Explaining/Explinability_Continue.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "rsN1g31Z7Vq3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion"
      ],
      "metadata": {
        "id": "qGe1mdrHtJ9P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The binary model feature analysis result"
      ],
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        "id": "uiG8TOpCBcFK"
      }
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      "source": [
        "text = '0.0318 ± 0.0039 l_peaks_heel , 0.0243 ± 0.0050 r_peaks_heel , 0.0156 ± 0.0020 L6_fwhm ,0.0135 ± 0.0022 l_max_peak, 0.0115 ± 0.0030 r_swings_time ,0.0099 ± 0.0018 R6_fwhm ,0.0094 ± 0.0023 L5_fwhm, 0.0086 ± 0.0024 l_swings_time, 0.0051 ± 0.0013 l_peaks_toe, 0.0048 ± 0.0018 R5_fwhm, 0.0046 ± 0.0020 r_max_peak, 0.0046 ± 0.0008 R7_fwhm, 0.0043 ± 0.0014 r_peaks_toe, 0.0039 ± 0.0012 L8_fwhm, 0.0032 ± 0.0011 R8_fwhm ,0.0026 ± 0.0027 L7_fwhm, 0.0025 ± 0.0008 r_total_fwhm, 0.0021 ± 0.0010 l_total_fwhm, 0.0018 ± 0.0007 l_strides_time ,0.0017 ± 0.0009 L2_fwhm ,0.0017 ± 0.0004 r_stances_time, 0.0017 ± 0.0010 R1_fwhm ,0.0015 ± 0.0005 L1_fwhm, 0.0015 ± 0.0012 L4_fwhm, 0.0014 ± 0.0006 L3_fwhm, 0.0013 ± 0.0004 l_stances_time, 0.0009 ± 0.0008 R2_fwhm ,0.0009 ± 0.0008 R4_fwhm, 0.0000 ± 0.0003 R3_fwhm, -0.0000 ± 0.0005 r_strides_time '"
      ],
      "metadata": {
        "id": "ckRfBnMwt7Xn"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "x = text.split(\",\")"
      ],
      "metadata": {
        "id": "H8EFW9sRuJuV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x[0].split(\"±\")[1].split(\" \")[2]"
      ],
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          "height": 35
        },
        "id": "td982RY2uukE",
        "outputId": "57a17591-6cf7-48d5-b5ee-b168f731e658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'l_peaks_heel'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 13
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    {
      "cell_type": "code",
      "source": [
        "importances = []\n",
        "labels=[]\n",
        "for row in x:\n",
        "  importances.append(float(row.split(\"±\")[0]))\n",
        "  labels.append(row.split(\"±\")[1].split(\" \")[2])"
      ],
      "metadata": {
        "id": "SnjfaRuTuxV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(dict(\n",
        "    r=importances,\n",
        "    theta=labels))\n",
        "fig = px.line_polar(df, r='r', theta='theta', line_close=True)\n",
        "fig.show()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 611
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        "id": "y-vL8uS9tLkj",
        "outputId": "0cb31876-dd22-4574-87b1-5a43ae930c56"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/plotly/express/_core.py:271: FutureWarning:\n",
            "\n",
            "The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "\n"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## The severity detection model featuer analysis results"
      ],
      "metadata": {
        "id": "UulTRusFBkIT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "txt=\"0.0276 ± 0.0030 l_peaks_heel, 0.0220 ± 0.0031 r_peaks_toe, 0.0220 ± 0.0029 r_peaks_heel, 0.0181 ± 0.0034 l_peaks_toe, 0.0168 ± 0.0010 l_max_peak, 0.0124 ± 0.0024 r_max_peak, 0.0101 ± 0.0038 L6_fwhm, 0.0092 ± 0.0015 R6_fwhm, 0.0075 ± 0.0034 L5_fwhm, 0.0072 ± 0.0018 r_swings_time, 0.0054 ± 0.0013 L8_fwhm, 0.0053 ± 0.0015 R7_fwhm, 0.0050 ± 0.0015 R5_fwhm, 0.0047 ± 0.0017 l_total_fwhm, 0.0043 ± 0.0017 l_swings_time, 0.0043 ± 0.0013 R8_fwhm, 0.0036 ± 0.0015 r_total_fwhm, 0.0034 ± 0.0010 R1_fwhm, 0.0031 ± 0.0021 l_stances_time, 0.0031 ± 0.0008 L1_fwhm, 0.0027 ± 0.0017 l_strides_time, 0.0025 ± 0.0006 R2_fwhm, 0.0021 ± 0.0004 r_strides_time, 0.0019 ± 0.0012 r_stances_time, 0.0019 ± 0.0014 L7_fwhm, 0.0018 ± 0.0012 R4_fwhm, 0.0018 ± 0.0016 L2_fwhm, 0.0017 ± 0.0011 L4_fwhm, 0.0014 ± 0.0015 L3_fwhm, 0.0014 ± 0.0011 R3_fwhm \""
      ],
      "metadata": {
        "id": "iU2impAywCAw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_2 = txt.split(\",\")\n",
        "importances_2 = []\n",
        "labels_2=[]\n",
        "for row in x_2:\n",
        "  importances_2.append(float(row.split(\"±\")[0]))\n",
        "  labels_2.append(row.split(\"±\")[1].split(\" \")[2])"
      ],
      "metadata": {
        "id": "_MOGjmYewkpo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary = dict(zip(labels, importances))"
      ],
      "metadata": {
        "id": "ojyf7m-ZxX0Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for l,v in zip(labels_2,importances_2):\n",
        "  dictionary[l] = [dictionary[l],v] "
      ],
      "metadata": {
        "id": "xgTr6Zo3xHWf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## from both binary and severity detection"
      ],
      "metadata": {
        "id": "3vmiYYfVUTR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dictionary"
      ],
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        "id": "OU84t_A3FHYI",
        "outputId": "5335950f-8dfa-4192-e061-3a5b91bb812a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "{'l_peaks_heel': [0.0318, 0.0276],\n",
              " 'r_peaks_heel': [0.0243, 0.022],\n",
              " 'L6_fwhm': [0.0156, 0.0101],\n",
              " 'l_max_peak': [0.0135, 0.0168],\n",
              " 'r_swings_time': [0.0115, 0.0072],\n",
              " 'R6_fwhm': [0.0099, 0.0092],\n",
              " 'L5_fwhm': [0.0094, 0.0075],\n",
