def generate_lookup(models: list, triples: np.array,
num_samples: int = 100, batch_size=4):
"""
Generate lookup by evaluating models per relation
It will be performed on TRAIN set for fariness(and
not all relations are present in test set. 12 of them are missing)
"""
lookup = dict()
np.random.seed(0)
triples = crop_per_relation(triples, num_samples)
# Get scores for all models
model_scores = dict()
model_names = []
for (model_name, model) in list(models.items()):
print(model_name)
model_names.append(model_name)
model_scores[model_name] = evaluation_per_relation(triples, model, batch_size)
# Aggregate scores into lookup table
# Select the model with best MRR
for i in range(len(triples.keys())):
lookup[i] = max(model_scores, key=lambda x, curr_rel=i: model_scores[x][curr_rel]['MRR'])
return lookup
def fig_cum_rewards(fout_name: str, cum_rewards: np.array):
""" 累積報酬の棒グラフを作図する。
"""
plt.figure()
categories = list(cum_rewards.keys())
ave = np.zeros(len(categories))
stdev = np.zeros(len(categories))
for i_cat, category in enumerate(categories):
ave[i_cat] = np.mean(cum_rewards[category])
stdev[i_cat] = np.std(cum_rewards[category])
plt.bar(np.array(range(len(categories))),
ave,
tick_label=categories,
yerr=stdev,
align="center",
capsize=10)
plt.ylabel("average cumulative rewards")
plt.savefig(fout_name)
