3.2 - Train Neural Net, two layers (16, 16).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c20a5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import adam_v2\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from utils import scoring_function\n",
    "\n",
    "pd.set_option('display.max_rows', 200)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "plt.rcParams['figure.figsize'] = (15, 8)\n",
    "plt.rc('axes', axisbelow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ace5eaa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(432587, 40) (144196, 40) (432587,) (144196,)\n"
     ]
    }
   ],
   "source": [
    "X_train = pd.read_feather('X_train_standard.f').set_index('sku')\n",
    "X_val = pd.read_feather('X_val_standard.f').set_index('sku')\n",
    "y_train = pd.read_feather('y_train.f').set_index('sku').iloc[:, 0]\n",
    "y_val = pd.read_feather('y_val.f').set_index('sku').iloc[:, 0]\n",
    "print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fd5b0ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-20 21:12:14.038684: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-09-20 21:12:14.420672: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "13519/13519 [==============================] - 26s 987us/step - loss: 3.3472 - val_loss: 3.1767\n",
      "Epoch 2/300\n",
      "13519/13519 [==============================] - 14s 1ms/step - loss: 3.1658 - val_loss: 3.1472\n",
      "Epoch 3/300\n",
      "13519/13519 [==============================] - 17s 1ms/step - loss: 3.1431 - val_loss: 3.1360\n",
      "Epoch 4/300\n",
      "13519/13519 [==============================] - 13s 973us/step - loss: 3.1354 - val_loss: 3.1316\n",
      "Epoch 5/300\n",
      "13519/13519 [==============================] - 12s 879us/step - loss: 3.1288 - val_loss: 3.1281\n",
      "Epoch 6/300\n",
      "13519/13519 [==============================] - 12s 891us/step - loss: 3.1273 - val_loss: 3.1237\n",
      "Epoch 7/300\n",
      "13519/13519 [==============================] - 12s 881us/step - loss: 3.1265 - val_loss: 3.1250\n",
      "Epoch 8/300\n",
      "13519/13519 [==============================] - 15s 1ms/step - loss: 3.1269 - val_loss: 3.1256\n",
      "Epoch 9/300\n",
      "13519/13519 [==============================] - 16s 1ms/step - loss: 3.1224 - val_loss: 3.1218\n",
      "Epoch 10/300\n",
      "13519/13519 [==============================] - 13s 995us/step - loss: 3.1220 - val_loss: 3.1237\n",
      "Epoch 11/300\n",
      "13519/13519 [==============================] - 11s 847us/step - loss: 3.1214 - val_loss: 3.1207\n",
      "Epoch 12/300\n",
      "13519/13519 [==============================] - 11s 845us/step - loss: 3.1210 - val_loss: 3.1222\n",
      "Epoch 13/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1202 - val_loss: 3.1198\n",
      "Epoch 14/300\n",
      "13519/13519 [==============================] - 11s 833us/step - loss: 3.1218 - val_loss: 3.1177\n",
      "Epoch 15/300\n",
      "13519/13519 [==============================] - 11s 833us/step - loss: 3.1184 - val_loss: 3.1181\n",
      "Epoch 16/300\n",
      "13519/13519 [==============================] - 11s 829us/step - loss: 3.1199 - val_loss: 3.1176\n",
      "Epoch 17/300\n",
      "13519/13519 [==============================] - 11s 841us/step - loss: 3.1202 - val_loss: 3.1203\n",
      "Epoch 18/300\n",
      "13519/13519 [==============================] - 11s 843us/step - loss: 3.1164 - val_loss: 3.1183\n",
      "Epoch 19/300\n",
      "13519/13519 [==============================] - 11s 837us/step - loss: 3.1154 - val_loss: 3.1210\n",
      "Epoch 20/300\n",
      "13519/13519 [==============================] - 11s 832us/step - loss: 3.1153 - val_loss: 3.1207\n",
      "Epoch 21/300\n",
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1170 - val_loss: 3.1165\n",
      "Epoch 22/300\n",
      "13519/13519 [==============================] - 12s 861us/step - loss: 3.1159 - val_loss: 3.1194\n",
      "Epoch 23/300\n",
      "13519/13519 [==============================] - 11s 847us/step - loss: 3.1171 - val_loss: 3.1149\n",
      "Epoch 24/300\n",
      "13519/13519 [==============================] - 11s 833us/step - loss: 3.1174 - val_loss: 3.1162\n",
      "Epoch 25/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1146 - val_loss: 3.1167\n",
      "Epoch 26/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1149 - val_loss: 3.1178\n",
      "Epoch 27/300\n",
      "13519/13519 [==============================] - 11s 828us/step - loss: 3.1161 - val_loss: 3.1165\n",
      "Epoch 28/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1161 - val_loss: 3.1147\n",
      "Epoch 29/300\n",
      "13519/13519 [==============================] - 11s 841us/step - loss: 3.1167 - val_loss: 3.1166\n",
      "Epoch 30/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1142 - val_loss: 3.1148\n",
      "Epoch 31/300\n",
      "13519/13519 [==============================] - 11s 830us/step - loss: 3.1144 - val_loss: 3.1188\n",
      "Epoch 32/300\n",
      "13519/13519 [==============================] - 11s 833us/step - loss: 3.1143 - val_loss: 3.1140\n",
      "Epoch 33/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1152 - val_loss: 3.1155\n",
      "Epoch 34/300\n",
      "13519/13519 [==============================] - 11s 843us/step - loss: 3.1138 - val_loss: 3.1142\n",
      "Epoch 35/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1163 - val_loss: 3.1174\n",
      "Epoch 36/300\n",
      "13519/13519 [==============================] - 11s 832us/step - loss: 3.1127 - val_loss: 3.1169\n",
      "Epoch 37/300\n",
      "13519/13519 [==============================] - 11s 847us/step - loss: 3.1120 - val_loss: 3.1133\n",
      "Epoch 38/300\n",
      "13519/13519 [==============================] - 11s 830us/step - loss: 3.1134 - val_loss: 3.1175\n",
      "Epoch 39/300\n",
      "13519/13519 [==============================] - 11s 832us/step - loss: 3.1141 - val_loss: 3.1155\n",
      "Epoch 40/300\n",
      "13519/13519 [==============================] - 11s 833us/step - loss: 3.1135 - val_loss: 3.1129\n",
      "Epoch 41/300\n",
      "13519/13519 [==============================] - 11s 828us/step - loss: 3.1137 - val_loss: 3.1160\n",
      "Epoch 42/300\n",
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1149 - val_loss: 3.1137\n",
      "Epoch 43/300\n",
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1150 - val_loss: 3.1137\n",
      "Epoch 44/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1119 - val_loss: 3.1152\n",
      "Epoch 45/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1110 - val_loss: 3.1162\n",
      "Epoch 46/300\n",
      "13519/13519 [==============================] - 11s 842us/step - loss: 3.1129 - val_loss: 3.1120\n",
      "Epoch 47/300\n",
      "13519/13519 [==============================] - 11s 837us/step - loss: 3.1123 - val_loss: 3.1131\n",
      "Epoch 48/300\n",
      "13519/13519 [==============================] - 12s 853us/step - loss: 3.1120 - val_loss: 3.1123\n",
      "Epoch 49/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1126 - val_loss: 3.1162\n",
      "Epoch 50/300\n",
      "13519/13519 [==============================] - 11s 827us/step - loss: 3.1130 - val_loss: 3.1136\n",
      "Epoch 51/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1116 - val_loss: 3.1129\n",
      "Epoch 52/300\n",
      "13519/13519 [==============================] - 11s 839us/step - loss: 3.1123 - val_loss: 3.1130\n",
      "Epoch 53/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1116 - val_loss: 3.1141\n",
      "Epoch 54/300\n",
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1136 - val_loss: 3.1134\n",
      "Epoch 55/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1121 - val_loss: 3.1145\n",
      "Epoch 56/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1170 - val_loss: 3.1137\n",
      "Epoch 57/300\n",
      "13519/13519 [==============================] - 11s 832us/step - loss: 3.1115 - val_loss: 3.1129\n",
      "Epoch 58/300\n",
      "13519/13519 [==============================] - 11s 830us/step - loss: 3.1123 - val_loss: 3.1133\n",
      "Epoch 59/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1134 - val_loss: 3.1134\n",
      "Epoch 60/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1102 - val_loss: 3.1119\n",
      "Epoch 61/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1121 - val_loss: 3.1124\n",
      "Epoch 62/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1122 - val_loss: 3.1119\n",
      "Epoch 63/300\n",
      "13519/13519 [==============================] - 11s 827us/step - loss: 3.1134 - val_loss: 3.1139\n",
      "Epoch 64/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1113 - val_loss: 3.1138\n",
      "Epoch 65/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1143 - val_loss: 3.1122\n",
      "Epoch 66/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1106 - val_loss: 3.1124\n",
      "Epoch 67/300\n",
      "13519/13519 [==============================] - 11s 827us/step - loss: 3.1118 - val_loss: 3.1135\n",
      "Epoch 68/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1111 - val_loss: 3.1117\n",
      "Epoch 69/300\n",
      "13519/13519 [==============================] - 11s 831us/step - loss: 3.1109 - val_loss: 3.1140\n",
      "Epoch 70/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1120 - val_loss: 3.1116\n",
      "Epoch 71/300\n",
      "13519/13519 [==============================] - 11s 832us/step - loss: 3.1122 - val_loss: 3.1122\n",
      "Epoch 72/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1144 - val_loss: 3.1121\n",
      "Epoch 73/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1126 - val_loss: 3.1120\n",
      "Epoch 74/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1111 - val_loss: 3.1125\n",
      "Epoch 75/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1113 - val_loss: 3.1119\n",
      "Epoch 76/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1118 - val_loss: 3.1114\n",
      "Epoch 77/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1108 - val_loss: 3.1124\n",
      "Epoch 78/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1124 - val_loss: 3.1134\n",
      "Epoch 79/300\n",
      "13519/13519 [==============================] - 11s 834us/step - loss: 3.1116 - val_loss: 3.1125\n",
      "Epoch 80/300\n",
      "13519/13519 [==============================] - 11s 828us/step - loss: 3.1094 - val_loss: 3.1135\n",
      "Epoch 81/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1110 - val_loss: 3.1112\n",
