Tucil2_13514047_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pembelajaran Mesin\n",
    "## Dataset Iris Internal Sklearn\n",
    "Oleh : Bervianto Leo P - 13514047 dan Muhammad Reifiza - 13514103"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Persiapan\n",
    "\n",
    "* Melakukan import yang diperlukan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import graphviz "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Mengambil atau men-load data iris internal dari sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Visualisasi hubungan antar fitur\n",
    "\n",
    "Agar mudah data yang digunakan yaitu pada seaborn sehingga struktur data sesuai dengan yang dibutuhkan untuk menggambar hubungan antar features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Iz63KFAEpvK8wggBVMj+SwLnc8K3qx+hodEarg2dLRAeD00pEU/zXL9snV+zd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\nq9dItB1IMS4pxiQl9nLXE/uNPht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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbed8bdcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "iris_features = sns.load_dataset(\"iris\")\n",
    "g = sns.PairGrid(iris_features, hue=\"species\")\n",
    "g.map_diag(plt.hist)\n",
    "g.map_offdiag(plt.scatter)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pembelajaran dengan Full Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Melakukan pembelajaran full training dengan DTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(iris.data, iris.target)\n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hasil Pohon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<path fill=\"#8139e5\" fill-opacity=\"0.976471\" stroke=\"black\" d=\"M708.5,-425C708.5,-425 572.5,-425 572.5,-425 566.5,-425 560.5,-419 560.5,-413 560.5,-413 560.5,-354 560.5,-354 560.5,-348 566.5,-342 572.5,-342 572.5,-342 708.5,-342 708.5,-342 714.5,-342 720.5,-348 720.5,-354 720.5,-354 720.5,-413 720.5,-413 720.5,-419 714.5,-425 708.5,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"568.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 4.85</text>\n",
       "<text text-anchor=\"start\" x=\"605\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.043</text>\n",
       "<text text-anchor=\"start\" x=\"599.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n",
       "<text text-anchor=\"start\" x=\"590\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 45]</text>\n",
       "<text text-anchor=\"start\" x=\"592\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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       "<!-- 2&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\"><title>2&#45;&gt;12</title>\n",
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       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.980392\" stroke=\"black\" d=\"M261,-306C261,-306 130,-306 130,-306 124,-306 118,-300 118,-294 118,-294 118,-235 118,-235 118,-229 124,-223 130,-223 130,-223 261,-223 261,-223 267,-223 273,-229 273,-235 273,-235 273,-294 273,-294 273,-300 267,-306 261,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"126\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.65</text>\n",
       "<text text-anchor=\"start\" x=\"160\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.041</text>\n",
       "<text text-anchor=\"start\" x=\"154.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
       "<text text-anchor=\"start\" x=\"145\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 47, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"143\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M339.172,-341.907C320.316,-331.879 299.999,-321.075 280.749,-310.837\"/>\n",
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       "<g id=\"node8\" class=\"node\"><title>7</title>\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M481,-306C481,-306 350,-306 350,-306 344,-306 338,-300 338,-294 338,-294 338,-235 338,-235 338,-229 344,-223 350,-223 350,-223 481,-223 481,-223 487,-223 493,-229 493,-235 493,-235 493,-294 493,-294 493,-300 487,-306 481,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"346\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 1.55</text>\n",
       "<text text-anchor=\"start\" x=\"380\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"378\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"368.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"367\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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       "<!-- 3&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\"><title>3&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M415.5,-341.907C415.5,-333.649 415.5,-324.864 415.5,-316.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"419,-316.021 415.5,-306.021 412,-316.021 419,-316.021\"/>\n",
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       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M109,-179.5C109,-179.5 12,-179.5 12,-179.5 6,-179.5 7.10543e-15,-173.5 7.10543e-15,-167.5 7.10543e-15,-167.5 7.10543e-15,-123.5 7.10543e-15,-123.5 7.10543e-15,-117.5 6,-111.5 12,-111.5 12,-111.5 109,-111.5 109,-111.5 115,-111.5 121,-117.5 121,-123.5 121,-123.5 121,-167.5 121,-167.5 121,-173.5 115,-179.5 109,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"32.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"19.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\n",
       "<text text-anchor=\"start\" x=\"10\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 47, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M148.662,-222.907C135.039,-211.101 120.173,-198.217 106.596,-186.45\"/>\n",
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       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M240,-179.5C240,-179.5 151,-179.5 151,-179.5 145,-179.5 139,-173.5 139,-167.5 139,-167.5 139,-123.5 139,-123.5 139,-117.5 145,-111.5 151,-111.5 151,-111.5 240,-111.5 240,-111.5 246,-111.5 252,-117.5 252,-123.5 252,-123.5 252,-167.5 252,-167.5 252,-173.5 246,-179.5 240,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"167.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"158\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"148.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"147\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M195.5,-222.907C195.5,-212.204 195.5,-200.615 195.5,-189.776\"/>\n",
