EnsemblePredictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "ensemble_file = np.load('output/resnet50-redemption/submissions_ensemble/probs.npy')\n",
    "ensemble_file.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_to_category_id =\\\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "test_by_product = pd.read_csv('input/split/test_by_product_id.csv')\n",
    "total = 0\n",
    "predictions = {}\n",
    "cur_idx = 0\n",
    "i = 0\n",
    "while i < 9285240:\n",
    "    row  = test_by_product.iloc[cur_idx]\n",
    "    num_imgs = row['num_imgs']\n",
    "    _id = row['_id']\n",
    "    for j in range(i, i+num_imgs*3):\n",
    "        if j >= 9285240:\n",
    "            break\n",
    "        if _id not in predictions:\n",
    "            predictions[_id] = ensemble_file[j].astype(np.uint16)\n",
    "        else:\n",
    "            predictions[_id] += ensemble_file[j].astype(np.uint16)\n",
    "    i+=num_imgs*3\n",
    "    cur_idx+=1\n",
    "classes_dict = pickle.load(open('src/classes_dict.pkl', 'rb'))\n",
    "for prediction in predictions:\n",
    "    predictions[prediction] = classes_dict[np.argmax(predictions[prediction])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_ls = [ { \"_id\": str(k), \"category_id\": predictions[k]} for k in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#prediction_df = pd.DataFrame(predictions,index=range(0,len(predictions)))\n",
    "#predictions = { int(k): predictions[k] for k in predictions}\n",
    "predictions_df = pd.DataFrame.from_dict(predictions_ls)\n",
    "#pd.DataFrame(predictions.values(), columns=[\"category_id\"], index=range(0,len(predictions)))\n",
    "sample_df = pd.read_csv('input/data/sample_submission.csv')\n",
    "#predictions_df[\"_id\"] = sample_df[\"_id\"]\n",
    "#predictions_df = predictions_df.set_index([\"_id\"])\n",
    "#predictions_df.to_csv('./submit.csv')\n",
    "predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_df = predictions_df.set_index([\"_id\"])\n",
    "predictions_df.to_csv('./submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "ensemble_files = [\n",
    "    'output/inception-redemption/submissions/probs.npy',\n",
    "    'output/resnet50-redemption/submissions/probs.npy',\n",
    "    'output/inception-redemption/submissions/probs2.npy',\n",
    "    #'HCK/10-26/code/probs.npy'\n",
    "    ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "itta = np.load(ensemble_files[2]).astype(np.uint16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "itta_probs_arr = np.zeros((1768182, 5270), np.uint16)\n",
    "for i, prob in enumerate(itta):\n",
    "    itta_probs_arr[i//3] += prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def loadU(efile):\n",
    "    return (255*np.load(efile)).astype(np.uint8)\n",
    "#e1_arr = np.load(ensemble_files[0]) #loadU(ensemble_files[0])\n",
    "e2_arr = loadU(ensemble_files[1])\n",
    "#e1_arr.shape, e2_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1768182, 5270)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hck_arr = np.load(ensemble_files[2])\n",
    "#hck_arr = (255*hck_arr).astype(np.uint8)\n",
    "hck_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "converted_classes_dict[5055]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category_id\n",
       "0           40\n",
       "1           16\n",
       "2           16\n",
       "3          616\n",
       "4          410"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = 1768182\n",
    "import pandas as pd\n",
    "new_hck_arr = np.zeros((num, 5270), np.uint8)\n",
    "for idx in range(hck_arr.shape[1]):\n",
    "    converted_idx = converted_classes_dict[idx]\n",
    "    #print(idx, 'to', converted_idx)\n",
