Cox_Model.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Cox Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pNl28plqm1p"
      },
      "source": [
        "## **Cox Proportional Hazards Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXeV6rHq_OEJ"
      },
      "source": [
        "pip install lifelines"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_sP1xUBle3i"
      },
      "source": [
        "### **Features Selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHp76rUi2Cjh"
      },
      "source": [
        "**Univariate Feature Selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcdI3nZanwo9"
      },
      "source": [
        "A first selection of the variables has been done by performing univariate analyses. Variables which were not associated with overall survival at 15% threshold have not been included in the multivariate analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K209N22_IlD"
      },
      "source": [
        "from lifelines import CoxPHFitter\n",
        "cph = CoxPHFitter()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FF51jldDrDlo"
      },
      "source": [
        "# Dataset containing all features as well as survival time and censorhsip variable\n",
        "x_train_model=pd.merge(x_train, y_train, how='inner', left_index=True, right_index=True, sort=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wVPTv2NSo81-"
      },
      "source": [
        "# Univariate analysis\r\n",
        "for variable in x_train.columns:\r\n",
        "  x_train_selection=x_train_model.loc[:,[f'{variable}','SurvivalTime','Event']]\r\n",
        "  cph.fit(x_train_selection, duration_col='SurvivalTime', event_col='Event')\r\n",
        "  cph.print_summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ie9U_Qbp2t1"
      },
      "source": [
        "The following 8 variables have not been included in the multivariate analysis: *original_shape_Compactness1, original_shape_Compactness2, original_shape_SphericalDisproportion, original_shape_Sphericity, original_firstorder_Energy, original_firstorder_Minimum, original_glcm_ClusterProminence, original_glrlm_LongRunLowGrayLevelEmphasis*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7Iscp_-z_fQ"
      },
      "source": [
        "**Multivariate Analysis** with selected variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dEEoMDn2XsUd",
        "outputId": "2029ad04-c43c-4cf2-8f98-5ce654514cc2"
      },
      "source": [
        "x_train_model_ = x_train_model.drop(columns=[\"original_shape_Compactness1\", \"original_shape_Compactness2\", \r\n",
        "                                             \"original_shape_SphericalDisproportion\", \"original_shape_Sphericity\", \r\n",
        "                                             \"original_firstorder_Energy\", \"original_firstorder_Minimum\", \r\n",
        "                                             \"original_glcm_ClusterProminence\", \"original_glrlm_LongRunLowGrayLevelEmphasis\"])\r\n",
        "\r\n",
        "cph.fit(x_train_model_, duration_col='SurvivalTime', event_col='Event')\r\n",
        "cph.print_summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lifelines/fitters/coxph_fitter.py:1583: ConvergenceWarning: Newton-Rhaphson failed to converge sufficiently. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model\n",
            "  \"Newton-Rhaphson failed to converge sufficiently. {0}\".format(CONVERGENCE_DOCS), exceptions.ConvergenceWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\toprule\n{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &                             exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\ncovariate                                    &       &            &           &                 &                 &                      &                                                 &       &      &           \\\\\n\\midrule\noriginal\\_shape\\_Maximum3DDiameter             &  0.01 &       1.01 &      0.27 &           -0.52 &            0.54 &                 0.59 &                                            1.72 &  0.04 & 0.97 &      0.04 \\\\\noriginal\\_shape\\_SurfaceArea                   & -0.04 &       0.96 &      0.52 &           -1.07 &            0.98 &                 0.34 &                                            2.66 & -0.09 & 0.93 &      0.10 \\\\\noriginal\\_shape\\_SurfaceVolumeRatio            &  0.05 &       1.06 &      0.45 &           -0.82 &            0.93 &                 0.44 &                                            2.54 &  0.12 & 0.90 &      0.15 \\\\\noriginal\\_shape\\_VoxelVolume                   &  0.08 &       1.09 &      0.89 &           -1.67 &            1.83 &                 0.19 &                                            6.25 &  0.09 & 0.93 &      0.11 \\\\\noriginal\\_firstorder\\_Entropy                  & -0.67 &       0.51 &      4.56 &           -9.61 &            8.28 &                 0.00 &                                         3925.61 & -0.15 & 0.88 &      0.18 \\\\\noriginal\\_firstorder\\_Kurtosis                 &  0.03 &       1.03 &      0.27 &           -0.50 &            0.56 &                 0.61 &                                            1.75 &  0.11 & 0.91 &      0.13 \\\\\noriginal\\_firstorder\\_Maximum                  & -0.25 &       0.78 &      4.95 &           -9.94 &            9.45 &                 0.00 &                                        12674.44 & -0.05 & 0.96 &      0.06 \\\\\noriginal\\_firstorder\\_Mean                     &  0.37 &       1.45 &      2.87 &           -5.25 &            6.00 &                 0.01 &                                          403.51 &  0.13 & 0.90 &      0.16 \\\\\noriginal\\_firstorder\\_MeanAbsoluteDeviation    &  0.13 &       1.14 &      2.22 &           -4.22 &            4.48 &                 0.01 &                                           88.43 &  0.06 & 0.95 &      0.07 \\\\\noriginal\\_firstorder\\_Median                   & -0.06 &       0.95 &      1.17 &           -2.35 &            2.24 &                 0.10 &                                            9.38 & -0.05 & 0.96 &      0.06 \\\\\noriginal\\_firstorder\\_Range                    &  0.24 &       1.27 &      5.37 &          -10.28 &           10.76 &                 0.00 &                                        46866.41 &  0.04 & 0.96 &      0.05 \\\\\noriginal\\_firstorder\\_RootMeanSquared          &  0.15 &       1.16 &      1.67 &           -3.13 &            3.42 &                 0.04 &                                           30.52 &  0.09 & 0.93 &      0.10 \\\\\noriginal\\_firstorder\\_Skewness                 &  0.04 &       1.04 &      0.38 &           -0.70 &            0.78 &                 0.50 &                                            2.19 &  0.11 & 0.91 &      0.13 \\\\\noriginal\\_firstorder\\_StandardDeviation        &  0.05 &       1.05 &      2.23 &           -4.33 &            4.43 &                 0.01 &                                           83.63 &  0.02 & 0.98 &      0.03 \\\\\noriginal\\_firstorder\\_Uniformity               &  0.05 &       1.05 &      3.56 &           -6.92 &            7.02 &                 0.00 &                                         1119.80 &  0.01 & 0.99 &      0.02 \\\\\noriginal\\_firstorder\\_Variance                 &  0.15 &       1.16 &      2.86 &           -5.45 &            5.75 &                 0.00 &                                          314.32 &  0.05 & 0.96 &      0.06 \\\\\noriginal\\_glcm\\_Autocorrelation                &  0.20 &       1.22 &      3.64 &           -6.93 &            7.32 &                 0.00 &                                         1517.15 &  0.05 & 0.96 &      0.06 \\\\\noriginal\\_glcm\\_ClusterShade                   & -0.02 &       0.98 &      0.34 &           -0.69 &            0.65 &                 0.50 &                                            1.92 & -0.06 & 0.95 &      0.07 \\\\\noriginal\\_glcm\\_ClusterTendency                & -0.14 &       0.87 &      1.95 &           -3.96 &            3.69 &                 0.02 &                                           40.09 & -0.07 & 0.94 &      0.08 \\\\\noriginal\\_glcm\\_Contrast                       &  0.39 &       1.47 &      4.08 &           -7.60 &            8.38 &                 0.00 &                                         4345.13 &  0.09 & 0.92 &      0.11 \\\\\noriginal\\_glcm\\_Correlation                    & -0.08 &       0.92 &      0.44 &           -0.95 &            0.79 &                 0.39 &                                            2.21 & -0.18 & 0.86 &      0.22 \\\\\noriginal\\_glcm\\_DifferenceEntropy              & -0.17 &       0.85 &      2.28 &           -4.64 &            4.31 &                 0.01 &                                           74.56 & -0.07 & 0.94 &      0.09 \\\\\noriginal\\_glcm\\_DifferenceAverage              & -1.44 &       0.24 &     11.37 &          -23.71 &           20.84 &                 0.00 &                                   1119853490.68 & -0.13 & 0.90 &      0.15 \\\\\noriginal\\_glcm\\_JointEnergy                    & -0.08 &       0.92 &      2.83 &           -5.62 &            5.46 &                 0.00 &                                          235.63 & -0.03 & 0.98 &      0.03 \\\\\noriginal\\_glcm\\_JointEntropy                   &  0.43 &       1.54 &      4.44 &           -8.26 &            9.12 &                 0.00 &                                         9159.85 &  0.10 & 0.92 &      0.12 \\\\\noriginal\\_glcm\\_Id                             & -7.52 &       0.00 &     51.95 &         -109.34 &           94.29 &                 0.00 &    89420435967420155407305347388490217684992.00 & -0.14 & 0.88 &      0.18 \\\\\noriginal\\_glcm\\_Idm                            &  6.24 &     512.80 &     48.02 &          -87.88 &          100.36 &                 0.00 & 38430642270815067272341085220701594686849024.00 &  0.13 & 0.90 &      0.16 \\\\\noriginal\\_glcm\\_Imc1                           & -0.01 &       0.99 &      1.00 &           -1.98 &            1.95 &                 0.14 &                                            7.06 & -0.01 & 0.99 &      0.01 \\\\\noriginal\\_glcm\\_Imc2                           &  0.04 &       1.04 &      0.40 &           -0.74 &            0.82 &                 0.48 &                                            2.28 &  0.10 & 0.92 &      0.12 \\\\\noriginal\\_glcm\\_Idmn                           & -0.08 &       0.93 &      1.12 &           -2.26 &            2.11 &                 0.10 &                                            8.25 & -0.07 & 0.95 &      0.08 \\\\\noriginal\\_glcm\\_Idn                            &  0.20 &       1.23 &      1.98 &           -3.67 &            4.08 &                 0.03 &                                           59.19 &  0.10 & 0.92 &      0.12 \\\\\noriginal\\_glcm\\_InverseVariance                & -0.24 &       0.79 &      3.07 &           -6.25 &            5.78 &                 0.00 &                                          322.68 & -0.08 & 0.94 &      0.09 \\\\\noriginal\\_glcm\\_MaximumProbability             & -0.05 &       0.95 &      0.68 &           -1.38 &            1.29 &                 0.25 &                                            3.63 & -0.07 & 0.95 &      0.08 \\\\\noriginal\\_glcm\\_SumAverage                     & -0.35 &       0.70 &      2.77 &           -5.78 &            5.07 &                 0.00 &                                          159.31 & -0.13 & 0.90 &      0.15 \\\\\noriginal\\_glcm\\_SumEntropy                     &  0.11 &       1.11 &      3.93 &           -7.60 &            7.82 &                 0.00 &                                         2477.64 &  0.03 & 0.98 &      0.03 \\\\\noriginal\\_glrlm\\_ShortRunEmphasis              &  0.50 &       1.65 &      1.97 &           -3.37 &            4.36 &                 0.03 &                                           78.63 &  0.25 & 0.80 &      0.32 \\\\\noriginal\\_glrlm\\_LongRunEmphasis               &  0.19 &       1.21 &      1.71 &           -3.15 &            3.53 &                 0.04 &                                           34.28 &  0.11 & 0.91 &      0.14 \\\\\noriginal\\_glrlm\\_GrayLevelNonUniformity        & -0.00 &       1.00 &      0.53 &           -1.04 &            1.04 &                 0.35 &                                            2.83 & -0.00 & 1.00 &      0.00 \\\\\noriginal\\_glrlm\\_RunLengthNonUniformity        & -0.02 &       0.98 &      0.43 &           -0.86 &            0.82 &                 0.42 &                                            2.26 & -0.05 & 0.96 &      0.06 \\\\\noriginal\\_glrlm\\_RunPercentage                 & -0.71 &       0.49 &      5.77 &          -12.02 &           10.59 &                 0.00 &                                        39878.51 & -0.12 & 0.90 &      0.15 \\\\\noriginal\\_glrlm\\_LowGrayLevelRunEmphasis       & -0.09 &       0.92 &      1.18 &           -2.41 &            2.23 &                 0.09 &                                            9.31 & -0.07 & 0.94 &      0.09 \\\\\noriginal\\_glrlm\\_HighGrayLevelRunEmphasis      &  0.42 &       1.52 &      4.52 &           -8.44 &            9.28 &                 0.00 &                                        10709.93 &  0.09 & 0.93 &      0.11 \\\\\noriginal\\_glrlm\\_ShortRunLowGrayLevelEmphasis  &  0.08 &       1.09 &      1.19 &           -2.25 &            2.42 &                 0.11 &                                           11.25 &  0.07 & 0.94 &      0.08 \\\\\noriginal\\_glrlm\\_ShortRunHighGrayLevelEmphasis & -0.29 &       0.75 &      2.72 &           -5.63 &            5.05 &                 0.00 &                                          156.10 & -0.11 & 0.92 &      0.13 \\\\\noriginal\\_glrlm\\_LongRunHighGrayLevelEmphasis  & -0.22 &       0.80 &      1.41 &           -2.97 &            2.54 &                 0.05 &                                           12.66 & -0.15 & 0.88 &      0.19 \\\\\nage                                          &  0.02 &       1.02 &      0.09 &           -0.16 &            0.21 &                 0.85 &                                            1.23 &  0.23 & 0.82 &      0.29 \\\\\nSourceDataset                                & -0.14 &       0.87 &      0.50 &           -1.12 &            0.83 &                 0.33 &                                            2.30 & -0.29 & 0.77 &      0.37 \\\\\nNstage\\_1                                     &  0.02 &       1.02 &      0.41 &           -0.78 &            0.81 &                 0.46 &                                            2.25 &  0.04 & 0.97 &      0.05 \\\\\nNstage\\_2                                     &  0.09 &       1.09 &      0.26 &           -0.42 &            0.59 &                 0.66 &                                            1.80 &  0.33 & 0.74 &      0.44 \\\\\nNstage\\_3                                     &  0.07 &       1.07 &      0.34 &           -0.60 &            0.73 &                 0.55 &                                            2.07 &  0.20 & 0.84 &      0.25 \\\\\nTstage\\_2                                     &  0.00 &       1.00 &      0.26 &           -0.51 &            0.52 &                 0.60 &                                            1.68 &  0.01 & 0.99 &      0.02 \\\\\nTstage\\_3                                     & -0.02 &       0.98 &      0.38 &           -0.75 &            0.72 &                 0.47 &                                            2.06 & -0.04 & 0.96 &      0.05 \\\\\nTstage\\_4                                     & -0.05 &       0.95 &      0.33 &           -0.70 &            0.61 &                 0.49 &                                            1.83 & -0.15 & 0.88 &      0.18 \\\\\nHistology\\_cat\\_1                              & -0.05 &       0.95 &      0.35 &           -0.73 &            0.63 &                 0.48 &                                            1.88 & -0.15 & 0.88 &      0.18 \\\\\nHistology\\_cat\\_2                              & -0.02 &       0.98 &      0.30 &           -0.60 &            0.55 &                 0.55 &                                            1.74 & -0.08 & 0.94 &      0.10 \\\\\nHistology\\_cat\\_3                              &  0.01 &       1.01 &      0.34 &           -0.66 &            0.67 &                 0.51 &                                            1.96 &  0.02 & 0.99 &      0.02 \\\\\n\\bottomrule\n\\end{tabular}\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'SurvivalTime'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'Event'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-825.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2021-02-11 14:26:42 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>original_shape_Maximum3DDiameter</th>\n",
              "      <td>0.01</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.52</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.72</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_SurfaceArea</th>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.52</td>\n",
              "      <td>-1.07</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.34</td>\n",
              "      <td>2.66</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_SurfaceVolumeRatio</th>\n",
              "      <td>0.05</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.45</td>\n",
              "      <td>-0.82</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2.54</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_VoxelVolume</th>\n",
              "      <td>0.08</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.89</td>\n",
              "      <td>-1.67</td>\n",
              "      <td>1.83</td>\n",
              "      <td>0.19</td>\n",
              "      <td>6.25</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Entropy</th>\n",
              "      <td>-0.67</td>\n",
              "      <td>0.51</td>\n",
              "      <td>4.56</td>\n",
              "      <td>-9.61</td>\n",
              "      <td>8.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3925.61</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Kurtosis</th>\n",
              "      <td>0.03</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Maximum</th>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.78</td>\n",
              "      <td>4.95</td>\n",
              "      <td>-9.94</td>\n",
              "      <td>9.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12674.44</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Mean</th>\n",
              "      <td>0.37</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.87</td>\n",
              "      <td>-5.25</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>403.51</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_MeanAbsoluteDeviation</th>\n",
              "      <td>0.13</td>\n",
              "      <td>1.14</td>\n",
              "      <td>2.22</td>\n",
              "      <td>-4.22</td>\n",
              "      <td>4.48</td>\n",
              "      <td>0.01</td>\n",
              "      <td>88.43</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Median</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-2.35</td>\n",
              "      <td>2.24</td>\n",
              "      <td>0.10</td>\n",
              "      <td>9.38</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Range</th>\n",
              "      <td>0.24</td>\n",
              "      <td>1.27</td>\n",
              "      <td>5.37</td>\n",
              "      <td>-10.28</td>\n",
              "      <td>10.76</td>\n",
              "      <td>0.00</td>\n",
              "      <td>46866.41</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_RootMeanSquared</th>\n",
              "      <td>0.15</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.67</td>\n",
              "      <td>-3.13</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.04</td>\n",
              "      <td>30.52</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Skewness</th>\n",
              "      <td>0.04</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-0.70</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.50</td>\n",
              "      <td>2.19</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_StandardDeviation</th>\n",
              "      <td>0.05</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.23</td>\n",
              "      <td>-4.33</td>\n",
              "      <td>4.43</td>\n",
              "      <td>0.01</td>\n",
              "      <td>83.63</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Uniformity</th>\n",
              "      <td>0.05</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.56</td>\n",
              "      <td>-6.92</td>\n",
              "      <td>7.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1119.80</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Variance</th>\n",
              "      <td>0.15</td>\n",
              "      <td>1.16</td>\n",
              "      <td>2.86</td>\n",
              "      <td>-5.45</td>\n",
              "      <td>5.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>314.32</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Autocorrelation</th>\n",
              "      <td>0.20</td>\n",
              "      <td>1.22</td>\n",
              "      <td>3.64</td>\n",
              "      <td>-6.93</td>\n",
              "      <td>7.32</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1517.15</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterShade</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.34</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.92</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterTendency</th>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.95</td>\n",
              "      <td>-3.96</td>\n",
              "      <td>3.69</td>\n",
              "      <td>0.02</td>\n",
              "      <td>40.09</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Contrast</th>\n",
              "      <td>0.39</td>\n",
              "      <td>1.47</td>\n",
              "      <td>4.08</td>\n",
              "      <td>-7.60</td>\n",
              "      <td>8.38</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4345.13</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Correlation</th>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.44</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.39</td>\n",
              "      <td>2.21</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_DifferenceEntropy</th>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.85</td>\n",
              "      <td>2.28</td>\n",
              "      <td>-4.64</td>\n",
              "      <td>4.31</td>\n",
              "      <td>0.01</td>\n",
              "      <td>74.56</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_DifferenceAverage</th>\n",
              "      <td>-1.44</td>\n",
              "      <td>0.24</td>\n",
              "      <td>11.37</td>\n",
              "      <td>-23.71</td>\n",
              "      <td>20.84</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.12e+09</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_JointEnergy</th>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.83</td>\n",
              "      <td>-5.62</td>\n",
              "      <td>5.46</td>\n",
              "      <td>0.00</td>\n",
              "      <td>235.63</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_JointEntropy</th>\n",
              "      <td>0.43</td>\n",
              "      <td>1.54</td>\n",
              "      <td>4.44</td>\n",
              "      <td>-8.26</td>\n",
              "      <td>9.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9159.85</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Id</th>\n",
              "      <td>-7.52</td>\n",
              "      <td>0.00</td>\n",
              "      <td>51.95</td>\n",
              "      <td>-109.34</td>\n",
              "      <td>94.29</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.94e+40</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idm</th>\n",
              "      <td>6.24</td>\n",
              "      <td>512.80</td>\n",
              "      <td>48.02</td>\n",
              "      <td>-87.88</td>\n",
              "      <td>100.36</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.84e+43</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Imc1</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-1.98</td>\n",
              "      <td>1.95</td>\n",
              "      <td>0.14</td>\n",
              "      <td>7.06</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Imc2</th>\n",
              "      <td>0.04</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.40</td>\n",
              "      <td>-0.74</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.48</td>\n",
              "      <td>2.28</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idmn</th>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.93</td>\n",
              "      <td>1.12</td>\n",
              "      <td>-2.26</td>\n",
              "      <td>2.11</td>\n",
              "      <td>0.10</td>\n",
              "      <td>8.25</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idn</th>\n",
              "      <td>0.20</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.98</td>\n",
              "      <td>-3.67</td>\n",