      "Epoch 82/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1115 - val_loss: 3.1132\n",
      "Epoch 83/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1107 - val_loss: 3.1112\n",
      "Epoch 84/300\n",
      "13519/13519 [==============================] - 11s 823us/step - loss: 3.1105 - val_loss: 3.1118\n",
      "Epoch 85/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1116 - val_loss: 3.1121\n",
      "Epoch 86/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1099 - val_loss: 3.1138\n",
      "Epoch 87/300\n",
      "13519/13519 [==============================] - 11s 827us/step - loss: 3.1136 - val_loss: 3.1117\n",
      "Epoch 88/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1110 - val_loss: 3.1146\n",
      "Epoch 89/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1096 - val_loss: 3.1138\n",
      "Epoch 90/300\n",
      "13519/13519 [==============================] - 12s 855us/step - loss: 3.1116 - val_loss: 3.1098\n",
      "Epoch 91/300\n",
      "13519/13519 [==============================] - 11s 823us/step - loss: 3.1106 - val_loss: 3.1112\n",
      "Epoch 92/300\n",
      "13519/13519 [==============================] - 11s 830us/step - loss: 3.1105 - val_loss: 3.1133\n",
      "Epoch 93/300\n",
      "13519/13519 [==============================] - 11s 827us/step - loss: 3.1130 - val_loss: 3.1111\n",
      "Epoch 94/300\n",
      "13519/13519 [==============================] - 11s 835us/step - loss: 3.1120 - val_loss: 3.1104\n",
      "Epoch 95/300\n",
      "13519/13519 [==============================] - 11s 828us/step - loss: 3.1121 - val_loss: 3.1130\n",
      "Epoch 96/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1114 - val_loss: 3.1106\n",
      "Epoch 97/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1105 - val_loss: 3.1121\n",
      "Epoch 98/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1106 - val_loss: 3.1127\n",
      "Epoch 99/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1100 - val_loss: 3.1107\n",
      "Epoch 100/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1104 - val_loss: 3.1143\n",
      "Epoch 101/300\n",
      "13519/13519 [==============================] - 11s 836us/step - loss: 3.1089 - val_loss: 3.1117\n",
      "Epoch 102/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1091 - val_loss: 3.1114\n",
      "Epoch 103/300\n",
      "13519/13519 [==============================] - 11s 826us/step - loss: 3.1095 - val_loss: 3.1103\n",
      "Epoch 104/300\n",
      "13519/13519 [==============================] - 11s 837us/step - loss: 3.1100 - val_loss: 3.1163\n",
      "Epoch 105/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1101 - val_loss: 3.1147\n",
      "Epoch 106/300\n",
      "13519/13519 [==============================] - 11s 837us/step - loss: 3.1107 - val_loss: 3.1122\n",
      "Epoch 107/300\n",
      "13519/13519 [==============================] - 11s 825us/step - loss: 3.1119 - val_loss: 3.1109\n",
      "Epoch 108/300\n",
      "13519/13519 [==============================] - 11s 824us/step - loss: 3.1094 - val_loss: 3.1140\n",
      "Epoch 109/300\n",
      "13519/13519 [==============================] - 11s 836us/step - loss: 3.1097 - val_loss: 3.1121\n",
      "Epoch 110/300\n",
      "13519/13519 [==============================] - 11s 822us/step - loss: 3.1107 - val_loss: 3.1129\n",
      "Epoch 00110: early stopping\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(10)\n",
    "tf.random.set_seed(20)\n",
    "random.seed(30)    \n",
    "\n",
    "epochs = 300\n",
    "\n",
    "# very simple NN\n",
    "model = Sequential()\n",
    "model.add(Dense(16, input_dim=X_train.shape[1], activation='relu'))\n",
    "model.add(Dense(16, input_dim=X_train.shape[1], activation='relu'))\n",
    "model.add(Dense(30, activation='softmax'))\n",
    "\n",
    "adam = adam_v2.Adam(learning_rate=0.0005) # default: 0.001\n",
    "model.compile(loss='categorical_crossentropy', optimizer=adam)\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', patience=20, verbose=1)\n",
    "h = model.fit(\n",
    "    X_train,\n",
    "    to_categorical(y_train, num_classes=30),\n",
    "    validation_data=(X_val, to_categorical(y_val, num_classes=30)),\n",
    "    epochs=epochs,\n",
    "    batch_size=32,\n",
    "    callbacks=[es]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19637ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.history['loss'])\n",
    "plt.plot(h.history['val_loss'])\n",
    "plt.legend(['Train', 'Val'])\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1fb84206",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90 3.1098172664642334\n"
     ]
    }
   ],
   "source": [
    "print(np.argmin(h.history['val_loss']) + 1, min(h.history['val_loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dbe35ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log loss (train): 3.1101946759436045\n",
      "Log loss (val): 3.1129438746426032\n",
      "RPS (train): 3.6575219208069845\n",
      "RPS (val): 3.671092105352449\n",
      "CPU times: user 9.26 s, sys: 1.76 s, total: 11 s\n",
      "Wall time: 8.23 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# predictions\n",
    "probs_train = model.predict(X_train)\n",
    "probs_val = model.predict(X_val)\n",
    "\n",
    "log_train = log_loss(y_train, probs_train)\n",
    "log_val = log_loss(y_val, probs_val)\n",
    "print('Log loss (train):', log_train)\n",
    "print('Log loss (val):', log_val)\n",
    "\n",
    "rps_train = scoring_function(y_train + 1, probs_train)\n",
    "rps_val = scoring_function(y_val + 1, probs_val)\n",
    "print('RPS (train):', rps_train)\n",
    "print('RPS (val):', rps_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc1515ce",
   "metadata": {},
   "source": [
    "### Save predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed83b169",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(probs_val).to_csv('3.2-probs-nn-two-layers-16-16.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caef6890-3f00-4263-8b76-f4f4eeb2fe38",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b62d23ba",
   "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>sold_quantity_17</th>\n",
       "      <th>sold_quantity_18</th>\n",
       "      <th>sold_quantity_19</th>\n",
       "      <th>sold_quantity_20</th>\n",
       "      <th>sold_quantity_21</th>\n",
       "      <th>sold_quantity_25</th>\n",
       "      <th>sold_quantity_26</th>\n",
       "      <th>sold_quantity_27</th>\n",
       "      <th>sold_quantity_28</th>\n",
       "      <th>sold_quantity_29</th>\n",
       "      <th>sold_quantity_30</th>\n",
       "      <th>minutes_active_29</th>\n",
       "      <th>minutes_active_30</th>\n",
       "      <th>roll_mean_sold_30_11</th>\n",
       "      <th>roll_mean_sold_29_10</th>\n",
       "      <th>roll_mean_sold_28_9</th>\n",
       "      <th>roll_mean_sold_27_8</th>\n",
       "      <th>roll_mean_sold_26_7</th>\n",
       "      <th>roll_mean_sold_25_6</th>\n",
       "      <th>roll_mean_sold_24_5</th>\n",
       "      <th>roll_mean_minutes_30_11</th>\n",
       "      <th>roll_mean_minutes_29_10</th>\n",
       "      <th>roll_mean_minutes_26_7</th>\n",
       "      <th>roll_mean_minutes_25_6</th>\n",
       "      <th>roll_mean_minutes_24_5</th>\n",
       "      <th>roll_mean_price_30_11</th>\n",
       "      <th>roll_mean_price_29_10</th>\n",
       "      <th>roll_mean_price_27_8</th>\n",
       "      <th>roll_mean_price_25_6</th>\n",
       "      <th>roll_mean_price_24_5</th>\n",
       "      <th>roll_sum_zero_sold_30_11</th>\n",
       "      <th>roll_sum_zero_sold_24_5</th>\n",
       "      <th>roll_sum_shipping_logistic_type_fulfillment_30_11</th>\n",
       "      <th>roll_sum_shipping_logistic_type_cross_docking_30_11</th>\n",
       "      <th>roll_sum_shipping_logistic_type_fulfillment_28_9</th>\n",
       "      <th>roll_sum_shipping_logistic_type_fulfillment_26_7</th>\n",
       "      <th>roll_sum_shipping_logistic_type_fulfillment_25_6</th>\n",
       "      <th>roll_sum_shipping_logistic_type_fulfillment_24_5</th>\n",
       "      <th>roll_sum_shipping_payment_free_shipping_28_9</th>\n",
       "      <th>target_stock</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sku</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>464801</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>0.097635</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>0.530162</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>-0.095625</td>\n",
       "      <td>-0.094022</td>\n",
       "      <td>-0.087551</td>\n",
       "      <td>-0.087259</td>\n",
       "      <td>-0.087144</td>\n",
       "      <td>-0.087088</td>\n",
       "      <td>-0.086720</td>\n",
       "      <td>0.737185</td>\n",
       "      <td>0.744720</td>\n",
       "      <td>0.763959</td>\n",
       "      <td>0.769021</td>\n",
       "      <td>0.774894</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>-0.004176</td>\n",
       "      <td>-0.00350</td>\n",
       "      <td>-0.003556</td>\n",
       "      <td>0.527432</td>\n",
       "      <td>0.314442</td>\n",
       "      <td>0.479567</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.50752</td>\n",
       "      <td>0.514066</td>\n",
       "      <td>0.520863</td>\n",
       "      <td>1.490386</td>\n",
       "      <td>-0.124437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645793</th>\n",
       "      <td>-0.000423</td>\n",
       "      <td>0.074500</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>0.018155</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>0.530162</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>-0.077280</td>\n",
       "      <td>-0.075858</td>\n",
       "      <td>-0.069648</td>\n",
       "      <td>-0.063206</td>\n",
       "      <td>-0.062798</td>\n",
       "      <td>-0.062530</td>\n",
       "      <td>-0.062069</td>\n",
       "      <td>0.737185</td>\n",
       "      <td>0.744720</td>\n",
       "      <td>0.763959</td>\n",
       "      <td>0.769021</td>\n",
       "      <td>0.774894</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>-0.004176</td>\n",
       "      <td>-0.00350</td>\n",
       "      <td>-0.003556</td>\n",
       "      <td>-0.073491</td>\n",
       "      <td>-0.266020</td>\n",
       "      <td>0.479567</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.50752</td>\n",
       "      <td>0.514066</td>\n",
       "      <td>0.520863</td>\n",
       "      <td>1.490386</td>\n",
