caliberate and predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import random\n",
    "from joblib import dump,load\n",
    "from scipy.special import expit\n",
    "from scipy.special import xlogy\n",
    "from scipy.optimize import fmin_bfgs\n",
    "from math import log\n",
    "\n",
    "from sklearn.utils.validation import indexable\n",
    "from sklearn.metrics import log_loss, make_scorer\n",
    "from sklearn.isotonic import IsotonicRegression\n",
    "from sklearn.base import RegressorMixin, BaseEstimator\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold, RepeatedStratifiedKFold, cross_val_predict\n",
    "from sklearn.calibration import column_or_1d, calibration_curve\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.neighbors import RadiusNeighborsClassifier, RadiusNeighborsRegressor, KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.svm import SVC, SVR, LinearSVC, LinearSVR\n",
    "\n",
    "from catboost import CatBoostClassifier, CatBoostRegressor\n",
    "\n",
    "RANDOM_SEED = 777\n",
    "np.random.seed(RANDOM_SEED)\n",
    "random.seed(RANDOM_SEED)\n",
    "os.environ['PYTHONHASHSEED'] = str(RANDOM_SEED)\n",
    "    \n",
    "import warnings\n",
    "warnings.filterwarnings(\n",
    "    action='ignore',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class _SigmoidCalibration(RegressorMixin, BaseEstimator):\n",
    "    \"\"\"Sigmoid regression model.\n",
    "    Attributes\n",
    "    ----------\n",
    "    a_ : float\n",
    "        The slope.\n",
    "    b_ : float\n",
    "        The intercept.\n",
    "    \"\"\"\n",
    "\n",
    "    def fit(self, X, y, sample_weight=None):\n",
    "        \"\"\"Fit the model using X, y as training data.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like of shape (n_samples,)\n",
    "            Training data.\n",
    "        y : array-like of shape (n_samples,)\n",
    "            Training target.\n",
    "        sample_weight : array-like of shape (n_samples,), default=None\n",
    "            Sample weights. If None, then samples are equally weighted.\n",
    "        Returns\n",
    "        -------\n",
    "        self : object\n",
    "            Returns an instance of self.\n",
    "        \"\"\"\n",
    "        X = column_or_1d(X)\n",
    "        y = column_or_1d(y)\n",
    "        X, y = indexable(X, y)\n",
    "\n",
    "        self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)\n",
    "        return self\n",
    "\n",
    "    def predict(self, T):\n",
    "        \"\"\"Predict new data by linear interpolation.\n",
    "        Parameters\n",
    "        ----------\n",
    "        T : array-like of shape (n_samples,)\n",
    "            Data to predict from.\n",
    "        Returns\n",
    "        -------\n",
    "        T_ : ndarray of shape (n_samples,)\n",
    "            The predicted data.\n",
    "        \"\"\"\n",
    "        # T = column_or_1d(T)\n",
    "        return expit(-(self.a_ * T + self.b_))\n",
    "\n",
    "\n",
    "def _sigmoid_calibration(predictions, y, sample_weight=None):\n",
    "    \"\"\"Probability Calibration with sigmoid method (Platt 2000)\n",
    "    Parameters\n",
    "    ----------\n",
    "    predictions : ndarray of shape (n_samples,)\n",
    "        The decision function or predict proba for the samples.\n",
    "    y : ndarray of shape (n_samples,)\n",
    "        The targets.\n",
    "    sample_weight : array-like of shape (n_samples,), default=None\n",
    "        Sample weights. If None, then samples are equally weighted.\n",
    "    Returns\n",
    "    -------\n",
    "    a : float\n",
    "        The slope.\n",
    "    b : float\n",
    "        The intercept.\n",
    "    References\n",
    "    ----------\n",
    "    Platt, \"Probabilistic Outputs for Support Vector Machines\"\n",
    "    \"\"\"\n",
    "    # predictions = column_or_1d(predictions)\n",
    "    # y = column_or_1d(y)\n",
    "\n",
    "    F = predictions  # F follows Platt's notations\n",
    "\n",
    "    # Bayesian priors (see Platt end of section 2.2):\n",
    "    # It corresponds to the number of samples, taking into account the\n",
    "    # `sample_weight`.\n",
    "    mask_negative_samples = y <= 0\n",
    "    if sample_weight is not None:\n",
    "        prior0 = (sample_weight[mask_negative_samples]).sum()\n",
    "        prior1 = (sample_weight[~mask_negative_samples]).sum()\n",
    "    else:\n",
    "        prior0 = float(np.sum(mask_negative_samples))\n",
    "        prior1 = y.shape[0] - prior0\n",
    "    T = np.zeros_like(y, dtype=np.float64)\n",
    "    T[y > 0] = (prior1 + 1.0) / (prior1 + 2.0)\n",
    "    T[y <= 0] = 1.0 / (prior0 + 2.0)\n",
    "    T1 = 1.0 - T\n",
    "\n",
    "    def objective(AB):\n",
    "        # From Platt (beginning of Section 2.2)\n",
    "        P = expit(-(AB[0] * F + AB[1]))\n",
    "        loss = -(xlogy(T, P) + xlogy(T1, 1.0 - P))\n",
    "        if sample_weight is not None:\n",
    "            return (sample_weight * loss).sum()\n",
    "        else:\n",
    "            return loss.sum()\n",
    "\n",
    "    def grad(AB):\n",
    "        # gradient of the objective function\n",
    "        P = expit(-(AB[0] * F + AB[1]))\n",
    "        TEP_minus_T1P = T - P\n",
    "        if sample_weight is not None:\n",
    "            TEP_minus_T1P *= sample_weight\n",
    "        dA = np.dot(TEP_minus_T1P, F)\n",
    "        dB = np.sum(TEP_minus_T1P)\n",
    "        return np.array([dA, dB])\n",
    "\n",
    "    AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))])\n",
    "    AB_ = fmin_bfgs(objective, AB0, fprime=grad, disp=False)\n",
    "    return AB_[0], AB_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('data/metadata.csv')\n",
    "sup_metadata = pd.read_csv('data/supplemental_metadata.csv')\n",
    "train_labels = pd.read_csv('data/train_labels.csv', index_col=\"sample_id\")\n",
    "val_labels = pd.read_csv('data/val_labels.csv',index_col='sample_id')\n",
