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main.py
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main.py
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"""[summary]
"""
#! /usr/bin/env python3
# coding=UTF-8
import sys
import pickle
import numpy as np
from sklearn import preprocessing
from src.linear_regression import LinearRegression
from src.decision_tree import DecisionTree
from src.c_semi import SemiClassifier
from src.global_variables import FED_NUM, file_path
from src.data_preprocess import split_test_train, statistics
try:
TRAIN_MODE = int(sys.argv[1])
except ValueError:
TRAIN_MODE = 1
all_real_label_id_features = []
all_ext_label_id_features = []
wrap_real_label_id_features = []
wrap_ext_label_id_features = []
all_train_y = []
all_train_x = []
all_train_id = []
all_train_x_u = []
all_train_y_u = []
all_train_id_u = []
all_test_y = []
all_test_x = []
all_test_id = []
for i in range(FED_NUM):
real_label_id_features, ext_label_id_features = pickle.load(
open("/data/icde_data/fed_with_sh_(5_30_200)ext%d.pkl" % i, "rb"))
np.random.seed(0)
np.random.shuffle(real_label_id_features)
statistics(real_label_id_features)
statistics(ext_label_id_features)
real_id_x_train, real_id_x_test, real_y_train, real_y_test = split_test_train(
real_label_id_features, test_size=0.3, random_state=0)
all_train_x.append(preprocessing.scale(real_id_x_train[:, 1:]))
all_train_id.append(real_id_x_train[:, 0])
all_train_y.append(real_y_train)
all_train_x_u.append(preprocessing.scale(ext_label_id_features[:, 2:]))
all_train_y_u.append(ext_label_id_features[:, 0])
all_train_id_u.append(ext_label_id_features[:, 1])
all_test_y.append(real_y_test)
all_test_id.append(real_id_x_test[:, 0])
all_test_x.append(preprocessing.scale(real_id_x_test[:, 1:]))
print("test:", len(all_test_x[0]))
from_fed = 0
to_fed = 4 # FED_NUM
if TRAIN_MODE == 5:
# Decision Tree
for i in range(from_fed, to_fed):
model = DecisionTree(
all_train_y[i],
all_train_x[i],
all_test_y[i],
all_test_x[i],
all_test_id[i])
model.fit(i, "/data/icde_data")
if TRAIN_MODE in [1, 3, 6]:
# LOCAL
for i in range(from_fed, to_fed):
if TRAIN_MODE == 1:
model = LinearRegression(
all_train_y[i],
all_train_x[i],
all_test_y[i],
all_test_x[i],
all_test_id[i])
model.fit(i, 8)
elif TRAIN_MODE == 6:
all_train_y[i] = np.concatenate(
(all_train_y[i], all_train_y_u[i]), axis=0)
all_train_x[i] = np.concatenate(
(all_train_x[i], all_train_x_u[i]), axis=0)
model = LinearRegression(
all_train_y[i],
all_train_x[i],
all_test_y[i],
all_test_x[i],
all_test_id[i])
model.fit(i, 8)
else:
# LOCAL+
model = SemiClassifier(
all_train_y[i],
all_train_x[i],
all_test_y[i],
all_test_x[i],
all_test_id[i],
all_train_x_u[i])
model.fit(i, file_path)
elif TRAIN_MODE in [2, 4]:
train_f = []
train_l = []
ext_f = []
ext_l = []
test_f = []
test_l = []
test_id = []
for i in range(from_fed, to_fed):
train_f.extend(all_train_x[i])
train_l.extend(all_train_y[i])
ext_f.extend(all_train_x_u[i])
ext_l.extend(all_train_y_u[i])
test_f.extend(all_test_x[i])
test_l.extend(all_test_y[i])
test_id.extend(all_test_id[i])
train_f = np.array(train_f)
train_l = np.array(train_l).reshape(-1, 1)
train_f_l = np.concatenate((train_f, train_l), axis=1)
np.random.shuffle(train_f_l)
train_f = train_f_l[:, :-1]
train_l = train_f_l[:, -1]
ext_f = np.array(ext_f)
np.random.shuffle(ext_f)
test_f = np.array(test_f)
test_l = np.array(test_l).reshape(-1, 1)
test_id = np.array(test_id).reshape(-1, 1)
test_id_f_l = np.concatenate((test_id, test_f, test_l), axis=1)
np.random.shuffle(test_id_f_l)
total_num = test_id_f_l.shape[0]
per_num = total_num // (to_fed - from_fed)
test_id = test_id_f_l[:per_num, 0]
test_f = test_id_f_l[:per_num, 1:-1]
test_l = test_id_f_l[:per_num, -1]
if TRAIN_MODE == 2:
# GLOBAL
model = LinearRegression(
train_l,
train_f,
np.array(test_l),
np.array(test_f),
np.array(test_id))
model.fit(FED_NUM, 8, file_path)
# model = Classifier(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id))
# model.fit(FED_NUM, 16)
# model = DecisionTree(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id))
# model.fit(FED_NUM, "/data/icde_data")
else:
# GLOBAL+
# model = GCSemi(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id), ext_f)
# model.fit(FED_NUM, file_path, 16)
# model = GCLSemi(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id), ext_f)
# model.fit(0, FED_NUM, file_path, 16)
# model = GSemi(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id), ext_f)
# model.fit(from_fed, to_fed, file_path, 16)
# model = DecisionTreeSemi(train_l, train_f, np.array(test_l), np.array(test_f), np.array(test_id), ext_f)
# model.fit(FED_NUM, "/data/icde_data")
train_l = np.concatenate((train_l, ext_l))
train_f = np.concatenate((train_f, ext_f))
model = LinearRegression(
train_l,
train_f,
np.array(test_l),
np.array(test_f),
np.array(test_id))
model.fit(FED_NUM, 8, file_path)