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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "pip install --quiet tensorflow-addons"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "KYNHuUp2TVZO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7a93710-70c1-4b9c-a3c5-8490e2c1d688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "def configure_device():\n",
        "    try:\n",
        "        tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()  # connect to tpu cluster\n",
        "        strategy = tf.distribute.TPUStrategy(tpu) # get strategy for tpu\n",
        "        print('Num of TPUs: ', strategy.num_replicas_in_sync)\n",
        "        device='TPU'\n",
        "    except: # otherwise detect GPUs\n",
        "        tpu = None\n",
        "        gpus = tf.config.list_logical_devices('GPU') # get logical gpus\n",
        "        ngpu = len(gpus)\n",
        "        if ngpu: # if number of GPUs are 0 then CPU\n",
        "            strategy = tf.distribute.MirroredStrategy(gpus) # single-GPU or multi-GPU\n",
        "            print(\"> Running on GPU\", end=' | ')\n",
        "            print(\"Num of GPUs: \", ngpu)\n",
        "            device='GPU'\n",
        "        else:\n",
        "            print(\"> Running on CPU\")\n",
        "            strategy = tf.distribute.get_strategy() # connect to single gpu or cpu\n",
        "            device='CPU'\n",
        "    return strategy, device, tpu"
      ],
      "metadata": {
        "id": "ASDwpoVPj1JC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strategy, device, tpu = configure_device()\n",
        "AUTO = tf.data.experimental.AUTOTUNE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJd40S9ElIFZ",
        "outputId": "262dbd53-cb0e-4fc2-c40c-c7aa8b99d7cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.84.110.186:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.84.110.186:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of TPUs:  8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df  = pd.read_csv('/content/drive/MyDrive/minidata.csv')\n",
        "# df = df.fillna(method='ffill')\n",
        "# df = df.fillna(method='bfill')"
      ],
      "metadata": {
        "id": "IBthvAfnOkUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.select_dtypes('int').columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIjSQ_wmaAKC",
        "outputId": "7eb7ae82-39ba-4691-e1eb-1d761b54de52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['D_63', 'D_64', 'B_31', 'D_126', 'target'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_features  = ['B_30', 'B_38', 'D_63', 'D_64', 'D_66', 'D_68', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126']\n",
        "cont_features = [x for x in list(df.columns) if x not in cat_features]\n",
        "len(cont_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yY-b8ZNePhN5",
        "outputId": "65d6f277-397b-4a52-f497-a217ce082156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "178"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(cat_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6v3NYYiNPYad",
        "outputId": "4e0250fb-05ad-4209-a0b0-66412df8b8d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ff = list(df.select_dtypes('int').columns)\n",
        "tt  = [x for x in list(df.columns) if x not in ff]\n",
        "print(len(ff))\n",
        "print(len(tt))\n",
        "print(df.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDAF7oACca4X",
        "outputId": "0e2b6ea6-6420-4a1f-ac3c-c5ddb6550f0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n",
            "184\n",
            "189\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def parse_tfr_element(element):\n",
        "  data = {}\n",
        "  for col in ff:\n",
        "    data[col] = tf.io.FixedLenFeature([], tf.int64)\n",
        "\n",
        "  for col in tt:\n",
        "    data[col] = tf.io.FixedLenFeature([], tf.float32)\n",
        "  \n",
        "    \n",
        "  content = tf.io.parse_single_example(element, data)\n",
        "  \n",
        "  my_arr = []\n",
        "  for col in list(df.columns):\n",
        "    my_arr.append(content[col])\n",
        "  return my_arr"
      ],
      "metadata": {
        "id": "R06ikeDac7Rl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_dataset_small(filename):\n",
        "  dataset = tf.data.TFRecordDataset(filename)\n",
        "  dataset = dataset.map(\n",
        "      parse_tfr_element\n",
        "  )\n",
        "  return dataset\n",
        "gspath = 'gs://kds-bbda14c6ba4300af481c96af7d145ddf21642544093f223c2ac8ef0b'\n",
        "filename = tf.io.gfile.glob(gspath + '/*.tfrecord')\n",
        "dataset_small = get_dataset_small(filename[0])"
      ],
      "metadata": {
        "id": "DbGpjNYLd8Eg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "arr = []\n",
        "for i in tqdm(dataset_small.take(100)):\n",
        "  temp = [j.numpy() for j in i]\n",
        "  arr.append(temp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkaeqUgYfdCY",
        "outputId": "2390b263-0252-43f8-b938-80e820b0405e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100it [00:31,  3.17it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array(arr)\n",
        "arr.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrtodLDHk2JC",
        "outputId": "a883b9ad-0c5d-4a77-d30e-a2bac8e91e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 189)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.DataFrame(arr,columns=list(df.columns))\n",
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "38vcfEeRk8P0",
        "outputId": "27a1b7f9-c53a-4065-ffe8-5d218bb651bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        P_2      D_39       B_1       B_2       R_1     S_3      D_41  \\\n",
              "0   0.62300  0.000249  0.006420  0.817400  0.000715  0.1614  0.009680   \n",
              "1   0.67200  0.184000  0.029130  1.007000  0.000747  0.4524  0.000583   \n",
              "2   0.07710  0.567400  0.026230  1.010000  0.009430  0.1617  0.008480   \n",
              "3   0.98700  0.008520  0.039340  0.812000  0.004604  0.2085  0.003180   \n",
              "4   0.05292  1.006000  0.108150  0.006660  0.501000  0.1520  0.981000   \n",
              "..      ...       ...       ...       ...       ...     ...       ...   \n",
              "95  0.82570  0.007103  0.006824  1.005000  0.009620  0.0479  0.004100   \n",
              "96 -0.04312  0.006996  0.855500  0.000773  0.006626  0.1827  0.692400   \n",
              "97  0.05817  0.241200  0.402800  0.027590  0.501000  0.2249  0.008250   \n",
              "98  0.54250  0.623000  0.214800  0.057400  1.001000  0.6740  0.888000   \n",
              "99  0.56450  0.001099  0.310800  0.040040  0.005510  0.1674  0.007940   \n",
              "\n",
              "         B_3     D_42     D_43  ...     D_137     D_138     D_139     D_140  \\\n",
              "0   0.011020  0.04172  0.03622  ...  0.004665  0.006330  0.008880  0.003250   \n",
              "1   0.009810  0.04172  0.12340  ...  0.004665  0.006330  1.003000  0.002089   \n",
              "2   0.011795  0.04172  0.15580  ...  0.004665  0.006330  0.000492  0.008250   \n",
              "3   0.013626  0.04172  0.10890  ...  0.004665  0.006330  0.009440  0.009400   \n",
              "4   0.034420  0.04172  0.29880  ...  0.004665  0.006330  0.000756  0.003809   \n",
              "..       ...      ...      ...  ...       ...       ...       ...       ...   \n",
              "95  0.005733  0.06744  0.10223  ...  0.004383  0.504400  0.008156  0.005363   \n",
              "96  0.241600  0.07180  0.10223  ...  0.002506  0.009544  0.005745  0.004364   \n",
              "97  0.870600  0.07180  0.10736  ...  0.002506  0.009544  1.004000  0.005604   \n",
