ms-get-oof-calib-cv.ipynb

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   "source": [
    "#  import stuff"
   ]
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "from tqdm import tqdm\n",
    "# import featuretools as ft\n",
    "from joblib import dump, load\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "from sklearn.preprocessing import minmax_scale, StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import make_scorer, log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score, train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from  sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline,Pipeline\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "from tensorflow.keras import layers,models,metrics,callbacks,losses\n",
    "from tensorflow.keras.activations import relu,sigmoid,swish,tanh,selu\n",
    "from tensorflow.keras.optimizers import SGD,Adam,RMSprop,Adadelta,Nadam\n",
    "from tensorflow.keras.models import Model, Sequential,load_model\n",
    "import tensorflow as tf\n",
    "\n",
    "RANDOM_SEED = 777\n",
    "np.random.seed(RANDOM_SEED)\n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "50bf17df",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('../input/is-this-crazy/metadata.csv')\n",
    "sup_metadata = pd.read_csv('../input/is-this-crazy/supplemental_metadata.csv')\n",
    "train_labels = pd.read_csv('../input/is-this-crazy/train_labels.csv', index_col=\"sample_id\")\n",
    "val_labels = pd.read_csv('../input/is-this-crazy/val_labels.csv',index_col='sample_id')\n",
    "# targets = pd.read_csv('targets.csv',index_col='sample_id')\n",
    "submission_format = pd.read_csv('../input/is-this-crazy/submission_format.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c8c4509",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "source": [
    "targets = pd.concat([train_labels,val_labels],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d34432b",
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     "start_time": "2022-04-07T18:38:33.231077",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load data"
   ]
  },
  {
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   "execution_count": 4,
   "id": "05bd72f4",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.1 s, sys: 117 ms, total: 1.21 s\n",
      "Wall time: 1.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "test_100 = pd.read_csv('../input/ms-gen-train-test-data/test_100.csv',index_col=0,header=[0,1])\n",
    "train_100 = pd.read_csv('../input/ms-gen-train-test-data/train_100.csv',index_col=0,header=[0,1])\n",
    "val_100 = pd.read_csv('../input/ms-gen-train-test-data/val_100.csv',index_col=0,header=[0,1])\n",
    "\n",
    "val_100.index = metadata[metadata.split == 'val'].sample_id\n",
    "\n",
    "val_100.sort_index(inplace=True,axis=1)\n",
    "test_100.sort_index(inplace=True,axis=1)\n",
    "train_100.sort_index(inplace=True,axis=1)"
   ]
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 33s, sys: 2.98 s, total: 1min 36s\n",
      "Wall time: 1min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_5 = pd.read_csv('../input/ms-gen-train-test-data/train_5.csv',index_col=0,header=[0,1])\n",
    "val_5 = pd.read_csv('../input/ms-gen-train-test-data/val_5.csv',index_col=0,header=[0,1])\n",
    "test_5 = pd.read_csv('../input/ms-gen-train-test-data/test_5.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_5.sort_index(inplace=True,axis=1)\n",
    "val_5.sort_index(inplace=True,axis=1)\n",
    "train_5.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ab088a6",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 49s, sys: 7.43 s, total: 4min 56s\n",
      "Wall time: 5min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_3 = pd.read_csv('../input/ms-gen-train-test-data/train_3.csv',index_col=0,header=[0,1])\n",
    "val_3 = pd.read_csv('../input/ms-gen-train-test-data/val_3.csv',index_col=0,header=[0,1])\n",
    "test_3 = pd.read_csv('../input/ms-gen-train-test-data/test_3.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_3.sort_index(inplace=True,axis=1)\n",
    "val_3.sort_index(inplace=True,axis=1)\n",
    "train_3.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39d67af5",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.56 s, sys: 51.1 ms, total: 2.61 s\n",
      "Wall time: 3.19 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train_50 = pd.read_csv('../input/ms-gen-train-test-data/train_50.csv',index_col=0,header=[0,1])\n",
    "test_50 = pd.read_csv('../input/ms-gen-train-test-data/test_50.csv',index_col=0,header=[0,1])\n",
    "val_50 = pd.read_csv('../input/ms-gen-train-test-data/val_50.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_50.sort_index(inplace=True,axis=1)\n",
    "train_50.sort_index(inplace=True,axis=1)\n",
    "val_50.sort_index(inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e083a40a",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   },
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    {
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       "(766, 1000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_200 = pd.read_csv('../input/ms-gen-train-test-data/train_200.csv',index_col=0,header=[0,1])\n",
    "val_200 = pd.read_csv('../input/ms-gen-train-test-data/val_200.csv',index_col=0,header=[0,1])\n",
    "test_200 = pd.read_csv('../input/ms-gen-train-test-data/test_200.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_200.sort_index(inplace=True,axis=1)\n",
    "val_200.sort_index(inplace=True,axis=1)\n",
    "train_200.sort_index(inplace=True,axis=1)\n",
    "\n",
    "train_200.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81207996",
   "metadata": {
    "execution": {
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   "source": [
    "train_500 = pd.read_csv('../input/ms-gen-train-test-data/train_500.csv',index_col=0,header=[0,1])\n",
    "val_500 = pd.read_csv('../input/ms-gen-train-test-data/val_500.csv',index_col=0,header=[0,1])\n",
    "test_500 = pd.read_csv('../input/ms-gen-train-test-data/test_500.csv',index_col=0,header=[0,1])\n",
    "\n",
    "test_500.sort_index(inplace=True,axis=1)\n",
    "val_500.sort_index(inplace=True,axis=1)\n",
    "train_500.sort_index(inplace=True,axis=1)"
   ]
  },
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   "cell_type": "code",
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       "((766, 400), (766, 1000), (766, 2100), (766, 4200), (766, 42000), (766, 70000))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_500.shape, train_200.shape, train_100.shape, train_50.shape, train_5.shape, train_3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab7def80",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "train_set_3 = pd.concat([train_3,val_3],axis=0)\n",
    "train_set_5 = pd.concat([train_5,val_5],axis=0)\n",
    "train_set_50 = pd.concat([train_50,val_50],axis=0)\n",