    "    new_hck_arr[:,[idx, converted_idx]] = hck_arr[:,[converted_idx, idx]]\n",
    "\n",
    "#rev_convert_dict = { converted_classes_dict[i] for i in converted_classes_dict }\n",
    "hck_predictions = np.argmax(new_hck_arr, axis=1)\n",
    "hck_predictions_df = pd.DataFrame(hck_predictions, columns=[\"category_id\"])\n",
    "#hck_predictions_df[\"category_id\"] = hck_predictions_df[\"category_id\"].apply(lambda x : converted_classes_dict[x])\n",
    "#for i in range(len(hck_predictions)):\n",
    "#    hck_predictions[i] = converted_classes_dict[hck_predictions[i]]\n",
    "#hck_predictions[0], hck_predictions[1], hck_predictions[2]\n",
    "hck_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#probs_arr0 = np.load(ensemble_files[0])#[:1768182]\n",
    "#probs_arr1 = np.load(ensemble_files[1])\n",
    "#probs_arr0 = (probs_arr0+probs_arr1)#/2\n",
    "#probs_arr0.shape\n",
    "\n",
    "#del probs_arr1\n",
    "#probs_arr0 = (probs_arr0+np.load(ensemble_files[2]))\n",
    "#np.save('HCK/10-26/code/probs.npy', probs_arr0)\n",
    "#probs_arr0.shape\n",
    "\n",
    "#probs_arr0 = probs_arr0/3\n",
    "#probs_arr0.shape\n",
    "\n",
    "#predictions = np.argmax(probs_arr0, axis=1)\n",
    "#predictions.shape\n",
    "#e1_predictions = np.argmax(e1_arr, axis=1)\n",
    "#e2_predictions = np.argmax(e2_arr, axis=1)\n",
    "predictions = np.argmax(e2_arr.astype(np.uint16)+new_hck_arr.astype(np.uint16), axis=1) #e1_arr+\n",
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "e1_predictions_df = pd.DataFrame(e1_predictions)\n",
    "e1_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "predictions_df = pd.DataFrame(predictions, columns=[\"category_id\"])\n",
    "\n",
    "e2_predictions = np.argmax(e2_arr, axis=1)\n",
    "e2_predictions_df = pd.DataFrame(e2_predictions)\n",
    "predictions_df.head(), hck_predictions_df.head(), e2_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "itta_predictions = np.argmax(itta_probs_arr, axis=1)\n",
    "itta_predictions_df = pd.DataFrame(itta_predictions, columns=[\"category_id\"])\n",
    "predictions_df = itta_predictions_df\n",
    "predictions_df[\"category_id\"] = predictions_df[\"category_id\"].apply(lambda x: classes_dict[x])\n",
    "sample_df = pd.read_csv('input/data/sample_submission.csv')\n",
    "predictions_df[\"_id\"] = sample_df[\"_id\"]\n",
    "predictions_df = predictions_df.set_index([\"_id\"])\n",
    "predictions_df.to_csv('./submit.csv')\n",
    "predictions_df.to_csv('ens_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 424: 1903,\n",
       " 425: 2838,\n",
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       " 428: 2155,\n",
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       " 993: 1889,\n",
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       " 996: 4751,\n",
       " 997: 4161,\n",
       " 998: 1573,\n",
       " 999: 3265,\n",
       " ...}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "def reorder_hck_labels_map(my_classes_dict, hck_classes_dict):\n",
    "    my_classes_reverse_lookup = { my_classes_dict[i] : i for i in my_classes_dict }\n",
    "    converted_classes_dict = {}\n",
    "    for label in hck_classes_dict:\n",
    "        converted_label = my_classes_reverse_lookup[hck_classes_dict[label]]\n",
    "        converted_classes_dict[label] = converted_label\n",
    "    return converted_classes_dict\n",
    "\n",
    "label_to_category_id =\\\n",
