main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import auxiliary_functions as aux\n",
    "import time\n",
    "from classification import train_randomforest_AUC\n",
    "from filters import runAutoFilter\n",
    "from filters import apply_chosenfilterstrategy\n",
    "from numpy import median\n",
    "from sklearn.model_selection import StratifiedKFold  # maintains class proportions when creating folds\n",
    "\n",
    "\n",
    "\n",
    "from filters import getscores_decisionstumpfilter\n",
    "\n",
    "# Creates a train and test set for each fold of a stratified kfold process,\n",
    "# then calls the train_classifier function and prints the average results\n",
    "def runClassificationExperiment(df, apply_undersampling, unusable_feature_threshold, filter_method, nfeatures):\n",
    "    kf = StratifiedKFold(n_splits=10, shuffle=False)\n",
    "    score_array = []\n",
    "    fold = 0\n",
    "    for train_index, test_index in kf.split(df.iloc[:, :-1], df.iloc[:, -1]):\n",
    "        fold = fold + 1\n",
    "        training_set = df.iloc[train_index, :]\n",
    "        test_set = df.iloc[test_index, :]\n",
    "        training_set, test_set = aux.removeLowFrequencyFeatures_TrainTest(training_set, test_set, unusable_feature_threshold)\n",
    "        \n",
    "        # count features\n",
    "        print(f'Features: {training_set.shape[1]}')\n",
    "\n",
    "        if filter_method == \"None\":\n",
    "            X_train, X_test = training_set.iloc[:, :-1], test_set.iloc[:, :-1]\n",
    "        elif filter_method == \"AutoFilter\":\n",
    "            # nfeatures_array = aux.set_nfeatures_array(df.shape[1])  # k values to be used (GenAge dataset)\n",
    "            nfeatures_array = aux.set_nfeatures_array(training_set.shape[1])  # k values to be used (GenAge dataset)\n",
    "            filter_method, nfeatures = runAutoFilter(training_set, apply_undersampling, nfeatures_array)\n",
    "            print(f'AutoFilter InternalCV end. Selected filter strategy : {filter_method} with k = {nfeatures}')\n",
    "            X_train, X_test = apply_chosenfilterstrategy(df, train_index, test_index, filter_method, nfeatures)\n",
    "        else:  # other filter methods\n",
    "            X_train, X_test = apply_chosenfilterstrategy(df, train_index, test_index, filter_method, nfeatures)\n",
    "\n",
    "        y_train, y_test = training_set.iloc[:, -1], test_set.iloc[:, -1]\n",
    "\n",
    "        #Training classifier - AUC Scoring\n",
    "        score = train_randomforest_AUC(X_train, X_test, y_train, y_test, apply_undersampling)\n",
    "        #score = train_randomforest_gmean(X_train, X_test, y_train, y_test, apply_undersampling) #for GMean scoring\n",
    "        score_array.append(score)\n",
    "        print(f\"Fold {fold}. AUC score: {round(score, 3)}\")\n",
    "\n",
    "    print(f\"Median AUC: {round(median(score_array),3)}\")\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Main function: runs a 10-fold cross-validation and returns the AUC results of Random Forest classifiers trained at each fold, and the median AUC value.\n",
    "The input datasets should be formatted as tab-separated spreadsheets with an index column as the first (leftmost) variable, and a binary (0,1) class value in the last (rightmost) position\n",
    "For our test datasets, there are some additional identifier variables which are removed when loading the dataset: STITCH_Code, InteractorsList, InteractorsCount\n",
    "The following experiment parameters that can be set by the user:\n",
    "\n",
    "unusable_feature_threshold: \n",
    "    Features with too few different values are removed in a preprocessing step, removing spurious variables\n",
    "    This works well for binary features, but is not recommended for features with numerical or categorical values\n",
    "    int value (recommended for our test datasets: 10). If set to a value < 0, the threshold filter is not applying\n",
    "    \n",
    "BRF_undersampling:\n",
    "    Whether the classifiers should be trained using balanced datasets to avoid a bias in favour of the majority class\n",
    "    True: undersample training set to a 1:1 ratio (uses BalancedRandomForestClassifier from imblearn.ensemble library).\n",
    "    False: trains classifier with unchanged class balance (Not recommended for imbalanced datasets)\n",
    "    \n",
    "filter_method: \n",
    "    Selected filter scoring strategy. Accepted values: \n",
    "    \"None\": Do not apply a filter to the dataset, and use all predictive features when training the classifier\n",
    "    \"AutoFilter\": Select a candidate filter and k value by testing all combinations in an internal cross-validation\n",
    "    \"InfoGain\": Apply Information Gain filter (uses native implementation from sklearn)\n",
    "    \"Chi2\": Apply Chi2 filter (uses native implementation from sklearn)\n",
    "    \"DecisionStump\": Apply Decision Stump filter (implemented in this project)\n",
    "    \"LogOddsRatio\": Apply Log Odds Ratio filter (implemented in this project)\n",
    "    \"AsymmetricOptimalPrediction\": Apply Asymmetric Optimal Prediction filter (implemented in this project)\n",
    "    \n",
    "nfeatures: number of predictive features to be selected when applying the filter method\n",
    "    int value, irrelevant for filter_method = None (no filter) and for filter_method = AutoFilter (chosen automatically)    \n",
    "\"\"\"\n",
    "def main(filepath, BRF_undersampling, unusable_feature_threshold, filter_method, nfeatures):\n",
    "    if filepath == \"\":\n",
    "        df = aux.load_dataset_dialog()\n",
    "    else:\n",
    "        df = aux.load_dataset_filepath(filepath)\n",
    "    df = aux.removeLowFrequencyFeatures(df, unusable_feature_threshold)  # Apply threshold filter to full dataset (reduce runtime)\n",
    "    runClassificationExperiment(df, BRF_undersampling, unusable_feature_threshold, filter_method, nfeatures)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = [\n",
    "    'C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans Protein Interactors dataset_v1.tsv',\n",
    "    'C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans GOTerms dataset_v1.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans Phenotypes dataset_v1.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans GenAge dataset_v1.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-2 datasets (45% minimum score threshold)\\CElegans Protein Interactors dataset_v2.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-2 datasets (45% minimum score threshold)\\CElegans GOTerms dataset_v2.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-2 datasets (45% minimum score threshold)\\CElegans Phenotypes dataset_v2.tsv',\n",
    "    # 'C Elegans dataset examples\\Version-2 datasets (45% minimum score threshold)\\CElegans GenAge dataset_v2.tsv',\n",
    "    # 'Drosophila datasets (validation experiment)\\Drosophila Protein Interactors .tsv',\n",
    "    # 'Drosophila datasets (validation experiment)\\Drosophila GO Terms.tsv',\n",
    "]\n",
    "\n",
    "filters = [\n",
    "    # \"None\",\n",
    "    # \"AutoFilter\",\n",
    "    \"InfoGain\",\n",
    "    # \"Chi2\",\n",
    "    # \"DecisionStump\",\n",
    "    # \"LogOddsRatio\",\n",
    "    # \"AsymmetricOptimalPrediction\",\n",
    "]\n",
    "\n",
    "# Test parameters\n",
    "# filename = 'C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans GOTerms dataset_v1.tsv' # set as empty (\"\") to have a file selection dialog\n",
    "#filename = \"\"  # set as empty (\"\") to have a file selection dialog\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = aux.load_dataset_dialog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       " 'GO:0035033': 0.4982432279270089,\n",
       " 'GO:0120111': 0.4983908045977012,\n",
       " 'GO:0004510': 0.4985057471264368,\n",
       " 'GO:0006587': 0.4985057471264368,\n",
       " 'GO:0000730': 0.4985057471264368,\n",
       " 'GO:0042148': 0.4985057471264368,\n",
       " 'GO:0022401': 0.4985057471264368,\n",
       " 'GO:0030203': 0.4985057471264368,\n",
       " 'GO:0090735': 0.4985057471264368,\n",
       " 'GO:0023058': 0.4985057471264368,\n",
       " 'GO:0015279': 0.4986206896551724,\n",
       " 'GO:0019226': 0.4986206896551724,\n",
       " 'GO:0003031': 0.4986206896551724,\n",
       " 'GO:0032413': 0.4986206896551724,\n",
       " 'GO:0034766': 0.4986206896551724,\n",
       " 'GO:0032410': 0.4986206896551724,\n",
       " 'GO:0034763': 0.4986206896551724,\n",
       " 'GO:0035637': 0.4986206896551724,\n",
       " 'GO:0051453': 0.4987532585288451,\n",
       " 'GO:0030641': 0.4987532585288451,\n",
       " 'GO:0060990': 0.4988665986625864,\n",
       " 'GO:0003896': 0.4988665986625864,\n",
       " 'GO:0006269': 0.4988665986625864,\n",
       " 'GO:0000972': 0.4988665986625864,\n",
       " 'GO:0015018': 0.4988665986625864,\n",
       " 'GO:0060988': 0.4988665986625864,\n",
       " 'GO:0030125': 0.49897993879632774,\n",
       " 'GO:0030118': 0.49897993879632774,\n",
       " 'GO:0030665': 0.49897993879632774,\n",
       " 'GO:0004500': 0.49937662926442256,\n",
       " 'GO:0004836': 0.49937662926442256,\n",
       " 'GO:0042420': 0.49937662926442256,\n",
       " 'GO:0042421': 0.49937662926442256,\n",
       " 'GO:0007220': 0.49937662926442256,\n",
       " 'GO:0051560': 0.49937662926442256,\n",
       " 'GO:0007596': 0.49937662926442256,\n",
       " 'GO:0030194': 0.49937662926442256,\n",
       " 'GO:0047710': 0.49937662926442256,\n",
       " 'GO:0051377': 0.49937662926442256,\n",
       " 'GO:0042424': 0.49937662926442256,\n",
       " 'GO:0019614': 0.49937662926442256,\n",
       " 'GO:0042415': 0.49937662926442256,\n",
       " 'GO:0050817': 0.49937662926442256,\n",
       " 'GO:0007599': 0.49937662926442256,\n",
       " 'GO:0090303': 0.49937662926442256,\n",
       " 'GO:0030193': 0.49937662926442256,\n",
       " ...}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getscores_decisionstumpfilter(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening the file: C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans Protein Interactors dataset_v1.tsv, for filter: InfoGain\n",
      "Features: 5303\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 1. AUC score: 0.813\n",
      "Features: 5562\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 2. AUC score: 0.818\n",
      "Features: 5518\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 3. AUC score: 0.57\n",
      "Features: 5558\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 4. AUC score: 0.749\n",
      "Features: 5678\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 5. AUC score: 0.581\n",
      "Features: 5607\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 6. AUC score: 0.719\n",
      "Features: 5571\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 7. AUC score: 0.87\n",
      "Features: 5532\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 8. AUC score: 0.652\n",
      "Features: 5420\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 9. AUC score: 0.594\n",
      "Features: 5593\n",
      "Warning, invalid k value. k: 0 features: 5789. Setting k as all.\n",
      "Fold 10. AUC score: 0.702\n",
      "Median AUC: 0.71\n",
      "total runtime: 377s\n",
      "\n",
      "\n",