              " 'l_swings_time': [0.0086, 0.0043],\n",
              " 'l_peaks_toe': [0.0051, 0.0181],\n",
              " 'R5_fwhm': [0.0048, 0.005],\n",
              " 'r_max_peak': [0.0046, 0.0124],\n",
              " 'R7_fwhm': [0.0046, 0.0053],\n",
              " 'r_peaks_toe': [0.0043, 0.022],\n",
              " 'L8_fwhm': [0.0039, 0.0054],\n",
              " 'R8_fwhm': [0.0032, 0.0043],\n",
              " 'L7_fwhm': [0.0026, 0.0019],\n",
              " 'r_total_fwhm': [0.0025, 0.0036],\n",
              " 'l_total_fwhm': [0.0021, 0.0047],\n",
              " 'l_strides_time': [0.0018, 0.0027],\n",
              " 'L2_fwhm': [0.0017, 0.0018],\n",
              " 'r_stances_time': [0.0017, 0.0019],\n",
              " 'R1_fwhm': [0.0017, 0.0034],\n",
              " 'L1_fwhm': [0.0015, 0.0031],\n",
              " 'L4_fwhm': [0.0015, 0.0017],\n",
              " 'L3_fwhm': [0.0014, 0.0014],\n",
              " 'l_stances_time': [0.0013, 0.0031],\n",
              " 'R2_fwhm': [0.0009, 0.0025],\n",
              " 'R4_fwhm': [0.0009, 0.0018],\n",
              " 'R3_fwhm': [0.0, 0.0014],\n",
              " 'r_strides_time': [-0.0, 0.0021],\n",
              " 'fwhm_values': [0.0037888888888888894, 0.004116666666666667]}"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_dict = dict(sorted(dictionary.items()))"
      ],
      "metadata": {
        "id": "IPwIN6Pp42aX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_dict"
      ],
      "metadata": {
        "id": "KlhKKWMZ8fex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_dict ['fwhm_values']= [fwhm_mean_1,fwhm_mean_2]"
      ],
      "metadata": {
        "id": "hluRwf-iH51U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cloumn1 = list(sorted_dict.keys())"
      ],
      "metadata": {
        "id": "s0HxVcQZI_Cs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l1= list( map(itemgetter(0), list(sorted_dict.values()) ))\n",
        "l2 = list( map(itemgetter(1), list(sorted_dict.values()) ))"
      ],
      "metadata": {
        "id": "eyt64lGPJeZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(list(zip(cloumn1, l1,l2)),\n",
        "               columns =['feature', 'binary','severity'])"
      ],
      "metadata": {
        "id": "-Gldya8QJdhM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "xBgIFn2eM4ei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PoGlwRbVKfiq",
        "outputId": "36590e04-4ad8-42a0-adcb-575be53bc620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           feature    binary  severity\n",
              "0          L1_fwhm  0.001500  0.003100\n",
              "1          L2_fwhm  0.001700  0.001800\n",
              "2          L3_fwhm  0.001400  0.001400\n",
              "3          L4_fwhm  0.001500  0.001700\n",
              "4          L5_fwhm  0.009400  0.007500\n",
              "5          L6_fwhm  0.015600  0.010100\n",
              "6          L7_fwhm  0.002600  0.001900\n",
              "7          L8_fwhm  0.003900  0.005400\n",
              "8          R1_fwhm  0.001700  0.003400\n",
              "9          R2_fwhm  0.000900  0.002500\n",
              "10         R3_fwhm  0.000000  0.001400\n",
              "11         R4_fwhm  0.000900  0.001800\n",
              "12         R5_fwhm  0.004800  0.005000\n",
              "13         R6_fwhm  0.009900  0.009200\n",
              "14         R7_fwhm  0.004600  0.005300\n",
              "15         R8_fwhm  0.003200  0.004300\n",
              "16      l_max_peak  0.013500  0.016800\n",
              "17    l_peaks_heel  0.031800  0.027600\n",
              "18     l_peaks_toe  0.005100  0.018100\n",
              "19  l_stances_time  0.001300  0.003100\n",
              "20  l_strides_time  0.001800  0.002700\n",
              "21   l_swings_time  0.008600  0.004300\n",
              "22    l_total_fwhm  0.002100  0.004700\n",
              "23      r_max_peak  0.004600  0.012400\n",
              "24    r_peaks_heel  0.024300  0.022000\n",
              "25     r_peaks_toe  0.004300  0.022000\n",
              "26  r_stances_time  0.001700  0.001900\n",
              "27  r_strides_time -0.000000  0.002100\n",
              "28   r_swings_time  0.011500  0.007200\n",
              "29    r_total_fwhm  0.002500  0.003600\n",
              "30     fwhm_values  0.003789  0.004117"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91e6b5bb-9683-41ce-9220-23ff8a006575\">\n",
              "    <div 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>feature</th>\n",
              "      <th>binary</th>\n",
              "      <th>severity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>L1_fwhm</td>\n",
              "      <td>0.001500</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>L2_fwhm</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>L3_fwhm</td>\n",
              "      <td>0.001400</td>\n",
              "      <td>0.001400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>L4_fwhm</td>\n",
              "      <td>0.001500</td>\n",
              "      <td>0.001700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>L5_fwhm</td>\n",
              "      <td>0.009400</td>\n",
              "      <td>0.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>L6_fwhm</td>\n",
              "      <td>0.015600</td>\n",
              "      <td>0.010100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>L7_fwhm</td>\n",
              "      <td>0.002600</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>L8_fwhm</td>\n",
