ML_Final_SVM_Details.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_Final_SVM_Details.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVMu3GoxtHs5"
      },
      "source": [
        "from sklearn.svm import SVC"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4jTXzLu4Dni"
      },
      "source": [
        "# SVM Training and Evaluation\n",
        "\n",
        "Our goal is to train a simple SVM."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DlYlv5WBvbc6",
        "outputId": "867cb9b6-254a-4655-81e7-b68370dad73a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ibV_aCod4NSW"
      },
      "source": [
        "Load in the train test splits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnF9TZ-2vuBa"
      },
      "source": [
        "import numpy as np\n",
        "X_train = np.load('drive/MyDrive/ML_Final/Upsampled_Data/X_train_upsample_final.npy')\n",
        "X_validate = np.load('drive/MyDrive/ML_Final/Upsampled_Data/X_validate_upsample_final.npy')\n",
        "X_test = np.load('drive/MyDrive/ML_Final/Upsampled_Data/X_test_upsample_final.npy')\n",
        "y_train = np.load('drive/MyDrive/ML_Final/Upsampled_Data/y_train_upsample_final.npy')\n",
        "y_validate = np.load('drive/MyDrive/ML_Final/Upsampled_Data/y_validate_upsample_final.npy')\n",
        "y_test = np.load('drive/MyDrive/ML_Final/Upsampled_Data/y_test_upsample_final.npy')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O03Di2DOHm-s",
        "outputId": "3f202130-38a8-4783-e058-d565693a527b"
      },
      "source": [
        "print(np.unique(y_train,return_counts=True))\n",
        "print(np.unique(y_test,return_counts=True))\n",
        "print(np.unique(y_validate,return_counts=True))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array(['anger', 'happiness', 'neutral', 'sadness', 'surprise'],\n",
            "      dtype='<U9'), array([189, 174, 200, 177, 164]))\n",
            "(array(['anger', 'happiness', 'neutral', 'sadness', 'surprise'],\n",
            "      dtype='<U9'), array([75, 73, 57, 57, 75]))\n",
            "(array(['anger', 'happiness', 'neutral', 'sadness', 'surprise'],\n",
            "      dtype='<U9'), array([36, 53, 43, 66, 61]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTyhjIPswxFM"
      },
      "source": [
        "We now have the data we need to train the SVM. We also need to encode the labels as numeric values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbUcs3xwwzhl"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "le.fit(y_train)\n",
        "y_train = le.transform(y_train)\n",
        "y_validate = le.transform(y_validate)\n",
        "y_test = le.transform(y_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKum7KBow0D6",
        "outputId": "3051d23a-81f2-40cc-adcf-44d33dcc3ca0"
      },
      "source": [
        "clf = SVC(C = 7, gamma = 'scale', decision_function_shape='ovr', kernel = 'poly', degree = 3)\n",
        "clf.fit(X_train, y_train)\n",
        "print(clf.score(X_validate, y_validate))\n",
        "clf.score(X_test, y_test)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6563706563706564\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6350148367952523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kDsLx8Q4fOv"
      },
      "source": [
        "We find that an SVM with C = 7, a polynomial kernel of degree 3, and an ovr decision function works best, achieving a validation accuracy of .65 and a test accuracy of .63. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylvWvVGtHkay",
        "outputId": "6c0fa87b-3076-4d37-ab26-0dee55d74aea"
      },
      "source": [
        "#Save the model\n",
        "\n",
        "from joblib import dump, load\n",
        "dump(clf, 'drive/MyDrive/ML_Final/Models/svm.joblib')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/MyDrive/ML_Final/Models/svm.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IbJNnkL4Wz4"
      },
      "source": [
        "Let's do some analysis of the quality of our model now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aqj7IpW4Yc6"
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlgpHaPh4ai4",
        "outputId": "c8008a87-47f9-4741-ee0a-20352ede33a0"
      },
      "source": [
        "y_test_pred = clf.predict(X_test)\n",
        "print(classification_report(y_test, y_test_pred))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.72      0.64        75\n",
            "           1       0.79      0.77      0.78        73\n",
            "           2       0.48      0.53      0.50        57\n",
            "           3       0.49      0.37      0.42        57\n",
            "           4       0.76      0.68      0.72        75\n",
            "\n",
            "    accuracy                           0.63       337\n",
            "   macro avg       0.62      0.61      0.61       337\n",
            "weighted avg       0.63      0.63      0.63       337\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBYEGe6f4yY3"
      },
      "source": [
        "We see that there is a bit of range in the quality of our scores. For example, our scores for class 1 reach into the .70's, however are only in the .40's and .50's for other classes. This suggests that our SVM may not be performing equally well across all classes. Next we can look at the confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VjyLFivrtm1",
