notebooks/Parkinson_SVM.ipynb

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  "metadata": {
    "colab": {
      "name": "Parkinson_SVM.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "wPH8N9UfaiXI"
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      "authorship_tag": "ABX9TyNDxJEPEZfsNGQbXoRz5IiC",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eecastillo/parkinson_analisis/blob/master/notebooks/Parkinson_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHuYJ9auVmqx"
      },
      "source": [
        "#Import scikit-learn dataset library\n",
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "import pickle\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "ABIgNDvwVsJJ",
        "outputId": "b6a6e21c-3b58-4231-9018-e0f915165351"
      },
      "source": [
        "df = pd.read_csv('./parkinson_dataset.csv')\n",
        "#df = pd.read_csv('./train_data.csv')\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>jitter_local</th>\n",
              "      <th>jitter_local_absolute</th>\n",
              "      <th>jitter_rap</th>\n",
              "      <th>jitter_ppq5</th>\n",
              "      <th>jitter_ddp</th>\n",
              "      <th>shimmer_local</th>\n",
              "      <th>shimer_local_db</th>\n",
              "      <th>shimmer_apq3</th>\n",
              "      <th>shimmer_apq5</th>\n",
              "      <th>shimmer_apq11</th>\n",
              "      <th>shimer_dda</th>\n",
              "      <th>ac</th>\n",
              "      <th>nth</th>\n",
              "      <th>htn</th>\n",
              "      <th>median_pitch</th>\n",
              "      <th>mean_pitch</th>\n",
              "      <th>standard_deviation</th>\n",
              "      <th>minimum_pitch</th>\n",
              "      <th>maximum_pitch</th>\n",
              "      <th>number_pulses</th>\n",
              "      <th>periods</th>\n",
              "      <th>mean_period</th>\n",
              "      <th>std_period</th>\n",
              "      <th>locally_unvoiced_frames</th>\n",
              "      <th>voice_breaks</th>\n",
              "      <th>degree_voice_break</th>\n",
              "      <th>class_information</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.067</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.202</td>\n",
              "      <td>2.033</td>\n",
              "      <td>0.178</td>\n",
              "      <td>1.074</td>\n",
              "      <td>1.336</td>\n",
              "      <td>1.576</td>\n",
              "      <td>3.223</td>\n",
              "      <td>0.996050</td>\n",
              "      <td>0.003967</td>\n",
              "      <td>24.204</td>\n",
              "      <td>186.266</td>\n",
              "      <td>186.300</td>\n",
              "      <td>0.859</td>\n",
              "      <td>184.502</td>\n",
              "      <td>187.880</td>\n",
              "      <td>183</td>\n",
              "      <td>182</td>\n",
              "      <td>0.005368</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.143</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.073</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.219</td>\n",
              "      <td>1.236</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.904</td>\n",
              "      <td>0.894</td>\n",
              "      <td>1.835</td>\n",
              "      <td>0.998471</td>\n",
              "      <td>0.001531</td>\n",
              "      <td>28.729</td>\n",
              "      <td>200.577</td>\n",
              "      <td>200.477</td>\n",
              "      <td>0.755</td>\n",
              "      <td>198.665</td>\n",
              "      <td>202.214</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>0.004988</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.087</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.260</td>\n",
              "      <td>1.338</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.948</td>\n",
              "      <td>1.017</td>\n",
              "      <td>1.891</td>\n",
              "      <td>0.998021</td>\n",
              "      <td>0.001984</td>\n",
              "      <td>27.349</td>\n",
              "      <td>202.868</td>\n",
              "      <td>202.425</td>\n",
              "      <td>1.860</td>\n",
              "      <td>197.220</td>\n",
              "      <td>206.060</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.075</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.224</td>\n",
              "      <td>1.086</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.747</td>\n",
              "      <td>0.823</td>\n",
              "      <td>1.667</td>\n",
              "      <td>0.999154</td>\n",
              "      <td>0.000847</td>\n",
              "      <td>32.114</td>\n",
              "      <td>204.027</td>\n",
              "      <td>204.090</td>\n",
              "      <td>0.931</td>\n",
              "      <td>202.324</td>\n",
