EventClassificationDNN/Analysis.py at master · dbullock1086/EventClassificationDNN · GitHub

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# Analysis
import numpy as np
import matplotlib as mpl
mpl.use('pdf')
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc

mpColors = ['red', 'darkorange', 'lawngreen', 'green', 'lightseagreen', 'cyan', 'royalblue', 'blue', 'blueviolet', 'magenta', 'hotpink']

def ClassificationAnalysis (MyModel, Test_X, Test_Y, BatchSize, SignalClassIndex=5):
    result = MyModel.Model.predict (Test_X, batch_size=BatchSize)

    fpr, tpr, _ = roc_curve (Test_Y[:,SignalClassIndex],
                             result[:,SignalClassIndex])
    roc_auc = auc (fpr, tpr)

    lw=2

    plt.plot (fpr,tpr,color=mpColors[SignalClassIndex],
              lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)

    print 'ROC', SignalClassIndex, 'AUC:', roc_auc
    pass


def MultiClassificationAnalysis (MyModel, Test_X, Test_Y, BatchSize):
    print 'Prediction Analysis.'
    result = MyModel.Model.predict (Test_X, batch_size=BatchSize)

    NClasses = Test_Y.shape[1]

    for ClassIndex in xrange(NClasses):
        ClassificationAnalysis (MyModel, Test_X, Test_Y, BatchSize, ClassIndex)
        pass

    lw=2

    plt.plot ([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim ([0.0, 1.0])
    plt.ylim ([0.0, 1.05])

    plt.xlabel ('False Positive Rate')
    plt.ylabel ('True Positive Rate')

    plt.legend (loc='lower right')

    plt.savefig (MyModel.OutDir + '/ROC.pdf')
    return result