logistic-regression.ipynb

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    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "from scipy import stats\n",
    "from scipy.stats import norm\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix, mean_squared_error\n",
    "import warnings\n",
    "import itertools\n",
    "import math\n",
    "from sympy import *\n",
    "from tabulate import tabulate\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('../input/breast-cancer/wisconsin.csv') # import data\n",
    "# df.columns = ['Sample Code Number', 'Clump_thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal_adhesion', 'Single_epithelial_cell_size', 'Bare_nuclei', 'Bland_chromatin', 'Normal_nucleoli', 'Mitoses', 'Class'] # adds column names to df\n",
    "\n",
    "render_graphs = True # want to see the graphs in output? change to True, else will hide all unimportant graphs\n",
    "show_detailed_output = False # want to see lots of text in output? change to True, else will hide all unimportant graphs"
   ]
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      "text/plain": [
       "\"\\nif (show_detailed_output):\\n    print('Finding possibilities...\\n')\\n\\npossibilities = list(itertools.combinations(df.columns, 2)) # find all possible unique graph combinations\\n\\nprint('-> Found', len(possibilities), 'possibilities\\n')\\n\\n#for col, col2 in possibilities: # for each possibility...\\n#    df[col] = np.log(df[col]) # transform the graph to log\\n#    data = pd.concat([df[col2], df[col]], axis=1) # merge the x and y axes into a graph\\n#    if (render_graphs):\\n#        data.plot.scatter(x=col, y=col2, xlim=(df[col].min(),df[col].max()), ylim=(df[col2].min(),df[col2].max())) # plot the scatter plot & scale it\\n\\nif (render_graphs & show_detailed_output):\\n    print('Rendering graphs...\\n')\\n\\nif (show_detailed_output):\\n    print('Finding best match to log...\\n')\\n    \\nmax_corr_coef = -1 # start at -1, everything else must be higher\\nbest_pair = None # no best pair at the start\\n\\nfor col, col2 in possibilities: # for each possibility...\\n    dff = df[[col, col2]] # make a new df (dff) but with x and y\\n    dff[col] = np.log(dff[col]) # transform the graph to log\\n    data = pd.concat([dff[col2], dff[col]], axis=1) # merge the x and y axes into a graph\\n    if (render_graphs):\\n        plt.figure(figsize=(8,6)) # set the graph size\\n        plt.scatter(data[col], data[col2]) # plot the scatter plot\\n        # plt.xscale('symlog') # scale x as log\\n        # plt.yscale('symlog') # scale y as log\\n        plt.title(f'{col} vs {col2}') # add a title\\n        plt.xlabel(col) # add x label\\n        plt.ylabel(col2) # add y label\\n        plt.show() # render the graph\\n    \\n    corr_coef = np.corrcoef(data[col], data[col2])[0, 1] # find how linear the relationship is\\n    if (show_detailed_output):\\n        print(f'^ Correlation for {col} vs {col2}: {corr_coef} ^\\n')\\n    \\n    if corr_coef > max_corr_coef: # if the correlation coefficient's larger than the previous...\\n        max_corr_coef = corr_coef # replace it to become the new largest\\n        best_pair = (col, col2) # replace the previous best pair to become new best pair\\n\\nif (render_graphs & show_detailed_output):\\n    print('Rendered graphs\\n')\\n\\nprint('-> Best match:', best_pair, max_corr_coef, '\\n')\\n\\nprint('-> Original graph:\\n')\\n\\nplt.figure(figsize=(8,6)) # set the graph size\\nplt.scatter(df[best_pair[0]], df[best_pair[1]]) # plot the scatter plot\\nplt.xscale('symlog') # scale x as log\\nplt.yscale('symlog') # scale y as log\\nplt.title(f'{best_pair[0]} vs {best_pair[1]}') # add a title\\nplt.xlabel(best_pair[0]) # add x label\\nplt.ylabel(best_pair[1]) # add y label\\nplt.show() # render the graph\\n\\nprint('\\n-> Transformed graph:\\n')\\n\\ndff = df[[best_pair[0], best_pair[1]]] # make a new df (dff) but with x and y\\ndff[best_pair[0]] = np.log(dff[best_pair[0]]) # transform the graph to log\\ndff[best_pair[1]] = np.log(dff[best_pair[1]]) # transform the graph to log\\np = np.poly1d(np.polyfit(dff[best_pair[0]], dff[best_pair[1]], 1)) # add a red line to best fit the linear data\\nplt.figure(figsize=(8,6)) # set the graph size\\nplt.scatter(dff[best_pair[0]], dff[best_pair[1]]) # plot the scatter plot\\nplt.plot(dff[best_pair[0]], p(dff[best_pair[0]]), 'r--') # plot the red line\\nplt.xscale('symlog') # scale x as log\\nplt.yscale('symlog') # scale y as log\\nplt.title(f'{best_pair[0]} vs {best_pair[1]}') # add a title\\nplt.xlabel(best_pair[0]) # add x label\\nplt.ylabel(best_pair[1]) # add y label\\nplt.show() # render the graph\\n\""
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   "source": [
    "# DAY 1 - Graphing all possibilities & finding the best relationships\n",
    "\n",
    "'''\n",
    "if (show_detailed_output):\n",
    "    print('Finding possibilities...\\n')\n",
    "\n",
    "possibilities = list(itertools.combinations(df.columns, 2)) # find all possible unique graph combinations\n",
    "\n",
    "print('-> Found', len(possibilities), 'possibilities\\n')\n",
    "\n",
    "#for col, col2 in possibilities: # for each possibility...\n",
    "#    df[col] = np.log(df[col]) # transform the graph to log\n",
    "#    data = pd.concat([df[col2], df[col]], axis=1) # merge the x and y axes into a graph\n",
    "#    if (render_graphs):\n",
    "#        data.plot.scatter(x=col, y=col2, xlim=(df[col].min(),df[col].max()), ylim=(df[col2].min(),df[col2].max())) # plot the scatter plot & scale it\n",
    "\n",
    "if (render_graphs & show_detailed_output):\n",
    "    print('Rendering graphs...\\n')\n",
    "\n",
    "if (show_detailed_output):\n",
    "    print('Finding best match to log...\\n')\n",
    "    \n",
    "max_corr_coef = -1 # start at -1, everything else must be higher\n",
    "best_pair = None # no best pair at the start\n",
    "\n",
    "for col, col2 in possibilities: # for each possibility...\n",
    "    dff = df[[col, col2]] # make a new df (dff) but with x and y\n",
    "    dff[col] = np.log(dff[col]) # transform the graph to log\n",
    "    data = pd.concat([dff[col2], dff[col]], axis=1) # merge the x and y axes into a graph\n",
    "    if (render_graphs):\n",
    "        plt.figure(figsize=(8,6)) # set the graph size\n",
    "        plt.scatter(data[col], data[col2]) # plot the scatter plot\n",
    "        # plt.xscale('symlog') # scale x as log\n",
