def factorization(
model_path='models/simple_factorization',
simple=True,
use_item_data = True,
item_data_output='data/model_data/item_data.csv',
model_params={'nmf':False,
'verbose': False}
):
"""builds and saves factorization model"""
get.model_data(**data_params)
mat = gl.SFrame.read_csv(data_params['output_path'])
if simple:
mat = mat[['user_id', 'item_id']]
if use_item_data:
get.item_data(item_data_output)
item_data = gl.SFrame.read_csv(item_data_output)
model = gl.recommender.ranking_factorization_recommender.create(
mat,
item_data=item_data, **model_params
)
else:
model = gl.recommender.ranking_factorization_recommender.create(
mat,
**model_params
)
save_model(model_path, model)
log.info('saved model as %s' % model_path)
def main():
# create csvs to train models
_load_data()
is_df = gl.SFrame.read_csv(IS_DF_PATH)
nmf_df = gl.SFrame.read_csv(NMF_DF_PATH)
pop_df = gl.SFrame.read_csv(POP_DF_PATH)
item_df = gl.SFrame(get.item_data(BEERS_PATH, BREWERS_PATH))
# list of beers to exclude from recs, by user
exclude_beers = pd.read_csv(FTISO_PATH)[['user_id', 'beer_id']]
exclude_beers.columns = ['user_id', 'item_id']
exclude_beers = gl.SFrame(exclude_beers)
# build & save models
build.is_model(is_df, IS_MODEL_PATH)
build.nmf_model(nmf_df, item_df, NMF_MODEL_PATH)
build.pop_model(pop_df, POP_MODEL_PATH)
# load all models
is_model = gl.load_model(IS_MODEL_PATH)
nmf_model = gl.load_model(NMF_MODEL_PATH)
pop_model = gl.load_model(POP_MODEL_PATH)
# get recommendations & export as json
users = pd.read_csv(USERS_PATH)['id'].to_frame(name='id')
users = gl.SFrame(users)
is_recs = is_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
nmf_recs = nmf_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
pop_recs = pop_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
# save recommendations
is_recs.export_json(IS_REC_PATH, orient='records')
nmf_recs.export_json(NMF_REC_PATH, orient='records')
pop_recs.export_json(POP_REC_PATH, orient='records')
def main():
# create csvs to train models
_load_data()
is_df = gl.SFrame.read_csv(IS_DF_PATH)
nmf_df = gl.SFrame.read_csv(NMF_DF_PATH)
pop_df = gl.SFrame.read_csv(POP_DF_PATH)
item_df = gl.SFrame(get.item_data(BEERS_PATH, BREWERS_PATH))
# list of beers to exclude from recs, by user
exclude_beers = pd.read_csv(FTISO_PATH)[['user_id', 'beer_id']]
exclude_beers.columns = ['user_id', 'item_id']
exclude_beers = gl.SFrame(exclude_beers)
# build & save models
build.is_model(is_df, IS_MODEL_PATH)
build.nmf_model(nmf_df, item_df, NMF_MODEL_PATH)
build.pop_model(pop_df, POP_MODEL_PATH)
# load all models
is_model = gl.load_model(IS_MODEL_PATH)
nmf_model = gl.load_model(NMF_MODEL_PATH)
pop_model = gl.load_model(POP_MODEL_PATH)
# get recommendations & export as json
users = pd.read_csv(USERS_PATH)['id'].to_frame(name='id')
users = gl.SFrame(users)
is_recs = is_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
nmf_recs = nmf_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
pop_recs = pop_model.recommend(users=users['id'],
exclude=exclude_beers,
diversity=3)
# save recommendations
is_recs.export_json(IS_REC_PATH, orient='records')
nmf_recs.export_json(NMF_REC_PATH, orient='records')
pop_recs.export_json(POP_REC_PATH, orient='records')
def build_nmf_model():
"""builds an nmf model from completed trades, including beer side data"""
# load data
df = get.model_data(sparse=4,
outlier=500,
iso_rate=0,
proposed_rate=0,
traded_rate=1,
trade_path=TRADE_PATH,
trade_item_path=TRADE_ITEM_PATH,
ftiso_path=FTISO_PATH)
mat = gl.SFrame(df[['user_id', 'item_id']])
item_data = gl.SFrame(get.item_data())
# build model
model = gl.recommender.ranking_factorization_recommender.create(
mat,
item_data=item_data,
nmf=False
)
return model
def build_nmf_model():
"""builds an nmf model from completed trades, including beer side data"""
# load data
df = get.model_data(sparse=4,
outlier=500,
iso_rate=0,
proposed_rate=0,
traded_rate=1,
trade_path=TRADE_PATH,
trade_item_path=TRADE_ITEM_PATH,
ftiso_path=FTISO_PATH)
mat = gl.SFrame(df[['user_id', 'item_id']])
item_data = gl.SFrame(get.item_data())
# build model
model = gl.recommender.ranking_factorization_recommender.create(mat, item_data=item_data, nmf=False)
# check recs
users = [3381, 14239, 6601, 8958, 1440, 880, 57]
ft, user_beers, beers = check.load_rec_data()
recommends = check.recs(users, model, ft, user_beers, beers)
# # check precision & recall
# train, test = gl.recommender.util.random_split_by_user(gl.SFrame(user_beers))
# model = gl.recommender.ranking_factorization_recommender.create(train, item_data=None, nmf=False, verbose=False)
# pr = model.evaluate(test, metric='precision_recall')
return model, recommends#, pr
