Get Image NIMA Scores.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# follow the steps and modified from the github eval_*.py\n",
    "# Github: https://github.com/titu1994/neural-image-assessment\n",
    "\n",
    "import numpy as np\n",
    "import argparse\n",
    "from tqdm import tqdm\n",
    "\n",
    "import tensorflow as tf\n",
    "import os\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "from utils.nasnet import NASNetMobile, preprocess_input\n",
    "from utils.score_utils import mean_score, std_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 5124965882682021193\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 3174131302\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 16157427006511274458\n",
      "physical_device_desc: \"device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_size = (224, 224)  # NASNet requires strict size set to 224x224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from zipfile import ZipFile\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dask import bag, threaded\n",
    "from dask.diagnostics import ProgressBar\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_img_from_zipped(zipped, filename):\n",
    "    try:\n",
    "        exfile = zipped.read(filename)\n",
    "        arr = np.frombuffer(exfile, np.uint8)\n",
    "        imz = cv2.imdecode(arr, flags=cv2.IMREAD_UNCHANGED)\n",
    "        imz = cv2.resize(imz, target_size, interpolation=cv2.INTER_AREA)\n",
    "    except:\n",
    "        print(filename, ' is invalid')\n",
    "        imz = None\n",
    "        \n",
    "    return imz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yield image_id, img\n",
    "def img_loader():\n",
    "    archives = ['imgs/train_jpg.zip', 'imgs/test_jpg.zip']\n",
    "    \n",
    "    invalid_img_ids = ['4f029e2a00e892aa2cac27d98b52ef8b13d91471f613c8d3c38e3f29d4da0b0c', \n",
    "                       '8513a91e55670c709069b5f85e12a59095b802877715903abef16b7a6f306e58', \n",
    "                       '60d310a42e87cdf799afcd89dc1b11ae3fdc3d0233747ec7ef78d82c87002e83', \n",
    "                       'b98b291bd04c3d92165ca515e00468fd9756af9a8f1df42505deed1dcfb5d7ae']\n",
    "    \n",
    "    for arch in archives:\n",
    "        print('Reading ', arch)\n",
    "        zipped = ZipFile(arch)\n",
    "        filenames = zipped.namelist()[1:]\n",
    "        \n",
    "        for file in tqdm(filenames):\n",
    "            img_id = file.replace('.jpg', '')\n",
    "            \n",
    "            if img_id in invalid_img_ids:\n",
    "                continue\n",
    "                \n",
    "            img = load_img_from_zipped(zipped, file)\n",
    "            if img is None:\n",
    "                continue\n",
    "                \n",
    "            yield img_id, img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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       " 'data/competition_files/test_jpg/4dc66c42736291971dc295771ae24dd53a84e26944362adbadebbfacc7a60077.jpg',\n",
       " 'data/competition_files/test_jpg/c7c5adbde2aaccfd33bcf6571dfc6b57870c552ab94267a59a115476ebae8cf1.jpg',\n",
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       " 'data/competition_files/test_jpg/19edd301f05ed3da8b07f33796087748d3aabb154ee3a254b2412165fdfba8af.jpg',\n",
       " 'data/competition_files/test_jpg/82938ecf41a5bf55f93564e7927981d317874ae378d4a2757fdccd9dc54ea08a.jpg',\n",
       " 'data/competition_files/test_jpg/323dadb7e623667dcdd27ef213656762ffa9c2a85c2d75519c5f5582c1b97666.jpg',\n",
       " 'data/competition_files/test_jpg/31fa6ed89ea8ab5d5bdd851c11df0a1a1586a4d19e084e9d65697334c537ad4f.jpg',\n",
       " 'data/competition_files/test_jpg/d8b0b1af3f032050aa92cad92970f8a38236977af7daac3f371b15a1298d1b8e.jpg',\n",
       " 'data/competition_files/test_jpg/1485bfd92f18eaeee2158693b2928283dab0e297ae3d6b400983dae632233a61.jpg',\n",
       " 'data/competition_files/test_jpg/9c3f87ae361508726eedcfcefb832ff32f4a27e68273a36db4a2cceec2a2382a.jpg',\n",
       " 'data/competition_files/test_jpg/0f8cd89e3a6d49f1f0a820915206ea99985fa4105b4bc6d29d0a7f7fcf8a5c77.jpg',\n",
       " 'data/competition_files/test_jpg/fac581263c7fab2499a62ecf389d7a48b4f93c46d27a6d76b6df1fbd8480c2d6.jpg',\n",
       " 'data/competition_files/test_jpg/ad3c0c7666f1e751511745d629282a37d3b8010b2a6678d8d7c1c59ea59870e4.jpg',\n",
       " 'data/competition_files/test_jpg/0448ec02a6d2e95c6eadb0452b27426619290f40f2b1ecf7b55de7347aa5bae1.jpg',\n",
       " 'data/competition_files/test_jpg/36bae13d95d729dbb29b26357b9d5244ae39b4a6512b31371f0c922657ac4c10.jpg',\n",
       " 'data/competition_files/test_jpg/df008f8dd7c900c5c2d58d927682ccd19469491159d9a3cef9808a89d2f3ddd0.jpg',\n",
       " 'data/competition_files/test_jpg/4373814ddcc4d1afe898e6114cef5439c72d139dc5cb1ecef4b1ce7172b1757d.jpg',\n",
       " 'data/competition_files/test_jpg/f2716fcf7521eebe3ce30b6250e857c0d1e3c66b8b93276a45cb3c860a7a258d.jpg',\n",
       " 'data/competition_files/test_jpg/ca0edfbf32caf803190bc5eeaff9aeea18a90ee3d0c1a02cca32aefcc8d02690.jpg',\n",