       "      <td>-0.116708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99516</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>0.064437</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>0.411154</td>\n",
       "      <td>0.427382</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.364686</td>\n",
       "      <td>0.530162</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>0.045019</td>\n",
       "      <td>0.027074</td>\n",
       "      <td>0.013903</td>\n",
       "      <td>0.002940</td>\n",
       "      <td>-0.008019</td>\n",
       "      <td>0.011142</td>\n",
       "      <td>0.005720</td>\n",
       "      <td>0.117517</td>\n",
       "      <td>0.128402</td>\n",
       "      <td>0.160092</td>\n",
       "      <td>0.169554</td>\n",
       "      <td>0.179085</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-0.474106</td>\n",
       "      <td>-0.652995</td>\n",
       "      <td>-0.792469</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>-0.744423</td>\n",
       "      <td>-0.69828</td>\n",
       "      <td>-0.676118</td>\n",
       "      <td>-0.654889</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.085792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538100</th>\n",
       "      <td>-0.000423</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.010009</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.114009</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.268610</td>\n",
       "      <td>0.530162</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>-0.016131</td>\n",
       "      <td>-0.021365</td>\n",
       "      <td>-0.039808</td>\n",
       "      <td>-0.045166</td>\n",
       "      <td>-0.050625</td>\n",
       "      <td>-0.044112</td>\n",
       "      <td>-0.049744</td>\n",
       "      <td>0.737185</td>\n",
       "      <td>0.744720</td>\n",
       "      <td>0.763959</td>\n",
       "      <td>0.769021</td>\n",
       "      <td>0.774894</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-0.874720</td>\n",
       "      <td>-0.459507</td>\n",
       "      <td>0.479567</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.50752</td>\n",
       "      <td>0.514066</td>\n",
       "      <td>0.520863</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.085792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557191</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>0.076460</td>\n",
       "      <td>0.530162</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>-0.107855</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.105404</td>\n",
       "      <td>-0.105506</td>\n",
       "      <td>-0.105208</td>\n",
       "      <td>-1.222632</td>\n",
       "      <td>-1.352601</td>\n",
       "      <td>-1.631688</td>\n",
       "      <td>-1.609169</td>\n",
       "      <td>-1.588786</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>0.928047</td>\n",
       "      <td>0.894904</td>\n",
       "      <td>0.479567</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.50752</td>\n",
       "      <td>0.514066</td>\n",
       "      <td>0.520863</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.070334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129187</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.502754</td>\n",
       "      <td>-0.120085</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.117577</td>\n",
       "      <td>-0.117785</td>\n",
       "      <td>-0.117533</td>\n",
       "      <td>-2.213161</td>\n",
       "      <td>-2.217213</td>\n",
       "      <td>-2.138135</td>\n",
       "      <td>-2.111925</td>\n",
       "      <td>-2.088475</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-2.677488</td>\n",
       "      <td>-2.781355</td>\n",
       "      <td>-2.700524</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>-2.602093</td>\n",
       "      <td>-2.50698</td>\n",
       "      <td>-2.461394</td>\n",
       "      <td>-2.418515</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.085792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6707</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.505581</td>\n",
       "      <td>-0.120085</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.117577</td>\n",
       "      <td>-0.117785</td>\n",
       "      <td>-0.117533</td>\n",
       "      <td>-2.213329</td>\n",
       "      <td>-2.217213</td>\n",
       "      <td>-2.138135</td>\n",
       "      <td>-2.111925</td>\n",
       "      <td>-2.088475</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-2.677488</td>\n",
       "      <td>-2.781355</td>\n",
       "      <td>-2.700524</td>\n",
       "      <td>-0.060230</td>\n",
       "      <td>-2.602093</td>\n",
       "      <td>-2.50698</td>\n",
       "      <td>-2.461394</td>\n",
       "      <td>-2.418515</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>0.084246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170355</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.967433</td>\n",
       "      <td>-0.120085</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.117577</td>\n",
       "      <td>-0.117785</td>\n",
       "      <td>-0.117533</td>\n",
       "      <td>-2.240817</td>\n",
       "      <td>-2.217213</td>\n",
       "      <td>-2.138135</td>\n",
       "      <td>-2.111925</td>\n",
       "      <td>-2.088475</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-2.677488</td>\n",
       "      <td>-2.781355</td>\n",
       "      <td>-2.700524</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>-2.602093</td>\n",
       "      <td>-2.50698</td>\n",
       "      <td>-2.461394</td>\n",
       "      <td>-2.418515</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.124437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246568</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.732601</td>\n",
       "      <td>-0.120085</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.117577</td>\n",
       "      <td>-0.117785</td>\n",
       "      <td>-0.117533</td>\n",
       "      <td>-2.226841</td>\n",
       "      <td>-2.217213</td>\n",
       "      <td>-2.138135</td>\n",
       "      <td>-2.111925</td>\n",
       "      <td>-2.088475</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-2.677488</td>\n",
       "      <td>-2.781355</td>\n",
       "      <td>-2.541519</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>-2.602093</td>\n",
       "      <td>-2.50698</td>\n",
       "      <td>-2.461394</td>\n",
       "      <td>-2.418515</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>-0.132166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49718</th>\n",
       "      <td>-0.096217</td>\n",
       "      <td>-0.090172</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.10158</td>\n",
       "      <td>-0.061326</td>\n",
       "      <td>-0.084454</td>\n",
       "      <td>-0.079735</td>\n",
       "      <td>-0.102219</td>\n",
       "      <td>-0.094906</td>\n",
       "      <td>-0.118098</td>\n",
       "      <td>-0.115690</td>\n",
       "      <td>-1.943069</td>\n",
       "      <td>-1.814924</td>\n",
       "      <td>-0.120085</td>\n",
       "      <td>-0.118242</td>\n",
       "      <td>-0.117391</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.117577</td>\n",
       "      <td>-0.117785</td>\n",
       "      <td>-0.117533</td>\n",
       "      <td>-2.231740</td>\n",
       "      <td>-2.217213</td>\n",
       "      <td>-2.138135</td>\n",
       "      <td>-2.111925</td>\n",
       "      <td>-2.088475</td>\n",
       "      <td>-0.004385</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>-0.004405</td>\n",
       "      <td>-0.00362</td>\n",
       "      <td>-0.003677</td>\n",
       "      <td>-2.677488</td>\n",
       "      <td>-2.781355</td>\n",
       "      <td>-2.700524</td>\n",
       "      <td>-0.297576</td>\n",
       "      <td>-2.602093</td>\n",
       "      <td>-2.50698</td>\n",
       "      <td>-2.461394</td>\n",
       "      <td>-2.418515</td>\n",
       "      <td>-0.686343</td>\n",
       "      <td>0.006956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551472 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sold_quantity_17  sold_quantity_18  sold_quantity_19  \\\n",
       "sku                                                            \n",
       "464801         -0.096217         -0.090172         -0.099056   \n",
       "645793         -0.000423          0.074500         -0.099056   \n",
       "99516          -0.096217         -0.090172         -0.099056   \n",
       "538100         -0.000423         -0.090172         -0.099056   \n",
       "557191         -0.096217         -0.090172         -0.099056   \n",
       "...                  ...               ...               ...   \n",
       "129187         -0.096217         -0.090172         -0.099056   \n",
       "6707           -0.096217         -0.090172         -0.099056   \n",
       "170355         -0.096217         -0.090172         -0.099056   \n",
       "246568         -0.096217         -0.090172         -0.099056   \n",
       "49718          -0.096217         -0.090172         -0.099056   \n",
       "\n",
       "        sold_quantity_20  sold_quantity_21  sold_quantity_25  \\\n",
       "sku                                                            \n",
       "464801          -0.10158          0.097635         -0.084454   \n",
       "645793          -0.10158          0.018155         -0.084454   \n",
       "99516           -0.10158         -0.061326          0.064437   \n",
       "538100          -0.10158         -0.061326         -0.010009   \n",
       "557191          -0.10158         -0.061326         -0.084454   \n",
       "...                  ...               ...               ...   \n",
       "129187          -0.10158         -0.061326         -0.084454   \n",
       "6707            -0.10158         -0.061326         -0.084454   \n",
       "170355          -0.10158         -0.061326         -0.084454   \n",
       "246568          -0.10158         -0.061326         -0.084454   \n",
       "49718           -0.10158         -0.061326         -0.084454   \n",
       "\n",
       "        sold_quantity_26  sold_quantity_27  sold_quantity_28  \\\n",
       "sku                                                            \n",
       "464801         -0.079735         -0.102219         -0.094906   \n",
       "645793         -0.079735         -0.102219          0.009552   \n",
       "99516          -0.079735          0.411154          0.427382   \n",
       "538100         -0.079735          0.000456          0.114009   \n",