    "        # plt.yscale('symlog') # scale y as log\n",
    "        plt.title(f'{col} vs {col2}') # add a title\n",
    "        plt.xlabel(col) # add x label\n",
    "        plt.ylabel(col2) # add y label\n",
    "        plt.show() # render the graph\n",
    "    \n",
    "    corr_coef = np.corrcoef(data[col], data[col2])[0, 1] # find how linear the relationship is\n",
    "    if (show_detailed_output):\n",
    "        print(f'^ Correlation for {col} vs {col2}: {corr_coef} ^\\n')\n",
    "    \n",
    "    if corr_coef > max_corr_coef: # if the correlation coefficient's larger than the previous...\n",
    "        max_corr_coef = corr_coef # replace it to become the new largest\n",
    "        best_pair = (col, col2) # replace the previous best pair to become new best pair\n",
    "\n",
    "if (render_graphs & show_detailed_output):\n",
    "    print('Rendered graphs\\n')\n",
    "\n",
    "print('-> Best match:', best_pair, max_corr_coef, '\\n')\n",
    "\n",
    "print('-> Original graph:\\n')\n",
    "\n",
    "plt.figure(figsize=(8,6)) # set the graph size\n",
    "plt.scatter(df[best_pair[0]], df[best_pair[1]]) # plot the scatter plot\n",
    "plt.xscale('symlog') # scale x as log\n",
    "plt.yscale('symlog') # scale y as log\n",
    "plt.title(f'{best_pair[0]} vs {best_pair[1]}') # add a title\n",
    "plt.xlabel(best_pair[0]) # add x label\n",
    "plt.ylabel(best_pair[1]) # add y label\n",
    "plt.show() # render the graph\n",
    "\n",
    "print('\\n-> Transformed graph:\\n')\n",
    "\n",
    "dff = df[[best_pair[0], best_pair[1]]] # make a new df (dff) but with x and y\n",
    "dff[best_pair[0]] = np.log(dff[best_pair[0]]) # transform the graph to log\n",
    "dff[best_pair[1]] = np.log(dff[best_pair[1]]) # transform the graph to log\n",
    "p = np.poly1d(np.polyfit(dff[best_pair[0]], dff[best_pair[1]], 1)) # add a red line to best fit the linear data\n",
    "plt.figure(figsize=(8,6)) # set the graph size\n",
    "plt.scatter(dff[best_pair[0]], dff[best_pair[1]]) # plot the scatter plot\n",
    "plt.plot(dff[best_pair[0]], p(dff[best_pair[0]]), 'r--') # plot the red line\n",
    "plt.xscale('symlog') # scale x as log\n",
    "plt.yscale('symlog') # scale y as log\n",
    "plt.title(f'{best_pair[0]} vs {best_pair[1]}') # add a title\n",
    "plt.xlabel(best_pair[0]) # add x label\n",
    "plt.ylabel(best_pair[1]) # add y label\n",
    "plt.show() # render the graph\n",
    "'''"
   ]
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     "data": {
      "text/plain": [
       "'\\n# print(df[df[\\'Class\\']==2]) # benign - no cancer\\n# print(df[df[\\'Class\\']==4]) # malignant - cancer\\n\\nprint(f\"-> ~{round((len(df[df[\\'Class\\']==4])/len(df[\\'Class\\']))*100)}% of the samples are malignant - cancerous\\n\")\\n\\nfor col in df.columns: # for each column...\\n    if (col != \\'Sample Code Number\\'): # exclude the sample ID\\n        if (render_graphs):\\n            x = df[df[\\'Class\\']==2][col] # set 1st variable color as benign - no cancer\\n            y = df[df[\\'Class\\']==4][col] # set 2nd variable color as malignant - cancer\\n            plt.hist([x, y], bins=np.linspace(min(x.min(), y.min()), max(x.max(), y.max()), 100), alpha=0.5, histtype=\\'barstacked\\', color=[\\'g\\', \\'r\\'], label=[\\'Benign\\', \\'Malignant\\']) # plot the histogram\\n            plt.legend(loc=\\'upper right\\') # set the legend location\\n            plt.title(col) # add a title\\n            plt.xlabel(col) # add x label\\n            plt.ylabel(\\'Count #\\') # add y label\\n            plt.show() # render the graph\\n'"
      ]
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   "source": [
    "# DAY 2 - Displaying data as histograms\n",
    "\n",
    "'''\n",
    "# print(df[df['Class']==2]) # benign - no cancer\n",
    "# print(df[df['Class']==4]) # malignant - cancer\n",
    "\n",
    "print(f\"-> ~{round((len(df[df['Class']==4])/len(df['Class']))*100)}% of the samples are malignant - cancerous\\n\")\n",
    "\n",
    "for col in df.columns: # for each column...\n",
    "    if (col != 'Sample Code Number'): # exclude the sample ID\n",
    "        if (render_graphs):\n",
    "            x = df[df['Class']==2][col] # set 1st variable color as benign - no cancer\n",
    "            y = df[df['Class']==4][col] # set 2nd variable color as malignant - cancer\n",
    "            plt.hist([x, y], bins=np.linspace(min(x.min(), y.min()), max(x.max(), y.max()), 100), alpha=0.5, histtype='barstacked', color=['g', 'r'], label=['Benign', 'Malignant']) # plot the histogram\n",
    "            plt.legend(loc='upper right') # set the legend location\n",
    "            plt.title(col) # add a title\n",
    "            plt.xlabel(col) # add x label\n",
    "            plt.ylabel('Count #') # add y label\n",
    "            plt.show() # render the graph\n",
    "'''"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name                  Correlation\n",
      "---------------------------  -------------\n",
      "Clump Thickness              71.48%\n",
      "Uniformity of Cell Size      82.08%\n",
      "Uniformity of Cell Shape     82.19%\n",
      "Marginal Adhesion            70.63%\n",
      "Single Epithelial Cell Size  69.1%\n",
      "Bare Nuclei                  82.27%\n",
      "Bland Chromatin              75.82%\n",
      "Normal Nucleoli              71.87%\n",
      "Mitoses                      42.34%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bo1IAAGCNu+F25FfuvwsAAKxwNtzCuKoUAABY4Wy4OXrfYQAAYJi74Rb+DRtuAADACGfDLYxuAwAAVjgbbuFJaYCrEwAAgBHuhpvfCwAAAOggZ8MtjP02AABghbPh9umo1N91AAAAtJez4QYAAGCN8+HGjhsAALDC3XALj0o5yw0AABjhbLh5XFcKAACMcTfcuDgBAAAY42y4AQAAWNPj87z5o48+UnV1tQ4fPqwBAwaoX79+XbWubseTEwAAgDUd3nGrr6/XihUrdOmll6pPnz5KSUnRsGHDNGDAAH3pS1/S9OnTtW3btu5Ya5fiHDcAAGBNh8Jt6dKlSklJ0Zo1a5SVlaUnnnhClZWV2r17t8rLy1VYWKiPP/5YEydO1JVXXqk33niju9bdZdhvAwAAVnRoVLpt2zY9//zzuvDCC9v88/T0dN1www0qKSnRmjVr9MILL+j888/vkoV2NS5OAAAA1nQo3B5++OHI7+vr69W7d+82XxcMBnXzzTd/vpV1MwalAADAmk5fVXrJJZeourq6K9fiC27ACwAArOh0uI0ePVoZGRnauXNnq+OVlZX6+te//rkX1u0YlQIAAGM6HW5r1qzR9ddfr4svvlibN2/W7t27dfXVVys1NVWxsbFducZuwVWlAADAms91H7fbb79dwWBQV1xxhZqbm3X55ZervLxc6enpXbW+bhO5OMHfZQAAALRbp3fcampqNHPmTN1xxx264IIL1LNnT11//fUmoq0Vyg0AABjR6XAbMmSInn/+eT366KOqqKjQ73//e91444365S9/2ZXr6zYMSgEAgDWdHpWuXr1a11xzTeT7K6+8Us8884yuuuoq7dmzR8XFxV2ywO7ihWelbLkBAAAjOr3jdnS0hY0ZM0ZbtmzR008//bkWdSpxVSkAALCi0+F2PCkpKdqyZUtX/7Vdjv02AABgTYfCraqqql2v69u3ryRp3759HV/