class CarModel():
def __init__(self, mode='svm'):
if mode != 'svm' and mode != 'xgboost':
raise ValueError('Unknown mode for CarModel')
self._f = Features()
self._model = None
self._mode = mode
self.input_shape = None
def prepare(self, data, mode='standard'):
self.input_shape = data.x_orig[0].shape
features = self._f.extract(data.x_orig)
self._f.fit_scaler(features, mode=mode)
x = self._f.normalize(features)
data.put_features(x)
self._colormap = data.colormap
return data
def fit(self, data, random_state=11, show=False):
if data.x is None:
raise ValueError('Dataset does not have input values')
x = data.x
y = data.y_orig
train, test = self._split_data(x, y)
self._train(train, test, show=show)
def predict(self, im, show=False):
f = self._f
im = common.cvt_color(im, color=self._colormap, src='RGB')
pred = self._model.predict(f.normalize(f.extract(np.array([im]), show=show)))
return pred[0]
def _split_data(self, x, y, test_size=0.1, random_state=11):
xtr, xt, ytr, yt = train_test_split(x, y, test_size=test_size, random_state=random_state)
return (xtr, ytr), (xt, yt)
def _one_hot_encode(self, y):
width = np.unique(y).shape[0]
height = y.shape[0]
one = np.zeros((height, width), dtype=np.int32)
one[range(height), y] = 1
return one
def _train(self, train, test, random_state=11, show=False):
x, y = train
xtest, ytest = test
if self._mode == 'xgboost':
self._model = XGBClassifier(
learning_rate=0.1,
n_estimators=150,
max_depth=5,
min_child_weight=1,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
scale_pos_weight=1,
seed=random_state)
self._model.fit(x, y, eval_metric='auc')
else:
self._model = LinearSVC(random_state=random_state)
#self._model = SVC(kernel='rbf', max_iter=25000, random_state=random_state)
#self._model = SVC(max_iter=25000, random_state=random_state)
self._model.fit(x, y)
pred = self._model.predict(xtest)
acc_msg = "Test accuracy: {0:.05f}"
print(acc_msg.format(metrics.accuracy_score(ytest, pred)))
if self._mode == 'xgboost':
pred_prob = self._model.predict_proba(xtest)
ytest_hot = self._one_hot_encode(ytest)
auc_msg = "AUC score: {0:.05f}"
print(auc_msg.format(metrics.roc_auc_score(ytest_hot, pred_prob)))
if self._mode == 'xgboost' and show == True:
importance = pd.Series(self._model.booster().get_fscore()).sort_values(ascending=False)
importance.plot(kind='bar', title='Feature Importance')
plt.show()
print(importance[:10])
def load(self, filename='data/model.p'):
with open(filename, 'rb') as fd:
self.__dict__ = pickle.load(fd)
def save(self, filename='data/model.p'):
with open(filename, 'wb') as fd:
pickle.dump(self.__dict__, fd)