    "train_set_100 = pd.concat([train_100,val_100],axis=0)\n",
    "train_set_200 = pd.concat([train_200,val_200],axis=0)\n",
    "train_set_500 = pd.concat([train_500,val_500],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4df0ece4",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "test_set_3 = pd.concat([val_3,test_3],axis=0)\n",
    "test_set_5 = pd.concat([val_5,test_5],axis=0)\n",
    "test_set_50 = pd.concat([val_50,test_50],axis=0)\n",
    "test_set_100 = pd.concat([val_100,test_100],axis=0)\n",
    "test_set_200 = pd.concat([val_200,test_200],axis=0)\n",
    "test_set_500 = pd.concat([val_500,test_500],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b446069",
   "metadata": {
    "papermill": {
     "duration": 0.05811,
     "end_time": "2022-04-07T18:45:29.350763",
     "exception": false,
     "start_time": "2022-04-07T18:45:29.292653",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# total abundance\n",
    "- this varies coz of min max scaling..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "504a85bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:29.474757Z",
     "iopub.status.busy": "2022-04-07T18:45:29.474084Z",
     "iopub.status.idle": "2022-04-07T18:45:29.476984Z",
     "shell.execute_reply": "2022-04-07T18:45:29.476483Z",
     "shell.execute_reply.started": "2022-04-06T15:53:51.048843Z"
    },
    "papermill": {
     "duration": 0.067882,
     "end_time": "2022-04-07T18:45:29.477127",
     "exception": false,
     "start_time": "2022-04-07T18:45:29.409245",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_mz(mz,df):\n",
    "    ''' returns list of columns for given mz'''\n",
    "    cols_list = []\n",
    "    for col in df.columns:\n",
    "        if col[0] == str(mz):\n",
    "            cols_list.append(col)\n",
    "    return cols_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bef3c2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:29.600711Z",
     "iopub.status.busy": "2022-04-07T18:45:29.597207Z",
     "iopub.status.idle": "2022-04-07T18:45:29.603460Z",
     "shell.execute_reply": "2022-04-07T18:45:29.602989Z",
     "shell.execute_reply.started": "2022-04-06T15:53:51.259351Z"
    },
    "papermill": {
     "duration": 0.067461,
     "end_time": "2022-04-07T18:45:29.603629",
     "exception": false,
     "start_time": "2022-04-07T18:45:29.536168",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_total_abundance(df):\n",
    "    ''' returns total abundance for each sample'''\n",
    "    tot_abund_df = pd.DataFrame(index=df.index)\n",
    "    tot_abund_df['tot_abund'] = df.sum(axis=1)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        tot_abund_df[f\"tot_abund_{mz}\"] = df[get_mz(mz,df)].sum(axis=1)\n",
    "    return tot_abund_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "002292ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:29.726423Z",
     "iopub.status.busy": "2022-04-07T18:45:29.725804Z",
     "iopub.status.idle": "2022-04-07T18:45:39.855208Z",
     "shell.execute_reply": "2022-04-07T18:45:39.855755Z",
     "shell.execute_reply.started": "2022-04-06T15:53:52.449019Z"
    },
    "papermill": {
     "duration": 10.194117,
     "end_time": "2022-04-07T18:45:39.855940",
     "exception": false,
     "start_time": "2022-04-07T18:45:29.661823",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 22.47it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 244.19it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 165.70it/s]\n",
      "100%|██████████| 100/100 [00:03<00:00, 33.03it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 205.36it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 264.36it/s]\n"
     ]
    }
   ],
   "source": [
    "tot_abund_3 = get_total_abundance(train_set_3)\n",
    "tot_abund_200 = get_total_abundance(train_set_200)\n",
    "tot_abund_50 = get_total_abundance(train_set_50)\n",
    "tot_abund_5 = get_total_abundance(train_set_5)\n",
    "tot_abund_100 = get_total_abundance(train_set_100)\n",
    "tot_abund_500 = get_total_abundance(train_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81eea865",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:40.102026Z",
     "iopub.status.busy": "2022-04-07T18:45:40.070790Z",
     "iopub.status.idle": "2022-04-07T18:45:49.733180Z",
     "shell.execute_reply": "2022-04-07T18:45:49.732521Z",
     "shell.execute_reply.started": "2022-04-06T15:53:57.423486Z"
    },
    "papermill": {
     "duration": 9.78458,
     "end_time": "2022-04-07T18:45:49.733331",
     "exception": false,
     "start_time": "2022-04-07T18:45:39.948751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:02<00:00, 36.79it/s]\n",
      "100%|██████████| 100/100 [00:04<00:00, 22.56it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 164.04it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 207.43it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 230.00it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 267.36it/s]\n"
     ]
    }
   ],
   "source": [
    "tot_abund_5_test = get_total_abundance(test_set_5)\n",
    "tot_abund_3_test = get_total_abundance(test_set_3)\n",
    "tot_abund_50_test = get_total_abundance(test_set_50)\n",
    "tot_abund_100_test = get_total_abundance(test_set_100)\n",
    "tot_abund_200_test = get_total_abundance(test_set_200)\n",
    "tot_abund_500_test = get_total_abundance(test_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ccb33ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:49.993216Z",
     "iopub.status.busy": "2022-04-07T18:45:49.992474Z",
     "iopub.status.idle": "2022-04-07T18:45:49.994784Z",
     "shell.execute_reply": "2022-04-07T18:45:49.994176Z",
     "shell.execute_reply.started": "2022-04-06T15:54:02.254735Z"
    },
    "papermill": {
     "duration": 0.132866,
     "end_time": "2022-04-07T18:45:49.994935",
     "exception": false,
     "start_time": "2022-04-07T18:45:49.862069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# def get_tempbins(mz):\n",
    "#     col_list = []\n",
    "#     for col in train_df.columns:\n",
    "#         if col[0] == str(mz):\n",
    "#             col_list.append(col)\n",
    "#     return col_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f348931",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:50.249241Z",
     "iopub.status.busy": "2022-04-07T18:45:50.248554Z",
     "iopub.status.idle": "2022-04-07T18:45:50.253348Z",
     "shell.execute_reply": "2022-04-07T18:45:50.253944Z",
     "shell.execute_reply.started": "2022-04-06T15:54:02.260748Z"
    },
    "papermill": {
     "duration": 0.133208,
     "end_time": "2022-04-07T18:45:50.254129",
     "exception": false,
     "start_time": "2022-04-07T18:45:50.120921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# drop if all values are zero\n",
    "def check_col(df):\n",
    "    col_with_zeros = []\n",
