def user_experiment(set_size=2000):
if set_size > 0:
print("+ Extraindo " + str(set_size) + " músicas")
extractSet(set_size=set_size)
print('+ Carregando dados no sistema')
groot = Radio(load_data_songs(), load_data_users())
print('+ Carregando Dados')
groot.post_preference_set(
preference_set=make_set_to_process(
song_set=groot.get_song_set(),
dict_set=statisticalOverview(
songSet=groot.get_song_set(),
preferenceSet=groot.get_preference_set(),
DEBUG=False
),
DEBUG=False
)
)
print('+ Treinando a árvore')
classifier, evaluate_results = plant_the_tree(
set_to_process=preprocessing_data(
data_set=groot.get_preference_set(),
all_features=groot.get_all_features(),
DEBUG=False
),
features=groot.get_song_features(),
important_feature=groot.get_important_feature(),
DEBUG=True,
ADMIN=False
)
groot.post_classifier(
new_classifier=classifier
)
print('+ Obtendo similaridade entre as músicas')
groot.post_distance_matrix(
new_distance_matrix=get_song_distance(
song_set=groot.get_song_set(),
song_features=groot.get_song_features(),
classifier_important=groot.get_feature_weight(),
DEBUG=False
)
)
print('+ Rádio Groot - Iniciando')
time.sleep(3)
os.system('clear||cls')
start_and_end = interface_menu(groot)
environment(
groot=groot,
song_stages=start_and_end
)
def admin_experiment(set_size=2000):
if set_size > 0:
print("+ Extraindo " + str(set_size) + " músicas")
extractSet(set_size=set_size)
print('+ Carregando dados no sistema')
groot = Radio(load_data_songs(), load_data_users())
print('+ Processando dados')
groot.post_preference_set(
preference_set=make_set_to_process(
song_set=groot.get_song_set(),
dict_set=statisticalOverview(
songSet=groot.get_song_set(),
preferenceSet=groot.get_preference_set(),
DEBUG=True
),
DEBUG=True
)
)
print('+ Treinando a árvore')
classifier, evaluate_results = plant_the_tree(
set_to_process=preprocessing_data(
data_set=groot.get_preference_set(),
all_features=groot.get_all_features(),
DEBUG=True
),
features=groot.get_song_features(),
important_feature=groot.get_important_feature(),
DEBUG=True,
ADMIN=True
)
groot.post_classifier(
new_classifier=classifier
)
print('+ Obtendo similaridade entre as músicas')
groot.post_distance_matrix(
new_distance_matrix=get_song_distance(
song_set=groot.get_song_set(),
song_features=groot.get_song_features(),
classifier_important=groot.get_feature_weight(),
DEBUG=True
)
)
print('+ Iniciando a busca')
similarity = environment(
groot=groot,
song_stages=random_choice(groot=groot),
DEBUG=True
)
print('+ Busca Terminada')
def experiment_cicles(cicles=5, set_size=500):
weight_df = pd.DataFrame(columns=list([]))
evaluate_df = pd.DataFrame(columns=list([]))
similarity_df = pd.DataFrame(columns=list())
for i in range(cicles):
print('- * - Iniciando o Ciclo: ', str(i))
if set_size > 0:
print("+ Extraindo " + str(set_size) + " músicas")
extractSet(set_size=set_size)
print('+ Carregando dados no sistema')
groot = Radio(load_data_songs(), load_data_users())
print('+ Processando dados')
groot.post_preference_set(
preference_set=make_set_to_process(
song_set=groot.get_song_set(),
dict_set=statisticalOverview(
songSet=groot.get_song_set(),
preferenceSet=groot.get_preference_set(),
DEBUG=False
),
DEBUG=False
)
)
print('+ Treinando a árvore')
classifier, evaluate_results = plant_the_tree(
set_to_process=preprocessing_data(
data_set=groot.get_preference_set(),
all_features=groot.get_all_features(),
DEBUG=False
),
features=groot.get_song_features(),
important_feature=groot.get_important_feature(),
DEBUG=False,
ADMIN=False
)
groot.post_classifier(
new_classifier=classifier
)
print('+ Obtendo similaridade entre as músicas')
groot.post_distance_matrix(
new_distance_matrix=get_song_distance(
song_set=groot.get_song_set(),
song_features=groot.get_song_features(),
classifier_important=groot.get_feature_weight(),
DEBUG=False
)
)
print('+ Iniciando a busca')
similarity = environment(
groot=groot,
song_stages=random_choice(groot=groot),
DEBUG=False
)
print('+ Busca Terminada')
print('Salvando informações')
#
weight_df = pd.concat([weight_df, pd.DataFrame(
[[i for i in list(groot.get_classifier().feature_importances_)]],
columns=[i for i in list(groot.get_song_features())],
)], sort=False)
#
evaluate_df = pd.concat([evaluate_df, pd.DataFrame(
[[i for i in evaluate_results.values()]],
columns=[x for x in evaluate_results],
)], sort=False)
#
similarity_df = pd.concat([similarity_df,
pd.DataFrame(
[[i for i in similarity.values()]],
columns=[x for x in similarity],
)], sort=False)
plot_feature_importance(weight_df)
plot_evaluations(evaluate_df)
plot_similarity(similarity_df['similaridade'].tolist())
plot_final_state(similarity_df['final_state'].tolist())
plot_nodes(similarity_df['total_visitas'].tolist())