      "Opening the file: C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans GOTerms dataset_v1.tsv, for filter: InfoGain\n",
      "Features: 7705\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 1. AUC score: 0.678\n",
      "Features: 7695\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 2. AUC score: 0.754\n",
      "Features: 7668\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 3. AUC score: 0.671\n",
      "Features: 7676\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 4. AUC score: 0.72\n",
      "Features: 7644\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 5. AUC score: 0.835\n",
      "Features: 7656\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 6. AUC score: 0.765\n",
      "Features: 7620\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 7. AUC score: 0.848\n",
      "Features: 7587\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 8. AUC score: 0.869\n",
      "Features: 7517\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 9. AUC score: 0.934\n",
      "Features: 7117\n",
      "Warning, invalid k value. k: 0 features: 7707. Setting k as all.\n",
      "Fold 10. AUC score: 0.828\n",
      "Median AUC: 0.796\n",
      "total runtime: 549s\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for filter_ in filters:\n",
    "    for filename in filenames:\n",
    "        unusable_feature_threshold = 10  # features with too few different values are removed in a preprocessing step\n",
    "        BRF_undersampling = False  # True: undersample the training set to a 1:1 ratio (uses BRF for RF classifiers).\n",
    "        filter_method = filter_\n",
    "        nfeatures = 0\n",
    "        print(f'Opening the file: {filename}, for filter: {filter_}')\n",
    "        start_time = time.time()\n",
    "        main(filename, BRF_undersampling, unusable_feature_threshold, filter_method, nfeatures)  # runs main function\n",
    "        end_time = time.time()\n",
    "        print(f'total runtime: {int(end_time-start_time)}s\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "cego = pd.read_csv('C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans GOTerms dataset_v1.tsv', sep='\\t')\n",
    "cepi = pd.read_csv('C Elegans dataset examples\\Version-1 datasets (no score threshold)\\CElegans Protein Interactors dataset_v1.tsv', sep='\\t')\n",
    "drgo = pd.read_csv('Drosophila datasets (validation experiment)\\Drosophila GO Terms.tsv', sep='\\t')\n",
    "drpi = pd.read_csv('Drosophila datasets (validation experiment)/Drosophila Protein Interactors .tsv', sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold  # maintains class proportions when creating folds\n",
    "def markFeatureAsLowFrequency(training_set, threshold):\n",
    "    features = []\n",
    "    if threshold > 0:\n",
    "        number_instances = training_set.shape[0]\n",
    "        for feature in training_set:\n",
    "            try:\n",
    "                mostFrequent = training_set[feature].value_counts(ascending=True)[0]\n",
    "            except:\n",
    "                mostFrequent = 0\n",
    "            if number_instances - mostFrequent < threshold:\n",
    "                features.append(feature)\n",
    "    return features\n",
    "def getFilteredFeatures(df, unusable_feature_threshold):\n",
    "    kf = StratifiedKFold(n_splits=10, shuffle=False)\n",
    "    fold = 0\n",
    "    allFoldFeatures = []\n",
    "    for train_index, test_index in kf.split(df.iloc[:, :-1], df.iloc[:, -1]):\n",
    "        fold = fold + 1\n",
    "        training_set = df.iloc[train_index, :]\n",
    "        lowFrequency = markFeatureAsLowFrequency(training_set, unusable_feature_threshold)\n",
    "        for low in lowFrequency:\n",
    "            if not (low in allFoldFeatures):\n",
    "                allFoldFeatures.append(low)\n",
    "        \n",
    "        print(f'{fold-1}, {len(lowFrequency)}, {len(allFoldFeatures)}')\n",
    "    return allFoldFeatures\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 1296, 1296\n",
      "1, 1306, 1308\n",
      "2, 1333, 1342\n",
      "3, 1325, 1367\n",
      "4, 1357, 1400\n",
      "5, 1345, 1412\n",
      "6, 1381, 1442\n",
      "7, 1414, 1486\n",
      "8, 1484, 1549\n",
      "9, 1884, 1913\n"
     ]
    }
   ],
   "source": [
    "filtered_go = getFilteredFeatures(cego, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "k | drop | cummulative\n",
    "--- | --- | ---\n",
    "0 | 1296 | 1296\n",
    "1 | 1306 | 1308\n",
    "2 | 1333 | 1342\n",
    "3 | 1325 | 1367\n",
    "4 | 1357 | 1400\n",
    "5 | 1345 | 1412\n",
    "6 | 1381 | 1442\n",
    "7 | 1414 | 1486\n",
    "8 | 1484 | 1549\n",
    "9 | 1884 | 1913"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 486, 486\n",
      "1, 227, 535\n",
      "2, 271, 658\n",
      "3, 231, 699\n",
      "4, 111, 703\n",
      "5, 182, 715\n",
      "6, 218, 730\n",
      "7, 257, 751\n",
      "8, 369, 791\n",
      "9, 196, 799\n"
     ]
    }
   ],
   "source": [
    "filtered_pi = getFilteredFeatures(cepi, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1913, 799)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(filtered_go), len(filtered_pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1120, 9001), (45, 9001), (1120, 5789), (45, 5789))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "remove = [\n",
    "    'Instance_Index',\n",
    "    'STITCH_Code',\n",
    "    'STITCH_Compound',\n",
    "    'STITCH Code',\n",
    "    'InteractorsList',\n",
    "    'InteractorsCount'\n",
    "]\n",
    "cepi.drop(columns=remove, errors='ignore',inplace=True)\n",
    "drpi.drop(columns=remove, errors='ignore',inplace=True)\n",
    "cego.drop(columns=remove, errors='ignore',inplace=True)\n",
    "drgo.drop(columns=remove, errors='ignore',inplace=True)\n",
    "\n",
    "(cego.shape, drgo.shape, cepi.shape, drpi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1120, 7088), (45, 7088), (1120, 4990), (45, 4990))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cepi.drop(columns=filtered_pi, errors='ignore',inplace=True)\n",
    "drpi.drop(columns=filtered_pi, errors='ignore',inplace=True)\n",
    "cego.drop(columns=filtered_go, errors='ignore',inplace=True)\n",
    "drgo.drop(columns=filtered_go, errors='ignore',inplace=True)\n",
    "\n",
    "(cego.shape, drgo.shape, cepi.shape, drpi.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6239.F07B10.6 = 0\n",
      "6239.F25C8.2a = 0\n",
      "6239.R13G10.2 = 0\n",
      "6239.T28F12.3 = 0\n",
      "6239.B0280.12b = 0\n",
      "6239.C06E1.4 = 0\n",
      "6239.K10D3.1 = 0\n",
      "6239.F33C8.1a = 0\n",
      "6239.F16B4.8 = 0\n",
      "6239.R05H5.2 = 0\n",
      "6239.F02E8.6 = 0\n",
      "6239.F09G8.4 = 0\n",
      "6239.F32A11.2 = 0\n",
      "6239.K10B3.10b = 0\n",
      "6239.K11C4.3c = 0\n",
      "6239.R31.1a = 0\n",
      "6239.C44E4.6.1 = 0\n",
      "6239.F44G4.8a = 0\n",
      "6239.Y105E8A.20b = 0\n",
      "6239.Y105E8A.28 = 0\n",
      "6239.F38E11.3 = 0\n",
      "6239.K07E3.8a = 0\n",
      "6239.Y45F10A.4 = 0\n",
      "6239.C54G4.6 = 0\n",
      "6239.F21C3.3 = 0\n",
      "6239.K02A11.3 = 0\n",
      "6239.Y19D10A.16 = 0\n",
      "6239.F55A11.11 = 0\n",
      "6239.C47D2.2 = 0\n",
      "6239.F49E8.4 = 0\n",
      "6239.R03A10.3 = 0\n",
      "6239.T04D3.3a = 0\n",
      "6239.C09F5.2a = 0\n",
      "6239.C47B2.6b = 0\n",
      "6239.Y56A3A.29a = 0\n",
      "6239.F47G4.6 = 0\n",
      "6239.W02D9.3 = 0\n",
      "6239.F47G4.3 = 0\n",
      "6239.B0207.12b = 0\n",
      "6239.F11A5.10 = 0\n",
      "6239.H35N03.1 = 0\n",
      "6239.R11G10.1a = 0\n",
      "6239.C47A4.2b = 0\n",
      "6239.F16H11.3 = 0\n",
      "6239.F36H2.2a = 0\n",
      "6239.F44D12.9c = 0\n",
      "6239.K02E11.1 = 0\n",
      "6239.ZK809.4a = 0\n",
      "6239.F18A1.5 = 0\n",
      "6239.H12C20.2a = 0\n",
      "6239.K08H10.4 = 0\n",
      "6239.R07B1.1 = 0\n",
      "6239.F56B3.5 = 0\n",
      "6239.T10F2.3 = 0\n",
      "6239.H27M09.1 = 0\n",
      "6239.R10E4.5d = 0\n",
      "6239.C01G8.2 = 0\n",
      "6239.D1046.3 = 0\n",
      "6239.F07B10.1a = 0\n",
      "6239.ZC190.1.2 = 0\n",
      "6239.Y6B3B.5b = 0\n",
      "6239.Y95B8A.10a = 0\n",
      "6239.ZK1128.8b = 0\n",
      "6239.C24A3.4 = 0\n",
      "6239.C05C12.3 = 0\n",
      "6239.F54D1.5 = 0\n",
      "6239.F54F2.1 = 0\n",
      "6239.R119.2 = 0\n",
      "6239.F55G1.9 = 0\n",
      "6239.M05B5.5a = 0\n",
      "6239.Y54E10A.14 = 0\n",
      "6239.K06A4.3 = 0\n",
      "6239.F41B4.4a = 0\n",
      "6239.W02A2.5 = 0\n",
      "6239.ZK867.2 = 0\n",
      "6239.ZK867.3 = 0\n",
      "6239.K04G2.8b = 0\n",
      "6239.F53B6.5 = 0\n",
      "6239.T06A4.1b = 0\n",
      "6239.T06A4.3a = 0\n",
      "6239.T27A8.3 = 0\n",
      "6239.Y18H1A.9 = 0\n",
      "6239.F39H11.2b = 0\n",
      "6239.K08D12.2 = 0\n",
      "6239.Y57G11C.11a = 0\n",
      "6239.C44B7.6a.1 = 0\n",
      "6239.F59B2.2 = 0\n",
      "6239.T16G12.6 = 0\n",
      "6239.T27A1.5a.2 = 0\n",
      "6239.B0365.3.1 = 0\n",
      "6239.C01G12.8 = 0\n",
      "6239.C02E7.1 = 0\n",
      "6239.C09H5.2a = 0\n",
      "6239.K11D9.2a = 0\n",
      "6239.Y105E8A.12a = 0\n",
      "6239.ZK256.1c = 0\n",
      "6239.F25H2.5.1 = 0\n",
      "6239.Y39H10A.3a = 0\n",
      "6239.Y48G1C.11 = 0\n",
      "6239.ZK945.9 = 0\n",
      "6239.F20B4.6 = 0\n",
      "6239.F48A11.1 = 0\n",
      "6239.F59G1.1b.2 = 0\n",
      "6239.T25G3.2.1 = 0\n",
      "6239.F08G5.2 = 0\n",
      "6239.F28B3.9 = 0\n",
      "6239.F55A11.5 = 0\n",
      "6239.F59F4.4 = 0\n",
      "6239.R07E3.5a = 0\n",
      "6239.T05H4.1 = 0\n",
      "6239.C10H11.8a = 0\n",
      "6239.W09C2.3a = 0\n",
      "6239.C27A7.5c = 0\n",
      "6239.F45E10.2b = 0\n",
      "6239.F52F12.1a = 0\n",
      "6239.Y46G5A.19b.2 = 0\n",
      "6239.Y17G7B.5a = 0\n",
      "6239.C01H6.5a = 0\n",
      "6239.C10F3.2 = 0\n",
      "6239.C18E3.2.1 = 0\n",
      "6239.ZK1128.5 = 0\n",
      "6239.C50C3.6 = 0\n",
      "6239.B0285.10 = 0\n",
      "6239.B0285.8 = 0\n",
      "6239.B0285.9 = 0\n",
      "6239.C28D4.2a = 0\n",
      "6239.C52B9.1a = 0\n",
      "6239.F22F7.5 = 0\n",
      "6239.F25G6.6 = 0\n",
      "6239.M02D8.4a = 0\n",
      "6239.VF36H2L.1 = 0\n",
      "6239.C01B10.8.2 = 0\n",
      "6239.F37C12.9.1 = 0\n",
      "6239.C18D1.4 = 0\n",
      "6239.C04F6.5 = 0\n",
      "6239.C06B3.4 = 0\n",
      "6239.C06B3.5 = 0\n",
      "6239.F11A5.12 = 0\n",
      "6239.F25G6.5 = 0\n",
      "6239.F56D1.5 = 0\n",
      "6239.T05F1.10 = 0\n",
      "6239.F13E6.5 = 0\n",
      "6239.T28D9.3d = 0\n",
      "6239.Y110A7A.16 = 0\n",
      "6239.Y38H6C.16 = 0\n",
      "6239.ZK84.2 = 0\n",
      "6239.C18A3.6a = 0\n",
      "6239.F18C12.1 = 0\n",
      "6239.F56H9.4a = 0\n",
      "6239.R144.13 = 0\n",
      "6239.T28F2.5 = 0\n",
      "6239.C06G3.7 = 0\n",
      "6239.T10F2.1a = 0\n",
      "6239.T01C3.6.1 = 0\n",
      "6239.ZK632.1a = 0\n",
      "6239.C47A10.5a = 0\n",
      "6239.F20H11.5 = 0\n",
      "6239.Y69A2AR.5 = 0\n",
      "6239.Y48C3A.14a = 0\n",
      "6239.C06G3.10 = 0\n",
      "6239.C33C12.3a = 0\n",
      "6239.C33C12.8 = 0\n",
      "6239.F11E6.1d = 0\n",
      "6239.T05H10.5b = 0\n",
      "6239.C07A9.7a = 0\n",
      "6239.D2092.3 = 0\n",
      "6239.F21F3.5 = 0\n",
      "6239.F33H2.7 = 0\n",
      "6239.K03B8.9.1 = 0\n",
      "6239.T22A3.4b = 0\n",
      "6239.ZC8.3 = 0\n",
      "6239.T05A8.4 = 0\n",
      "6239.T05E11.6 = 0\n",
