Please cite us if you use the software
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Example-2 (How to plot via matplotlib)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "⚠️ This example is deprecated. You can use `plot` method from `version 3.0`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Environment check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Checking that the notebook is running on Google Colab or not." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "try:\n", " import google.colab\n", " !{sys.executable} -m pip -q -q install pycm\n", "except:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hide_output": true }, "outputs": [], "source": [ "!{sys.executable} -m pip -q -q install matplotlib;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import itertools\n", "from pycm import ConfusionMatrix\n", "\n", "def plot_confusion_matrix(cm,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function modified to plots the ConfusionMatrix object.\n", " Normalization can be applied by setting `normalize=True`.\n", " \n", " Code Reference : \n", " http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n", " \n", " \"\"\"\n", "\n", " plt_cm = []\n", " for i in cm.classes :\n", " row=[]\n", " for j in cm.classes:\n", " row.append(cm.table[i][j])\n", " plt_cm.append(row)\n", " plt_cm = np.array(plt_cm)\n", " if normalize:\n", " plt_cm = plt_cm.astype('float') / plt_cm.sum(axis=1)[:, np.newaxis] \n", " plt.imshow(plt_cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(cm.classes))\n", " plt.xticks(tick_marks, cm.classes, rotation=45)\n", " plt.yticks(tick_marks, cm.classes)\n", "\n", " fmt = '.2f' if normalize else 'd'\n", " thresh = plt_cm.max() / 2.\n", " for i, j in itertools.product(range(plt_cm.shape[0]), range(plt_cm.shape[1])):\n", " plt.text(j, i, format(plt_cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if plt_cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('Actual')\n", " plt.xlabel('Predict')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cm = ConfusionMatrix(matrix={0: {0:13, 1:0, 2:0}, 1:{0:0, 1:10, 2:6}, 2: {0:0, 1:0, 2:9}})" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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