diff --git a/models/face-recognition-ensemble-model.txt b/models/face-recognition-ensemble-model.txt
new file mode 100644
index 0000000..f210933
--- /dev/null
+++ b/models/face-recognition-ensemble-model.txt
@@ -0,0 +1,1976 @@
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+
+
+Tree=94
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+
+Tree=95
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+
+Tree=96
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+
+Tree=97
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+
+Tree=98
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+
+Tree=99
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+
+Tree=100
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+shrinkage=0.1
+
+
+Tree=101
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+decision_type=2 2 2 2 2 2 2 2 2
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+right_child=2 -3 4 -5 5 8 -8 -9 -10
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+internal_count=224 117 107 97 87 60 77 57 40
+shrinkage=0.1
+
+
+end of trees
+
+feature importances:
+VGG-Face_cosine=224
+Facenet_euclidean_l2=146
+Facenet_cosine=136
+OpenFace_cosine=120
+OpenFace_euclidean_l2=102
+DeepFace_cosine=82
+VGG-Face_euclidean_l2=6
+
+parameters:
+[boosting: gbdt]
+[objective: multiclass]
+[metric: multi_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data: ]
+[valid: ]
+[num_iterations: 100]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 0]
+[max_depth: -1]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 1]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[early_stopping_round: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[forcedbins_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[verbosity: 1]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[histogram_pool_size: -1]
+[data_random_seed: 1]
+[output_model: LightGBM_model.txt]
+[snapshot_freq: -1]
+[input_model: ]
+[output_result: LightGBM_predict_result.txt]
+[initscore_filename: ]
+[valid_data_initscores: ]
+[pre_partition: 0]
+[enable_bundle: 1]
+[max_conflict_rate: 0]
+[is_enable_sparse: 1]
+[sparse_threshold: 0.8]
+[use_missing: 1]
+[zero_as_missing: 0]
+[two_round: 0]
+[save_binary: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[predict_raw_score: 0]
+[predict_leaf_index: 0]
+[predict_contrib: 0]
+[num_iteration_predict: -1]
+[pred_early_stop: 0]
+[pred_early_stop_freq: 10]
+[pred_early_stop_margin: 10]
+[convert_model_language: ]
+[convert_model: gbdt_prediction.cpp]
+[num_class: 2]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[max_position: 20]
+[lambdamart_norm: 1]
+[label_gain: ]
+[metric_freq: 1]
+[is_provide_training_metric: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+
+end of parameters
+
+pandas_categorical:null
diff --git a/tests/Ensemble-Face-Recognition.ipynb b/tests/Ensemble-Face-Recognition.ipynb
new file mode 100644
index 0000000..10f5528
--- /dev/null
+++ b/tests/Ensemble-Face-Recognition.ipynb
@@ -0,0 +1,1354 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import itertools\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import confusion_matrix,accuracy_score, roc_curve, auc\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "tqdm.pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ref: https://github.com/serengil/deepface/tree/master/tests/dataset\n",
+    "idendities = {\n",
+    "    \"Angelina\": [\"img1.jpg\", \"img2.jpg\", \"img4.jpg\", \"img5.jpg\", \"img6.jpg\", \"img7.jpg\", \"img10.jpg\", \"img11.jpg\"],\n",
+    "    \"Scarlett\": [\"img8.jpg\", \"img9.jpg\", \"img47.jpg\", \"img48.jpg\", \"img49.jpg\", \"img50.jpg\", \"img51.jpg\"],\n",
+    "    \"Jennifer\": [\"img3.jpg\", \"img12.jpg\", \"img53.jpg\", \"img54.jpg\", \"img55.jpg\", \"img56.jpg\"],\n",
+    "    \"Mark\": [\"img13.jpg\", \"img14.jpg\", \"img15.jpg\", \"img57.jpg\", \"img58.jpg\"],\n",
+    "    \"Jack\": [\"img16.jpg\", \"img17.jpg\", \"img59.jpg\", \"img61.jpg\", \"img62.jpg\"],\n",
+    "    \"Elon\": [\"img18.jpg\", \"img19.jpg\", \"img67.jpg\"],\n",
+    "    \"Jeff\": [\"img20.jpg\", \"img21.jpg\"],\n",
+    "    \"Marissa\": [\"img22.jpg\", \"img23.jpg\"],\n",
+    "    \"Sundar\": [\"img24.jpg\", \"img25.jpg\"],\n",
+    "    \"Katy\": [\"img26.jpg\", \"img27.jpg\", \"img28.jpg\", \"img42.jpg\", \"img43.jpg\", \"img44.jpg\", \"img45.jpg\", \"img46.jpg\"],\n",
+    "    \"Matt\": [\"img29.jpg\", \"img30.jpg\", \"img31.jpg\", \"img32.jpg\", \"img33.jpg\"],\n",
+    "    \"Leonardo\": [\"img34.jpg\", \"img35.jpg\", \"img36.jpg\", \"img37.jpg\"],\n",
+    "    \"George\": [\"img38.jpg\", \"img39.jpg\", \"img40.jpg\", \"img41.jpg\"]\n",
+    "    \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Positive samples\n",
+    "Find different photos of same people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "positives = []\n",
+    "\n",
+    "for key, values in idendities.items():\n",
+    "    \n",
+    "    #print(key)\n",
+    "    for i in range(0, len(values)-1):\n",
+    "        for j in range(i+1, len(values)):\n",
+    "            #print(values[i], \" and \", values[j])\n",
+    "            positive = []\n",
+    "            positive.append(values[i])\n",
+    "            positive.append(values[j])\n",
+    "            positives.append(positive)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "positives = pd.DataFrame(positives, columns = [\"file_x\", \"file_y\"])\n",
+    "positives[\"decision\"] = \"Yes\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(167, 3)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "positives.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Negative samples\n",
+    "Compare photos of different people"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples_list = list(idendities.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "negatives = []\n",
+    "\n",
+    "for i in range(0, len(idendities) - 1):\n",
+    "    for j in range(i+1, len(idendities)):\n",
+    "        #print(samples_list[i], \" vs \",samples_list[j]) \n",
+    "        cross_product = itertools.product(samples_list[i], samples_list[j])\n",
+    "        cross_product = list(cross_product)\n",
+    "        #print(cross_product)\n",
+    "        \n",
+    "        for cross_sample in cross_product:\n",
+    "            #print(cross_sample[0], \" vs \", cross_sample[1])\n",
+    "            negative = []\n",
+    "            negative.append(cross_sample[0])\n",
+    "            negative.append(cross_sample[1])\n",
+    "            negatives.append(negative)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "negatives = pd.DataFrame(negatives, columns = [\"file_x\", \"file_y\"])\n",
+    "negatives[\"decision\"] = \"No\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "negatives = negatives.sample(positives.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(167, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negatives.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Merge Positives and Negative Samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([positives, negatives]).reset_index(drop = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(334, 3)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Yes    167\n",
