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  # Load libraries
 import pandas
 from pandas.tools.plotting import scatter_matrix
 import matplotlib.pyplot as plt
 from sklearn import model_selection
 from sklearn.metrics import classification_report
 from sklearn.metrics import confusion_matrix
 from sklearn.metrics import accuracy_score
 from sklearn.linear_model import LogisticRegression, RidgeClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
 from sklearn.neural_network import MLPClassifier
 from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
 from sklearn.gaussian_process import GaussianProcessClassifier
 from sklearn.gaussian_process.kernels import RBF
 from sklearn.svm import SVC
 from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, ExtraTreesClassifier
 from sklearn.cluster import SpectralClustering
 from sklearn.mixture import GaussianMixture
 from sklearn.cluster import KMeans
 from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
 from sklearn.calibration import CalibratedClassifierCV
 from pandas.tools.plotting import andrews_curves
 from pandas.tools.plotting import parallel_coordinates
 from pandas.tools.plotting import radviz
 import seaborn as sns
 import numpy as np
 
 # Load dataset
 #url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
 #names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
 path = "./data/JetSummary_p250_e20_1000events_r05.csv"
 #names = ['n_Total', 'n_Above_0p001', 'n_Above_0p01', 'n_Above_0p1', 'n_Above_1', 'n_Above_10', 'eta_avg', 'eta_std', 'phi_avg', 'phi_std', 'Delta_eta_avg', 'Delta_eta_std', 'Delta_phi_avg', 'Delta_phi_std', 'Delta_eta_avg_w', 'Delta_eta_std_w', 'Delta_phi_avg_w', 'Delta_phi_std_w', 'towerenergy_sum','class']
 names = ['n_track','charge_tot','eta','vertex','class']
 dataset = pandas.read_csv(path, names=names)
 
 # shape
 #print((dataset.shape)[0])
 
 # head
 #print(dataset.head(20))
 
 # descriptions and visualizations
 #print(dataset.describe())
 #andrews_curves(dataset, 'class')
 #parallel_coordinates(dataset, 'class')
 #radviz(dataset, 'class')
 
 # class distribution
 #print(dataset.groupby('class').size())
 
 # box and whisker plots
 #dataset.plot(kind='box', subplots=True, layout=(19,19), sharex=False, sharey=False)
 #plt.show()
 
 # histograms
 #dataset.hist()
 #plt.show()
 
 # scatter plot matrix
 #sns.pairplot(dataset, hue="class")
 #plt.show()
 #plt.savefig('destination_path.eps', format='eps', dpi=1000)
 
 # Split-out validation dataset
 array = dataset.values
 #X = array[:,0:19]
 #Y = array[:,19]
 X = array[:,0:4]
 Y = array[:,4]
 validation_size = 0.60
 seed = 7
 X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
 
 ding = np.column_stack((X_train,Y_train))
 print(ding)
 print(ding.dtype)
 dong = np.column_stack((X_validation,Y_validation))
 
 #np.savetxt("./data/JetSummary_1000_training.csv", ding, delimiter=", ")
 #np.savetxt("./data/JetSummary_1000_validation.csv", dong, fmt='%i, %i, %i, %i, %i, %i, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %s')
 #np.savetxt("./data/JetSummary_1000_training.csv", ding, fmt='%i, %i, %i, %i, %i, %i, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %s')
 
 
 # Test options and evaluation metric
 seed = 7
 scoring = 'accuracy'
 
 # Spot Check Algorithms
 models = []
 models.append(('LR', LogisticRegression()))
 models.append(('LDA', LinearDiscriminantAnalysis()))
 #models.append(('QDA', QuadraticDiscriminantAnalysis()))
 models.append(('KNN', KNeighborsClassifier()))
 models.append(('CART', DecisionTreeClassifier()))
 models.append(('GNB', GaussianNB()))
 #models.append(('SVM', SVC()))
 models.append(('SVMlin', SVC(kernel="linear", C=0.025)))
 #models.append(('SVMpoly', SVC(kernel="poly")))
 #models.append(('SVMg2', SVC(gamma=2, C=1)))
 #models.append(('Neural', MLPClassifier()))
 models.append(('RFC', RandomForestClassifier()))
 models.append(('ADA', AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)))
 models.append(('ADA3', AdaBoostClassifier(base_estimator=None, n_estimators=100, learning_rate=0.5, algorithm='SAMME.R', random_state=None)))
 #models.append(('EXT', ExtraTreesClassifier()))
 #models.append(('RC', RidgeClassifier()))
 #models.append(('RNeigh', RadiusNeighborsClassifier(200)))
 #models.append(('ccCV', CalibratedClassifierCV()))
 #models.append(('DTC', DecisionTreeClassifier()))
 #models.append(('ETC', ExtraTreeClassifier()))
 
 
 
 # evaluate each model in turn
 results = []
 names = []
 for name, model in models:
     kfold = model_selection.KFold(n_splits=10, random_state=seed)
     cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
     results.append(cv_results)
     names.append(name)
     msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
     print(msg)
 
 # Compare Algorithms
 #fig = plt.figure()
 #fig.suptitle('Algorithm Comparison')
 #ax = fig.add_subplot(111)
 #plt.boxplot(results)
 #ax.set_xticklabels(names)
 
 # Make predictions on validation dataset
 ada = AdaBoostClassifier()
 ada.fit(X_train, Y_train)
 predictions = ada.predict(X_validation)
 
 #LR = LogisticRegression(class_weight ={'DIS':3,'tau':1})
 #LR = LogisticRegression()
 #LR.fit(X_train, Y_train)
 #predictions = LR.predict(X_validation)
 print(accuracy_score(Y_validation, predictions))
 print(confusion_matrix(Y_validation, predictions))
 print(classification_report(Y_validation, predictions))
 
 
 plt.show()

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