def train_initial_model():
dataset = get_movielens_dataset(variant='100K')
train, test = random_train_test_split(dataset, random_state=np.random.RandomState(42))
model = ImplicitFactorizationModel(loss='adaptive_hinge',
embedding_dim=128, # latent dimensionality
n_iter=10, # number of epochs of training
batch_size=1024, # minibatch size
l2=1e-9, # strength of L2 regularization
learning_rate=1e-3,
use_cuda=torch.cuda.is_available())
print('Fitting the model')
model.fit(train, verbose=True)
print(type(model))
model_file = open('models/filmclub.model', 'wb')
pickle.dump(model, model_file)
model_file.close()
dataset.num_users = 1000000
dataset_file = open('data/dataset.pkl', 'wb')
pickle.dump(dataset, dataset_file)
dataset_file.close()
train_rmse = rmse_score(model, train)
test_rmse = rmse_score(model, test)
print('Train RMSE {:.3f}, test RMSE {:.3f}'.format(train_rmse, test_rmse))
def train_spotlight_models(train, test, dataset_testing, embedding_dims, n_iters, batch_sizes, l2s, learning_rates, is_save = False):
"""
takes train, test, dataset_testing datasets as spotlight.interactions.
train multiple spotlight models using ExplicitFactorizationModel, with given parameters.
parameters are given in embedding_dims, n_iters, batch_sizes, l2s, learning_rates.
return predictions of train, test, dataset_testing datasets as well as rmse on train and test.
"""
# initialize train_rmses and test_rmses, these store rmse on train and test set
train_rmses = np.array([])
test_rmses = np.array([])
# initialize preds_train_trains, preds_train_tests, preds_tests; these store predictions of models on train, test and dataset_testing datasets
preds_train_trains = []
preds_train_tests = []
preds_tests = []
# traverse all parameter combinations
# embedding_din, n_iter, batch_size, l2 regularization, learning_rate
for embedding_dim in embedding_dims:
for n_iter in n_iters:
for batch_size in batch_sizes:
for l2 in l2s:
for learning_rate in learning_rates:
# initialize model with parameter, ues GPU is torch.cuda.is_available() returns True, otherwise use CPU
model = ExplicitFactorizationModel(loss='regression',
embedding_dim=embedding_dim, # latent dimensionality
n_iter=n_iter, # number of epochs of training
batch_size=batch_size, # minibatch size
l2=l2, # strength of L2 regularization
learning_rate=learning_rate,
use_cuda=torch.cuda.is_available())
# print which model is being trained
print("embedding_dim={}, n_iter={}, batch_size={}, l2={}, learning_rate={}".format(embedding_dim, n_iter, batch_size, l2, learning_rate))
# fit model
model.fit(train, verbose=True)
# find rmse on train
train_rmse = rmse_score(model, train)
# find rmse on test
test_rmse = rmse_score(model, test)
# store rmse on train and test sets
train_rmses = np.append(train_rmses, train_rmse)
test_rmses = np.append(test_rmses, test_rmse)
# print train and test rmses
print('Train RMSE {:.3f}, test RMSE {:.3f}'.format(train_rmse, test_rmse))
# if is_save given, save the models to disk
if is_save:
torch.save(model, "models/embedding_dim={}, n_iter={}, batch_size={}, l2={}, learning_rate={}".format(embedding_dim, n_iter, batch_size, l2, learning_rate))
# find predictions of train, test and dataset_testing datasets
preds_train_train = model.predict(train.user_ids,train.item_ids)
preds_train_test = model.predict(test.user_ids,test.item_ids)
preds_test = model.predict(dataset_testing.user_ids,dataset_testing.item_ids)
#store those predictions
preds_train_trains.append(preds_train_train)
preds_train_tests.append(preds_train_test)
preds_tests.append(preds_test)
# return stored predictions on train, test, dataset_testing; return rmses on train and test
return preds_train_trains, preds_train_tests, preds_tests, train_rmses, test_rmses
def fit(self, num_epochs, report_int=1):
for t in range(num_epochs):
self.step_mcmc()
## REPORTING ###
if t % report_int == 0:
rmse_train = rmse_score(self, self.train)
rmse_test = rmse_score(self, self.test)
print(
f'step: {t} \t rmse train: {rmse_train:.2f}, test: {rmse_test:.2f}'
)
return rmse_train, rmse_test
def test_bloom(compression_ratio, expected_rmse):