+       "No     167\n",
+       "Name: decision, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.decision.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.file_x = \"deepface/tests/dataset/\"+df.file_x\n",
+    "df.file_y = \"deepface/tests/dataset/\"+df.file_y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DeepFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from deepface import DeepFace\n",
+    "from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VGG-Face loaded\n",
+      "Facenet loaded\n",
+      "OpenFace loaded\n",
+      "FbDeepFace loaded\n"
+     ]
+    }
+   ],
+   "source": [
+    "pretrained_models = {}\n",
+    "\n",
+    "pretrained_models[\"VGG-Face\"] = VGGFace.loadModel()\n",
+    "print(\"VGG-Face loaded\")\n",
+    "\n",
+    "pretrained_models[\"Facenet\"] = Facenet.loadModel()\n",
+    "print(\"Facenet loaded\")\n",
+    "\n",
+    "pretrained_models[\"OpenFace\"] = OpenFace.loadModel() \n",
+    "print(\"OpenFace loaded\")\n",
+    "\n",
+    "pretrained_models[\"DeepFace\"] = FbDeepFace.loadModel()\n",
+    "print(\"FbDeepFace loaded\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "instances = df[[\"file_x\", \"file_y\"]].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ['VGG-Face', 'Facenet', 'OpenFace', 'DeepFace']\n",
+    "metrics = ['cosine', 'euclidean_l2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [14:34<00:00,  2.62s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [14:18<00:00,  2.57s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [09:56<00:00,  1.78s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [10:47<00:00,  1.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [06:34<00:00,  1.18s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [07:15<00:00,  1.30s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [07:31<00:00,  1.35s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Already built model is passed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Verification: 100%|██████████| 334/334 [07:46<00:00,  1.40s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "if True:\n",
+    "    for model in models:\n",
+    "        for metric in metrics:\n",
+    "\n",
+    "            resp_obj = DeepFace.verify(instances\n",
+    "                                       , model_name = model\n",
+    "                                       , model = pretrained_models[model]\n",
+    "                                       , distance_metric = metric)\n",
+    "\n",
+    "            distances = []\n",
+    "\n",
+    "            for i in range(0, len(instances)):\n",
+    "                distance = round(resp_obj[\"pair_%s\" % (i+1)][\"distance\"], 4)\n",
+    "                distances.append(distance)\n",
+    "\n",
+    "            df['%s_%s' % (model, metric)] = distances\n",
+    "    \n",
+    "    df.to_csv(\"face-recognition-pivot.csv\", index = False)\n",
+    "else:\n",
+    "    df = pd.read_csv(\"face-recognition-pivot.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_raw = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>file_x</th>\n",
+       "      <th>file_y</th>\n",
+       "      <th>decision</th>\n",
+       "      <th>VGG-Face_cosine</th>\n",
+       "      <th>VGG-Face_euclidean_l2</th>\n",
+       "      <th>Facenet_cosine</th>\n",
+       "      <th>Facenet_euclidean_l2</th>\n",
+       "      <th>OpenFace_cosine</th>\n",
+       "      <th>OpenFace_euclidean_l2</th>\n",
+       "      <th>DeepFace_cosine</th>\n",
+       "      <th>DeepFace_euclidean_l2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>deepface/tests/dataset/img8.jpg</td>\n",
+       "      <td>deepface/tests/dataset/img9.jpg</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.3147</td>\n",
+       "      <td>0.7933</td>\n",
+       "      <td>0.1976</td>\n",
+       "      <td>0.6287</td>\n",
+       "      <td>0.0997</td>\n",
+       "      <td>0.4466</td>\n",
+       "      <td>0.1695</td>\n",
+       "      <td>0.5822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>deepface/tests/dataset/img8.jpg</td>\n",
+       "      <td>deepface/tests/dataset/img47.jpg</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.3638</td>\n",
+       "      <td>0.8530</td>\n",
+       "      <td>0.1976</td>\n",
+       "      <td>0.6287</td>\n",
+       "      <td>0.0931</td>\n",
+       "      <td>0.4314</td>\n",
+       "      <td>0.1869</td>\n",
+       "      <td>0.6114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>deepface/tests/dataset/img8.jpg</td>\n",
+       "      <td>deepface/tests/dataset/img48.jpg</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.3068</td>\n",
+       "      <td>0.7834</td>\n",
+       "      <td>0.2593</td>\n",
+       "      <td>0.7201</td>\n",
+       "      <td>0.1319</td>\n",
+       "      <td>0.5136</td>\n",
+       "      <td>0.2194</td>\n",
+       "      <td>0.6624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>deepface/tests/dataset/img8.jpg</td>\n",
+       "      <td>deepface/tests/dataset/img49.jpg</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.2353</td>\n",
+       "      <td>0.6860</td>\n",
+       "      <td>0.1797</td>\n",
+       "      <td>0.5996</td>\n",
+       "      <td>0.1472</td>\n",
+       "      <td>0.5426</td>\n",
+       "      <td>0.1904</td>\n",
+       "      <td>0.6170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>deepface/tests/dataset/img8.jpg</td>\n",
+       "      <td>deepface/tests/dataset/img50.jpg</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>0.3583</td>\n",
+       "      <td>0.8465</td>\n",
+       "      <td>0.2400</td>\n",
+       "      <td>0.6928</td>\n",
+       "      <td>0.1320</td>\n",
+       "      <td>0.5138</td>\n",
+       "      <td>0.1380</td>\n",
+       "      <td>0.5253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            file_x                            file_y decision  \\\n",
+       "0  deepface/tests/dataset/img8.jpg   deepface/tests/dataset/img9.jpg      Yes   \n",
+       "1  deepface/tests/dataset/img8.jpg  deepface/tests/dataset/img47.jpg      Yes   \n",
+       "2  deepface/tests/dataset/img8.jpg  deepface/tests/dataset/img48.jpg      Yes   \n",
+       "3  deepface/tests/dataset/img8.jpg  deepface/tests/dataset/img49.jpg      Yes   \n",
+       "4  deepface/tests/dataset/img8.jpg  deepface/tests/dataset/img50.jpg      Yes   \n",
+       "\n",
+       "   VGG-Face_cosine  VGG-Face_euclidean_l2  Facenet_cosine  \\\n",
+       "0           0.3147                 0.7933          0.1976   \n",
+       "1           0.3638                 0.8530          0.1976   \n",
+       "2           0.3068                 0.7834          0.2593   \n",
+       "3           0.2353                 0.6860          0.1797   \n",
+       "4           0.3583                 0.8465          0.2400   \n",
+       "\n",
+       "   Facenet_euclidean_l2  OpenFace_cosine  OpenFace_euclidean_l2  \\\n",
+       "0                0.6287           0.0997                 0.4466   \n",
+       "1                0.6287           0.0931                 0.4314   \n",