       " 'data/competition_files/test_jpg/416a0f7f5c68443915578d9df028e52eef4624175547ecb4bf19fcb5c42d1f1a.jpg',\n",
       " 'data/competition_files/test_jpg/0a11aac2e6411d87535a4524b0b159d1376ced9ab9af10458572f67a57bf04f1.jpg',\n",
       " 'data/competition_files/test_jpg/af93369e93cc6b553f2866bc8179cc79ee700af408b70e2ae4c09dc4769d4d53.jpg',\n",
       " ...]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for\n",
    "zipped = ZipFile('imgs/test_jpg.zip')\n",
    "filenames = zipped.namelist()[1:]\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights loaded\n",
      "Reading  imgs/train_jpg_0.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 278167/278167 [2:47:17<00:00, 27.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading  imgs/train_jpg_1.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 278166/278166 [2:47:35<00:00, 27.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading  imgs/train_jpg_2.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 278166/278166 [2:47:55<00:00, 27.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading  imgs/train_jpg_3.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 278166/278166 [2:47:49<00:00, 27.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading  imgs/train_jpg_4.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 278166/278166 [2:49:36<00:00, 27.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading  imgs/test_jpg.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 465829/465829 [4:48:41<00:00, 26.89it/s]\n"
     ]
    }
   ],
   "source": [
    "DEBUG = False\n",
    "\n",
    "with tf.device('/GPU:0'):\n",
    "    base_model = NASNetMobile((224, 224, 3), include_top=False, pooling='avg', weights=None)\n",
    "    x = Dropout(0.75)(base_model.output)\n",
    "    x = Dense(10, activation='softmax')(x)\n",
    "    model = Model(base_model.input, x)\n",
    "    model.load_weights('weights/nasnet_weights.h5')\n",
    "    print('Weights loaded')\n",
    "    \n",
    "    img_ids = []\n",
    "    score_list = []\n",
    "\n",
    "    for img_id, img in img_loader():\n",
    "        \n",
    "        x = img_to_array(img)\n",
    "        x = np.expand_dims(x, axis=0)\n",
    "        x = preprocess_input(x)\n",
    "\n",
    "        scores = model.predict(x, batch_size=1, verbose=0)[0]\n",
    "        img_ids.append(img_id)\n",
    "        score_list.append(scores)\n",
    "        \n",
    "        if DEBUG:\n",
    "            print(img_ids)\n",
    "            print(score_list)\n",
    "            plt.imshow(img)\n",
    "            plt.show()\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|                                                                                      | 0/1856656 [00:00<?, ?it/s]\n",
      " 11%|███████▋                                                            | 209329/1856656 [00:00<00:00, 2078189.78it/s]\n",
      " 22%|██████████████▉                                                     | 407254/1856656 [00:00<00:00, 2021320.35it/s]\n",
      " 33%|██████████████████████▎                                             | 607876/1856656 [00:00<00:00, 2011403.40it/s]\n",
      " 45%|██████████████████████████████▎                                     | 826657/1856656 [00:00<00:00, 2046609.24it/s]\n",
      " 56%|█████████████████████████████████████▋                             | 1045105/1856656 [00:00<00:00, 2075069.13it/s]\n",
      " 68%|█████████████████████████████████████████████▎                     | 1256212/1856656 [00:00<00:00, 2078515.53it/s]\n",
      " 79%|████████████████████████████████████████████████████▊              | 1463154/1856656 [00:00<00:00, 2075070.36it/s]\n",
      " 89%|███████████████████████████████████████████████████████████▋       | 1654619/1856656 [00:00<00:00, 2035576.10it/s]\n",
      " 99%|██████████████████████████████████████████████████████████████████▍| 1842458/1856656 [00:00<00:00, 1997181.18it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████| 1856656/1856656 [00:00<00:00, 1990976.92it/s]"
     ]
    }
   ],
   "source": [
    "img_ids = [img_id.replace('data/competition_files/test_jpg/', '') for img_id in tqdm(img_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['7abf6c4aa44b9a0d04414c2e350d528e0a83c300f809bd4299226ba2f43e69d6',\n",
       " '1ce2716b4022d40c38d18dfbdfb37bbcdadd2a494dc4ce28339f68ab0c8f7cca',\n",
       " 'bfc4ee7103ffa563152b94b25a29c28d6967ce8e8990072120902313963fd807']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_ids[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([0.02021847, 0.05123571, 0.12512006, 0.2761127 , 0.31151772,\n",
       "        0.13658957, 0.04995457, 0.02059884, 0.00557685, 0.00307551],\n",
       "       dtype=float32),\n",
       " array([0.04311303, 0.08751361, 0.16680562, 0.28526697, 0.2504091 ,\n",
       "        0.10246787, 0.0404527 , 0.01804111, 0.00350323, 0.00242668],\n",
       "       dtype=float32),\n",
       " array([0.00881213, 0.02849251, 0.07964744, 0.21630877, 0.34663433,\n",