              "      <td>206.182</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>0.004900</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.150</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.240</td>\n",
              "      <td>1.049</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.698</td>\n",
              "      <td>0.777</td>\n",
              "      <td>1.599</td>\n",
              "      <td>0.999295</td>\n",
              "      <td>0.000706</td>\n",
              "      <td>32.816</td>\n",
              "      <td>207.519</td>\n",
              "      <td>207.468</td>\n",
              "      <td>0.880</td>\n",
              "      <td>205.407</td>\n",
              "      <td>209.927</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>0.004820</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.077</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.230</td>\n",
              "      <td>1.241</td>\n",
              "      <td>0.109</td>\n",
              "      <td>0.487</td>\n",
              "      <td>0.715</td>\n",
              "      <td>1.568</td>\n",
              "      <td>1.461</td>\n",
              "      <td>0.998378</td>\n",
              "      <td>0.001631</td>\n",
              "      <td>30.960</td>\n",
              "      <td>224.812</td>\n",
              "      <td>226.435</td>\n",
              "      <td>6.224</td>\n",
              "      <td>218.455</td>\n",
              "      <td>243.523</td>\n",
              "      <td>221</td>\n",
              "      <td>220</td>\n",
              "      <td>0.004416</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2</td>\n",
              "      <td>1.137</td>\n",
              "      <td>0.000102</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.506</td>\n",
              "      <td>1.929</td>\n",
              "      <td>6.993</td>\n",
              "      <td>0.620</td>\n",
              "      <td>3.394</td>\n",
              "      <td>4.156</td>\n",
              "      <td>6.189</td>\n",
              "      <td>10.183</td>\n",
              "      <td>0.933691</td>\n",
              "      <td>0.078360</td>\n",
              "      <td>13.196</td>\n",
              "      <td>111.318</td>\n",
              "      <td>111.054</td>\n",
              "      <td>4.301</td>\n",
              "      <td>97.914</td>\n",
              "      <td>119.037</td>\n",
              "      <td>110</td>\n",
              "      <td>109</td>\n",
              "      <td>0.009014</td>\n",
              "      <td>0.000370</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "      <td>0.606</td>\n",
              "      <td>0.000065</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.269</td>\n",
              "      <td>0.486</td>\n",
              "      <td>5.351</td>\n",
              "      <td>0.481</td>\n",
              "      <td>2.444</td>\n",
              "      <td>3.295</td>\n",
              "      <td>4.568</td>\n",
              "      <td>7.333</td>\n",
              "      <td>0.962727</td>\n",
              "      <td>0.040464</td>\n",
              "      <td>15.913</td>\n",
              "      <td>93.483</td>\n",
              "      <td>92.935</td>\n",
              "      <td>3.659</td>\n",
              "      <td>80.332</td>\n",
              "      <td>98.876</td>\n",
              "      <td>92</td>\n",
              "      <td>91</td>\n",
              "      <td>0.010768</td>\n",
              "      <td>0.000451</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2</td>\n",
              "      <td>0.765</td>\n",
              "      <td>0.000081</td>\n",
              "      <td>0.204</td>\n",
              "      <td>0.311</td>\n",
              "      <td>0.611</td>\n",
              "      <td>5.314</td>\n",
              "      <td>0.461</td>\n",
              "      <td>2.492</td>\n",
              "      <td>3.469</td>\n",
              "      <td>5.992</td>\n",
              "      <td>7.477</td>\n",
              "      <td>0.940527</td>\n",
              "      <td>0.065536</td>\n",
              "      <td>12.954</td>\n",
              "      <td>93.239</td>\n",
              "      <td>93.811</td>\n",
              "      <td>3.686</td>\n",
              "      <td>88.629</td>\n",
              "      <td>104.526</td>\n",
              "      <td>93</td>\n",
              "      <td>92</td>\n",
              "      <td>0.010657</td>\n",
              "      <td>0.000411</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2</td>\n",
              "      <td>0.374</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.215</td>\n",
              "      <td>0.477</td>\n",
              "      <td>5.634</td>\n",
              "      <td>0.499</td>\n",
              "      <td>2.627</td>\n",
              "      <td>3.240</td>\n",
              "      <td>6.694</td>\n",
              "      <td>7.882</td>\n",
              "      <td>0.982695</td>\n",
              "      <td>0.017775</td>\n",