              "      <td>4.08</td>\n",
              "      <td>0.03</td>\n",
              "      <td>59.19</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_InverseVariance</th>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.79</td>\n",
              "      <td>3.07</td>\n",
              "      <td>-6.25</td>\n",
              "      <td>5.78</td>\n",
              "      <td>0.00</td>\n",
              "      <td>322.68</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_MaximumProbability</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.68</td>\n",
              "      <td>-1.38</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.25</td>\n",
              "      <td>3.63</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_SumAverage</th>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.70</td>\n",
              "      <td>2.77</td>\n",
              "      <td>-5.78</td>\n",
              "      <td>5.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>159.31</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_SumEntropy</th>\n",
              "      <td>0.11</td>\n",
              "      <td>1.11</td>\n",
              "      <td>3.93</td>\n",
              "      <td>-7.60</td>\n",
              "      <td>7.82</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2477.64</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunEmphasis</th>\n",
              "      <td>0.50</td>\n",
              "      <td>1.65</td>\n",
              "      <td>1.97</td>\n",
              "      <td>-3.37</td>\n",
              "      <td>4.36</td>\n",
              "      <td>0.03</td>\n",
              "      <td>78.63</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LongRunEmphasis</th>\n",
              "      <td>0.19</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.71</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>3.53</td>\n",
              "      <td>0.04</td>\n",
              "      <td>34.28</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_GrayLevelNonUniformity</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.53</td>\n",
              "      <td>-1.04</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2.83</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_RunLengthNonUniformity</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.43</td>\n",
              "      <td>-0.86</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.26</td>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_RunPercentage</th>\n",
              "      <td>-0.71</td>\n",
              "      <td>0.49</td>\n",
              "      <td>5.77</td>\n",
              "      <td>-12.02</td>\n",
              "      <td>10.59</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39878.51</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1.18</td>\n",
              "      <td>-2.41</td>\n",
              "      <td>2.23</td>\n",
              "      <td>0.09</td>\n",
              "      <td>9.31</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n",
              "      <td>0.42</td>\n",
              "      <td>1.52</td>\n",
              "      <td>4.52</td>\n",
              "      <td>-8.44</td>\n",
              "      <td>9.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10709.93</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n",
              "      <td>0.08</td>\n",
              "      <td>1.09</td>\n",
              "      <td>1.19</td>\n",
              "      <td>-2.25</td>\n",
              "      <td>2.42</td>\n",
              "      <td>0.11</td>\n",
              "      <td>11.25</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.72</td>\n",
              "      <td>-5.63</td>\n",
              "      <td>5.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>156.10</td>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n",
              "      <td>-0.22</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.41</td>\n",
              "      <td>-2.97</td>\n",
              "      <td>2.54</td>\n",
              "      <td>0.05</td>\n",
              "      <td>12.66</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.02</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.09</td>\n",
              "      <td>-0.16</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SourceDataset</th>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-1.12</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.33</td>\n",
              "      <td>2.30</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_1</th>\n",
              "      <td>0.02</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.41</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.46</td>\n",
              "      <td>2.25</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_2</th>\n",
              "      <td>0.09</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.66</td>\n",
              "      <td>1.80</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_3</th>\n",
              "      <td>0.07</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.34</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.55</td>\n",
              "      <td>2.07</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_3</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.38</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.47</td>\n",
              "      <td>2.06</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_4</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.70</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.83</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_1</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-0.73</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.48</td>\n",
              "      <td>1.88</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_2</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-0.60</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.74</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_3</th>\n",
              "      <td>0.01</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.34</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.96</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Concordance</th>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>1763.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>26.43 on 56 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 300 total observations, 138 right-censored observations>\n",
              "             duration col = 'SurvivalTime'\n",
              "                event col = 'Event'\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 300\n",
              "number of events observed = 162\n",
              "   partial log-likelihood = -825.73\n",
              "         time fit was run = 2021-02-11 14:26:42 UTC\n",
              "\n",
              "---\n",
              "                                               coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
              "covariate                                                                                                                                            \n",
              "original_shape_Maximum3DDiameter               0.01       1.01       0.27            -0.52             0.54                 0.59                 1.72\n",
              "original_shape_SurfaceArea                    -0.04       0.96       0.52            -1.07             0.98                 0.34                 2.66\n",
              "original_shape_SurfaceVolumeRatio              0.05       1.06       0.45            -0.82             0.93                 0.44                 2.54\n",
              "original_shape_VoxelVolume                     0.08       1.09       0.89            -1.67             1.83                 0.19                 6.25\n",
              "original_firstorder_Entropy                   -0.67       0.51       4.56            -9.61             8.28                 0.00              3925.61\n",
              "original_firstorder_Kurtosis                   0.03       1.03       0.27            -0.50             0.56                 0.61                 1.75\n",
              "original_firstorder_Maximum                   -0.25       0.78       4.95            -9.94             9.45                 0.00             12674.44\n",
              "original_firstorder_Mean                       0.37       1.45       2.87            -5.25             6.00                 0.01               403.51\n",
              "original_firstorder_MeanAbsoluteDeviation      0.13       1.14       2.22            -4.22             4.48                 0.01                88.43\n",
              "original_firstorder_Median                    -0.06       0.95       1.17            -2.35             2.24                 0.10                 9.38\n",
              "original_firstorder_Range                      0.24       1.27       5.37           -10.28            10.76                 0.00             46866.41\n",
              "original_firstorder_RootMeanSquared            0.15       1.16       1.67            -3.13             3.42                 0.04                30.52\n",
              "original_firstorder_Skewness                   0.04       1.04       0.38            -0.70             0.78                 0.50                 2.19\n",
              "original_firstorder_StandardDeviation          0.05       1.05       2.23            -4.33             4.43                 0.01                83.63\n",
              "original_firstorder_Uniformity                 0.05       1.05       3.56            -6.92             7.02                 0.00              1119.80\n",
              "original_firstorder_Variance                   0.15       1.16       2.86            -5.45             5.75                 0.00               314.32\n",
              "original_glcm_Autocorrelation                  0.20       1.22       3.64            -6.93             7.32                 0.00              1517.15\n",
              "original_glcm_ClusterShade                    -0.02       0.98       0.34            -0.69             0.65                 0.50                 1.92\n",
              "original_glcm_ClusterTendency                 -0.14       0.87       1.95            -3.96             3.69                 0.02                40.09\n",
              "original_glcm_Contrast                         0.39       1.47       4.08            -7.60             8.38                 0.00              4345.13\n",
              "original_glcm_Correlation                     -0.08       0.92       0.44            -0.95             0.79                 0.39                 2.21\n",
              "original_glcm_DifferenceEntropy               -0.17       0.85       2.28            -4.64             4.31                 0.01                74.56\n",
              "original_glcm_DifferenceAverage               -1.44       0.24      11.37           -23.71            20.84                 0.00             1.12e+09\n",
              "original_glcm_JointEnergy                     -0.08       0.92       2.83            -5.62             5.46                 0.00               235.63\n",
              "original_glcm_JointEntropy                     0.43       1.54       4.44            -8.26             9.12                 0.00              9159.85\n",
              "original_glcm_Id                              -7.52       0.00      51.95          -109.34            94.29                 0.00             8.94e+40\n",
              "original_glcm_Idm                              6.24     512.80      48.02           -87.88           100.36                 0.00             3.84e+43\n",
              "original_glcm_Imc1                            -0.01       0.99       1.00            -1.98             1.95                 0.14                 7.06\n",
              "original_glcm_Imc2                             0.04       1.04       0.40            -0.74             0.82                 0.48                 2.28\n",
              "original_glcm_Idmn                            -0.08       0.93       1.12            -2.26             2.11                 0.10                 8.25\n",
              "original_glcm_Idn                              0.20       1.23       1.98            -3.67             4.08                 0.03                59.19\n",
              "original_glcm_InverseVariance                 -0.24       0.79       3.07            -6.25             5.78                 0.00               322.68\n",
              "original_glcm_MaximumProbability              -0.05       0.95       0.68            -1.38             1.29                 0.25                 3.63\n",
              "original_glcm_SumAverage                      -0.35       0.70       2.77            -5.78             5.07                 0.00               159.31\n",
              "original_glcm_SumEntropy                       0.11       1.11       3.93            -7.60             7.82                 0.00              2477.64\n",
              "original_glrlm_ShortRunEmphasis                0.50       1.65       1.97            -3.37             4.36                 0.03                78.63\n",
              "original_glrlm_LongRunEmphasis                 0.19       1.21       1.71            -3.15             3.53                 0.04                34.28\n",
              "original_glrlm_GrayLevelNonUniformity         -0.00       1.00       0.53            -1.04             1.04                 0.35                 2.83\n",
              "original_glrlm_RunLengthNonUniformity         -0.02       0.98       0.43            -0.86             0.82                 0.42                 2.26\n",
              "original_glrlm_RunPercentage                  -0.71       0.49       5.77           -12.02            10.59                 0.00             39878.51\n",
              "original_glrlm_LowGrayLevelRunEmphasis        -0.09       0.92       1.18            -2.41             2.23                 0.09                 9.31\n",
              "original_glrlm_HighGrayLevelRunEmphasis        0.42       1.52       4.52            -8.44             9.28                 0.00             10709.93\n",
              "original_glrlm_ShortRunLowGrayLevelEmphasis    0.08       1.09       1.19            -2.25             2.42                 0.11                11.25\n",
              "original_glrlm_ShortRunHighGrayLevelEmphasis  -0.29       0.75       2.72            -5.63             5.05                 0.00               156.10\n",
              "original_glrlm_LongRunHighGrayLevelEmphasis   -0.22       0.80       1.41            -2.97             2.54                 0.05                12.66\n",
              "age                                            0.02       1.02       0.09            -0.16             0.21                 0.85                 1.23\n",
              "SourceDataset                                 -0.14       0.87       0.50            -1.12             0.83                 0.33                 2.30\n",
              "Nstage_1                                       0.02       1.02       0.41            -0.78             0.81                 0.46                 2.25\n",
              "Nstage_2                                       0.09       1.09       0.26            -0.42             0.59                 0.66                 1.80\n",
              "Nstage_3                                       0.07       1.07       0.34            -0.60             0.73                 0.55                 2.07\n",
              "Tstage_2                                       0.00       1.00       0.26            -0.51             0.52                 0.60                 1.68\n",
              "Tstage_3                                      -0.02       0.98       0.38            -0.75             0.72                 0.47                 2.06\n",
              "Tstage_4                                      -0.05       0.95       0.33            -0.70             0.61                 0.49                 1.83\n",
              "Histology_cat_1                               -0.05       0.95       0.35            -0.73             0.63                 0.48                 1.88\n",
              "Histology_cat_2                               -0.02       0.98       0.30            -0.60             0.55                 0.55                 1.74\n",
              "Histology_cat_3                                0.01       1.01       0.34            -0.66             0.67                 0.51                 1.96\n",
              "\n",
              "                                                 z    p   -log2(p)\n",
              "covariate                                                         \n",
              "original_shape_Maximum3DDiameter              0.04 0.97       0.04\n",
              "original_shape_SurfaceArea                   -0.09 0.93       0.10\n",
              "original_shape_SurfaceVolumeRatio             0.12 0.90       0.15\n",
              "original_shape_VoxelVolume                    0.09 0.93       0.11\n",
              "original_firstorder_Entropy                  -0.15 0.88       0.18\n",
              "original_firstorder_Kurtosis                  0.11 0.91       0.13\n",
              "original_firstorder_Maximum                  -0.05 0.96       0.06\n",
              "original_firstorder_Mean                      0.13 0.90       0.16\n",
              "original_firstorder_MeanAbsoluteDeviation     0.06 0.95       0.07\n",
              "original_firstorder_Median                   -0.05 0.96       0.06\n",
              "original_firstorder_Range                     0.04 0.96       0.05\n",
              "original_firstorder_RootMeanSquared           0.09 0.93       0.10\n",
              "original_firstorder_Skewness                  0.11 0.91       0.13\n",
              "original_firstorder_StandardDeviation         0.02 0.98       0.03\n",
              "original_firstorder_Uniformity                0.01 0.99       0.02\n",
              "original_firstorder_Variance                  0.05 0.96       0.06\n",
              "original_glcm_Autocorrelation                 0.05 0.96       0.06\n",
              "original_glcm_ClusterShade                   -0.06 0.95       0.07\n",
              "original_glcm_ClusterTendency                -0.07 0.94       0.08\n",
              "original_glcm_Contrast                        0.09 0.92       0.11\n",
              "original_glcm_Correlation                    -0.18 0.86       0.22\n",
              "original_glcm_DifferenceEntropy              -0.07 0.94       0.09\n",
              "original_glcm_DifferenceAverage              -0.13 0.90       0.15\n",
              "original_glcm_JointEnergy                    -0.03 0.98       0.03\n",
              "original_glcm_JointEntropy                    0.10 0.92       0.12\n",
              "original_glcm_Id                             -0.14 0.88       0.18\n",
              "original_glcm_Idm                             0.13 0.90       0.16\n",
              "original_glcm_Imc1                           -0.01 0.99       0.01\n",
              "original_glcm_Imc2                            0.10 0.92       0.12\n",
              "original_glcm_Idmn                           -0.07 0.95       0.08\n",
              "original_glcm_Idn                             0.10 0.92       0.12\n",
              "original_glcm_InverseVariance                -0.08 0.94       0.09\n",
              "original_glcm_MaximumProbability             -0.07 0.95       0.08\n",
              "original_glcm_SumAverage                     -0.13 0.90       0.15\n",
              "original_glcm_SumEntropy                      0.03 0.98       0.03\n",
              "original_glrlm_ShortRunEmphasis               0.25 0.80       0.32\n",
              "original_glrlm_LongRunEmphasis                0.11 0.91       0.14\n",
              "original_glrlm_GrayLevelNonUniformity        -0.00 1.00       0.00\n",
              "original_glrlm_RunLengthNonUniformity        -0.05 0.96       0.06\n",
              "original_glrlm_RunPercentage                 -0.12 0.90       0.15\n",
              "original_glrlm_LowGrayLevelRunEmphasis       -0.07 0.94       0.09\n",
              "original_glrlm_HighGrayLevelRunEmphasis       0.09 0.93       0.11\n",
              "original_glrlm_ShortRunLowGrayLevelEmphasis   0.07 0.94       0.08\n",
              "original_glrlm_ShortRunHighGrayLevelEmphasis -0.11 0.92       0.13\n",
              "original_glrlm_LongRunHighGrayLevelEmphasis  -0.15 0.88       0.19\n",
              "age                                           0.23 0.82       0.29\n",
              "SourceDataset                                -0.29 0.77       0.37\n",
              "Nstage_1                                      0.04 0.97       0.05\n",
              "Nstage_2                                      0.33 0.74       0.44\n",
              "Nstage_3                                      0.20 0.84       0.25\n",
              "Tstage_2                                      0.01 0.99       0.02\n",
              "Tstage_3                                     -0.04 0.96       0.05\n",
              "Tstage_4                                     -0.15 0.88       0.18\n",
              "Histology_cat_1                              -0.15 0.88       0.18\n",
              "Histology_cat_2                              -0.08 0.94       0.10\n",
              "Histology_cat_3                               0.02 0.99       0.02\n",
              "---\n",
              "Concordance = 0.74\n",
              "Partial AIC = 1763.46\n",
              "log-likelihood ratio test = 26.43 on 56 df\n",
              "-log2(p) of ll-ratio test = 0.00"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Puo28w3_2NMh"
      },
      "source": [
        "We have the following warning message: \"*Newton-Rhaphson failed to converge sufficiently*\". It may be due to ***multicollinearity*** which occurs when some features are highly linearly related. \r\n",
        "Here, the Newton-Raphson's method is used to maximized the Cox partial likelihood."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPrwyS9S3bP0"
      },
      "source": [
        "Computation of the **Correlation matrix** of continuous features to have insight into which of the features are highly correlated"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-ugYhWnyU5ha",
        "outputId": "de19c5df-0c23-488b-8689-284ff1ac5ad0"
      },
      "source": [
        "x_train_model_numeric = x_train_model_.drop(columns=['SourceDataset','SurvivalTime','Event','Nstage_1','Nstage_2','Nstage_3',\r\n",
        "                                                     'Tstage_2','Tstage_3','Tstage_3','Tstage_4',\r\n",
        "                                                     'Histology_cat_1','Histology_cat_2', 'Histology_cat_3'])\r\n",
        "\r\n",
        "# Compute the correlation matrix\r\n",
        "corr = x_train_model_numeric.corr()\r\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>original_shape_Maximum3DDiameter</th>\n",
              "      <th>original_shape_SurfaceArea</th>\n",
              "      <th>original_shape_SurfaceVolumeRatio</th>\n",
              "      <th>original_shape_VoxelVolume</th>\n",
              "      <th>original_firstorder_Entropy</th>\n",
              "      <th>original_firstorder_Kurtosis</th>\n",
              "      <th>original_firstorder_Maximum</th>\n",
              "      <th>original_firstorder_Mean</th>\n",
              "      <th>original_firstorder_MeanAbsoluteDeviation</th>\n",
              "      <th>original_firstorder_Median</th>\n",
              "      <th>original_firstorder_Range</th>\n",
              "      <th>original_firstorder_RootMeanSquared</th>\n",
              "      <th>original_firstorder_Skewness</th>\n",
              "      <th>original_firstorder_StandardDeviation</th>\n",
              "      <th>original_firstorder_Uniformity</th>\n",
              "      <th>original_firstorder_Variance</th>\n",
              "      <th>original_glcm_Autocorrelation</th>\n",
              "      <th>original_glcm_ClusterShade</th>\n",
              "      <th>original_glcm_ClusterTendency</th>\n",
              "      <th>original_glcm_Contrast</th>\n",
              "      <th>original_glcm_Correlation</th>\n",
              "      <th>original_glcm_DifferenceEntropy</th>\n",
              "      <th>original_glcm_DifferenceAverage</th>\n",
              "      <th>original_glcm_JointEnergy</th>\n",
              "      <th>original_glcm_JointEntropy</th>\n",
              "      <th>original_glcm_Id</th>\n",
              "      <th>original_glcm_Idm</th>\n",
              "      <th>original_glcm_Imc1</th>\n",
              "      <th>original_glcm_Imc2</th>\n",
              "      <th>original_glcm_Idmn</th>\n",
              "      <th>original_glcm_Idn</th>\n",
              "      <th>original_glcm_InverseVariance</th>\n",
              "      <th>original_glcm_MaximumProbability</th>\n",
              "      <th>original_glcm_SumAverage</th>\n",
              "      <th>original_glcm_SumEntropy</th>\n",
              "      <th>original_glrlm_ShortRunEmphasis</th>\n",
              "      <th>original_glrlm_LongRunEmphasis</th>\n",
              "      <th>original_glrlm_GrayLevelNonUniformity</th>\n",