    "targets = pd.read_csv('targets.csv',index_col='sample_id')\n",
    "submission_format = pd.read_csv('data/submission_format.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1059, 11), (804, 11))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_50 = pd.read_csv('oof_/oof_train_50.csv')\n",
    "test_50 = pd.read_csv('oof_/oof_test_50.csv')\n",
    "train_50.shape, test_50.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1352641494914019\n"
     ]
    }
   ],
   "source": [
    "print(log_loss(targets['basalt'], train_50['basalt']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bas = 'basalt'\n",
    "io = \"iron_oxide\"\n",
    "phy = 'phyllosilicate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(random_state=RANDOM_SEED,n_splits=20,shuffle=True)\n",
    "rskf = RepeatedStratifiedKFold(n_splits=20,n_repeats=100,random_state=RANDOM_SEED)\n",
    "log_loss_scorer = make_scorer(log_loss, greater_is_better=False, needs_proba=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_SigmoidCalibration()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib = _SigmoidCalibration()\n",
    "calib.fit(train_50[bas],targets[bas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.018744\n",
       "1      0.027265\n",
       "2      0.999936\n",
       "3      0.020038\n",
       "4      0.019633\n",
       "         ...   \n",
       "799    0.036685\n",
       "800    0.025246\n",
       "801    0.018817\n",
       "802    0.020031\n",
       "803    0.095789\n",
       "Name: basalt, Length: 804, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib.predict(test_50[bas])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.11984382848220254, 0.06223535225896785)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = cross_val_score(calib,train_50[bas],targets[bas],cv=rskf,scoring=log_loss_scorer)\n",
    "cv.mean(), cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11962449234819525\n"
     ]
    }
   ],
   "source": [
    "tot_loss = []\n",
    "for i, (train_id,test_id) in enumerate(skf.split(train_50[bas],targets[bas])):\n",
    "    train_50_ = train_50.iloc[train_id]\n",
    "    test_50_ = train_50.iloc[test_id]\n",
    "    calib.fit(train_50_[bas],targets[bas].iloc[train_id])\n",
    "    preds = calib.predict(test_50_[bas])\n",
    "    loss = log_loss(targets[bas].iloc[test_id],preds)\n",
    "    # print(i,loss)\n",
    "    tot_loss.append(loss)\n",
    "print(np.array(tot_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check log loss score for baseline dummy model\n",
    "def logloss_cross_val(clf, X, y):\n",
    "    # Generate a score for each label class\n",
    "    log_loss_cv = {}\n",
    "    for col in y.columns:\n",
    "        y_col = y[col]  # take one label at a time\n",
    "        X_col = X[col]\n",
    "        log_loss_cv[col] = np.mean(\n",
    "            cross_val_score(clf, X_col.values, y_col, cv=skf,scoring=log_loss_scorer))\n",
    "    avg_log_loss = np.mean(list(log_loss_cv.values()))\n",
    "    std_log_loss = np.std(list(log_loss_cv.values()))\n",
    "    print('Average log loss: {}'.format(avg_log_loss))\n",
    "    return log_loss_cv, avg_log_loss, std_log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average log loss: -0.13180907389875685\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'basalt': -0.11962449234819525,\n",
       "  'carbonate': -0.10698640865213513,\n",
       "  'chloride': -0.14295069535314314,\n",
       "  'iron_oxide': -0.18055743873864755,\n",
       "  'oxalate': -0.007585031776448882,\n",
       "  'oxychlorine': -0.1473750928685112,\n",
       "  'phyllosilicate': -0.19003568142311916,\n",
       "  'silicate': -0.1746920522113929,\n",
       "  'sulfate': -0.16353783070255207,\n",
       "  'sulfide': -0.08474601491342323},\n",
       " -0.13180907389875685,\n",
       " 0.05233547010451446)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logloss_cross_val(calib,train_50,targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.050432674433534636, 0.11962449234819525, 0.18881631026285586)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = cross_val_score(calib,train_50[bas],targets[bas], cv=skf, scoring=log_loss_scorer)\n",
    "-cv.mean()-cv.std(), -cv.mean(), -cv.mean()+cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.13493157515767454 > 0.11962449234819525"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02477712, 0.0185275 , 0.06173367, ..., 0.01717328, 0.02094265,\n",
       "       0.02168105])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_preds = cross_val_predict(calib,train_50[bas],targets[bas],cv=skf)\n",
    "cv_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20209a5be80>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob_true, prob_pred = calibration_curve(targets[bas], train_50[bas],strategy='uniform')\n",
    "prob_true_1, prob_preds_1 = calibration_curve(targets[bas], cv_preds,strategy='uniform')\n",
    "plt.plot(prob_true,prob_pred,label = 'uncalibrated')\n",
    "plt.plot(prob_true_1,prob_preds_1,label='calibrated')\n",
    "plt.plot([0,0.7],[0,0.7],'k--')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8847969782813976"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_1 = targets[bas].sum()/len(targets[bas])\n",
    "prob_1 = 1 - prob_1\n",
    "prob_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.11520302, 0.11520302, 0.11520302, ..., 0.11520302, 0.11520302,\n",
       "       0.11520302])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_weight = np.ones_like(targets[bas])\n",
    "sample_weight = np.where(targets[bas]==1, prob_1, 1 - prob_1)\n",
    "sample_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso = IsotonicRegression(y_max=0.95,y_min=0.01,out_of_bounds='clip')\n",
    "iso_cv = cross_val_score(iso,train_50[bas],targets[bas],cv=rskf,scoring=log_loss_scorer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1223097178359193, 0.06124368601998579)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-iso_cv.mean(), iso_cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average log loss: -0.1281700674627801\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'basalt': -0.12172061513303603,\n",