              "98  0.566000  0.07180  0.25050  ...  0.002506  0.009544  1.004000  1.009000   \n",
              "99  0.216900  0.07180  0.03546  ...  0.002506  0.009544  1.004000  0.003675   \n",
              "\n",
              "       D_141    D_142     D_143     D_144     D_145  target  \n",
              "0   0.004635  0.32960  0.002626  0.002804  0.007355     0.0  \n",
              "1   0.931000  0.32960  1.003000  1.191000  0.186600     0.0  \n",
              "2   0.008590  0.32960  0.007107  0.004430  0.000937     1.0  \n",
              "3   0.002674  0.32960  0.009590  0.007310  0.001398     0.0  \n",
              "4   0.002684  0.32960  0.008000  0.002956  0.002918     1.0  \n",
              "..       ...      ...       ...       ...       ...     ...  \n",
              "95  0.003405  0.44780  0.003664  0.006040  0.008550     0.0  \n",
              "96  0.004852  0.44780  0.002655  0.004390  0.005530     1.0  \n",
              "97  0.988300  0.68900  1.009000  0.001042  0.276900     1.0  \n",
              "98  0.888000  0.10144  1.006000  0.007618  0.095100     1.0  \n",
              "99  0.912600  0.44240  1.002000  0.009260  1.097000     0.0  \n",
              "\n",
              "[100 rows x 189 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P_2</th>\n",
              "      <th>D_39</th>\n",
              "      <th>B_1</th>\n",
              "      <th>B_2</th>\n",
              "      <th>R_1</th>\n",
              "      <th>S_3</th>\n",
              "      <th>D_41</th>\n",
              "      <th>B_3</th>\n",
              "      <th>D_42</th>\n",
              "      <th>D_43</th>\n",
              "      <th>...</th>\n",
              "      <th>D_137</th>\n",
              "      <th>D_138</th>\n",
              "      <th>D_139</th>\n",
              "      <th>D_140</th>\n",
              "      <th>D_141</th>\n",
              "      <th>D_142</th>\n",
              "      <th>D_143</th>\n",
              "      <th>D_144</th>\n",
              "      <th>D_145</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.62300</td>\n",
              "      <td>0.000249</td>\n",
              "      <td>0.006420</td>\n",
              "      <td>0.817400</td>\n",
              "      <td>0.000715</td>\n",
              "      <td>0.1614</td>\n",
              "      <td>0.009680</td>\n",
              "      <td>0.011020</td>\n",
              "      <td>0.04172</td>\n",
              "      <td>0.03622</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.008880</td>\n",
              "      <td>0.003250</td>\n",
              "      <td>0.004635</td>\n",
              "      <td>0.32960</td>\n",
              "      <td>0.002626</td>\n",
              "      <td>0.002804</td>\n",
              "      <td>0.007355</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.67200</td>\n",
              "      <td>0.184000</td>\n",
              "      <td>0.029130</td>\n",
              "      <td>1.007000</td>\n",
              "      <td>0.000747</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.000583</td>\n",
              "      <td>0.009810</td>\n",
              "      <td>0.04172</td>\n",
              "      <td>0.12340</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>1.003000</td>\n",
              "      <td>0.002089</td>\n",
              "      <td>0.931000</td>\n",
              "      <td>0.32960</td>\n",
              "      <td>1.003000</td>\n",
              "      <td>1.191000</td>\n",
              "      <td>0.186600</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.07710</td>\n",
              "      <td>0.567400</td>\n",
              "      <td>0.026230</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>0.009430</td>\n",
              "      <td>0.1617</td>\n",
              "      <td>0.008480</td>\n",
              "      <td>0.011795</td>\n",
              "      <td>0.04172</td>\n",
              "      <td>0.15580</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.000492</td>\n",
              "      <td>0.008250</td>\n",
              "      <td>0.008590</td>\n",
              "      <td>0.32960</td>\n",
              "      <td>0.007107</td>\n",
              "      <td>0.004430</td>\n",
              "      <td>0.000937</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.98700</td>\n",
              "      <td>0.008520</td>\n",
              "      <td>0.039340</td>\n",
              "      <td>0.812000</td>\n",
              "      <td>0.004604</td>\n",
              "      <td>0.2085</td>\n",
              "      <td>0.003180</td>\n",
              "      <td>0.013626</td>\n",
              "      <td>0.04172</td>\n",
              "      <td>0.10890</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.009440</td>\n",
              "      <td>0.009400</td>\n",
              "      <td>0.002674</td>\n",
              "      <td>0.32960</td>\n",
              "      <td>0.009590</td>\n",
              "      <td>0.007310</td>\n",
              "      <td>0.001398</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.05292</td>\n",
              "      <td>1.006000</td>\n",
              "      <td>0.108150</td>\n",
              "      <td>0.006660</td>\n",
              "      <td>0.501000</td>\n",
              "      <td>0.1520</td>\n",
              "      <td>0.981000</td>\n",
              "      <td>0.034420</td>\n",
              "      <td>0.04172</td>\n",
              "      <td>0.29880</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>0.006330</td>\n",
              "      <td>0.000756</td>\n",
              "      <td>0.003809</td>\n",
              "      <td>0.002684</td>\n",
              "      <td>0.32960</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>0.002956</td>\n",
              "      <td>0.002918</td>\n",
              "      <td>1.0</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>0.82570</td>\n",
              "      <td>0.007103</td>\n",
              "      <td>0.006824</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.009620</td>\n",
              "      <td>0.0479</td>\n",
              "      <td>0.004100</td>\n",
              "      <td>0.005733</td>\n",
              "      <td>0.06744</td>\n",
              "      <td>0.10223</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004383</td>\n",
              "      <td>0.504400</td>\n",
              "      <td>0.008156</td>\n",
              "      <td>0.005363</td>\n",
              "      <td>0.003405</td>\n",
              "      <td>0.44780</td>\n",
              "      <td>0.003664</td>\n",
              "      <td>0.006040</td>\n",
              "      <td>0.008550</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>-0.04312</td>\n",
              "      <td>0.006996</td>\n",
              "      <td>0.855500</td>\n",
              "      <td>0.000773</td>\n",
              "      <td>0.006626</td>\n",
              "      <td>0.1827</td>\n",
              "      <td>0.692400</td>\n",
              "      <td>0.241600</td>\n",
              "      <td>0.07180</td>\n",
              "      <td>0.10223</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.009544</td>\n",
              "      <td>0.005745</td>\n",
              "      <td>0.004364</td>\n",
              "      <td>0.004852</td>\n",
              "      <td>0.44780</td>\n",
              "      <td>0.002655</td>\n",
              "      <td>0.004390</td>\n",
              "      <td>0.005530</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.05817</td>\n",
              "      <td>0.241200</td>\n",
              "      <td>0.402800</td>\n",
              "      <td>0.027590</td>\n",
              "      <td>0.501000</td>\n",
              "      <td>0.2249</td>\n",
              "      <td>0.008250</td>\n",