       "557191         -0.079735         -0.102219         -0.094906   \n",
       "...                  ...               ...               ...   \n",
       "129187         -0.079735         -0.102219         -0.094906   \n",
       "6707           -0.079735         -0.102219         -0.094906   \n",
       "170355         -0.079735         -0.102219         -0.094906   \n",
       "246568         -0.079735         -0.102219         -0.094906   \n",
       "49718          -0.079735         -0.102219         -0.094906   \n",
       "\n",
       "        sold_quantity_29  sold_quantity_30  minutes_active_29  \\\n",
       "sku                                                             \n",
       "464801         -0.118098         -0.115690           0.530162   \n",
       "645793         -0.118098         -0.115690           0.530162   \n",
       "99516           0.277992          0.364686           0.530162   \n",
       "538100          0.277992          0.268610           0.530162   \n",
       "557191         -0.118098          0.076460           0.530162   \n",
       "...                  ...               ...                ...   \n",
       "129187         -0.118098         -0.115690          -1.943069   \n",
       "6707           -0.118098         -0.115690          -1.943069   \n",
       "170355         -0.118098         -0.115690          -1.943069   \n",
       "246568         -0.118098         -0.115690          -1.943069   \n",
       "49718          -0.118098         -0.115690          -1.943069   \n",
       "\n",
       "        minutes_active_30  roll_mean_sold_30_11  roll_mean_sold_29_10  \\\n",
       "sku                                                                     \n",
       "464801           0.533972             -0.095625             -0.094022   \n",
       "645793           0.533972             -0.077280             -0.075858   \n",
       "99516            0.533972              0.045019              0.027074   \n",
       "538100           0.533972             -0.016131             -0.021365   \n",
       "557191           0.533972             -0.107855             -0.118242   \n",
       "...                   ...                   ...                   ...   \n",
       "129187          -1.502754             -0.120085             -0.118242   \n",
       "6707            -1.505581             -0.120085             -0.118242   \n",
       "170355          -1.967433             -0.120085             -0.118242   \n",
       "246568          -1.732601             -0.120085             -0.118242   \n",
       "49718           -1.814924             -0.120085             -0.118242   \n",
       "\n",
       "        roll_mean_sold_28_9  roll_mean_sold_27_8  roll_mean_sold_26_7  \\\n",
       "sku                                                                     \n",
       "464801            -0.087551            -0.087259            -0.087144   \n",
       "645793            -0.069648            -0.063206            -0.062798   \n",
       "99516              0.013903             0.002940            -0.008019   \n",
       "538100            -0.039808            -0.045166            -0.050625   \n",
       "557191            -0.117391            -0.117325            -0.105404   \n",
       "...                     ...                  ...                  ...   \n",
       "129187            -0.117391            -0.117325            -0.117577   \n",
       "6707              -0.117391            -0.117325            -0.117577   \n",
       "170355            -0.117391            -0.117325            -0.117577   \n",
       "246568            -0.117391            -0.117325            -0.117577   \n",
       "49718             -0.117391            -0.117325            -0.117577   \n",
       "\n",
       "        roll_mean_sold_25_6  roll_mean_sold_24_5  roll_mean_minutes_30_11  \\\n",
       "sku                                                                         \n",
       "464801            -0.087088            -0.086720                 0.737185   \n",
       "645793            -0.062530            -0.062069                 0.737185   \n",
       "99516              0.011142             0.005720                 0.117517   \n",
       "538100            -0.044112            -0.049744                 0.737185   \n",
       "557191            -0.105506            -0.105208                -1.222632   \n",
       "...                     ...                  ...                      ...   \n",
       "129187            -0.117785            -0.117533                -2.213161   \n",
       "6707              -0.117785            -0.117533                -2.213329   \n",
       "170355            -0.117785            -0.117533                -2.240817   \n",
       "246568            -0.117785            -0.117533                -2.226841   \n",
       "49718             -0.117785            -0.117533                -2.231740   \n",
       "\n",
       "        roll_mean_minutes_29_10  roll_mean_minutes_26_7  \\\n",
       "sku                                                       \n",
       "464801                 0.744720                0.763959   \n",
       "645793                 0.744720                0.763959   \n",
       "99516                  0.128402                0.160092   \n",
       "538100                 0.744720                0.763959   \n",
       "557191                -1.352601               -1.631688   \n",
       "...                         ...                     ...   \n",
       "129187                -2.217213               -2.138135   \n",
       "6707                  -2.217213               -2.138135   \n",
       "170355                -2.217213               -2.138135   \n",
       "246568                -2.217213               -2.138135   \n",
       "49718                 -2.217213               -2.138135   \n",
       "\n",
       "        roll_mean_minutes_25_6  roll_mean_minutes_24_5  roll_mean_price_30_11  \\\n",
       "sku                                                                             \n",
       "464801                0.769021                0.774894               0.000237   \n",
       "645793                0.769021                0.774894               0.000237   \n",
       "99516                 0.169554                0.179085              -0.004385   \n",
       "538100                0.769021                0.774894              -0.004385   \n",
       "557191               -1.609169               -1.588786              -0.004385   \n",
       "...                        ...                     ...                    ...   \n",
       "129187               -2.111925               -2.088475              -0.004385   \n",
       "6707                 -2.111925               -2.088475              -0.004385   \n",
       "170355               -2.111925               -2.088475              -0.004385   \n",
       "246568               -2.111925               -2.088475              -0.004385   \n",
       "49718                -2.111925               -2.088475              -0.004385   \n",
       "\n",
       "        roll_mean_price_29_10  roll_mean_price_27_8  roll_mean_price_25_6  \\\n",
       "sku                                                                         \n",
       "464801               0.000191             -0.004176              -0.00350   \n",
       "645793               0.000191             -0.004176              -0.00350   \n",
       "99516               -0.004378             -0.004405              -0.00362   \n",
       "538100              -0.004378             -0.004405              -0.00362   \n",
       "557191              -0.004378             -0.004405              -0.00362   \n",
       "...                       ...                   ...                   ...   \n",
       "129187              -0.004378             -0.004405              -0.00362   \n",
       "6707                -0.004378             -0.004405              -0.00362   \n",
       "170355              -0.004378             -0.004405              -0.00362   \n",
       "246568              -0.004378             -0.004405              -0.00362   \n",
       "49718               -0.004378             -0.004405              -0.00362   \n",
       "\n",
       "        roll_mean_price_24_5  roll_sum_zero_sold_30_11  \\\n",
       "sku                                                      \n",
       "464801             -0.003556                  0.527432   \n",
       "645793             -0.003556                 -0.073491   \n",
       "99516              -0.003677                 -0.474106   \n",
       "538100             -0.003677                 -0.874720   \n",
       "557191             -0.003677                  0.928047   \n",
       "...                      ...                       ...   \n",
       "129187             -0.003677                 -2.677488   \n",
       "6707               -0.003677                 -2.677488   \n",
       "170355             -0.003677                 -2.677488   \n",
       "246568             -0.003677                 -2.677488   \n",
       "49718              -0.003677                 -2.677488   \n",
       "\n",
       "        roll_sum_zero_sold_24_5  \\\n",
       "sku                               \n",
       "464801                 0.314442   \n",
       "645793                -0.266020   \n",
       "99516                 -0.652995   \n",
       "538100                -0.459507   \n",
       "557191                 0.894904   \n",
       "...                         ...   \n",
       "129187                -2.781355   \n",
       "6707                  -2.781355   \n",
       "170355                -2.781355   \n",
       "246568                -2.781355   \n",
       "49718                 -2.781355   \n",
       "\n",
       "        roll_sum_shipping_logistic_type_fulfillment_30_11  \\\n",
       "sku                                                         \n",
       "464801                                           0.479567   \n",
       "645793                                           0.479567   \n",
       "99516                                           -0.792469   \n",
       "538100                                           0.479567   \n",
       "557191                                           0.479567   \n",
       "...                                                   ...   \n",
       "129187                                          -2.700524   \n",
       "6707                                            -2.700524   \n",