       "  'carbonate': -0.09496921190852708,\n",
       "  'chloride': -0.13935485285448548,\n",
       "  'iron_oxide': -0.17561779220939824,\n",
       "  'oxalate': -0.019394723345030396,\n",
       "  'oxychlorine': -0.1317600018985851,\n",
       "  'phyllosilicate': -0.19747350006878342,\n",
       "  'silicate': -0.16488759865274286,\n",
       "  'sulfate': -0.1641398840052941,\n",
       "  'sulfide': -0.07238249455191795},\n",
       " -0.1281700674627801,\n",
       " 0.05091410927337648)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logloss_cross_val(iso,train_50,targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe calibrating these is a bad idea..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_50 = pd.read_csv('oof_/oof_train_50.csv')\n",
    "test_50 = pd.read_csv('oof_/oof_test_50.csv')\n",
    "\n",
    "train_time_50 = pd.read_csv('oof_/oof_train_temp_50.csv')\n",
    "test_time_50 = pd.read_csv('oof_/oof_test_temp_50.csv')\n",
    "\n",
    "train_100 = pd.read_csv('oof_/oof_train_100.csv')\n",
    "test_100 = pd.read_csv('oof_/oof_test_100.csv')\n",
    "\n",
    "train_time_100 = pd.read_csv('oof_/oof_train_temp_100.csv')\n",
    "test_time_100 = pd.read_csv('oof_/oof_test_temp_100.csv')\n",
    "\n",
    "train_200 = pd.read_csv('oof_/oof_train_200.csv')\n",
    "test_200 = pd.read_csv('oof_/oof_test_200.csv')\n",
    "\n",
    "train_time_200 = pd.read_csv('oof_/oof_train_temp_200.csv')\n",
    "test_time_200 = pd.read_csv('oof_/oof_test_temp_200.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (12,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_files = [train_50,train_time_50,train_100,train_time_100,train_200,train_time_200]\n",
    "test_files = [test_50,test_time_50,test_100,test_time_100,test_200,test_time_200]\n",
    "\n",
    "train_file_names = ['train_50','train_time_50','train_100','train_time_100','train_200','train_time_200']\n",
    "test_file_names = ['test_50','test_time_50','test_100','test_time_100','test_200','test_time_200']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['basalt', 'carbonate', 'chloride', 'iron_oxide', 'oxalate',\n",
       "       'oxychlorine', 'phyllosilicate', 'silicate', 'sulfate', 'sulfide'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'basalt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tar_df(target):\n",
    "    tar_df = pd.DataFrame(columns = train_file_names)\n",
    "    for i,train in enumerate(train_files):\n",
    "        tar_df[train_file_names[i]] = train[target]\n",
    "    return tar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tar_df_test(target):\n",
    "    tar_df = pd.DataFrame(columns = test_file_names)\n",
    "    for i,train in enumerate(test_files):\n",
    "        tar_df[test_file_names[i]] = train[target]\n",
    "    return tar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(get_tar_df_test(target).corr(),annot=True,cmap='rocket_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(get_tar_df('basalt').corr(),annot=True,cmap='magma_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = CatBoostClassifier(verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_loss_scorer = make_scorer(log_loss,greater_is_better=False, needs_proba=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt\n",
      "0.12980351837749066 0.08827490274124304\n",
      "carbonate\n",
      "0.08341759512409103 0.07434784093756425\n",
      "chloride\n",
      "0.13630134429221058 0.10637324194218142\n",
      "iron_oxide\n",
      "0.18391150976863774 0.06730442082775667\n",
      "oxalate\n",
      "0.007781641912242234 0.020263184617718834\n",
      "oxychlorine\n",
      "0.12949837161506178 0.0886036101045427\n",
      "phyllosilicate\n",
      "0.19021371333843745 0.07726575280015728\n",
      "silicate\n",
      "0.1734391441546083 0.0887063762631961\n",
      "sulfate\n",
      "0.16677188178037966 0.09734424343509929\n",
      "sulfide\n",
      "0.0821994384172133 0.06626779330088867\n",
      "agg loss: 0.12833381587803727\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "agg_loss = []\n",
    "for target in targets.columns:\n",
    "    print(target)\n",
    "    tar_df = get_tar_df(target)\n",
    "    calib_preds = np.zeros((tar_df.shape[0],tar_df.shape[1]))\n",
    "    for i,col in enumerate(tar_df.columns):\n",
    "        calib = _SigmoidCalibration()\n",
    "        preds = cross_val_predict(calib,tar_df[col],targets[target],cv=skf)\n",
    "        calib_preds[:,i] = preds\n",
    "    cv = cross_val_score(clf,calib_preds,targets[target],cv=skf,scoring=log_loss_scorer)\n",
    "    print(-cv.mean(),cv.std())\n",
    "    agg_loss.append(-cv.mean())\n",
    "print('agg loss:',np.array(agg_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt\n",
      "carbonate\n",
      "chloride\n",
      "iron_oxide\n",
      "oxalate\n",
      "oxychlorine\n",
      "phyllosilicate\n",
      "silicate\n",
      "sulfate\n",
      "sulfide\n",
      "0.07581474076370877\n",
      "Wall time: 3.72 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "agg_loss = []\n",
    "for target in targets.columns:\n",
    "    print(target)\n",
    "    tar_df = get_tar_df(target)\n",
    "    calib_preds = np.zeros((tar_df.shape[0],tar_df.shape[1]))\n",
    "    for i,col in enumerate(tar_df.columns):\n",
    "        calib = _SigmoidCalibration()\n",
    "        preds = cross_val_predict(calib,tar_df[col],targets[target],cv=skf)\n",
    "        calib_preds[:,i] = preds\n",
    "\n",
    "        # print(calib_preds)\n",
    "    # cv = cross_val_score(clf,calib_preds,targets[target],cv=skf,scoring=log_loss_scorer)\n",
    "    # print(-cv.mean(),cv.std())\n",
    "    # agg_loss.append(-cv.mean())\n",
    "print(log_loss(targets[target],calib_preds.mean(axis=1)))\n",
    "# print('agg loss:',np.array(agg_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04625068, 0.0463625 , 0.04633841, 0.04623735, 0.04645336,\n",