              "      <th>original_glrlm_RunLengthNonUniformity</th>\n",
              "      <th>original_glrlm_RunPercentage</th>\n",
              "      <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n",
              "      <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n",
              "      <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n",
              "      <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n",
              "      <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>original_shape_Maximum3DDiameter</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.873098</td>\n",
              "      <td>-0.631682</td>\n",
              "      <td>0.778222</td>\n",
              "      <td>-0.517604</td>\n",
              "      <td>0.485807</td>\n",
              "      <td>0.477864</td>\n",
              "      <td>0.483936</td>\n",
              "      <td>-0.410241</td>\n",
              "      <td>0.428488</td>\n",
              "      <td>0.496094</td>\n",
              "      <td>-0.434021</td>\n",
              "      <td>-0.484912</td>\n",
              "      <td>-0.357023</td>\n",
              "      <td>0.531525</td>\n",
              "      <td>-0.302514</td>\n",
              "      <td>0.598807</td>\n",
              "      <td>-0.152939</td>\n",
              "      <td>-0.244392</td>\n",
              "      <td>-0.395961</td>\n",
              "      <td>0.423478</td>\n",
              "      <td>-0.562465</td>\n",
              "      <td>-0.503483</td>\n",
              "      <td>0.476386</td>\n",
              "      <td>-0.500397</td>\n",
              "      <td>0.572306</td>\n",
              "      <td>0.575070</td>\n",
              "      <td>-0.322542</td>\n",
              "      <td>-0.098532</td>\n",
              "      <td>0.584027</td>\n",
              "      <td>0.652907</td>\n",
              "      <td>0.522775</td>\n",
              "      <td>0.454791</td>\n",
              "      <td>0.579089</td>\n",
              "      <td>-0.489627</td>\n",
              "      <td>-0.612788</td>\n",
              "      <td>0.497306</td>\n",
              "      <td>0.703748</td>\n",
              "      <td>0.686436</td>\n",
              "      <td>-0.584467</td>\n",
              "      <td>-0.250724</td>\n",
              "      <td>0.628312</td>\n",
              "      <td>-0.299613</td>\n",
              "      <td>0.474408</td>\n",
              "      <td>0.585460</td>\n",
              "      <td>0.032349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_SurfaceArea</th>\n",
              "      <td>0.873098</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.653477</td>\n",
              "      <td>0.943666</td>\n",
              "      <td>-0.519178</td>\n",
              "      <td>0.485211</td>\n",
              "      <td>0.557718</td>\n",
              "      <td>0.484047</td>\n",
              "      <td>-0.394664</td>\n",
              "      <td>0.432869</td>\n",
              "      <td>0.571116</td>\n",
              "      <td>-0.426418</td>\n",
              "      <td>-0.482664</td>\n",
              "      <td>-0.334401</td>\n",
              "      <td>0.530784</td>\n",
              "      <td>-0.285847</td>\n",
              "      <td>0.611411</td>\n",
              "      <td>-0.181897</td>\n",
              "      <td>-0.234516</td>\n",
              "      <td>-0.377788</td>\n",
              "      <td>0.402810</td>\n",
              "      <td>-0.547205</td>\n",
              "      <td>-0.485235</td>\n",
              "      <td>0.462424</td>\n",
              "      <td>-0.502426</td>\n",
              "      <td>0.552018</td>\n",
              "      <td>0.555243</td>\n",
              "      <td>-0.313926</td>\n",
              "      <td>-0.106066</td>\n",
              "      <td>0.557954</td>\n",
              "      <td>0.643016</td>\n",
              "      <td>0.524320</td>\n",
              "      <td>0.438659</td>\n",
              "      <td>0.585145</td>\n",
              "      <td>-0.497025</td>\n",
              "      <td>-0.599231</td>\n",
              "      <td>0.486413</td>\n",
              "      <td>0.848956</td>\n",
              "      <td>0.886036</td>\n",
              "      <td>-0.563329</td>\n",
              "      <td>-0.232338</td>\n",
              "      <td>0.642114</td>\n",
              "      <td>-0.280951</td>\n",
              "      <td>0.493900</td>\n",
              "      <td>0.585071</td>\n",
              "      <td>-0.017626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_SurfaceVolumeRatio</th>\n",
              "      <td>-0.631682</td>\n",
              "      <td>-0.653477</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.610894</td>\n",
              "      <td>0.604269</td>\n",
              "      <td>-0.362313</td>\n",
              "      <td>-0.379320</td>\n",
              "      <td>-0.593890</td>\n",
              "      <td>0.484648</td>\n",
              "      <td>-0.570628</td>\n",
              "      <td>-0.375187</td>\n",
              "      <td>0.554593</td>\n",
              "      <td>0.510749</td>\n",
              "      <td>0.432342</td>\n",
              "      <td>-0.596855</td>\n",
              "      <td>0.410911</td>\n",
              "      <td>-0.594866</td>\n",
              "      <td>0.213781</td>\n",
              "      <td>0.350441</td>\n",
              "      <td>0.590750</td>\n",
              "      <td>-0.594196</td>\n",
              "      <td>0.678302</td>\n",
              "      <td>0.694299</td>\n",
              "      <td>-0.546565</td>\n",
              "      <td>0.600255</td>\n",
              "      <td>-0.715481</td>\n",
              "      <td>-0.713112</td>\n",
              "      <td>0.349951</td>\n",
              "      <td>0.125131</td>\n",
              "      <td>-0.778277</td>\n",
              "      <td>-0.815380</td>\n",
              "      <td>-0.691626</td>\n",
              "      <td>-0.584155</td>\n",
              "      <td>-0.602545</td>\n",
              "      <td>0.577648</td>\n",
              "      <td>0.694442</td>\n",
              "      <td>-0.551579</td>\n",
              "      <td>-0.535566</td>\n",
              "      <td>-0.552963</td>\n",
              "      <td>0.708983</td>\n",
              "      <td>0.383714</td>\n",
              "      <td>-0.594257</td>\n",
              "      <td>0.441996</td>\n",
              "      <td>-0.411223</td>\n",
              "      <td>-0.599629</td>\n",
              "      <td>-0.006960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_VoxelVolume</th>\n",
              "      <td>0.778222</td>\n",
              "      <td>0.943666</td>\n",
              "      <td>-0.610894</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.512563</td>\n",
              "      <td>0.487016</td>\n",
              "      <td>0.500003</td>\n",
              "      <td>0.453066</td>\n",
              "      <td>-0.396806</td>\n",
              "      <td>0.397957</td>\n",
              "      <td>0.503510</td>\n",
              "      <td>-0.407431</td>\n",
              "      <td>-0.460052</td>\n",
              "      <td>-0.341334</td>\n",
              "      <td>0.522597</td>\n",
              "      <td>-0.298058</td>\n",
              "      <td>0.542490</td>\n",
              "      <td>-0.163251</td>\n",
              "      <td>-0.251419</td>\n",
              "      <td>-0.369018</td>\n",
              "      <td>0.365761</td>\n",
              "      <td>-0.532368</td>\n",
              "      <td>-0.466085</td>\n",
              "      <td>0.456104</td>\n",
              "      <td>-0.499135</td>\n",
              "      <td>0.528602</td>\n",
              "      <td>0.531427</td>\n",
              "      <td>-0.320692</td>\n",
              "      <td>-0.098991</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>0.587760</td>\n",
              "      <td>0.491187</td>\n",
              "      <td>0.431302</td>\n",
              "      <td>0.520984</td>\n",
              "      <td>-0.493301</td>\n",
              "      <td>-0.584068</td>\n",
              "      <td>0.489115</td>\n",
              "      <td>0.934321</td>\n",
              "      <td>0.875987</td>\n",
              "      <td>-0.546164</td>\n",
              "      <td>-0.201632</td>\n",
              "      <td>0.570943</td>\n",
              "      <td>-0.249645</td>\n",
              "      <td>0.411277</td>\n",
              "      <td>0.574245</td>\n",
              "      <td>0.004208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Entropy</th>\n",
              "      <td>-0.517604</td>\n",
              "      <td>-0.519178</td>\n",
              "      <td>0.604269</td>\n",
              "      <td>-0.512563</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.696390</td>\n",
              "      <td>-0.169316</td>\n",
              "      <td>-0.820474</td>\n",
              "      <td>0.909681</td>\n",
              "      <td>-0.691941</td>\n",
              "      <td>-0.086091</td>\n",
              "      <td>0.876584</td>\n",
              "      <td>0.850178</td>\n",
              "      <td>0.887635</td>\n",
              "      <td>-0.966701</td>\n",
              "      <td>0.821856</td>\n",
              "      <td>-0.522815</td>\n",
              "      <td>-0.066512</td>\n",
              "      <td>0.799038</td>\n",
              "      <td>0.721909</td>\n",
              "      <td>-0.234536</td>\n",
              "      <td>0.941019</td>\n",
              "      <td>0.855940</td>\n",
              "      <td>-0.889731</td>\n",
              "      <td>0.987007</td>\n",
              "      <td>-0.941998</td>\n",
              "      <td>-0.944335</td>\n",
              "      <td>0.400132</td>\n",
              "      <td>0.358425</td>\n",
              "      <td>-0.717617</td>\n",
              "      <td>-0.830314</td>\n",
              "      <td>-0.829298</td>\n",
              "      <td>-0.886244</td>\n",
              "      <td>-0.572732</td>\n",
              "      <td>0.995903</td>\n",
              "      <td>0.949601</td>\n",
              "      <td>-0.801817</td>\n",
              "      <td>-0.588079</td>\n",
              "      <td>-0.279184</td>\n",
              "      <td>0.927234</td>\n",
              "      <td>0.389505</td>\n",
              "      <td>-0.540717</td>\n",
              "      <td>0.450367</td>\n",
              "      <td>-0.237753</td>\n",
              "      <td>-0.778907</td>\n",
              "      <td>0.143891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Kurtosis</th>\n",
              "      <td>0.485807</td>\n",
              "      <td>0.485211</td>\n",
              "      <td>-0.362313</td>\n",
              "      <td>0.487016</td>\n",
              "      <td>-0.696390</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.294522</td>\n",
              "      <td>0.514501</td>\n",
              "      <td>-0.624172</td>\n",
              "      <td>0.375795</td>\n",
              "      <td>0.260351</td>\n",
              "      <td>-0.540567</td>\n",
              "      <td>-0.710491</td>\n",
              "      <td>-0.620360</td>\n",
              "      <td>0.682191</td>\n",
              "      <td>-0.515396</td>\n",
              "      <td>0.444508</td>\n",
              "      <td>0.162563</td>\n",
              "      <td>-0.497272</td>\n",
              "      <td>-0.389315</td>\n",
              "      <td>0.075072</td>\n",
              "      <td>-0.650213</td>\n",
              "      <td>-0.508777</td>\n",
              "      <td>0.610873</td>\n",
              "      <td>-0.657568</td>\n",
              "      <td>0.618012</td>\n",
              "      <td>0.623681</td>\n",
              "      <td>-0.230778</td>\n",
              "      <td>-0.361583</td>\n",
              "      <td>0.439331</td>\n",
              "      <td>0.543938</td>\n",
              "      <td>0.510945</td>\n",
              "      <td>0.547134</td>\n",
              "      <td>0.447262</td>\n",
              "      <td>-0.681167</td>\n",
              "      <td>-0.707898</td>\n",
              "      <td>0.604427</td>\n",
              "      <td>0.573612</td>\n",
              "      <td>0.217207</td>\n",
              "      <td>-0.627625</td>\n",
              "      <td>-0.221798</td>\n",
              "      <td>0.493762</td>\n",
              "      <td>-0.256327</td>\n",
              "      <td>0.263952</td>\n",
              "      <td>0.650730</td>\n",
              "      <td>-0.120810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Maximum</th>\n",
              "      <td>0.477864</td>\n",
              "      <td>0.557718</td>\n",
              "      <td>-0.379320</td>\n",
              "      <td>0.500003</td>\n",
              "      <td>-0.169316</td>\n",
              "      <td>0.294522</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.258426</td>\n",
              "      <td>-0.106007</td>\n",
              "      <td>0.216781</td>\n",
              "      <td>0.972858</td>\n",
              "      <td>-0.153000</td>\n",
              "      <td>-0.032737</td>\n",
              "      <td>-0.037606</td>\n",
              "      <td>0.207588</td>\n",
              "      <td>0.000523</td>\n",
              "      <td>0.398445</td>\n",
              "      <td>-0.061817</td>\n",
              "      <td>0.019716</td>\n",
              "      <td>-0.058959</td>\n",
              "      <td>0.176224</td>\n",
              "      <td>-0.202211</td>\n",
              "      <td>-0.150574</td>\n",
              "      <td>0.178195</td>\n",
              "      <td>-0.165473</td>\n",
              "      <td>0.231201</td>\n",
              "      <td>0.236324</td>\n",
              "      <td>-0.178690</td>\n",
              "      <td>-0.012154</td>\n",
              "      <td>0.376800</td>\n",
              "      <td>0.439501</td>\n",
              "      <td>0.207168</td>\n",
              "      <td>0.166522</td>\n",
              "      <td>0.362106</td>\n",
              "      <td>-0.162044</td>\n",
              "      <td>-0.270393</td>\n",
              "      <td>0.213872</td>\n",
              "      <td>0.370650</td>\n",
              "      <td>0.509335</td>\n",
              "      <td>-0.253419</td>\n",
              "      <td>-0.078548</td>\n",
              "      <td>0.440329</td>\n",
              "      <td>-0.105321</td>\n",
              "      <td>0.420530</td>\n",
              "      <td>0.286276</td>\n",
              "      <td>0.081424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Mean</th>\n",
              "      <td>0.483936</td>\n",
              "      <td>0.484047</td>\n",
              "      <td>-0.593890</td>\n",
              "      <td>0.453066</td>\n",
              "      <td>-0.820474</td>\n",
              "      <td>0.514501</td>\n",
              "      <td>0.258426</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.767442</td>\n",
              "      <td>0.954266</td>\n",
              "      <td>0.173850</td>\n",
              "      <td>-0.969729</td>\n",
              "      <td>-0.758347</td>\n",
              "      <td>-0.730186</td>\n",
              "      <td>0.751792</td>\n",
              "      <td>-0.703004</td>\n",
              "      <td>0.712073</td>\n",
              "      <td>-0.228348</td>\n",
              "      <td>-0.713909</td>\n",
              "      <td>-0.644689</td>\n",
              "      <td>0.231874</td>\n",
              "      <td>-0.803505</td>\n",
              "      <td>-0.777736</td>\n",
              "      <td>0.666197</td>\n",
              "      <td>-0.845984</td>\n",
              "      <td>0.838773</td>\n",
              "      <td>0.840654</td>\n",
              "      <td>-0.405377</td>\n",
              "      <td>-0.226711</td>\n",
              "      <td>0.704240</td>\n",
              "      <td>0.798128</td>\n",
              "      <td>0.810571</td>\n",
              "      <td>0.723444</td>\n",
              "      <td>0.784367</td>\n",
              "      <td>-0.836576</td>\n",
              "      <td>-0.802270</td>\n",
              "      <td>0.614075</td>\n",
              "      <td>0.459499</td>\n",
              "      <td>0.315307</td>\n",
              "      <td>-0.802209</td>\n",
              "      <td>-0.544283</td>\n",
              "      <td>0.707943</td>\n",
              "      <td>-0.611950</td>\n",
              "      <td>0.525309</td>\n",
              "      <td>0.646165</td>\n",
              "      <td>-0.107266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_MeanAbsoluteDeviation</th>\n",
              "      <td>-0.410241</td>\n",
              "      <td>-0.394664</td>\n",
              "      <td>0.484648</td>\n",
              "      <td>-0.396806</td>\n",
              "      <td>0.909681</td>\n",
              "      <td>-0.624172</td>\n",
              "      <td>-0.106007</td>\n",
              "      <td>-0.767442</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.613818</td>\n",
              "      <td>0.001705</td>\n",
              "      <td>0.870460</td>\n",
              "      <td>0.738331</td>\n",
              "      <td>0.988645</td>\n",
              "      <td>-0.819457</td>\n",
              "      <td>0.973050</td>\n",
              "      <td>-0.376925</td>\n",
              "      <td>-0.185510</td>\n",
              "      <td>0.952444</td>\n",
              "      <td>0.789675</td>\n",
              "      <td>-0.163992</td>\n",
              "      <td>0.893199</td>\n",
              "      <td>0.851898</td>\n",
              "      <td>-0.726198</td>\n",
              "      <td>0.895866</td>\n",
              "      <td>-0.838607</td>\n",
              "      <td>-0.834044</td>\n",
              "      <td>0.345602</td>\n",
              "      <td>0.320411</td>\n",
              "      <td>-0.700796</td>\n",
              "      <td>-0.752626</td>\n",
              "      <td>-0.790627</td>\n",
              "      <td>-0.727710</td>\n",
              "      <td>-0.443356</td>\n",
              "      <td>0.903451</td>\n",
              "      <td>0.824911</td>\n",
              "      <td>-0.651617</td>\n",
              "      <td>-0.483713</td>\n",
              "      <td>-0.185369</td>\n",
              "      <td>0.795631</td>\n",
              "      <td>0.490467</td>\n",
              "      <td>-0.404223</td>\n",
              "      <td>0.536337</td>\n",
              "      <td>-0.129769</td>\n",
              "      <td>-0.620687</td>\n",
              "      <td>0.156567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Median</th>\n",
              "      <td>0.428488</td>\n",
              "      <td>0.432869</td>\n",
              "      <td>-0.570628</td>\n",
              "      <td>0.397957</td>\n",
              "      <td>-0.691941</td>\n",
              "      <td>0.375795</td>\n",
              "      <td>0.216781</td>\n",
              "      <td>0.954266</td>\n",
              "      <td>-0.613818</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.154629</td>\n",
              "      <td>-0.894749</td>\n",
              "      <td>-0.677775</td>\n",
              "      <td>-0.576333</td>\n",
              "      <td>0.625186</td>\n",
              "      <td>-0.570813</td>\n",
              "      <td>0.727548</td>\n",
              "      <td>-0.463516</td>\n",
              "      <td>-0.598451</td>\n",
              "      <td>-0.551493</td>\n",
              "      <td>0.222781</td>\n",
              "      <td>-0.672256</td>\n",
              "      <td>-0.676657</td>\n",
              "      <td>0.551802</td>\n",
              "      <td>-0.728116</td>\n",
              "      <td>0.733959</td>\n",
              "      <td>0.735970</td>\n",
              "      <td>-0.351002</td>\n",
              "      <td>-0.169062</td>\n",
              "      <td>0.616883</td>\n",
              "      <td>0.704130</td>\n",
              "      <td>0.721821</td>\n",
              "      <td>0.631967</td>\n",
              "      <td>0.802186</td>\n",
              "      <td>-0.716920</td>\n",
              "      <td>-0.681095</td>\n",
              "      <td>0.511717</td>\n",
              "      <td>0.370787</td>\n",
              "      <td>0.312430</td>\n",
              "      <td>-0.699692</td>\n",
              "      <td>-0.550933</td>\n",
              "      <td>0.701308</td>\n",
              "      <td>-0.614837</td>\n",
              "      <td>0.570359</td>\n",
              "      <td>0.560885</td>\n",
              "      <td>-0.070635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Range</th>\n",
              "      <td>0.496094</td>\n",
              "      <td>0.571116</td>\n",
              "      <td>-0.375187</td>\n",
              "      <td>0.503510</td>\n",
              "      <td>-0.086091</td>\n",
              "      <td>0.260351</td>\n",
              "      <td>0.972858</td>\n",
              "      <td>0.173850</td>\n",
              "      <td>0.001705</td>\n",
              "      <td>0.154629</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.048946</td>\n",
              "      <td>-0.013752</td>\n",
              "      <td>0.082048</td>\n",
              "      <td>0.143341</td>\n",
              "      <td>0.115114</td>\n",
              "      <td>0.479307</td>\n",
              "      <td>-0.133486</td>\n",
              "      <td>0.133727</td>\n",
              "      <td>0.039030</td>\n",
              "      <td>0.179862</td>\n",
              "      <td>-0.111402</td>\n",
              "      <td>-0.056061</td>\n",
              "      <td>0.117906</td>\n",
              "      <td>-0.075656</td>\n",
              "      <td>0.153019</td>\n",
              "      <td>0.159793</td>\n",
              "      <td>-0.126943</td>\n",
              "      <td>-0.003648</td>\n",
              "      <td>0.313912</td>\n",
              "      <td>0.378633</td>\n",
              "      <td>0.127038</td>\n",
              "      <td>0.098942</td>\n",
              "      <td>0.422860</td>\n",
              "      <td>-0.076843</td>\n",
              "      <td>-0.204088</td>\n",
              "      <td>0.165875</td>\n",
              "      <td>0.366030</td>\n",
              "      <td>0.530460</td>\n",
              "      <td>-0.187595</td>\n",
              "      <td>-0.050239</td>\n",
              "      <td>0.516835</td>\n",
              "      <td>-0.073831</td>\n",
              "      <td>0.529051</td>\n",
              "      <td>0.286415</td>\n",
              "      <td>0.103321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_RootMeanSquared</th>\n",
              "      <td>-0.434021</td>\n",
              "      <td>-0.426418</td>\n",
              "      <td>0.554593</td>\n",
              "      <td>-0.407431</td>\n",
              "      <td>0.876584</td>\n",
              "      <td>-0.540567</td>\n",
              "      <td>-0.153000</td>\n",
              "      <td>-0.969729</td>\n",
              "      <td>0.870460</td>\n",
              "      <td>-0.894749</td>\n",
              "      <td>-0.048946</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.787791</td>\n",
              "      <td>0.855312</td>\n",
              "      <td>-0.791908</td>\n",
              "      <td>0.830813</td>\n",
              "      <td>-0.588891</td>\n",
              "      <td>0.099005</td>\n",
              "      <td>0.836048</td>\n",
              "      <td>0.723007</td>\n",
              "      <td>-0.201600</td>\n",
              "      <td>0.855929</td>\n",
              "      <td>0.830868</td>\n",
              "      <td>-0.702288</td>\n",
              "      <td>0.892330</td>\n",
              "      <td>-0.859469</td>\n",
              "      <td>-0.858719</td>\n",
              "      <td>0.381602</td>\n",
              "      <td>0.270644</td>\n",
              "      <td>-0.714959</td>\n",
              "      <td>-0.790076</td>\n",
              "      <td>-0.833669</td>\n",
              "      <td>-0.747587</td>\n",
              "      <td>-0.676612</td>\n",
              "      <td>0.889579</td>\n",
              "      <td>0.821723</td>\n",
              "      <td>-0.631004</td>\n",
              "      <td>-0.452314</td>\n",
              "      <td>-0.244936</td>\n",
              "      <td>0.813958</td>\n",
              "      <td>0.569941</td>\n",
              "      <td>-0.588337</td>\n",
              "      <td>0.632470</td>\n",
              "      <td>-0.371151</td>\n",
              "      <td>-0.628272</td>\n",
              "      <td>0.135961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Skewness</th>\n",
              "      <td>-0.484912</td>\n",
              "      <td>-0.482664</td>\n",
              "      <td>0.510749</td>\n",
              "      <td>-0.460052</td>\n",
              "      <td>0.850178</td>\n",
              "      <td>-0.710491</td>\n",
              "      <td>-0.032737</td>\n",
              "      <td>-0.758347</td>\n",
              "      <td>0.738331</td>\n",
              "      <td>-0.677775</td>\n",
              "      <td>-0.013752</td>\n",
