Application of Machine Learning in Metagenomics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12a5d3b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, f_classif, VarianceThreshold, mutual_info_classif, RFE\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc\n",
    "from sklearn.preprocessing import LabelEncoder, label_binarize\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dde3a9c4-e6b2-4d4f-9de9-913ba8b659b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reading data ##\n",
    "data = pd.read_csv('Data/abundance.txt', delimiter='\\t', low_memory=False).T\n",
    "\n",
    "## Labeling rows ##\n",
    "df_initial = pd.DataFrame(data)\n",
    "df_initial.columns = df_initial.iloc[0]\n",
    "df_initial = df_initial[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c207bf41",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Grouping deseases ##\n",
    "df_grouped_diseases = df_initial\n",
    "\n",
    "df_grouped_diseases['disease'] = df_grouped_diseases['disease'].replace(['nd', ' -', '-', 'n', -1, 'leaness'], 'not_diagnosed')\n",
    "df_grouped_diseases['disease'] = df_grouped_diseases['disease'].replace(['obese', 'overweight'], 'obesity')\n",
    "df_grouped_diseases['disease'] = df_grouped_diseases['disease'].replace(['large_adenoma', 'small_adenoma', 'ibd_crohn_disease', 'ibd_ulcerative_colitis', 'stec2-positive', 'adenoma'], 'intestinal diseases')\n",
    "df_grouped_diseases['disease'] = df_grouped_diseases['disease'].replace(['impaired_glucose_tolerance', 't2d'], 'diabetes')\n",
    "\n",
    "## Removing useless rows ##\n",
    "df_grouped_diseases = df_grouped_diseases[df_grouped_diseases['disease'] != 'y']\n",
    "df_grouped_diseases = df_grouped_diseases[df_grouped_diseases['disease'] != 'underweight']\n",
    "df_grouped_diseases = df_grouped_diseases[df_grouped_diseases['disease'] != 'n_relative']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34141e56-36ef-4e8c-8e16-17dc17765672",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Reducing number of not diagnosed patients ##\n",
    "df_reduced_not_diagnosed = df_grouped_diseases\n",
    "\n",
    "#How many not diagnosed patients to keep\n",
    "n = 250\n",
    "\n",
    "sampled_rows = df_reduced_not_diagnosed[df_reduced_not_diagnosed['disease'] == 'not_diagnosed'].sample(n)\n",
    "df_reduced_not_diagnosed = df_reduced_not_diagnosed[(df_reduced_not_diagnosed['disease'] != 'not_diagnosed') | df_reduced_not_diagnosed.index.isin(sampled_rows.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6c9366e-7682-4a7c-bf85-602e72df75ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Labeling target row ##\n",
    "labelEncoder = LabelEncoder()\n",
    "target = df_reduced_not_diagnosed['disease']\n",
    "targetEncoded = labelEncoder.fit_transform(target)\n",
    "\n",
    "## Saving desease names for later ##\n",
    "label_mapping = {i: val for i, val in enumerate(labelEncoder.classes_)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbcf65fa-27b9-4c34-b2a1-d4f4a31799ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing useless features ##\n",
    "df_reduced = df_reduced_not_diagnosed\n",
    "\n",
    "features_to_keep = ['age', 'gender', 'married', 'height']\n",
    "df_reduced = df_reduced[features_to_keep + list(df_reduced.columns[210:])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39d8f869",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Grouping and labeling null values ##\n",
    "df_fixedNA = df_reduced\n",
    "\n",
    "df_fixedNA = df_fixedNA.replace(['nd', 'na', '-', ' -'], np.nan)\n",
    "df_fixedNA = df_fixedNA.applymap(lambda x: float(x) if isinstance(x, (int, float, str)) and str(x).replace('.', '', 1).isdigit() else x)\n",
    "\n",
    "non_numeric_columns = df_fixedNA.select_dtypes(exclude=[np.number]).columns\n",
    "df_fixedNA[non_numeric_columns] = df_fixedNA[non_numeric_columns].applymap(lambda x: '-1' if pd.isna(x) else x)\n",
    "\n",
    "numeric_columns = df_fixedNA.select_dtypes(include=[np.number]).columns\n",
    "\n",
    "## ZAMJENJIVANJE NULL SA 0 U NUMERIČKIM STUPCIMA ##\n",
    "df_fixedNA[numeric_columns] = df_fixedNA[numeric_columns].applymap(lambda x: 0 if pd.isna(x) else x)\n",
    "\n",
    "## ZAMJENJIVANJE NULL SA SREDNJOM VRIJEDNOSTI U NUMERIČKIM STUPCIMA ##\n",
    "#for column in numeric_columns:\n",
    "#    mean_value = df_fixedNA[column].mean()\n",
    "#    df_fixedNA[column].fillna(mean_value, inplace=True)\n",
    "\n",
    "## BRISANJE STUPCA S NULL VRIJEDNOSTIMA ##\n",
    "#df_fixedNA = df_fixedNA.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbbb1c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Labeling non numeric features ##\n",
    "df_labeled = df_fixedNA\n",
    "\n",
    "for n in non_numeric_columns:\n",
    "    df_labeled[n] = df_labeled[n].astype(str)  \n",
    "    df_labeled[n] = labelEncoder.fit_transform(df_labeled[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fae77e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Micanje prekoreliranih stupaca ##\n",
    "df_removed_overcorrelated = df_labeled\n",
    "\n",
    "correlation_matrix = df_removed_overcorrelated.iloc[:, :210].corr().abs()\n",
    "\n",
    "correlation_threshold = 0.95\n",
    "upper = correlation_matrix.where(np.triu(np.ones(correlation_matrix.shape), k = 1).astype(bool))\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > correlation_threshold)]\n",
    "df_removed_overcorrelated = df_removed_overcorrelated.drop(columns=to_drop)\n",
    "\n",
    "df_removed_overcorrelated = df_removed_overcorrelated.iloc[:, 210:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d3634da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Clostridiales_noname',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Clostridiales_noname|g__Pseudoflavonifractor',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Clostridiales_noname|g__Pseudoflavonifractor|s__Pseudoflavonifractor_capillosus',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Clostridiales_noname|g__Pseudoflavonifractor|s__Pseudoflavonifractor_capillosus|t__GCF_000169255',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae|g__Eubacterium',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae|g__Eubacterium|s__Eubacterium_eligens',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae|g__Eubacterium|s__Eubacterium_eligens|t__GCF_000146185',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae|g__Eubacterium|s__Eubacterium_hallii',\n",
      "       'k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Eubacteriaceae|g__Eubacterium|s__Eubacterium_hallii|t__GCF_000173975',\n",
      "       ...\n",
      "       'k__Bacteria|p__Firmicutes|c__Negativicutes|o__Selenomonadales|f__Veillonellaceae|g__Megasphaera|s__Megasphaera_micronuciformis',\n",
      "       'k__Bacteria|p__Fusobacteria|c__Fusobacteriia|o__Fusobacteriales|f__Fusobacteriaceae|g__Fusobacterium|s__Fusobacterium_mortiferum',\n",