              "      <td>0.870600</td>\n",
              "      <td>0.07180</td>\n",
              "      <td>0.10736</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.009544</td>\n",
              "      <td>1.004000</td>\n",
              "      <td>0.005604</td>\n",
              "      <td>0.988300</td>\n",
              "      <td>0.68900</td>\n",
              "      <td>1.009000</td>\n",
              "      <td>0.001042</td>\n",
              "      <td>0.276900</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>0.54250</td>\n",
              "      <td>0.623000</td>\n",
              "      <td>0.214800</td>\n",
              "      <td>0.057400</td>\n",
              "      <td>1.001000</td>\n",
              "      <td>0.6740</td>\n",
              "      <td>0.888000</td>\n",
              "      <td>0.566000</td>\n",
              "      <td>0.07180</td>\n",
              "      <td>0.25050</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.009544</td>\n",
              "      <td>1.004000</td>\n",
              "      <td>1.009000</td>\n",
              "      <td>0.888000</td>\n",
              "      <td>0.10144</td>\n",
              "      <td>1.006000</td>\n",
              "      <td>0.007618</td>\n",
              "      <td>0.095100</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.56450</td>\n",
              "      <td>0.001099</td>\n",
              "      <td>0.310800</td>\n",
              "      <td>0.040040</td>\n",
              "      <td>0.005510</td>\n",
              "      <td>0.1674</td>\n",
              "      <td>0.007940</td>\n",
              "      <td>0.216900</td>\n",
              "      <td>0.07180</td>\n",
              "      <td>0.03546</td>\n",
              "      <td>...</td>\n",
              "      <td>0.002506</td>\n",
              "      <td>0.009544</td>\n",
              "      <td>1.004000</td>\n",
              "      <td>0.003675</td>\n",
              "      <td>0.912600</td>\n",
              "      <td>0.44240</td>\n",
              "      <td>1.002000</td>\n",
              "      <td>0.009260</td>\n",
              "      <td>1.097000</td>\n",
              "      <td>0.0</td>\n",
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              "<p>100 rows × 189 columns</p>\n",
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          "metadata": {},
          "execution_count": 111
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    },
    {
      "cell_type": "code",
      "source": [
        "def get_X_from_groups(feature_set, groups):\n",
        "    result = []\n",
        "    for group in groups:\n",
        "        result.append(feature_set[group])\n",
        "    return result\n",
        "\n",
        "def get_X_from_features(feature_set, cont_features, cat_features):\n",
        "    groups = [cont_features]\n",
        "    groups.extend(cat_features)\n",
        "    return get_X_from_groups(feature_set, groups)"
      ],
      "metadata": {
        "id": "tOeMtlp3rwlX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cont_features.remove('target')\n",
        "len(cont_features)"
      ],
      "metadata": {
        "id": "Evnbpfumm7dA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9975baaa-5a57-4882-e73c-fbdcc4fc7374"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cats = []\n",
        "for col in cat_features:\n",
        "  cats.append(df1[col].unique().shape[0])"
      ],
      "metadata": {
        "id": "Y3MmIcXVnT1x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFkfimS_no9v",
        "outputId": "23e28682-c662-490c-fd69-22a30229c1a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 7, 5, 4, 1, 6, 2, 2, 7, 2, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = get_X_from_features(df1.drop(columns=['target'],axis=1),cont_features,cat_features)\n",
        "y = df1['target']"
      ],
      "metadata": {
        "id": "cUH9wtpHmO2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf \n",
        "import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow_addons as tfa"
      ],
      "metadata": {
        "id": "UD8b0YwcTp1m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1.drop(columns=['target'],axis=1)\n",
        "y = y.to_numpy()"
      ],
      "metadata": {
        "id": "4uYGLNB9mTZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class TransformerBlock(layers.Layer):\n",
        "    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n",
        "        super(TransformerBlock, self).__init__()\n",
        "        # parametreleri\n",
        "        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.ffn = keras.Sequential(\n",
        "            [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n",
        "        )\n",
        "        # batch-layer\n",
        "        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n",
        "        self.dropout1 = layers.Dropout(rate)\n",
        "        self.dropout2 = layers.Dropout(rate)\n",
        "\n",
        "    def call(self, inputs, training):\n",
        "        attn_output = self.att(inputs, inputs)\n",
        "        attn_output = self.dropout1(attn_output, training=training)\n",
        "        out1 = self.layernorm1(inputs + attn_output)\n",
        "        ffn_output = self.ffn(out1)\n",
        "        ffn_output = self.dropout2(ffn_output, training=training)\n",
        "        return self.layernorm2(out1 + ffn_output)\n",
        "\n",
        "class FTTransformer(keras.Model):\n",
        "\n",
        "    def __init__(self, \n",
        "            categories,\n",
        "            num_continuous,\n",
        "            dim,\n",
        "            dim_out,\n",
        "            depth,\n",
        "            embedding_dim,\n",
        "            heads,\n",
        "            attn_dropout,\n",
        "            ff_dropout,\n",
        "            mlp_hidden,\n",
        "            normalize_continuous = True):\n",
        "\n",
        "        super(FTTransformer, self).__init__()\n",
        "\n",
        "        # --> continuous inputs\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.normalize_continuous = normalize_continuous\n",
        "        if normalize_continuous:\n",
        "            self.continuous_normalization = layers.LayerNormalization()\n",
        "\n",
        "        # --> categorical inputs\n",
        "\n",
        "        # embedding\n",
        "        self.embedding_layers = []\n",
        "        for number_of_classes in categories:\n",
        "            self.embedding_layers.append(layers.Embedding(input_dim = number_of_classes, output_dim = dim))\n",
        "        # self.embedding_layers_cont = []\n",
        "        # for number_of_classes in range(177):\n",
        "        #     self.embedding_layers_cont.append(layers.Embedding(input_dim = 10, output_dim = dim))\n",
        "        self.embb = layers.Embedding(input_dim = 1000, output_dim = dim)\n",
        "        self.flatten_output = layers.Flatten()\n",
        "\n",
        "        # concatenation\n",
        "        self.embedded_concatenation = layers.Concatenate(axis=1)\n",
        "        self.cont_embedded_concatenation = layers.Concatenate(axis=1)\n",
        "\n",
        "        # adding transformers\n",
        "        self.transformers = []\n",
        "        for _ in range(depth):\n",
        "            self.transformers.append(TransformerBlock(dim, heads, dim))\n",
        "        self.flatten_transformer_output = layers.Flatten()\n",
        "\n",
        "        # --> MLP\n",
        "        self.pre_concatenation = layers.Concatenate(axis=1)\n",
        "\n",
        "        # mlp layers\n",
        "        self.mlp_layers = []\n",
        "        for size, activation in mlp_hidden:\n",