    "    for id,col in enumerate(tqdm(df.columns)):\n",
    "        if df[col].nunique() == 1:\n",
    "            col_with_zeros.append(id)\n",
    "    return col_with_zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0eeb570",
   "metadata": {
    "papermill": {
     "duration": 0.125133,
     "end_time": "2022-04-07T18:45:50.504841",
     "exception": false,
     "start_time": "2022-04-07T18:45:50.379708",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63173730",
   "metadata": {
    "papermill": {
     "duration": 0.124649,
     "end_time": "2022-04-07T18:45:50.758936",
     "exception": false,
     "start_time": "2022-04-07T18:45:50.634287",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# get gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed2211bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:51.018415Z",
     "iopub.status.busy": "2022-04-07T18:45:51.017689Z",
     "iopub.status.idle": "2022-04-07T18:45:51.462177Z",
     "shell.execute_reply": "2022-04-07T18:45:51.461530Z",
     "shell.execute_reply.started": "2022-04-06T15:54:02.27468Z"
    },
    "papermill": {
     "duration": 0.574089,
     "end_time": "2022-04-07T18:45:51.462345",
     "exception": false,
     "start_time": "2022-04-07T18:45:50.888256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set_200.loc[:5,get_mz(0,train_set_200)].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11630a80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:51.729482Z",
     "iopub.status.busy": "2022-04-07T18:45:51.728346Z",
     "iopub.status.idle": "2022-04-07T18:45:51.996640Z",
     "shell.execute_reply": "2022-04-07T18:45:51.997166Z",
     "shell.execute_reply.started": "2022-04-06T15:54:02.689424Z"
    },
    "papermill": {
     "duration": 0.405238,
     "end_time": "2022-04-07T18:45:51.997375",
     "exception": false,
     "start_time": "2022-04-07T18:45:51.592137",
     "status": "completed"
    },
    "tags": []
   },
   "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"
    }
   ],
   "source": [
    "train_set_500.loc[:50,get_mz(0,train_set_500)].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6637883c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:52.258720Z",
     "iopub.status.busy": "2022-04-07T18:45:52.258009Z",
     "iopub.status.idle": "2022-04-07T18:45:52.562583Z",
     "shell.execute_reply": "2022-04-07T18:45:52.563161Z",
     "shell.execute_reply.started": "2022-04-06T15:54:02.945954Z"
    },
    "papermill": {
     "duration": 0.435983,
     "end_time": "2022-04-07T18:45:52.563345",
     "exception": false,
     "start_time": "2022-04-07T18:45:52.127362",
     "status": "completed"
    },
    "tags": []
   },
   "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"
    }
   ],
   "source": [
    "train_set_50.loc[:5,get_mz(0,train_set_50)].plot(legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d82040a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:52.837343Z",
     "iopub.status.busy": "2022-04-07T18:45:52.836344Z",
     "iopub.status.idle": "2022-04-07T18:45:53.915886Z",
     "shell.execute_reply": "2022-04-07T18:45:53.916433Z",
     "shell.execute_reply.started": "2022-04-06T15:54:03.243829Z"
    },
    "papermill": {
     "duration": 1.220829,
     "end_time": "2022-04-07T18:45:53.916629",
     "exception": false,
     "start_time": "2022-04-07T18:45:52.695800",
     "status": "completed"
    },
    "tags": []
   },
   "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"
    }
   ],
   "source": [
    "train_set_5.loc[:5,get_mz(0,train_set_5)].plot(legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4603c03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:54.222554Z",
     "iopub.status.busy": "2022-04-07T18:45:54.218278Z",
     "iopub.status.idle": "2022-04-07T18:45:56.243894Z",
     "shell.execute_reply": "2022-04-07T18:45:56.243308Z"
    },
    "papermill": {
     "duration": 2.193517,
     "end_time": "2022-04-07T18:45:56.244051",
     "exception": false,
     "start_time": "2022-04-07T18:45:54.050534",
     "status": "completed"
    },
    "tags": []
   },
   "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"
    }
   ],
   "source": [
    "train_set_3.loc[:5,get_mz(0,train_set_3)].plot(legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5e462bd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:56.532323Z",
     "iopub.status.busy": "2022-04-07T18:45:56.531377Z",
     "iopub.status.idle": "2022-04-07T18:45:56.534430Z",
     "shell.execute_reply": "2022-04-07T18:45:56.533864Z",
     "shell.execute_reply.started": "2022-04-06T15:54:07.204252Z"
    },
    "papermill": {
     "duration": 0.150257,
     "end_time": "2022-04-07T18:45:56.534581",
     "exception": false,
     "start_time": "2022-04-07T18:45:56.384324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_grads(df,bin_size):\n",
    "    ''' returns gradients for each mz'''\n",
    "    grad_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        # df_mz_col1 = df_mz.iloc[:,0].values\n",
    "        df_mz = df_mz.diff(axis=1)\n",
    "        # df_mz.iloc[:,0] = df_mz_col1\n",
    "        df_mz.dropna(axis='columns',inplace=True)\n",
    "        # df_mz = df_mz/bin_size\n",
    "        df_mz.columns = [col[0]+ '_' + col[1] + '_' + 'grad' for col in df_mz.columns]\n",
    "        grad_df = pd.concat([grad_df,df_mz],axis=1)\n",
    "\n",
    "    return  grad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6cd3dd73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:45:56.820194Z",
     "iopub.status.busy": "2022-04-07T18:45:56.819265Z",
     "iopub.status.idle": "2022-04-07T18:46:26.740643Z",
     "shell.execute_reply": "2022-04-07T18:46:26.740031Z",
     "shell.execute_reply.started": "2022-04-06T15:54:07.414142Z"
    },
    "papermill": {
     "duration": 30.06758,
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:15<00:00,  6.60it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 86.63it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 52.38it/s]\n",
      "100%|██████████| 100/100 [00:09<00:00, 10.96it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 95.92it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 66.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.7 s, sys: 471 ms, total: 30.1 s\n",
      "Wall time: 29.9 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grad_3 = get_grads(train_set_3,3)\n",
    "grad_200 = get_grads(train_set_200,200)\n",
    "grad_50 = get_grads(train_set_50,50)\n",
    "grad_5 = get_grads(train_set_5,5)\n",
    "grad_500 = get_grads(train_set_500,500)\n",
    "grad_100 = get_grads(train_set_100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "efacf840",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:46:27.183792Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:11<00:00,  8.36it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 91.56it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 58.82it/s]\n",