              "      <td>0.003900</td>\n",
              "      <td>0.005400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>R1_fwhm</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.003400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>R2_fwhm</td>\n",
              "      <td>0.000900</td>\n",
              "      <td>0.002500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>R3_fwhm</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>R4_fwhm</td>\n",
              "      <td>0.000900</td>\n",
              "      <td>0.001800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>R5_fwhm</td>\n",
              "      <td>0.004800</td>\n",
              "      <td>0.005000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>R6_fwhm</td>\n",
              "      <td>0.009900</td>\n",
              "      <td>0.009200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>R7_fwhm</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.005300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>R8_fwhm</td>\n",
              "      <td>0.003200</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>l_max_peak</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.016800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>l_peaks_heel</td>\n",
              "      <td>0.031800</td>\n",
              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>l_peaks_toe</td>\n",
              "      <td>0.005100</td>\n",
              "      <td>0.018100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>l_stances_time</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>l_strides_time</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>0.002700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>l_swings_time</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>l_total_fwhm</td>\n",
              "      <td>0.002100</td>\n",
              "      <td>0.004700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>r_max_peak</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>r_peaks_heel</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>r_peaks_toe</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>r_stances_time</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>r_strides_time</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>r_swings_time</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>r_total_fwhm</td>\n",
              "      <td>0.002500</td>\n",
              "      <td>0.003600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>fwhm_values</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
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      "cell_type": "code",
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        "parquet_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/parquet_files/\""
      ],
      "metadata": {
        "id": "VtQSwHJpH3sv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_parquet(parquet_path+'features_importance.gzip')"
      ],
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        "id": "ZnxIcVg8H2_Y"
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      "execution_count": null,
      "outputs": []
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      "source": [],
      "metadata": {
        "id": "F-TyJtZyH01W"
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    },
    {
      "cell_type": "code",
      "source": [
        "l1= list( map(itemgetter(0), list(sorted_dict.values()) ))\n",
        "l2 = list( map(itemgetter(1), list(sorted_dict.values()) ))"
      ],
      "metadata": {
        "id": "f0RmXTIy-6tp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l11 = np.append(l1[:16],[l1[22],l1[29]])\n",
        "l21 = np.append(l2[:16],[l2[22],l2[29]])"
      ],
      "metadata": {
        "id": "Nl3c90o5_R_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fwhm_mean_1 = np.mean(l11)\n",
        "fwhm_mean_2 = np.mean(l21)"
      ],
      "metadata": {
        "id": "ISeViwS95Bny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dic = dict(zip(   np.append( list(sorted_dict.keys())[16:22], list(sorted_dict.keys())[23:29])   , l))"
      ],
      "metadata": {
        "id": "pAKRs6q45BSn"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dic['FWHM values'] = [fwhm_mean_1,fwhm_mean_2]"
      ],
      "metadata": {
        "id": "TlqxfzOs9_Tj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2= pd.DataFrame ( list (zip ( list(dic.keys()), list (map(itemgetter(0), list(dic.values()) )), list( map(itemgetter(1), list(dic.values()) ) )) ) ,\n",
        "                   columns = ['feature', 'binary', 'severity'])"
      ],
      "metadata": {
        "id": "hGO9Omg0M5_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "mHOXabdWNyUQ",
        "outputId": "857cf75c-ed9a-4a16-9c3c-072d9ff83fd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           feature    binary  severity\n",
              "0       l_max_peak  0.013500  0.016800\n",
              "1     l_peaks_heel  0.031800  0.027600\n",
              "2      l_peaks_toe  0.005100  0.018100\n",
              "3   l_stances_time  0.001300  0.003100\n",
              "4   l_strides_time  0.001800  0.002700\n",
              "5    l_swings_time  0.008600  0.004300\n",
              "6       r_max_peak  0.004600  0.012400\n",
              "7     r_peaks_heel  0.024300  0.022000\n",
              "8      r_peaks_toe  0.004300  0.022000\n",
