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