      "100%|██████████| 100/100 [00:07<00:00, 13.70it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 107.76it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 76.48it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24 s, sys: 452 ms, total: 24.4 s\n",
      "Wall time: 24.3 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grad_3_test = get_grads(test_set_3,3)\n",
    "grad_200_test = get_grads(test_set_200,200)\n",
    "grad_50_test = get_grads(test_set_50,50)\n",
    "grad_5_test = get_grads(test_set_5,5)\n",
    "grad_500_test = get_grads(test_set_500,500)\n",
    "grad_100_test = get_grads(test_set_100,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfb0821",
   "metadata": {
    "papermill": {
     "duration": 0.281604,
     "end_time": "2022-04-07T18:46:52.069679",
     "exception": false,
     "start_time": "2022-04-07T18:46:51.788075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# add metadata\n",
    "- mean,varience of all mzs\n",
    "- mean,varience of a tempbin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf7d1972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:46:52.641484Z",
     "iopub.status.busy": "2022-04-07T18:46:52.640371Z",
     "iopub.status.idle": "2022-04-07T18:46:52.642831Z",
     "shell.execute_reply": "2022-04-07T18:46:52.643271Z",
     "shell.execute_reply.started": "2022-04-06T15:54:31.743357Z"
    },
    "papermill": {
     "duration": 0.290914,
     "end_time": "2022-04-07T18:46:52.643460",
     "exception": false,
     "start_time": "2022-04-07T18:46:52.352546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_mean_std(df):\n",
    "    ''' returns mean and std for each mz'''\n",
    "    mean_std_df = pd.DataFrame(index=df.index)\n",
    "    for mz in tqdm(range(0,100)):\n",
    "        df_mz = df.loc[:,get_mz(mz,df)]\n",
    "        df_mz[f'{mz}_mean'] = df_mz.mean(axis=1)\n",
    "        df_mz[f'{mz}_std'] = df_mz.std(axis=1)\n",
    "        mean_std_df = pd.concat([mean_std_df,df_mz],axis=1)\n",
    "    return mean_std_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a20e7a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:46:53.220503Z",
     "iopub.status.busy": "2022-04-07T18:46:53.219652Z",
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     "shell.execute_reply": "2022-04-07T18:47:23.638736Z",
     "shell.execute_reply.started": "2022-04-06T15:54:31.751957Z"
    },
    "papermill": {
     "duration": 30.713492,
     "end_time": "2022-04-07T18:47:23.642772",
     "exception": false,
     "start_time": "2022-04-07T18:46:52.929280",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:15<00:00,  6.28it/s]\n",
      "100%|██████████| 100/100 [00:09<00:00, 10.80it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 57.82it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 74.35it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 86.81it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 105.47it/s]\n"
     ]
    }
   ],
   "source": [
    "mean_std_3 = get_mean_std(train_set_3)\n",
    "mean_std_5 = get_mean_std(train_set_5)\n",
    "mean_std_50 = get_mean_std(train_set_50)\n",
    "mean_std_100 = get_mean_std(train_set_100)\n",
    "mean_std_200 = get_mean_std(train_set_200)\n",
    "mean_std_500 = get_mean_std(train_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b9e3370",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:47:24.359459Z",
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     "iopub.status.idle": "2022-04-07T18:47:51.477469Z",
     "shell.execute_reply": "2022-04-07T18:47:51.477976Z",
     "shell.execute_reply.started": "2022-04-06T15:54:44.326741Z"
    },
    "papermill": {
     "duration": 27.477789,
     "end_time": "2022-04-07T18:47:51.478167",
     "exception": false,
     "start_time": "2022-04-07T18:47:24.000378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:13<00:00,  7.26it/s]\n",
      "100%|██████████| 100/100 [00:08<00:00, 11.67it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 61.79it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 82.73it/s]\n",
      "100%|██████████| 100/100 [00:01<00:00, 98.19it/s]\n",
      "100%|██████████| 100/100 [00:00<00:00, 111.95it/s]\n"
     ]
    }
   ],
   "source": [
    "mean_std_3_test = get_mean_std(test_set_3)\n",
    "mean_std_5_test = get_mean_std(test_set_5)\n",
    "mean_std_50_test = get_mean_std(test_set_50)\n",
    "mean_std_100_test = get_mean_std(test_set_100)\n",
    "mean_std_200_test = get_mean_std(test_set_200)\n",
    "mean_std_500_test = get_mean_std(test_set_500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd6d16f1",
   "metadata": {
    "papermill": {
     "duration": 0.428111,
     "end_time": "2022-04-07T18:47:52.334011",
     "exception": false,
     "start_time": "2022-04-07T18:47:51.905900",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c27235bf",
   "metadata": {
    "papermill": {
     "duration": 0.427791,
     "end_time": "2022-04-07T18:47:53.188331",
     "exception": false,
     "start_time": "2022-04-07T18:47:52.760540",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**next up**\n",
    "- quantile transform, guassian rank transform , feature tools and nns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "344198fd",
   "metadata": {
    "papermill": {
     "duration": 0.427878,
     "end_time": "2022-04-07T18:47:54.042578",
     "exception": false,
     "start_time": "2022-04-07T18:47:53.614700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83c9f753",
   "metadata": {
    "papermill": {
     "duration": 0.459439,
     "end_time": "2022-04-07T18:47:54.928058",
     "exception": false,
     "start_time": "2022-04-07T18:47:54.468619",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "724e66d9",
   "metadata": {
    "papermill": {
     "duration": 0.428302,
     "end_time": "2022-04-07T18:47:55.782707",
     "exception": false,
     "start_time": "2022-04-07T18:47:55.354405",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "51a1e4d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:47:56.636325Z",
     "iopub.status.busy": "2022-04-07T18:47:56.635239Z",
     "iopub.status.idle": "2022-04-07T18:47:56.639998Z",
     "shell.execute_reply": "2022-04-07T18:47:56.640558Z",
     "shell.execute_reply.started": "2022-04-06T15:54:55.138219Z"
    },
    "papermill": {
     "duration": 0.431989,
     "end_time": "2022-04-07T18:47:56.640757",
     "exception": false,
     "start_time": "2022-04-07T18:47:56.208768",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED)\n",
    "log_loss_scorer = make_scorer(log_loss, needs_proba=True)\n",
    "phy = 'phyllosilicate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a7fe519",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:47:57.500181Z",
     "iopub.status.busy": "2022-04-07T18:47:57.497394Z",