       "170355                                          -2.700524   \n",
       "246568                                          -2.541519   \n",
       "49718                                           -2.700524   \n",
       "\n",
       "        roll_sum_shipping_logistic_type_cross_docking_30_11  \\\n",
       "sku                                                           \n",
       "464801                                          -0.297576     \n",
       "645793                                          -0.297576     \n",
       "99516                                           -0.297576     \n",
       "538100                                          -0.297576     \n",
       "557191                                          -0.297576     \n",
       "...                                                   ...     \n",
       "129187                                          -0.297576     \n",
       "6707                                            -0.060230     \n",
       "170355                                          -0.297576     \n",
       "246568                                          -0.297576     \n",
       "49718                                           -0.297576     \n",
       "\n",
       "        roll_sum_shipping_logistic_type_fulfillment_28_9  \\\n",
       "sku                                                        \n",
       "464801                                          0.494024   \n",
       "645793                                          0.494024   \n",
       "99516                                          -0.744423   \n",
       "538100                                          0.494024   \n",
       "557191                                          0.494024   \n",
       "...                                                  ...   \n",
       "129187                                         -2.602093   \n",
       "6707                                           -2.602093   \n",
       "170355                                         -2.602093   \n",
       "246568                                         -2.602093   \n",
       "49718                                          -2.602093   \n",
       "\n",
       "        roll_sum_shipping_logistic_type_fulfillment_26_7  \\\n",
       "sku                                                        \n",
       "464801                                           0.50752   \n",
       "645793                                           0.50752   \n",
       "99516                                           -0.69828   \n",
       "538100                                           0.50752   \n",
       "557191                                           0.50752   \n",
       "...                                                  ...   \n",
       "129187                                          -2.50698   \n",
       "6707                                            -2.50698   \n",
       "170355                                          -2.50698   \n",
       "246568                                          -2.50698   \n",
       "49718                                           -2.50698   \n",
       "\n",
       "        roll_sum_shipping_logistic_type_fulfillment_25_6  \\\n",
       "sku                                                        \n",
       "464801                                          0.514066   \n",
       "645793                                          0.514066   \n",
       "99516                                          -0.676118   \n",
       "538100                                          0.514066   \n",
       "557191                                          0.514066   \n",
       "...                                                  ...   \n",
       "129187                                         -2.461394   \n",
       "6707                                           -2.461394   \n",
       "170355                                         -2.461394   \n",
       "246568                                         -2.461394   \n",
       "49718                                          -2.461394   \n",
       "\n",
       "        roll_sum_shipping_logistic_type_fulfillment_24_5  \\\n",
       "sku                                                        \n",
       "464801                                          0.520863   \n",
       "645793                                          0.520863   \n",
       "99516                                          -0.654889   \n",
       "538100                                          0.520863   \n",
       "557191                                          0.520863   \n",
       "...                                                  ...   \n",
       "129187                                         -2.418515   \n",
       "6707                                           -2.418515   \n",
       "170355                                         -2.418515   \n",
       "246568                                         -2.418515   \n",
       "49718                                          -2.418515   \n",
       "\n",
       "        roll_sum_shipping_payment_free_shipping_28_9  target_stock  \n",
       "sku                                                                 \n",
       "464801                                      1.490386     -0.124437  \n",
       "645793                                      1.490386     -0.116708  \n",
       "99516                                      -0.686343     -0.085792  \n",
       "538100                                     -0.686343     -0.085792  \n",
       "557191                                     -0.686343     -0.070334  \n",
       "...                                              ...           ...  \n",
       "129187                                     -0.686343     -0.085792  \n",
       "6707                                       -0.686343      0.084246  \n",
       "170355                                     -0.686343     -0.124437  \n",
       "246568                                     -0.686343     -0.132166  \n",
       "49718                                      -0.686343      0.006956  \n",
       "\n",
       "[551472 rows x 40 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.read_feather('X_test_standard.f').set_index('sku')\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7782a120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sold_quantity_17                                       0\n",
       "sold_quantity_18                                       0\n",
       "sold_quantity_19                                       0\n",
       "sold_quantity_20                                       0\n",
       "sold_quantity_21                                       0\n",
       "sold_quantity_25                                       0\n",
       "sold_quantity_26                                       0\n",
       "sold_quantity_27                                       0\n",
       "sold_quantity_28                                       0\n",
       "sold_quantity_29                                       0\n",
       "sold_quantity_30                                       0\n",
       "minutes_active_29                                      0\n",
       "minutes_active_30                                      0\n",
       "roll_mean_sold_30_11                                   0\n",
       "roll_mean_sold_29_10                                   0\n",
       "roll_mean_sold_28_9                                    0\n",
       "roll_mean_sold_27_8                                    0\n",
       "roll_mean_sold_26_7                                    0\n",
       "roll_mean_sold_25_6                                    0\n",
       "roll_mean_sold_24_5                                    0\n",
       "roll_mean_minutes_30_11                                0\n",
       "roll_mean_minutes_29_10                                0\n",
       "roll_mean_minutes_26_7                                 0\n",
       "roll_mean_minutes_25_6                                 0\n",
       "roll_mean_minutes_24_5                                 0\n",
       "roll_mean_price_30_11                                  0\n",
       "roll_mean_price_29_10                                  0\n",
       "roll_mean_price_27_8                                   0\n",
       "roll_mean_price_25_6                                   0\n",
       "roll_mean_price_24_5                                   0\n",
       "roll_sum_zero_sold_30_11                               0\n",
       "roll_sum_zero_sold_24_5                                0\n",
       "roll_sum_shipping_logistic_type_fulfillment_30_11      0\n",
       "roll_sum_shipping_logistic_type_cross_docking_30_11    0\n",
       "roll_sum_shipping_logistic_type_fulfillment_28_9       0\n",
       "roll_sum_shipping_logistic_type_fulfillment_26_7       0\n",
       "roll_sum_shipping_logistic_type_fulfillment_25_6       0\n",
       "roll_sum_shipping_logistic_type_fulfillment_24_5       0\n",
       "roll_sum_shipping_payment_free_shipping_28_9           0\n",
       "target_stock                                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many NAs \n",
    "X_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77b90eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.49 s, sys: 1.39 s, total: 9.88 s\n",
      "Wall time: 7.08 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "final_y_pred_nn = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cbe1cac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.14760213e-03, 1.71141755e-02, 2.43373681e-02, ...,\n",
       "        1.58151016e-02, 1.16905477e-02, 7.09263142e-03],\n",
       "       [3.47780553e-03, 1.08269900e-02, 2.05788705e-02, ...,\n",
       "        1.38102239e-02, 9.98794567e-03, 5.23595279e-03],\n",
       "       [1.00686535e-01, 2.05102652e-01, 1.51706174e-01, ...,\n",
       "        3.69169097e-03, 2.84222886e-03, 1.74273481e-03],\n",
       "       ...,\n",
       "       [5.83326328e-04, 2.77547073e-03, 6.22398173e-03, ...,\n",
       "        4.87173460e-02, 4.54919860e-02, 3.62462290e-02],\n",
       "       [2.34685396e-03, 7.21130008e-03, 1.27811376e-02, ...,\n",
       "        4.61968072e-02, 4.78049777e-02, 4.46558148e-02],\n",
       "       [4.40580443e-05, 3.21988191e-04, 1.25636871e-03, ...,\n",
       "        5.68320081e-02, 5.27581275e-02, 3.92932147e-02]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_y_pred_nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a3514b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(551472, 30)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_y_pred_nn.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782f4c57",