        "            self.mlp_layers.append(layers.Dense(size, activation=activation))\n",
        "\n",
        "        self.output_layer = layers.Dense(dim_out)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        continuous_inputs  = inputs[0]\n",
        "        categorical_inputs = inputs[1:]\n",
        "        # print(continuous_inputs.shape, type(categorical_inputs))\n",
        "        # --> continuous\n",
        "        if self.normalize_continuous:\n",
        "            continuous_inputs = self.continuous_normalization(continuous_inputs)\n",
        "    \n",
        "        # cont_embedding_outputs = []\n",
        "        # print(continuous_inputs.transpoghjse()[0].shape)\n",
        "        # for continuous_input, embedding_layer in zip(tf.unstack(tf.transpose(continuous_inputs)), self.embedding_layers_cont):\n",
        "        #     cont_embedding_outputs.append(tf.expand_dims(embedding_layer(continuous_input),axis=1))\n",
        "        # continuous_inputs = self.cont_embedded_concatenation(cont_embedding_outputs)\n",
        "        continuous_inputs = self.embb(continuous_inputs)\n",
        "        # print(cont_embedding_outputs[0].shape)\n",
        "        # --> categorical\n",
        "        embedding_outputs = []\n",
        "        for categorical_input, embedding_layer in zip(categorical_inputs, self.embedding_layers):\n",
        "            embedding_outputs.append(embedding_layer(categorical_input))\n",
        "        categorical_inputs = self.embedded_concatenation(embedding_outputs)\n",
        "        # print(embedding_outputs[0].shape)\n",
        "        # print(embedding_outputs[0].shape)\n",
        "        # print(categorical_inputs.shape)\n",
        "        trans_input = self.pre_concatenation([continuous_inputs, categorical_inputs])\n",
        "        \n",
        "        for transformer in self.transformers:\n",
        "            trans_input = transformer(trans_input)\n",
        "        mlp_input = self.flatten_transformer_output(trans_input)\n",
        "        # print(categorical_inputs.shape)\n",
        "        # --> MLP\n",
        "        # print(mlp_input.shape)\n",
        "        for mlp_layer in self.mlp_layers:\n",
        "            mlp_input = mlp_layer(mlp_input)\n",
        "        return self.output_layer(mlp_input)\n",
        "  "
      ],
      "metadata": {
        "id": "uSELv6K2Tzmf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with strategy.scope():\n",
        "  model = FTTransformer(\n",
        "        categories = cats, # number of unique elements in each categorical feature\n",
        "        num_continuous = 177,      # number of numerical features\n",
        "        dim = 16,                # embedding/transformer dimension\n",
        "        dim_out = 1,             # dimension of the model output\n",
        "        depth = 6,  \n",
        "        embedding_dim=256,             # number of transformer layers in the stack\n",
        "        heads = 8,               # number of attention heads\n",
        "        attn_dropout = 0.1,      # attention layer dropout in transformers\n",
        "        ff_dropout = 0.1,        # feed-forward layer dropout in transformers\n",
        "        mlp_hidden = [(1024, 'relu'),(256, 'relu'),(128, 'relu'),(64, 'relu'), (32, 'relu')] # mlp layer dimensions and activations\n",
        "    )\n",
        "\n",
        "  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "  history = model.fit(X,y,epochs=5,validation_split=0.2,batch_size=32,verbose=1)"
      ],
      "metadata": {
        "id": "ypGqWh4ssl7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90891fb3-62fb-472d-a8b8-7a7765e6c42b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_36/layer_normalization_468/gamma:0', 'ft_transformer_36/layer_normalization_468/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_36/layer_normalization_468/gamma:0', 'ft_transformer_36/layer_normalization_468/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_36/layer_normalization_468/gamma:0', 'ft_transformer_36/layer_normalization_468/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_36/layer_normalization_468/gamma:0', 'ft_transformer_36/layer_normalization_468/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 45s 8s/step - loss: 2.3151 - accuracy: 0.8500 - val_loss: 7.7125 - val_accuracy: 0.5000\n",
            "Epoch 2/5\n",
            "3/3 [==============================] - 1s 349ms/step - loss: 2.3137 - accuracy: 0.8500 - val_loss: 7.7125 - val_accuracy: 0.5000\n",
            "Epoch 3/5\n",
            "3/3 [==============================] - 1s 344ms/step - loss: 2.3137 - accuracy: 0.8500 - val_loss: 7.7125 - val_accuracy: 0.5000\n",
            "Epoch 4/5\n",
            "3/3 [==============================] - 1s 356ms/step - loss: 2.3137 - accuracy: 0.8500 - val_loss: 7.7125 - val_accuracy: 0.5000\n",
            "Epoch 5/5\n",
            "3/3 [==============================] - 1s 350ms/step - loss: 2.3137 - accuracy: 0.8500 - val_loss: 7.7125 - val_accuracy: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold\n",
        "kfold = KFold(n_splits=5, shuffle=True)\n",
        "acc_per_fold=[]\n",
        "loss_per_fold = []\n",
        "# K-fold Cross Validation model evaluation\n",
        "fold_no = 1\n",
        "with strategy.scope():\n",
        "  for train, test in kfold.split(df2, y):\n",
        "    train = list(train)\n",
        "    test = list(test)\n",
        "    train_df = df2.iloc[train]\n",
        "    test_df = df2.iloc[test]\n",
        "    train_y = y[train]\n",
        "    test_y = y[test]\n",
        "    train_X = get_X_from_features(train_df,cont_features,cat_features)\n",
        "    test_X = get_X_from_features(test_df,cont_features,cat_features)\n",
        "    \n",
        "    model = FTTransformer(\n",
        "        categories = cats, # number of unique elements in each categorical feature\n",
        "        num_continuous = 177,      # number of numerical features\n",
        "        dim = 16,                # embedding/transformer dimension\n",
        "        dim_out = 1,             # dimension of the model output\n",
        "        depth = 6,  \n",
        "        embedding_dim=256,             # number of transformer layers in the stack\n",
        "        heads = 8,               # number of attention heads\n",
        "        attn_dropout = 0.1,      # attention layer dropout in transformers\n",
        "        ff_dropout = 0.1,        # feed-forward layer dropout in transformers\n",
        "        mlp_hidden = [(64, 'relu'),(32, 'relu'), (16, 'relu')] # mlp layer dimensions and activations\n",
        "    )\n",
        "\n",
        "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    \n",
        "    print('------------------------------------------------------------------------')\n",
        "    print(f'Training for fold {fold_no} ...')\n",
        "    history = model.fit(train_X,train_y,epochs=3,validation_split=0.2,batch_size=32,verbose=1)\n",
        "    \n",
        "\n",
        "    # Generate generalization metrics\n",
        "    scores = model.evaluate(test_X, test_y, verbose=0)\n",
        "    print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')\n",
        "    acc_per_fold.append(scores[1] * 100)\n",
        "    loss_per_fold.append(scores[0])\n",
        "\n",
        "    # Increase fold number\n",
        "    fold_no = fold_no + 1"
      ],
      "metadata": {
        "id": "HNqzdmcqmuYe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "678335a0-7dd3-4751-8e49-4c1f2a6ace6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "------------------------------------------------------------------------\n",
            "Training for fold 1 ...\n",