     "iopub.status.idle": "2022-04-07T18:47:57.507379Z",
     "shell.execute_reply": "2022-04-07T18:47:57.506848Z",
     "shell.execute_reply.started": "2022-04-06T15:54:55.144859Z"
    },
    "papermill": {
     "duration": 0.438957,
     "end_time": "2022-04-07T18:47:57.507534",
     "exception": false,
     "start_time": "2022-04-07T18:47:57.068577",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, penalty='l1', random_state=777, solver='liblinear')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_clf = LogisticRegression(penalty='l1',C=1, solver='liblinear',random_state=RANDOM_SEED)\n",
    "logreg_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7803789a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:47:58.377444Z",
     "iopub.status.busy": "2022-04-07T18:47:58.376718Z",
     "iopub.status.idle": "2022-04-07T18:47:58.378486Z",
     "shell.execute_reply": "2022-04-07T18:47:58.379006Z",
     "shell.execute_reply.started": "2022-04-06T15:54:55.161852Z"
    },
    "papermill": {
     "duration": 0.444198,
     "end_time": "2022-04-07T18:47:58.379202",
     "exception": false,
     "start_time": "2022-04-07T18:47:57.935004",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_imp_coefs(clf,df,target_col):\n",
    "    \"\"\"\n",
    "    Returns the df with imp cols based on logreg coefs...\n",
    "    \"\"\"\n",
    "    clf.fit(df,targets[target_col])\n",
    "    coefs = clf.coef_\n",
    "    coefs = pd.Series(coefs.ravel())\n",
    "    coefs = coefs.where(coefs != 0.0).dropna().sort_values(ascending=False).index\n",
    "    # imp_fts_5[target_col] = coefs\n",
    "    \n",
    "    cv_score = cross_val_score(clf,df,targets[target_col],cv=skf,scoring=log_loss_scorer)\n",
    "    print(cv_score,cv_score.mean())\n",
    "    # print(target_col)\n",
    "    # print(coefs)\n",
    "    # print(\"------------------------------------\")\n",
    "    \n",
    "    return df.iloc[:,coefs],coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f2b481a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:47:59.373667Z",
     "iopub.status.busy": "2022-04-07T18:47:59.372948Z",
     "iopub.status.idle": "2022-04-07T18:47:59.374621Z",
     "shell.execute_reply": "2022-04-07T18:47:59.375120Z",
     "shell.execute_reply.started": "2022-04-06T15:55:05.214413Z"
    },
    "papermill": {
     "duration": 0.558485,
     "end_time": "2022-04-07T18:47:59.375294",
     "exception": false,
     "start_time": "2022-04-07T18:47:58.816809",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# X_500 = pd.concat([train_set_500,grad_500,tot_abund_500,mean_std_500],axis=1)\n",
    "# X_200 = pd.concat([train_set_200,grad_200,tot_abund_200,mean_std_200],axis=1)\n",
    "# X_50 = pd.concat([train_set_50,grad_50,tot_abund_50,mean_std_50],axis=1)\n",
    "# X_5 = pd.concat([train_set_5,grad_5,tot_abund_5,mean_std_5],axis=1)\n",
    "# X_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c6734791",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:00.254928Z",
     "iopub.status.busy": "2022-04-07T18:48:00.254185Z",
     "iopub.status.idle": "2022-04-07T18:48:00.261685Z",
     "shell.execute_reply": "2022-04-07T18:48:00.262348Z",
     "shell.execute_reply.started": "2022-04-06T16:04:56.296007Z"
    },
    "papermill": {
     "duration": 0.448388,
     "end_time": "2022-04-07T18:48:00.262531",
     "exception": false,
     "start_time": "2022-04-07T18:47:59.814143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_imp_coefs_for_tar(clf,df_set,tar):\n",
    "    imp_df_1 = dict()\n",
    "    imp_fts_1 = dict()\n",
    "    \n",
    "    imp_df_org,imp_fts_1['org_set'] = get_imp_coefs(clf,df_set[0],tar)\n",
    "    imp_df_tot_abund,imp_fts_1['tot_abund_set'] = get_imp_coefs(clf,df_set[1],tar)\n",
    "    imp_df_grad,imp_fts_1['grad_set'] = get_imp_coefs(clf,df_set[2],tar)\n",
    "    imp_df_mean_std,imp_fts_1['mean_std_set'] = get_imp_coefs(clf,df_set[3],tar)\n",
    "\n",
    "    imp_df_1 = pd.concat([imp_df_org,imp_df_tot_abund,imp_df_grad,imp_df_mean_std],axis=1)\n",
    "    print(imp_df_1.shape)\n",
    "\n",
    "    return imp_df_1,imp_fts_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cf4eaa99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:01.160947Z",
     "iopub.status.busy": "2022-04-07T18:48:01.160254Z",
     "iopub.status.idle": "2022-04-07T18:48:01.164762Z",
     "shell.execute_reply": "2022-04-07T18:48:01.165310Z"
    },
    "papermill": {
     "duration": 0.449784,
     "end_time": "2022-04-07T18:48:01.165484",
     "exception": false,
     "start_time": "2022-04-07T18:48:00.715700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#  selecting fts each on additional datasets is best strategy\n",
    "\n",
    "# c=5 l1 penality\n",
    "# .25 from cb_clf on imp fts of x_500\n",
    "# on whole x_500 directly fit on cb_clf (no imp fts) 0.26...\n",
    "\n",
    "\n",
    "# .23 from cb_clf on individual imp fts of x_500 (73 fts)\n",
    "\n",
    "# x_50 (131 fts) C =1\n",
    "# (array([0.15715623, 0.19957427, 0.30083273, 0.2696438 , 0.18046425]),\n",
    "#  0.22153425492556167,\n",
    "#  0.054619214043050285)\n",
    "# c = 5\n",
    "# (array([0.16005222, 0.20716925, 0.30667596, 0.2675076 , 0.17654597]),\n",
    "#  0.22359020020625892,\n",
    "#  0.05541094180104098)\n",
    "\n",
    "# X_5 (321) c = 5\n",
    "# (array([0.2062453 , 0.23841005, 0.33270952, 0.28029341, 0.21285483]),\n",
    "#  0.2541026221564036,\n",
    "#  0.04712316089806101)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb3f27c",
   "metadata": {
    "papermill": {
     "duration": 0.445261,
     "end_time": "2022-04-07T18:48:02.049590",
     "exception": false,
     "start_time": "2022-04-07T18:48:01.604329",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## get oof_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e14d18da",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-04-06T15:55:24.668927Z"
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     "exception": false,
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     "status": "completed"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "777"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NFOLDS = 5\n",
    "ntrain = 1059\n",
    "ntest = 804\n",
    "RANDOM_SEED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45e831c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:03.827499Z",
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     "shell.execute_reply.started": "2022-04-06T15:55:25.188844Z"
    },
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     "end_time": "2022-04-07T18:48:03.833246",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_set_3 = [train_set_3,tot_abund_3,grad_3,mean_std_3]\n",
    "df_set_5 = [train_set_5,tot_abund_5,grad_5,mean_std_5]\n",
    "df_set_50 = [train_set_50,tot_abund_50,grad_50,mean_std_50]\n",