   "metadata": {},
   "source": [
    "## Creating DataFrame with predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e34e42a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 135 ms, sys: 35.9 ms, total: 171 ms\n",
      "Wall time: 174 ms\n"
     ]
    },
    {
     "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009148</td>\n",
       "      <td>0.017114</td>\n",
       "      <td>0.024337</td>\n",
       "      <td>0.028336</td>\n",
       "      <td>0.033023</td>\n",
       "      <td>0.037369</td>\n",
       "      <td>0.046440</td>\n",
       "      <td>0.055468</td>\n",
       "      <td>0.058103</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>0.047821</td>\n",
       "      <td>0.043026</td>\n",
       "      <td>0.041469</td>\n",
       "      <td>0.048662</td>\n",
       "      <td>0.054276</td>\n",
       "      <td>0.050846</td>\n",
       "      <td>0.045493</td>\n",
       "      <td>0.038657</td>\n",
       "      <td>0.032825</td>\n",
       "      <td>0.028910</td>\n",
       "      <td>0.032031</td>\n",
       "      <td>0.029343</td>\n",
       "      <td>0.028675</td>\n",
       "      <td>0.026190</td>\n",
       "      <td>0.021073</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0.013821</td>\n",
       "      <td>0.015815</td>\n",
       "      <td>0.011691</td>\n",
       "      <td>0.007093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003478</td>\n",
       "      <td>0.010827</td>\n",
       "      <td>0.020579</td>\n",
       "      <td>0.026291</td>\n",
       "      <td>0.033360</td>\n",
       "      <td>0.038820</td>\n",
       "      <td>0.049437</td>\n",
       "      <td>0.060064</td>\n",
       "      <td>0.063440</td>\n",
       "      <td>0.060843</td>\n",
       "      <td>0.051683</td>\n",
       "      <td>0.047398</td>\n",
       "      <td>0.042181</td>\n",
       "      <td>0.049839</td>\n",
       "      <td>0.054609</td>\n",
       "      <td>0.051280</td>\n",
       "      <td>0.047422</td>\n",
       "      <td>0.038141</td>\n",
       "      <td>0.031664</td>\n",
       "      <td>0.028114</td>\n",
       "      <td>0.031657</td>\n",
       "      <td>0.029375</td>\n",
       "      <td>0.028452</td>\n",
       "      <td>0.025030</td>\n",
       "      <td>0.019503</td>\n",
       "      <td>0.014565</td>\n",
       "      <td>0.012914</td>\n",
       "      <td>0.013810</td>\n",
       "      <td>0.009988</td>\n",
       "      <td>0.005236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.100687</td>\n",
       "      <td>0.205103</td>\n",
       "      <td>0.151706</td>\n",
       "      <td>0.097559</td>\n",
       "      <td>0.074883</td>\n",
       "      <td>0.056147</td>\n",
       "      <td>0.047282</td>\n",
       "      <td>0.041032</td>\n",
       "      <td>0.035807</td>\n",
       "      <td>0.025101</td>\n",
       "      <td>0.019521</td>\n",
       "      <td>0.015853</td>\n",
       "      <td>0.011892</td>\n",
       "      <td>0.014474</td>\n",
       "      <td>0.013199</td>\n",
       "      <td>0.011450</td>\n",
       "      <td>0.012133</td>\n",
       "      <td>0.006985</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.007026</td>\n",
       "      <td>0.007932</td>\n",
       "      <td>0.006140</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.005508</td>\n",
       "      <td>0.004612</td>\n",
       "      <td>0.003258</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.002842</td>\n",
       "      <td>0.001743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011515</td>\n",
       "      <td>0.054058</td>\n",
       "      <td>0.086754</td>\n",
       "      <td>0.080007</td>\n",
       "      <td>0.084526</td>\n",
       "      <td>0.077281</td>\n",
       "      <td>0.074711</td>\n",
       "      <td>0.070278</td>\n",
       "      <td>0.068597</td>\n",
       "      <td>0.049815</td>\n",
       "      <td>0.040826</td>\n",
       "      <td>0.034830</td>\n",
       "      <td>0.026048</td>\n",
       "      <td>0.030785</td>\n",
       "      <td>0.029048</td>\n",
       "      <td>0.025298</td>\n",
       "      <td>0.024944</td>\n",
       "      <td>0.015598</td>\n",
       "      <td>0.014237</td>\n",
       "      <td>0.013747</td>\n",
       "      <td>0.015647</td>\n",
       "      <td>0.012402</td>\n",
       "      <td>0.012462</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>0.008707</td>\n",
       "      <td>0.006453</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.006521</td>\n",
       "      <td>0.004899</td>\n",
       "      <td>0.003037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003763</td>\n",
       "      <td>0.009313</td>\n",
       "      <td>0.016293</td>\n",
       "      <td>0.021080</td>\n",
       "      <td>0.026772</td>\n",
       "      <td>0.029407</td>\n",
       "      <td>0.040801</td>\n",
       "      <td>0.050222</td>\n",
       "      <td>0.056143</td>\n",
       "      <td>0.053658</td>\n",
       "      <td>0.046722</td>\n",
       "      <td>0.044139</td>\n",
       "      <td>0.041223</td>\n",
       "      <td>0.049088</td>\n",
       "      <td>0.054417</td>\n",
       "      <td>0.051266</td>\n",
       "      <td>0.052455</td>\n",
       "      <td>0.041802</td>\n",
       "      <td>0.036090</td>\n",
       "      <td>0.030300</td>\n",
       "      <td>0.036469</td>\n",
       "      <td>0.035820</td>\n",
       "      <td>0.033995</td>\n",
       "      <td>0.031453</td>\n",
       "      <td>0.025441</td>\n",
       "      <td>0.020342</td>\n",
       "      <td>0.019722</td>\n",
       "      <td>0.018639</td>\n",
       "      <td>0.014820</td>\n",
       "      <td>0.008345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551467</th>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.001076</td>\n",
       "      <td>0.002756</td>\n",
       "      <td>0.005182</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.009292</td>\n",
       "      <td>0.012481</td>\n",
       "      <td>0.015886</td>\n",
       "      <td>0.021118</td>\n",
       "      <td>0.021988</td>\n",
       "      <td>0.022748</td>\n",
       "      <td>0.028799</td>\n",
       "      <td>0.031389</td>\n",
       "      <td>0.043328</td>\n",
       "      <td>0.038329</td>\n",
       "      <td>0.047303</td>\n",
       "      <td>0.053314</td>\n",
       "      <td>0.048187</td>\n",
       "      <td>0.042433</td>\n",
       "      <td>0.053818</td>\n",
       "      <td>0.059904</td>\n",
       "      <td>0.064704</td>\n",
       "      <td>0.058130</td>\n",
       "      <td>0.057809</td>\n",
       "      <td>0.054459</td>\n",
       "      <td>0.047478</td>\n",
       "      <td>0.058334</td>\n",
       "      <td>0.054957</td>\n",
       "      <td>0.038044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551468</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.002447</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.006910</td>\n",
       "      <td>0.008933</td>\n",
       "      <td>0.012754</td>\n",
       "      <td>0.015869</td>\n",
       "      <td>0.017846</td>\n",
       "      <td>0.022854</td>\n",
       "      <td>0.026106</td>\n",
       "      <td>0.041430</td>\n",
       "      <td>0.035355</td>\n",
       "      <td>0.046883</td>\n",
       "      <td>0.056903</td>\n",
       "      <td>0.048703</td>\n",
       "      <td>0.046660</td>\n",
       "      <td>0.053899</td>\n",
       "      <td>0.066559</td>\n",
       "      <td>0.064964</td>\n",
       "      <td>0.062761</td>\n",
       "      <td>0.058291</td>\n",
       "      <td>0.059401</td>\n",
       "      <td>0.056617</td>\n",
       "      <td>0.070027</td>\n",
       "      <td>0.063039</td>\n",
       "      <td>0.048387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551469</th>\n",
       "      <td>0.000583</td>\n",
       "      <td>0.002775</td>\n",
       "      <td>0.006224</td>\n",
       "      <td>0.011016</td>\n",
       "      <td>0.013416</td>\n",
       "      <td>0.013703</td>\n",
       "      <td>0.020440</td>\n",
       "      <td>0.022841</td>\n",
       "      <td>0.026475</td>\n",
       "      <td>0.031541</td>\n",
       "      <td>0.029045</td>\n",
       "      <td>0.029613</td>\n",
       "      <td>0.029426</td>\n",
       "      <td>0.038534</td>\n",
       "      <td>0.043577</td>\n",
       "      <td>0.041390</td>\n",
       "      <td>0.047545</td>\n",
       "      <td>0.047674</td>\n",
       "      <td>0.040056</td>\n",
       "      <td>0.036704</td>\n",
       "      <td>0.047091</td>\n",
       "      <td>0.049569</td>\n",
       "      <td>0.053500</td>\n",
       "      <td>0.051959</td>\n",
       "      <td>0.050742</td>\n",
       "      <td>0.042313</td>\n",
       "      <td>0.041791</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.045492</td>\n",
       "      <td>0.036246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551470</th>\n",
       "      <td>0.002347</td>\n",
       "      <td>0.007211</td>\n",
       "      <td>0.012781</td>\n",
       "      <td>0.017974</td>\n",
       "      <td>0.019835</td>\n",
       "      <td>0.018744</td>\n",
       "      <td>0.025510</td>\n",
       "      <td>0.028011</td>\n",
       "      <td>0.032228</td>\n",
       "      <td>0.034587</td>\n",
       "      <td>0.031708</td>\n",
       "      <td>0.030690</td>\n",
       "      <td>0.028753</td>\n",
       "      <td>0.038525</td>\n",
       "      <td>0.042076</td>\n",
       "      <td>0.038329</td>\n",
       "      <td>0.043069</td>\n",
       "      <td>0.041720</td>\n",
       "      <td>0.036303</td>\n",
       "      <td>0.031686</td>\n",
       "      <td>0.040192</td>\n",
       "      <td>0.044192</td>\n",
       "      <td>0.046519</td>\n",
       "      <td>0.046593</td>\n",
       "      <td>0.045425</td>\n",
       "      <td>0.037534</td>\n",
       "      <td>0.038801</td>\n",
       "      <td>0.046197</td>\n",
       "      <td>0.047805</td>\n",
       "      <td>0.044656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551471</th>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.001256</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.006088</td>\n",
       "      <td>0.007221</td>\n",
       "      <td>0.012382</td>\n",
       "      <td>0.015093</td>\n",
       "      <td>0.020908</td>\n",
       "      <td>0.023278</td>\n",
       "      <td>0.024023</td>\n",
       "      <td>0.025683</td>\n",
       "      <td>0.027460</td>\n",