       "       0.04640565])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i, (train_id,test_id) in skf.split(tar_df,targets[target]):\n",
    "    print(i)\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "\n",
    "    for col in tar_df.columns:\n",
    "        X = tar_df[col]\n",
    "        calib = _SigmoidCalibration()\n",
    "        X_train = X.iloc[train_id]\n",
    "        X_test = X.iloc[test_id]\n",
    "        y_train = targets[target].iloc[train_id]\n",
    "        y_test = targets[target].iloc[test_id]\n",
    "        calib.fit(X_train,y_train)\n",
    "        preds = calib.predict(X_test)\n",
    "\n",
    "        oof_train[test_id] = y_preds\n",
    "        oof_test_skf[fold,:] = calib.predict(X_test.values)[:,1]\n",
    "\n",
    "    calib_preds[test_id] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 5.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2,3],[4,5,6]])\n",
    "a.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr  0.3052962341440854\n",
    "# lr  0.3239982196626524\n",
    "# knn 0.5894523367682062/0.35634749615550243\n",
    "# knn 0.35634749615550243\n",
    "# rf 0.527098185929098/0.527098185929098\n",
    "# svc 0.23970403199340756\n",
    "# cb 0.12555419536242826"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt 0.12944059009512446\n",
      "carbonate 0.08364542405130632\n",
      "chloride 0.14165062518401939\n",
      "iron_oxide 0.18852733322713666\n",
      "oxalate 0.004195500456769655\n",
      "oxychlorine 0.1300305975179676\n",
      "phyllosilicate 0.18962404078105488\n",
      "silicate 0.16533412856218363\n",
      "sulfate 0.15576284199089865\n",
      "sulfide 0.06126346725818371\n",
      "agg_loss: 0.1249474549124645\n"
     ]
    }
   ],
   "source": [
    "# average the predictions\n",
    "tot_loss = []\n",
    "for target in targets.columns:\n",
    "    tar_df_avg = get_tar_df(target).mean(axis=1)\n",
    "    loss = log_loss(targets[target],tar_df_avg)\n",
    "    print(target, loss)\n",
    "    tot_loss.append(loss)\n",
    "print('agg_loss:',np.array(tot_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# average the predictions\n",
    "tot_loss = []\n",
    "df = pd.DataFrame(columns=targets.columns,index= train_100.index)\n",
    "for target in targets.columns:\n",
    "    tar_df_avg = get_tar_df(target).mean(axis=1)\n",
    "    df[target] = tar_df_avg\n",
    "    # loss = log_loss(targets[target],tar_df_avg_test)\n",
    "    # print(target, loss)\n",
    "    # tot_loss.append(loss)\n",
    "# print('agg_loss:',np.array(tot_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# average the predictions\n",
    "tot_loss = []\n",
    "for target in targets.columns:\n",
    "    tar_df_avg_test = get_tar_df_test(target).mean(axis=1)\n",
    "    submission_format[target] = tar_df_avg_test\n",
    "    # loss = log_loss(targets[target],tar_df_avg_test)\n",
    "    # print(target, loss)\n",
    "    # tot_loss.append(loss)\n",
    "# print('agg_loss:',np.array(tot_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt\n",
      "0.025260423445174335\n",
      "carbonate\n",
      "0.014948593671174808\n",
      "chloride\n",
      "0.031817053188323514\n",
      "iron_oxide\n",
      "0.056269076985415285\n",
      "oxalate\n",
      "0.0004982270342918026\n",
      "oxychlorine\n",
      "0.0243314308609783\n",
      "phyllosilicate\n",
      "0.04636228177469431\n",
      "silicate\n",
      "0.034479506224458896\n",
      "sulfate\n",
      "0.028582462563677583\n",
      "sulfide\n",
      "0.004958678811871422\n",
      "agg_loss: 0.026750773456006023\n"
     ]
    }
   ],
   "source": [
    "tot_loss = []\n",
    "for tar in targets.columns:\n",
    "    print(tar)\n",
    "    loss = log_loss(val_labels[tar],submission_format[:293].drop('sample_id',axis=1)[tar])\n",
    "    print(loss)\n",
    "    tot_loss.append(loss)\n",
    "print('agg_loss:',np.array(tot_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_format.to_csv('submissions/avg_preds.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_clf = CatBoostClassifier(verbose=False,random_seed=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold loss 0:  0.11889924489742884\n",
      "fold loss 1:  0.1715906085146575\n",
      "fold loss 2:  0.07299240449609418\n",
      "fold loss 3:  0.054773206772004764\n",
      "fold loss 4:  0.03197460374852056\n",
      "fold loss 5:  0.12714170694768653\n",
      "fold loss 6:  0.1563996138627068\n",
      "fold loss 7:  0.07010585495782817\n",
      "fold loss 8:  0.1915561025086712\n",
      "fold loss 9:  0.10587917729968462\n",
      "fold loss 10:  0.09298151983424494\n",
      "fold loss 11:  0.05260179184002491\n",
      "fold loss 12:  0.19615768944019873\n",
      "fold loss 13:  0.012466679518407107\n",
      "fold loss 14:  0.0500980363652995\n",
      "fold loss 15:  0.3879279649684213\n",
      "fold loss 16:  0.2024173521677505\n",
      "fold loss 17:  0.12723188119396087\n",
      "fold loss 18:  0.2853124397355027\n",
      "fold loss 19:  0.0832453147516212\n",
      "cv loss for basalt: 0.12958765969103575\n",
      "cv std for basalt: 0.08917783530034022\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "carbonate\n",
      "fold loss 0:  0.014330312999402368\n",
      "fold loss 1:  0.07409627263192212\n",
      "fold loss 2:  0.20870733763196536\n",
      "fold loss 3:  0.12352240500337472\n",
      "fold loss 4:  0.03940152232504708\n",
      "fold loss 5:  0.06678600970572555\n",
      "fold loss 6:  0.027762839081974536\n",
      "fold loss 7:  0.0049958521617010756\n",
      "fold loss 8:  0.05565721212279947\n",
      "fold loss 9:  0.02673951035965081\n",
      "fold loss 10:  0.03946121323962127\n",
      "fold loss 11:  0.020324816125889954\n",
      "fold loss 12:  0.1734338358716069\n",
      "fold loss 13:  0.228822326178333\n",
      "fold loss 14:  0.16513116872718267\n",
      "fold loss 15:  0.21193622160238423\n",
      "fold loss 16:  0.009769388851379577\n",