              "      <td>0.787791</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.719278</td>\n",
              "      <td>-0.817471</td>\n",
              "      <td>0.645963</td>\n",
              "      <td>-0.633143</td>\n",
              "      <td>0.102527</td>\n",
              "      <td>0.638320</td>\n",
              "      <td>0.544064</td>\n",
              "      <td>-0.157693</td>\n",
              "      <td>0.776909</td>\n",
              "      <td>0.680635</td>\n",
              "      <td>-0.724008</td>\n",
              "      <td>0.825326</td>\n",
              "      <td>-0.777223</td>\n",
              "      <td>-0.782350</td>\n",
              "      <td>0.248980</td>\n",
              "      <td>0.420433</td>\n",
              "      <td>-0.587520</td>\n",
              "      <td>-0.690102</td>\n",
              "      <td>-0.711120</td>\n",
              "      <td>-0.697616</td>\n",
              "      <td>-0.670475</td>\n",
              "      <td>0.850481</td>\n",
              "      <td>0.810934</td>\n",
              "      <td>-0.653917</td>\n",
              "      <td>-0.553767</td>\n",
              "      <td>-0.267019</td>\n",
              "      <td>0.762311</td>\n",
              "      <td>0.380493</td>\n",
              "      <td>-0.645265</td>\n",
              "      <td>0.434343</td>\n",
              "      <td>-0.414028</td>\n",
              "      <td>-0.704878</td>\n",
              "      <td>0.141711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_StandardDeviation</th>\n",
              "      <td>-0.357023</td>\n",
              "      <td>-0.334401</td>\n",
              "      <td>0.432342</td>\n",
              "      <td>-0.341334</td>\n",
              "      <td>0.887635</td>\n",
              "      <td>-0.620360</td>\n",
              "      <td>-0.037606</td>\n",
              "      <td>-0.730186</td>\n",
              "      <td>0.988645</td>\n",
              "      <td>-0.576333</td>\n",
              "      <td>0.082048</td>\n",
              "      <td>0.855312</td>\n",
              "      <td>0.719278</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.791616</td>\n",
              "      <td>0.977690</td>\n",
              "      <td>-0.288009</td>\n",
              "      <td>-0.233400</td>\n",
              "      <td>0.960725</td>\n",
              "      <td>0.774206</td>\n",
              "      <td>-0.118001</td>\n",
              "      <td>0.867488</td>\n",
              "      <td>0.825486</td>\n",
              "      <td>-0.703820</td>\n",
              "      <td>0.870736</td>\n",
              "      <td>-0.802592</td>\n",
              "      <td>-0.797399</td>\n",
              "      <td>0.307864</td>\n",
              "      <td>0.331680</td>\n",
              "      <td>-0.657093</td>\n",
              "      <td>-0.699423</td>\n",
              "      <td>-0.757530</td>\n",
              "      <td>-0.701734</td>\n",
              "      <td>-0.364813</td>\n",
              "      <td>0.882340</td>\n",
              "      <td>0.790963</td>\n",
              "      <td>-0.625636</td>\n",
              "      <td>-0.444192</td>\n",
              "      <td>-0.125819</td>\n",
              "      <td>0.757318</td>\n",
              "      <td>0.494392</td>\n",
              "      <td>-0.313812</td>\n",
              "      <td>0.534176</td>\n",
              "      <td>-0.038332</td>\n",
              "      <td>-0.571115</td>\n",
              "      <td>0.161385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Uniformity</th>\n",
              "      <td>0.531525</td>\n",
              "      <td>0.530784</td>\n",
              "      <td>-0.596855</td>\n",
              "      <td>0.522597</td>\n",
              "      <td>-0.966701</td>\n",
              "      <td>0.682191</td>\n",
              "      <td>0.207588</td>\n",
              "      <td>0.751792</td>\n",
              "      <td>-0.819457</td>\n",
              "      <td>0.625186</td>\n",
              "      <td>0.143341</td>\n",
              "      <td>-0.791908</td>\n",
              "      <td>-0.817471</td>\n",
              "      <td>-0.791616</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.710384</td>\n",
              "      <td>0.526597</td>\n",
              "      <td>0.064708</td>\n",
              "      <td>-0.689265</td>\n",
              "      <td>-0.616102</td>\n",
              "      <td>0.222490</td>\n",
              "      <td>-0.884650</td>\n",
              "      <td>-0.769783</td>\n",
              "      <td>0.965295</td>\n",
              "      <td>-0.946466</td>\n",
              "      <td>0.905884</td>\n",
              "      <td>0.910581</td>\n",
              "      <td>-0.374408</td>\n",
              "      <td>-0.370669</td>\n",
              "      <td>0.661090</td>\n",
              "      <td>0.789544</td>\n",
              "      <td>0.720547</td>\n",
              "      <td>0.936342</td>\n",
              "      <td>0.566336</td>\n",
              "      <td>-0.964422</td>\n",
              "      <td>-0.934845</td>\n",
              "      <td>0.874323</td>\n",
              "      <td>0.597897</td>\n",
              "      <td>0.277025</td>\n",
              "      <td>-0.922595</td>\n",
              "      <td>-0.338585</td>\n",
              "      <td>0.544272</td>\n",
              "      <td>-0.395662</td>\n",
              "      <td>0.245504</td>\n",
              "      <td>0.836964</td>\n",
              "      <td>-0.101014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Variance</th>\n",
              "      <td>-0.302514</td>\n",
              "      <td>-0.285847</td>\n",
              "      <td>0.410911</td>\n",
              "      <td>-0.298058</td>\n",
              "      <td>0.821856</td>\n",
              "      <td>-0.515396</td>\n",
              "      <td>0.000523</td>\n",
              "      <td>-0.703004</td>\n",
              "      <td>0.973050</td>\n",
              "      <td>-0.570813</td>\n",
              "      <td>0.115114</td>\n",
              "      <td>0.830813</td>\n",
              "      <td>0.645963</td>\n",
              "      <td>0.977690</td>\n",
              "      <td>-0.710384</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.267568</td>\n",
              "      <td>-0.184891</td>\n",
              "      <td>0.985521</td>\n",
              "      <td>0.805583</td>\n",
              "      <td>-0.146249</td>\n",
              "      <td>0.820824</td>\n",
              "      <td>0.819339</td>\n",
              "      <td>-0.626282</td>\n",
              "      <td>0.814891</td>\n",
              "      <td>-0.752621</td>\n",
              "      <td>-0.744146</td>\n",
              "      <td>0.308634</td>\n",
              "      <td>0.260734</td>\n",
              "      <td>-0.642137</td>\n",
              "      <td>-0.659827</td>\n",
              "      <td>-0.723658</td>\n",
              "      <td>-0.644828</td>\n",
              "      <td>-0.347849</td>\n",
              "      <td>0.818287</td>\n",
              "      <td>0.717757</td>\n",
              "      <td>-0.557367</td>\n",
              "      <td>-0.384417</td>\n",
              "      <td>-0.110787</td>\n",
              "      <td>0.700571</td>\n",
              "      <td>0.555571</td>\n",
              "      <td>-0.281165</td>\n",
              "      <td>0.590748</td>\n",
              "      <td>-0.024420</td>\n",
              "      <td>-0.515813</td>\n",
              "      <td>0.157107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Autocorrelation</th>\n",
              "      <td>0.598807</td>\n",
              "      <td>0.611411</td>\n",
              "      <td>-0.594866</td>\n",
              "      <td>0.542490</td>\n",
              "      <td>-0.522815</td>\n",
              "      <td>0.444508</td>\n",
              "      <td>0.398445</td>\n",
              "      <td>0.712073</td>\n",
              "      <td>-0.376925</td>\n",
              "      <td>0.727548</td>\n",
              "      <td>0.479307</td>\n",
              "      <td>-0.588891</td>\n",
              "      <td>-0.633143</td>\n",
              "      <td>-0.288009</td>\n",
              "      <td>0.526597</td>\n",
              "      <td>-0.267568</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.436308</td>\n",
              "      <td>-0.279189</td>\n",
              "      <td>-0.281505</td>\n",
              "      <td>0.247946</td>\n",
              "      <td>-0.488467</td>\n",
              "      <td>-0.438460</td>\n",
              "      <td>0.454232</td>\n",
              "      <td>-0.528235</td>\n",
              "      <td>0.570586</td>\n",
              "      <td>0.579180</td>\n",
              "      <td>-0.259994</td>\n",
              "      <td>-0.176436</td>\n",
              "      <td>0.509219</td>\n",
              "      <td>0.625289</td>\n",
              "      <td>0.529278</td>\n",
              "      <td>0.483585</td>\n",
              "      <td>0.987396</td>\n",
              "      <td>-0.531051</td>\n",
              "      <td>-0.584751</td>\n",
              "      <td>0.459234</td>\n",
              "      <td>0.492918</td>\n",
              "      <td>0.465564</td>\n",
              "      <td>-0.582092</td>\n",
              "      <td>-0.403332</td>\n",
              "      <td>0.992243</td>\n",
              "      <td>-0.460021</td>\n",
              "      <td>0.920744</td>\n",
              "      <td>0.661135</td>\n",
              "      <td>-0.022105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterShade</th>\n",
              "      <td>-0.152939</td>\n",
              "      <td>-0.181897</td>\n",
              "      <td>0.213781</td>\n",
              "      <td>-0.163251</td>\n",
              "      <td>-0.066512</td>\n",
              "      <td>0.162563</td>\n",
              "      <td>-0.061817</td>\n",
              "      <td>-0.228348</td>\n",
              "      <td>-0.185510</td>\n",
              "      <td>-0.463516</td>\n",
              "      <td>-0.133486</td>\n",
              "      <td>0.099005</td>\n",
              "      <td>0.102527</td>\n",
              "      <td>-0.233400</td>\n",
              "      <td>0.064708</td>\n",
              "      <td>-0.184891</td>\n",
              "      <td>-0.436308</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.161851</td>\n",
              "      <td>-0.075718</td>\n",
              "      <td>-0.137224</td>\n",
              "      <td>-0.073526</td>\n",
              "      <td>-0.029180</td>\n",
              "      <td>0.066693</td>\n",
              "      <td>-0.039824</td>\n",
              "      <td>-0.019264</td>\n",
              "      <td>-0.023066</td>\n",
              "      <td>0.026383</td>\n",
              "      <td>-0.116299</td>\n",
              "      <td>-0.010583</td>\n",
              "      <td>-0.047947</td>\n",
              "      <td>-0.026384</td>\n",
              "      <td>0.006927</td>\n",
              "      <td>-0.449174</td>\n",
              "      <td>-0.051409</td>\n",
              "      <td>-0.010685</td>\n",
              "      <td>0.024034</td>\n",
              "      <td>-0.032595</td>\n",
              "      <td>-0.255467</td>\n",
              "      <td>0.033547</td>\n",
              "      <td>0.285088</td>\n",
              "      <td>-0.376598</td>\n",
              "      <td>0.301363</td>\n",
              "      <td>-0.453301</td>\n",
              "      <td>-0.081187</td>\n",
              "      <td>-0.143943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterTendency</th>\n",
              "      <td>-0.244392</td>\n",
              "      <td>-0.234516</td>\n",
              "      <td>0.350441</td>\n",
              "      <td>-0.251419</td>\n",
              "      <td>0.799038</td>\n",
              "      <td>-0.497272</td>\n",
              "      <td>0.019716</td>\n",
              "      <td>-0.713909</td>\n",
              "      <td>0.952444</td>\n",
              "      <td>-0.598451</td>\n",
              "      <td>0.133727</td>\n",
              "      <td>0.836048</td>\n",
              "      <td>0.638320</td>\n",
              "      <td>0.960725</td>\n",
              "      <td>-0.689265</td>\n",
              "      <td>0.985521</td>\n",
              "      <td>-0.279189</td>\n",
              "      <td>-0.161851</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722614</td>\n",
              "      <td>-0.030805</td>\n",
              "      <td>0.762865</td>\n",
              "      <td>0.749928</td>\n",
              "      <td>-0.605236</td>\n",
              "      <td>0.788479</td>\n",
              "      <td>-0.706461</td>\n",
              "      <td>-0.700124</td>\n",
              "      <td>0.232354</td>\n",
              "      <td>0.312099</td>\n",
              "      <td>-0.562358</td>\n",
              "      <td>-0.596116</td>\n",
              "      <td>-0.676549</td>\n",
              "      <td>-0.623120</td>\n",
              "      <td>-0.367693</td>\n",
              "      <td>0.807210</td>\n",
              "      <td>0.676316</td>\n",
              "      <td>-0.528780</td>\n",
              "      <td>-0.352709</td>\n",
              "      <td>-0.063258</td>\n",
              "      <td>0.659093</td>\n",
              "      <td>0.586389</td>\n",
              "      <td>-0.287163</td>\n",
              "      <td>0.614260</td>\n",
              "      <td>-0.052807</td>\n",
              "      <td>-0.489805</td>\n",
              "      <td>0.157821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Contrast</th>\n",
              "      <td>-0.395961</td>\n",
              "      <td>-0.377788</td>\n",
              "      <td>0.590750</td>\n",
              "      <td>-0.369018</td>\n",
              "      <td>0.721909</td>\n",
              "      <td>-0.389315</td>\n",
              "      <td>-0.058959</td>\n",
              "      <td>-0.644689</td>\n",
              "      <td>0.789675</td>\n",
              "      <td>-0.551493</td>\n",
              "      <td>0.039030</td>\n",
              "      <td>0.723007</td>\n",
              "      <td>0.544064</td>\n",
              "      <td>0.774206</td>\n",
              "      <td>-0.616102</td>\n",
              "      <td>0.805583</td>\n",
              "      <td>-0.281505</td>\n",
              "      <td>-0.075718</td>\n",
              "      <td>0.722614</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.602305</td>\n",
              "      <td>0.867347</td>\n",
              "      <td>0.959810</td>\n",
              "      <td>-0.552213</td>\n",
              "      <td>0.768489</td>\n",
              "      <td>-0.802463</td>\n",
              "      <td>-0.786404</td>\n",
              "      <td>0.596633</td>\n",
              "      <td>-0.054630</td>\n",
              "      <td>-0.847703</td>\n",
              "      <td>-0.801665</td>\n",
              "      <td>-0.810599</td>\n",
              "      <td>-0.618898</td>\n",
              "      <td>-0.324584</td>\n",
              "      <td>0.696875</td>\n",
              "      <td>0.715611</td>\n",
              "      <td>-0.526328</td>\n",
              "      <td>-0.365234</td>\n",
              "      <td>-0.250079</td>\n",
              "      <td>0.727081</td>\n",
              "      <td>0.374164</td>\n",
              "      <td>-0.284007</td>\n",
              "      <td>0.443686</td>\n",
              "      <td>-0.024844</td>\n",
              "      <td>-0.501480</td>\n",
              "      <td>0.138825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Correlation</th>\n",
              "      <td>0.423478</td>\n",
              "      <td>0.402810</td>\n",
              "      <td>-0.594196</td>\n",
              "      <td>0.365761</td>\n",
              "      <td>-0.234536</td>\n",
              "      <td>0.075072</td>\n",
              "      <td>0.176224</td>\n",
              "      <td>0.231874</td>\n",
              "      <td>-0.163992</td>\n",
              "      <td>0.222781</td>\n",
              "      <td>0.179862</td>\n",
              "      <td>-0.201600</td>\n",
              "      <td>-0.157693</td>\n",
              "      <td>-0.118001</td>\n",
              "      <td>0.222490</td>\n",
              "      <td>-0.146249</td>\n",
              "      <td>0.247946</td>\n",
              "      <td>-0.137224</td>\n",
              "      <td>-0.030805</td>\n",
              "      <td>-0.602305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.485455</td>\n",
              "      <td>-0.580218</td>\n",
              "      <td>0.233731</td>\n",
              "      <td>-0.308951</td>\n",
              "      <td>0.474470</td>\n",
              "      <td>0.462260</td>\n",
              "      <td>-0.713320</td>\n",
              "      <td>0.552051</td>\n",
              "      <td>0.631095</td>\n",
              "      <td>0.573892</td>\n",
              "      <td>0.466259</td>\n",
              "      <td>0.312089</td>\n",
              "      <td>0.223806</td>\n",
              "      <td>-0.178962</td>\n",
              "      <td>-0.405265</td>\n",
              "      <td>0.296669</td>\n",
              "      <td>0.259994</td>\n",
              "      <td>0.349674</td>\n",
              "      <td>-0.444868</td>\n",
              "      <td>-0.061591</td>\n",
              "      <td>0.233171</td>\n",
              "      <td>-0.115019</td>\n",
              "      <td>0.081893</td>\n",
              "      <td>0.331643</td>\n",
              "      <td>-0.026338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_DifferenceEntropy</th>\n",
              "      <td>-0.562465</td>\n",
              "      <td>-0.547205</td>\n",
              "      <td>0.678302</td>\n",
              "      <td>-0.532368</td>\n",
              "      <td>0.941019</td>\n",
              "      <td>-0.650213</td>\n",
              "      <td>-0.202211</td>\n",
              "      <td>-0.803505</td>\n",
              "      <td>0.893199</td>\n",
              "      <td>-0.672256</td>\n",
              "      <td>-0.111402</td>\n",
              "      <td>0.855929</td>\n",
              "      <td>0.776909</td>\n",
              "      <td>0.867488</td>\n",
              "      <td>-0.884650</td>\n",
              "      <td>0.820824</td>\n",
              "      <td>-0.488467</td>\n",
              "      <td>-0.073526</td>\n",
              "      <td>0.762865</td>\n",
              "      <td>0.867347</td>\n",
              "      <td>-0.485455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.961906</td>\n",
              "      <td>-0.814359</td>\n",
              "      <td>0.961612</td>\n",
              "      <td>-0.975276</td>\n",
              "      <td>-0.971411</td>\n",
              "      <td>0.613383</td>\n",
              "      <td>0.115782</td>\n",
              "      <td>-0.849409</td>\n",
              "      <td>-0.914879</td>\n",
              "      <td>-0.895297</td>\n",
              "      <td>-0.833947</td>\n",
              "      <td>-0.530263</td>\n",
              "      <td>0.918845</td>\n",
              "      <td>0.955587</td>\n",
              "      <td>-0.771970</td>\n",
              "      <td>-0.561407</td>\n",
              "      <td>-0.315164</td>\n",
              "      <td>0.940156</td>\n",
              "      <td>0.371842</td>\n",
              "      <td>-0.507234</td>\n",
              "      <td>0.443981</td>\n",
              "      <td>-0.192466</td>\n",
              "      <td>-0.754045</td>\n",
              "      <td>0.145071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_DifferenceAverage</th>\n",
              "      <td>-0.503483</td>\n",
              "      <td>-0.485235</td>\n",
              "      <td>0.694299</td>\n",
              "      <td>-0.466085</td>\n",
              "      <td>0.855940</td>\n",
              "      <td>-0.508777</td>\n",
              "      <td>-0.150574</td>\n",
              "      <td>-0.777736</td>\n",
              "      <td>0.851898</td>\n",
              "      <td>-0.676657</td>\n",
              "      <td>-0.056061</td>\n",
              "      <td>0.830868</td>\n",
              "      <td>0.680635</td>\n",
              "      <td>0.825486</td>\n",
              "      <td>-0.769783</td>\n",
              "      <td>0.819339</td>\n",
              "      <td>-0.438460</td>\n",
              "      <td>-0.029180</td>\n",
              "      <td>0.749928</td>\n",
              "      <td>0.959810</td>\n",
              "      <td>-0.580218</td>\n",
              "      <td>0.961906</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.698618</td>\n",
              "      <td>0.894680</td>\n",
              "      <td>-0.930772</td>\n",
              "      <td>-0.920828</td>\n",
              "      <td>0.626130</td>\n",
              "      <td>0.025978</td>\n",
              "      <td>-0.895974</td>\n",
              "      <td>-0.909467</td>\n",
              "      <td>-0.904031</td>\n",
              "      <td>-0.756322</td>\n",
              "      <td>-0.482598</td>\n",
              "      <td>0.833662</td>\n",
              "      <td>0.865108</td>\n",
              "      <td>-0.663382</td>\n",
              "      <td>-0.467025</td>\n",
              "      <td>-0.307433</td>\n",
              "      <td>0.870588</td>\n",
              "      <td>0.397689</td>\n",
              "      <td>-0.444175</td>\n",
              "      <td>0.472657</td>\n",
              "      <td>-0.157090</td>\n",
              "      <td>-0.648112</td>\n",
              "      <td>0.143112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_JointEnergy</th>\n",
              "      <td>0.476386</td>\n",
              "      <td>0.462424</td>\n",
              "      <td>-0.546565</td>\n",
              "      <td>0.456104</td>\n",
              "      <td>-0.889731</td>\n",
              "      <td>0.610873</td>\n",
              "      <td>0.178195</td>\n",
              "      <td>0.666197</td>\n",
              "      <td>-0.726198</td>\n",
              "      <td>0.551802</td>\n",
              "      <td>0.117906</td>\n",
              "      <td>-0.702288</td>\n",
              "      <td>-0.724008</td>\n",
              "      <td>-0.703820</td>\n",
              "      <td>0.965295</td>\n",
              "      <td>-0.626282</td>\n",
              "      <td>0.454232</td>\n",
              "      <td>0.066693</td>\n",
              "      <td>-0.605236</td>\n",
              "      <td>-0.552213</td>\n",
              "      <td>0.233731</td>\n",
              "      <td>-0.814359</td>\n",
              "      <td>-0.698618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.879439</td>\n",
              "      <td>0.855978</td>\n",
              "      <td>0.859365</td>\n",
              "      <td>-0.412902</td>\n",
              "      <td>-0.274835</td>\n",
              "      <td>0.591428</td>\n",
              "      <td>0.715813</td>\n",
              "      <td>0.573538</td>\n",
              "      <td>0.964489</td>\n",
              "      <td>0.492200</td>\n",
              "      <td>-0.888626</td>\n",
              "      <td>-0.882628</td>\n",
              "      <td>0.948088</td>\n",
              "      <td>0.510075</td>\n",
              "      <td>0.199998</td>\n",
              "      <td>-0.901859</td>\n",
              "      <td>-0.293981</td>\n",
              "      <td>0.460759</td>\n",
              "      <td>-0.346096</td>\n",
              "      <td>0.161755</td>\n",
              "      <td>0.875173</td>\n",
              "      <td>-0.069231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_JointEntropy</th>\n",
              "      <td>-0.500397</td>\n",
              "      <td>-0.502426</td>\n",
              "      <td>0.600255</td>\n",
              "      <td>-0.499135</td>\n",
              "      <td>0.987007</td>\n",
              "      <td>-0.657568</td>\n",
              "      <td>-0.165473</td>\n",
              "      <td>-0.845984</td>\n",
              "      <td>0.895866</td>\n",
              "      <td>-0.728116</td>\n",
              "      <td>-0.075656</td>\n",
              "      <td>0.892330</td>\n",
              "      <td>0.825326</td>\n",
              "      <td>0.870736</td>\n",
              "      <td>-0.946466</td>\n",
              "      <td>0.814891</td>\n",
              "      <td>-0.528235</td>\n",
              "      <td>-0.039824</td>\n",
              "      <td>0.788479</td>\n",
              "      <td>0.768489</td>\n",
              "      <td>-0.308951</td>\n",
              "      <td>0.961612</td>\n",
              "      <td>0.894680</td>\n",
              "      <td>-0.879439</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.968658</td>\n",
              "      <td>-0.969628</td>\n",
              "      <td>0.521511</td>\n",
              "      <td>0.237118</td>\n",
              "      <td>-0.742192</td>\n",
              "      <td>-0.852025</td>\n",
              "      <td>-0.850521</td>\n",
              "      <td>-0.898943</td>\n",
              "      <td>-0.579194</td>\n",
              "      <td>0.982734</td>\n",