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_29/layer_normalization_377/gamma:0', 'ft_transformer_29/layer_normalization_377/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_29/layer_normalization_377/gamma:0', 'ft_transformer_29/layer_normalization_377/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_29/layer_normalization_377/gamma:0', 'ft_transformer_29/layer_normalization_377/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_29/layer_normalization_377/gamma:0', 'ft_transformer_29/layer_normalization_377/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 27s 27s/step - loss: 0.6941 - accuracy: 0.6562 - val_loss: 0.6230 - val_accuracy: 0.6250\n",
            "Epoch 2/3\n",
            "1/1 [==============================] - 1s 712ms/step - loss: 0.4882 - accuracy: 0.7188 - val_loss: 1.9360 - val_accuracy: 0.8750\n",
            "Epoch 3/3\n",
            "1/1 [==============================] - 1s 694ms/step - loss: 3.0382 - accuracy: 0.7500 - val_loss: 0.4799 - val_accuracy: 0.8750\n",
            "Score for fold 1: loss of 0.5756421685218811; accuracy of 80.0000011920929%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 2 ...\n",
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_30/layer_normalization_390/gamma:0', 'ft_transformer_30/layer_normalization_390/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_30/layer_normalization_390/gamma:0', 'ft_transformer_30/layer_normalization_390/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_30/layer_normalization_390/gamma:0', 'ft_transformer_30/layer_normalization_390/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_30/layer_normalization_390/gamma:0', 'ft_transformer_30/layer_normalization_390/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 31s 31s/step - loss: 3.8562 - accuracy: 0.7500 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 2/3\n",
            "1/1 [==============================] - 1s 717ms/step - loss: 3.8562 - accuracy: 0.7500 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 3/3\n",
            "1/1 [==============================] - 1s 724ms/step - loss: 3.8562 - accuracy: 0.7500 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Score for fold 2: loss of 1.542490839958191; accuracy of 90.00000357627869%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 3 ...\n",
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_31/layer_normalization_403/gamma:0', 'ft_transformer_31/layer_normalization_403/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_31/layer_normalization_403/gamma:0', 'ft_transformer_31/layer_normalization_403/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_31/layer_normalization_403/gamma:0', 'ft_transformer_31/layer_normalization_403/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_31/layer_normalization_403/gamma:0', 'ft_transformer_31/layer_normalization_403/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 28s 28s/step - loss: 3.4011 - accuracy: 0.7812 - val_loss: 1.9281 - val_accuracy: 0.8750\n",
            "Epoch 2/3\n",
            "1/1 [==============================] - 1s 711ms/step - loss: 3.3742 - accuracy: 0.7812 - val_loss: 1.9281 - val_accuracy: 0.8750\n",
            "Epoch 3/3\n",
            "1/1 [==============================] - 1s 698ms/step - loss: 3.3742 - accuracy: 0.7812 - val_loss: 1.9281 - val_accuracy: 0.8750\n",
            "Score for fold 3: loss of 4.627472877502441; accuracy of 69.9999988079071%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 4 ...\n",
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_32/layer_normalization_416/gamma:0', 'ft_transformer_32/layer_normalization_416/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_32/layer_normalization_416/gamma:0', 'ft_transformer_32/layer_normalization_416/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_32/layer_normalization_416/gamma:0', 'ft_transformer_32/layer_normalization_416/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_32/layer_normalization_416/gamma:0', 'ft_transformer_32/layer_normalization_416/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 20 calls to <function Model.make_train_function.<locals>.train_function at 0x7fba5b101290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 20 calls to <function Model.make_train_function.<locals>.train_function at 0x7fba5b101290> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 27s 27s/step - loss: 0.4602 - accuracy: 0.7500 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 2/3\n",
            "1/1 [==============================] - 1s 690ms/step - loss: 2.8922 - accuracy: 0.8125 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 3/3\n",
            "1/1 [==============================] - 1s 686ms/step - loss: 2.8922 - accuracy: 0.8125 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Score for fold 4: loss of 4.627472877502441; accuracy of 69.9999988079071%\n",
            "------------------------------------------------------------------------\n",
            "Training for fold 5 ...\n",
            "Epoch 1/3\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_33/layer_normalization_429/gamma:0', 'ft_transformer_33/layer_normalization_429/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_33/layer_normalization_429/gamma:0', 'ft_transformer_33/layer_normalization_429/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_33/layer_normalization_429/gamma:0', 'ft_transformer_33/layer_normalization_429/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['ft_transformer_33/layer_normalization_429/gamma:0', 'ft_transformer_33/layer_normalization_429/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_train_function.<locals>.train_function at 0x7fba51400170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_train_function.<locals>.train_function at 0x7fba51400170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 28s 28s/step - loss: 3.3742 - accuracy: 0.7812 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 2/3\n",
            "1/1 [==============================] - 1s 703ms/step - loss: 3.3742 - accuracy: 0.7812 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Epoch 3/3\n",
            "1/1 [==============================] - 1s 684ms/step - loss: 3.3742 - accuracy: 0.7812 - val_loss: 3.8562 - val_accuracy: 0.7500\n",
            "Score for fold 5: loss of 3.084981679916382; accuracy of 80.0000011920929%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights('fttransformer.h5', overwrite=True)"
      ],
      "metadata": {
        "id": "soUxIfJAJJcQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp gatedtab.h5 drive/MyDrive/"
      ],
      "metadata": {
        "id": "hsUoVkkoJV13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cbbce72-282a-41cc-f5be-9950cd3fef45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp: cannot stat 'gatedtab.h5': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "05Qdu0gWsnbi",
        "outputId": "148de885-b1fa-4b6d-d31a-cad93a1e5d8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install --quiet shap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlEQ3q8U-jkJ",