      "fold loss 17:  0.061975433359399014\n",
      "fold loss 18:  0.03711326615096519\n",
      "fold loss 19:  0.024517983434428278\n",
      "cv loss for carbonate: 0.08072424637823766\n",
      "cv std for carbonate: 0.07325121479336252\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "chloride\n",
      "fold loss 0:  0.0626717722890869\n",
      "fold loss 1:  0.4565303306392429\n",
      "fold loss 2:  0.1650965489441362\n",
      "fold loss 3:  0.10190985210173112\n",
      "fold loss 4:  0.1400796861133369\n",
      "fold loss 5:  0.0767590061421501\n",
      "fold loss 6:  0.13788356542034313\n",
      "fold loss 7:  0.13473049587225072\n",
      "fold loss 8:  0.1168070173570256\n",
      "fold loss 9:  0.21463957931846986\n",
      "fold loss 10:  0.1787579186954434\n",
      "fold loss 11:  0.05736280653215774\n",
      "fold loss 12:  0.016862498037030965\n",
      "fold loss 13:  0.06435427018359414\n",
      "fold loss 14:  0.12145263104125566\n",
      "fold loss 15:  0.033955547305291364\n",
      "fold loss 16:  0.06204118169128064\n",
      "fold loss 17:  0.3677501060263977\n",
      "fold loss 18:  0.20026204465182673\n",
      "fold loss 19:  0.03368947570445544\n",
      "cv loss for chloride: 0.13717981670332535\n",
      "cv std for chloride: 0.10768476070663753\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "iron_oxide\n",
      "fold loss 0:  0.1952614114910297\n",
      "fold loss 1:  0.10140285519223738\n",
      "fold loss 2:  0.15022600323248395\n",
      "fold loss 3:  0.24852223174057814\n",
      "fold loss 4:  0.14349990005418328\n",
      "fold loss 5:  0.17632655983719164\n",
      "fold loss 6:  0.13044011743062636\n",
      "fold loss 7:  0.12802167490402738\n",
      "fold loss 8:  0.10552183244174021\n",
      "fold loss 9:  0.11167557035716687\n",
      "fold loss 10:  0.29656005037800454\n",
      "fold loss 11:  0.27065076347109446\n",
      "fold loss 12:  0.24347478641792877\n",
      "fold loss 13:  0.11447276959355089\n",
      "fold loss 14:  0.2928365794306969\n",
      "fold loss 15:  0.15027830298845446\n",
      "fold loss 16:  0.16682375975583694\n",
      "fold loss 17:  0.15378931989705036\n",
      "fold loss 18:  0.23029108811838384\n",
      "fold loss 19:  0.12474144805955929\n",
      "cv loss for iron_oxide: 0.1767408512395913\n",
      "cv std for iron_oxide: 0.0627232542950197\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "oxalate\n",
      "fold loss 0:  0.00012711090066368772\n",
      "fold loss 1:  0.00011614869715547143\n",
      "fold loss 2:  0.00012260607623306395\n",
      "fold loss 3:  0.00010849817134519684\n",
      "fold loss 4:  0.0001332832617962978\n",
      "fold loss 5:  0.0001301817132165469\n",
      "fold loss 6:  0.00012616087514173404\n",
      "fold loss 7:  0.00010169529506960254\n",
      "fold loss 8:  0.011565082097936797\n",
      "fold loss 9:  0.05281558079021341\n",
      "fold loss 10:  0.08089206632664234\n",
      "fold loss 11:  0.0005543902557052009\n",
      "fold loss 12:  0.0001237525680715524\n",
      "fold loss 13:  0.00014611238091241755\n",
      "fold loss 14:  0.0016799533874036832\n",
      "fold loss 15:  0.004779744124181509\n",
      "fold loss 16:  0.0003000056085344631\n",
      "fold loss 17:  0.000149459548664624\n",
      "fold loss 18:  0.00020553556722814156\n",
      "fold loss 19:  0.00012178782909027452\n",
      "cv loss for oxalate: 0.0077149577737603\n",
      "cv std for oxalate: 0.020374801439216335\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "oxychlorine\n",
      "fold loss 0:  0.07548497948152605\n",
      "fold loss 1:  0.09826778816610277\n",
      "fold loss 2:  0.28147762348418087\n",
      "fold loss 3:  0.21933184856339868\n",
      "fold loss 4:  0.05527379068288833\n",
      "fold loss 5:  0.4171234678293147\n",
      "fold loss 6:  0.20603345720579264\n",
      "fold loss 7:  0.14233166035625056\n",
      "fold loss 8:  0.07422547199480722\n",
      "fold loss 9:  0.11843437388424408\n",
      "fold loss 10:  0.04512689290998976\n",
      "fold loss 11:  0.045608814516724716\n",
      "fold loss 12:  0.06766840464404232\n",
      "fold loss 13:  0.16089761183778173\n",
      "fold loss 14:  0.14461026341806746\n",
      "fold loss 15:  0.210701015447022\n",
      "fold loss 16:  0.04851679450962169\n",
      "fold loss 17:  0.2335496748209715\n",
      "fold loss 18:  0.015776878166252586\n",
      "fold loss 19:  0.06446998861956485\n",
      "cv loss for oxychlorine: 0.1362455400269272\n",
      "cv std for oxychlorine: 0.09795928170888073\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "phyllosilicate\n",
      "fold loss 0:  0.062347792568265024\n",
      "fold loss 1:  0.18115627583740962\n",
      "fold loss 2:  0.1536202937205768\n",
      "fold loss 3:  0.15066753798807658\n",
      "fold loss 4:  0.23849595508740973\n",
      "fold loss 5:  0.12075867171888223\n",
      "fold loss 6:  0.20292404745758683\n",
      "fold loss 7:  0.12391981373454966\n",
      "fold loss 8:  0.2255107415139123\n",
      "fold loss 9:  0.11828378838401765\n",
      "fold loss 10:  0.3148555450224431\n",
      "fold loss 11:  0.3522409624487123\n",
      "fold loss 12:  0.17740863447841015\n",
      "fold loss 13:  0.2880089873704789\n",
      "fold loss 14:  0.11378811182442769\n",
      "fold loss 15:  0.2021513088185297\n",
      "fold loss 16:  0.22215278255309248\n",
      "fold loss 17:  0.22105941417797068\n",
      "fold loss 18:  0.15699397441691967\n",
      "fold loss 19:  0.07271848093369827\n",
      "cv loss for phyllosilicate: 0.18495315600276843\n",
      "cv std for phyllosilicate: 0.07459643684930438\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "silicate\n",
      "fold loss 0:  0.11313822628631878\n",
      "fold loss 1:  0.2403635895351742\n",
      "fold loss 2:  0.2635230488247967\n",
      "fold loss 3:  0.4121661900857794\n",
      "fold loss 4:  0.10184463782479605\n",
      "fold loss 5:  0.19613742973127596\n",
      "fold loss 6:  0.18701801192911202\n",
