def modeling():
options = [{
'title': 'iris',
'id': 1,
}]
print(options)
elements = [
{
'title': 'Network layers number',
'id': 'layers_n',
'type': '',
'default': 1,
},
{
'title': 'Neuron number',
'id': 'nn',
'type': '',
'default': 10,
},
{
'title': 'Activation functions list',
'id': 'func',
'type': '',
'default': 'sigmoid',
},
{
'title': 'Metrics',
'id': 'metrics',
'type': '',
'default': 'accuracy',
},
{
'title': 'Loss',
'id': 'loss',
'type': '',
'default': 'categorical_crossentropy',
},
{
'title': 'Epoch number',
'id': 'ep',
'type': '',
'default': '100',
},
{
'title': 'Datasets',
'id': 'dataset',
'type': '',
'options': options,
'default': options[0]['id'],
},
]
return_url = "/"
if request.args.get('result'):
dataset_to_comps = [
'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
] # more tables???
model_info, datasets = get_model_info(base, request.args.get('models'))
neurons = request.args.get('nn').split(',')
input_dim = [len(dataset_to_comps)] + [0] * (len(neurons) - 1)
activation = request.args.get('func').split(',')
etl = ETL(manager=base)
load_data_instr = {"category_name": 'Iris Fisher'}
path = 'local_files/iris.csv'
etl.load_supervised_data(path=path,
ctg_name=load_data_instr["category_name"])
# x1 = base.get_raw_data(RateName=dataset_to_comps[0])
# x1 = pd.DataFrame(x1[2].float_value)
# x2 = base.get_raw_data(RateName=dataset_to_comps[1])
# x2 = pd.DataFrame(x2[2].float_value)
# x3 = base.get_raw_data(RateName=dataset_to_comps[2])
# x3 = pd.DataFrame(x3[2].float_value)
# x4 = base.get_raw_data(RateName=dataset_to_comps[3])
# x4 = pd.DataFrame(x4[2].float_value)
X = pd.read_csv(path)
y = X['species']
X = X.drop('species', axis=1)
X = X.as_matrix()
train_X, test_X, train_y, test_y = train_test_split(X,
y,
train_size=0.7,
random_state=42)
train_y_ohe = np.array(get_dummies(train_y), dtype=np.float64)
test_y_ohe = np.array(get_dummies(test_y), dtype=np.float64)
# build_args = {
# 'build_args': [
# {'neurons': neurons[i], 'input_dim': input_dim[i], 'activation': activation[i], 'init': 'normal'} for i in range(len(neurons))
## {'neurons' : 16, 'input_dim' : 4, 'init' : 'normal', 'activation' : 'relu'},
## {'neurons' : 3, 'input_dim' : 0, 'init' : 'normal', 'activation' : 'sigmoid'}
# ],
# 'compile_args': {
# 'loss': request.args.get('loss'),
# 'optimizer': 'adam',
# 'metrics': request.args.get('metrics')
# }
# }
# compile_args = {
# 'loss': request.args.get('loss'),
# 'optimizer': 'adam',
# 'metrics': request.args.get('metrics')
# }
# fit_args = {'nb_epoch': request.args.get('ep'), 'batch_size': 1, 'verbose': 0}
# evaluate_args = {'verbose': 0}
# predict_args = {}
build_args = {
'build_args': [{
'neurons': 16,
'input_dim': 4,
'init': 'normal',
'activation': 'relu'
}, {
'neurons': 3,
'input_dim': 0,
'init': 'normal',
'activation': 'sigmoid'
}],
'compile_args': {
'loss': 'categorical_crossentropy',
'optimizer': 'adam',
'metrics': 'accuracy'
}
}
compile_args = {
'loss': 'categorical_crossentropy',
'optimizer': 'adam',
'metrics': 'accuracy'
}
fit_args = {'epochs': 100, 'batch_size': 1, 'verbose': 1}
evaluate_args = {'verbose': 0}
predict_args = {}
print(build_args)
m = KerasClassifier(name='iris', args=build_args)
history = m.fit(train_X, train_y_ohe, fit_args=fit_args)
loss, accuracy = m.evaluate(test_X, test_y_ohe, evaluate_args)
prediction = m.predict(train_X)
loss_data = history.history['loss'][1:]
return render_template("modeling.html",
elements=elements,
return_url=return_url,
loss=request.args.get('loss'),
loss_data=list(
zip(list(range(len(loss_data) - 1)),
loss_data)))
else:
return render_template(
"input.html",
elements=elements,
return_url=return_url,
)
import numpy as np
import pandas as pd
import datetime
from models.models import *
if __name__ == '__main__':
# Connection to DataBase and assemble Scheme
DB = DBManager()
etl = ETL(manager=DB)
##### LOADING DATA FROM VARIOUS SOURCES
# Download local files for superviesd learning
load_data_instr = {"category_name": 'Iris Fisher'}
etl.load_supervised_data(path='local_files/iris.csv', ctg_name=load_data_instr["category_name"])
# Define categories for JapanExchange_Derivatives_ex2
cats = [Category(name='futures', description='azaza'),
Category(name='call', description='azaza'),
Category(name='put', description='azaza'),
Category(name='cbr', description='azaza')]
DB.session.add_all(cats)
# Import Future Data
c, r, rh = etl.get_Kospi_data_ex1('../Kospi Quotes Eikon Loader.xlsx')
# Download file 'rb_e20161027.txt.csv'
etl.get_JapanExchange_Derivatives_ex2('../rb_e20161027.txt.csv')
# Import data from pdf