       "        0.19870226, 0.07647053, 0.03049389, 0.00986233, 0.00457572],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('nima_nasnet_img_ids.pickle', 'wb') as handle:\n",
    "    pickle.dump(img_ids, handle)\n",
    "\n",
    "with open('nima_nasnet_scores.pickle', 'wb') as handle:\n",
    "    pickle.dump(score_list, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1856656, 10)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list = np.array(score_list)\n",
    "score_list.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature engineering\n",
    "res = pd.DataFrame()\n",
    "res.loc[:, 'image'] = img_ids\n",
    "for i in range(10):\n",
    "    res.loc[:, 'raw_pred_{}'.format(i)] = score_list[:,i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['raw_pred_0',\n",
       " 'raw_pred_1',\n",
       " 'raw_pred_2',\n",
       " 'raw_pred_3',\n",
       " 'raw_pred_4',\n",
       " 'raw_pred_5',\n",
       " 'raw_pred_6',\n",
       " 'raw_pred_7',\n",
       " 'raw_pred_8',\n",
       " 'raw_pred_9']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_feature_cols = ['raw_pred_{}'.format(i) for i in range(10)]\n",
    "raw_feature_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.loc[:, 'mean'] = res[raw_feature_cols].mean(axis=1)\n",
    "res.loc[:, 'med'] = res[raw_feature_cols].median(axis=1)\n",
    "res.loc[:, 'std'] = res[raw_feature_cols].std(axis=1)\n",
    "res.loc[:, 'max'] = res[raw_feature_cols].max(axis=1)\n",
    "res.loc[:, 'min'] = res[raw_feature_cols].min(axis=1)\n",
    "res.loc[:, '1_quartile'] = res[raw_feature_cols].quantile(.25, axis=1)\n",
    "res.loc[:, '3_quartile'] = res[raw_feature_cols].quantile(.75, axis=1)\n",
    "res.loc[:, '13_quartile_diff'] = res.loc[:, '3_quartile']-res.loc[:, '1_quartile']\n",
    "res.loc[:, 'max_min_diff'] = res.loc[:, 'max'] - res.loc[:, 'min']\n",
    "res.loc[:, 'non_max_mean'] = (res[raw_feature_cols].sum(axis=1)-res.loc[:, 'max'])/9\n",
    "res.loc[:, 'max_non_max_mean_diff'] = res.loc[:, 'max']-res.loc[:, 'non_max_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>raw_pred_0</th>\n",
       "      <th>raw_pred_1</th>\n",
       "      <th>raw_pred_2</th>\n",
       "      <th>raw_pred_3</th>\n",
       "      <th>raw_pred_4</th>\n",
       "      <th>raw_pred_5</th>\n",
       "      <th>raw_pred_6</th>\n",
       "      <th>raw_pred_7</th>\n",
       "      <th>raw_pred_8</th>\n",
       "      <th>raw_pred_9</th>\n",
       "      <th>mean</th>\n",
       "      <th>med</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>1_quartile</th>\n",
       "      <th>3_quartile</th>\n",
       "      <th>13_quartile_diff</th>\n",
       "      <th>max_min_diff</th>\n",
       "      <th>non_max_mean</th>\n",
       "      <th>max_non_max_mean_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856e74b8c46edcf0c0e23444eab019bfda63687bb70a34...</td>\n",
       "      <td>0.020218</td>\n",
       "      <td>0.051236</td>\n",
       "      <td>0.125120</td>\n",
       "      <td>0.276113</td>\n",
       "      <td>0.311518</td>\n",
       "      <td>0.136590</td>\n",
       "      <td>0.049955</td>\n",
       "      <td>0.020599</td>\n",
       "      <td>0.005577</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.050595</td>\n",
       "      <td>0.112342</td>\n",
       "      <td>0.311518</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>0.020314</td>\n",
       "      <td>0.133722</td>\n",
       "      <td>0.113409</td>\n",
       "      <td>0.308442</td>\n",
       "      <td>0.076498</td>\n",
       "      <td>0.235020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122d198cf11ab32d2346bff455d6702f1ea519df957cea...</td>\n",
       "      <td>0.043113</td>\n",
       "      <td>0.087514</td>\n",
       "      <td>0.166806</td>\n",
       "      <td>0.285267</td>\n",
       "      <td>0.250409</td>\n",
       "      <td>0.102468</td>\n",
       "      <td>0.040453</td>\n",
       "      <td>0.018041</td>\n",
       "      <td>0.003503</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.065313</td>\n",
       "      <td>0.102145</td>\n",
       "      <td>0.285267</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.023644</td>\n",
       "      <td>0.150721</td>\n",
       "      <td>0.127077</td>\n",
       "      <td>0.282840</td>\n",
       "      <td>0.079415</td>\n",
       "      <td>0.205852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2809fd6afd6d3cae4dd4ad93a7f905a0db32292f4df4b3...</td>\n",
       "      <td>0.008812</td>\n",
       "      <td>0.028493</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.216309</td>\n",
       "      <td>0.346634</td>\n",
       "      <td>0.198702</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.030494</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.053482</td>\n",
       "      <td>0.115737</td>\n",
       "      <td>0.346634</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>0.014520</td>\n",
       "      <td>0.168939</td>\n",
       "      <td>0.154419</td>\n",
       "      <td>0.342059</td>\n",