              "      <td>19.361</td>\n",
              "      <td>97.264</td>\n",
              "      <td>97.274</td>\n",
              "      <td>0.960</td>\n",
              "      <td>95.165</td>\n",
              "      <td>99.424</td>\n",
              "      <td>96</td>\n",
              "      <td>95</td>\n",
              "      <td>0.010280</td>\n",
              "      <td>0.000104</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID  jitter_local  ...  degree_voice_break  class_information\n",
              "0   1         0.135  ...                 0.0                  1\n",
              "1   1         0.143  ...                 0.0                  1\n",
              "2   1         0.162  ...                 0.0                  1\n",
              "3   1         0.140  ...                 0.0                  1\n",
              "4   1         0.150  ...                 0.0                  1\n",
              "5   1         0.208  ...                 0.0                  1\n",
              "6   2         1.137  ...                 0.0                  1\n",
              "7   2         0.606  ...                 0.0                  1\n",
              "8   2         0.765  ...                 0.0                  1\n",
              "9   2         0.374  ...                 0.0                  1\n",
              "\n",
              "[10 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWhzIqfyGNOU",
        "outputId": "26c2a86d-3d87-4735-eca3-7a22e9d27c1e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1208, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXislJJJElJw"
      },
      "source": [
        "# Separación de sujeto de prueba"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBvnXzmPEqKD"
      },
      "source": [
        "sujeto_prueba = pd.DataFrame(df.iloc[1])\n",
        "sujeto_prueba = sujeto_prueba.transpose()\n",
        "#df = df.drop(labels=[0:5], axis=0)\n",
        "df.drop(df.index[[0,1,2,3,4,5]], inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "9jFb7fcpKEnk",
        "outputId": "dd65d989-ff18-49f6-caf2-bac867cbc6d9"
      },
      "source": [
        "sujeto_prueba"
      ],
      "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>ID</th>\n",
              "      <th>jitter_local</th>\n",
              "      <th>jitter_local_absolute</th>\n",
              "      <th>jitter_rap</th>\n",
              "      <th>jitter_ppq5</th>\n",
              "      <th>jitter_ddp</th>\n",
              "      <th>shimmer_local</th>\n",
              "      <th>shimer_local_db</th>\n",
              "      <th>shimmer_apq3</th>\n",
              "      <th>shimmer_apq5</th>\n",
              "      <th>shimmer_apq11</th>\n",
              "      <th>shimer_dda</th>\n",
              "      <th>ac</th>\n",
              "      <th>nth</th>\n",
              "      <th>htn</th>\n",
              "      <th>median_pitch</th>\n",
              "      <th>mean_pitch</th>\n",
              "      <th>standard_deviation</th>\n",
              "      <th>minimum_pitch</th>\n",
              "      <th>maximum_pitch</th>\n",
              "      <th>number_pulses</th>\n",
              "      <th>periods</th>\n",
              "      <th>mean_period</th>\n",
              "      <th>std_period</th>\n",
              "      <th>locally_unvoiced_frames</th>\n",
              "      <th>voice_breaks</th>\n",
              "      <th>degree_voice_break</th>\n",
              "      <th>class_information</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.073</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.219</td>\n",
              "      <td>1.236</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.904</td>\n",
              "      <td>0.894</td>\n",
              "      <td>1.835</td>\n",
              "      <td>0.998471</td>\n",
              "      <td>0.001531</td>\n",
              "      <td>28.729</td>\n",
              "      <td>200.577</td>\n",
              "      <td>200.477</td>\n",
              "      <td>0.755</td>\n",
              "      <td>198.665</td>\n",
              "      <td>202.214</td>\n",
              "      <td>194.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>0.004988</td>\n",
              "      <td>0.00002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ID  jitter_local  ...  degree_voice_break  class_information\n",
              "1  1.0         0.143  ...                 0.0                1.0\n",
              "\n",
              "[1 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "QxaZZYTsGRKk",
        "outputId": "c42e0903-c969-4f98-c0a9-6d4ffac727e1"
      },
      "source": [
        "sujeto_prueba_X = sujeto_prueba.drop(columns=['class_information','ID'])\n",
        "sujeto_prueba_X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>jitter_local</th>\n",
              "      <th>jitter_local_absolute</th>\n",
              "      <th>jitter_rap</th>\n",
              "      <th>jitter_ppq5</th>\n",
