<Type1>540_Model Building

import pandas as pd
import numpy as np
import h5py
import matplotlib
import csv
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.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib


#读取csv文件
filename="D:\\NCSU Research\\GEARS_Jul9\\GEARS\\540\\data.complete.csv"
train=pd.read_csv(filename)

#建模
#x_columns = [x for x in train.columns if x not in ["label_new"]]
#X=train[x_columns]
#y=train["label_new"]




clf = RandomForestClassifier(n_estimators=100, criterion="entropy",oob_score=True) #oob:out of bag 通过袋外样本，可以评估这个树的准确度，估算算法的泛化能力(explanation of oob)

if __name__=="__main__":
    x_columns = [x for x in train.columns if x not in ["label_new"]]
    X=train[x_columns]
    y=train["label_new"]


    Xd_train, Xd_test, y_train, y_test = train_test_split(X, y,test_size=0.25)
    clf = clf.fit(Xd_train, y_train)
    y_predicted = clf.predict(Xd_test)
    accuracy = np.mean(y_predicted == y_test) * 100

    print ("y_test\n",y_test)
    print ("y_predicted\n",y_predicted)
    print ("accuracy:",accuracy)
    print(clf.oob_score_)

    #joblib.dump(clf,"train_model_540.m") #preserve the model

    joblib.dump(clf,"train_model_540.m") #preserve the model

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