data_balancing.ipynb

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        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
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        "id": "PtrC3gIcbRRa"
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      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import librosa as lb\n",
        "\n",
        "import os\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "status": "completed"
        },
        "tags": [],
        "id": "2fQ3ZzDLbRRc"
      },
      "source": [
        "# Handeling Class Imbalance"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pid  disease\n",
              "0  101     URTI\n",
              "1  102  Healthy\n",
              "2  103   Asthma\n",
              "3  104     COPD\n",
              "4  105     URTI"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "diagnosis",
              "summary": "{\n  \"name\": \"diagnosis\",\n  \"rows\": 126,\n  \"fields\": [\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 101,\n        \"max\": 226,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          174,\n          120,\n          217\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disease\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Healthy\",\n          \"Bronchiectasis\",\n          \"URTI\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "diagnosis=pd.read_csv('/content/drive/MyDrive/Respiratory_Sound_Database/patient_diagnosis.csv',names=['pid','disease'])\n",
        "diagnosis.head()"
      ]
    },
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uXP3rX/9SQECAunbtekfrHTt2rBo1aqTIyEhFRETIz8+PJ6wAAAArBtO13zMBbiEjI0Oenp4KG7ZA9kbnPPskTosq5qoAAMDNXPv8Tk9Pl4eHx037MmMIAAAASQRDAAAAmBEMAQAAIIlgCAAAADOCIQAAACQRDAEAAGBGMAQAAIAkgiEAAADMCIYAAACQRDAEAACAGcEQAAAAkiQHWxeA0mfbxN63fNYiAAAofZgxBAAAgCSCIQAAAMwIhgAAAJBEMAQAAIAZwRAAAACSCIYAAAAwIxgCAABAEsEQAAAAZgRDAAAASOLJJyiEVmNXyN7obOsyAAAoUxKnRdm6BGYMAQAA8CeCIQAAACQRDAEAAGBGMAQAAIAkgiEAAADMCIYAAACQRDAEAACAGcEQAAAAkgiGAAAAMCMYAgAAQBLBEAAAAGYEQwAAAEgiGAIAAMCMYGgjp06d0rBhw1S9enUZjUYFBgaqS5cu2rx5s6XPd999p4cfflje3t5ycnJSaGioZs6cqZycHKuxDAaDZfH09FSLFi20ZcsWS3t0dLSlvVy5cqpUqZIefPBBvffee8rNzS22YwYAACUbwdAGUlJS1LhxY23ZskXTpk3TgQMHtG7dOrVp00ZDhgyRJK1evVqtW7dWlSpVFB8fr0OHDmnEiBGaOHGinnjiCZlMJqsxlyxZorS0NCUkJKh8+fLq3Lmzfv75Z0t7hw4dlJaWppSUFH399ddq06aNRowYoc6dO+vq1avFevwAAKBkcrB1AXejwYMHy2AwaOfOnXJ1dbWsr1evnvr3768LFy7o2Wef1SOPPKJ3333X0j5gwABVqlRJjzzyiFauXKlevXpZ2ry8vOTn5yc/Pz/Nnz9flStX1saNG/Xcc89JkoxGo/z8/CRJlStXVqNGjXT//ferXbt2Wrp0qQYMGFBMRw8AAEoqZgyL2e+//65169ZpyJAhVqHwGi8vL23YsEG//fabYmJirmvv0qWLatWqpRUrVtxwH87OzpKky5cv37SWtm3bKiwsTHFxcXm2Z2dnKyMjw2oBAABlV6GD4fvvv68WLVooICBAx48flyTNnj1bn3/+eZEVVxb99NNPMplMCgkJuWGfI0eOSJLq1KmTZ3tISIilz99dvHhRY8eOlb29vVq3bn3LekJCQpSSkpJn26RJk+Tp6WlZAgMDbzkeAAAovQoVDOfPn68XX3xRDz/8sP744w/LzRBeXl6aPXt2UdZX5vz92sCi6tu7d2+5ubnJ3d1d//73v7V48WLVr18/X/swGAx5tsXGxio9Pd2ypKam5rseAABQ+hQqGP7zn//UwoUL9corr8je3t6yPjw8XAcOHCiy4sqimjVrymAw6NChQzfsU6tWLUlScnJynu3JycmWPtfMmjVLSUlJOnXqlE6dOqV+/frlq57k5GTdc889ebYZjUZ5eHhYLQAAoOwqVDD85Zdf1LBhw+vWG41GXbhw4baLKst8fHwUGRmpt99+O89z9ccff+ihhx6Sj4+PZsyYcV37mjVrdPToUfXu3dtqvZ+fn4KDg1WhQoV817JlyxYdOHBAPXr0KPiBAACAMqdQwfCee+5RUlLSdevXrVt3w+vi8D9vv/22cnJydN999+nf//63jh49quTkZM2dO1fNmjWTq6ur/vWvf+nzzz/XwIEDtX//fqWkpGjx4sWKjo5Wz5499fjjjxdon9nZ2Tp16pT+7//+T3v27NFbb72lrl27qnPnzoqKirpDRwoAAEqTQv1czYsvvqghQ4YoKytLJpNJO3fu1IoVKzRp0iQtWrSoqGssc6pXr649e/bozTff1KhRo5SWlqYKFSqocePGmj9/viSpZ8+eio+P15tvvqmWLVsqKytLNWvW1CuvvKKRI0fe8LrAG1m3bp38/f3l4OAgb29vhYWFae7cuerXr5/s7Lg5HQAASAZTQe5w+IsPP/xQ48eP17FjxyRJAQEBev311/XMM88UaYEoOTIyMuTp6amwYQtkb3S2dTkAAJQpidPuzDd41z6/09PTb3m/QKF/4LpPnz7q06ePLl68qMzMTFWsWLGwQwEAAKAEKNR3iJcuXdLFixclSS4uLrp06ZJmz56tDRs2FGlxAAAAKD6FCoZdu3bV8uXLJf15F+19992nGTNmqGvXrpZr5AAAAFC6FCoY7tmzRy1btpQkffrpp/Lz89Px48e1fPlyzZ07t0gLBAAAQPEoVDC8ePGi3N3dJUkbNmxQ9+7dZWdnp/vvv9/yeDwAAACULoUKhsHBwfrss8+Umpqq9evX66GHHpIknTlzhqdjAAAAlFKFCoavvfaaYmJiFBQUpKZNm6pZs2aS/pw9zOuJKAAAACj5CvVzNT179tQDDzygtLQ0hYWFWda3a9dO3bp1K7LiAAAAUHwK/TuGfn5+8vPzs1p333333XZBAAAAsI1CB8Pdu3dr5cqVOnHihC5fvmzVFhcXd9uFAQAAoHgV6hrDjz/+WM2bN1dycrJWr16tK1eu6IcfftCWLVvk6elZ1DUCAACgGBQqGL711luaNWuWvvjiCzk6OmrOnDk6dOiQHn/8cVWtWrWoawQAAEAxMJhMJlNBN3J1ddUPP/ygoKAg+fr6auvWrQoNDVVycrLatm2rtLS0O1ErbKwgD+EGAAAlQ0E+vws1Y+jt7a3z589LkipXrqyDBw9K+vPxeNeeoQwAAIDSpVA3n7Rq1UobN25UaGioHnvsMY0YMUJbtmzRxo0b1a5du6KuEQAAAMWgUMFw3rx5ysrKkiS98sorKleunL777jv16NFDY8eOLdICAQAAUDwKdY0h7k5cYwgAQOlzx68xlKRjx45p7Nix6t27t86cOSNJ+vrrr/XDDz8UdkgAAADYUKGC4TfffKPQ0FDt2LFDcXFxyszMlCTt27dP48aNK9ICAQAAUDwKFQzHjBmjiRMnauPGjXJ0dLSsb9u2rb7//vsiKw4AAADFp1DB8MCBA+rWrdt16ytWrKhff/31tosCAABA8SvUXcleXl5KS0vTPffcY7V+7969qly5cpEUhpKr1dgVsjc6F3i7xGlRd6AaAABQVAo1Y/jEE09o9OjROnXqlAwGg3Jzc5WQkKCYmBhFRfHhDwAAUBoV+lnJISEhCgwMVGZmpurWratWrVqpefPm/I4hAABAKVWor5IdHR21cOFCvfbaazpw4IAyMzPVsGFD1axZs6jrAwAAQDEpVDC8JjAwUIGBgcrJydGBAwd07tw5eXt7F1VtAAAAKEaF+ip55MiRWrx4sSQpJydHrVu3VqNGjRQYGKitW7cWZX0AAAAoJoUKhp9++qnCwsIkSV988YV+/vlnHTp0SC+88IJeeeWVIi0QAAAAxaNQwfDXX3+Vn5+fJOmrr77S448/rlq1aql///46cOBAkRYIAACA4lGoYFipUiX9+OOPysnJ0bp16/Tggw9Kki5evCh7e/siLRAAAADFo1A3nzz99NN6/PHH5e/vL4PBoPbt20uSduzYoZCQkCItEAAAAMWjUMFw/Pjxuvfee5WamqrHHntMRqNRkmRvb68xY8YUaYEAAAAoHoX+uZqePXtet65fv363VQwAAABsJ9/BcO7cuRo4cKCcnJw0d+7cm/YdPnz4bRcGAACA4pXvYDhr1iz16dNHTk5OmjVr1g37GQwGgiEAAEAplO+7kn/55Rf5+vpa/r7R8vPPP9+xYu820dHRevTRR/NsCwoKksFgkMFgkIuLi0JDQ7Vo0SLLdtfa8lqCgoIkSRERERo5cmTxHAwAACjx8j1j+OKLL+arn8Fg0IwZMwpdEPJvwoQJevbZZ3Xx4kWtWrVKzz77rCpXrqw5c+Zo8uTJln7+/v5asmSJOnToIEn8pBAAAMhTvoPh3r17rV7v2bNHV69eVe3atSVJR44ckb29vRo3bly0FeKG3N3dLT80Pnr0aE2dOlUbN25Ux44d5enpadXXy8vL0hcAACAv+Q6G8fHxlr9nzpwpd3d3LVu2TN7e3pKkc+fO6emnn1bLli2LvkrcVG5urlavXq1z587J0dGxyMbNzs5Wdna25XVGRkaRjQ0AAEqeQj35ZMaMGZo0aZIlFEqSt7e3Jk6cyNfIxWj06NFyc3OT0WhUz5495e3trQEDBhTZ+JMmTZKnp6dlCQwMLLKxAQBAyVOoYJiRkaGzZ89et/7s2bM6f/78bReF/HnppZeUlJSkLVu2qGnTppo1a5aCg4OLbPzY2Filp6dbltTU1CIbGwAAlDyF+oHrbt266emnn9aMGTN03333SfrzcXgvvfSSunfvXqQF4sbKly+v4OBgBQcHa9WqVQoNDVV4eLjq1q1bJOMbjUbLU20AAEDZV6gZwwULFqhjx4568sknVa1aNVWrVk1PPvmkOnTooHfeeaeoa0Q+BAYGqlevXoqNjbV1KQAAoJQq1Iyhi4uL3nnnHU2bNk3Hjh2TJNWoUUOurq5FWhyk9PR0JSUlWa279nuSfzdixAjde++92r17t8LDw4uhOgAAUJYU+lnJkuTq6qr69esXVS3Iw9atW9WwYUOrdc8880yefevWrauHHnpIr732mr766qviKA8AAJQhBpPJZLJ1ESgdMjIy5OnpqbBhC2RvdC7w9onTou5AVQAA4GaufX6np6fLw8Pjpn0LdY0hAAAAyh6CIQAAACQRDAEAAGBGMAQAAIAkgiEAAADMCIYAAACQRDAEAACAGcEQAAAAkgiGAAAAMCMYAgAAQBLBEAAAAGYEQwAAAEiSHGxdAEqfbRN73/