      "fold loss 7:  0.14955377881892065\n",
      "fold loss 8:  0.05902966766608158\n",
      "fold loss 9:  0.13896525638511306\n",
      "fold loss 10:  0.19492264374193508\n",
      "fold loss 11:  0.2438814878944846\n",
      "fold loss 12:  0.0930810539309922\n",
      "fold loss 13:  0.27028397348027566\n",
      "fold loss 14:  0.12501070182818513\n",
      "fold loss 15:  0.08900576394885092\n",
      "fold loss 16:  0.04557205526158954\n",
      "fold loss 17:  0.20883029396391692\n",
      "fold loss 18:  0.0862584788948755\n",
      "fold loss 19:  0.2682152802436829\n",
      "cv loss for silicate: 0.17434007851380787\n",
      "cv std for silicate: 0.08910618609879192\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "sulfate\n",
      "fold loss 0:  0.12228758280540661\n",
      "fold loss 1:  0.1174418742186256\n",
      "fold loss 2:  0.23780993500528927\n",
      "fold loss 3:  0.42734326660335004\n",
      "fold loss 4:  0.33794333265286336\n",
      "fold loss 5:  0.200153972846572\n",
      "fold loss 6:  0.17797540408012355\n",
      "fold loss 7:  0.04316072086101466\n",
      "fold loss 8:  0.09107094605458801\n",
      "fold loss 9:  0.04480225534461929\n",
      "fold loss 10:  0.08355819577517493\n",
      "fold loss 11:  0.1837888744352394\n",
      "fold loss 12:  0.054363065284307725\n",
      "fold loss 13:  0.3742260213558315\n",
      "fold loss 14:  0.13716946161998644\n",
      "fold loss 15:  0.17859477008862235\n",
      "fold loss 16:  0.09682964888035042\n",
      "fold loss 17:  0.1673700736528929\n",
      "fold loss 18:  0.09736084769756363\n",
      "fold loss 19:  0.13698850210018448\n",
      "cv loss for sulfate: 0.16551193756813032\n",
      "cv std for sulfate: 0.10446321139419536\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "sulfide\n",
      "fold loss 0:  0.03764768394554103\n",
      "fold loss 1:  0.005440164488709635\n",
      "fold loss 2:  0.07251449388840842\n",
      "fold loss 3:  0.09630213653674835\n",
      "fold loss 4:  0.04375081386692568\n",
      "fold loss 5:  0.03363983989719065\n",
      "fold loss 6:  0.14068409604718538\n",
      "fold loss 7:  0.06663380049095757\n",
      "fold loss 8:  0.008573409503290899\n",
      "fold loss 9:  0.16283283676692534\n",
      "fold loss 10:  0.012901308705196174\n",
      "fold loss 11:  0.14228936330773362\n",
      "fold loss 12:  0.054536978627261944\n",
      "fold loss 13:  0.007595559515581321\n",
      "fold loss 14:  0.007214610648798075\n",
      "fold loss 15:  0.04612428775844327\n",
      "fold loss 16:  0.14360909309765696\n",
      "fold loss 17:  0.06544590665206938\n",
      "fold loss 18:  0.09747569867850946\n",
      "fold loss 19:  0.11327158898751653\n",
      "cv loss for sulfide: 0.06792418357053248\n",
      "cv std for sulfide: 0.05021666765499867\n",
      "-----------------------------------------------------\n",
      "=============================================================================\n",
      "0.12609224274681166\n"
     ]
    }
   ],
   "source": [
    "agg_loss = []\n",
    "for target in targets.columns: \n",
    "    print(target)\n",
    "    oof_train = np.zeros((ntrain,))\n",
    "    oof_test = np.zeros((ntest,))\n",
    "    oof_test_skf = np.empty((NFOLDS, ntest))\n",
    "    tar_df = get_tar_df(target)\n",
    "    test_tar_df = get_tar_df_test(target)\n",
    "    X_test = test_tar_df\n",
    "    cv_loss = []\n",
    "    for fold, (train_id,test_id) in enumerate(skf.split(tar_df,targets[target])):\n",
    "        X = tar_df.iloc[train_id]\n",
    "        y = targets[target].iloc[train_id]\n",
    "        X_val = tar_df.iloc[test_id]\n",
    "        y_val = targets[target].iloc[test_id]\n",
    "\n",
    "        cb_clf.fit(X,y)\n",
    "        preds = cb_clf.predict_proba(X_val)[:,1]\n",
    "        pred_loss = log_loss(y_val,preds)\n",
    "        print(f'fold loss {fold}: ',pred_loss)\n",
    "        cv_loss.append(pred_loss)\n",
    "\n",
    "        oof_train[test_id] = preds\n",
    "        oof_test_skf[fold,:] = cb_clf.predict_proba(X_test.values)[:,1]\n",
    "\n",
    "        loss = np.array(cv_loss).mean()\n",
    "        std = np.array(cv_loss).std()\n",
    "    print(f'cv loss for {target}:',loss)\n",
    "    print(f'cv std for {target}:',std)\n",
    "    print('-----------------------------------------------------')\n",
    "    agg_loss.append(loss)\n",
    "\n",
    "    oof_test[:] = oof_test_skf.mean(axis=0)\n",
    "    oof_test_df[target] = oof_test\n",
    "    oof_train_df[target] = oof_train\n",
    "    print(\"=============================================================================\")\n",
    "\n",
    "print(np.array(agg_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_id</th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.001755</td>\n",
       "      <td>0.007722</td>\n",
       "      <td>0.024148</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.004228</td>\n",
       "      <td>0.127824</td>\n",
       "      <td>0.050391</td>\n",
       "      <td>0.943614</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>0.981521</td>\n",
       "      <td>0.002043</td>\n",
       "      <td>0.056500</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.005858</td>\n",
       "      <td>0.008629</td>\n",
       "      <td>0.004390</td>\n",
       "      <td>0.018624</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049180</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.008525</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.971591</td>\n",
       "      <td>0.028617</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.012755</td>\n",
       "      <td>0.000118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031535</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.033933</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.005090</td>\n",
       "      <td>0.003958</td>\n",
       "      <td>0.002708</td>\n",
       "      <td>0.887684</td>\n",
