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coarse_tuning.py
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coarse_tuning.py
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## author@ Wenhui Yu email: jianlin.ywh@alibaba-inc.com 2021.02.16
## Low-pass Collaborative Filtering Network (LCFN) and baselines
from train_model import train_model
from print_save import print_params, save_params
from get_hyperparameters import get_hyperparameter
import tensorflow as tf
import numpy as np
import random as rd
import time
def coarse_tuning(path_excel_dir, para_name, para, data, lr, lamda, min_num_coarse, max_num_coarse):
## tuning settings
x_cen, y_cen = 1, 1
score_matrix = np.zeros((3,3))
hyper_matrix = np.array([[[lr * (10 ** i), lamda * (10 ** j)] for i in range(-1, 2)] for j in range(-1, 2)])
num_matrix = np.zeros((3,3))
## coarse tuning
for i in range(2):
for x_curr, y_curr in [[x_cen, y_cen], [x_cen - 1, y_cen], [x_cen, y_cen - 1], [x_cen + 1, y_cen], [x_cen, y_cen + 1]]:
if (num_matrix[x_curr, y_curr] < min_num_coarse or (x_curr == x_cen and y_curr == y_cen)) and (num_matrix[x_curr, y_curr] < 0.5 or score_matrix[x_curr, y_curr] >= 0.7 * score_matrix.max()):
para[3: 5] = hyper_matrix[x_curr, y_curr]
print_params(para_name, para)
path_excel = path_excel_dir + str(int(time.time())) + str(int(rd.uniform(100, 900))) + '.xlsx'
save_params(para_name, para, path_excel)
score = train_model(para, data, path_excel)
if para[2] not in ['GCMC', 'NGCF', 'SCF', 'CGMC', 'LightGCN']: tf.reset_default_graph()
score_matrix[x_curr, y_curr] = (score_matrix[x_curr, y_curr] * num_matrix[x_curr, y_curr] + score)/(num_matrix[x_curr, y_curr] + 1)
num_matrix[x_curr, y_curr] += 1
print(score_matrix)
print(num_matrix)
x_argmax, y_argmax = np.where(score_matrix == score_matrix.max())
x_argmax, y_argmax = x_argmax[0], y_argmax[0]
print('When \eta and \lambda is: ', hyper_matrix[x_argmax, y_argmax])
print('the model achieves the best performance: ', score_matrix.max())
while num_matrix[x_cen, y_cen] < max_num_coarse or score_matrix.max() != score_matrix[x_cen, y_cen]:
x_cen, y_cen = np.where(score_matrix == score_matrix.max())
x_cen, y_cen = x_cen[0], y_cen[0]
## extending the matrices
if y_cen == 0:
y_cen += 1
pad = np.zeros(score_matrix.shape[0])
score_matrix = np.c_[pad, score_matrix]
num_matrix = np.c_[pad, num_matrix]
hyper_matrix = np.concatenate((np.zeros((hyper_matrix.shape[0], 1, 2)), hyper_matrix), axis=1)
for i in range(hyper_matrix.shape[0]):
hyper_matrix[i, 0, 0] = hyper_matrix[i, 1, 0] / 10
hyper_matrix[i, 0, 1] = hyper_matrix[i, 1, 1]
if y_cen == score_matrix.shape[1] - 1:
pad = np.zeros(score_matrix.shape[0])
score_matrix = np.c_[score_matrix, pad]
num_matrix = np.c_[num_matrix, pad]
hyper_matrix = np.concatenate((hyper_matrix, np.zeros((hyper_matrix.shape[0], 1, 2))), axis=1)
for i in range(hyper_matrix.shape[0]):
hyper_matrix[i, -1, 0] = hyper_matrix[i, -1-1, 0] * 10
hyper_matrix[i, -1, 1] = hyper_matrix[i, -1-1, 1]
if x_cen == 0:
x_cen += 1
pad = np.zeros((1, score_matrix.shape[1]))
score_matrix = np.r_[pad, score_matrix]
num_matrix = np.r_[pad, num_matrix]
hyper_matrix = np.concatenate((np.zeros((1, hyper_matrix.shape[1], 2)), hyper_matrix), axis=0)
for i in range(hyper_matrix.shape[1]):
hyper_matrix[0, i, 0] = hyper_matrix[1, i, 0]
hyper_matrix[0, i, 1] = hyper_matrix[1, i, 1] / 10
if x_cen == score_matrix.shape[0] - 1:
pad = np.zeros((1, score_matrix.shape[1]))
score_matrix = np.r_[score_matrix, pad]
num_matrix = np.r_[num_matrix, pad]
hyper_matrix = np.concatenate((hyper_matrix, np.zeros((1, hyper_matrix.shape[1], 2))), axis=0)
for i in range(hyper_matrix.shape[1]):
hyper_matrix[-1, i, 0] = hyper_matrix[-1-1, i, 0]
hyper_matrix[-1, i, 1] = hyper_matrix[-1-1, i, 1] * 10
## finding the best performance
for x_curr, y_curr in [[x_cen, y_cen], [x_cen - 1, y_cen], [x_cen, y_cen - 1], [x_cen + 1, y_cen], [x_cen, y_cen + 1]]:
if (num_matrix[x_curr, y_curr] < min_num_coarse or (x_curr == x_cen and y_curr == y_cen)) and (num_matrix[x_curr, y_curr] < 0.5 or score_matrix[x_curr, y_curr] >= 0.7 * score_matrix.max()):
para[3: 5] = hyper_matrix[x_curr, y_curr]
print_params(para_name, para)
path_excel = path_excel_dir + str(int(time.time())) + str(int(rd.uniform(100, 900))) + '.xlsx'
save_params(para_name, para, path_excel)
score = train_model(para, data, path_excel)
if para[2] not in ['GCMC', 'NGCF', 'SCF', 'CGMC', 'LightGCN']: tf.reset_default_graph()
score_matrix[x_curr, y_curr] = (score_matrix[x_curr, y_curr] * num_matrix[x_curr, y_curr] + score)/(num_matrix[x_curr, y_curr] + 1)
num_matrix[x_curr, y_curr] += 1
print(score_matrix)
print(num_matrix)
x_argmax, y_argmax = np.where(score_matrix == score_matrix.max())
x_argmax, y_argmax = x_argmax[0], y_argmax[0]
print('When \eta and \lambda is: ', hyper_matrix[x_argmax, y_argmax])
print('the model achieves the best performance: ', score_matrix.max())