Ih3AAAoPRhxhAAAACSCIYAAAAwIxgCAABAEsEQAAAAZgRDAAAASCIYAgAAwIxgCAAAAEkEQwAAAJjxA9cosFZjV8je6GzrMopE4rQoW5cAAECJwYwhAAAAJBEMAQAAYEYwBAAAgCSCIQAAAMwIhgAAAJBEMAQAAIAZwRAAAACSCIYAAAAwIxgCAABAEsEQAAAAZgRDAAAASCIYAgAAwIxgCAAAAEkEwxuKiIjQyJEjb9rHYDDos88+K5Z6Cis/xwEAACDZOBhGR0fLYDBYFl9fX3Xo0EH79++3ZVn5lpaWpo4dOxbZeHciaMbFxemNN94o0jEBAEDZZPMZww4dOigtLU1paWnavHmzHBwc1Llz5xv2v3LlSjFWd3N+fn4yGo22LuOmfHx85O7ubusyAABAKWDzYGg0GuXn5yc/Pz81aNBAY8aMUWpqqs6ePauUlBQZDAZ98sknat26tZycnPThhx8qNzdXEyZMUJUqVWQ0GtWgQQOtW7fOMua17eLi4tSmTRu5uLgoLCxM27dvt9p3QkKCIiIi5OLiIm9vb0VGRurcuXOW9tzcXL388svy8fGRn5+fxo8fb7X932f4UlNT9fjjj8vLy0s+Pj7q2rWrUlJSrLZ57733VK9ePRmNRvn7+2vo0KGSpKCgIElSt27dZDAYLK+PHTumrl27qlKlSnJzc1OTJk20adMmqzHfeecd1axZU05OTqpUqZJ69uxpafv7V8k36wsAAO5uNg+Gf5WZmakPPvhAwcHB8vX1tawfM2aMRowYoeTkZEVGRmrOnDmaMWOGpk+frv379ysyMlKPPPKIjh49ajXeK6+8opiYGCUlJalWrVrq3bu3rl69KklKSkpSu3btVLduXW3fvl3ffvutunTpopycHMv2y5Ytk6urq3bs2KGpU6dqwoQJ2rhxY561X7lyRZGRkXJ3d9d//vMfJSQkyM3NTR06dNDly5clSfPnz9eQIUM0cOBAHThwQGvWrFFwcLAkadeuXZKkJUuWKC0tzfI6MzNTDz/8sDZv3qy9e/eqQ4cO6tKli06cOCFJ2r17t4YPH64JEybo8OHDWrdunVq1apVnjQXpK0nZ2dnKyMiwWgAAQNnlYOsC1q5dKzc3N0nShQsX5O/vr7Vr18rO7n+ZdeTIkerevbvl9fTp0zV69Gg98cQTkqQpU6YoPj5es2fP1ttvv23pFxMTo06dOkmSXn/9ddWrV08//fSTQkJCNHXqVIWHh+udd96x9K9Xr55VbfXr19e4ceMkSTVr1tS8efO0efNmPfjgg9cdxyeffKLc3FwtWrRIBoNB0p8hz8vLS1u3btVDDz2kiRMnatSoURoxYoRluyZNmkiSKlSoIEny8vKSn5+fpT0sLExhYWGW12+88YZWr16tNWvWaOjQoTpx4oRcXV3VuXNnubu7q1q1amrYsGGe57ogfSVp0qRJev3112/YDgAAyhabzxi2adNGSUlJSkpK0s6dOxUZGamOHTvq+PHjlj7h4eGWvzMyMnTy5Em1aNHCapwWLVooOTnZal39+vUtf/v7+0uSzpw5I+l/M4Y389ftr41xbfu/27dvn3766Se5u7vLzc1Nbm5u8vHxUVZWlo4dO6YzZ87o5MmTt9zn32VmZiomJkZ16tSRl5eX3NzclJycbJkxfPDBB1WtWjVVr15dffv21YcffqiLFy/mOVZB+kpSbGys0tPTLUtqamqBagcAAKWLzYOhq6urgoODFRwcrCZNmmjRokW6cOGCFi5caNWnMMqVK2f5+9osXm5uriTJ2dm5QNtfG+Pa9n+XmZmpxo0bW0LuteXIkSN68skn87W/vMTExGj16tV666239J///EdJSUkKDQ21fD3t7u6uPXv2aMWKFfL399drr72msLAw/fHHH9eNVZC+0p/Xf3p4eFgtAACg7LJ5MPw7g8EgOzs7Xbp0Kc92Dw8PBQQEKCEhwWp9QkKC6tatm+/91K9fX5s3b76tWv+qUaNGOnr0qCpWrGgJutcWT09Pubu7Kygo6Kb7LFeunNU1jtKfxxUdHa1u3bopNDRUfn5+193Q4uDgoPbt22vq1Knav3+/UlJStGXLljz3UZC+AADg7mLzawyzs7N16tQpSdK5c+c0b948ZWZmqkuXLjfc5qWXXtK4ceNUo0YNNWjQQEuWLFFSUpI+/PDDfO83NjZWoaGhGjx4sJ5//nk5OjoqPj5ejz32mMqXL1/g4+jTp4+mTZumrl27Wu6YPn78uOLi4vTyyy+rSpUqGj9+vJ5//nlVrFhRHTt21Pnz55WQkKBhw4ZJkiU4tmjRQkajUd7e3qpZs6bi4uLUpUsXGQwGvfrqq1azlmvXrtXPP/+sVq1aydvbW1999ZVyc3NVu3bt62osSF8AAHD3sXkwXLduneX6P3d3d4WEhGjVqlWKiIi4bmbsmuHDhys9PV2jRo3SmTNnVLduXa1Zs0Y1a9bM935r1aqlDRs26B//+Ifuu+8+OTs7q2nTpurdu3ehjsPFxUXbtm3T6NGj1b17d50/f16VK1dWu3btLF/B9uvXT1lZWZo1a5ZiYmJUvnx5q5+LmTFjhl588UUtXLhQlStXVkpKimbOnKn+/furefPmKl++vEaPHm11d7CXl5fi4uI0fvx4ZWVlqWbNmlqxYsV1N9IUtC8AALj7GEwmk8nWRaB0yMjIkKenp8KGLZC9sXDXTJY0idOibF0CAAB31LXP7/T09FveL1DirjEEAACAbRAMAQAAIIlgCAAAADOCIQAAACQRDAEAAGBGMAQAAIAkgiEAAADMCIYAAACQRDAEAACAGcEQAAAAkgiGAAAAMHOwdQEofbZN7H3LZy0CAIDShxlDAAAASCIYAgAAwIxgCAAAAEkEQwAAAJgRDAEAACCJYAgAAAAzgiEAAAAkEQwBAABgxg9co8BajV0he6OzrcvIt8RpUbYuAQCAUoEZQwAAAEgiGAIAAMCMYAgAAABJBEMAAACYEQwBAAAgiWAIAAAAM4IhAAAAJBEMAQAAYEYwBAAAgCSCIQAAAMwIhgAAAJBEMAQAAIAZwRAAAACSCIZlWlBQkGbPnm3rMgAAQClRpoNhdHS0DAaDDAaDHB0dFRwcrAkTJujq1au2Lq1Y7Nq1SwMHDrR1GQAAoJRwsHUBd1qHDh20ZMkSZWdn66uvvtKQIUNUrlw5xcbG2rq0O65ChQq2LgEAAJQiZXrGUJKMRqP8/PxUrVo1DRo0SO3bt9eaNWsUHR2tRx99VNOnT5e/v798fX01ZMgQXblyxbJtdna2YmJiVLlyZbm6uqpp06baunWrpX38+PFq0KCB1f5mz56toKAgy+tr+3nrrbdUqVIleXl5WWYtX3rpJfn4+KhKlSpasmSJ1TgHDhxQ27Zt5ezsLF9fXw0cOFCZmZnXjXuz+v/+VfLMmTMVGhoqV1dXBQYGavDgwVZjAgCAu1uZD4Z/5+zsrMuXL0uS4uPjdezYMcXHx2vZsmVaunSpli5dauk7dOhQbd++XR9//LH279+vxx57TB06dNDRo0cLtM8tW7bo5MmT2rZtm2bOnKlx48apc+fO8vb21o4dO/T888/rueee03//+19J0oULFxQZGSlvb2/t2rVLq1at0qZNmzR06FCrcW9V/9/Z2dlp7ty5+uGHH7Rs2TJt2bJFL7/88g37Z2dnKyMjw2oBAABl110TDE0mkzZt2qT169erbdu2kiRvb2/NmzdPISEh6ty5szp16qTNmzdLkk6cOKElS5Zo1apVatmypWrUqKGYmBg98MAD183u3YqPj4/mzp2r2rVrq3///qpdu7YuXryof/zjH6pZs6ZiY2Pl6Oiob7/9VpL00UcfKSsrS8uXL9e9996rtm3bat68eXr//fd1+vRpy7g3qz8vI0eOVJs2bRQUFKS2bdtq4sSJWrly5Q37T5o0SZ6enpYlMDCwQMcNAABKlzIfDNeuXSs3Nzc5OTmpY8eO6tWrl8aPHy9Jqlevnuzt7S19/f39debMGUl/fpWbk5OjWrVqyc3NzbJ88803OnbsWIFqqFevnuzs/neqK1WqpNDQUMtre3t7+fr6WvadnJyssLAwubq6Wvq0aNFCubm5Onz4sNW4N6o/L5s2bVK7du1UuXJlubu7q2/fvvrtt9908eLFPPvHxsYqPT3dsqSmphbouAEAQOlS5m8+adOmjebPny9HR0cFBATIweF/h1yuXDmrvgaDQbm5uZKkzMxM2dvbKzEx0Sp8SZKbm5ukP7+aNZlMVm1/vcbvZvu52b7zqyBjpKSkqHPnzho0aJDefPNN+fj46Ntvv9Uzzzyjy5cvy8XF5bptjEajjEZjgWoCAAClV5kPhq6urgoODi7wdg0bNlROTo7OnDmjli1b5tmnQoUKOnXqlEwmkwwGgyQpKSnpdsqVJNWpU0dLly7VhQsXLLOGCQkJsrOzU+3atQs1ZmJionJzczVjxgzL7OXNvkYGAAB3nzL/VXJh1apVS3369FFUVJTi4uL0yy+/aOfOnZo0aZK+/PJLSVJERITOnj2rqVOn6tixY3r77bf19ddf3/a++/TpIycnJ/Xr108HDx5UfHy8hg0bpr59+6pSpUqFGjM4OFhXrlzRP//5T/388896//33tWDBgtuuFQAAlB0Ew5tYsmSJoqKiNGrUKNWuXVuPPvqodu3apapVq0r6c2bvnXfe0dtvv62wsDDt3LlTMTExt71fFxcXrV+/Xr///ruaNGminj17ql27dpo3b16hxwwLC9PMmTM1ZcoU3Xvvvfrwww81adKk264VAACUHQbT3y+SA24gIyNDnp6eChu2QPZGZ1uXk2+J06JsXQIAADZz7fM7PT1dHh4eN+3LjCEAAAAkEQwBAABgRjAEAACAJIIhAAAAzAiGAAAAkEQwBAAAgBnBEAAAAJIIhgAAADAjGAIAAEASwRAAAABmBEMAAABIkhxsXQBKn20Te9/yWYsAAKD0YcYQAAAAkgiGAAAAMCMYAgAAQBLBEAAAAGYEQwAAAEgiGAIAAMCMYAgAAABJBEMAAACYEQwBAAAgiSefoBBajV0he6OzrcvIt8RpUbYuAQCAUoEZQwAAAEgiGAIAAMCMYAgAAABJBEMAAACYEQwBAAAgiWAIAAAAM4IhAAAAJBEMAQAAYEYwBAAAgCSCIQAAAMwIhgAAAJBEMAQAAIAZwfAOiYiI0MiRI2/ax2Aw6LPPPsv3mNHR0Xr00UcLtI+lS5fKy8sr3/sAAAB3r1IdDKOjo2UwGCyLr6+vOnTooP3799u6tHxJS0tTx44dC719XFyc3njjDcvroKAgzZ4926pPr169dOTIkULvAwAA3D1KdTCUpA4dOigtLU1paWnavHmzHBwc1Llz5xv2v3LlSjFWd3N+fn4yGo2F3t7Hx0fu7u437ePs7KyKFSsWeh8AAODuUeqDodFolJ+fn/z8/NSgQQONGTNGqampOnv2rFJSUmQwGPTJJ5+odevWcnJy0ocffqjc3FxNmDBBVapUkdFoVIMGDbRu3TrLmNe2i4uLU5s2beTi4qKwsDBt377dat8JCQmKiIiQi4uLvL29FRkZqXPnzlnac3Nz9fLLL8vHx0d+fn4aP3681fZ//yr5wIEDatu2rZydneXr66uBAwcqMzPzhsf+16+SIyIidPz4cb3wwguWGVTp+q+S9+3bpzZt2sjd3V0eHh5q3Lixdu/eXcCzDgAAyqJSHwz/KjMzUx988IGCg4Pl6+trWT9mzBiNGDFCycnJioyM1Jw5czRjxgxNnz5d+/fvV2RkpB555BEdPXrUarxXXnlFMTExSkpKUq1atdS7d29dvXpVkpSUlKR27dqpbt262r59u7799lt16dJFOTk5lu2XLVsmV1dX7dixQ1OnTtWECRO0cePGPGu/cOGCIiMj5e3trV27dmnVqlXatGmThg4dmq9jj4uLU5UqVTRhwgTLDGpe+vTpoypVqmjXrl1KTEzUmDFjVK5cuTz7ZmdnKyMjw2oBAABll4OtC7hda9eulZubm6Q/w5W/v7/Wrl0rO7v/Zd6RI0eqe/fultfTp0/X6NGj9cQTT0iSpkyZovj4eM2ePVtvv/22pV9MTIw6deokSXr99ddVr149/fTTTwoJCdHUqVMVHh6ud955x9K/Xr16VrXVr19f48aNkyTVrFlT8+bN0+bNm/Xggw9edxwfffSRsrKytHz5crm6ukqS5s2bpy5dumjKlCmqVKnSTc+Dj4+P7O3t5e7uLj8/vxv2O3HihF566SWFhIRY6rqRSZMm6fXXX7/pfgEAQNlR6mcM27Rpo6SkJCUlJWnnzp2KjIxUx44ddfz4cUuf8PBwy98ZGRk6efKkWrRoYTVOixYtlJycbLWufv36lr/9/f0