              "9   r_stances_time  0.001700  0.001900\n",
              "10  r_strides_time -0.000000  0.002100\n",
              "11   r_swings_time  0.011500  0.007200\n",
              "12     FWHM values  0.003789  0.004117"
            ],
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              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>binary</th>\n",
              "      <th>severity</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>l_max_peak</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.016800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>l_peaks_heel</td>\n",
              "      <td>0.031800</td>\n",
              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>l_peaks_toe</td>\n",
              "      <td>0.005100</td>\n",
              "      <td>0.018100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>l_stances_time</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>l_strides_time</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>0.002700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>l_swings_time</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>r_max_peak</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>r_peaks_heel</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>r_peaks_toe</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>r_stances_time</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>r_strides_time</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>r_swings_time</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['Max Peak per total siganl', 'Max peak per cycle for Heel sensor', 'Max Peak per cycle for Toes sensor', \\\n",
        "          'Peak time interval (Stance)', 'Cycle time interval (Stride)' , 'Rest time interval (Swing)',\\\n",
        "          'FWHM values per peak']\n",
        "Values_binary = [np.mean([ df['binary'][0],df['binary'][6]]),\\\n",
        "                 np.mean([ df['binary'][1],df['binary'][7]]),\\\n",
        "                 np.mean([ df['binary'][2],df['binary'][8]]),\\\n",
        "                 np.mean([ df['binary'][3],df['binary'][9]]),\\\n",
        "                 np.mean([ df['binary'][4],df['binary'][10]]),\\\n",
        "                 np.mean([ df['binary'][5],df['binary'][11]]),\\\n",
        "                 df['binary'][12]]\n",
        "                 "
      ],
      "metadata": {
        "id": "LxrON-YReSu_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_parquet(parquet_path+'less_features_importance.gzip')"
      ],
      "metadata": {
        "id": "s8EitCvoODVe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df2.sort_values('binary')"
      ],
      "metadata": {
        "id": "5_ee8ZJkY1kb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "labels = ['Max Peak per total siganl', 'Max peak per cycle for Heel sensor', 'Max Peak per cycle for Toes sensor', \\\n",
        "          'Peak time interval (Stance)', 'Cycle time interval (Stride)' , 'Rest time interval (Swing)',\\\n",
        "          'FWHM values per peak']"
      ],
      "metadata": {
        "id": "kDSe-Rnph3h_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.replace({'r_max_peak':'Max Peak per total siganl (R)',\n",
        "                    'r_peaks_heel':'Max peak per cycle for Heel sensor (R)',\n",
        "                    'r_peaks_toe':'Max Peak per cycle for Toes sensor (R)',\n",
        "                    'r_stances_time':'Peak time interval (Stance) (R)',\n",
        "                    'r_strides_time':'Cycle time interval (Stride) (R)',\n",
        "                    'r_swings_time':'Rest time interval (Swing) (R)',\n",
        "                    'l_max_peak':'Max Peak per total siganl (L)',\n",
        "                    'l_peaks_heel':'Max peak per cycle for Heel sensor (L)',\n",
        "                    'l_peaks_toe':'Max Peak per cycle for Toes sensor (L)',\n",
        "                    'l_stances_time':'Peak time interval (Stance) (L)',\n",
        "                    'l_strides_time':'Cycle time interval (Stride) (L)',\n",
        "                    'l_swings_time':'Rest time interval (Swing) (L)',\n",
        "                    'FWHM values':'FWHM values per peak',\n",
        "                    },inplace=True)"
      ],
      "metadata": {
        "id": "SoQlyWWBhnTf"
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      "execution_count": null,
      "outputs": []
    },
    {
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        "df2"
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          "height": 457
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        "id": "AY1RP_yvjP2l",
        "outputId": "2bc4763c-951c-4e13-9477-48db12e46ef5"
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          "data": {
            "text/plain": [
              "                                   feature    binary  severity\n",
              "0            Max Peak per total siganl (L)  0.013500  0.016800\n",
              "1   Max peak per cycle for Heel sensor (L)  0.031800  0.027600\n",
              "2   Max Peak per cycle for Toes sensor (L)  0.005100  0.018100\n",
              "3          Peak time interval (Stance) (L)  0.001300  0.003100\n",
              "4         Cycle time interval (Stride) (L)  0.001800  0.002700\n",
              "5           Rest time interval (Swing) (L)  0.008600  0.004300\n",
              "6            Max Peak per total siganl (R)  0.004600  0.012400\n",