              "      <th>jitter_ddp</th>\n",
              "      <th>shimmer_local</th>\n",
              "      <th>shimer_local_db</th>\n",
              "      <th>shimmer_apq3</th>\n",
              "      <th>shimmer_apq5</th>\n",
              "      <th>shimmer_apq11</th>\n",
              "      <th>shimer_dda</th>\n",
              "      <th>ac</th>\n",
              "      <th>nth</th>\n",
              "      <th>htn</th>\n",
              "      <th>median_pitch</th>\n",
              "      <th>mean_pitch</th>\n",
              "      <th>standard_deviation</th>\n",
              "      <th>minimum_pitch</th>\n",
              "      <th>maximum_pitch</th>\n",
              "      <th>number_pulses</th>\n",
              "      <th>periods</th>\n",
              "      <th>mean_period</th>\n",
              "      <th>std_period</th>\n",
              "      <th>locally_unvoiced_frames</th>\n",
              "      <th>voice_breaks</th>\n",
              "      <th>degree_voice_break</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.143</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.073</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.219</td>\n",
              "      <td>1.236</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.904</td>\n",
              "      <td>0.894</td>\n",
              "      <td>1.835</td>\n",
              "      <td>0.998471</td>\n",
              "      <td>0.001531</td>\n",
              "      <td>28.729</td>\n",
              "      <td>200.577</td>\n",
              "      <td>200.477</td>\n",
              "      <td>0.755</td>\n",
              "      <td>198.665</td>\n",
              "      <td>202.214</td>\n",
              "      <td>194.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>0.004988</td>\n",
              "      <td>0.00002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   jitter_local  jitter_local_absolute  ...  voice_breaks  degree_voice_break\n",
              "1         0.143               0.000007  ...           0.0                 0.0\n",
              "\n",
              "[1 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLapZxYMGgcx",
        "outputId": "d8bdc946-cf2e-4fcb-c352-914c4279acfc"
      },
      "source": [
        "sujeto_prueba_X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFBHTvW-Eqij"
      },
      "source": [
        "# Resto de algoritmo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bilrfkEiV61i"
      },
      "source": [
        "X = df.drop(columns=['class_information','ID'])\n",
        "y = df['class_information']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "xH33ZxzvZuvZ",
        "outputId": "868105a3-e0a9-49d5-f0a9-60476e832048"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>jitter_local</th>\n",
              "      <th>jitter_local_absolute</th>\n",
              "      <th>jitter_rap</th>\n",
              "      <th>jitter_ppq5</th>\n",
              "      <th>jitter_ddp</th>\n",
              "      <th>shimmer_local</th>\n",
              "      <th>shimer_local_db</th>\n",
              "      <th>shimmer_apq3</th>\n",
              "      <th>shimmer_apq5</th>\n",
              "      <th>shimmer_apq11</th>\n",
              "      <th>shimer_dda</th>\n",
              "      <th>ac</th>\n",
              "      <th>nth</th>\n",
              "      <th>htn</th>\n",
              "      <th>median_pitch</th>\n",
              "      <th>mean_pitch</th>\n",
              "      <th>standard_deviation</th>\n",
              "      <th>minimum_pitch</th>\n",
              "      <th>maximum_pitch</th>\n",
              "      <th>number_pulses</th>\n",
              "      <th>periods</th>\n",
              "      <th>mean_period</th>\n",
              "      <th>std_period</th>\n",
              "      <th>locally_unvoiced_frames</th>\n",
              "      <th>voice_breaks</th>\n",
              "      <th>degree_voice_break</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.135</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.067</td>\n",
              "      <td>0.078</td>\n",
              "      <td>0.202</td>\n",
              "      <td>2.033</td>\n",
              "      <td>0.178</td>\n",
              "      <td>1.074</td>\n",
              "      <td>1.336</td>\n",
              "      <td>1.576</td>\n",
              "      <td>3.223</td>\n",
              "      <td>0.996050</td>\n",
              "      <td>0.003967</td>\n",
              "      <td>24.204</td>\n",
              "      <td>186.266</td>\n",
              "      <td>186.300</td>\n",
              "      <td>0.859</td>\n",
              "      <td>184.502</td>\n",
              "      <td>187.880</td>\n",
              "      <td>183</td>\n",
              "      <td>182</td>\n",
              "      <td>0.005368</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.143</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.073</td>\n",
              "      <td>0.081</td>\n",