      "6239.T28H10.3 = 0\n",
      "6239.C45G9.2 = 0\n",
      "6239.F36A2.2 = 0\n",
      "6239.T26G10.9 = 0\n",
      "6239.Y54E5A.6 = 0\n",
      "6239.F38A1.8 = 0\n",
      "6239.D1081.2 = 0\n",
      "6239.C03B1.13 = 0\n",
      "6239.C35A11.4 = 0\n",
      "6239.C53A3.2 = 0\n",
      "6239.F14E5.1 = 0\n",
      "6239.F44E7.2 = 0\n",
      "6239.K09H11.7 = 0\n",
      "6239.M01F1.5.1 = 0\n",
      "6239.Y110A2AL.13 = 0\n",
      "6239.Y113G7B.17.1 = 0\n",
      "6239.C06E1.10 = 0\n",
      "6239.F22B8.7 = 0\n",
      "6239.B0285.6 = 0\n",
      "6239.F31F6.6 = 0\n",
      "6239.F35B12.2 = 0\n",
      "6239.F55A12.4a = 0\n",
      "6239.F55E10.6 = 0\n",
      "6239.K08E5.2a = 0\n",
      "6239.B0035.2 = 0\n",
      "6239.F11G11.7 = 0\n",
      "6239.K07A12.7 = 0\n",
      "6239.T15H9.7a = 0\n",
      "6239.ZK652.9 = 0\n",
      "6239.Y43C5A.5a = 0\n",
      "6239.C54E4.5 = 0\n",
      "6239.F40F4.7 = 0\n",
      "6239.C44E4.4 = 0\n",
      "6239.E04F6.3 = 0\n",
      "6239.F33A8.5.1 = 0\n",
      "6239.H04M03.4 = 0\n",
      "6239.M03A8.1 = 0\n",
      "6239.B0272.4 = 0\n",
      "6239.C29F3.1 = 0\n",
      "6239.C32E8.9 = 0\n",
      "6239.F43H9.1 = 0\n",
      "6239.T08B2.7a = 0\n",
      "6239.Y105E8A.4 = 0\n",
      "6239.Y71D11A.3b = 0\n",
      "6239.C03F11.3 = 0\n",
      "6239.C48B4.2 = 0\n",
      "6239.F26F4.3 = 0\n",
      "6239.T05G5.5d = 0\n",
      "6239.Y116A8C.14 = 0\n",
      "6239.Y116A8C.16c = 0\n",
      "6239.T01E8.3 = 0\n",
      "6239.F41E6.4a = 0\n",
      "6239.F54F3.1a = 0\n",
      "6239.C52E4.2c = 0\n",
      "6239.F13G3.9 = 0\n",
      "6239.F35C8.6 = 0\n",
      "6239.K03E6.6 = 0\n",
      "6239.R10H10.5 = 0\n",
      "6239.T21F2.1a = 0\n",
      "6239.Y73B6BL.13 = 0\n",
      "6239.C06A1.5 = 0\n",
      "6239.C16A3.1c = 0\n",
      "6239.F33D11.4 = 0\n",
      "6239.C15A11.3 = 0\n",
      "6239.F56A8.1b = 0\n",
      "6239.T13H5.1c = 0\n",
      "6239.T28D6.9 = 0\n",
      "6239.K09F6.7 = 0\n",
      "6239.M05D6.7 = 0\n",
      "6239.Y71H2B.3 = 0\n",
      "6239.ZC395.3b = 0\n",
      "6239.F08A8.1a.2 = 0\n",
      "6239.F08A8.2 = 0\n",
      "6239.F08A8.3 = 0\n",
      "6239.F08A8.4.2 = 0\n",
      "6239.F59F4.1 = 0\n",
      "6239.Y23H5A.7a = 0\n",
      "6239.B0252.4b = 0\n",
      "6239.C34D4.12 = 0\n",
      "6239.D1009.2a = 0\n",
      "6239.F59E10.2 = 0\n",
      "6239.T27D1.1 = 0\n",
      "6239.Y17G7B.9 = 0\n",
      "6239.Y63D3A.4 = 0\n",
      "6239.C10F3.4b = 0\n",
      "6239.C34D4.4a = 0\n",
      "6239.W01F3.3b = 0\n",
      "6239.F44F1.3 = 0\n",
      "6239.LLC1.1 = 0\n",
      "6239.T04A8.16 = 0\n",
      "6239.W04A4.4 = 0\n",
      "6239.W05G11.4 = 0\n",
      "6239.Y47H10A.1 = 0\n",
      "6239.Y77E11A.10 = 0\n",
      "6239.C01G10.7 = 0\n",
      "6239.C15B12.1 = 0\n",
      "6239.C15B12.8 = 0\n",
      "6239.C17E4.3 = 0\n",
      "6239.Y37E3.17b = 0\n",
      "6239.C23H5.7 = 0\n",
      "6239.F34D6.3 = 0\n",
      "6239.F38E11.12 = 0\n",
      "6239.ZC84.2 = 0\n",
      "6239.C33G3.4 = 0\n",
      "6239.C47E12.8 = 0\n",
      "6239.C01G5.6 = 0\n",
      "6239.C02G6.2 = 0\n",
      "6239.C08B6.14 = 0\n",
      "6239.C28F5.4 = 0\n",
      "6239.F18E2.1a = 0\n",
      "6239.F21A3.11 = 0\n",
      "6239.F44E7.4a = 0\n",
      "6239.H25K10.1 = 0\n",
      "6239.Y105C5B.15 = 0\n",
      "6239.Y105C5B.3 = 0\n",
      "6239.Y70C5C.1 = 0\n",
      "6239.F11A10.8 = 0\n",
      "6239.F26E4.3.1 = 0\n",
      "6239.M01F1.7 = 0\n",
      "6239.Y54F10AR.1 = 0\n",
      "6239.Y71G12B.17 = 0\n",
      "6239.B0491.8a = 0\n",
      "6239.C07H4.2 = 0\n",
      "6239.E04F6.11a = 0\n",
      "6239.R07B7.1 = 0\n",
      "6239.T06F4.2b = 0\n",
      "6239.T27D12.2a = 0\n",
      "6239.ZC477.9a = 0\n",
      "6239.F53H8.4 = 0\n",
      "6239.H21P03.3b = 0\n",
      "6239.M01A10.2c = 0\n",
      "6239.C55B7.1 = 0\n",
      "6239.F13H8.12b = 0\n",
      "6239.M01D7.7a = 0\n",
      "6239.T28B8.5 = 0\n",
      "6239.Y43F8C.13 = 0\n",
      "6239.C17D12.1a = 0\n",
      "6239.C37H5.6a = 0\n",
      "6239.F09B12.2 = 0\n",
      "6239.F43C9.3 = 0\n",
      "6239.K02G10.1 = 0\n",
      "6239.R13F6.5 = 0\n",
      "6239.T05E11.4 = 0\n",
      "6239.T22E7.2 = 0\n",
      "6239.Y20F4.3 = 0\n",
      "6239.Y47H9C.2 = 0\n",
      "6239.F08C6.4a = 0\n",
      "6239.C13C4.4 = 0\n",
      "6239.C45E5.1 = 0\n",
      "6239.K08B12.3 = 0\n",
      "6239.M106.4b = 0\n",
      "6239.Y46G5A.4 = 0\n",
      "6239.Y55B1AL.3a = 0\n",
      "6239.Y71G12B.12a.2 = 0\n",
      "6239.F31E3.3 = 0\n",
      "6239.T12B3.3 = 0\n",
      "6239.C29H12.1 = 0\n",
      "6239.Y102A5A.1 = 0\n",
      "6239.Y17G7B.15a = 0\n",
      "6239.C06E2.3 = 0\n",
      "6239.C06E2.7 = 0\n",
      "6239.F56F11.4b = 0\n",
      "6239.F57B9.10a = 0\n",
      "6239.K02B12.1 = 0\n",
      "6239.Y49E10.1.1 = 0\n",
      "6239.F43G6.1a = 0\n",
      "6239.T03D8.3 = 0\n",
      "6239.B0331.2 = 0\n",
      "6239.C26H9A.1b = 0\n",
      "6239.C43F9.6 = 0\n",
      "6239.F35H10.4.1 = 0\n",
      "6239.F39C12.2b = 0\n",
      "6239.F57F5.4a.1 = 0\n",
      "6239.VW02B12L.1.1 = 0\n",
      "6239.W05H5.3 = 0\n",
      "6239.ZK637.8a = 0\n",
      "6239.F47F6.1b = 0\n",
      "6239.Y110A7A.13 = 0\n",
      "6239.ZK593.4 = 0\n",
      "6239.D1054.2.2 = 0\n",
      "6239.F14D12.4a = 0\n",
      "6239.F32A6.5c = 0\n",
      "6239.F41G4.3a = 0\n",
      "6239.F57H12.2 = 0\n",
      "6239.F10F2.2 = 0\n",
      "6239.T03F6.5 = 0\n",
      "6239.T14B4.2 = 0\n",
      "6239.C16A11.4a = 0\n",
      "6239.C41G7.4 = 0\n",
      "6239.C43E11.3a = 0\n",
      "6239.C44B9.4 = 0\n",
      "6239.C49F5.2 = 0\n",
      "6239.F01D4.5a = 0\n",
      "6239.F02D10.7 = 0\n",
      "6239.F34D6.4 = 0\n",
      "6239.K09F5.5 = 0\n",
      "6239.K12H6.11 = 0\n",
      "6239.R05D3.11 = 0\n",
      "6239.R11E3.4 = 0\n",
      "6239.T12D8.1 = 0\n",
      "6239.T12F5.4 = 0\n",
      "6239.T24C12.2 = 0\n",
      "6239.W01C8.3 = 0\n",
      "6239.Y24D9A.2 = 0\n",
      "6239.Y41D4B.12a = 0\n",
      "6239.Y43F4B.3 = 0\n",
      "6239.Y50D7A.2 = 0\n",
      "6239.C53B7.7 = 0\n",
      "6239.F12A10.4 = 0\n",
      "6239.F18A12.1 = 0\n",
      "6239.F18A12.4 = 0\n",
      "6239.F18A12.5 = 0\n",
      "6239.F18A12.6 = 0\n",
      "6239.F39E9.4 = 0\n",
      "6239.F45E4.7a = 0\n",
      "6239.F54F11.2a = 0\n",
      "6239.T06D4.4 = 0\n",
      "6239.T25B6.2 = 0\n",
      "6239.Y37E11B.4 = 0\n",
      "6239.ZK970.1b = 0\n",
      "6239.C14B1.4 = 0\n",
      "6239.F33D4.2a = 0\n",
      "6239.K04G11.4 = 0\n",
      "6239.ZC302.2a = 0\n",
      "6239.C28C12.9a.1 = 0\n",
      "6239.F21H11.3 = 0\n",
      "6239.W10C8.1 = 0\n",
      "6239.C15C7.7 = 0\n",
      "6239.T19A6.2a = 0\n",
      "6239.Y67D2.4a = 0\n",
      "6239.F01G10.2 = 0\n",
      "6239.F01G10.3 = 0\n",
      "6239.F14B4.3 = 0\n",
      "6239.F54C8.1 = 0\n",
      "6239.ZC373.5 = 0\n",
      "6239.C02C2.3 = 0\n",
      "6239.C15A7.1 = 0\n",
      "6239.C15A7.4 = 0\n",
      "6239.C31H5.3b = 0\n",
      "6239.C33G3.3 = 0\n",
      "6239.C35C5.5 = 0\n",
      "6239.C40C9.2 = 0\n",
      "6239.F08H9.6 = 0\n",
      "6239.F09E8.7a = 0\n",
      "6239.F11C7.1 = 0\n",
      "6239.F18G5.4 = 0\n",
      "6239.F21A3.7a = 0\n",
      "6239.F25G6.3a = 0\n",
      "6239.F28F8.1 = 0\n",
      "6239.F53E10.2b = 0\n",
      "6239.F58H7.3 = 0\n",
      "6239.F59B1.9 = 0\n",
      "6239.K03F8.2 = 0\n",
      "6239.K11G12.2 = 0\n",
      "6239.K11G12.7 = 0\n",
      "6239.R01E6.4 = 0\n",
      "6239.R03E1.3 = 0\n",
      "6239.R06A4.10c = 0\n",
      "6239.R13A5.4 = 0\n",
      "6239.T01H10.7 = 0\n",
      "6239.T05B4.1 = 0\n",
      "6239.T08G11.5.2 = 0\n",
      "6239.Y110A7A.3 = 0\n",
      "6239.BE0003N10.1 = 0\n",
      "6239.R13F6.9 = 0\n",
      "6239.ZK370.2 = 0\n",
      "6239.C07G1.5.1 = 0\n",
      "6239.C01H6.6.2 = 0\n",
      "6239.C24H11.6 = 0\n",
      "6239.C33D12.2 = 0\n",
      "6239.C52D10.12.1 = 0\n",
      "6239.R04E5.2 = 0\n",
      "6239.R13G10.4 = 0\n",
      "6239.ZC64.3a = 0\n",
      "6239.C29F7.6 = 0\n",
      "6239.C34B7.2 = 0\n",
      "6239.C47D12.8 = 0\n",
      "6239.F40A3.3a = 0\n",
      "6239.H13N06.4a = 0\n",
      "6239.Y38C1BA.2b = 0\n",
      "6239.B0212.4b = 0\n",
      "6239.C03H5.2 = 0\n",
      "6239.F44C8.7 = 0\n",
      "6239.F54E7.1a = 0\n",
      "6239.M02B1.1a = 0\n",
      "6239.ZC250.3 = 0\n",
      "6239.ZK896.9a = 0\n",
      "6239.K12H4.2 = 0\n",
      "6239.B0432.8.1 = 0\n",
      "6239.CD4.2 = 0\n",
      "6239.Y71G12B.15.2 = 0\n",
      "6239.B0285.4 = 0\n",
      "6239.D2089.5 = 0\n",
      "6239.ZK550.4.2 = 0\n",
      "6239.T10B11.3 = 0\n",
      "6239.T05G5.10a = 0\n",
      "6239.T18D3.3 = 0\n",
      "6239.Y105E8A.3 = 0\n",
      "6239.Y32H12A.7.1 = 0\n",
      "6239.Y39E4A.2b = 0\n",
      "6239.K02D10.5 = 0\n",
      "6239.T09B4.10.1 = 0\n",
      "6239.T14G12.2 = 0\n",
      "6239.Y22F5A.3b = 0\n",
      "6239.F15D4.1 = 0\n",
      "6239.F53H4.1 = 0\n",
      "6239.F53H4.6 = 0\n",
      "6239.T04G9.4 = 0\n",
      "6239.T28H10.1 = 0\n",
      "6239.ZC101.3 = 0\n",
      "6239.ZK899.8c = 0\n",
      "6239.C07G1.8 = 0\n",
      "6239.C34C6.5a = 0\n",
      "6239.DY3.5 = 0\n",
      "6239.Y34D9A.6 = 0\n",
      "6239.Y94H6A.7 = 0\n",
      "6239.ZC334.7 = 0\n",
      "6239.ZK121.1a = 0\n",
      "6239.C02A12.1 = 0\n",
      "6239.F10C2.4 = 0\n",
      "6239.F12F6.3.2 = 0\n",
      "6239.F37B1.4 = 0\n",
      "6239.T10B11.2 = 0\n",
      "6239.Y37B11A.2 = 0\n",
      "6239.Y45G12C.2 = 0\n",
      "6239.F49E2.1b = 0\n",
      "6239.C39B5.6 = 0\n",
      "6239.F28H1.3.3 = 0\n",
      "6239.C30H6.7a = 0\n",
      "6239.C34C6.6a = 0\n",
      "6239.F02E9.4b = 0\n",
      "6239.F27E11.3a = 0\n",
      "6239.F54D5.1a = 0\n",
      "6239.T23D8.1 = 0\n",
      "6239.T04A11.4 = 0\n",
      "6239.F33H2.5 = 0\n",
      "6239.B0035.3 = 0\n",
      "6239.C18E9.11b = 0\n",
      "6239.K02A4.1 = 0\n",
      "6239.W08D2.4a = 0\n",
      "6239.Y37A1B.12 = 0\n",
      "6239.Y37A1B.13 = 0\n",
      "6239.Y39E4A.3b = 0\n",
      "6239.Y43F4A.4 = 0\n",
      "6239.B0207.3b = 0\n",
      "6239.C55H1.2 = 0\n",
      "6239.DY3.7 = 0\n",
      "6239.E02C12.5 = 0\n",
      "6239.F18E2.5b = 0\n",
      "6239.F18G5.3 = 0\n",
      "6239.F35H12.3 = 0\n",
      "6239.F38E1.5 = 0\n",
      "6239.F48C11.1 = 0\n",
      "6239.F53B1.7 = 0\n",
      "6239.T07A9.7 = 0\n",
      "6239.ZK154.7 = 0\n",
      "6239.C32D5.12 = 0\n",
      "6239.F08F3.10 = 0\n",
      "6239.F08F3.4 = 0\n",
      "6239.F25H2.13.2 = 0\n",
      "6239.M03C11.2 = 0\n",
      "6239.R01H2.5 = 0\n",
      "6239.Y54G2A.50 = 0\n",
      "6239.C02D5.1 = 0\n",
      "6239.C37A2.3 = 0\n",
      "6239.T10E9.9 = 0\n",
      "6239.ZK792.8 = 0\n",
      "6239.F43G9.3 = 0\n",
      "6239.R13H4.4b = 0\n",
      "6239.Y48G8AL.15 = 0\n",
      "6239.C44E12.1 = 0\n",
      "6239.K12H4.7a = 0\n",
      "6239.Y48E1B.10 = 0\n",
      "6239.ZC168.1 = 0\n",
      "6239.ZK377.1 = 0\n",
      "6239.ZK546.11 = 0\n",
      "6239.C41D11.6 = 0\n",
      "6239.C44H9.4 = 0\n",
      "6239.EEED8.8 = 0\n",
      "6239.R03D7.2 = 0\n",
      "6239.Y44A6D.5 = 0\n",
      "6239.Y53F4B.10 = 0\n",
      "6239.C34G6.5 = 0\n",
      "6239.F37A4.8 = 0\n",
      "6239.K01A6.2a = 0\n",
      "6239.Y23H5B.6 = 0\n",
      "6239.D2023.5 = 0\n",
      "6239.R186.6 = 0\n",
      "6239.Y59H11AM.3 = 0\n",
      "6239.C44B7.10.1 = 0\n",
      "6239.F07F6.7 = 0\n",
      "6239.F40F9.6a = 0\n",
      "6239.F19F10.1 = 0\n",
      "6239.F19F10.5 = 0\n",
      "6239.T08H4.3 = 0\n",
      "6239.K04G11.5 = 0\n",
