274 lines
9.2 KiB
Python
Executable File
274 lines
9.2 KiB
Python
Executable File
#!/usr/bin/env python3
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#
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# Copyright 2015 Carnegie Mellon University
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import numpy as np
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import pandas as pd
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from scipy.interpolate import interp1d
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from sklearn import cross_validation
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from sklearn.cross_validation import KFold
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from sklearn.metrics import accuracy_score
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from sklearn.metrics.pairwise import chi2_kernel
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from sklearn.svm import SVC
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import matplotlib as mpl
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mpl.use('Agg')
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import matplotlib.pyplot as plt
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plt.style.use('bmh')
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import os
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import sys
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import argparse
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from scipy import arange
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument('--workDir', type=str, default='reps')
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parser.add_argument('--lfwPairs', type=str,
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default=os.path.expanduser("~/openface/data/lfw/pairs.txt"))
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args = parser.parse_args()
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print("Loading embeddings.")
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fname = "{}/labels.csv".format(args.workDir)
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paths = pd.read_csv(fname, header=None).as_matrix()[:, 1]
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paths = map(os.path.basename, paths) # Get the filename.
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# Remove the extension.
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paths = map(lambda path: os.path.splitext(path)[0], paths)
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fname = "{}/reps.csv".format(args.workDir)
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rawEmbeddings = pd.read_csv(fname, header=None).as_matrix()
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embeddings = dict(zip(*[paths, rawEmbeddings]))
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pairs = loadPairs(args.lfwPairs)
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classifyExp(args.workDir, pairs, embeddings)
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plotClassifyExp(args.workDir)
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def loadPairs(pairsFname):
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print(" + Reading pairs.")
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pairs = []
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with open(pairsFname, 'r') as f:
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for line in f.readlines()[1:]:
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pair = line.strip().split()
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pairs.append(pair)
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assert(len(pairs) == 6000)
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return np.array(pairs)
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def getEmbeddings(pair, embeddings):
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if len(pair) == 3:
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name1 = "{}_{}".format(pair[0], pair[1].zfill(4))
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name2 = "{}_{}".format(pair[0], pair[2].zfill(4))
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actual_same = True
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elif len(pair) == 4:
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name1 = "{}_{}".format(pair[0], pair[1].zfill(4))
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name2 = "{}_{}".format(pair[2], pair[3].zfill(4))
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actual_same = False
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else:
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raise Exception(
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"Unexpected pair length: {}".format(len(pair)))
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(x1, x2) = (embeddings[name1], embeddings[name2])
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return (x1, x2, actual_same)
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def writeROC(fname, thresholds, embeddings, pairsTest):
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with open(fname, "w") as f:
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f.write("threshold,tp,tn,fp,fn,tpr,fpr\n")
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tp = tn = fp = fn = 0
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for threshold in thresholds:
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tp = tn = fp = fn = 0
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for pair in pairsTest:
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(x1, x2, actual_same) = getEmbeddings(pair, embeddings)
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diff = x1 - x2
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dist = np.dot(diff.T, diff)
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predict_same = dist < threshold
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if predict_same and actual_same:
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tp += 1
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elif predict_same and not actual_same:
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fp += 1
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elif not predict_same and not actual_same:
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tn += 1
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elif not predict_same and actual_same:
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fn += 1
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if tp + fn == 0:
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tpr = 0
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else:
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tpr = float(tp) / float(tp + fn)
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if fp + tn == 0:
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fpr = 0
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else:
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fpr = float(fp) / float(fp + tn)
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f.write(",".join([str(x)
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for x in [threshold, tp, tn, fp, fn, tpr, fpr]]))
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f.write("\n")
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if tpr == 1.0 and fpr == 1.0:
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# No further improvements.
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f.write(",".join([str(x)
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for x in [4.0, tp, tn, fp, fn, tpr, fpr]]))
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return
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def evalThresholdAccuracy(embeddings, pairs, threshold):
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y_true = []
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y_predict = []
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for pair in pairs:
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(x1, x2, actual_same) = getEmbeddings(pair, embeddings)
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diff = x1 - x2
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dist = np.dot(diff.T, diff)
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predict_same = dist < threshold
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y_predict.append(predict_same)
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y_true.append(actual_same)
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y_true = np.array(y_true)
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y_predict = np.array(y_predict)
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accuracy = accuracy_score(y_true, y_predict)
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return accuracy
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def findBestThreshold(thresholds, embeddings, pairsTrain):
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bestThresh = bestThreshAcc = 0
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for threshold in thresholds:
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accuracy = evalThresholdAccuracy(embeddings, pairsTrain, threshold)
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if accuracy >= bestThreshAcc:
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bestThreshAcc = accuracy
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bestThresh = threshold
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else:
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# No further improvements.
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return bestThresh
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return bestThresh
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def classifyExp(workDir, pairs, embeddings):
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print(" + Computing accuracy.")