              "7   Max peak per cycle for Heel sensor (R)  0.024300  0.022000\n",
              "8   Max Peak per cycle for Toes sensor (R)  0.004300  0.022000\n",
              "9          Peak time interval (Stance) (R)  0.001700  0.001900\n",
              "10        Cycle time interval (Stride) (R) -0.000000  0.002100\n",
              "11          Rest time interval (Swing) (R)  0.011500  0.007200\n",
              "12                    FWHM values per peak  0.003789  0.004117"
            ],
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              "      <th>0</th>\n",
              "      <td>Max Peak per total siganl (L)</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.016800</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Max peak per cycle for Heel sensor (L)</td>\n",
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              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Max Peak per cycle for Toes sensor (L)</td>\n",
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              "      <td>0.018100</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Peak time interval (Stance) (L)</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cycle time interval (Stride) (L)</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>0.002700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rest time interval (Swing) (L)</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Max Peak per total siganl (R)</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Max peak per cycle for Heel sensor (R)</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Max Peak per cycle for Toes sensor (R)</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Peak time interval (Stance) (R)</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cycle time interval (Stride) (R)</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Rest time interval (Swing) (R)</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values per peak</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_df = df2.sort_values(by=['binary'], ascending=True)"
      ],
      "metadata": {
        "id": "egf-MLDVnKYL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories = sorted_df['feature'].to_list()\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df['binary']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Binary Calssification'\n",
        "))\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df['severity']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Severity Detedction'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "  polar=dict(\n",
        "    radialaxis=dict(\n",
        "      visible=True\n",
        "    )),\n",
        "  showlegend=True\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 542
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        "id": "IlIHevuavej5",
        "outputId": "19be135c-27d3-499d-e058-812c503b7374"
      },
      "execution_count": null,
      "outputs": [
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "sorted_df_l=sorted_df.drop([6,7,8,9,10,11])"
      ],
      "metadata": {
        "id": "yKzcq4fncZoP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories = sorted_df_l['feature'].to_list()\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df_l['binary']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Binary Calssification'\n",
        "))\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df_l['severity']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Severity Detedction'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "  polar=dict(\n",
        "    radialaxis=dict(\n",
        "      visible=True\n",
        "    )),\n",
        "  showlegend=True\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 542
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        "id": "Gmd1tDXpoDew",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_df_r=sorted_df.drop([0,1,2,3,4,5])"
      ],
      "metadata": {
        "id": "bAmtlheqoO0c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categories = sorted_df_r['feature'].to_list()\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df_r['binary']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Binary Calssification'\n",
        "))\n",
        "fig.add_trace(go.Scatterpolar(\n",
        "      r=sorted_df_r['severity']*100,\n",
        "      theta=categories,\n",
        "      fill='toself',\n",
        "      name='Severity Detedction'\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "  polar=dict(\n",
        "    radialaxis=dict(\n",
        "      visible=True\n",
        "    )),\n",
        "  showlegend=True\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "3lvGa_xkoTJa",
        "outputId": "d3858c5e-3539-4731-9bcc-cfe0375d195a"
      },
      "execution_count": null,
      "outputs": [
        {
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    {
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      "source": [
        "parquet_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/parquet_files/\"\n",
        "df = pd.read_parquet(parquet_path+'less_features_importance.gzip')"
      ],
      "metadata": {
        "id": "YQVLulNMk-NG"
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      "execution_count": null,
      "outputs": []
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    {
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      "source": [
