Vehicle_Classification_LDA_PCA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DfcYl2Y8PYte"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "OC3jNOciPv2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdoWGh_aPxUD",
        "outputId": "b486e6d1-c12d-4fc3-df1b-bf85f69f6615"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = '/content/drive/My Drive/Colab Notebooks/Emergency_non_emergency_vehicles'\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RuzDIXlNQk2P",
        "outputId": "b0e7c654-968e-4b92-e064-91598e96978e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/Colab Notebooks/Emergency_non_emergency_vehicles'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_img(image):\n",
        "  img =[]\n",
        "  for i in os.listdir(image):\n",
        "    img1 = cv2.imread(os.path.join(image,i))\n",
        "    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
        "    img1 = cv2.resize(img1, (128,128))\n",
        "    img.append(img1)\n",
        "\n",
        "  return img\n"
      ],
      "metadata": {
        "id": "GAH5bQakQ31v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img2=load_img(image)"
      ],
      "metadata": {
        "id": "7UrlJ8sgRArN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img2[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZX8ccGaSRGtB",
        "outputId": "59d38e7c-78ac-44e9-9d61-c3928ae5aad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_array2(image):\n",
        "    arrays = np.array(load_img(image))\n",
        "    arrays = np.reshape(arrays, (arrays.shape[0], arrays.shape[1]*arrays.shape[2]))\n",
        "    return arrays"
      ],
      "metadata": {
        "id": "TzWFQzRKRRDo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arrays = get_array2(image)"
      ],
      "metadata": {
        "id": "eoIJTf59RWlA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "arrays.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWUHEf8gRaVE",
        "outputId": "ddacac9f-d241-4dd0-ae42-4e7d21301c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 16384)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = np.array([0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1])\n",
        "X = arrays\n",
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLktW-vXSh9M",
        "outputId": "4a355eeb-7604-46c0-f425-b8509543211c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20,)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.2)"
      ],
      "metadata": {
        "id": "EAGJVf61Ul4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_Train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLW1_91MWBSQ",
        "outputId": "1cfa46d5-0427-4050-f727-9814bd605b46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 16384)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca1 = PCA(0.90)\n",
        "pca1.fit(X_Train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_8DfXGwW5w9",
        "outputId": "e66a3417-6fd6-4c7e-dfa0-b051f6f90eaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=0.9)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pca1.components_.shape) #Dimensions \n",
        "# print(pca1.components_.T) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3PoCtj4ZiDJ",
        "outputId": "d0a4979f-c5d7-4810-c0cb-37cad21767b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11, 16384)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pca1.explained_variance_)\n",
        "print(pca1.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "weDOjDm2Zsh3",
        "outputId": "8cc5afca-615b-4cc5-825e-1e3a3533829d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[24772995.7054017   7842627.11059861  7645096.76023129  6020645.60251433\n",
            "  4792628.96557915  4028371.19147318  3814855.04703908  2830852.24486634\n",
            "  2604814.38228063  2323135.53947928  2161982.62593933]\n",
            "[0.32762562 0.10371961 0.10110725 0.07962371 0.06338305 0.05327566\n",
            " 0.05045188 0.03743834 0.03444896 0.03072373 0.02859246]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "plt.plot(np.cumsum(pca1.explained_variance_ratio_)) #plotting for variability of features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "wHmJu0MBZ26d",
        "outputId": "8b2d9e45-f245-4a83-a906-753ae39bcc1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f575ab79ad0>]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": [
        "plt.imshow(pca1.components_[0].reshape(128,128),cmap=plt.cm.bone)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "9xv4WbncZ76b",
        "outputId": "e7ce6a54-fdab-4ca2-b08c-a57d96ab6082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f575a6da710>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": [
        "projected = pca1.fit_transform(X_Train)\n",
        "projected.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRRvHjAIaITw",
        "outputId": "31379b98-ca10-4287-9058-62cf89b30dbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = projected\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gi-D_9mgas9E",
        "outputId": "47b77c4c-4b23-4f2b-fdbe-a6a36c71db28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "y0 = np.zeros(8, dtype = bool)\n",
        "y1 = np.ones(8, dtype = bool)\n",
        "\n",
        "y = np.concatenate((y0, y1), axis = 0)\n",
        "print(y)"
      ],
      "metadata": {
        "id": "2jUvaWIHbgUA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "122f343b-dc33-42b5-939b-31ccbe492b54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False False False False False False False False  True  True  True  True\n",
            "  True  True  True  True]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.reshape((16, 1)) #(m,n_x)\n",
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bLOUppEbkOQ",
        "outputId": "01fa64bc-d733-4310-8a67-5f352d7d0b12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "rIfnM35lbteu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda = LDA(n_components = 1)\n",
        "lda.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbFJr9gvcXzV",
        "outputId": "09800c5f-7732-41fe-9656-39bfabbc9d4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis(n_components=1)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z = lda.transform(X)\n",
        "z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtiAsTOiceGp",
        "outputId": "a6c263f3-58d8-4a33-bfdd-9e20cbe051d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.6201706 ],\n",
              "       [-0.31151847],\n",
              "       [-2.1040403 ],\n",
              "       [-2.16542547],\n",
              "       [-2.42476005],\n",
              "       [ 0.71278409],\n",
              "       [-1.9613875 ],\n",
              "       [-2.34010858],\n",
              "       [ 1.48703892],\n",
              "       [ 0.35067397],\n",
              "       [ 0.87933485],\n",
              "       [ 2.66620528],\n",
              "       [ 2.02857742],\n",
              "       [ 2.07486509],\n",
              "       [ 0.81715857],\n",
              "       [ 0.91077278]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lda.predict(X)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scG2h5aqcjmO",
        "outputId": "242df252-cf30-44b1-9adf-9677840dde1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, False, False,  True, False, False,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score"
      ],
      "metadata": {
        "id": "Mtv-GiBTcoQl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_pred,y)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NBP8D1s_Edx",
        "outputId": "4267e707-e144-4f52-b5fc-42d1a8d0f22c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7, 0],\n",
              "       [1, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CyDPpZCct5U",
        "outputId": "8085b991-e244-4d97-9cf4-a14d51cb0fca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9375"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    }
  ]
}