neural_networks/simple-neural-network/Iris_Perceptron.ipynb

{
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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from NeuralNetwork import NeuralNetwork\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "text": "Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n0   1            5.1           3.5            1.4           0.2  Iris-setosa\n1   2            4.9           3.0            1.4           0.2  Iris-setosa\n2   3            4.7           3.2            1.3           0.2  Iris-setosa\n3   4            4.6           3.1            1.5           0.2  Iris-setosa\n4   5            5.0           3.6            1.4           0.2  Iris-setosa \n\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "iris_db = pd.read_csv(\"Iris.csv\")\n",
    "print(iris_db.head(),\"\\n\")\n",
    "\n",
    "data_plot = iris_db.iloc[0:150, [1, 3]].values\n",
    "plt.scatter(data_plot[:50, 0], data_plot[:50, 1], color='red', marker='+', label='setosa')\n",
    "plt.scatter(data_plot[50:100, 0], data_plot[50:100, 1], color='blue', marker='.', label='versicolor')\n",
    "plt.scatter(data_plot[100:150, 0], data_plot[100:150, 1], color='green', marker='x', label='virginica')\n",
    "plt.xlabel('Petal Length Cm')\n",
    "plt.ylabel('Sepal Length Cm')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Id               150.0\nSepalLengthCm      7.9\nSepalWidthCm       4.4\nPetalLengthCm      6.9\nPetalWidthCm       2.5\nSpecies            2.0\ndtype: float64\n\nOnce the values are normalized:\n Id               150.0\nSepalLengthCm      1.0\nSepalWidthCm       1.0\nPetalLengthCm      1.0\nPetalWidthCm       1.0\nSpecies            1.0\ndtype: float64\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n136  137       0.797468      0.772727       0.811594          0.96      1.0\n43    44       0.632911      0.795455       0.231884          0.24      0.0\n83    84       0.759494      0.613636       0.739130          0.64      0.5\n42    43       0.556962      0.727273       0.188406          0.08      0.0\n79    80       0.721519      0.590909       0.507246          0.40      0.5",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>SepalLengthCm</th>\n      <th>SepalWidthCm</th>\n      <th>PetalLengthCm</th>\n      <th>PetalWidthCm</th>\n      <th>Species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>137</td>\n      <td>0.797468</td>\n      <td>0.772727</td>\n      <td>0.811594</td>\n      <td>0.96</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>44</td>\n      <td>0.632911</td>\n      <td>0.795455</td>\n      <td>0.231884</td>\n      <td>0.24</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>84</td>\n      <td>0.759494</td>\n      <td>0.613636</td>\n      <td>0.739130</td>\n      <td>0.64</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>43</td>\n      <td>0.556962</td>\n      <td>0.727273</td>\n      <td>0.188406</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>80</td>\n      <td>0.721519</td>\n      <td>0.590909</td>\n      <td>0.507246</td>\n      <td>0.40</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "classes = [\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\"]\n",
    "data = iris_db.replace(classes,list(range(3)))\n",
    "print(data.max(axis=0))\n",
    "\n",
    "cols = list(data.max(axis=0)[1:].index)\n",
    "vals = list(data.max(axis=0)[1:])\n",
    "\n",
    "for idx_col in range(len(cols)):\n",
    "    data[cols[idx_col]] /= vals[idx_col]\n",
    "\n",
    "print(\"\\nOnce the values are normalized:\\n\",data.max(axis=0))\n",
    "train_data, test_data = train_test_split(data, test_size=0.2)\n",
    "#train_data.head()\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[0.96202532, 0.68181818, 0.95652174, 0.84      ],\n       [0.72151899, 0.63636364, 0.5942029 , 0.52      ],\n       [0.59493671, 0.72727273, 0.1884058 , 0.08      ],\n       [0.97468354, 0.68181818, 0.88405797, 0.92      ],\n       [0.70886076, 0.56818182, 0.56521739, 0.44      ]])"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "# The training set, with 150 examples consisting of 4\n",
    "# input values and 1 output value\n",
    "\n",
    "training_inputs = np.squeeze(train_data.iloc[:, list(range(1,5))].values)\n",
    "training_outputs = np.array([train_data[\"Species\"]]).T\n",
    "#training_outputs[:5]\n",
    "training_inputs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Random starting synaptic weights: \n[[-0.16595599]\n [ 0.44064899]\n [-0.99977125]\n [-0.39533485]]\nSynaptic weights after training: \n[[-5.11961173]\n [-9.53559271]\n [ 5.80313919]\n [10.42515623]]\n"
    }
   ],
   "source": [
    "# Initialize the single neuron neural network\n",
    "input_size = 4\n",
    "neural_network = NeuralNetwork(input_size)\n",
    "\n",
    "print(\"Random starting synaptic weights: \")\n",
    "print(neural_network.synaptic_weights)\n",
    "\n",
    "# Train the neural network\n",
