conv1d-generic-wine-regressor.ipynb

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    "ExecuteTime": {
     "end_time": "2020-09-19T17:12:59.078496Z",
     "start_time": "2020-09-19T17:12:58.986302Z"
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       "            setTimeout(function() {\n",
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       "                var nbb_formatted_code = \"%load_ext nb_black\\n%load_ext autoreload\\n\\n%autoreload 2\";\n",
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       "            }, 500);\n",
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   "source": [
    "%load_ext nb_black\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"import os\\nfrom pathlib import Path\\n\\nfrom requests import get\\nimport pandas as pd\\nimport numpy as np\\n\\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\\nfrom sklearn.preprocessing import LabelEncoder\\nfrom tensorflow.keras.utils import plot_model\\nfrom tensorflow.keras.callbacks import EarlyStopping\\n\\nimport logging\\n\\nlogging.basicConfig(level=logging.WARN)\";\n",
       "                var nbb_formatted_code = \"import os\\nfrom pathlib import Path\\n\\nfrom requests import get\\nimport pandas as pd\\nimport numpy as np\\n\\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error\\nfrom sklearn.preprocessing import LabelEncoder\\nfrom tensorflow.keras.utils import plot_model\\nfrom tensorflow.keras.callbacks import EarlyStopping\\n\\nimport logging\\n\\nlogging.basicConfig(level=logging.WARN)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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   ],
   "source": [
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "from requests import get\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.WARN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.470749Z",
     "start_time": "2020-09-19T17:13:00.291326Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"from xplainet.input_utils import preproc_dataset\\nfrom xplainet.model import build_model\\nfrom xplainet.random_utils import setup_seed, SEED\\nfrom sklearn.model_selection import StratifiedShuffleSplit\\n\\nimport matplotlib.pyplot as plt\\nfrom matplotlib.pyplot import imshow\\n\\n%matplotlib inline\\n\\nfrom zipfile import ZipFile\";\n",
       "                var nbb_formatted_code = \"from xplainet.input_utils import preproc_dataset\\nfrom xplainet.model import build_model\\nfrom xplainet.random_utils import setup_seed, SEED\\nfrom sklearn.model_selection import StratifiedShuffleSplit\\n\\nimport matplotlib.pyplot as plt\\nfrom matplotlib.pyplot import imshow\\n\\n%matplotlib inline\\n\\nfrom zipfile import ZipFile\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xplainet.input_utils import preproc_dataset\n",
    "from xplainet.model import build_model\n",
    "from xplainet.random_utils import setup_seed, SEED\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from zipfile import ZipFile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.493695Z",
     "start_time": "2020-09-19T17:13:00.471666Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"setup_seed()\";\n",
       "                var nbb_formatted_code = \"setup_seed()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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   "source": [
    "setup_seed()"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.577850Z",
     "start_time": "2020-09-19T17:13:00.494655Z"
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   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"def download(url, out, force=False, verify=True):\\n    out.parent.mkdir(parents=True, exist_ok=True)\\n    if force and out.exists():\\n        print(f\\\"Removing file at {str(out)}\\\")\\n        out.unlink()\\n\\n    if out.exists():\\n        print(\\\"File already exists.\\\")\\n        return\\n    print(f\\\"Downloading {url} at {str(out)} ...\\\")\\n    # open in binary mode\\n    with out.open(mode=\\\"wb\\\") as file:\\n        # get request\\n        response = get(url, verify=verify)\\n        for chunk in response.iter_content(100000):\\n            # write to file\\n            file.write(chunk)\";\n",
       "                var nbb_formatted_code = \"def download(url, out, force=False, verify=True):\\n    out.parent.mkdir(parents=True, exist_ok=True)\\n    if force and out.exists():\\n        print(f\\\"Removing file at {str(out)}\\\")\\n        out.unlink()\\n\\n    if out.exists():\\n        print(\\\"File already exists.\\\")\\n        return\\n    print(f\\\"Downloading {url} at {str(out)} ...\\\")\\n    # open in binary mode\\n    with out.open(mode=\\\"wb\\\") as file:\\n        # get request\\n        response = get(url, verify=verify)\\n        for chunk in response.iter_content(100000):\\n            # write to file\\n            file.write(chunk)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "def download(url, out, force=False, verify=True):\n",
    "    out.parent.mkdir(parents=True, exist_ok=True)\n",
    "    if force and out.exists():\n",
    "        print(f\"Removing file at {str(out)}\")\n",
    "        out.unlink()\n",
    "\n",
    "    if out.exists():\n",
    "        print(\"File already exists.\")\n",
    "        return\n",
    "    print(f\"Downloading {url} at {str(out)} ...\")\n",
    "    # open in binary mode\n",
    "    with out.open(mode=\"wb\") as file:\n",
    "        # get request\n",
    "        response = get(url, verify=verify)\n",
    "        for chunk in response.iter_content(100000):\n",
    "            # write to file\n",
    "            file.write(chunk)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.626416Z",
     "start_time": "2020-09-19T17:13:00.578797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"def plot_history(history):\\n    loss_list = [s for s in history.history.keys() if \\\"loss\\\" in s and \\\"val\\\" not in s]\\n    val_loss_list = [s for s in history.history.keys() if \\\"loss\\\" in s and \\\"val\\\" in s]\\n    acc_list = [s for s in history.history.keys() if \\\"AUC\\\" in s and \\\"val\\\" not in s]\\n    val_acc_list = [s for s in history.history.keys() if \\\"AUC\\\" in s and \\\"val\\\" in s]\\n\\n    if len(loss_list) == 0:\\n        print(\\\"Loss is missing in history\\\")\\n        return\\n\\n    ## As loss always exists\\n    epochs = range(1, len(history.history[loss_list[0]]) + 1)\\n\\n    ## Loss\\n    plt.figure(1)\\n    for l in loss_list:\\n        plt.plot(\\n            epochs,\\n            history.history[l],\\n            \\\"b\\\",\\n            label=\\\"Training loss (\\\"\\n            + str(str(format(history.history[l][-1], \\\".5f\\\")) + \\\")\\\"),\\n        )\\n    for l in val_loss_list:\\n        plt.plot(\\n            epochs,\\n            history.history[l],\\n            \\\"g\\\",\\n            label=\\\"Validation loss (\\\"\\n            + str(str(format(history.history[l][-1], \\\".5f\\\")) + \\\")\\\"),\\n        )\\n\\n    plt.title(\\\"Loss\\\")\\n    plt.xlabel(\\\"Epochs\\\")\\n    plt.ylabel(\\\"Loss\\\")\\n    plt.legend()\\n\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"def plot_history(history):\\n    loss_list = [s for s in history.history.keys() if \\\"loss\\\" in s and \\\"val\\\" not in s]\\n    val_loss_list = [s for s in history.history.keys() if \\\"loss\\\" in s and \\\"val\\\" in s]\\n    acc_list = [s for s in history.history.keys() if \\\"AUC\\\" in s and \\\"val\\\" not in s]\\n    val_acc_list = [s for s in history.history.keys() if \\\"AUC\\\" in s and \\\"val\\\" in s]\\n\\n    if len(loss_list) == 0:\\n        print(\\\"Loss is missing in history\\\")\\n        return\\n\\n    ## As loss always exists\\n    epochs = range(1, len(history.history[loss_list[0]]) + 1)\\n\\n    ## Loss\\n    plt.figure(1)\\n    for l in loss_list:\\n        plt.plot(\\n            epochs,\\n            history.history[l],\\n            \\\"b\\\",\\n            label=\\\"Training loss (\\\"\\n            + str(str(format(history.history[l][-1], \\\".5f\\\")) + \\\")\\\"),\\n        )\\n    for l in val_loss_list:\\n        plt.plot(\\n            epochs,\\n            history.history[l],\\n            \\\"g\\\",\\n            label=\\\"Validation loss (\\\"\\n            + str(str(format(history.history[l][-1], \\\".5f\\\")) + \\\")\\\"),\\n        )\\n\\n    plt.title(\\\"Loss\\\")\\n    plt.xlabel(\\\"Epochs\\\")\\n    plt.ylabel(\\\"Loss\\\")\\n    plt.legend()\\n\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    loss_list = [s for s in history.history.keys() if \"loss\" in s and \"val\" not in s]\n",
    "    val_loss_list = [s for s in history.history.keys() if \"loss\" in s and \"val\" in s]\n",
    "    acc_list = [s for s in history.history.keys() if \"AUC\" in s and \"val\" not in s]\n",
    "    val_acc_list = [s for s in history.history.keys() if \"AUC\" in s and \"val\" in s]\n",
    "\n",
    "    if len(loss_list) == 0:\n",
    "        print(\"Loss is missing in history\")\n",
    "        return\n",
    "\n",
    "    ## As loss always exists\n",
    "    epochs = range(1, len(history.history[loss_list[0]]) + 1)\n",
    "\n",
    "    ## Loss\n",
    "    plt.figure(1)\n",
    "    for l in loss_list:\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history.history[l],\n",
    "            \"b\",\n",
    "            label=\"Training loss (\"\n",
    "            + str(str(format(history.history[l][-1], \".5f\")) + \")\"),\n",
    "        )\n",