       "      <td>0.072596</td>\n",
       "      <td>0.274038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5ef4a19afe4ad593464931734ff43c1112cf94c6bdb459...</td>\n",
       "      <td>0.024279</td>\n",
       "      <td>0.057675</td>\n",
       "      <td>0.134440</td>\n",
       "      <td>0.278446</td>\n",
       "      <td>0.297135</td>\n",
       "      <td>0.131150</td>\n",
       "      <td>0.048500</td>\n",
       "      <td>0.020092</td>\n",
       "      <td>0.005347</td>\n",
       "      <td>0.002937</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.053087</td>\n",
       "      <td>0.109445</td>\n",
       "      <td>0.297135</td>\n",
       "      <td>0.002937</td>\n",
       "      <td>0.021139</td>\n",
       "      <td>0.133618</td>\n",
       "      <td>0.112479</td>\n",
       "      <td>0.294199</td>\n",
       "      <td>0.078096</td>\n",
       "      <td>0.219039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c37787b5cc6c3052130c6f390aa5b57462b558a204d5c4...</td>\n",
       "      <td>0.029881</td>\n",
       "      <td>0.065804</td>\n",
       "      <td>0.139384</td>\n",
       "      <td>0.271839</td>\n",
       "      <td>0.283863</td>\n",
       "      <td>0.128138</td>\n",
       "      <td>0.049918</td>\n",
       "      <td>0.021757</td>\n",
       "      <td>0.005840</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.057861</td>\n",
       "      <td>0.104646</td>\n",
       "      <td>0.283863</td>\n",
       "      <td>0.003576</td>\n",
       "      <td>0.023788</td>\n",
       "      <td>0.136572</td>\n",
       "      <td>0.112784</td>\n",
       "      <td>0.280286</td>\n",
       "      <td>0.079571</td>\n",
       "      <td>0.204292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0f8ae17e177ed82363ed3dba7d277ed6227ac0c935cb52...</td>\n",
       "      <td>0.033061</td>\n",
       "      <td>0.072587</td>\n",
       "      <td>0.152462</td>\n",
       "      <td>0.285778</td>\n",
       "      <td>0.275863</td>\n",
       "      <td>0.113779</td>\n",
       "      <td>0.042014</td>\n",
       "      <td>0.017784</td>\n",
       "      <td>0.004123</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.057300</td>\n",
       "      <td>0.106680</td>\n",
       "      <td>0.285778</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.021603</td>\n",
       "      <td>0.142791</td>\n",
       "      <td>0.121188</td>\n",
       "      <td>0.283227</td>\n",
       "      <td>0.079358</td>\n",
       "      <td>0.206420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ba126be25858022d3cddf07d27288f9d35c495458ec49a...</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.031624</td>\n",
       "      <td>0.086884</td>\n",
       "      <td>0.235003</td>\n",
       "      <td>0.352156</td>\n",
       "      <td>0.183095</td>\n",
       "      <td>0.065776</td>\n",
       "      <td>0.025425</td>\n",
       "      <td>0.007272</td>\n",
       "      <td>0.003472</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.048700</td>\n",
       "      <td>0.118527</td>\n",
       "      <td>0.352156</td>\n",
       "      <td>0.003472</td>\n",
       "      <td>0.013326</td>\n",
       "      <td>0.159043</td>\n",
       "      <td>0.145717</td>\n",
       "      <td>0.348684</td>\n",
       "      <td>0.071983</td>\n",
       "      <td>0.280173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4cc05cb70bcdde73e34718020f2ef4c69063af4098602b...</td>\n",
       "      <td>0.009584</td>\n",
       "      <td>0.027701</td>\n",
       "      <td>0.072714</td>\n",
       "      <td>0.191522</td>\n",
       "      <td>0.320179</td>\n",
       "      <td>0.214804</td>\n",
       "      <td>0.096026</td>\n",
       "      <td>0.042491</td>\n",
       "      <td>0.016647</td>\n",
       "      <td>0.008331</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.057603</td>\n",
       "      <td>0.106904</td>\n",
       "      <td>0.320179</td>\n",
       "      <td>0.008331</td>\n",
       "      <td>0.019410</td>\n",
       "      <td>0.167648</td>\n",
       "      <td>0.148238</td>\n",
       "      <td>0.311848</td>\n",
       "      <td>0.075536</td>\n",
       "      <td>0.244644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28519dc39d1da01b36c544d31154f251cd6774f55599b7...</td>\n",
       "      <td>0.012794</td>\n",
       "      <td>0.037735</td>\n",
       "      <td>0.104254</td>\n",
       "      <td>0.261892</td>\n",
       "      <td>0.335709</td>\n",
       "      <td>0.157786</td>\n",
       "      <td>0.057503</td>\n",
       "      <td>0.022838</td>\n",
       "      <td>0.006433</td>\n",
       "      <td>0.003054</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.116676</td>\n",
       "      <td>0.335709</td>\n",
       "      <td>0.003054</td>\n",
       "      <td>0.015305</td>\n",
       "      <td>0.144403</td>\n",
       "      <td>0.129098</td>\n",
       "      <td>0.332655</td>\n",
       "      <td>0.073810</td>\n",
