default=False)
parser.add_argument('--item_wins',
type=bool,
nargs='?',
const=True,
default=False)
parser.add_argument('--item_fails',
type=bool,
nargs='?',
const=True,
default=False)
parser.add_argument('--iter', type=int, nargs='?', default=1000)
options = parser.parse_args()
DATASET_NAME = options.dataset
CSV_FOLDER, CSV_ALL, CONFIG_FILE, Q_NPZ, SKILL_WINS, SKILL_FAILS = dataio.build_new_paths(
DATASET_NAME)
config = dataio.get_config(CONFIG_FILE)
experiment_args = vars(options)
df_train, df_val, df_test = dataio.get_data(DATASET_NAME)
try:
skill_wins = load_npz(SKILL_WINS)
skill_fails = load_npz(SKILL_FAILS)
except:
skill_wins = None
skill_fails = None
short_legend, full_legend, latex_legend, active_agents = dataio.get_legend(
experiment_args)
EXPERIMENT_FOLDER = os.path.join(CSV_FOLDER, short_legend)
parser.add_argument('--attempts',
type=bool,
nargs='?',
const=True,
default=False)
parser.add_argument('--tw_kc', type=bool, nargs='?', const=True, default=False)
parser.add_argument('--tw_items',
type=bool,
nargs='?',
const=True,
default=False)
options = parser.parse_args()
experiment_args = vars(options)
DATASET_NAME = options.dataset
CSV_FOLDER = dataio.build_new_paths(DATASET_NAME)
# Build legend
short_legend, full_legend, latex_legend, active_agents = dataio.get_legend(
experiment_args)
EXPERIMENT_FOLDER = os.path.join(CSV_FOLDER, "results", short_legend)
dataio.prepare_folder(EXPERIMENT_FOLDER)
maxRuns = 5
for run_id in range(maxRuns):
dataio.prepare_folder(os.path.join(EXPERIMENT_FOLDER, str(run_id)))
# Load sparsely encoded datasets
X = csr_matrix(load_npz(options.X_file))
all_users = np.unique(X[:, 0].toarray().flatten())
y = X[:, 3].toarray().flatten()