    "    for l in val_loss_list:\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history.history[l],\n",
    "            \"g\",\n",
    "            label=\"Validation loss (\"\n",
    "            + str(str(format(history.history[l][-1], \".5f\")) + \")\"),\n",
    "        )\n",
    "\n",
    "    plt.title(\"Loss\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bank marketing : loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.662004Z",
     "start_time": "2020-09-19T17:13:00.628717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File already exists.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\\\"\\ndataset_name = \\\"wine-quality\\\"\\nout = Path(os.getcwd() + \\\"/data/\\\" + dataset_name + \\\".csv\\\")\\n\\ndownload(url, out)\\n\\ntarget = \\\"quality\\\"\\nto_remove = []\\ntrain = pd.read_csv(out, sep=\\\";\\\", low_memory=False)\";\n",
       "                var nbb_formatted_code = \"url = \\\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\\\"\\ndataset_name = \\\"wine-quality\\\"\\nout = Path(os.getcwd() + \\\"/data/\\\" + dataset_name + \\\".csv\\\")\\n\\ndownload(url, out)\\n\\ntarget = \\\"quality\\\"\\nto_remove = []\\ntrain = pd.read_csv(out, sep=\\\";\\\", low_memory=False)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
    "dataset_name = \"wine-quality\"\n",
    "out = Path(os.getcwd() + \"/data/\" + dataset_name + \".csv\")\n",
    "\n",
    "download(url, out)\n",
    "\n",
    "target = \"quality\"\n",
    "to_remove = []\n",
    "train = pd.read_csv(out, sep=\";\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.691543Z",
     "start_time": "2020-09-19T17:13:00.663716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"train.shape\";\n",
       "                var nbb_formatted_code = \"train.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.718718Z",
     "start_time": "2020-09-19T17:13:00.692816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"train.columns\";\n",
       "                var nbb_formatted_code = \"train.columns\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "train.columns"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.759341Z",
     "start_time": "2020-09-19T17:13:00.719697Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building tailored column\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/.cache/poetry/xplainet-KS9Ipbc1-py3.7/lib/python3.7/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/work/.cache/poetry/xplainet-KS9Ipbc1-py3.7/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  iloc._setitem_with_indexer(indexer, value)\n",
      "/work/.cache/poetry/xplainet-KS9Ipbc1-py3.7/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"if \\\"Set\\\" not in train.columns:\\n    print(\\\"Building tailored column\\\")\\n    train_valid_index, test_index = next(\\n        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\\n            range(train[target].shape[0]), train[target].values\\n        )\\n    )\\n    train_index, valid_index = next(\\n        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\\n            train_valid_index, train[target].values[train_valid_index]\\n        )\\n    )\\n    train[\\\"Set\\\"] = \\\"train\\\"\\n    train[\\\"Set\\\"][valid_index] = \\\"valid\\\"\\n    train[\\\"Set\\\"][test_index] = \\\"test\\\"\\n    # train.to_csv((out.parent / \\\"train_bench.csv\\\").as_posix(), index=False)\";\n",
       "                var nbb_formatted_code = \"if \\\"Set\\\" not in train.columns:\\n    print(\\\"Building tailored column\\\")\\n    train_valid_index, test_index = next(\\n        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\\n            range(train[target].shape[0]), train[target].values\\n        )\\n    )\\n    train_index, valid_index = next(\\n        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\\n            train_valid_index, train[target].values[train_valid_index]\\n        )\\n    )\\n    train[\\\"Set\\\"] = \\\"train\\\"\\n    train[\\\"Set\\\"][valid_index] = \\\"valid\\\"\\n    train[\\\"Set\\\"][test_index] = \\\"test\\\"\\n    # train.to_csv((out.parent / \\\"train_bench.csv\\\").as_posix(), index=False)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if \"Set\" not in train.columns:\n",
    "    print(\"Building tailored column\")\n",
    "    train_valid_index, test_index = next(\n",
    "        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\n",
    "            range(train[target].shape[0]), train[target].values\n",
    "        )\n",
    "    )\n",
    "    train_index, valid_index = next(\n",
    "        StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=SEED).split(\n",
    "            train_valid_index, train[target].values[train_valid_index]\n",
    "        )\n",
    "    )\n",
    "    train[\"Set\"] = \"train\"\n",
    "    train[\"Set\"][valid_index] = \"valid\"\n",
    "    train[\"Set\"][test_index] = \"test\"\n",
    "    # train.to_csv((out.parent / \"train_bench.csv\").as_posix(), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.787811Z",
     "start_time": "2020-09-19T17:13:00.760685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"train_indices = train[train.Set == \\\"train\\\"].index\\nvalid_indices = train[train.Set == \\\"valid\\\"].index\\ntest_indices = train[train.Set == \\\"test\\\"].index\";\n",
       "                var nbb_formatted_code = \"train_indices = train[train.Set == \\\"train\\\"].index\\nvalid_indices = train[train.Set == \\\"valid\\\"].index\\ntest_indices = train[train.Set == \\\"test\\\"].index\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_indices = train[train.Set == \"train\"].index\n",
    "valid_indices = train[train.Set == \"valid\"].index\n",
    "test_indices = train[train.Set == \"test\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.849754Z",
     "start_time": "2020-09-19T17:13:00.788767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'constant_cols': [],\n",
       " 'bool_cols': [],\n",
       " 'num_cols': ['citric acid',\n",
       "  'volatile acidity',\n",
       "  'pH',\n",
       "  'chlorides',\n",
       "  'fixed acidity',\n",
       "  'sulphates',\n",
       "  'total sulfur dioxide',\n",
       "  'residual sugar',\n",
       "  'density'],\n",
       " 'cat_cols': ['free sulfur dioxide', 'alcohol'],\n",
       " 'num_encoder': [Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=-0.019274336038921036,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.10203064783174609,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=2.7247534703867524,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.007052178701545107,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=4.430469800926217,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.35252137039077047,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=2.6992470791190675,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.7544668475173683,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.9898825436334188,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())])],\n",
       " 'bool_encoder': [],\n",
       " 'max_nb': 64,\n",
       " 'cat_encoder': [SafeLabelEncoder(), SafeLabelEncoder()]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"input_train, params = preproc_dataset(train.loc[train_indices], target, [\\\"Set\\\"])\\nparams\";\n",
       "                var nbb_formatted_code = \"input_train, params = preproc_dataset(train.loc[train_indices], target, [\\\"Set\\\"])\\nparams\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_train, params = preproc_dataset(train.loc[train_indices], target, [\"Set\"])\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.875159Z",
     "start_time": "2020-09-19T17:13:00.850717Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1310"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"len(train_indices)\";\n",
       "                var nbb_formatted_code = \"len(train_indices)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "len(train_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.909535Z",
     "start_time": "2020-09-19T17:13:00.876339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"input_valid, _ = preproc_dataset(train.loc[valid_indices], target, [\\\"Set\\\"], params)\\ninput_test, _ = preproc_dataset(train.loc[test_indices], target, [\\\"Set\\\"], params)\";\n",
       "                var nbb_formatted_code = \"input_valid, _ = preproc_dataset(train.loc[valid_indices], target, [\\\"Set\\\"], params)\\ninput_test, _ = preproc_dataset(train.loc[test_indices], target, [\\\"Set\\\"], params)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_valid, _ = preproc_dataset(train.loc[valid_indices], target, [\"Set\"], params)\n",
    "input_test, _ = preproc_dataset(train.loc[test_indices], target, [\"Set\"], params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.932748Z",
     "start_time": "2020-09-19T17:13:00.910702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# target_encoder = LabelEncoder()\";\n",
       "                var nbb_formatted_code = \"# target_encoder = LabelEncoder()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.958546Z",
     "start_time": "2020-09-19T17:13:00.933697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"# train[target] = target_encoder.fit_transform(train[target].values.reshape(-1))\\ny_train = train[target].values[train_indices]\\ny_valid = train[target].values[valid_indices]\\ny_test = train[target].values[test_indices]\";\n",
