{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Substance Dependence from Multi-Site Data\n",
    "\n",
    "This notebook explores an example using data from the ENIGMA Addiction Consortium. Within this notebook we will be trying to predict between participants with any drug dependence (alcohol, cocaine, etc...), vs. healthy controls. The data for this is sources from a number of individual studies from all around the world and with different scanners etc... making this a challenging problem with its own unique considerations. Structural FreeSurfer ROIs are used. The raw data cannot be made available due to data use agreements.\n",
    "\n",
    "The key idea explored in this notebook is a particular tricky problem introduced by case-only sites, which are subject's data from site's with only case's. This introduces a confound where you cannot easily tell if the classifier is learning to predict site or the dependence status of interest.\n",
    "\n",
    "Featured in this notebook as well are some helpful\n",
    "code snippets for converting from BPt versions earlier than BPt 2.0 to valid BPt 2.0+ code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import BPt as bp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from warnings import simplefilter\n",
    "from sklearn.exceptions import ConvergenceWarning\n",
    "simplefilter(\"ignore\", category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading / Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a general tip it can be useful to wrap something like a series of steps that load multiple DataFrames and merge them into a function. That said, it is often useful when first writing the function to try it taking advantage of the interactive-ness of the jupyter-notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_base_df():\n",
    "    '''Loads and merges a DataFrame from multiple raw files'''\n",
    "    \n",
    "    na_vals = [' ', '  ', 'nan', 'NaN']\n",
    "    \n",
    "    # Load first part of data\n",
    "    d1 = pd.read_excel('/home/sage/Downloads/e1.xlsx', na_values=na_vals)\n",
    "    d2 = pd.read_excel('/home/sage/Downloads/e2.xlsx', na_values=na_vals)\n",
    "    df = pd.concat([d1, d2])\n",
    "\n",
    "    df['Subject'] = df['Subject'].astype('str')\n",
    "    df.rename({'Subject': 'subject'}, axis=1, inplace=True)\n",
    "    df.set_index('subject', inplace=True)\n",
    "\n",
    "    # Load second part\n",
    "    df2 = pd.read_excel('/home/sage/Downloads/e3.xlsx', na_values=na_vals)\n",
    "    df2['Subject ID'] = df2['Subject ID'].astype('str')\n",
    "    df2.rename({'Subject ID': 'subject'}, axis=1, inplace=True)\n",
    "    df2.set_index('subject', inplace=True)\n",
    "\n",
    "    # Merge\n",
    "    data = df2.merge(df, on='subject', how='outer')\n",
    "    \n",
    "    # Rename age and sex\n",
    "    data = data.rename({'Sex_y': 'Sex', 'Age_y': 'Age'}, axis=1)\n",
    "    \n",
    "    # Remove subject name to obsficate\n",
    "    data.index = list(range(len(data.index)))\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3525, 224)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = load_base_df()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting NaN threshold to: 1762.5\n",
      "Dropped 20 Columns\n",
      "Dropped 3 Columns\n",
      "Dropped 36 Columns\n",
      "Num. categorical variables in dataset: 3\n",
      "to binary cols: ['Dependent any drug', 'Handedness', 'Sex']\n",
      "Dropped 479 Rows\n",
      "scope covars =  ['Age', 'Education', 'Handedness', 'ICV', 'Sex']\n",
      "Setting NaN threshold to: 82.5\n",
      "Dropped 38 Rows\n",
      "Dropped 5 Rows\n"
     ]
    }
   ],
   "source": [
    "# Cast to dataset\n",
    "data = bp.Dataset(df)\n",
    "data.verbose = 1\n",
    "\n",
    "# Drop non relevant columns\n",
    "data.drop_cols_by_nan(threshold=.5, inplace=True)\n",
    "data.drop_cols(scope='Dependent', inclusions='any drug', inplace=True)\n",
    "data.drop_cols(exclusions=['Half', '30 days', 'Site ',\n",
    "                           'Sex_', 'Age_', 'Primary Drug', 'ICV.'], inplace=True)\n",
    "\n",
    "# Set binary vars as categorical\n",
    "data.auto_detect_categorical(inplace=True)\n",
    "data.to_binary(scope='category', inplace=True)\n",
    "print('to binary cols:', data.get_cols('category'))\n",
    "\n",
    "# Set target and drop any NaNs\n",
    "data.set_role('Dependent any drug', 'target', inplace=True)\n",
    "data.drop_nan_subjects('target', inplace=True)\n",
    "\n",
    "# Save this set of vars under scope covars\n",
    "data = data.add_scope(['ICV', 'Sex', 'Age', 'Education', 'Handedness'], 'covars')\n",
    "print('scope covars = ', data.get_cols('covars'))\n",
    "\n",
    "# Set site as non input\n",
    "data = data.set_role('Site', 'non input')\n",
    "data = data.ordinalize(scope='non input')\n",
    "\n",
    "# Drop subjects with too many NaN's and big outliers\n",
    "data.drop_subjects_by_nan(threshold=.5, scope='all', inplace=True)\n",
    "data.filter_outliers_by_std(n_std=10, scope='float', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Legacy Equivilent pre BPt 2.0 loading code**\n",
    "\n",
    "```\n",
    "ML = BPt_ML('Enigma_Alc',\n",
    "             log_dr = None,\n",
    "             n_jobs = 8)\n",
    "             \n",
    "ML.Set_Default_Load_Params(subject_id = 'subject',\n",
    "                           na_values = [' ', '  ', 'nan', 'NaN'],\n",
    "                           drop_na = .5)\n",
    "                           \n",
    "ML.Load_Data(df=df,\n",
    "             drop_keys = ['Unnamed:', 'Site', 'Half', 'PI', 'Dependent',\n",
    "                          'Surface Area', 'Thickness', 'ICV', 'Subcortical',\n",
    "                          'Sex', 'Age', 'Primary Drug', 'Education', 'Handedness'],\n",
    "             inclusion_keys=None,\n",
    "             unique_val_warn=None,\n",
    "             clear_existing=True)\n",
    "\n",
    "ML.Load_Targets(df=df,\n",
    "                col_name = 'Dependent any drug',\n",
    "                data_type = 'b')\n",
    "\n",
    "ML.Load_Covars(df=df,\n",
    "               col_name = ['ICV', 'Sex', 'Age'],\n",
    "               drop_na = False,\n",
    "               data_type = ['f', 'b', 'f'])\n",
    "\n",
    "ML.Load_Covars(df = df,\n",
    "               col_name = ['Education', 'Handedness'],\n",
    "               data_type = ['f', 'b'],\n",
    "               drop_na = False,\n",
    "               filter_outlier_std = 10)\n",
    "\n",
    "ML.Load_Strat(df=df,\n",
    "              col_name=['Sex', 'Site'],\n",
    "              binary_col=[True, False]\n",
    "              )\n",
    "\n",
    "ML.Prepare_All_Data()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what we prepared. We can see that visually the Dataset is grouped by role."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependent any drug: 3003 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 3003 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot('Site')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 3003 rows\n",
      "Dependent any drug: 3003 rows\n",
      "Plotting 3003 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot_bivar('Site', 'Dependent any drug')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That bi-variate plot is a little hard to read... we can make it bigger though easy enough. Let's say we also wanted to save it (we need to add show=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 3003 rows\n",
      "Dependent any drug: 3003 rows\n",
      "Plotting 3003 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "data.plot_bivar('Site', 'Dependent any drug', show=False)\n",
    "plt.savefig('site_by_drug.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay next, we are going to define a custom validation strategy to use, which is essentially going to preserve subjects within the same train and test fold based on site.\n",
    "\n",
    "\n",
    "**Legacy Code**\n",
    "```\n",
    "from BPt import CV\n",
    "group_site = CV(groups='Site')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CVStrategy(groups='Site')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_site = bp.CVStrategy(groups='Site')\n",
    "group_site"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then can use this CV strategy as an argument when defining the train test split.\n",
