class Beermind(object):
def __init__(self, model_dir, data_dir):
self.model_dir = model_dir
self.data_dir = data_dir
logging.info("Loading datasets[0]...")
with open(self.data_dir / 'beer' / 'beer-top.pkl', 'rb') as fp:
beer_top = pickle.load(fp)[0]
logging.info("Loading datasets[1]...")
with open(self.data_dir / 'beer' / 'beer-core.pkl', 'rb') as fp:
beer_core = pickle.load(fp)[0]
self.top_encoding = OneHotEncoding(include_start_token=True,
include_stop_token=True)
top_sequences = [CharacterSequence.from_string(r.text) for r in beer_top]
self.top_encoding.build_encoding(top_sequences)
top_cats = [SingletonSequence(r.beer.style) for r in beer_top]
self.cat_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.cat_encoding.build_encoding(top_cats)
self.rat_encoding = IdentityEncoding(1)
self.core_encoding = OneHotEncoding(include_start_token=True,
include_stop_token=True)
core_sequences = [CharacterSequence.from_string(r.text) for r in beer_core]
self.core_encoding.build_encoding(core_sequences)
core_users = [SingletonSequence(r.user) for r in beer_core]
self.user_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.user_encoding.build_encoding(core_users)
core_items = [SingletonSequence(r.beer.name) for r in beer_core]
self.item_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.item_encoding.build_encoding(core_items)
logging.info("Loading models[0]...")
self.catnet = CharacterRNN('catnet',
len(self.top_encoding) + len(self.cat_encoding),
len(self.top_encoding),
n_layers=2,
n_hidden=1024)
self.catnet.load_parameters(self.model_dir / 'catnet.pkl')
self.catnet.compile_method('log_probability')
self.catratnet = CharacterRNN('catratnet',
len(self.top_encoding) + len(self.rat_encoding) + len(self.cat_encoding),
len(self.top_encoding),
n_layers=2,
n_hidden=1024)
self.catratnet.load_parameters(self.model_dir / 'catratnet.pkl')
self.catratnet.compile_method('generate_with_concat')
logging.info("Loading models[1]...")
self.useritemnet = CharacterRNN('useritemnet',
len(self.core_encoding) + len(self.user_encoding) + len(self.item_encoding),
len(self.core_encoding),
n_layers=2,
n_hidden=1024)
self.useritemnet.load_parameters(self.model_dir / 'useritemnet.pkl')
self.useritemnet.compile_method('generate_with_concat')
def generate(self, category, rating, length, temperature=1.0):
rating = self.transform_rating(rating)
results = self.catratnet.generate_with_concat(
np.eye(len(self.top_encoding))[self.top_encoding.encode("<STR>")],
np.concatenate([np.eye(len(self.cat_encoding))[self.cat_encoding.encode(category)],
[rating]]),
length,
temperature
)
return str(NumberSequence(results.argmax(axis=1)).decode(self.top_encoding))
def category_probability(self, review):
num_review = CharacterSequence(review).encode(self.top_encoding).seq.ravel()
Xs = num_review[:-1]
idx = num_review[1:][:, None]
X = np.tile(np.eye(len(self.top_encoding))[Xs, None], (1, len(self.cat_encoding), 1))
S = X.shape[0]
cats = np.tile(np.eye(len(self.cat_encoding))[None], (S, 1, 1))
X, cats = X.astype(np.float32), cats.astype(np.float32)
X = np.concatenate([X, cats], -1)
result = self.catnet.log_probability(X, idx)
denom = logsumexp(result, axis=1)
probs = np.exp(result - denom[:, None])
data = {}
for i, cat in enumerate(self.cat_encoding.backward_mapping):
data[cat] = probs[:, i].tolist()
return data
def users(self):
return self.user_encoding.backward_mapping
def items(self):
return self.item_encoding.backward_mapping
def generate_useritemnet(self, user, item, length, temperature=1.0):
results = self.useritemnet.generate_with_concat(
np.eye(len(self.core_encoding))[self.top_encoding.encode("<STR>")],
np.concatenate([np.eye(len(self.user_encoding))[self.user_encoding.encode(user)],
np.eye(len(self.item_encoding))[self.item_encoding.encode(item)]
]),
length,
temperature
)
return str(NumberSequence(results.argmax(axis=1)).decode(self.core_encoding))
def transform_rating(self, rating):
return (rating - 3.0) / 2.0
def inverse_transform_rating(self, rating):
return rating * 2 + 3
def __init__(self, model_dir, data_dir):
self.model_dir = model_dir
self.data_dir = data_dir
logging.info("Loading datasets[0]...")
with open(self.data_dir / 'beer' / 'beer-top.pkl', 'rb') as fp:
beer_top = pickle.load(fp)[0]
logging.info("Loading datasets[1]...")
with open(self.data_dir / 'beer' / 'beer-core.pkl', 'rb') as fp:
beer_core = pickle.load(fp)[0]
self.top_encoding = OneHotEncoding(include_start_token=True,
include_stop_token=True)
top_sequences = [CharacterSequence.from_string(r.text) for r in beer_top]
self.top_encoding.build_encoding(top_sequences)
top_cats = [SingletonSequence(r.beer.style) for r in beer_top]
self.cat_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.cat_encoding.build_encoding(top_cats)
self.rat_encoding = IdentityEncoding(1)
self.core_encoding = OneHotEncoding(include_start_token=True,
include_stop_token=True)
core_sequences = [CharacterSequence.from_string(r.text) for r in beer_core]
self.core_encoding.build_encoding(core_sequences)
core_users = [SingletonSequence(r.user) for r in beer_core]
self.user_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.user_encoding.build_encoding(core_users)
core_items = [SingletonSequence(r.beer.name) for r in beer_core]
self.item_encoding = OneHotEncoding(include_start_token=False,
include_stop_token=False)
self.item_encoding.build_encoding(core_items)
logging.info("Loading models[0]...")
self.catnet = CharacterRNN('catnet',
len(self.top_encoding) + len(self.cat_encoding),
len(self.top_encoding),
n_layers=2,
n_hidden=1024)
self.catnet.load_parameters(self.model_dir / 'catnet.pkl')
self.catnet.compile_method('log_probability')
self.catratnet = CharacterRNN('catratnet',
len(self.top_encoding) + len(self.rat_encoding) + len(self.cat_encoding),
len(self.top_encoding),
n_layers=2,
n_hidden=1024)
self.catratnet.load_parameters(self.model_dir / 'catratnet.pkl')
self.catratnet.compile_method('generate_with_concat')
logging.info("Loading models[1]...")
self.useritemnet = CharacterRNN('useritemnet',
len(self.core_encoding) + len(self.user_encoding) + len(self.item_encoding),
len(self.core_encoding),
n_layers=2,
n_hidden=1024)
self.useritemnet.load_parameters(self.model_dir / 'useritemnet.pkl')
self.useritemnet.compile_method('generate_with_concat')