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ensemble
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Şefik Serangil
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tests/Ensemble-Face-Recognition.py
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249
tests/Ensemble-Face-Recognition.py
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import pandas as pd
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import numpy as np
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import itertools
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from sklearn import metrics
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from sklearn.metrics import confusion_matrix,accuracy_score, roc_curve, auc
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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tqdm.pandas()
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#--------------------------
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#Data set
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# Ref: https://github.com/serengil/deepface/tree/master/tests/dataset
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idendities = {
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"Angelina": ["img1.jpg", "img2.jpg", "img4.jpg", "img5.jpg", "img6.jpg", "img7.jpg", "img10.jpg", "img11.jpg"],
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"Scarlett": ["img8.jpg", "img9.jpg", "img47.jpg", "img48.jpg", "img49.jpg", "img50.jpg", "img51.jpg"],
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"Jennifer": ["img3.jpg", "img12.jpg", "img53.jpg", "img54.jpg", "img55.jpg", "img56.jpg"],
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"Mark": ["img13.jpg", "img14.jpg", "img15.jpg", "img57.jpg", "img58.jpg"],
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"Jack": ["img16.jpg", "img17.jpg", "img59.jpg", "img61.jpg", "img62.jpg"],
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"Elon": ["img18.jpg", "img19.jpg", "img67.jpg"],
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"Jeff": ["img20.jpg", "img21.jpg"],
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"Marissa": ["img22.jpg", "img23.jpg"],
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"Sundar": ["img24.jpg", "img25.jpg"],
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"Katy": ["img26.jpg", "img27.jpg", "img28.jpg", "img42.jpg", "img43.jpg", "img44.jpg", "img45.jpg", "img46.jpg"],
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"Matt": ["img29.jpg", "img30.jpg", "img31.jpg", "img32.jpg", "img33.jpg"],
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"Leonardo": ["img34.jpg", "img35.jpg", "img36.jpg", "img37.jpg"],
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"George": ["img38.jpg", "img39.jpg", "img40.jpg", "img41.jpg"]
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}
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#--------------------------
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#Positives
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positives = []
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for key, values in idendities.items():
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#print(key)
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for i in range(0, len(values)-1):
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for j in range(i+1, len(values)):
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#print(values[i], " and ", values[j])
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positive = []
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positive.append(values[i])
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positive.append(values[j])
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positives.append(positive)
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positives = pd.DataFrame(positives, columns = ["file_x", "file_y"])
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positives["decision"] = "Yes"
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print(positives.shape)
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#--------------------------
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#Negatives
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samples_list = list(idendities.values())
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negatives = []
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for i in range(0, len(idendities) - 1):
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for j in range(i+1, len(idendities)):
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#print(samples_list[i], " vs ",samples_list[j])
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cross_product = itertools.product(samples_list[i], samples_list[j])
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cross_product = list(cross_product)
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#print(cross_product)
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for cross_sample in cross_product:
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#print(cross_sample[0], " vs ", cross_sample[1])
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negative = []
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negative.append(cross_sample[0])
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negative.append(cross_sample[1])
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negatives.append(negative)
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negatives = pd.DataFrame(negatives, columns = ["file_x", "file_y"])
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negatives["decision"] = "No"
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negatives = negatives.sample(positives.shape[0])
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print(negatives.shape)
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#--------------------------
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#Merge positive and negative ones
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df = pd.concat([positives, negatives]).reset_index(drop = True)
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print(df.decision.value_counts())
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df.file_x = "deepface/tests/dataset/"+df.file_x
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df.file_y = "deepface/tests/dataset/"+df.file_y
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#--------------------------
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#DeepFace
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from deepface import DeepFace
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from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
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pretrained_models = {}
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pretrained_models["VGG-Face"] = VGGFace.loadModel()
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print("VGG-Face loaded")
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pretrained_models["Facenet"] = Facenet.loadModel()
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print("Facenet loaded")
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pretrained_models["OpenFace"] = OpenFace.loadModel()
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print("OpenFace loaded")
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pretrained_models["DeepFace"] = FbDeepFace.loadModel()
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print("FbDeepFace loaded")
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instances = df[["file_x", "file_y"]].values.tolist()
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models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
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metrics = ['cosine', 'euclidean_l2']
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if True:
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for model in models:
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for metric in metrics:
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resp_obj = DeepFace.verify(instances