    "df_set_100 = [train_set_100,tot_abund_100,grad_100,mean_std_100]\n",
    "df_set_200 = [train_set_200,tot_abund_200,grad_200,mean_std_200]\n",
    "df_set_500 = [train_set_500,tot_abund_500,grad_500,mean_std_500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d756f499",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:04.748324Z",
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     "shell.execute_reply.started": "2022-04-06T15:55:27.464056Z"
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     "exception": false,
     "start_time": "2022-04-07T18:48:04.277069",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_test_set_3 = [test_set_3,tot_abund_3_test,grad_3_test,mean_std_3_test]\n",
    "df_test_set_5 = [test_set_5,tot_abund_5_test,grad_5_test,mean_std_5_test]\n",
    "df_test_set_50 = [test_set_50,tot_abund_50_test,grad_50_test,mean_std_50_test]\n",
    "df_test_set_100 = [test_set_100,tot_abund_100_test,grad_100_test,mean_std_100_test]\n",
    "df_test_set_200 = [test_set_200,tot_abund_200_test,grad_200_test,mean_std_200_test]\n",
    "df_test_set_500 = [test_set_500,tot_abund_500_test,grad_500_test,mean_std_500_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "467b78f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:05.738309Z",
     "iopub.status.busy": "2022-04-07T18:48:05.737588Z",
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     "shell.execute_reply.started": "2022-04-06T16:05:00.574344Z"
    },
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     "duration": 0.496643,
     "end_time": "2022-04-07T18:48:05.742772",
     "exception": false,
     "start_time": "2022-04-07T18:48:05.246129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_oof(clf, x_train, y_train, x_test):\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "    cv_loss = []\n",
    "\n",
    "    for i, (train_index, test_index) in enumerate(skf.split(x_train,y_train)):\n",
    "        x_tr = x_train.iloc[train_index]\n",
    "        y_tr = y_train.iloc[train_index]\n",
    "        x_te = x_train.iloc[test_index]\n",
    "        y_te = y_train.iloc[test_index]\n",
    "\n",
    "        clf.fit(x_tr.values, y_tr)\n",
    "\n",
    "        oof_train[test_index] = clf.predict_proba(x_te.values)[:,1]\n",
    "        fold_loss = log_loss(y_te, clf.predict_proba(x_te.values)[:,1])\n",
    "        cv_loss.append(fold_loss)\n",
    "        print(f\"logloss for fold {i}: {fold_loss}\")\n",
    "\n",
    "        oof_test_skf[i, :] = clf.predict_proba(x_test.values)[:,1]\n",
    "\n",
    "    print(\"CV ccv loss \",np.array(cv_loss).mean(),\"CV ccv std\", np.array(cv_loss).std())\n",
    "    \n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1), np.array(cv_loss).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0a8c8b5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:06.627362Z",
     "iopub.status.busy": "2022-04-07T18:48:06.626717Z",
     "iopub.status.idle": "2022-04-07T18:48:06.641465Z",
     "shell.execute_reply": "2022-04-07T18:48:06.642094Z",
     "shell.execute_reply.started": "2022-04-06T16:05:00.658964Z"
    },
    "papermill": {
     "duration": 0.453902,
     "end_time": "2022-04-07T18:48:06.642304",
     "exception": false,
     "start_time": "2022-04-07T18:48:06.188402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "oof_train_df = pd.DataFrame(index=metadata[metadata.split != 'test'].sample_id, columns=targets.columns)\n",
    "oof_test_df = pd.DataFrame(index=metadata[metadata.split != 'train'].sample_id, columns=targets.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "47b01f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:07.534311Z",
     "iopub.status.busy": "2022-04-07T18:48:07.533409Z",
     "iopub.status.idle": "2022-04-07T18:48:07.541917Z",
     "shell.execute_reply": "2022-04-07T18:48:07.542490Z",
     "shell.execute_reply.started": "2022-04-06T16:06:15.181721Z"
    },
    "papermill": {
     "duration": 0.455724,
     "end_time": "2022-04-07T18:48:07.542734",
     "exception": false,
     "start_time": "2022-04-07T18:48:07.087010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cvon_df(df_train_set,df_test_set,freq):\n",
    "    imp_df  = dict()\n",
    "    imp_fts = dict()\n",
    "    \n",
    "    agg_loss = []\n",
    "    for target in targets.columns:\n",
    "        print('target:',target)\n",
    "        \n",
    "        # select the best features for each target\n",
    "        logreg_clf = LogisticRegression(penalty='l1',C=1, solver='liblinear',random_state=RANDOM_SEED)\n",
    "        print('logreg cv score:')\n",
    "\n",
    "        imp_df[target],imp_fts[target] = get_imp_coefs_for_tar(logreg_clf,df_train_set,target) # cv logreg\n",
    "\n",
    "        X = imp_df[target]    # x is selected fts for the given target.\n",
    "        X_test = pd.concat([\n",
    "            df_test_set[0].iloc[:,imp_fts[target]['org_set']],\n",
    "            df_test_set[1].iloc[:,imp_fts[target]['tot_abund_set']],\n",
    "            df_test_set[2].iloc[:,imp_fts[target]['grad_set']],\n",
    "            df_test_set[3].iloc[:,imp_fts[target]['mean_std_set']]\n",
    "        ],axis=1)\n",
    "\n",
    "        # save train and test sets \n",
    "        X.to_csv('X_'+freq+'_imp_v1_'+target+'.csv')\n",
    "        X_test.to_csv('X_test_'+freq+'_imp_v1_'+target+'.csv')\n",
    "        \n",
    "        print(\"Caliberated catboost CV:\")\n",
    "        cb_clf = CatBoostClassifier(random_seed=RANDOM_SEED,verbose=False,eval_metric='Logloss')\n",
    "        calb_clf = CalibratedClassifierCV(cb_clf,cv=skf,method='sigmoid')   # calibrating this way is calibrating on less data\n",
    "\n",
    "        oof_train, oof_test, loss = get_oof(calb_clf,X,targets[target],X_test)\n",
    "        agg_loss.append(loss)\n",
    "#         print(loss)\n",
    "\n",
    "        oof_train_df[target] = oof_train\n",
    "        oof_test_df[target] = oof_test\n",
    "        print('------------------------------------------------------------------------')\n",
    "    \n",
    "    dump(imp_df,'imp_df_'+ freq + '_v1.joblib')\n",
    "    dump(imp_df,'imp_fts_'+ freq + '_v1.joblib')\n",
    "    \n",
    "    print('AGG Loss:',agg_loss)\n",
    "    print(\"AGG Loss mean\",np.array(agg_loss).mean())\n",
    "    \n",
    "    return oof_train_df,oof_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7b2cb1da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:08.427424Z",
     "iopub.status.busy": "2022-04-07T18:48:08.426763Z",
     "iopub.status.idle": "2022-04-07T18:48:08.430307Z",
     "shell.execute_reply": "2022-04-07T18:48:08.430928Z"
    },
    "papermill": {
     "duration": 0.447569,
     "end_time": "2022-04-07T18:48:08.431109",
     "exception": false,