RKeJxkhsAADCmQ+E2duxY3XTTTdq2bdtxX1NbW6tVq1Zp+PDh+v3vf/+5F9jdAsxKAQCAER26OGHSpEk644wzdMUVVyg+Pl6pqalKSkpSfHy83nvvPb322mt69dVXNWbMGN11111R/QQFRqUAAMCaDoXb7373O+3du1cLFy7UgAEDNGjQIB08eFAffPCB+vfvr6lTpyo7O1vDhw/vrvV2HWalAADAmA6FW1JSkiorK5Wdna0PPvhAixcv1sCBA7trbacEk1IAAGBFh85x+/GPf6ycnBxdcsklCgQCeuihh7Rt2zZ98MEH3bW+bhMZlRJuAADAiA6F2y233KKXXnpJV155pTzPU3FxsTIzM9WnTx8NGzZM11xzjZYsWaI///nP3bXeLsOkFAAAWNPhJyeMGDFCI0aM0IMPPqjy8nKdfvrp+te//qXKykpVVlbqySef1KJFi1RfX98d6+0y3pE9twCXJwAAACM6/cirN954I/L7jIwMZWRkRL73LG1n0W0AAMCILn9ygmTj3mjcDgQAAFjTLeFmgaVNQQAAAMnhcAuzsDsIAAAgEW6MSgEAgBnOhpupCygAAADkcLiFMSkFAABWEG4MSwEAgBHOhhuTUgAAYI274RZ5cgIAAIANzoZbGOEGAACscDbcGJUCAABr3A238G/YcgMAAEY4G25hXFUKAACscDbcwjfg5T5uAADACnfDze8FAAAAdJCz4RbGhhsAALDC3XALb7lRbgAAwAhnw41RKQAAsMbZcAvj4gQAAGCFs+EWuarU53UAAAC0l7vh5vcCAAAAOsjdcDtSboxKAQCAFVEVbsXFxUpJSVF8fLwyMjK0devWdr1v3bp1CgQCmjx5cic+lXIDAAA2RE24rV+/Xvn5+SosLNT27ds1cuRIZWdna//+/Sd83549e/STn/xEl1xySYc+zxNPTgAAALZETbgtXbpU06dPV15eni644AKVlJTotNNO0+rVq4/7nubmZk2dOlW33367zj333A59nsdJbgAAwJioCLempiZVVFQoKysrciwmJkZZWVkqLy8/7vt+8YtfaODAgfre97530s9obGxUXV1d5KulpUUSg1IAAGBHVITbwYMH1dzcrFAo1Op4KBRSdXV1m+/ZvHmzHnjgAa1atapdn1FUVKSEhITI13//+19JUoBZKQAAMCIqwq2j6uvrdd1112nVqlXq379/u94zb9481dbWRr6Sk8/u5lUCAAB0rR5+L0CS+vfvr9jYWNXU1LQ6XlNTo8TExGNe/+9//1t79uxRTk5O5Fh49NmjRw/t2rVL5513Xqv3BINBBYPByPcxMZ80K/ttAADAiqjYcYuLi1NqaqrKysoix1paWlRWVqbMzMxjXj906FC98sorqqysjHx94xvf0GWXXabKykoNHjz4pJ8ZeXIC5QYAAIyIih03ScrPz9e0adOUlpam9PR0LVu2TA0NDcrLy5Mk5ebmKjk5WUVFRYqPj9fw4cNbvf/MM8+UpGOOHw8XlQIAAGuiJtymTJmiAwcOqKCgQNXV1Ro1apRKS0sjFyxUVVVFxptdIfLkBIalAADAiIDnuXlHs7SCp1Sx8P9U/nqV/mfoyUerAAAAfouKc9z84WSvAgAAw5wNNzf3GQEAgGXOhlsYV5UCAAArnA238IYbFycAAAAr3A03RqUAAMAYZ8MtjFEpAACwwuFw48kJAADAFmfDjVEpAACwxtlwC+PiBAAAYIWz4Ra5qpRuAwAARrgbbsxKAQCAMe6G25Ff2XADAABWOBtuEZQbAAAwwtlw+3RSSrkBAAAb3A03cY4bAACwxdlwC+OqUgAAYIWz4cbFCQAAwBpnw41JKQAAsMbdcDsiwKwUAAAY4Wy4MSoFAADWuBtuPDkBAAAY42y4hTEpBQAAVjgbbp+OSik3AABgg7vhxqQUAAAY4264HfmV/TYAAGCFs+EGAABgjbPhFh6VcnECAACwwt1w83sBAAAAHeRsuIXx5AQAAGCFu+EWHpX6uwoAAIB2czfcGJYCAABjHA63TzApBQAAVjgbbpGrShmWAgAAI9wNN78XAAAA0EHuhtuRLTdGpQAAwApnwy2MbgMAAFY4G26RUSnlBgAAjHA33DjJDQAAGONsuIVxVSkAALCCcKPbAACAEU6Gm8ecFAAAGORkuB2NDTcAAGCFk+HWasONWSkAADDCzXDzewEAAACd4GS4HY39NgAAYIWT4Xb0xQlMSgEAgBVuhpvfCwAAAOgEN8PtqHLjBrwAAMAKJ8OtFboNAAAY4WS4eeIcNwAAYI+b4cZJbgAAwCAnw+1obLgBAAArCDdmpQAAwAgnw41RKQAAsMjJcDsa+20AAMAKJ8ONq0oBAIBFboYbo1IAAGCQm+F21O95cgIAALDCyXA7GqNSAABghZPh5jErBQAABrkZbn4vAAAAoBOcDLejMSoFAABWOBluR09KuTgBAABY4WS4MSsFAAAWuRluR2FUCgAArHAy3Fo9OcHHdQAAAHREVIVbcXGxUlJSFB8fr4yMDG3duvW4r121apUuueQS9e3bV3379lVWVtYJX3807gYCAAAsippwW79+vfLz81VYWKjt27dr5MiRys7O1v79+9t8/bPPPqtrr71WzzzzjMrLyzV48GBNnDhR+/bt69DnBpiVAgAAIwJelNyNNiMjQ2PHjtXy5cslSS0tLRo8eLBuueUWzZ0796Tvb25uVt++fbV8+XLl5uae8LXvNjRp1G1Pau+yq/Xee4d05pkJXfJvAAAA6E5RsePW1NSkiooKZWVlRY7FxMQoKytL5eXl7fo7Dh8+rI8++kj9+vVr888bGxtVV1cX+QIAALAmKsLt4MGDam5uVigUanU8FAqpurq6XX/HnDlzlJSU1Cr+jlZUVKSEhAQlJCTovC9/OXKcSSkAALAiKsLt81qyZInWrVunxx9/XPHx8W2+Zt68eaqtrVVtba3efPPNyHHOcQMAAFb08HsBktS/f3/Fxsaqpqam1fGamholJiae8L2/+tWvtGTJEv3tb3/TiBEjjvu6YDCoYDAoSWoMNH7+RQMAAJxiUbHjFhcXp9TUVJWVlUWOtbS0qKysTJmZmcd931133aWFCxeqtLRUaWlp7f48j0cnAAAAg6Jix02S8vPzNW3aNKWlpSk9PV3Lli1TQ0OD8vLyJEm5ublKTk5WUVGRJOnOO+9UQUGB1q5dq5SUlMi5cGeccYbOOOMM3/4dAAAA3SVqwm3KlCk6cOCACgoKVF1drVGjRqm0tDRywUJVVZViYj7dIFyxYoWampr0rW99q9XfU1hYqJ///Ocn/jA23AAAgEFRcx+3U6mm7kONLfyj9i67WrW1terTp4/fSwIAADipqDjHDQAAACfnZLi5t8cIAAC+CNwMN05yAwAABrkZbnQbAAAwyMlwAwAAsMjJcGPDDQAAWORmuDErBQAABjkZbgAAABY5GW5suAEAAIucDDcAAACLCDcAAAAjnAw3RqUAAMAiN8ONG4IAAACDnAw3AAAAi5wMN0alAADAIjfDze8FAAAAdIKb4caWGwAAMMjJcAMAALDIyXBjvw0AAFjkZrhRbgAAwCAnww0AAMAiR8ONLTcAAGCPk+HGqBQAAFjkZLgBAABY5GS4seEGAAAscjPcKDcAAGCQm+HGnhsAADDIyXADAACwyMlwY1QKAAAsItwAAACMcDLcAAAALHIy3Lg4AQAAWORmuNFtAADAICfDDQAAwCLCDQAAwAgnw41RKQAAsMjJcAMAALDIyXDjqlIAAGCRm+FGtwEAAIPcDDe/FwAAANAJToYbAACARU6Gm8esFAAAGORmuPm9AAAAgE5wMtwAAAAscjLcmJQCAACLnAw3hqUAAMAiR8MNAADAHifDjVEpAACwyM1w83sBAAAAneBmuFFuAADAICfDDQAAwCInw40nJwAAAIvcDDe/FwAAANAJToYbAACARU6GG5NSAABgkZvhxrAUAAAY5GS4AQAAWORmuLHhBgAADHIy3Og2AABgkZPhBgAAYJGT4cZVpQAAwCI3w41hKQAAMMjNcKPbAACAQU6GGwAAgEVOhhsbbgAAwCI3w41ZKQAAMCiqwq24uFgpKSmKj49XRkaGtm7desLXP/rooxo6dKji4+N10UUXaePGjadopQAAAKde1ITb+vXrlZ+fr8LCQm3fvl0jR45Udna29u/f3+brt2zZomuvvVbf+9739PLLL2vy5MmaPHmyduzYcdLPYr8NAABYFPCiZG6YkZGhsWPHavny5ZKklpYWDR48WLfccovmzp17zOuvyPuJPmxs1K3z50eOzZ07TylDUnTzTTed8LN27KvTPaX/0t5lV6u2tlZ9+vTp2n8MAABAN+jScPM8T/X19R1+X1NTk0KhkH7729/qqquuihy/6aabVFtbq3Xr1h3zngsXbFQgJrbTa21pPKx9K67X3r17CTcAABAVevfurUAgcNw/79Jwq6urU0JCQlf9dQAAAE452SQwKnbc3nnnHQ0dOlSbNm1Senp65PiCBQv097//XU8//fQx7znrrLNUUlKib3/725Fjq1at0pIlS/Tvf//7mNc3NjaqsbGx1Wemp6frtddeU3JycofXjO5XV1enwYMHsysapfj5RD9+RtGNn0/08+NndLIdtx5d+WGBQKBT/7D4+HjFxsbq/fffb/X+Q4cOKTk5uc2/c9CgQaqvr2/1Z3V1dUpKSurQGnr37s3/YKJcnz59+BlFMX4+0Y+fUXTj5xP9oulnFBVXlcbFxSk1NVVlZWWRYy0tLSorK1NmZmab78nMzGz1eknatGnTcV8PAABgXZfuuH0e+fn5mjZtmtLS0pSenq5ly5apoaFBeXl5kqTc3FwlJyerqKhIkjRz5kxdeumluvvuuzVp0iStW7dOL730klauXOnnPwMAAKDbRE24TZkyRQcOHFBBQYGqq6s1atQolZaWKhQKSZKqqqoUE/PpBuG4ceO0du1a3XbbbZo/f77OP/98PfHEExo+fHi7Pi8YDLb6FdEnGAyqsLCQn1GU4ucT/fgZRTd+PtEvGn9GUXMft1MtfAUs93EDAABWRMU5bgAAADg5wg0AAMAIwg0AAMAIwg0AAMAIZ8MtfNuQAQMGKCMjQ1u3bvV5RZCkoqIijR07Vr1799bAgQM1efJk7dq1y+9l4QSWLFmiQCCgWbNm+b0UHLFv3z5997vf1VlnnaVevXrpoosu0ksvveT3snBEc3OzFixYoCFDhqhXr14677zztHDhQjl6rWBUeP7555WTk6OkpCQFAgE98cQTrf7c8zwVFBRo0KBB6tWrl7KysvTGG2/4slYnw239+vWaP3++JOmFF17QyJEjlZ2drf379/u8Mjz33HOaMWOGXnzxRW3atEkfffSRJk6cqIaGBr+XhjZs27ZN9913n0aMGOH3UnDEe++9p/Hjx6tnz57685//rNdee0133323+vbt6/fScMSdd96pFStWaPny5Xr99dd155136q677tK9997r99Kc1dDQoJEjR6q4uLjNP7/rrrt0zz33qKSkRP/4xz90+umnKzs7Wx9++OEpXqkkz0Hp6ene9OnTPUlebW2t19zc7CUlJXlFRUV+Lw2fsX//fk+S99xzz/m9FHxGfX29d/7553ubNm3yLr30Um/mzJl+Lwme582ZM8e7+OKL/V4GTmDSpEneDTfc0OrYN7/5TW/q1Kk+rQhHk+Q9/vjjke9bWlq8xMRE75e//GXk2KFDh7xgMOg9/PDDp3x9zu24NTU1qaKiQhMmTIgci4mJUVZWlsrLy/1bGNpUW1srSerXr5/PK8FnzZgxQ5MmTVJWVpbfS8FRnnrqKaWlpenb3/62Bg4cqNGjR2vVqlV+LwtHGTdunMrKyrR7925J0j//+U9t3rxZX/va13xeGdry9ttvq7q6utX/1yUkJCgjI8OXboiaJyecKgcPHlRzc7MGDhzY6ngoFNLOnTt9WhXa0tLSolmzZmn8+PHtfiIGTo1169Zp+/bt2rZtm99LwWe89dZbWrFihfLz8zV//nxt27ZNP/rRjxQXF6dp06b5vTxImjt3rurq6jR06FDFxsaqublZixYt0tSpU/1eGtpQXV0tSZEnOYWFQqHIn51KzoUb7JgxY4Z27NihzZs3+70UHGXv3r2aOXOmNm3apPj4eL+Xg89oaWlRWlqaFi9eLEkaPXq0duzYoZKSEsItSjzyyCN66KGHtHbtWl144YWqrKzUrFmzlJSUxM8IJ+XcqLR///6KjY095kKEmpoaJSYm+rQqfNYPf/hD/elPf9Izzzyjs88+2+/l4CgVFRXav3+/xowZox49eqhHjx567rnndM8996hHjx5qbm72e4lOGzRokC644IJWx4YNG6aqqiqfVoTP+ulPf6q5c+fqmmuu0UUXXaTrrrtOs2fPVlFRkd9LQxvCbVBTU9PquF/d4Fy4xcXFKTU1Vc8991zkWEtLi8rKypSZmenjyiB9csn1D3