def F_model(self, row, data, dummy_dict, Var, category_key):
df = data.iloc[row]
C_var = [9, 10, 11, 12]
C_tar = Var[5]
df = df[[Var[i] for i in C_var]]
test = []
for k in C_var:
test.extend(test_dummy(df[Var[k]], dummy_dict[Var[k]]))
test = np.array(test).reshape(1, -1)
model = pickle.load(open("./Model/F_model.pickle", "rb"))
return model.predict(test)[0]
def G_model(self, row, data, dummy_dict, Var, category_key):
ex = data.iloc[row]
var = [0, 1, 2, 5, 7, 9, 10, 11, 13, 14, 15]
tar = Var[6]
test = list(ex[[Var[i] for i in [2, 5]]].values)
key = [i for i in var if i in category_key]
for k in key:
test.extend(test_dummy(ex[Var[k]], dummy_dict[Var[k]]))
test = np.array(test).reshape(1, -1)
model = pickle.load(open("./Model/G_model.pickle", "rb"))
return model.predict(test)[0]
def K_model(self, row, data, dummy_dict, Var, category_key):
ex = data.iloc[row]
var = [0, 2, 3, 4, 5, 6, 7, 8, 13, 14, 15]
tar = Var[10]
test = list(ex[Var[2:7]].values)
key = [i for i in var if i in category_key]
for k in key:
test.extend(test_dummy(ex[Var[k]], dummy_dict[Var[k]]))
test = np.array(test).reshape(1, -1)
model = pickle.load(open("./Model/K_model.pickle", "rb"))
pred = model.predict(test)
return dummy_dict[tar][pred][0]
def E_model(self, row, data, dummy_dict, Var, category_key):
ex = data.iloc[row]
var = [1, 7, 9, 10, 11, 12, 14, 15]
tar = Var[4]
test = list(ex[Var[2:4] + Var[5:7]].values)
key = [i for i in var if i in category_key]
for k in key:
test.extend(test_dummy(ex[Var[k]], dummy_dict[Var[k]]))
test = np.array(test).reshape(1, -1)
model = pickle.load(open("./Model/E_model.pickle", "rb"))
pred = model.predict(data=test)
return pred[0]
def P_model(self, row, data, dummy_dict, Var, category_key):
ex = data.iloc[row]
var = [1, 4, 5, 6, 7, 8, 9, 10, 11]
tar = Var[15]
test = list(ex[Var[4:7]].values)
key = [i for i in var if i in category_key]
for k in key:
test.extend(test_dummy(ex[Var[k]], dummy_dict[Var[k]]))
test = np.array(test).reshape(1, -1)
model = pickle.load(open("./Model/P_model.pickle", "rb"))
pred = np.argmax(model.predict(X=test), 1)
return dummy_dict[tar][pred][0]