lab14_Williams.ipynb

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      "source": [
        "Lab 14: Artificial Neural Networks \n",
        "\n",
        "Kobie Williams \n",
        "\n",
        "4/12/2022 \n",
        "\n",
        "The purpose of this lab is to experiment with artificial neural networks for classification. "
      ],
      "metadata": {
        "id": "l3KSE1kf-AAJ"
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      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "-pfZuwiS96UO"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "from sklearn.neural_network import MLPClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://drive.google.com/uc?export=download&id=15OZ7JhQaX2SEqqQAUbyller5PThdmC7M')"
      ],
      "metadata": {
        "id": "a5FxfvcO-dJ1"
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      "execution_count": 96,
      "outputs": []
    },
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      "source": [
        "df.head(5)"
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    {
      "cell_type": "code",
      "source": [
        "y = df['10']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFP9vC0WcMPc",
        "outputId": "1c733f7d-6612-4d74-ae31-b33437d707a3"
      },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1.0\n",
              "1       0.0\n",
              "2       1.0\n",
              "3       1.0\n",
              "4       1.0\n",
              "       ... \n",
              "4995    0.0\n",
              "4996    0.0\n",
              "4997    0.0\n",
              "4998    1.0\n",
              "4999    0.0\n",
              "Name: 10, Length: 5000, dtype: float64"
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['0','1','2','3','4','5','6','7','8','9']]"
      ],
      "metadata": {
        "id": "IZqnwGsZ-kKe"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "\n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X,y, test_size=0.2, random_state=25) "
      ],
      "metadata": {
        "id": "6IvH856h-xBU"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Make a copy of the data for normalization \n",
        "Xtrain_norm = Xtrain.copy() \n",
        "Xtrain_mean = Xtrain.mean().copy() \n",
        "Xtrain_std = Xtrain.std().copy() \n",
        "\n",
        "\n",
        "#z-score normalization\n",
        "#Xtrain_norm will change after every time \n",
        "Xtrain_norm = (Xtrain_norm - Xtrain_mean) / Xtrain_std \n",
        "\n",
        "Xtest_norm = (Xtest - Xtrain_mean) / Xtrain_std \n",
        "print(Xtrain_norm.mean())\n",
        "print(Xtrain_norm.std()) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKuO0mG0dj1t",
        "outputId": "e4f1bde1-753d-412e-cc5d-932d192cef1a"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0   -4.991901e-14\n",
            "1   -3.419487e-17\n",
            "2    4.447415e-16\n",
            "3   -1.170619e-15\n",
            "4   -1.157794e-11\n",
            "5    5.669909e-16\n",
            "6   -2.262079e-18\n",
            "7    3.648193e-16\n",
            "8    1.101202e-17\n",
            "9   -1.873501e-19\n",
            "dtype: float64\n",
            "0    1.0\n",
            "1    1.0\n",
            "2    1.0\n",
            "3    1.0\n",
            "4    1.0\n",
            "5    1.0\n",
            "6    1.0\n",
            "7    1.0\n",
            "8    1.0\n",
            "9    1.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier(hidden_layer_sizes=(4))  \n"
      ],
      "metadata": {
        "id": "RcCY1VnZdqie"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp.fit(Xtrain_norm, ytrain) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2eo6lPPesHT",
        "outputId": "472485c8-b7ea-4dd0-81b0-fd7b8503f47c"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(hidden_layer_sizes=4)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = mlp.predict(Xtest_norm) "
      ],
      "metadata": {
        "id": "WeTMMZ2Hexl6"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report \n",
        "print(classification_report(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBhHEmQMHhCJ",
        "outputId": "01ef4eb6-8107-42e8-f9c1-4b0dc447c8a6"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.80      0.76      0.78       484\n",
            "         1.0       0.78      0.82      0.80       516\n",
            "\n",
            "    accuracy                           0.79      1000\n",
            "   macro avg       0.79      0.79      0.79      1000\n",
            "weighted avg       0.79      0.79      0.79      1000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(mlp.loss_curve_) \n",
        "plt.xlabel('Epoch') \n",
        "plt.ylabel('Loss') \n",
        "plt.title('Loss Curve') \n",
        "plt.show() \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qzMfEuDFJTxm",
        "outputId": "e0b29020-31e3-4bb5-bb14-4f9682cd11bc"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = mlp.predict(Xtrain_norm)\n",
        "print(classification_report(ytrain, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-ckBSO9Qbd3",
        "outputId": "9ca49c38-e707-4b57-c9f2-abdcdf63f8e7"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.80      0.79      0.79      2010\n",