       "                var nbb_formatted_code = \"# train[target] = target_encoder.fit_transform(train[target].values.reshape(-1))\\ny_train = train[target].values[train_indices]\\ny_valid = train[target].values[valid_indices]\\ny_test = train[target].values[test_indices]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train[target] = target_encoder.fit_transform(train[target].values.reshape(-1))\n",
    "y_train = train[target].values[train_indices]\n",
    "y_valid = train[target].values[valid_indices]\n",
    "y_test = train[target].values[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:00.998721Z",
     "start_time": "2020-09-19T17:13:00.959728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'constant_cols': [],\n",
       " 'bool_cols': [],\n",
       " 'num_cols': ['citric acid',\n",
       "  'volatile acidity',\n",
       "  'pH',\n",
       "  'chlorides',\n",
       "  'fixed acidity',\n",
       "  'sulphates',\n",
       "  'total sulfur dioxide',\n",
       "  'residual sugar',\n",
       "  'density'],\n",
       " 'cat_cols': ['free sulfur dioxide', 'alcohol'],\n",
       " 'num_encoder': [Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=-0.019274336038921036,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.10203064783174609,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=2.7247534703867524,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.007052178701545107,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=4.430469800926217,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.35252137039077047,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=2.6992470791190675,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.7544668475173683,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())]),\n",
       "  Pipeline(steps=[('fillna',\n",
       "                   SimpleImputer(fill_value=0.9898825436334188,\n",
       "                                 strategy='constant')),\n",
       "                  ('scaler', StandardScaler())])],\n",
       " 'bool_encoder': [],\n",
       " 'max_nb': 64,\n",
       " 'cat_encoder': [SafeLabelEncoder(), SafeLabelEncoder()]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"params\";\n",
       "                var nbb_formatted_code = \"params\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.116077Z",
     "start_time": "2020-09-19T17:13:00.999652Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/.cache/poetry/xplainet-KS9Ipbc1-py3.7/lib/python3.7/site-packages/tensorflow_addons/utils/resource_loader.py:103: UserWarning: You are currently using TensorFlow 2.3.0 and trying to load a custom op (custom_ops/activations/_activation_ops.so).\n",
      "TensorFlow Addons has compiled its custom ops against TensorFlow 2.2.0, and there are no compatibility guarantees between the two versions. \n",
      "This means that you might get segfaults when loading the custom op, or other kind of low-level errors.\n",
      " If you do, do not file an issue on Github. This is a known limitation.\n",
      "\n",
      "It might help you to fallback to pure Python ops with TF_ADDONS_PY_OPS . To do that, see https://github.com/tensorflow/addons#gpucpu-custom-ops \n",
      "\n",
      "You can also change the TensorFlow version installed on your system. You would need a TensorFlow version equal to or above 2.2.0 and strictly below 2.3.0.\n",
      " Note that nightly versions of TensorFlow, as well as non-pip TensorFlow like `conda install tensorflow` or compiled from source are not supported.\n",
      "\n",
      "The last solution is to find the TensorFlow Addons version that has custom ops compatible with the TensorFlow installed on your system. To do that, refer to the readme: https://github.com/tensorflow/addons\n",
      "  UserWarning,\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"model = build_model(\\n    params,\\n    lconv_dim=[4],\\n    lconv_num_dim=[8],\\n    emb_size=16,\\n    activation_num_first_layer=None,  # \\\"tanh\\\",\\n    output_activation=None,\\n    output_dim=1,  # np.unique(y_train).shape[0],\\n)\";\n",
       "                var nbb_formatted_code = \"model = build_model(\\n    params,\\n    lconv_dim=[4],\\n    lconv_num_dim=[8],\\n    emb_size=16,\\n    activation_num_first_layer=None,  # \\\"tanh\\\",\\n    output_activation=None,\\n    output_dim=1,  # np.unique(y_train).shape[0],\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model(\n",
    "    params,\n",
    "    lconv_dim=[4],\n",
    "    lconv_num_dim=[8],\n",
    "    emb_size=16,\n",
    "    activation_num_first_layer=None,  # \"tanh\",\n",
    "    output_activation=None,\n",
    "    output_dim=1,  # np.unique(y_train).shape[0],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.145080Z",
     "start_time": "2020-09-19T17:13:01.117095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"explainable_model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_num (InputLayer)          [(None, 9)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_cat (InputLayer)          [(None, 2)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_num_input (Reshape)     (None, 9, 1)         0           input_num[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "large_emb (Embedding)           (None, 2, 16)        1040        input_cat[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "block_num_0_conv (LocallyConnec (None, 9, 8)         72          reshape_num_input[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_cat_0_conv (LocallyConnec (None, 2, 4)         128         large_emb[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "block_num_0_activation (Activat (None, 9, 8)         0           block_num_0_conv[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_cat_0_activation (Activat (None, 2, 4)         0           block_cat_0_conv[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "reshape_num_output (Reshape)    (None, 72)           0           block_num_0_activation[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "reshape_cat_output (Reshape)    (None, 8)            0           block_cat_0_activation[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 80)           0           reshape_num_output[0][0]         \n",
      "                                                                 reshape_cat_output[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 1)            81          concatenate[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 1,321\n",
      "Trainable params: 1,321\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"model.summary()\";\n",
       "                var nbb_formatted_code = \"model.summary()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.168543Z",
     "start_time": "2020-09-19T17:13:01.146136Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"#!pip install pydot graphviz\";\n",
       "                var nbb_formatted_code = \"#!pip install pydot graphviz\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!pip install pydot graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.330445Z",
     "start_time": "2020-09-19T17:13:01.169589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"plot_model(\\n    model,\\n    # to_file=\\\"model.png\\\",\\n    show_shapes=True,\\n    show_layer_names=True,\\n    rankdir=\\\"TB\\\",\\n    expand_nested=False,\\n    dpi=96,\\n)\";\n",
       "                var nbb_formatted_code = \"plot_model(\\n    model,\\n    # to_file=\\\"model.png\\\",\\n    show_shapes=True,\\n    show_layer_names=True,\\n    rankdir=\\\"TB\\\",\\n    expand_nested=False,\\n    dpi=96,\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(\n",
    "    model,\n",
    "    # to_file=\"model.png\",\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    "    rankdir=\"TB\",\n",
    "    expand_nested=False,\n",
    "    dpi=96,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.358243Z",
     "start_time": "2020-09-19T17:13:01.333039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1310,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"y_train.shape\";\n",
       "                var nbb_formatted_code = \"y_train.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.385411Z",
     "start_time": "2020-09-19T17:13:01.360178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"counts = np.unique(y_train, return_counts=True)[1]\\ncounts = counts.sum() / counts\";\n",
       "                var nbb_formatted_code = \"counts = np.unique(y_train, return_counts=True)[1]\\ncounts = counts.sum() / counts\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = np.unique(y_train, return_counts=True)[1]\n",
    "counts = counts.sum() / counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:01.411764Z",
     "start_time": "2020-09-19T17:13:01.386374Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 145.55555555555554,\n",
       " 1: 28.47826086956522,\n",
       " 2: 2.347670250896057,\n",
       " 3: 2.5387596899224807,\n",
       " 4: 7.844311377245509,\n",
       " 5: 93.57142857142857}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"class_weight = {}\\nfor i, counts in enumerate(counts):\\n    class_weight[i] = counts\\nclass_weight\";\n",
       "                var nbb_formatted_code = \"class_weight = {}\\nfor i, counts in enumerate(counts):\\n    class_weight[i] = counts\\nclass_weight\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_weight = {}\n",
    "for i, counts in enumerate(counts):\n",
    "    class_weight[i] = counts\n",
    "class_weight"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-14T10:13:28.824624Z",
     "start_time": "2020-07-14T10:13:28.800384Z"
    }
   },
   "source": [
    "class_weight = {\n",