        "df"
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      "metadata": {
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          "data": {
            "text/plain": [
              "                                   feature    binary  severity\n",
              "0            Max Peak per total siganl (L)  0.013500  0.016800\n",
              "1   Max peak per cycle for Heel sensor (L)  0.031800  0.027600\n",
              "2   Max Peak per cycle for Toes sensor (L)  0.005100  0.018100\n",
              "3          Peak time interval (Stance) (L)  0.001300  0.003100\n",
              "4         Cycle time interval (Stride) (L)  0.001800  0.002700\n",
              "5           Rest time interval (Swing) (L)  0.008600  0.004300\n",
              "6            Max Peak per total siganl (R)  0.004600  0.012400\n",
              "7   Max peak per cycle for Heel sensor (R)  0.024300  0.022000\n",
              "8   Max Peak per cycle for Toes sensor (R)  0.004300  0.022000\n",
              "9          Peak time interval (Stance) (R)  0.001700  0.001900\n",
              "10        Cycle time interval (Stride) (R) -0.000000  0.002100\n",
              "11          Rest time interval (Swing) (R)  0.011500  0.007200\n",
              "12                    FWHM values per peak  0.003789  0.004117"
            ],
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              "      <th>0</th>\n",
              "      <td>Max Peak per total siganl (L)</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.016800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Max peak per cycle for Heel sensor (L)</td>\n",
              "      <td>0.031800</td>\n",
              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Max Peak per cycle for Toes sensor (L)</td>\n",
              "      <td>0.005100</td>\n",
              "      <td>0.018100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Peak time interval (Stance) (L)</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cycle time interval (Stride) (L)</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>0.002700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rest time interval (Swing) (L)</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Max Peak per total siganl (R)</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Max peak per cycle for Heel sensor (R)</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Max Peak per cycle for Toes sensor (R)</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Peak time interval (Stance) (R)</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cycle time interval (Stride) (R)</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Rest time interval (Swing) (R)</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values per peak</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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        "df.replace({'Max Peak per total siganl (R)':'Max Peak per <br> total siganl (R)',\n",
        "                    'Max peak per cycle for Heel sensor (R)':'Max peak per cycle <br> for Heel sensor (R)',\n",
        "                    'Max Peak per cycle for Toes sensor (R)':'Max Peak per cycle <br> for Toes sensor (R)',\n",
        "                    'Peak time interval (Stance) (R)':'Peak time interval <br> (Stance) (R)',\n",
        "                    'Cycle time interval (Stride) (R)':'Cycle time interval <br> (Stride) (R)',\n",
        "                    'Rest time interval (Swing) (R)':'Rest time interval <br> (Swing) (R)',\n",
        "                    'Max Peak per total siganl (L)':'Max Peak per total <br> siganl (L)',\n",
        "                    'Max peak per cycle for Heel sensor (L)':'Max peak per cycle <br> for Heel sensor (L)',\n",
        "                    'Max Peak per cycle for Toes sensor (L)':'Max Peak per cycle <br> for Toes sensor (L)',\n",
        "                    'Peak time interval (Stance) (L)':'Peak time interval <br> (Stance) (L)',\n",
        "                    'Cycle time interval (Stride) (L)':'Cycle time interval <br> (Stride) (L)',\n",
        "                    'Rest time interval (Swing) (L)':'Rest time interval <br> (Swing) (L)',\n",
        "                    'FWHM values':'FWHM values per peak',\n",
        "                    },inplace=True)"
      ],
      "metadata": {
        "id": "fY5BwF8-r1h4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['feature'].to_list()[6:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YZ65_Cvmuo4",
        "outputId": "e6d9b334-43e8-45dd-adeb-dc9ed3e64b11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Max Peak per total siganl (R)',\n",
              " 'Max peak per cycle for Heel sensor (R)',\n",
              " 'Max Peak per cycle for Toes sensor (R)',\n",
              " 'Peak time interval (Stance) (R)',\n",
              " 'Cycle time interval (Stride) (R)',\n",
              " 'Rest time interval (Swing) (R)',\n",
              " 'FWHM values per peak']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.sort_values('binary')"
      ],
      "metadata": {
        "id": "ujvq-holrtG_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "YplN1kqds0KX",
        "outputId": "2b0a54d9-45ba-4a2c-99e2-1b0c01ae3f4b"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "                                        feature    binary  severity\n",
              "0            Max Peak per total <br> siganl (L)  0.013500  0.016800\n",
