def evaluate_model(metrics, categorical, model, y_test, X_test):
y_pred = model.predict(X_test, verbose=1)
if (categorical): ##Check this out, weird##
y_pred_coded = (y_pred == y_pred.max(axis=1)[:, None]).astype(int)
metric = []
metric.append(
['f1score',
f1_score(y_test, y_pred_coded, average='weighted')])
metric.append([
'precision',
precision_score(y_test, y_pred_coded, average='weighted')
])
metric.append(
['recall',
recall_score(y_test, y_pred_coded, average='weighted')])
metric.append(['accuracy', accuracy_score(y_test, y_pred_coded)])
print(metric)
metrics.append(metric)
else:
y_pred_coded = np.where(y_pred > 0.5, 1, 0)
y_pred_coded = y_pred_coded.flatten()
metric = []
metric.append(['f1score', f1_score(y_test, y_pred_coded)])
metric.append(['precision', precision_score(y_test, y_pred_coded)])
metric.append(['recall', recall_score(y_test, y_pred_coded)])
metric.append(['accuracy', accuracy_score(y_test, y_pred_coded)])
print(metric)
metrics.append(metric)
return metrics, y_pred
def evaluate_model(metrics, model, y_test, X_test):
y_pred=model.predict(X_test,verbose=1)
y_pred_coded=np.where(y_pred>0.5,1,0)
y_pred_coded=y_pred_coded.flatten()
metric=[]
metric.append(['f1score',f1_score(y_test,y_pred_coded)])
metric.append(['precision',precision_score(y_test,y_pred_coded)])
metric.append(['recall',recall_score(y_test,y_pred_coded)])
metric.append(['accuracy',accuracy_score(y_test,y_pred_coded)])
metrics.append(metric)
return metrics, y_pred
def ExtraTrees(X_train, X_test, y_train, y_test):
model = ExtraTreesClassifier(random_state=0, n_estimators=150, bootstrap=True, oob_score=True, warm_start=True)
model.fit(X_train, y_train)
# use the model to make predictions with the test data
y_pred = model.predict(X_test)
metrics = []
metrics.append(['f1score',f1_score(y_pred, y_test)])
#metrics.append(['precision',precision_score(y_pred, y_test)])
#metrics.append(['recall',recall_score(y_pred, y_test)])
metrics.append(['accuracy',accuracy_score(y_pred, y_test)])
print(metrics)
return model
def ExtraTrees(X_train, X_test, y_train, y_test):
model = load('Models/exttree_inst3_round_4.joblib')
model.fit(X_train, y_train)
# use the model to make predictions with the test data
y_pred = model.predict(X_test)
metrics = []
metrics.append(['f1score', f1_score(y_pred, y_test)])
#metrics.append(['precision',precision_score(y_pred, y_test)])
#metrics.append(['recall',recall_score(y_pred, y_test)])
metrics.append(['accuracy', accuracy_score(y_pred, y_test)])
print(metrics)
return model
def get_metrics():
metrics = []
# Separate losses.
# metrics.append(rot_loss)
# metrics.append(trans_loss)
# Metrics.
metrics.append(met.rot_angle_error)
metrics.append(met.tilt_error)
metrics.append(met.pan_error)
metrics.append(met.roll_error)
# metrics.append(trans_error)
# metrics.append(trans_error_x)
# metrics.append(trans_error_y)
# metrics.append(trans_error_z)
return metrics