      "       'k__Bacteria|p__Fusobacteria|c__Fusobacteriia|o__Fusobacteriales|f__Fusobacteriaceae|g__Fusobacterium|s__Fusobacterium_mortiferum|t__GCF_000158195',\n",
      "       'k__Bacteria|p__Proteobacteria|c__Epsilonproteobacteria|o__Campylobacterales|f__Campylobacteraceae|g__Campylobacter|s__Campylobacter_concisus',\n",
      "       'k__Bacteria|p__Proteobacteria|c__Epsilonproteobacteria|o__Campylobacterales|f__Campylobacteraceae|g__Campylobacter|s__Campylobacter_concisus|t__GCF_000017725',\n",
      "       'k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales|f__Corynebacteriaceae|g__Corynebacterium|s__Corynebacterium_pseudogenitalium|t__GCF_000156615',\n",
      "       'k__Bacteria|p__Actinobacteria|c__Actinobacteria|o__Actinomycetales|f__Propionibacteriaceae|g__Propionibacterium|s__Propionibacterium_acnes',\n",
      "       'k__Bacteria|p__Firmicutes|c__Bacilli|o__Bacillales|f__Staphylococcaceae|g__Staphylococcus|s__Staphylococcus_epidermidis|t__Staphylococcus_epidermidis_unclassified',\n",
      "       'k__Bacteria|p__Fusobacteria|c__Fusobacteriia|o__Fusobacteriales|f__Leptotrichiaceae|g__Leptotrichia',\n",
      "       'k__Bacteria|p__Proteobacteria|c__Epsilonproteobacteria|o__Campylobacterales|f__Campylobacteraceae|g__Campylobacter|s__Campylobacter_gracilis'],\n",
      "      dtype='object', name='dataset_name', length=150)\n"
     ]
    }
   ],
   "source": [
    "## Feature selection ##\n",
    "df_feature_selection = df_removed_overcorrelated\n",
    "\n",
    "num_features_to_select = 150\n",
    "\n",
    "## Korelacijska metoda - prvi način ##\n",
    "#selector = SelectKBest(f_classif, k = num_features_to_select)\n",
    "\n",
    "## Info gain metoda - drugi način ##\n",
    "selector = SelectKBest(mutual_info_classif, k=num_features_to_select)\n",
    "\n",
    "selected_features = selector.fit_transform(df_feature_selection, targetEncoded)\n",
    "df_feature_selection = pd.DataFrame(data=selected_features, columns=df_feature_selection.columns[selector.get_support()])\n",
    "\n",
    "print(df_feature_selection.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "92fee735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Spliting data ##\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_feature_selection, targetEncoded, test_size = 0.2)\n",
    "\n",
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d3d01001",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## RF calssifier ##\n",
    "\n",
    "    model = RandomForestClassifier(n_estimators=500)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f387360",
   "metadata": {},
   "outputs": [],
   "source": [
    "## SVM classifier ## \n",
    "\n",
    "#from sklearn.svm import SVC\n",
    "\n",
    "#model = SVC(kernel = 'rbf', probability=True)\n",
    "#model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33fee78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7929515418502202"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "992b6170-351f-48e9-9554-6a3726db68d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "diseases = [label_mapping[i] for i in np.unique(targetEncoded)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "70515ad2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for cancer: 0.33 (2/6)\n",
      "Accuracy for cirrhosis: 0.81 (17/21)\n",
      "Accuracy for diabetes: 0.78 (42/54)\n",
      "Accuracy for intestinal diseases: 0.84 (46/55)\n",
      "Accuracy for not_diagnosed: 0.72 (33/46)\n",
      "Accuracy for obesity: 0.89 (40/45)\n"
     ]
    }
   ],
   "source": [
    "## Accuracy per desease ##\n",
    "\n",
    "for i, class_name in enumerate(np.unique(targetEncoded)):\n",
    "    indices = np.where(y_test == class_name)\n",
    "    accuracy = accuracy_score(y_test[indices], y_pred[indices])\n",
    "    correct = sum(y_test[indices] == y_pred[indices])\n",
    "    total = len(y_test[indices])\n",
    "    disease_name = diseases[i]\n",
    "    print(f'Accuracy for {disease_name}: {accuracy:.2f} ({correct}/{total})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cee80635-0a4b-482f-a619-0795407c8441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Gravical display of accuracy score ##\n",
    "\n",
    "accuracies = []\n",
    "\n",
    "for i, class_name in enumerate(np.unique(targetEncoded)):\n",
    "    indices = np.where(y_test == class_name)\n",
    "    accuracy = accuracy_score(y_test[indices], y_pred[indices])\n",
    "    accuracies.append(accuracy)\n",
    "    correct = sum(y_test[indices] == y_pred[indices])\n",
    "    total = len(y_test[indices])\n",
    "    disease_name = diseases[i]\n",
    "\n",
    "colors = plt.cm.Blues(np.array(accuracies))\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(diseases, accuracies, color=colors)\n",
    "plt.xlabel('Bolesti')\n",
    "plt.ylabel('Točnost')\n",
    "plt.title('Točnost po bolestima')\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "for bar, acc in zip(bars, accuracies):\n",
    "    plt.text(bar.get_x() + bar.get_width() / 2 - 0.1, bar.get_height() + 0.02, f'{acc:.2%}', fontsize=9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "091759d0-60de-4ed0-8d9a-24f22335829d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Confusion matrix ##\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "df_confusion_matrix = pd.DataFrame(cm, index=diseases, columns=diseases)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df_confusion_matrix, annot=True, cmap='Blues', fmt='g', vmax=50)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c037b5c-ef57-4ce2-92d6-0b1460c9cf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_score = model.predict_proba(x_test)\n",
    "\n",
    "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "for i, disease_name in enumerate(diseases):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    plt.plot(fpr[i], tpr[i], lw=2, label=f'{disease_name} (AUC = {roc_auc[i]:.2f})')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Krivulje po bolestima')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "90b9a739-3c54-4491-999b-9983d545a490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_score = model.predict_proba(x_test)\n",
    "\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "for i in range(len(np.unique(targetEncoded))):\n",
    "    fpr[i], tpr[i], _ = roc_curve((y_test == i).astype(int), y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(len(np.unique(targetEncoded)))]))\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "\n",
    "for i in range(len(np.unique(targetEncoded))):\n",
    "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "mean_tpr /= len(np.unique(targetEncoded))\n",
    "\n",
    "mean_tpr[0] = 0.0\n",
    "\n",