    "{0: 1000021794, 1: 1000012764, 2: 1000012776, 3: 1000012768, 4: 1000012755, 5: 1000012738, 6: 1000012732, 7: 1000013163, 8: 1000012712, 9: 1000012718, 10: 1000016200, 11: 1000016202, 12: 1000016204, 13: 1000016210, 14: 1000021524, 15: 1000016212, 16: 1000016220, 17: 1000016206, 18: 1000016214, 19: 1000016216, 20: 1000016222, 21: 1000016226, 22: 1000016228, 23: 1000016230, 24: 1000016232, 25: 1000016234, 26: 1000016236, 27: 1000016238, 28: 1000016240, 29: 1000016224, 30: 1000016242, 31: 1000016244, 32: 1000016248, 33: 1000016246, 34: 1000016250, 35: 1000016256, 36: 1000016258, 37: 1000016260, 38: 1000016262, 39: 1000016266, 40: 1000016252, 41: 1000016264, 42: 1000016268, 43: 1000016270, 44: 1000016272, 45: 1000016274, 46: 1000016288, 47: 1000016276, 48: 1000016278, 49: 1000016280, 50: 1000016282, 51: 1000016284, 52: 1000016286, 53: 1000016290, 54: 1000016296, 55: 1000016292, 56: 1000016294, 57: 1000016302, 58: 1000016306, 59: 1000016308, 60: 1000016298, 61: 1000016300, 62: 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1000011046, 5237: 1000017677, 5238: 1000017681, 5239: 1000011048, 5240: 1000003524, 5241: 1000003462, 5242: 1000003443, 5243: 1000003447, 5244: 1000003441, 5245: 1000003439, 5246: 1000003437, 5247: 1000003452, 5248: 1000003454, 5249: 1000003456, 5250: 1000003460, 5251: 1000003427, 5252: 1000003429, 5253: 1000022247, 5254: 1000003407, 5255: 1000003409, 5256: 1000012938, 5257: 1000012936, 5258: 1000003434, 5259: 1000003432, 5260: 1000003528, 5261: 1000003424, 5262: 1000003414, 5263: 1000003416, 5264: 1000003420, 5265: 1000003412, 5266: 1000003530, 5267: 1000003402, 5268: 1000003404, 5269: 1000003400}\n",
    "classes_dict = pickle.load(open('src/classes_dict.pkl', 'rb'))\n",
    "#hck_img_files = pickle.load(open('HCK/10-26/code/img_files', 'rb'))\n",
    "converted_classes_dict = reorder_hck_labels_map(classes_dict, label_to_category_id)\n",
    "converted_classes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hck_probs_arr = np.load(ensemble_files[2])\n",
    "hck_probs_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "cur_label = -1\n",
    "cur_probs = None\n",
    "cur_count = 0\n",
    "cur_idx = 0\n",
    "total_count = len(img_files)\n",
    "hck_reduced_probs_arr = np.zeros((1768182, 5270), np.float32)\n",
    "for i, (fpath, probs) in enumerate(tqdm(zip(img_files, hck_probs_arr))):\n",
    "    label = int(fpath.split('/')[-1].split('-')[0])\n",
    "    if cur_label == -1:\n",
    "        cur_label = label\n",
    "        cur_probs = probs\n",
    "        cur_label = label\n",
    "    elif cur_label == label:\n",
    "        cur_probs += probs\n",
    "        cur_count += 1\n",
    "    elif cur_label != label:\n",
    "        hck_reduced_probs_arr[cur_idx] = cur_probs/cur_count\n",
    "        cur_count = 1\n",
    "        cur_probs = probs\n",
    "        cur_label = label\n",
    "        cur_idx += 1\n",
    "        \n",
    "     if i == total_count-1:\n",
    "        hck_reduced_probs_arr[cur_idx] = cur_probs/cur_count\n",
    "        cur_idx += 1\n",
    "#assert(cur_idx == 1768182)\n",
    "hck_reduced_probs_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for idx in hck_probs_arr.shape[1]:\n",
    "    converted_idx = converted_classes_dict[idx]\n",
    "    print(idx, 'to', converted_idx)\n",
    "    hck_probs_arr[:,[idx, converted_idx]] = hck_probs_arr[:,[converted_idx, idx]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "predictions_dict = list(map(lambda x: classes_dict[x], predictions.tolist()))\n",
    "predictions_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "predictions_df = pd.DataFrame(predictions_dict, columns=[\"category_id\"])\n",
    "sample_df = pd.read_csv('input/data/sample_submission.csv')\n",
    "predictions_df[\"_id\"] = sample_df[\"_id\"]\n",
    "predictions_df = predictions_df.set_index([\"_id\"])\n",
    "predictions_df.to_csv('./submit.csv')\n",
    "#predictions_df.head()"
   ]
  }
 ],
 "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",
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