      "6239.R03E9.4 = 0\n",
      "6239.R02F2.9 = 0\n",
      "6239.T27A3.6 = 0\n",
      "6239.C33D12.6 = 0\n",
      "6239.D1037.4 = 0\n",
      "6239.H06I04.2 = 0\n",
      "6239.K02E10.1 = 0\n",
      "6239.T05H10.6b = 0\n",
      "6239.Y11D7A.4 = 0\n",
      "6239.C48D5.2a = 0\n",
      "6239.C50C3.7 = 0\n",
      "6239.C32F10.5 = 0\n",
      "6239.F13H10.3b = 0\n",
      "6239.K07E3.3 = 0\n",
      "6239.K10C2.4.1 = 0\n",
      "6239.Y51F10.4a = 0\n",
      "6239.B0303.9 = 0\n",
      "6239.T04C10.2b = 0\n",
      "6239.W06A7.3f = 0\n",
      "6239.C07A9.11 = 0\n",
      "6239.C07A9.4 = 0\n",
      "6239.C13D9.7 = 0\n",
      "6239.C13D9.8 = 0\n",
      "6239.C18B2.6 = 0\n",
      "6239.C24G7.1 = 0\n",
      "6239.C24G7.4 = 0\n",
      "6239.C41C4.5e.1 = 0\n",
      "6239.C50H2.3a = 0\n",
      "6239.E02H4.1 = 0\n",
      "6239.F02D10.5 = 0\n",
      "6239.F23B2.3 = 0\n",
      "6239.F25D1.4 = 0\n",
      "6239.F28A12.1 = 0\n",
      "6239.T28D9.7 = 0\n",
      "6239.T28F2.7 = 0\n",
      "6239.T28F4.2 = 0\n",
      "6239.Y97E10B.7a = 0\n",
      "6239.ZK770.1 = 0\n",
      "6239.Y65B4A.8.1 = 0\n",
      "6239.C56A3.8a = 0\n",
      "6239.F01F1.12a.1 = 0\n",
      "6239.F49H6.5 = 0\n",
      "6239.Y54G9A.6 = 0\n",
      "6239.ZC8.6 = 0\n",
      "6239.F11A10.7 = 0\n",
      "6239.F42A6.7d.1 = 0\n",
      "6239.F56D1.7 = 0\n",
      "6239.F57B9.3 = 0\n",
      "6239.R06C1.4 = 0\n",
      "6239.R09B3.2 = 0\n",
      "6239.T06A10.1a = 0\n",
      "6239.T07F10.3 = 0\n",
      "6239.ZK616.1.1 = 0\n",
      "6239.ZK616.61 = 0\n",
      "6239.B0457.1a = 0\n",
      "6239.C06A8.4 = 0\n",
      "6239.C42D4.18 = 0\n",
      "6239.C42D4.6 = 0\n",
      "6239.C52D10.6.1 = 0\n",
      "6239.C52D10.8 = 0\n",
      "6239.C52D10.9 = 0\n",
      "6239.F44G3.6.2 = 0\n",
      "6239.F46A9.4 = 0\n",
      "6239.F47H4.10 = 0\n",
      "6239.F54D10.1 = 0\n",
      "6239.F56B3.12 = 0\n",
      "6239.K08H2.1 = 0\n",
      "6239.R12H7.3 = 0\n",
      "6239.R12H7.5 = 0\n",
      "6239.Y105C5B.13 = 0\n",
      "6239.Y37H2C.2 = 0\n",
      "6239.Y47D7A.1 = 0\n",
      "6239.Y47D7A.8 = 0\n",
      "6239.Y60A3A.18 = 0\n",
      "6239.B0286.2a = 0\n",
      "6239.F26H9.7 = 0\n",
      "6239.Y54G2A.31 = 0\n",
      "6239.Y55B1BM.1a = 0\n",
      "6239.C09G4.4 = 0\n",
      "6239.C09G4.5 = 0\n",
      "6239.C05D11.1 = 0\n",
      "6239.C07A12.7a.1 = 0\n",
      "6239.F15C11.2a = 0\n",
      "6239.F19B6.2a = 0\n",
      "6239.K08H10.1k = 0\n",
      "6239.F23F1.1 = 0\n",
      "6239.M117.3 = 0\n",
      "6239.F37B1.1 = 0\n",
      "6239.F56A4.3 = 0\n",
      "6239.F56B3.10 = 0\n",
      "6239.R07B1.4 = 0\n",
      "6239.R13D7.7 = 0\n",
      "6239.Y45G12C.3 = 0\n",
      "6239.B0414.3 = 0\n",
      "6239.C18G1.5 = 0\n",
      "6239.C30G7.1 = 0\n",
      "6239.C52E4.5b = 0\n",
      "6239.D2030.1.1 = 0\n",
      "6239.F22F1.1 = 0\n",
      "6239.F59A7.4 = 0\n",
      "6239.M163.3 = 0\n",
      "6239.Y73B6BL.9a = 0\n",
      "6239.ZC101.2e = 0\n",
      "6239.F11A10.3a = 0\n",
      "6239.F21G4.1 = 0\n",
      "6239.F26A3.8a = 0\n",
      "6239.F53A2.5 = 0\n",
      "6239.B0280.17 = 0\n",
      "6239.F11G11.9 = 0\n",
      "6239.F53E4.1a = 0\n",
      "6239.H12D21.4 = 0\n",
      "6239.H12D21.5 = 0\n",
      "6239.T08D10.1 = 0\n",
      "6239.Y53H1A.2b = 0\n",
      "6239.Y53H1A.5a = 0\n",
      "6239.ZC395.7 = 0\n",
      "6239.C36F7.4e = 0\n",
      "6239.C01G8.5b = 0\n",
      "6239.F42A10.2b = 0\n",
      "6239.F38B7.1a = 0\n",
      "6239.T07A9.2 = 0\n",
      "6239.W03C9.7a.1 = 0\n",
      "6239.B0250.9 = 0\n",
      "6239.C15H9.5.2 = 0\n",
      "6239.Y97E10AL.3 = 0\n",
      "6239.ZK688.5a = 0\n",
      "6239.K10B3.6d.2 = 0\n",
      "6239.T05H10.7a = 0\n",
      "6239.T09B9.3 = 0\n",
      "6239.T22G5.1 = 0\n",
      "6239.ZC155.4 = 0\n",
      "6239.C36B1.1d = 0\n",
      "6239.E03A3.2 = 0\n",
      "6239.F18C5.2 = 0\n",
      "6239.F26E4.10a = 0\n",
      "6239.K02F3.12a = 0\n",
      "6239.K12H4.8 = 0\n",
      "6239.T04A11.6 = 0\n",
      "6239.W04D2.1a = 0\n",
      "6239.F37D6.6 = 0\n",
      "6239.F25B3.1 = 0\n",
      "6239.C02B10.6 = 0\n",
      "6239.F09G8.3 = 0\n",
      "6239.F36H12.10 = 0\n",
      "6239.K11G9.6 = 0\n",
      "6239.T08G5.10 = 0\n",
      "6239.Y105E8A.16.1 = 0\n",
      "6239.Y37D8A.18 = 0\n",
      "6239.ZK643.2 = 0\n",
      "6239.Y50D4C.6 = 0\n",
      "6239.4R79.1a = 0\n",
      "6239.M18.3 = 0\n",
      "6239.D1009.1a = 0\n",
      "6239.F28D1.9 = 0\n",
      "6239.T26A5.9.2 = 0\n",
      "6239.Y50E8A.6 = 0\n",
      "6239.F55A12.7 = 0\n",
      "6239.R160.1a = 0\n",
      "6239.C23G10.2a = 0\n",
      "6239.W03G11.3 = 0\n",
      "6239.Y48C3A.16 = 0\n",
      "6239.F01D4.4.1 = 0\n",
      "6239.T27A8.1 = 0\n",
      "6239.ZK909.4 = 0\n",
      "6239.T21C9.12 = 0\n",
      "6239.F59B2.5a = 0\n",
      "6239.H34C03.2 = 0\n",
      "6239.K08E7.7 = 0\n",
      "6239.K11D12.4 = 0\n",
      "6239.W01A11.5 = 0\n",
      "6239.Y48G9A.10 = 0\n",
      "6239.C52B9.8 = 0\n",
      "6239.F54G8.4 = 0\n",
      "6239.B0524.2 = 0\n",
      "6239.D1054.1 = 0\n",
      "6239.K10G9.1 = 0\n",
      "6239.T07A5.3 = 0\n",
      "6239.ZK54.1a = 0\n",
      "6239.C05D9.1.1 = 0\n",
      "6239.T04D1.4 = 0\n",
      "6239.C34E10.5.3 = 0\n",
      "6239.F35G12.1a = 0\n",
      "6239.F44B9.7 = 0\n",
      "6239.F44B9.8 = 0\n",
      "6239.F53H8.2 = 0\n",
      "6239.F07A5.3 = 0\n",
      "6239.F11C1.3 = 0\n",
      "6239.R07B1.3 = 0\n",
      "6239.C07E3.9 = 0\n",
      "6239.C25A1.9a = 0\n",
      "6239.F25B4.7 = 0\n",
      "6239.R07E3.4 = 0\n",
      "6239.T12A2.1 = 0\n",
      "6239.W02D3.6 = 0\n",
      "6239.Y44E3B.1a.1 = 0\n",
      "6239.C06A6.4b.1 = 0\n",
      "6239.C10C5.3 = 0\n",
      "6239.C10C5.4 = 0\n",
      "6239.C10C5.5 = 0\n",
      "6239.F32H2.4 = 0\n",
      "6239.F02E9.2a = 0\n",
      "6239.C36B1.3 = 0\n",
      "6239.D2085.4 = 0\n",
      "6239.Y39A1C.2 = 0\n",
      "6239.D2013.10 = 0\n",
      "6239.T07F10.4a = 0\n",
      "6239.Y71A12C.2 = 0\n",
      "6239.F57B10.3a = 0\n",
      "6239.F58F6.4 = 0\n",
      "6239.K03H1.2 = 0\n",
      "6239.Y67D2.6 = 0\n",
      "6239.F22E12.1 = 0\n",
      "6239.F23F12.3 = 0\n",
      "6239.F28F8.6.1 = 0\n",
      "6239.F09G2.8b = 0\n",
      "6239.T05C3.6a = 0\n",
      "6239.Y51A2D.13b = 0\n",
      "6239.C15F1.7a = 0\n",
      "6239.C18E3.8 = 0\n",
      "6239.M60.2.2 = 0\n",
      "6239.K02D7.4 = 0\n",
      "6239.W02B12.6a = 0\n",
      "6239.F09G2.4 = 0\n",
      "6239.F10B5.8 = 0\n",
      "6239.F19F10.12 = 0\n",
      "6239.T22G5.6 = 0\n",
      "6239.C31H2.1a = 0\n",
      "6239.F55C5.5 = 0\n",
      "6239.C56G3.1b = 0\n",
      "6239.H04M03.1 = 0\n",
      "6239.C18E9.6 = 0\n",
      "6239.F23B2.13 = 0\n",
      "6239.F26F12.7 = 0\n",
      "6239.F55H2.1b = 0\n",
      "6239.T14G8.1 = 0\n",
      "6239.C01B10.9 = 0\n",
      "6239.C27A12.9 = 0\n",
      "6239.F13H10.5 = 0\n",
      "6239.F43G6.5 = 0\n",
      "6239.T15H9.6 = 0\n",
      "6239.Y71G10AR.4 = 0\n",
      "6239.C27H6.2 = 0\n",
      "6239.Y111B2A.22a = 0\n",
      "6239.F09C3.2 = 0\n",
      "6239.F45E1.4 = 0\n",
      "6239.F58F9.1 = 0\n",
      "6239.Y110A7A.12 = 0\n",
      "6239.E04A4.4a = 0\n",
      "6239.T24D1.1b = 0\n",
      "6239.Y54G11A.9 = 0\n",
      "6239.C25F9.10a = 0\n",
      "6239.C25F9.15 = 0\n",
      "6239.C25F9.4 = 0\n",
      "6239.C25F9.9 = 0\n",
      "6239.F17A2.3 = 0\n",
      "6239.F54E12.2.1 = 0\n",
      "6239.K09A11.5 = 0\n",
      "6239.M04C3.5c = 0\n",
      "6239.T05A12.4a = 0\n",
      "6239.Y116A8C.13a = 0\n",
      "6239.F48F7.1b = 0\n",
      "6239.T07D3.7a = 0\n",
      "6239.T22B3.2b = 0\n",
      "6239.F52C9.3 = 0\n",
      "6239.R09H10.3a = 0\n",
      "6239.ZK697.8 = 0\n",
      "6239.C18A3.8 = 0\n",
      "6239.B0564.1a = 0\n",
      "6239.F44G4.1 = 0\n",
      "6239.Y6D11A.1 = 0\n",
      "6239.ZK856.1 = 0\n",
      "6239.Y25C1A.5 = 0\n",
      "6239.C30F12.7 = 0\n",
      "6239.C37E2.1.1 = 0\n",
      "6239.F35G12.2.2 = 0\n",
      "6239.D1005.3 = 0\n",
      "6239.F17A9.3 = 0\n",
      "6239.F23F12.9a = 0\n",
      "6239.Y51H4A.4 = 0\n",
      "6239.D1022.1a = 0\n",
      "6239.T28D9.2a = 0\n",
      "6239.Y65B4BR.4a = 0\n",
      "6239.Y39B6A.3b = 0\n",
      "6239.EGAP2.3.1 = 0\n",
      "6239.E01A2.5 = 0\n",
      "6239.F56H1.5 = 0\n",
      "6239.C41C4.6 = 0\n",
      "6239.Y38A8.3 = 0\n",
      "6239.F57B9.2 = 0\n",
      "6239.F40F4.3.1 = 0\n",
      "6239.F40F4.4a = 0\n",
      "6239.K04D7.4a = 0\n",
      "6239.T22G5.2 = 0\n",
      "6239.W02D3.7 = 0\n",
      "6239.Y6B3B.10 = 0\n",
      "6239.C04G2.6 = 0\n",
      "6239.F48E8.6 = 0\n",
      "6239.T10H9.4 = 0\n",
      "6239.D1025.2 = 0\n",
      "6239.F52B5.3 = 0\n",
      "6239.T07D4.5 = 0\n",
      "6239.D2024.7.1 = 0\n",
      "6239.F22D3.6 = 0\n",
      "6239.R10E11.3a.2 = 0\n",
      "6239.T27A3.2 = 0\n",
      "6239.F45E12.3 = 0\n",
      "6239.T01C3.7.1 = 0\n",
      "6239.Y108G3AL.1.2 = 0\n",
      "6239.ZK520.4a = 0\n",
      "6239.F36D4.5a = 0\n",
      "6239.F10G8.3.2 = 0\n",
      "6239.F01F1.9 = 0\n",
      "6239.C43C3.1 = 0\n",
      "6239.F25E2.4 = 0\n",
      "6239.F52E10.5 = 0\n",
      "6239.R04E5.10a = 0\n",
      "6239.F13E6.4 = 0\n",
      "6239.F45H7.6 = 0\n",
      "6239.Y92H12A.2 = 0\n",
      "6239.F55H12.5 = 0\n",
      "6239.B0252.2 = 0\n",
      "6239.B0511.13a = 0\n",
      "6239.R05D11.9 = 0\n",
      "6239.W03G1.7a = 0\n",
      "6239.C40C9.1 = 0\n",
      "6239.F36F2.5 = 0\n",
      "6239.K01D12.4b = 0\n",
      "6239.Y57A10A.10 = 0\n",
      "6239.Y57A10A.28 = 0\n",
      "6239.R03H10.6 = 0\n",
      "6239.R03H10.7 = 0\n",
      "6239.JC8.10b = 0\n",
      "6239.T25B9.10b = 0\n",
      "6239.Y17G7B.4 = 0\n",
      "6239.F54F2.2a.3 = 0\n",
      "6239.F19H6.4 = 0\n",
      "6239.F42F12.3 = 0\n",
      "6239.ZK1251.3 = 0\n",
      "6239.R12E2.10 = 0\n",
      "6239.C03D6.5 = 0\n",
      "6239.F10G7.3 = 0\n",
      "6239.H43I07.2 = 0\n",
      "6239.C08B11.7 = 0\n",
      "6239.T22D1.9.1 = 0\n",
      "6239.C04F12.9 = 0\n",
      "6239.F59A6.6c = 0\n",
      "6239.K12D12.5 = 0\n",
      "6239.T09F3.4 = 0\n",
      "6239.T09F3.5 = 0\n",
      "6239.ZK1290.6 = 0\n",
      "6239.B0207.1 = 0\n",
      "6239.B0244.2 = 0\n",
      "6239.B0280.11 = 0\n",
      "6239.C07E3.4 = 0\n",
      "6239.C15H7.3 = 0\n",
      "6239.C17H12.3.1 = 0\n",
      "6239.C17H12.5 = 0\n",
      "6239.C24D10.1 = 0\n",
      "6239.C29E6.3 = 0\n",
      "6239.C33F10.8 = 0\n",
      "6239.C55B7.3 = 0\n",
      "6239.F10G8.4b = 0\n",
      "6239.F20H11.4 = 0\n",
      "6239.F25H5.7 = 0\n",
      "6239.F35C11.2 = 0\n",
      "6239.F36H1.3 = 0\n",
      "6239.F38H4.4 = 0\n",
      "6239.F40G12.10 = 0\n",
      "6239.F47B3.1 = 0\n",
      "6239.F47B3.7 = 0\n",
      "6239.F54F12.1 = 0\n",
      "6239.F55F8.7 = 0\n",
      "6239.K07F5.6 = 0\n",
      "6239.K09F6.3 = 0\n",
      "6239.M05B5.1 = 0\n",
      "6239.R09E10.2 = 0\n",
      "6239.T04F3.3 = 0\n",
      "6239.T21E3.1 = 0\n",
      "6239.T22C1.8 = 0\n",
      "6239.T27A3.5 = 0\n",
      "6239.T28F4.3 = 0\n",
      "6239.W01B6.6 = 0\n",
      "6239.W03F11.4 = 0\n",
      "6239.Y113G7C.1 = 0\n",
      "6239.Y116A8C.37 = 0\n",
      "6239.Y18H1A.1 = 0\n",
      "6239.C01F1.3a = 0\n",
      "6239.F53B1.4 = 0\n",
      "6239.E02H1.3 = 0\n",
      "6239.F35B12.5a = 0\n",
      "6239.C04F12.4.2 = 0\n",
      "6239.C04G2.7 = 0\n",
      "6239.D2023.6 = 0\n",
      "6239.C56C10.8.1 = 0\n",
      "6239.F25C8.5 = 0\n",