        "outputId": "2ba640b5-ed8e-4b46-bf51-07947c4042b2"
      },
      "source": [
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "conf_norm = normalize(confusion_matrix(y_test,y_test_pred), norm=\"l1\")\n",
        "conf_norm = np.round(conf_norm, 2)\n",
        "print(conf_norm)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.72 0.04 0.04 0.17 0.03]\n",
            " [0.04 0.77 0.08 0.07 0.04]\n",
            " [0.18 0.07 0.53 0.05 0.18]\n",
            " [0.3  0.11 0.21 0.37 0.02]\n",
            " [0.13 0.03 0.15 0.01 0.68]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAEwEYPKsvFj"
      },
      "source": [
        "CNN confusion matrix for reference:\n",
        "![CNN 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)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1E0IuxhkvYG-",
        "outputId": "7f70a353-6e08-4602-8110-8ebeaadb9727"
      },
      "source": [
        "bad_data = []\n",
        "for i in range(len(y_test)):\n",
        "  if y_test_pred[i] != y_test[i]:\n",
        "    #bad_data.append()\n",
        "    print(i, 'has been classified as ', y_test_pred[i], 'and should be ', y_test[i])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 has been classified as  3 and should be  1\n",
            "2 has been classified as  2 and should be  4\n",
            "5 has been classified as  0 and should be  3\n",
            "8 has been classified as  0 and should be  4\n",
            "12 has been classified as  1 and should be  0\n",
            "19 has been classified as  2 and should be  1\n",
            "20 has been classified as  2 and should be  0\n",
            "21 has been classified as  3 and should be  0\n",
            "33 has been classified as  0 and should be  4\n",
            "34 has been classified as  2 and should be  1\n",
            "36 has been classified as  0 and should be  2\n",
            "39 has been classified as  1 and should be  3\n",
            "41 has been classified as  1 and should be  3\n",
            "42 has been classified as  3 and should be  1\n",
            "43 has been classified as  1 and should be  0\n",
            "45 has been classified as  1 and should be  0\n",
            "46 has been classified as  2 and should be  3\n",
            "48 has been classified as  2 and should be  3\n",
            "50 has been classified as  2 and should be  3\n",
            "51 has been classified as  0 and should be  3\n",
            "52 has been classified as  2 and should be  3\n",
            "58 has been classified as  0 and should be  3\n",
            "59 has been classified as  0 and should be  3\n",
            "60 has been classified as  0 and should be  3\n",
            "63 has been classified as  0 and should be  3\n",
            "65 has been classified as  0 and should be  3\n",
            "66 has been classified as  0 and should be  4\n",
            "67 has been classified as  0 and should be  2\n",
            "73 has been classified as  2 and should be  1\n",
            "74 has been classified as  4 and should be  2\n",
            "75 has been classified as  2 and should be  4\n",
            "82 has been classified as  2 and should be  3\n",
            "84 has been classified as  2 and should be  3\n",
            "86 has been classified as  0 and should be  3\n",
            "89 has been classified as  3 and should be  0\n",
            "90 has been classified as  3 and should be  0\n",
            "91 has been classified as  3 and should be  0\n",
            "92 has been classified as  3 and should be  0\n",
            "93 has been classified as  3 and should be  0\n",
            "94 has been classified as  3 and should be  0\n",
            "97 has been classified as  3 and should be  0\n",
            "99 has been classified as  3 and should be  0\n",
            "100 has been classified as  3 and should be  0\n",
            "110 has been classified as  3 and should be  2\n",
            "114 has been classified as  0 and should be  4\n",
            "115 has been classified as  4 and should be  1\n",
            "117 has been classified as  3 and should be  0\n",
            "119 has been classified as  2 and should be  4\n",
            "121 has been classified as  3 and should be  0\n",
            "127 has been classified as  0 and should be  2\n",
            "131 has been classified as  0 and should be  4\n",
            "132 has been classified as  1 and should be  3\n",
            "134 has been classified as  1 and should be  3\n",
            "140 has been classified as  3 and should be  2\n",
            "141 has been classified as  0 and should be  2\n",
            "143 has been classified as  2 and should be  4\n",
            "144 has been classified as  0 and should be  2\n",
            "146 has been classified as  2 and should be  1\n",
            "154 has been classified as  2 and should be  4\n",
            "155 has been classified as  2 and should be  3\n",
            "156 has been classified as  0 and should be  3\n",
            "158 has been classified as  2 and should be  3\n",
            "159 has been classified as  0 and should be  3\n",
            "160 has been classified as  2 and should be  3\n",
            "161 has been classified as  4 and should be  3\n",
            "162 has been classified as  0 and should be  3\n",
            "164 has been classified as  4 and should be  0\n",