    "\n",
    "**Legacy Code**\n",
    "```\n",
    "ML.Train_Test_Split(test_size =.2, cv=group_site)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing test split on: 3003 subjects.\n",
      "random_state: 5\n",
      "Test split size: 0.2\n",
      "\n",
      "Performed train/test split\n",
      "Train size: 2379\n",
      "Test size:  624\n"
     ]
    }
   ],
   "source": [
    "data = data.set_test_split(size=.2, cv_strategy=group_site, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependent any drug: 2379 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependent any drug: 624 rows\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot('target', subjects='train')\n",
    "data.plot('target', subjects='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running ML\n",
    "\n",
    "Next, we are going to evaluate some different machine learning models on the problem we have defined. Notably not much has changed here with respect to the old version, except some cosmetic changes like Problem_Spec to ProblemSpec. Also we have a new `evaluate` function instead of calling Evaluate via the ML object (`ML.Evaluate`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProblemSpec(n_jobs=16, scorer=['matthews', 'roc_auc', 'balanced_accuracy'],\n",
       "            subjects='train')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This just holds some commonly used values\n",
    "ps = bp.ProblemSpec(subjects='train',\n",
    "                    scorer=['matthews', 'roc_auc', 'balanced_accuracy'],\n",
    "                    n_jobs=16)\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ModelPipeline\n",
      "-------------\n",
      "imputers=\\\n",
      "[Imputer(obj='mean', scope='float'),\n",
      " Imputer(obj='median', scope='category')]\n",
      "\n",
      "scalers=\\\n",
      "Scaler(obj='standard')\n",
      "\n",
      "model=\\\n",
      "Model(obj='elastic', params=1)\n",
      "\n",
      "param_search=\\\n",
      "ParamSearch(cv=CV(cv_strategy=CVStrategy()), n_iter=64,\n",
      "            search_type='DiscreteOnePlusOne')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define a ModelPipeline to use with imputation, scaling and an elastic net\n",
    "pipe = bp.ModelPipeline(imputers=[bp.Imputer(obj='mean', scope='float'),\n",
    "                                  bp.Imputer(obj='median', scope='category')],\n",
    "                        scalers=bp.Scaler('standard'), \n",
    "                        model=bp.Model('elastic', params=1),\n",
    "                        param_search=bp.ParamSearch(\n",
    "                            search_type='DiscreteOnePlusOne', n_iter=64))\n",
    "pipe.print_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-fold CV random splits\n",
    "\n",
    "Let's start by evaluating this model is a fairly naive way, just 3 folds of CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting target = Dependent any drug\n",
      "Using problem_type = binary\n",
      "Using scope = all (defining a total of 163 features).\n",
      "Evaluating 2379 total data points.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "096378b8b9d045239c0e5fce543c3999",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train size: 1586 - Val size: 793\n",
      "Fit fold in 15.781 seconds.\n",
      "matthews: 0.45819711214039605\n",
      "roc_auc: 0.8219843414397561\n",
      "balanced_accuracy: 0.7347294394789718\n",
      "\n",
      "Train size: 1586 - Val size: 793\n",
      "Fit fold in 12.219 seconds.\n",
      "matthews: 0.43270247791412836\n",
      "roc_auc: 0.8060915258007118\n",
      "balanced_accuracy: 0.7243174488434163\n",
      "\n",
      "Train size: 1586 - Val size: 793\n",
      "Fit fold in 11.532 seconds.\n",
      "matthews: 0.4005078667838128\n",
      "roc_auc: 0.7789128332642631\n",
      "balanced_accuracy: 0.7066203895565686\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.43046915227944577, 'roc_auc': 0.8023295668349103, 'balanced_accuracy': 0.721889092626319}\n",
       "std_scores = {'matthews': 0.023604421407805696, 'roc_auc': 0.01778394323677345, 'balanced_accuracy': 0.0116032282095078}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_X_transform_df', 'get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary',\n",
       "            scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score),\n",
       "                    'matthews': make_scorer(matthews_corrcoef),\n",
       "                    'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)},\n",
       "            subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=bp.CV(splits=3))\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notably this is likely going to give us overly optimistic results. Let's look at just the Site's drawn from the first evaluation fold to get a feel for why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 1586 rows\n",
      "Dependent any drug: 1586 rows\n",
      "Plotting 1586 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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apZTKVran77SxezSOyqBBA7+l2Z3dad+273WvTfzffGpW68q5cxdsiEzZadDA0TS7swft2/ZLbPv6qx9peddLPNzhTR7u8CarfttqY4SezzjE5c0daLL3cO3b38Xo0OsHNJ04cZY1f/xJ8eKFbYhK2e1G58WT3dowc/ZHzJz9Ec3u0kpVGaI3aFV2qt+geuKStUl9PGIyffo9rmOpc6j6DaoRUDBf6juq9NMZtMpuy5dtpEjRQKpWLWd3KMrN/PD9Ijq068+ggaM5f/6i3eF4Nh+H61sqRKS0iKwQkd0isktEXrfaA0VkiYgcsP4sZLWnef0wTfZe5tKly4wNnUPPV7vYHYpyM10fac2vi0cxc/YIgoML8snH39kdkmfL3Cv7OKCvMaY60BB4RUSqA28By4wxIcAy6zmkY/0wTfZe5t9/T3Hs6Gkebt+fe1r15NSpMDo//BZnz0TYHZqyWeHCBfHxceBwOOjUuSU7/zxod0ieLROXSzDGnDDGbLEeRwJ7cC4R0w6YZO02CWhvPU7z+mG6nr2XqVy5DKv+GJv4/J5WPZn+0wcUKuRvY1TKHZw5fY7gIoUAWLZkI5VCSqdyhLqpNIyyEZEeOK/Arwq1lntJad9yQB1gPVA0yYTSk0BR6/GN1g+74eRTTfYe7o2+X7Jxw24iIiJp1fwlXu7ZmYc7aYHxnO6NvqOSnBcv83LPTmzcsJt9ew+DCCVLBvPO0OftDtOjmTTceE26jtfNiEh+YCbQyxhzIekAC2OMEZF0zxL0mhq0ObXguLo5Q7zdISg3dItPowyPkanwwkyXc87fYx5O9fNExA/4GVhkjPncatsHNDfGnLC6aVYaY6qIyBjr8Q/J97vR+2ufvVJKpUcm9tlbS8SMB/ZcTfSWeUA363E3YG6S9jStH6bdOB5uyMCx/PbbVgID/Zk9bwQA+/Ye5r1hE4mOjqFEycKM+Phl8ufPY3OkKjudPBHGwAFjCT97AQQ6dWnO40/ew+KFG/j26zn88/cJvp8+hBo1y9sdqufK3EvlO4EngR0iss1qexsYAcwQkeeAw8DVYXa/AG2Ag1jrh2VvuC4SkddFZKc1nrRXCq9rDVoXPdShKd+G9r+mbeiQ8fTq04VZcz+kVav6TJywwKbolF18fH3o1/8RZv/8Ad9NG8y0qcv46+AxKoWUYuSoV6lXv7LdIXq+TJxBa4z53RgjxphaV8u2GmN+McaEGWNaGWNCjDF3G2PCrf2NMeYVY0xFY8ytqS0UCfYUHK8JdAduB2oDD4pIpWS7aQ1aF9WvX5WAgGtnSh4+dJJ69asC0KhxTZYu3mhHaMpGwcEFqVa9HAD58uWhQoUSnD59jgoVS1CuvFb4zBQeVqnKjiv7asB6Y0y0MSYO+A3omGwfrUGbARUrlWTFss0ALF60gZMnw22OSNnp2LEz7N1zmFtrVbQ7FK9iRFze3IEdyX4n0FREgkQkL85+p+QDftNcg3bc2NlZFrCneff97kyftoyunQYTFXUJPz+9NZNTRUfF0Pf1r3hjwGN63yaz+YrrmxvI9ixgjNkjIh8Bi4EoYBukb3xc0rGrOvTyP+UrlGDMuDcBOHToBKtXbbc5ImWH2Ng4+vT6ijYPNuLu1vXtDsf7uMkVu6tsuUFrjBlvjKlnjGkGnAP2J9tFa9BmQFjYeQASEhIIHT2Xzl10klVOY4xh6OAJVKhQnKeevs/ucLyTh/XZ2/L7XkSKGGNOi0gZnP31DZPtMg/oKSLTgDvQGrQ31L/f12zasIeIiIvc3eI1Xu7Zkejoy0yfuhSAVq3r075jM5ujVNlt65YD/DxvDSGVS9Glw2AAXu3ViSuxcYwY/h3nwiPp+dJIqlQtw+ix/VJ5N5Ui98jhLrOrBu1qnGUJY4E+xphlGa1Bq904KiU6g1alJDNm0JYbsMDlnHPowwds/2qwqwZt0xTaMlSDNrfP7ZkQmfI2MfFr7Q7BFjqpKhu4SfeMq3SYhlJe6OqkqmrVyxEVdYlHOg2lYaMaiZOq3hs60e4QPZ+PJnullM2CgwsSHFwQuHZSVaPGNe0NzJt42GgcTfZKeTmdVJVFtBtHKeUudFJVFtJkr5RyBzqpKmu5yzIIrtJkr5QX0klV2UBv0Cql7KaTqrKBduMopexWt15ltu+emOJrre6ul73BeCtN9kq5jx3huryzul6D4Ex4E8/K9ZrslfJWUZGXGPfRdI7+fRIR6D7gEXLl9mPCJz8SeyUOHx8HT/d9mIrVy9odqkcyemXvJCITgAeB08aYmlbbJ0Bb4ArwF841byJSOPY+4EvABxhnjBmRVXEq5a2mfDmbWndU5fX3nyYuNo7LMbH835BJdHzmXmo3qsa2tbv54ZufGfRVmlYmUVd52GicrFzieCLOhcySWgLUNMbUwrms8YDkB4mID/A1ztKE1YFHRaR6FsaplNeJvniJfdv/pvmDdwDg6+dLvgJ5EBEuRcdY+8RQqLC/nWF6Nh9xfXMDWXZlb4xZJSLlkrUtTvJ0HdAphUNvBw4aY/4GsJY5bgfszqJQlfI6Z06EU6BgPkI/mMaRg8cpV6UUT77enidea8/HfcYw9ev5mIQE3hn9mt2heiyHLdVA0s/OcJ8Ffk2h3aWShHBtWcLQ0NAsCFEpzxQfn8Ch/cdo1b4xw//Xl9y35GL+d8tZNucPHn+tHaNmDeHxV9sz9sPpdofqsURc39yBLcleRAYCccD3GXkfY0yoMaa+MaZ+jx49Mic4pbxAYHAAgcEBVKrhvPl6e4vaHNp/lNW/bqLBXbUAuKNlbf7ac8TOMD2aJvtUiMjTOG/cPm5SrpyiJQmVyqCCQf4EFinI8SOnAdi1aT8lyxWlUGF/9mz9y9m2+QDFSmXGGMScSURc3txBtg69tEbZ9AfuMsZE32C3jUCIiJTHmeQfAR7LphCV8hrdenfk22HfERcXT5ESQfQY8Aj1mtRkypdzSIiPxy+XH8/172x3mB7L0/rss6wsoYj8ADQHCgOngHdwjr7JDYRZu60zxrwoIiVwDrFsYx3bBvgC59DLCcaY4S58pJYlVNfZeGaB3SEoN9QgOONlAiuPXeVyztnfvZntl/dZORrn0RSax99g3+NAmyTPfwF+ScvnPbbytzTFp3KGsMsBdoeg3NCiezP+Hm7SO+MynUHrgQ5Nmsj5HTvwLVCAGu8MBeDoTz8R8ed2HL6+5A4Opmy3p/HNmzfxmCvhYewaOpTiD7al2D332BS5ykp9aoRwR3AhIq7E8sKarQA8VakMjYoEYYwh4kosn+48QPjlK4nHVPbPzxd31OaDP/fy+6mwG721SoGHTaC1deilSqegRo0Jee3a8dH+1atR452hVB/yDrmLFOXkr9eOav33xx/xr1EjO8NU2Wzx8VMM3Lzrmraf/jnGS2u28vLabaw/E84TFf8b++AAnqtcjs1h57I5Uu+go3FUlitQuTI+efNd0+ZfvQbi4wNAvgoViI347z/giG1byR1UmDwlSmRrnCp77Tx3gcjYuGvaouPjEx/f4uND0lt07cqW4PdTYURcic2uEL2KJntlu7A//sC/hrOwdHxMDCcXLqL4gw/aHJWyy9OVyvJdswa0LB7M5IOHAQjKnYvGRYL4+d8TNkfnuRw+4vLmDjTZe5kTvyxAfBwE3uFcE+XEz/Mpcvfd+Nxyi82RKbtMPHiYJ1ZtZPmJMzxUxvnr7sWqFRi//5AOYcsAT7uy1xu0XuTsmjWc/3MHlfv0TpzIEfXPP5zbsoVjs2YSHx0NIjj8fCnSoqXN0arstvzEGd6vW50pfx2hsn9+BtSuAkCAnx+3Fy5EvDGsPa3r/7sqM5P4DVYJHgp0B85Yu71tjVRERAYAzwHxwGvGmEWpfYYmey9xfudOTi1eROW+/XDkyp3YXuWN/omPj8+fhyP3LZroc5ASeW/huLXKZaMigfwbdQmAbqs3Je7Tt2YI68+Ea6JPo0y+Yp8IfAVMTtY+0hjz6bWfK9VxTjatAZQAlopIZWNMPDehyd4D/T1uLJH79hF38SJ/vtmfEm0f4uTCX0mIi+PAFyMB503aso8/YXOkKju9VasKtQIDCPDz5bu7GjDl4BFuDy5Eqbx5SABOX7rMqN0H7Q7Ta2Tm0MuUVgm+iXbANGPMZeAfETmIc7XgtTc7SJO9B6rwfPfr2go3aZLqcSXaPpQV4Sg3MeLPfde1LTp2KtXjPtt5ICvC8XppubIXkR5A0tUaQ40xrizV21NEngI2AX2NMedwrgK8Lsk+N1wZOCmvSfbjmui9ZpUSN7k7prxOWkbZWIk9reuwfwu8h3MpmPeAz3AuDZ8uXpPsc6qTJ8IZPGA8YWEXEBEe7tyMx568O/H1yRMXMfKTH1n++0gKFSpgY6QqOznPi3GEhZ23zou7eOzJ1nw9aha/rdiGiBAY5M+w4c9SpEghu8P1SFk9ysYYk/izTETGAj9bT9O1MnC21qC12l8FXsF5F3mBMaZ/CsdqDVoX+fg66NO/C9WqlyUqKobHOr/HHY2qU7FSCU6eCGfdH7spVjzQ7jBVNnOeF12t8+ISj3V+lzsaVafbs/fzymsdAZj63RJCv53PoHeesjlaz5TVyV5Eihtjrk6E6ADstB7PA6aKyOc4b9CGABtSe79srUErIi1w3lyobYypAXya/CCtQZs2wcEFqVbdWaAiX75bKF+hOGdOO2fPfvrRdF7v28lt1tNW2efa8yKPdV5EkD9/nsR9Ll264jZjwD1RZo6zt1YJXgtUEZGjIvIc8LGI7BCRP4EWQG8AY8wuYAbOUq0LgVdSG4kDLl7ZizNbPA5UMMa8KyJlgGLGmBt+m9zg7vJLwAjrLjLGmNMpHKo1aNPp+LGz7NtzhJq1KrBi+VaKFC1IlaqlUz9QebWk5wXAV1/O5Od5a8ifPy+h/3vD5ug8VyaPxnF5lWBr/+GAK0u/J3L1yv4boBFwNaBInFffaVUZaCoi60XkNxFpkMI+6apBO2HsvHSE4z2io2Lo1+sb+r3VFR8fBxNCf+Glnu3sDkvZzHlefE2/tx5NvKrv+frDLFz2Gfc/2JDpU5fbHKHncvi4vrkDV5P9HcaYV4AYAGv4T650fJ4vEAg0BN4AZkgG+hiS1qB9tnvOHVYYGxtHv17fcv8DDWnVuh5H/z3DsWNn6dpxGG1av8npU+d4rNN7nD1z3u5QVTZynhdfJ54XybV5oCHLlmy2ITLv4K3LJcRafekGQESCgYR0fN5RYJZVe3aDiCTgrGR1Jsk+WoM2DYwxDBsyifIVivPk08516kMql2L56pGJ+7Rp/Sbfzxiko3FyEOd58T/rvPivUsfhw6coW7YoACtXbKVc+WJ2hejxPO1emKvJfhQwGygiIsOBTsDgdHzeHJw3GlaISGWcvw7OJttHa9CmwbYtB1kwby0hlUvSteMwAHr26kDTZrVsjkzZaduWA9Z5UYquHd8BoGevh5kzczWHD53E4RCKFw9ioI7ESTcPy/Wu16AVkapAK5yzVJYZY/aksn9KNWinABOA24ArQD9jzPLMqEEbHbdaF/BTKdDJdup6eX3vzHCqbr7gD5dzzsoHMv55GeXqaJwpxpgngb0ptKXoBneXAa5bsCUzatDm9tGJIep6F2OP2h2C8lKedmXvajfONfXsrP776+/4qGw38O2v+W3lJgKDApg3/wsAFi5cw9dfTefvv44xfcYIat5ayd4gVba7fDmWF54exZUrccTHJ9CqdW16vNKG94ZMZc+uf8EYypQrwpD3Hydv3typv6G6jq+H/Wi8abgiMkBEIoFaInJBRCKt56eBudkSobqpDh2aEzr22tsnISFlGDWqP/Xr61y0nCpXLl++Gd+TqTPf5Psf+7P2j73s2H6I3v07MnXmm0yd9RZFixXix6mr7A7VYznEuLy5g5te2RtjPgQ+FJEPjTEDsikmlQb1G9Tg2NFr56ZVrFjKpmiUuxCRxCv2uLh44uLiEYH8+Z0Vy4wxXL4c63l9EW4kMydVZYebJnsRqWqM2Qv8KCJ1k79ujNmSZZEppTIkPj6Bp7p+ytEjZ+j0SFNq1ioHwLuDvmfN6t2Ur1iMXv3a2xqjJ/OwXpxU++z74FyD+bMkbUl/k2jJI6XclI+Pg+9/6k/khWj69xrPXweOUzGkBEPef5z4+AQ+/fAnlizcQtsODe0O1SO5S/eMq1L7chonIsWMMS2MMS1wLm52Eefqa52yOjilVMYV8M9LvQYhrP0jcTAdPj4OWt9Xl+VLt9sYmWdziOubO0gt2Y/GOR4eEWkGfAhMAs6T9oX4lVLZ5Fz4RSIvRAMQE3OF9ev2UbZcEf494pysboxh9cqdlCtf1M4wPZqvuL65g9S6cXyMMVerEHfFWUprJjBTRLZlaWTKJf36fM6GjbuIOBdJi7u60/PVrgQEFGD4++MID7/ASy9+QNWq5Rg7fojdoapsdPbMeYYN+p6E+AQSjOHue+pwZ7Pq9Og2iqiLMRgMIZVL8ubgLnaH6rHEw7pxbjqDVkR2ArcZY+JEZC/Qwxiz6uprSYuS2C3e7PSsf3mVLfadP2R3CMoNVS/4YIavt7usWOVyzpnRopnt1/epXdn/APwmImeBS8BqABGphLMrx234iNt87yi3csjuAJSX8qrROMaY4SKyDCgOLDb//QxwAK9mdXBKqfTr0f598uTNjcPhwMfHwaeTevPP/uOM/ugnYi5dpkjxQHoPe5y81th7lTaeNhon1eUSjDHrUmjbn9pxKdWgFZHbcN70vQWIA15OqdqViHQDBllP3zfGTErt85RS13vvm5fwL5g/8fk3H8yg22ttqVm3IkvnrWfOdyt47MX7bYzQc7nLjVdXZWsNWuBjYJgx5jZgiPX8GiISiHOFzDtwlih8R0R0lTOlMsHxI2eoUcdZnvC2OyqzdsUOmyPyXN429DLdrBu54cmbAX/rcQBwPIVD7wWWGGPCrYpYS7j+S0MplQpBGPZaKH2fGsni2WsBKF2hKBtW7QTgj2V/cvZ0hI0RejavWhsnC/QCFonIpzi/aBqnsE+aatDinOHLmDFj6NGjR6YGq5Qn+yC0J0FFAogIj2TYq2MoWa4IPQd1Zdxnc5gxfim3N6uOr6+bFEj1QO5yxe6q7E72LwG9jTEzRaQLzurpd6f3zYwxofw3ucs9vj6VchNBRQIAKBhYgDua38qBXUdo/0QLhv7fCwAcO3KGTX/ctAaRuglPG42T3fF2A2ZZj3/E2SefnNagVSqDYi5d5lJUTOLjbev3UaZicSLCIwFISEjgpwlLuLdDIzvD9GjajXNzx4G7gJU4F1E7kMI+i4APktyUvQfQ5ZWVSoOI8It81P9/gHP1y6b31qVuo6rMn7aKX3/6A4CGLW6lVduUrreUKzyteInLNWjT/MYp16DdB3yJ80smBufQy80iUh940RjzvHXss8Db1lsNN8b8L7XPuxi73D2+PpVbyeXjn/pOKsfJ5aif4R7319aucDnnjGrUwvYe/iy7sr9JDdrryhkaYzYBzyd5PgFnYXKllHJL7tI946rs7sZRmezkiXCGvD2J8LALiAgdOjXhsSdbsm/vv3zw7lSuXI7Dx8fBW4Mfpeat5ewOV2WTwQNDWbVyK4GB/sye/xEA/XqP4tChEwBEXoimgH9efpr9oZ1hejQdjaOylY+vD73feJhq1csQFRXDE10+pGHjanz52Wx6vPQAdzatye+rdjLqs1mETuxjd7gqm7Rr35RHH2vNwLdGJ7Z9OvK1xMeffPQd+fPntSM0r+FhXfYeF69KJjg4gGrVywCQL98tlK9QjNOnIhCBqIvO0RgXL16isDUMT+UM9RtUIyDJMglJGWNYtHA9bR5IaZqLcpWnzaDVK3svcvxYGHv3/EvNWuXo92ZnXnnh//ji01kkmAT+990bdoen3MTmTXsJCgqgbLlidofi0XwcntVnr1f2XiI6OoY3eo+h35udyZ8/Dz9OX0XfNzvxy7IP6NO/M+8OmWJ3iMpN/LpgLW0e0PH1GeVIw+YO3CUOlQGxsfG80SuU+x+4nZat6wDw87x1tLzb+bj1vXXZteOwnSEqNxEXF8/SpRu5934tMp5RnjapSpO9hzPG8N6QKZSvUIwnuv238kRwcEE2b3TOWdu4fh+lywbbFaJyI+vW7qR8+RIUKxZkdygeLzP77EVkgoictqoDXm0LFJElInLA+rOQ1S4iMkpEDorInyJS15V4tc/ew23b+hcL5q+nUkhJHn14OACvvN6OQcMe59MRM4iPSyBXbj8GvfO4zZGq7NS/71ds3LCHiIhIWjXvySs9O9GxU3N+/UW7cDJLJt94nQh8BUxO0vYWsMwYM0JE3rKevwncD4RY2x3At9afN5VlM2iz26W4Nd7xF1GZyhBvdwjKDeX1bZrhVP3BtiUu55y3b2ud6ueJSDng5yTFnvYBzY0xJ0SkOLDSGFNFRMZYj39Ivt/N3l+v7JVSKh2yYUhl0SQJ/CRQ1Hp8o2XgNdl7s5Mnwhg0YBzhYRdA4OHOd/H4k/fw+afTWbVyG35+vpQqXYRh7z+Hv79OoskpTp4IZ/CA8YRZM6sf7tyMx578757O5ImLGPnJjyz/fSSFChWwMVLPlZZkn7T2hiXUWqLdJcYYI5KxO71ZluxFpDTO/qeiONeaDzXGfGmVHZwOlAMOAV2silTJj9c6tC7w8fWhb/+uVKtejqioSzzaeRgNG9WgYaMavNarE76+Pnzx2QwmjP2ZXn272B2uyiY+vg769O9CtepliYqK4bHO73FHo+pUrFSCkyfCWffHbooVD7Q7TI/mk4Zkn6z2hqtOiUjxJN04p632dC0Dn5WjceKAvsaY6kBD4BURqc5/Nx1CgGXW82toHVrXBQcXpFr1cgDky5eHChWKc/p0BI3vrJlYhahW7YqcOnXd96nyYs7zoixwdWZ1cc6cdp4Dn340ndf7dkLETaZ2eqhsmEE7D2cNEKw/5yZpf8oaldMQOJ9afz1kbQ3aE8aYLdbjSGAPzn6ldsDVq/RJQPsUDtc6tOlw7NhZ9u45wq21KlzTPmfWapo0vdWmqJTdjh87y749R6hZqwIrlm+lSNGCVKlaOvUD1U1l5jh7a0n4tUAVETkqIs8BI4DWInIAZ0W/EdbuvwB/AweBscDLrsSbLX321l3mOsB6bnzTISmX6tAm7Qf7v2/681z3dpkYtWeJjoqhX6+veOOtR8mfP09i+9gx8/Hx9aHNgzrcLidynhff0O+trvj4OJgQ+gvfjO1td1hewS8TfxjdZEn4Vinsa4BX0voZWZ7sRSQ/MBPoZYy5kPSnY0ZvOiTtB8vJQy9jY+Po2+sr2jzQiFat6ye2z539O6t/286Y8W/oT/YcKDY2jn69vuX+BxrSqnU9Duw/yrFjZ+nacRgAp0+d47FO7zFl2kAKB+tCeWnlLgucuSpLk72I+OFM9N8bY67Wnr3RTYekjuGscnVVKZylDFUyxhiGDfkf5SuU4Mmn701s/2P1DiZN+JVxk94kT57cNkao7OA8LyZRvkJxnnz6HgBCKpdi+eqRifu0af0m388YpKNx0sldlkFwVVaOxhFgPLDHGPN5kpeu3nQYwbU3HZLSOrQu2rblAD/PW0NI5VJ06TgEgFd7PczHH0zlSmwsLz7/KeC8STvonW43eyvlRbZtOciCeWsJqVwy8Uq+Z68ONG1Wy+bIvEdaRuO4g6ysQdsEWA3sABKs5rdx9tvPAMoAh3EOvQzPaB3aXed+9qyvWZUtSufXXzXqev5+qc9oTc3/9i9yOec8U/le278asrIG7e/Ajf6CKd100Dq06RQVeYmvP5jBv3+fAISeg7pSokwRPhs0mTMnzhFcvBD9hj9Ffp1UlWNcvhxLj25fEHsljrj4eFq1rsMLPR9gxtTf+GHKCo7+e5Ylq0dQsFDKBU5U6nw9bBlJnUHrBcaPnEOdhlXo/2E3YmPjuBITy8yJS6nVIISOT7Vi1uRlzJq8nKd6Pmh3qCqb5Mrly7cTXiNv3tzExcbz/FOf07hpdWrXqUCTu2ry4jNf2h2ix/PxsD57D/tuUslFXbzE7q1/c/dDzkXv/Px8yVcgDxtW76J5mwYANG/TgA2rdt7sbZSXERHy5nV2YcXFxRMXF4+IUKVaaUqU1OWNM4OnFS/RK3sPd/p4OP6F8vHVe9M4dPA4FaqU4rk+7YkIjySwsD8AhYIKEBEeaXOkKrvFxyfwZJePOHrkDJ0fbUbNWuXsDsmreNrQS3f50lHpFB+fwN/7jnFvx8Z8Nrkvt+TJzazJy6/ZR0R0nH0O5OPjYOrMASxY9j67dhzm4IHjdofkVTyt4Lgmew8XVCSAoOAAKtd0roPSqGUt/t53jIKBBQg/ewGA8LMXCNAbcTlWAf+81Lu9Mmt/3213KF7FR4zLmzvQZO/hCgX5U7hoQY4dds5N+3PjAUqXL0qDpjVY+ctGAFb+spHbm9awM0yVzc6FRxJ5IRqAmJgrbFi7l3LlU1qZRKWXr8P1zR1on70XeL5vB75453viYuMpWjKQnoMewRjDpwMns2zeBoKLFaLv8KfsDlNlo7NnLjB04BQS4hNIMIa7761L0+a3Mu27lUz531LCzl7g0Y4fcGfTGgx6V0tWpoe7dM+4ymvKEuqkKpUSnVSlUpIZk6oWHv3V5ZxzX6n7bf9q8Jor+5CAYnaHoNxQLoe/3SEoL6Vr46hsNXhgKKtWbiUw0J/Z8z8CoF/vURw65FxFOvJCNAX88/LT7A/tDFPZ7MKFiwwa9H/s338YEeGDD16nTp2qdofl0dykK95ldpQlfA9nAZMEnCtePm2MuW5MmJYldE279k159LHWDHxrdGLbpyNfS3z8yUffkT+/LpOQ0w0fPpamTesyatQArlyJJSbmst0heTxP67O3oyzhJ8aYWsaY24CfgSHJD9SyhK6r36AaAQVTHlZpjGHRwvW0eaBxNkel3ElkZBQbN+6kUyfnUse5cvnh769DcTPKz2Fc3txBVi6EdgI4YT2OFJE9QEljTNLBvvlwXvUnl1iWEEBErpYl/CGr4vVGmzftJSgogLLl9H5GTnb06CkCAwMYMOAL9u49RI0aFRk4sAd5895id2geTa/sU5CsLCEiMlxE/gUeJ4Ure9JQllBENonIpnGhs5K/nOP9umAtbR7QcoQ5XVxcPLt3/8Wjj7ZhzpwvyZPnFkJDf7I7LI+nM2iTSV6WEMAYM9AYUxr4HuiZ3vc2xoQaY+obY+o/36Nj5gTsJeLi4lm6dCP33t/Q7lCUzYoVK0yxYoWpXbsKAPfddye7d/9lc1Sez9MWQsvSOG5QljCp74GHU2g/BpRO8ryU1aZctG7tTsqXL0GxYrrCYU4XHFyIYsUK8/ffRwFYu3Y7FSuWTuUolRoR1zd3kO1lCUUkxBhzwHraDtibwuFaltBF/ft+xcYNe4iIiKRV85680rMTHTs159dftAtH/Wfw4Bfo1+8zYmPjKF26KB9+2MvukDyeu3TPuMqOsoTPAVWstsM4SxEey2hZwisJm9zjlrdyKzqpSqWscoZT9ZazC1zOOXULP2D7V4PXLJdg2OMdfxGVqbzl/FaZyyHVM5x8t4a5vkRLnaAHbU/2OoPWy0ycOI+fflyCiBBSuSwffvgquXPnsjssZbPJk+fz449LMAY6d25Nt25t7Q7J49mevdPIXW4Uq0xw6lQYUyb/zE8zP2X+z6NIiI9nwYLVdoelbLZ//2F+/HEJM2Z8wpw5I1