       "      <td>0.034639</td>\n",
       "      <td>0.044127</td>\n",
       "      <td>0.041715</td>\n",
       "      <td>0.048273</td>\n",
       "      <td>0.049780</td>\n",
       "      <td>0.043371</td>\n",
       "      <td>0.041215</td>\n",
       "      <td>0.051828</td>\n",
       "      <td>0.054693</td>\n",
       "      <td>0.060669</td>\n",
       "      <td>0.056951</td>\n",
       "      <td>0.056032</td>\n",
       "      <td>0.051292</td>\n",
       "      <td>0.049782</td>\n",
       "      <td>0.056832</td>\n",
       "      <td>0.052758</td>\n",
       "      <td>0.039293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551472 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1         2         3         4         5         6         7   \\\n",
       "0       0.009148  0.017114  0.024337  0.028336  0.033023  0.037369  0.046440   \n",
       "1       0.003478  0.010827  0.020579  0.026291  0.033360  0.038820  0.049437   \n",
       "2       0.100687  0.205103  0.151706  0.097559  0.074883  0.056147  0.047282   \n",
       "3       0.011515  0.054058  0.086754  0.080007  0.084526  0.077281  0.074711   \n",
       "4       0.003763  0.009313  0.016293  0.021080  0.026772  0.029407  0.040801   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "551467  0.000035  0.000290  0.001076  0.002756  0.005182  0.006427  0.009292   \n",
       "551468  0.000002  0.000020  0.000146  0.000556  0.001649  0.002447  0.004031   \n",
       "551469  0.000583  0.002775  0.006224  0.011016  0.013416  0.013703  0.020440   \n",
       "551470  0.002347  0.007211  0.012781  0.017974  0.019835  0.018744  0.025510   \n",
       "551471  0.000044  0.000322  0.001256  0.002991  0.006088  0.007221  0.012382   \n",
       "\n",
       "              8         9         10        11        12        13        14  \\\n",
       "0       0.055468  0.058103  0.057534  0.047821  0.043026  0.041469  0.048662   \n",
       "1       0.060064  0.063440  0.060843  0.051683  0.047398  0.042181  0.049839   \n",
       "2       0.041032  0.035807  0.025101  0.019521  0.015853  0.011892  0.014474   \n",
       "3       0.070278  0.068597  0.049815  0.040826  0.034830  0.026048  0.030785   \n",
       "4       0.050222  0.056143  0.053658  0.046722  0.044139  0.041223  0.049088   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "551467  0.012481  0.015886  0.021118  0.021988  0.022748  0.028799  0.031389   \n",
       "551468  0.006910  0.008933  0.012754  0.015869  0.017846  0.022854  0.026106   \n",
       "551469  0.022841  0.026475  0.031541  0.029045  0.029613  0.029426  0.038534   \n",
       "551470  0.028011  0.032228  0.034587  0.031708  0.030690  0.028753  0.038525   \n",
       "551471  0.015093  0.020908  0.023278  0.024023  0.025683  0.027460  0.034639   \n",
       "\n",
       "              15        16        17        18        19        20        21  \\\n",
       "0       0.054276  0.050846  0.045493  0.038657  0.032825  0.028910  0.032031   \n",
       "1       0.054609  0.051280  0.047422  0.038141  0.031664  0.028114  0.031657   \n",
       "2       0.013199  0.011450  0.012133  0.006985  0.006652  0.007026  0.007932   \n",
       "3       0.029048  0.025298  0.024944  0.015598  0.014237  0.013747  0.015647   \n",
       "4       0.054417  0.051266  0.052455  0.041802  0.036090  0.030300  0.036469   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "551467  0.043328  0.038329  0.047303  0.053314  0.048187  0.042433  0.053818   \n",
       "551468  0.041430  0.035355  0.046883  0.056903  0.048703  0.046660  0.053899   \n",
       "551469  0.043577  0.041390  0.047545  0.047674  0.040056  0.036704  0.047091   \n",
       "551470  0.042076  0.038329  0.043069  0.041720  0.036303  0.031686  0.040192   \n",
       "551471  0.044127  0.041715  0.048273  0.049780  0.043371  0.041215  0.051828   \n",
       "\n",
       "              22        23        24        25        26        27        28  \\\n",
       "0       0.029343  0.028675  0.026190  0.021073  0.015412  0.013821  0.015815   \n",
       "1       0.029375  0.028452  0.025030  0.019503  0.014565  0.012914  0.013810   \n",
       "2       0.006140  0.006354  0.005508  0.004612  0.003258  0.003427  0.003692   \n",
       "3       0.012402  0.012462  0.010593  0.008707  0.006453  0.006374  0.006521   \n",
       "4       0.035820  0.033995  0.031453  0.025441  0.020342  0.019722  0.018639   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "551467  0.059904  0.064704  0.058130  0.057809  0.054459  0.047478  0.058334   \n",
       "551468  0.066559  0.064964  0.062761  0.058291  0.059401  0.056617  0.070027   \n",
       "551469  0.049569  0.053500  0.051959  0.050742  0.042313  0.041791  0.048717   \n",
       "551470  0.044192  0.046519  0.046593  0.045425  0.037534  0.038801  0.046197   \n",
       "551471  0.054693  0.060669  0.056951  0.056032  0.051292  0.049782  0.056832   \n",
       "\n",
       "              29        30  \n",
       "0       0.011691  0.007093  \n",
       "1       0.009988  0.005236  \n",
       "2       0.002842  0.001743  \n",
       "3       0.004899  0.003037  \n",
       "4       0.014820  0.008345  \n",
       "...          ...       ...  \n",
       "551467  0.054957  0.038044  \n",
       "551468  0.063039  0.048387  \n",
       "551469  0.045492  0.036246  \n",
       "551470  0.047805  0.044656  \n",
       "551471  0.052758  0.039293  \n",
       "\n",
       "[551472 rows x 30 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X_test_sub = pd.DataFrame(final_y_pred_nn, columns=range(1, 31))\n",
    "X_test_sub = X_test_sub.astype(float)\n",
    "X_test_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f09bae97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check probabilites for random skus\n",
    "for _ in range(1, 5):\n",
    "    random_sku = X_test_sub.sample(1)\n",
    "    plt.plot(range(1, 31), random_sku.values.squeeze())\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ad60b423",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "      ..\n",
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "1.0    1\n",
       "Length: 42915, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sub.sum(axis=1).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7559d305-0f1a-4ee4-9d20-5da8b9864bc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_test_sub.values.reshape(-1))\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5c852750-926d-46a4-b276-167422624b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_sub.idxmax(axis=1).plot.hist(bins=30)\n",
    "plt.xticks(range(1, 31))\n",
    "plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf2a3aa",
   "metadata": {},
   "source": [
    "Pretty different from LGBM predictions.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "054b8e22-6462-40bf-9de0-786d4b9be099",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>551472.000000</td>\n",
       "      <td>551472.000000</td>\n",
       "      <td>551472.000000</td>\n",
       "      <td>551472.000000</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "      <td>5.514720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.041519</td>\n",
       "      <td>0.042969</td>\n",
       "      <td>0.039269</td>\n",
       "      <td>0.033159</td>\n",
       "      <td>3.275507e-02</td>\n",
       "      <td>3.042589e-02</td>\n",
       "      <td>3.419103e-02</td>\n",
       "      <td>3.746800e-02</td>\n",
       "      <td>4.084180e-02</td>\n",
       "      <td>3.713907e-02</td>\n",
       "      <td>3.316577e-02</td>\n",
       "      <td>3.095540e-02</td>\n",
       "      <td>2.793179e-02</td>\n",
       "      <td>3.500702e-02</td>\n",
       "      <td>3.937471e-02</td>\n",
       "      <td>3.781813e-02</td>\n",
       "      <td>3.914118e-02</td>\n",
       "      <td>3.339893e-02</td>\n",
       "      <td>3.041476e-02</td>\n",
       "      <td>2.789151e-02</td>\n",
       "      <td>3.346232e-02</td>\n",
       "      <td>3.440946e-02</td>\n",
       "      <td>3.548341e-02</td>\n",
       "      <td>3.372752e-02</td>\n",
       "      <td>3.045006e-02</td>\n",
       "      <td>2.656431e-02</td>\n",
       "      <td>2.595150e-02</td>\n",
       "      <td>2.798318e-02</td>\n",
       "      <td>2.665676e-02</td>\n",
       "      <td>2.047522e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.093415</td>\n",
       "      <td>0.067152</td>\n",
       "      <td>0.047043</td>\n",
       "      <td>0.032465</td>\n",
       "      <td>2.750811e-02</td>\n",
       "      <td>2.287149e-02</td>\n",
       "      <td>2.150388e-02</td>\n",
       "      <td>2.092256e-02</td>\n",
       "      <td>1.962511e-02</td>\n",
       "      <td>1.579530e-02</td>\n",
       "      <td>1.274424e-02</td>\n",
       "      <td>1.194399e-02</td>\n",
       "      <td>1.022831e-02</td>\n",
       "      <td>1.231907e-02</td>\n",
       "      <td>1.460633e-02</td>\n",
       "      <td>1.552498e-02</td>\n",
       "      <td>1.681575e-02</td>\n",
       "      <td>1.573777e-02</td>\n",
       "      <td>1.470007e-02</td>\n",
       "      <td>1.417069e-02</td>\n",
       "      <td>1.765077e-02</td>\n",
       "      <td>1.999741e-02</td>\n",
       "      <td>2.148920e-02</td>\n",
       "      <td>2.124724e-02</td>\n",
       "      <td>2.065727e-02</td>\n",
       "      <td>1.966340e-02</td>\n",
       "      <td>1.976030e-02</td>\n",
       "      <td>2.169539e-02</td>\n",
       "      <td>2.323736e-02</td>\n",
       "      <td>2.071977e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.959386e-31</td>\n",
       "      <td>3.615619e-27</td>\n",
       "      <td>1.388425e-21</td>\n",
       "      <td>3.667198e-18</td>\n",
       "      <td>3.323436e-14</td>\n",
       "      <td>2.526447e-14</td>\n",
       "      <td>3.651418e-19</td>\n",
       "      <td>3.401964e-19</td>\n",
       "      <td>4.864451e-27</td>\n",
       "      <td>5.884443e-10</td>\n",
       "      <td>2.487311e-16</td>\n",
       "      <td>3.108792e-11</td>\n",
       "      <td>2.787327e-15</td>\n",
       "      <td>3.009565e-12</td>\n",
       "      <td>4.105628e-22</td>\n",
       "      <td>6.100181e-14</td>\n",
       "      <td>8.927707e-17</td>\n",
       "      <td>3.446428e-14</td>\n",
       "      <td>2.307557e-25</td>\n",
       "      <td>4.173526e-15</td>\n",
       "      <td>2.653174e-19</td>\n",
       "      <td>2.395721e-17</td>\n",
       "      <td>7.377509e-25</td>\n",
       "      <td>3.312603e-26</td>\n",
       "      <td>1.788172e-21</td>\n",