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folds = KFold(n=6000, n_folds=10, shuffle=False)
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thresholds = arange(0, 4, 0.01)
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if os.path.exists("{}/accuracies.txt".format(workDir)):
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print("{}/accuracies.txt already exists. Skipping processing.".format(workDir))
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else:
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accuracies = []
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with open("{}/accuracies.txt".format(workDir), "w") as f:
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f.write('fold, threshold, accuracy\n')
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for idx, (train, test) in enumerate(folds):
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fname = "{}/l2-roc.fold-{}.csv".format(workDir, idx)
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writeROC(fname, thresholds, embeddings, pairs[test])
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bestThresh = findBestThreshold(
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thresholds, embeddings, pairs[train])
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accuracy = evalThresholdAccuracy(
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embeddings, pairs[test], bestThresh)
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accuracies.append(accuracy)
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f.write('{}, {:0.2f}, {:0.2f}\n'.format(
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idx, bestThresh, accuracy))
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f.write('\navg, {:0.4f} +/- {:0.4f}\n'.format(np.mean(accuracies),
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np.std(accuracies)))
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def getAUC(fprs, tprs):
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sortedFprs, sortedTprs = zip(*sorted(zip(*(fprs, tprs))))
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sortedFprs = list(sortedFprs)
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sortedTprs = list(sortedTprs)
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if sortedFprs[-1] != 1.0:
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sortedFprs.append(1.0)
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sortedTprs.append(sortedTprs[-1])
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return np.trapz(sortedTprs, sortedFprs)
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def plotClassifyExp(workDir):
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print("Plotting.")
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fig, ax = plt.subplots(1, 1)
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fs = []
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for i in range(10):
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rocData = pd.read_csv("{}/l2-roc.fold-{}.csv".format(workDir, i))
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fs.append(interp1d(rocData['fpr'], rocData['tpr']))
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x = np.linspace(0, 1, 1000)
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fnFoldPlot, = plt.plot(x, fs[-1](x), color='grey', alpha=0.5)
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openbrData = pd.read_csv("comparisons/openbr.v1.1.0.DET.csv")
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openbrData['Y'] = 1 - openbrData['Y']
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# brPlot = openbrData.plot(x='X', y='Y', legend=True, ax=ax)
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brPlot, = plt.plot(openbrData['X'], openbrData['Y'])
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brAUC = getAUC(openbrData['X'], openbrData['Y'])
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fprs = []
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tprs = []
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for fpr in np.linspace(0, 1, 1000):
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tpr = 0.0
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for f in fs:
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v = f(fpr)
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if math.isnan(v):
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v = 0.0
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tpr += v
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tpr /= 10.0
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fprs.append(fpr)
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tprs.append(tpr)
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fnMeanPlot, = plt.plot(fprs, tprs)
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fnAUC = getAUC(fprs, tprs)
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humanData = pd.read_table(
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"comparisons/kumar_human_crop.txt", header=None, sep=' ')
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humanPlot, = plt.plot(humanData[1], humanData[0])
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humanAUC = getAUC(humanData[1], humanData[0])
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deepfaceData = pd.read_table(
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"comparisons/deepface_ensemble.txt", header=None, sep=' ')
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dfPlot, = plt.plot(deepfaceData[1], deepfaceData[0], '--',
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alpha=0.75)
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deepfaceAUC = getAUC(deepfaceData[1], deepfaceData[0])
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baiduData = pd.read_table(
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"comparisons/BaiduIDLFinal.TPFP", header=None, sep=' ')
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bPlot, = plt.plot(baiduData[1], baiduData[0])
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baiduAUC = getAUC(baiduData[1], baiduData[0])
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eigData = pd.read_table(
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"comparisons/eigenfaces-original-roc.txt", header=None, sep=' ')
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eigPlot, = plt.plot(eigData[1], eigData[0])
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eigAUC = getAUC(eigData[1], eigData[0])
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ax.legend([humanPlot, bPlot, dfPlot, brPlot, eigPlot, fnMeanPlot, fnFoldPlot],
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['Human, Cropped [AUC={:.3f}]'.format(humanAUC),
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'Baidu [{:.3f}]'.format(baiduAUC),
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'DeepFace Ensemble [{:.3f}]'.format(deepfaceAUC),
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'OpenBR v1.1.0 [{:.3f}]'.format(brAUC),
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'Eigenfaces (img-restrict) [{:.3f}]'.format(eigAUC),
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'OpenFace nn4.v1 [{:.3f}]'.format(fnAUC),
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'OpenFace nn4.v1 folds'],
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loc='lower right')
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plt.plot([0, 1], color='k', linestyle=':')
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plt.xlabel("False Positive Rate")
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plt.ylabel("True Positive Rate")
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# plt.ylim(ymin=0,ymax=1)
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plt.xlim(xmin=0, xmax=1)
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plt.grid(b=True, which='major', color='k', linestyle='-')
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plt.grid(b=True, which='minor', color='k', linestyle='-', alpha=0.2)
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plt.minorticks_on()
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fig.savefig(os.path.join(workDir, "roc.pdf"))
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if __name__ == '__main__':
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main()
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