              "      <td>0.958310</td>\n",
              "      <td>-0.807138</td>\n",
              "      <td>-0.555909</td>\n",
              "      <td>-0.264094</td>\n",
              "      <td>0.949704</td>\n",
              "      <td>0.364644</td>\n",
              "      <td>-0.538186</td>\n",
              "      <td>0.433060</td>\n",
              "      <td>-0.230076</td>\n",
              "      <td>-0.784787</td>\n",
              "      <td>0.152261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Id</th>\n",
              "      <td>0.572306</td>\n",
              "      <td>0.552018</td>\n",
              "      <td>-0.715481</td>\n",
              "      <td>0.528602</td>\n",
              "      <td>-0.941998</td>\n",
              "      <td>0.618012</td>\n",
              "      <td>0.231201</td>\n",
              "      <td>0.838773</td>\n",
              "      <td>-0.838607</td>\n",
              "      <td>0.733959</td>\n",
              "      <td>0.153019</td>\n",
              "      <td>-0.859469</td>\n",
              "      <td>-0.777223</td>\n",
              "      <td>-0.802592</td>\n",
              "      <td>0.905884</td>\n",
              "      <td>-0.752621</td>\n",
              "      <td>0.570586</td>\n",
              "      <td>-0.019264</td>\n",
              "      <td>-0.706461</td>\n",
              "      <td>-0.802463</td>\n",
              "      <td>0.474470</td>\n",
              "      <td>-0.975276</td>\n",
              "      <td>-0.930772</td>\n",
              "      <td>0.855978</td>\n",
              "      <td>-0.968658</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999539</td>\n",
              "      <td>-0.624760</td>\n",
              "      <td>-0.100175</td>\n",
              "      <td>0.824054</td>\n",
              "      <td>0.920115</td>\n",
              "      <td>0.866923</td>\n",
              "      <td>0.895687</td>\n",
              "      <td>0.611296</td>\n",
              "      <td>-0.925097</td>\n",
              "      <td>-0.977967</td>\n",
              "      <td>0.823410</td>\n",
              "      <td>0.535524</td>\n",
              "      <td>0.307169</td>\n",
              "      <td>-0.985108</td>\n",
              "      <td>-0.371893</td>\n",
              "      <td>0.574177</td>\n",
              "      <td>-0.446300</td>\n",
              "      <td>0.260924</td>\n",
              "      <td>0.815983</td>\n",
              "      <td>-0.125006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idm</th>\n",
              "      <td>0.575070</td>\n",
              "      <td>0.555243</td>\n",
              "      <td>-0.713112</td>\n",
              "      <td>0.531427</td>\n",
              "      <td>-0.944335</td>\n",
              "      <td>0.623681</td>\n",
              "      <td>0.236324</td>\n",
              "      <td>0.840654</td>\n",
              "      <td>-0.834044</td>\n",
              "      <td>0.735970</td>\n",
              "      <td>0.159793</td>\n",
              "      <td>-0.858719</td>\n",
              "      <td>-0.782350</td>\n",
              "      <td>-0.797399</td>\n",
              "      <td>0.910581</td>\n",
              "      <td>-0.744146</td>\n",
              "      <td>0.579180</td>\n",
              "      <td>-0.023066</td>\n",
              "      <td>-0.700124</td>\n",
              "      <td>-0.786404</td>\n",
              "      <td>0.462260</td>\n",
              "      <td>-0.971411</td>\n",
              "      <td>-0.920828</td>\n",
              "      <td>0.859365</td>\n",
              "      <td>-0.969628</td>\n",
              "      <td>0.999539</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.618494</td>\n",
              "      <td>-0.108239</td>\n",
              "      <td>0.814026</td>\n",
              "      <td>0.916432</td>\n",
              "      <td>0.865184</td>\n",
              "      <td>0.897938</td>\n",
              "      <td>0.619552</td>\n",
              "      <td>-0.927881</td>\n",
              "      <td>-0.981050</td>\n",
              "      <td>0.824732</td>\n",
              "      <td>0.539947</td>\n",
              "      <td>0.307618</td>\n",
              "      <td>-0.986688</td>\n",
              "      <td>-0.368002</td>\n",
              "      <td>0.583014</td>\n",
              "      <td>-0.442088</td>\n",
              "      <td>0.270411</td>\n",
              "      <td>0.819078</td>\n",
              "      <td>-0.123610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Imc1</th>\n",
              "      <td>-0.322542</td>\n",
              "      <td>-0.313926</td>\n",
              "      <td>0.349951</td>\n",
              "      <td>-0.320692</td>\n",
              "      <td>0.400132</td>\n",
              "      <td>-0.230778</td>\n",
              "      <td>-0.178690</td>\n",
              "      <td>-0.405377</td>\n",
              "      <td>0.345602</td>\n",
              "      <td>-0.351002</td>\n",
              "      <td>-0.126943</td>\n",
              "      <td>0.381602</td>\n",
              "      <td>0.248980</td>\n",
              "      <td>0.307864</td>\n",
              "      <td>-0.374408</td>\n",
              "      <td>0.308634</td>\n",
              "      <td>-0.259994</td>\n",
              "      <td>0.026383</td>\n",
              "      <td>0.232354</td>\n",
              "      <td>0.596633</td>\n",
              "      <td>-0.713320</td>\n",
              "      <td>0.613383</td>\n",
              "      <td>0.626130</td>\n",
              "      <td>-0.412902</td>\n",
              "      <td>0.521511</td>\n",
              "      <td>-0.624760</td>\n",
              "      <td>-0.618494</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.642557</td>\n",
              "      <td>-0.530157</td>\n",
              "      <td>-0.569758</td>\n",
              "      <td>-0.476983</td>\n",
              "      <td>-0.495626</td>\n",
              "      <td>-0.256568</td>\n",
              "      <td>0.362848</td>\n",
              "      <td>0.577844</td>\n",
              "      <td>-0.511746</td>\n",
              "      <td>-0.221824</td>\n",
              "      <td>-0.155716</td>\n",
              "      <td>0.622539</td>\n",
              "      <td>-0.021854</td>\n",
              "      <td>-0.248369</td>\n",
              "      <td>0.045232</td>\n",
              "      <td>-0.024716</td>\n",
              "      <td>-0.515514</td>\n",
              "      <td>0.085813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Imc2</th>\n",
              "      <td>-0.098532</td>\n",
              "      <td>-0.106066</td>\n",
              "      <td>0.125131</td>\n",
              "      <td>-0.098991</td>\n",
              "      <td>0.358425</td>\n",
              "      <td>-0.361583</td>\n",
              "      <td>-0.012154</td>\n",
              "      <td>-0.226711</td>\n",
              "      <td>0.320411</td>\n",
              "      <td>-0.169062</td>\n",
              "      <td>-0.003648</td>\n",
              "      <td>0.270644</td>\n",
              "      <td>0.420433</td>\n",
              "      <td>0.331680</td>\n",
              "      <td>-0.370669</td>\n",
              "      <td>0.260734</td>\n",
              "      <td>-0.176436</td>\n",
              "      <td>-0.116299</td>\n",
              "      <td>0.312099</td>\n",
              "      <td>-0.054630</td>\n",
              "      <td>0.552051</td>\n",
              "      <td>0.115782</td>\n",
              "      <td>0.025978</td>\n",
              "      <td>-0.274835</td>\n",
              "      <td>0.237118</td>\n",
              "      <td>-0.100175</td>\n",
              "      <td>-0.108239</td>\n",
              "      <td>-0.642557</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.047264</td>\n",
              "      <td>-0.094354</td>\n",
              "      <td>-0.154092</td>\n",
              "      <td>-0.179375</td>\n",
              "      <td>-0.199440</td>\n",
              "      <td>0.395223</td>\n",
              "      <td>0.158344</td>\n",
              "      <td>-0.123408</td>\n",
              "      <td>-0.247275</td>\n",
              "      <td>-0.078243</td>\n",
              "      <td>0.093682</td>\n",
              "      <td>0.188394</td>\n",
              "      <td>-0.216045</td>\n",
              "      <td>0.183836</td>\n",
              "      <td>-0.221522</td>\n",
              "      <td>-0.106953</td>\n",
              "      <td>0.022468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idmn</th>\n",
              "      <td>0.584027</td>\n",
              "      <td>0.557954</td>\n",
              "      <td>-0.778277</td>\n",
              "      <td>0.501899</td>\n",
              "      <td>-0.717617</td>\n",
              "      <td>0.439331</td>\n",
              "      <td>0.376800</td>\n",
              "      <td>0.704240</td>\n",
              "      <td>-0.700796</td>\n",
              "      <td>0.616883</td>\n",
              "      <td>0.313912</td>\n",
              "      <td>-0.714959</td>\n",
              "      <td>-0.587520</td>\n",
              "      <td>-0.657093</td>\n",
              "      <td>0.661090</td>\n",
              "      <td>-0.642137</td>\n",
              "      <td>0.509219</td>\n",
              "      <td>-0.010583</td>\n",
              "      <td>-0.562358</td>\n",
              "      <td>-0.847703</td>\n",
              "      <td>0.631095</td>\n",
              "      <td>-0.849409</td>\n",
              "      <td>-0.895974</td>\n",
              "      <td>0.591428</td>\n",
              "      <td>-0.742192</td>\n",
              "      <td>0.824054</td>\n",
              "      <td>0.814026</td>\n",
              "      <td>-0.530157</td>\n",
              "      <td>-0.047264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960832</td>\n",
              "      <td>0.830854</td>\n",
              "      <td>0.634734</td>\n",
              "      <td>0.534241</td>\n",
              "      <td>-0.693723</td>\n",
              "      <td>-0.766021</td>\n",
              "      <td>0.568683</td>\n",
              "      <td>0.456721</td>\n",
              "      <td>0.423757</td>\n",
              "      <td>-0.767564</td>\n",
              "      <td>-0.394547</td>\n",
              "      <td>0.527296</td>\n",
              "      <td>-0.473664</td>\n",
              "      <td>0.313785</td>\n",
              "      <td>0.582220</td>\n",
              "      <td>-0.068102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idn</th>\n",
              "      <td>0.652907</td>\n",
              "      <td>0.643016</td>\n",
              "      <td>-0.815380</td>\n",
              "      <td>0.587760</td>\n",
              "      <td>-0.830314</td>\n",
              "      <td>0.543938</td>\n",
              "      <td>0.439501</td>\n",
              "      <td>0.798128</td>\n",
              "      <td>-0.752626</td>\n",
              "      <td>0.704130</td>\n",
              "      <td>0.378633</td>\n",
              "      <td>-0.790076</td>\n",
              "      <td>-0.690102</td>\n",
              "      <td>-0.699423</td>\n",
              "      <td>0.789544</td>\n",
              "      <td>-0.659827</td>\n",
              "      <td>0.625289</td>\n",
              "      <td>-0.047947</td>\n",
              "      <td>-0.596116</td>\n",
              "      <td>-0.801665</td>\n",
              "      <td>0.573892</td>\n",
              "      <td>-0.914879</td>\n",
              "      <td>-0.909467</td>\n",
              "      <td>0.715813</td>\n",
              "      <td>-0.852025</td>\n",
              "      <td>0.920115</td>\n",
              "      <td>0.916432</td>\n",
              "      <td>-0.569758</td>\n",
              "      <td>-0.094354</td>\n",
              "      <td>0.960832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.884135</td>\n",
              "      <td>0.753516</td>\n",
              "      <td>0.649187</td>\n",
              "      <td>-0.809647</td>\n",
              "      <td>-0.885228</td>\n",
              "      <td>0.686437</td>\n",
              "      <td>0.549008</td>\n",
              "      <td>0.464893</td>\n",
              "      <td>-0.882440</td>\n",
              "      <td>-0.390360</td>\n",
              "      <td>0.643657</td>\n",
              "      <td>-0.470964</td>\n",
              "      <td>0.398660</td>\n",
              "      <td>0.710353</td>\n",
              "      <td>-0.076726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_InverseVariance</th>\n",
              "      <td>0.522775</td>\n",
              "      <td>0.524320</td>\n",
              "      <td>-0.691626</td>\n",
              "      <td>0.491187</td>\n",
              "      <td>-0.829298</td>\n",
              "      <td>0.510945</td>\n",
              "      <td>0.207168</td>\n",
              "      <td>0.810571</td>\n",
              "      <td>-0.790627</td>\n",
              "      <td>0.721821</td>\n",
              "      <td>0.127038</td>\n",
              "      <td>-0.833669</td>\n",
              "      <td>-0.711120</td>\n",
              "      <td>-0.757530</td>\n",
              "      <td>0.720547</td>\n",
              "      <td>-0.723658</td>\n",
              "      <td>0.529278</td>\n",
              "      <td>-0.026384</td>\n",
              "      <td>-0.676549</td>\n",
              "      <td>-0.810599</td>\n",
              "      <td>0.466259</td>\n",
              "      <td>-0.895297</td>\n",
              "      <td>-0.904031</td>\n",
              "      <td>0.573538</td>\n",
              "      <td>-0.850521</td>\n",
              "      <td>0.866923</td>\n",
              "      <td>0.865184</td>\n",
              "      <td>-0.476983</td>\n",
              "      <td>-0.154092</td>\n",
              "      <td>0.830854</td>\n",
              "      <td>0.884135</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.625376</td>\n",
              "      <td>0.570559</td>\n",
              "      <td>-0.812322</td>\n",
              "      <td>-0.814939</td>\n",
              "      <td>0.483071</td>\n",
              "      <td>0.515801</td>\n",
              "      <td>0.384231</td>\n",
              "      <td>-0.777493</td>\n",
              "      <td>-0.374576</td>\n",
              "      <td>0.549323</td>\n",
              "      <td>-0.445343</td>\n",
              "      <td>0.326210</td>\n",
              "      <td>0.502624</td>\n",
              "      <td>-0.148818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_MaximumProbability</th>\n",
              "      <td>0.454791</td>\n",
              "      <td>0.438659</td>\n",
              "      <td>-0.584155</td>\n",
              "      <td>0.431302</td>\n",
              "      <td>-0.886244</td>\n",
              "      <td>0.547134</td>\n",
              "      <td>0.166522</td>\n",
              "      <td>0.723444</td>\n",
              "      <td>-0.727710</td>\n",
              "      <td>0.631967</td>\n",
              "      <td>0.098942</td>\n",
              "      <td>-0.747587</td>\n",
              "      <td>-0.697616</td>\n",
              "      <td>-0.701734</td>\n",
              "      <td>0.936342</td>\n",
              "      <td>-0.644828</td>\n",
              "      <td>0.483585</td>\n",
              "      <td>0.006927</td>\n",
              "      <td>-0.623120</td>\n",
              "      <td>-0.618898</td>\n",
              "      <td>0.312089</td>\n",
              "      <td>-0.833947</td>\n",
              "      <td>-0.756322</td>\n",
              "      <td>0.964489</td>\n",
              "      <td>-0.898943</td>\n",
              "      <td>0.895687</td>\n",
              "      <td>0.897938</td>\n",
              "      <td>-0.495626</td>\n",
              "      <td>-0.179375</td>\n",
              "      <td>0.634734</td>\n",
              "      <td>0.753516</td>\n",
              "      <td>0.625376</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.528761</td>\n",
              "      <td>-0.885512</td>\n",
              "      <td>-0.890351</td>\n",
              "      <td>0.908785</td>\n",
              "      <td>0.466731</td>\n",
              "      <td>0.189360</td>\n",
              "      <td>-0.931303</td>\n",
              "      <td>-0.319484</td>\n",
              "      <td>0.473543</td>\n",
              "      <td>-0.378007</td>\n",
              "      <td>0.173099</td>\n",
              "      <td>0.851068</td>\n",
              "      <td>-0.073702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_SumAverage</th>\n",
              "      <td>0.579089</td>\n",
              "      <td>0.585145</td>\n",
              "      <td>-0.602545</td>\n",
              "      <td>0.520984</td>\n",
              "      <td>-0.572732</td>\n",
              "      <td>0.447262</td>\n",
              "      <td>0.362106</td>\n",
              "      <td>0.784367</td>\n",
              "      <td>-0.443356</td>\n",
              "      <td>0.802186</td>\n",
              "      <td>0.422860</td>\n",
              "      <td>-0.676612</td>\n",
              "      <td>-0.670475</td>\n",
              "      <td>-0.364813</td>\n",
              "      <td>0.566336</td>\n",
              "      <td>-0.347849</td>\n",
              "      <td>0.987396</td>\n",
              "      <td>-0.449174</td>\n",
              "      <td>-0.367693</td>\n",
              "      <td>-0.324584</td>\n",
              "      <td>0.223806</td>\n",
              "      <td>-0.530263</td>\n",
              "      <td>-0.482598</td>\n",
              "      <td>0.492200</td>\n",
              "      <td>-0.579194</td>\n",
              "      <td>0.611296</td>\n",
              "      <td>0.619552</td>\n",
              "      <td>-0.256568</td>\n",
              "      <td>-0.199440</td>\n",
              "      <td>0.534241</td>\n",
              "      <td>0.649187</td>\n",
              "      <td>0.570559</td>\n",
              "      <td>0.528761</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.585777</td>\n",
              "      <td>-0.617952</td>\n",
              "      <td>0.484002</td>\n",
              "      <td>0.483030</td>\n",
              "      <td>0.436087</td>\n",
              "      <td>-0.617833</td>\n",
              "      <td>-0.493004</td>\n",
              "      <td>0.974014</td>\n",
              "      <td>-0.549973</td>\n",
              "      <td>0.892409</td>\n",
              "      <td>0.662526</td>\n",
              "      <td>-0.021370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_SumEntropy</th>\n",
              "      <td>-0.489627</td>\n",
              "      <td>-0.497025</td>\n",
              "      <td>0.577648</td>\n",
              "      <td>-0.493301</td>\n",
              "      <td>0.995903</td>\n",
              "      <td>-0.681167</td>\n",
              "      <td>-0.162044</td>\n",
              "      <td>-0.836576</td>\n",
              "      <td>0.903451</td>\n",
              "      <td>-0.716920</td>\n",
              "      <td>-0.076843</td>\n",
              "      <td>0.889579</td>\n",
              "      <td>0.850481</td>\n",
              "      <td>0.882340</td>\n",
              "      <td>-0.964422</td>\n",
              "      <td>0.818287</td>\n",
              "      <td>-0.531051</td>\n",
              "      <td>-0.051409</td>\n",
              "      <td>0.807210</td>\n",
              "      <td>0.696875</td>\n",
              "      <td>-0.178962</td>\n",
              "      <td>0.918845</td>\n",
              "      <td>0.833662</td>\n",
              "      <td>-0.888626</td>\n",
              "      <td>0.982734</td>\n",
              "      <td>-0.925097</td>\n",
              "      <td>-0.927881</td>\n",
              "      <td>0.362848</td>\n",
              "      <td>0.395223</td>\n",
              "      <td>-0.693723</td>\n",
              "      <td>-0.809647</td>\n",
              "      <td>-0.812322</td>\n",
              "      <td>-0.885512</td>\n",
              "      <td>-0.585777</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.930416</td>\n",
              "      <td>-0.794105</td>\n",
              "      <td>-0.574282</td>\n",
              "      <td>-0.265405</td>\n",
              "      <td>0.910893</td>\n",
              "      <td>0.399680</td>\n",
              "      <td>-0.546181</td>\n",
              "      <td>0.459966</td>\n",
              "      <td>-0.256025</td>\n",
              "      <td>-0.768954</td>\n",
              "      <td>0.144223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunEmphasis</th>\n",
              "      <td>-0.612788</td>\n",
              "      <td>-0.599231</td>\n",
              "      <td>0.694442</td>\n",
              "      <td>-0.584068</td>\n",
              "      <td>0.949601</td>\n",
              "      <td>-0.707898</td>\n",
              "      <td>-0.270393</td>\n",
              "      <td>-0.802270</td>\n",
              "      <td>0.824911</td>\n",
              "      <td>-0.681095</td>\n",
              "      <td>-0.204088</td>\n",
              "      <td>0.821723</td>\n",
              "      <td>0.810934</td>\n",
              "      <td>0.790963</td>\n",
              "      <td>-0.934845</td>\n",
              "      <td>0.717757</td>\n",
              "      <td>-0.584751</td>\n",
              "      <td>-0.010685</td>\n",
              "      <td>0.676316</td>\n",
              "      <td>0.715611</td>\n",
              "      <td>-0.405265</td>\n",
              "      <td>0.955587</td>\n",
              "      <td>0.865108</td>\n",
              "      <td>-0.882628</td>\n",
              "      <td>0.958310</td>\n",
              "      <td>-0.977967</td>\n",
              "      <td>-0.981050</td>\n",
              "      <td>0.577844</td>\n",
              "      <td>0.158344</td>\n",
              "      <td>-0.766021</td>\n",
              "      <td>-0.885228</td>\n",
              "      <td>-0.814939</td>\n",
              "      <td>-0.890351</td>\n",
              "      <td>-0.617952</td>\n",
              "      <td>0.930416</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.853881</td>\n",
              "      <td>-0.606632</td>\n",
              "      <td>-0.322905</td>\n",
              "      <td>0.982863</td>\n",
              "      <td>0.350127</td>\n",
              "      <td>-0.599137</td>\n",
              "      <td>0.418796</td>\n",
              "      <td>-0.275485</td>\n",
              "      <td>-0.859969</td>\n",
              "      <td>0.112990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LongRunEmphasis</th>\n",
              "      <td>0.497306</td>\n",
              "      <td>0.486413</td>\n",
              "      <td>-0.551579</td>\n",
              "      <td>0.489115</td>\n",
              "      <td>-0.801817</td>\n",
              "      <td>0.604427</td>\n",
              "      <td>0.213872</td>\n",
              "      <td>0.614075</td>\n",
              "      <td>-0.651617</td>\n",
              "      <td>0.511717</td>\n",
              "      <td>0.165875</td>\n",
              "      <td>-0.631004</td>\n",
              "      <td>-0.653917</td>\n",
              "      <td>-0.625636</td>\n",
              "      <td>0.874323</td>\n",
              "      <td>-0.557367</td>\n",
              "      <td>0.459234</td>\n",
              "      <td>0.024034</td>\n",
              "      <td>-0.528780</td>\n",
              "      <td>-0.526328</td>\n",
              "      <td>0.296669</td>\n",
              "      <td>-0.771970</td>\n",
              "      <td>-0.663382</td>\n",
              "      <td>0.948088</td>\n",
              "      <td>-0.807138</td>\n",
              "      <td>0.823410</td>\n",
              "      <td>0.824732</td>\n",
              "      <td>-0.511746</td>\n",
              "      <td>-0.123408</td>\n",
              "      <td>0.568683</td>\n",
              "      <td>0.686437</td>\n",
              "      <td>0.483071</td>\n",
              "      <td>0.908785</td>\n",
              "      <td>0.484002</td>\n",
              "      <td>-0.794105</td>\n",
              "      <td>-0.853881</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.493274</td>\n",
              "      <td>0.207867</td>\n",
              "      <td>-0.888722</td>\n",
              "      <td>-0.264891</td>\n",
              "      <td>0.460299</td>\n",
              "      <td>-0.318024</td>\n",
              "      <td>0.159340</td>\n",
              "      <td>0.942360</td>\n",
              "      <td>-0.053679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_GrayLevelNonUniformity</th>\n",
              "      <td>0.703748</td>\n",
              "      <td>0.848956</td>\n",
              "      <td>-0.535566</td>\n",
              "      <td>0.934321</td>\n",
              "      <td>-0.588079</td>\n",
              "      <td>0.573612</td>\n",
              "      <td>0.370650</td>\n",
              "      <td>0.459499</td>\n",
              "      <td>-0.483713</td>\n",
              "      <td>0.370787</td>\n",
              "      <td>0.366030</td>\n",
              "      <td>-0.452314</td>\n",