lSWfOnJH0vxnDm/nr9tfGuLb93yUnJyssLMwSCq/VlJubq8OHD990PwXx4osvasCAAWrfvr0mT56sY8eO3bBvbGys0tPTLUtqamqR1QEAAEqeUh8MXV1dFRwcrODgYDVp0kSLFi3ShQsXtHDhQqs+hfHXr1ivXbOXm5sr6c+bOgqy/bUxrm1vK+PHj9cPP/ygTp06acuWLapbt65Wr16dZ1+j0SgPDw+rBQAAlF2lPhj+ncFgkJ2dnS5dupRnu4eHhwICApSQkGC1PiEhQXXr1s33furXr6/NmzffVq1/VadOHe3bt08XLlywqsnOzk61a9fO1xiOjo5W1zjeSK1atfTCCy9ow4YN6t69u5YsWVLougEAQNlR6oNhdna2Tp06pVOnTik5OVnDhg1TZmamunTpcsNtXnrpJU2ZMkWffPKJDh8+rDFjxigpKUkjRozI935jY2O1a9cuDR48WPv379ehQ4c0f/58/frrr4U6jj59+sjJyUn9+vXTwYMHFR8fr2HDhqlv3763vL7wmqCgIG3btk3/93//l2cdly5d0tChQ7V161YdP35cCQkJ2rVrl+rUqVOomgEAQNlS6m8+WbduneX6P3d3d4WEhGjVqlWKiIhQSkpKntsMHz5c6enpGjVqlM6cOaO6detqzZo1N70R4+9q1aqlDRs26B//+Ifuu+8+OTs7q2nTpurdu3ehjsPFxUXr16/XiBEj1KRJE7m4uKhHjx6aOXNmvseYMGGCnnvuOdWoUUPZ2dkymUxW7fb29vrtt98UFRWl06dPq3z58urevTs3mAAAAEmSwfT39ADcQEZGhjw9PRU2bIHsjbe+xrKkSJwWZesSAACwmWuf3+np6be8X6DUf5UMAACAokEwBAAAgCSCIQAAAMwIhgAAAJBEMAQAAIAZwRAAAACSCIYAAAAwIxgCAABAEsEQAAAAZgRDAAAASCIYAgAAwMzB1gWg9Nk2sfctn7UIAABKH2YMAQAAIIlgCAAAADOCIQAAACRxjSEKwGQySZIyMjJsXAkAAMiva5/b1z7Hb4ZgiHz77bffJEmBgYE2rgQAABTU+fPn5enpedM+BEPkm4+PjyTpxIkTt3xj3c0yMjIUGBio1NRU7t6+Cc5T/nCe8ofzdGuco/wpi+fJZDLp/PnzCggIuGVfgiHyzc7uz0tSPT09y8x/LHeSh4cH5ykfOE/5w3nKH87TrXGO8qesnaf8Tuhw8wkAAAAkEQwBAABgRjBEvhmNRo0bN05Go9HWpZRonKf84TzlD+cpfzhPt8Y5yp+7/TwZTPm5dxkAAABlHjOGAAAAkEQwBAAAgBnBEAAAAJIIhgAAADAjGAIAAEASTz7BTfz666967733tH37dp06dUqS5Ofnp+bNmys6OloVKlSwcYUAAKAo8XM1yNOuXbsUGRkpFxcXtW/fXpUqVZIknT59Wps3b9bFixe1fv16hYeH27hSlCY7d+687n80mjVrpvvuu8/GlaG04b2EonL58mV99tlneU6CdO3aVY6OjjausHgRDJGn+++/X2FhYVqwYIEMBoNVm8lk0vPPP6/9+/dr+/btNqqwZOEflps7c+aMevTooYSEBFWtWtXqfzROnDihFi1a6N///rcqVqxo40pLBkLPjfFeKhjeSzf3008/KTIyUidPnlTTpk2t3k87duxQlSpV9PXXXys4ONjGlRYfgiHy5OzsrL179yokJCTP9kOHDqlhw4a6dOlSMVdW8vAPy6317NlTJ0+e1JIlS1S7dm2rtsOHD6t///4KCAjQqlWrbFRhyUDouTXeS/nDeyl/HnzwQbm6umr58uXy8PCwasvIyFBUVJQuXbqk9evX26hCGzABeQgKCjItW7bshu3Lli0zVatWrfgKKsHat29v6tq1qyk9Pf26tvT0dFPXrl1NDz30kA0qKznc3NxMe/bsuWH77t27TW5ubsVYUcnUo0cPU7NmzUyHDh26ru3QoUOm5s2bm3r27GmDykoO3kv5w3spf5ydnU0HDhy4Yfv+/ftNzs7OxViR7XHzCfIUExOjgQMHKjExUe3atbvuGsOFCxdq+vTpNq6yZEhISNDOnTuv+79NSfLw8NAbb7yhpk2b2qCyksNoNCojI+OG7efPn79rn0v6V+vXr9e2bduumwmTpNq1a2vu3LmKiIgo/sJKEN5L+cN7KX+8vLyUkpKie++9N8/2lJQUeXl5FW9RNsbP1SBPQ4YM0bJly7Rjxw716NFDzZo1U7NmzdSjRw/t2LFDS5cu1eDBg21dZolw7R+WG7kb/2H5u169eqlfv35avXq11Yd6RkaGVq9eraefflq9e/e2YYUlA6Hn1ngv5Q/vpfwZMGCAoqKiNGvWLO3fv1+nT5/W6dOntX//fs2aNUvR0dEaOHCgrcssXraeskTJd/nyZdPJkydNJ0+eNF2+fNnW5ZQ4r776qsnb29s0c+ZM0759+0ynTp0ynTp1yrRv3z7TzJkzTT4+PqZx48bZukybysrKMj3//PMmR0dHk52dncnJycnk5ORksrOzMzk6OpoGDRpkysrKsnWZNjd48GBTtWrVTHFxcVaXJqSnp5vi4uJMQUFBpqFDh9qwQtu70XvJYDDwXvoL3kv5N3nyZJO/v7/JYDCY7OzsTHZ2diaDwWDy9/c3TZkyxdblFTtuPgGKwJQpUzRnzhydOnXKche3yWSSn5+fRo4cqZdfftnGFZYMGRkZSkxMtLpDsnHjxnl+DX83ys7O1siRI/Xee+/p6tWrlrvZL1++LAcHBz3zzDOaNWsWMz368720e/dunT59WpJUqVIlhYeH814y471UcL/88ovVv0333HOPjSuyDYIhUIT4hwVFgQBdcI6Ojtq3b5/q1Klj61JKFN5LKCiCIXCHpaamaty4cXrvvfdsXYpNXbp0SYmJifLx8VHdunWt2rKysrRy5UpFRUXZqLqSIzk5Wd9//72aNWumkJAQHTp0SHPmzFF2draeeuoptW3b1tYl2tSLL76Y5/o5c+boqaeekq+vryRp5syZxVlWiXfhwgWtXLlSP/30kwICAvTEE09YztXdbM+ePfL29rb8T/z777+vBQsW6MSJE6pWrZqGDh2qJ554wsZVFi+CIXCH7du3T40aNVJOTo6tS7GZI0eO6KGHHtKJEydkMBj0wAMPaMWKFQoICJD0593uAQEBd/U5kqR169apa9eucnNz08WLF7V69WpFRUUpLCxMubm5+uabb7Rhw4a7Ohza2dkpLCzsuhu6vvnmG4WHh8vV1VUGg0FbtmyxTYElRN26dfXtt9/Kx8dHqampatWqlc6dO6datWrp2LFjcnBw0Pfff3/Xf6sRFhamGTNmqH379lq0aJGGDx+uZ599VnXq1NHhw4e1aNEizZkzR/3797d1qcWGYAjcpjVr1ty0/eeff9aoUaPu6tDTrVs3XblyRUuXLtUff/yhkSNH6scff9TWrVtVtWpVgqFZ8+bN1bZtW02cOFEff/yxBg8erEGDBunNN9+UJMXGxioxMVEbNmywcaW2M3nyZL377rtatGiRVUAuV66c9u3bd91s9N3Kzs5Op06dUsWKFfXUU0/pl19+0VdffSVPT09lZmaqW7duqlChgj766CNbl2pTLi4uSk5OVrVq1dSoUSMNGjRIzz77rKX9o48+0ptvvqkffvjBhlUWMxvd9AKUGdfuZDMYDDdc7OzsbF2mTVWsWNG0f/9+y+vc3FzT888/b6patarp2LFjplOnTt3158hkMpk8PDxMR48eNZlMJlNOTo7JwcHB6secDxw4YKpUqZKtyisxdu7caapVq5Zp1KhRll9KcHBwMP3www82rqzkMBgMptOnT5tMJpOpevXqpg0bNli1JyQkmAIDA21RWoni6+tr2r17t8lk+vPfqaSkJKv2n3766a77gWt+xxC4Tf7+/oqLi1Nubm6ey549e2xdos1dunRJDg7/+z19g8Gg+fPnq0uXLmrdurWOHDliw+pKlmt3tdvZ2cnJyUmenp6WNnd3d6Wnp9uqtBKjSZMmSkxM1NmzZxUeHq6DBw9e90x3/O+9lJWVJX9/f6u2ypUr6+zZs7Yoq0Tp2LGj5s+fL0lq3bq1Pv30U6v2lStX3nWPM+XJJ8Btaty4sRITE9W1a9c82w0Gg0x3+RUbISEh2r1793V3jM6bN0+S9Mgjj9iirBInKChIR48eVY0aNSRJ27dvV9WqVS3tJ06cuO4D/m7l5uamZcuW6eOPP1b79u3v+ssQ8tKuXTs5ODgoIyNDhw8ftnq6x/Hjx7n5RH/+1FiLFi3UunVrhYeHa8aMGdq6davlGsPvv/9eq1evtnWZxYpgCNyml156SRcuXLhhe3BwsOLj44uxopKnW7duWrFihfr27Xtd27x585Sbm6sFCxbYoLKSZdCgQVYB5++P6fr666/v6htP8vLEE0/ogQceUGJioqpVq2brckqMcePGWb12c3Ozev3FF1+oZcuWxVlSiRQQEKC9e/dq8uTJ+uKLL2QymbRz506lpqaqRYsWSkhIUHh4uK3LLFbcfAIAAABJPCsZAAAAZgRDAAAASCIYAgAAwIxgCAAAAEkEQwAAAJgRDAEAtyUlJUUGg0FJSUm2LgXAbSIYAgAAQBLBEABKvdzcXE2dOlXBwcEyGo2qWrWq3nzzTUnSgQMH1LZtWzk7O8vX11cDBw5UZmamZduIiAiNHDnSarxHH31U0dHRltdBQUF666231L9/f7m7u6tq1ap69913Le333HOPJKlhw4YyGAyKiIi4Y8cK4M4iGAJAKRcbG6vJkyfr1Vdf1Y8//qiPPvpIlSpV0oULFxQZGSlvb2/t2rVLq1at0qZNmzR06NAC72PGjBkKDw/X3r17NXjwYA0aNEiHDx+WJO3cuVOStGnTJqWlpSkuLq5Ijw9A8eGReABQip0/f15z5szRvHnz1K9fP0lSjRo19MADD2jhwoXKysrS8uXL5erqKunPRxB26dJFU6ZMUaVKlfK9n4cffliDBw+WJI0ePVqzZs1SfHy8ateurQoVKkiSfH195efnV8RHCKA4MWMIAKVYcnKysrOz1a5duzzbwsLCLKFQklq0aKHc3FzLbF9+1a9f3/K3wWCQn5+fzpw5U/jCAZRIBEMAKMWcnZ1va3s7OzuZTCardVeuXLmuX7ly5axeGwwG5ebm3ta+AZQ8BEMAKMVq1qwpZ2dnbd68+bq2OnXqaN++fbpw4YJlXUJCguzs7FS7dm1JUoUKFZSWlmZpz8nJ0cGDBwtUg6Ojo2VbAKUbwRAASjEnJyeNHj1aL7/8spYvX65jx47p+++/1+LFi9WnTx85OTmpX79+OnjwoOLj4zVs2DD17dvXcn1h27Zt9eWXX+rLL7/UoUOHNGjQIP3xxx8FqqFixYpydnbWunXrdPr0aaWnp9+BIwVQHAiGAFDKvfrqqxo1apRee+011alTR7169dKZM2fk4uKi9evX6/fff1eTJk3Us2dPtWvXTvPmzbNs279/f/Xr109RUVFq3bq1qlevrjZt2hRo/w4ODpo7d67+9a9/KSAgQF27di3qQwRQTAymv19cAgAAgLsSM4YAAACQRDAEAACAGcEQAAAAkgiGAAAAMCMYAgAAQBLBEAAAAGYEQwAAAEgiGAIAAMCMYAgAAABJBEMAAACYEQwBAAAgSfp/iXwzqhsM+S4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(diagnosis.disease)\n",
        "plt.xticks(rotation=90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.016448,
          "end_time": "2020-09-24T12:01:43.346775",
          "exception": false,
          "start_time": "2020-09-24T12:01:43.330327",
          "status": "completed"
        },
        "tags": [],
        "id": "JSUjrRnpbRRe"
      },
      "source": [
        "> We will try to extract **Id** of each processed audio file and then merge them with their respective **class label** so we can split files in to train and validation folder in **stratified manner**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:43.382779Z",