              "      <td>0.219</td>\n",
              "      <td>1.236</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.612</td>\n",
              "      <td>0.904</td>\n",
              "      <td>0.894</td>\n",
              "      <td>1.835</td>\n",
              "      <td>0.998471</td>\n",
              "      <td>0.001531</td>\n",
              "      <td>28.729</td>\n",
              "      <td>200.577</td>\n",
              "      <td>200.477</td>\n",
              "      <td>0.755</td>\n",
              "      <td>198.665</td>\n",
              "      <td>202.214</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>0.004988</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.162</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.087</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.260</td>\n",
              "      <td>1.338</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.948</td>\n",
              "      <td>1.017</td>\n",
              "      <td>1.891</td>\n",
              "      <td>0.998021</td>\n",
              "      <td>0.001984</td>\n",
              "      <td>27.349</td>\n",
              "      <td>202.868</td>\n",
              "      <td>202.425</td>\n",
              "      <td>1.860</td>\n",
              "      <td>197.220</td>\n",
              "      <td>206.060</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>0.000046</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.140</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.075</td>\n",
              "      <td>0.089</td>\n",
              "      <td>0.224</td>\n",
              "      <td>1.086</td>\n",
              "      <td>0.094</td>\n",
              "      <td>0.556</td>\n",
              "      <td>0.747</td>\n",
              "      <td>0.823</td>\n",
              "      <td>1.667</td>\n",
              "      <td>0.999154</td>\n",
              "      <td>0.000847</td>\n",
              "      <td>32.114</td>\n",
              "      <td>204.027</td>\n",
              "      <td>204.090</td>\n",
              "      <td>0.931</td>\n",
              "      <td>202.324</td>\n",
              "      <td>206.182</td>\n",
              "      <td>200</td>\n",
              "      <td>199</td>\n",
              "      <td>0.004900</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.150</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.097</td>\n",
              "      <td>0.240</td>\n",
              "      <td>1.049</td>\n",
              "      <td>0.091</td>\n",
              "      <td>0.533</td>\n",
              "      <td>0.698</td>\n",
              "      <td>0.777</td>\n",
              "      <td>1.599</td>\n",
              "      <td>0.999295</td>\n",
              "      <td>0.000706</td>\n",
              "      <td>32.816</td>\n",
              "      <td>207.519</td>\n",
              "      <td>207.468</td>\n",
              "      <td>0.880</td>\n",
              "      <td>205.407</td>\n",
              "      <td>209.927</td>\n",
              "      <td>204</td>\n",
              "      <td>203</td>\n",
              "      <td>0.004820</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   jitter_local  jitter_local_absolute  ...  voice_breaks  degree_voice_break\n",
              "0         0.135               0.000007  ...             0                 0.0\n",
              "1         0.143               0.000007  ...             0                 0.0\n",
              "2         0.162               0.000008  ...             0                 0.0\n",
              "3         0.140               0.000007  ...             0                 0.0\n",
              "4         0.150               0.000007  ...             0                 0.0\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhl__v6GbnQ8",
        "outputId": "cc820e85-ec86-428c-e788-3ca23bebd00b"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1208, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69nVM75hb4yo",
        "outputId": "da602c7d-ddb4-4c6a-dd2c-4339fd6f35d5"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: class_information, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rlAleaYW2E9"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80xR54YUW2rF"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=109) # 70% training and 30% test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkJ-mQiPW8cf"
      },
      "source": [
        "#Import svm model\n",
        "from sklearn import svm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5bDWA0MHO0I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53786939-10a0-4212-b9bd-8c33a05015a2"
      },
      "source": [
        "pip show sklearn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: sklearn\n",
            "Version: 0.0\n",
            "Summary: A set of python modules for machine learning and data mining\n",
            "Home-page: https://pypi.python.org/pypi/scikit-learn/\n",
            "Author: UNKNOWN\n",
