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@denismazzucato
Last active January 17, 2019 16:41
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled",
"version": "0.3.2",
"provenance": [],
"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/gist/denismazzu96/a7fdb5d95bdbe2979b60dcb7816e7462/untitled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "26LrV9tXZqCI",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Decision Tree Classifier - Iris Dataset\n",
"\n",
"Io e il mio compagno Paolo Eccher abbiamo implementato con questo piccolo script gli **alberi di decisione** per la **classificazione**, abbiamo scelto il dataset **Iris** da sklearn (vedi https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html).\n",
"\n",
"Il dataset Iris si compone da 3 diversi tipi di iridi (setosa, versicolour e virginica) lunghezza dei petali e dei sepali.\n",
"\n",
"Lo script python alla fine della sua esecuzione mostra 3 grafici con le aree di decisione dell'albero di decisione usando la **paired features**.\n"
]
},
{
"metadata": {
"id": "z3KtIiAnYsF-",
"colab_type": "code",
"outputId": "316e1159-93df-423f-fa25-155d829eec22",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"# Parametri\n",
"n_classi = 3\n",
"colori_grafico = \"ryb\"\n",
"passi_grafico = 0.02\n",
"\n",
"# scarico dataset Iris\n",
"iris = load_iris()\n",
"\n",
"for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3]]):\n",
" # Prendiamo solo le due corrispondenti features\n",
" X = iris.data[:, pair]\n",
" y = iris.target\n",
"\n",
" # Training dell'albero di decisione per problemi di classificazione\n",
" clf = DecisionTreeClassifier().fit(X, y)\n",
"\n",
" # Traccio i limiti di decisione\n",
" plt.subplot(2, 3, pairidx + 1)\n",
"\n",
" # definisco le coordinate dei bounds\n",
" x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
" y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, passi_grafico),\n",
" np.arange(y_min, y_max, passi_grafico))\n",
" plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n",
"\n",
" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
" cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)\n",
"\n",
" plt.xlabel(iris.feature_names[pair[0]])\n",
" plt.ylabel(iris.feature_names[pair[1]])\n",
"\n",
" # Disegno i grafici\n",
" for i, color in zip(range(n_classi), colori_grafico):\n",
" idx = np.where(y == i)\n",
" plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],\n",
" cmap=plt.cm.RdYlBu, edgecolor='black', s=15)\n",
"\n",
"plt.suptitle(\"Aree di Decisione\")\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
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wsJARI0bQv7/kyFGr1Tz77LN06dKFlStX0rVrV4fzr1y5wttvv01ubi5z5swhOTkZmUzW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\nxI8aBBEREfGjBkFERET8qEEQERERP/8PBAFd0kEqHrgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x396 with 3 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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