          "iopub.status.busy": "2020-09-24T12:01:43.382115Z",
          "iopub.status.idle": "2020-09-24T12:01:43.386228Z",
          "shell.execute_reply": "2020-09-24T12:01:43.385673Z"
        },
        "papermill": {
          "duration": 0.024365,
          "end_time": "2020-09-24T12:01:43.386386",
          "exception": false,
          "start_time": "2020-09-24T12:01:43.362021",
          "status": "completed"
        },
        "tags": [],
        "id": "Rg74WQZ7bRRe"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "def extractId(filename):\n",
        "    return filename.split('_')[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:43.425272Z",
          "iopub.status.busy": "2020-09-24T12:01:43.424419Z",
          "iopub.status.idle": "2020-09-24T12:01:44.839961Z",
          "shell.execute_reply": "2020-09-24T12:01:44.839360Z"
        },
        "papermill": {
          "duration": 1.438303,
          "end_time": "2020-09-24T12:01:44.840093",
          "exception": false,
          "start_time": "2020-09-24T12:01:43.401790",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4EgRkqrRbRRe",
        "outputId": "b1375889-da66-4711-bd19-86796ded6350"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pid                       filename\n",
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              "1  147   147_2b2_Ar_mc_AKGC417L_4.wav\n",
              "2  147  147_1b2_Tc_mc_AKGC417L_10.wav\n",
              "3  147   147_2b3_Al_mc_AKGC417L_3.wav\n",
              "4  147   147_1b2_Tc_mc_AKGC417L_8.wav"
            ],
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              "    <tr>\n",
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              "      <td>147</td>\n",
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              "\n",
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              "        document.querySelector('#df-e692ee55-e941-4bd9-8da0-3499e14c173b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-e692ee55-e941-4bd9-8da0-3499e14c173b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "\n",
              "<div id=\"df-924169b6-968a-400f-a3d7-38e415d4ba24\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-924169b6-968a-400f-a3d7-38e415d4ba24')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-924169b6-968a-400f-a3d7-38e415d4ba24 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "files_df",
              "summary": "{\n  \"name\": \"files_df\",\n  \"rows\": 6898,\n  \"fields\": [\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 126,\n        \"samples\": [\n          \"106\",\n          \"134\",\n          \"165\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6898,\n        \"samples\": [\n          \"130_2b3_Tc_mc_AKGC417L_4.wav\",\n          \"130_1p2_Pl_mc_AKGC417L_5.wav\",\n          \"188_1b1_Pl_sc_Meditron_2.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "path='/content/drive/MyDrive/processed_audio_files/'\n",
        "length=len(os.listdir(path))\n",
        "index=range(length)\n",
        "i=0\n",
        "files_df=pd.DataFrame(index=index,columns=['pid','filename'])\n",
        "for f in os.listdir(path):\n",
        "    files_df.iloc[i]['pid']=extractId(f)\n",
        "    files_df.iloc[i]['filename']=f\n",
        "    i+=1\n",
        "files_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:44.881549Z",
          "iopub.status.busy": "2020-09-24T12:01:44.880498Z",
          "iopub.status.idle": "2020-09-24T12:01:44.884523Z",
          "shell.execute_reply": "2020-09-24T12:01:44.883886Z"
        },
        "papermill": {
          "duration": 0.027857,
          "end_time": "2020-09-24T12:01:44.884639",
          "exception": false,
          "start_time": "2020-09-24T12:01:44.856782",
          "status": "completed"
        },
        "tags": [],
        "id": "gNfC88dUbRRe"
      },
      "outputs": [],
      "source": [
        "files_df.pid=files_df.pid.astype('int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:44.926322Z",
          "iopub.status.busy": "2020-09-24T12:01:44.925564Z",
          "iopub.status.idle": "2020-09-24T12:01:44.946352Z",
          "shell.execute_reply": "2020-09-24T12:01:44.946917Z"
        },
        "papermill": {
          "duration": 0.046029,
          "end_time": "2020-09-24T12:01:44.947089",
          "exception": false,
          "start_time": "2020-09-24T12:01:44.901060",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2HJNPlWXbRRf",
        "outputId": "4e2fdf88-c184-4d16-fd1a-de75cb625b4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pid                       filename disease\n",
              "0  147   147_1b2_Tc_mc_AKGC417L_4.wav    COPD\n",
              "1  147   147_2b2_Ar_mc_AKGC417L_4.wav    COPD\n",
              "2  147  147_1b2_Tc_mc_AKGC417L_10.wav    COPD\n",
              "3  147   147_2b3_Al_mc_AKGC417L_3.wav    COPD\n",
              "4  147   147_1b2_Tc_mc_AKGC417L_8.wav    COPD"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b67a545-7c3e-4860-b3cf-629d51bfcd70\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pid</th>\n",
              "      <th>filename</th>\n",
              "      <th>disease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>147</td>\n",
              "      <td>147_1b2_Tc_mc_AKGC417L_4.wav</td>\n",
              "      <td>COPD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>147</td>\n",
              "      <td>147_2b2_Ar_mc_AKGC417L_4.wav</td>\n",
              "      <td>COPD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>147</td>\n",
              "      <td>147_1b2_Tc_mc_AKGC417L_10.wav</td>\n",
              "      <td>COPD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>147</td>\n",
              "      <td>147_2b3_Al_mc_AKGC417L_3.wav</td>\n",
              "      <td>COPD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>147</td>\n",
              "      <td>147_1b2_Tc_mc_AKGC417L_8.wav</td>\n",