            "Author-email: UNKNOWN\n",
            "License: None\n",
            "Location: /usr/local/lib/python3.7/dist-packages\n",
            "Requires: scikit-learn\n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLQZYvJ9XBUk"
      },
      "source": [
        "#Create a svm Classifier\n",
        "clf = svm.SVC(C=10,gamma=0.0001,kernel='rbf') # Linear Kernel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0lRtObGXEDr",
        "outputId": "dfd05f52-b7f8-4aa8-c842-42c887ade558"
      },
      "source": [
        "#Train the model using the training sets\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4uzmKxWXHeS"
      },
      "source": [
        "#Predict the response for test dataset\n",
        "y_pred = clf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4g5yHT2i4fJ"
      },
      "source": [
        "Reto 1 sesion 4 ML bedu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlDfhfYYi9Bi"
      },
      "source": [
        "y_pred_train = clf.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Edj0FudcjdNx"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_boston\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aB6fAuvPikx4",
        "outputId": "f00b0639-46cc-4994-e27d-8a4ce5caa49a"
      },
      "source": [
        "MSE = mean_squared_error(y_train,y_pred_train)\n",
        "print(\"Entrenamiento: MSE =\"+str(MSE))\n",
        "\n",
        "y_test_predict = clf.predict(X_test)\n",
        "MSE = (mean_squared_error(y_test, y_pred))\n",
        "print(\"Pruebas: MSE =\"+str(MSE))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Entrenamiento: MSE =0.15384615384615385\n",
            "Pruebas: MSE =0.29201101928374656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ip4cxu5DXI_k"
      },
      "source": [
        "#Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hq5nBZXPXO28",
        "outputId": "cd2593e9-1491-4161-fa0d-34a90d74ed65"
      },
      "source": [
        "# Model Accuracy: how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7079889807162535\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMHR-MdUXYU8",
        "outputId": "489cec28-485f-4977-b905-b270fac4d9ab"
      },
      "source": [
        "# Model Precision: what percentage of positive tuples are labeled as such?\n",
        "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
        "\n",
        "# Model Recall: what percentage of positive tuples are labelled as such?\n",
        "print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.7233009708737864\n",
            "Recall: 0.7525252525252525\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxrrc890AYNT"
      },
      "source": [
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import roc_curve, auc\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRchQlD2AZqW",
        "outputId": "4fa01595-c7da-44fb-efe9-a5ac75471ee0"
      },
      "source": [
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "cnf_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[108,  57],\n",
              "       [ 49, 149]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "ZrTZo-0DAbK7",
        "outputId": "d41332fa-e7bc-47b1-bd08-85f160ef6b6f"
      },
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots(figsize=(6, 6))\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Blues\" ,fmt='g', cbar=False, annot_kws={\"size\": 20})\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.ylabel('Realidad', fontsize=15, y=0.5)\n",
        "plt.xlabel('Estimado por el modelo', fontsize=15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1jAHUrHCYXX"
      },
      "source": [
        "y_train_pred = clf.decision_function(X_train)  \n",
        "y_test_pred = clf.decision_function(X_test)  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lMrJI_-XA1xH",
        "outputId": "40398b62-0d0c-48bd-a8dc-0558f1118ae1"
      },
      "source": [
        "train_fpr, train_tpr, tr_thresholds = roc_curve(y_train, y_train_pred)\n",
        "test_fpr, test_tpr, te_thresholds = roc_curve(y_test, y_test_pred)\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.plot(test_fpr, test_tpr,color='darkorange', label=\" AUC TEST =\"+str(auc(test_fpr, test_tpr)))\n",
        "plt.plot([0,1],[0,1],color='navy',linestyle='--')\n",
        "plt.legend()\n",
        "plt.xlabel(\"True Positive Rate\")\n",
        "plt.ylabel(\"False Positive Rate\")\n",
        "plt.title(\"AUC(ROC curve)\")\n",
        "plt.grid(color='black', linestyle='-', linewidth=0.5)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPH8N9UfaiXI"
      },
      "source": [
        "#cross validation to get the best C, gamma and kernel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gloUwMt0astA"
      },
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CeMjTMtatzs"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5,random_state=0) # 50% training and 50% test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01yyyqSma4_L"
      },
      "source": [
        "# Set the parameters by cross-validation\n",
