IMG_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-03 11:58:51.703139: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-03 11:58:52.869071: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2023-05-03 11:58:52.869150: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "2023-05-03 11:58:56.000896: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2023-05-03 11:58:56.001673: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2023-05-03 11:58:56.001721: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/pn7hao/miniconda3/lib/python3.8/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: pytorch\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits,load_iris\n",
    "from sklearn.datasets import fetch_openml\n",
    "from utils import *\n",
    "from collections import defaultdict\n",
    "import time\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "import functools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percs = [0.4, 0.5, 0.6]\n",
    "# for perc in percs:\n",
    "#     mnist = fetch_openml('mnist_784')\n",
    "#     # Split the data into features (X) and labels (y)\n",
    "#     X = mnist.data.astype('float64')\n",
    "#     y = mnist.target\n",
    "#     df = pd.DataFrame(X) \n",
    "#     new_df = missing(df,Type='monotone_img',perc=perc,perc_del=0.5, im_width=28,im_height=28)\n",
    "#     new_df = apply_normalize(new_df)\n",
    "#     df['label'] = y \n",
    "#     new_df['label'] = y\n",
    "#     corr_gt = np.corrcoef(df.drop('label',axis=1).T)\n",
    "#     new_df.to_csv(f'DATA/mnist_ms_nm_{perc}.csv',index=False)\n",
    "#     np.save(f'DATA/corr_gt_{perc}.npy', corr_gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_df = new_df.rename(columns={f\"pixel{i}\": i for i in range(1, 785)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse_ignore_nan(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculates the Root Mean Squared Error (RMSE) between two matrices, ignoring NaN values.\n",
    "\n",
    "    Parameters:\n",
    "    y_true (numpy.ndarray): The true values.\n",
    "    y_pred (numpy.ndarray): The predicted values.\n",
    "\n",
    "    Returns:\n",
    "    float: The RMSE between the true and predicted values, ignoring NaN values.\n",
    "    \"\"\"\n",
    "    # Create a mask of valid elements\n",
    "    y_true_clean = np.nan_to_num(y_true, nan=-1)\n",
    "    y_pred_clean = np.nan_to_num(y_pred, nan=-1)\n",
    "    mask = ~(np.isnan(y_true_clean) | np.isnan(y_pred_clean))\n",
    "\n",
    "    # Extract only valid elements\n",
    "    y_true_valid = y_true_clean[mask]\n",
    "    y_pred_valid = y_pred_clean[mask]\n",
    "    \n",
    "    # Calculate RMSE on valid elements\n",
    "    rmse = np.sqrt(mean_squared_error(y_true_valid, y_pred_valid))\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 307720/307720 [28:39<00:00, 178.98it/s]\n",
      "100%|██████████| 307720/307720 [25:12<00:00, 203.45it/s]\n",
      "100%|██████████| 307720/307720 [23:04<00:00, 222.25it/s]\n"
     ]
    }
   ],
   "source": [
    "for perc in percs:\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_dpers = corr_with_dpers(new_df)\n",
    "    corr_dpers = corr_dpers.drop('label',axis=0)\n",
    "    corr_dpers = corr_dpers.drop('label',axis=1)\n",
    "    np.save(f'experiments/Corr/corr_dpers_{perc}.npy', corr_dpers)\n",
    "    np.save(f'experiments/Rmse/rmse_dpers_{perc}.npy',rmse_ignore_nan(corr_gt,corr_dpers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "perc = 0.4\n",
    "corr_gt = np.load(f'DATA/corr_gt_{perc}.npy') # REMEMBER that : No Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv') # Have Label , must remove\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [59:52<00:00, 1197.34s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_mice = np.corrcoef(impute_with_mice(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/corr_mice_{perc}.npy', corr_mice)\n",
    "    np.save(f'experiments/Rmse/rmse_mice_{perc}.npy',rmse_ignore_nan(corr_gt,corr_mice))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1/3 [13:35<27:10, 815.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 2/3 [38:25<20:12, 1212.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [1:12:51<00:00, 1457.16s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_missforest = np.corrcoef(impute_with_missforest(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/corr_missforest_{perc}.npy', corr_missforest)\n",
    "    np.save(f'experiments/Rmse/rmse_missforest_{perc}.npy',rmse_ignore_nan(corr_gt,corr_missforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "  0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SoftImpute] Max Singular Value of X_init = 1592.278784\n",
      "[SoftImpute] Iter 1: observed MAE=0.044478 rank=708\n",
      "[SoftImpute] Stopped after iteration 1 for lambda=31.845576\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1/3 [00:20<00:40, 20.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SoftImpute] Max Singular Value of X_init = 1517.419023\n",
      "[SoftImpute] Iter 1: observed MAE=0.042604 rank=707\n",
      "[SoftImpute] Stopped after iteration 1 for lambda=30.348380\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 2/3 [00:39<00:19, 19.90s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SoftImpute] Max Singular Value of X_init = 1471.822236\n",
      "[SoftImpute] Iter 1: observed MAE=0.041458 rank=708\n",
      "[SoftImpute] Stopped after iteration 1 for lambda=29.436445\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [01:00<00:00, 20.26s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_softimpute = np.corrcoef(soft_impute_als_df(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/corr_softimpute_{perc}.npy', corr_softimpute)\n",