              "      <td>COPD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b67a545-7c3e-4860-b3cf-629d51bfcd70')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6b67a545-7c3e-4860-b3cf-629d51bfcd70 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b67a545-7c3e-4860-b3cf-629d51bfcd70');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2d4d66fd-0442-48bd-a8f0-b67bb61b505d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2d4d66fd-0442-48bd-a8f0-b67bb61b505d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2d4d66fd-0442-48bd-a8f0-b67bb61b505d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6898,\n  \"fields\": [\n    {\n      \"column\": \"pid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 101,\n        \"max\": 226,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          106,\n          134,\n          165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6898,\n        \"samples\": [\n          \"117_1b2_Tc_mc_LittC2SE_2.wav\",\n          \"111_1b3_Tc_sc_Meditron_8.wav\",\n          \"186_2b3_Tc_mc_AKGC417L_4.wav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disease\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Bronchiolitis\",\n          \"Bronchiectasis\",\n          \"COPD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data=pd.merge(files_df,diagnosis,on='pid')\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:44.986184Z",
          "iopub.status.busy": "2020-09-24T12:01:44.985125Z",
          "iopub.status.idle": "2020-09-24T12:01:45.148157Z",
          "shell.execute_reply": "2020-09-24T12:01:45.147563Z"
        },
        "papermill": {
          "duration": 0.183694,
          "end_time": "2020-09-24T12:01:45.148285",
          "exception": false,
          "start_time": "2020-09-24T12:01:44.964591",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "laFLww3lbRRf",
        "outputId": "6c1aa5b7-1f19-4fbf-8c2d-fb22897d38ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   0., 1000., 2000., 3000., 4000., 5000., 6000., 7000.]),\n",
              " [Text(0.0, 0, '0'),\n",
              "  Text(1000.0, 0, '1000'),\n",
              "  Text(2000.0, 0, '2000'),\n",
              "  Text(3000.0, 0, '3000'),\n",
              "  Text(4000.0, 0, '4000'),\n",
              "  Text(5000.0, 0, '5000'),\n",
              "  Text(6000.0, 0, '6000'),\n",
              "  Text(7000.0, 0, '7000')])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WY87OzrLZbJKkcuXK6bHHHtPcuXPl4uJiXy9Jbm5uysjIcFgHAACQnwiVd4ikpCStXLlSLi4u+TZmRkaGMjIy7O/T0tLybWwAAHBnIVQWYwkJCfL09FRmZqYuXrwoSZo2bVq+jT9hwgS9+uqr+TYeAAC4c/GgTjFWtWpVxcfHa8uWLRo9erSioqI0ePDgfBs/JiZGqamp9uXo0aP5NjYAALizECqLIG9vb6Wmpt6w/syZM/Lx8bG/d3FxUWhoqGrVqqWJEyfKyckpX88sWq1WeXt7OywAAADZIVQWQVWrVtX27dtvWL99+3ZVqVLlptuNHTtWU6ZM0bFjxwqyPAAAgBsQKoug/v37a9++fRoyZIh27dqlvXv3atq0aVqwYIGGDx9+0+0aN26siIgIvfnmm7exWgAAAEJlkVS5cmWtX79ev/32m9q2batGjRrp66+/1qJFi9SuXbtbbvvcc8/p448/5v5HAABwW1kMwzAKuwgUD2lpafLx8VHk4A/kZHXLlzG3Te6ZL+MAAIDsXfv8Tk1NLdDnIzhTCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA05wLuwAUP+tfjy7Q3w4FAADFD2cqAQAAYBqhEgAAAKYRKgEAAGAaoRIAAACmESoBAABgGqESAAAAphEqAQAAYBqhEgAAAKbx5efIteZjF8jJ6lbg+9k2uWeB7wMAAOQPzlQCAADANEIlAAAATCNUAgAAwDRCJQAAAEwjVAIAAMA0QiUAAABMI1QCAADANEIlAAAATCNUAgAAwDRCJQAAAEwjVAIAAMA0QiUAAABMI1QCAADANEJlEbdu3TpZLBadOXPmlv1CQkI0ffr021ITAADA9QiVedS7d2917tz5hvU5DYF5NW/ePPn6+hbI2AAAAHlFqAQAAIBphMoCtmHDBjVr1kxubm4KDg7WkCFDdO7cOXv7559/rvr168vLy0s2m03//Oc/lZKSku1Y69at05NPPqnU1FRZLBZZLBaNGzfO3n7+/Hn16dNHXl5eqlChgj766CN7W+vWrTVo0CCH8U6cOCEXFxetWbMmfw8aAADcdQiVBejAgQNq166dHn30Ue3atUtfffWVNmzY4BDuLl++rNdee007d+7Ut99+q6SkJPXu3Tvb8Zo0aaLp06fL29tbycnJSk5O1ogRI+ztU6dOVf369bVjxw4NGDBA/fv31969eyVJffv21ZdffqmMjAx7/y+++ELlypVT69ats91fRkaG0tLSHBYAAIDs5DlUfv7552ratKmCgoJ0+PBhSdL06dP13Xff5VtxRd2yZcvk6enpsLRv397ePmHCBHXv3l3Dhg1TWFiYmjRpohkzZuizzz7TxYsXJUl9+vRR+/btVblyZd17772aMWOGli9frvT09Bv25+LiIh8fH1ksFtlsNtlsNnl6etrbH3zwQQ0YMEChoaEaPXq0SpcurdjYWElSly5dJMnhn8+8efPUu3dvWSyWbI9vwoQJ8vHxsS/BwcHmJw0AANyR8hQqZ82apeeff14PPvigzpw5o8zMTEmSr6/vXfUEcqtWrRQfH++wfPzxx/b2nTt3at68eQ6hMyoqSllZWTp06JAkadu2berYsaMqVKggLy8vtWjRQpJ05MiRXNcTERFhf30teF67lO7q6qoePXrok08+kSRt375du3fvvulZUUmKiYlRamqqfTl69GiuawIAAHcH57xs9O6772r27Nnq3LmzJk6caF9fv359h8uxdzoPDw+FhoY6rPvvf/9rf52enq5nnnlGQ4YMuWHbChUq6Ny5c4qKilJUVJTmz5+vMmXK6MiRI4qKitKlS5dyXU/JkiUd3lssFmVlZdnf9+3bV7Vr19Z///tfzZ07V61bt1bFihVvOp7VapXVas11HQAA4O6Tp1B56NAh1alT54b1VqvV4SGUu13dunW1Z8+eG4LnNQkJCTp58qQmTpxov7S8devWW47p4uJiPzOcW+Hh4apfv75mz56tL7/8UjNnzszTOAAAANfL0+XvSpUqKT4+/ob1K1asUPXq1c3WdMcYPXq0fv75Zw0aNEjx8fHav3+/vvvuO/uDOhUqVJCLi4veffddHTx4UEuXLtVrr712yzFDQkKUnp6uNWvW6M8//9T58+dzVVPfvn01ceJEGYahRx55JM/HBgAA8Fd5CpXPP/+8Bg4cqK+++kqGYWjz5s164403FBMTo1GjRuV3jcVWRESEfvrpJ+3bt0/NmjVTnTp19PLLLysoKEiSVKZMGc2bN0+LFi1SjRo1NHHiRE2ZMuWWYzZp0kTPPvusunXrpjJlymjSpEm5qik6OlrOzs6Kjo6Wq6trno8NAADgryyGYRh52XD+/PkaN26cDhw4IEkKCgrSq6++qqeeeipfC0T+SkpK0j333KMtW7aobt26udo2LS1NPj4+ihz8gZysbgVU4f/ZNrlnge8DAIA73bXP79TUVHl7exfYfvJ0T6Ukde/eXd27d9f58+eVnp6usmXL5mddyGeXL1/WyZMnNXbsWN177725DpQAAAC3kqfL3xcuXLDfy+fu7q4LFy5o+vTp+vHHH/O1OOSfuLg4BQYGasuWLfrggw8KuxwAAHCHydOZyk6dOqlLly569tlndebMGTVs2FAuLi76888/NW3aNPXv3z+/64RJLVu2VB7vdAAAAPhbeTpTuX37djVr1kyS9M0338hms+nw4cP67LPPNGPGjHwtEAAAAEVfnkLl+fPn5eXlJUn68ccf1aVLF5UoUUL33nuv/ScbAQAAcPfIU6gMDQ3Vt99+q6NHj2rlypV64IEHJEkpKSkF+lQRAAAAiqY8hcqXX35ZI0aMUEhIiBo1aqTGjRtLunrWMrtf2gEAAMCdLU8P6nTt2lX33XefkpOTFRkZaV/fpk0bfqUFAADgLpTn76m02Wyy2WwO6xo2bGi6IAAAABQ/eQ6VW7du1ddff60jR47o0qVLDm2LFy82XRgAAACKjzzdU7lw4UI1adJEiYmJWrJkiS5fvqxff/1Va9eulY+PT37XCAAAgCIuT7/9HRERoWeeeUYDBw6Ul5eXdu7cqUqVKumZZ55RYGCgXn311YKoFYXsdv12KAAAyD+36/M7T2cqDxw4oA4dOkiSXFxcdO7cOVksFj333HP66KOP8rVAAAAAFH15CpV+fn46e/asJKlcuXLavXu3JOnMmTP23wQHAADA3SNPD+o0b95cq1atUnh4uB577DENHTpUa9eu1apVq9SmTZv8rhEAAABFXJ5C5cyZM3Xx4kVJ0osvvqiSJUvq559/1qOPPqqxY8fma4EAAAAo+vL0oA7uTjyoAwBA8VOkH9SRrj6sM3bsWEVHRyslJUWStHz5cv3666/5VhwAAACKhzyFyp9++knh4eHatGmTFi9erPT0dEnSzp079corr+RrgQAAACj68hQqx4wZo9dff12rVq2Si4uLfX3r1q31yy+/5FtxAAAAKB7yFCoTEhL0yCOP3LC+bNmy+vPPP00XBQAAgOIlT09/+/r6Kjk5WZUqVXJYv2PHDpUrVy5fCkPR1XzsAjlZ3Qpk7G2TexbIuAAAoGDl6UzlP/7xD40ePVrHjx+XxWJRVlaW4uLiNGLECPXsSSgAAAC42+QpVL755puqVq2agoODlZ6erho1aqh58+Zq0qQJ31MJAABwF8rT5W8XFxfNnj1bL7/8shISEpSenq46deooLCwsv+sDAABAMZCnUHlNcHCwgoODlZmZqYSEBJ0+fVp+fn75VRsAAACKiTxd/h42bJjmzJkjScrMzFSLFi1Ut25dBQcHa926dflZHwAAAIqBPIXKb775RpGRkZKkf//73zp48KB+++03Pffcc3rxxRfztUAAAAAUfXkKlX/++adsNpsk6YcfftDjjz+uKlWqqE+fPkpISMjXAgEAAFD05SlUBgQEaM+ePcrMzNSKFSt0//33S5LOnz8vJyenfC0QAAAARV+eHtR58skn9fjjjyswMFAWi0Vt27aVJG3atEnVqlXL1wIBAABQ9OUpVI4bN061atXS0aNH9dhjj8lqtUqSnJycNGbMmHwtEAAAAEVfnr9SqGvXrjes69Wrl6liAAAAUDzlOFTOmDFD/fr1k6urq2bMmHHLvkOGDDFdGAAAAIqPHIfKt99+W927d5erq6vefvvtm/azWCyEyiIiJCREw4YN07Bhwwq7FAAAcIfLcag8dOhQtq+Lst69e+vTTz+VJJUsWVIVKlRQz5499cILL8jZ2dSPCRULW7ZskYeHR2GXAQAA7gI5TlbPP/98jvpZLBZNnTo1zwXlt3bt2mnu3LnKyMjQDz/8oIEDB6pkyZKKiYkp7NIKXJkyZQq7BAAAcJfI8fdU7tixw2GZM2eOPvzwQ61bt07r1q3TRx99pDlz5mjHjh0FWW+uWa1W2Ww2VaxYUf3791fbtm21dOlS9e7dW507d9aUKVMUGBioUqVKaeDAgbp8+bJ924yMDI0YMULlypWTh4eHGjVq5PAzlOPGjVPt2rUd9jd9+nSFhITY31/bz5tvvqmAgAD5+vpq/PjxunLlikaOHCl/f3+VL19ec+fOdRgnISFBrVu3lpubm0qVKqV+/fopPT39hnFvVX9ISIimT59ufz9t2jSFh4fLw8NDwcHBGjBggMOYAAAAeZXjM5WxsbH219OmTZOXl5c+/fRT+fn5SZJOnz6tJ598Us2aNcv/KvORm5ubTp48KenqMQUGBio2Nla///67unXrptq1a+vpp5+WJA0aNEh79uzRwoULFRQUpCVLlqhdu3ZKSEhQWFhYjve5du1alS9fXuvXr1dcXJyeeuop/fzzz2revLk2bdqkr776Ss8884zuv/9+lS9fXufOnVNUVJQaN26sLVu2KCUlRX379tWgQYM0b948+7h/V//1SpQooRkzZqhSpUo6ePCgBgwYoFGjRun999/Ptn9GRoYyMjLs79PS0nJ8zAAA4O6Sp1/UmTp1qiZMmGAPlJLk5+en119/vUhd+v4rwzC0evVqrVy5Uq1bt5Z0teaZM2eqWrVqeuihh9ShQwetWbNGknTkyBHNnTtXixYtUrNmzXTPPfdoxIgRuu+++244q/h3/P39NWPGDFWtWlV9+vRR1apVdf78eb3wwgsKCwtTTEyMXFxctGHDBknSl19+qYsXL+qzzz5TrVq11Lp1a82cOVOff/65/vjjD/u4t6o/O8OGDVOrVq0UEhKi1q1b6/XXX9fXX3990/4TJkyQj4+PfQkODs7VcQMAgLtHnkJlWlqaTpw4ccP6EydO6OzZs6aLyk/Lli2Tp6enXF1d1b59e3Xr1k3jxo2TJNWsWdPhZyUDAwOVkpIi6erl58zMTFWpUkWenp725aefftKBAwdyVUPNmjVVosT/TXVAQIDCw8Pt752cnFSqVCn7vhMTExUZGenwkE3Tpk2VlZWlvXv3Oox7s/qzs3r1arVp00blypWTl5eXevTooZMnT+r8+fPZ9o+JiVFqaqp9OXr0aK6OGwAA3D3y9Aj0I488oieffFJTp05Vw4YNJV39icaRI0eqS5cu+VqgWa1atdKsWbPk4uKioKAgh6e+S5Ys6dDXYrEoKytLkpSeni4nJydt27btht8z9/T0lHT1crJhGA5tf72n8Vb7udW+cyo3YyQlJemhhx5S//799cYbb8jf318bNmzQU089pUuXLsnd3f2GbaxWq/3XkgAAAG4lT6Hygw8+0IgRI/TPf/7THqKcnZ311FNPafLkyflaoFkeHh4KDQ3N9XZ16tRRZmamUlJSbnqfaJkyZXT8+HEZhiGLxSJJio+PN1OuJKl69eqaN2+ezp07Zz9bGRcXpxIlSqhq1ap5GnPbtm3KysrS1KlT7WdNb3XpGwAAIDfydPnb3d1d77//vk6ePGl/GvzUqVN6//3375jvRaxSpYq6d++unj17avHixTp06JA2b96sCRMm6Pvvv5cktWzZUidOnNCkSZN04MABvffee1q+fLnpfV/7kvlevXpp9+7dio2N1eDBg9WjRw8FBATkaczQ0FBdvnxZ7777rg4ePKjPP/9cH3zwgelaAQAApDyGyms8PDwUERGhiIiIOyZM/tXcuXPVs2dPDR8+XFWrVlXnzp21ZcsWVahQQdLVM4rvv/++3nvvPUVGRmrz5s0aMWKE6f26u7tr5cqVOnXqlBo0aKCuXbuqTZs2mjlzZp7HjIyM1LRp0/TWW2+pVq1amj9/viZMmGC6VgAAAEmyGNffFAjcRFpamnx8fBQ5+AM5Wd0KZB/bJvcskHEBALhbXfv8Tk1Nlbe3d4Htx9SZSgAAAEAiVAIAACAfECoBAABgGqESAAAAphEqAQAAYBqhEgAAAKYRKgEAAGAaoRIAAACmESoBAABgGqESAAAAphEqAQAAYBqhEgAAAKY5F3YBKH7Wvx5doD9IDwAAih/OVAIAAMA0QiUAAABMI1QCAADANEIlAAAATCNUAgAAwDRCJQAAAEwjVAIAAMA0QiUAAABM48vPkWvNxy6Qk9Xtpu3bJve8jdUAAICigDOVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QeRMtW7bUsGHDbtnHYrHo22+/vS315FVOjgMAAMCsQg2VvXv3lsVisS+lSpVSu3bttGvXrsIsK8eSk5PVvn37fBuvIELq4sWL9dprr+XrmAAAANcr9DOV7dq1U3JyspKTk7VmzRo5OzvroYceumn/y5cv38bqbs1ms8lqtRZ2Gbfk7+8vLy+vwi4DAADc4Qo9VFqtVtlsNtlsNtWuXVtjxozR0aNHdeLECSUlJcliseirr75SixYt5Orqqvnz5ysrK0vjx49X+fLlZbVaVbt2ba1YscI+5rXtFi9erFatWsnd3V2RkZHauHGjw77j4uLUsmVLubu7y8/PT1FRUTp9+rS9PSsrS6NGjZK/v79sNpvGjRvnsP31ZxaPHj2qxx9/XL6+vvL391enTp2UlJTksM0nn3yimjVrymq1KjAwUIMGDZIkhYSESJIeeeQRWSwW+/sDBw6oU6dOCggIkKenpxo0aKDVq1c7jPn+++8rLCxMrq6uCggIUNeuXe1t11/+vlXf62VkZCgtLc1hAQAAyE6hh8q/Sk9P1xdffKHQ0FCVKlXKvn7MmDEaOnSoEhMTFRUVpXfeeUdTp07VlClTtGvXLkVFRenhhx/W/v37HcZ78cUXNWLECMXHx6tKlSqKjo7WlStXJEnx8fFq06aNatSooY0bN2rDhg3q2LGjMjMz7dt/+umn8vDw0KZNmzRp0iSNHz9eq1atyrb2y5cvKyoqSl5eXvrPf/6juLg4eXp6ql27drp06ZIkadasWRo4cKD69eunhIQELV26VKGhoZKkLVu2SJLmzp2r5ORk+/v09HQ9+OCDWrNmjXbs2KF27dqpY8eOOnLkiCRp69atGjJkiMaPH6+9e/dqxYoVat68ebY15qavJE2YMEE+Pj72JTg4+Ob/8AAAwN3NKES9evUynJycDA8PD8PDw8OQZAQGBhrbtm0zDMMwDh06ZEgypk+f7rBdUFCQ8cYbbzisa9CggTFgwACH7T7++GN7+6+//mpIMhITEw3DMIzo6GijadOmN62tRYsWxn333XfDPkaPHm1/L8lYsmSJYRiG8fnnnxtVq1Y1srKy7O0ZGRmGm5ubsXLlSnvdL7744k33+dfxbqVmzZrGu+++axiGYfzrX/8yvL29jbS0tJsex9ChQ3PU93oXL140UlNT7cvRo0cNSUbk4A+MuiM+vekCAACKjtTUVEOSkZqaWqD7KfQzla1atVJ8fLzi4+O1efNmRUVFqX379jp8+LC9T/369e2v09LSdOzYMTVt2tRhnKZNmyoxMdFhXUREhP11YGCgJCklJUXS/52pvJW/bn9tjGvbX2/nzp36/fff5eXlJU9PT3l6esrf318XL17UgQMHlJKSomPHjv3tPq+Xnp6uESNGqHr16vL19ZWnp6cSExPtZyrvv/9+VaxYUZUrV1aPHj00f/58nT9/PtuxctNXunprgre3t8MCAACQnUIPlR4eHgoNDVVoaKgaNGigjz/+WOfOndPs2bMd+uRFyZIl7a8tFoukq/dJSpKbm1uutr82xrXtr5eenq569erZA/K1Zd++ffrnP/+Zo/1lZ8SIEVqyZInefPNN/ec//1F8fLzCw8Ptl9S9vLy0fft2LViwQIGBgXr55ZcVGRmpM2fO3DBWbvoCAADkRqGHyutZLBaVKFFCFy5cyLbd29tbQUFBiouLc1gfFxenGjVq5Hg/ERERWrNmjala/6pu3brav3+/ypYtaw/J1xYfHx95eXkpJCTklvssWbKkwz2d0tXj6t27tx555BGFh4fLZrPd8PCPs7Oz2rZtq0mTJmnXrl1KSkrS2rVrs91HbvoCAADklHNhF5CRkaHjx49Lkk6fPq2ZM2cqPT1dHTt2vOk2I0eO1CuvvKJ77rlHtWvX1ty5cxUfH6/58+fneL8xMTEKDw/XgAED9Oyzz8rFxUWxsbF67LHHVLp06VwfR/fu3TV58mR16tTJ/mT64cOHtXjxYo0aNUrly5fXuHHj9Oyzz6ps2bJq3769zp49q7i4OA0ePFiS7KGzadOmslqt8vPzU1hYmBYvXqyOHTvKYrHopZdecjhbumzZMh08eFDNmzeXn5+ffvjhB2VlZalq1ao31JibvgAAALlR6KFyxYoV9vsdvby8VK1aNS1atEgtW7a84YzcNUOGDFFqaqqGDx+ulJQU1ahRQ0uXLlVYWFiO91ulShX9+OOPeuGFF9SwYUO5ubmpUaNGio6OztNxuLu7a/369Ro9erS6dOmis2fPqly5cmrTpo39XsRevXrp4sWLevvttzVixAiVLl3a4St9pk6dqueff16zZ89WuXLllJSUpGnTpqlPnz5q0qSJSpcurdGjRzt8tY+vr68WL16scePG6eLFiwoLC9OCBQtUs2bNG2rMTV8AAIDcsBiGYRR2ESge0tLS5OPjo8jBH8jJevN7RLdN7nkbqwIAALdy7fM7NTW1QB+6LXL3VAIAAKD4IVQCAADANEIlAAAATCNUAgAAwDRCJQAAAEwjVAIAAMA0QiUAAABMI1QCAADANEIlAAAATCNUAgAAwDRCJQAAAExzLuwCUPysfz26QH87FAAAFD+cqQQAAIBphEoAAACYRqgEAACAaYRKAAAAmEaoBAAAgGmESgAAAJhGqAQAAIBphEoAAACYRqhErjUfu0D1Rn5W2GUAAIAihFAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QWYT17t1bnTt3zrYtJCREFotFFotF7u7uCg8P18cff2zf7lpbdktISIgkqWXLlho2bNjtORgAAHBHI1QWY+PHj1dycrJ2796tJ554Qk8//bSWL1+ud955R8nJyfZFkubOnWt/v2XLlkKuHAAA3GmcC7sA5J2Xl5dsNpskafTo0Zo0aZJWrVql9u3by8fHx6Gvr6+vvW9OZWRkKCMjw/4+LS3NfNEAAOCOxJnKO0BWVpb+9a9/6fTp03Jxccm3cSdMmCAfHx/7EhwcnG9jAwCAOwuhshgbPXq0PD09ZbVa1bVrV/n5+alv3775Nn5MTIxSU1Pty9GjR/NtbAAAcGchVBZjI0eOVHx8vNauXatGjRrp7bffVmhoaL6Nb7Va5e3t7bAAAABkh3sqi7HSpUsrNDRUoaGhWrRokcLDw1W/fn3VqFGjsEsDAAB3Gc5U3iGCg4PVrVs3xcTEFHYpAADgLsSZyiIuNTVV8fHxDutKlSqVbd+hQ4eqVq1a2rp1q+rXr38bqgMAALiKUFnErVu3TnXq1HFY99RTT2Xbt0aNGnrggQf08ssv64cffrgd5QEAAEiSLIZhGIVdBIqHtLQ0+fj4KHLwB3Kyumnb5J6FXRIAAPgb1z6/U1NTC/ShW+6pBAAAgGmESgAAAJhGqAQAAIBphEoAAACYRqgEAACAaYRKAAAAmEaoBAAAgGmESgAAAJhGqAQAAIBphEoAAACYRqgEAACAac6FXQCKn/WvRxfob4cCAIDihzOVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJUAAAAwjVAJAAAA0wiVAAAAMI1QCQAAANMIlQAAADCNUAkAAADTCJVFTO/evdW5c+fCLgMAACBXCJX5YOPGjXJyclKHDh1yvE1SUpIsFovi4+MLrjAAAIDbhFCZD+bMmaPBgwdr/fr1OnbsWGGXAwAAcNsRKk1KT0/XV199pf79+6tDhw6aN2+eve306dPq3r27ypQpIzc3N4WFhWnu3LmSpEqVKkmS6tSpI4vFopYtWzqMO2XKFAUGBqpUqVIaOHCgLl++bG8LCQnR66+/rp49e8rT01MVK1bU0qVLdeLECXXq1Emenp6KiIjQ1q1b7ducPHlS0dHRKleunNzd3RUeHq4FCxYU3MQAAIC7CqHSpK+//lrVqlVT1apV9cQTT+iTTz6RYRiSpJdeekl79uzR8uXLlZiYqFmzZql06dKSpM2bN0uSVq9ereTkZC1evNg+ZmxsrA4cOKDY2Fh9+umnmjdvnkNYlaS3335bTZs21Y4dO9ShQwf16NFDPXv21BNPPKHt27frnnvuUc+ePe21XLx4UfXq1dP333+v3bt3q1+/furRo4e9juxkZGQoLS3NYQEAAMiWAVOaNGliTJ8+3TAMw7h8+bJRunRpIzY21jAMw+jYsaPx5JNPZrvdoUOHDEnGjh07HNb36tXLqFixonHlyhX7uscee8zo1q2b/X3FihWNJ554wv4+OTnZkGS89NJL9nUbN240JBnJyck3rb1Dhw7G8OHDb9r+yiuvGJJuWFJTU2+6DQAAKFpSU1Nvy+c3ZypN2Lt3rzZv3qzo6GhJkrOzs7p166Y5c+ZIkvr376+FCxeqdu3aGjVqlH7++eccjVuzZk05OTnZ3wcGBiolJcWhT0REhP11QECAJCk8PPyGdde2y8zM1Guvvabw8HD5+/vL09NTK1eu1JEjR25aR0xMjFJTU+3L0aNHc1Q/AAC4+zgXdgHF2Zw5c3TlyhUFBQXZ1xmGIavVqpkzZ6p9+/Y6fPiwfvjhB61atUpt2rTRwIEDNWXKlFuOW7JkSYf3FotFWVlZN+1jsVhuuu7adpMnT9Y777yj6dOnKzw8XB4eHho2bJguXbp00zqsVqusVustawUAAJC4pzLPrly5os8++0xTp05VfHy8fdm5c6eCgoLsD8GUKVNGvXr10hdffKHp06fro48+kiS5uLhIunoG8XaIi4tTp06d9MQTTygyMlKVK1fWvn37bsu+AQDAnY8zlXm0bNkynT59Wk899ZR8fHwc2h599FHNmTNHx44dU7169VSzZk1lZGRo2bJlql69uiSpbNmycnNz04oVK1S+fHm5urreME5+CgsL0zfffKOff/5Zfn5+mjZtmv744w/VqFGjwPYJAADuHpypzKM5c+aobdu22QbBRx99VFu3bpWzs7NiYmIUERGh5s2by8nJSQsXLpR09f7LGTNm6MMPP1RQUJA6depUoPWOHTtWdevWVVRUlFq2bCmbzcYv9wAAgHxjMYz//50zwN9IS0uTj4+PUlNT5e3tXdjlAACAHLhdn9+cqQQAAIBphEoAAACYRqgEAACAaYRKAAAAmEaoBAAAgGmESgAAAJhGqAQAAIBphEoAAACYRqgEAACAaYRKAAAAmEaoBAAAgGmESgAAAJhGqAQAAIBphEoAAACYRqgEAACAac6FXQCKD8MwJElpaWmFXAkAAMipa5/b1z7HCwqhEjl28uRJSVJwcHAhVwIAAHLr7Nmz8vHxKbDxCZXIMX9/f0nSkSNHCvSP8k6Xlpam4OBgHT16VN7e3oVdTrHGXOYP5jF/MI/5g3nMP9fm8siRI7JYLAoKCirQ/REqkWMlSly9BdfHx4d/0fOBt7c385hPmMv8wTzmD+YxfzCP+ed2fW7zoA4AAABMI1QCAADANEIlcsxqteqVV16R1Wot7FKKNeYx/zCX+YN5zB/MY/5gHvPP7Z5Li1HQz5cDAADgjseZSgAAAJhGqAQAAIBphEoAAACYRqgEAACAaYRK5Nh7772nkJAQubq6qlGjRtq8eXNhl1Ro1q9fr44dOyooKEgWi0XffvutQ7thGHr55ZcVGBgoNzc3tW3bVvv373foc+rUKXXv3l3e3t7y9fXVU089pfT0dIc+u3btUrNmzeTq6qrg4GBNmjSpoA/ttpowYYIaNGggLy8vlS1bVp07d9bevXsd+ly8eFEDBw5UqVKl5OnpqUcffVR//PGHQ58jR46oQ4cOcnd3V9myZTVy5EhduXLFoc+6detUt25dWa1WhYaGat68eQV9eLfNrFmzFBERYf+y6MaNG2v58uX2duYwbyZOnCiLxaJhw4bZ1zGXOTNu3DhZLBaHpVq1avZ25jHn/ve//+mJJ55QqVKl5ObmpvDwcG3dutXeXqQ+bwwgBxYuXGi4uLgYn3zyifHrr78aTz/9tOHr62v88ccfhV1aofjhhx+MF1980Vi8eLEhyViyZIlD+8SJEw0fHx/j22+/NXbu3Gk8/PDDRqVKlYwLFy7Y+7Rr186IjIw0fvnlF+M///mPERoaakRHR9vbU1NTjYCAAKN79+7G7t27jQULFhhubm7Ghx9+eLsOs8BFRUUZc+fONXbv3m3Ex8cbDz74oFGhQgUjPT3d3ufZZ581goODjTVr1hhbt2417r33XqNJkyb29itXrhi1atUy2rZta+zYscP44YcfjNKlSxsxMTH2PgcPHjTc3d2N559/3tizZ4/x7rvvGk5OTsaKFStu6/EWlKVLlxrff/+9sW/fPmPv3r3GCy+8YJQsWdLYvXu3YRjMYV5s3rzZCAkJMSIiIoyhQ4fa1zOXOfPKK68YNWvWNJKTk+3LiRMn7O3MY86cOnXKqFixotG7d29j06ZNxsGDB42VK1cav//+u71PUfq8IVQiRxo2bGgMHDjQ/j4zM9MICgoyJkyYUIhVFQ3Xh8qsrCzDZrMZkydPtq87c+aMYbVajQULFhiGYRh79uwxJBlbtmyx91m+fLlhsViM//3vf4ZhGMb7779v+Pn5GRkZGfY+o0ePNqpWrVrAR1R4UlJSDEnGTz/9ZBjG1XkrWbKksWjRInufxMREQ5KxceNGwzCuBvwSJUoYx48ft/eZNWuW4e3tbZ+7UaNGGTVr1nTYV7du3YyoqKiCPqRC4+fnZ3z88cfMYR6cPXvWCAsLM1atWmW0aNHCHiqZy5x75ZVXjMjIyGzbmMecGz16tHHffffdtL2ofd5w+Rt/69KlS9q2bZvatm1rX1eiRAm1bdtWGzduLMTKiqZDhw7p+PHjDvPl4+OjRo0a2edr48aN8vX1Vf369e192rZtqxIlSmjTpk32Ps2bN5eLi4u9T1RUlPbu3avTp0/fpqO5vVJTUyVJ/v7+kqRt27bp8uXLDnNZrVo1VahQwWEuw8PDFRAQYO8TFRWltLQ0/frrr/Y+fx3jWp878e83MzNTCxcu1Llz59S4cWPmMA8GDhyoDh063HC8zGXu7N+/X0FBQapcubK6d++uI0eOSGIec2Pp0qWqX7++HnvsMZUtW1Z16tTR7Nmz7e1F7fOGUIm/9eeffyozM9PhX25JCggI0PHjxwupqqLr2pzcar6OHz+usmXLOrQ7OzvL39/foU92Y/x1H3eSrKwsDRs2TE2bNlWtWrUkXT1OFxcX+fr6OvS9fi7/bp5u1ictLU0XLlwoiMO57RISEuTp6Smr1apnn31WS5YsUY0aNZjDXFq4cKG2b9+uCRMm3NDGXOZco0aNNG/ePK1YsUKzZs3SoUOH1KxZM509e5Z5zIWDBw9q1qxZCgsL08qVK9W/f38NGTJEn376qaSi93njnItjA4ACM3DgQO3evVsbNmwo7FKKpapVqyo+Pl6pqan65ptv1KtXL/3000+FXVaxcvToUQ0dOlSrVq2Sq6trYZdTrLVv397+OiIiQo0aNVLFihX19ddfy83NrRArK16ysrJUv359vfnmm5KkOnXqaPfu3frggw/Uq1evQq7uRpypxN8qXbq0nJycbngy748//pDNZiukqoqua3Nyq/my2WxKSUlxaL9y5YpOnTrl0Ce7Mf66jzvFoEGDtGzZMsXGxqp8+fL29TabTZcuXdKZM2cc+l8/l383Tzfr4+3tfcd8wLm4uCg0NFT16tXThAkTFBkZqXfeeYc5zIVt27YpJSVFdevWlbOzs5ydnfXTTz9pxowZcnZ2VkBAAHOZR76+vqpSpYp+//13/iZzITAwUDVq1HBYV716dfutBEXt84ZQib/l4uKievXqac2aNfZ1WVlZWrNmjRo3blyIlRVNlSpVks1mc5ivtLQ0bdq0yT5fjRs31pkzZ7Rt2zZ7n7Vr1yorK0uNGjWy91m/fr0uX75s77Nq1SpVrVpVfn5+t+loCpZhGBo0aJCWLFmitWvXqlKlSg7t9erVU8mSJR3mcu/evTpy5IjDXCYkJDj8R3PVqlXy9va2/8e4cePGDmNc63Mn//1mZWUpIyODOcyFNm3aKCEhQfHx8falfv366t69u/01c5k36enpOnDggAIDA/mbzIWmTZve8DVr+/btU8WKFSUVwc+bXD3Wg7vWwoULDavVasybN8/Ys2eP0a9fP8PX19fhyby7ydmzZ40dO3YYO3bsMCQZ06ZNM3bs2GEcPnzYMIyrX/Hg6+trfPfdd8auXbuMTp06ZfsVD3Xq1DE2bdpkbNiwwQgLC3P4ioczZ84YAQEBRo8ePYzdu3cbCxcuNNzd3e+orxTq37+/4ePjY6xbt87hq0fOnz9v7/Pss88aFSpUMNauXWts3brVaNy4sdG4cWN7+7WvHnnggQeM+Ph4Y8WKFUaZMmWy/eqRkSNHGomJicZ77713R331yJgxY4yffvrJOHTokLFr1y5jzJgxhsViMX788UfDMJhDM/769LdhMJc5NXz4cGPdunXGoUOHjLi4OKNt27ZG6dKljZSUFMMwmMec2rx5s+Hs7Gy88cYbxv79+4358+cb7u7uxhdffGHvU5Q+bwiVyLF3333XqFChguHi4mI0bNjQ+OWXXwq7pEITGxtrSLph6dWrl2EYV7/m4aWXXjICAgIMq9VqtGnTxti7d6/DGCdPnjSio6MNT09Pw9vb23jyySeNs2fPOvTZuXOncd999xlWq9UoV66cMXHixNt1iLdFdnMoyZg7d669z4ULF4wBAwYYfn5+hru7u/HII48YycnJDuMkJSUZ7du3N9zc3IzSpUsbw4cPNy5fvuzQJzY21qhdu7bh4uJiVK5c2WEfxV2fPn2MihUrGi4uLkaZMmWMNm3a2AOlYTCHZlwfKpnLnOnWrZsRGBhouLi4GOXKlTO6devm8N2KzGPO/fvf/zZq1aplWK1Wo1q1asZHH33k0F6UPm8shmEYOT+vCQAAANyIeyoBAABgGqESAAAAphEqAQAAYBqhEgAAAKYRKgEAAGAaoRIAAACmESoBAABgGqESAAAAphEqAQAAYBqhEgBgSlJSkiwWi+Lj4wu7FACFiFAJAAAA0wiVAFDMZWVladKkSQoNDZXValWFChX0xhtvSJISEhLUunVrubm5qVSpUurXr5/S09Pt27Zs2VLDhg1zGK9z587q3bu3/X1ISIjefPNN9enTR15eXqpQoYI++ugje3ulSpUkSXXq1JHFYlHLli0L7FgBFF2ESgAo5mJiYjRx4kS99NJL2rNnj7788ksFBATo3LlzioqKkp+fn7Zs2aJFixZp9erVGjRoUK73MXXqVNWvX187duzQgAED1L9/f+3du1eStHnzZknS6tWrlZycrMWLF+fr8QEoHpwLuwAAQN6dPXtW77zzjmbOnKlevXpJku655x7dd999mj17ti5evKjPPvtMHh4ekqSZM2eqY8eOeuuttxQQEJDj/Tz44IMaMGCAJGn06NF6++23FRsbq6pVq6pMmTKSpFKlSslms+XzEQIoLjhTCQDFWGJiojIyMtSmTZts2yIjI+2BUpKaNm2qrKws+1nGnIqIiLC/tlgsstlsSklJyXvhAO44hEoAKMbc3NxMbV+iRAkZhuGw7vLlyzf0K1mypMN7i8WirKwsU/sGcGchVAJAMRYWFiY3NzetWbPmhrbq1atr586dOnfunH1dXFycSpQooapVq0qSypQpo+TkZHt7Zmamdu/enasaXFxc7NsCuHsRKgGgGHN1ddXo0aM1atQoffbZZzpw4IB++eUXzZkzR927d5erq6t69eql3bt3KzY2VoMHD1aPHj3s91O2bt1a33//vb7//nv99ttv6t+/v86cOZOrGsqWLSs3NzetWLFCf/zxh1JTUwvgSAEUdYRKACjmXnrpJQ0fPlwvv/yyqlevrm7duiklJUXu7u5auXKlTp06pQYNGqhr165q06aNZs6cad+2T58+6tWrl3r27KkWLVqocuXKatWqVa727+zsrBkzZujDDz9UUFCQOnXqlN+HCKAYsBjX30wDAAAA5BJnKgEAAGAaoRIAAACmESoBAABgGqESAAAAphEqAQAAYBqhEgAAAKYRKgEAAGAaoRIAAACmESoBAABgGqESAAAAphEqAQAAYNr/Azxr9U7gTwxRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(data.disease)\n",
        "plt.xticks(rotation=360)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.017687,
          "end_time": "2020-09-24T12:01:45.184192",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.166505",
          "status": "completed"
        },
        "tags": [],