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       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M371,-179.5C371,-179.5 282,-179.5 282,-179.5 276,-179.5 270,-173.5 270,-167.5 270,-167.5 270,-123.5 270,-123.5 270,-117.5 276,-111.5 282,-111.5 282,-111.5 371,-111.5 371,-111.5 377,-111.5 383,-117.5 383,-123.5 383,-123.5 383,-167.5 383,-167.5 383,-173.5 377,-179.5 371,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"298.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"289\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"279.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"278\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M384.622,-222.907C375.976,-211.542 366.572,-199.178 357.896,-187.774\"/>\n",
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       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M549.5,-187C549.5,-187 413.5,-187 413.5,-187 407.5,-187 401.5,-181 401.5,-175 401.5,-175 401.5,-116 401.5,-116 401.5,-110 407.5,-104 413.5,-104 413.5,-104 549.5,-104 549.5,-104 555.5,-104 561.5,-110 561.5,-116 561.5,-116 561.5,-175 561.5,-175 561.5,-181 555.5,-187 549.5,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"409.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) ≤ 5.45</text>\n",
       "<text text-anchor=\"start\" x=\"446\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"444\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"434.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"429\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M438.398,-222.907C443.313,-214.195 448.558,-204.897 453.637,-195.893\"/>\n",
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       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M462,-68C462,-68 365,-68 365,-68 359,-68 353,-62 353,-56 353,-56 353,-12 353,-12 353,-6 359,-0 365,-0 365,-0 462,-0 462,-0 468,-0 474,-6 474,-12 474,-12 474,-56 474,-56 474,-62 468,-68 462,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"385.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"376\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"366.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"361\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M456.179,-103.726C450.742,-94.9703 444.987,-85.7032 439.523,-76.9051\"/>\n",
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       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M593,-68C593,-68 504,-68 504,-68 498,-68 492,-62 492,-56 492,-56 492,-12 492,-12 492,-6 498,-0 504,-0 504,-0 593,-0 593,-0 599,-0 605,-6 605,-12 605,-12 605,-56 605,-56 605,-62 599,-68 593,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"520.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"511\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"501.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"500\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\n",
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       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M710,-306C710,-306 571,-306 571,-306 565,-306 559,-300 559,-294 559,-294 559,-235 559,-235 559,-229 565,-223 571,-223 571,-223 710,-223 710,-223 716,-223 722,-229 722,-235 722,-235 722,-294 722,-294 722,-300 716,-306 710,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"567\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal length (cm) ≤ 5.95</text>\n",
       "<text text-anchor=\"start\" x=\"605\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"603\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"593.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 2]</text>\n",
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       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\n",
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       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M845,-298.5C845,-298.5 752,-298.5 752,-298.5 746,-298.5 740,-292.5 740,-286.5 740,-286.5 740,-242.5 740,-242.5 740,-236.5 746,-230.5 752,-230.5 752,-230.5 845,-230.5 845,-230.5 851,-230.5 857,-236.5 857,-242.5 857,-242.5 857,-286.5 857,-286.5 857,-292.5 851,-298.5 845,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"770.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"757.5\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 43</text>\n",
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       "<g id=\"node15\" class=\"node\"><title>14</title>\n",
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       "<text text-anchor=\"start\" x=\"612.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7fdbaa58f6a0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                         feature_names=iris.feature_names,  \n",
    "                         class_names=iris.target_names,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Full training dengan ANN (Multi Layer Perceptron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "ann.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Menampilkan weigth pada hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-0.13550239,  0.3597881 , -0.81630916, -0.3227893 , -0.57684476],\n",
       "        [-0.66570776, -0.51233452, -0.25219808, -0.16857787,  0.06338741],\n",
       "        [-0.1319547 ,  0.30246194, -0.48262707,  0.6174627 , -0.77177221],\n",
       "        [ 0.27837206, -0.13504058,  0.09584001, -0.5872452 , -0.49299781]]),\n",
       " array([[ 0.73066898, -0.76834821],\n",
       "        [-0.85350401, -0.61135478],\n",
       "        [ 0.7001833 , -0.74371656],\n",
       "        [-0.14608018,  0.84784599],\n",
       "        [ 0.06141013,  0.35528709]]),\n",
       " array([[ 0.73312753, -1.05537667,  0.5480383 ],\n",