              "      <td>-0.553767</td>\n",
              "      <td>-0.444192</td>\n",
              "      <td>0.597897</td>\n",
              "      <td>-0.384417</td>\n",
              "      <td>0.492918</td>\n",
              "      <td>-0.032595</td>\n",
              "      <td>-0.352709</td>\n",
              "      <td>-0.365234</td>\n",
              "      <td>0.259994</td>\n",
              "      <td>-0.561407</td>\n",
              "      <td>-0.467025</td>\n",
              "      <td>0.510075</td>\n",
              "      <td>-0.555909</td>\n",
              "      <td>0.535524</td>\n",
              "      <td>0.539947</td>\n",
              "      <td>-0.221824</td>\n",
              "      <td>-0.247275</td>\n",
              "      <td>0.456721</td>\n",
              "      <td>0.549008</td>\n",
              "      <td>0.515801</td>\n",
              "      <td>0.466731</td>\n",
              "      <td>0.483030</td>\n",
              "      <td>-0.574282</td>\n",
              "      <td>-0.606632</td>\n",
              "      <td>0.493274</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.741332</td>\n",
              "      <td>-0.545907</td>\n",
              "      <td>-0.208822</td>\n",
              "      <td>0.529680</td>\n",
              "      <td>-0.247476</td>\n",
              "      <td>0.354319</td>\n",
              "      <td>0.560118</td>\n",
              "      <td>-0.031632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_RunLengthNonUniformity</th>\n",
              "      <td>0.686436</td>\n",
              "      <td>0.886036</td>\n",
              "      <td>-0.552963</td>\n",
              "      <td>0.875987</td>\n",
              "      <td>-0.279184</td>\n",
              "      <td>0.217207</td>\n",
              "      <td>0.509335</td>\n",
              "      <td>0.315307</td>\n",
              "      <td>-0.185369</td>\n",
              "      <td>0.312430</td>\n",
              "      <td>0.530460</td>\n",
              "      <td>-0.244936</td>\n",
              "      <td>-0.267019</td>\n",
              "      <td>-0.125819</td>\n",
              "      <td>0.277025</td>\n",
              "      <td>-0.110787</td>\n",
              "      <td>0.465564</td>\n",
              "      <td>-0.255467</td>\n",
              "      <td>-0.063258</td>\n",
              "      <td>-0.250079</td>\n",
              "      <td>0.349674</td>\n",
              "      <td>-0.315164</td>\n",
              "      <td>-0.307433</td>\n",
              "      <td>0.199998</td>\n",
              "      <td>-0.264094</td>\n",
              "      <td>0.307169</td>\n",
              "      <td>0.307618</td>\n",
              "      <td>-0.155716</td>\n",
              "      <td>-0.078243</td>\n",
              "      <td>0.423757</td>\n",
              "      <td>0.464893</td>\n",
              "      <td>0.384231</td>\n",
              "      <td>0.189360</td>\n",
              "      <td>0.436087</td>\n",
              "      <td>-0.265405</td>\n",
              "      <td>-0.322905</td>\n",
              "      <td>0.207867</td>\n",
              "      <td>0.741332</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.296990</td>\n",
              "      <td>-0.157784</td>\n",
              "      <td>0.489997</td>\n",
              "      <td>-0.196061</td>\n",
              "      <td>0.449251</td>\n",
              "      <td>0.294365</td>\n",
              "      <td>0.007283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_RunPercentage</th>\n",
              "      <td>-0.584467</td>\n",
              "      <td>-0.563329</td>\n",
              "      <td>0.708983</td>\n",
              "      <td>-0.546164</td>\n",
              "      <td>0.927234</td>\n",
              "      <td>-0.627625</td>\n",
              "      <td>-0.253419</td>\n",
              "      <td>-0.802209</td>\n",
              "      <td>0.795631</td>\n",
              "      <td>-0.699692</td>\n",
              "      <td>-0.187595</td>\n",
              "      <td>0.813958</td>\n",
              "      <td>0.762311</td>\n",
              "      <td>0.757318</td>\n",
              "      <td>-0.922595</td>\n",
              "      <td>0.700571</td>\n",
              "      <td>-0.582092</td>\n",
              "      <td>0.033547</td>\n",
              "      <td>0.659093</td>\n",
              "      <td>0.727081</td>\n",
              "      <td>-0.444868</td>\n",
              "      <td>0.940156</td>\n",
              "      <td>0.870588</td>\n",
              "      <td>-0.901859</td>\n",
              "      <td>0.949704</td>\n",
              "      <td>-0.985108</td>\n",
              "      <td>-0.986688</td>\n",
              "      <td>0.622539</td>\n",
              "      <td>0.093682</td>\n",
              "      <td>-0.767564</td>\n",
              "      <td>-0.882440</td>\n",
              "      <td>-0.777493</td>\n",
              "      <td>-0.931303</td>\n",
              "      <td>-0.617833</td>\n",
              "      <td>0.910893</td>\n",
              "      <td>0.982863</td>\n",
              "      <td>-0.888722</td>\n",
              "      <td>-0.545907</td>\n",
              "      <td>-0.296990</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.352471</td>\n",
              "      <td>-0.581286</td>\n",
              "      <td>0.423358</td>\n",
              "      <td>-0.258878</td>\n",
              "      <td>-0.881855</td>\n",
              "      <td>0.099219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n",
              "      <td>-0.250724</td>\n",
              "      <td>-0.232338</td>\n",
              "      <td>0.383714</td>\n",
              "      <td>-0.201632</td>\n",
              "      <td>0.389505</td>\n",
              "      <td>-0.221798</td>\n",
              "      <td>-0.078548</td>\n",
              "      <td>-0.544283</td>\n",
              "      <td>0.490467</td>\n",
              "      <td>-0.550933</td>\n",
              "      <td>-0.050239</td>\n",
              "      <td>0.569941</td>\n",
              "      <td>0.380493</td>\n",
              "      <td>0.494392</td>\n",
              "      <td>-0.338585</td>\n",
              "      <td>0.555571</td>\n",
              "      <td>-0.403332</td>\n",
              "      <td>0.285088</td>\n",
              "      <td>0.586389</td>\n",
              "      <td>0.374164</td>\n",
              "      <td>-0.061591</td>\n",
              "      <td>0.371842</td>\n",
              "      <td>0.397689</td>\n",
              "      <td>-0.293981</td>\n",
              "      <td>0.364644</td>\n",
              "      <td>-0.371893</td>\n",
              "      <td>-0.368002</td>\n",
              "      <td>-0.021854</td>\n",
              "      <td>0.188394</td>\n",
              "      <td>-0.394547</td>\n",
              "      <td>-0.390360</td>\n",
              "      <td>-0.374576</td>\n",
              "      <td>-0.319484</td>\n",
              "      <td>-0.493004</td>\n",
              "      <td>0.399680</td>\n",
              "      <td>0.350127</td>\n",
              "      <td>-0.264891</td>\n",
              "      <td>-0.208822</td>\n",
              "      <td>-0.157784</td>\n",
              "      <td>0.352471</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.369253</td>\n",
              "      <td>0.990360</td>\n",
              "      <td>-0.293488</td>\n",
              "      <td>-0.297764</td>\n",
              "      <td>-0.038763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_HighGrayLevelRunEmphasis</th>\n",
              "      <td>0.628312</td>\n",
              "      <td>0.642114</td>\n",
              "      <td>-0.594257</td>\n",
              "      <td>0.570943</td>\n",
              "      <td>-0.540717</td>\n",
              "      <td>0.493762</td>\n",
              "      <td>0.440329</td>\n",
              "      <td>0.707943</td>\n",
              "      <td>-0.404223</td>\n",
              "      <td>0.701308</td>\n",
              "      <td>0.516835</td>\n",
              "      <td>-0.588337</td>\n",
              "      <td>-0.645265</td>\n",
              "      <td>-0.313812</td>\n",
              "      <td>0.544272</td>\n",
              "      <td>-0.281165</td>\n",
              "      <td>0.992243</td>\n",
              "      <td>-0.376598</td>\n",
              "      <td>-0.287163</td>\n",
              "      <td>-0.284007</td>\n",
              "      <td>0.233171</td>\n",
              "      <td>-0.507234</td>\n",
              "      <td>-0.444175</td>\n",
              "      <td>0.460759</td>\n",
              "      <td>-0.538186</td>\n",
              "      <td>0.574177</td>\n",
              "      <td>0.583014</td>\n",
              "      <td>-0.248369</td>\n",
              "      <td>-0.216045</td>\n",
              "      <td>0.527296</td>\n",
              "      <td>0.643657</td>\n",
              "      <td>0.549323</td>\n",
              "      <td>0.473543</td>\n",
              "      <td>0.974014</td>\n",
              "      <td>-0.546181</td>\n",
              "      <td>-0.599137</td>\n",
              "      <td>0.460299</td>\n",
              "      <td>0.529680</td>\n",
              "      <td>0.489997</td>\n",
              "      <td>-0.581286</td>\n",
              "      <td>-0.369253</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.427725</td>\n",
              "      <td>0.927886</td>\n",
              "      <td>0.659637</td>\n",
              "      <td>-0.027433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunLowGrayLevelEmphasis</th>\n",
              "      <td>-0.299613</td>\n",
              "      <td>-0.280951</td>\n",
              "      <td>0.441996</td>\n",
              "      <td>-0.249645</td>\n",
              "      <td>0.450367</td>\n",
              "      <td>-0.256327</td>\n",
              "      <td>-0.105321</td>\n",
              "      <td>-0.611950</td>\n",
              "      <td>0.536337</td>\n",
              "      <td>-0.614837</td>\n",
              "      <td>-0.073831</td>\n",
              "      <td>0.632470</td>\n",
              "      <td>0.434343</td>\n",
              "      <td>0.534176</td>\n",
              "      <td>-0.395662</td>\n",
              "      <td>0.590748</td>\n",
              "      <td>-0.460021</td>\n",
              "      <td>0.301363</td>\n",
              "      <td>0.614260</td>\n",
              "      <td>0.443686</td>\n",
              "      <td>-0.115019</td>\n",
              "      <td>0.443981</td>\n",
              "      <td>0.472657</td>\n",
              "      <td>-0.346096</td>\n",
              "      <td>0.433060</td>\n",
              "      <td>-0.446300</td>\n",
              "      <td>-0.442088</td>\n",
              "      <td>0.045232</td>\n",
              "      <td>0.183836</td>\n",
              "      <td>-0.473664</td>\n",
              "      <td>-0.470964</td>\n",
              "      <td>-0.445343</td>\n",
              "      <td>-0.378007</td>\n",
              "      <td>-0.549973</td>\n",
              "      <td>0.459966</td>\n",
              "      <td>0.418796</td>\n",
              "      <td>-0.318024</td>\n",
              "      <td>-0.247476</td>\n",
              "      <td>-0.196061</td>\n",
              "      <td>0.423358</td>\n",
              "      <td>0.990360</td>\n",
              "      <td>-0.427725</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.336730</td>\n",
              "      <td>-0.353854</td>\n",
              "      <td>-0.027804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n",
              "      <td>0.474408</td>\n",
              "      <td>0.493900</td>\n",
              "      <td>-0.411223</td>\n",
              "      <td>0.411277</td>\n",
              "      <td>-0.237753</td>\n",
              "      <td>0.263952</td>\n",
              "      <td>0.420530</td>\n",
              "      <td>0.525309</td>\n",
              "      <td>-0.129769</td>\n",
              "      <td>0.570359</td>\n",
              "      <td>0.529051</td>\n",
              "      <td>-0.371151</td>\n",
              "      <td>-0.414028</td>\n",
              "      <td>-0.038332</td>\n",
              "      <td>0.245504</td>\n",
              "      <td>-0.024420</td>\n",
              "      <td>0.920744</td>\n",
              "      <td>-0.453301</td>\n",
              "      <td>-0.052807</td>\n",
              "      <td>-0.024844</td>\n",
              "      <td>0.081893</td>\n",
              "      <td>-0.192466</td>\n",
              "      <td>-0.157090</td>\n",
              "      <td>0.161755</td>\n",
              "      <td>-0.230076</td>\n",
              "      <td>0.260924</td>\n",
              "      <td>0.270411</td>\n",
              "      <td>-0.024716</td>\n",
              "      <td>-0.221522</td>\n",
              "      <td>0.313785</td>\n",
              "      <td>0.398660</td>\n",
              "      <td>0.326210</td>\n",
              "      <td>0.173099</td>\n",
              "      <td>0.892409</td>\n",
              "      <td>-0.256025</td>\n",
              "      <td>-0.275485</td>\n",
              "      <td>0.159340</td>\n",
              "      <td>0.354319</td>\n",
              "      <td>0.449251</td>\n",
              "      <td>-0.258878</td>\n",
              "      <td>-0.293488</td>\n",
              "      <td>0.927886</td>\n",
              "      <td>-0.336730</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.373071</td>\n",
              "      <td>0.022276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LongRunHighGrayLevelEmphasis</th>\n",
              "      <td>0.585460</td>\n",
              "      <td>0.585071</td>\n",
              "      <td>-0.599629</td>\n",
              "      <td>0.574245</td>\n",
              "      <td>-0.778907</td>\n",
              "      <td>0.650730</td>\n",
              "      <td>0.286276</td>\n",
              "      <td>0.646165</td>\n",
              "      <td>-0.620687</td>\n",
              "      <td>0.560885</td>\n",
              "      <td>0.286415</td>\n",
              "      <td>-0.628272</td>\n",
              "      <td>-0.704878</td>\n",
              "      <td>-0.571115</td>\n",
              "      <td>0.836964</td>\n",
              "      <td>-0.515813</td>\n",
              "      <td>0.661135</td>\n",
              "      <td>-0.081187</td>\n",
              "      <td>-0.489805</td>\n",
              "      <td>-0.501480</td>\n",
              "      <td>0.331643</td>\n",
              "      <td>-0.754045</td>\n",
              "      <td>-0.648112</td>\n",
              "      <td>0.875173</td>\n",
              "      <td>-0.784787</td>\n",
              "      <td>0.815983</td>\n",
              "      <td>0.819078</td>\n",
              "      <td>-0.515514</td>\n",
              "      <td>-0.106953</td>\n",
              "      <td>0.582220</td>\n",
              "      <td>0.710353</td>\n",
              "      <td>0.502624</td>\n",
              "      <td>0.851068</td>\n",
              "      <td>0.662526</td>\n",
              "      <td>-0.768954</td>\n",
              "      <td>-0.859969</td>\n",
              "      <td>0.942360</td>\n",
              "      <td>0.560118</td>\n",
              "      <td>0.294365</td>\n",
              "      <td>-0.881855</td>\n",
              "      <td>-0.297764</td>\n",
              "      <td>0.659637</td>\n",
              "      <td>-0.353854</td>\n",
              "      <td>0.373071</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.032349</td>\n",
              "      <td>-0.017626</td>\n",
              "      <td>-0.006960</td>\n",
              "      <td>0.004208</td>\n",
              "      <td>0.143891</td>\n",
              "      <td>-0.120810</td>\n",
              "      <td>0.081424</td>\n",
              "      <td>-0.107266</td>\n",
              "      <td>0.156567</td>\n",
              "      <td>-0.070635</td>\n",
              "      <td>0.103321</td>\n",
              "      <td>0.135961</td>\n",
              "      <td>0.141711</td>\n",
              "      <td>0.161385</td>\n",
              "      <td>-0.101014</td>\n",
              "      <td>0.157107</td>\n",
              "      <td>-0.022105</td>\n",
              "      <td>-0.143943</td>\n",
              "      <td>0.157821</td>\n",
              "      <td>0.138825</td>\n",
              "      <td>-0.026338</td>\n",
              "      <td>0.145071</td>\n",
              "      <td>0.143112</td>\n",
              "      <td>-0.069231</td>\n",
              "      <td>0.152261</td>\n",
              "      <td>-0.125006</td>\n",
              "      <td>-0.123610</td>\n",
              "      <td>0.085813</td>\n",
              "      <td>0.022468</td>\n",
              "      <td>-0.068102</td>\n",
              "      <td>-0.076726</td>\n",
              "      <td>-0.148818</td>\n",
              "      <td>-0.073702</td>\n",
              "      <td>-0.021370</td>\n",
              "      <td>0.144223</td>\n",
              "      <td>0.112990</td>\n",
              "      <td>-0.053679</td>\n",
              "      <td>-0.031632</td>\n",
              "      <td>0.007283</td>\n",
              "      <td>0.099219</td>\n",
              "      <td>-0.038763</td>\n",
              "      <td>-0.027433</td>\n",
              "      <td>-0.027804</td>\n",
              "      <td>0.022276</td>\n",
              "      <td>-0.059722</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              original_shape_Maximum3DDiameter  ...       age\n",
              "original_shape_Maximum3DDiameter                                      1.000000  ...  0.032349\n",
              "original_shape_SurfaceArea                                            0.873098  ... -0.017626\n",
              "original_shape_SurfaceVolumeRatio                                    -0.631682  ... -0.006960\n",
              "original_shape_VoxelVolume                                            0.778222  ...  0.004208\n",
              "original_firstorder_Entropy                                          -0.517604  ...  0.143891\n",
              "original_firstorder_Kurtosis                                          0.485807  ... -0.120810\n",
              "original_firstorder_Maximum                                           0.477864  ...  0.081424\n",
              "original_firstorder_Mean                                              0.483936  ... -0.107266\n",
              "original_firstorder_MeanAbsoluteDeviation                            -0.410241  ...  0.156567\n",
              "original_firstorder_Median                                            0.428488  ... -0.070635\n",
              "original_firstorder_Range                                             0.496094  ...  0.103321\n",
              "original_firstorder_RootMeanSquared                                  -0.434021  ...  0.135961\n",
              "original_firstorder_Skewness                                         -0.484912  ...  0.141711\n",
              "original_firstorder_StandardDeviation                                -0.357023  ...  0.161385\n",
              "original_firstorder_Uniformity                                        0.531525  ... -0.101014\n",
              "original_firstorder_Variance                                         -0.302514  ...  0.157107\n",
              "original_glcm_Autocorrelation                                         0.598807  ... -0.022105\n",
              "original_glcm_ClusterShade                                           -0.152939  ... -0.143943\n",
              "original_glcm_ClusterTendency                                        -0.244392  ...  0.157821\n",
              "original_glcm_Contrast                                               -0.395961  ...  0.138825\n",
              "original_glcm_Correlation                                             0.423478  ... -0.026338\n",
              "original_glcm_DifferenceEntropy                                      -0.562465  ...  0.145071\n",
              "original_glcm_DifferenceAverage                                      -0.503483  ...  0.143112\n",
              "original_glcm_JointEnergy                                             0.476386  ... -0.069231\n",
              "original_glcm_JointEntropy                                           -0.500397  ...  0.152261\n",
              "original_glcm_Id                                                      0.572306  ... -0.125006\n",
              "original_glcm_Idm                                                     0.575070  ... -0.123610\n",
              "original_glcm_Imc1                                                   -0.322542  ...  0.085813\n",
              "original_glcm_Imc2                                                   -0.098532  ...  0.022468\n",
              "original_glcm_Idmn                                                    0.584027  ... -0.068102\n",
              "original_glcm_Idn                                                     0.652907  ... -0.076726\n",
              "original_glcm_InverseVariance                                         0.522775  ... -0.148818\n",
              "original_glcm_MaximumProbability                                      0.454791  ... -0.073702\n",
              "original_glcm_SumAverage                                              0.579089  ... -0.021370\n",
              "original_glcm_SumEntropy                                             -0.489627  ...  0.144223\n",
              "original_glrlm_ShortRunEmphasis                                      -0.612788  ...  0.112990\n",
              "original_glrlm_LongRunEmphasis                                        0.497306  ... -0.053679\n",
              "original_glrlm_GrayLevelNonUniformity                                 0.703748  ... -0.031632\n",
              "original_glrlm_RunLengthNonUniformity                                 0.686436  ...  0.007283\n",
              "original_glrlm_RunPercentage                                         -0.584467  ...  0.099219\n",
              "original_glrlm_LowGrayLevelRunEmphasis                               -0.250724  ... -0.038763\n",
              "original_glrlm_HighGrayLevelRunEmphasis                               0.628312  ... -0.027433\n",
              "original_glrlm_ShortRunLowGrayLevelEmphasis                          -0.299613  ... -0.027804\n",
              "original_glrlm_ShortRunHighGrayLevelEmphasis                          0.474408  ...  0.022276\n",
              "original_glrlm_LongRunHighGrayLevelEmphasis                           0.585460  ... -0.059722\n",
              "age                                                                   0.032349  ...  1.000000\n",
              "\n",
              "[46 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBM9GMVXpH4Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "outputId": "099a63ac-7fc4-428a-ee22-62d01e1fd31b"
      },
      "source": [
        "#Set up the matplotlib figure\r\n",
        "f, ax = plt.subplots(figsize=(30, 15))\r\n",
        "\r\n",
        "#Generate a mask for the upper triangle\r\n",
        "mask = np.triu(np.ones_like(corr, dtype=bool))\r\n",
        "\r\n",
        "#Generate a custom diverging colormap\r\n",
        "cmap = sns.diverging_palette(220, 20, as_cmap=True)\r\n",
        "sns.heatmap(corr, cmap=cmap, mask=mask, vmin=0, vmax=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ae3ac09e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akIfqplb3eyx"
      },
      "source": [
        "We can notice that some features are highly correlated."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nz46YEHFh267"
      },
      "source": [
        "To assess the multicollinearity, we can compute the **Variance Inflation Factor** (VIF). Its measures how much the variance of an estimated regression coefficient is increased because of collinearity. When VIF is high (>10), there is high multicollinearity and instability of the\r\n",
        "coefficients."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "11qD_wFnoyts",
        "outputId": "67e4f8c5-e366-48e5-fb20-909daa0b9af7"
      },
      "source": [
        "# Compute VIF\r\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\r\n",
        "\r\n",
        "vif = pd.DataFrame()\r\n",
        "vif[\"variables\"] = x_train_model_numeric.columns\r\n",
        "vif[\"VIF\"] = [variance_inflation_factor(x_train_model_numeric.values, i) for i in range(x_train_model_numeric.shape[1])]\r\n",
        "vif"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>variables</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>original_shape_Maximum3DDiameter</td>\n",
              "      <td>7.358174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>original_shape_SurfaceArea</td>\n",
              "      <td>31.329542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>original_shape_SurfaceVolumeRatio</td>\n",
              "      <td>29.040151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>original_shape_VoxelVolume</td>\n",
              "      <td>77.513886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>original_firstorder_Entropy</td>\n",
              "      <td>2617.574770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>original_firstorder_Kurtosis</td>\n",
