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bug in ensemble

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Sefik Ilkin Serengil 2022-03-26 11:19:26 +00:00
parent 4500e32fbe
commit b13cca851f
1 changed files with 25 additions and 19 deletions
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44
tests/Ensemble-Face-Recognition.py
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@ -2,7 +2,7 @@ import pandas as pd
import numpy as np
import itertools
from sklearn import metrics
from sklearn.metrics import confusion_matrix,accuracy_score, roc_curve, auc
from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc, roc_auc_score
import matplotlib.pyplot as plt
from tqdm import tqdm
tqdm.pandas()
@ -45,8 +45,6 @@ for key, values in idendities.items():
positives = pd.DataFrame(positives, columns = ["file_x", "file_y"])
positives["decision"] = "Yes"
positives = positives.sample(12)
print(positives.shape)
#--------------------------
#Negatives
@ -85,31 +83,30 @@ print(df.decision.value_counts())
df.file_x = "dataset/"+df.file_x
df.file_y = "dataset/"+df.file_y
print(df.head())
#--------------------------
#DeepFace
from deepface import DeepFace
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
pretrained_models = {}
pretrained_models["VGG-Face"] = VGGFace.loadModel()
print("VGG-Face loaded")
pretrained_models["Facenet"] = Facenet.loadModel()
print("Facenet loaded")
pretrained_models["OpenFace"] = OpenFace.loadModel()
print("OpenFace loaded")
pretrained_models["DeepFace"] = FbDeepFace.loadModel()
print("FbDeepFace loaded")
instances = df[["file_x", "file_y"]].values.tolist()
models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']
metrics = ['cosine', 'euclidean_l2']
if True:
pretrained_models = {}
pretrained_models["VGG-Face"] = VGGFace.loadModel()
print("VGG-Face loaded")
pretrained_models["Facenet"] = Facenet.loadModel()
print("Facenet loaded")
pretrained_models["OpenFace"] = OpenFace.loadModel()
print("OpenFace loaded")
pretrained_models["DeepFace"] = FbDeepFace.loadModel()
print("FbDeepFace loaded")
for model in models:
for metric in metrics:
@ -152,6 +149,7 @@ for model in models:
figure_idx = figure_idx + 1
plt.show()
#--------------------------
#Pre-processing for modelling
@ -184,6 +182,10 @@ x_train = df_train.drop(columns=[target_name]).values
y_test = df_test[target_name].values
x_test = df_test.drop(columns=[target_name]).values
#print("target distribution:")
#print(df_train[target_name].value_counts())
#print(df_test[target_name].value_counts())
#--------------------------
#LightGBM
@ -201,7 +203,7 @@ params = {
, 'metric': 'multi_logloss'
}
gbm = lgb.train(params, lgb_train, num_boost_round=250, early_stopping_rounds = 15 , valid_sets=lgb_test)
gbm = lgb.train(params, lgb_train, num_boost_round=500, early_stopping_rounds = 50, valid_sets=lgb_test)
gbm.save_model("face-recognition-ensemble-model.txt")
@ -215,6 +217,8 @@ for prediction in predictions:
prediction_class = np.argmax(prediction)
prediction_classes.append(prediction_class)
y_test = list(y_test)
cm = confusion_matrix(y_test, prediction_classes)
print(cm)
@ -235,6 +239,7 @@ print("Accuracy: ", 100*accuracy,"%")
ax = lgb.plot_importance(gbm, max_num_features=20)
plt.show()
"""
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin'
@ -246,13 +251,14 @@ for i in range(0, gbm.num_trees()):
if i == 2:
break
"""
#--------------------------
#ROC Curve
y_pred_proba = predictions[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)
auc = metrics.roc_auc_score(y_test, y_pred_proba)
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
auc = roc_auc_score(y_test, y_pred_proba)
plt.figure(figsize=(7,3))
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))