+       "2                0.7201           0.1319                 0.5136   \n",
+       "3                0.5996           0.1472                 0.5426   \n",
+       "4                0.6928           0.1320                 0.5138   \n",
+       "\n",
+       "   DeepFace_cosine  DeepFace_euclidean_l2  \n",
+       "0           0.1695                 0.5822  \n",
+       "1           0.1869                 0.6114  \n",
+       "2           0.2194                 0.6624  \n",
+       "3           0.1904                 0.6170  \n",
+       "4           0.1380                 0.5253  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x1080 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(15, 15))\n",
+    "\n",
+    "figure_idx = 1\n",
+    "for model in models:\n",
+    "    for metric in metrics:\n",
+    "        \n",
+    "        feature = '%s_%s' % (model, metric)\n",
+    "        \n",
+    "        ax1 = fig.add_subplot(4, 2, figure_idx)\n",
+    "        \n",
+    "        df[df.decision == \"Yes\"][feature].plot(kind='kde', title = feature, label = 'Yes', legend = True)\n",
+    "        df[df.decision == \"No\"][feature].plot(kind='kde', title = feature, label = 'No', legend = True)\n",
+    "        \n",
+    "        figure_idx = figure_idx + 1\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pre-processing for model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = []\n",
+    "for model in models:\n",
+    "    for metric in metrics:\n",
+    "        feature = '%s_%s' % (model, metric)\n",
+    "        columns.append(feature)\n",
+    "\n",
+    "columns.append(\"decision\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[df.decision == 'Yes'].index, 'decision'] = 1\n",
+    "df.loc[df[df.decision == 'No'].index, 'decision'] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VGG-Face_cosine</th>\n",
+       "      <th>VGG-Face_euclidean_l2</th>\n",
+       "      <th>Facenet_cosine</th>\n",
+       "      <th>Facenet_euclidean_l2</th>\n",
+       "      <th>OpenFace_cosine</th>\n",
+       "      <th>OpenFace_euclidean_l2</th>\n",
+       "      <th>DeepFace_cosine</th>\n",
+       "      <th>DeepFace_euclidean_l2</th>\n",
+       "      <th>decision</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.3147</td>\n",
+       "      <td>0.7933</td>\n",
+       "      <td>0.1976</td>\n",
+       "      <td>0.6287</td>\n",
+       "      <td>0.0997</td>\n",
+       "      <td>0.4466</td>\n",
+       "      <td>0.1695</td>\n",
+       "      <td>0.5822</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.3638</td>\n",
+       "      <td>0.8530</td>\n",
+       "      <td>0.1976</td>\n",
+       "      <td>0.6287</td>\n",
+       "      <td>0.0931</td>\n",
+       "      <td>0.4314</td>\n",
+       "      <td>0.1869</td>\n",
+       "      <td>0.6114</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.3068</td>\n",
+       "      <td>0.7834</td>\n",
+       "      <td>0.2593</td>\n",
+       "      <td>0.7201</td>\n",
+       "      <td>0.1319</td>\n",
+       "      <td>0.5136</td>\n",
+       "      <td>0.2194</td>\n",
+       "      <td>0.6624</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.2353</td>\n",
+       "      <td>0.6860</td>\n",
+       "      <td>0.1797</td>\n",
+       "      <td>0.5996</td>\n",
+       "      <td>0.1472</td>\n",
+       "      <td>0.5426</td>\n",
+       "      <td>0.1904</td>\n",
+       "      <td>0.6170</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.3583</td>\n",
+       "      <td>0.8465</td>\n",
+       "      <td>0.2400</td>\n",
+       "      <td>0.6928</td>\n",
+       "      <td>0.1320</td>\n",
+       "      <td>0.5138</td>\n",
+       "      <td>0.1380</td>\n",
+       "      <td>0.5253</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   VGG-Face_cosine  VGG-Face_euclidean_l2  Facenet_cosine  \\\n",
+       "0           0.3147                 0.7933          0.1976   \n",
+       "1           0.3638                 0.8530          0.1976   \n",
+       "2           0.3068                 0.7834          0.2593   \n",
+       "3           0.2353                 0.6860          0.1797   \n",
+       "4           0.3583                 0.8465          0.2400   \n",
+       "\n",
+       "   Facenet_euclidean_l2  OpenFace_cosine  OpenFace_euclidean_l2  \\\n",
+       "0                0.6287           0.0997                 0.4466   \n",
+       "1                0.6287           0.0931                 0.4314   \n",
+       "2                0.7201           0.1319                 0.5136   \n",
+       "3                0.5996           0.1472                 0.5426   \n",
+       "4                0.6928           0.1320                 0.5138   \n",
+       "\n",
+       "   DeepFace_cosine  DeepFace_euclidean_l2  decision  \n",
+       "0           0.1695                 0.5822         1  \n",
+       "1           0.1869                 0.6114         1  \n",
+       "2           0.2194                 0.6624         1  \n",
+       "3           0.1904                 0.6170         1  \n",
+       "4           0.1380                 0.5253         1  "
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train, df_test = train_test_split(df, test_size=0.30, random_state=17)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_name = \"decision\"\n",
+    "\n",
+    "y_train = df_train[target_name].values\n",
+    "x_train = df_train.drop(columns=[target_name]).values\n",
+    "\n",
+    "y_test = df_test[target_name].values\n",
+    "x_test = df_test.drop(columns=[target_name]).values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LightGBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lightgbm as lgb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = df.drop(columns=[target_name]).columns.tolist()\n",
+    "\n",
+    "lgb_train = lgb.Dataset(x_train, y_train, feature_name = features)\n",
+    "lgb_test = lgb.Dataset(x_test, y_test, feature_name = features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = {\n",
+    "    'task': 'train'\n",
+    "    , 'boosting_type': 'gbdt'\n",
+    "    , 'objective': 'multiclass'\n",
+    "    , 'num_class': 2\n",
+    "    , 'metric': 'multi_logloss'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\tvalid_0's multi_logloss: 0.607564\n",
+      "Training until validation scores don't improve for 15 rounds\n",
+      "[2]\tvalid_0's multi_logloss: 0.530974\n",
+      "[3]\tvalid_0's multi_logloss: 0.46737\n",
+      "[4]\tvalid_0's multi_logloss: 0.413739\n",
+      "[5]\tvalid_0's multi_logloss: 0.368084\n",
+      "[6]\tvalid_0's multi_logloss: 0.32886\n",
+      "[7]\tvalid_0's multi_logloss: 0.295086\n",
+      "[8]\tvalid_0's multi_logloss: 0.265713\n",
+      "[9]\tvalid_0's multi_logloss: 0.240001\n",
+      "[10]\tvalid_0's multi_logloss: 0.217589\n",
+      "[11]\tvalid_0's multi_logloss: 0.197774\n",
+      "[12]\tvalid_0's multi_logloss: 0.180478\n",
+      "[13]\tvalid_0's multi_logloss: 0.164216\n",
+      "[14]\tvalid_0's multi_logloss: 0.150409\n",
+      "[15]\tvalid_0's multi_logloss: 0.137384\n",
+      "[16]\tvalid_0's multi_logloss: 0.1265\n",