       "      <td>0.006394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001981</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>0.013286</td>\n",
       "      <td>0.975912</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.995968</td>\n",
       "      <td>0.911565</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.011994</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1054</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.007287</td>\n",
       "      <td>0.001787</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.004180</td>\n",
       "      <td>0.010216</td>\n",
       "      <td>0.170141</td>\n",
       "      <td>0.009838</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.007147</td>\n",
       "      <td>0.028450</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.026348</td>\n",
       "      <td>0.010358</td>\n",
       "      <td>0.718225</td>\n",
       "      <td>0.002472</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.000971</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.002073</td>\n",
       "      <td>0.998896</td>\n",
       "      <td>0.004451</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.000078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1057</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>0.976715</td>\n",
       "      <td>0.009909</td>\n",
       "      <td>0.014316</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.007116</td>\n",
       "      <td>0.030909</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.015269</td>\n",
       "      <td>0.000084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002823</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.007536</td>\n",
       "      <td>0.002922</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>0.992332</td>\n",
       "      <td>0.002556</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1059 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample_id    basalt  carbonate  chloride  iron_oxide   oxalate  \\\n",
       "0          NaN  0.003611   0.001755  0.007722    0.024148  0.000032   \n",
       "1          NaN  0.001864   0.981521  0.002043    0.056500  0.000149   \n",
       "2          NaN  0.049180   0.001246  0.008525    0.001503  0.000039   \n",
       "3          NaN  0.031535   0.048991  0.006177    0.033933  0.000065   \n",
       "4          NaN  0.001981   0.001098  0.013286    0.975912  0.000091   \n",
       "...        ...       ...        ...       ...         ...       ...   \n",
       "1054       NaN  0.000768   0.001208  0.007287    0.001787  0.000057   \n",
       "1055       NaN  0.004711   0.000480  0.007147    0.028450  0.000068   \n",
       "1056       NaN  0.002182   0.000971  0.000967    0.002073  0.998896   \n",
       "1057       NaN  0.003698   0.976715  0.009909    0.014316  0.000031   \n",
       "1058       NaN  0.002823   0.002337  0.007536    0.002922  0.000075   \n",
       "\n",
       "      oxychlorine  phyllosilicate  silicate   sulfate   sulfide  \n",
       "0        0.004228        0.127824  0.050391  0.943614  0.000055  \n",
       "1        0.005858        0.008629  0.004390  0.018624  0.000084  \n",
       "2        0.971591        0.028617  0.004296  0.012755  0.000118  \n",
       "3        0.005090        0.003958  0.002708  0.887684  0.006394  \n",
       "4        0.995968        0.911565  0.000788  0.011994  0.000137  \n",
       "...           ...             ...       ...       ...       ...  \n",
       "1054     0.004180        0.010216  0.170141  0.009838  0.000075  \n",
       "1055     0.026348        0.010358  0.718225  0.002472  0.000046  \n",
       "1056     0.004451        0.002688  0.000660  0.004988  0.000078  \n",
       "1057     0.007116        0.030909  0.001399  0.015269  0.000084  \n",
       "1058     0.003402        0.992332  0.002556  0.002086  0.000047  \n",
       "\n",
       "[1059 rows x 11 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cal_tar_df(target):\n",
    "    tar_df = get_tar_df(target)\n",
    "    sig_cal = _SigmoidCalibration()\n",
    "    cal_df = pd.DataFrame(columns=tar_df.columns,index=tar_df.index)\n",
    "    for col in tar_df.columns:\n",
    "        cv_preds = cross_val_predict(sig_cal,tar_df[col],targets[target],cv=skf)\n",
    "        cal_df[col] = cv_preds\n",
    "    return cal_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.014049\n",
       "1       0.006449\n",
       "2       0.070275\n",
       "3       0.014067\n",
       "4       0.004511\n",
       "          ...   \n",
       "1054    0.008114\n",
       "1055    0.010747\n",
       "1056    0.004204\n",
       "1057    0.008556\n",
       "1058    0.009203\n",
       "Length: 1059, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_tar_df('basalt').mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11371960647741605"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(targets['basalt'],get_cal_tar_df('basalt').mean(axis=1))\n",
    "# can this be trusted..........."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt 0.11371960647741605\n",
      "carbonate 0.09186352875168836\n",
      "chloride 0.13854233311903216\n",
      "iron_oxide 0.17899450712264148\n",
      "oxalate 0.006679389658804797\n",
      "oxychlorine 0.13219531163383963\n",
      "phyllosilicate 0.18568571270054718\n",
      "silicate 0.16869142852479768\n",
      "sulfate 0.16109848210107122\n",
      "sulfide 0.07581474076370877\n",
      "0.12532850408535473\n"
     ]
    }
   ],
   "source": [
    "agg_loss = []\n",
    "for tar in targets.columns:\n",
    "    loss = log_loss(targets[tar],get_cal_tar_df(tar).mean(axis=1))\n",
    "    agg_loss.append(loss)\n",
    "    print(tar,loss)\n",
    "print(np.array(agg_loss).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basalt\n",
      "carbonate\n",
      "chloride\n",
      "iron_oxide\n",
      "oxalate\n",
      "oxychlorine\n",
      "phyllosilicate\n",
      "silicate\n",
      "sulfate\n",
      "sulfide\n"
     ]
    }
   ],
   "source": [
    "cal_list = {}\n",
    "for target in targets.columns:\n",
