def uppc_experiment(**kwargs):
exp_data = {}
exp_data.update(kwargs)
sparseness = kwargs['sparseness']
index = kwargs['index']
extend = kwargs['extend']
matrix_type = kwargs['matrix_type']
matrix = create_sparse_matrix(
load_training_data(sparseness, index, extend, matrix_type=matrix_type))
test_userId, test_itemId, test_rating = \
load_data(csv_file_path(sparseness, index, training_set=False, matrix_type=matrix_type))
R = np.corrcoef(matrix)
R = np.nan_to_num(R, nan=-1)
def predict(user, item):
i = matrix[:, item]
i[i >= 0] = 1
return np.matmul(R[user], matrix[:, item]) / np.sum(
np.abs(np.matmul(R[user], i)))
test_predict = np.array(
list(map(lambda u, i: predict(u, i), test_userId, test_itemId)))
mae = np.mean(np.abs(test_predict - test_rating))
rmse = np.sqrt(np.mean(np.square(test_predict - test_rating)))
exp_data["mae"] = float(mae)
exp_data["rmse"] = float(rmse)
exp_data['datetime'] = datetime.now()
print(exp_data)
auto_insert_database(database_remote_config, exp_data,
f'uppc_{matrix_type}')
def main_1():
sparseness = 5
index = 1
extend_near_num = 1
userId, itemId, rating = load_data(csv_file_path(sparseness, index))
result = calculate_distance(csv_file_path(sparseness, index))
distance = convert_distance_result(result)
file = extend_csv_file_path(sparseness, index, extend_near_num)
print(file)
u, i, r = extend_array(extend_near_num, distance, userId, itemId, rating)
save_data(file, u, i, r)
def save_extend_array(sparseness, index, extend_near_nums, matrix_type):
if isinstance(extend_near_nums, int) is True:
extend_near_nums = (extend_near_nums, )
userId, itemId, rating = load_data(
csv_file_path(sparseness, index, matrix_type=matrix_type))
result = calculate_distance(
csv_file_path(sparseness, index, matrix_type=matrix_type))
distance = convert_distance_result(result)
for e in extend_near_nums:
extend_array_and_save(sparseness,
index,
e,
distance,
userId,
itemId,
rating,
matrix_type=matrix_type)
def experiment(experiment_data: ExperimentData, last_activation, matrix_type):
sparseness = experiment_data.sparseness # 5
index = experiment_data.data_index # 3
mf_dim = experiment_data.mf_dim # 32
epochs = experiment_data.epochs # 30
batch_size = experiment_data.batch_size # 128
layers = experiment_data.layers # [32, 16]
reg_layers = experiment_data.reg_layers # [0, 0]
learning_rate = experiment_data.learning_rate # 0.007
extend_near_num = experiment_data.extend_near_num # 5
learner = experiment_data.learner # adagrad
optimizer = {
'adagrad': Adagrad(lr=learning_rate),
'rmsprop': RMSprop(lr=learning_rate),
'adam': Adam(lr=learning_rate),
'sgd': SGD(lr=learning_rate)
}[learner]
matrix_type = matrix_type
dataset_name = 'sparseness%s_%s' % (sparseness, index)
model_out_file = '%s_NeuMF_%d_%s_%s.h5' % (dataset_name, mf_dim, layers,
datetime.now())
userId, itemId, rating = load_training_data(sparseness,
index,
extend_near_num,
matrix_type=matrix_type)
test_userId, test_itemId, test_rating = \
load_data(csv_file_path(sparseness, index, training_set=False, matrix_type=matrix_type))
early_stop = keras.callbacks.EarlyStopping(monitor='mean_absolute_error',
min_delta=0.0002,
patience=10)
model = custom_model.NeuMF.get_model(num_users=user_num,
num_items=ws_num,
layers=layers,
reg_layers=reg_layers,
mf_dim=mf_dim,
last_activation=last_activation)
model.compile(optimizer=optimizer,
loss='mae',
metrics=[metrics.mae, metrics.mse])
model.fit([userId, itemId],
rating,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stop],
verbose=0,
shuffle=True)
mkdir('./Trained')
model.save('./Trained/{}'.format(model_out_file))
loss, mae, mse = model.evaluate([test_userId, test_itemId],
test_rating,
steps=1)
# print('loss: ', loss)
# print('mae: ', mae)
# print('rmse', np.sqrt(mse))
experiment_data.model = model_out_file
experiment_data.loss = loss
experiment_data.mae = mae
experiment_data.rmse = np.sqrt(mse)
exp_data = experiment_data.to_dict()
exp_data['datetime'] = datetime.now()
exp_data['last_activation'] = last_activation
print(exp_data)
auto_insert_database(database_config, exp_data, f'ncf_{matrix_type}')
def experiment(**kwargs):
sparseness = kwargs['sparseness']
index = kwargs['index']
epochs = kwargs['epochs']
batch_size = kwargs['batch_size']
mf_dim = kwargs['mf_dim']
regs = kwargs['regs']
last_activation = kwargs['last_activation']
learning_rate = kwargs['learning_rate']
extend_near_num = kwargs['extend_near_num']
learner = kwargs['learner']
matrix_type = kwargs['matrix_type']
exp_data = {
'sparseness': sparseness,
'index': index,