/4Qz3++ON6+umnNWTIEL+XhM+4/PLL9corr6iysjLylZaWpqlTp6qyslKxsbF+L9Fp48ePP+YWOrt379aXvvQln1aEzzp8+LBiYlr/5zc2NlYtLS0+rQgnMmTIECUmJqqsrCxyrK6uTv/4xz986QYnR6X5+fnKzc2VJO3atUv333+/GhoalJeX5/PKMGPGDK1du1ZPPvmkevfuHTl/ICEhQb169fJ5dZCk3r17H3PO4emnn66zzjqLcxGjwOzZszVu3DgtXrxYV199tbZu3aqVK1dG7l0J/+Xk5GjRokU655xzdOGFF+rll1/W0qVLdcMNN/i9NGe9//77evPNNyPfv/3226qsrFS/fv10zjnnaNasWbrjjjt0/vnna8iQIVqwYIGSkpI0efLkU7/YU34da5S45557vLPPPtvr2bOnl56e7r344ot+LwneJ5dht/W1Zs0av5eGE+B2INHlj3/8ozd8+HAvGAx6Q4cO9VauXOn3knCUuro6b+bMmd4555zjxcfHe+eee6536623eo2NjX4vzVnPPPNMm//tmTZtmud5n9wSZMGCBV4oFPKCwaB3+eWXe7t27fJlrQHP41bNAAAAFjh3jhsAAIBVhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBsAAIARhBuAL7SHH35YvXr10jvvvBM5lpeXpxEjRqi2ttbHlQFAx/GQeQBfaJ7nadSoUfrf//1f3XvvvSosLNTq1av14osvKjk52e/lAUCH9PB7AQDQnQKBgBYtWqRvfetbSkxM1L333qsXXniBaANgEjtuAJwwZswYvfrqq/rrX/+qSy+91O/lAECncI4bgC+80tJS7dy5U83NzQqFQn4vBwA6jR03AF9o27dv14QJE3TffffpwQcfVJ8+ffToo4/6vSwA6BTOcQPwhbVnzx5NmjRJ8+fP17XXXqtzzz1XmZmZ2r59u8aMGeP38gCgw9hxA/CF9O6772rcuHGaMGGCSkpKIscnTZqk5uZmlZaW+rg6AOgcwg0AAMAILk4AAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAw4v8D8HlSJe1TsLcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ibLgxKgUAANa4G26Hf+X+uwAAwApnwy2Cq0oBAIAVzoabo/cdBgAAhrkbbpHfsOEGAACMcDbcIug2AABghbPhFpmUBrg6AQAAGOFuuPm9AAAAgE5yNtwi2G8DAABWOBtuH49K/V0HAABARzkbbgAAANY4H27suAEAACvcDbfIqJSz3AAAgBHOhpvHdaUAAMAYd8ONixMAAIAxzoYbAACANb1O5s2HDh1SbW2tDh48qEGDBmnAgAHdta4ex5MTAACANZ3ecWtsbNTKlSt18cUXq1+/fkpLS9PIkSM1aNAgnX322Zo5c6a2bt3aE2vtVpzjBgAArOlUuC1fvlxpaWm6//77lZOTo0cffVTV1dXatWuXKisrVVxcrA8//FCXX365rrjiCr322ms9te5uw34bAACwolOj0q1bt+rZZ5/V+eef3+6fZ2Zm6vrrr1dZWZnuv/9+Pffccxo+fHi3LLS7cXECAACwplPh9uCDD0Z/39jYqL59+7b7umAwqJtuuunkVtbDGJQCAABrunxV6UUXXaTa2truXIsvuAEvAACwosvhNm7cOGVlZWnHjh1tjldXV+vLX/7ySS+sxzEqBQAAxnQ53O6//35dd911uvDCC7Vp0ybt2rVLV111ldLT0xUfH9+da+wRXFUKAACsOan7uN1+++0KBoO67LLLFA6Hdemll6qyslKZmZndtb4eE704wd9lAAAAdFiXd9zq6uo0e/Zs3XHHHTrvvPPUu3dvXXfddSairQ3KDQAAGNHlcBs2bJieffZZPfTQQ6qqqtLvf/973XDDDfrpT3/anevrMQxKAQCANV0ela5Zs0ZXX3119PsrrrhCTz31lK688kq99dZbKi0t7ZYF9hQvMitlyw0AABjR5R23I6MtYvz48dq8ebOefPLJk1rUp4mrSgEAgBVdDrdjSUtL0+bNm7v7r+127LcBAABrOhVuNTU1HXpd//79JUl79uzp/Io+JR4nuQEAAGM6FW4TJkzQjTfeqK1btx7zNfX19Vq9erVGjRql3//+9ye9wJ4WYFYKAACM6NTFCVOmTNFpp52myy67TImJiUpPT1dKSooSExP17rvv6pVXXtHLL7+s8ePH66677orpJygwKgUAANZ0Ktx+85vfaPfu3Vq0aJEGDRqkIUOGaP/+/Xr//fc1cOBATZ8+Xbm5uRo1alRPrbf7MCsFAADGdCrcUlJSVF1drdzcXL3//vtasmSJBg8e3FNr+1QwKQUAAFZ06hy373//+8rLy9NFF12kQCCg3/72t9q6davef//9nlpfj4mOSgk3AABgRKfC7eabb9YLL7ygK664Qp7nqbS0VNnZ2erXr59Gjhypq6++WkuXLtWf//znnlpvt2FSCgAArOn0kxNGjx6t0aNH64EHHlBlZaVOPfVU/etf/1J1dbWqq6v12GOPafHixWpsbOyJ9XYb7/CeW4DLEwAAgBFdfuTVa6+9Fv19VlaWsrKyot97lraz6DYAAGBEtz85QbJxbzRuBwIAAKzpkXCzwNKmIAAAgORwuEVY2B0EAACQCDdGpQAAwAxnw83UBRQAAAByONwimJQCAAArCDeGpQAAwAhnw41JKQAAsMbdcIs+OQEAAMAGZ8MtgnADAABWOBtujEoBAIA17oZb5DdsuQEAACOcDbcIrioFAABWOBtukRvwch83AABghbvh5vcCAAAAOsnZcItgww0AAFjhbrhFttwoNwAAYISz4caoFAAAWONsuEVwcQIAALDC2XCLXlXq8zoAAAA6yt1w83sBAAAAneRuuB0uN0alAADAipgKt9LSUqWlpSkxMVFZWVnasmVLh963bt06BQIBTZ06tQufSrkBAAAbYibc1q9fr8LCQhUXF2vbtm0aM2aMcnNztXfv3uO+76233tIPfvADXXTRRZ36PE88OQEAANgSM+G2fPlyzZw5UwUFBTrvvPNUVlamU045RWvWrDnme8LhsKZPn67bb79d55xzTqc+z+MkNwAAYExMhFtLS4uqqqqUk5MTPRYXF6ecnBxVVlYe830/+clPNHjwYH3rW9864Wc0NzeroaEh+tXa2iqJQSkAALAjJsJt//79CofDCoVCbY6HQiHV1ta2+55Nmzbpl7/8pVavXt2hzygpKVFSUlL067///a8kKcCsFAAAGBET4dZZjY2Nuvbaa7V69WoNHDiwQ++ZP3++6uvro1+pqWf28CoBAAC6Vy+/FyBJAwcOVHx8vOrq6tocr6urU3Jy8lGv//e//6233npLeXl50WOR0WevXr20c+dOnXvuuW3eEwwGFQwGo9/HxX3UrOy3AQAAK2Jixy0hIUHp6emqqKiIHmttbVVFRYWys7OPev2IESP00ksvqbq6Ovr1la98RZdccomqq6s1dOjQE35m9MkJlBsAADAiJnbcJKmwsFAzZsxQRkaGMjMztWLFCjU1NamgoECSlJ+fr9TUVJWUlCgxMVGjRo1q8/7TTz9dko46fixcVAoAAKyJmXCbNm2a9u3bp6KiItXW1mrs2LEqLy+PXrBQU1MTHW92h+iTExiWAgAAIwKe5+YdzTKKHlfVov9T5as1+p8RJx6tAgAA+C0mznHzh5O9CgAADHM23NzcZwQAAJY5G24RXFUKAACscDbcIhtuXJwAAACscDfcGJUCAABjnA23CEalAADACofDjScnAAAAW5wNN0alAADAGmfDLYKLEwAAgBXOhlv0qlK6DQAAGOFuuDErBQAAxrgbbod/ZcMNAABY4Wy4RVFuAADACGfD7eNJKeUGAABscDfcxDluAADAFmfDLYKrSgEAgBXOhhsXJwAAAGucDTcmpQAAwBp3w+2wALNSAABghLPhxqgUAABY42648eQEAABgjLPhFsGkFAAAWOFsuH08KqXcAACADe6GG5NSAABgjLvhdvhX9tsAAIAVzoYbAACANc6GW2RUysUJAADACnfDze8FAAAAdJKz4RbBkxMAAIAV7oZbZFTq7yoAAAA6zN1wY1gKAACMcTjcPsKkFAAAWOFsuEWvKmVYCgAAjHA33PxeAAAAQCe5G26Ht9wYlQIAACucDbcIug0AAFjhbLhFR6WUGwAAMMLdcOMkNwAAYIyz4RbBVaUAAMAKwo1uAwAARjgZbh5zUgAAYJCT4XYkNtwAAIAVToZbmw03ZqUAAMAIN8PN7wUAAAB0gZPhdiT22wAAgBVOhtuRFycwKQUAAFa4GW5+LwAAAKAL3Ay3I8qNG/ACAAArnAy3Nug2AABghJPh5olz3AAAgD1uhhsnuQEAAIOcDLcjseEGAACsINyYlQIAACOcDDdGpQAAwCInw+1I7LcBAAArnAw3rioFAAAWuRlujEoBAIBBbobbEb/nyQkAAMAKJ8PtSIxKAQCAFU6Gm8esFAAAGORmuPm9AAAAgC5wMtyOxKgUAABY4WS4HTkp5eIEAABghZPhxqwUAABY5Ga4HYFRKQAAsMLJcGvz5AQf1wEAANAZMRVupaWlSktLU2JiorKysrRly5Zjvnb16tW66KKL1L9/f/Xv3185OTnHff2RuBsIAACwKGbCbf369SosLFRxcbG2bdumMWPGKDc3V3v37m339U8//bSuueYaPfXUU6qsrNTQoUN1+eWXa8+ePZ363ACzUgAAYETAi5G70WZlZWnChAm69957JUmtra0aOnSobr75Zs2bN++E7w+Hw+rfv7/uvfde5efnH/e17zS1aOxtj2n3iqv07rsHdPrpSd3ybwAAAOhJMbHj1tLSoqqqKuXk5ESPxcXFKScnR5WVlR36Ow4ePKhDhw5pwIAB7f55c3OzGhoaol8AAADWxES47d+/X+FwWKFQqM3xUCik2traDv0dt9xyi1JSUtrE35FKSkqUlJSkpKQknfu5z0WPMykFAABWxES4naylS5dq3bp1euSRR5SYmNjua+bPn6/6+nrV19fr9ddfjx7nHDcAAGBFL78XIEkDBw5UfHy86urq2hyvq6tTcnLycd/7s5/9TEuXLtXf/vY3jR49+pivCwaDCgaDkqTmQPPJLxoAAOBTFhM7bgkJCUpPT1dFRUX0WGtrqyoqKpSdnX3M9911111atGiRysvLlZGR0eHP83h0AgAAMCgmdtwkqbCwUDNmzFBGRoYyMzO1YsUKNTU1qaCgQJKUn5+v1NRUlZSUSJLuvPNOFRUVae3atUpLS4ueC3faaafptNNO8+3fAQAA0FNiJtymTZumffv2qaioSLW1tRo7dqzKy8ujFyzU1NQoLu7jDcKVK1eqpaVFX/va19r8PcXFxfrxj398/A9jww0AABgUM/dx+zTVNXygCcV/1O4VV6m+vl79+vXze0kAAAAnFBPnuAEAAODEnAw39/YYAQDAZ4Gb4cZJbgAAwCA3w41uAwAABjkZbgAAABY5GW5suAEAAIvcDDdmpQAAwCAnww0AAMAiJ8ONDTcAAGCRk+EGAABgEeEGAABghJPhxqgUAABY5Ga4cUMQAABgkJPhBgAAYJGT4caoFAAAWORmuPm9AAAAgC5wM9zYcgMAAAY5GW4AAAAWORlu7LcBAACL3Aw3yg0AABjkZLgBAABY5Gi4seUGAADscTLcGJUCAACLnAw3AAAAi5wMNzbcAACARW6GG+UGAAAMcjPc2HMDAAAGORluAAAAFjkZboxKAQCARYQbAACAEU6GGwAAgEVOhhsXJwAAAIvcDDe6DQAAGORkuAEAAFhEuAEAABjhZLgxKgUAABY5GW4AAAAWORluXFUKAAAscjPc6DYAAGCQm+Hm9wIAAAC6wMlwAwAAsMjJcPOYlQIAAIPcDDe/FwAAANAFToYbAACARU6GG5NSAABgkZPhxrAUAABY5Gi4AQAA2ONkuDEqBQAAFrkZbn4vAAAAoAvcDDfKDQAAGORkuAEAAFjkZLjx5AQAAGCRm+Hm9wIAAAC6wMlwAwAAsMjJcGNSCgAALHIz3BiWAgAAg5wMNwAAAIvcDDc23AAAgEFOhhvdBgAALHIy3AAAACxyMty4qhQAAFjkZrgxLAUAAAa5GW50GwAAMMjJcAMAALDIyXBjww0AAFjkZrgxKwUAAAbFVLiVlpYqLS1NiYmJysrK0pYtW477+oceekgjRoxQYmKiLrjgAj3xxBOf0koBAAA+fTETbuvXr1dhYaGKi4u1bds2jRkzRrm5udq7d2+7r9+8ebOuueYafetb39KLL76oqVOnaurUqdq+ffsJP4v9NgAAYFHAi5G5YVZWliZMmKB7771XktTa2qqhQ4fq5ptv1rx58yRJh8Ktqni1TpK0bNlyfdD8gW5dsCD6d8ybN19pw9