m5chOHD5+wOyyP52k3aDXZe5n4+HhiYq4QFxfPpZgrFCkSaHdIymZ//32UWrUqkydPbnx9fWjQoAZLlqyzOyyPJ2nY3IEmey9StGgQzz7bnpYtutO0yTMUyJ+XJk3q2B2WsllISBk2b9rNuXMXuHTpMqt+28zJE2ftDsvj+YjrmzvQZO9Fzp+/yLJlG1i6bAyrVk/g0qUY5s1daXdYymYVK5bm+e4def65YXTv/i5Vq5XH4aP/6WeUduMo26xds51SpYoQGBiAn58vre9pxNatKU1jUDlNp053M3PWZ3z33XAC/PNTrlwJu0PyeNqNo2xTvEQw27fv59KlyxhjWLv2TypULGV3WMoNhIVFAHD8+BmWLFnHgw82szcgL+BpyV6HXnqR2rUrc8+9jenYoQ++vj5Uq1aerl3vtTss5QZef+1jIiIi8fX1ZfCQHvj757M7JI+nM2htopOqVEq85fxWmSszJlUdOO/6pKqQAJ1UlWmi487YHYJyS5rs1fXyZULmy+watCJyCIgE4oE4Y0x9q5DTdKAccAjoYow5l57395pkn1OdPBHOkAETCAu7gAh07NyMx568m9Ffz2P2T6spVMhZkahnr440aXarzdGq7PLfeRGZ5LxoBcC075cz44cVOBwOmjS7lV79OtkcrWfKolE2LYwxScfFvgUsM8aMEJG3rOdvpueN7ahBexswGrgFZ+nCl40xG1I4XmvQusDH10Hv/p2pVr0sUVExPN75PRo2qg7A40/dzVPPaJ99TnT9efE+DRtVIyzsAiuXb2ParCHkyuVHeNgFu0P1WNk0uqUd0Nx6PAlYibsle/6rQbtFRAoAm63ygh8Dw4wxv4pIG+t586QHJqlBWx/nF8VmEZmX3p8v3iw4uCDBwQUByJfvFspXKM7p0xG2xqTsd6PzYvZPq3nm+fvIlcsPgMAgXRU0vdJyZS8iPYAeSZpCjTGhyXYzwGIRMcAY6/WiVolXgJM4L57TJcu+nIwxJ4wxW6zHkcAenKUFDXD1DAsAjqdweGINWivBX61Bq27i+LGz7NvzLzVrlQdg+tQVdOkwlKGDJnLhfJTN0Sm7OM+LI9SsVZ7Dh06xZfNBnnrkA57v9gm7dhyyOzyPlZahl0mr6llb8kQP0MQYUxe4H3hFRK4ZH2ucow3SfaPAjhq0vYBPrBq0n5JyUZI016CdMHZeZoftUaKjYujX61v6vtWV/Pnz0Llrc+Yt/IBpM4dQODiAzz/50e4QlQ2c58XoxPMiPj6BC+ejmPTDAHr17cSbfcfoiKV0yuwatMaYY9afp4HZwO3AKREpDmD9eTrd8ab3QFelUIP2JaC3VYO2N85qVumS9Nvy2e4PZU7AHig2No5+vb6lzQN30Kp1XQCCCvvj4+PA4XDQsVNTdu34x+YoVXZznhejrzkvihQtRMu76yAi1KxVHodDiDh30eZIPVNmJnsRyWd1dyMi+XBW59sJzAO6Wbt1A+amO970HuiKG9Sg7QZcffwjzm+v5LQGrYuMMbw7ZBLlKxTniafvSWw/cyYi8fHypVupGHLdDyPlxZznxWTrvGid2N6i1W1s2rAPgMOHThEbG09Ba8SWSptMnkFbFPhdRLYDG4AFxpiFwAigtYgcAO62nqcv3iwsSyg47x6HG2N6JWnfA7xkjFkpIq2Aj40x9ZIdGwhsBupaTVuAesaY8Bt9XlTcqhz5W3Tr5gM899THVKpcEod1x6hnr44s/GUD+/f+CwIlShRm4NAnEm/Y5Sw58rSwzotPkp0XHbijYTWGDp7E/r3/4ufnQ69+nbm9YVWbo81++XzvyvDAyZOX5rl8chXL85Dtk6rsqEF7AfgS50igGJxDLzdntAZtTk32KjV6WqjrZUayP5WGZF/Um5N9dktLPUiVc9QJqmx3CMotZbzg+OkY15N9kVvsT/Y6g9YLREVeYsyHMzj69wkQ4cW3u7J9/T6Wz1uHv9Uf+8gLbajTuJrNkSq7tGz5HPny5cHhcODj48OsWSPtDsnj+dgdQBppsvcCk76Yw20Nq9Dng27ExcZxOSaW7ev30eaRZrR9rIXd4Sk3MWnScAIDA+wOw2u4S1ESV+l69h4u+uIl9mz7mxZt7wDA18+XfAXy2ByVUjmBZ61or1f2Hu708XD8C+bj2+HTOHLgOOWrlqJbr/YALPrpD1b/upkKVUvxxKsPkd8/r73BKls999wQRISuXe+ja1edkJ5R4iZJ3FWa7D1cfHwC/+w/xtN9OhBSoywTR85h7pTl3NupCQ8/0xoEZoQu5Lv/m8eLAx+xO1xlkx9++JiiRYMIC4vgmWcGU6FCKRo0qGl3WB5NxLM6RjwrWnWdoCIBBAYHEFKjLAB3tKjFoX3HKBhYAIc1g7Zlu4Yc3P1vKu+kvFnRokEABAUVpHXrRvz5536bI/IGntWNo8newxUM8ieoaEGOH3YumbFz0wFKli/KubP/LV278bcdlK5QzK4Qlc2io2O4eDE68fEff2wlJKSszVF5PsHh8uYOtBvHCzzTuwNfDfueuNh4ipQI5MWBjzBx5BwOHziGiBBcvBDP9+9sd5jKJmFhEbzyynAA4uPjefDBu2jWrF4qR6nUeFo3jk6qUl5NJ1WplGV8UtWF2KUu5xx/v7tt78vxmiv7GoWK2x2CckOX4yPsDkG5odyZMCNKR+OobDV44BhWrdxKYKA/s+d/nNj+/XeLmDZ1MT4OB83uqkOfNx6zMUqV3YYMHMtvv1nnxTznQon79h7mvWETiY6OoUTJwoz4+GXy59c5Genlack+yzqdROQWEdkgIttFZJeIDLPay4vIehE5KCLTRSTXDY4fYO2zT0S0kOoNtGvfjG9Dry1JuWH9LlYs28TMOSOY8/MndHv2AZuiU3Z5qENTvg3tf03b0CHj6dWnC7PmfkirVvWZOGGBTdF5BxEflzd3kJV3GC4DLY0xtYHbgPtEpCHwETDSGFMJOAc8l/xAEakOPALUwFmO8Btxl38xN1O/QTUCCl67Hvn0aUt5rvtDiXVGg4J0inxOU79+VQIC8l3TdvjQSerVdy5n3KhxTZYu3mhHaF5Eh14CznqJxpirJXD8rM0ALYGfrPZJQPsUDm8HTDPGXDbG/AMcJOUiJyoFhw+dZMvmfTzWdTBPP/kuO3f8ZXdIyg1UrFSSFcs2A7B40QZOnrxheQjlAknD/9xBVleq8hGRbTjrJi4B/gIijDFx1i4p1pYlHTVox4XOSv5yjhUfF8/58xf5ftq79H3jMfr1HqV1RhXvvt+d6dOW0bXTYKKiLuHnp7fsMsaRhs1+Wfr/tjEmHrhNRAriLKCbqSVxrArtoQBXEjZrNrMULRbI3a0bICLcWqsS4hDOnYskMNDf7tCUjcpXKMGYcc77O4cOnWD1qu02R+TZ3OWK3VXZ8pVjjIkAVgCNgIIicvVL5ka1ZbUGbQa0bFWfDet3A3DonxPExsZRqFABm6NSdgsLOw9AQkICoaPn0rlLS5sj8mwi4vLmDrKyLGEwEGuMiRCRPMBinDdnuwEzjTHTRGQ08Kcx5ptkx9YApuLspy8BLANCrF8KKcqpV/b9+/4fGzfsISIiksCgAF7p+TBtH2rK4EFj2LfnMH5+vvTt/zh3NKxhd6i2uMkp49X69/uaTRv2EBFxkcAgf17u2ZHo6MtMn7oUgFat6/N67y5uk4iyW26f2zP8F78cv8HlnJMZn5dRWZnsa+G8AeuD8xfEDGPMuyJSAZgGBAJbgSeMMZdF5CGgvjFmiHX8QOBZIA7oZYz59Wafl2B25chkr27OaA1alQIfqZkJyX5jGpJ9A+9N9tlNk71KiSZ7lZLMSPZXEja5fHLlctS3Pdm7x21ilW4D3/6KOxs/Tdu2rye2RURE8uyzQ7n33ld49tmhnD9/8YbHK+808O2vadL4GR5q2yuxbeHCNbR98HVqVOvEzh0H7QvOa+g4e5WN2ndoQejYwde0jR07m0YNa7Fo0dc0aliLsWN1WGpO06FD8+vOi5CQMowa1Z/69avbE5SX8bQljt0jCpVuDRrUoGDAtSNtli/bQLv2zQFo1745y5ZusCEyZaf6DWoQEHDtzOqKFUtRvkJK01pU+njWlb3OqvBCYWERFCkSCEBwcCHCwiLsDUgpL+TwsPXsNdl7OXca56uUd/GsZO9Z0SqXBAUV5PRp57onp0+HExioC6Epldl0bRxlu5YtGzB3zkoA5s5ZSctWuoacUpnPs/rsdZy9h+vb53M2bNxJxLlIgoIC6PnqI7RqdQd9en/K8RNnKVEimJEj+1KwYM5cLiGnjrPv1+dzNmzcleS86EpAQAGGvz+O8PAL+Pvno2rVcowdP8TuUG2RGePsDXtcPrmEarZnfE32yqvl1GSvbi4zkj3sT8PJlfGatxnlNcle/UdEelgrgiqVSM+LnE377L1TD7sDUG5Jz4scTJO9UkrlAJrslVIqB9Bk7520X1alRM+LHExv0CqlVA6gV/ZKKZUDaLJXSqkcQJO9BxOR+0Rkn4gcFJG3Ung9t4hMt15fLyLlbAhTZTMRmSAip0Vk5w1eFxEZZZ0Xf4pI3eyOUWU/TfYeSkR8gK+B+4HqwKMikrwqxXPAOWNMJWAkzoLvyvtNBO67yev3AyHW1gP4NhtiUjbTZO+5bgcOGmP+NsZcwVnEvV2yfdrhLPoO8BPQSnS9Y69njFkFhN9kl3bAZOO0DigoIsWzJzplF032nqsk8G+S50etthT3McbEAeeBoGyJTrkzV84d5WU02SulVA6gyd5zHQNKJ3leympLcR8R8QUCgLBsiU65M1fOHeVlNNl7ro1AiIiUF5FcwCPAvGT7zAO6WY87AcuNzqJTzvPiKWtUTkPgvDHmhN1BqaylNWg9lDEmTkR6AosAH2CCMWaXiLwLbDLGzAPGA1NE5CDOG3aP2Bexyi4i8gPQHCgsIkeBdwA/AGPMaOAXoA1wEIgGnrEnUpWddLkEpZTKAbQbRymlcgBN9koplQNosldKqRxAk71SSuUAmuyVUioH0GSvUiQi8SKyTUR2ich2EekrIradLyJySEQKp/PY9iksEpdtRGSoiPSz6/OVAk326sYuGWNuM8bUAFrjXCnxHZtjSq/2OFcGdSvWrGalsoUme5UqY8xpnEvh9rRmXfqIyCcistFaD/0FABFpLiKrRGSBtc7+6Ku/BkTkHhFZKyJbRORHEclvtR8SkWFW+w4RqWq1B4nIYuuXxTggcbVOEXlCRDZYvzzGWMs9IyIXRWS49UtknYgUFZHGwEPAJ9b+FZP+3USkrbXW/1YRWSoiRa32oda68CtF5G8Rec1qf1dEeiU5friIvJ7830xEBorIfhH5HaiSpH2liHwhIpuA10Vkooh0SvL6RetPh4h8IyJ7RWSJiPySdD+l0kqTvXKJMeZvnDN1i+BcJ/+8MaYB0ADoLiLlrV1vB17FeSVdEehodb8MAu42xtQFNgF9krz9Wav9W+Bqd8c7wO/WL4vZQBkAEakGdAXuNMbcBsQDj1vH5APWGWNqA6uA7saYNTiXB3jD+qXyV7K/2u9AQ2NMHZzLRPdP8lpV4F7r7/SOiPgBE4CnrFgcOGclf5f0DUWkntV+G86Zqg2SfWYuY0x9Y8xn3FhHoBzOf8cngUY32VepVOnPSJUe9wC1klxpBuAshHEF2GB9MVydtt8EiMGZtP6wltPPBaxN8n6zrD8340xyAM2uPjbGLBCRc1Z7K6AesNF6rzzAaeu1K8DPSd6rtQt/l1LAdGs991zAP0leW2CMuQxcFpHTQFFjzCERCROROkBRYKsxJvnick2B2caYaOvfIfmaRdNdiKsJ8KMxJgE4KSIrXDhGqRvSZK9cIiIVcF5Fn8bZpfKqMWZRsn2aA8nX3zDW/kuMMY/e4O0vW3/Gk/o5KcAkY8yAFF6LTbLQmyvvBfB/wOfGmHlW/ENTiCv5+40DngaK4bzST6uoJI/jsH5hW78UcqXj/ZRKlXbjqFSJSDAwGvjKSqaLgJesbg1EpLKI5LN2v91aidOBs7vld2AdcKeIVLL2zycilVP52FXAY9b+9wOFrPZlQCcRKWK9FigiZVN5r0igwA1eC+C/5X273WCf5GbjLPvXAOe/RUqxtxeRPCJSAGh7k/c6hPOXCjjvLfhZj/8AHrb67oviXNhMqXTTK3t1I3lEZBvO5BMHTAE+t14bh7M/eYs4+1LO4BzxAs6ll78CKgErcHZnJIjI08APIpLb2m8QsP8mnz/M2n8XsAY4AmCM2S0ig4DF1hdKLPAKcPgm7zUNGGvdZO2UrN9+KPCj1U20HCifwvHXMMZcsbpVIowx8Sm8vkVEpgPbcf4S2niTtxsLzBWR7cBC/rvqn4mzy2o3zqpSW3BWGlMqXXTVS5VprG6QfsaYB20OJUtZXzJbgM7GmANZ+Dn5jTEXRSQI2IDzpvTJrPo85d30yl6pNBDn5Kyfcf5iybJEb/lZRAri7Md/TxO9ygi9sldKqRxAb9AqpVQOoMleKaVyAE32SimVA2iyV0qpHECTvVJK5QD/D5rYLpMAK6P3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 793 rows\n",
      "Dependent any drug: 793 rows\n",
      "Plotting 793 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot_bivar('Site', 'Dependent any drug', subjects=results.train_subjects[0])\n",
    "data.plot_bivar('Site', 'Dependent any drug', subjects=results.val_subjects[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preserving Groups by Site\n",
    "\n",
    "We can see that for example the 149 subjects from site 1 in the validation set had 269 subjects also from site 1 to potentially memorize site effects from! The confound is a site with only cases. One way we can account for this is through something we've already hinted at, using the group site cv from earlier. We can create a new CV object with this attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CV(cv_strategy=CVStrategy(groups='Site'))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_cv = bp.CV(splits=3, cv_strategy=group_site)\n",
    "site_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5dbfc07f21b24324a748ba0e1998d195",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.2806937010851929, 'roc_auc': 0.724342679133859, 'balanced_accuracy': 0.6448295419977427}\n",
       "std_scores = {'matthews': 0.09542590294675868, 'roc_auc': 0.04969484135272, 'balanced_accuracy': 0.0595558011571011}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=site_cv)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now make the same plots as before, confirming now that the sites are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 1471 rows\n",
      "Dependent any drug: 1471 rows\n",
      "Plotting 1471 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 908 rows\n",
      "Dependent any drug: 908 rows\n",
      "Plotting 908 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot_bivar('Site', 'Dependent any drug', subjects=results.train_subjects[0])\n",