+      "[17]\tvalid_0's multi_logloss: 0.116161\n",
+      "[18]\tvalid_0's multi_logloss: 0.107604\n",
+      "[19]\tvalid_0's multi_logloss: 0.0996502\n",
+      "[20]\tvalid_0's multi_logloss: 0.0928946\n",
+      "[21]\tvalid_0's multi_logloss: 0.0861664\n",
+      "[22]\tvalid_0's multi_logloss: 0.0809035\n",
+      "[23]\tvalid_0's multi_logloss: 0.0756792\n",
+      "[24]\tvalid_0's multi_logloss: 0.0715751\n",
+      "[25]\tvalid_0's multi_logloss: 0.0671947\n",
+      "[26]\tvalid_0's multi_logloss: 0.0640758\n",
+      "[27]\tvalid_0's multi_logloss: 0.0605712\n",
+      "[28]\tvalid_0's multi_logloss: 0.058598\n",
+      "[29]\tvalid_0's multi_logloss: 0.0557225\n",
+      "[30]\tvalid_0's multi_logloss: 0.0539703\n",
+      "[31]\tvalid_0's multi_logloss: 0.0516381\n",
+      "[32]\tvalid_0's multi_logloss: 0.0504265\n",
+      "[33]\tvalid_0's multi_logloss: 0.0484533\n",
+      "[34]\tvalid_0's multi_logloss: 0.0468254\n",
+      "[35]\tvalid_0's multi_logloss: 0.0462074\n",
+      "[36]\tvalid_0's multi_logloss: 0.0447064\n",
+      "[37]\tvalid_0's multi_logloss: 0.0433531\n",
+      "[38]\tvalid_0's multi_logloss: 0.043166\n",
+      "[39]\tvalid_0's multi_logloss: 0.042303\n",
+      "[40]\tvalid_0's multi_logloss: 0.0412656\n",
+      "[41]\tvalid_0's multi_logloss: 0.0414055\n",
+      "[42]\tvalid_0's multi_logloss: 0.0405468\n",
+      "[43]\tvalid_0's multi_logloss: 0.0410451\n",
+      "[44]\tvalid_0's multi_logloss: 0.0403277\n",
+      "[45]\tvalid_0's multi_logloss: 0.0399882\n",
+      "[46]\tvalid_0's multi_logloss: 0.0404895\n",
+      "[47]\tvalid_0's multi_logloss: 0.0399342\n",
+      "[48]\tvalid_0's multi_logloss: 0.0405385\n",
+      "[49]\tvalid_0's multi_logloss: 0.040076\n",
+      "[50]\tvalid_0's multi_logloss: 0.0400383\n",
+      "[51]\tvalid_0's multi_logloss: 0.0396479\n",
+      "[52]\tvalid_0's multi_logloss: 0.0404013\n",
+      "[53]\tvalid_0's multi_logloss: 0.0400753\n",
+      "[54]\tvalid_0's multi_logloss: 0.0402145\n",
+      "[55]\tvalid_0's multi_logloss: 0.0410454\n",
+      "[56]\tvalid_0's multi_logloss: 0.0407894\n",
+      "[57]\tvalid_0's multi_logloss: 0.0416614\n",
+      "[58]\tvalid_0's multi_logloss: 0.0414465\n",
+      "[59]\tvalid_0's multi_logloss: 0.0417404\n",
+      "[60]\tvalid_0's multi_logloss: 0.0415578\n",
+      "[61]\tvalid_0's multi_logloss: 0.0429162\n",
+      "[62]\tvalid_0's multi_logloss: 0.0427614\n",
+      "[63]\tvalid_0's multi_logloss: 0.0431202\n",
+      "[64]\tvalid_0's multi_logloss: 0.0429881\n",
+      "[65]\tvalid_0's multi_logloss: 0.0443909\n",
+      "[66]\tvalid_0's multi_logloss: 0.0442781\n",
+      "Early stopping, best iteration is:\n",
+      "[51]\tvalid_0's multi_logloss: 0.0396479\n"
+     ]
+    }
+   ],
+   "source": [
+    "gbm = lgb.train(params, lgb_train, num_boost_round=250, early_stopping_rounds = 15 , valid_sets=lgb_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<lightgbm.basic.Booster at 0x24ddd87b5f8>"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gbm.save_model(\"face-recognition-ensemble-model.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = gbm.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "accuracy:  97.94 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction_classes = []\n",
+    "classified = 0\n",
+    "\n",
+    "index = 0\n",
+    "for prediction in predictions:\n",
+    "    prediction_class = np.argmax(prediction)\n",
+    "    prediction_classes.append(prediction_class)\n",
+    "    \n",
+    "    actual = y_test[index]\n",
+    "    \n",
+    "    #print(\"prediction is \",prediction_class,\" whereas actual is \",actual)\n",
+    "    if actual == prediction_class:\n",
+    "        classified = classified + 1\n",
+    "    \n",
+    "    index = index + 1\n",
+    "\n",
+    "#print(classified,\" instances are classified in \",len(predictions),\" instances\") \n",
+    "print(\"accuracy: \",round(100*classified/len(predictions),2),\"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm = confusion_matrix(y_test, prediction_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[58,  1],\n",
+       "       [ 1, 37]], dtype=int64)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tn, fp, fn, tp = cm.ravel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(58, 1, 1, 37)"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tn, fp, fn, tp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recall = tp / (tp + fn)\n",
+    "precision = tp / (tp + fp)\n",
+    "accuracy = (tp + tn)/(tn + fp +  fn + tp)\n",
+    "f1 = 2 * (precision * recall) / (precision + recall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision:  97.36842105263158 %\n",
+      "Recall:  97.36842105263158 %\n",
+      "F1 score  97.36842105263158 %\n",
+      "Accuracy:  97.9381443298969 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Precision: \", 100*precision,\"%\")\n",
+    "print(\"Recall: \", 100*recall,\"%\")\n",
+    "print(\"F1 score \",100*f1, \"%\")\n",
+    "print(\"Accuracy: \", 100*accuracy,\"%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interpretability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rvAEDBtC2bduvlJ999tlcdtllSCovu+uuuzjssMPKE3iHDh3WWpxWmAohWX+FpC6SHpf0fHrskbPtXEmzJL0oaWwq21bSJEnPpf26p/JbJV0t6V+S3pR0eE47IyU9K2mmpDGpeCywraQZkn5bQ3xf2TfF/FJOnXMkXZied5X0jxTz85K2rdResaS/peebSpoi6QVJ1wHKqXe0pGdSfNdJap7K/yhpuqSXc44FSaWSxqQ+Z5WNS20i4p8RsSS9nAZsUZf9zGz1TJw4kU6dOrHjjjtWKH/99df55JNPKC4uZpddduH222/PU4RWKPK+DA60kjQjPZ8bEUOA+cDAiFgmaTtgAtBP0mDgUGC3iFgiqext6vXAyRExR9JuwLXAPmlbR+DbQHdgInCPpEHAdsCuZMlwoqQBwCigV0T0rS7YGvZ9u4ZjvBMYGxH3SWpJ9iapurfKFwBPRMRFkg4ETkr99gB+AOwZESskXQsMA24Hzo+Ij1PyniqpT0TMTO0tiIidJZ0KnEO2vL06TgD+XtUGSSeVxdeuXXtG9165mk03Xpu1ypZ+LdOUx6OkpKT8+QcffMDixYtZtGgRkyZN4rzzzuO3v/0tJSUlLFu2jCeffJKNN96Yt956i9mzZzNu3DiWL1/OaaedhiS23HLL/B1IA1q0aFGFcbKvKoRkvbSK5LgecI2kvsAqYPtUvh9wS9msLyWoImAP4O6cZaQNctq6PyK+AF6RtFkqG5QeL6TXRWQJuKaEW2a19pW0IdApIu5LMS9L5dW1PwA4LNV9SNInqXxfYBfg2bRvK7I3NQBHpsTZguzNyQ5AWbL+a/r5XFm7dSXpaKAfUOXVLRFxPdkbJTpv0zXGzSqEX6fC8LPeK/F4fKkpj0fpsOIvn5eW0qZNG4qKith000356KOPOP300wFYsGABZ5xxBs888wy77bYbO+64I4MHDwayGXjLli0pLi6uood1X0lJSaM9tvpSqP97zgY+BHYkm4UuS+UColLdZsDCGmbDn+c8V87PSyLiutyK6aKv2lS37xZUPK3QslKfq6PyMZa1c1tE/LxSv1uTzZi/FRGfSLo1p2/48vhXsRr/3pL2A84H9oqIz2ur32q95sxOF9JY9scn9490U+fx+KrevXtXuHCsS5cuTJ8+nXbt2nHIIYdw+umns3LlSpYvX87TTz/N2WefncdoLd8K8pw1sDHwfpoRHwM0T+VTgBGSWgNIahsRnwJzJR2RyiRpx6oazTE5tVOU9ukkqQPwGbDh19z3Q6BDOue8AXAQQIpvnqRDU/0NyuKvxmNky9ukZf9vpPKpwOGpLyS1lbQVsBGwGPhvWjkYXEv8tZK0E3AdcHBE+DJUs3owdOhQdt99d2bPns0RRxzBTTfdVG3dHj16sP/++9OnTx923XVXTjzxxCo/8mVNR6HOrK8F7k0J+J9kyYiImJSWxqdLWg48DPyCLLn9UdIvyZbQ/wy8WF3jETElnQN+Ki0pLwKOjog3JD2ZLhT7e0SMXI1950u6CHgamAu8lrPbMcB1afsK4Ajgi2rCGwNMkPQ88ChpeT0iXknHN0VSs9TOaRExTdILwMvAm8CT1R33avgt2fJ+2amFtyPi4Hpo16zJmjBhQvnzqpZ9S0tLK7weOXIkI0d+5U+QNVGKqGrF1Wz1dOvWLWbPnp3vMAqGz8FV5PGoyONRUVMeD0nPRUS/2uoV6jK4mZmZJYW6DJ53knoDd1Qq/jwidstHPPVF0vHAWZWKn4yI0/IRj5mZ1c7JuhoRMQuo9vPW66qIuAW4Jd9xmJlZ3XkZ3MzMrMA5WZuZmRU4J2szM7MC52RtZmZW4JyszczMCpyTtZmZWYFzsjYzMytwTtZmZmYFzsnazMyswDlZm5mZFTgnazMzswLnZG1mZlbgnKzNzMwKnJO1mZlZgXOyNjNbDSNGjKBDhw706tWrvOxXv/oVffr0oW/fvgwaNIj33nuvfFtJSQl9+/alZ8+e7LXXXvkI2RoBRUS+Y7BGoPM2XaPZkVflO4yC8bPeKxk3y18XX6YxjEfp2AMBeOyxxygqKmL48OG89NJLAHz66adstNFGAFx99dW88sorjB8/noULF7LHHnswadIkOnfuzPz58+nQoQMlJSUUFxfn61AKTlMeD0nPRUS/2uo16Zm1pFWSZuQ8uuQxlk0knboW+jlY0qiG7sessRowYABt27atUFaWqAEWL16MJADuuusuDjvsMDp37gxAhw4d1l6g1qis229119zSiOib7yCSTYBTgWsbspOImAhMbMg+zJqi888/n9tvv52NN96Yf/7znwC8/vrrrFixguLiYj777DPOOusshg8fnudIbV3U1JP1V6TZ9R1Am1R0ekT8K207FzgG+AL4e0SMkrQt8AegPbAE+FFEvCbpVuBToB/wTeDciLgntTMSOBLYALgvIi4AxgLbSpoBPBIRI6uJr6oY+gLjgdbAG8CIiPhE0pnAycBK4JWIOErScUC/iDj9a8RYOZaTgJMA2rVrz+jeK+s+0I3cZq2ypV/LNIbxKCkpKX/+wQcfsHjx4gplAwcOZODAgdx5552cc845HH/88bz11lvMnj2bcePGsXz5ck477TQk8Y1vfKPCvk3dokWLPB61aOrJulVKjgBzI2IIMB8YGBHLJG0HTAD6SRoMHArsFhFLJJWtg10PnBwRcyTtRjYz3idt6wh8G+hONpu9R9IgYDtgV0DAREkDgFFAr5pm+jXEcDtwRkQ8Kuki4ALgJ6nNrSPic0mbVNNsnWOMiMdyd4yI69Px03mbrrGun5OsT43hHG19agzjUTqs+MvnpaW0adOmyvOsW2+9NQceeCC33XYb06ZNY8cdd2Tw4MEATJw4kZYtW1JUVNRkz9FWpSmfs66rJn3OmrQMnh5DUtl6wA2SZgF3Azuk8v2AWyJiCUBEfCypCNgDuDsl/evIkl+Z+yPii4h4BdgslQ1KjxeA58mS5HZ1jLeqGDYGNomIR1Od24AB6flM4E5JR5PNrqtS3zGaNTlz5swpfz5x4kS6d+8OwCGHHMLjjz/OypUrWbJkCU8//TQ9evTIV5i2Dlu33+o2jLOBD4Edyd7MLEvlAipfOt8MWFjDbPjznOfK+XlJRFyXW7GOF7dVFUNNDiRL3AcDv5LUc01irEmr9ZozO10ta9lMIXcm1tQ1pvEYOnQoJSUlLFiwgC222IIxY8bw8MMPM3v2bJo1a8ZWW23F+PHjAejRowf7778/ffr0oVmzZpx44on06tXLS7622pysv2pjYF5EfCHpWKB5Kp8CjJZ0V9kSdJrZzpV0RETcrewS0D4R8WIN7U8G/kfSnRGxSFInYAXwGbBhLbFVF8Mnkr4TEY+Tnc9+VFIzYMuI+KekJ4AfAkV1HIMqY4yI+XXc36zRmjBhwlfKTjjhhGrrjxw5kpEjq7wExazOnKy/6lrgXklHAP8EFgNExKR0Idd0ScuBh4FfAMOAP0r6JdkS+p+BapN1REyR1AN4Kn28YxFwdES8IelJSS+RXTj2lf/dNcRwLDBeUmvgTeB4sjcZf0rL5AKuiIiFZR8pqUl1MZKdzzczs7XMN0WxetGtW7eYPXt2vsMoGL5gpiKPR0Uej4qa8nj4pihmZmaNxGovg0v6Btm50JkNEI8BknqTfdY71+cRsVs+4jEzs/yqU7KWVEJ2RXELYAbwH0mPRsRPGzC2JisiZgGFcmc1MzPLs7oug28cEZ8Ch5F9zncXss/8mpmZWQOra7JuIakj2e0n/9aA8ZiZmVkldU3WF5F99vaNiHhW0jbAnFr2MTMzs3pQp3PWEXE32a03y16/CXy/oYIyMzOzL9VpZi1pe0lT0w07kNQn3QTEzMzMGlhdl8FvAH5OdltM0se2jmqooMzMzOxLdU3WrSPimUpl6/aX05qZma0j6pqsF0jalvSNT5IOB95vsKjMzMysXF3vYHYacD3QXdK7wFyyL7AwMzOzBlZrsk5ftdgvIvaT1AZoFhGfNXxoZmZmBnVYBo+IL4DT0/PFTtRmZmZrV13PWT8i6RxJW0pqW/Zo0MjMzMwMqPs56xHp52k5ZQFsU7/hmJmZWWV1mllHxNZVPJyozaygjRgxgg4dOtCrV6/yspEjR9K9e3f69OnDkCFDWLhwYfm2Sy65hK5du9KtWzcmT56cj5DNqqSIqL2SNLyq8oi4vd4jsnVS5226RrMjr8p3GAXjZ71XMm7Wan9dfKO1NsejdOyB5c8fe+wxioqKGD58OC+99BIAU6ZMYZ999qFFixacd955AFx66aW88sorDB06lGeeeYb33nuP/fbbj9dff53mzZvXe4wlJSUUFxfXe7vrqqY8HpKei4h+tdWr6znrb+U8vgNcSPb91gVL0haSHpA0R9Ibkq6StH4993GhpHclzUiPsfXZfkOQ1E/S1fmOw2xtGDBgAG3bVry8ZtCgQbRokb1x6N+/P/PmzQPggQce4KijjmKDDTZg6623pmvXrjzzTOV7QZnlR12/yOOM3NeSNgbuaJCI6oEkAX8F/hgRh0hqTvY58YuBkfXc3RURcXk9t9lgImI6MD3fcZgVgptvvpkf/OAHALz77rv079+/fNsWW2zBu+++m6/QzCr4uutSS4Dt6jOQerYPsCwibgGIiFWSzgbmSpoLfBfYANgauCsixgBIOho4E1gfeBo4Ne27CLgKOAhYChwSER9W17mk0cD3gFbAv4AfR0RI6gqMB9oDq4AjIuINSSPJvit8A+C+iLighraHA+eQXeA3MyKOkbQVcHNq9z/A8RHxtqQjgAtSX/+NiAGSioFzIuIgSRcCnckuFOwMXBkRV9c0FpViOQk4CaBdu/aM7u070JbZrFW29GuZtTkeJSUlFV5/8MEHLF68+Cvlf/rTn1i4cCGdOnWipKSEefPm8eqrr5bXe//993n55Zdp165dvce4aNGir8TTlHk8alenZC3pQdKtRsmWzncg5yszC1BP4Lncgoj4VNLbZMe8K9CL7E3Hs5IeAhYDPwD2jIgVkq4lu0vb7UAbYFpEnC/pMuBHwK9T02enxAZwXkRMBq6JiIsAJN1BluQfBO4ExkbEfZJaAs0kDSJ747MrIGCipAER8Vjlg5LUEzg/xbgg5+Nz1wC3R8RtkkYAVwOHAqOB70bEu5I2qWasugN7AxsCsyX9Eehaw1jkjun1ZCsWdN6ma/gc7Zd8zrqitXrOelhxxdelpbRp06bCOdHbbruNl19+malTp9K6dWsAnnrqKYDyepdccgmDBg1i9913r/cYm/I52qp4PGpX1/89ucu8K4G3ImJeA8RTX8SXby6qKn8kIj4CkPRX4Ntkx7ULWfKGbFY8P+23HPhbev4cMDCnzaqWwfeWdC7QGmgLvCypBOgUEfcBRMSy1P8gYBDwQtq3iCx5fyVZk60Y3BMRC1IbH6fy3YHD0vM7gMvS8yeBWyX9hey0QFUeiojPgc8lzQc2A/atYSzM1mmTJk3i0ksv5dFHHy1P1AAHH3wwP/zhD/npT3/Ke++9x5w5c9h1113zGKnZl+qarA+IiPNyCyRdWrmsgLwMfD+3QNJGwJZkS8KVE3mQJfLbIuLnVbS3Ir68bH4VNYxbmjFfS3aL1nfSUnPL1H6VuwCXRMR1NR7Rl3Vrv3w/1YmIkyXtBhwIzJDUt4q6n+c8Lzu2msaiSq3Wa87snKtwm7qSkpKvzPCasnyNx9ChQykpKWHBggVsscUWjBkzhksuuYTPP/+cgQOz99z9+/dn/Pjx9OzZkyOPPJIddtiBFi1a8Ic//KFBrgQ3+zrqejX4wCrKBtdnIPVsKtC67CNn6QKzccCtZEvfA9Nd2FqRLRc/mfY5XFKHtE/bdC54dbVMPxdIKgIOh2wZHpgn6dDU/gaSWgOTgRGpLpI6lcVQzXEdKWnTshhT+b/48vvFhwFPpO3bRsTTETEaWED2ZqUu6msszPJqwoQJvP/++6xYsYJ58+Zxwgkn8O9//5t33nmHGTNmMGPGDMaPH19e//zzz+eNN95g9uzZDB5cyH/irKmpMVlLOkXSLKCbpJk5j7nAzLUT4upLs+AhwBGS5gCvA8uAX6QqT5AtF88A7o2I6RHxCvBLYIqkmcAjQMev0fdC4AZgFnA/8GzO5mOAM1OrZZv4AAAdLklEQVT7/wK+GRFTgLuAp9JY30N2/riqtl8mu6L9UUkvAr9Lm84Ejk/tHgOclcp/K2mWpJfIltVfrOMx1MtYmJlZ/ajxpijpI1rfAC4BRuVs+iznfOk6RdJxZEvUp+c7lsakW7duMXv27HyHUTB8wUxFHo+KPB4VNeXxqOtNUWo8Zx0R/wX+CwxNjXYgW+YtklQUEW/XR7BmZmZWvbp+dOt7ZEuum5NdFbwV8CrZR6TWKRFxK9m564KVzklPrWLTvmVXsZuZWdNR16vBfw30B/4RETtJ2ps027b6lxJyVVdum5lZE1TXq8FXpATSTFKziPgnTiZmZmZrRV1n1gvTR4seB+5MN8/wvRTNzMzWgrrOrA8h+3zyT4BJwBtk9742MzOzBlbXb91anG6KsV26/3RrwLf2MTMzWwvqNLOW9COym3WU3RKzE9kNP8zMzKyB1XUZ/DRgT+BTgIiYA1R3S0wzMzOrR3VN1p9HxPKyF5JaULcvlDAzM7M1VNdk/aikXwCtJA0k+y7rBxsuLDMzMytT12Q9CvgP2ZdT/Bh4mOyLHszMzKyB1Xg1uKTOEfF2RHxB9k1SN6ydsMzMzKxMbTPr8iu+Jd3bwLGYmZlZFWpL1sp5vk1DBmJmZmZVqy1ZRzXPzczMbC2pLVnvKOlTSZ8BfdLzTyV9JunTtRGgmTVOI0aMoEOHDvTq1au87OOPP2bgwIFst912DBw4kE8++QSAO++8kz59+tCnTx/22GMPXnzxxXyFbZYXNSbriGgeERtFxIYR0SI9L3u90doK0swan+OOO45JkyZVKBs7diz77rsvc+bMYd9992Xs2LEAbL311jz66KPMnDmTX/3qV5x00kn5CNksb+r6rVtfi6QtgD8AO5C9MfgbMDL3Biv10MeFwI/IPloGMCkiRtVX+w1J0qKIKJK0OXB1RBxeRZ0S4JyImN7AsRSnfg6SNAw4L21aBJwSETVOZZauWEWXUQ81ZIjrlJ/1XslxHo9yueNROvZAAAYMGEBpaWmFeg888AAlJSUAHHvssRQXF3PppZeyxx57lNfp378/8+bNWytxmxWKun7OerVJEvBX4P6I2A7YHigCLm6A7q6IiL7psU4k6lwR8V5ViTqP5gJ7RUQf4H+A6/McjzURH374IR07dgSgY8eOzJ8//yt1brrpJgYPHry2QzPLq4acWe8DLIuIWwAiYpWks4G5kuYC3wU2ALYG7oqIMQCSjgbOBNYHngZOTfsuAq4CDgKWAodExIfVdS5pNNnXeLYC/gX8OCJCUldgPNAeWAUcERFvSBoJHJliui8iLqih7WpjjIiiVOdw4KCIOE7SZqnPsivqT4mIf+W01wX4W0T0ktQKuIVsNeLVFH9ZvUHAmBTjG8DxEbGohmMtSfHtDWwCnBARj1d3XGVyYwOmAVtUMw4nAScBtGvXntG9/RXnZTZrlc0mLZM7HmUzZ4APPviAxYsXl5etXLmywvbKr1944QV+//vfc/XVV1coX9csWrRonY6/vnk8ateQybon8FxuQUR8Kunt1O+uQC+y78l+VtJDwGLgB8CeEbFC0rXAMOB2oA0wLSLOl3QZ2dL3r1PTZ6cECnBeREwGromIiwAk3UGW5B8E7gTGRsR9kloCzVIS3C7FJGCipAER8Vjlg5LUo4YYq3M18GhEDJHUnGyFoTqnAEsioo+kPsDzqd92ZHeN2y99Zel5wE+Bi2o4VoAWEbGrpAOAC4D9aui7KicAf69qQ0RcT5p1d96ma4yb1aBnVdYpP+u9Eo/Hl3LHo3RYcXl5aWkpbdq0obg4K+vUqRPdunWjY8eOvP/++2y++ebl22bOnMk111zDI488wvbbb7+Wj6B+lZSUlB+XeTzqoiH/moiqP+5VVv5IRHwEIOmvwLeBlcAuZMkbspli2TrYcrJz3pC9CRiY0+YVEXF5pX72lnQu0BpoC7ycZpqdIuI+gIhYlvofBAwCXkj7FpEl768ka2DfGmKszj7A8NTnKuC/NdQdQJbciYiZkmam8v5ks+0nU7/rA09Vd6x8maz/mn4+B3SpJc4KJO1Nlqy/vTr7mX1dBx98MLfddhujRo3itttu45BDDgHg7bff5rDDDuOOO+5Y5xO12dfRkMn6ZeD7uQWSNgK2JFt+rpzIgyyR3xYRP6+ivRURUbbPKmqIPc2YrwX6RcQ76SK0llS8yUuFXYBLIuK6arZXrltdjLnH1LIObVWnujc5j0TE0AqF1R9rmc/TzxrH7CudZbP6G4HBZW+qatJqvebMThcOWTZTyJ1BNnVVjcfQoUMpKSlhwYIFbLHFFowZM4ZRo0Zx5JFHctNNN9G5c2fuvvtuAC666CI++ugjTj31VABatGjB9OkNes2lWUFpsAvMgKlAa0nDAdLy7zjgVrKl74GS2qZztIcCT6Z9DpfUIe3TVtJWX6PvsmS1QFIRcDhky/DAPEmHpvY3kNQamAyMSHWR1KkshmqOq7oYP5TUQ1IzYEilfU4pG4f0pqU6j5EtqyOpF9AnlU8D9kzn3JHUWtL21R3rmpDUmWxGfkxEvL6m7ZlVZcKECbz//vusWLGCefPmccIJJ7DpppsydepU5syZw9SpU2nbti0AN954I5988gkzZsxgxowZTtTW5DRYsk6z4CHAEZLmAK8Dy4BfpCpPAHcAM4B7I2J6RLxCdl52Slr+fQTo+DX6Xkj2pSOzyO5v/mzO5mOAM1P7/wK+GRFTgLuApyTNAu4BNqym7ZpiHEW2VP9/wPs5u51FtlQ9i2w5umcN4f8RKEptnws8k/r9D3AcMCFtmwZ0r+VYv67RwKbAtZJmSPJfRjOzPNKXK8trsVPpOLJl29PXeufWILp16xazZ8/OdxgFwxfMVOTxqMjjUVFTHg9Jz0VEv9rqNeQyuJmZmdWDvHy2JCJuJTt3XbAkbUp2rrmyfetywVWhkvRd4NJKxXMjYkhV9c3MLP/8QdBqpITcN99x1Lf0GfTJ+Y7DzMzqzsvgZmZmBc7J2szMrMA5WZuZmRU4J2szM7MC52RtZmZW4JyszczMCpyTtZmZWYFzsjYzMytwTtZmZmYFzsnazMyswDlZm5mZFTgnazMzswLnZG1mZlbgnKzNrIIrrriCnj170qtXL4YOHcqyZcsYNmwY3bp1o1evXowYMYIVK1bkO0yzJsXJ2szKvfvuu1x99dVMnz6dl156iVWrVvHnP/+ZYcOG8dprrzFr1iyWLl3KjTfemO9QzZqURvl91pJWAbOA9YCVwG3AlRHxRT33UwJ0BJamol9HxD312Ud9k3QysCQibq/PdpeuWEWXUQ/VZ5PrtJ/1Xslx69B4lI49sPz5ypUrWbp0Keuttx5Llixh8803Z9CgQeXbd911V+bNm5ePMM2arEaZrIGlEdEXQFIH4C5gY+CCBuhrWERMb4B2G0REjM93DFa4OnXqxDnnnEPnzp1p1aoVgwYNqpCoV6xYwR133MFVV12VxyjNmh5FRL5jqHeSFkVEUc7rbYBngXZkS/9jgWJgA+APEXFdqjcSODKV3xcRF0jqAkwCngZ2Al4HhkfEkjSzPqdyspZ0P7Al0BK4KiKuT+X7A78BmgMLImJfSW2A3wO9yd48XRgRD1RzXM2BS4HvAgHcEBG/l7QvcHna/1nglIj4XNJY4GCy1YUpEXGOpAuBRRFxeYr/aWBvYBPghIh4PPVT5RhViuck4CSAdu3a7zL6yhuq/gdpgjZrBR8urb1eoejdaWMAPvvsMy644AJGjx5NUVERF154IXvttRcDBw4E4PLLL6dly5acfvrpq9X+okWLKCoqqr1iE+HxqKgpj8fee+/9XET0q61eY51ZVxARb0pqBnQADgH+GxHfkrQB8KSkKcB26bErIGCipAHA20A3skT2pKSbgVPJkiPAnZLK/izvGxEfASMi4mNJrYBnJd1L9ibhBmBARMyV1Dbtcz7wfxExQtImwDOS/hERi6s4lJOArYGdImKlpLaSWgK3pr5fl3Q7cEr6OQToHhGR2q5Ki4jYVdIBZCsP+wEnVDVGETG30rheD1wP0HmbrjFuVpP4daqTn/Veybo0HqXDigG4++672WmnnTj00EMBeO+995g2bRrFxcWMGTOGFi1a8Je//IVmzVbvcpeSkhKKi4vrOep1l8ejIo9H7ZrSBWZKPwcBwyXNIJtVbkqWpAelxwvA80D3VA7wTkQ8mZ7/Cfh2TrvDIqJvenyUys6U9CIwjWyGvR3QH3isLOFFxMc58YxK8ZSQzcY7V3MM+wHjI2JlThvdgLkR8XqqcxswAPgUWAbcKOkwYEk1bf41/XwO6FLLGFkj17lzZ6ZNm8aSJUuICKZOnUqPHj248cYbmTx5MhMmTFjtRG1ma27deeu/BtIy+CpgPlnSPiMiJleq813gksrLvWkZvPK5gmrPHUgqJkuqu+cslbdM/Va1n4DvR8TsuhxKFW2oqopp5r0rsC9wFHA6sE8VVT9PP1fx5e9DlWNUk1brNWd2zkVKTV1JSUn5bHVdsttuu3H44Yez884706JFC3baaSdOOukk2rRpw1ZbbcXuu+8OwGGHHcbo0aPzHK1Z09Hok7Wk9sB44Jq0HDyZbJn4/yJihaTtgXeBycD/SLozIhZJ6gSUfZi0s6TdI+IpYCjwRA1dbgx8khJ1d7IZNcBTwB8kbV22DJ5mxpOBMySdkeLbKSJeqKbtKcDJkkrKlsGB14AukrpGxL+BY4BHJRUBrSPiYUnTgH+vxrBVOUbVLM1bIzNmzBjGjBlToWzlypV5isbMoPEm61ZpCbfso1t3AL9L224kW+59XpKA/wCHRsQUST2Ap7JiFgFHk804XwWOlXQdMAf4Yw19TyJLqDOB2WRL4UTEf9IFWX9N58/nAwOB/wGuBGameEqBg6pp+0Zg+1R3BdkFZtdIOh64W1LZBWbjgbbAA+mctoCz6zRyNYzRauxvZmb1qFEm64hoXsO2L4BfpEflbVcBFT6TkpbBv4iIk6uoX1xF2efA4Gr6/jvw90plS4EfVxdvpborgZ+mR275VLIr1XO9T3axXOU2Lqwq/ohYQDpnXdMYmZnZ2ucrRczMzApco5xZ16eIKAV6rc0+08Vul1YqnhsRQ9ZmHGZmVhicrAtQugq7zldim5lZ4+ZlcDMzswLnZG1mZlbgnKzNzMwKnJO1mZlZgXOyNjMzK3BO1mZmZgXOydrMzKzAOVmbmZkVOCdrMzOzAudkbWZmVuCcrM3MzAqck7WZmVmBc7I2MzMrcE7WttYtXLiQww8/nO7du9OjRw+eeuqpfIdkZlbQ/BWZttadddZZ7L///txzzz0sX76cJUuW5DskM7OC1mDJWlIJcEn6buaysp8A2wNXpEcPYCHwKXBBRDyW6u0PXARsBCwDZgMjI+LtKvpZBczKKTo0Ikob4JDqlaQLgUURcbmki4DHIuIfleoUA+dExEFrIZ4S4BzgFeBuYFtgFfBgRIyqbf+lK1bRZdRD1W4vHXsgAJ9++imPPfYYt956KwDrr78+66+//hpGb2bWuDXkMvgE4KhKZUel8oeA6yNi24jYBTgD2AZAUi/g98CxEdE9IvoCdwJdqulnaUT0zXmU1v+hNKyIGF05UefZ5RHRHdgJ2FPS4Ppq+M0336R9+/Ycf/zx7LTTTpx44oksXry4vpo3M2uUGjJZ3wMcJGkDAEldgM3JZtZPRcTEsooR8VJE3Jpengf8JiJezdk+sWzWXReSukh6XNLz6bFHzrZzJc2S9KKksalsW0mTJD2X9uteQ9vtJd0r6dn02DOVXyjpnJx6L6VjRtJwSTNTn3dU0eatkg5Pz/eX9JqkJ4DDcuq0kXRz6vMFSYfUdKySiiWVSLontXenJNU2dhGxJCL+mZ4vB54Htqhtv7pauXIlzz//PKeccgovvPACbdq0YezYsfXVvJlZo9Rgy+AR8ZGkZ4D9gQfIZtX/C/QkSwDV6QlcvhpdtZI0Iz2fGxFDgPnAwIhYJmk7stl8vzRDPBTYLSKWSGqb9rseODki5kjaDbgW2Kea/q4CroiIJyR1BiaTLedXSVJP4Hxgz4hYkNNnVXVbAjekvv9NNl5lzgf+LyJGSNoEeEbSP6o71rTPTmTj+R7wJLAn8ER1/VcRzybA99IxV7X9JOAkgHbt2jO698pq2yopKQHg448/pl27dixdupSSkhK23XZb7rrrLvbdd9+6hrVOWLRoUfkxm8ejMo9HRR6P2jX0BWZlS+FlyXoEcHRuBUn3AdsBr0fEYZW2bQpMBVqTLZtXlcSXpqXyXOsB10jqS3bedftUvh9wS0QsAYiIjyUVAXsAd+dMPDeo4Zj2A3bIqbuRpA1rqL8PcE9ELCjrs4a63cnecMwBkPQnUjIEBgEH58zeWwKdyRJxVccK8ExEzEttzSA7lVCnZC2pBdm/39UR8WZVdSLierI3OnTepmuMm1X9r1PpsOLy51dccQUdO3akW7dulJSU8J3vfIfi4uJq910XlZSUNLpjWhMej4o8HhV5PGrX0Mn6fuB3knYGWkXE85J2AgaUVYiIIZL68eVs+mVgZ+DFiPgI6JsSVJGkLYEHU73xETG+mn7PBj4EdiRb6l+WygVEpbrNgIVVJPzqNAN2j4iluYWSVlLxtELLGvqsSXV1BXw/ImZX6vdCqj5WgM9znq9i9f69rwfmRMSVdancar3mzE4XkdXm97//PcOGDWP58uVss8023HLLLasRlplZ09Ogn7OOiEVACXAz2SwN4C6yi5YOzqnaOuf5ZcD5knpU3h4R7+RcSFZdogbYGHg/Ir4AjgGap/IpwAhJrQEktY2IT4G5ko5IZZK0Yw1tTwFOL3uRZrQApWRvMkhvTrZO5VOBI9MqATUtgwOvAVtL2ja9HpqzbTJwRtl55/Smp6Zj/dok/Tq1+5M1basqffv2Zfr06cycOZP777+fb3zjGw3RjZlZo7E2booygWzW92eANCM9CDhZ0puSngJ+Cfw6bZ8FnAXcni6MepLsnPBdq9HntcCxkqaRLQsvTm1PAiYC09OycNmS8jDgBEkvks3sD6mh7TPJzn/PlPQKcHIqvxdom9o9BXg99fkycDHwaGr/d9U1HBHLyJa9H0oXmL2Vs/l/yJb3Z0p6Kb2u9li/LklbkJ0f3wF4XtIMSSeuSZtmZrZmFLE6K7RmVevWrVvMnj279opNhM/BVeTxqMjjUVFTHg9Jz0VEv9rq+XajZmZmBc63G62GpPOBIyoV3x0RF+cjnvqSrr7fulLxebl3mjMzs8LiZF2NlJTX6cRclfQ5dDMzW4d4GdzMzKzAOVmbmZkVOCdrMzOzAudkbWZmVuCcrM3MzAqck7WZmVmBc7I2MzMrcE7WZmZmBc7J2szMrMA5WZuZmRU4J2szM7MC52RtZmZW4JyszczMCpyTtZmZWYFzsjYzMytwTtZmZmYFzsnazMyswDlZm5mZFThFRL5jsEZA0mfA7HzHUUDaAQvyHUQB8XhU5PGoqCmPx1YR0b62Si3WRiTWJMyOiH75DqJQSJru8fiSx6Mij0dFHo/aeRnczMyswDlZm5mZFTgna6sv1+c7gALj8ajI41GRx6Mij0ctfIGZmZlZgfPM2szMrMA5WZuZmRU4J2tbY5L2lzRb0r8ljcp3PPkgqVTSLEkzJE1PZW0lPSJpTvr5jXzH2VAk3SxpvqSXcsqqPH5lrk6/LzMl7Zy/yBtGNeNxoaR30+/IDEkH5Gz7eRqP2ZK+m5+oG4akLSX9U9Krkl6WdFYqb7K/H1+Hk7WtEUnNgT8Ag4EdgKGSdshvVHmzd0T0zfm86ChgakRsB0xNrxurW4H9K5VVd/yDge3S4yTgj2spxrXpVr46HgBXpN+RvhHxMED6/3IU0DPtc236f9VYrAR+FhE9gP7AaemYm/Lvx2pzsrY1tSvw74h4MyKWA38GDslzTIXiEOC29Pw24NA8xtKgIuIx4ONKxdUd/yHA7ZGZBmwiqePaiXTtqGY8qnMI8OeI+Dwi5gL/Jvt/1ShExPsR8Xx6/hnwKtCJJvz78XU4Wdua6gS8k/N6XipragKYIuk5SSelss0i4n3I/mABHfIWXX5Ud/xN+Xfm9LS0e3POaZEmMx6SugA7AU/j34/V4mRta0pVlDXFzwPuGRE7ky3hnSZpQL4DKmBN9Xfmj8C2QF/gfWBcKm8S4yGpCLgX+ElEfFpT1SrKGt14rC4na1tT84Atc15vAbyXp1jyJiLeSz/nA/eRLWN+WLZ8l37Oz1+EeVHd8TfJ35mI+DAiVkXEF8ANfLnU3ejHQ9J6ZIn6zoj4ayr278dqcLK2NfUssJ2krSWtT3ahzMQ8x7RWSWojacOy58Ag4CWycTg2VTsWeCA/EeZNdcc/ERiervrtD/y3bDm0Mat03nUI2e8IZONxlKQNJG1NdmHVM2s7voYiScBNwKsR8bucTf79WA3+1i1bIxGxUtLpwGSgOXBzRLyc57DWts2A+7K/SbQA7oqISZKeBf4i6QTgbeCIPMbYoCRNAIqBdpLmARcAY6n6+B8GDiC7kGoJcPxaD7iBVTMexZL6ki3plgI/BoiIlyX9BXiF7Mrp0yJiVT7ibiB7AscAsyTNSGW/oAn/fnwdvt2omZlZgfMyuJmZWYFzsjYzMytwTtZmZmYFzsnazMyswDlZm5mZFTgnazOrkaRVOd8UNSPdMnJ129hE0qn1H115+wev7W98k3RoE/7SGlvL/NEtM6uRpEURUbSGbXQB/hYRvVZzv+aF+JljSS2AG8mO6Z58x2ONn2fWZrbaJDWX9FtJz6YvpvhxKi+SNFXS88q+37vsG9jGAtummflvJRVL+ltOe9dIOi49L5U0WtITwBGStpU0KX1JyuOSulcRz3GSrknPb5X0x/Qdym9K2it9ccarkm7N2WeRpHEp1qmS2qfyvpKmpeO6L+d7lksk/UbSo8B5wMHAb9MxbSvpR2k8XpR0r6TWOfFcLelfKZ7Dc2I4N43Ti5LGprJaj9eaHt/BzMxq0yrnzlNzI2IIcALZbSC/JWkD4ElJU8i+LWlIRHwqqR0wTdJEsu8q7hX/397dhNgYxXEc//5mQzFdlGRFiYSYopRGxkvKBhsbksKCohSWLCbZWCrlpbxEGSMpGy9ZTNkIM2a8pCwQJQojjZfI3+KcyzXu3BnXS9f4fTbPveeec89znsX933PPc88/ogFAUlM/fb6PiMZc9zKwISLuS5oN7AMW9NN+ZK6zFDhH2kVrPXBNUkNE3ASGAe0RsVXSTtIuY5uAY8DmiGiT1JzLt+T3HRER8/J5TaRkZi2pOyIO5se78jXam9uNBRqByaTtNE9LWkJKCzk7It5KGpXrHqhivDbIOVibWX/eFYNsicXA9JJZYoG0p/UTYLdS1rHPpNSGY6roswW+ZmqaA7Tm7VwBhgyg/bmICEm3gGcRcSu/3x1gPHAzn19Lrn8cOCOpQArIbbn8KNDa+7z6MC0H6RHAcNIWvEVncwKPu5KK12MRcDgi3gJExMtfGK8Ncg7WZlYNkWafF74rTD9ljwZmRsRHSQ+BoWXaf+L7ZbjedXrysQ7oLvNloT8f8vFzyePi874+9wZyA09PhdeOAMsjojNfh6Yy5wPfUkCqTJ/VjtcGOa9Zm1k1LgAblVIfImmSUsaxAvA8B+r5wLhc/w1QX9L+ETBFKdNUAVhYrpOc9/iBpBW5H0ma8ZvGUAcUfxlYCVyJiNfAK0lzc/lqoK1cY34cUz3wNF+TVQPo/yKwtmRte9QfHq/9wxyszawah0hZotol3Qb2k2asJ4BZkq6TAtY9gIh4QVrXvi1pT0Q8Bk4BXblNR4W+VgHrJHUCd4BlFer+jB5gqqQbpDXh5ly+hnTjWBfQUFLe20lgu6QOSROAHcBV4BJ53JVExHnS+vX1fE/AtvzSnxqv/cP81y0z+y/pN/wlzexv8czazMysxnlmbWZmVuM8szYzM6txDtZmZmY1zsHazMysxjlYm5mZ1TgHazMzsxr3BXqXyWoMb83LAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = lgb.plot_importance(gbm, max_num_features=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = [20, 20]\n",
+    "\n",
+    "for i in range(0, gbm.num_trees()):\n",