    "neural_network.train(training_inputs, training_outputs, 100)\n",
    "\n",
    "print(\"Synaptic weights after training: \")\n",
    "print(neural_network.synaptic_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Real Specie:  2.0  Predicted output:  1.9  ->  2.0  ->  Iris-virginica\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  1.0  Predicted output:  1.5  ->  2.0  ->  Iris-virginica\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  1.0  Predicted output:  0.2  ->  0.0  ->  Iris-setosa\nReal Specie:  1.0  Predicted output:  0.7  ->  1.0  ->  Iris-versicolor\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  2.0  Predicted output:  1.4  ->  1.0  ->  Iris-versicolor\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  1.0  Predicted output:  0.7  ->  1.0  ->  Iris-versicolor\nReal Specie:  1.0  Predicted output:  0.8  ->  1.0  ->  Iris-versicolor\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  2.0  Predicted output:  1.7  ->  2.0  ->  Iris-virginica\nReal Specie:  1.0  Predicted output:  0.2  ->  0.0  ->  Iris-setosa\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  1.0  Predicted output:  0.5  ->  0.0  ->  Iris-setosa\nReal Specie:  2.0  Predicted output:  1.2  ->  1.0  ->  Iris-versicolor\nReal Specie:  2.0  Predicted output:  1.8  ->  2.0  ->  Iris-virginica\nReal Specie:  2.0  Predicted output:  1.9  ->  2.0  ->  Iris-virginica\nReal Specie:  2.0  Predicted output:  1.5  ->  2.0  ->  Iris-virginica\nReal Specie:  1.0  Predicted output:  1.1  ->  1.0  ->  Iris-versicolor\nReal Specie:  2.0  Predicted output:  1.9  ->  2.0  ->  Iris-virginica\nReal Specie:  2.0  Predicted output:  1.9  ->  2.0  ->  Iris-virginica\nReal Specie:  2.0  Predicted output:  1.4  ->  1.0  ->  Iris-versicolor\nReal Specie:  1.0  Predicted output:  0.7  ->  1.0  ->  Iris-versicolor\nReal Specie:  1.0  Predicted output:  0.4  ->  0.0  ->  Iris-setosa\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nReal Specie:  0.0  Predicted output:  0.0  ->  0.0  ->  Iris-setosa\nAccuracy: 0.7333333333333333\n"
    }
   ],
   "source": [
    "classes = [\"Iris-setosa\",\"Iris-versicolor\",\"Iris-virginica\"]\n",
    "features = list(data.max(axis=0)[1:].index)\n",
    "results = []\n",
    "for test_indx in test_data[\"Id\"]:\n",
    "    parameters = [test_data[feature][test_indx-1] for feature in features]\n",
    "    train_test,specie = parameters[:4],parameters[4]*2\n",
    "    prediction = round(neural_network.think(np.array(train_test))[0]*2,1)\n",
    "    prediction_class = round(prediction,0)\n",
    "    results.append(1 if specie == prediction_class else 0)\n",
    "    print(\"Real Specie: \",specie,\" Predicted output: \", prediction,\" -> \",prediction_class,\" -> \",classes[int(prediction_class)])\n",
    "\n",
    "accuracy = sum(results) / len(results)\n",
    "print(\"Accuracy: \"+str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n136  137       0.797468      0.772727       0.811594          0.96      1.0\n43    44       0.632911      0.795455       0.231884          0.24      0.0\n83    84       0.759494      0.613636       0.739130          0.64      0.5\n42    43       0.556962      0.727273       0.188406          0.08      0.0\n79    80       0.721519      0.590909       0.507246          0.40      0.5\n56    57       0.797468      0.750000       0.681159          0.64      0.5\n11    12       0.607595      0.772727       0.231884          0.08      0.0\n22    23       0.582278      0.818182       0.144928          0.08      0.0\n125  126       0.911392      0.727273       0.869565          0.72      1.0\n27    28       0.658228      0.795455       0.217391          0.08      0.0\n76    77       0.860759      0.636364       0.695652          0.56      0.5\n90    91       0.696203      0.590909       0.637681          0.48      0.5\n7      8       0.632911      0.772727       0.217391          0.08      0.0\n103  104       0.797468      0.659091       0.811594          0.72      1.0\n67    68       0.734177      0.613636       0.594203          0.40      0.5\n40    41       0.632911      0.795455       0.188406          0.12      0.0\n71    72       0.772152      0.636364       0.579710          0.52      0.5\n133  134       0.797468      0.636364       0.739130          0.60      1.0\n111  112       0.810127      0.613636       0.768116          0.76      1.0\n142  143       0.734177      0.613636       0.739130          0.76      1.0\n138  139       0.759494      0.681818       0.695652          0.72      1.0\n53    54       0.696203      0.522727       0.579710          0.52      0.5\n122  123       0.974684      0.636364       0.971014          0.80      1.0\n140  141       0.848101      0.704545       0.811594          0.96      1.0\n134  135       0.772152      0.590909       0.811594          0.56      1.0\n94    95       0.708861      0.613636       0.608696          0.52      0.5\n58    59       0.835443      0.659091       0.666667          0.52      0.5\n17    18       0.645570      0.795455       0.202899          0.12      0.0\n3      4       0.582278      0.704545       0.217391          0.08      0.0\n1      2       0.620253      0.681818       0.202899          0.08      0.