        "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
        "                     'C': [1, 10, 100, 1000]},\n",
        "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtOPpkeha97L"
      },
      "source": [
        "scores = ['precision', 'recall']\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bsK3Re3a-U4"
      },
      "source": [
        "for score in scores:\n",
        "    print(\"# Tuning hyper-parameters for %s\" % score)\n",
        "    print()\n",
        "\n",
        "    clf = GridSearchCV(\n",
        "        SVC(), tuned_parameters, scoring='%s_macro' % score\n",
        "    )\n",
        "    clf.fit(X_train, y_train)\n",
        "\n",
        "    print(\"Best parameters set found on development set:\")\n",
        "    print()\n",
        "    print(clf.best_params_)\n",
        "    print()\n",
        "    print(\"Grid scores on development set:\")\n",
        "    print()\n",
        "    means = clf.cv_results_['mean_test_score']\n",
        "    stds = clf.cv_results_['std_test_score']\n",
        "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
        "        print(\"%0.3f (+/-%0.03f) for %r\"\n",
        "              % (mean, std * 2, params))\n",
        "    print()\n",
        "\n",
        "    print(\"Detailed classification report:\")\n",
        "    print()\n",
        "    print(\"The model is trained on the full development set.\")\n",
        "    print(\"The scores are computed on the full evaluation set.\")\n",
        "    print()\n",
        "    y_true, y_pred = y_test, clf.predict(X_test)\n",
        "    print(classification_report(y_true, y_pred))\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCQMA26nBsnE"
      },
      "source": [
        "#practical"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOiI7CumBwZy"
      },
      "source": [
        "df_audio=pd.read_csv('./audio_new_ethan_test.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "H7waQx7MCmfB",
        "outputId": "619fd0e3-28e8-4c5f-ffbe-e72cfe73fcf9"
      },
      "source": [
        "df_audio"
      ],
      "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>Jitter (local):</th>\n",
              "      <th>Jitter (local, absolute):</th>\n",
              "      <th>Jitter (rap):</th>\n",
              "      <th>Jitter (ppq5):</th>\n",
              "      <th>Jitter (ddp):</th>\n",
              "      <th>Shimmer (local):</th>\n",
              "      <th>Shimmer (local, dB):</th>\n",
              "      <th>Shimmer (apq3):</th>\n",
              "      <th>Shimmer (apq5):</th>\n",
              "      <th>Shimmer (apq11):</th>\n",
              "      <th>Shimmer (dda):</th>\n",
              "      <th>Mean autocorrelation:</th>\n",
              "      <th>Mean noise-to-harmonics ratio:</th>\n",
              "      <th>Mean harmonics-to-noise ratio:</th>\n",
              "      <th>Median pitch:</th>\n",
              "      <th>Mean pitch:</th>\n",
              "      <th>Standard deviation:</th>\n",
              "      <th>Minimum pitch:</th>\n",
              "      <th>Maximum pitch:</th>\n",
              "      <th>Number of pulses:</th>\n",
              "      <th>Number of periods:</th>\n",
              "      <th>Mean period:</th>\n",
              "      <th>Standard deviation of period:</th>\n",
              "      <th>Fraction of locally unvoiced frames:</th>\n",
              "      <th>Number of voice breaks:</th>\n",
              "      <th>Degree of voice breaks:</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00804</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00437</td>\n",
              "      <td>0.01396</td>\n",
              "      <td>0.0754</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.04216</td>\n",
              "      <td>0.04382</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.12647</td>\n",
              "      <td>0.932847</td>\n",
              "      <td>0.084179</td>\n",
              "      <td>14.788</td>\n",
              "      <td>100.089</td>\n",
              "      <td>100.073</td>\n",
              "      <td>2.287</td>\n",
              "      <td>87.308</td>\n",
              "      <td>123.896</td>\n",
              "      <td>1746.0</td>\n",
              "      <td>1739.0</td>\n",
              "      <td>0.009993</td>\n",
              "      <td>0.00024</td>\n",
              "      <td>0.47692</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.45129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Jitter (local):   ...  Degree of voice breaks: \n",
              "0           0.00804  ...                   0.45129\n",
              "\n",
              "[1 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "id": "07ZUgUxOB5do",
        "outputId": "098c70fd-51c0-48e2-8ddb-a60b46c1b7f6"
      },
      "source": [
        "df_audio.columns = list(X.columns)\n",
        "df_audio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>jitter_local</th>\n",