       "      <td>0.261899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6511abdb5be3ea579bc0f03c899542818231e39d9d1295...</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.066193</td>\n",
       "      <td>0.138753</td>\n",
       "      <td>0.266936</td>\n",
       "      <td>0.281225</td>\n",
       "      <td>0.129927</td>\n",
       "      <td>0.051812</td>\n",
       "      <td>0.023241</td>\n",
       "      <td>0.006528</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.059003</td>\n",
       "      <td>0.102807</td>\n",
       "      <td>0.281225</td>\n",
       "      <td>0.004029</td>\n",
       "      <td>0.025269</td>\n",
       "      <td>0.136547</td>\n",
       "      <td>0.111277</td>\n",
       "      <td>0.277196</td>\n",
       "      <td>0.079864</td>\n",
       "      <td>0.201362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  raw_pred_0  raw_pred_1  \\\n",
       "0  856e74b8c46edcf0c0e23444eab019bfda63687bb70a34...    0.020218    0.051236   \n",
       "1  122d198cf11ab32d2346bff455d6702f1ea519df957cea...    0.043113    0.087514   \n",
       "2  2809fd6afd6d3cae4dd4ad93a7f905a0db32292f4df4b3...    0.008812    0.028493   \n",
       "3  5ef4a19afe4ad593464931734ff43c1112cf94c6bdb459...    0.024279    0.057675   \n",
       "4  c37787b5cc6c3052130c6f390aa5b57462b558a204d5c4...    0.029881    0.065804   \n",
       "5  0f8ae17e177ed82363ed3dba7d277ed6227ac0c935cb52...    0.033061    0.072587   \n",
       "6  ba126be25858022d3cddf07d27288f9d35c495458ec49a...    0.009292    0.031624   \n",
       "7  4cc05cb70bcdde73e34718020f2ef4c69063af4098602b...    0.009584    0.027701   \n",
       "8  28519dc39d1da01b36c544d31154f251cd6774f55599b7...    0.012794    0.037735   \n",
       "9  6511abdb5be3ea579bc0f03c899542818231e39d9d1295...    0.031354    0.066193   \n",
       "\n",
       "   raw_pred_2  raw_pred_3  raw_pred_4  raw_pred_5  raw_pred_6  raw_pred_7  \\\n",
       "0    0.125120    0.276113    0.311518    0.136590    0.049955    0.020599   \n",
       "1    0.166806    0.285267    0.250409    0.102468    0.040453    0.018041   \n",
       "2    0.079647    0.216309    0.346634    0.198702    0.076471    0.030494   \n",
       "3    0.134440    0.278446    0.297135    0.131150    0.048500    0.020092   \n",
       "4    0.139384    0.271839    0.283863    0.128138    0.049918    0.021757   \n",
       "5    0.152462    0.285778    0.275863    0.113779    0.042014    0.017784   \n",
       "6    0.086884    0.235003    0.352156    0.183095    0.065776    0.025425   \n",
       "7    0.072714    0.191522    0.320179    0.214804    0.096026    0.042491   \n",
       "8    0.104254    0.261892    0.335709    0.157786    0.057503    0.022838   \n",
       "9    0.138753    0.266936    0.281225    0.129927    0.051812    0.023241   \n",
       "\n",
       "   raw_pred_8  raw_pred_9  mean       med       std       max       min  \\\n",
       "0    0.005577    0.003076   0.1  0.050595  0.112342  0.311518  0.003076   \n",
       "1    0.003503    0.002427   0.1  0.065313  0.102145  0.285267  0.002427   \n",
       "2    0.009862    0.004576   0.1  0.053482  0.115737  0.346634  0.004576   \n",
       "3    0.005347    0.002937   0.1  0.053087  0.109445  0.297135  0.002937   \n",
       "4    0.005840    0.003576   0.1  0.057861  0.104646  0.283863  0.003576   \n",
       "5    0.004123    0.002551   0.1  0.057300  0.106680  0.285778  0.002551   \n",
       "6    0.007272    0.003472   0.1  0.048700  0.118527  0.352156  0.003472   \n",
       "7    0.016647    0.008331   0.1  0.057603  0.106904  0.320179  0.008331   \n",
       "8    0.006433    0.003054   0.1  0.047619  0.116676  0.335709  0.003054   \n",
       "9    0.006528    0.004029   0.1  0.059003  0.102807  0.281225  0.004029   \n",
       "\n",
       "   1_quartile  3_quartile  13_quartile_diff  max_min_diff  non_max_mean  \\\n",
       "0    0.020314    0.133722          0.113409      0.308442      0.076498   \n",
       "1    0.023644    0.150721          0.127077      0.282840      0.079415   \n",
       "2    0.014520    0.168939          0.154419      0.342059      0.072596   \n",
       "3    0.021139    0.133618          0.112479      0.294199      0.078096   \n",
       "4    0.023788    0.136572          0.112784      0.280286      0.079571   \n",
       "5    0.021603    0.142791          0.121188      0.283227      0.079358   \n",
       "6    0.013326    0.159043          0.145717      0.348684      0.071983   \n",
       "7    0.019410    0.167648          0.148238      0.311848      0.075536   \n",
       "8    0.015305    0.144403          0.129098      0.332655      0.073810   \n",
       "9    0.025269    0.136547          0.111277      0.277196      0.079864   \n",
       "\n",
       "   max_non_max_mean_diff  \n",
       "0               0.235020  \n",
       "1               0.205852  \n",
       "2               0.274038  \n",
       "3               0.219039  \n",
       "4               0.204292  \n",
       "5               0.206420  \n",
       "6               0.280173  \n",
       "7               0.244644  \n",