            "169 has been classified as  0 and should be  4\n",
            "171 has been classified as  1 and should be  2\n",
            "173 has been classified as  4 and should be  2\n",
            "174 has been classified as  3 and should be  1\n",
            "176 has been classified as  2 and should be  4\n",
            "178 has been classified as  2 and should be  1\n",
            "181 has been classified as  1 and should be  4\n",
            "183 has been classified as  2 and should be  4\n",
            "186 has been classified as  3 and should be  1\n",
            "190 has been classified as  1 and should be  2\n",
            "192 has been classified as  0 and should be  4\n",
            "194 has been classified as  1 and should be  4\n",
            "195 has been classified as  3 and should be  4\n",
            "199 has been classified as  4 and should be  0\n",
            "214 has been classified as  2 and should be  0\n",
            "215 has been classified as  3 and should be  0\n",
            "216 has been classified as  2 and should be  0\n",
            "236 has been classified as  4 and should be  2\n",
            "239 has been classified as  4 and should be  1\n",
            "240 has been classified as  2 and should be  4\n",
            "241 has been classified as  2 and should be  4\n",
            "242 has been classified as  2 and should be  3\n",
            "246 has been classified as  1 and should be  2\n",
            "251 has been classified as  2 and should be  3\n",
            "254 has been classified as  0 and should be  1\n",
            "255 has been classified as  3 and should be  2\n",
            "265 has been classified as  2 and should be  1\n",
            "272 has been classified as  2 and should be  4\n",
            "273 has been classified as  1 and should be  3\n",
            "274 has been classified as  0 and should be  3\n",
            "275 has been classified as  1 and should be  3\n",
            "281 has been classified as  0 and should be  1\n",
            "288 has been classified as  4 and should be  2\n",
            "289 has been classified as  2 and should be  3\n",
            "290 has been classified as  0 and should be  3\n",
            "291 has been classified as  0 and should be  3\n",
            "293 has been classified as  3 and should be  1\n",
            "294 has been classified as  0 and should be  2\n",
            "295 has been classified as  0 and should be  2\n",
            "298 has been classified as  4 and should be  2\n",
            "299 has been classified as  4 and should be  2\n",
            "301 has been classified as  1 and should be  2\n",
            "302 has been classified as  0 and should be  4\n",
            "311 has been classified as  0 and should be  3\n",
            "312 has been classified as  0 and should be  3\n",
            "313 has been classified as  0 and should be  3\n",
            "314 has been classified as  4 and should be  2\n",
            "315 has been classified as  4 and should be  2\n",
            "317 has been classified as  0 and should be  2\n",
            "319 has been classified as  4 and should be  1\n",
            "321 has been classified as  0 and should be  2\n",
            "322 has been classified as  0 and should be  1\n",
            "324 has been classified as  2 and should be  4\n",
            "325 has been classified as  0 and should be  4\n",
            "326 has been classified as  0 and should be  4\n",
            "330 has been classified as  4 and should be  2\n",
            "331 has been classified as  4 and should be  2\n",
            "334 has been classified as  0 and should be  2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3-alt6zZGxZ"
      },
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "train_sizes, train_scores, valid_scores = learning_curve(clf, X_test, y_test)\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQr7ShjF5V5w"
      },
      "source": [
        "Let's plot some learning curves now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhYCho_raGF7"
      },
      "source": [
        "#Code taken from https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.datasets import load_digits\n",
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "\n",
        "def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n",
        "                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    \"\"\"\n",
        "    Generate 3 plots: the test and training learning curve, the training\n",
        "    samples vs fit times curve, the fit times vs score curve.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
        "        An object of that type which is cloned for each validation.\n",
        "\n",
        "    title : string\n",
        "        Title for the chart.\n",
        "\n",
        "    X : array-like, shape (n_samples, n_features)\n",
        "        Training vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X for classification or regression;\n",
        "        None for unsupervised learning.\n",
        "\n",
        "    axes : array of 3 axes, optional (default=None)\n",
        "        Axes to use for plotting the curves.\n",
        "\n",
        "    ylim : tuple, shape (ymin, ymax), optional\n",
        "        Defines minimum and maximum yvalues plotted.\n",
        "\n",