J00403Hveztu9p0N3l/9LuFVepvr5e/fr166F/FQAAQPfplnDzPE+NjY1dfn9LS4tCoZB+/etf68orr4wev/HGG1VfX69169ZJkho+OKSJJU+e7HIlSa3NB7Vn5XXavXs34QYAAHzXt29fBQKB476mW8KtoaFBSUlJJ/vXAAAAOKsjU8CY2HF7++23NWLECG3cuFGZmZnR4wsXLtTf//53Pfnk0btsZ5xxhsrKyvT1r389emz16tVaunSp/v3vfx/1+ubmZjU3N7f5zMzMTL3yyitKTU3t8trRcxoaGjR06FB2RWMUP5/Yx88otvHziW1+/Hw6suPWqzs+KBAInNQ/KjExUfHx8Xrvvffa/D0HDhxQampqu3/3kCFD1NjY2ObPGhoalJKS0qm19O3bl//BxLh+/frxM4ph/HxiHz+j2MbPJ7bF2s8nJq4qTUhIUHp6uioqKqLHWltbVVFRoezs7Hbfk52d3eb1krRx48Zjvh4AAMC6btlx6w6FhYWaMWOGMjIylJmZqRUrVqipqUkFBQWSpPz8fKWmpqqkpESSNHv2bF188cVatmyZpkyZonXr1umFF17QqlWr/PxnAAAA9JiYCbdp06Zp3759KioqUm1trcaOHavy8nKFQiFJUk1NjeLiPt4gnDhxotauXavbbrtNCxYs0PDhw/Xoo49q1KhRHfq8YDDY5lfEnmAwqOLiYn5GMYqfT+zjZxTb+PnEtlj9+cTMfdw+bZErYbmPGwAAsCImznEDAADAiRFuAAAARhBuAAAARhBuAAAARjgbbpHbhgwaNEhZWVnasmWLzyuCJJWUlGjChAnq27evBg8erKlTp2rnzp1+LwvHsHTpUgUCAc2ZM8fvpeAIe/bs0Te/+U2dccYZ6tOnjy644AK98MILfi8Lh4XDYS1cuFDDhg1Tnz59dO6552rRokVy9FpB3z377LPKy8tTSkqKAoGAHn300TZ/7nmeioqKNGTIEPXp00c5OTl67bXX/FmsHA239evXa8GCBZKk5557TmPGjFFubq727t3r88rwzDPPaNasWXr++ee1ceNGHTp0SJdffrmampr8Xho+YevWrbrvvvs0evRov5eCI7z77ruaNGmSevfurT//+c965ZVXtGzZMvXv39/vpeGwO++8UytXrtS9996rV199VXfeeafuuusu3XPPPX4vzUlNTU0aM2aMSktL2/3zu+66S3fffbfKysr0j3/8Q6eeeqpyc3P1wQcffMorPcxzUGZmpjdz5kxPkldfX++Fw2EvJSXFKykp8Xtp+IS9e/d6krxnnnnG76XgCI2Njd7w4cO9jRs3ehdffLE3e/Zsv5eEw2655Rbvwgsv9HsZOI4pU6Z4119/fZtjX/3qV73p06f7tCJESPIeeeSR6Petra1ecnKy99Of/jR67MCBA14wGPQefPBBH1boec7tuLW0tKiqqkqTJ0+OHouLi1NOTo4qKyv9WxjaVV9fL0kaMGCAzyvBkWbNmqUpU6YoJyfH76XgEx5//HFlZGTo61//ugYPHqxx48Zp9erVfi8LR5g4caIqKiq0a9cuSdI///lPbdq0SV/60pd8Xhk+6c0331RtbW2b/69LSkpSVlaWb80QM09O+LTs379f4XBYgwcPbnM8FAppx44dPq0K7WltbdWcOXM0adKkDj8RAz1v3bp12rZtm7Zu3er3UtCON954QytXrlRhYaEWLFigrVu36nvf+54SEhI0Y8YMv5cHSfPmzVNDQ4NGjBih+Ph4hcNhLV68WNOnT/d7afiE2tpaSYo+xSkiFApF/+zT5ly4wY5Zs2Zp+/bt2rRpk99LwWG7d+/W7NmztXHjRiUmJvq9HLSjtbVVGRkZWrJkiSRp3Lhx2r59u8rKygi3GPG73/1Ov/3tb7V27Vqdf/75qq6u1pw5c5SSksLPCCfk3Kh04MCBio+PP+pChLq6OiUnJ/u0KnzSd7/7Xf3pT3/SU089pTPPPNPv5eCwqqoq7d27V+PHj1evXr3Uq1cvPfPMM7r77rvVq1cvhcNhv5fovCFDhui8885rc2zkyJGqqanxaUX4pB/+8IeaN2+err76al1wwQW69tprNXfuXJWUlPi9NHxCpAvq6uraHPezGZwLt4SEBKWnp+uZZ56JHmttbVVFRYWys7N9XBmkjy67/u53v6tHHnlETz75pIYNG+b3knCESy+9VC+99JKqq6ujXxkZGZo+fbqqq6sVHx/v9xKdN2nSpKNuobNr1y6dffbZPq0In3Tw4EHFxbX9z298fLxaW1t9WhGOZdiwYUpOTlZFRUX0WENDg/7xj3/41gxOjkoLCwuVn58vSdq5c6d+8YtfqKmpSQUFBT6vDLNmzdLatWv12GOPqW/fvtFzCJKSktSnTx+fV4e+ffsedb7hqaeeqjPOOIPzEGPE3LlzNXHiRC1ZskRXXXWVtmzZolWrVkXvXQn/5eXlafHixTrrrLN0/vnn68UXX9Ty5ct1/fXX+700J7333nt6/fXXo9+/+eabqq6u1oABA3TWWWdpzpw5uuOOOzR8+HANGzZMCxcuVEpKiqZOnerPgn25ljUG3H333d6ZZ57p9e7d28vMzPSef/55v5cE76NLsdv7uv/++/1eGo6B24HEnj/+8Y/eqFGjvGAw6I0YMcJbtWqV30vCERoaGrzZs2d7Z511lpeYmOidc8453q233uo1Nzf7vTQnPfXUU+3+d2fGjBme5310S5CFCxd6oVDICwaD3qWXXurt3LnTt/UGPI9bNQMAAFjg3DluAAAAVhFuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAAAARhBuAD6zHnzwQfXp00dvv/129FhBQYFGjx6t+vp6H1cGAF3DQ+YBfGZ5nqexY8fqi1/8ou655x4VFxdrzZo1ev7555Wamur38gCg03r5vQAA6CmBQECLFy/W1772NSUnJ+uee+7Rc889R7QBMIsdNwCfeePHj9fLL7+sv/71r7r44ov9Xg4AdBnnuAH4TCsvL9eOHTsUDocVCoX8Xg4AnBR23AB8Zm3btk2TJ0/WfffdpwceeED9+vXTQw895PeyAKDLOMcNwGfSW2+9pSlTpmjBggW65pprdM455yg7O1vbtm3T+PHj/V4eAHQJO24APnPeeecdTZw4UZMnT1ZZWVn0+JQpUxQOh1VeXu7j6gCg6wg3AAAAI7g4AQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwAjCDQAAwIj/D/j4WtP0S/LJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VAgAAa9wNt4O/cv9dAABghbPhFsFVpQAAwApnw83R+w4DAADD3A23yG/YcAMAAEY4G24RdBsAALDC2XCLTEoDXJ0AAACMcDfc/F4AAABAFzkbbhHstwEAACucDbdDo1J/1wEAANBZzoYbAACANc6HGztuAADACnfDLTIq5Sw3AABghLPh5nFdKQAAMMbdcOPiBAAAYIyz4QYAAGBNr+N58yeffKLa2lodOHBAAwcO1GmnnXai1tXjeHICAACwpss7bo2NjVq+fLkuvvhi9evXT2lpaRo+fLgGDhyos846S9OnT9eWLVt6Yq0nFOe4AQAAa7oUbkuXLlVaWpoefPBB5eTk6IknnlB1dbV27typyspKFRcX69NPP9Xll1+uK664Qm+++WZPrfuEYb8NAABY0aVR6ZYtW/TCCy/o/PPP7/DPMzMzdcMNN6isrEwPPvigXnzxRZ177rknZKEnGhcnAAAAa7oUbo888kj0942Njerbt2+HrwsGg7r55puPb2U9jEEpAACwpttXlV500UWqra09kWvxBTfgBQAAVnQ73MaMGaOsrCxt37693fHq6mp97WtfO+6F9ThGpQAAwJhuh9uDDz6o66+/XhdeeKE2btyonTt36qqrrlJ6erri4+NP5Bp7BFeVAgAAa47rPm533HGHgsGgLrvsMoXDYV166aWqrKxUZmbmiVpfj4lenODvMgAAADqt2ztudXV1mjlzpu68806dd9556t27t66//noT0dYO5QYAAIzodrgNHTpUL7zwgh599FFVVVXp97//vW688Ub9/Oc/P5Hr6zEMSgEAgDXdHpWuWrVKV199dfT7K664Qs8995yuvPJKvfvuuyotLT0hC+wpXmRWypYbAAAwots7bm2jLWLs2LHatGmTnn322eNa1OeJq0oBAIAV3Q63I0lLS9OmTZtO9F97wrHfBgAArOlSuNXU1HTqdf3795ck7d69u+sr+px4nOQGAACM6VK4jRs3TjfddJO2bNlyxNfU19dr5cqVGjFihH7/+98f9wJ7WoBZKQAAMKJLFydMmjRJp5xyii677DIlJiYqPT1dKSkpSkxM1AcffKDXX39dr732msaOHau77747pp+gwKgUAABY06Vw++1vf6tdu3ZpwYIFGjhwoAYPHqx9+/bpo48+0oABAzR16lTl5uZqxIgRPbXeE4dZKQAAMKZL4ZaSkqLq6mrl5ubqo48+0qJFizRo0KCeWtvngkkpAACwokvnuP3whz9UXl6eLrroIgUCAT388MPasmWLPvroo55aX4+JjkoJNwAAYESXwu2WW27Ryy+/rCuuuEKe56m0tFTZ2dnq16+fhg8frquvvlqLFy/Wn/70p55a7wnDpBQAAFjT5ScnjBw5UiNHjtRDDz2kyspKnXzyyfrnP/+p6upqVVdX68knn9TChQvV2NjYE+s9YbyDe24BLk8AAABGdPuRV2+++Wb091lZWcrKyop+71nazqLbAACAESf8yQmSjXujcTsQAABgTY+EmwWWNgUBAAAkh8MtwsLuIAAAgES4MSoFAABmOBtupi6gAAAAkMPhFsGkFAAAWEG4MSwFAABGOBtuTEoBAIA17oZb9MkJAAAANjgbbhGEGwAAsMLZcGNUCgAArHE33CK/YcsNAAAY4Wy4RXBVKQAAsMLZcIvcgJf7uAEAACvcDTe/FwAAANBFzoZbBBtuAADACnfDLbLlRrkBAAAjnA03RqUAAMAaZ8MtgosTAACAFc6GW/SqUp/XAQAA0FnuhpvfCwAAAOgid8PtYLkxKgUAAFbEVLiVlpYqLS1NiYmJysrK0ubNmzv1vjVr1igQCGjy5Mnd+FTKDQAA2BAz4bZ27VoVFhaquLhYW7du1ahRo5Sbm6s9e/Yc9X3vvvuufvSjH+miiy7q0ud54skJAADAlpgJt6VLl2r69OkqKCjQeeedp7KyMp100klatWrVEd8TDoc1depU3XHHHTr77LO79HkeJ7kBAABjYiLcWlpaVFVVpZycnOixuLg45eTkqLKy8ojv+9nPfqZBgwbpO9/5zjE/o7m5WQ0NDdGv1tZWSQxKAQCAHTERbvv27VM4HFYoFGp3PBQKqba2tsP3bNy4UQ888IBWrlzZqc8oKSlRUlJS9Os///mPJCnArBQAABgRE+HWVY2Njbruuuu0cuVKDRgwoFPvmTt3rurr66Nfqaln9PAqAQAATqxefi9AkgYMGKD4+HjV1dW1O15XV6fk5OTDXv+vf/1L7777rvLy8qLHIqPPXr16aceOHTrnnHPavScYDCoYDEa/j4v7rFnZbwMAAFbExI5bQkKC0tPTVVFRET3W2tqqiooKZWdnH/b6YcOG6dVXX1V1dXX06+tf/7ouueQSVVdXa8iQIcf8zOiTEyg3AABgREzsuElSYWGhpk2bpoyMDGVmZmrZsmVqampSQUGBJCk/P1+pqakqKSlRYmKiRowY0e79p556qiQddvxIuKgUAABYEzPhNmXKFO3du1dFRUWqra3V6NGjVV5eHr1goaamJjrePBGiT05gWAoAAIwIeJ6bdzTLKHpKVQv+T5Vv1Oh/hh17tAoAAOC3mDjHzR9O9ioAADDM2XBzc58RAABY5my4RXBVKQAAsMLZcItsuHFxAgAAsMLdcGNUCgAAjHE23CIYlQIAACscDjeenAAAAGxxNtwYlQIAAGucDbcILk4AAABWOBtu0atK6TYAAGCEu+HGrBQAABjjbrgd/JUNNwAAYIWz4RZFuQEAACOcDbdDk1LKDQAA2OBuuIlz3AAAgC3OhlsEV5UCAAArnA03Lk4AAADWOBtuTEoBAIA17obbQQFmpQAAwAhnw41RKQAAsMbdcOPJCQAAwBhnwy2CSSkAALDC2XA7NCql3AAAgA3uhhuTUgAAYIy74XbwV/bbAACAFc6GGwAAgDXOhltkVMrFCQAAwAp3w83vBQAAAHSRs+EWwZMTAACAFe6GW2RU6u8qAAAAOs3dcGNYCgAAjHE43D7DpBQAAFjhbLhFryplWAoAAIxwN9z8XgAAAEAXuRtuB7fcGJUCAAArnA23CLoNAABY4Wy4RUellBsAADDC3XDjJDcAAGCMs+EWwVWlAADACsKNbgMAAEY4GW4ec1IAAGCQk+HWFhtuAADACifDrd2GG7NSAABghJvh5vcCAAAAusHJcGuL/TYAAGCFk+HW9uIEJqUAAMAKN8PN7wUAAAB0g5vh1qbcuAEvAACwwslwa4duAwAARjgZbp44xw0AANjjZrhxkhsAADDIyXBriw03AABgBeHGrBQAABjhZLgxKgUAABY5GW5tsd8GAACscDLcuKoUAABY5Ga4MSoFAAAGuRlubX7PkxMAAIAVToZbW4xKAQCAFU6Gm8esFAAAGORmuPm9AAAAgG5wMtzaYlQKAACscDLc2k5KuTgBAABY4WS4MSsFAAAWuRlubTAqBQAAVjgZbu2enODjOgAAALoipsKttLRUaWlpSkxMVFZWljZv3nzE165cuVIXXXSR+vfvr/79+ysnJ+eor2+Lu4EAAACLYibc1q5dq8LCQhUXF2vr1q0aNWqUcnNztWfPng5f//zzz+uaa67Rc889p8rKSg0ZMkSXX365du/e3aXPDTArBQAARgS8GLkbbVZWlsaNG6f77rtPktTa2qohQ4bolltu0Zw5c475/nA4rP79++u+++5Tfn7+UV/7flOLRt/+pHYtu0offLBfp56adEL+DQAAAD0pJnbcWlpaVFVVpZycnOixuLg45eTkqLKyslN/x4EDB/TJJ5/otNNO6/DPm5ub1dDQEP0CAACwJibCbd++fQqHwwqFQu2Oh0Ih1dbWdurvuPXWW5WSktIu/toqKSlRUlKSkpKSdM6XvhQ9zqQUAABYERPhdrwWL16sNWvW6PHHH1diYmKHr5k7d67q6+tVX1+vt956K3qcc9wAAIAVvfxegCQNGDBA8fHxqqura3e8rq5OycnJR33vL37xCy1evFh//etfNXLkyCO+LhgMKhgMSpKaA83Hv2gAAIDPWUzsuCUkJCg9PV0VFRXRY62traqoqFB2dvYR33f33XdrwYIFKi8vV0ZGRqc/z+PRCQAAwKCY2HGTpMLCQk2bNk0ZGRnKzMzUsmXL1NTUpIKCAklSfn6+UlNTVVJSIkm66667VFRUpNWrVystLS16Ltwpp5yiU045xbd/BwAAQE+JmXCbMmWK9u7dq6KiItXW1mr06NEqLy+PXrBQU1OjuLhDG4TLly9XS0uLvvnNb7b7e4qLi/XTn/706B/GhhsAADAoZu7j9nmqa/hY44qf1q5lV6m+vl79+vXze0kAAADHFBPnuAEAAODYnAw39/YYAQDAF4Gb4cZJbgAAwCA3w41uAwAABjkZbgAAABY5GW5suAEAAIvcDDdmpQAAwCAnww0AAMAiJ8ONDTcAAGCRk+EGAABgEeEGAABghJPhxqgUAABY5Ga4cUMQAABgkJPhBgAAYJGT4caoFAAAWORmuPm9AAAAgG5wM9zYcgMAAAY5GW4AAAAWORlu7LcBAACL3Aw3yg0AABjkZLgBAABY5Gi4seUGAADscTLcGJUCAACLnAw3AAAAi5wMNzbcAACARW6GG+UGAAAMcjPc2HMDAAAGORluAAAAFjkZboxKAQCARYQbAACAEU6GGwAAgEVOhhsXJwAAAIvcDDe6DQAAGORkuAEAAFhEuAEAABjhZLgxKgUAABY5GW4AAAAWORluXFUKAAAscjPc6DYAAGCQm+Hm9wIAAAC6wclwAwAAsMjJcPOYlQIAAIPcDDe/FwAAANANToYbAACARU6GG5NSAABgkZPhxrAUAABY5Gi4AQAA2ONkuDEqBQAAFrkZbn4vAAAAoBvcDDfKDQAAGORkuAEAAFjkZLjx5AQAAGCRm+Hm9wIAAAC6wclwAwAAsMjJcGNSCgAALHIz3BiWAgAAg5wMNwAAAIvcDDc23AAAgEFOhhvdBgAALHIy3AAAACxyMty4qhQAAFjkZrgxLAUAAAa5GW50GwAAMMjJcAMAALDIyXBjww0AAFjkZrgxKwUAAAbFVLiVlpYqLS1NiYmJysrK0ubNm4/6+kcffVTDhg1TYmKiLrjgAj3zzDOf00oBAAA+fzETbmvXrlVhYaGKi4u1detWjRo1Srm5udqzZ0+Hr9+0aZOuueYafec739Err7yiyZMna/Lkydq2bdsxP4v9NgAAYFHAi5G5YVZWlsaNG6f77rtPktTa2qohQ4bolltu0Zw5c/TxJ2E9v+NQxC1ZslQfN3+s2+bNix6bM2eu0oam6eabbjrqZ23b3aB7yv+pXcuuUn19vfr169cz/ygAAIAT6LjDzfM8NTY2HtciWlpaFAqF9Jvf/EZXXnll9PhNN92k+vp6rVmzRnsbP9Ylv9hwXJ/TVmvzAe1efr127dpFuAEAAN/17dtXgUDgqK857nBraGhQUlLS8fwVAAAAzuvMFDAmdtzee+89DRs2TOvXr1dmZmb0+Pz58/W3v/1Nzz777GHvOf3001VWVqZvfetb0WMrV67U4sWL9a9//euw1zc3N6u5ubndZ2ZmZur1119Xamrqca0fPaOhoUFDhgxhVzRG8fOJbfx8Yh8/o9jmx8+nMztuvY73QwKBwHH/gxITExUfH68PP/yw3d+1f/9+paamdvj3Dx48WI2Nje3+rKGhQSkpKV1aT9++ffkfTIzr168fP6MYxs8ntvHziX38jGJbrP18YuKq0oSEBKWnp6uioiJ6rLW1VRUVFcrOzu7wPdnZ2e1eL0nr168/4usBAACsO+4dtxOlsLBQ06ZNU0ZGhjIzM7Vs2TI1NTWpoKBAkpSfn6/U1FSVlJRIkmbOnKmLL75YS5Ys0aRJk7RmzRq9/PLLWrFihZ//DAAAgB4TM+E2ZcoU7d27V0VFRaqtrdXo0aNVXl6uUCgkSaqpqVFc3KENwvHjx2v16tW6/fbbNW/ePJ177rl64oknNGLEiE59XjAYbPcrYk8wGFRxcTE/oxjFzye28fOJffyMYlus/nxi5j5un7fI1bDcxw0AAFgRE+e4AQAA4NgINwAAACMINwAAACMINwAAACOcDbfIbUMGDhyorKwsbd682ecVQZJKSko0btw49e3bV4MGDdLkyZO1Y8cOv5eFI1i8eLECgYBmzZrl91LQxu7du/Xtb39bp59+uvr06aMLLrhAL7/8st/LgqRwOKz58+dr6NCh6tOnj8455xwtWLBAjl4nGBNeeOEF5eXlKSUlRYFAQE888US7P/c8T0VFRRo8eLD69OmjnJwcvfnmm/4sVo6G29q1azVv3jxJ0osvvqhRo0YpNzdXe/bs8Xll2LBhg2bMmKGXXnpJ69ev1yeffKLLL79cTU1Nfi8N/2XLli26//77NXLkSL+XgjY++OADTZgwQb1799af/vQnvf7661qyZIn69+/v99Ig6a677tLy5ct133336Y033tBdd92lu+++W/fee6/fS3NWU1OTRo0apdLS0g7//O6779Y999yjsrIy/f3vf9fJJ5+s3Nxcffzxx5/zSg/yHJSZmelNnz7dk+TV19d74XDYS0lJ8UpKSvxeGv7Lnj17PEnehg0b/F4K2mhsbPTOPfdcb/369d7FF1/szZw50+8l4aBbb73Vu/DCC/1eBo5g0qRJ3g033NDu2De+8Q1v6tSpPq0IbUnyHn/88ej3ra2tXnJysvfzn/88emz//v1eMBj0HnnkER9W6HnO7bi1tLSoqqpKEydOjB6Li4tTTk6OKisr/VsYOlRfXy9JOu2003xeCdqaMWOGJk2apJycHL+Xgv/y1FNPKSMjQ9/61rc0aNAgjRkzRitXrvR7WTho/Pjxqqio0M6dOyVJ//jHP7Rx40Z99atf9Xll6Mg777yj2tradv9fl5SUpKysLN+aIWaenPB52bdvn8LhsAYNGtTueCgU0vbt231aFTrS2tqqWbNmacKECZ1+IgZ63po1a7R161Zt2bLF76WgA2+//baWL1+uwsJCzZs3T1u2bNEPfvADJSQkaNq0aX4vz3lz5sxRQ0ODhg0bpvj4eIXDYS1cuFBTp071e2noQG1trSRFn+IUEQqFon/2eXMu3GDHjBkztG3bNm3cuNHvpeCgXbt2aebMmVq/fr0SExP9Xg460NraqoyMDC1atEiSNGbMGG3btk1lZWWEWwz43e9+p4cfflirV6/W+eefr+rqas2aNUspKSn8fNApzo1KBwwYoPj4+MMuRKirq1NycrJPq8J/+/73v68//vGPeu6553TGGWf4vRwcVFVVpT179mjs2LHq1auXevXqpQ0bNuiee+5Rr169FA6H/V6i8wYPHqzzzjuv3bHhw4erpqbGpxWhrR//+MeaM2eOrr76al1wwQW67rrrNHv2bJWUlPi9NHQg0gV1dXXtjvvZDM6FW0JCgtLT07Vhw4bosdbWVlVUVCg7O9vHlUH67LLr73//+3r88cf17LPPaujQoX4vCW1ceumlevXVV1VdXR39ysjI0NSpU1VdXa34+Hi/l+i8CRMmHHYLnZ07d+qss87yaUVo68CBA4qLa/+f3vj4eLW2tvq0IhzN0KFDlZycrIqKiuixhoYG/f3vf/etGZwclRYWFio/P1+StGPHDv3qV79SU1OTCgoKfF4ZZsyYodWrV+vJJ59U3759o+cQJCUlqU+fPj6vDn379j3sfMOTTz5Zp59+OuchxojZs2dr/PjxWrRoka666ipt3rxZK1asiN67Ev7Ky8vTwoULdeaZZ+r888/XK6+8oqVLl+qGG27we2nO+vDDD/XWW29Fv3/nnXdUXV2t0047TWeeeaZmzZqlO++8U+eee66GDh2q+fPnKyUlRZMnT/Znwb5cyxoD7rnnHu+MM87wevfu7WVmZnovvfSS30uC99ml2B19Pfjgg34vDUfA7UBiz9NPP+2NGDHCCwaD3rBhw7wVK1b4vSQc1NDQ4M2cOdM788wzvcTERO/ss8/2brvtNq+5udnvpTnrueee6/C/O9OmTfM877NbgsyfP98LhUJeMBj0Lr30Um/Hjh2+rTfgedyuGQAAwALnznEDAACwinADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnADAAAwgnAD8IX0yCOPqE+fPnrvvfeixwoKCjRy5EjV19f7uDIA6D4eMg/gC8nzPI0ePVr/+7//q3vvvVfFxcVatWqVXnrpJaWmpvq9PADoll5+LwAAekIgENDChQv1zW9+U8nJybr33nv14osvEm0ATGPHDcAX2tixY/Xaa6/pL3/5iy6++GK/lwMAx4Vz3AB8YZWXl2v79u0Kh8MKhUJ+LwcAjhs7bgC+kLZu3aqJEyfq/vvv10MPPaR+/frp0Ucf9XtZAHBcOMcNwBfOu+++q0mTJmnevHm65pprdPbZZys7O1tbt27V2LFj/V4eAHQbO24AvlDef/99jR8/XhMnTlRZWVn0+KRJkxQOh1VeXu7j6gDg+BBuAAAARnBxAgAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBGEGwAAgBH/HyF5WwR6Hwg5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DAY 3 - Calculating logistic functions and correlations\n",
    "\n",
    "\"\"\"\n",
    "possibilities = list(itertools.combinations(df.columns, 2)) # find all possible unique graph combinations\n",
    "for col, col2 in possibilities: # for each possibility...\n",
    "    if col != 'Sample Code Number': # exclude the sample ID\n",
    "        x = df[col][df[col] > 0]\n",
    "        y = df[col2][df[col2] > 0]\n",
    "        \n",
    "        polynomial = np.poly1d(np.polyfit(np.log10(x), np.log10(y), 1)) # find best fit of slope and intercept\n",
    "        slope = polynomial.coefficients[0] # fitted slope\n",
    "        intercept = polynomial.coefficients[1] # fitted intercept\n",
    "        \n",
    "        if (show_detailed_output):\n",
    "            print(f\"\\n🔻 log(y) = {slope:.4f} * log(x) + {intercept:.4f} 🔻\") # equation: log(y) = m*log(x) + c\n",
    "        \n",
    "        y_fit = 10**polynomial(np.log10(x)) # find the fitted y data\n",