        "outputId": "e24034b2-a8ef-44c6-a127-21d5ed922307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |▋                               | 10 kB 30.4 MB/s eta 0:00:01\r\u001b[K     |█▏                              | 20 kB 12.2 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 30 kB 9.2 MB/s eta 0:00:01\r\u001b[K     |██▎                             | 40 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |██▉                             | 51 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |████                            | 71 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |████▋                           | 81 kB 5.3 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 92 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 102 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 112 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████                         | 122 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████▌                        | 133 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████                        | 143 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████▋                       | 153 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 163 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 174 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 184 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 194 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 204 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 215 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 225 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 235 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 245 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 256 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 266 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▌                | 276 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 286 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 296 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 307 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 317 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 327 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 337 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 348 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████▏           | 358 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 368 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 378 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▉          | 389 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 399 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 409 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▋        | 419 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 430 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 440 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 450 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 460 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 471 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 481 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 491 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 501 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 512 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 522 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 532 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 542 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████ | 552 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▋| 563 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 569 kB 5.0 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "\n",
        "# print the JS visualization code to the notebook\n",
        "shap.initjs()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 43
        },
        "id": "g_MwlC-03X3Q",
        "outputId": "067e9a18-1647-48cb-d84f-526a4cf09a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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p=this.xlabel.selectAll(\"option\").data(f);p.enter().append(\"option\").merge(p).attr(\"value\",function(t){return t}).text(function(t){return t}),p.exit().remove();var h=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";f=(0,y.map)(this.usedFeatures,function(e){return[t.props.featureNames[e],t.props.featureNames[e]+\" effects\"]}),f.unshift([\"model output value\",h]);var d=this.ylabel.selectAll(\"option\").data(f);d.enter().append(\"option\").merge(d).attr(\"value\",function(t){return t[0]}).text(function(t){return t[1]}),d.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var t=this,e=!0,n=!1,r=void 0;try{for(var i,o=this.props.explanations[Symbol.iterator]();!(e=(i=o.next()).done);e=!0){var a=i.value,c=!0,s=!1,l=void 0;try{for(var 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f=this._processPendingState(l,a),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,a):this._compositeType===_.PureClass&&(p=!m(s,l)||!m(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,a,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=a)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=c({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var u=r[a];c(o,\"function\"==typeof u?u.call(n,o,t,e):u)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(y(r,i))v.receiveComponent(n,i,t,this._processChildContext(e));else{var a=v.getHostNode(n);v.unmountComponent(n,!1);var u=d.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==d.EMPTY);this._renderedComponent=c;var s=v.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){l.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t,e=this._instance;return t=e.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==_.StatelessFunctional){f.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{f.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||t===!1||s.isValidElement(t)?void 0:u(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n?u(\"110\"):void 0;var r=e.getPublicInstance(),i=n.refs===g?n.refs={}:n.refs;i[t]=r},detachRef:function(t){var e=this.getPublicInstance().refs;delete e[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===_.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=x},function(t,e,n){\"use strict\";var r=n(4),i=n(358),o=n(163),a=n(24),u=n(11),c=n(371),s=n(387),l=n(167),f=n(395);n(1);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&(G[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML?v(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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q)q.hasOwnProperty(n)&&t._wrapperState.listeners.push(k.trapBubbledEvent(n,q[n],e));break;case\"source\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topError\",\"error\",e),k.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topReset\",\"reset\",e),k.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[k.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function f(){N.postUpdateWrapper(this)}function p(t){Z.call(X,t)||($.test(t)?void 0:v(\"65\",t),X[t]=!0)}function h(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function d(t){var e=t.type;p(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var v=n(2),g=n(3),m=n(332),y=n(334),_=n(20),b=n(82),x=n(21),w=n(156),C=n(22),M=n(83),k=n(51),E=n(157),T=n(4),S=n(351),P=n(352),N=n(158),A=n(355),O=(n(9),n(364)),I=n(369),D=(n(8),n(54)),R=(n(0),n(94),n(80),n(96),n(1),E),L=C.deleteListener,U=T.getNodeFromInstance,F=k.listenTo,j=M.registrationNameModules,B={string:!0,number:!0},W=\"style\",V=\"__html\",z={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},H=11,q={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},Y={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},K={listing:!0,pre:!0,textarea:!0},G=g({menuitem:!0},Y),$=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,X={},Z={}.hasOwnProperty,Q=1;d.displayName=\"ReactDOMComponent\",d.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=Q++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(l,this);break;case\"input\":S.mountWrapper(this,o,e),o=S.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"option\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o);break;case\"select\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this);break;case\"textarea\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o),t.getReactMountReady().enqueue(l,this)}i(this,o);var a,f;null!=e?