     "start_time": "2022-04-07T18:48:07.983540",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# oof_train_3, oof_test_3 = cvon_df(df_set_3,df_test_set_3,'3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "009ee3c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:09.309128Z",
     "iopub.status.busy": "2022-04-07T18:48:09.308376Z",
     "iopub.status.idle": "2022-04-07T18:48:09.311727Z",
     "shell.execute_reply": "2022-04-07T18:48:09.311163Z"
    },
    "papermill": {
     "duration": 0.446694,
     "end_time": "2022-04-07T18:48:09.311886",
     "exception": false,
     "start_time": "2022-04-07T18:48:08.865192",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# oof_train_3.to_csv('oof_train_3_v1.csv')\n",
    "# oof_test_3.to_csv('oof_test_3_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66bf281f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T18:48:10.222797Z",
     "iopub.status.busy": "2022-04-07T18:48:10.222028Z",
     "iopub.status.idle": "2022-04-07T19:26:54.501726Z",
     "shell.execute_reply": "2022-04-07T19:26:54.502268Z"
    },
    "papermill": {
     "duration": 2324.750425,
     "end_time": "2022-04-07T19:26:54.502481",
     "exception": false,
     "start_time": "2022-04-07T18:48:09.752056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target: basalt\n",
      "logreg cv score:\n",
      "[0.29618601 0.34251172 0.33147642 0.3045623  0.30368469] 0.31568422627501647\n",
      "[0.29744037 0.34430501 0.31667005 0.31142916 0.31604542] 0.3171780015725899\n",
      "[0.30391862 0.33366086 0.33822246 0.30078298 0.29904712] 0.31512640635640254\n",
      "[0.29616164 0.34246994 0.33219393 0.30454738 0.30363236] 0.31580104914857976\n",
      "(1059, 74)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.1358830946320037\n",
      "logloss for fold 1: 0.14939754737414068\n",
      "logloss for fold 2: 0.15679552664177998\n",
      "logloss for fold 3: 0.17641164294523326\n",
      "logloss for fold 4: 0.1711870593070472\n",
      "CV ccv loss  0.15793497418004096 CV ccv std 0.014679757747536229\n",
      "------------------------------------------------------------------------\n",
      "target: carbonate\n",
      "logreg cv score:\n",
      "[0.29346799 0.24537882 0.24149702 0.33570706 0.25428965] 0.2740681090345786\n",
      "[0.3120115  0.26306459 0.27503564 0.34983382 0.29931206] 0.299851523042478\n",
      "[0.28829173 0.23241007 0.25131178 0.31154878 0.25044888] 0.26680224657321505\n",
      "[0.27824139 0.22833238 0.22317038 0.31812348 0.23722489] 0.25701850420125016\n",
      "(1059, 81)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.15524245415484939\n",
      "logloss for fold 1: 0.07466358847770015\n",
      "logloss for fold 2: 0.07682282710308329\n",
      "logloss for fold 3: 0.14408818955924496\n",
      "logloss for fold 4: 0.07879766733837118\n",
      "CV ccv loss  0.10592294532664978 CV ccv std 0.035913075043959714\n",
      "------------------------------------------------------------------------\n",
      "target: chloride\n",
      "logreg cv score:\n",
      "[0.32790486 0.26108448 0.28901421 0.25388521 0.332007  ] 0.29277915149790273\n",
      "[0.3383186  0.31096058 0.32036654 0.30039875 0.31239248] 0.316487389359151\n",
      "[0.3306376  0.26868631 0.29903706 0.26094747 0.34835547] 0.3015327802647978\n",
      "[0.32789891 0.26106562 0.28901972 0.25385538 0.33200993] 0.29276991079323256\n",
      "(1059, 73)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.20357540370972074\n",
      "logloss for fold 1: 0.13349749750584983\n",
      "logloss for fold 2: 0.1806930347104376\n",
      "logloss for fold 3: 0.11618205612189036\n",
      "logloss for fold 4: 0.201426436941318\n",
      "CV ccv loss  0.1670748857978433 CV ccv std 0.03581980449567109\n",
      "------------------------------------------------------------------------\n",
      "target: iron_oxide\n",
      "logreg cv score:\n",
      "[0.36697975 0.42177068 0.43775229 0.4081568  0.38291864] 0.4035156330707735\n",
      "[0.47436915 0.49217155 0.5226787  0.51035066 0.52571041] 0.5050560910604643\n",
      "[0.3570388  0.41913029 0.42256884 0.4035552  0.36790162] 0.3940389505484023\n",
      "[0.36697854 0.42177754 0.43775123 0.40814973 0.38292778] 0.4035169635119054\n",
      "(1059, 74)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.19842237351629924\n",
      "logloss for fold 1: 0.20800301569491791\n",
      "logloss for fold 2: 0.24765832815913963\n",
      "logloss for fold 3: 0.2141420025194537\n",
      "logloss for fold 4: 0.19500762503861735\n",
      "CV ccv loss  0.21264666898568557 CV ccv std 0.01877808440340594\n",
      "------------------------------------------------------------------------\n",
      "target: oxalate\n",
      "logreg cv score:\n",
      "[0.00984188 0.01327078 0.04604236 0.0258621  0.01698352] 0.022400127487326768\n",
      "[0.03069733 0.05427495 0.06385451 0.04062313 0.0419145 ] 0.0462728844890055\n",
      "[0.00850089 0.01168469 0.03650979 0.0261592  0.01293296] 0.019157507588507434\n",
      "[0.00984435 0.01326709 0.04604432 0.02586922 0.01701134] 0.0224072634223316\n",
      "(1059, 36)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.01069442365250605\n",
      "logloss for fold 1: 0.012550668019230098\n",
      "logloss for fold 2: 0.019230600647567715\n",
      "logloss for fold 3: 0.02023896086814507\n",
      "logloss for fold 4: 0.01102300896534706\n",
      "CV ccv loss  0.014747532430559197 CV ccv std 0.0041323036184406975\n",
      "------------------------------------------------------------------------\n",
      "target: oxychlorine\n",
      "logreg cv score:\n",
      "[0.29223283 0.36762584 0.26074789 0.27288936 0.25765996] 0.2902311738531026\n",
      "[0.40150426 0.43452255 0.39911104 0.34843203 0.36007038] 0.3887280521420037\n",
      "[0.29885401 0.37330214 0.25905494 0.27805557 0.25262616] 0.2923785637415853\n",
      "[0.29223145 0.36762809 0.26076288 0.27289099 0.2576738 ] 0.2902374431694611\n",
      "(1059, 64)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.16571009648373827\n",
      "logloss for fold 1: 0.19472570110561369\n",
      "logloss for fold 2: 0.11052049747114855\n",
      "logloss for fold 3: 0.18192739561484778\n",
      "logloss for fold 4: 0.11149487815172458\n",
      "CV ccv loss  0.1528757137654146 CV ccv std 0.03540192351225343\n",
      "------------------------------------------------------------------------\n",
      "target: phyllosilicate\n",
      "logreg cv score:\n",
      "[0.41060183 0.44999417 0.53982509 0.42846251 0.42077978] 0.4499326785584552\n",
      "[0.47799821 0.52840075 0.57539049 0.55072067 0.48576207] 0.523654438328584\n",
      "[0.43253261 0.45842482 0.54470879 0.43034695 0.42504537] 0.4582117071982325\n",
      "[0.41072349 0.44967956 0.53982238 0.42846487 0.4207783 ] 0.4498937179834068\n",
      "(1059, 71)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.21717945156376917\n",
