Exemplos de prepare_file_output em Python

Exemplo n.º 1

Arquivo: get_result_ver_5.py Projeto: bartosz-wozniak/SmogDetector

#('gamma',0.0), #   bigger -> more conservative
#('min_child_weight',1), 
('objective','reg:linear'),
('eval_metric', 'rmse'), 
]

watchlist  = [(dtrain,'train'),(dtest,'eval')]
num_round = 1000
evals_result = {}
for i_max_depth in range(1, 2):
    param[0]=('max_depth',i_max_depth) #zmiana glebokosci drzewa
    print param
    #bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=evals_result, early_stopping_rounds=100)
    bst = xgb.train(param, dtrain, num_round, watchlist, feval=common.feval_class_round, evals_result=evals_result, early_stopping_rounds=100)    
    preds = bst.predict(dtest,ntree_limit=bst.best_ntree_limit)
    preds_class = common.round_all(preds);
    print(sk.metrics.classification_report(dtest.get_label(),preds_class))
    f1.write("{0}\n".format(round(sk.metrics.accuracy_score(dtest.get_label(),preds_class),2)));
    f2.write('Regresja na klasach,'+str(i_max_depth)+','+common.prepare_file_output(dtest.get_label(),preds_class)+"\n");
f1.close();
f2.close();

#      PLOT 
#ax = xgb.plot_tree(bst,num_trees=0)
#xgb.plot_importance(bst)
#plt.show()
#bst.dump_model('dump_raw.txt');

Exemplo n.º 2

Arquivo: get_result_ver_3.py Projeto: bartosz-wozniak/SmogDetector

num_round = 1000
evals_result = {}
for i_max_depth in range(1, 11):
    param[0] = ('max_depth', i_max_depth)  #zmiana glebokosci drzewa
    print param
    #bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=evals_result, early_stopping_rounds=100)
    bst = xgb.train(param,
                    dtrain,
                    num_round,
                    watchlist,
                    feval=common.feval_value,
                    evals_result=evals_result,
                    early_stopping_rounds=100)
    preds = bst.predict(dtest, ntree_limit=bst.best_ntree_limit)
    preds_class = common.value_to_class_all(preds)
    dtest_labels_class = common.value_to_class_all(dtest.get_label())
    f1.write("{0}\n".format(
        round(sk.metrics.accuracy_score(dtest_labels_class, preds_class), 2)))
    print(sk.metrics.classification_report(dtest_labels_class, preds_class))
    f2.write('Regresja,' + str(i_max_depth) + ',' +
             common.prepare_file_output(dtest_labels_class, preds_class) +
             "\n")
f1.close()
f2.close()

#      PLOT
#ax = xgb.plot_tree(bst,num_trees=0)
#xgb.plot_importance(bst)
#plt.show()
#bst.dump_model('dump_raw.txt');

Exemplo n.º 3

Arquivo: get_result_ver_2.py Projeto: bartosz-wozniak/SmogDetector

]

watchlist  = [(dtrain,'train'),(dtest,'eval')]
num_round = 1000
evals_result = {}
for i_max_depth in range(4, 11):
    param[0]=('max_depth',i_max_depth) #zmiana glebokosci drzewa
    print param
    #bst = xgb.train(param, dtrain, num_round, watchlist, evals_result=evals_result, early_stopping_rounds=100)
    bst = xgb.train(param, dtrain, num_round, watchlist, feval=common.feval_value, evals_result=evals_result, early_stopping_rounds=100)    
    preds = bst.predict(dtest,ntree_limit=bst.best_ntree_limit)
    preds_class= common.value_to_class_all(preds)
    labels_class=common.value_to_class_all(dtest.get_label())
    print(sk.metrics.classification_report(labels_class,preds_class))
    f1.write("{0}\n".format(round(sk.metrics.accuracy_score(labels_class,preds_class),2)));
    common.prepare_file_output(labels_class,preds_class);
f1.close()


#      MAKE PREDICTION
#preds = bst.predict(dtest)

#      PLOT 
#ax = xgb.plot_tree(bst,num_trees=0)
#xgb.plot_importance(bst)
#plt.show()
#bst.dump_model('dump_raw.txt');

Exemplo n.º 4

Arquivo: get_result_ver_8.py Projeto: bartosz-wozniak/SmogDetector

    ('eval_metric', 'mlogloss'),
    ('eval_metric', 'merror')  #2 metryki
]

watchlist = [(dtrain, 'train'), (dtest, 'eval')]
num_round = 1000
evals_result = {}
#for i_max_depth in range(1, 11):
#param[0]=('max_depth',i_max_depth) #zmiana glebokosci drzewa
print param
bst = xgb.train(param,
                dtrain,
                num_round,
                watchlist,
                evals_result=evals_result,
                early_stopping_rounds=100)
preds = bst.predict(dtest)
print(sk.metrics.classification_report(dtest.get_label(), preds))
f1.write("{0}\n".format(
    round(sk.metrics.accuracy_score(dtest.get_label(), preds), 2)))
f2.write('Klasyfikacja funkcja linowa,' + str(1) + ',' +
         common.prepare_file_output(dtest.get_label(), preds) + "\n")
f1.close()
f2.close()

#      PLOT
#ax = xgb.plot_tree(bst,num_trees=0)
#xgb.plot_importance(bst)
#plt.show()
#bst.dump_model('dump_raw.txt');