      "6239.C11G6.1a = 0\n",
      "6239.D1022.7b.1 = 0\n",
      "6239.F13E6.3 = 0\n",
      "6239.F21G4.4 = 0\n",
      "6239.T06A10.4 = 0\n",
      "6239.Y116A8C.22 = 0\n",
      "6239.Y43H11AL.1b.1 = 0\n",
      "6239.Y51H1A.4 = 0\n",
      "6239.F08F8.8 = 0\n",
      "6239.Y48G9A.3 = 0\n",
      "6239.K08E3.4 = 0\n",
      "6239.C40H5.5a = 0\n",
      "6239.W02B12.2 = 0\n",
      "6239.F10B5.7 = 0\n",
      "6239.F44E2.6a = 0\n",
      "6239.C05D11.6b = 0\n",
      "6239.F15B10.2 = 0\n",
      "6239.C33H5.18b = 0\n",
      "6239.C02B8.4 = 0\n",
      "6239.D2005.5 = 0\n",
      "6239.T06D8.9.2 = 0\n",
      "6239.EEED8.6 = 0\n",
      "6239.F56H1.7 = 0\n",
      "6239.K07C11.1 = 0\n",
      "6239.B0344.2 = 0\n",
      "6239.C29F3.2 = 0\n",
      "6239.F46B3.5 = 0\n",
      "6239.F52E4.6 = 0\n",
      "6239.K02E2.2 = 0\n",
      "6239.T05C12.10 = 0\n",
      "6239.F37D6.1 = 0\n",
      "6239.F36F2.6 = 0\n",
      "6239.C18E3.7a = 0\n",
      "6239.T28B8.3a = 0\n",
      "6239.F26A3.3 = 0\n",
      "6239.F31E8.2b = 0\n",
      "6239.Y57G11C.43 = 0\n",
      "6239.Y54G2A.4 = 0\n",
      "6239.C44B7.7 = 0\n",
      "6239.F32D8.6 = 0\n",
      "6239.T06D8.8.1 = 0\n",
      "6239.Y116A8C.12 = 0\n",
      "6239.K09H9.6.2 = 0\n",
      "6239.W02B12.3a.1 = 0\n",
      "6239.F26E4.1.1 = 0\n",
      "6239.K06A5.7.2 = 0\n",
      "6239.ZK637.11.2 = 0\n",
      "6239.H19N07.4a.2 = 0\n",
      "6239.T07D4.3.2 = 0\n",
      "6239.R10E11.2.2 = 0\n",
      "6239.R10E11.8.1 = 0\n",
      "6239.Y38F2AL.4 = 0\n",
      "6239.DY3.4a = 0\n",
      "6239.C01B4.6 = 0\n",
      "6239.C28H8.11a.2 = 0\n",
      "6239.F40F11.1.1 = 0\n",
      "6239.F55F10.3 = 0\n",
      "6239.E01F3.1e = 0\n",
      "6239.B0238.11.1 = 0\n",
      "6239.F45E4.9.1 = 0\n",
      "6239.F47D12.4a.2 = 0\n",
      "6239.Y48B6A.14.2 = 0\n",
      "6239.Y62E10A.5a.2 = 0\n",
      "6239.C45B2.4a.2 = 0\n",
      "6239.F11H8.2 = 0\n",
      "6239.T21C12.1a = 0\n",
      "6239.Y57G11C.12b.4 = 0\n",
      "6239.C24H11.7 = 0\n",
      "6239.F29F11.1.2 = 0\n",
      "6239.T14F9.3.1 = 0\n",
      "6239.W01A11.6.2 = 0\n",
      "6239.B0228.7.2 = 0\n",
      "6239.K02D7.1.3 = 0\n",
      "6239.K12C11.1.2 = 0\n",
      "6239.M7.1.1 = 0\n",
      "6239.ZK112.6 = 0\n",
      "6239.B0491.7.1 = 0\n",
      "6239.C08B6.5 = 0\n",
      "6239.F59E12.8 = 0\n",
      "6239.T25E4.2 = 0\n",
      "6239.F09E5.3.2 = 0\n",
      "6239.T20G5.6.2 = 0\n",
      "6239.F02D8.4.1 = 0\n",
      "6239.Y116A8A.9b.1 = 0\n",
      "6239.Y37E11AL.7 = 0\n",
      "6239.F10E7.7.1 = 0\n",
      "6239.H32K16.1 = 0\n",
      "6239.Y38H6C.17 = 0\n",
      "6239.Y43F4B.7.2 = 0\n",
      "6239.C10C6.6.1 = 0\n",
      "6239.T24H7.5b = 0\n",
      "6239.Y48G1C.10 = 0\n",
      "6239.T06C12.10.2 = 0\n",
      "6239.F43E2.5.2 = 0\n",
      "6239.C01C10.3.1 = 0\n",
      "6239.F49H12.6a.2 = 0\n",
      "6239.K07B1.5b.1 = 0\n",
      "6239.M79.2 = 0\n",
      "6239.T06E8.1.2 = 0\n",
      "6239.Y38C1AA.1a.1 = 0\n",
      "6239.ZK40.1.1 = 0\n",
      "6239.R05C11.3.2 = 0\n",
      "6239.Y67D8C.10b.4 = 0\n",
      "6239.K05F1.6a.1 = 0\n",
      "6239.Y82E9BR.16a.1 = 0\n",
      "6239.Y18H1A.4.1 = 0\n",
      "6239.F21D5.5.1 = 0\n",
      "6239.Y48G8AL.1.1 = 0\n",
      "6239.B0361.10.3 = 0\n",
      "6239.T27A10.3a.2 = 0\n",
      "6239.T28A8.7.2 = 0\n",
      "6239.F33D11.12.3 = 0\n",
      "6239.T23F6.4.1 = 0\n",
      "6239.C17E4.11.1 = 0\n",
      "6239.C24A8.3 = 0\n",
      "6239.F52F12.9 = 0\n",
      "6239.W04D2.5.1 = 0\n",
      "6239.Y34B4A.7.1 = 0\n",
      "6239.Y53C10A.12.2 = 0\n",
      "6239.Y53C10A.3 = 0\n",
      "6239.R144.3b.2 = 0\n",
      "6239.ZK637.10.1 = 0\n",
      "6239.T12A2.15a = 0\n",
      "6239.Y46H3C.4 = 0\n",
      "6239.Y56A3A.27.2 = 0\n",
      "6239.C42D8.5a.1 = 0\n",
      "6239.F36D3.3 = 0\n",
      "6239.R06F6.4.2 = 0\n",
      "6239.F18A12.8a.1 = 0\n",
      "6239.ZK20.6.2 = 0\n",
      "6239.T26G10.8 = 0\n",
      "6239.Y37E11B.5.1 = 0\n",
      "6239.T13F2.2.2 = 0\n",
      "6239.F54D8.2.5 = 0\n",
      "6239.C03B1.7a = 0\n",
      "6239.K08F9.1 = 0\n",
      "6239.K07B1.2.2 = 0\n",
      "6239.F56A11.5 = 0\n",
      "6239.R107.1.1 = 0\n",
      "6239.C16A3.9.1 = 0\n",
      "6239.R02D3.5.2 = 0\n",
      "6239.C24G6.6.4 = 0\n",
      "6239.F09F7.4a.1 = 0\n",
      "6239.R06F6.9.2 = 0\n",
      "6239.T05G5.6.2 = 0\n",
      "6239.Y18D10A.20.2 = 0\n",
      "6239.Y56A3A.3.1 = 0\n",
      "6239.Y38A8.2.2 = 0\n",
      "6239.F32D1.5.1 = 0\n",
      "6239.C48B4.1.2 = 0\n",
      "6239.C53D5.6.1 = 0\n",
      "6239.F58F9.7.3 = 0\n",
      "6239.T01B7.4.1 = 0\n",
      "6239.W02A2.7.2 = 0\n",
      "6239.Y116A8C.34a.3 = 0\n",
      "6239.K07A1.12.2 = 0\n",
      "6239.D2096.4.1 = 0\n",
      "6239.F43G9.1.2 = 0\n",
      "6239.Y55F3AR.3 = 0\n",
      "6239.C06G4.2a.2 = 0\n",
      "6239.F47F6.9 = 0\n",
      "6239.H25P06.4 = 0\n",
      "6239.Y53H1B.6 = 0\n",
      "6239.F18E3.7a.2 = 0\n",
      "6239.F14H8.6c = 0\n",
      "6239.Y76B12C.1 = 0\n",
      "6239.D2013.8a.2 = 0\n",
      "6239.R06C7.3.3 = 0\n",
      "6239.C02G6.1 = 0\n",
      "6239.C08F8.4 = 0\n",
      "6239.C54G7.2.2 = 0\n",
      "6239.ZK550.1 = 0\n",
      "6239.C50F4.13.1 = 0\n",
      "6239.F53A2.7.1 = 0\n",
      "6239.Y71H2B.10a.1 = 0\n",
      "6239.B0414.6.2 = 0\n",
      "6239.C14C11.6.1 = 0\n",
      "6239.F13H8.3.2 = 0\n",
      "6239.K07C5.1.2 = 0\n",
      "6239.R13A1.4 = 0\n",
      "6239.T12F5.3.2 = 0\n",
      "6239.Y38A10A.6.2 = 0\n",
      "6239.Y54E10A.9c.2 = 0\n",
      "6239.Y71H2AM.19.1 = 0\n",
      "6239.ZC317.1 = 0\n",
      "6239.H32C10.1.1 = 0\n",
      "6239.Y54E2A.4 = 0\n",
      "6239.Y75B8A.14.1 = 0\n",
      "6239.F52B11.1a.2 = 0\n",
      "6239.C25D7.6.2 = 0\n",
      "6239.C30F8.2.3 = 0\n",
      "6239.F32D1.10.1 = 0\n",
      "6239.R10E4.4.2 = 0\n",
      "6239.Y39G10AR.14.2 = 0\n",
      "6239.ZK970.4.3 = 0\n",
      "6239.F26F4.10a.2 = 0\n",
      "6239.F07F6.4.2 = 0\n",
      "6239.ZK1248.3a.1 = 0\n",
      "6239.F40G9.3.1 = 0\n",
      "6239.K07D4.3.2 = 0\n",
      "6239.R12E2.3.2 = 0\n",
      "6239.C05D12.2 = 0\n",
      "6239.C17E4.9.3 = 0\n",
      "6239.F55F3.3.2 = 0\n",
      "6239.W09D10.1.2 = 0\n",
      "6239.K02B12.7.2 = 0\n",
      "6239.ZK1127.5.1 = 0\n",
      "6239.C16A11.3 = 0\n",
      "6239.T26A5.7a.1 = 0\n",
      "6239.W01C8.4b = 0\n",
      "6239.Y108F1.3 = 0\n",
      "6239.Y2H9A.1.2 = 0\n",
      "6239.Y50D7A.11 = 0\n",
      "6239.C49D10.10 = 0\n",
      "6239.F18A12.3 = 0\n",
      "6239.F20D12.7 = 0\n",
      "6239.F26G1.6 = 0\n",
      "6239.F40B5.1 = 0\n",
      "6239.F40B5.3a = 0\n",
      "6239.F41C6.4b = 0\n",
      "6239.K02F6.9 = 0\n",
      "6239.T06D4.3 = 0\n",
      "6239.T16A9.4.1 = 0\n",
      "6239.Y116A8C.4.1 = 0\n",
      "6239.Y116A8C.5 = 0\n",
      "6239.ZK1248.1 = 0\n",
      "6239.C01A2.3.1 = 0\n",
      "6239.F22F7.1a.1 = 0\n",
      "6239.F22F7.2.2 = 0\n",
      "6239.Y55F3AR.1.3 = 0\n",
      "6239.Y45F3A.3a = 0\n",
      "6239.Y106G6H.5.1 = 0\n",
      "6239.B0272.3.2 = 0\n",
      "6239.C26E6.4.2 = 0\n",
      "6239.F09F7.3.2 = 0\n",
      "6239.R09B5.6.1 = 0\n",
      "6239.B0491.4 = 0\n",
      "6239.C04C3.2 = 0\n",
      "6239.F15E6.2 = 0\n",
      "6239.F17E9.7 = 0\n",
      "6239.F17E9.8 = 0\n",
      "6239.F27B3.2 = 0\n",
      "6239.F48E3.7 = 0\n",
      "6239.T01H10.1 = 0\n",
      "6239.T01H10.2 = 0\n",
      "6239.T01H10.3 = 0\n",
      "6239.T01H10.5 = 0\n",
      "6239.T01H10.6 = 0\n",
      "6239.T26H10.1 = 0\n",
      "6239.Y105C5B.16 = 0\n",
      "6239.K01G5.4.1 = 0\n",
      "6239.M02F4.3 = 0\n",
      "6239.Y54E10BR.5.2 = 0\n",
      "6239.Y55F3AM.8 = 0\n",
      "6239.F18E9.5b.2 = 0\n",
      "6239.C06H2.3.1 = 0\n",
      "6239.K06H6.3 = 0\n",
      "6239.ZK370.7.1 = 0\n",
      "6239.B0491.5.2 = 0\n",
      "6239.F58A4.10.2 = 0\n",
      "6239.C15C7.1.2 = 0\n",
      "6239.F55A4.1.1 = 0\n",
      "6239.K10C8.3a.1 = 0\n",
      "6239.Y66D12A.9.1 = 0\n",
      "6239.F54C9.1.3 = 0\n",
      "6239.Y22F5A.2 = 0\n",
      "6239.T15B7.2.1 = 0\n",
      "6239.M18.5.1 = 0\n",
      "6239.K06H7.4.1 = 0\n",
      "6239.C14C10.5 = 0\n",
      "6239.F23B12.5.2 = 0\n",
      "6239.C50H2.1 = 0\n",
      "6239.F45D3.5.1 = 0\n",
      "6239.C34F6.8.1 = 0\n",
      "6239.F59B8.2.1 = 0\n",
      "6239.T04A11.1 = 0\n",
      "6239.T04A11.2 = 0\n",
      "6239.T04A11.5 = 0\n",
      "6239.T19D2.2.3 = 0\n",
      "6239.F22E12.4a.1 = 0\n",
      "6239.T13F2.1a.3 = 0\n",
      "6239.C26C6.2.2 = 0\n",
      "6239.Y95B8A.5.1 = 0\n",
      "6239.C50F4.16.1 = 0\n",
      "6239.F33H2.1.1 = 0\n",
      "6239.C17C3.12a.2 = 0\n",
      "6239.T25B9.9.2 = 0\n",
      "6239.Y87G2A.3 = 0\n",
      "6239.H06H21.10a = 0\n",
      "6239.R11A5.4a.2 = 0\n",
      "6239.C10G8.5a.1 = 0\n",
      "6239.H12D21.7.1 = 0\n",
      "6239.T13G4.4.2 = 0\n",
      "6239.Y106G6H.3.2 = 0\n",
      "6239.Y113G7A.4a.2 = 0\n",
      "6239.ZK686.1.2 = 0\n",
      "6239.C05C10.2b.2 = 0\n",
      "6239.C44H9.2 = 0\n",
      "6239.K08D10.5 = 0\n",
      "6239.R11H6.1.2 = 0\n",
      "6239.B0511.6.2 = 0\n",
      "6239.ZK945.2.2 = 0\n",
      "6239.Y59H11AM.2 = 0\n",
      "6239.ZK1320.9.2 = 0\n",
      "6239.F07F6.8.2 = 0\n",
      "6239.F11H8.1.1 = 0\n",
      "6239.K09A9.3.2 = 0\n",
      "6239.C55F2.1b.2 = 0\n",
      "6239.F52D1.1.2 = 0\n",
      "6239.K07A1.11.2 = 0\n",
      "6239.C37F5.1 = 0\n",
      "6239.C42D8.4 = 0\n",
      "6239.C50A2.4 = 0\n",
      "6239.C52B9.2b = 0\n",
      "6239.M02A10.2b = 0\n",
      "6239.F26D11.1.1 = 0\n",
      "6239.T23H2.5.1 = 0\n",
      "6239.B0412.4.1 = 0\n",
      "6239.ZC262.8 = 0\n",
      "6239.ZK688.3.1 = 0\n",
      "6239.C34E11.1.2 = 0\n",
      "6239.C27C12.5b = 0\n",
      "6239.F55G1.12 = 0\n",
      "6239.F58G6.8 = 0\n",
      "6239.Y57G11C.44a = 0\n",
      "6239.R05G6.7.2 = 0\n",
      "6239.T05D4.1.2 = 0\n",
      "6239.Y57A10C.6.1 = 0\n",
      "6239.C50B8.1.1 = 0\n",
      "6239.C53H9.2a.2 = 0\n",
      "6239.H28G03.1a.1 = 0\n",
      "6239.Y73B6BL.6b.8 = 0\n",
      "6239.F13D2.1 = 0\n",
      "6239.F52C6.12.3 = 0\n",
      "6239.T08D2.2 = 0\n",
      "6239.F38E9.5.2 = 0\n",
      "6239.D2013.7.2 = 0\n",
      "6239.F52D10.3a.2 = 0\n",
      "6239.M117.2.1 = 0\n",
      "6239.C35B1.1.1 = 0\n",
      "6239.F37B1.6 = 0\n",
      "6239.F55A11.6c = 0\n",
      "6239.F56A4.4 = 0\n",
      "6239.T25G3.4.2 = 0\n",
      "6239.F47E1.2.2 = 0\n",
      "6239.F47E1.4 = 0\n",
      "6239.F53B1.8 = 0\n",
      "6239.K02G10.5 = 0\n",
      "6239.Y32F6B.1 = 0\n",
      "6239.Y70G10A.3.1 = 0\n",
      "6239.C44B7.1.1 = 0\n",
      "6239.F45E12.2.2 = 0\n",
      "6239.F56H1.4.2 = 0\n",
      "6239.C37C3.2a.3 = 0\n",
      "6239.F23F1.9.1 = 0\n",
      "6239.T27F6.5a.2 = 0\n",
      "6239.Y41E3.4a.2 = 0\n",
      "6239.B0238.10.2 = 0\n",
      "6239.K07H8.3.2 = 0\n",
      "6239.D2096.12.2 = 0\n",
      "6239.F13H10.4a.2 = 0\n",
      "6239.T01E8.6.1 = 0\n",
      "6239.F23H11.9a.1 = 0\n",
      "6239.K11D2.3a.1 = 0\n",
      "6239.F10C2.6.1 = 0\n",
      "6239.F59A3.1.1 = 0\n",
      "6239.Y38F2AR.9.1 = 0\n",
      "6239.F36A2.13.2 = 0\n",
      "6239.F07A11.4 = 0\n",
      "6239.F09F3.9.1 = 0\n",
      "6239.C06H2.1.1 = 0\n",
      "6239.F58F12.1.1 = 0\n",
      "6239.T12D8.2.2 = 0\n",
      "6239.C05D11.7b.2 = 0\n",
      "6239.C38C10.2.2 = 0\n",
      "6239.F09E5.8.2 = 0\n",
      "6239.E01A2.10 = 0\n",
      "6239.E01A2.7 = 0\n",
      "6239.K05F6.11 = 0\n",
      "6239.C01G10.10 = 0\n",
      "6239.C38D4.9.1 = 0\n",
