image data
X_train, X_test = normalize_data(X_train, X_test)
# Generate or load trained model
model = generate_model(X_train, Y_train)
# Evaluate model
evaluate_model(model, X_train, Y_train, X_test, Y_test)
# Save model to disk
save_model(name="svm", model=model)
def __init__(self):
self.loans = get_data()
self.loans["Unique title"] = [
"".join([i for i in title
if i != "-" and not i.isdigit()]).rstrip().lower()
for title in self.loans["Loan title"]
]
self.loans["Unique title"] = [
a[:-5] if (a[-5:] == " loan"
and a[:-5] in self.loans["Unique title"].values) else a
for a in self.loans["Unique title"]
]
self.live_loans = self.loans[self.loans["Loan status"].isin(
["Live", "Late", "Processing"])]
self.i = 0
self.total_remaining_principal = self.live_loans[
"Principal remaining"].sum()
else:
d1 = date(2011,7,28)
d2 = d1 + td(days=8)
d1 = date(2014,3,28)
d2 = date(2014,3,28)
delta = d2 - d1
for i in range(delta.days + 1):
my_date = d1 + td(days=i)
fecha = my_date.strftime("%d/%m/%Y")
print fecha
buscar(fecha)
filename = os.path.join(config.base_folder, "visitas.db")
db = dataset.connect("sqlite:///" + filename)
table = db['visitas']
print "Getting data from our json file"
items = lib.get_data()
print "Uploading data from our json file"
for i in items:
if not table.find_one(sha1=i['sha1']):
print i['sha1'], i['date']
table.insert(i)
print "Recreating website"
lib.recreate_website()
#!/usr/bin/env python3
from datetime import datetime
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
from lib import get_data
if __name__ == "__main__":
data = get_data()
data["due_date"] = [datetime.strptime(a, "%Y-%m-%d") for a in data["due_date"]]
data = data.sort_values(by=["due_date", "loan_part_id"])
data["profit"] = data["pay_interest"] - data["lender_fee"]
data["income"] = data["pay_principal"] + data["profit"]
data["profit_cumulative"] = data["profit"].cumsum()
data["income_cumulative"] = data["income"].cumsum()
register_matplotlib_converters()
plt.plot(data["due_date"], data["income_cumulative"], label="Total")
plt.plot(data["due_date"], data["profit_cumulative"], label="Interest")
plt.xlabel("Date")
plt.ylabel("Income / £")
plt.legend()
plt.title("Cumulative income forecast")
plt.show()