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>SepalLengthCm</th>\n      <th>SepalWidthCm</th>\n      <th>PetalLengthCm</th>\n      <th>PetalWidthCm</th>\n      <th>Species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>136</th>\n      <td>137</td>\n      <td>0.797468</td>\n      <td>0.772727</td>\n      <td>0.811594</td>\n      <td>0.96</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>44</td>\n      <td>0.632911</td>\n      <td>0.795455</td>\n      <td>0.231884</td>\n      <td>0.24</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>84</td>\n      <td>0.759494</td>\n      <td>0.613636</td>\n      <td>0.739130</td>\n      <td>0.64</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>43</td>\n      <td>0.556962</td>\n      <td>0.727273</td>\n      <td>0.188406</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>80</td>\n      <td>0.721519</td>\n      <td>0.590909</td>\n      <td>0.507246</td>\n      <td>0.40</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>57</td>\n      <td>0.797468</td>\n      <td>0.750000</td>\n      <td>0.681159</td>\n      <td>0.64</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>0.607595</td>\n      <td>0.772727</td>\n      <td>0.231884</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>23</td>\n      <td>0.582278</td>\n      <td>0.818182</td>\n      <td>0.144928</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>125</th>\n      <td>126</td>\n      <td>0.911392</td>\n      <td>0.727273</td>\n      <td>0.869565</td>\n      <td>0.72</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28</td>\n      <td>0.658228</td>\n      <td>0.795455</td>\n      <td>0.217391</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>77</td>\n      <td>0.860759</td>\n      <td>0.636364</td>\n      <td>0.695652</td>\n      <td>0.56</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>91</td>\n      <td>0.696203</td>\n      <td>0.590909</td>\n      <td>0.637681</td>\n      <td>0.48</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>0.632911</td>\n      <td>0.772727</td>\n      <td>0.217391</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>104</td>\n      <td>0.797468</td>\n      <td>0.659091</td>\n      <td>0.811594</td>\n      <td>0.72</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>68</td>\n      <td>0.734177</td>\n      <td>0.613636</td>\n      <td>0.594203</td>\n      <td>0.40</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>41</td>\n      <td>0.632911</td>\n      <td>0.795455</td>\n      <td>0.188406</td>\n      <td>0.12</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>72</td>\n      <td>0.772152</td>\n      <td>0.636364</td>\n      <td>0.579710</td>\n      <td>0.52</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>134</td>\n      <td>0.797468</td>\n      <td>0.636364</td>\n      <td>0.739130</td>\n      <td>0.60</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>112</td>\n      <td>0.810127</td>\n      <td>0.613636</td>\n      <td>0.768116</td>\n      <td>0.76</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>143</td>\n      <td>0.734177</td>\n      <td>0.613636</td>\n      <td>0.739130</td>\n      <td>0.76</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>138</th>\n      <td>139</td>\n      <td>0.759494</td>\n      <td>0.681818</td>\n      <td>0.695652</td>\n      <td>0.72</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>54</td>\n      <td>0.696203</td>\n      <td>0.522727</td>\n      <td>0.579710</td>\n      <td>0.52</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>122</th>\n      <td>123</td>\n      <td>0.974684</td>\n      <td>0.636364</td>\n      <td>0.971014</td>\n      <td>0.80</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>140</th>\n      <td>141</td>\n      <td>0.848101</td>\n      <td>0.704545</td>\n      <td>0.811594</td>\n      <td>0.96</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>134</th>\n      <td>135</td>\n      <td>0.772152</td>\n      <td>0.590909</td>\n      <td>0.811594</td>\n      <td>0.56</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>95</td>\n      <td>0.708861</td>\n      <td>0.613636</td>\n      <td>0.608696</td>\n      <td>0.52</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>59</td>\n      <td>0.835443</td>\n      <td>0.659091</td>\n      <td>0.666667</td>\n      <td>0.52</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>0.645570</td>\n      <td>0.795455</td>\n      <td>0.202899</td>\n      <td>0.12</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0.582278</td>\n      <td>0.704545</td>\n      <td>0.217391</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.620253</td>\n      <td>0.681818</td>\n      <td>0.202899</td>\n      <td>0.08</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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