    "    0: 1,\n",
    "    1: 1,\n",
    "}\n",
    "class_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.383977Z",
     "start_time": "2020-09-19T17:13:01.412737Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "2/2 - 0s - loss: 32.2739 - val_loss: 32.1564\n",
      "Epoch 2/2000\n",
      "2/2 - 0s - loss: 32.0166 - val_loss: 31.8979\n",
      "Epoch 3/2000\n",
      "2/2 - 0s - loss: 31.7611 - val_loss: 32.0914\n",
      "Epoch 4/2000\n",
      "2/2 - 0s - loss: 31.9802 - val_loss: 32.0888\n",
      "Epoch 5/2000\n",
      "2/2 - 0s - loss: 31.9778 - val_loss: 32.0855\n",
      "Epoch 6/2000\n",
      "2/2 - 0s - loss: 31.9747 - val_loss: 32.0864\n",
      "Epoch 7/2000\n",
      "2/2 - 0s - loss: 31.9755 - val_loss: 32.0818\n",
      "Epoch 8/2000\n",
      "2/2 - 0s - loss: 31.9713 - val_loss: 32.0768\n",
      "Epoch 9/2000\n",
      "2/2 - 0s - loss: 31.9668 - val_loss: 32.0788\n",
      "Epoch 10/2000\n",
      "2/2 - 0s - loss: 31.9685 - val_loss: 32.0728\n",
      "Epoch 11/2000\n",
      "2/2 - 0s - loss: 31.9631 - val_loss: 32.0664\n",
      "Epoch 12/2000\n",
      "2/2 - 0s - loss: 31.9573 - val_loss: 32.0692\n",
      "Epoch 13/2000\n",
      "2/2 - 0s - loss: 31.9598 - val_loss: 32.0620\n",
      "Epoch 14/2000\n",
      "2/2 - 0s - loss: 31.9534 - val_loss: 32.0545\n",
      "Epoch 15/2000\n",
      "2/2 - 0s - loss: 31.9466 - val_loss: 32.0579\n",
      "Epoch 16/2000\n",
      "2/2 - 0s - loss: 31.9496 - val_loss: 32.0498\n",
      "Epoch 17/2000\n",
      "2/2 - 0s - loss: 31.9423 - val_loss: 32.0414\n",
      "Epoch 18/2000\n",
      "2/2 - 0s - loss: 31.9347 - val_loss: 32.0453\n",
      "Epoch 19/2000\n",
      "2/2 - 0s - loss: 31.9381 - val_loss: 32.0364\n",
      "Epoch 20/2000\n",
      "2/2 - 0s - loss: 31.9301 - val_loss: 32.0272\n",
      "Epoch 21/2000\n",
      "2/2 - 0s - loss: 31.9216 - val_loss: 32.0316\n",
      "Epoch 22/2000\n",
      "2/2 - 0s - loss: 31.9255 - val_loss: 32.0219\n",
      "Epoch 23/2000\n",
      "2/2 - 0s - loss: 31.9166 - val_loss: 32.0119\n",
      "Epoch 24/2000\n",
      "2/2 - 0s - loss: 31.9074 - val_loss: 32.0165\n",
      "Epoch 25/2000\n",
      "2/2 - 0s - loss: 31.9118 - val_loss: 32.0058\n",
      "Epoch 26/2000\n",
      "2/2 - 0s - loss: 31.9022 - val_loss: 31.9949\n",
      "Epoch 27/2000\n",
      "2/2 - 0s - loss: 31.8923 - val_loss: 32.0001\n",
      "Epoch 28/2000\n",
      "2/2 - 0s - loss: 31.8970 - val_loss: 31.9888\n",
      "Epoch 29/2000\n",
      "2/2 - 0s - loss: 31.8867 - val_loss: 31.9772\n",
      "Epoch 30/2000\n",
      "2/2 - 0s - loss: 31.8762 - val_loss: 31.9828\n",
      "Epoch 31/2000\n",
      "2/2 - 0s - loss: 31.8812 - val_loss: 31.9708\n",
      "Epoch 32/2000\n",
      "2/2 - 0s - loss: 31.8703 - val_loss: 31.9586\n",
      "Epoch 33/2000\n",
      "2/2 - 0s - loss: 31.8591 - val_loss: 31.9645\n",
      "Epoch 34/2000\n",
      "2/2 - 0s - loss: 31.8645 - val_loss: 31.9517\n",
      "Epoch 35/2000\n",
      "2/2 - 0s - loss: 31.8530 - val_loss: 31.9387\n",
      "Epoch 36/2000\n",
      "2/2 - 0s - loss: 31.8411 - val_loss: 31.9451\n",
      "Epoch 37/2000\n",
      "2/2 - 0s - loss: 31.8469 - val_loss: 31.9316\n",
      "Epoch 38/2000\n",
      "2/2 - 0s - loss: 31.8346 - val_loss: 31.9180\n",
      "Epoch 39/2000\n",
      "2/2 - 0s - loss: 31.8223 - val_loss: 31.9245\n",
      "Epoch 40/2000\n",
      "2/2 - 0s - loss: 31.8283 - val_loss: 31.9104\n",
      "Epoch 41/2000\n",
      "2/2 - 0s - loss: 31.8155 - val_loss: 31.8961\n",
      "Epoch 42/2000\n",
      "2/2 - 0s - loss: 31.8025 - val_loss: 31.9031\n",
      "Epoch 43/2000\n",
      "2/2 - 0s - loss: 31.8088 - val_loss: 31.8884\n",
      "Epoch 44/2000\n",
      "2/2 - 0s - loss: 31.7955 - val_loss: 31.8735\n",
      "Epoch 45/2000\n",
      "2/2 - 0s - loss: 31.7818 - val_loss: 31.8808\n",
      "Epoch 46/2000\n",
      "2/2 - 0s - loss: 31.7884 - val_loss: 31.8656\n",
      "Epoch 47/2000\n",
      "2/2 - 0s - loss: 31.7745 - val_loss: 31.8501\n",
      "Epoch 48/2000\n",
      "2/2 - 0s - loss: 31.7603 - val_loss: 31.8577\n",
      "Epoch 49/2000\n",
      "2/2 - 0s - loss: 31.7671 - val_loss: 31.8418\n",
      "Epoch 50/2000\n",
      "2/2 - 0s - loss: 31.7526 - val_loss: 31.8255\n",
      "Epoch 51/2000\n",
      "2/2 - 0s - loss: 31.7377 - val_loss: 31.8334\n",
      "Epoch 52/2000\n",
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      "Epoch 53/2000\n",
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      "Epoch 54/2000\n",
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      "Epoch 55/2000\n",
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      "Epoch 56/2000\n",
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      "Epoch 57/2000\n",
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      "Epoch 58/2000\n",
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      "Epoch 59/2000\n",
      "2/2 - 0s - loss: 31.6809 - val_loss: 31.7448\n",
      "Epoch 60/2000\n",
      "2/2 - 0s - loss: 31.6641 - val_loss: 31.7537\n",
      "Epoch 61/2000\n",
      "2/2 - 0s - loss: 31.6724 - val_loss: 31.7351\n",
      "Epoch 62/2000\n",
      "2/2 - 0s - loss: 31.6550 - val_loss: 31.7162\n",
      "Epoch 63/2000\n",
      "2/2 - 0s - loss: 31.6377 - val_loss: 31.7254\n",
      "Epoch 64/2000\n",
      "2/2 - 0s - loss: 31.6462 - val_loss: 31.7061\n",
      "Epoch 65/2000\n",
      "2/2 - 0s - loss: 31.6282 - val_loss: 31.6863\n",
      "Epoch 66/2000\n",
      "2/2 - 0s - loss: 31.6100 - val_loss: 31.6959\n",
      "Epoch 67/2000\n",
      "2/2 - 0s - loss: 31.6188 - val_loss: 31.6753\n",
      "Epoch 68/2000\n",
      "2/2 - 0s - loss: 31.6001 - val_loss: 31.6543\n",
      "Epoch 69/2000\n",
      "2/2 - 0s - loss: 31.5811 - val_loss: 31.6646\n",
      "Epoch 70/2000\n",
      "2/2 - 0s - loss: 31.5904 - val_loss: 31.6434\n",
      "Epoch 71/2000\n",
      "2/2 - 0s - loss: 31.5709 - val_loss: 31.6217\n",
      "Epoch 72/2000\n",
      "2/2 - 0s - loss: 31.5510 - val_loss: 31.6322\n",
      "Epoch 73/2000\n",
      "2/2 - 0s - loss: 31.5608 - val_loss: 31.6098\n",
      "Epoch 74/2000\n",
      "2/2 - 0s - loss: 31.5404 - val_loss: 31.5870\n",
      "Epoch 75/2000\n",
      "2/2 - 0s - loss: 31.5197 - val_loss: 31.5983\n",
      "Epoch 76/2000\n",
      "2/2 - 0s - loss: 31.5299 - val_loss: 31.5749\n",
      "Epoch 77/2000\n",
      "2/2 - 0s - loss: 31.5087 - val_loss: 31.5511\n",
      "Epoch 78/2000\n",
      "2/2 - 0s - loss: 31.4871 - val_loss: 31.5628\n",
      "Epoch 79/2000\n",
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      "Epoch 80/2000\n",
      "2/2 - 0s - loss: 31.4757 - val_loss: 31.5142\n",
      "Epoch 81/2000\n",
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      "Epoch 82/2000\n",
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      "Epoch 83/2000\n",
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      "Epoch 84/2000\n",
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      "Epoch 85/2000\n",
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      "Epoch 86/2000\n",
      "2/2 - 0s - loss: 31.4059 - val_loss: 31.4376\n",
      "Epoch 87/2000\n",
      "2/2 - 0s - loss: 31.3817 - val_loss: 31.4503\n",
      "Epoch 88/2000\n",
      "2/2 - 0s - loss: 31.3937 - val_loss: 31.4239\n",
      "Epoch 89/2000\n",
      "2/2 - 0s - loss: 31.3689 - val_loss: 31.3970\n",
      "Epoch 90/2000\n",
      "2/2 - 0s - loss: 31.3437 - val_loss: 31.4103\n",
      "Epoch 91/2000\n",
      "2/2 - 0s - loss: 31.3562 - val_loss: 31.3829\n",
      "Epoch 92/2000\n",
      "2/2 - 0s - loss: 31.3303 - val_loss: 31.3550\n",
      "Epoch 93/2000\n",
      "2/2 - 0s - loss: 31.3040 - val_loss: 31.3686\n",
      "Epoch 94/2000\n",
      "2/2 - 0s - loss: 31.3170 - val_loss: 31.3395\n",
      "Epoch 95/2000\n",
      "2/2 - 0s - loss: 31.2901 - val_loss: 31.3098\n",
      "Epoch 96/2000\n",
      "2/2 - 0s - loss: 31.2625 - val_loss: 31.3244\n",
      "Epoch 97/2000\n",
      "2/2 - 0s - loss: 31.2762 - val_loss: 31.2941\n",
      "Epoch 98/2000\n",
      "2/2 - 0s - loss: 31.2481 - val_loss: 31.2633\n",
      "Epoch 99/2000\n",
      "2/2 - 0s - loss: 31.2194 - val_loss: 31.2786\n",
      "Epoch 100/2000\n",
      "2/2 - 0s - loss: 31.2336 - val_loss: 31.2471\n",
      "Epoch 101/2000\n",
      "2/2 - 0s - loss: 31.2044 - val_loss: 31.2150\n",
      "Epoch 102/2000\n",
      "2/2 - 0s - loss: 31.1746 - val_loss: 31.2310\n",
      "Epoch 103/2000\n",
      "2/2 - 0s - loss: 31.1893 - val_loss: 31.1984\n",
      "Epoch 104/2000\n",
      "2/2 - 0s - loss: 31.1589 - val_loss: 31.1652\n",
      "Epoch 105/2000\n",
      "2/2 - 0s - loss: 31.1279 - val_loss: 31.1816\n",
      "Epoch 106/2000\n",
      "2/2 - 0s - loss: 31.1432 - val_loss: 31.1477\n",
      "Epoch 107/2000\n",
      "2/2 - 0s - loss: 31.1115 - val_loss: 31.1131\n",
      "Epoch 108/2000\n",
      "2/2 - 0s - loss: 31.0792 - val_loss: 31.1303\n",
      "Epoch 109/2000\n",
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      "Epoch 110/2000\n",
      "2/2 - 0s - loss: 31.0622 - val_loss: 31.0593\n",
      "Epoch 111/2000\n",
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      "Epoch 112/2000\n",
      "2/2 - 0s - loss: 31.0450 - val_loss: 31.0404\n",
      "Epoch 113/2000\n",
      "2/2 - 0s - loss: 31.0108 - val_loss: 31.0029\n",
      "Epoch 114/2000\n",
      "2/2 - 0s - loss: 30.9756 - val_loss: 31.0215\n",
      "Epoch 115/2000\n",
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      "Epoch 116/2000\n",
      "2/2 - 0s - loss: 30.9571 - val_loss: 30.9448\n",
      "Epoch 117/2000\n",