    "\n",
    "        if (render_graphs):\n",
    "            plt.figure(figsize=(8,6)) # set the graph size\n",
    "            plt.scatter(x, y, label='Data') # plot the scatter plot\n",
    "            plt.plot(x, y_fit, 'r*-', label='Fitted Line') # plot the fitted line\n",
    "            plt.title(f'{col} vs {col2}') # add a title\n",
    "            plt.yscale('log') # scale x as log\n",
    "            plt.xscale('log') # scale y as log\n",
    "            plt.xlabel(col) # add x label\n",
    "            plt.ylabel(col2) # add y label\n",
    "            plt.legend() # show the legend\n",
    "            plt.show() # render the graph\n",
    "\"\"\"\n",
    "\n",
    "table = []\n",
    "\n",
    "for col in df.columns:\n",
    "    if (col != 'Sample Code Number') & (col != 'Class'): # exclude the sample ID\n",
    "        correlation_matrix = np.corrcoef(df[col], df['Class']) # find the correlation\n",
    "        table.append([col, f'{round(correlation_matrix[0][1] * 100, 2)}%'])\n",
    "\n",
    "print(tabulate(table, headers=['Column Name', 'Correlation']))\n",
    "\n",
    "for col in df.columns:\n",
    "    if col != 'Sample Code Number': # exclude the sample ID\n",
    "        x = symbols('x') # define the x symbol\n",
    "        p = 0 # start p as nothing\n",
    "\n",
    "        for patient in df[col]: # for each patient...\n",
    "            p = p + patient # add the patient to the equation\n",
    "\n",
    "        p = 1 / (1 + exp(-p * x)) # make the equation\n",
    "        if (show_detailed_output):\n",
    "            print('\\n', col, 'equation:', p)\n",
    "        if (render_graphs):\n",
    "            plot(p) # render the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26ba8d84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T04:39:31.149752Z",
     "iopub.status.busy": "2024-08-12T04:39:31.149314Z",
     "iopub.status.idle": "2024-08-12T04:39:31.158852Z",
     "shell.execute_reply": "2024-08-12T04:39:31.157608Z"
    },
    "papermill": {
     "duration": 0.023808,
     "end_time": "2024-08-12T04:39:31.162143",
     "exception": false,
     "start_time": "2024-08-12T04:39:31.138335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ninputs = df.iloc[:, 1:-1]\\noutput = df.iloc[:, -1]\\nfit = LogisticRegression(penalty=\\'none\\').fit(inputs, output)\\n\\ncoef = fit.coef_\\ncoef = np.delete(fit.coef_, 3)\\nintercept = fit.intercept_.flatten()\\n\\nif (show_detailed_output):\\n    print(\"COEFFICIENTS: {0}\".format(coef))\\n    print(\"INTERCEPT: {0}\".format(intercept), \\'\\n\\')\\n\\nb0, b1, x = symbols(\\'b0, b1, x\\')\\nb0 = fit.intercept_.flatten()\\nb1, b2, b3, b4, b5, b6, b7, b8 = coef\\n\\ntable = []\\n\\nrows = len(df)\\ncorrectRows = 0\\nfor index, row in df.iterrows():\\n    num = (b0 + b1 * row[1] + b2 * row[2] + b3 * row[3] + b4 * row[4] + b5 * row[5] + b6 * row[6] + b7 * row[7] + b8 * row[8])\\n    p = 1 / (1 + exp(-num))\\n    above0 = (0 > num)[0]\\n    if (((row[\\'Class\\']==2) & (not above0)) or ((row[\\'Class\\']==4) & above0)):\\n        correct = True\\n        correctRows = correctRows + 1\\n    else:\\n        correct = False\\n    if (row[\\'Class\\']==2):\\n        type = \\'Benign\\'\\n    else:\\n        type = \\'Malignant\\'\\n    # print(f\\'{type} and {\"above 0\" if (above0) else \"below 0\"}:\\', \\'matches\\' if correct else \\'does not match\\')\\n    table.append([type, True if above0 else False, correct])\\n    # print(correct, under0, p, num[0])\\n\\nprint(f\\'Correct: {correctRows}/{rows} ({round((correctRows/rows)*100, 2)}%)\\n\\')\\n\\nif (show_detailed_output):\\n    print(tabulate(table, headers=[\\'Type\\', \\'Above 0\\', \\'Matches\\']))\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DAY 5 - Finding and plotting Sigmoid function equation\n",
    "\n",
    "'''\n",
    "inputs = df.iloc[:, 1:-1]\n",
    "output = df.iloc[:, -1]\n",
    "fit = LogisticRegression(penalty='none').fit(inputs, output)\n",
    "\n",
    "coef = fit.coef_\n",
    "coef = np.delete(fit.coef_, 3)\n",
    "intercept = fit.intercept_.flatten()\n",
    "\n",
    "if (show_detailed_output):\n",
    "    print(\"COEFFICIENTS: {0}\".format(coef))\n",
    "    print(\"INTERCEPT: {0}\".format(intercept), '\\n')\n",
    "\n",
    "b0, b1, x = symbols('b0, b1, x')\n",
    "b0 = fit.intercept_.flatten()\n",
    "b1, b2, b3, b4, b5, b6, b7, b8 = coef\n",
    "\n",
    "table = []\n",
    "\n",
    "rows = len(df)\n",
    "correctRows = 0\n",
    "for index, row in df.iterrows():\n",
    "    num = (b0 + b1 * row[1] + b2 * row[2] + b3 * row[3] + b4 * row[4] + b5 * row[5] + b6 * row[6] + b7 * row[7] + b8 * row[8])\n",
    "    p = 1 / (1 + exp(-num))\n",
    "    above0 = (0 > num)[0]\n",
    "    if (((row['Class']==2) & (not above0)) or ((row['Class']==4) & above0)):\n",
    "        correct = True\n",
    "        correctRows = correctRows + 1\n",
    "    else:\n",
    "        correct = False\n",
    "    if (row['Class']==2):\n",
    "        type = 'Benign'\n",
    "    else:\n",
    "        type = 'Malignant'\n",
    "    # print(f'{type} and {\"above 0\" if (above0) else \"below 0\"}:', 'matches' if correct else 'does not match')\n",
    "    table.append([type, True if above0 else False, correct])\n",
    "    # print(correct, under0, p, num[0])\n",
    "\n",
    "print(f'Correct: {correctRows}/{rows} ({round((correctRows/rows)*100, 2)}%)\\n')\n",
    "\n",
    "if (show_detailed_output):\n",
    "    print(tabulate(table, headers=['Type', 'Above 0', 'Matches']))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0beaaa7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T04:39:31.182366Z",
     "iopub.status.busy": "2024-08-12T04:39:31.181913Z",
     "iopub.status.idle": "2024-08-12T04:40:41.920278Z",
     "shell.execute_reply": "2024-08-12T04:40:41.918670Z"
    },
    "papermill": {
     "duration": 70.751624,
     "end_time": "2024-08-12T04:40:41.923006",
     "exception": false,
     "start_time": "2024-08-12T04:39:31.171382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> Found 256 possibilities\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "121b445da1a14d51a814d0d5a8541ddb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/256 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-> Least AIC is -563.811182516834 at []\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DAY 6 - Making models of all possibilities and getting accuracies\n",