      "logloss for fold 1: 0.2393615287526272\n",
      "logloss for fold 2: 0.3230034539758006\n",
      "logloss for fold 3: 0.24110057801117601\n",
      "logloss for fold 4: 0.20654166500609966\n",
      "CV ccv loss  0.24543733546189453 CV ccv std 0.04094778825839997\n",
      "------------------------------------------------------------------------\n",
      "target: silicate\n",
      "logreg cv score:\n",
      "[0.34570543 0.35204092 0.32637611 0.34530693 0.34437207] 0.3427602920974856\n",
      "[0.36239786 0.35246824 0.34373231 0.35005483 0.3551161 ] 0.3527538683315579\n",
      "[0.35248618 0.35701319 0.33338603 0.35354716 0.34109883] 0.3475062782006725\n",
      "[0.3457168  0.35212159 0.32640817 0.34550653 0.34438725] 0.34282806964526924\n",
      "(1059, 68)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.25196104909760286\n",
      "logloss for fold 1: 0.20162755042342784\n",
      "logloss for fold 2: 0.15283520741417375\n",
      "logloss for fold 3: 0.17992876354227202\n",
      "logloss for fold 4: 0.1791009070989211\n",
      "CV ccv loss  0.19309069551527952 CV ccv std 0.03325071842257057\n",
      "------------------------------------------------------------------------\n",
      "target: sulfate\n",
      "logreg cv score:\n",
      "[0.37581412 0.32789106 0.30231648 0.35106315 0.31550575] 0.3345181126282285\n",
      "[0.39187597 0.4082938  0.56332907 0.45507159 0.40986362] 0.44568680777142033\n",
      "[0.40704799 0.34943374 0.36049931 0.37404849 0.35870463] 0.36994683162147657\n",
      "[0.37580665 0.32788139 0.30232754 0.35107084 0.31550757] 0.334518799038306\n",
      "(1059, 75)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.22384894462688168\n",
      "logloss for fold 1: 0.21582191305795556\n",
      "logloss for fold 2: 0.11431807736368695\n",
      "logloss for fold 3: 0.2054254514140197\n",
      "logloss for fold 4: 0.14102714834223817\n",
      "CV ccv loss  0.1800883069609564 CV ccv std 0.0440121622203988\n",
      "------------------------------------------------------------------------\n",
      "target: sulfide\n",
      "logreg cv score:\n",
      "[0.15153869 0.16740655 0.14186937 0.136492   0.14016926] 0.14749517389430455\n",
      "[0.18500622 0.16664159 0.16464513 0.15967224 0.15542162] 0.16627736034417134\n",
      "[0.14847687 0.16158962 0.14258704 0.13429706 0.12263312] 0.14191674133338597\n",
      "[0.15154069 0.16742103 0.14184064 0.1365184  0.1401715 ] 0.1474984507466049\n",
      "(1059, 54)\n",
      "Caliberated catboost CV:\n",
      "logloss for fold 0: 0.09511173461491146\n",
      "logloss for fold 1: 0.12298101069329703\n",
      "logloss for fold 2: 0.07811420690465849\n",
      "logloss for fold 3: 0.05441557787822379\n",
      "logloss for fold 4: 0.10098219183606406\n",
      "CV ccv loss  0.09032094438543097 CV ccv std 0.02298474965450829\n",
      "------------------------------------------------------------------------\n",
      "AGG Loss: [0.15793497418004096, 0.10592294532664978, 0.1670748857978433, 0.21264666898568557, 0.014747532430559197, 0.1528757137654146, 0.24543733546189453, 0.19309069551527952, 0.1800883069609564, 0.09032094438543097]\n",
      "AGG Loss mean 0.1520140002809755\n"
     ]
    }
   ],
   "source": [
    "oof_train_200, oof_test_200 = cvon_df(df_set_200,df_test_set_200,'200')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "43cf3d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T19:26:55.433410Z",
     "iopub.status.busy": "2022-04-07T19:26:55.432757Z",
     "iopub.status.idle": "2022-04-07T19:26:55.478357Z",
     "shell.execute_reply": "2022-04-07T19:26:55.477769Z"
    },
    "papermill": {
     "duration": 0.516394,
     "end_time": "2022-04-07T19:26:55.478518",
     "exception": false,
     "start_time": "2022-04-07T19:26:54.962124",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "oof_train_200.to_csv('oof_train_200_v1.csv')\n",
    "oof_test_200.to_csv('oof_test_200_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df78490b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T19:26:56.409544Z",
     "iopub.status.busy": "2022-04-07T19:26:56.408882Z",
     "iopub.status.idle": "2022-04-07T19:26:56.410940Z",
     "shell.execute_reply": "2022-04-07T19:26:56.411533Z"
    },
    "papermill": {
     "duration": 0.471838,
     "end_time": "2022-04-07T19:26:56.411735",
     "exception": false,
     "start_time": "2022-04-07T19:26:55.939897",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# oof_train_5, oof_test_5 = cvon_df(df_set_5,df_test_set_5,'5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "676f2db8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T19:26:57.344814Z",
     "iopub.status.busy": "2022-04-07T19:26:57.344096Z",
     "iopub.status.idle": "2022-04-07T19:26:57.348265Z",
     "shell.execute_reply": "2022-04-07T19:26:57.347735Z"
    },
    "papermill": {
     "duration": 0.470863,
     "end_time": "2022-04-07T19:26:57.348408",
     "exception": false,
     "start_time": "2022-04-07T19:26:56.877545",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# oof_train_5.to_csv('oof_train_5_v1.csv')\n",
    "# oof_test_5.to_csv('oof_test_5_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "38f1459f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T19:26:58.308704Z",
     "iopub.status.busy": "2022-04-07T19:26:58.307993Z",
     "iopub.status.idle": "2022-04-07T19:26:58.310755Z",
     "shell.execute_reply": "2022-04-07T19:26:58.310116Z",
     "shell.execute_reply.started": "2022-04-06T16:06:16.119071Z"
    },
    "papermill": {
     "duration": 0.472099,
     "end_time": "2022-04-07T19:26:58.310916",
     "exception": false,
     "start_time": "2022-04-07T19:26:57.838817",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# oof_train_500, oof_test_500 = cvon_df(df_set_500,df_test_set_500,'500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e1d67ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-07T19:26:59.279128Z",
     "iopub.status.busy": "2022-04-07T19:26:59.278030Z",
     "iopub.status.idle": "2022-04-07T19:26:59.282460Z",
     "shell.execute_reply": "2022-04-07T19:26:59.281843Z",
     "shell.execute_reply.started": "2022-04-06T16:34:54.66965Z"
    },
    "papermill": {
     "duration": 0.496633,
     "end_time": "2022-04-07T19:26:59.282643",
     "exception": false,
     "start_time": "2022-04-07T19:26:58.786010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# oof_train_500.to_csv('oof_train_500_v1.csv')\n",
    "# oof_test_500.to_csv('oof_test_500_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "188ce005",
   "metadata": {
    "papermill": {
     "duration": 0.485631,
     "end_time": "2022-04-07T19:27:00.254971",
     "exception": false,
     "start_time": "2022-04-07T19:26:59.769340",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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