       "8               0.261899  \n",
       "9               0.201362  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns=100\n",
    "res.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>image</th>\n",
       "      <th>mean</th>\n",
       "      <th>med</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>1_quartile</th>\n",
       "      <th>3_quartile</th>\n",
       "      <th>13_quartile_diff</th>\n",
       "      <th>max_min_diff</th>\n",
       "      <th>non_max_mean</th>\n",
       "      <th>max_non_max_mean_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856e74b8c46edcf0c0e23444eab019bfda63687bb70a34...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.050595</td>\n",
       "      <td>0.112342</td>\n",
       "      <td>0.311518</td>\n",
       "      <td>0.003076</td>\n",
       "      <td>0.020314</td>\n",
       "      <td>0.133722</td>\n",
       "      <td>0.113409</td>\n",
       "      <td>0.308442</td>\n",
       "      <td>0.076498</td>\n",
       "      <td>0.235020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122d198cf11ab32d2346bff455d6702f1ea519df957cea...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.065313</td>\n",
       "      <td>0.102145</td>\n",
       "      <td>0.285267</td>\n",
       "      <td>0.002427</td>\n",
       "      <td>0.023644</td>\n",
       "      <td>0.150721</td>\n",
       "      <td>0.127077</td>\n",
       "      <td>0.282840</td>\n",
       "      <td>0.079415</td>\n",
       "      <td>0.205852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2809fd6afd6d3cae4dd4ad93a7f905a0db32292f4df4b3...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.053482</td>\n",
       "      <td>0.115737</td>\n",
       "      <td>0.346634</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>0.014520</td>\n",
       "      <td>0.168939</td>\n",
       "      <td>0.154419</td>\n",
       "      <td>0.342059</td>\n",
       "      <td>0.072596</td>\n",
       "      <td>0.274038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image  mean       med  \\\n",
       "0  856e74b8c46edcf0c0e23444eab019bfda63687bb70a34...   0.1  0.050595   \n",
       "1  122d198cf11ab32d2346bff455d6702f1ea519df957cea...   0.1  0.065313   \n",
       "2  2809fd6afd6d3cae4dd4ad93a7f905a0db32292f4df4b3...   0.1  0.053482   \n",
       "\n",
       "        std       max       min  1_quartile  3_quartile  13_quartile_diff  \\\n",
       "0  0.112342  0.311518  0.003076    0.020314    0.133722          0.113409   \n",
       "1  0.102145  0.285267  0.002427    0.023644    0.150721          0.127077   \n",
       "2  0.115737  0.346634  0.004576    0.014520    0.168939          0.154419   \n",
       "\n",
       "   max_min_diff  non_max_mean  max_non_max_mean_diff  \n",
       "0      0.308442      0.076498               0.235020  \n",
       "1      0.282840      0.079415               0.205852  \n",
       "2      0.342059      0.072596               0.274038  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = res.drop(raw_feature_cols, axis=1)\n",
    "res.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "null percentage: 7.48877229577285\n",
      "image in both df and res\n",
      " True     1390827\n",
      "False     112597\n",
      "Name: image, dtype: int64\n",
      "                                                     image  mean       med  \\\n",
      "1503404  2be1b7fddec6a779e5770a39d3a8322a750e4874d4f6ef...   0.1  0.068083   \n",
      "1503405  682fca314e99afb85b8577a6466da8da0bc94e8764f3ce...   0.1  0.057518   \n",
      "1503406  53e10cf76c59451ed9dbe2d313a0c178a7c0db8a01b96e...   0.1  0.053425   \n",
      "1503407  a49b727bb8d37f7a0b1a0f100dbd6ff69b2c914ef9dab6...   0.1  0.100741   \n",
      "1503408  5bdbe60d03ed200de9b27ff1816879c1abdd25300db0c4...   0.1  0.063268   \n",
      "1503409  8b2aba840ba311537a936630be8d3722649006cfefe468...   0.1  0.051062   \n",
      "1503410  cf6a421ff8ce70f0b0faae386b64d6607ecbc89b40cd74...   0.1  0.062044   \n",
      "1503411  4c60a49ca031a0d6f37ae9aee6167bdb0a23d9dbc67d9a...   0.1  0.051695   \n",
      "1503412                                                 -1  -1.0 -1.000000   \n",
      "1503413  7d04440d912c9efa92f61709648afd832c4739f2ccf524...   0.1  0.052766   \n",
      "1503414                                                 -1  -1.0 -1.000000   \n",
      "1503415  3ae04467454e1568362e69f091c3b98feb318568f535a9...   0.1  0.058757   \n",
      "1503416  b0c33fc45166111b88a191d269f66a20133aed810942a3...   0.1  0.068950   \n",
      "1503417  f9941e2ccad92809ef0be765df36446007e69a7a3df3b5...   0.1  0.061835   \n",
      "1503418  6b9979e53e930045faa64e508398733117184bfb04c2f0...   0.1  0.051958   \n",
      "1503419  416d3bbcbd7cacd0b3155f17ca7498bcabb47c9d4c7d18...   0.1  0.052821   \n",
      "1503420  27cac7439590927378bb115b1c1709df9ea6b1260bf455...   0.1  0.082219   \n",
      "1503421  58dc43987f7b42214e52924618cf9ce8014c6dbacb8239...   0.1  0.051353   \n",
      "1503422  b512662550d0f4663a3761463c40e430cad854f731efd1...   0.1  0.056428   \n",