+    "    ax = lgb.plot_tree(gbm, tree_index = i)\n",
+    "    plt.show()\n",
+    "    \n",
+    "    if i == 2:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ROC Curve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_proba = predictions[::,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
+    "auc = metrics.roc_auc_score(y_test, y_pred_proba)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x24e6e35e438>]"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7,3))\n",
+    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/dataset/face-recognition-pivot.csv b/tests/dataset/face-recognition-pivot.csv
new file mode 100644
index 0000000..c6c6438
--- /dev/null
+++ b/tests/dataset/face-recognition-pivot.csv
@@ -0,0 +1,322 @@
+file_x,file_y,decision,VGG-Face_cosine,VGG-Face_euclidean_l2,Facenet_cosine,Facenet_euclidean_l2,OpenFace_cosine,OpenFace_euclidean_l2,DeepFace_cosine,DeepFace_euclidean_l2
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img9.jpg,Yes,0.3147,0.7933,0.1976,0.6287,0.0997,0.4466,0.1695,0.5822
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img47.jpg,Yes,0.3638,0.853,0.1976,0.6287,0.0931,0.4314,0.1869,0.6114
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img48.jpg,Yes,0.3068,0.7834,0.2593,0.7201,0.1319,0.5136,0.2194,0.6624
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img49.jpg,Yes,0.2353,0.686,0.1797,0.5996,0.1472,0.5426,0.1904,0.617
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img50.jpg,Yes,0.3583,0.8465,0.24,0.6928,0.132,0.5138,0.138,0.5253
+deepface/tests/dataset/img8.jpg,deepface/tests/dataset/img51.jpg,Yes,0.3446,1.1425,0.1666,0.5772,0.1413,0.5317,0.1656,0.5756
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+deepface/tests/dataset/img9.jpg,deepface/tests/dataset/img48.jpg,Yes,0.3537,0.8411,0.1723,0.5871,0.1234,0.4969,0.1795,0.5992
+deepface/tests/dataset/img9.jpg,deepface/tests/dataset/img49.jpg,Yes,0.2072,0.6437,0.1954,0.6251,0.1529,0.553,0.1311,0.5121
+deepface/tests/dataset/img9.jpg,deepface/tests/dataset/img50.jpg,Yes,0.2662,0.7296,0.2576,0.7177,0.1531,0.5533,0.1205,0.491
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+deepface/tests/dataset/img47.jpg,deepface/tests/dataset/img48.jpg,Yes,0.345,0.8307,0.1613,0.5679,0.1419,0.5328,0.1649,0.5742
+deepface/tests/dataset/img47.jpg,deepface/tests/dataset/img49.jpg,Yes,0.257,0.717,0.1944,0.6236,0.1089,0.4667,0.2415,0.695
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+deepface/tests/dataset/img48.jpg,deepface/tests/dataset/img49.jpg,Yes,0.2917,0.7639,0.232,0.6812,0.1067,0.462,0.2183,0.6608
+deepface/tests/dataset/img48.jpg,deepface/tests/dataset/img50.jpg,Yes,0.3985,0.8927,0.2745,0.7409,0.2245,0.6701,0.2181,0.6605
+deepface/tests/dataset/img48.jpg,deepface/tests/dataset/img51.jpg,Yes,0.3408,0.8255,0.1586,0.5633,0.1734,0.5888,0.2082,0.6452
+deepface/tests/dataset/img49.jpg,deepface/tests/dataset/img50.jpg,Yes,0.2073,0.6439,0.2437,0.6982,0.1738,0.5896,0.1949,0.6243
+deepface/tests/dataset/img49.jpg,deepface/tests/dataset/img51.jpg,Yes,0.2694,0.7341,0.2076,0.6444,0.1414,0.5318,0.2283,0.6758
+deepface/tests/dataset/img50.jpg,deepface/tests/dataset/img51.jpg,Yes,0.2505,0.7079,0.2299,0.6781,0.1894,0.6155,0.1715,0.5857
+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img2.jpg,Yes,0.2342,0.6844,0.2508,0.7082,0.0844,0.4109,0.2417,0.6952
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+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img5.jpg,Yes,0.2963,0.7699,0.2696,0.7343,0.0965,0.4393,0.2306,0.679
+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img6.jpg,Yes,0.254,0.7128,0.2164,0.6579,0.0691,0.3718,0.2365,0.6877
+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img7.jpg,Yes,0.3104,0.7879,0.2112,0.65,0.1027,0.4531,0.2385,0.6906
+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img10.jpg,Yes,0.3363,0.8202,0.2129,0.6525,0.0661,0.3635,0.2472,0.7031
+deepface/tests/dataset/img1.jpg,deepface/tests/dataset/img11.jpg,Yes,0.3083,0.7852,0.2042,0.639,0.1626,0.5703,0.2001,0.6326
+deepface/tests/dataset/img2.jpg,deepface/tests/dataset/img4.jpg,Yes,0.1397,0.5285,0.1957,0.6256,0.2497,0.7066,0.1349,0.5194
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+deepface/tests/dataset/img2.jpg,deepface/tests/dataset/img7.jpg,Yes,0.2,0.6325,0.1642,0.5731,0.1049,0.4581,0.098,0.4428
+deepface/tests/dataset/img2.jpg,deepface/tests/dataset/img10.jpg,Yes,0.2077,0.6444,0.1593,0.5644,0.0589,0.3431,0.1118,0.4729
+deepface/tests/dataset/img2.jpg,deepface/tests/dataset/img11.jpg,Yes,0.2349,0.6854,0.1869,0.6114,0.1029,0.4536,0.1548,0.5564
+deepface/tests/dataset/img4.jpg,deepface/tests/dataset/img5.jpg,Yes,0.1991,0.6311,0.1199,0.4898,0.2891,0.7604,0.1797,0.5995
+deepface/tests/dataset/img4.jpg,deepface/tests/dataset/img6.jpg,Yes,0.1937,0.6224,0.1772,0.5954,0.2199,0.6632,0.1788,0.598
+deepface/tests/dataset/img4.jpg,deepface/tests/dataset/img7.jpg,Yes,0.245,0.7,0.1663,0.5767,0.3435,0.8289,0.1971,0.6279
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+deepface/tests/dataset/img4.jpg,deepface/tests/dataset/img11.jpg,Yes,0.2569,0.7168,0.1909,0.618,0.2874,0.7582,0.1737,0.5894
+deepface/tests/dataset/img5.jpg,deepface/tests/dataset/img6.jpg,Yes,0.1858,0.6095,0.1818,0.6029,0.13,0.5099,0.1742,0.5903
+deepface/tests/dataset/img5.jpg,deepface/tests/dataset/img7.jpg,Yes,0.2639,0.7264,0.1754,0.5923,0.1174,0.4846,0.1523,0.5519
+deepface/tests/dataset/img5.jpg,deepface/tests/dataset/img10.jpg,Yes,0.2013,0.6344,0.1143,0.478,0.1228,0.4957,0.1942,0.6232
+deepface/tests/dataset/img5.jpg,deepface/tests/dataset/img11.jpg,Yes,0.3348,0.8183,0.1975,0.6285,0.2071,0.6436,0.1692,0.5818
+deepface/tests/dataset/img6.jpg,deepface/tests/dataset/img7.jpg,Yes,0.192,0.6196,0.1275,0.505,0.1004,0.4482,0.094,0.4335
+deepface/tests/dataset/img6.jpg,deepface/tests/dataset/img10.jpg,Yes,0.214,0.6542,0.1237,0.4974,0.0517,0.3216,0.11,0.4691
+deepface/tests/dataset/img6.jpg,deepface/tests/dataset/img11.jpg,Yes,0.2755,0.7423,0.1772,0.5953,0.1383,0.526,0.1771,0.5951
+deepface/tests/dataset/img7.jpg,deepface/tests/dataset/img10.jpg,Yes,0.3425,0.8276,0.1708,0.5845,0.0956,0.4374,0.1552,0.5571
+deepface/tests/dataset/img7.jpg,deepface/tests/dataset/img11.jpg,Yes,0.2912,0.7632,0.2449,0.6998,0.148,0.544,0.1894,0.6154
+deepface/tests/dataset/img10.jpg,deepface/tests/dataset/img11.jpg,Yes,0.2535,0.712,0.1371,0.5237,0.0609,0.349,0.1851,0.6085
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+deepface/tests/dataset/img38.jpg,deepface/tests/dataset/img41.jpg,Yes,0.1073,0.4632,0.1257,0.5014,0.7157,1.1964,0.2452,0.7002
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+deepface/tests/dataset/img40.jpg,deepface/tests/dataset/img41.jpg,Yes,0.1706,0.5841,0.2017,0.6352,0.3985,1.0649,0.2423,0.6961
+deepface/tests/dataset/img24.jpg,deepface/tests/dataset/img25.jpg,Yes,0.1197,0.4893,0.1419,0.5327,0.1666,0.5773,0.2083,0.6454
+deepface/tests/dataset/img20.jpg,deepface/tests/dataset/img21.jpg,Yes,0.0641,0.3581,0.1052,0.4586,0.1118,0.4729,0.2209,0.6646
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+deepface/tests/dataset/img16.jpg,deepface/tests/dataset/img59.jpg,Yes,0.1796,0.5993,0.3095,0.7868,0.4173,0.9136,0.247,0.7028