interactions = movielens.get_movielens_dataset('100K')
train, test = random_train_test_split(interactions,
random_state=RANDOM_STATE)
user_embeddings = BloomEmbedding(interactions.num_users,
32,
compression_ratio=compression_ratio,
num_hash_functions=2)
item_embeddings = BloomEmbedding(interactions.num_items,
32,
compression_ratio=compression_ratio,
num_hash_functions=2)
network = BilinearNet(interactions.num_users,
interactions.num_items,
user_embedding_layer=user_embeddings,
item_embedding_layer=item_embeddings)
model = ExplicitFactorizationModel(loss='regression',
n_iter=10,
batch_size=1024,
learning_rate=1e-2,
l2=1e-5,
representation=network,
use_cuda=CUDA)
model.fit(train)
print(model)
rmse = rmse_score(model, test)
print(rmse)
assert rmse - EPSILON < expected_rmse
def test_explicit_serialization(data):
train, test = data
model = ExplicitFactorizationModel(loss='regression',
n_iter=3,
batch_size=1024,
learning_rate=1e-3,
l2=1e-5,
use_cuda=CUDA)
model.fit(train)
rmse_original = rmse_score(model, test)
rmse_recovered = rmse_score(_reload(model), test)
assert rmse_original == rmse_recovered
def best_params_spotlight(losses,
n_iters,
batch_sizes,
l2s,
learning_rates,
embedding_dims,
train_data,
t=Timer()):
rmses = dict()
params = dict()
t.start()
for loss in losses:
params['loss'] = loss
for n_iter in n_iters:
params['n_iter'] = n_iter
for batch_size in batch_sizes:
params['batch_size'] = batch_size
for l2 in l2s:
params['l2'] = l2
for learning_rate in learning_rates:
params['learning_rate'] = learning_rate
for embedding_dim in embedding_dims:
params['embedding_dim'] = embedding_dim
model = ExplicitFactorizationModel(
loss='regression',
embedding_dim=
embedding_dim, # latent dimensionality
n_iter=n_iter, # number of epochs of training
batch_size=batch_size, # minibatch size
l2=l2, # strength of L2 regularization
learning_rate=learning_rate,
use_cuda=torch.cuda.is_available())
params['model'] = model
train_tr_data, test_tr_data = random_train_test_split(
train_data,
random_state=np.random.RandomState(42))
model.fit(train_tr_data, verbose=True)
rmse = rmse_score(model, test_tr_data)
rmses[rmse] = params
print(
"-----------Time: {}, Loss: {}, n_iter: {}, l2: {}, batch_size: {}, learning_rate: {}, embedding_dim: {}, rmse: {}-------------\n\n"
.format(t.stop(), loss, n_iter, l2, batch_size,
learning_rate, embedding_dim, rmse))
# restart timer
t.start()
def load_spotlight_models(train, test, dataset_testing, verbose=False):
"""
Loads pretrained spotlight models from the folder in the directory.
Takes train, test datasets and dataset_testing to generate predictions and calculate rmse
"""
# initialize predictions, stores predictions on train, test and dataset_testing datasets
preds_train_trains = []
preds_train_tests = []
preds_tests = []
# initialize rmses, stores rmses on train and test dataset
train_rmses = np.array([])
test_rmses = np.array([])
# for each file in the "models" folder in the directory
for file in glob.glob("models/*"):
# prinr filenames, if given True
if verbose:
print(file)
# load model
model = torch.load(file)
# calculate and store rmses on train and test datasets
train_rmse = rmse_score(model, train)
test_rmse = rmse_score(model, test)
train_rmses = np.append(train_rmses, train_rmse)
test_rmses = np.append(test_rmses, test_rmse)
# make predictions on train, test and dataset_testing datasets
preds_train_train = model.predict(train.user_ids,train.item_ids)
preds_train_test = model.predict(test.user_ids,test.item_ids)
preds_test = model.predict(dataset_testing.user_ids,dataset_testing.item_ids)
# store predictions
preds_train_trains.append(preds_train_train)
preds_train_tests.append(preds_train_test)
preds_tests.append(preds_test)
# return predictions on train, test and dataset_testing datasets; return rmse on train and test datasets
return preds_train_trains, preds_train_tests, preds_tests, train_rmses, test_rmses
def resultados_factorizacion_explicito(self):
"""
Método resultados_factorizacion_explicito. Calcula las métricas del modelo de factorización explícito.
Este método solo se utiliza en la interfaz de texto.