      "6239.F35B12.10 = 0\n",
      "6239.F36A2.3.2 = 0\n",
      "6239.VF13D12L.3 = 0\n",
      "6239.Y56A3A.2.1 = 0\n",
      "6239.C02A12.8 = 0\n",
      "6239.C43F9.10 = 0\n",
      "6239.C47E12.2.2 = 0\n",
      "6239.C49D10.4 = 0\n",
      "6239.K02B2.3.1 = 0\n",
      "6239.F33A8.3.1 = 0\n",
      "6239.F46F11.2.2 = 0\n",
      "6239.M01E11.5.2 = 0\n",
      "6239.R74.3a = 0\n",
      "6239.Y39A1C.3.2 = 0\n",
      "6239.Y55F3AM.2 = 0\n",
      "6239.F08F3.2a.2 = 0\n",
      "6239.K05C4.1.2 = 0\n",
      "6239.K08D12.1.1 = 0\n",
      "6239.F31D4.1.1 = 0\n",
      "6239.Y53F4B.9.2 = 0\n",
      "6239.C33G3.1b.2 = 0\n",
      "6239.C04H5.6 = 0\n",
      "6239.F56D2.6a.1 = 0\n",
      "6239.F23F12.13 = 0\n",
      "6239.E04F6.4 = 0\n",
      "6239.B0361.5b.1 = 0\n",
      "6239.VF13D12L.1.2 = 0\n",
      "6239.C23F12.1a = 0\n",
      "6239.C28H8.8 = 0\n",
      "6239.Y66H1B.2a.2 = 0\n",
      "6239.F55B12.4.3 = 0\n",
      "6239.F58G6.1.2 = 0\n",
      "6239.F25B5.5.1 = 0\n",
      "6239.Y67H2A.1.2 = 0\n",
      "6239.C14B9.6b = 0\n",
      "6239.T01D3.3b.1 = 0\n",
      "6239.F28C6.4a.3 = 0\n",
      "6239.T03G6.3.1 = 0\n",
      "6239.Y71G10AR.1 = 0\n",
      "6239.C08B6.9.2 = 0\n",
      "6239.C36A4.4.1 = 0\n",
      "6239.K06B9.2 = 0\n",
      "6239.C03C10.3.2 = 0\n",
      "6239.F19G12.2 = 0\n",
      "6239.Y67D8C.5 = 0\n",
      "6239.C52E4.4.2 = 0\n",
      "6239.F23F12.6.2 = 0\n",
      "6239.F29G9.5.1 = 0\n",
      "6239.C25F9.14 = 0\n",
      "6239.C25F9.5.2 = 0\n",
      "6239.F19B2.5.2 = 0\n",
      "6239.F59A7.8 = 0\n",
      "6239.M04C3.1a = 0\n",
      "6239.M04C3.2a.1 = 0\n",
      "6239.T22D1.6 = 0\n",
      "6239.Y113G7B.14 = 0\n",
      "6239.Y37D8A.9b.2 = 0\n",
      "6239.ZK757.3a.2 = 0\n",
      "6239.W06E11.4.1 = 0\n",
      "6239.Y45G12B.3.2 = 0\n",
      "6239.T02G5.13a.2 = 0\n",
      "6239.F40F4.2.1 = 0\n",
      "6239.W02D3.5.2 = 0\n",
      "6239.ZK742.5.2 = 0\n",
      "6239.F23H12.1.2 = 0\n",
      "6239.F52A8.5.1 = 0\n",
      "6239.F29C4.5 = 0\n",
      "6239.Y46G5A.1a.2 = 0\n",
      "6239.K04G7.10.1 = 0\n",
      "6239.F10C1.7a.1 = 0\n",
      "6239.F10C1.8a = 0\n",
      "6239.F37B4.2.1 = 0\n",
      "6239.W10G6.3.2 = 0\n",
      "6239.F59G1.5.1 = 0\n",
      "6239.C34D4.14 = 0\n",
      "6239.F55H12.2 = 0\n",
      "6239.ZK792.7 = 0\n",
      "6239.C47E12.5a.2 = 0\n",
      "6239.T03F1.1.1 = 0\n",
      "6239.C14A4.1.1 = 0\n",
      "6239.K07E8.3.2 = 0\n",
      "6239.ZK370.8.1 = 0\n",
      "6239.F42A9.2.1 = 0\n",
      "6239.F53F10.5.1 = 0\n",
      "6239.ZK20.5.1 = 0\n",
      "6239.T01G1.4.1 = 0\n",
      "6239.W06H3.2a.1 = 0\n",
      "6239.C53H9.1.2 = 0\n",
      "6239.K06B9.5 = 0\n",
      "6239.R02D3.1 = 0\n",
      "6239.T01H3.1.2 = 0\n",
      "6239.Y82E9BR.3.3 = 0\n",
      "6239.C50F4.7 = 0\n",
      "6239.F07B7.9 = 0\n",
      "6239.F17E9.12 = 0\n",
      "6239.F22B3.1 = 0\n",
      "6239.C06A5.5 = 0\n",
      "6239.Y111B2A.18.2 = 0\n",
      "6239.Y48G1BL.4 = 0\n",
      "6239.Y76G2A.1 = 0\n",
      "6239.F26F4.1.2 = 0\n",
      "6239.F45G2.1 = 0\n",
      "6239.T08B2.10.2 = 0\n",
      "6239.C50C3.9a.2 = 0\n",
      "6239.T24F1.6b = 0\n",
      "6239.R11A5.7.1 = 0\n",
      "6239.W01A8.6 = 0\n",
      "6239.F38E11.7 = 0\n",
      "6239.R08B4.1b.2 = 0\n",
      "6239.D1022.3 = 0\n",
      "6239.D1022.4 = 0\n",
      "6239.D1022.5 = 0\n",
      "6239.C33H5.16 = 0\n",
      "6239.C40A11.5 = 0\n",
      "6239.C46A5.1 = 0\n",
      "6239.F20B6.1 = 0\n",
      "6239.F20B6.6 = 0\n",
      "6239.F44F4.2.2 = 0\n",
      "6239.F47B3.2 = 0\n",
      "6239.F47B3.6 = 0\n",
      "6239.F54F11.3 = 0\n",
      "6239.F57H12.5 = 0\n",
      "6239.H06I04.5 = 0\n",
      "6239.K07F5.8 = 0\n",
      "6239.K09F6.4 = 0\n",
      "6239.M04G7.2 = 0\n",
      "6239.M05D6.3 = 0\n",
      "6239.T04F3.5 = 0\n",
      "6239.VW02B12L.2 = 0\n",
      "6239.R05D11.3.1 = 0\n",
      "6239.B0035.7 = 0\n",
      "6239.F07B7.10 = 0\n",
      "6239.F07B7.3 = 0\n",
      "6239.F08G2.2 = 0\n",
      "6239.F17E9.13 = 0\n",
      "6239.F35H10.1 = 0\n",
      "6239.F45F2.4 = 0\n",
      "6239.F35G12.12.1 = 0\n",
      "6239.F49C12.8.1 = 0\n"
     ]
    }
   ],
   "source": [
    "summation = []\n",
    "for feature in test_set:\n",
    "    summation.append(test_set[feature].sum())\n",
    "for i,x in enumerate(summation):\n",
    "    if x==0:\n",
    "        print(f'{test_set.columns[i]} = 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1120, 5789), (45, 5789))"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = aux.removeLowFrequencyFeatures(cepi, 10)\n",
    "training_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['6239.H13N06.6b', '6239.C41G7.9b', '6239.F15A8.5a', '6239.T07C4.8',\n",
       "       '6239.Y39G8B.5', '6239.F07B10.5', '6239.F07B10.6', '6239.ZC482.5',\n",
       "       '6239.C12C8.2a', '6239.ZK1127.10.1',\n",
       "       ...\n",
       "       '6239.T28F3.1a.2', '6239.F10G7.8.3', '6239.F35G12.12.1',\n",
       "       '6239.F49C12.8.1', '6239.T12G3.1a.1', '6239.EEED8.9.2',\n",
       "       '6239.F53H1.4a.1', '6239.C01F6.1.1', '6239.F52C12.6', 'Class'],\n",
       "      dtype='object', length=5789)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cepi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import detectshift as ds\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "random_seed=0\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "#DetectShift parameters\n",
    "task='class'\n",
    "test=.1 #test size fraction\n",
    "B=500 #number of permutations\n",
    "\n",
    "#Shift parameters\n",
    "gamma=1\n",
    "delta=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 1, 1, 1],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drpi.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GenData2(gamma, delta, n = 1000):\n",
    "    ys = np.random.binomial(1, .5, n)  \n",
    "    yt = np.random.binomial(1, .5 + delta, n)\n",
    "    Xs=[]\n",
    "    Xt=[]\n",
    "    for i in range(n):\n",
    "        Xs.append(np.random.normal(ys[i],1,3).tolist())\n",
    "        Xt.append(np.random.normal(yt[i]+gamma,1,3).tolist())  \n",
    "    Xs=np.array(Xs)\n",
    "    Xt=np.array(Xt)\n",
    "    Xs = Xs.reshape(n,-1)\n",
    "    Xt = Xt.reshape(n,-1)\n",
    "    ys = ys.reshape(-1)\n",
    "    yt = yt.reshape(-1)\n",
    "    return Xs, ys, Xt, yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1115</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Class\n",
       "0         1\n",
       "1         1\n",
       "2         1\n",
       "3         1\n",
       "4         1\n",
       "...     ...\n",
       "1115      0\n",
       "1116      1\n",
       "1117      1\n",
       "1118      0\n",
       "1119      1\n",
       "\n",
       "[1120 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cepi[['Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xs, ys, Xt, yt =  cepi.iloc[:,:-1], cepi[['Class']], drpi.iloc[:,:-1], drpi[['Class']], "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xs_train, Xs_test, ys_train, ys_test, Zs_train, Zs_test, \\\n",
    "Xt_train, Xt_test, yt_train, yt_test, Zt_train, Zt_test = ds.tools.prep_data(Xs, ys, Xt, yt, test=test, task=task, random_state=random_seed)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training classifiers to estimate the R-N derivative\n",
    "totshift_model = ds.tools.KL(boost=True)\n",
    "totshift_model.fit(Zs_train, Zt_train)\n",
    "covshift_model = ds.tools.KL(boost=True)\n",
    "covshift_model.fit(Xs_train, Xt_train)\n",
    "\n",
    "#Estimating the conditional distribution\n",
    "cd_model = ds.cdist.cde_class(boost=True)\n",
    "cd_model.fit(pd.concat([Xs_train, Xt_train], axis=0), \n",
    "             pd.concat([ys_train, yt_train], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating p-value for total shift...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:00<00:00, 26668.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating p-value for label shift...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/500 [00:00<?, ?it/s]c:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\detectshift\\tools.py:126: RuntimeWarning: divide by zero encountered in log\n",
      "  kts+=pt*np.log(pt/ps)\n",
      "c:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\detectshift\\tools.py:126: RuntimeWarning: invalid value encountered in scalar multiply\n",
      "  kts+=pt*np.log(pt/ps)\n",
      "100%|██████████| 500/500 [00:00<00:00, 12372.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating p-value for covariate shift...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:00<00:00, 22931.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating p-value for concept shift type 1...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [02:19<00:00,  3.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Calculating p-value for concept shift type 2...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 4/500 [00:03<06:29,  1.27it/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 5788 elements, new values have 5789 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[59], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m out \u001b[39m=\u001b[39m ds\u001b[39m.\u001b[39;49mtests\u001b[39m.\u001b[39;49mShiftDiagnostics(Xs_test, ys_test, Xt_test, yt_test,\n\u001b[0;32m      2\u001b[0m                                 totshift_model\u001b[39m=\u001b[39;49mtotshift_model, covshift_model\u001b[39m=\u001b[39;49mcovshift_model, labshift_model\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m      3\u001b[0m                                 cd_model\u001b[39m=\u001b[39;49mcd_model, task\u001b[39m=\u001b[39;49mtask, B\u001b[39m=\u001b[39;49mB, verbose\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\detectshift\\tests.py:298\u001b[0m, in \u001b[0;36mShiftDiagnostics\u001b[1;34m(Xs_test, ys_test, Xt_test, yt_test, totshift_model, covshift_model, labshift_model, cd_model, task, n_bins, B, verbose)\u001b[0m\n\u001b[0;32m    294\u001b[0m conc1\u001b[39m=\u001b[39mLocalPermut(Xs_test, ys_test, Xt_test, yt_test, \n\u001b[0;32m    295\u001b[0m                   totshift_model, labshift_model, task, n_bins, B\u001b[39m=\u001b[39mB, verbose \u001b[39m=\u001b[39m verbose)\n\u001b[0;32m    297\u001b[0m \u001b[39mif\u001b[39;00m verbose: \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39mCalculating p-value for concept shift type 2...\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 298\u001b[0m conc2\u001b[39m=\u001b[39mCondRand(Xs_test, ys_test, Xt_test, yt_test, \n\u001b[0;32m    299\u001b[0m                cd_model, totshift_model, covshift_model, B\u001b[39m=\u001b[39;49mB, verbose \u001b[39m=\u001b[39;49m verbose)\n\u001b[0;32m    301\u001b[0m \u001b[39mreturn\u001b[39;00m {\u001b[39m'\u001b[39m\u001b[39mtot\u001b[39m\u001b[39m'\u001b[39m:tot, \u001b[39m'\u001b[39m\u001b[39mlab\u001b[39m\u001b[39m'\u001b[39m:lab, \u001b[39m'\u001b[39m\u001b[39mcov\u001b[39m\u001b[39m'\u001b[39m:cov, \u001b[39m'\u001b[39m\u001b[39mconc1\u001b[39m\u001b[39m'\u001b[39m:conc1, \u001b[39m'\u001b[39m\u001b[39mconc2\u001b[39m\u001b[39m'\u001b[39m:conc2}\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\detectshift\\tests.py:236\u001b[0m, in \u001b[0;36mCondRand\u001b[1;34m(Xs, ys, Xt, yt, cd_model, totshift_model, covshift_model, B, verbose)\u001b[0m\n\u001b[0;32m    234\u001b[0m     yt \u001b[39m=\u001b[39m cd_model\u001b[39m.