              "      <td>7.615059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>original_firstorder_Maximum</td>\n",
              "      <td>3447.061600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>original_firstorder_Mean</td>\n",
              "      <td>1068.634467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>original_firstorder_MeanAbsoluteDeviation</td>\n",
              "      <td>652.344237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>original_firstorder_Median</td>\n",
              "      <td>183.104135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>original_firstorder_Range</td>\n",
              "      <td>4071.601427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>original_firstorder_RootMeanSquared</td>\n",
              "      <td>365.055604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>original_firstorder_Skewness</td>\n",
              "      <td>14.912731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>original_firstorder_StandardDeviation</td>\n",
              "      <td>579.084513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>original_firstorder_Uniformity</td>\n",
              "      <td>1586.538373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>original_firstorder_Variance</td>\n",
              "      <td>1179.536661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>original_glcm_Autocorrelation</td>\n",
              "      <td>1748.601272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>original_glcm_ClusterShade</td>\n",
              "      <td>15.425639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>original_glcm_ClusterTendency</td>\n",
              "      <td>525.775025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>original_glcm_Contrast</td>\n",
              "      <td>2816.286131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>original_glcm_Correlation</td>\n",
              "      <td>22.552970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>original_glcm_DifferenceEntropy</td>\n",
              "      <td>727.024393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>original_glcm_DifferenceAverage</td>\n",
              "      <td>22437.872959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>original_glcm_JointEnergy</td>\n",
              "      <td>1023.579546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>original_glcm_JointEntropy</td>\n",
              "      <td>2861.027620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>original_glcm_Id</td>\n",
              "      <td>453499.854944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>original_glcm_Idm</td>\n",
              "      <td>374714.621616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>original_glcm_Imc1</td>\n",
              "      <td>133.995265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>original_glcm_Imc2</td>\n",
              "      <td>20.912638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>original_glcm_Idmn</td>\n",
              "      <td>195.271138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>original_glcm_Idn</td>\n",
              "      <td>575.735738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>original_glcm_InverseVariance</td>\n",
              "      <td>1325.128518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>original_glcm_MaximumProbability</td>\n",
              "      <td>62.561595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>original_glcm_SumAverage</td>\n",
              "      <td>1032.808703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>original_glcm_SumEntropy</td>\n",
              "      <td>2314.568540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>original_glrlm_ShortRunEmphasis</td>\n",
              "      <td>445.932090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>original_glrlm_LongRunEmphasis</td>\n",
              "      <td>329.653356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>original_glrlm_GrayLevelNonUniformity</td>\n",
              "      <td>27.413517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>original_glrlm_RunLengthNonUniformity</td>\n",
              "      <td>21.890140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>original_glrlm_RunPercentage</td>\n",
              "      <td>4066.525217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>original_glrlm_LowGrayLevelRunEmphasis</td>\n",
              "      <td>177.819462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>original_glrlm_HighGrayLevelRunEmphasis</td>\n",
              "      <td>2548.683450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>original_glrlm_ShortRunLowGrayLevelEmphasis</td>\n",
              "      <td>194.103882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>original_glrlm_ShortRunHighGrayLevelEmphasis</td>\n",
              "      <td>925.397420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>original_glrlm_LongRunHighGrayLevelEmphasis</td>\n",
              "      <td>226.580082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>age</td>\n",
              "      <td>1.224793</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       variables            VIF\n",
              "0               original_shape_Maximum3DDiameter       7.358174\n",
              "1                     original_shape_SurfaceArea      31.329542\n",
              "2              original_shape_SurfaceVolumeRatio      29.040151\n",
              "3                     original_shape_VoxelVolume      77.513886\n",
              "4                    original_firstorder_Entropy    2617.574770\n",
              "5                   original_firstorder_Kurtosis       7.615059\n",
              "6                    original_firstorder_Maximum    3447.061600\n",
              "7                       original_firstorder_Mean    1068.634467\n",
              "8      original_firstorder_MeanAbsoluteDeviation     652.344237\n",
              "9                     original_firstorder_Median     183.104135\n",
              "10                     original_firstorder_Range    4071.601427\n",
              "11           original_firstorder_RootMeanSquared     365.055604\n",
              "12                  original_firstorder_Skewness      14.912731\n",
              "13         original_firstorder_StandardDeviation     579.084513\n",
              "14                original_firstorder_Uniformity    1586.538373\n",
              "15                  original_firstorder_Variance    1179.536661\n",
              "16                 original_glcm_Autocorrelation    1748.601272\n",
              "17                    original_glcm_ClusterShade      15.425639\n",
              "18                 original_glcm_ClusterTendency     525.775025\n",
              "19                        original_glcm_Contrast    2816.286131\n",
              "20                     original_glcm_Correlation      22.552970\n",
              "21               original_glcm_DifferenceEntropy     727.024393\n",
              "22               original_glcm_DifferenceAverage   22437.872959\n",
              "23                     original_glcm_JointEnergy    1023.579546\n",
              "24                    original_glcm_JointEntropy    2861.027620\n",
              "25                              original_glcm_Id  453499.854944\n",
              "26                             original_glcm_Idm  374714.621616\n",
              "27                            original_glcm_Imc1     133.995265\n",
              "28                            original_glcm_Imc2      20.912638\n",
              "29                            original_glcm_Idmn     195.271138\n",
              "30                             original_glcm_Idn     575.735738\n",
              "31                 original_glcm_InverseVariance    1325.128518\n",
              "32              original_glcm_MaximumProbability      62.561595\n",
              "33                      original_glcm_SumAverage    1032.808703\n",
              "34                      original_glcm_SumEntropy    2314.568540\n",
              "35               original_glrlm_ShortRunEmphasis     445.932090\n",
              "36                original_glrlm_LongRunEmphasis     329.653356\n",
              "37         original_glrlm_GrayLevelNonUniformity      27.413517\n",
              "38         original_glrlm_RunLengthNonUniformity      21.890140\n",
              "39                  original_glrlm_RunPercentage    4066.525217\n",
              "40        original_glrlm_LowGrayLevelRunEmphasis     177.819462\n",
              "41       original_glrlm_HighGrayLevelRunEmphasis    2548.683450\n",
              "42   original_glrlm_ShortRunLowGrayLevelEmphasis     194.103882\n",
              "43  original_glrlm_ShortRunHighGrayLevelEmphasis     925.397420\n",
              "44   original_glrlm_LongRunHighGrayLevelEmphasis     226.580082\n",
              "45                                           age       1.224793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WBHoAW6QdZhY"
      },
      "source": [
        "For several variables, the VIF is > 100, meaning that more of 99% of their variance is explained by the other variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqiuvXRLfGzs"
      },
      "source": [
        "Afer analysing the pairwise correlations and the VIFs, I decided to remove the following variables from the model: \r\n",
        "\r\n",
        "*original_glcm_Id,original_glcm_Idm, original_firstorder_Range, original_glcm_JointEntropy, original_glrlm_HighGrayLevelRunEmphasis, original_firstorder_Entropy, original_firstorder_Uniformity, original_firstorder_Variance, original_glcm_Autocorrelation, original_glcm_DifferenceAverage, original_glcm_DifferenceEntropy, original_glcm_Idn, original_firstorder_MeanAbsoluteDeviation, original_firstorder_RootMeanSquared, original_glrlm_RunPercentage, original_glrlm_ShortRunLowGrayLevelEmphasis, original_glcm_SumEntropy, original_glcm_SumAverage, original_firstorder_StandardDeviation, original_firstorder_Mean, original_glcm_JointEnergy,original_shape_VoxelVolume,original_glrlm_ShortRunEmphasis, original_glrlm_LongRunHighGrayLevelEmphasis,\r\n",
        "original_shape_SurfaceArea,original_glcm_Contrast, original_glcm_Imc1, original_glrlm_LongRunEmphasis.*\r\n",
        "\r\n",
        "Pairwise correlations between the remaining variables are <0.85 and VIFs are < 10."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQFAH7okfuoG"
      },
      "source": [
        "x_train_model_2 = x_train_model_.drop(columns=[\"original_glcm_Id\",\"original_glcm_Idm\",\"original_firstorder_Range\", \r\n",
        "                                               \"original_glcm_JointEntropy\", \"original_glrlm_HighGrayLevelRunEmphasis\",\r\n",
        "                                               \"original_firstorder_Entropy\",\"original_firstorder_Uniformity\", \r\n",
        "                                               \"original_firstorder_Variance\",\"original_glcm_Autocorrelation\",\r\n",
        "                                               \"original_glcm_DifferenceAverage\",\"original_glcm_DifferenceEntropy\",\r\n",
        "                                               \"original_glcm_Idn\",\"original_firstorder_MeanAbsoluteDeviation\",\r\n",
        "                                               \"original_firstorder_RootMeanSquared\",\"original_glrlm_RunPercentage\",\r\n",
        "                                               \"original_glrlm_ShortRunLowGrayLevelEmphasis\",\"original_glcm_SumEntropy\",\r\n",
        "                                               \"original_glcm_SumAverage\",\"original_firstorder_StandardDeviation\",\r\n",
        "                                               \"original_firstorder_Mean\",\"original_glcm_JointEnergy\",\"original_shape_VoxelVolume\",\r\n",
        "                                               \"original_glrlm_ShortRunEmphasis\", \"original_glrlm_LongRunHighGrayLevelEmphasis\",\r\n",
        "                                               \"original_shape_SurfaceArea\",\"original_glcm_Contrast\",\"original_glcm_Imc1\",\r\n",
        "                                               \"original_glrlm_LongRunEmphasis\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kSsoZhaUrzA"
      },
      "source": [
        "### **Sequential Backward Selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GERviFssAugM"
      },
      "source": [
        "Selected features are included in a multivariate model and the less significant are removed one by one, to keep only features significant at 5% threshold."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QUhzIM3J_n__",
        "outputId": "ee3055ff-3e1a-4c0c-a678-7615663fae4d"
      },
      "source": [
        "#This model contains 17 radiomics features and 5 clinical variables.\r\n",
        "cph.fit(x_train_model_2, duration_col='SurvivalTime', event_col='Event')\r\n",
        "cph.print_summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\toprule\n{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\ncovariate                                    &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n\\midrule\noriginal\\_shape\\_Maximum3DDiameter             &  0.03 &       1.03 &      0.15 &           -0.27 &            0.32 &                 0.77 &                 1.37 &  0.17 & 0.86 &      0.21 \\\\\noriginal\\_shape\\_SurfaceVolumeRatio            &  0.23 &       1.26 &      0.20 &           -0.16 &            0.62 &                 0.85 &                 1.85 &  1.15 & 0.25 &      2.01 \\\\\noriginal\\_firstorder\\_Kurtosis                 &  0.07 &       1.07 &      0.13 &           -0.19 &            0.32 &                 0.83 &                 1.37 &  0.51 & 0.61 &      0.72 \\\\\noriginal\\_firstorder\\_Maximum                  &  0.06 &       1.06 &      0.13 &           -0.20 &            0.32 &                 0.82 &                 1.37 &  0.44 & 0.66 &      0.60 \\\\\noriginal\\_firstorder\\_Median                   &  0.28 &       1.32 &      0.30 &           -0.31 &            0.87 &                 0.73 &                 2.38 &  0.92 & 0.36 &      1.48 \\\\\noriginal\\_firstorder\\_Skewness                 &  0.23 &       1.25 &      0.18 &           -0.13 &            0.58 &                 0.88 &                 1.78 &  1.25 & 0.21 &      2.24 \\\\\noriginal\\_glcm\\_ClusterShade                   & -0.02 &       0.98 &      0.15 &           -0.32 &            0.28 &                 0.72 &                 1.33 & -0.13 & 0.90 &      0.15 \\\\\noriginal\\_glcm\\_ClusterTendency                &  0.53 &       1.70 &      0.25 &            0.05 &            1.01 &                 1.05 &                 2.74 &  2.16 & 0.03 &      5.01 \\\\\noriginal\\_glcm\\_Correlation                    & -0.22 &       0.80 &      0.27 &           -0.76 &            0.31 &                 0.47 &                 1.37 & -0.81 & 0.42 &      1.26 \\\\\noriginal\\_glcm\\_Imc2                           &  0.03 &       1.03 &      0.19 &           -0.35 &            0.41 &                 0.71 &                 1.51 &  0.17 & 0.86 &      0.21 \\\\\noriginal\\_glcm\\_Idmn                           &  0.10 &       1.11 &      0.28 &           -0.45 &            0.65 &                 0.64 &                 1.91 &  0.36 & 0.72 &      0.47 \\\\\noriginal\\_glcm\\_InverseVariance                &  0.31 &       1.36 &      0.24 &           -0.17 &            0.79 &                 0.84 &                 2.19 &  1.25 & 0.21 &      2.25 \\\\\noriginal\\_glcm\\_MaximumProbability             &  0.27 &       1.30 &      0.17 &           -0.08 &            0.61 &                 0.93 &                 1.84 &  1.52 & 0.13 &      2.97 \\\\\noriginal\\_glrlm\\_GrayLevelNonUniformity        &  0.20 &       1.22 &      0.17 &           -0.14 &            0.53 &                 0.87 &                 1.70 &  1.14 & 0.26 &      1.96 \\\\\noriginal\\_glrlm\\_RunLengthNonUniformity        & -0.02 &       0.98 &      0.17 &           -0.35 &            0.31 &                 0.70 &                 1.36 & -0.13 & 0.90 &      0.16 \\\\\noriginal\\_glrlm\\_LowGrayLevelRunEmphasis       & -0.03 &       0.97 &      0.13 &           -0.29 &            0.23 &                 0.75 &                 1.26 & -0.21 & 0.84 &      0.26 \\\\\noriginal\\_glrlm\\_ShortRunHighGrayLevelEmphasis &  0.04 &       1.04 &      0.14 &           -0.24 &            0.33 &                 0.79 &                 1.39 &  0.30 & 0.76 &      0.39 \\\\\nage                                          &  0.24 &       1.27 &      0.09 &            0.06 &            0.41 &                 1.06 &                 1.51 &  2.62 & 0.01 &      6.83 \\\\\nSourceDataset                                & -1.33 &       0.27 &      0.43 &           -2.17 &           -0.48 &                 0.11 &                 0.62 & -3.06 & 0.00 &      8.82 \\\\\nNstage\\_1                                     &  0.23 &       1.25 &      0.36 &           -0.48 &            0.93 &                 0.62 &                 2.53 &  0.63 & 0.53 &      0.92 \\\\\nNstage\\_2                                     &  0.60 &       1.83 &      0.22 &            0.18 &            1.03 &                 1.19 &                 2.80 &  2.77 & 0.01 &      7.49 \\\\\nNstage\\_3                                     &  0.48 &       1.61 &      0.27 &           -0.06 &            1.01 &                 0.94 &                 2.76 &  1.73 & 0.08 &      3.58 \\\\\nTstage\\_2                                     &  0.21 &       1.24 &      0.26 &           -0.30 &            0.72 &                 0.74 &                 2.06 &  0.81 & 0.42 &      1.26 \\\\\nTstage\\_3                                     &  0.23 &       1.25 &      0.35 &           -0.45 &            0.91 &                 0.63 &                 2.48 &  0.65 & 0.52 &      0.96 \\\\\nTstage\\_4                                     &  0.04 &       1.05 &      0.30 &           -0.55 &            0.64 &                 0.58 &                 1.90 &  0.15 & 0.88 &      0.18 \\\\\nHistology\\_cat\\_1                              & -0.37 &       0.69 &      0.29 &           -0.94 &            0.21 &                 0.39 &                 1.23 & -1.26 & 0.21 &      2.26 \\\\\nHistology\\_cat\\_2                              & -0.15 &       0.86 &      0.27 &           -0.69 &            0.39 &                 0.50 &                 1.47 & -0.55 & 0.59 &      0.77 \\\\\nHistology\\_cat\\_3                              &  0.07 &       1.07 &      0.29 &           -0.49 &            0.64 &                 0.61 &                 1.89 &  0.25 & 0.80 &      0.32 \\\\\n\\bottomrule\n\\end{tabular}\n",
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'SurvivalTime'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'Event'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-789.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2021-02-11 14:26:44 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>original_shape_Maximum3DDiameter</th>\n",
              "      <td>0.03</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.15</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_shape_SurfaceVolumeRatio</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.20</td>\n",
              "      <td>-0.16</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.85</td>\n",
              "      <td>1.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>2.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Kurtosis</th>\n",
              "      <td>0.07</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.83</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Maximum</th>\n",
              "      <td>0.06</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.20</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Median</th>\n",
              "      <td>0.28</td>\n",
              "      <td>1.32</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.73</td>\n",
              "      <td>2.38</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Skewness</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.78</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.21</td>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterShade</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.15</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.33</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterTendency</th>\n",
              "      <td>0.53</td>\n",
              "      <td>1.70</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.01</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.74</td>\n",
              "      <td>2.16</td>\n",
              "      <td>0.03</td>\n",
              "      <td>5.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Correlation</th>\n",
              "      <td>-0.22</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.76</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.37</td>\n",
              "      <td>-0.81</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Imc2</th>\n",
              "      <td>0.03</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.71</td>\n",
              "      <td>1.51</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_Idmn</th>\n",
              "      <td>0.10</td>\n",
              "      <td>1.11</td>\n",
              "      <td>0.28</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.91</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_InverseVariance</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.24</td>\n",
              "      <td>-0.17</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.84</td>\n",
              "      <td>2.19</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.21</td>\n",
              "      <td>2.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_MaximumProbability</th>\n",
              "      <td>0.27</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.93</td>\n",
              "      <td>1.84</td>\n",
              "      <td>1.52</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_GrayLevelNonUniformity</th>\n",
              "      <td>0.20</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.70</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_RunLengthNonUniformity</th>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.17</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.36</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_LowGrayLevelRunEmphasis</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.13</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.26</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_ShortRunHighGrayLevelEmphasis</th>\n",
              "      <td>0.04</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.14</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.24</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.51</td>\n",
              "      <td>2.62</td>\n",
              "      <td>0.01</td>\n",
              "      <td>6.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SourceDataset</th>\n",
              "      <td>-1.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.43</td>\n",
              "      <td>-2.17</td>\n",
              "      <td>-0.48</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.62</td>\n",
              "      <td>-3.06</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>8.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_1</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.36</td>\n",
              "      <td>-0.48</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.62</td>\n",
              "      <td>2.53</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_2</th>\n",
              "      <td>0.60</td>\n",
              "      <td>1.83</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.18</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1.19</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.77</td>\n",
              "      <td>0.01</td>\n",
              "      <td>7.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_3</th>\n",
              "      <td>0.48</td>\n",
              "      <td>1.61</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.94</td>\n",
              "      <td>2.76</td>\n",
              "      <td>1.73</td>\n",
              "      <td>0.08</td>\n",
              "      <td>3.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_2</th>\n",
              "      <td>0.21</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.74</td>\n",
              "      <td>2.06</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_3</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.63</td>\n",
              "      <td>2.48</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tstage_4</th>\n",
              "      <td>0.04</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.30</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.90</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_1</th>\n",
              "      <td>-0.37</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.29</td>\n",
              "      <td>-0.94</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.23</td>\n",
              "      <td>-1.26</td>\n",
              "      <td>0.21</td>\n",