'epochs': epochs,
'batch_size': batch_size,
'mf_dim': mf_dim,
'regs': regs,
'last_activation': last_activation,
'learning_rate': learning_rate,
'extend_near_num': extend_near_num,
'learner': learner
}
optimizer = {
'adagrad': Adagrad(lr=learning_rate),
'rmsprop': RMSprop(lr=learning_rate),
'adam': Adam(lr=learning_rate),
'sgd': SGD(lr=learning_rate)
}[learner]
dataset_name = 'sparseness%s_%s' % (sparseness, index)
model_out_file = '%s_GMF_%s_extend_%s_%s.h5' % (
dataset_name, regs, extend_near_num, datetime.now())
# load file
userId, itemId, rating = load_training_data(sparseness,
index,
extend_near_num,
matrix_type=matrix_type)
test_userId, test_itemId, test_rating = \
load_data(csv_file_path(sparseness, index, training_set=False, matrix_type=matrix_type))
# load end
early_stop = keras.callbacks.EarlyStopping(monitor='mean_absolute_error',
min_delta=0.0002,
patience=10)
model = custom_model.GMF.get_model(num_users=user_num,
num_items=ws_num,
latent_dim=mf_dim,
regs=regs,
last_activation=last_activation)
model.compile(optimizer=optimizer,
loss='mae',
metrics=[metrics.mae, metrics.mse])
model.fit([userId, itemId],
rating,
batch_size=batch_size,
epochs=epochs,
callbacks=[early_stop],
verbose=0,
shuffle=True)
mkdir('./Trained')
model.save('./Trained/{}'.format(model_out_file))
loss, mae, mse = model.evaluate([test_userId, test_itemId],
test_rating,
steps=1)
# print('loss: ', loss)
# print('mae: ', mae)
# print('rmse', np.sqrt(mse))
exp_data['model'] = model_out_file
exp_data['mae'] = float(mae)
exp_data['rmse'] = float(np.sqrt(mse))
exp_data['datetime'] = datetime.now()
print(exp_data)
auto_insert_database(database_config, exp_data, f'gmf_{matrix_type}')
return exp_data
def experiment(**kwargs):
sparseness = kwargs['sparseness']
index = kwargs['index']
epochs = kwargs['epochs']
batch_size = kwargs['batch_size']
layers = kwargs['layers']
reg_layers = kwargs['reg_layers']
fake_layers = kwargs['fake_layers']
fake_reg_layers = kwargs['fake_reg_layers']
last_activation = kwargs['last_activation']
fake_last_activation = kwargs['fake_last_activation']
learning_rate = kwargs['learning_rate']
extend_near_num = kwargs['extend_near_num']
learner = kwargs['learner']
exp_data = {
'sparseness': sparseness,
'index': index,
'epochs': epochs,
'batch_size': batch_size,
'layers': layers,
'reg_layers': reg_layers,
'fake_layers': fake_layers,
'fake_reg_layers': fake_reg_layers,
'last_activation': last_activation,
'fake_last_activation': fake_last_activation,
'learning_rate': learning_rate,
'extend_near_num': extend_near_num,
'learner': learner
}
optimizer = {'adagrad': Adagrad(lr=learning_rate),
'rmsprop': RMSprop(lr=learning_rate),
'adam': Adam(lr=learning_rate),
'sgd': SGD(lr=learning_rate)}[learner]
dataset_name = 'sparseness%s_%s' % (sparseness, index)
model_out_file = '%s_exMLP_%s_%s_%s.h5' % (dataset_name, layers, fake_layers, datetime.now())
userId, itemId, rating = load_data(csv_file_path(sparseness, index))
result = calculate_distance(csv_file_path(sparseness, index))
distance = convert_distance_result(result)
fake_user_id, userId, itemId, rating = extend_array(extend_near_num, distance, userId, itemId, rating)
test_userId, test_itemId, test_rating = load_data(csv_file_path(sparseness, index, training_set=False))
# early_stop = keras.callbacks.EarlyStopping(monitor='mean_absolute_error', min_delta=0.0002, patience=10)
model = custom_model.extend_mlp_model.get_model(num_users=user_num, num_items=ws_num,
layers=layers, reg_layers=reg_layers,
fake_layers=fake_layers, fake_reg_layers=fake_reg_layers,
last_activation=last_activation,
fake_last_activation=fake_last_activation)
model.compile(optimizer=optimizer)
model.fit(x=[fake_user_id, userId, itemId, rating],
y=None,
batch_size=batch_size, epochs=epochs,
verbose=1,
shuffle=False)
mkdir('./Trained')
model.save('./Trained/{}'.format(model_out_file))
# prediction, fake_prediction = model.predict([np.zeros(len(test_userId)), test_userId, test_itemId])
# mae = np.mean(np.abs(prediction - test_rating))
# rmse = np.sqrt(np.mean(np.square(prediction - test_rating)))
_, _, mae, rmse = model.evaluate([np.zeros(len(test_rating)), test_userId, test_itemId, test_rating], steps=1)
# print('loss: ', loss)
# print('mae: ', mae)
# print('rmse', np.sqrt(mse))
exp_data['model'] = model_out_file
exp_data['mae'] = float(mae)
exp_data['rmse'] = float(rmse)
exp_data['datetime'] = datetime.now()
exp_data['last_activation'] = last_activation
print(exp_data)
auto_insert_database(database_config, exp_data, 'exmlp_rt')