"""
global train, test, modelo
# Se calculan las métricas
rmse = rmse_score(modelo, test)
mrr = mrr_score(modelo, test, train=train).mean()
precision, recall = precision_recall_score(modelo, test, train=train, k=10)
# Se imprimen las métricas
imprimir_resultados_dl(mrr, precision.mean(), recall.mean(), rmse)
def build_model(data, loss, embedding_dim, n_iter, batch_size, l2,
learning_rate, **kwargs):
model = ExplicitFactorizationModel(
loss=loss,
embedding_dim=embedding_dim, # latent dimensionality
n_iter=n_iter, # number of epochs of training
batch_size=batch_size, # minibatch size
l2=l2, # strength of L2 regularization
learning_rate=learning_rate,
use_cuda=torch.cuda.is_available())
train, test = random_train_test_split(
data, random_state=np.random.RandomState(42))
model.fit(train, verbose=True)
test_rmse = rmse_score(model, test)
return test_rmse
def test_poisson():
interactions = movielens.get_movielens_dataset('100K')
train, test = random_train_test_split(interactions,
random_state=RANDOM_STATE)
model = ExplicitFactorizationModel(loss='poisson',
n_iter=10,
batch_size=1024,
learning_rate=1e-3,
l2=1e-6)
model.fit(train)
rmse = rmse_score(model, test)
assert rmse < 1.0
def test_poisson():
interactions = movielens.get_movielens_dataset('100K')
train, test = random_train_test_split(interactions,
random_state=RANDOM_STATE)
model = ExplicitFactorizationModel(loss='poisson',
n_iter=10,
batch_size=1024,
learning_rate=1e-3,
l2=1e-6)
model.fit(train)
rmse = rmse_score(model, test)
assert rmse < 1.0
def obtener_metricas_gui(self):
"""
Método obtener_metricas_gui. Obtiene las métricas del modelo escogido.
Este método solo se utiliza en la interfaz web.
Returns
-------
metricas_devueltas: dict
diccionario con las métricas del modelo
"""
global train, test, modelo
# Se guardan las métricas en un diccionario para su futura muestra en la interfaz web
metricas = dict()
# Se calculan las métricas y se guardan en el diccionario formateadas
if self.opcion_modelo == 1:
rmse = rmse_score(modelo, test)
mrr = mrr_score(modelo, test, train=train).mean()
precision, recall = precision_recall_score(modelo, test, train=train, k=10)
metricas_devueltas = {"RMSE": format(rmse, '.4f'), "MRR": format(mrr, '.4f'), "Precisión k": format(precision.mean(), '.4f'), "Recall k": format(recall.mean(), '.4f')}
metricas_a_guardar = {"RMSE": [format(rmse, '.4f')], "MRR": [format(mrr, '.4f')], "Precisión k": [format(precision.mean(), '.4f')], "Recall k": [format(recall.mean(), '.4f')]}
elif self.opcion_modelo == 2:
mrr = mrr_score(modelo, test, train=train).mean()
precision, recall = precision_recall_score(modelo, test, train=train, k=10)
metricas_devueltas = {"MRR": format(mrr, '.4f'), "Precisión k": format(precision.mean(), '.4f'), "Recall k": format(recall.mean(), '.4f')}
metricas_a_guardar = {"MRR": [format(mrr, '.4f')], "Precisión k": [format(precision.mean(), '.4f')], "Recall k": [format(recall.mean(), '.4f')]}
else:
mrr = sequence_mrr_score(modelo, test).mean()
metricas_devueltas = {"MRR": format(mrr, '.4f')}
metricas_a_guardar = {"MRR": [format(mrr, '.4f')]}
# Se guardan las métricas en un archivo .csv
guardar_resultados(metricas_a_guardar)
return metricas_devueltas
def test_logistic():
interactions = movielens.get_movielens_dataset('100K')
# Convert to binary
interactions.ratings = (interactions.ratings > 3).astype(np.float32)
# Convert from (0, 1) to (-1, 1)
interactions.ratings = interactions.ratings * 2 - 1
train, test = random_train_test_split(interactions,
random_state=RANDOM_STATE)
model = ExplicitFactorizationModel(loss='logistic',
n_iter=10,
batch_size=1024,
learning_rate=1e-3,
l2=1e-6,
use_cuda=CUDA)
model.fit(train)
rmse = rmse_score(model, test)
assert rmse - EPSILON < 1.05
from spotlight.interactions import Interactions
user_ids = np.array(lite['user']).astype(np.int32)
item_ids = np.array(lite['item']).astype(np.int32)
ratings = np.array(lite['rating']).astype(np.float32)
times = np.array(lite['time']).astype(np.int32)
dataset = Interactions(user_ids, item_ids, ratings, times)
# Prepare train test
train, test = user_based_train_test_split(dataset)
# train, test = random_train_test_split(dataset)
# Test baseline
model = ExplicitFactorizationModel(n_iter=20)