            "         1.0       0.79      0.80      0.79      1990\n",
            "\n",
            "    accuracy                           0.79      4000\n",
            "   macro avg       0.79      0.79      0.79      4000\n",
            "weighted avg       0.79      0.79      0.79      4000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier(hidden_layer_sizes=(4), learning_rate_init=100)\n",
        "mlp.fit(Xtrain_norm, ytrain)\n",
        "plt.plot(mlp.loss_curve_)\n",
        "plt.xlabel('Epoch') \n",
        "plt.ylabel('Loss') \n",
        "plt.title('Loss Curve') \n",
        "plt.show() \n",
        "y_pred = mlp.predict(Xtrain_norm)\n",
        "print(classification_report(ytrain, y_pred))\n",
        "y_pred = mlp.predict(Xtest_norm)\n",
        "print(classification_report(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "U8xVMMFMKJtY",
        "outputId": "b4f77371-90a7-4c1b-89d2-74fe69b03b47"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.50      1.00      0.67      2010\n",
            "         1.0       0.00      0.00      0.00      1990\n",
            "\n",
            "    accuracy                           0.50      4000\n",
            "   macro avg       0.25      0.50      0.33      4000\n",
            "weighted avg       0.25      0.50      0.34      4000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.48      1.00      0.65       484\n",
            "         1.0       0.00      0.00      0.00       516\n",
            "\n",
            "    accuracy                           0.48      1000\n",
            "   macro avg       0.24      0.50      0.33      1000\n",
            "weighted avg       0.23      0.48      0.32      1000\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier(hidden_layer_sizes=(4), learning_rate_init=0.00001)\n",
        "mlp.fit(Xtrain_norm, ytrain)\n",
        "plt.plot(mlp.loss_curve_)\n",
        "plt.xlabel('Epoch') \n",
        "plt.ylabel('Loss') \n",
        "plt.title('Loss Curve') \n",
        "plt.show() \n",
        "y_pred = mlp.predict(Xtrain_norm)\n",
        "print(classification_report(ytrain, y_pred))\n",
        "y_pred = mlp.predict(Xtest_norm)\n",
        "print(classification_report(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "kcSeBQsxKQ3Z",
        "outputId": "e1e6831a-3118-4f00-9589-ffa62b4e9023"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.65      0.25      0.37      2010\n",
            "         1.0       0.53      0.86      0.66      1990\n",
            "\n",
            "    accuracy                           0.56      4000\n",
            "   macro avg       0.59      0.56      0.51      4000\n",
            "weighted avg       0.59      0.56      0.51      4000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.63      0.24      0.35       484\n",
            "         1.0       0.55      0.87      0.67       516\n",
            "\n",
            "    accuracy                           0.56      1000\n",
            "   macro avg       0.59      0.56      0.51      1000\n",
            "weighted avg       0.59      0.56      0.52      1000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier(hidden_layer_sizes=(4,8), learning_rate_init=0.001)\n",
        "mlp.fit(Xtrain_norm, ytrain)\n",
        "plt.plot(mlp.loss_curve_)\n",
        "plt.xlabel('Epoch') \n",
        "plt.ylabel('Loss') \n",
        "plt.title('Loss Curve') \n",
        "plt.show() \n",
        "y_pred = mlp.predict(Xtrain_norm)\n",
        "print(classification_report(ytrain, y_pred))\n",
        "y_pred = mlp.predict(Xtest_norm)\n",
        "print(classification_report(ytest, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "q1eolt44M0Fp",
        "outputId": "162ca2d2-d3a6-4476-eb8f-e6bc43b5d42e"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.82      0.80      0.81      2010\n",
            "         1.0       0.80      0.82      0.81      1990\n",
            "\n",
            "    accuracy                           0.81      4000\n",
            "   macro avg       0.81      0.81      0.81      4000\n",
            "weighted avg       0.81      0.81      0.81      4000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.80      0.77      0.78       484\n",
            "         1.0       0.79      0.81      0.80       516\n",
            "\n",
            "    accuracy                           0.79      1000\n",
            "   macro avg       0.79      0.79      0.79      1000\n",
            "weighted avg       0.79      0.79      0.79      1000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oKNC6lxLNd2S"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test 1) When the learning rate is too high, it makes the loss curve not optimized. \n",
        "\n",
        "Test 2) When the learning rate is too low, we don't see enough change in the loss curve per the change in epochs. \n",
        "\n",
        "Test 3) When there are more layers it made the loss curve more optimal but not as optimal that we'd like it to be. \n",
        "\n",
        "Overall, with more layers it adds to training time, but when you decrease the learning rate the training time goes down. "
      ],
      "metadata": {
        "id": "cy7_tDoNNjHK"
      }
    }
  ]
}