       "        [ 1.07104013,  0.54370328, -0.48102273]])]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.coefs_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Vector bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.49111382,  0.76466795, -0.30467704,  0.31406152,  0.61464091]),\n",
       " array([-0.34159867,  0.34533261]),\n",
       " array([-0.11660927, -0.11662606, -0.11643325])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.intercepts_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pembelajaran dengan Split Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Membagi data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pembelajaran dengan DTL dan akurasinya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi: 100.0 %\n"
     ]
    }
   ],
   "source": [
    "split = tree.DecisionTreeClassifier()\n",
    "split = split.fit(X_train, y_train)\n",
    "y_predict = split.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_predict)\n",
    "print('Akurasi: {} %'.format(accuracy * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fungsi untuk menggambarkan confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\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 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",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hasil klasifikasi dengan DTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00         6\n",
      " versicolor       1.00      1.00      1.00         6\n",
      "  virginica       1.00      1.00      1.00         3\n",
      "\n",
      "avg / total       1.00      1.00      1.00        15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predict, target_names=iris.target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Confusion Matrix pada DTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[6 0 0]\n",
      " [0 6 0]\n",
      " [0 0 3]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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cuVLo8pIQhRBmSaPCvSqSEIUQZsmsZrsRQgg1mdV8iEIIoSZJiEIIYSBNZiGE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdbaa58f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, y_predict)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=iris.target_names,\n",
    "                      title='Confusion matrix DT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pembelajaran dengan ANN dan akurasinya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi: 20.0 %\n"
     ]
    }
   ],
   "source": [
    "ann_split = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "ann_split.fit(X_train, y_train)\n",
    "y_ann_predict = ann_split.predict(X_test)\n",
    "accuracy_ann = accuracy_score(y_test, y_ann_predict)\n",
    "print('Akurasi: {} %'.format(accuracy_ann * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hasil klasifikasi dengan ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       0.00      0.00      0.00         6\n",
      " versicolor       0.00      0.00      0.00         6\n",
      "  virginica       0.20      1.00      0.33         3\n",
      "\n",
      "avg / total       0.04      0.20      0.07        15\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_ann_predict, target_names=iris.target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Confusion Matrix pada ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[0 0 6]\n",
      " [0 0 6]\n",
      " [0 0 3]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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GLODoynlERRSPetDpCt7MTRLiE8RgMLAxdD0fB36Ck5MTrdu0oVv3nqxY/q3W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\nhBA5/h//QPMOkZdB3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdba8aaf9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix_ann = confusion_matrix(y_test, y_ann_predict)\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix_ann, classes=iris.target_names,\n",
    "                      title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pembelajaran dengan 10-fold cross validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pembelajaran dengan DTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.96 (+/- 0.09)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(clf, iris.data, iris.target, cv=10)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pembelajaran dengan ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.33 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(ann, iris.data, iris.target, cv=10)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melakukan Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['full_train_ann.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(clf, 'full_train_dtl.pkl')\n",
    "joblib.dump(ann, 'full_train_ann.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Melakukan Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model_dtl = joblib.load('full_train_dtl.pkl') \n",
    "loaded_model_ann = joblib.load('full_train_ann.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict New Instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* New Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value for sepal length (cm): 5.1\n",
      "Value for sepal width (cm): 3.5\n",
      "Value for petal length (cm): 1.4\n",
      "Value for petal width (cm): 0.2\n",
      "['5.1', '3.5', '1.4', '0.2']\n"
     ]
    }
   ],
   "source": [
    "new_instance = []\n",
    "for iris_attr in iris.feature_names:\n",
    "    value = input(\"Value for \"+iris_attr+\": \")\n",
    "    new_instance.append(value)\n",
    "\n",
    "print(new_instance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Predict with DTL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance = []\n",
    "instance.append(new_instance)\n",
    "loaded_model_dtl.predict(instance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Predict with ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance_in_float = [float(i) for i in new_instance]\n",
    "instance_ann = []\n",
    "instance_ann.append(instance_in_float)\n",
    "loaded_model_ann.predict(instance_ann)"
   ]
  }
 ],
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