model.fit(train, verbose=True)
print('RMSE', rmse_score(model, test))
from scipy.sparse import coo_matrix
ratings = coo_matrix((dataset.ratings, (dataset.user_ids, dataset.item_ids)),
shape=(dataset.num_users, dataset.num_items)).tocsr()
train_seq = train.to_sequence(SEQ_LEN)
test_seq = test.to_sequence(SEQ_LEN)
model = ExplicitSequenceModel(n_iter=30, representation='lstm', batch_size=1)
model.fit(train_seq, ratings, verbose=True)
SEQ_ID = 0
user_batch = train_seq.user_ids[SEQ_ID]
item_batch = train_seq.sequences[SEQ_ID]
from spotlight.cross_validation import random_train_test_split from spotlight.datasets.movielens import get_movielens_dataset from spotlight.evaluation import rmse_score from spotlight.factorization.explicit import ExplicitFactorizationModel dataset = get_movielens_dataset(variant='100K') train, test = random_train_test_split(dataset) model = ExplicitFactorizationModel(n_iter=1) model.fit(train) rmse = rmse_score(model, test) print(rmse)
def trainModelUntilOverfit(dataset, modelSteps, modelIterations,
numberDataSplits, embedding_dim, learning_rate):
numUsers = dataset.num_users
numMovies = dataset.num_items
train, test = random_train_test_split(dataset, 0.2)
print('Split into \n {} and \n {}.'.format(train, test))
#add random seed
seed = np.random.RandomState(seed=55555)
model = ExplicitFactorizationModel(n_iter=modelIterations,
embedding_dim=embedding_dim,
learning_rate=learning_rate,
random_state=seed)
rmseResults = np.empty((modelSteps * numberDataSplits, 2))
indexPreviousClosest = ["0"]
if (numberDataSplits > 1):
arraySplits = dataSplit(train, numberDataSplits)
print("Data set split into", len(arraySplits), "*", (arraySplits[1]))
# Each model step fits the entire dataset
arrayOfSteps = []
splitCounter = 0
fullStepCounter = 0 # increases each time the entire data set has been visited
currentStep = 0 # increases at every split of the data set (does not reset)
for i in range(modelSteps * numberDataSplits):
print("\nStarting step", fullStepCounter)
print("Data split", splitCounter)
if (numberDataSplits == 1):
model.fit(train, verbose=True)
elif (numberDataSplits > 1):
print(arraySplits[splitCounter])
model.fit(arraySplits[splitCounter], verbose=True)
else:
print("Invalid number of data splits")
break
#predictions for any user are made for all items, matrix has shape (944, 1683)
modelPredict = np.empty((numUsers, numMovies))
for userIndex in range(numUsers):
modelPredict[userIndex, :] = model.predict(userIndex)
# We take the transpose for tsne formatting (should be more rows than columns)
modelPredict = modelPredict.T
#Measure the model's effectiveness (how good predictions are):
rmse = rmse_score(model, test)
rmseTrain = rmse_score(model, train)
rmseTest = rmse_score(model, test)
print("RMSE TEST:", rmseTest, "\n")
rmseResults[i, :] = [rmseTrain, rmseTest]
arrayOfSteps += [i]
if (stopTraining(rmseResults, arrayOfSteps)):
rmseResults = rmseResults[:len(arrayOfSteps)]
break
if (numberDataSplits > 1):
splitCounter += 1
if (splitCounter >= len(arraySplits)):
splitCounter = 0
fullStepCounter += 1
currentStep += 1
return (model, rmseResults)
embedding_dim=5, # latent dimensionality
n_iter=10, # number of epochs of training
batch_size=256, # minibatch size
l2=1e-9, # strength of L2 regularization
learning_rate=2e-2,
use_cuda=torch.cuda.is_available())
# model = ImplicitFactorizationModel(loss='bpr',
# embedding_dim=128, # latent dimensionality
# n_iter=10, # number of epochs of training
# batch_size=256, # minibatch size
# l2=1e-9, # strength of L2 regularization
# learning_rate=1e-2,
# use_cuda=torch.cuda.is_available())
from spotlight.cross_validation import random_train_test_split
train, test = random_train_test_split(dataset,
random_state=np.random.RandomState(42))
print('Split into \n {} and \n {}.'.format(train, test))
model.fit(train, verbose=True)
torch.save(model, 'spotlight.model')
from spotlight.evaluation import rmse_score
train_rmse = rmse_score(model, train)
test_rmse = rmse_score(model, test)
print('Train RMSE {:.3f}, test RMSE {:.3f}'.format(train_rmse, test_rmse))
predictions = model.predict(test.user_ids, test.item_ids)
print(((predictions > 0.5) == (test.ratings > 0)).sum() / len(predictions))