       "      <td>8.205613e-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000463</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.003582</td>\n",
       "      <td>0.005870</td>\n",
       "      <td>8.779542e-03</td>\n",
       "      <td>1.054308e-02</td>\n",
       "      <td>1.561902e-02</td>\n",
       "      <td>1.961628e-02</td>\n",
       "      <td>2.491087e-02</td>\n",
       "      <td>2.526899e-02</td>\n",
       "      <td>2.437442e-02</td>\n",
       "      <td>2.315298e-02</td>\n",
       "      <td>2.191622e-02</td>\n",
       "      <td>2.842904e-02</td>\n",
       "      <td>3.172387e-02</td>\n",
       "      <td>2.859323e-02</td>\n",
       "      <td>2.748683e-02</td>\n",
       "      <td>2.017408e-02</td>\n",
       "      <td>1.756057e-02</td>\n",
       "      <td>1.525845e-02</td>\n",
       "      <td>1.734755e-02</td>\n",
       "      <td>1.491652e-02</td>\n",
       "      <td>1.465700e-02</td>\n",
       "      <td>1.290263e-02</td>\n",
       "      <td>1.028424e-02</td>\n",
       "      <td>7.523805e-03</td>\n",
       "      <td>7.130728e-03</td>\n",
       "      <td>7.703605e-03</td>\n",
       "      <td>5.922820e-03</td>\n",
       "      <td>3.899105e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.002388</td>\n",
       "      <td>0.007827</td>\n",
       "      <td>0.014755</td>\n",
       "      <td>0.019491</td>\n",
       "      <td>2.357495e-02</td>\n",
       "      <td>2.449209e-02</td>\n",
       "      <td>3.020070e-02</td>\n",
       "      <td>3.409456e-02</td>\n",
       "      <td>3.866166e-02</td>\n",
       "      <td>3.570614e-02</td>\n",
       "      <td>3.252674e-02</td>\n",
       "      <td>3.050002e-02</td>\n",
       "      <td>2.833743e-02</td>\n",
       "      <td>3.686162e-02</td>\n",
       "      <td>4.250943e-02</td>\n",
       "      <td>4.134883e-02</td>\n",
       "      <td>4.381262e-02</td>\n",
       "      <td>3.873951e-02</td>\n",
       "      <td>3.486476e-02</td>\n",
       "      <td>3.072040e-02</td>\n",
       "      <td>3.657893e-02</td>\n",
       "      <td>3.809961e-02</td>\n",
       "      <td>3.788114e-02</td>\n",
       "      <td>3.550550e-02</td>\n",
       "      <td>3.019520e-02</td>\n",
       "      <td>2.512150e-02</td>\n",
       "      <td>2.360611e-02</td>\n",
       "      <td>2.336671e-02</td>\n",
       "      <td>1.899517e-02</td>\n",
       "      <td>1.098274e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.024766</td>\n",
       "      <td>0.054387</td>\n",
       "      <td>0.069071</td>\n",
       "      <td>0.060370</td>\n",
       "      <td>5.672196e-02</td>\n",
       "      <td>4.934414e-02</td>\n",
       "      <td>5.250463e-02</td>\n",
       "      <td>5.563167e-02</td>\n",
       "      <td>5.741603e-02</td>\n",
       "      <td>5.067921e-02</td>\n",
       "      <td>4.341499e-02</td>\n",
       "      <td>3.994042e-02</td>\n",
       "      <td>3.595198e-02</td>\n",
       "      <td>4.419332e-02</td>\n",
       "      <td>5.034767e-02</td>\n",
       "      <td>4.936436e-02</td>\n",
       "      <td>5.111697e-02</td>\n",
       "      <td>4.586032e-02</td>\n",
       "      <td>4.248039e-02</td>\n",
       "      <td>4.010253e-02</td>\n",
       "      <td>4.897484e-02</td>\n",
       "      <td>5.234923e-02</td>\n",
       "      <td>5.487802e-02</td>\n",
       "      <td>5.241864e-02</td>\n",
       "      <td>4.814581e-02</td>\n",
       "      <td>4.219664e-02</td>\n",
       "      <td>4.147600e-02</td>\n",
       "      <td>4.624284e-02</td>\n",
       "      <td>4.530324e-02</td>\n",
       "      <td>3.510839e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.992824</td>\n",
       "      <td>0.386677</td>\n",
       "      <td>0.246703</td>\n",
       "      <td>0.189021</td>\n",
       "      <td>1.478699e-01</td>\n",
       "      <td>1.354398e-01</td>\n",
       "      <td>1.118243e-01</td>\n",
       "      <td>1.055088e-01</td>\n",
       "      <td>1.431599e-01</td>\n",
       "      <td>7.084284e-01</td>\n",
       "      <td>7.825551e-02</td>\n",
       "      <td>7.649904e-02</td>\n",
       "      <td>5.823806e-02</td>\n",
       "      <td>6.414008e-02</td>\n",
       "      <td>7.816473e-02</td>\n",
       "      <td>7.541338e-02</td>\n",
       "      <td>7.472886e-02</td>\n",
       "      <td>2.570391e-01</td>\n",
       "      <td>7.141150e-02</td>\n",
       "      <td>6.748613e-02</td>\n",
       "      <td>6.733298e-02</td>\n",
       "      <td>1.548335e-01</td>\n",
       "      <td>8.636047e-02</td>\n",
       "      <td>1.852598e-01</td>\n",
       "      <td>8.461534e-02</td>\n",
       "      <td>1.052070e-01</td>\n",
       "      <td>2.551028e-01</td>\n",
       "      <td>1.159660e-01</td>\n",
       "      <td>4.647162e-01</td>\n",
       "      <td>1.963219e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  1              2              3              4   \\\n",
       "count  551472.000000  551472.000000  551472.000000  551472.000000   \n",
       "mean        0.041519       0.042969       0.039269       0.033159   \n",
       "std         0.093415       0.067152       0.047043       0.032465   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000463       0.001701       0.003582       0.005870   \n",
       "50%         0.002388       0.007827       0.014755       0.019491   \n",
       "75%         0.024766       0.054387       0.069071       0.060370   \n",
       "max         0.992824       0.386677       0.246703       0.189021   \n",
       "\n",
       "                 5             6             7             8             9   \\\n",
       "count  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05   \n",
       "mean   3.275507e-02  3.042589e-02  3.419103e-02  3.746800e-02  4.084180e-02   \n",
       "std    2.750811e-02  2.287149e-02  2.150388e-02  2.092256e-02  1.962511e-02   \n",
       "min    4.959386e-31  3.615619e-27  1.388425e-21  3.667198e-18  3.323436e-14   \n",
       "25%    8.779542e-03  1.054308e-02  1.561902e-02  1.961628e-02  2.491087e-02   \n",
       "50%    2.357495e-02  2.449209e-02  3.020070e-02  3.409456e-02  3.866166e-02   \n",
       "75%    5.672196e-02  4.934414e-02  5.250463e-02  5.563167e-02  5.741603e-02   \n",
       "max    1.478699e-01  1.354398e-01  1.118243e-01  1.055088e-01  1.431599e-01   \n",
       "\n",
       "                 10            11            12            13            14  \\\n",
       "count  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05   \n",
       "mean   3.713907e-02  3.316577e-02  3.095540e-02  2.793179e-02  3.500702e-02   \n",
       "std    1.579530e-02  1.274424e-02  1.194399e-02  1.022831e-02  1.231907e-02   \n",
       "min    2.526447e-14  3.651418e-19  3.401964e-19  4.864451e-27  5.884443e-10   \n",
       "25%    2.526899e-02  2.437442e-02  2.315298e-02  2.191622e-02  2.842904e-02   \n",
       "50%    3.570614e-02  3.252674e-02  3.050002e-02  2.833743e-02  3.686162e-02   \n",
       "75%    5.067921e-02  4.341499e-02  3.994042e-02  3.595198e-02  4.419332e-02   \n",
       "max    7.084284e-01  7.825551e-02  7.649904e-02  5.823806e-02  6.414008e-02   \n",
       "\n",
       "                 15            16            17            18            19  \\\n",
       "count  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05   \n",
       "mean   3.937471e-02  3.781813e-02  3.914118e-02  3.339893e-02  3.041476e-02   \n",
       "std    1.460633e-02  1.552498e-02  1.681575e-02  1.573777e-02  1.470007e-02   \n",
       "min    2.487311e-16  3.108792e-11  2.787327e-15  3.009565e-12  4.105628e-22   \n",
       "25%    3.172387e-02  2.859323e-02  2.748683e-02  2.017408e-02  1.756057e-02   \n",
       "50%    4.250943e-02  4.134883e-02  4.381262e-02  3.873951e-02  3.486476e-02   \n",
       "75%    5.034767e-02  4.936436e-02  5.111697e-02  4.586032e-02  4.248039e-02   \n",
       "max    7.816473e-02  7.541338e-02  7.472886e-02  2.570391e-01  7.141150e-02   \n",
       "\n",
       "                 20            21            22            23            24  \\\n",
       "count  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05   \n",
       "mean   2.789151e-02  3.346232e-02  3.440946e-02  3.548341e-02  3.372752e-02   \n",
       "std    1.417069e-02  1.765077e-02  1.999741e-02  2.148920e-02  2.124724e-02   \n",
       "min    6.100181e-14  8.927707e-17  3.446428e-14  2.307557e-25  4.173526e-15   \n",
       "25%    1.525845e-02  1.734755e-02  1.491652e-02  1.465700e-02  1.290263e-02   \n",
       "50%    3.072040e-02  3.657893e-02  3.809961e-02  3.788114e-02  3.550550e-02   \n",
       "75%    4.010253e-02  4.897484e-02  5.234923e-02  5.487802e-02  5.241864e-02   \n",
       "max    6.748613e-02  6.733298e-02  1.548335e-01  8.636047e-02  1.852598e-01   \n",
       "\n",
       "                 25            26            27            28            29  \\\n",
       "count  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05  5.514720e+05   \n",
       "mean   3.045006e-02  2.656431e-02  2.595150e-02  2.798318e-02  2.665676e-02   \n",
       "std    2.065727e-02  1.966340e-02  1.976030e-02  2.169539e-02  2.323736e-02   \n",
       "min    2.653174e-19  2.395721e-17  7.377509e-25  3.312603e-26  1.788172e-21   \n",
       "25%    1.028424e-02  7.523805e-03  7.130728e-03  7.703605e-03  5.922820e-03   \n",
       "50%    3.019520e-02  2.512150e-02  2.360611e-02  2.336671e-02  1.899517e-02   \n",
       "75%    4.814581e-02  4.219664e-02  4.147600e-02  4.624284e-02  4.530324e-02   \n",
       "max    8.461534e-02  1.052070e-01  2.551028e-01  1.159660e-01  4.647162e-01   \n",
       "\n",
       "                 30  \n",
       "count  5.514720e+05  \n",
       "mean   2.047522e-02  \n",
       "std    2.071977e-02  \n",
       "min    8.205613e-20  \n",
       "25%    3.899105e-03  \n",
       "50%    1.098274e-02  \n",
       "75%    3.510839e-02  \n",
       "max    1.963219e-01  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sub.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b0d502a-3adf-495c-9243-b5749e246e8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 44s, sys: 723 ms, total: 1min 45s\n",
      "Wall time: 1min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "X_test_sub.to_csv(\n",
    "    '3.2-nn-two-layers-16-16.csv.gz',\n",
    "    compression='gzip',\n",
    "    index=False,\n",
    "    header=False\n",
    ")"
   ]
  }
 ],
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  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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