              "      <td>2.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_2</th>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.27</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.47</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Histology_cat_3</th>\n",
              "      <td>0.07</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.29</td>\n",
              "      <td>-0.49</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.89</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Concordance</th>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>1635.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>98.49 on 28 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>30.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 300 total observations, 138 right-censored observations>\n",
              "             duration col = 'SurvivalTime'\n",
              "                event col = 'Event'\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 300\n",
              "number of events observed = 162\n",
              "   partial log-likelihood = -789.70\n",
              "         time fit was run = 2021-02-11 14:26:44 UTC\n",
              "\n",
              "---\n",
              "                                               coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
              "covariate                                                                                                                                            \n",
              "original_shape_Maximum3DDiameter               0.03       1.03       0.15            -0.27             0.32                 0.77                 1.37\n",
              "original_shape_SurfaceVolumeRatio              0.23       1.26       0.20            -0.16             0.62                 0.85                 1.85\n",
              "original_firstorder_Kurtosis                   0.07       1.07       0.13            -0.19             0.32                 0.83                 1.37\n",
              "original_firstorder_Maximum                    0.06       1.06       0.13            -0.20             0.32                 0.82                 1.37\n",
              "original_firstorder_Median                     0.28       1.32       0.30            -0.31             0.87                 0.73                 2.38\n",
              "original_firstorder_Skewness                   0.23       1.25       0.18            -0.13             0.58                 0.88                 1.78\n",
              "original_glcm_ClusterShade                    -0.02       0.98       0.15            -0.32             0.28                 0.72                 1.33\n",
              "original_glcm_ClusterTendency                  0.53       1.70       0.25             0.05             1.01                 1.05                 2.74\n",
              "original_glcm_Correlation                     -0.22       0.80       0.27            -0.76             0.31                 0.47                 1.37\n",
              "original_glcm_Imc2                             0.03       1.03       0.19            -0.35             0.41                 0.71                 1.51\n",
              "original_glcm_Idmn                             0.10       1.11       0.28            -0.45             0.65                 0.64                 1.91\n",
              "original_glcm_InverseVariance                  0.31       1.36       0.24            -0.17             0.79                 0.84                 2.19\n",
              "original_glcm_MaximumProbability               0.27       1.30       0.17            -0.08             0.61                 0.93                 1.84\n",
              "original_glrlm_GrayLevelNonUniformity          0.20       1.22       0.17            -0.14             0.53                 0.87                 1.70\n",
              "original_glrlm_RunLengthNonUniformity         -0.02       0.98       0.17            -0.35             0.31                 0.70                 1.36\n",
              "original_glrlm_LowGrayLevelRunEmphasis        -0.03       0.97       0.13            -0.29             0.23                 0.75                 1.26\n",
              "original_glrlm_ShortRunHighGrayLevelEmphasis   0.04       1.04       0.14            -0.24             0.33                 0.79                 1.39\n",
              "age                                            0.24       1.27       0.09             0.06             0.41                 1.06                 1.51\n",
              "SourceDataset                                 -1.33       0.27       0.43            -2.17            -0.48                 0.11                 0.62\n",
              "Nstage_1                                       0.23       1.25       0.36            -0.48             0.93                 0.62                 2.53\n",
              "Nstage_2                                       0.60       1.83       0.22             0.18             1.03                 1.19                 2.80\n",
              "Nstage_3                                       0.48       1.61       0.27            -0.06             1.01                 0.94                 2.76\n",
              "Tstage_2                                       0.21       1.24       0.26            -0.30             0.72                 0.74                 2.06\n",
              "Tstage_3                                       0.23       1.25       0.35            -0.45             0.91                 0.63                 2.48\n",
              "Tstage_4                                       0.04       1.05       0.30            -0.55             0.64                 0.58                 1.90\n",
              "Histology_cat_1                               -0.37       0.69       0.29            -0.94             0.21                 0.39                 1.23\n",
              "Histology_cat_2                               -0.15       0.86       0.27            -0.69             0.39                 0.50                 1.47\n",
              "Histology_cat_3                                0.07       1.07       0.29            -0.49             0.64                 0.61                 1.89\n",
              "\n",
              "                                                 z      p   -log2(p)\n",
              "covariate                                                           \n",
              "original_shape_Maximum3DDiameter              0.17   0.86       0.21\n",
              "original_shape_SurfaceVolumeRatio             1.15   0.25       2.01\n",
              "original_firstorder_Kurtosis                  0.51   0.61       0.72\n",
              "original_firstorder_Maximum                   0.44   0.66       0.60\n",
              "original_firstorder_Median                    0.92   0.36       1.48\n",
              "original_firstorder_Skewness                  1.25   0.21       2.24\n",
              "original_glcm_ClusterShade                   -0.13   0.90       0.15\n",
              "original_glcm_ClusterTendency                 2.16   0.03       5.01\n",
              "original_glcm_Correlation                    -0.81   0.42       1.26\n",
              "original_glcm_Imc2                            0.17   0.86       0.21\n",
              "original_glcm_Idmn                            0.36   0.72       0.47\n",
              "original_glcm_InverseVariance                 1.25   0.21       2.25\n",
              "original_glcm_MaximumProbability              1.52   0.13       2.97\n",
              "original_glrlm_GrayLevelNonUniformity         1.14   0.26       1.96\n",
              "original_glrlm_RunLengthNonUniformity        -0.13   0.90       0.16\n",
              "original_glrlm_LowGrayLevelRunEmphasis       -0.21   0.84       0.26\n",
              "original_glrlm_ShortRunHighGrayLevelEmphasis  0.30   0.76       0.39\n",
              "age                                           2.62   0.01       6.83\n",
              "SourceDataset                                -3.06 <0.005       8.82\n",
              "Nstage_1                                      0.63   0.53       0.92\n",
              "Nstage_2                                      2.77   0.01       7.49\n",
              "Nstage_3                                      1.73   0.08       3.58\n",
              "Tstage_2                                      0.81   0.42       1.26\n",
              "Tstage_3                                      0.65   0.52       0.96\n",
              "Tstage_4                                      0.15   0.88       0.18\n",
              "Histology_cat_1                              -1.26   0.21       2.26\n",
              "Histology_cat_2                              -0.55   0.59       0.77\n",
              "Histology_cat_3                               0.25   0.80       0.32\n",
              "---\n",
              "Concordance = 0.72\n",
              "Partial AIC = 1635.39\n",
              "log-likelihood ratio test = 98.49 on 28 df\n",
              "-log2(p) of ll-ratio test = 30.07"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gSsksfKDIjV"
      },
      "source": [
        "**Final Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fBJtZSoDWV1"
      },
      "source": [
        "The following variables have been removed in the following order: \r\n",
        "1)original_glcm_ClusterShade, 2)original_glrlm_RunLengthNonUniformity, \r\n",
        "3) original_shape_Maximum3DDiameter, 4)original_glcm_Imc2, \r\n",
        "5)original_glrlm_LowGrayLevelRunEmphasis, \r\n",
        "6) original_glrlm_ShortRunHighGrayLevelEmphasis, 7)original_glcm_Idmn,\r\n",
        "8)original_firstorder_Kurtosis, 9) Tstage_2, Tstage_3, Tstage_4, \r\n",
        "10)original_glcm_Correlation, 11)original_firstorder_Skewness, \r\n",
        "12)original_glcm_InverseVariance, 13)original_shape_SurfaceVolumeRatio, \r\n",
        "14)original_glcm_MaximumProbability, 15)Histology_cat_1, Histology_cat_2,Histology_cat_3, 16)original_firstorder_Maximum. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "AEYh13Lt101d",
        "outputId": "5108f53d-f6bc-430c-febb-879d421fd1a3"
      },
      "source": [
        "x_train_model_final = x_train_model_2.drop(columns=[\"original_glcm_ClusterShade\",\"original_glrlm_RunLengthNonUniformity\",\n",
        "                                                    \"original_shape_Maximum3DDiameter\",\"original_glcm_Imc2\",\n",
        "                                                    \"original_glrlm_LowGrayLevelRunEmphasis\",\n",
        "                                                    \"original_glrlm_ShortRunHighGrayLevelEmphasis\",\"original_glcm_Idmn\",\n",
        "                                                    \"original_firstorder_Kurtosis\",\"Tstage_2\",\"Tstage_3\",\"Tstage_4\",\n",
        "                                                    \"original_glcm_Correlation\",\"original_firstorder_Skewness\", \n",
        "                                                    \"original_glcm_InverseVariance\",\"original_shape_SurfaceVolumeRatio\",\n",
        "                                                    \"original_glcm_MaximumProbability\",\"Histology_cat_1\",\"Histology_cat_2\",\n",
        "                                                    \"Histology_cat_3\",\"original_firstorder_Maximum\"])\n",
        "\n",
        "cph.fit(x_train_model_final, duration_col='SurvivalTime', event_col='Event')\n",
        "cph.print_summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/latex": "\\begin{tabular}{lrrrrrrrrrr}\n\\toprule\n{} &  coef &  exp(coef) &  se(coef) &  coef lower 95\\% &  coef upper 95\\% &  exp(coef) lower 95\\% &  exp(coef) upper 95\\% &     z &    p &  -log2(p) \\\\\ncovariate                             &       &            &           &                 &                 &                      &                      &       &      &           \\\\\n\\midrule\noriginal\\_firstorder\\_Median            &  0.41 &       1.51 &      0.14 &            0.15 &            0.68 &                 1.16 &                 1.98 &  3.02 & 0.00 &      8.65 \\\\\noriginal\\_glcm\\_ClusterTendency         &  0.31 &       1.36 &      0.11 &            0.09 &            0.53 &                 1.10 &                 1.70 &  2.78 & 0.01 &      7.52 \\\\\noriginal\\_glrlm\\_GrayLevelNonUniformity &  0.22 &       1.24 &      0.08 &            0.06 &            0.37 &                 1.06 &                 1.45 &  2.73 & 0.01 &      7.32 \\\\\nage                                   &  0.22 &       1.25 &      0.08 &            0.06 &            0.39 &                 1.06 &                 1.48 &  2.67 & 0.01 &      7.03 \\\\\nSourceDataset                         & -1.08 &       0.34 &      0.23 &           -1.54 &           -0.63 &                 0.21 &                 0.53 & -4.68 & 0.00 &     18.42 \\\\\nNstage\\_1                              &  0.18 &       1.19 &      0.35 &           -0.51 &            0.86 &                 0.60 &                 2.37 &  0.50 & 0.62 &      0.70 \\\\\nNstage\\_2                              &  0.48 &       1.62 &      0.20 &            0.10 &            0.87 &                 1.11 &                 2.38 &  2.47 & 0.01 &      6.21 \\\\\nNstage\\_3                              &  0.42 &       1.52 &      0.25 &           -0.07 &            0.91 &                 0.93 &                 2.48 &  1.68 & 0.09 &      3.42 \\\\\n\\bottomrule\n\\end{tabular}\n",
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>lifelines.CoxPHFitter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration col</th>\n",
              "      <td>'SurvivalTime'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>event col</th>\n",
              "      <td>'Event'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>baseline estimation</th>\n",
              "      <td>breslow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of observations</th>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>number of events observed</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>partial log-likelihood</th>\n",
              "      <td>-795.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time fit was run</th>\n",
              "      <td>2021-02-11 14:26:44 UTC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th style=\"min-width: 12px;\"></th>\n",
              "      <th style=\"min-width: 12px;\">coef</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">se(coef)</th>\n",
              "      <th style=\"min-width: 12px;\">coef lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">coef upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) lower 95%</th>\n",
              "      <th style=\"min-width: 12px;\">exp(coef) upper 95%</th>\n",
              "      <th style=\"min-width: 12px;\">z</th>\n",
              "      <th style=\"min-width: 12px;\">p</th>\n",
              "      <th style=\"min-width: 12px;\">-log2(p)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>original_firstorder_Median</th>\n",
              "      <td>0.41</td>\n",
              "      <td>1.51</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.68</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.98</td>\n",
              "      <td>3.02</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>8.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glcm_ClusterTendency</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.70</td>\n",
              "      <td>2.78</td>\n",
              "      <td>0.01</td>\n",
              "      <td>7.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>original_glrlm_GrayLevelNonUniformity</th>\n",
              "      <td>0.22</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.45</td>\n",
              "      <td>2.73</td>\n",
              "      <td>0.01</td>\n",
              "      <td>7.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.22</td>\n",
              "      <td>1.25</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.48</td>\n",
              "      <td>2.67</td>\n",
              "      <td>0.01</td>\n",
              "      <td>7.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SourceDataset</th>\n",
              "      <td>-1.08</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-1.54</td>\n",
              "      <td>-0.63</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.53</td>\n",
              "      <td>-4.68</td>\n",
              "      <td>&lt;0.005</td>\n",
              "      <td>18.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_1</th>\n",
              "      <td>0.18</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.35</td>\n",
              "      <td>-0.51</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.60</td>\n",
              "      <td>2.37</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_2</th>\n",
              "      <td>0.48</td>\n",
              "      <td>1.62</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.11</td>\n",
              "      <td>2.38</td>\n",
              "      <td>2.47</td>\n",
              "      <td>0.01</td>\n",
              "      <td>6.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nstage_3</th>\n",
              "      <td>0.42</td>\n",
              "      <td>1.52</td>\n",
              "      <td>0.25</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.93</td>\n",
              "      <td>2.48</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.09</td>\n",
              "      <td>3.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Concordance</th>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Partial AIC</th>\n",
              "      <td>1607.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log-likelihood ratio test</th>\n",
              "      <td>86.00 on 8 df</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-log2(p) of ll-ratio test</th>\n",
              "      <td>48.24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "<lifelines.CoxPHFitter: fitted with 300 total observations, 138 right-censored observations>\n",
              "             duration col = 'SurvivalTime'\n",
              "                event col = 'Event'\n",
              "      baseline estimation = breslow\n",
              "   number of observations = 300\n",
              "number of events observed = 162\n",
              "   partial log-likelihood = -795.94\n",
              "         time fit was run = 2021-02-11 14:26:44 UTC\n",
              "\n",
              "---\n",
              "                                        coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
              "covariate                                                                                                                                     \n",
              "original_firstorder_Median              0.41       1.51       0.14             0.15             0.68                 1.16                 1.98\n",
              "original_glcm_ClusterTendency           0.31       1.36       0.11             0.09             0.53                 1.10                 1.70\n",
              "original_glrlm_GrayLevelNonUniformity   0.22       1.24       0.08             0.06             0.37                 1.06                 1.45\n",
              "age                                     0.22       1.25       0.08             0.06             0.39                 1.06                 1.48\n",
              "SourceDataset                          -1.08       0.34       0.23            -1.54            -0.63                 0.21                 0.53\n",
              "Nstage_1                                0.18       1.19       0.35            -0.51             0.86                 0.60                 2.37\n",
              "Nstage_2                                0.48       1.62       0.20             0.10             0.87                 1.11                 2.38\n",
              "Nstage_3                                0.42       1.52       0.25            -0.07             0.91                 0.93                 2.48\n",
              "\n",
              "                                          z      p   -log2(p)\n",
              "covariate                                                    \n",
              "original_firstorder_Median             3.02 <0.005       8.65\n",
              "original_glcm_ClusterTendency          2.78   0.01       7.52\n",
              "original_glrlm_GrayLevelNonUniformity  2.73   0.01       7.32\n",
              "age                                    2.67   0.01       7.03\n",
              "SourceDataset                         -4.68 <0.005      18.42\n",
              "Nstage_1                               0.50   0.62       0.70\n",
              "Nstage_2                               2.47   0.01       6.21\n",
              "Nstage_3                               1.68   0.09       3.42\n",
              "---\n",
              "Concordance = 0.71\n",
              "Partial AIC = 1607.88\n",
              "log-likelihood ratio test = 86.00 on 8 df\n",
              "-log2(p) of ll-ratio test = 48.24"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-OuZqL3OG3S"
      },
      "source": [
        "The final model contains 6 variables: *median voxel intensity, cluster tendency, gray level non-uniformity, age, dataset of origin and the N-tumoral stage*.\r\n",
        "\r\n",
        "By default, the **Breslow's** method is used to estimate the baseline hazard function and the **Efron's** method is used handle situations in which there are ties in the survival time data. It is suitable to our data because we have very few tied event times."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGOUf0JstIb4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81c01f20-01d5-4720-bc7f-3e7b17e6bc34"
      },
      "source": [
        "from lifelines.utils import concordance_index\n",
        "concordance=concordance_index(x_train_model_final['SurvivalTime'], -cph.predict_partial_hazard(x_train_model_final), x_train_model_final['Event']) \n",
        "print(concordance)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7054153223255519\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLk5gpIVX0oz"
      },
      "source": [
        "The **concordance index** on the train data is equal to 0.71"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeV0-pdiXcs8"
      },
      "source": [
        "### **Predictions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0fP4cHnbP0K"
      },
      "source": [
        "**On Train Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HACCJVr-EWA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd8cc01c-f028-463d-e1e3-f8e93f293812"
      },
      "source": [
        "# Computation of the expected lifetime\n",
        "prediction_train_cph=cph.predict_expectation(x_train_model_final)\n",
        "prediction_train_cph.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     300.000000\n",
              "mean     1462.079897\n",
              "std       761.869072\n",
              "min       192.612174\n",
              "25%       867.822673\n",
              "50%      1241.302830\n",
              "75%      2072.424649\n",
              "max      3192.857298\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCvXc6T0bVNt"
      },
      "source": [
        "**On Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESJ1Nr60bjvl"
      },
      "source": [
        "#Only variables selected in the final model are kept to make the predictions on the expected lifetime\r\n",
        "x_test_model = x_test[[\"original_firstorder_Median\",\"original_glcm_ClusterTendency\",\"original_glrlm_GrayLevelNonUniformity\",\"age\",\r\n",
        "                       \"SourceDataset\", \"Nstage_1\",\"Nstage_2\",\"Nstage_3\"]]\r\n",
        "\r\n",
        "prediction_test_cph=cph.predict_expectation(x_test_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmSbbj0Ng8Ny"
      },
      "source": [
        "**Submission**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4JtNZXtd9ue",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f3e92e47-dc36-409a-cd4a-0290deb374cc"
      },
      "source": [
        "output=pd.DataFrame(data=prediction_test_cph)\n",
        "output=output.rename(columns={0:'Survival_Time'})\n",
        "output.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survival_Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1466.030711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>810.801772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>175.268645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>786.217413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1336.306616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2122.452550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3254.733920</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survival_Time\n",
              "count     125.000000\n",
              "mean     1466.030711\n",
              "std       810.801772\n",
              "min       175.268645\n",
              "25%       786.217413\n",
              "50%      1336.306616\n",
              "75%      2122.452550\n",
              "max      3254.733920"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLTai5Kjfylx"
      },
      "source": [
        "output.to_csv('/content/drive/My Drive/submission_test.csv')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}