      "1503423                                                 -1  -1.0 -1.000000   \n",
      "\n",
      "              std       max       min  1_quartile  3_quartile  \\\n",
      "1503404  0.104742  0.298520  0.001848    0.018909    0.159383   \n",
      "1503405  0.106656  0.289881  0.002339    0.021626    0.144121   \n",
      "1503406  0.113335  0.339911  0.006697    0.016384    0.168336   \n",
      "1503407  0.094926  0.251570  0.000765    0.010682    0.156507   \n",
      "1503408  0.107584  0.301523  0.001761    0.018889    0.152713   \n",
      "1503409  0.114362  0.337693  0.004414    0.015129    0.157739   \n",
      "1503410  0.097934  0.282331  0.006301    0.029974    0.142892   \n",
      "1503411  0.113378  0.302493  0.001674    0.017767    0.140885   \n",
      "1503412 -1.000000 -1.000000 -1.000000   -1.000000   -1.000000   \n",
      "1503413  0.111928  0.328838  0.004194    0.016478    0.155232   \n",
      "1503414 -1.000000 -1.000000 -1.000000   -1.000000   -1.000000   \n",
      "1503415  0.103489  0.283314  0.003818    0.024658    0.136262   \n",
      "1503416  0.101574  0.286608  0.002542    0.022432    0.152504   \n",
      "1503417  0.105735  0.295886  0.002159    0.021224    0.150669   \n",
      "1503418  0.110960  0.312115  0.003338    0.020310    0.138042   \n",
      "1503419  0.108660  0.299293  0.003544    0.017625    0.134158   \n",
      "1503420  0.101286  0.279591  0.000465    0.008688    0.157942   \n",
      "1503421  0.111716  0.314167  0.003388    0.019918    0.136534   \n",
      "1503422  0.106085  0.283544  0.003181    0.022982    0.137124   \n",
      "1503423 -1.000000 -1.000000 -1.000000   -1.000000   -1.000000   \n",
      "\n",
      "         13_quartile_diff  max_min_diff  non_max_mean  max_non_max_mean_diff  \n",
      "1503404          0.140474      0.296672      0.077942               0.220578  \n",
      "1503405          0.122495      0.287542      0.078902               0.210979  \n",
      "1503406          0.151952      0.333214      0.073343               0.266568  \n",
      "1503407          0.145825      0.250806      0.083159               0.168411  \n",
      "1503408          0.133824      0.299762      0.077609               0.223914  \n",
      "1503409          0.142611      0.333279      0.073590               0.264104  \n",
      "1503410          0.112918      0.276030      0.079741               0.202590  \n",
      "1503411          0.123118      0.300820      0.077501               0.224993  \n",
      "1503412         -1.000000     -1.000000     -1.000000              -1.000000  \n",
      "1503413          0.138754      0.324644      0.074574               0.254264  \n",
      "1503414         -1.000000     -1.000000     -1.000000              -1.000000  \n",
      "1503415          0.111604      0.279496      0.079632               0.203683  \n",
      "1503416          0.130072      0.284066      0.079266               0.207342  \n",
      "1503417          0.129445      0.293727      0.078235               0.217651  \n",
      "1503418          0.117732      0.308777      0.076432               0.235684  \n",
      "1503419          0.116532      0.295749      0.077856               0.221437  \n",
      "1503420          0.149254      0.279126      0.080045               0.199545  \n",
      "1503421          0.116616      0.310780      0.076204               0.237964  \n",
      "1503422          0.114142      0.280362      0.079606               0.203937  \n",
      "1503423         -1.000000     -1.000000     -1.000000              -1.000000  \n",
      "null percentage: 8.380372828152105\n",
      "image in both df and res\n",
      " True     465829\n",
      "False     42609\n",
      "Name: image, dtype: int64\n",
      "                                                    image  mean       med  \\\n",
      "508418  04dff7e0df23be9c8622eca2c54f7b91be8e014f975517...   0.1  0.061964   \n",
      "508419  373eedd4c940f529bfde7701f6396e664159040a2399de...   0.1  0.053050   \n",
      "508420  12b1b0d561172506f2d5d98cb2a3faefd4344670c68f09...   0.1  0.052421   \n",
      "508421  b42ac51f39b0463890fcde11166b2b6515ad770454d511...   0.1  0.053982   \n",
      "508422  0c24c514182d71940ea53e18c6d66f716e7d35b3468438...   0.1  0.051151   \n",
      "508423  ce764550bd36b1a9de6b8e17d2dbed690e904ef88a332e...   0.1  0.057704   \n",
      "508424  31ae264fe8548af40d2dc9edc82a735bbee8c1e8308ac1...   0.1  0.053227   \n",
      "508425  07d357272fad535b97a76c4db5e913f41fc0385906577e...   0.1  0.054591   \n",
      "508426                                                 -1  -1.0 -1.000000   \n",
      "508427  2302800b095fddbceec098179cf0ca8ea82974d0bc8cb0...   0.1  0.055820   \n",
      "508428  e99f991ebb930bde1a922e0013bc979ada6411e7ec6cf5...   0.1  0.066205   \n",
      "508429  4a9e994812d88a171c9f035252e029f13c7ff6a56e81d7...   0.1  0.062636   \n",
      "508430  8b1a35170f652c6829663f23ddd49a1f7227963bcf2470...   0.1  0.053231   \n",
