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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled0.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/mlynarik/pbpython/blob/master/First\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KXIWBltLBsxQ",
"colab_type": "code",
"outputId": "bace5d5b-004c-4230-c5c0-4f23277db799",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
}
},
"source": [
"import numpy as np\n",
"import sklearn.preprocessing as pp\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.utils.multiclass import unique_labels\n",
"\n",
"X = np.array([0,1,0,1])\n",
"Y = pp.binarize([X],0.5)[0]\n",
"\n",
"print(confusion_matrix(X,Y))\n",
"\n",
"def plot_confusion_matrix(y_true, y_pred, classes,\n",
" normalize=False,\n",
" title=None,\n",
" cmap=plt.cm.Blues):\n",
" \"\"\"\n",
" This function prints and plots the confusion matrix.\n",
" Normalization can be applied by setting `normalize=True`.\n",
" \"\"\"\n",
" if not title:\n",
" if normalize:\n",
" title = 'Normalized confusion matrix'\n",
" else:\n",
" title = 'Confusion matrix, without normalization'\n",
"\n",
" # Compute confusion matrix\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" # Only use the labels that appear in the data\n",
" if normalize:\n",
" cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
" print(\"Normalized confusion matrix\")\n",
" else:\n",
" print('Confusion matrix, without normalization')\n",
"\n",
" print(cm)\n",
"\n",
" fig, ax = plt.subplots()\n",
" im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" ax.figure.colorbar(im, ax=ax)\n",
" # We want to show all ticks...\n",
" ax.set(xticks=np.arange(cm.shape[1]),\n",
" yticks=np.arange(cm.shape[0]),\n",
" # ... and label them with the respective list entries\n",
" xticklabels=classes, yticklabels=classes,\n",
" title=title,\n",
" ylabel='True label',\n",
" xlabel='Predicted label')\n",
"\n",
" # Rotate the tick labels and set their alignment.\n",
" plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
" rotation_mode=\"anchor\")\n",
"\n",
" # Loop over data dimensions and create text annotations.\n",
" fmt = '.2f' if normalize else 'd'\n",
" thresh = cm.max() / 2.\n",
" for i in range(cm.shape[0]):\n",
" for j in range(cm.shape[1]):\n",
" ax.text(j, i, format(cm[i, j], fmt),\n",
" ha=\"center\", va=\"center\",\n",
" color=\"white\" if cm[i, j] > thresh else \"black\")\n",
" fig.tight_layout()\n",
" return ax\n",
"\n",
"plot_confusion_matrix(X,Y,['Event','Non-Event'], normalize=True)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
],
"execution_count": 98,
"outputs": [
{
"output_type": "stream",
"text": [
"[[2 0]\n",
" [0 2]]\n",
"Normalized confusion matrix\n",
"[[1. 0.]\n",
" [0. 1.]]\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fae3d9cb9b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 98
},
{
"output_type": "display_data",
"data": {
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/Bm4CjicLjgDjgOsl9YmIqalJ3TMiXi6Qzwd8PlNGi/LHS4ayx45b0K12LaaOuYiLbriP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\nZmaJA6KZWeKAaGaW/H+Hnsi6Mm4BLwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QmJf5qu4Mm_L",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}