    "    np.save(f'experiments/Rmse/rmse_softimpute_{perc}.npy',rmse_ignore_nan(corr_gt,corr_softimpute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "  0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after 1 iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1/3 [14:35<29:11, 875.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after 1 iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 2/3 [31:38<16:01, 961.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after criterion < threshold\n",
      "Stopped after 1 iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [50:39<00:00, 1013.10s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_pca = np.corrcoef(impute_with_pca(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/corr_pca_{perc}.npy', corr_pca)\n",
    "    np.save(f'experiments/Rmse/rmse_pca_{perc}.npy',rmse_ignore_nan(corr_gt,corr_pca))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_knn = np.corrcoef(impute_with_knn(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/corr_knn_{perc}.npy', corr_knn)\n",
    "    np.save(f'experiments/Rmse/rmse_knn_{perc}.npy',rmse_ignore_nan(corr_gt,corr_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GINN is running on cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:00<00:00,  4.85it/s]\n"
     ]
    }
   ],
   "source": [
    "mnist = fetch_openml('mnist_784')\n",
    "from tqdm import tqdm\n",
    "percs = [0.4]\n",
    "for perc in tqdm(percs):\n",
    "    X = mnist.data.astype('float32')\n",
    "    y = mnist.target.astype('int')\n",
    "    df = pd.DataFrame(X) \n",
    "    # try :\n",
    "    #     df = df.drop('label',axis=1)\n",
    "    # except:\n",
    "    #     pass\n",
    "    df['label'] = y\n",
    "    df = df.sample(100)\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    # new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    imputed_ginn = ginn_imputate(apply_normalize(df),Type='monotone_img',perc=perc)\n",
    "    corr_ginn = np.corrcoef(imputed_ginn.drop('label',axis=1).T)\n",
    "    # np.save(f'experiments/Corr/corr_ginn_{perc}.npy', corr_ginn)\n",
    "    # np.save(f'experiments/Rmse/rmse_ginn_{perc}.npy',rmse_ignore_nan(corr_gt,corr_ginn))\n",
    "    print(rmse_ignore_nan(corr_gt,corr_ginn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [1:28:35<00:00, 5315.70s/it]\n",
      "100%|██████████| 1/1 [1:08:32<00:00, 4112.54s/it]t]\n",
      "100%|██████████| 1/1 [1:07:27<00:00, 4047.65s/it]t]\n",
      "100%|██████████| 3/3 [5:06:21<00:00, 6127.21s/it]  \n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm # 306p 21s\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_EM = np.corrcoef(impute_with_EM(new_df.drop('label',axis=1), n_iter=1).T)\n",
    "    np.save(f'experiments/Corr/MNIST/EM/corr_{perc}.npy', corr_EM)\n",
    "    np.save(f'experiments/Rmse/MNIST/EM/rmse_{perc}.npy',rmse_ignore_nan(corr_gt,corr_EM))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/3 [00:00<?, ?it/s]2023-04-29 10:49:36.268666: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2023-04-29 10:49:36.268742: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2023-04-29 10:49:36.268804: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (hp): /proc/driver/nvidia/version does not exist\n",
      "2023-04-29 10:49:36.312359: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# from tqdm import tqdm # 13p 51s\n",
    "# percs = [0.4, 0.5, 0.6]\n",
    "# for perc in tqdm(percs):\n",
    "#     corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "#     new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "#     corr_GAIN = np.corrcoef(gain_impute(new_df.drop('label',axis=1),epochs=10).T)\n",
    "#     np.save(f'experiments/Corr/MNIST/GAIN/corr_{perc}_v2.npy', corr_GAIN)\n",
    "#     np.save(f'experiments/Rmse/MNIST/GAIN/rmse_{perc}_v2.npy',rmse_ignore_nan(corr_gt,corr_GAIN))\n",
    "\n",
    "\n",
    "from tqdm import tqdm # 13p 51s\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "path = '/home/pn7hao/Desktop/SecondYear/Research/DeepLearning/'\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(path + f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(path + f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_GAIN = np.corrcoef(gain_imputed_v2(new_df.drop('label',axis=1),\n",
    "                                iterations=100,hint_rate=0.9,batch_size=64,alpha=0.5).T)\n",
    "    np.save(path + f'experiments/Corr/MNIST/GAIN/corr_{perc}.npy', corr_GAIN)\n",
    "    np.save(path + f'experiments/Rmse/MNIST/GAIN/rmse_{perc}.npy',rmse_ignore_nan(corr_gt,corr_GAIN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:50<00:00, 16.99s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm # 51s\n",
    "percs = [0.4, 0.5, 0.6]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_MeanImpute = np.corrcoef(impute_with_meanImpute(new_df.drop('label',axis=1)).T)\n",
    "    np.save(f'experiments/Corr/MNIST/MeanImpute/corr_{perc}.npy', corr_MeanImpute)\n",
    "    np.save(f'experiments/Rmse/MNIST/MeanImpute/rmse_{perc}.npy',rmse_ignore_nan(corr_gt,corr_MeanImpute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm # 51s\n",
    "from impyute.imputation.cs import fast_knn\n",
    "percs = [0.4]\n",
    "for perc in tqdm(percs):\n",
    "    corr_gt = np.load(f'DATA/corr_gt_{perc}.npy')\n",
    "    new_df = pd.read_csv(f'DATA/mnist_ms_nm_{perc}.csv')\n",
    "    corr_MeanImpute = np.corrcoef(fast_knn(new_df.drop('label',axis=1),k=10).T)\n",
    "    # np.save(f'experiments/Corr/MNIST/MeanImpute/corr_{perc}.npy', corr_MeanImpute)\n",
    "    # np.save(f'experiments/Rmse/MNIST/MeanImpute/rmse_{perc}.npy',rmse_ignore_nan(corr_gt,corr_MeanImpute))\n",
    "    print(corr_MeanImpute)\n",
    "    print(rmse_ignore_nan(corr_gt,corr_MeanImpute))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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