      "2/2 - 0s - loss: 30.9204 - val_loss: 30.9639\n",
      "Epoch 118/2000\n",
      "2/2 - 0s - loss: 30.9387 - val_loss: 30.9240\n",
      "Epoch 119/2000\n",
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      "Epoch 120/2000\n",
      "2/2 - 0s - loss: 30.8629 - val_loss: 30.9038\n",
      "Epoch 121/2000\n",
      "2/2 - 0s - loss: 30.8818 - val_loss: 30.8626\n",
      "Epoch 122/2000\n",
      "2/2 - 0s - loss: 30.8429 - val_loss: 30.8205\n",
      "Epoch 123/2000\n",
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      "Epoch 124/2000\n",
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      "Epoch 125/2000\n",
      "2/2 - 0s - loss: 30.7819 - val_loss: 30.7543\n",
      "Epoch 126/2000\n",
      "2/2 - 0s - loss: 30.7402 - val_loss: 30.7761\n",
      "Epoch 127/2000\n",
      "2/2 - 0s - loss: 30.7608 - val_loss: 30.7313\n",
      "Epoch 128/2000\n",
      "2/2 - 0s - loss: 30.7182 - val_loss: 30.6854\n",
      "Epoch 129/2000\n",
      "2/2 - 0s - loss: 30.6748 - val_loss: 30.7081\n",
      "Epoch 130/2000\n",
      "2/2 - 0s - loss: 30.6964 - val_loss: 30.6611\n",
      "Epoch 131/2000\n",
      "2/2 - 0s - loss: 30.6520 - val_loss: 30.6136\n",
      "Epoch 132/2000\n",
      "2/2 - 0s - loss: 30.6067 - val_loss: 30.6374\n",
      "Epoch 133/2000\n",
      "2/2 - 0s - loss: 30.6291 - val_loss: 30.5889\n",
      "Epoch 134/2000\n",
      "2/2 - 0s - loss: 30.5829 - val_loss: 30.5394\n",
      "Epoch 135/2000\n",
      "2/2 - 0s - loss: 30.5357 - val_loss: 30.5639\n",
      "Epoch 136/2000\n",
      "2/2 - 0s - loss: 30.5591 - val_loss: 30.5131\n",
      "Epoch 137/2000\n",
      "2/2 - 0s - loss: 30.5107 - val_loss: 30.4613\n",
      "Epoch 138/2000\n",
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      "Epoch 139/2000\n",
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2/2 - 0s - loss: 0.3699 - val_loss: 0.6841\n",
      "Epoch 1884/2000\n",
      "2/2 - 0s - loss: 0.3697 - val_loss: 0.6842\n",
      "Epoch 1885/2000\n",
      "2/2 - 0s - loss: 0.3698 - val_loss: 0.6842\n",
      "Epoch 1886/2000\n",
      "2/2 - 0s - loss: 0.3696 - val_loss: 0.6840\n",
      "Epoch 1887/2000\n",
      "2/2 - 0s - loss: 0.3695 - val_loss: 0.6840\n",
      "Epoch 1888/2000\n",
      "2/2 - 0s - loss: 0.3695 - val_loss: 0.6837\n",
      "Epoch 1889/2000\n",
      "2/2 - 0s - loss: 0.3694 - val_loss: 0.6835\n",
      "Epoch 1890/2000\n",
      "2/2 - 0s - loss: 0.3693 - val_loss: 0.6837\n",
      "Epoch 1891/2000\n",
      "2/2 - 0s - loss: 0.3693 - val_loss: 0.6836\n",
      "Epoch 1892/2000\n",
      "2/2 - 0s - loss: 0.3692 - val_loss: 0.6836\n",
      "Epoch 1893/2000\n",
      "2/2 - 0s - loss: 0.3690 - val_loss: 0.6837\n",
      "Epoch 1894/2000\n",
      "2/2 - 0s - loss: 0.3691 - val_loss: 0.6838\n",
      "Epoch 1895/2000\n",
      "2/2 - 0s - loss: 0.3689 - val_loss: 0.6839\n",
      "Epoch 1896/2000\n",
      "2/2 - 0s - loss: 0.3688 - val_loss: 0.6839\n",
      "Epoch 1897/2000\n",
      "2/2 - 0s - loss: 0.3688 - val_loss: 0.6841\n",
      "Epoch 1898/2000\n",
      "2/2 - 0s - loss: 0.3687 - val_loss: 0.6842\n",
      "Epoch 1899/2000\n",
      "2/2 - 0s - loss: 0.3685 - val_loss: 0.6841\n",
      "Epoch 1900/2000\n",
      "2/2 - 0s - loss: 0.3686 - val_loss: 0.6845\n",
      "Epoch 1901/2000\n",
      "2/2 - 0s - loss: 0.3685 - val_loss: 0.6849\n",
      "Epoch 1902/2000\n",
      "2/2 - 0s - loss: 0.3683 - val_loss: 0.6846\n",
      "Epoch 1903/2000\n",
      "2/2 - 0s - loss: 0.3684 - val_loss: 0.6845\n",
      "Epoch 1904/2000\n",
      "2/2 - 0s - loss: 0.3682 - val_loss: 0.6844\n",
      "Epoch 1905/2000\n",
      "2/2 - 0s - loss: 0.3681 - val_loss: 0.6843\n",
      "Epoch 1906/2000\n",
      "2/2 - 0s - loss: 0.3682 - val_loss: 0.6839\n",
      "Epoch 01906: early stopping\n",
      "CPU times: user 52.3 s, sys: 3.18 s, total: 55.5 s\n",
      "Wall time: 24.9 s\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"%%time\\nhistory = model.fit(\\n    input_train,\\n    y_train.reshape(-1, 1),\\n    epochs=2000,\\n    batch_size=1024,\\n    validation_data=(input_valid, y_valid.reshape(-1, 1),),\\n    verbose=2,\\n    callbacks=[EarlyStopping(monitor=\\\"val_loss\\\", patience=50, verbose=1)],\\n    #class_weight=None#class_weight\\n)\";\n",
       "                var nbb_formatted_code = \"%%time\\nhistory = model.fit(\\n    input_train,\\n    y_train.reshape(-1, 1),\\n    epochs=2000,\\n    batch_size=1024,\\n    validation_data=(input_valid, y_valid.reshape(-1, 1),),\\n    verbose=2,\\n    callbacks=[EarlyStopping(monitor=\\\"val_loss\\\", patience=50, verbose=1)],\\n    #class_weight=None#class_weight\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "history = model.fit(\n",
    "    input_train,\n",
    "    y_train.reshape(-1, 1),\n",
    "    epochs=2000,\n",
    "    batch_size=1024,\n",
    "    validation_data=(input_valid, y_valid.reshape(-1, 1),),\n",
    "    verbose=2,\n",
    "    callbacks=[EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1)],\n",
    "    #class_weight=None#class_weight\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.534785Z",
     "start_time": "2020-09-19T17:13:26.384979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"plot_history(history)\";\n",
       "                var nbb_formatted_code = \"plot_history(history)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.673961Z",
     "start_time": "2020-09-19T17:13:26.535799Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5686252625413644"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"model_auc = mean_absolute_error(\\n    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\\n)\\nmodel_auc\";\n",
       "                var nbb_formatted_code = \"model_auc = mean_absolute_error(\\n    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\\n)\\nmodel_auc\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_auc = mean_absolute_error(\n",
    "    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\n",
    ")\n",
    "model_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.726747Z",
     "start_time": "2020-09-19T17:13:26.675067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6838507386196302"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"model_auc = mean_squared_error(\\n    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\\n)\\nmodel_auc\";\n",
       "                var nbb_formatted_code = \"model_auc = mean_squared_error(\\n    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\\n)\\nmodel_auc\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_auc = mean_squared_error(\n",
    "    y_pred=model.predict(input_valid).reshape(-1), y_true=y_valid\n",
    ")\n",
    "model_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.839953Z",
     "start_time": "2020-09-19T17:13:26.727656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49195781350135803"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"model_auc = mean_absolute_error(\\n    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\\n)\\nmodel_auc\";\n",
       "                var nbb_formatted_code = \"model_auc = mean_absolute_error(\\n    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\\n)\\nmodel_auc\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_auc = mean_absolute_error(\n",
    "    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\n",
    ")\n",
    "model_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.892230Z",
     "start_time": "2020-09-19T17:13:26.841113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5065062456342929"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"model_auc = mean_squared_error(\\n    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\\n)\\nmodel_auc\";\n",
       "                var nbb_formatted_code = \"model_auc = mean_squared_error(\\n    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\\n)\\nmodel_auc\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_auc = mean_squared_error(\n",
    "    y_pred=model.predict(input_test).reshape(-1), y_true=y_test\n",
    ")\n",
    "model_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:26.915863Z",
     "start_time": "2020-09-19T17:13:26.893229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"from xplainet.model import predict, encode\";\n",
       "                var nbb_formatted_code = \"from xplainet.model import predict, encode\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xplainet.model import predict, encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.030664Z",
     "start_time": "2020-09-19T17:13:26.916775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"probs, explanations = predict(model, input_test)\";\n",
       "                var nbb_formatted_code = \"probs, explanations = predict(model, input_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs, explanations = predict(model, input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.148387Z",
     "start_time": "2020-09-19T17:13:27.031511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 33;\n",
       "                var nbb_unformatted_code = \"probs, encoded_output = encode(model, input_test)\";\n",
       "                var nbb_formatted_code = \"probs, encoded_output = encode(model, input_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs, encoded_output = encode(model, input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.171924Z",
     "start_time": "2020-09-19T17:13:27.149365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 34;\n",
       "                var nbb_unformatted_code = \"y_test.shape\";\n",