(a=e._namespaceURI,f=e._tag):n._tag&&(a=n._namespaceURI,f=n._tag),(null==a||a===b.svg&&\"foreignobject\"===f)&&(a=b.html),a===b.html&&(\"svg\"===this._tag?a=b.svg:\"math\"===this._tag&&(a=b.mathml)),this._namespaceURI=a;var p;if(t.useCreateElement){var h,d=n._ownerDocument;if(a===b.html)if(\"script\"===this._tag){var v=d.createElement(\"div\"),g=this._currentElement.type;v.innerHTML=\"<\"+g+\"></\"+g+\">\",h=v.removeChild(v.firstChild)}else h=o.is?d.createElement(this._currentElement.type,o.is):d.createElement(this._currentElement.type);else h=d.createElementNS(a,this._currentElement.type);T.precacheNode(this,h),this._flags|=R.hasCachedChildNodes,this._hostParent||w.setAttributeForRoot(h),this._updateDOMProperties(null,o,t);var y=_(h);this._createInitialChildren(t,o,r,y),p=y}else{var x=this._createOpenTagMarkupAndPutListeners(t,o),C=this._createContentMarkup(t,o,r);p=!C&&Y[this._tag]?x+\"/>\":x+\">\"+C+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"select\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(m.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return p},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(j.hasOwnProperty(r))i&&o(this,r,i,t);else{r===W&&(i&&(i=this._previousStyleCopy=g({},e.style)),i=y.createMarkupForStyles(i,this));var a=null;null!=this._tag&&h(this._tag,e)?z.hasOwnProperty(r)||(a=w.createMarkupForCustomAttribute(r,i)):a=w.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+w.createMarkupForRoot()),n+=\" \"+w.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=D(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return K[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&_.queueHTML(r,i.__html);else{var o=B[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&_.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)_.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=S.getHostProps(this,o),a=S.getHostProps(this,a);break;case\"option\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"select\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"textarea\":o=A.getHostProps(this,o),a=A.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":S.updateWrapper(this);break;case\"textarea\":A.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(f,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(r===W){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else j.hasOwnProperty(r)?t[r]&&L(this,r):h(this._tag,t)?z.hasOwnProperty(r)||w.deleteValueForAttribute(U(this),r):(x.properties[r]||x.isCustomAttribute(r))&&w.deleteValueForProperty(U(this),r);for(r in e){var c=e[r],s=r===W?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(r===W)if(c?c=this._previousStyleCopy=g({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(j.hasOwnProperty(r))c?o(this,r,c,n):s&&L(this,r);else if(h(this._tag,e))z.hasOwnProperty(r)||w.setValueForAttribute(U(this),r,c);else if(x.properties[r]||x.isCustomAttribute(r)){var l=U(this);null!=c?w.setValueForProperty(l,r,c):w.deleteValueForProperty(l,r)}}a&&y.setValueForStyles(U(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=B[typeof t.children]?t.children:null,o=B[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return U(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"html\":case\"head\":case\"body\":v(\"66\",this._tag)}this.unmountChildren(t),T.uncacheNode(this),C.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return U(this)}},g(d.prototype,d.Mixin,O.Mixin),t.exports=d},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(96),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" \";if(t.useCreateElement){var c=n._ownerDocument,s=c.createComment(u);return o.precacheNode(this,s),i(s)}return t.renderToStaticMarkup?\"\":\"<!--\"+u+\"-->\"},receiveComponent:function(){},getHostNode:function(){return o.getNodeFromInstance(this)},unmountComponent:function(){o.uncacheNode(this)}}),t.exports=a},function(t,e,n){\"use strict\";var r={useCreateElement:!0,useFiber:!1};t.exports=r},function(t,e,n){\"use strict\";var r=n(81),i=n(4),o={dangerouslyProcessChildrenUpdates:function(t,e){var n=i.getNodeFromInstance(t);r.processUpdates(n,e)}};t.exports=o},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&f.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=c.executeOnChange(e,t);l.asap(r,this);var i=e.name;if(\"radio\"===e.type&&null!=i){for(var a=s.getNodeFromInstance(this),u=a;u.parentNode;)u=u.parentNode;for(var f=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<f.length;p++){var h=f[p];if(h!==a&&h.form===a.form){var d=s.getInstanceFromNode(h);d?void 0:o(\"90\"),l.asap(r,d)}}}return n}var o=n(2),a=n(3),u=n(156),c=n(85),s=n(4),l=n(11),f=(n(0),n(1),{getHostProps:function(t,e){var n=c.getValue(e),r=c.getChecked(e),i=a({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange});return i},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&u.setValueForProperty(s.getNodeFromInstance(t),\"checked\",n||!1);var r=s.getNodeFromInstance(t),i=c.getValue(e);if(null!=i){var o=\"\"+i;o!==r.value&&(r.value=o)}else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=s.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=f},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(158),c=(n(1),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){var n=a.getNodeFromInstance(t);n.setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length,a=o+r;return{start:o,end:a}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(392),l=n(168),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(2),i=n(3),o=n(81),a=n(20),u=n(4),c=n(54),s=(n(0),n(96),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \",s=\" /react-text \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var l=n._ownerDocument,f=l.createComment(o),p=l.createComment(s),h=a(l.createDocumentFragment());return a.queueChild(h,a(f)),this._stringText&&a.queueChild(h,a(l.createTextNode(this._stringText))),a.queueChild(h,a(p)),u.precacheNode(this,f),this._closingComment=p,h}var d=c(this._stringText);return t.renderToStaticMarkup?d:\"<!--\"+o+\"-->\"+d+\"<!--\"+s+\"-->\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n?r(\"67\",this._domID):void 0,8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(2),a=n(3),u=n(85),c=n(4),s=n(11),l=(n(0),n(1),{getHostProps:function(t,e){null!=e.dangerouslySetInnerHTML?o(\"91\"):void 0;var n=a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange});return n},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a?o(\"92\"):void 0,Array.isArray(c)&&(c.length<=1?void 0:o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;\n",
              "n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t?void 0:c(\"33\"),\"_hostNode\"in e?void 0:c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in t?void 0:c(\"35\"),\"_hostNode\"in e?void 0:c(\"35\");for(;e;){if(e===t)return!0;e=e._hostParent}return!1}function o(t){return\"_hostNode\"in t?void 0:c(\"36\"),t._hostParent}function a(t,e,n){for(var r=[];t;)r.push(t),t=t._hostParent;var i;for(i=r.length;i-- >0;)e(r[i],\"captured\",n);for(i=0;i<r.length;i++)e(r[i],\"bubbled\",n)}function u(t,e,n,i,o){for(var a=t&&e?r(t,e):null,u=[];t&&t!==a;)u.push(t),t=t._hostParent;for(var c=[];e&&e!