    "data.plot_bivar('Site', 'Dependent any drug', subjects=results.val_subjects[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another piece we might want to look at in this case is the weighted_mean_scores. That is, each of the splits has train and test sets of slightly different sizes, so how does the metric change if we weight it by the number of validation subjects in that fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'matthews': 0.2962912181056522,\n",
       " 'roc_auc': 0.731185680809635,\n",
       " 'balanced_accuracy': 0.6545156761514691}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.weighted_mean_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1125e8677974cdb9d85d73fd28d5bea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.4198523568381287, 'roc_auc': 0.8021293794313956, 'balanced_accuracy': 0.71695304741449}\n",
       "std_scores = {'matthews': 0.05886752017766923, 'roc_auc': 0.020630146396151645, 'balanced_accuracy': 0.030556740029522838}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=3)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other options?\n",
    "\n",
    "What other options do we have to handle site with CV besides just the group splitting?\n",
    "\n",
    "1. Explicit leave-out-site CV: Where every train set is composed of all sites but one, and the validation set is the left out site.\n",
    "\n",
    "2. Use the train-only parameter in CV.\n",
    "\n",
    "3. Only evaluate subjects from sites with both cases and controls.\n",
    "\n",
    "Let's try option 1 first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leave-out-site CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "564eb1338e244b9491eda2b16f4042de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only one class present in y_true. ROC AUC score is not defined in that case.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/sklearn/metrics/_classification.py:870: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
     ]
    }
   ],
   "source": [
    "# All we need to do for leave-out-site is specify that splits = Site\n",
    "leave_out_site_cv = bp.CV(splits='Site')\n",
    "\n",
    "# This will actually raise an error\n",
    "try:\n",
    "    results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                          problem_spec=ps, cv=leave_out_site_cv)\n",
    "except Exception as exc:\n",
    "    print(exc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uh oh looks like this option actually failed. What happened? Well basically since we have sites with only one class, we are trying to compute metrics that require two classes. Let's try again this time just using scorers that can work in the case of only one class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a23ef370f42f4cf0a1018d0b94638683",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/22 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "({'accuracy': 0.6236689337343281},\n",
       " {'accuracy': 0.19902767857831596},\n",
       " {'accuracy': 0.6658259773013872})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data, scorer='accuracy',\n",
    "                      problem_spec=ps, cv=leave_out_site_cv)\n",
    "\n",
    "results.mean_scores, results.std_scores, results.weighted_mean_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem here is accuracy is likely a pretty bad metric, especially for the sites which have both cases and controls, but a skewed distribution. In general using the group preserving by Site is likely a better option."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train-only subjects\n",
    "\n",
    "One optional way of handling these subjects from sites with only cases or only controls is to just never evaluate them. To instead always treat them as train subjects. We can notably then either use random splits or group preserving by CV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_imbalanced(df):\n",
    "    as_int = df['Dependent any drug'].astype('int')\n",
    "    return np.sum(as_int) == len(as_int)\n",
    "\n",
    "# Apply function to check if each site has all the same target variable\n",
    "is_imbalanced = data.groupby('Site').apply(check_imbalanced)\n",
    "\n",
    "# Note these are internal values, not the original ones\n",
    "imbalanced_sites = np.flatnonzero(is_imbalanced)\n",
    "\n",
    "# We can specify just these subjects using the ValueSubset wrapper\n",
    "imbalanced_subjs = bp.ValueSubset('Site', values=imbalanced_sites, decode_values=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f247edb6a5934227a6e1e32d7909a80f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.024294039432900597, 'roc_auc': 0.5292721521624003, 'balanced_accuracy': 0.5116503104881627}\n",
       "std_scores = {'matthews': 0.0818023189639453, 'roc_auc': 0.053461117751205205, 'balanced_accuracy': 0.04147838671486171}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now define our new version of the group site cv\n",
    "site_cv_tr_only = bp.CV(splits=3, cv_strategy=group_site,\n",
    "                        train_only_subjects=imbalanced_subjs)\n",
    "\n",
    "# And evaluate\n",
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=site_cv_tr_only)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The take-away here is maybe a little bleak. Essentially it is saying that some of the high scores we got earlier might have been in large part due to scores from the imbalanced sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b31f4d77d6bf4e098dc5e140f4c6acbb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.23754373855512448, 'roc_auc': 0.6581411778771414, 'balanced_accuracy': 0.6117532215243421}\n",
       "std_scores = {'matthews': 0.028251718001276915, 'roc_auc': 0.00840487420017697, 'balanced_accuracy': 0.01031391488070083}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And we can also run a version with just random splits\n",
    "tr_only_cv = bp.CV(splits=3, train_only_subjects=imbalanced_subjs)\n",
    "\n",
    "# And evaluate\n",
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=tr_only_cv)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this version with random splits does better, we are still notably ignoring the distribution of case to control in the sites with both cases and controls. Based on the group preserving results what it seems like is going on is that when subjects from the same site are split across validation folds, the classifier might be able to just memorize Site instead of the effect of interest. Lets use Site 29 as an example.\n",
    "\n",
    "Let's look at just fold 1 first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 70 rows\n",
      "Dependent any drug: 70 rows\n",
      "Plotting 70 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Site: 30 rows\n",
      "Dependent any drug: 30 rows\n",
      "Plotting 30 overlap valid subjects.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predict</th>\n",
       "      <th>predict_proba</th>\n",
       "      <th>decision_function</th>\n",
       "      <th>y_true</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3458</th>\n",
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       "      <td>-1.451580</td>\n",
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       "    <tr>\n",
       "      <th>3459</th>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.964846</td>\n",
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       "      <td>0.681535</td>\n",
       "      <td>-0.760837</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>-2.509949</td>\n",
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       "      <td>0.959444</td>\n",
       "      <td>-3.163663</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.877308</td>\n",
       "      <td>-1.967185</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3502</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.892309</td>\n",
       "      <td>-2.114550</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3503</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.946427</td>\n",
       "      <td>-2.871650</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3507</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.968260</td>\n",
       "      <td>-3.417921</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.907378</td>\n",