       "                var nbb_formatted_code = \"y_test.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.202933Z",
     "start_time": "2020-09-19T17:13:27.172909Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 80)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"encoded_output.shape\";\n",
       "                var nbb_formatted_code = \"encoded_output.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoded_output.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.227848Z",
     "start_time": "2020-09-19T17:13:27.203861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 11)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"explanations.shape\";\n",
       "                var nbb_formatted_code = \"explanations.shape\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanations.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.259471Z",
     "start_time": "2020-09-19T17:13:27.229157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 37;\n",
       "                var nbb_unformatted_code = \"import matplotlib.pyplot as plt\\n\\n# plt.rcdefaults()\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\ndef explain_plot(importances, columns):\\n    selection = np.argsort(-np.absolute(importances))[:10]\\n    # indexes = np.argsort(importances)\\n    performance = importances[selection]\\n    #     print(performance.shape)512, 256, 128, 64, 32, 1\\n    y_pos = np.arange(performance.shape[0])\\n\\n    plt.barh(y_pos, performance, align=\\\"center\\\", alpha=0.5)\\n    plt.yticks(y_pos, columns[selection])\\n    # plt.xlabel('Usage')\\n    plt.title(\\\"Feature importance\\\")\\n\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"import matplotlib.pyplot as plt\\n\\n# plt.rcdefaults()\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\ndef explain_plot(importances, columns):\\n    selection = np.argsort(-np.absolute(importances))[:10]\\n    # indexes = np.argsort(importances)\\n    performance = importances[selection]\\n    #     print(performance.shape)512, 256, 128, 64, 32, 1\\n    y_pos = np.arange(performance.shape[0])\\n\\n    plt.barh(y_pos, performance, align=\\\"center\\\", alpha=0.5)\\n    plt.yticks(y_pos, columns[selection])\\n    # plt.xlabel('Usage')\\n    plt.title(\\\"Feature importance\\\")\\n\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plt.rcdefaults()\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def explain_plot(importances, columns):\n",
    "    selection = np.argsort(-np.absolute(importances))[:10]\n",
    "    # indexes = np.argsort(importances)\n",
    "    performance = importances[selection]\n",
    "    #     print(performance.shape)512, 256, 128, 64, 32, 1\n",
    "    y_pos = np.arange(performance.shape[0])\n",
    "\n",
    "    plt.barh(y_pos, performance, align=\"center\", alpha=0.5)\n",
    "    plt.yticks(y_pos, columns[selection])\n",
    "    # plt.xlabel('Usage')\n",
    "    plt.title(\"Feature importance\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.287546Z",
     "start_time": "2020-09-19T17:13:27.261080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['citric acid', 'volatile acidity', 'pH', 'chlorides',\n",
       "       'fixed acidity', 'sulphates', 'total sulfur dioxide',\n",
       "       'residual sugar', 'density', 'free sulfur dioxide', 'alcohol'],\n",
       "      dtype='<U20')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 38;\n",
       "                var nbb_unformatted_code = \"all_cols = np.array(params[\\\"bool_cols\\\"] + params[\\\"num_cols\\\"] + params[\\\"cat_cols\\\"])\\nall_cols\";\n",
       "                var nbb_formatted_code = \"all_cols = np.array(params[\\\"bool_cols\\\"] + params[\\\"num_cols\\\"] + params[\\\"cat_cols\\\"])\\nall_cols\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_cols = np.array(params[\"bool_cols\"] + params[\"num_cols\"] + params[\"cat_cols\"])\n",
    "all_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-18T18:48:54.151671Z",
     "start_time": "2020-09-18T18:48:54.127488Z"
    }
   },
   "source": [
    "## Explain global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.313019Z",
     "start_time": "2020-09-19T17:13:27.288424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 39;\n",
       "                var nbb_unformatted_code = \"# Looks like this is broken for now.\";\n",
       "                var nbb_formatted_code = \"# Looks like this is broken for now.\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looks like this is broken for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.445383Z",
     "start_time": "2020-09-19T17:13:27.314088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 40;\n",
       "                var nbb_unformatted_code = \"probs_train, explanations_train = predict(model, input_train)\";\n",
       "                var nbb_formatted_code = \"probs_train, explanations_train = predict(model, input_train)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_train, explanations_train = predict(model, input_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.471205Z",
     "start_time": "2020-09-19T17:13:27.446333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 41;\n",
       "                var nbb_unformatted_code = \"global_explain = np.abs(explanations_train).sum(axis=0)\\nglobal_explain = global_explain / np.abs(global_explain).sum()\";\n",
       "                var nbb_formatted_code = \"global_explain = np.abs(explanations_train).sum(axis=0)\\nglobal_explain = global_explain / np.abs(global_explain).sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_explain = np.abs(explanations_train).sum(axis=0)\n",
    "global_explain = global_explain / np.abs(global_explain).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.496082Z",
     "start_time": "2020-09-19T17:13:27.472163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.09062034, 0.0906155 , 0.09061714, 0.0906186 , 0.09061475,\n",
       "       0.0906136 , 0.0906184 , 0.09061984, 0.09061975, 0.09091277,\n",
       "       0.09352931])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 42;\n",
       "                var nbb_unformatted_code = \"global_explain\";\n",
       "                var nbb_formatted_code = \"global_explain\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.624277Z",
     "start_time": "2020-09-19T17:13:27.497133Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 43;\n",
       "                var nbb_unformatted_code = \"explain_plot(global_explain, all_cols)\";\n",
       "                var nbb_formatted_code = \"explain_plot(global_explain, all_cols)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explain_plot(global_explain, all_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explain local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:27.647248Z",
     "start_time": "2020-09-19T17:13:27.625347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 44;\n",
       "                var nbb_unformatted_code = \"# Looks like this is broken for now.\";\n",
       "                var nbb_formatted_code = \"# Looks like this is broken for now.\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looks like this is broken for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:29.836479Z",
     "start_time": "2020-09-19T17:13:27.648179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.069087982177734\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.2148051261901855\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.317386150360107\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.355174541473389\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.241655349731445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.353858470916748\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.16807746887207\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zpuy5p9jPAnYDDpP0vvygiHgZeFnSuNR0Qq77VrL7mwPTsUdLGlRlzGZm1gM6vDQaES9Kujc9gPIb4BxgH2AhWbV2TkT8r6QXgXXpYZAZwE+Aa1NldwuVK7EDgbMlvQGsAtoqwi8DNwIvAHOBTSvE+4akC8iS8zPAYxWO2+Z84EpJDwP3AX8ts8/1lJw7WeKdRXZ/9FlJp5BVtHuWjD2J7NJvAPmK72KyJ27nK7up+ALZU7BmZtZHFBGV97K6MWr0mJg07bpah2Fm1mW1+K5RSfPaeVjS3yxjZmbF5kRoZmaF5kRoZmaF5vUIG8yIwU11t5aXmVkjc0VoZmaF5kRoZmaF5kRoZmaF5nuEDaZ15Rqmzlpa6zDMzLqs3p5zcEVoZmaF5kRoZmaF5kRoZmaF5kRoZmaF5kRYI5LuktSSe92cVvkwM7M+5ERoZmaF5kTYy1Kl95ikKyQ9KukaSZvUOi4zM8v4c4R94z3AKRFxr6RLgM+l9iskvZa2NwTWlxssaSIwEWDYliN7O1Yzs0JxRdg3noqIe9P25cC4tH1CRIyNiLHA4e0NjojpEdESES2Dhgzr5VDNzIrFibBvRIXXZmZWI06EfWMbSfuk7eOB39UyGDMze5MTYd94HPi8pEeBYcBPaxyPmZklflimb6yNiE+UtB2YfxERTwJj+iogMzPLuCI0M7NCc0XYy1zpmZnVNyfCBjNicFPdreVlZtbIfGnUzMwKzYnQzMwKzYnQzMwKzfcIG0zryjVMnbW01mGYmXVZvT3n4IrQzMwKzYnQzMwKzYnQzMwKzYnQzMwKzYmwDEnnSZrcg/PdLGlo+vlc5RFmZtZXnAj7QEQcHhEvA0N5c3V6MzOrA06EiaSvSFoq6XfAe1LbdpJukTRP0j2SdkjtMyT9UNJ9kv4s6ZjUvpWk2ZIWSFoiaf/U/qSk4cC3ge1S/xRJv5B0VC6GKyQd2dfnbmZWZP4cISBpD+BjwFiy92Q+MA+YDpwaEX+Q9D7gJ8DBadhWwDhgB2AmcA3Zoru3RsSFkgYAm5Qc6svAmIgYm477fuBM4AZJQ4B9gU+ViW8iMBFg2JYje+akzcwMcCJssz9wfUS8CiBpJtBElpiultS230a5MTdExHrgEUkjUtsc4BJJA1P/go4OGhF3S/qJpC2AjwLXRsTaMvtNJ0vKjBo9Jrp4jmZmVoYvjbZvA+DliBib+9kx1/96blsAETEbOAB4Bpgh6cQqjvML4BPAScAlPRO6mZlVy4kwMxs4StLGkjYDPgS8Cjwh6VgAZXbtaBJJ2wKtEfEz4GJg95JdXgE2K2mbAZwBEBGPdPM8zMysk5wIgYiYD1wFLAR+Q3aJE+AE4BRJC4GHgUoPshwILJT0EHAc8IOS47wI3JsepJmS2lqBR4FLe+ZszMysMxThW061JGkTYDGwe0SsqLT/qNFjYtK063o/MDOzXlKLL92WNC8iWsr1uSKsIUnjyarBH1WTBM3MrOf5qdEaiojfAtvWOg4zsyJzImwwIwY31d1aXmZmjcyXRs3MrNCcCM3MrNCcCM3MrNB8j7DBtK5cw9RZS2sdhplZl9Xbcw6uCM3MrNCcCM3MrNCcCM3MrNCcCM3MrNAKmQjTCvPHlGlvlrSkk3ONlHRNO313SSr73XZmZlYf/NRoN0h6W0Q8C/xDUjUzs8ZQiIpQ0omSFklaKOmy1HyApPsk/bmd6rBJ0qWSFkt6SNJBqX2CpJmS7gBuz1eRaT3DX0p6VNL1wMa5+Q6RdL+k+ZKulrRpav+2pEdSfN/t9TfDzMzeot9XhJLeC5wL7BsRyyRtDnwP2AoYB+wAzARKL29+HoiI2FnSDsBtkto+/LI7sEtELJfUnBvzWeDViNhR0i7A/BTD8BTD+IhYLelLwCRJ04CjgR0iIiQNbeccJgITAYZtObI7b4eZmZUoQkV4MHB1RCwDiIjlqf2GiFifVoUfUWbcOODyNOYx4C9AWyKclZsn74DcmEXAotS+N7AT2aK8C4BPka06sQJYA/xc0keAV8udQERMj4iWiGgZNGRY1SduZmaV9fuKsAOv57bVybGrO7m/yJLnx/+hQ9oL+Cey+4ynkSVuMzPrI0WoCO8AjpX0doB0abQa9wAnpDGjgW2AxyuMmQ0cn8aMAXZJ7Q8A+0l6d+obJGl0uk84JCJuBs4Edq36rMzMrEf0+4owIh6WdCFwt6R1wENVDv0J8FNJi4G1wISIeF3qsHj8KXCppEfJVp6fl2J4QdIE4EpJG6V9zwVeAX4tqYmsapzUubMzM7PuUkTUOgbrhFGjx8SkadfVOgwzsy6rxZduS5oXEWU/112ES6NmZmbtciI0M7NC6/f3CPubEYOb6m4tLzOzRuaK0MzMCs2J0MzMCs2J0MzMCs33CBtM68o1TJ21tNZhmJl1Wb095+CK0MzMCs2J0MzMCs2J0MzMCs2J0MzMCq1hE6GkiyXtVKZ9gqQfd2PeVd2LzMzMGkldPDWqbEkHRcT6asdExL/2Ykg1JWlARKyrdRxmZkVQs4pQUrOkxyX9AlgCjJJ0tqQ5khZJOj/tN0jSTZIWSloi6bjUfpeklrR9kqSlkh4E9ssdY4akY3KvV6V/N5V0u6T5khZLOrJCrO3F8KSk4Wm7RdJdaXsLSbMkPZwq17/k9rtB0rzUNzEfm6T/lLQQ2Kfbb7CZmVWl1hXh9sCnIuIBSYek13uRrc03U9IBwBbAsxFxBICkIfkJJG0FnA/sAawA7qTymoNrgKMjYmVKUA9Imhntr0l1aEcxlPE14I6I+JakQ4FTcn0nR8RySRsDcyRdGxEvAoOA30fEWaWTpYQ5EWDYliMrHNrMzDqj1vcI/xIRD6TtQ9LPQ8B8YAeyxLgY+ICk70jaPyJWlMzxPuCuiHghIv4GXFXFcQV8U9Ii4LfA1sCIDvavFEOpccAvASLiFuClXN/pqep7ABiVzhFgHXBtuckiYnpEtEREy6Ahwyoc2szMOqPWFeHq3LaAb0XE/yvdSdLuwOHANyTdHhEXVDn/WlKyl7QBsGFqP4Gs0twjIt6Q9CTQ1N4kEbG0nRj+Pn9H43PncSAwHtgnIl5Nl1Lbxq3xfUEzs75X64ow71bgZEmbAkjaWtKWkkYCr0bE5cAUYPeScb8H3i/p7ZIGAsfm+p4ku2QK8GFgYNoeAjyfkuBBwLYdBdZBDPn5P5obci/wL2nsIUBbGTcEeCklwR2AvTs6rpmZ9b5aV4R/FxG3SdoRuD97iJRVwCeAdwNTJK0H3gA+WzLuOUnnAfcDLwMLct0/A36dLkXewpsV6BXA/0haDMwFHqsQ3s7txHA+8HNJXwfuyu1/PnClpE+muP4XeCXFcKqkR4HHyS6PmplZDan950OsqyRtBKyLiLWS9gF+GhFje2LuUaPHxKRp1/XEVGZmNVGLL92WNC8iWsr11U1F2M9sA/wq3Zf8G/DpGsdjZmbtcCLsBRHxB2C3WsdhZmaVORE2mBGDm+puLS8zs0ZWT0+NmpmZ9TknQjMzKzQnQjMzKzTfI2wwrSvXMHXW0lqHYWbWZfX2nIMrQjMzKzQnQjMzKzQnQjMzKzQnQjMzK7TCJEJJp0o6MW1PSCtKtLfvBZLG93YcJe3Nkpb0xjHNzKx9hXlqNCIuyr2cACwBni3dT9KAiPhqH8VhZmY11i8rQkknSlokaaGky1LbeZImSzoGaAGukLRA0saSnkyrz88HjpU0I+2HpD0l3ZfmelDSZiXH2lTS7ZLmS1os6chq4kjbe6S+hcDn++bdMTOzvH5XEUp6L3AusG9ELJO0eb4/Iq6RdBowOSLmpjEAL0bE7un1oenfDYGrgOMiYo6kwcBrJYdcAxwdESslDQcekDQT2KmjOJJLgdMiYrakKT3zDpiZWWf0x4rwYODqiFgGEBHLqxx3VZm29wDPRcScNNfKiFhbso+Ab0paBPwW2BoYUSkOSUOBoRExOzVd1l5gkiZKmitp7uoVL1V5OmZmVo3+mAi7anXlXco6AdgC2CMtvtsKNPVUUAARMT0iWiKiZdCQYT05tZlZ4fXHRHgH2X2+twO0c0nyFWCzMu2lHge2krRnmmszSaWXk4cAz0fEG5IOAratJo6IeBl4WdK41HRCFfGYmVkP63f3CCPiYUkXAndLWgc8RPaUaN4M4CJJrwH7dDDX3yQdB/xI0sZk9wfHA6tyu10B/I+kxcBc4LFOxHEScImkAG7rwumamVk3KSJqHYN1wqjRY2LStOtqHYaZWZfV4ku3Jc2LiJZyff3x0qiZmVnVnAjNzKzQnAjNzKzQ+t3DMv3diMFNdbeopZlZI3NFaGZmheZEaGZmheZEaGZmheZ7hA2mdeUaps5aWuswzMy6rV6ed3BFaGZmheZEaGZmheZEaGZmheZEaGZmhdapRCjpdEmPSrqitwKqMo7zJE1O2ztIWiDpIUnb9dD8T6bV5pF0XxfnOFXSiWXamyUt6W6MZmbWMzr71OjngPER8XS+UdLbyqzc3leOAq6JiG9UO6Az8UbEvl0JKiIu6so4MzPrW1VXhJIuAt4F/EbSmakqu0zSvcBlkraQdK2kOelnvzRukKRLJD2YqrYjy8y9laTZqbJbImn/1L4qt88xkmaUjDscOAP4rKQ7S6stSZMlnZe275L0fUlzgS+WzPN2SbdJeljSxYByfavSv5I0JcW3OK1TiKQfSPpq2v7ndB4blFSte0haKGkh8Pnc3APSnHMkLZL0mWr/e5iZWc+ouiKMiFMlHQocFBHLUoLZCRgXEa9J+m9gakT8TtI2wK3AjsBXgDsi4mRJQ4EHJf02Ilbnpj8euDUiLpQ0ANikyphuTgl6VUR8V1JzhSEbtrMe1deA30XEBZKOAE4ps89HgLHArsBwYI6k2cC/pe17gB8Ch0fEekn5sZcCp0XEbElTcu2nACsiYk9JGwH3SrotIp7ID5Y0EZgIMGzLkRVO0czMOqO7H6ifGRGvpe3xwE65BDBY0qbAIcCH26ojoAnYBng0N88cspXaBwI3RMSCbsbVnqvaaT+ALNERETdJeqnMPuOAKyNiHdAq6W5gz4iYKenTwGzgzIj4U35QSv5DI2J2aroMOCxtHwLsIumY9HoIsD3wlkQYEdOB6ZAtzFvtyZqZWWXdTYT5qm4DYO+IWJPfQVlm/GhEPN7eJKlSOgA4Apgh6XsR8Qsg/0u/qYp41vLWy72lY1bTO3YGXgQ6W64J+EJE3NrzIZmZWTV68uMTtwFfaHshaWzavBX4QkqISNqtdKCkbYHWiPgZcDGwe+pqlbSjpA2Ao6uIoRXYMt3z2wj4YJWxzya7PIukw4BhZfa5Bzgu3dfbgqyKfDDFfhawG3CYpPflB0XEy8DLksalphNy3beS3d8cmI49WtKgKmM2M7Me0JPfNXo6ME3SojTvbOBU4OvA94FFKaE9wT8mqAOBsyW9AawC2j528GXgRuAFYC6waUcBRMQbki4AHgSeAR6rMvbzgSslPQzcB/y1zD7XA/sAC8kq1XPIEu8sYHJEPCvpFLKKds+SsSeRXfoNsj8Y2lwMNAPz0x8KL5A9BWtmZn1EEb7l1EhGjR4Tk6ZdV+swzMy6rS+/dFvSvHYelvQ3y5iZWbE5EZqZWaF5PcIGM2JwU92s4WVm1h+4IjQzs0JzIjQzs0JzIjQzs0LzPcIG07pyDVNnLa11GGZmfao3n41wRWhmZoXmRGhmZoXmRGhmZoXmRGhmZoXmRFiBpCclDe/CuBm5dQar2b9Z0pLOHsfMzLrHidDMzArNiTBH0g2S5kl6WNLEMv0nSlokaaGky1Jbs6Q7UvvtkrbJDTlA0n2S/txWHSozRdISSYslHddHp2dmZmX4c4RvdXJELJe0MTBH0rVtHZLeC5wL7BsRyyRtnrp+BPxXRPyXpJOBH/LmmoJbAeOAHYCZwDXAR4CxwK7A8HSc2R0FlZLyRIBhW47sifM0M7PEFeFbnS5pIfAAMArYPtd3MHB1RCwDiIjlqX0f4L/T9mVkia/NDRGxPiIeAUaktnHAlRGxLiJagbuB0oV83yIipkdES0S0DBoyrBunZ2ZmpVwRJpIOBMYD+0TEq5LuApq6Oe3r+UN0cy4zM+sFrgjfNAR4KSXBHYC9S/rvAI6V9HaA3KXR+4CPpe0TgHsqHOce4DhJAyRtARwAPNgTJ2BmZp3nivBNtwCnSnoUeJzs8ujfRcTDki4E7pa0DngImAB8AbhU0tnAC8BJFY5zPdnl1IVAAOdExP9Kau7BczEzsyopImodg3XCqNFjYtK062odhplZn+rul25LmhcRLeX6fGnUzMwKzYnQzMwKzfcIG8yIwU29ui6XmVnRuCI0M7NCcyI0M7NCcyI0M7NCcyI0M7NCcyI0M7NCcyI0M7NCcyI0M7NCcyI0M7NCcyI0M7NC85duNxhJr5CtjmHtGw4sq3UQdc7vUWV+jyprpPdo24jYolyHv2Kt8Tze3jeoW0bSXL9HHfN7VJnfo8r6y3vkS6NmZlZoToRmZlZoToSNZ3qtA2gAfo8q83tUmd+jyvrFe+SHZczMrNBcEZqZWaE5EZqZWaE5ETYQSYdKelzSHyV9udbx1BtJl0h6XtKSWsdSrySNknSnpEckPSzpi7WOqd5IapL0oKSF6T06v9Yx1SNJAyQ9JOnGWsfSXU6EDULSAGAacBiwE/BxSTvVNqq6MwM4tNZB1Lm1wFkRsROwN/B5/3/0D14HDo6IXYGxwKGS9q5tSHXpi8CjtQ6iJzgRNo69gD9GxJ8j4m/AL4EjaxxTXYmI2cDyWsdRzyLiuYiYn7ZfIftFtnVto6ovkVmVXg5MP36qMEfSO4AjgItrHUtPcCJsHFsDT+VeP41/gVk3SGoGdgN+X+NQ6k667LcAeB6YFRF+j97q+8A5wPoax9EjnAjNCkjSpsC1wBkRsbLW8dSbiFgXEWOBdwB7SRpT45DqhqQPAs9HxLxax9JTnAgbxzPAqNzrd6Q2s06RNJAsCV4REdfVOp56FhEvA3fie895+wEflvQk2S2agyVdXtuQuseJsHHMAbaX9E5JGwIfA2bWOCZrMJIE/Bx4NCK+V+t46pGkLSQNTdsbAx8AHqtpUHUkIv4tIt4REc1kv4fuiIhP1DisbnEibBARsRY4DbiV7AGHX0XEw7WNqr5IuhK4H3iPpKclnVLrmOrQfsAnyf6KX5B+Dq91UHVmK+BOSYvI/gCdFREN/xEBa5+/Ys3MzArNFaGZmRWaE6GZmRWaE6GZmRWaE6GZmRWaE6GZmRWaE6GZmRWaE6GZmRXa/wcX7r5Tb4HeXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.189202308654785\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.033139705657959\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.238567352294922\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.336091041564941\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.319075107574463\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.081655502319336\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.374035358428955\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.789801597595215\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.231739521026611\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZFZkrQjMzKzRXhA1mWP++dXn93cysUbkiNDOzQnMiNDOzQnMiNDOzQvM9wgbTsnwl509fXOswzMwqarTnGFwRmplZoTkRmplZoTkRmplZoTkRmplZoTkR1oikuyQ15143SVpYy5jMzIrIidDMzArNibCbpUrvUUlTJC2SdG2aesnMzOqAP0fYMz4AHB8R90m6HDgxrZ8i6fW0vB6wplxnSROACQCDhg7v7ljNzArFFWHPeCoi7kvLVwF7p+WjI2J0RIwGDqrUOSIuiYjmiGjuN2BQN4dqZlYsToQ9I9p5bWZmNeJE2DO2kLRHWj4KuLeWwZiZ2ducCHvGY8BXJC0CBgE/q3E8ZmaW+GGZnrEqIj5Xsm6//IuIeBIY1VMBmZlZxhWhmZkVmivCbuZKz8ysvjkRNphh/fs23FxfZmb1zJdGzcys0JwIzcys0JwIzcys0HyPsMG0LF/J+dMX1zoMM7OKGu05BleEZmZWaE6EZmZWaE6EZmZWaE6EZmZWaA2bCCVdJmm7MuvHSbqwE+Ou6FxkZmbWSOriqVFJAhQRZWdoLycivtCNIdWUpN4RsbrWcZiZFUHNKkJJTZIek/RLYCEwQtIpkmZKmi/prNSun6SbJc2TtFDS2LT+LknNaXm8pMWSHgT2yu1jsqQjcq9XpH83kvQHSXMkLZB0aDuxVorhSUmD03KzpLvS8hBJ0yU9nCrXv+Xa3Shpdto2IR+bpP+RNA/Y491RmJlZd6h1Rbg1cGxEPCBp//R6V0DANEn7AkOAZyPiYABJA/IDSNoMOAvYGVgG3Ak81M5+VwKHR8TylKAekDQtIirNHH9AWzGUcQZwR0R8X9IBwPG5bcdFxFJJGwAzJV0XEUuAfsCfIuKbpYOlhDkBYNDQ4e3s2szMOqLW9wj/FhEPpOX9089DwBxgG7LEuAD4mKT/krRPRCwrGWM34K6IeCEi/gFcXcV+BXxP0nzg98DmwLA22rcXQ6m9gV8DRMStwEu5bSelqu8BYEQ6RoDVwHXlBouISyKiOSKa+w0Y1M6uzcysI2pdEb6aWxbw/Yj4f6WNJO0EHAR8V9IfIuLsKsdfRUr2knoB66X1R5NVmjtHxJuSngT6VhokIhZXiOGt8dvqnzuO/YAxwB4R8Vq6lNrab6XvC5qZ9bxaV4R5twHHSdoIQNLmkoZKGg68FhFXAecBO5X0+xPwYUmbSuoDfCa37UmyS6YAhwB90vIA4PmUBD8CbNlWYG3EkB//07ku9wH/mvruD7SWcQOAl1IS3AbYva39mplZ96t1RfiWiLhd0rbAH7OHSFkBfA54P3CepDXAm8CXS/o9J+lM4I/Ay8Dc3OZLgd+kS5G38nYFOgX4raQFwCzg0XbC+1CFGM4Cfi7pO8BdufZnAVMlfT7F9XfglRTDCZIWAY+RXR41M7MaUuXnQ2xtSVofWB0RqyTtAfwsIkZ3xdgjRo6KiRdd3xVDmZl1i3r80m1JsyOiudy2uqkI1zFbAP+b7kv+A/hijeMxM7MKnAi7QUT8Gdix1nGYmVn7nAgbzLD+fevysoOZWaOqp6dGzczMepwToZmZFZoToZmZFZrvETaYluUrOX/64lqHYWZWUaM9x+CK0MzMCs2J0MzMCs2J0MzMCs2J0MzMCq2QibB05vrc+iZJCzs41nBJ11bYdpekst9tZ2Zm9cFPjXaCpPdExLPAu5KqmZk1hkJUhJKOkTRf0jxJV6bV+0q6X9JfK1SHfSVdIWmBpIfSvIVIGidpmqQ7gD/kq0hJG0j6taRFkm4ANsiNt7+kP0qaI+ma3LyL50p6JMX3g24/GWZm9g7rfEUo6YPAt4E9I+JFSZsAPwQ2A/YGtgGmAaWXN78CRER8KE2ie7uk1g/H7ARsHxFLJTXl+nyZbALfbSVtD8xJMQxOMYyJiFclfQuYKOki4HBgm4gISQMrHMMEYALAoKHDO3M6zMysRBEqwo8C10TEiwARsTStvzEi1kTEI8CwMv32Bq5KfR4F/ga0JsLpuXHy9s31mQ/MT+t3B7YD7pM0FzgW2BJYBqwkm9z3U8Br5Q4gIi6JiOaIaO43YFC5JmZmtpbW+YqwDW/kltXBvq+23+QdRJY8P/uuDdKuwL+Q3Wf8KlniNjOzHlKEivAO4DOSNgVIl0arcQ9wdOozkmyy3cfa6TMDOCr1GQVsn9Y/AOwl6f1pWz9JI9N9wgERcQtwMrBD1UdlZmZdYp2vCCPiYUnnAHdLWg08VGXXnwI/k7QAWAWMi4g3pDaLx58BV0haBCwCZqcYXpA0Dpgqaf3U9tvAK8BvJPUlqxonduzozMyssxQRtY7BOmDEyFEx8aLrax2GmVlF9fil25JmR0TZz3UX4dKomZlZRU6EZmZWaOv8PcJ1zbD+fevysoOZWaNyRWhmZoXmRGhmZoXmRGhmZoXme4QNpmX5Ss6fvrjWYZiZVdRozzG4IjQzs0JzIjQzs0JzIjQzs0JzIjQzs0IrTCKUdIKkY9LyOEkVZ7iVdLakMd0dR8n6t2a6NzOznlOYp0Yj4uLcy3HAQuDZ0naSekfE6T0Uh5mZ1dg6WRFKOkbSfEnzJF2Z1p0paZKkI4BmYIqkuZI2kPSkpP+SNIds7sLJqR2SdpF0fxrrQUkbl+xrI0l/kDRH0gJJh1YTR1reOW2bB3ylZ86OmZnlrXMVoaQPks31t2dEvFg6EW9EXCvpq8CkiJiV+gAsiYid0usD0r/rAVcDYyNipqT+wOslu1wJHB4RyyUNBh6QNA3Yrq04kiuAr0bEDEnntXFME4AJAIOGVryia2Zma2FdrAg/ClwTES8CRMTSKvtdXWbdB4DnImJmGmt5RKwqaSPge5LmA78HNgeGtReHpIHAwIiYkVZdWSmwiLgkIpojornfgEFVHo6ZmVVjnasIO+HVtex3NDAE2Dki3pT0JNC3y6IyM7NutS5WhHeQ3efbFKDCJclXgI3LrC/1GLCZpF3SWBtLKv3jYQDwfEqCHwG2rCaOiHgZeFnS3mnV0VXEY2ZmXWydqwgj4mFJ5wB3S1oNPET2lGjeZOBiSa8De7Qx1j8kjQV+ImkDsvuDY4AVuWZTgN9KWgDMAh7tQBzjgcslBXD7WhyumZl1kiKi1jFYB4wYOSomXnR9rcMwM6uoHr90W9LsiGgut21dvDRqZmZWNSdCMzMrtHXuHuG6blj/vnV52cHMrFG5IjQzs0JzIjQzs0JzIjQzs0LzPcIG07J8JedPX1zrMMzMKmq05xhcEZqZWaE5EZqZWaE5EZqZWaE5EZqZWaG1mQglDZR0YnuDSGqSdFSV7RZ2JMAK4+Rned8mzTT/kKStOjt2GvPJNMkuku5fyzFOkHRMmfVdcg7MzKxrtFcRDgTaTYRAE9BuIuwmhwHXRsSOEfGXajqUmUqpoojYc22CioiLI+KXa9PXzMx6TnuJ8Fxgq1RxnafMeZIWSlqQpihqbbdPandyqnrukTQn/bSZTCRtJmlG6r9Q0j5p/YpcmyMkTS7pdxDwDeDLku4srbYkTZJ0Zlq+S9IFkmYBXy8ZZ1NJt0t6WNJlZLPOt25bkf4te+ySfiTp9LT88XQcvUqq1p0lzZM0D/hKbuzeacyZkuZL+lI7/x9mZtbF2quMTgNGRcRoAEmfBkYDOwCDgZmSZqR2kyLiE6ndhsDHImKlpK2BqUDZ6S+So4DbIuIcSb2BDasJPiJukXQxsCIifiCpqZ0u61WYhuMM4N6IOFvSwcDxZdp8ivLH/m9p+R7gx8BBEbFGUr7vFcBXI2KGpPNy648HlkXELpLWB+6TdHtEPNHesZuZWdfo6Afq9wamRsRqoEXS3cAuwPKSdn2ACyWNBlYD7X26cibZBLV9gBsjYm4H46rW1RXW70uW6IiImyW9VKZN2WOPiGmSvgjMAE4uvTwraSAwMCJmpFVXAgem5f2B7SUdkV4PALYGnigZYwIwAWDQ0OHVHquZmVWhu54aPRloIauemoH12mqcksS+wDPA5NxDJvlZg/tWsd9VvPOYSvu8WsUYa+NDwBKgo1lKwNciYnT6+eeIeNdM9RFxSUQ0R0RzvwGDuiJeMzNL2kuErwAb517fA4xN97aGkCWvB8u0GwA8FxFrgM8DvdvaiaQtgZaIuBS4DNgpbWqRtK2kXsDhVRxPCzA03fNbH/hEFX0gq+aOSrEcCJTLNmWPPcX+TWBH4EBJu+U7RcTLwMuS9k6rjs5tvo3s/maftO+RkvpVGbOZmXWBNi+NRsQSSfelB1B+B5wK7AHMI6vWTo2Iv0taAqxOD4NMBn4KXJcqu1tpvxLbDzhF0pvACqC1IjwNuAl4AZgFbNROvG9KOpssOT8DPNrOfludBUyV9DBwP/B/ZdrcQMmxkyXe6WT3R5+VdDxZRbtLSd/xZJd+A8hXfJeRPXE7R9lNxRfInoI1M7Meoohov5XVjREjR8XEi66vdRhmZhXV45duS5pd4WFJf7OMmZkVmxOhmZkVmhOhmZkVmifmbTDD+vety+vvZmaNyhWhmZkVmhOhmZkVmhOhmZkVmu8RNpiW5Ss5f/riWodhZtauRnmewRWhmZkVmhOhmZkVmhOhmZkVmhOhmZkVWocSoaSTJC2SNKW7AqoyjjMlTUrL20iaK+khSVt10fhPShqclu9fyzFOyM2rmF/flGbzMDOzOtDRp0ZPBMZExNP5lZLeExGrui6sDjkMuDYivltth47EGxF7rk1QEXHx2vQzM7OeVXVFKOli4H3A7ySdnKqyKyXdB1wpaYik6yTNTD97pX79JF0u6cFUtR1aZuzNJM1Ild1CSfuk9StybY6QNLmk30HAN8gmt72ztNqSNEnSmWn5LkkXSJoFfL1knE0l3S7pYUmXkc0c37ptRfpXks5L8S2QNDat/5Gk09Pyx9Nx9CqpWneWNC/N1/iV3Ni905gzJc2X9KVq/z/MzKxrVF0RRsQJkg4APhIRL6YEsx2wd0S8LulXwPkRca+kLchmX98W+A/gjog4TtJAslndfx8R+cl6jwJui4hzJPUGNqwypltSgl4RET+Q1NROl/UqzEd1BnBvRJwt6WDg+DJtPgWMBnYABgMzJc0A/i0t3wP8GDgoItZk8+y+5QrgqxExQ9J5ufXHA8siYhdJ6wP3Sbo9Ip7Id5Y0AZgAMGjo8HYO0czMOqKzH6ifFhGvp+UxwHa5BNBf0kbA/sAhrdUR0BfYAliUG2cm2QzufYAbI2JuJ+Oq5OoK6/clS3RExM2SXirTZm9gakSsBlok3Q3sEhHTJH0RmAGcHBF/yXdKyX9gRMxIq64EDkzL+wPbSzoivR4AbA28IxFGxCXAJZBNzFvtwZqZWfs6mwjzVV0vYPeIWJlvoCwzfjoiHqs0SKqU9gUOBiZL+mFE/BLI/9LvW0U8q3jn5d7SPq/SPT4ELAE6Wq4J+FpE3Nb1IZmZWTW68uMTtwNfa30haXRavA34WkqISNqxtKOkLYGWiLgUuAzYKW1qkbStpF7A4VXE0AIMTff81gc+UWXsM8guzyLpQGBQmTb3AGPTfb0hZFXkgyn2bwI7AgdK2i3fKSJeBl6WtHdadXRu821k9zf7pH2PlNSvypjNzKwLdOV3jZ4EXCRpfhp3BnAC8B3gAmB+SmhP8O4EtR9wiqQ3gRVA68cOTgNuAl4AZgEbtRVARLwp6WzgQeAZ4NEqYz8LmCrpYeB+4P/KtLkB2AOYR1apnkqWeKcDkyLiWUnHk1W0u5T0HU926TfI/mBodRnQBMxJfyi8QPYUrJmZ9RBF+JZTIxkxclRMvOj6WodhZtauevrSbUmzKzws6W+WMTOzYnMiNDOzQvN8hA1mWP++dXW5wcys0bkiNDOzQnMiNDOzQnMiNDOzQvM9wgbTsnwl509fXOswzMx6VHc+G+GK0MzMCs2J0MzMCs2J0MzMCs2J0MzMCs2JsB2SnpQ0eC36Tc7NM1hN+yZJCzu6HzMz6xwnQjMzKzQnwhxJN0qaLelhSRPKbD9G0nxJ8yRdmdY1Sbojrf+DpC1yXfaVdL+kv7ZWh8qcJ2mhpAWSxvbQ4ZmZWRn+HOE7HRcRSyVtAMyUdF3rBkkfBL4N7BkRL0raJG36CfCLiPiFpOOAH/P2nIKbAXsD2wDTgGuBTwGjgR2AwWk/M9oKKiXlCQCDhg7viuM0M7PEFeE7nSRpHvAAMALYOrfto8A1EfEiQEQsTev3AH6Vlq8kS3ytboyINRHxCDAsrdsbmBoRqyOiBbgbKJ3I9x0i4pKIaI6I5n4DBnXi8MzMrJQrwkTSfsAYYI+IeE3SXUDfTg77Rn4XnRzLzMy6gSvCtw0AXkpJcBtg95LtdwCfkbQpQO7S6P3AkWn5aOCedvZzDzBWUm9JQ4B9gQe74gDMzKzjXBG+7VbgBEmLgMfILo++JSIelnQOcLek1cBDwDjga8AVkk4BXgDGt7OfG8gup84DAjg1Iv4uqakLj8XMzKqkiKh1DNYBI0aOiokXXV/rMMzMelRnv3Rb0uyIaC63zZdGzcys0JwIzcys0HyPsMEM69+3W+flMjMrGleEZmZWaE6EZmZWaE6EZmZWaE6EZmZWaE6EZmZWaE6EZmZWaE6EZmZWaE6EZmZWaE6EZmZWaP7S7QYj6RWy2THq1WDgxVoH0QbH13n1HqPj65x1Nb4tI2JIuQ3+irXG81ilb1CvB5JmOb61V+/xQf3H6Pg6p4jx+dKomZkVmhOhmZkVmhNh47mk1gG0w/F1Tr3HB/Ufo+PrnMLF54dlzMys0FwRmplZoTkRmplZoTkR1ilJB0h6TNLjkk4rs319SVen7X+S1FRn8Y2T9IKkuennCz0c3+WSnpe0sMJ2Sfpxin++pJ3qLL79JC3Lnb/TezC2EZLulPSIpIclfb1Mm1qfv2pirOU57CvpQUnzUnxnlWlTs/dwlfHV9D2cYugt6SFJN5XZ1nXnLyL8U2c/QG/gL8D7gPWAecB2JW1OBC5Oy0cCV9dZfOOAC2t4DvcFdgIWVth+EPA7QMDuwJ/qLL79gJtqdO42A3ZKyxsDi8v8/9b6/FUTYy3PoYCN0nIf4E/A7iVtavkeria+mr6HUwwTgV+V+3/syvPnirA+7Qo8HhF/jYh/AL8GDi1pcyjwi7R8LfAvklRH8dVURMwAlrbR5FDgl5F5ABgoabOeia6q+GomIp6LiDlp+RVgEbB5SbNan79qYqyZdF5WpJd90k/pk4k1ew9XGV9NSXovcDBwWYUmXXb+nAjr0+bAU7nXT/PuN/lbbSJiFbAM2LRHoqsuPoBPp8tm10oa0TOhVa3aY6ilPdKlq99J+mAtAkiXm3Ykqxjy6ub8tREj1PAcpst6c4HngekRUfEc1uA9XE18UNv38AXAqcCaCtu77Pw5EVp3+S3QFBHbA9N5+y83q84csu9G3AH4CXBjTwcgaSPgOuAbEbG8p/dfjXZirOk5jIjVETEaeC+wq6RRPbn/9lQRX83ew5I+ATwfEbN7Yn9OhPXpGSD/19d707qybSS9BxgALOmR6KqILyKWRMQb6eVlwM49FFu1qjnHNRMRy1svXUXELUAfSYN7av+S+pAlmCkRcX2ZJjU/f+3FWOtzmIvjZeBO4ICSTbV8D7+lUnw1fg/vBRwi6UmyWy8flXRVSZsuO39OhPVpJrC1pH+WtB7ZjeBpJW2mAcem5SOAOyLdNa6H+EruFx1Cdg+nnkwDjklPP+4OLIuI52odVCtJ/9R6v0PSrmTv1R75JZn2+3NgUUT8sEKzmp6/amKs8TkcImlgWt4A+BjwaEmzmr2Hq4mvlu/hiPi3iHhvRDSR/X65IyI+V9Ksy86fZ5+oQxGxStJXgdvIntC8PCIelnQ2MCsippH9ErhS0uNkD10cWWfxnSTpEGBVim9cT8UHIGkq2VODgyU9DZxB9kAAEXExcAvZk4+PA68B4+ssviOAL0taBbwOHNmDf+jsBXweWJDuIQH8O7BFLr6anr8qY6zlOdwM+IWk3mQJ+H8j4qZ6eQ9XGV9N38PldNf581esmZlZofnSqJmZFZoToZmZFZoToZmZFZoToZmZFZoToZmZFZoToZmZFZoToZmZFdr/B+PwAbAUQlVuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.700886249542236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.2146196365356445\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.227941989898682\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.416680335998535\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 45;\n",
       "                var nbb_unformatted_code = \"for i in range(20):\\n    explain_plot(explanations[i], all_cols)\\n    print(probs[i].item())\";\n",
       "                var nbb_formatted_code = \"for i in range(20):\\n    explain_plot(explanations[i], all_cols)\\n    print(probs[i].item())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    explain_plot(explanations[i], all_cols)\n",
    "    print(probs[i].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:29.860177Z",
     "start_time": "2020-09-19T17:13:29.837525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 46;\n",
       "                var nbb_unformatted_code = \"from sklearn.metrics import confusion_matrix\";\n",
       "                var nbb_formatted_code = \"from sklearn.metrics import confusion_matrix\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:29.978177Z",
     "start_time": "2020-09-19T17:13:29.861624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 47;\n",
       "                var nbb_unformatted_code = \"probs_test, explanations_train = predict(model, input_test)\";\n",
       "                var nbb_formatted_code = \"probs_test, explanations_train = predict(model, input_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_test, explanations_train = predict(model, input_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:30.111028Z",
     "start_time": "2020-09-19T17:13:29.979272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 48;\n",
       "                var nbb_unformatted_code = \"# plt.barh(y_pos, performance, align=\\\"center\\\", alpha=0.5)\\n# plt.yticks(y_pos, columns[selection])\\nplt.ylabel(\\\"Predicted value\\\")\\nplt.xlabel(\\\"Truth value\\\")\\n\\nplt.title(\\\"Scatter plot of prediction vs truth\\\")\\nplt.scatter(y_test, probs_test)\\nplt.plot([np.min(y_test), np.max(y_test)], [np.min(y_test), np.max(y_test)])\\nplt.grid(True)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# plt.barh(y_pos, performance, align=\\\"center\\\", alpha=0.5)\\n# plt.yticks(y_pos, columns[selection])\\nplt.ylabel(\\\"Predicted value\\\")\\nplt.xlabel(\\\"Truth value\\\")\\n\\nplt.title(\\\"Scatter plot of prediction vs truth\\\")\\nplt.scatter(y_test, probs_test)\\nplt.plot([np.min(y_test), np.max(y_test)], [np.min(y_test), np.max(y_test)])\\nplt.grid(True)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.barh(y_pos, performance, align=\"center\", alpha=0.5)\n",
    "# plt.yticks(y_pos, columns[selection])\n",
    "plt.ylabel(\"Predicted value\")\n",
    "plt.xlabel(\"Truth value\")\n",
    "\n",
    "plt.title(\"Scatter plot of prediction vs truth\")\n",
    "plt.scatter(y_test, probs_test)\n",
    "plt.plot([np.min(y_test), np.max(y_test)], [np.min(y_test), np.max(y_test)])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:30.138203Z",
     "start_time": "2020-09-19T17:13:30.112180Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [5.7662053]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 49;\n",
       "                var nbb_unformatted_code = \"probs_test\";\n",
       "                var nbb_formatted_code = \"probs_test\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:30.263644Z",
     "start_time": "2020-09-19T17:13:30.139364Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 50;\n",
       "                var nbb_unformatted_code = \"plt.hist(y_test.reshape(-1) - probs_test.reshape(-1), bins=10)\\nplt.xlabel(\\\"Error value\\\")\\nplt.ylabel(\\\"Counts\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(y_test.reshape(-1) - probs_test.reshape(-1), bins=10)\\nplt.xlabel(\\\"Error value\\\")\\nplt.ylabel(\\\"Counts\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_test.reshape(-1) - probs_test.reshape(-1), bins=10)\n",
    "plt.xlabel(\"Error value\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-19T17:13:30.379028Z",
     "start_time": "2020-09-19T17:13:30.264718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 51;\n",
       "                var nbb_unformatted_code = \"plt.hist(np.abs(y_test.reshape(-1) - probs_test.reshape(-1)), bins=10)\\nplt.xlabel(\\\"Error value (absolute)\\\")\\nplt.ylabel(\\\"Counts\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.hist(np.abs(y_test.reshape(-1) - probs_test.reshape(-1)), bins=10)\\nplt.xlabel(\\\"Error value (absolute)\\\")\\nplt.ylabel(\\\"Counts\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.abs(y_test.reshape(-1) - probs_test.reshape(-1)), bins=10)\n",
    "plt.xlabel(\"Error value (absolute)\")\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}