      "508431  4425b90a296fb86adbbfd472b5d75e5e2a0e997a3f5e80...   0.1  0.047717   \n",
      "508432  b53cf37997e9996f8473f413cff975c7afc49603695d03...   0.1  0.060997   \n",
      "508433  d8415e65ba910227706b14181e85df1b0b445790bbb640...   0.1  0.068106   \n",
      "508434  c0b8fd605dd0261194a1ebab4f0674915c242a397b1e68...   0.1  0.072176   \n",
      "508435  26349b576628b7da187a8d161cb961a119769bda83e638...   0.1  0.053287   \n",
      "508436                                                 -1  -1.0 -1.000000   \n",
      "508437  9c1ecc23ab04d543903bfd6a880d949fc1691cd7190dbb...   0.1  0.074535   \n",
      "\n",
      "             std       max       min  1_quartile  3_quartile  \\\n",
      "508418  0.111942  0.316313  0.001077    0.015506    0.155030   \n",
      "508419  0.109042  0.316558  0.005683    0.019808    0.147085   \n",
      "508420  0.111602  0.294111  0.002213    0.019291    0.138129   \n",
      "508421  0.110973  0.298490  0.001963    0.019018    0.142853   \n",
      "508422  0.112326  0.295453  0.002278    0.019342    0.135106   \n",
      "508423  0.103381  0.304431  0.008584    0.022779    0.150456   \n",
      "508424  0.112363  0.335138  0.006593    0.016479    0.164446   \n",
      "508425  0.112162  0.311835  0.001529    0.018146    0.148365   \n",
      "508426 -1.000000 -1.000000 -1.000000   -1.000000   -1.000000   \n",
      "508427  0.106332  0.309146  0.004882    0.021445    0.150541   \n",
      "508428  0.105977  0.303889  0.001656    0.018124    0.161539   \n",
      "508429  0.100140  0.268116  0.004022    0.026205    0.140777   \n",
      "508430  0.110343  0.292748  0.002415    0.020300    0.138332   \n",
      "508431  0.116684  0.303157  0.001591    0.016570    0.135546   \n",
      "508432  0.109625  0.315413  0.001009    0.013753    0.171934   \n",
      "508433  0.105966  0.302267  0.000500    0.009437    0.166362   \n",
      "508434  0.100293  0.288811  0.001400    0.015750    0.181224   \n",
      "508435  0.107991  0.299154  0.004181    0.018127    0.137127   \n",
      "508436 -1.000000 -1.000000 -1.000000   -1.000000   -1.000000   \n",
      "508437  0.098692  0.280400  0.002096    0.018959    0.165733   \n",
      "\n",
      "        13_quartile_diff  max_min_diff  non_max_mean  max_non_max_mean_diff  \n",
      "508418          0.139523      0.315237      0.075965               0.240348  \n",
      "508419          0.127277      0.310874      0.075938               0.240620  \n",
      "508420          0.118839      0.291898      0.078432               0.215679  \n",
      "508421          0.123836      0.296526      0.077946               0.220544  \n",
      "508422          0.115764      0.293175      0.078283               0.217170  \n",
      "508423          0.127677      0.295848      0.077285               0.227146  \n",
      "508424          0.147967      0.328545      0.073874               0.261264  \n",
      "508425          0.130219      0.310306      0.076463               0.235372  \n",
      "508426         -1.000000     -1.000000     -1.000000              -1.000000  \n",
      "508427          0.129096      0.304265      0.076762               0.232385  \n",
      "508428          0.143414      0.302233      0.077346               0.226543  \n",
      "508429          0.114572      0.264094      0.081320               0.186796  \n",
      "508430          0.118032      0.290333      0.078584               0.214165  \n",
      "508431          0.118976      0.301566      0.077427               0.225730  \n",
      "508432          0.158180      0.314403      0.076065               0.239347  \n",
      "508433          0.156925      0.301767      0.077526               0.224741  \n",
      "508434          0.165474      0.287411      0.079021               0.209790  \n",
      "508435          0.119001      0.294973      0.077872               0.221283  \n",
      "508436         -1.000000     -1.000000     -1.000000              -1.000000  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "508437          0.146774      0.278304      0.079956               0.200444  \n"
     ]
    }
   ],
   "source": [
    "for file in ['train.csv', 'test.csv']:\n",
    "    if file == 'train.csv':\n",
    "        df = pd.read_csv(file, usecols=['image', 'activation_date'], parse_dates=['activation_date']) \\\n",
    "               .sort_values('activation_date') \\\n",
    "               .reset_index(drop=True) \\\n",
    "               .drop('activation_date', axis=1)\n",
    "    else:\n",
    "        df = pd.read_csv(file, usecols=['image'])\n",
    "        \n",
    "    print('null percentage:', df.isnull().sum().max() / df.shape[0] * 100.)\n",
    "    print('image in both df and res\\n', df.image.isin(res.image).value_counts())\n",
    "    df = df.merge(res, on='image', how='left').fillna(-1)\n",
    "    \n",
    "    print(df.tail(20))\n",
    "    df.to_csv(file.replace(\".csv\", \"_nasnet_nima_features.csv\"), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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