    "    print(target)\n",
    "    tar_df = get_tar_df(target)\n",
    "    test_df = get_tar_df_test(target)\n",
    "    sig_cal = _SigmoidCalibration()\n",
    "    cal_df = pd.DataFrame(columns=tar_df.columns,index=tar_df.index)\n",
    "    cal_test_df = pd.DataFrame(columns=test_df.columns,index=test_df.index)\n",
    "    for i in range(len(tar_df.columns)):\n",
    "        cv_preds = cross_val_predict(sig_cal,tar_df.iloc[:,i],targets[target],cv=skf)\n",
    "        cal_df.iloc[:,i] = cv_preds\n",
    "        sig_cal.fit(tar_df.iloc[:,i],targets[target])\n",
    "        test_col_preds = sig_cal.predict(test_df.iloc[:,0])\n",
    "        cal_test_df.iloc[:,i] = test_col_preds\n",
    "    cal_list[target] = cal_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tar in targets.columns:\n",
    "    submission_format[tar] = cal_list[tar].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_format.to_csv('submissions/calb_avg_preds.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "oof_test_df.sample_id = metadata[metadata.split != 'train'].sample_id\n",
    "oof_train_df.sample_id = metadata[metadata.split != 'test'].sample_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oof_test_df['sample_id'] = submission_format['sample_id']\n",
    "oof_test_df.to_csv('submissions/cb_preds.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tar in targets.columns:\n",
    "    submission_format[tar] = oof_test_df[tar]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_id</th>\n",
       "      <th>basalt</th>\n",
       "      <th>carbonate</th>\n",
       "      <th>chloride</th>\n",
       "      <th>iron_oxide</th>\n",
       "      <th>oxalate</th>\n",
       "      <th>oxychlorine</th>\n",
       "      <th>phyllosilicate</th>\n",
       "      <th>silicate</th>\n",
       "      <th>sulfate</th>\n",
       "      <th>sulfide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S0767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S0768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S0769</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S0770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>S1565</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.011389</td>\n",
       "      <td>0.051463</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>0.008052</td>\n",
       "      <td>0.091713</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.000223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>S1566</td>\n",
       "      <td>0.001710</td>\n",
       "      <td>0.002062</td>\n",
       "      <td>0.019702</td>\n",
       "      <td>0.981612</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.985512</td>\n",
       "      <td>0.049364</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>0.002610</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>S1567</td>\n",
       "      <td>0.003572</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.988541</td>\n",
       "      <td>0.997656</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.030486</td>\n",
       "      <td>0.993354</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>S1568</td>\n",
       "      <td>0.001498</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>0.000942</td>\n",
       "      <td>0.030787</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.984616</td>\n",
       "      <td>0.002707</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>S1569</td>\n",
       "      <td>0.002974</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.979513</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.993119</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.000052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>804 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    sample_id    basalt  carbonate  chloride  iron_oxide   oxalate  \\\n",
       "0       S0766       NaN        NaN       NaN         NaN       NaN   \n",
       "1       S0767       NaN        NaN       NaN         NaN       NaN   \n",
       "2       S0768       NaN        NaN       NaN         NaN       NaN   \n",
       "3       S0769       NaN        NaN       NaN         NaN       NaN   \n",
       "4       S0770       NaN        NaN       NaN         NaN       NaN   \n",
       "..        ...       ...        ...       ...         ...       ...   \n",
       "799     S1565  0.001653   0.001499  0.011389    0.051463  0.000077   \n",
       "800     S1566  0.001710   0.002062  0.019702    0.981612  0.000040   \n",
       "801     S1567  0.003572   0.001285  0.988541    0.997656  0.000066   \n",
       "802     S1568  0.001498   0.000872  0.000942    0.030787  0.000035   \n",
       "803     S1569  0.002974   0.002959  0.979513    0.002240  0.000054   \n",
       "\n",
       "     oxychlorine  phyllosilicate  silicate   sulfate   sulfide  \n",
       "0            NaN             NaN       NaN       NaN       NaN  \n",
       "1            NaN             NaN       NaN       NaN       NaN  \n",
       "2            NaN             NaN       NaN       NaN       NaN  \n",
       "3            NaN             NaN       NaN       NaN       NaN  \n",
       "4            NaN             NaN       NaN       NaN       NaN  \n",
       "..           ...             ...       ...       ...       ...  \n",
       "799     0.005921        0.008052  0.091713  0.002178  0.000223  \n",
       "800     0.985512        0.049364  0.000779  0.002610  0.000072  \n",
       "801     0.030486        0.993354  0.002665  0.001387  0.000053  \n",
       "802     0.004857        0.004918  0.984616  0.002707  0.000139  \n",
       "803     0.002589        0.993119  0.000878  0.030316  0.000052  \n",
       "\n",
       "[804 rows x 11 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calib_avg_preds = pd.read_csv('submissions/calb_avg_preds')  # 3rd\n",
    "# cb_preds = pd.read_csv('submissions/cb_preds.csv')   # 2nd \n",
    "# avg_preds = pd.read_csv('submissions/avg_preds.csv')   # 1st"
   ]
  }
 ],
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