        "id": "HgFD7KCUbRRf"
      },
      "source": [
        "> We can see that classes are very **skewed**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:45.233630Z",
          "iopub.status.busy": "2020-09-24T12:01:45.232915Z",
          "iopub.status.idle": "2020-09-24T12:01:45.247413Z",
          "shell.execute_reply": "2020-09-24T12:01:45.246797Z"
        },
        "papermill": {
          "duration": 0.045219,
          "end_time": "2020-09-24T12:01:45.247529",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.202310",
          "status": "completed"
        },
        "tags": [],
        "id": "75WJcgLrbRRf"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain,Xval,ytrain,yval=train_test_split(data,data.disease,stratify=data.disease,random_state=42,test_size=0.25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.017759,
          "end_time": "2020-09-24T12:01:45.283654",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.265895",
          "status": "completed"
        },
        "tags": [],
        "id": "KK0tW7ITbRRf"
      },
      "source": [
        "* Above i used the **stratify** arg of **train_test_split** and set it to disease to stratify data based on **class labels**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:45.328403Z",
          "iopub.status.busy": "2020-09-24T12:01:45.327471Z",
          "iopub.status.idle": "2020-09-24T12:01:45.353519Z",
          "shell.execute_reply": "2020-09-24T12:01:45.352941Z"
        },
        "papermill": {
          "duration": 0.051587,
          "end_time": "2020-09-24T12:01:45.353630",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.302043",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_SWYkZ-bRRf",
        "outputId": "3f94c4f9-9725-4902-dda4-1d2499a0549c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disease\n",
              "COPD              0.832979\n",
              "Healthy           0.046588\n",
              "Pneumonia         0.041369\n",
              "URTI              0.035183\n",
              "Bronchiolitis     0.023197\n",
              "Bronchiectasis    0.015078\n",
              "LRTI              0.004639\n",
              "Asthma            0.000967\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "Xtrain.disease.value_counts()/Xtrain.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:45.400963Z",
          "iopub.status.busy": "2020-09-24T12:01:45.400052Z",
          "iopub.status.idle": "2020-09-24T12:01:45.404848Z",
          "shell.execute_reply": "2020-09-24T12:01:45.404149Z"
        },
        "papermill": {
          "duration": 0.032468,
          "end_time": "2020-09-24T12:01:45.404965",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.372497",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8rXirV7bRRf",
        "outputId": "3e9623ec-b301-4516-9f38-abcf1e5d6f97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disease\n",
              "COPD              0.833043\n",
              "Healthy           0.046957\n",
              "Pneumonia         0.041159\n",
              "URTI              0.035362\n",
              "Bronchiolitis     0.023188\n",
              "Bronchiectasis    0.015072\n",
              "LRTI              0.004638\n",
              "Asthma            0.000580\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "Xval.disease.value_counts()/Xval.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.019065,
          "end_time": "2020-09-24T12:01:45.482454",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.463389",
          "status": "completed"
        },
        "tags": [],
        "id": "y0FsSJ1lbRRg"
      },
      "source": [
        "# Visualizing MFCCS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:45.563046Z",
          "iopub.status.busy": "2020-09-24T12:01:45.562312Z",
          "iopub.status.idle": "2020-09-24T12:01:45.790102Z",
          "shell.execute_reply": "2020-09-24T12:01:45.790779Z"
        },
        "papermill": {
          "duration": 0.25061,
          "end_time": "2020-09-24T12:01:45.790958",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.540348",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE4pFcIqbRRg",
        "outputId": "721b6631-08dd-4461-d25f-c3801b968955"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'MFCC')]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "path='/content/drive/MyDrive/processed_audio_files/'\n",
        "\n",
        "import librosa.display\n",
        "file=path + Xtrain.iloc[193].filename\n",
        "sound,sample_rate=lb.load(file)\n",
        "mfccs = lb.feature.mfcc(y=sound, sr=sample_rate, n_mfcc=40)\n",
        "fig, ax = plt.subplots()\n",
        "img = librosa.display.specshow(mfccs, x_axis='time', ax=ax)\n",
        "fig.colorbar(img, ax=ax)\n",
        "ax.set(title='MFCC')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.020285,
          "end_time": "2020-09-24T12:01:45.833553",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.813268",
          "status": "completed"
        },
        "tags": [],
        "id": "QolOeTyjbRRg"
      },
      "source": [
        "* Here I am going to output **train & test datasets** so that i can use the **filenames** in then in another notebook where i will try other **feature extraction** methods inclusive of MFCC's and adopt the one or many based on **performance**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-24T12:01:45.878635Z",
          "iopub.status.busy": "2020-09-24T12:01:45.877964Z",
          "iopub.status.idle": "2020-09-24T12:01:46.351425Z",
          "shell.execute_reply": "2020-09-24T12:01:46.350736Z"
        },
        "papermill": {
          "duration": 0.497149,
          "end_time": "2020-09-24T12:01:46.351547",
          "exception": false,
          "start_time": "2020-09-24T12:01:45.854398",
          "status": "completed"
        },
        "tags": [],
        "id": "jAScYR43bRRg"
      },
      "outputs": [],
      "source": [
        "Xtrain.to_csv('train.csv')\n",
        "Xval.to_csv('val.csv')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.6"
    },
    "papermill": {
      "duration": 16.804692,
      "end_time": "2020-09-24T12:01:46.481402",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2020-09-24T12:01:29.676710",
      "version": "2.1.0"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}