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, model_name = model
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, model = pretrained_models[model]
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, distance_metric = metric)
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distances = []
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for i in range(0, len(instances)):
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distance = round(resp_obj["pair_%s" % (i+1)]["distance"], 4)
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distances.append(distance)
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df['%s_%s' % (model, metric)] = distances
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df.to_csv("face-recognition-pivot.csv", index = False)
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else:
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df = pd.read_csv("face-recognition-pivot.csv")
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df_raw = df.copy()
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#--------------------------
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#Distribution
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fig = plt.figure(figsize=(15, 15))
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figure_idx = 1
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for model in models:
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for metric in metrics:
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feature = '%s_%s' % (model, metric)
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ax1 = fig.add_subplot(4, 2, figure_idx)
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df[df.decision == "Yes"][feature].plot(kind='kde', title = feature, label = 'Yes', legend = True)
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df[df.decision == "No"][feature].plot(kind='kde', title = feature, label = 'No', legend = True)
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figure_idx = figure_idx + 1
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plt.show()
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#--------------------------
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#Pre-processing for modelling
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columns = []
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for model in models:
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for metric in metrics:
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feature = '%s_%s' % (model, metric)
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columns.append(feature)
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columns.append("decision")
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df = df[columns]
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df.loc[df[df.decision == 'Yes'].index, 'decision'] = 1
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df.loc[df[df.decision == 'No'].index, 'decision'] = 0
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print(df.head())
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#--------------------------
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#Train test split
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from sklearn.model_selection import train_test_split
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df_train, df_test = train_test_split(df, test_size=0.30, random_state=17)
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target_name = "decision"
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y_train = df_train[target_name].values
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x_train = df_train.drop(columns=[target_name]).values
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y_test = df_test[target_name].values
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x_test = df_test.drop(columns=[target_name]).values
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#--------------------------
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#LightGBM
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import lightgbm as lgb
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features = df.drop(columns=[target_name]).columns.tolist()
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lgb_train = lgb.Dataset(x_train, y_train, feature_name = features)
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lgb_test = lgb.Dataset(x_test, y_test, feature_name = features)
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params = {
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'task': 'train'
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, 'boosting_type': 'gbdt'
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, 'objective': 'multiclass'
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, 'num_class': 2
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, 'metric': 'multi_logloss'
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}
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gbm = lgb.train(params, lgb_train, num_boost_round=250, early_stopping_rounds = 15 , valid_sets=lgb_test)
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gbm.save_model("face-recognition-ensemble-model.txt")
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#--------------------------
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#Evaluation
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predictions = gbm.predict(x_test)
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cm = confusion_matrix(y_test, prediction_classes)
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print(cm)
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tn, fp, fn, tp = cm.ravel()
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recall = tp / (tp + fn)
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precision = tp / (tp + fp)
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accuracy = (tp + tn)/(tn + fp + fn + tp)
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f1 = 2 * (precision * recall) / (precision + recall)
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print("Precision: ", 100*precision,"%")
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print("Recall: ", 100*recall,"%")
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print("F1 score ",100*f1, "%")
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print("Accuracy: ", 100*accuracy,"%")
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#--------------------------
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#Interpretability
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ax = lgb.plot_importance(gbm, max_num_features=20)
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plt.show()
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import os
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os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin'
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plt.rcParams["figure.figsize"] = [20, 20]
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for i in range(0, gbm.num_trees()):
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ax = lgb.plot_tree(gbm, tree_index = i)
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plt.show()
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if i == 2:
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break
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#--------------------------
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#ROC Curve
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y_pred_proba = predictions[::,1]
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fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)
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auc = metrics.roc_auc_score(y_test, y_pred_proba)
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plt.figure(figsize=(7,3))
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plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
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#--------------------------
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