    "roc_auc_macro = auc(all_fpr, mean_tpr)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(all_fpr, mean_tpr, lw=2, label=f'ROC krivulja (AUC = {roc_auc_macro:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Krivulja za sve ')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2aa0384e-ffc1-4831-a8b0-992e78990d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [ 334  335  336  337  338  339  340  527  528  671  756  757  933  934\n",
      "  935  936  971  972 1045 1046 1048 1049 1050 1063 1064 1066 1099 1100\n",
      " 1106 1107 1124 1125 1148 1149 1150 1189 1195 1196 1218 1219 1220 1221\n",
      " 1233 1234 1235 1236 1237 1282 1283 1287 1288 1289 1293 1294 1295 1296\n",
      " 1299 1300 1301 1317 1318 1326 1333 1334 1351 1352 1353 1354 1355 1360\n",
      " 1361 1362 1363 1379 1380 1388 1389 1390 1391 1392 1393 1394 1395 1405\n",
      " 1406 1409 1410 1411 1412 1413 1414 1415 1426 1446 1447 1448 1449 1468\n",
      " 1469 1478 1490 1491 1492 1493 1499 1500 1503 1504 1516 1519 1543 1554\n",
      " 1555 1579 1580 1583 1584 1587 1588 1589 1590 1591 1611 1612 1613 1614\n",
      " 1619 1620 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1637\n",
      " 1638 1639 1640 1641 1655 1656 1671 1675 1678 1679 1682 1683 1690 1691\n",
      " 1700 1701 1730 1743 1754 1757 1758 1759 1760 1770 1771 1772 1773 1774\n",
      " 1775 1782 1783 1790 1791 1798 1801 1802 1817 1818 1852 1853 1872 1873\n",
      " 1874 1885 1886 1905 1906 1911 1912 1913 1916 1917 1918 1926 1927 1936\n",
      " 1937 1938 1939 1964 1965 1966 1967 1974 1975 1976 1977 1978 1981 1984\n",
      " 1985 1987 1989 1990 1992 1993 1994 1995 1998 1999 2000 2001 2002 2003\n",
      " 2004 2005 2006 2009 2010 2013 2014 2017 2018 2027 2028 2049 2050 2051\n",
      " 2054 2055 2056 2057 2058 2097 2098 2110 2111 2142 2149 2150 2151 2152\n",
      " 2159 2160 2161 2162 2163 2166 2167 2172 2173 2174 2182 2183 2186 2187\n",
      " 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2201 2202 2210 2233\n",
      " 2234 2251 2252 2253 2254 2255 2258 2259 2262 2263 2268 2269 2270 2271\n",
      " 2272 2273 2274 2275 2278 2279 2280 2281 2291 2292 2293 2294 2295 2296\n",
      " 2297 2298 2299 2304 2305 2308 2309 2310 2311 2312 2313 2314 2315 2316\n",
      " 2333 2334 2340 2341 2342 2345 2346 2347 2348 2351 2352 2353 2354 2355\n",
      " 2356 2357 2358 2359 2360 2361 2362 2364 2365 2366 2368 2369 2374 2375\n",
      " 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392\n",
      " 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2408 2409\n",
      " 2410 2411 2414 2419 2426 2427 2428 2429 2431 2432 2433 2434 2435 2436\n",
      " 2437 2438 2439 2440 2441 2442 2443 2444 2447 2448 2449 2450 2451 2452\n",
      " 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2469 2470\n",
      " 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484\n",
      " 2485 2486 2487 2488 2489 2490 2491 2494 2495 2496 2497 2504 2505 2506\n",
      " 2507 2510 2511 2515 2516 2517 2518 2520 2521 2522 2523 2524 2525 2526\n",
      " 2527 2528 2529 2530 2531 2532 2547 2548 2549 2550 2551 2552 2553 2554\n",
      " 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2569 2570\n",
      " 2571 2572 2575 2576 2577 2578 2580 2581 2587 2588 2590 2591 2593 2594\n",
      " 2595 2597 2600 2601 2602 2608 2609 2610 2611 2612 2613 2614 2618 2619\n",
      " 2620 2621 2622 2626 2627 2628 2629 2635 2636 2637 2638 2641 2642 2645\n",
      " 2646 2647 2648 2649 2650 2651 2652 2656 2657 2658 2661 2662 2663 2664\n",
      " 2665 2666 2667 2668 2669 2670 2671 2672 2675 2676 2679 2680 2685 2686\n",
      " 2687 2688 2689 2690 2691 2692 2698 2699 2703 2704 2705 2706 2707 2708\n",
      " 2713 2714 2715 2716 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728\n",
      " 2729 2738 2739 2740 2741 2746 2747 2749 2750 2753 2754 2755 2756 2757\n",
      " 2758 2759 2760 2761 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773\n",
      " 2774 2775 2776 2777 2778 2781 2782 2783 2784 2785 2786 2789 2790 2791\n",
      " 2792 2793 2794 2796 2797 2798 2799 2813 2814 2815 2816 2817 2818 2819\n",
      " 2820 2821 2822 2823 2824 2825 2826 2827 2828 2838 2839 2840 2841 2842\n",
      " 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856\n",
      " 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2871 2872\n",
      " 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886\n",
      " 2887 2888 2889 2890 2891 2892 2896 2897 2898 2899 2900 2901 2902 2903\n",
      " 2904 2905 2906 2907 2908 2915 2916 2917 2918 2931 2932 2933 2943 2944\n",
      " 2945 2946 2947 2952 2953 2954 2955 2956 2957 2960 2961 2962 2963 2964\n",
      " 2965 2966 2967 2981 2982 2983] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n",
      "/usr/lib64/python3.12/site-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in divide\n",
      "  f = msb / msw\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Uspješnost u ovisnosti o broju stupaca ##\n",
    "\n",
    "num_features_to_select = range(1, 251, 10)\n",
    "accuracies = []\n",
    "\n",
    "for num_features in num_features_to_select:\n",
    "    selector = SelectKBest(f_classif, k=num_features)\n",
    "    selected_features = selector.fit_transform(df_removed_overcorrelated, target)\n",
    "    x_train, x_test, y_train, y_test = train_test_split(selected_features, targetEncoded, test_size=0.2)\n",
    "\n",
    "    model = RandomForestClassifier(n_estimators=500)\n",
    "    model.fit(x_train, y_train)\n",
    "\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "plt.plot(num_features_to_select, accuracies)\n",
    "plt.xlabel(\"Broj odabranih značajki\")\n",
    "plt.ylabel(\"Točnost modela\")\n",
    "plt.title(\"Uspješnost modela ovisno o broju značajki\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63566a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/PRQKBZ577jmdOvDw8EDbtm0r7YI4efIkhgwZAmdnZ1haWkKpVGLMmDEoLi7GX3/9ZdD16Nq1K5RKJezt7fHEE0+gYcOG+Pbbb6XWH62vvvoK3bt3h52dHaysrKBUKrFq1SqcPXtW75h9+vSBvb299Njd3R1ubm7StTTmGmn/AntwgPrTTz8NW1tb7N27V2d7Veu3MkuWLMGxY8d0fnx8fJCTk4MjR45g+PDhsLOzk8pbWloiPDwcf//9N86fP69zrH/96186j3ft2oWioiKMGTNG532g0WjQq1cv6X3g5OSEpk2b4p133sGyZctw8uTJMrsQjWHo9bWyssJzzz2HrVu3Sl2IxcXFWLduHYYOHVrhBAdjPsuGDBmi87hNmzbIy8tDamqqUfGWxZjPn4qMGjVKp1vHz88PISEhZX7ePljXP/30E2xtbfVm6GrPp6L4H9bDfqZqW0cdHR2lz5vXXnsNGRkZUv1otWzZEm3bttXZNmrUKGRlZeHEiRPlvkZRURHeeustBAcHQ6VSwcrKCiqVChcuXCjzs+bB+IDK3xvaeirve9NUfvzxR2g0GkyYMMGo/fr37w8PDw+sWbNG2rZr1y5cv35d71gff/wxOnToAI1GI30u7927t8xrVdn3c3kSEhIwatQoeHh4SPXeq1cvAKi0Tu5nVgmZtqKLi4vLfL6oqEinezEqKgrvvvsuDh8+jP79+8PZ2Rl9+/Ytc9yLh4eH3ms5OztLTdFluXHjBm7dugWVSgWlUqnzk5KSIi3P0bRpU+zZswdubm74z3/+g6ZNm6Jp06Y64z+0HvxQ1jYP3717FwCkeDw9PfX29fLyQklJCW7evAmgtN967Nix+N///odu3brByckJY8aMQUpKSrnnZAgnJyedx9rpx+Vtz8vLMyr2mzdvoqSkRK9OAP16unHjBoQQcHd316uDw4cPl7tECgAkJSWhR48euHbtGj744APExcXh2LFjWL58OYB/rnll1q5di2PHjuGnn37CCy+8gLNnz+LZZ5/VKbN161aMGDEC3t7eWL9+PQ4dOoRjx45hwoQJ0vW5X1lfzmq1WorJmGuUkZEBKysruLq66mxXKBTw8PDQe49XtX4r06RJE3Tq1EnnR61W4+bNmxBClPu+0J7D/R4sq+2mfeSRR/TeB5s3b5beBwqFAnv37kW/fv2wdOlSdOjQAa6urpgyZUqVu1qMub7a+t60aROA0i+J5OTkSid/GPNZZshniDHvhwfPFTDs86ci5b1vH3xtGxsbvdlzGRkZ8PDw0Bun4+bmBisrqwrjf1gP85l69OhRhIWFAQA+++wz/Prrrzh27Bjmzp0LQP/zpqLf7YrOcfr06Zg3bx6efPJJfPfddzhy5AiOHTuGtm3bVvqZZuh7Q/tved+bppKWlgYvLy+d5N8QVlZWCA8Px7Zt26Ru/ZiYGHh6eqJfv35SuWXLluHFF19Ely5dsGXLFhw+fBjHjh3DE088Uea1qux3qyx37txBjx49cOTIEbzxxhvYt28fjh07hq1bt1a6r955GVyyBmjHAl27dk1vXJAQAsnJydKAaqC0UqZPn47p06fj1q1b2LNnD/773/+iX79+uHr1qs5f+ykpKTqtAkVFRcjIyKjwzeXi4gJnZ2fs3LmzzOfvb+Ho0aMHevTogeLiYvz222/46KOPEBkZCXd3dzzzzDMGXwNtPMnJyXrPXb9+HRYWFtJSCy4uLoiOjkZ0dDSSkpKwfft2zJkzB6mpqeXGXJ0MjV0IAYVCUeaH3IPbXFxcoFAoEBcXV+a4l4rGwnzzzTfIycnB1q1b4efnJ23XDu42VFBQkPS+69OnD4qLi/G///0PX3/9tfRX/Pr16+Hv74/NmzfrfJE8OAjZUA0bNjT4Gjk7O6OoqAhpaWk6H7RCCKSkpOhNIa9pDRs2hIWFRbnvC6C0nu/34Jex9vmvv/5apy7L4ufnh1WrVgEA/vrrL3z55ZdYsGABCgoK8PHHHxsdvzHXNzg4GJ07d8aaNWvwwgsvYM2aNfDy8pK+qMtjzGeZKeMta1/AsM+fipT3vn3w87asSUbOzs44cuSI9DmhlZqaiqKiIr33yv20rckP/t5V9Ifb/R7mM3XTpk1QKpX4/vvvdVq1v/nmmzLLV/S7XdH30vr16zFmzBi89dZbOtvT09PRoEGDCmM09L2hff3yvjcro9Fo9CaaaGO8n6urKw4cOICSkhKjk7Lx48fjnXfewaZNmzBy5Ehs374dkZGROi1c69evR+/evbFy5UqdfU05Du6nn37C9evXsW/fPqlVDIDBE2juZ1YtZI899hgUCgU2b96s99zOnTuRlZWlM1j2fg0aNMDw4cPxn//8B5mZmXqzbDZs2KDz+Msvv0RRUVGFC8EOGjQIGRkZKC4u1vvLv1OnTmUOnrS0tESXLl2kVpiKmp7L0qJFC3h7e2Pjxo06M3NycnKwZcsWaebTg3x9ffHSSy8hNDRU5zXvb3WpbobGrp3Bt3XrVp3Wl+zsbHz33Xc6xxw0aBCEELh27VqZddC6dety49F+mN+ftAkh8Nlnnz3UeS5duhQNGzbEa6+9JnWHKRQKqFQqnS+QlJSUMmdZGsKYa6SdmbZ+/Xqd7Vu2bEFOTo70vFxsbW3RpUsXbN26Vee9WFJSgvXr16NRo0Y6EyTK0q9fP1hZWeHSpUtlvg/u/0Ptfs2bN8err76K1q1bV/n3wtjrO378eBw5cgQHDhzAd999h7Fjxxq1JmFln2Wmjvd+Vf38edAXX3yhs/+VK1dw8OBBgxbe7tu3L+7cuaOXyKxdu1Z6vjzaGXynT5/W2b59+/ZKX/dBxn6mahePvr+u7969i3Xr1pV5/D///BOnTp3S2bZx40bY29vrzMgt63Ue/EP0hx9+wLVr1yo9J0PfG9p6Ku97szKNGzfGX3/9pZMYZ2Rk6MycBEq7HvPy8qq08G1QUBC6dOmCNWvWYOPGjcjPz9driS7rWp0+fdqoGcOVKet7BgA++eQTo49lVi1kTZs2xUsvvYR33nkHt27dwoABA2BtbY1jx47h7bffRqdOnTBq1Cip/ODBg6U1olxdXXHlyhVER0fDz88PAQEBOsfeunUrrKysEBoaKs2ybNu2LUaMGFFuPM888ww2bNiAAQMGYOrUqejcuTOUSiX+/vtv/Pzzzxg6dCieeuopfPzxx/jpp58wcOBA+Pr6Ii8vT5rxVF4CWR4LCwssXboUo0ePxqBBg/DCCy8gPz9fuiZvv/02AOD27dvo06cPRo0ahcDAQNjb2+PYsWPYuXMnhg0bJh2vdevW2Lp1K1auXImOHTvCwsKi3C+vh2Vo7ADw+uuv44knnkBoaCheeeUVFBcXY8mSJbC1tZVmxgBA9+7d8fzzz2P8+PH47bff0LNnT9ja2iI5ORkHDhxA69at8eKLL5YZT2hoKFQqFZ599lnMmjULeXl5WLlypUFdLhVp2LAhoqKiMGvWLGzcuBHPPfccBg0ahK1btyIiIgLDhw/H1atX8frrr8PT07PKq/obeo1CQ0PRr18/zJ49G1lZWejevbs0c6p9+/YIDw9/qPM1hcWLFyM0NBR9+vTBjBkzoFKpsGLFCvzxxx/44osvDFqOZdGiRZg7dy4SEhKksXw3btzA0aNHYWtri4ULF+L06dN46aWX8PTTTyMgIAAqlQo//fQTTp8+jTlz5kjHa926NTZt2oTNmzejSZMm0Gg05Sb3xl7fZ599FtOnT8ezzz6L/Px8gxafNeazrDIP834w5ne4IqmpqXjqqafw73//G7dv38b8+fOh0WikGaEVGTNmDJYvX46xY8fi8uXLaN26NQ4cOIC33noLAwYMqPAz9ZFHHkGLFi0wY8YMFBUVoWHDhti2bRsOHDhQ6es+7GfqwIEDsWzZMowaNQrPP/88MjIy8O6775bbiu/l5YUhQ4ZgwYIF8PT0xPr16xEbG4slS5ZUmPQOGjQIMTExCAwMRJs2bXD8+HG88847Bq2NZeh7IygoCM899xyio6OhVCrx+OOP448//sC7775r0AKt4eHh+OSTT/Dcc8/h3//+NzIyMrB06VK9fZ999lmsWbMGkydPxvnz59GnTx+UlJTgyJEjCAoKqrR3acKECXjhhRdw/fp1hISE6DWSDBo0CK+//jrmz5+PXr164fz581i0aBH8/f0NSiwNERISgoYNG2Ly5MmYP38+lEolNmzYoJdsG8Tg4f81pKSkRKxcuVJ06tRJ2NjYCJVKJQICAsTs2bN1ZmgJIcR7770nQkJChIuLi1CpVMLX11dMnDhRXL58WSqjnRFx/PhxMXjwYGFnZyfs7e3Fs88+K27cuKFzvF69eonevXvrbCssLBTvvvuuaNu2rdBoNMLOzk4EBgaKF154QZpBdujQIfHUU08JPz8/oVarhbOzs+jVq5fO7DTtLI533nlH75wBiPnz5+ts++abb0SXLl2ERqMRtra2om/fvuLXX3+Vns/LyxOTJ08Wbdq0EQ4ODsLa2lq0aNFCzJ8/X2emXGZmphg+fLho0KCBUCgUFc4OEaJ0NsvAgQPLjPE///mPzrbyzqmy2LW2b98u2rRpI9Xd22+/Xe4MltWrV4suXboIW1tbYW1tLZo2bSrGjBkjfvvtN6lMWbMsv/vuO6nuvL29xcyZM8WPP/5o0Iwr7YytY8eO6T139+5d4evrKwICAqRZOW+//bZo3LixUKvVIigoSHz22Wdlnk9Z11II/ZlExlyju3fvitmzZws/Pz+hVCqFp6enePHFF8XNmzf1XuNh6/dB2llWX331VYXl4uLixGOPPSbVYdeuXcV3332nU6aiay5E6XurT58+wsHBQajVauHn5yeGDx8u9uzZI4QQ4saNG2LcuHEiMDBQ2NraCjs7O9GmTRvx/vvv68yeunz5sggLCxP29vYCQKWz5gy9vlqjRo0SAET37t3LfP7Bujbms+zBWehlzSw0Nt4HGfo7/CDte2HdunViypQpwtXVVajVatGjRw+d31UhSn9fbW1tyzxORkaGmDx5svD09BRWVlbCz89PREVFiby8PJ1yfn5+Yty4cTrb/vrrLxEWFiYcHByEq6urePnll8UPP/xQ6e+8KT5TV69eLVq0aCHUarVo0qSJWLx4sVi1apVe/Wh/D7/++mvRsmVLoVKpROPGjcWyZct0YiprluXNmzfFxIkThZubm7CxsRGPPvqoiIuLK3dm44MMfW/k5+eLV155Rbi5uQmNRiO6du0qDh06pPfeLW8G6+effy6CgoKERqMRwcHBYvPmzWV+Rt+9e1e89tprIiAgQKhUKuHs7Cwee+wxcfDgQZ3r9eBnoxBC3L59W1hbW5c7UzM/P1/MmDFDeHt7C41GIzp06CC++eYbvTiM+X4u6/P34MGDolu3bsLGxka4urqKSZMmiRMnTlQ6Q/ZBinsvWGctWLAACxcuRFpaWoVjD4DS1YCbNm1aY/dhIyKiqnNycsKECRPKXTCZyNSmTZuGdevWGTwu0Rhm1WUpl7/++gtxcXH4/fff8dxzz8kdDhERVeD06dPYsWMHbt68iW7duskdDtUDqampOHToELZu3Vpt7zkmZCgd3/Ldd99hzJgxiIiIkDscIiKqwNSpU3Hu3DnMmDFDZ3wXUXXZsWMHXnrpJXTt2rXMJa1Moc53WRIRERGZO7Na9oKIiIioPmJCRkRERCQzJmREREREMuOg/jKUlJTg+vXrsLe3r3TBSiIiIjIPQghkZ2dX6R6ZcmNCVobr16/Dx8dH7jCIiIioCq5evWrQ3QvMCROyMmhvGn716lWDbhNhqMLCQuzevRthYWFQKpUmOy5VHevEvLA+zAvrw7ywPiqXlZUFHx8f6Xu8NmFCVgZtN6WDg4PJEzIbGxs4ODjwl8lMsE7MC+vDvLA+zAvrw3C1cbhR7epgJSIiIqqDmJARERERyYwJGREREZHMmJARERERyYwJGREREZHMmJARERERyYwJGREREZHMZE/IVqxYAX9/f2g0GnTs2BFxcXEVlt+wYQPatm0LGxsbeHp6Yvz48cjIyNAps2XLFgQHB0OtViM4OBjbtm2rzlMgIiIieiiyJmSbN29GZGQk5s6di5MnT6JHjx7o378/kpKSyix/4MABjBkzBhMnTsSff/6Jr776CseOHcOkSZOkMocOHcLIkSMRHh6OU6dOITw8HCNGjMCRI0dq6rSIiIiIjCLrSv3Lli3DxIkTpYQqOjoau3btwsqVK7F48WK98ocPH0bjxo0xZcoUAIC/vz9eeOEFLF26VCoTHR2N0NBQREVFAQCioqKwf/9+REdH44svvigzjvz8fOTn50uPs7KyAJSuilxYWGiak713vPv/JfmxTswL68O8sD7MC+ujcrX52siWkBUUFOD48eOYM2eOzvawsDAcPHiwzH1CQkIwd+5c7NixA/3790dqaiq+/vprDBw4UCpz6NAhTJs2TWe/fv36ITo6utxYFi9ejIULF+pt3717N2xsbIw4K8PExsaa/Jj0cFgn5oX1YV5YH+aF9VG+3NxcuUOoMtkSsvT0dBQXF8Pd3V1nu7u7O1JSUsrcJyQkBBs2bMDIkSORl5eHoqIiDBkyBB999JFUJiUlxahjAqWtaNOnT5cea29OGhYWZvJ7WcbGxiI0NJT3ITMTrBPzwvowL6wP88L6qJy2h6s2kv3m4g/eAFQIUe5NQc+cOYMpU6bgtddeQ79+/ZCcnIyZM2di8uTJWLVqVZWOCQBqtRpqtVpvu1KprJY3fXUdtyZ9eewqsvIKMbSdN1zt9a9dbVMX6qQuYX2YF9aHeWF9lK82XxfZEjIXFxdYWlrqtVylpqbqtXBpLV68GN27d8fMmTMBAG3atIGtrS169OiBN954A56envDw8DDqmGS8q5m5mLXlNABgyc5z6NfSA6O7+KFrE6cKE18iIiIqm2yzLFUqFTp27KjXFx4bG4uQkJAy98nNzYWFhW7IlpaWAEpbwQCgW7duesfcvXt3ucck411IzQYAWFooUFgs8P3pZDz72WH0XbYfqw4k4lZugcwREhER1S6ydllOnz4d4eHh6NSpE7p164ZPP/0USUlJmDx5MoDSsV3Xrl3D2rVrAQCDBw/Gv//9b6xcuVLqsoyMjETnzp3h5eUFAJg6dSp69uyJJUuWYOjQofj222+xZ88eHDhwQLbzrGsS0nIAAE+09MCLvZti49EkfHPyGhLScvD692ewdOc5DGrjhdFdfdHepwFbzYiIiCoha0I2cuRIZGRkYNGiRUhOTkarVq2wY8cO+Pn5AQCSk5N11iQbN24csrOz8X//93945ZVX0KBBAzz22GNYsmSJVCYkJASbNm3Cq6++innz5qFp06bYvHkzunTpUuPnV1clppcmZP4utmjl7Yi3nmqNqP6B+Db+OtYfvoJzKdnYcuJvbDnxN4I8HTC6iy+ebO8NO7XsQxaJiIjMkuzfkBEREYiIiCjzuZiYGL1tL7/8Ml5++eUKjzl8+HAMHz7cFOFRGe5PyLTsNUo819UPo7v44uTVW9hwOAnfn76Os8lZePWbP7B4x1kMbe+N0V180dLLUa7QiYiIzJLsCRnVPlJC5mqr95xCoUAH34bo4NsQ8wYFYcuJa9hw5AoS0nKw8UgSNh5JQjufBniuqx8GtfGERmlZ0+ETERGZHSZkZJTcgiIk384DADRx0U/I7tfARoWJj/pjQvfGOJSQgQ1HkrDrjxTEX72F+Ku3sOi7PzG8ow9GdfFFMze7mgifiIjILDEhI6NoW8ecbFVoYKMyaB+FQoGQpi4IaeqCtOx8fPnbVXxxNAl/37yL1b8mYvWviejaxAmju/ihX0sPqKxkv+c9ERFRjWJCRkYpa/yYMVzt1fhPn2aY3KspfrmQhg2Hk/DTuRs4nJCJwwmZcLFT4elOPhjV2Rc+Tqa/bRUREZE5YkJGRklMe7iETMvSQoE+LdzQp4Ubrt+6i03HrmLT0SSkZudj5b5L+Hj/JfQMcMXoLr54LNANVpZsNauLhBDIuluEG9l5uJGVh+s3c3A6VYGOWXlo5Fx7V9wmIjIWEzIyysO2kJXFq4E1poc2x8uPNcPes6nYcOQK4i6kY/9fadj/Vxo8HDR4prMPnnnEFx6OGpO9LlWvO/lFuJFVmmilZuXf+38+bmTnIVX7/6w85BeVPLCnJTa88wvaNHLE40HueDzIHUGe9lzPjojqNCZkZJSEewlZZQP6q0JpaYEnWnngiVYeuJKRg41Hk/DVb38jJSsP0Xsu4KOfLqJvoBtGd/VDj2YusLDgF7Qc8gqL/0mutAlXdr5e8pVTUGzwMRvYKOFur4GrvQp/p6TjSo4Cp/++jdN/38ay2L/g3cAaocGlyVlnfyeOMySiOocJGRlMCIGEtDsAgCau1Tsr0s/ZFlH9gzA9tDl2/pGCDUeScDQxE7vP3MDuMzfg62SDZzv7YkSnRnC2q/03NzcHBUUlSM0uTbRS7yVXN+4lWqn3JV9ZeUUGH9NebQU3BzXcHTRwd9CU/t9ec+9x6XZXe7W0/ElhYSF27NiBzj37Iu7iTcSevYG4C2m4dusuYg5eRszBy7BXW6FXC1eEBrujd3M3ONqwa5OIaj8mZGSwzJwCZOUVQaEA/JxrZsC92soSQ9t5Y2g7b1y4kY0NR5Kw5cTfSMrMxZKd57As9jz6t/LE6C6+6OzPm5uXpai4BBk5BTqtWqn3dR9qE7CMHMPvQapRWsDDQQO3e4mWu736n4RLm3zZq2FbxbszuNipMeIRH4x4xAd5hcX49WI69py9gT1nU5GWnY/vTyfj+9PJsLRQoHNjJzwe7I7Hg9zg52z6llsioprAhIwMph0/5uVoLcuCrgHu9lgwpCVmPxGI705fx4YjSTh19Ra2n7qO7aeuo5mbHUZ38cWw9o3qRatJSYlAZm5BpWO00u/ko0QYdkyVpcV9SZUabg+0Zrk7qOHmoIG92qrGkl+N0hJ9g9zRN8gdb5YInL52G3vO3MCeszdwLiUbhxIycCghA69/fwbN3e1Kx50Fu6Ndowbs1iaiWoMJGRlMGj9Wxgr9NclaZYkRnXwwopMP/rh2GxuOJOHb+Gu4mHoHC787gyU7z2FwGy+M7uqHto0ca12rmRACt+8WljtGS9uilZqdjyIDMy1LCwXc7EuTKW1rlja5khIuew0a2CjN+npZWCjQzqcB2vk0wIx+LZCUkXuv5ewGjiRm4q8bd/DXjTtYse8SXOzU6BvohseD3fFoMxdYq3hXCCIyX0zIyGDVMcPyYbXydsTiYa3x3wGB+Cb+Ojbcu7n5V8f/xlfH/0ZLLweM7uKHoe28qtx9ZipCiHszD++N0cq+vwvxXsJ1b1uB3szDsikUpd175bZm3dvmZKuCZR1sLfJ1tsGER/0x4VF/3M4txL6/UrHnbCr2nUtF+p18bP7tKjb/dhVqKwv0CHDB40HueCzIDW72nK1LROaFCRkZzFRrkFUHe40S4V398FwXX5xIull6c/Pfk/Hn9Sz8d9vveGvHWTzZ3guju/ghyNPB5K9/t6D4nxas7PsGxWsTrnstXLlGzDx0slXB7b7WLHftmC37f8ZpOdupoOQabQAARxulNN6woKgExy5nIvbMDcSeuYFrt+5iz9nSZA0A2vk0kGZtNne3M+tWQSKqH5iQkcESpS5L873vpEKhQEc/J3T0c8K8QcHYcuJvbDiShMT0HKw/nIT1h5PQwbcBRnfxw8A2nqisEyu/qBipWfnS7MP7uwzvb+HKNmbmocZKp5vQrYxWLTcHNdRW7GKrKpWVBbo3c0H3Zi6YPzgY529kY8+ZG4g9m4pT9+6lGn/1Ft7ZdR4+TtZ4PMgdoUHueMTfiQkuEcmCCRkZpLhEIDGj+tYgqw4NbVWY1KMJJj7qj0OX7t3c/M8UnEi6hRNJt7Do+zMY1t4LigwFbh5JQnpOkV4L183cQoNfz1ppCQ9HTYWtWm4Oatio+GtXkxQKBQI9HBDo4YCXHgtAalYe9p5LReyZGzhwMR1XM+9iza+XsebXy7DXWKFPi9JxZ71buMJBU/cnh5RFeweFa5l3cCkLSEjLgbezHexk7vavz3Lyi3AtMweXsoDzKdnwdrIz+zGfZBz+dpFBrt+6i4KiEqgsLeDVwFrucIyiUCgQ0swFIc1ckJqdh69++xsbjyTh2q27WHPwCgBL4K9z5e6vsrKQWrN0l3bQbeGyq8GZh1R1bg4aPNvZF8929kVuQRHiLqRjz5kb+OlcKjJyCqRZu1YWCnRt4ozHg9zQN8i9ztxb1fg7KFjhwz9/BQDYqizLXN7E/b7JIW72Gk6gMEJeYWkrvPb2YTrrAEr1ko87+dpWeCt8+OchAKWfTff/AWgOs6Kp6piQkUG03ZV+zja1enC4m73mn5ub/5WGjUeu4K+rN9Dcxx2eDazL/IJxtOZfoXWVjcoK/Vp6oF9LDxSXCMRfvYnYM6nYc/YGLqbewYGL6ThwMR0LvjuDQA97adxZa29Hs1tS425B8QNd6ya4g4K1ElYlBchXKJGdV4ScgmIkpOdIM67L4yB1y9+XvNnf12LsoIarfd3uli8oKkHanQfW/dMmXPclX7fvGt4Kb6u2hDWKUGypws3cQhQUleDvm3fx9827Fe5nrbSUkjMPtt6bLV59Mog5zrB8GJYWCvQJdMOjTRtix44dGDCgHZTK+tk9RaUsLf4ZfzinfyAS03Ow92zppIBjlzNxLiUb51Ky8dFPF+Fmr0bfIHeEBrshpKlLta7LJ+cdFCxRcu/3ox8KheK+2cD5uHH7wVhKY7xbWIysvCJk5d3BhdQ7FcZhyMQVFzsVrMxoXF91LLSstrKAh6PmXov7A9fD/p+WLrWFuFcffVCisEBadr5B74u7hcW4nJGLyxm5FcbB8a3yYkJGBpESMpnXICOqKf4utpjUowkm9WiCmzkFpUtqnEnFvvOpSM3OxxdHk/DF0SRYKy1Ll9QIdsdjgW5wMfBWXkXFJUi/UyB9cZY3OzdTxjsoFBb+s/yKjcoKjV2s0LiCP8qEEMjOL9JrESo9F91lXgqKS5CZU4DMnAKcS8ku95j3L+1SXpLg7qCBs63qoVotq2OhZaWlQkqoykt+3Rw0cNAY1qVYWPhPa5rayhKNGtqgUcOKu9LLajl98D2WcjsPdwuLkZ1XhOy8O7hYSSLd0EZZRvKsu66hi52aE2SMxISMDKLtomjqYr4zLImqS0NbFZ5q3whPtW+E/KJiHEnILF2Q9swNXL+dJ91jVaEAOvg2vHcT9IbIultkki92c7yDQlkUCgUcNEo4aJRo5mZfbjkhBG7lFj6wFt99SUP2P4sfF5cIpGXnIy07H38gq9xjWlko4FrB4sdONirc1CZcD7nQsoUCcLXXbcFyv9cdeH/y28BaKXvXtrXKEn7OthXeVszYNRJv5hbiZm5hpYm0s626zOT5/gTV1Z73ItZiQkYG0d5UnC1kVN+prSzRs7krejZ3xcIhLfHn9SzpbgF/XMvC8Ss3cfzKTYOOZWmhgKt2Yd/7xvfUtjsoGEuhUKChrQoNbVUI9Ci/XHGJQGZOgV4L24OJQ/qd0mQq+XYekm/nPVRsLnYqnSTL7b560NaJs526Vo+lfZBCoYC9Rgl7jRLN3Mr/o9vYu4ik3ymtmz+vl51IB3rYY2dkz+o6rVqHCRlVKq+wGNdulQ4arStjyIhMQaFQoJW3I1p5OyLy8eZIvn1vAdozN3A+JRvOdqpyWwfcHNRwtq1bX+ymZnmv1au0FcWx3HKGdP9m5hSgoY2q3DFapePV1FBZsZutPAqFAg1sVGhgo0ILj/JbQA3t/q1tM/arGxMyqlRSZi6EKB3w6WyrkjscIrPl6WiN8K5+CO/qJ3co9YqVZemgeA9H3hLLHFhYKOBiVzqOrKVX+eWEMLDPvp7gnwJUqYS0fxaErUvdJkREJB9+n+hiQkaVqmtLXhAREZkbJmRUKe2AfnO+hyUREVFtxoSMKsUWMiIiourFhIwqxYSMiIioejEhowrdzi2UbgHChIyIiKh6MCGjCiVmlLaOuTsYfrsVIiIiMg4TMqpQYvq9FfrZOkZERFRtmJBRhaQ1yDjDkoiIqNowIaMKaW8q3oQtZERERNWGCRlVKDGNMyyJiIiqGxMyKpcQgkteEBER1QAmZFSuG1n5uFtYDCsLBXycbOQOh4iIqM5iQkblSrg3w9LXyQZKS75ViIiIqgu/ZalcCRw/RkREVCOYkFG5OH6MiIioZjAho3JJCZkrEzIiIqLqxISMysUWMiIioprBhIzKVFhcgqTMXABAExeu0k9ERFSdmJBRma5m5qK4RMBGZQl3B7Xc4RAREdVpTMioTPfPsFQoFDJHQ0REVLcxIaMycfwYERFRzWFCRmXiTcWJiIhqjuwJ2YoVK+Dv7w+NRoOOHTsiLi6u3LLjxo2DQqHQ+2nZsqVUJiYmpswyeXl5NXE6dUbivVX6ueQFERFR9ZM1Idu8eTMiIyMxd+5cnDx5Ej169ED//v2RlJRUZvkPPvgAycnJ0s/Vq1fh5OSEp59+Wqecg4ODTrnk5GRoNJqaOKU6458uS86wJCIiqm6yJmTLli3DxIkTMWnSJAQFBSE6Oho+Pj5YuXJlmeUdHR3h4eEh/fz222+4efMmxo8fr1NOoVDolPPw8KiJ06kzcvKLcCMrHwDHkBEREdUEK7leuKCgAMePH8ecOXN0toeFheHgwYMGHWPVqlV4/PHH4efnp7P9zp078PPzQ3FxMdq1a4fXX38d7du3L/c4+fn5yM/Plx5nZWUBAAoLC1FYWGjoKVVKeyxTHrM6XEgpPX9nWxVsrMw/3odRW+qkvmB9mBfWh3lhfVSuNl8b2RKy9PR0FBcXw93dXWe7u7s7UlJSKt0/OTkZP/74IzZu3KizPTAwEDExMWjdujWysrLwwQcfoHv37jh16hQCAgLKPNbixYuxcOFCve27d++GjY2NEWdlmNjYWJMf05ROpCsAWMLRIh87duyQO5waYe51Ut+wPswL68O8sD7Kl5ubK3cIVSZbQqb14BpXQgiD1r2KiYlBgwYN8OSTT+ps79q1K7p27So97t69Ozp06ICPPvoIH374YZnHioqKwvTp06XHWVlZ8PHxQVhYGBwcHIw4m4oVFhYiNjYWoaGhUCqVJjuuqV36+RJw4RI6BDTCgAEtK9+hFqstdVJfsD7MC+vDvLA+Kqft4aqNZEvIXFxcYGlpqdcalpqaqtdq9iAhBFavXo3w8HCoVKoKy1pYWOCRRx7BhQsXyi2jVquhVuuvRq9UKqvlTV9dxzWVpMy7AICmbvZmHacpmXud1DesD/PC+jAvrI/y1ebrItugfpVKhY4dO+o1vcbGxiIkJKTCfffv34+LFy9i4sSJlb6OEALx8fHw9PR8qHjrEy4KS0REVLNk7bKcPn06wsPD0alTJ3Tr1g2ffvopkpKSMHnyZAClXYnXrl3D2rVrdfZbtWoVunTpglatWukdc+HChejatSsCAgKQlZWFDz/8EPHx8Vi+fHmNnFNtJ4SQbpvUlGuQERER1QhZE7KRI0ciIyMDixYtQnJyMlq1aoUdO3ZIsyaTk5P11iS7ffs2tmzZgg8++KDMY966dQvPP/88UlJS4OjoiPbt2+OXX35B586dq/186oL0OwXIzi+CQgH4Opt+QgMRERHpk31Qf0REBCIiIsp8LiYmRm+bo6NjhbMo3n//fbz//vumCq/e0XZXNmpoDbWVpczREBER1Q+y3zqJzIt0yySu0E9ERFRjmJCRDt5UnIiIqOYxISMdiWmcYUlERFTTmJCRDqmFjDMsiYiIagwTMpIUlwhcyWALGRERUU1jQkaSazfvorBYQGVlAS9Ha7nDISIiqjeYkJEkQTvD0tkWFhaV30+UiIiITIMJGUl4yyQiIiJ5MCEjSSIH9BMREcmCCRlJErjkBRERkSyYkJGELWRERETyYEJGAIC8wmJcu3UXAG+bREREVNOYkBEA4PK99cccrZVoaKOUORoiIqL6hQkZAdC9ZZJCwSUviIiIahITMgLAWyYRERHJiQkZAfhnhmUTzrAkIiKqcUzICACQqF2lnwP6iYiIahwTMgLAVfqJiIjkxISMcDOnADdzCwEAjV1sZI6GiIio/mFCRki8t+SFp6MGNiormaMhIiKqf5iQkbTkBWdYEhERyYMJGSFBGtDPhIyIiEgOTMjovgH9nGFJREQkByZkxDXIiIiIZMaErJ4rKRHSfSzZZUlERCQPJmT1XEpWHvIKS6C0VKBRQ2u5wyEiIqqXmJDVc9rxY75ONrCy5NuBiIhIDvwGrucS0njLJCIiIrkxIavnEtK5BhkREZHcmJDVc7yHJRERkfyYkNVzTMiIiIjkx4SsHisoKsHVzFwAXIOMiIhITkzI6rGkzByUCMBObQVXe7Xc4RAREdVbTMjqMe0K/f4utlAoFDJHQ0REVH8xIavHOH6MiIjIPDAhq8eYkBEREZkHJmT1GNcgIyIiMg9MyOoxtpARERGZByZk9VR2XiHSsvMBMCEjIiKSGxOyekrbOuZqr4a9RilzNERERPUbE7J6it2VRERE5oMJWT2lXYOMK/QTERHJjwlZPcUWMiIiIvPBhKyeSpSWvLCTORIiIiJiQlYPCSGQkHYHAFvIiIiIzAETsnooLTsfOQXFsFAAvk42codDRERU7zEhq4e0K/T7ONlAZcW3ABERkdxk/zZesWIF/P39odFo0LFjR8TFxZVbdty4cVAoFHo/LVu21Cm3ZcsWBAcHQ61WIzg4GNu2bavu06hVOKCfiIjIvMiakG3evBmRkZGYO3cuTp48iR49eqB///5ISkoqs/wHH3yA5ORk6efq1atwcnLC008/LZU5dOgQRo4cifDwcJw6dQrh4eEYMWIEjhw5UlOnZfaYkBEREZkXWROyZcuWYeLEiZg0aRKCgoIQHR0NHx8frFy5sszyjo6O8PDwkH5+++033Lx5E+PHj5fKREdHIzQ0FFFRUQgMDERUVBT69u2L6OjoGjor8yetQcYZlkRERGbBSq4XLigowPHjxzFnzhyd7WFhYTh48KBBx1i1ahUef