              "1   Max peak per cycle <br> for Heel sensor (L)  0.031800  0.027600\n",
              "2   Max Peak per cycle <br> for Toes sensor (L)  0.005100  0.018100\n",
              "3          Peak time interval <br> (Stance) (L)  0.001300  0.003100\n",
              "4         Cycle time interval <br> (Stride) (L)  0.001800  0.002700\n",
              "5           Rest time interval <br> (Swing) (L)  0.008600  0.004300\n",
              "6            Max Peak per <br> total siganl (R)  0.004600  0.012400\n",
              "7   Max peak per cycle <br> for Heel sensor (R)  0.024300  0.022000\n",
              "8   Max Peak per cycle <br> for Toes sensor (R)  0.004300  0.022000\n",
              "9          Peak time interval <br> (Stance) (R)  0.001700  0.001900\n",
              "10        Cycle time interval <br> (Stride) (R) -0.000000  0.002100\n",
              "11          Rest time interval <br> (Swing) (R)  0.011500  0.007200\n",
              "12                         FWHM values per peak  0.003789  0.004117"
            ],
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              "      <th>0</th>\n",
              "      <td>Max Peak per total &lt;br&gt; siganl (L)</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.016800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Max peak per cycle &lt;br&gt; for Heel sensor (L)</td>\n",
              "      <td>0.031800</td>\n",
              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Max Peak per cycle &lt;br&gt; for Toes sensor (L)</td>\n",
              "      <td>0.005100</td>\n",
              "      <td>0.018100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Peak time interval &lt;br&gt; (Stance) (L)</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cycle time interval &lt;br&gt; (Stride) (L)</td>\n",
              "      <td>0.001800</td>\n",
              "      <td>0.002700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rest time interval &lt;br&gt; (Swing) (L)</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Max Peak per &lt;br&gt; total siganl (R)</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Max peak per cycle &lt;br&gt; for Heel sensor (R)</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Max Peak per cycle &lt;br&gt; for Toes sensor (R)</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Peak time interval &lt;br&gt; (Stance) (R)</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cycle time interval &lt;br&gt; (Stride) (R)</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Rest time interval &lt;br&gt; (Swing) (R)</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values per peak</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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              "  </tbody>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
      "source": [
        "df_2 = df [6:]"
      ],
      "metadata": {
        "id": "j5sWfN08tC_f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "df_2"
      ],
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        "id": "tTZR9QD_tMa2",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        feature    binary  severity\n",
              "6            Max Peak per <br> total siganl (R)  0.004600  0.012400\n",
              "7   Max peak per cycle <br> for Heel sensor (R)  0.024300  0.022000\n",
              "8   Max Peak per cycle <br> for Toes sensor (R)  0.004300  0.022000\n",
              "9          Peak time interval <br> (Stance) (R)  0.001700  0.001900\n",
              "10        Cycle time interval <br> (Stride) (R) -0.000000  0.002100\n",
              "11          Rest time interval <br> (Swing) (R)  0.011500  0.007200\n",
              "12                         FWHM values per peak  0.003789  0.004117"
            ],
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              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>binary</th>\n",
              "      <th>severity</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Max Peak per &lt;br&gt; total siganl (R)</td>\n",
              "      <td>0.004600</td>\n",
              "      <td>0.012400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Max peak per cycle &lt;br&gt; for Heel sensor (R)</td>\n",
              "      <td>0.024300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Max Peak per cycle &lt;br&gt; for Toes sensor (R)</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.022000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Peak time interval &lt;br&gt; (Stance) (R)</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.001900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cycle time interval &lt;br&gt; (Stride) (R)</td>\n",
              "      <td>-0.000000</td>\n",
              "      <td>0.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Rest time interval &lt;br&gt; (Swing) (R)</td>\n",
              "      <td>0.011500</td>\n",
              "      <td>0.007200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values per peak</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-4b31abe7-b10e-4ea3-b482-24e10e158334');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_2=df_2.sort_values('binary')"
      ],
      "metadata": {
        "id": "t6URXlNgtQy1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "features=df_2['feature'].to_list()\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(name='Binary Classification', x=features, y=df_2['binary'].to_list()\n",