\u001b[39msample(Xt)    \n\u001b[0;32m    235\u001b[0m     Zt_new \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([Xt\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m), yt\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)  \n\u001b[1;32m--> 236\u001b[0m     Zt_new\u001b[39m.\u001b[39;49mcolumns \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(X_names)\u001b[39m+\u001b[39m\u001b[39mlist\u001b[39m(y_names) \n\u001b[0;32m    237\u001b[0m     totperm\u001b[39m.\u001b[39mappend(totshift_model\u001b[39m.\u001b[39mpredict(Zt_new))\n\u001b[0;32m    239\u001b[0m perm \u001b[39m=\u001b[39m (np\u001b[39m.\u001b[39marray(totperm) \u001b[39m-\u001b[39m np\u001b[39m.\u001b[39marray(covshift))\u001b[39m.\u001b[39mtolist()\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:6002\u001b[0m, in \u001b[0;36mNDFrame.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   6000\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   6001\u001b[0m     \u001b[39mobject\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__getattribute__\u001b[39m(\u001b[39mself\u001b[39m, name)\n\u001b[1;32m-> 6002\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__setattr__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name, value)\n\u001b[0;32m   6003\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[0;32m   6004\u001b[0m     \u001b[39mpass\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\pandas\\_libs\\properties.pyx:69\u001b[0m, in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\pandas\\core\\generic.py:730\u001b[0m, in \u001b[0;36mNDFrame._set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    725\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    726\u001b[0m \u001b[39mThis is called from the cython code when we set the `index` attribute\u001b[39;00m\n\u001b[0;32m    727\u001b[0m \u001b[39mdirectly, e.g. `series.index = [1, 2, 3]`.\u001b[39;00m\n\u001b[0;32m    728\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    729\u001b[0m labels \u001b[39m=\u001b[39m ensure_index(labels)\n\u001b[1;32m--> 730\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_mgr\u001b[39m.\u001b[39;49mset_axis(axis, labels)\n\u001b[0;32m    731\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clear_item_cache()\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:225\u001b[0m, in \u001b[0;36mBaseBlockManager.set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    223\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mset_axis\u001b[39m(\u001b[39mself\u001b[39m, axis: AxisInt, new_labels: Index) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    224\u001b[0m     \u001b[39m# Caller is responsible for ensuring we have an Index object.\u001b[39;00m\n\u001b[1;32m--> 225\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_set_axis(axis, new_labels)\n\u001b[0;32m    226\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes[axis] \u001b[39m=\u001b[39m new_labels\n",
      "File \u001b[1;32mc:\\Users\\Ewerton\\Documents\\github\\auto_filter\\.venv\\Lib\\site-packages\\pandas\\core\\internals\\base.py:70\u001b[0m, in \u001b[0;36mDataManager._validate_set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[39mpass\u001b[39;00m\n\u001b[0;32m     69\u001b[0m \u001b[39melif\u001b[39;00m new_len \u001b[39m!=\u001b[39m old_len:\n\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m     71\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mLength mismatch: Expected axis has \u001b[39m\u001b[39m{\u001b[39;00mold_len\u001b[39m}\u001b[39;00m\u001b[39m elements, new \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     72\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mvalues have \u001b[39m\u001b[39m{\u001b[39;00mnew_len\u001b[39m}\u001b[39;00m\u001b[39m elements\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     73\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 5788 elements, new values have 5789 elements"
     ]
    }
   ],
   "source": [
    "out = ds.tests.ShiftDiagnostics(Xs_test, ys_test, Xt_test, yt_test,\n",
    "                                totshift_model=totshift_model, covshift_model=covshift_model, labshift_model=None,\n",
    "                                cd_model=cd_model, task=task, B=B, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pval</th>\n",
       "      <th>kl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tot</th>\n",
       "      <td>0.001996</td>\n",
       "      <td>1.322811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lab</th>\n",
       "      <td>0.750499</td>\n",
       "      <td>0.000804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cov</th>\n",
       "      <td>0.001996</td>\n",
       "      <td>0.79148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conc1</th>\n",
       "      <td>0.001996</td>\n",
       "      <td>1.322007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conc2</th>\n",
       "      <td>0.005988</td>\n",
       "      <td>0.531331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           pval        kl\n",
       "tot    0.001996  1.322811\n",
       "lab    0.750499  0.000804\n",
       "cov    0.001996   0.79148\n",
       "conc1  0.001996  1.322007\n",
       "conc2  0.005988  0.531331"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(out).T.iloc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13212915413765"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kts=0\n",
    "ys = np.array([0,0,0,0,0,0,0])\n",
    "yt = np.array([0,0,0,0,0,0,1])\n",
    "Y=np.unique(ys)\n",
    "\n",
    "for y in Y:\n",
    "    pt=np.mean(np.array(yt).squeeze()==y)\n",
    "    ps=np.mean(np.array(ys).squeeze()==y)\n",
    "    kts+=pt*np.log(pt/ps)\n",
    "    \n",
    "kts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    0.774107\n",
       "1    0.225893\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cego.iloc[:,-1].value_counts()/len(cego)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    0.774107\n",
       "1    0.225893\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cepi.iloc[:,-1].value_counts()/len(cepi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    0.511111\n",
       "1    0.488889\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drgo.iloc[:,-1].value_counts()/len(drgo)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    0.511111\n",
       "1    0.488889\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drpi.iloc[:,-1].value_counts()/len(drpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1004"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cego.iloc[:,10].value_counts(ascending=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(cego.iloc[:,10].value_counts())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1120, 5794)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cepi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 5793)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drpi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 9005)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drgo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1294, 0)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_0(df):\n",
    "    count = 0\n",
    "    for c in df.columns:\n",
    "        if (df[c].sum() < 10):\n",
    "            count += 1 \n",
    "    return count\n",
    "\n",
    "get_0(cego),get_0(cepi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1120, 9001)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cego.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log2\n",
    "\n",
    "# calculate the kl divergence\n",
    "def kl_divergence(p, q):\n",
    "    val = []\n",
    "    min = 2**(-20)\n",
    "    for i in range(len(p)):\n",
    "        if p[i]==0:\n",
    "            p[i]=min\n",
    "        if q[i]==0:\n",
    "            q[i]=min\n",
    "        val.append(p[i] * np.log2(p[i]/q[i]))\n",
    "    return sum(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = cepi.iloc[:,0].value_counts().values/cepi.iloc[:,0].value_counts().values.sum()\n",
    "p2 = drpi.iloc[:,0].value_counts().values/drpi.iloc[:,0].value_counts().values.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>( p2 || p1 )</th>\n",
       "      <th>( p1 || p2 )</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4603</th>\n",
       "      <td>GO:0000188</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5248</th>\n",
       "      <td>GO:0071733</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>GO:0007257</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2444</th>\n",
       "      <td>GO:0036425</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4444</th>\n",
       "      <td>GO:2000582</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>GO:0008584</td>\n",
       "      <td>0.35343</td>\n",
       "      <td>0.250840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7900</th>\n",
       "      <td>GO:0046546</td>\n",
       "      <td>0.35343</td>\n",
       "      <td>0.250840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7903</th>\n",
       "      <td>GO:0051728</td>\n",
       "      <td>0.39158</td>\n",
       "      <td>0.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7904</th>\n",
       "      <td>GO:0060184</td>\n",
       "      <td>0.39158</td>\n",
       "      <td>0.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2846</th>\n",
       "      <td>GO:0051729</td>\n",
       "      <td>0.39158</td>\n",
       "      <td>0.252578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  ( p2 || p1 )  ( p1 || p2 )\n",
       "4603  GO:0000188       0.00000      0.000000\n",
       "5248  GO:0071733       0.00000      0.000000\n",
       "738   GO:0007257       0.00000      0.000000\n",
       "2444  GO:0036425       0.00000      0.000000\n",
       "4444  GO:2000582       0.00000      0.000000\n",
       "...          ...           ...           ...\n",
       "2832  GO:0008584       0.35343      0.250840\n",
       "7900  GO:0046546       0.35343      0.250840\n",
       "7903  GO:0051728       0.39158      0.252578\n",
       "7904  GO:0060184       0.39158      0.252578\n",
       "2846  GO:0051729       0.39158      0.252578\n",
       "\n",
       "[9000 rows x 3 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_instances = cego.shape[0]\n",
    "df2_instances = drgo.shape[0]\n",
    "result=[]\n",
    "\n",
    "for col in cego.columns[:-1]:\n",
    "    p1 = cego[col].value_counts().reindex([0,1], fill_value=0).values/df1_instances\n",
    "    p2 = drgo[col].value_counts().reindex([0,1], fill_value=0).values/df2_instances\n",
    "    \n",
    "    result.append([col, kl_divergence(p2,p1), kl_divergence(p1,p2)])\n",
    "\n",
    "_test = pd.DataFrame(result, columns=['feature', '( p2 || p1 )', '( p1 || p2 )'])\n",
    "_test.sort_values(by='( p2 || p1 )')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GO:0047748\n",
      "GO:0047749\n"
     ]
    }
   ],
   "source": [
    "for feature in _test[_test['( p2 || p1 )']==0]['feature']:\n",
    "    if cego[feature].value_counts().values.shape[0] > 1:\n",
    "        print(feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 3.020448485776899e-08,\n",
       " 4.1066544693714154e-08,\n",
       " 1.5370146335487358e-07,\n",
       " 3.257879758792833e-07,\n",
       " 5.526361208810313e-07,\n",
       " 6.667760663271109e-07,\n",
       " 1.02405345749968e-06,\n",
       " 1.1525102627079871e-06,\n",