              "      <th>jitter_local_absolute</th>\n",
              "      <th>jitter_rap</th>\n",
              "      <th>jitter_ppq5</th>\n",
              "      <th>jitter_ddp</th>\n",
              "      <th>shimmer_local</th>\n",
              "      <th>shimer_local_db</th>\n",
              "      <th>shimmer_apq3</th>\n",
              "      <th>shimmer_apq5</th>\n",
              "      <th>shimmer_apq11</th>\n",
              "      <th>shimer_dda</th>\n",
              "      <th>ac</th>\n",
              "      <th>nth</th>\n",
              "      <th>htn</th>\n",
              "      <th>median_pitch</th>\n",
              "      <th>mean_pitch</th>\n",
              "      <th>standard_deviation</th>\n",
              "      <th>minimum_pitch</th>\n",
              "      <th>maximum_pitch</th>\n",
              "      <th>number_pulses</th>\n",
              "      <th>periods</th>\n",
              "      <th>mean_period</th>\n",
              "      <th>std_period</th>\n",
              "      <th>locally_unvoiced_frames</th>\n",
              "      <th>voice_breaks</th>\n",
              "      <th>degree_voice_break</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00804</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00437</td>\n",
              "      <td>0.01396</td>\n",
              "      <td>0.0754</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.04216</td>\n",
              "      <td>0.04382</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.12647</td>\n",
              "      <td>0.932847</td>\n",
              "      <td>0.084179</td>\n",
              "      <td>14.788</td>\n",
              "      <td>100.089</td>\n",
              "      <td>100.073</td>\n",
              "      <td>2.287</td>\n",
              "      <td>87.308</td>\n",
              "      <td>123.896</td>\n",
              "      <td>1746.0</td>\n",
              "      <td>1739.0</td>\n",
              "      <td>0.009993</td>\n",
              "      <td>0.00024</td>\n",
              "      <td>0.47692</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.45129</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   jitter_local  jitter_local_absolute  ...  voice_breaks  degree_voice_break\n",
              "0       0.00804                0.00008  ...           6.0             0.45129\n",
              "\n",
              "[1 rows x 26 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9GeDsp2B7Yj"
      },
      "source": [
        "y_pred = clf.predict(df_audio)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q00LXknRB9Hp",
        "outputId": "19fcc296-4314-4fa0-f634-b84f8ae18d33"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEj9moUkCtZ2",
        "outputId": "6369d581-085c-4083-aae1-9dd8f2137280"
      },
      "source": [
        "if y_pred[0] == 1:\n",
        "  print(\"Patient has some form of parkinson\")\n",
        "else:\n",
        "  print(\"Patient does not have parkinson\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Patient does not have parkinson\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A0G0W68Hko6C"
      },
      "source": [
        "# Save ML SVM trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHJRLIBCksls",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "5342e0ea-2599-4d9e-fe94-142377afc64b"
      },
      "source": [
        "filename = 'parkinson_svm_model.sav'\n",
        "pickle.dump(clf, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-f429063e7237>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#filename = 'parkinson_svm_model.sav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://github.com/eecastillo/parkinson_analisis/blob/master/python_code/parkinson_svm_model.sav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'clf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXqRYLv0le9q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "cb598106-11e3-499a-b2bf-c584dc1d9a28"
      },
      "source": [
        "filename = 'https://github.com/eecastillo/parkinson_analisis/blob/master/python_code/parkinson_svm_model.sav'\n",
        "\n",
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "prediction=loaded_model.predict(df_audio)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-10af85e76fa0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://github.com/eecastillo/parkinson_analisis/blob/master/python_code/parkinson_svm_model.sav'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mloaded_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloaded_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_audio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'https://github.com/eecastillo/parkinson_analisis/blob/master/python_code/parkinson_svm_model.sav'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0MHJaSYlvzn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3818989-cbe6-4a5f-97c0-67165248399c"
      },
      "source": [
        "prediction"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}