       "      <td>-2.282028</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3517</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.976704</td>\n",
       "      <td>-3.735907</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3521</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.665222</td>\n",
       "      <td>-0.686655</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3523</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.699328</td>\n",
       "      <td>-0.844100</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.951770</td>\n",
       "      <td>-2.982337</td>\n",
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       "      <td>-0.421364</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.955454</td>\n",
       "      <td>-3.065661</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3437</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.934065</td>\n",
       "      <td>-2.650873</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3442</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.912519</td>\n",
       "      <td>-2.344782</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3443</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.927844</td>\n",
       "      <td>-2.554032</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3447</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.916637</td>\n",
       "      <td>-2.397506</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3453</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.945561</td>\n",
       "      <td>-2.854691</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3455</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.745451</td>\n",
       "      <td>-1.074496</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      predict  predict_proba  decision_function  y_true\n",
       "3458      0.0       0.810241          -1.451580     0.0\n",
       "3459      0.0       0.902262          -2.222618     0.0\n",
       "3462      0.0       0.976899          -3.744522     0.0\n",
       "3470      0.0       0.954009          -3.032217     0.0\n",
       "3478      0.0       0.964846          -3.312243     0.0\n",
       "3479      0.0       0.900526          -2.203087     0.0\n",
       "3482      0.0       0.946305          -2.869253     0.0\n",
       "3484      0.0       0.681535          -0.760837     0.0\n",
       "3485      0.0       0.924836          -2.509949     0.0\n",
       "3486      0.0       0.957875          -3.124076     0.0\n",
       "3494      0.0       0.870815          -1.908185     0.0\n",
       "3497      0.0       0.959444          -3.163663     0.0\n",
       "3498      0.0       0.877308          -1.967185     0.0\n",
       "3502      0.0       0.892309          -2.114550     0.0\n",
       "3503      0.0       0.946427          -2.871650     1.0\n",
       "3507      0.0       0.968260          -3.417921     0.0\n",
       "3509      0.0       0.961510          -3.218103     0.0\n",
       "3512      0.0       0.907378          -2.282028     0.0\n",
       "3517      0.0       0.976704          -3.735907     0.0\n",
       "3521      0.0       0.665222          -0.686655     0.0\n",
       "3523      0.0       0.699328          -0.844100     0.0\n",
       "3423      0.0       0.951770          -2.982337     0.0\n",
       "3429      0.0       0.603810          -0.421364     0.0\n",
       "3434      0.0       0.955454          -3.065661     0.0\n",
       "3437      0.0       0.934065          -2.650873     0.0\n",
       "3442      0.0       0.912519          -2.344782     0.0\n",
       "3443      0.0       0.927844          -2.554032     0.0\n",
       "3447      0.0       0.916637          -2.397506     0.0\n",
       "3453      0.0       0.945561          -2.854691     0.0\n",
       "3455      0.0       0.745451          -1.074496     0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The train and val subjects from fold 0\n",
    "tr_subjs = results.train_subjects[0]\n",
    "val_subjs = results.val_subjects[0]\n",
    "\n",
    "# An object specifying just site 29's subjects\n",
    "site29 = bp.ValueSubset('Site', 29, decode_values=True)\n",
    "\n",
    "# Get the intersection w/ intersection obj\n",
    "site_29_tr = bp.Intersection([tr_subjs, site29])\n",
    "site_29_val = bp.Intersection([val_subjs, site29])\n",
    "\n",
    "# Plot\n",
    "data.plot_bivar('Site', 'Dependent any drug', subjects=bp.Intersection([tr_subjs, site29]))\n",
    "data.plot_bivar('Site', 'Dependent any drug', subjects=bp.Intersection([val_subjs, site29]))\n",
    "\n",
    "# Grab just the subjects as actual index\n",
    "val_subjs = data.get_subjects(site_29_val)\n",
    "\n",
    "# Get a dataframe with the predictions made for just fold0\n",
    "preds_df_fold0 = results.get_preds_dfs()[0]\n",
    "\n",
    "# Let's see predictions for just these validation subjects\n",
    "preds_df_fold0.loc[val_subjs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that it has learned to just predict 0 for every subject, which because of the imbalance is right most of the time, but really doesn't tell us if it has learned to predict site or substance dependence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop imbalanced subjects\n",
    "The last option was just don't use the imbalanced subjects at all. We will do this by indexing only the balanced sites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the ~ before the previously defined is_imbalanced\n",
    "balanced_sites = np.flatnonzero(~is_imbalanced)\n",
    "\n",
    "# We can specify just these subjects using the ValueSubset wrapper\n",
    "balanced_subjs = bp.ValueSubset('Site', values=balanced_sites, decode_values=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d17d0d434dd452eb75b6b7019897c19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.23079311975258734, 'roc_auc': 0.6494071534373412, 'balanced_accuracy': 0.6141868842892168}\n",
       "std_scores = {'matthews': 0.09467372271946288, 'roc_auc': 0.07189605086562514, 'balanced_accuracy': 0.05165558889230518}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects=Intersection(['train', ValueSubset(name=Site, values=[ 2  3  4  5  6  7 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27], decode_values=False)]), target='Dependent any drug')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate with site preserving cv from earlier but now using just the balanced train subjects\n",
    "train_just_balanced = bp.Intersection(['train', balanced_subjs])\n",
    "\n",
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      subjects=train_just_balanced,\n",
    "                      problem_spec=ps, cv=site_cv)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This actually seems to show that the imbalanced site confound with doesn't just corrupt cross validated results, but also actively impedes the model learning the real relationship. That we are better off just not using subjects with from sites with only cases. Yikes. \n",
    "\n",
    "Likewise, we can do the random split strategy again too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66a60e3de9a74942965d31bf28c2776e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.3099741843096915, 'roc_auc': 0.7185050035507189, 'balanced_accuracy': 0.6557306499974717}\n",
       "std_scores = {'matthews': 0.05501720339208484, 'roc_auc': 0.030948171887409684, 'balanced_accuracy': 0.02748782703232899}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects=Intersection(['train', ValueSubset(name=Site, values=[ 2  3  4  5  6  7 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27], decode_values=False)]), target='Dependent any drug')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      subjects=train_just_balanced,\n",
    "                      problem_spec=ps, cv=bp.CV(splits=3))\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we could run it with a leave-out-site CV. Now we can use the normal metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "165db5b66d3d424da2e0badb0a3d019a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/sklearn/metrics/_classification.py:870: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.14755625865911742, 'roc_auc': 0.6060559623948263, 'balanced_accuracy': 0.5636346829506975}\n",