       " 1.3988159442182265e-06,\n",
       " 1.7923793011405441e-06,\n",
       " 2.198904916983599e-06,\n",
       " 2.2061947189820803e-06,\n",
       " 4.130384469824009e-06,\n",
       " 4.641842513521078e-06,\n",
       " 4.871098597492626e-06,\n",
       " 6.419746007021478e-06,\n",
       " 6.564704264546556e-06,\n",
       " 7.921254034582575e-06,\n",
       " 8.273497487122725e-06,\n",
       " 8.416536981270886e-06,\n",
       " 8.891849857744312e-06,\n",
       " 1.2230999897772012e-05,\n",
       " 1.241421557806547e-05,\n",
       " 1.3737122313645534e-05,\n",
       " 1.3987058409021137e-05,\n",
       " 1.4440994665443872e-05,\n",
       " 1.4686738311292394e-05,\n",
       " 1.5957331139100986e-05,\n",
       " 1.6250895413795832e-05,\n",
       " 1.7344936139032912e-05,\n",
       " 1.8339049713922244e-05,\n",
       " 1.8358259243477816e-05,\n",
       " 1.9917198983294623e-05,\n",
       " 2.0990901718929804e-05,\n",
       " 2.141103877497199e-05,\n",
       " 2.1596985877841632e-05,\n",
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       " 0.016843510163414045,\n",
       " ...]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = sorted(_test['( p2 || p1 )'].unique())\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cego['GO:0047748']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(GO:0047749\n",
       " 0    896\n",
       " 1    224\n",
       " Name: count, dtype: int64,\n",
       " GO:0047749\n",
       " 0    36\n",
       " 1     9\n",
       " Name: count, dtype: int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'GO:0047749'\n",
    "cego[a].value_counts(),drgo[a].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 1 into shape (2,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m p2\u001b[39m.\u001b[39;49mreshape(\u001b[39m2\u001b[39;49m,)\n",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 1 into shape (2,)"
     ]
    }
   ],
   "source": [
    "p2.reshape(2,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.model_selection import StratifiedKFold  # maintains class proportions when creating folds\n",
    "\n",
    "\n",
    "kf = StratifiedKFold(n_splits=10, shuffle=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('GO:0005507', 'GO:0005507')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cego.columns[2],drgo.columns[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1120, 69)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cego.shape[0], cego['GO:0004500'].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drgo.shape[0], drgo['GO:0004500'].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.060085836909871244, 0.9399141630901288)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = cego.shape[0]+drgo.shape[0]\n",
    "positive = cego['GO:0004500'].sum() + drgo['GO:0004500'].sum()\n",
    "positive/total, (total-positive)/total\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import numpy as np\n",
    "def myChi2(obs):\n",
    "    f_exp = scipy.stats.contingency.expected_freq(obs.reshape(2,2)).ravel()\n",
    "    chi = scipy.stats.chisquare(f_obs=obs, f_exp=f_exp)\n",
    "    independency = scipy.stats.chi2_contingency(obs.reshape(2,2), correction=False).pvalue\n",
    "    return chi, independency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>statistic</th>\n",
       "      <th>dependency</th>\n",
       "      <th>independency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>GO:0047749</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>GO:0047748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>GO:0007611</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.989261e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4469</th>\n",
       "      <td>GO:0051051</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.989261e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>GO:0046189</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.987479e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>GO:0008584</td>\n",
       "      <td>31.246812</td>\n",
       "      <td>7.541708e-07</td>\n",
       "      <td>2.272203e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>GO:0046546</td>\n",
       "      <td>31.246812</td>\n",
       "      <td>7.541708e-07</td>\n",
       "      <td>2.272203e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>GO:0060184</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>GO:0051729</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>GO:0051728</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6818 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  statistic    dependency  independency\n",
       "1700  GO:0047749   0.000000  1.000000e+00  1.000000e+00\n",
       "1699  GO:0047748   0.000000  1.000000e+00  1.000000e+00\n",
       "684   GO:0007611   0.000002  1.000000e+00  9.989261e-01\n",
       "4469  GO:0051051   0.000002  1.000000e+00  9.989261e-01\n",
       "4174  GO:0046189   0.000002  1.000000e+00  9.987479e-01\n",
       "...          ...        ...           ...           ...\n",
       "2510  GO:0008584  31.246812  7.541708e-07  2.272203e-08\n",
       "6244  GO:0046546  31.246812  7.541708e-07  2.272203e-08\n",
       "6248  GO:0060184  38.395437  2.330857e-08  5.776730e-10\n",
       "2524  GO:0051729  38.395437  2.330857e-08  5.776730e-10\n",
       "6247  GO:0051728  38.395437  2.330857e-08  5.776730e-10\n",
       "\n",
       "[6818 rows x 4 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_instances = cego.shape[0]\n",
    "df2_instances = drgo.shape[0]\n",
    "result=[]\n",
    "\n",
    "for col in cego.columns[:-1]:\n",
    "    positive1 = cego[col].sum()\n",
    "    positive2 = drgo[col].sum()\n",
    "    if ((positive1 > 0) & (positive1 < df1_instances) & (positive2 > 0) & (positive2 < df2_instances)):\n",
    "        observed = np.array([[df1_instances-positive1, positive1],[df2_instances-positive2,positive2]])\n",
    "        chi,independency=myChi2(observed.ravel())\n",
    "        result.append([col, chi.statistic, chi.pvalue, independency])\n",
    "_test = pd.DataFrame(result, columns=['feature', 'statistic', 'dependency', 'independency'])\n",
    "_test.sort_values(by='statistic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>statistic</th>\n",
       "      <th>dependency</th>\n",
       "      <th>independency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>GO:0044325</td>\n",
       "      <td>6.635378</td>\n",
       "      <td>8.447374e-02</td>\n",
       "      <td>9.997300e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>GO:0040002</td>\n",
       "      <td>6.641753</td>\n",
       "      <td>8.423666e-02</td>\n",
       "      <td>9.961587e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>GO:1990498</td>\n",
       "      <td>6.656814</td>\n",
       "      <td>8.367910e-02</td>\n",
       "      <td>9.877734e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>GO:0000387</td>\n",
       "      <td>6.656814</td>\n",
       "      <td>8.367910e-02</td>\n",
       "      <td>9.877734e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5848</th>\n",
       "      <td>GO:0016863</td>\n",
       "      <td>6.656814</td>\n",
       "      <td>8.367910e-02</td>\n",
       "      <td>9.877734e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2510</th>\n",
       "      <td>GO:0008584</td>\n",
       "      <td>31.246812</td>\n",
       "      <td>7.541708e-07</td>\n",
       "      <td>2.272203e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6244</th>\n",
       "      <td>GO:0046546</td>\n",
       "      <td>31.246812</td>\n",
       "      <td>7.541708e-07</td>\n",
       "      <td>2.272203e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>GO:0051729</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6247</th>\n",
       "      <td>GO:0051728</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6248</th>\n",
       "      <td>GO:0060184</td>\n",
       "      <td>38.395437</td>\n",
       "      <td>2.330857e-08</td>\n",
       "      <td>5.776730e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>878 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  statistic    dependency  independency\n",
       "937   GO:0044325   6.635378  8.447374e-02  9.997300e-03\n",
       "605   GO:0040002   6.641753  8.423666e-02  9.961587e-03\n",
       "2183  GO:1990498   6.656814  8.367910e-02  9.877734e-03\n",
       "1788  GO:0000387   6.656814  8.367910e-02  9.877734e-03\n",
       "5848  GO:0016863   6.656814  8.367910e-02  9.877734e-03\n",
       "...          ...        ...           ...           ...\n",
       "2510  GO:0008584  31.246812  7.541708e-07  2.272203e-08\n",
       "6244  GO:0046546  31.246812  7.541708e-07  2.272203e-08\n",
       "2524  GO:0051729  38.395437  2.330857e-08  5.776730e-10\n",
       "6247  GO:0051728  38.395437  2.330857e-08  5.776730e-10\n",
       "6248  GO:0060184  38.395437  2.330857e-08  5.776730e-10\n",
       "\n",
       "[878 rows x 4 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_test[_test['independency']<0.01].sort_values(by='statistic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1052.70386266,   67.29613734],\n",
       "       [  42.29613734,    2.70386266]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats.contingency import expected_freq\n",
    "observed = np.array([[cego.shape[0]-cego['GO:0004500'].sum(), cego['GO:0004500'].sum()],[drgo.shape[0]-drgo['GO:0004500'].sum(), drgo['GO:0004500'].sum()]])\n",
    "expected_freq(observed)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=1.1882402295012373, pvalue=0.7558261502558508)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myChi2(observed.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=array([926.07214612,  66.05714286]), pvalue=array([2.10930069e-203, 4.38037542e-016]))"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chisquare(f_obs.reshape(2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chi2ContingencyResult(statistic=1.1882402295012373, pvalue=0.2756848104299716, dof=1, expected_freq=array([[1052.70386266,   67.29613734],\n",
       "       [  42.29613734,    2.70386266]]))"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "scipy.stats.chi2_contingency(observed, correction=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "_one = pd.read_csv(r'C:\\Users\\Ewerton\\Downloads\\7227.protein_chemical.links.v5.0.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(644408, 20087229)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(_one.chemical.unique()),len(_one.chemical),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[0,1,2],[3,4,5],[3,4,5]]).ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 4, 2)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observed.size,sum(observed.shape),observed.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=1.1882775657657683, pvalue=0.7558171869270677)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from scipy.stats import chisquare\n",
    "\n",
    "import numpy as np\n",
    "f_exp = np.array([1052.7039, 67.2961, 42.2961, 2.7039])\n",
    "f_obs = np.array([1051, 69, 44, 1])\n",
    "\n",
    "chisquare(f_obs=f_obs, f_exp=f_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1989.610371,  127.189629,   79.939629,    5.110371])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_exp"
   ]
  }
 ],
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