+deepface/tests/dataset/img16.jpg,deepface/tests/dataset/img61.jpg,Yes,0.1779,0.5965,0.1863,0.6105,0.1407,0.5305,0.1643,0.5732
+deepface/tests/dataset/img16.jpg,deepface/tests/dataset/img62.jpg,Yes,0.106,0.4604,0.2243,0.6698,0.3857,0.8783,0.1953,0.6249
+deepface/tests/dataset/img17.jpg,deepface/tests/dataset/img59.jpg,Yes,0.2519,0.7099,0.2846,0.7544,0.3877,0.8806,0.2994,0.7739
+deepface/tests/dataset/img17.jpg,deepface/tests/dataset/img61.jpg,Yes,0.2507,0.708,0.1992,0.6313,0.1867,0.6111,0.2101,0.6482
+deepface/tests/dataset/img17.jpg,deepface/tests/dataset/img62.jpg,Yes,0.2533,0.7118,0.2672,0.731,0.3356,0.8193,0.252,0.7099
+deepface/tests/dataset/img59.jpg,deepface/tests/dataset/img61.jpg,Yes,0.192,0.6196,0.4417,0.9399,0.3558,0.8435,0.1808,0.6014
+deepface/tests/dataset/img59.jpg,deepface/tests/dataset/img62.jpg,Yes,0.1123,0.4738,0.2974,0.7713,0.5393,1.0386,0.1934,0.6219
+deepface/tests/dataset/img61.jpg,deepface/tests/dataset/img62.jpg,Yes,0.1251,0.5002,0.2245,0.6701,0.4072,0.9024,0.1757,0.5928
+deepface/tests/dataset/img3.jpg,deepface/tests/dataset/img12.jpg,Yes,0.2533,0.7118,0.4062,0.9014,0.1908,0.6178,0.2337,0.6837
+deepface/tests/dataset/img3.jpg,deepface/tests/dataset/img53.jpg,Yes,0.1655,0.5754,0.184,0.6066,0.1465,0.5412,0.243,0.6971
+deepface/tests/dataset/img3.jpg,deepface/tests/dataset/img54.jpg,Yes,0.1982,0.6297,0.406,0.9011,0.1132,0.4758,0.1824,0.6041
+deepface/tests/dataset/img3.jpg,deepface/tests/dataset/img55.jpg,Yes,0.1835,0.6057,0.1366,0.5227,0.1755,0.5924,0.1697,0.5825
+deepface/tests/dataset/img3.jpg,deepface/tests/dataset/img56.jpg,Yes,0.1652,0.5748,0.1833,0.6054,0.1803,0.6005,0.2061,0.642
+deepface/tests/dataset/img12.jpg,deepface/tests/dataset/img53.jpg,Yes,0.372,0.8626,0.3933,0.8869,0.1068,0.4621,0.1633,0.5715
+deepface/tests/dataset/img12.jpg,deepface/tests/dataset/img54.jpg,Yes,0.2153,0.6561,0.2694,0.734,0.1943,0.6234,0.1881,0.6133
+deepface/tests/dataset/img12.jpg,deepface/tests/dataset/img55.jpg,Yes,0.3551,0.8428,0.4726,0.9722,0.1054,0.4591,0.1265,0.5029
+deepface/tests/dataset/img12.jpg,deepface/tests/dataset/img56.jpg,Yes,0.2826,0.7518,0.4761,0.9758,0.1364,0.5224,0.1908,0.6177
+deepface/tests/dataset/img53.jpg,deepface/tests/dataset/img54.jpg,Yes,0.3363,0.8202,0.4627,0.962,0.1964,0.6267,0.174,0.5898
+deepface/tests/dataset/img53.jpg,deepface/tests/dataset/img55.jpg,Yes,0.187,0.6116,0.1625,0.5701,0.1312,0.5123,0.1439,0.5365
+deepface/tests/dataset/img53.jpg,deepface/tests/dataset/img56.jpg,Yes,0.1385,0.5263,0.141,0.5311,0.1285,0.507,0.2005,0.6332
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+deepface/tests/dataset/img54.jpg,deepface/tests/dataset/img56.jpg,Yes,0.2571,0.717,0.3912,0.8846,0.1802,0.6002,0.1648,0.574
+deepface/tests/dataset/img55.jpg,deepface/tests/dataset/img56.jpg,Yes,0.2217,0.6658,0.1433,0.5353,0.1677,0.5792,0.1505,0.5486
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+deepface/tests/dataset/img34.jpg,deepface/tests/dataset/img36.jpg,Yes,0.1553,0.5573,0.1861,0.6101,0.399,0.8933,0.2923,0.7646
+deepface/tests/dataset/img34.jpg,deepface/tests/dataset/img37.jpg,Yes,0.104,0.4562,0.1192,0.4882,0.4158,0.912,0.2853,0.7554
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+deepface/tests/dataset/img22.jpg,deepface/tests/dataset/img23.jpg,Yes,0.3637,0.8528,0.3501,0.8368,0.1651,0.5746,0.1649,0.5742
+deepface/tests/dataset/img22.jpg,deepface/tests/dataset/img63.jpg,Yes,0.2768,0.744,0.2387,0.691,0.1212,0.4924,0.168,0.5797
+deepface/tests/dataset/img22.jpg,deepface/tests/dataset/img64.jpg,Yes,0.2684,0.7327,0.3427,0.8279,0.1495,0.5469,0.2475,0.7035
+deepface/tests/dataset/img22.jpg,deepface/tests/dataset/img65.jpg,Yes,0.4131,0.9089,0.4066,0.9018,0.2039,0.6386,0.3097,0.787
+deepface/tests/dataset/img22.jpg,deepface/tests/dataset/img66.jpg,Yes,0.3381,0.8223,0.502,1.002,0.1663,0.5768,0.3472,0.8333
+deepface/tests/dataset/img23.jpg,deepface/tests/dataset/img63.jpg,Yes,0.1662,0.5766,0.1551,0.557,0.0829,0.4072,0.112,0.4732
+deepface/tests/dataset/img23.jpg,deepface/tests/dataset/img64.jpg,Yes,0.2661,0.7295,0.2743,0.7406,0.1013,0.4502,0.2109,0.6494
+deepface/tests/dataset/img23.jpg,deepface/tests/dataset/img65.jpg,Yes,0.2766,0.7438,0.2725,0.7383,0.2187,0.6613,0.2542,0.713
+deepface/tests/dataset/img23.jpg,deepface/tests/dataset/img66.jpg,Yes,0.175,0.5917,0.2991,0.7735,0.235,0.6856,0.228,0.6752
+deepface/tests/dataset/img63.jpg,deepface/tests/dataset/img64.jpg,Yes,0.2031,0.6373,0.196,0.6261,0.0849,0.412,0.2245,0.6701
+deepface/tests/dataset/img63.jpg,deepface/tests/dataset/img65.jpg,Yes,0.3022,0.7774,0.2646,0.7275,0.1529,0.553,0.2365,0.6878
+deepface/tests/dataset/img63.jpg,deepface/tests/dataset/img66.jpg,Yes,0.2105,0.6488,0.3178,0.7972,0.1468,0.5419,0.2381,0.69
+deepface/tests/dataset/img64.jpg,deepface/tests/dataset/img65.jpg,Yes,0.37,0.8602,0.2176,0.6597,0.1199,0.4896,0.2104,0.6487
+deepface/tests/dataset/img64.jpg,deepface/tests/dataset/img66.jpg,Yes,0.2383,0.6904,0.3096,0.7869,0.1356,0.5209,0.2226,0.6672
+deepface/tests/dataset/img65.jpg,deepface/tests/dataset/img66.jpg,Yes,0.1934,0.6219,0.2835,0.7531,0.2622,0.7242,0.2352,0.6858
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+deepface/tests/dataset/img29.jpg,deepface/tests/dataset/img31.jpg,Yes,0.1525,0.5523,0.1588,0.5636,0.5027,1.0027,0.2,0.6324
+deepface/tests/dataset/img29.jpg,deepface/tests/dataset/img32.jpg,Yes,0.1807,0.6011,0.1997,0.632,0.4602,0.9594,0.3084,0.7854
+deepface/tests/dataset/img29.jpg,deepface/tests/dataset/img33.jpg,Yes,0.1757,0.5927,0.2406,0.6937,0.3446,0.8302,0.1679,0.5795
+deepface/tests/dataset/img30.jpg,deepface/tests/dataset/img31.jpg,Yes,0.1141,0.4776,0.1654,0.5751,0.3189,0.7986,0.1897,0.6159
+deepface/tests/dataset/img30.jpg,deepface/tests/dataset/img32.jpg,Yes,0.1567,0.5597,0.1757,0.5929,0.1712,0.5851,0.242,0.6957
+deepface/tests/dataset/img30.jpg,deepface/tests/dataset/img33.jpg,Yes,0.1548,0.5565,0.2074,0.644,0.1744,0.5906,0.2601,0.7213
+deepface/tests/dataset/img31.jpg,deepface/tests/dataset/img32.jpg,Yes,0.1402,0.5295,0.1009,0.4493,0.2098,0.6478,0.2023,0.6361
+deepface/tests/dataset/img31.jpg,deepface/tests/dataset/img33.jpg,Yes,0.0895,0.4232,0.1873,0.6121,0.1871,0.6118,0.229,0.6768
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+deepface/tests/dataset/img18.jpg,deepface/tests/dataset/img67.jpg,Yes,0.2197,0.6629,0.2387,0.6909,0.3072,0.7838,0.1839,0.6065
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+deepface/tests/dataset/img26.jpg,deepface/tests/dataset/img28.jpg,Yes,0.343,0.8282,0.3556,0.8433,0.1662,0.5766,0.205,0.6403
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+deepface/tests/dataset/img26.jpg,deepface/tests/dataset/img43.jpg,Yes,0.3254,0.8067,0.2825,0.7517,0.2876,0.7585,0.3443,0.8299
+deepface/tests/dataset/img26.jpg,deepface/tests/dataset/img44.jpg,Yes,0.3645,0.8539,0.2248,0.6706,0.2646,0.7274,0.2572,0.7173
+deepface/tests/dataset/img26.jpg,deepface/tests/dataset/img45.jpg,Yes,0.283,0.7523,0.2537,0.7124,0.3277,0.8096,0.2726,0.7384
+deepface/tests/dataset/img26.jpg,deepface/tests/dataset/img46.jpg,Yes,0.447,0.9456,0.4372,0.9351,0.3544,0.8419,0.3079,0.7848
+deepface/tests/dataset/img27.jpg,deepface/tests/dataset/img28.jpg,Yes,0.2847,0.7546,0.2178,0.6601,0.1205,0.491,0.232,0.6811
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