==a;)c.push(e),e=e._hostParent;var s;for(s=0;s<u.length;s++)n(u[s],\"bubbled\",i);for(s=c.length;s-- 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r(){C||(C=!0,y.EventEmitter.injectReactEventListener(m),y.EventPluginHub.injectEventPluginOrder(u),y.EventPluginUtils.injectComponentTree(p),y.EventPluginUtils.injectTreeTraversal(d),y.EventPluginHub.injectEventPluginsByName({SimpleEventPlugin:w,EnterLeaveEventPlugin:c,ChangeEventPlugin:a,SelectEventPlugin:x,BeforeInputEventPlugin:o}),y.HostComponent.injectGenericComponentClass(f),y.HostComponent.injectTextComponentClass(v),y.DOMProperty.injectDOMPropertyConfig(i),y.DOMProperty.injectDOMPropertyConfig(s),y.DOMProperty.injectDOMPropertyConfig(b),y.EmptyComponent.injectEmptyComponentFactory(function(t){return new h(t)}),y.Updates.injectReconcileTransaction(_),y.Updates.injectBatchingStrategy(g),y.Component.injectEnvironment(l))}var i=n(331),o=n(333),a=n(335),u=n(337),c=n(338),s=n(341),l=n(343),f=n(346),p=n(4),h=n(348),d=n(356),v=n(354),g=n(357),m=n(361),y=n(362),_=n(367),b=n(372),x=n(373),w=n(374),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof 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v={_enabled:!0,_handleTopLevel:null,WINDOW_HANDLE:s.canUseDOM?window:null,setHandleTopLevel:function(t){v._handleTopLevel=t},setEnabled:function(t){v._enabled=!!t},isEnabled:function(){return v._enabled},trapBubbledEvent:function(t,e,n){return n?c.listen(n,e,v.dispatchEvent.bind(null,t)):null},trapCapturedEvent:function(t,e,n){return n?c.capture(n,e,v.dispatchEvent.bind(null,t)):null},monitorScrollValue:function(t){var e=a.bind(null,t);c.listen(window,\"scroll\",e)},dispatchEvent:function(t,e){if(v._enabled){var n=i.getPooled(t,e);try{p.batchedUpdates(o,n)}finally{i.release(n)}}}};t.exports=v},function(t,e,n){\"use strict\";var r=n(21),i=n(22),o=n(50),a=n(86),u=n(159),c=n(51),s=n(161),l=n(11),f={Component:a.injection,DOMProperty:r.injection,EmptyComponent:u.injection,EventPluginHub:i.injection,EventPluginUtils:o.injection,EventEmitter:c.injection,HostComponent:s.injection,Updates:l.injection};t.exports=f},function(t,e,n){\"use strict\";var r=n(385),i=/\\/?>/,o=/^<\\!\\-\\-/,a={CHECKSUM_ATTR_NAME:\"data-react-checksum\",addChecksumToMarkup:function(t){var e=r(t);return o.test(t)?t:t.replace(i,\" \"+a.CHECKSUM_ATTR_NAME+'=\"'+e+'\"$&')},canReuseMarkup:function(t,e){var n=e.getAttribute(a.CHECKSUM_ATTR_NAME);n=n&&parseInt(n,10);var i=r(t);return i===n}};t.exports=a},function(t,e,n){\"use strict\";function r(t,e,n){return{type:\"INSERT_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:n,afterNode:e}}function i(t,e,n){return{type:\"MOVE_EXISTING\",content:null,fromIndex:t._mountIndex,fromNode:p.getHostNode(t),toIndex:n,afterNode:e}}function o(t,e){return{type:\"REMOVE_NODE\",content:null,fromIndex:t._mountIndex,fromNode:e,toIndex:null,afterNode:null}}function a(t){return{type:\"SET_MARKUP\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function u(t){return{type:\"TEXT_CONTENT\",content:t,fromIndex:null,fromNode:null,toIndex:null,afterNode:null}}function c(t,e){return e&&(t=t||[],t.push(e)),t}function s(t,e){f.processChildrenUpdates(t,e)}var l=n(2),f=n(86),p=(n(40),n(9),n(15),n(24)),h=n(342),d=(n(8),n(388)),v=(n(0),{Mixin:{_reconcilerInstantiateChildren:function(t,e,n){return h.instantiateChildren(t,e,n)},_reconcilerUpdateChildren:function(t,e,n,r,i,o){var a,u=0;return a=d(e,u),h.updateChildren(t,a,n,r,i,this,this._hostContainerInfo,o,u),a},mountChildren:function(t,e,n){var r=this._reconcilerInstantiateChildren(t,e,n);this._renderedChildren=r;var i=[],o=0;for(var a in r)if(r.hasOwnProperty(a)){var u=r[a],c=0,s=p.mountComponent(u,e,this,this._hostContainerInfo,n,c);u._mountIndex=o++,i.push(s)}return i},updateTextContent:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[u(t)];s(this,r)},updateMarkup:function(t){var e=this._renderedChildren;h.unmountChildren(e,!1);for(var n in e)e.hasOwnProperty(n)&&l(\"118\");var r=[a(t)];s(this,r)},updateChildren:function(t,e,n){this._updateChildren(t,e,n)},_updateChildren:function(t,e,n){var r=this._renderedChildren,i={},o=[],a=this._reconcilerUpdateChildren(r,t,o,i,e,n);if(a||r){var u,l=null,f=0,h=0,d=0,v=null;for(u in a)if(a.hasOwnProperty(u)){var g=r&&r[u],m=a[u];g===m?(l=c(l,this.moveChild(g,v,f,h)),h=Math.max(g._mountIndex,h),g._mountIndex=f):(g&&(h=Math.max(g._mountIndex,h)),l=c(l,this._mountChildAtIndex(m,o[d],v,f,e,n)),d++),f++,v=p.getHostNode(m)}for(u in i)i.hasOwnProperty(u)&&(l=c(l,this._unmountChild(r[u],i[u])));l&&s(this,l),this._renderedChildren=a}},unmountChildren:function(t){var e=this._renderedChildren;h.unmountChildren(e,t),this._renderedChildren=null},moveChild:function(t,e,n,r){if(t._mountIndex<r)return i(t,e,n)},createChild:function(t,e,n){return r(n,e,t._mountIndex)},removeChild:function(t,e){return o(t,e)},_mountChildAtIndex:function(t,e,n,r,i,o){return t._mountIndex=r,this.createChild(t,n,e)},_unmountChild:function(t,e){var n=this.removeChild(t,e);return t._mountIndex=null,n}}});t.exports=v},function(t,e,n){\"use strict\";function r(t){return!(!t||\"function\"!=typeof t.attachRef||\"function\"!=typeof t.detachRef)}var i=n(2),o=(n(0),{addComponentAsRefTo:function(t,e,n){r(n)?void 0:i(\"119\"),n.attachRef(e,t)},removeComponentAsRefFrom:function(t,e,n){r(n)?void 0:i(\"120\");var o=n.getPublicInstance();o&&o.refs[e]===t.getPublicInstance()&&n.detachRef(e)}});t.exports=o},function(t,e,n){\"use strict\";var r=\"SECRET_DO_NOT_PASS_THIS_OR_YOU_WILL_BE_FIRED\";t.exports=r},function(t,e,n){\"use strict\";function r(t){this.reinitializeTransaction(),this.renderToStaticMarkup=!1,this.reactMountReady=o.getPooled(null),this.useCreateElement=t}var i=n(3),o=n(155),a=n(17),u=n(51),c=n(162),s=(n(9),n(53)),l=n(88),f={initialize:c.getSelectionInformation,close:c.restoreSelection},p={initialize:function(){var t=u.isEnabled();return u.setEnabled(!1),t},close:function(t){u.setEnabled(t)}},h={initialize:function(){this.reactMountReady.reset()},close:function(){this.reactMountReady.notifyAll()}},d=[f,p,h],v={getTransactionWrappers:function(){return d},getReactMountReady:function(){return this.reactMountReady},getUpdateQueue:function(){return l},checkpoint:function(){return this.reactMountReady.checkpoint()},rollback:function(t){this.reactMountReady.rollback(t)},destructor:function(){o.release(this.reactMountReady),this.reactMountReady=null}};i(r.prototype,s,v),a.addPoolingTo(r),t.exports=r},function(t,e,n){\"use strict\";function r(t,e,n){\"function\"==typeof t?t(e.getPublicInstance()):o.addComponentAsRefTo(e,t,n)}function i(t,e,n){\"function\"==typeof t?t(null):o.removeComponentAsRefFrom(e,t,n)}var o=n(365),a={};a.attachRefs=function(t,e){if(null!==e&&\"object\"==typeof e){var n=e.ref;null!=n&&r(n,t,e._owner)}},a.shouldUpdateRefs=function(t,e){var n=null,r=null;null!==t&&\"object\"==typeof 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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def f(inp):\n",
        "    inp = pd.DataFrame(inp,columns=list(df.columns))\n",
        "    X = get_X_from_features(inp.drop(columns=['target'],axis=1),cont_features,cat_features)\n",
        "    return model.predict(X).flatten()\n",
        "\n",
        "explainer = shap.KernelExplainer(f, df1.iloc[:50,:])"
      ],
      "metadata": {
        "id": "c-jE-DYB-eqn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap_values50 = explainer.shap_values(df1.iloc[:50,:], nsamples=500)\n",
        "fig = shap.summary_plot(shap_values50, df1.iloc[:50,:],show=False)\n",
        "plt.savefig('shap.png', dpi=600, bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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          ]
        },
        "id": "VYyUnM00_MMD",
        "outputId": "1aaa3feb-3277-4b07-fe44-74e43046a26d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/50 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n",
            "The default of 'normalize' will be set to False in version 1.2 and deprecated in version 1.4.\n",
            "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
            "\n",
            "from sklearn.pipeline import make_pipeline\n",
            "\n",
            "model = make_pipeline(StandardScaler(with_mean=False), LassoLarsIC())\n",
            "\n",
            "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
            "\n",
            "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
            "model.fit(X, y, **kwargs)\n",
            "\n",
            "Set parameter alpha to: original_alpha * np.sqrt(n_samples). \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x684 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4IiZvcTB6SbZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}