       "std_scores = {'matthews': 0.17026794856315272, 'roc_auc': 0.12014100381868857, 'balanced_accuracy': 0.076467544201846}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores', 'fis_', 'coef_']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'get_fis', 'get_coef_', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects=Intersection(['train', ValueSubset(name=Site, values=[ 2  3  4  5  6  7 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27], decode_values=False)]), target='Dependent any drug')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=pipe, dataset=data,\n",
    "                      subjects=train_just_balanced,\n",
    "                      problem_spec=ps, cv=leave_out_site_cv)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results Summary\n",
    "\n",
    "To summarize, of all of the above different iterations. Only the results from the versions where subjects from case-only sites are treated as train-only or dropped and evaluated group-preserving by site are likely un-biased! The leave-out-site CV results should be okay too, but let's skip those for now.\n",
    "\n",
    "Train Only Scores:\n",
    "mean_scores = {'matthews': 0.024294039432900597, 'roc_auc': 0.5292721521624003, 'balanced_accuracy': 0.5116503104881627}\n",
    "\n",
    "Dropped Scores:\n",
    "mean_scores = {'matthews': 0.23079311975258734, 'roc_auc': 0.6494071534373412, 'balanced_accuracy': 0.6141868842892168}\n",
    "\n",
    "By these interm results we can with some confidence conclude dropping subjects is the way to go, unless we want to dig deeper..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disentangle Target and Site?\n",
    "\n",
    "Can information on dependance from case-only sites be some how disentangled from site?\n",
    "\n",
    "This actually leads to a follow up a question. Namely is there any way to extract usable information from case-only site subjects? This question I actually investigated more deeply in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25248 The idea of train-only subjects is actually from this paper, though it still has some important differences then the way we formulated it. \n",
    "\n",
    "The key piece in the paper which we haven't tried to do yet is centered around the problem of the classifier learning site related information instead of dependence related information in the train-only setup. We essentially need a way of forcing the classifier to learn this. The way proposed in the paper proposes that maybe we can by restricting the features provided to the classifier, give it only features related to dependence and not those related to site. In order to do this, we need to add a feature selection step.\n",
    "\n",
    "There are different ways to compose the problem, but we will specify the feature selection step as a seperate layer of nesting, importantly one that is evaluated according to the same site_cv_tr_only that we will be evaluating with. Remember the goal here is to select features which help us learn to predict useful information from case only sites, i.e., with feature only related to Dependence and not site!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same imputer and scaler\n",
    "imputers = [bp.Imputer(obj='mean', scope='float'),\n",
    "            bp.Imputer(obj='median', scope='category')]\n",
    "scaler = bp.Scaler('standard')\n",
    "\n",
    "# This object will be used to set features as an array of binary hyper-parameters\n",
    "feat_selector = bp.FeatSelector('selector', params=1)\n",
    "\n",
    "# This param search is responsible for optimizing the selected features from feat_selector\n",
    "param_search = bp.ParamSearch('RandomSearch',\n",
    "                              n_iter=16,\n",
    "                              cv=site_cv_tr_only)\n",
    "\n",
    "# We create a nested elastic net model to optimize - no particular CV should be okay\n",
    "random_search = bp.ParamSearch('RandomSearch', n_iter=16)\n",
    "elastic_search = bp.Model('elastic', params=1,\n",
    "                          param_search=random_search)\n",
    "\n",
    "# Put it all together in a pipeline\n",
    "fs_pipe = bp.Pipeline(imputers + [scaler, feat_selector, elastic_search],\n",
    "                      param_search=param_search)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This may take a while to evaluate as we now have two levels of parameters to optimize, e.g., for each choice\n",
    "of features, we need to train a nested elastic net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1f8a66a76bc4caca6e291375574ebbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.02346740168044174, 'roc_auc': 0.4992373334460878, 'balanced_accuracy': 0.511308005873143}\n",
       "std_scores = {'matthews': 0.09763704971436771, 'roc_auc': 0.06654364664577839, 'balanced_accuracy': 0.04908589456453536}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=fs_pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=site_cv_tr_only)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunetly this doesn't seem to work. Notably we still arn't quite following the method from the paper, which performs a meta-analysis on the results from the feature search to find overlapping subsets of features. As of the time of writing this example, a simmilar strategy has not been able to be easily integrated into BPt.\n",
    "\n",
    "The above method still has a few problems, one being severe overfitting. Namely, because a number of subjects are reserved in train-only roles, the same small number of subjects are being used over and over in cross-validation. \n",
    "\n",
    "We can confirm this intutiton by comparing the best score achieved internally for each set of features w/ how they score in the outer cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.034082343012732844, 0.1394079745869329)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-results.estimators[0].best_score_, results.scores['matthews'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15037612204051184, -0.09944769119313912)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-results.estimators[1].best_score_, results.scores['matthews'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10307484584144071, 0.030441921647531428)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-results.estimators[2].best_score_, results.scores['matthews'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These values are quite different to say the least. Part of the problem again is that such a small amount of data is being used to determine generalization. What if instead of a the 3-fold group preserving train only feature selection CV, we instead used a leave-out-site scheme on site with train only? I doubt it will help, but let's see..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c054d65a6bf74615b7f4c680cff13e18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.03667173630581328, 'roc_auc': 0.5248868808677055, 'balanced_accuracy': 0.5178404766273731}\n",
       "std_scores = {'matthews': 0.11389312167894736, 'roc_auc': 0.06057589628452694, 'balanced_accuracy': 0.05676567744096608}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leave_out_site_tr_only_cv = bp.CV(splits='Site',\n",
    "                                  train_only_subjects=imbalanced_subjs)\n",
    "\n",
    "fs_pipe.param_search = bp.ParamSearch(\n",
    "    'RandomSearch', n_iter=16,\n",
    "    cv=leave_out_site_tr_only_cv)\n",
    "\n",
    "results = bp.evaluate(pipeline=fs_pipe, dataset=data,\n",
    "                      problem_spec=ps, cv=site_cv_tr_only)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does a little better. But only slightly. What if we also used this leave-out site strategy for the outer loop? This would be to address the issue that the outer CV has the same problem as the inner, where the validation set is overly specific and small in some folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting target = Dependent any drug\n",
      "Using problem_type = binary\n",
      "Using scope = all defining an initial total of 163 features.\n",
      "Evaluating 2379 total data points.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59378c11530a4015ae9f61b0ed7afc25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Folds:   0%|          | 0/17 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape = (2082, 163) (number of data points x number of feature)\n",
