<Type1>540_Model Testing

import pandas as pd
import numpy as np
import h5py
import matplotlib
#matplotlib.use('agg')
import matplotlib.pyplot as plt
from keras.models import Model, Sequential
from keras.layers import Dense, Input, concatenate,Conv1D, Conv2D, MaxPooling2D, Conv2DTranspose,MaxPooling1D, Cropping2D, Multiply, subtract, Flatten, Reshape, Permute, LSTM, TimeDistributed,Dropout,BatchNormalization,UpSampling1D
from keras.optimizers import SGD
from keras.callbacks import ModelCheckpoint,EarlyStopping
import tensorflow as tf
from keras.models import load_model
from keras import optimizers
from keras import regularizers
from math import sqrt
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import pickle
from sklearn.preprocessing import StandardScaler
import time
from sklearn import preprocessing
from numpy import argmax
from keras.utils import to_categorical
from tabulate import tabulate
from numpy import array
from sklearn.externals import joblib
from sklearn.metrics import confusion_matrix,classification_report

filename="D:\\NCSU Research\\GEARS_Jul9\\GEARS\\540\\data_test.complete.csv"
test=pd.read_csv(filename)


clf=joblib.load("train_model_540.m")


x_columns = [x for x in test.columns if x not in ["label_new"]]
X=test[x_columns]
y=test["label_new"]


y_predicted = clf.predict(X)
accuracy = np.mean(y_predicted == y) * 100
confusion_m=confusion_matrix(y, y_predicted)
print ("y_predicted\n",y_predicted)
print ("accuracy:",accuracy)
print(confusion_m)
print(classification_report(y,y_predicted))