    "# WARNING - Long process\n",
    "\n",
    "df2 = df.drop('Class', axis=1).drop('Sample Code Number', axis=1).drop('Uniformity of Cell Shape', axis=1)\n",
    "\n",
    "\"\"\"\n",
    "possibilities = list(itertools.combinations(df2.columns, 8)) # find all possible unique graph combinations\n",
    "\n",
    "print('-> Found', len(possibilities), 'possibilities\\n')\n",
    "\n",
    "for col1, col2, col3, col4, col5, col6, col7, col8 in possibilities:\n",
    "    print(col1, 'x', col2, 'x', col3, 'x', col4, 'x', col5, 'x', col6, 'x', col7, 'x', col8)\n",
    "\n",
    "possibilities = list(itertools.permutations(df2.columns, 8)) # find all possible unique graph combinations\n",
    "\n",
    "print('-> Found', len(possibilities), 'possibilities\\n')\n",
    "\n",
    "for col1, col2, col3, col4, col5, col6, col7, col8 in possibilities[:10]:\n",
    "    print(col1, 'x', col2, 'x', col3, 'x', col4, 'x', col5, 'x', col6, 'x', col7, 'x', col8)\n",
    "\"\"\"\n",
    "\n",
    "def combinations(original_list):\n",
    "    num_subsets = 2 ** len(original_list)\n",
    "    subsets = []\n",
    "    for subset_index in range(num_subsets):\n",
    "        subset = []\n",
    "        for index in range(len(original_list)):\n",
    "            if (subset_index & (1 << index)) != 0:\n",
    "                subset.append(original_list[index])\n",
    "        subsets.append(subset)\n",
    "    return subsets\n",
    "\n",
    "possibilities2 = combinations(df2.columns)\n",
    "\n",
    "print('-> Found', len(possibilities2), 'possibilities\\n')\n",
    "    \n",
    "least_aic = 100\n",
    "least_possibility = []\n",
    "least_possibilities = pd.DataFrame(columns=['Possibility', 'AIC'])\n",
    "\n",
    "if (show_detailed_output):\n",
    "    print('Running long process...\\n')\n",
    "\n",
    "def calculate_AIC(n,mse,num_parameters):\n",
    "    aic = n * log(mse) + 2 * num_parameters\n",
    "    return aic\n",
    "\n",
    "for i in tqdm(range(len(possibilities2))):\n",
    "    possibility = possibilities2[i]\n",
    "    if (show_detailed_output):\n",
    "        print(possibility)\n",
    "    Z = []\n",
    "    if (len(possibility) > 0):\n",
    "        strings = ()\n",
    "        for col in possibility:\n",
    "            strings = strings + (f'[\"{possibility[0]}\"]',)\n",
    "        string = \"+\".join(strings)\n",
    "        string = f'df2[{string}]'\n",
    "        exec(f'X = {string}')\n",
    "    else:\n",
    "        exec(f'X = df2')\n",
    "\n",
    "    for j in range(len(df2)):\n",
    "        d = []\n",
    "        for col in df2.columns:\n",
    "            d.append(df2.loc[j][col])\n",
    "        Z += [d]\n",
    "\n",
    "    sc = StandardScaler()\n",
    "    sc.fit(X)\n",
    "    x = sc.transform(X)\n",
    "    X = Z\n",
    "    y = df[['Class']]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)\n",
    "    lr = LogisticRegression()\n",
    "    lr.fit(X_train, y_train.values.ravel())\n",
    "    y_pred = lr.predict(X_test)\n",
    "    mse = mean_squared_error(y_test, y_pred) / 4\n",
    "    n = len(y_test)\n",
    "    num_parameters = len(lr.coef_[0]) + 1\n",
    "\n",
    "    aic = calculate_AIC(n, mse, num_parameters)\n",
    "\n",
    "    if (show_detailed_output):\n",
    "        print(f\"Train Accuracy: {round(metrics.accuracy_score(y_train, lr.predict(X_train)) * 100, 5)}%\")\n",
    "        print(f\"Train Confusion matrix: {metrics.confusion_matrix(y_train, lr.predict(X_train))}\")\n",
    "        print(f\"Train Classificiation report: {metrics.classification_report(y_train, lr.predict(X_train))}\")\n",
    "        print(f\"Test Accuracy: {round(metrics.accuracy_score(y_test, lr.predict(X_test)) * 100, 5)}%\")\n",
    "        print(f\"Test Confusion matrix: {metrics.confusion_matrix(y_test,lr.predict(X_test))}\")\n",
    "        print(f\"Test Classification report: {metrics.classification_report(y_test, lr.predict(X_test))}\")\n",
    "        print(f\"Intercept: {lr.intercept_}\")\n",
    "        print(f'AIC: {aic}\\n')\n",
    "\n",
    "    least_possibilities.loc[len(least_possibilities.index)] = [possibility, aic] \n",
    "\n",
    "    if (aic < least_aic):\n",
    "        least_aic = aic\n",
    "        least_possibility = possibility\n",
    "\n",
    "if (show_detailed_output):\n",
    "    print('\\nFinished long process')\n",
    "print(f'\\n-> Least AIC is {least_aic} at {least_possibility}\\n')\n",
    "least_possibilities = least_possibilities.sort_values(by=['AIC'], ascending=True)\n",
    "if (show_detailed_output):\n",
    "    print(least_possibilities[2:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5bc4d49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T04:40:41.943223Z",
     "iopub.status.busy": "2024-08-12T04:40:41.942822Z",
     "iopub.status.idle": "2024-08-12T04:40:43.092155Z",
     "shell.execute_reply": "2024-08-12T04:40:43.090122Z"
    },
    "papermill": {
     "duration": 1.163045,
     "end_time": "2024-08-12T04:40:43.095118",
     "exception": false,
     "start_time": "2024-08-12T04:40:41.932073",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Possibility                AIC\n",
      "66         [Uniformity of Cell Size, Normal Nucleoli]  -544.210293152246\n",
      "10  [Uniformity of Cell Size, Single Epithelial Ce...  -544.210293152246\n",
      "34         [Uniformity of Cell Size, Bland Chromatin]  -544.210293152246\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DAY 7 - Find and graph the best possibilities\n",
    "\n",
    "print(least_possibilities[2:5])\n",
    "\n",
    "for row in least_possibilities[2:5]['Possibility']:\n",
    "    dff = df[[row[0], row[1]]]\n",
    "    data = pd.concat([dff[row[1]], dff[row[0]]], axis=1)\n",
    "\n",
    "    benign_data = data[df['Class'] == 2]\n",
    "    malignant_data = data[df['Class'] == 4]\n",
    "\n",
    "    if (render_graphs):\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        plt.scatter(benign_data[row[0]], benign_data[row[1]], alpha=0.5, color='y', label='Benign')\n",
    "        plt.scatter(malignant_data[row[0]], malignant_data[row[1]], alpha=0.5, color='r', label='Malignant')\n",
    "        plt.title(f'{row[0]} vs {row[1]}')\n",
    "        plt.xlabel(row[0])\n",
    "        plt.ylabel(row[1])\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  }
 ],
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