        "),\n",
        "    go.Bar(name='Severity Detection', x=features, y=df_2['severity'].to_list()\n",
        ")\n",
        "])\n",
        "# Change the bar mode\n",
        "fig.update_layout(barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "kZ6KdHFZktdi",
        "outputId": "6eca05cd-28ae-47b9-d556-53e216c1237b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "df_3 = df.drop([6,7,8,9,10,11])\n",
        "df_3"
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              "                                        feature    binary  severity\n",
              "0            Max Peak per total <br> siganl (L)  0.013500  0.016800\n",
              "1   Max peak per cycle <br> for Heel sensor (L)  0.031800  0.027600\n",
              "2   Max Peak per cycle <br> for Toes sensor (L)  0.005100  0.018100\n",
              "3          Peak time interval <br> (Stance) (L)  0.001300  0.003100\n",
              "4         Cycle time interval <br> (Stride) (L)  0.001800  0.002700\n",
              "5           Rest time interval <br> (Swing) (L)  0.008600  0.004300\n",
              "12                         FWHM values per peak  0.003789  0.004117"
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              "      <td>Max Peak per total &lt;br&gt; siganl (L)</td>\n",
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              "      <th>1</th>\n",
              "      <td>Max peak per cycle &lt;br&gt; for Heel sensor (L)</td>\n",
              "      <td>0.031800</td>\n",
              "      <td>0.027600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Max Peak per cycle &lt;br&gt; for Toes sensor (L)</td>\n",
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              "      <th>3</th>\n",
              "      <td>Peak time interval &lt;br&gt; (Stance) (L)</td>\n",
              "      <td>0.001300</td>\n",
              "      <td>0.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cycle time interval &lt;br&gt; (Stride) (L)</td>\n",
              "      <td>0.001800</td>\n",
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              "      <td>Rest time interval &lt;br&gt; (Swing) (L)</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.004300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>FWHM values per peak</td>\n",
              "      <td>0.003789</td>\n",
              "      <td>0.004117</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df_3=df_3.sort_values('binary')"
      ],
      "metadata": {
        "id": "WVCa9eC1tr8n"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
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        "features=df_3['feature'].to_list()\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(name='Binary Classification', x=features, y=df_3['binary'].to_list()\n",
        "),\n",
        "    go.Bar(name='Severity Detection', x=features, y=df_3['severity'].to_list()\n",
        ")\n",
        "])\n",
        "# Change the bar mode\n",
        "fig.update_layout(barmode='group')\n",
        "fig.show()"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WTg2TdQ4uL3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "fbtPa0SGDm1c"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SnWD4kKKDo6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fECODnHaD26D",
        "outputId": "2b8bbe28-c8ab-44f1-c05e-3d4258e6d068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parquet_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Data/parquet_files/\"\n",
        "models_path = \"/content/drive/MyDrive/ITMO-Master's/Thesis/3rd_semester/Models/\""
      ],
      "metadata": {
        "id": "lRMuOC7OD7P_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "d = pd.read_parquet(parquet_path+'scaled_stances_statics.gzip')"
      ],
      "metadata": {
        "id": "Bqzqz56kD8GY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in d.columns:\n",
        "  print(col)"
      ],
      "metadata": {
        "id": "nDTd6r1QEZVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d['level']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qc78JQtEfAq",
        "outputId": "9ff5bc18-3967-4d6b-8bfd-83d7ee7352dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        3.0\n",
              "1        3.0\n",
              "2        3.0\n",
              "3        3.0\n",
              "4        3.0\n",
              "        ... \n",
              "18221    2.5\n",
              "18222    2.5\n",
              "18223    2.5\n",
              "18224    2.5\n",
              "18225    2.5\n",
              "Name: level, Length: 18226, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.mean(d.loc[d['level'] == 3.0]['l_peaks_toe'])"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132.535404284727"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
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        "np.mean(d.loc[d['level'] == 2.5]['l_peaks_toe'])"
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        "colab": {
          "base_uri": "https://localhost:8080/"
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      "outputs": [
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "array([  0.  ,   0.  ,   0.  , ..., 481.36, 486.31, 491.26])"
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    {
      "cell_type": "code",
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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  ]
}