        "    cv : int, cross-validation generator or an iterable, optional\n",
        "        Determines the cross-validation splitting strategy.\n",
        "        Possible inputs for cv are:\n",
        "\n",
        "          - None, to use the default 5-fold cross-validation,\n",
        "          - integer, to specify the number of folds.\n",
        "          - :term:`CV splitter`,\n",
        "          - An iterable yielding (train, test) splits as arrays of indices.\n",
        "\n",
        "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
        "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
        "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
        "\n",
        "        Refer :ref:`User Guide <cross_validation>` for the various\n",
        "        cross-validators that can be used here.\n",
        "\n",
        "    n_jobs : int or None, optional (default=None)\n",
        "        Number of jobs to run in parallel.\n",
        "        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
        "        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
        "        for more details.\n",
        "\n",
        "    train_sizes : array-like, shape (n_ticks,), dtype float or int\n",
        "        Relative or absolute numbers of training examples that will be used to\n",
        "        generate the learning curve. If the dtype is float, it is regarded as a\n",
        "        fraction of the maximum size of the training set (that is determined\n",
        "        by the selected validation method), i.e. it has to be within (0, 1].\n",
        "        Otherwise it is interpreted as absolute sizes of the training sets.\n",
        "        Note that for classification the number of samples usually have to\n",
        "        be big enough to contain at least one sample from each class.\n",
        "        (default: np.linspace(0.1, 1.0, 5))\n",
        "    \"\"\"\n",
        "    if axes is None:\n",
        "        _, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
        "\n",
        "    axes[0].set_title(title)\n",
        "    if ylim is not None:\n",
        "        axes[0].set_ylim(*ylim)\n",
        "    axes[0].set_xlabel(\"Training examples\")\n",
        "    axes[0].set_ylabel(\"Score\")\n",
        "\n",
        "    train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
        "        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n",
        "                       train_sizes=train_sizes,\n",
        "                       return_times=True)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    fit_times_mean = np.mean(fit_times, axis=1)\n",
        "    fit_times_std = np.std(fit_times, axis=1)\n",
        "\n",
        "    # Plot learning curve\n",
        "    axes[0].grid()\n",
        "    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                         train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                         color=\"r\")\n",
        "    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                         test_scores_mean + test_scores_std, alpha=0.1,\n",
        "                         color=\"g\")\n",
        "    axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "                 label=\"Training score\")\n",
        "    axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "                 label=\"Cross-validation score\")\n",
        "    axes[0].legend(loc=\"best\")\n",
        "\n",
        "    # Plot n_samples vs fit_times\n",
        "    axes[1].grid()\n",
        "    axes[1].plot(train_sizes, fit_times_mean, 'o-')\n",
        "    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n",
        "                         fit_times_mean + fit_times_std, alpha=0.1)\n",
        "    axes[1].set_xlabel(\"Training examples\")\n",
        "    axes[1].set_ylabel(\"fit_times\")\n",
        "    axes[1].set_title(\"Scalability of the model\")\n",
        "\n",
        "    # Plot fit_time vs score\n",
        "    axes[2].grid()\n",
        "    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n",
        "    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n",
        "                         test_scores_mean + test_scores_std, alpha=0.1)\n",
        "    axes[2].set_xlabel(\"fit_times\")\n",
        "    axes[2].set_ylabel(\"Score\")\n",
        "    axes[2].set_title(\"Performance of the model\")\n",
        "\n",
        "    return plt"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "iutd7OejbnRj",
        "outputId": "401e1448-1eca-4059-a8f3-2f855e73377b"
      },
      "source": [
        "fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
        "\n",
        "title = \"Learning Curves (SVM)\"\n",
        "#estimator = GaussianNB()\n",
        "# Cross validation with 100 iterations to get smoother mean test and train\n",
        "# score curves, each time with 20% data randomly selected as a validation set.\n",
        "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
        "estimator = clf\n",
        "#estimator = clf\n",
        "\n",
        "plot_learning_curve(estimator, title, np.concatenate((X_train, X_validate, X_test), axis = 0), np.concatenate((y_train, y_validate, y_test)), axes = axes, ylim=(0.4, 1.01), cv = cv,\n",
        "                    n_jobs=4)\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}