      "Val shape = (297, 163) (number of data points x number of feature)\n",
      "Fit fold in 433.108 seconds.\n",
      "matthews: 0.27971196025033845\n",
      "roc_auc: 0.6950904392764857\n",
      "balanced_accuracy: 0.6410575858250277\n",
      "Train shape = (2338, 163) (number of data points x number of feature)\n",
      "Val shape = (41, 163) (number of data points x number of feature)\n",
      "Fit fold in 488.837 seconds.\n",
      "matthews: 0.0\n",
      "roc_auc: 0.4492753623188406\n",
      "balanced_accuracy: 0.5\n",
      "Train shape = (2345, 163) (number of data points x number of feature)\n",
      "Val shape = (34, 163) (number of data points x number of feature)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/sklearn/metrics/_classification.py:870: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit fold in 448.277 seconds.\n",
      "matthews: -0.1604088343952626\n",
      "roc_auc: 0.46315789473684216\n",
      "balanced_accuracy: 0.42280701754385963\n",
      "Train shape = (2338, 163) (number of data points x number of feature)\n",
      "Val shape = (41, 163) (number of data points x number of feature)\n",
      "Fit fold in 470.407 seconds.\n",
      "matthews: -0.019138755980861243\n",
      "roc_auc: 0.47368421052631576\n",
      "balanced_accuracy: 0.49043062200956933\n",
      "Train shape = (2327, 163) (number of data points x number of feature)\n",
      "Val shape = (52, 163) (number of data points x number of feature)\n",
      "Fit fold in 448.843 seconds.\n",
      "matthews: 0.3354656500760583\n",
      "roc_auc: 0.6979166666666666\n",
      "balanced_accuracy: 0.6636904761904762\n",
      "Train shape = (2009, 163) (number of data points x number of feature)\n",
      "Val shape = (370, 163) (number of data points x number of feature)\n",
      "Fit fold in 437.145 seconds.\n",
      "matthews: 0.020177568979434148\n",
      "roc_auc: 0.49300137342567435\n",
      "balanced_accuracy: 0.5099646415943426\n",
      "Train shape = (2308, 163) (number of data points x number of feature)\n",
      "Val shape = (71, 163) (number of data points x number of feature)\n",
      "Fit fold in 451.353 seconds.\n",
      "matthews: 0.0\n",
      "roc_auc: 0.5223285486443381\n",
      "balanced_accuracy: 0.5\n",
      "Train shape = (2319, 163) (number of data points x number of feature)\n",
      "Val shape = (60, 163) (number of data points x number of feature)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sage/anaconda3/envs/bpt/lib/python3.9/site-packages/sklearn/metrics/_classification.py:870: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit fold in 454.574 seconds.\n",
      "matthews: -0.12598815766974242\n",
      "roc_auc: 0.5039682539682541\n",
      "balanced_accuracy: 0.44047619047619047\n",
      "Train shape = (2313, 163) (number of data points x number of feature)\n",
      "Val shape = (66, 163) (number of data points x number of feature)\n",
      "Fit fold in 458.078 seconds.\n",
      "matthews: 0.053121271525973054\n",
      "roc_auc: 0.5089947089947089\n",
      "balanced_accuracy: 0.5253968253968253\n",
      "Train shape = (2310, 163) (number of data points x number of feature)\n",
      "Val shape = (69, 163) (number of data points x number of feature)\n",
      "Fit fold in 452.421 seconds.\n",
      "matthews: 0.2622746101092192\n",
      "roc_auc: 0.6256366723259762\n",
      "balanced_accuracy: 0.6294567062818336\n",
      "Train shape = (2242, 163) (number of data points x number of feature)\n",
      "Val shape = (137, 163) (number of data points x number of feature)\n",
      "Fit fold in 442.038 seconds.\n",
      "matthews: -0.11597093720775255\n",
      "roc_auc: 0.5342528735632184\n",
      "balanced_accuracy: 0.4574712643678161\n",
      "Train shape = (2338, 163) (number of data points x number of feature)\n",
      "Val shape = (41, 163) (number of data points x number of feature)\n",
      "Fit fold in 456.392 seconds.\n",
      "matthews: 0.2313602894352951\n",
      "roc_auc: 0.6515151515151514\n",
      "balanced_accuracy: 0.6257575757575757\n",
      "Train shape = (2352, 163) (number of data points x number of feature)\n",
      "Val shape = (27, 163) (number of data points x number of feature)\n",
      "Fit fold in 462.279 seconds.\n",
      "matthews: -0.12507270025494252\n",
      "roc_auc: 0.37912087912087916\n",
      "balanced_accuracy: 0.43956043956043955\n",
      "Train shape = (2301, 163) (number of data points x number of feature)\n",
      "Val shape = (78, 163) (number of data points x number of feature)\n",
      "Fit fold in 453.980 seconds.\n",
      "matthews: 0.12403989506994083\n",
      "roc_auc: 0.5671052631578948\n",
      "balanced_accuracy: 0.5565789473684211\n",
      "Train shape = (2279, 163) (number of data points x number of feature)\n",
      "Val shape = (100, 163) (number of data points x number of feature)\n",
      "Fit fold in 442.766 seconds.\n",
      "matthews: 0.06294058066909786\n",
      "roc_auc: 0.4231578947368421\n",
      "balanced_accuracy: 0.5368421052631579\n",
      "Train shape = (2320, 163) (number of data points x number of feature)\n",
      "Val shape = (59, 163) (number of data points x number of feature)\n",
      "Fit fold in 450.422 seconds.\n",
      "matthews: -0.005703586973049607\n",
      "roc_auc: 0.48160919540229885\n",
      "balanced_accuracy: 0.4982758620689655\n",
      "Train shape = (2345, 163) (number of data points x number of feature)\n",
      "Val shape = (34, 163) (number of data points x number of feature)\n",
      "Fit fold in 460.327 seconds.\n",
      "matthews: 0.10213764617139019\n",
      "roc_auc: 0.6423611111111112\n",
      "balanced_accuracy: 0.5451388888888888\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BPtEvaluator\n",
       "------------\n",
       "mean_scores = {'matthews': 0.05405567645912566, 'roc_auc': 0.5360103823230292, 'balanced_accuracy': 0.5284061852113758}\n",
       "std_scores = {'matthews': 0.14703361948297453, 'roc_auc': 0.0926704447511229, 'balanced_accuracy': 0.07192589246275141}\n",
       "\n",
       "Saved Attributes: ['estimators', 'preds', 'timing', 'train_subjects', 'val_subjects', 'feat_names', 'ps', 'mean_scores', 'std_scores', 'weighted_mean_scores', 'scores']\n",
       "\n",
       "Available Methods: ['get_preds_dfs', 'permutation_importance']\n",
       "\n",
       "Evaluated with:\n",
       "ProblemSpec(n_jobs=16, problem_type='binary', scorer={'balanced_accuracy': make_scorer(balanced_accuracy_score), 'matthews': make_scorer(matthews_corrcoef), 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}, subjects='train', target='Dependent any drug')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = bp.evaluate(pipeline=fs_pipe,\n",
    "                      dataset=data,\n",
    "                      problem_spec=ps,\n",
    "                      cv=leave_out_site_tr_only_cv,\n",
    "                      eval_verbose=1)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing the results for each site seperately with the verbose option really makes some of the problem clear, namely that a number of small sites don't seem to generalize well. This is simmilar to what we saw in the case of using leave-out-site with case-only subjects dropped, where we had a huge std in roc auc between folds.\n",
    "\n",
    "Another potential problem with doing leave-out-site is that the way the underlying data was collected, different sites will have subjects with dependence to different substances. This can certainly cause issues, as say our classifier only learns to classify alcohol dependence (the strongest signal), then it will do poorly on sites for other substances. Which is good in one way, that it lets us know the classifier is not generalizing, but it doesn't solve the generalization problem.\n",
    "\n",
    "Anyways, that will end this example for now. Leaving the problem of how to best exploit information from case-only sites as an open question (though if interested, a more faithful reproduction of the method presented in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25248 might prove to be fruitful. Specifically, in the end only using a few, maybe 3-4, features, which is notably quite different then what we are trying above, i.e., trying just 16 random sets of features)."
   ]
  }
 ],
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