/xx+Pn5SdsOHTqEadOm6ZTr169fhQlZfn4+8vPzpcdZWVkAgMLCQhQWFhoUiyG0xzLlMasiIS0bAODbQC17LHIzlzqhUqwP88L6MC+sj8rV5msjW0KWnp6O4uJiuLu762x3d3dHSkpKpfsnJyfjxx9/xMaNG3W2p6SkGH3MxYsXY+HChXrbd+/eDRsb089CjI2NNfkxDVUsgMsZlgAUSDx9BLfOyxaKWZGzTkgf68O8sD7MC+ujfLm5uXKHUGWyJWRaCoVC57EQQm9bWWJiYtCgQQM8+eSTD33MqKgoTJ8+XXqclZUFHx8fhIWFwcHBodJYDFVYWIjY2FiEhoZCqZTnht5XMnJRcvgANEoLPDu0PywsKr/WdZk51An9g/VhXlgf5oX1UTltD1dtJFtC5uLiAktLS72Wq9TUVL0WrgcJIbB69WqEh4dDpVLpPOfh4WH0MdVqNdRqtd52pVJZLW/66jquIa7eKu2abexsC7VaVUnp+kPOOiF9rA/zwvowL6yP8tXm61LlQf25ubk4d+4cTp8+rfNjKJVKhY4dO+o1vcbGxiIkJKTCfffv34+LFy9i4sSJes9169ZN75i7d++u9Jj1RYJ0yyTOsCQiIjIXRreQpaWlYfz48fjxxx/LfL64uNjgY02fPh3h4eHo1KkTunXrhk8//RRJSUmYPHkygNKuxGvXrmHt2rU6+61atQpdunRBq1at9I45depU9OzZE0uWLMHQoUPx7bffYs+ePThw4IARZ1l3JaaX3jKpiQtnWBIREZkLo1vIIiMjcfPmTRw+fBjW1tbYuXMnPv/8cwQEBGD79u1GHWvkyJGIjo7GokWL0K5dO/zyyy/YsWOHNGsyOTlZb02y27dvY8uWLWW2jgFASEgINm3ahDVr1qBNmzaIiYnB5s2b0aVLF2NPtU7SLnnBNciIiIjMh9EtZD/99BO+/fZbPPLII7CwsICfnx9CQ0Ph4OCAxYsXY+DAgUYdLyIiAhEREWU+FxMTo7fN0dGx0lkUw4cPx/Dhw42Ko76QFoVllyUREZHZMLqFLCcnB25ubgAAJycnpKWlAQBat26NEydOmDY6MqncgiIk384DADRhCxkREZHZMDoha9GiBc6fL128ql27dvjkk09w7do1fPzxx/D09DR5gGQ6l9NLWxYb2ijRwIYzLImIiMyF0V2WkZGRSE5OBgDMnz8f/fr1w4YNG6BSqcrsYiTzwXtYEhERmSejE7LRo0dL/2/fvj0uX76Mc+fOwdfXFy4uLiYNjkxLmmHJe1gSERGZlYdeGNbGxgYdOnQwRSxUzTjDkoiIyDwZlJDdf1uhyixbtqzKwVD1khaFZUJGRERkVgxKyE6ePGnQwQy5ByXJQwiBhLTSLksueUFERGReDErIfv755+qOg6rZzdxCZOUVASi9jyURERGZjyrfy/LixYvYtWsX7t69C6C0BYbMl3ZAv3cDa2iUljJHQ0RERPczOiHLyMhA37590bx5cwwYMEBaAmPSpEl45ZVXTB4gmcalNN5UnIiIyFwZnZBNmzYNSqUSSUlJsLGxkbaPHDkSO3fuNGlwZDpcg4yIiMh8Gb3sxe7du7Fr1y40atRIZ3tAQACuXLlissDItBK55AUREZHZqtK9LO9vGdNKT0+HWq02SVBkemwhIyIiMl9GJ2Q9e/bE2rVrpccKhQIlJSV455130KdPH5MGR6ZRUiKQmKFdg4yr9BMREZkbo7ss33nnHfTu3Ru//fYbCgoKMGvWLPz555/IzMzEr7/+Wh0x0kO6fvsuCopKoLK0gHdDa7nDISIiogcY3UIWHByM06dPo3PnzggNDUVOTg6GDRuGkydPomnTptURIz0k7S2T/JxtYGnBxXuJiIjMTZXuZenh4YGFCxeaOhaqJhw/RkREZN4MSshOnz5t8AHbtGlT5WCoekgJGdcgIyIiMksGJWTt2rWDQqGAEELnfpXa1fnv31ZcXGziEOlh8abiRERE5s2gMWSJiYlISEhAYmIitmzZAn9/f6xYsQLx8fGIj4/HihUr0LRpU2zZsqW646Uq0N42yZ8zLImIiMySQS1kfn5+0v+ffvppfPjhhxgwYIC0rU2bNvDx8cG8efPw5JNPmjxIqrr8omL8fbP0fqO8bRIREZF5MnqW5e+//w5/f3+97f7+/jhz5oxJgiLTuZKRCyEAe40VnG1VcodDREREZTA6IQsKCsIbb7yBvLw8aVt+fj7eeOMNBAUFmTQ4enjaJS+auNjqjPUjIiIi82H0shcff/wxBg8eDB8fH7Rt2xYAcOrUKSgUCnz//fcmD5AeDpe8ICIiMn9GJ2SdO3dGYmIi1q9fj3PnzkEIgZEjR2LUqFGwteWXvrnhgH4iIiLzV6WFYW1sbPD888+bOhaqBlyDjIiIyPxVKSG7dOkSoqOjcfbsWSgUCgQFBWHq1Km8dZIZSuQaZERERGbP6EH9u3btQnBwMI4ePYo2bdqgVatWOHLkCFq2bInY2NjqiJGq6PbdQqTfKQDAMWRERETmzOgWsjlz5mDatGl4++239bbPnj0boaGhJguOHo62dczdQQ1bdZUaQ4mIiKgGGN1CdvbsWUycOFFv+4QJE7gOmZn5Z0A/W8eIiIjMmdEJmaurK+Lj4/W2x8fHw83NzRQxkYkkpmmXvOAMSyIiInNmdD/Wv//9bzz//PNISEhASEgIFAoFDhw4gCVLluCVV16pjhipinhTcSIiotrB6IRs3rx5sLe3x3vvvYeoqCgAgJeXFxYsWIApU6aYPECqOmmGJZe8ICIiMmtGJ2QKhQLTpk3DtGnTkJ2dDQCwt7c3eWD0cIQQXKWfiIiolnioqXdMxMzXjax85BYUw9JCAR8nG7nDISIiogoYnZBlZGTgtddew88//4zU1FSUlJToPJ+ZmWmy4KjqEu7NsPR1soHS0ui5G0RERFSDjE7InnvuOVy6dAkTJ06Eu7s7FApFdcRFD4ndlURERLWH0QnZgQMHcODAAbRt27Y64iET+WfJCyZkRERE5s7ovqzAwEDcvXu3OmIhE+IMSyIiotrD6IRsxYoVmDt3Lvbv34+MjAxkZWXp/JB5SGCXJRERUa1hdJdlgwYNcPv2bTz22GM624UQUCgUKC4uNllwVDWFxSVIyswFADThKv1ERERmz+iEbPTo0VCpVNi4cSMH9Zupq5m5KC4RsFZawt1BLXc4REREVAmjE7I//vgDJ0+eRIsWLaojHjKB+2dYMmEmIiIyf0aPIevUqROuXr1aHbGQiUgJGQf0ExER1QpGt5C9/PLLmDp1KmbOnInWrVtDqVTqPN+mTRuTBUdVc+nekhdNOaCfiIioVjA6IRs5ciQAYMKECdI2hULBQf1mJPHeKv1sISMiIqodjE7IEhMTqyMOMqF/xpBxhiUREVFtYPQYMj8/vwp/jLVixQr4+/tDo9GgY8eOiIuLq7B8fn4+5s6dCz8/P6jVajRt2hSrV6+Wno+JiYFCodD7ycvLMzq22ignvwg3svIBAP7ObCEjIiKqDYxuITOlzZs3IzIyEitWrED37t3xySefoH///jhz5gx8fX3L3GfEiBG4ceMGVq1ahWbNmiE1NRVFRUU6ZRwcHHD+/HmdbRqNptrOw5xoW8ecbVVwtFFWUpqIiIjMgawJ2bJlyzBx4kRMmjQJABAdHY1du3Zh5cqVWLx4sV75nTt3Yv/+/UhISICTkxMAoHHjxnrlFAoFPDw8qjV2c8VbJhEREdU+siVkBQUFOH78OObMmaOzPSwsDAcPHixzn+3bt6NTp05YunQp1q1bB1tbWwwZMgSvv/46rK2tpXJ37tyBn58fiouL0a5dO7z++uto3759ubHk5+cjPz9feqy9BVRhYSEKCwsf5jR1aI9lymM+6MKN0tj9nGyq9XXqipqoEzIc68O8sD7MC+ujcrX52siWkKWnp6O4uBju7u46293d3ZGSklLmPgkJCThw4AA0Gg22bduG9PR0REREIDMzUxpHFhgYiJiYGLRu3RpZWVn44IMP0L17d5w6dQoBAQFlHnfx4sVYuHCh3vbdu3fDxsbmIc9UX2xsrMmPqfXrBQsAFshPT8KOHVeq7XXqmuqsEzIe68O8sD7MC+ujfLm5uXKHUGUKIYQwZocmTZrg2LFjcHZ21tl+69YtdOjQAQkJCQYd5/r16/D29sbBgwfRrVs3afubb76JdevW4dy5c3r7hIWFIS4uDikpKXB0dAQAbN26FcOHD0dOTo5OK5lWSUkJOnTogJ49e+LDDz8sM5ayWsh8fHyQnp4OBwcHg87HEIWFhYiNjUVoaKje+m2m8q+PD+P0tSwsf7YtwoLdK9+hnquJOiHDsT7MC+vDvLA+KpeVlQUXFxfcvn3bpN/fNcHoFrLLly+XudZYfn4+rl27ZvBxXFxcYGlpqdcalpqaqtdqpuXp6Qlvb28pGQOAoKAgCCHw999/l9kCZmFhgUceeQQXLlwoNxa1Wg21Wv+ej0qlslre9NV1XCEEEjNK/zoI8HDkL6wRqqtOqGpYH+aF9WFeWB/lq83XxeCEbPv27dL/d+3apZMUFRcXY+/evWUOsC+PSqVCx44dERsbi6eeekraHhsbi6FDh5a5T/fu3fHVV1/hzp07sLMrXWPrr7/+goWFBRo1alTmPkIIxMfHo3Xr1gbHVltl5BQgO68ICgXg62T6rlYiIiKqHgYnZE8++SSA0hmMY8eO1XlOqVSicePGeO+994x68enTpyM8PBydOnVCt27d8OmnnyIpKQmTJ08GAERFReHatWtYu3YtAGDUqFF4/fXXMX78eCxcuBDp6emYOXMmJkyYIHVXLly4EF27dkVAQACysrLw4YcfIj4+HsuXLzcqttpIO8OyUUNraJSWMkdDREREhjI4ISspKQEA+Pv749ixY3BxcXnoFx85ciQyMjKwaNEiJCcno1WrVtixY4e0wGxycjKSkpKk8nZ2doiNjcXLL7+MTp06wdnZGSNGjMAbb7whlbl16xaef/55aZxZ+/bt8csvv6Bz584PHa+5S0i7d8skrtBPRERUq5jk1km3bt1CgwYNqhRAREQEIiIiynwuJiZGb1tgYGCFM0zef/99vP/++1WKpbZL0K5BxpuKExER1SpG3zppyZIl2Lx5s/T46aefhpOTE7y9vXHq1CmTBkfGSUzT3sOSCRkREVFtYnRC9sknn8DHxwdA6QD8PXv2YOfOnejfvz9mzpxp8gDJcP/cVJwJGRERUW1idJdlcnKylJB9//33GDFiBMLCwtC4cWN06dLF5AGSYYpLBK7cW/KCCRkREVHtYnQLWcOGDXH16lUApfeWfPzxxwGULi9R1vpkVDOu37qLguISqKws4N1Af4FcIiIiMl9Gt5ANGzYMo0aNQkBAADIyMtC/f38AQHx8PJo1a2byAMkwl7QzLJ1tYWGhkDkaIiIiMobRCdn777+Pxo0b4+rVq1i6dKm0QGtycnK5syWp+nH8GBERUe1ldEKmVCoxY8YMve2RkZGmiIeqSErIXJmQERER1TZGJ2QAcOnSJURHR+Ps2bNQKBQICgpCZGQkmjRpYur4yEBsISMiIqq9jB7Uv2vXLgQHB+Po0aNo06YNWrVqhSNHjiA4OLjCBVupeiWkcVFYIiKi2sroFrI5c+Zg2rRpePvtt/W2z549G6GhoSYLjgyTV1iM67fvAgCauPK2SURERLWN0S1kZ8+excSJE/W2T5gwAWfOnDFJUGScyxk5EAJwtFaioY1S7nCIiIjISEYnZK6uroiPj9fbHh8fDzc3N1PEREa6/5ZJCgWXvCAiIqptjO6y/Pe//43nn38eCQkJCAkJgUKhwIEDB7BkyRK88sor1REjVYI3FSciIqrdjE7I5s2bB3t7e7z33nuIiooCAHh5eWHBggWYMmWKyQOkynGGJRERUe1mdEKmUCgwbdo0TJs2DdnZ2QAAe3t7kwdGhuMaZERERLVbldYh02IiZh4SpS5LzrAkIiKqjYwe1H/jxg2Eh4fDy8sLVlZWsLS01PmhmnUrtwCZOQUAgMYuNjJHQ0RERFVhdAvZuHHjkJSUhHnz5sHT05Oz+mSmHdDv6aiBjeqhGjyJiIhIJkZ/gx84cABxcXFo165dNYRDxrp/yQsiIiKqnYzusvTx8YEQojpioSrgDEsiIqLaz+iELDo6GnPmzMHly5erIRwyljSgn7dMIiIiqrWM7rIcOXIkcnNz0bRpU9jY2ECp1L1VT2ZmpsmCo8pdSrsDgIvCEhER1WZGJ2TR0dHVEAZVRUmJwOUMdlkSERHVdkYnZGPHjq2OOKgKUrLykFdYAisLBRo1tJY7HCIiIqqiSseQ3b59W+fxjh07sGvXLr1yu3fvxo8//mi6yKhS2vFjvs42sLI0ejggERERmYlKv8U3bNiAcePGISsrCwAwZ84cFBcX65UrKSnBnDlzTB8hlYs3FSciIqobKu2yjIiIgIWFBVq3bo0rV67gwoULCA4O1isXGBiIixcvVkuQVDbtGmScYUlERFS7VdpClpOTg19++QVt2rQBADg6OiIhIUGv3MWLF2Fry5aampSQXjrDkgP6iYiIardKE7I1a9bgiSeewHfffQcAGDJkCCIjI3Hp0iWpzMWLF/HKK69gyJAh1Rcp6eGisERERHVDpV2WL730ks7jd955B0888QQCAwPRqFEjAMDff/+NHj164N13362eKElPQVEJrmbmAuAYMiIiotrO6GUvHB0dcfDgQcTGxuLUqVOwtrZGmzZt0LNnz+qIj8qRlJmLEgHYqizhaq+WOxwiIiJ6CEYnZACgUCgQFhaGsLAwU8dDBpK6K11toVAoZI6GiIiIHkaVFq/av38/Bg8ejGbNmiEgIABDhgxBXFycqWOjCiSma2+ZxBmWREREtZ3RCdn69evx+OOPw8bGBlOmTMFLL70Ea2tr9O3bFxs3bqyOGKkMCWkc0E9ERFRXGN1l+eabb2Lp0qWYNm2atG3q1KlYtmwZXn/9dYwaNcqkAVLZpEVhXZmQERER1XZGt5AlJCRg8ODBetuHDBmCxMREkwRFleOSF0RERHWH0QmZj48P9u7dq7d979698PHxMUlQVLHsvEKkZecDABozISMiIqr1DO6ynDBhAj744AO88sormDJlCuLj4xESEgKFQoEDBw4gJiYGH3zwQXXGSvdcTi9df8zFTg0HjVLmaIiIiOhhGZyQff7553j77bfx4osvwsPDA++99x6+/PJLAEBQUBA2b96MoUOHVlug9A/tLZM4foyIiKhuMDghE0JI/3/qqafw1FNPVUtAVDntDEuu0E9ERFQ3GDWGjAuQmgcO6CciIqpbjFr2onnz5pUmZZmZmQ8VEFWOCRkREVHdYlRCtnDhQjg6OlZXLGQAIYSUkHEMGRERUd1gVEL2zDPPwM3NrbpiIQOk3cnHnfwiWCgAHycbucMhIiIiEzB4DBnHj5mHxHsD+n2cbKC2spQ5GiIiIjIFgxOy+2dZknwSOH6MiIiozjG4y7KkpKQ64yADcUA/ERFR3WP0rZNMbcWKFfD394dGo0HHjh0RFxdXYfn8/HzMnTsXfn5+UKvVaNq0KVavXq1TZsuWLQgODoZarUZwcDC2bdtWnadQo7gGGRERUd0ja0K2efNmREZGYu7cuTh58iR69OiB/v37Iykpqdx9RowYgb1792LVqlU4f/48vvjiCwQGBkrPHzp0CCNHjkR4eDhOnTqF8PBwjBgxAkeOHKmJU6p2ifdW6fd3sZM5EiIiIjIVo2ZZmtqyZcswceJETJo0CQAQHR2NXbt2YeXKlVi8eLFe+Z07d2L//v1ISEiAk5MTAKBx48Y6ZaKjoxEaGoqoqCgAQFRUFPbv34/o6Gh88cUXZcaRn5+P/Px86XFWVhYAoLCwEIWFhQ99nlraY1X1mEXFJUjKLL2PpU8DlUljq68etk7ItFgf5oX1YV5YH5WrzddGtoSsoKAAx48fx5w5c3S2h4WF4eDBg2Xus337dnTq1AlLly7FunXrYGtriyFDhuD111+HtbU1gNIWsmnTpuns169fP0RHR5cby+LFi7Fw4UK97bt374aNjemXloiNja3Sful5QGGxFZQWAid+/RnxnPhqMlWtE6oerA/zwvowL6yP8uXm5sodQpXJlpClp6ejuLgY7u7uOtvd3d2RkpJS5j4JCQk4cOAANBoNtm3bhvT0dERERCAzM1MaR5aSkmLUMYHSVrTp06dLj7OysuDj44OwsDA4ODhU9RT1FBYWIjY2FqGhoVAqlUbvv++vNODkSTR1tceggSEmi6s+e9g6IdNifZgX1od5YX1UTtvDVRvJ2mUJ6K9vJoQod82zkpISKBQKbNiwQbpjwLJlyzB8+HAsX75caiUz5pgAoFaroVar9bYrlcpqedNX9bhJN0u7VZu42fGX0cSqq66palgf5oX1YV5YH+WrzddFtkH9Li4usLS01Gu5Sk1N1Wvh0vL09IS3t7fO7ZuCgoIghMDff/8NAPDw8DDqmLXJPwP6OcOSiIioLpEtIVOpVOjYsaNeX3hsbCxCQsrujuvevTuuX7+OO3fuSNv++usvWFhYoFGjRgCAbt266R1z9+7d5R6zNvlnDTLOsCQiIqpLZF32Yvr06fjf//6H1atX4+zZs5g2bRqSkpIwefJkAKVju8aMGSOVHzVqFJydnTF+/HicOXMGv/zyC2bOnIkJEyZI3ZVTp07F7t27sWTJEpw7dw5LlizBnj17EBkZKccpmpT2tkm8qTgREVHdIusYspEjRyIjIwOLFi1CcnIyWrVqhR07dsDPzw8AkJycrLMmmZ2dHWJjY/Hyyy+jU6dOcHZ2xogRI/DGG29IZUJCQrBp0ya8+uqrmDdvHpo2bYrNmzejS5cuNX5+ppRbUITrt/MAcFFYIiKiukb2Qf0RERGIiIgo87mYmBi9bYGBgZVO+R0+fDiGDx9uivDMxuX00qm8DW2UaGCjkjkaIiIiMiXZb51EhuE9LImIiOouJmS1BG+ZREREVHcxIaslEtI5oJ+IiKiuYkJWS2i7LDmgn4iIqO5hQlYLCCGQcG/JC3+2kBEREdU5TMhqgZu5hbh9t/QO9o2dmZARERHVNUzIagHtgH7vBtbQKC1ljoaIiIhMjQlZLSB1V3L8GBERUZ3EhKwW4BpkREREdRsTslogkUteEBER1WlMyGoBdlkSERHVbUzIzFxJiUBihnYNMq7ST0REVBcxITNz12/fRUFRCZSWCng3tJY7HCIiIqoGTMjMnHb8mJ+zLSwtFDJHQ0RERNWBCZmZ4wxLIiKiuo8JmZnTDujnDEsiIqK6iwmZmUvgTcWJiIjqPCZkZk572yR/zrAkIiKqs5iQmbH8omL8ffMuAI4hIyIiqsuYkJmxpIxcCAHYq63gYqeSOxwiIiKqJkzIzJh2/Ji/qy0UCi55QUREVFcxITNjiRzQT0REVC8wITNjCWkc0E9ERFQfMCEzY4n3dVkSERFR3cWEzIyxy5KIiKh+YEJmpm7fLUT6nQIAQGMmZERERHUaEzIzdfle65i7gxp2aiuZoyEiIqLqxITMTPGm4kRERPUHEzIzxRmWRERE9QcTMjPFm4oTERHVH0zIzBS7LImIiOoPJmRmSAjBNciIiIjqESZkZig1Ox+5BcWwtFDA18lG7nCIiIiomjEhM0OX7g3o93WygdKSVURERFTX8dveDHH8GBERUf3ChMwMJaYxISMiIqpPmJCZIbaQERER1S9MyMwQbypORERUvzAhMzOFxSVIyswFADRx5Sr9RERE9QETMjNzNTMXRSUC1kpLuDuo5Q6HiIiIagATMjNz//gxhUIhczRERERUE5iQmRmu0E9ERFT/MCEzM7ypOBERUf3DhMzMcA0yIiKi+ocJmZmRlrzgDEsiIqJ6gwmZGcnJL0JKVh4AwN+ZLWRERET1hewJ2YoVK+Dv7w+NRoOOHTsiLi6u3LL79u2DQqHQ+zl37pxUJiYmpswyeXl5NXE6D0XbOuZsq4KjjVLmaIiIiKimWMn54ps3b0ZkZCRWrFiB7t2745NPPkH//v1x5swZ+Pr6lrvf+fPn4eDgID12dXXVed7BwQHnz5/X2abRaEwbfDXgLZOIiIjqJ1kTsmXLlmHixImYNGkSACA6Ohq7du3CypUrsXjx4nL3c3NzQ4MGDcp9XqFQwMPDw9ThVjsmZERERPWTbAlZQUEBjh8/jjlz5uhsDwsLw8GDByvct3379sjLy0NwcDBeffVV9OnTR+f5O3fuwM/PD8XFxWjXrh1ef/11tG/fvtzj5efnIz8/X3qclZUFACgsLERhYaGxp1Yu7bHKO+al1GwAgJ+TtUlfl8pXWZ1QzWJ9mBfWh3lhfVSuNl8b2RKy9PR0FBcXw93dXWe7u7s7UlJSytzH09MTn376KTp27Ij8/HysW7cOffv2xb59+9CzZ08AQGBgIGJiYtC6dWtkZWXhgw8+QPfu3XHq1CkEBASUedzFixdj4cKFett3794NGxubhzxTfbGxsWVuj79kCUCBzCvnsGPHWZO/LpWvvDohebA+zAvrw7ywPsqXm5srdwhVphBCCDle+Pr16/D29sbBgwfRrVs3afubb76JdevW6QzUr8jgwYOhUCiwffv2Mp8vKSlBhw4d0LNnT3z44YdllimrhczHxwfp6ek6Y9UeVmFhIWJjYxEaGgqlUnfQvhACHd/6Gdl5RdjxUggC3LnsRU2oqE6o5rE+zAvrw7ywPiqXlZUFFxcX3L5926Tf3zVBthYyFxcXWFpa6rWGpaam6rWaVaRr165Yv359uc9bWFjgkUcewYULF8oto1aroVbr38hbqVRWy5u+rOOm38lHdl4RFAqgibsDlEpLk78ula+66pqqhvVhXlgf5oX1Ub7afF1kW/ZCpVKhY8eOek2vsbGxCAkJMfg4J0+ehKenZ7nPCyEQHx9fYRlzoB3Q793AGhomY0RERPWKrLMsp0+fjvDwcHTq1AndunXDp59+iqSkJEyePBkAEBUVhWvXrmHt2rUASmdhNm7cGC1btkRBQQHWr1+PLVu2YMuWLdIxFy5ciK5duyIgIABZWVn48MMPER8fj+XLl8tyjobiLZOIiIjqL1kTspEjRyIjIwOLFi1CcnIyWrVqhR07dsDPzw8AkJycjKSkJKl8QUEBZsyYgWvXrsHa2hotW7bEDz/8gAEDBkhlbt26heeffx4pKSlwdHRE+/bt8csvv6Bz5841fn7G4E3FiYiI6i9ZEzIAiIiIQERERJnPxcTE6DyeNWsWZs2aVeHx3n//fbz//vumCq/GJKbfAcB7WBIREdVHst86iUolsMuSiIio3mJCZgaKSwSuZJSuncKEjIiIqP5hQmYGrt+6i4LiEqisLODVwFrucIiIiKiGMSEzA9oB/Y2dbWBpoZA5GiIiIqppTMjMQGLavQH9LhzQT0REVB8xITMD2hYyf1eOHyMiIqqPmJCZAe0q/RzQT0REVD8xITMD2iUvuCgsERFR/cSETGZ5hcW4fvsuALaQERER1VdMyGR2JSMXQgAOGis42arkDoeIiIhkwIRMZvffMkmh4JIXRERE9RETMpld4vgxIiKieo8Jmcw4w5KIiIiYkMkskWuQERER1XtMyGTGFjIiIiJiQiajW7kFyMwpAMCEjIiIqD5jQiYjbeuYp6MGNiormaMhIiIiuTAhk5F2hX62jhEREdVvTMhkxPFjREREBDAhkxUTMiIiIgKYkMkq4V5C1oRLXhAREdVrTMhkUlIicFlqIbOTORoiIiKSExMymdzIzsPdwmJYWSjg09Ba7nCIiIhIRkzIZKKdYenrbAMrS1YDERFRfcZMQCbS+DEO6CciIqr3mJDJJJFrkBEREdE9TMhkkph+BwAH9BMRERETMtlwDTIiIiLSYkImg4KiEly9eRcA0JRrkBEREdV7TMhkcPXmXRSXCNiqLOFqr5Y7HCIiIpIZEzIZSAvCutpCoVDIHA0RERHJjQmZDBIzcgFwQD8RERGVYkImg8sZHNBPRERE/2BCJoPE9NIWMi4KS0RERAATMllcvtdl2YQzLImIiAhMyGpcXhGQmp0PAGjMFjIiIiICE7Ial5ZX+q+LnRoOGqW8wRAREZFZYEJWw1LzSpe54PgxIiIi0mJCVsNSSxfo5wxLIiIikjAhq2Fp2hYyDugnIiKie5iQ1bDUu6UJGVvIiIiISIsJWQ0SQiD13qB+tpARERGRFhOyGpR+pwD5xQpYKAAfJxu5wyEiIiIzwYSsBiXeu2WSdwNrqK0sZY6GiIiIzIWV3AHUJz4NbfCvxsVo3cpP7lCIiIjIjDAhq0Gejhr09BQY0MVX7lCIiIjIjLDLkoiIiEhmsidkK1asgL+/PzQaDTp27Ii4uLhyy+7btw8KhULv59y5czrltmzZguDgYKjVagQHB2Pbtm3VfRpEREREVSZrQrZ582ZERkZi7ty5OHnyJHr06IH+/fsjKSmpwv3Onz+P5ORk6ScgIEB67tChQxg5ciTCw8Nx6tQphIeHY8SIEThy5Eh1nw4RERFRlciakC1btgwTJ07EpEmTEBQUhOjoaPj4+GDlypUV7ufm5gYPDw/px9LynxmL0dHRCA0NRVRUFAIDAxEVFYW+ffsiOjq6ms+GiIiIqGpkG9RfUFCA48ePY86cOTrbw8LCcPDgwQr3bd++PfLy8hAcHIxXX30Vffr0kZ47dOgQpk2bplO+X79+FSZk+fn5yM/Plx5nZWUBAAoLC1FYWGjoKVVKeyxTHpMeDuvEvLA+zAvrw7ywPipXm6+NbAlZeno6iouL4e7urrPd3d0dKSkpZe7j6emJTz/9FB07dkR+fj7WrVuHvn37Yt++fejZsycAICUlxahjAsDixYuxcOFCve27d++GjY3pF3CNjY01+THp4bBOzAvrw7ywPswL66N8ubm5codQZbIve6FQKHQeCyH0tmm1aNECLVq0kB5369YNV69exbvvvislZMYeEwCioqIwffp06XFWVhZ8fHwQFhYGBwcHo86nIoWFhYiNjUVoaCiUSqXJjktVxzoxL6wP88L6MC+sj8ppe7hqI9kSMhcXF1haWuq1XKWmpuq1cFWka9euWL9+vfTYw8PD6GOq1Wqo1Wq97Uqlslre9NV1XKo61ol5YX2YF9aHeWF9lK82XxfZBvWrVCp07NhRr+k1NjYWISEhBh/n5MmT8PT0lB5369ZN75i7d+826phERERENUnWLsvp06cjPDwcnTp1Qrdu3fDpp58iKSkJkydPBlDalXjt2jWsXbsWQOkMysaNG6Nly5YoKCjA+vXrsWXLFmzZskU65tSpU9GzZ08sWbIEQ4cOxbfffos9e/bgwIEDspwjERERUWVkTchGjhyJjIwMLFq0CMnJyWjVqhV27NgBP7/Sez0mJyfrrElWUFCAGTNm4Nq1a7C2tkbLli3xww8/YMCAAVKZkJAQbNq0Ca+++irmzZuHpk2bYvPmzejSpUuNnx8RERGRIWQf1B8REYGIiIgyn4uJidF5PGvWLMyaNavSYw4fPhzDhw83RXhERERE1U72WycRERER1Xeyt5CZIyEEANNPny0sLERubi6ysrJq9UyQuoR1Yl5YH+aF9WFeWB+V035va7/HaxMmZGXIzs4GAPj4+MgcCRERERkrOzsbjo6OcodhFIWojWlkNSspKcH169dhb29f4YKyxtIuOHv16lWTLjhLVcc6MS+sD/PC+jAvrI/KCSGQnZ0NLy8vWFjUrlFZbCErg4WFBRo1alRtx3dwcOAvk5lhnZgX1od5YX2YF9ZHxWpby5hW7UofiYiIiOogJmREREREMmNCVoPUajXmz59f5n0zSR6sE/PC+jAvrA/zwvqo2zion4iIiEhmbCEjIiIikhkTMiIiIiKZMSEjIiIikhkTMiIiIiKZMSEjIiIikhkTshq0YsUK+Pv7Q6PRoGPHjoiLi5M7pDpn8eLFeOSRR2Bvbw83Nzc8+eSTOH/+vE4ZIQQWLFgALy8vWFtbo3fv3vjzzz91yuTn5+Pll1+Gi4sLbG1tMWTIEPz99981eSp10uLFi6FQKBAZGSltY33UvGvXruG5556Ds7MzbGxs0K5dOxw/flx6nnVSc4qKivDqq6/C398f1tbWaNKkCRYtWoSSkhKpDOujnhBUIzZt2iSUSqX47LPPxJkzZ8TUqVOFra2tuHLlityh1Sn9+vUTa9asEX/88YeIj48XAwcOFL6+vuLOnTtSmbffflvY29uLLVu2iN9//12MHDlSeHp6iqysLKnM5MmThbe3t4iNjRUnTpwQffr0EW3bthVFRUVynFadcPToUdG4cWPRpk0bMXXqVGk766NmZWZmCj8/PzFu3Dhx5MgRkZiYKPbs2SMuXrwolWGd1Jw33nhDODs7i++//14kJiaKr776StjZ2Yno6GipDOujfmBCVkM6d+4sJk+erLMtMDBQzJkzR6aI6ofU1FQBQOzfv18IIURJSYnw8PAQb7/9tlQmLy9PODo6io8//lgIIcStW7eEUqkUmzZtkspcu3ZNWFhYiJ07d9bsCdQR2dnZIiAgQMTGxopevXpJCRnro+bNnj1bPProo+U+zzqpWQMHDhQTJkzQ2TZs2DDx3HPPCSFYH/UJuyxrQEFBAY4fP46wsDCd7WFhYTh48KBMUdUPt2/fBgA4OTkBABITE5GSkqJTF2q1Gr169ZLq4vjx4ygsLNQp4+XlhVatWrG+qug///kPBg4ciMcff1xnO+uj5m3fvh2dOnXC008/DTc3N7Rv3x6fffaZ9DzrpGY9+uij2Lt3L/766y8AwKlTp3DgwAEMGDAAAOujPrGSO4D6ID09HcXFxXB3d9fZ7u7ujpSUFJmiqvuEEJg+fToeffRRtGrVCgCk611WXVy5ckUqo1Kp0LBhQ70yrC/jbdq0CSdOnMCxY8f0nmN91LyEhASsXLkS06dPx3//+18cPXoUU6ZMgVqtxpgxY1gnNWz27Nm4ffs2AgMDYWlpieLiYrz55pt49tlnAfB3pD5hQlaDFAqFzmMhhN42Mp2XXnoJp0+fxoEDB/Seq0pdsL6Md/XqVUydOhW7d++GRqMptxzro+aUlJSgU6dOeOuttwAA7du3x59//omVK1dizJgxUjnWSc3YvHkz1q9fj40bN6Jly5aIj49HZGQkvLy8MHbsWKkc66PuY5dlDXBxcYGlpaXeXyqpqal6f/WQabz88svYvn07fv75ZzRq1Eja7uHhAQAV1oWHhwcKCgpw8+bNcsuQYY4fP47U1FR07NgRVlZWsLKywv79+/Hhhx/CyspKup6sj5rj6emJ4OBgnW1BQUFISkoCwN+RmjZz5kzMmTMHzzzzDFq3bo3w8HBMmzYNixcvBsD6qE+YkNUAlUqFjh07IjY2Vmd7bGwsQkJCZIqqbhJC4KWXXsLWrVvx008/wd/fX+d5f39/eHh46NRFQUEB9u/fL9VFx44doVQqdcokJyfjjz/+YH0ZqW/fvvj9998RHx8v/XTq1AmjR49GfHw8mjRpwvqoYd27d9dbCuavv/6Cn58fAP6O1LTc3FxYWOh+FVtaWkrLXrA+6hGZJhPUO9plL1atWiXOnDkjIiMjha2trbh8+bLcodUpL774onB0dBT79u0TycnJ0k9ubq5U5u233xaOjo5i69at4vfffxfPPvtsmVPIGzVqJPbs2SNOnDghHnvsMU4hN5H7Z1kKwfqoaUePHhVWVlbizTffFBcuXBAbNmwQNjY2Yv369VIZ1knNGTt2rPD29paWvdi6datwcXERs2bNksqwPuoHJmQ1aPny5cLPz0+oVCrRoUMHaSkGMh0AZf6sWbNGKlNSUiLmz58vPDw8hFqtFj179hS///67znHu3r0rXnrpJeHk5CSsra3FoEGDRFJSUg2fTd30YELG+qh53333nWjVqpVQq9UiMDBQfPrppzrPs05qTlZWlpg6darw9fUVGo1GNGnSRMydO1fk5+dLZVgf9YNCCCHkbKEjIiIiqu84hoyIiIhIZkzIiIiIiGTGhIyIiIhIZkzIiIiIiGTGhIyIiIhIZkzIiIiIiGTGhIyIiIhIZkzIiIhq2Lfffos///wTAPD9998jPj5e3oCISHZMyIioXAqFAt98802NvuaCBQvQrl27CsuMGzcOTz75ZI3Ec799+/ZBoVDg1q1bAICYmBg0aNDA6OMEBgZi1KhRiIuLw9y5c9GiRQsAwOXLl6FQKGo8QTPkmhNR9WJCRlSLjRs3DgqFQvpxdnbGE088gdOnT5vk+MnJyejfv3+VY5MjaaoNWrRogeeeew59+/bFypUrYW1tDQDw8fFBcnIyWrVqVaPxzJgxA3v37q3R1yQiXUzIiGq5J554AsnJyUhOTsbevXthZWWFQYMGVbhPYWGhQcf28PCAWq02RZj0gJkzZ6KgoAAhISHSNktLS3h4eMDKyqpGY7Gzs4Ozs3ONviYR6WJCRlTLqdVqeHh4wMPDA+3atcPs2bNx9epVpKWlAfinG+zLL79E7969odFosH79epSUlGDRokVo1KgR1Go12rVrh507d+ocu7Iuy6+//hqtW7eGtbU1nJ2d8fjjjyMnJwcLFizA559/jm+//VZqvdu3bx8AYPbs2WjevDlsbGzQpEkTzJs3r8wE8ZNPPoGPjw9sbGzw9NNPS92EZdm5cyceffRRNGjQAM7Ozhg0aBAuXbpU4XXLz8/HlClT4ObmBo1Gg0cffRTHjh3TKbNjxw40b94c1tbW6NOnDy5fvlzhMctqFYyMjETv3r2lxyUlJViyZAmaNWsGtVoNX19fvPnmmwB0uyxLSkrQqFEjfPzxxzrHO3HiBBQKBRISEgAAy5YtQ+vWrWFrawsfHx9ERETgzp07Unltt+quXbsQFBQEOzs7KYnXerDL8tixYwgNDYWLiwscHR3Rq1cvnDhxosJzJ6KHw4SMqA65c+cONmzYgGbNmum1eMyePRtTpkzB2bNn0a9fP3zwwQd477338O677+L06dPo168fhgwZggsXLhj0WsnJyXj22WcxYcIEnD17Fvv27cOwYcMghMCMGTMwYsQIndY7bUuQvb09YmJicObMGXzwwQf47LPP8P777+sc++LFi/jyyy/x3XffYefOnYiPj8d//vOfcmPJycnB9OnTcezYMezduxcWFhZ46qmnUFJSUu4+s2bNwpYtW/D555/jxIkTaNasGfr164fMzEwAwNWrVzFs2DAMGDAA8fHxmDRpEubMmWPQtalIVFQUlixZgnnz5uHMmTPYuHEj3N3d9cpZWFjgmWeewYYNG3S2b9y4Ed26dUOTJk2kch9++CH++OMPfP755/jpp58wa9YsnX1yc3Px7rvvYt26dfjll1+QlJSEGTNmlBtjdnY2xo4di7i4OBw+fBgBAQEYMGAAsrOzH/r8iagcgohqrbFjxwpLS0tha2srbG1tBQDh6ekpjh8/LpVJTEwUAER0dLTOvl5eXuLNN9/U2fbII4+IiIgI6TEAsW3btjJf+/jx4wKAuHz5crmxDR06tNJzWLp0qejYsaP0eP78+cLS0lJcvXpV2vbjjz8KCwsLkZycbNCxU1NTBQDx+++/l/n8nTt3hFKpFBs2bJC2FRQUCC8vL7F06VIhhBBRUVEiKChIlJSUSGVmz54tAIibN28KIYRYs2aNcHR0rPCcp06dKnr16iWEECIrK0uo1Wrx2WeflRmXtq5OnjwphBDixIkTQqFQSNe4uLhYeHt7i+XLl5d77l9++aVwdnaWHq9Zs0YAEBcvXpS2LV++XLi7u0uP58+fL9q2bVvuMYuKioS9vb347rvvyi1DRA+HLWREtVyfPn0QHx+P+Ph4HDlyBGFhYejfvz+uXLmiU65Tp07S/7OysnD9+nV0795dp0z37t1x9uxZg163bdu26Nu3L1q3bo2nn34an332GW7evFnpfl9//TUeffRReHh4wM7ODvPmzUNSUpJOGV9fXzRq1Eh63K1bN5SUlOD8+fNlHvPSpUsYNWoUmjRpAgcHB/j7+wOA3nHvL19YWKhz/kqlEp07d5bO/+zZs+jatSsUCoVOHA/j7NmzyM/PR9++fQ0q3759ewQGBuKLL74AAOzfvx+pqakYMWKEVObnn39GaGgovL29YW9vjzFjxiAjIwM5OTlSGRsbGzRt2lR67OnpidTU1HJfNzU1FZMnT0bz5s3h6OgIR0dH3Llzp9zrSUQPjwkZUS1na2uLZs2aoVmzZujcuTNWrVqFnJwcfPbZZ3rlHnR/sgEAQgi9beWxtLREbGwsfvzxRwQHB+Ojjz5CixYtkJiYWO4+hw8fxjPPPIP+/fvj+++/x8mTJzF37lwUFBRU+FramMqLbfDgwcjIyMBnn32GI0eO4MiRIwBQ7nGFEGUe7/7z15YxhoWFhd5+94+P086mNMbo0aOxceNGAKXdlf369YOLiwsA4MqVKxgwYABatWqFLVu24Pjx41i+fLne6yqVSp1jKhSKCs9v3LhxOH78OKKjo3Hw4EHEx8fD2dm50noioqpjQkZUxygUClhYWODu3bvllnFwcICXlxcOHDigs/3gwYMICgoy6rW6d++OhQsX4uTJk1CpVNi2bRsAQKVSobi4WKf8r7/+Cj8/P8ydOxedOnVCQECAXkseUNqydf36denxoUOHYGFhgebNm+uVzcjIwNmzZ/Hqq6+ib9++CAoKqrSlrlmzZlCpVDrnX1hYiN9++006/+DgYBw+fFhnvwcfP8jV1VVnsDwAnTXFAgICYG1tbdQSE6NGjcLvv/+O48eP4+uvv8bo0aOl53777TcUFRXhvffeQ9euXdG8eXOd61ZVcXFxmDJlCgYMGICWLVtCrVYjPT39oY9LROWr2bnVRGRy+fn5SElJAQDcvHkT//d//4c7d+5g8ODBFe43c+ZMzJ8/H02bNkW7du2wZs0axMfH6w0iL8+RI0ewd+9ehIWFwc3NDUeOHEFaWpqU0DRu3Bi7du3C+fPn4ezsDEdHRzRr1gxJSUnYtGkTHnnkEfzwww9SAnc/jUaDsWPH4t1330VWVhamTJmCESNGwMPDQ69sw4YN4ezsjE8//RSenp5ISkqqdPC9ra0tXnzxRcycORNOTk7w9fXF0qVLkZubi4kTJwIAJk+ejPfeew/Tp0/HCy+8gOPHjyMmJqbC4z722GN45513sHbtWnTr1g3r16/HH3/8gfbt20vnNXv2bMyaNQsqlQrdu3dHWloa/vzzT+l1H+Tv74+QkBBMnDgRRUVFGDp0qPRc06ZNUVRUhI8++giDBw/Gr7/+qjcrsyqaNWuGdevWoVOnTsjKysLMmTOr1LpHREaQcwAbET2csWPHCgDSj729vXjkkUfE119/LZV5cKC4VnFxsVi4cKHw9vYWSqVStG3bVvz44486ZVDBoP4zZ86Ifv36CVdXV6FWq0Xz5s3FRx99JD2fmpoqQkNDhZ2dnQAgfv75ZyGEEDNnzhTOzs7Czs5OjBw5Urz//vs6A+O1A8xXrFghvLy8hEajEcOGDROZmZk6533/4PnY2FgRFBQk1Gq1aNOmjdi3b1+FsQshxN27d8XLL78sXFxchFqtFt27dxdHjx7VKfPdd9+JZs2aCbVaLXr06CFWr15d4aB+IYR47bXXhLu7u3B0dBTTpk0TL730kjSoX3vd33jjDeHn5yeUSqXw9fUVb731lhCi/Lpavny5ACDGjBmjdx7Lli0Tnp6ewtraWvTr10+sXbu20hi3bdsm7v/4f3BQ/4kTJ0SnTp2EWq0WAQEB4quvvhJ+fn7i/fffL/d6EtHDUQhRhYESRFTn5efnQ6PRIDY2Fo8//rjc4VA1ioqKQlxcnF4XNhHVHHZZEpGerKwsbN26FRYWFggMDJQ7HKomQggkJCRg7969UrcqEcmDg/qJSM/8+fMxe/ZsLFmyRGf5Capbbt++jeDgYKhUKvz3v/+VOxyieo1dlkREREQyYwsZERERkcyYkBERERHJjAkZERERkcyYkBERERHJjAkZERERkcyYkBERERHJjAkZERERkcyYkBERERHJ7P8BkT41oSS+DtAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Uspješnost u ovisnosti o broju stabla odlučivanja ##\n",
    "\n",
    "num_estimators = range(1, 1001, 100)\n",
    "accuracies = []\n",
    "\n",
    "for n_estimators in num_estimators:\n",
    "    rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n",
    "    rf_model.fit(x_train, y_train)\n",
    "    \n",
    "    accuracy = rf_model.score(x_test, y_test)\n",
    "    accuracies.append(accuracy)\n",
    "\n",
    "\n",
    "plt.plot(num_estimators, accuracies)\n",
    "plt.xlabel(\"Broj stabla odlučivanja\")\n",
    "plt.ylabel(\"Točnost modela\")\n",
    "plt.title(\"Uspješnost modela Random Forest ovisno o broju stabla odlučivanja\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}