Prediction_with_HMM.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Prediction_with_HMM.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMN6/K9SJQY57kjZjX+gQVv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AnilOsmanTur/Spatio-Temporal-Event-Prediction/blob/main/Prediction_with_HMM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c78b_zFaIkhy"
      },
      "source": [
        "Prediction with HMM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4X-jTJaIaHu",
        "outputId": "b9ee4132-38b1-4315-d459-0afd5f3bc826"
      },
      "source": [
        "!pip install hmmlearn\n",
        "import numpy as np\n",
        "from hmmlearn import hmm\n",
        "np.random.seed(42) # to make the code reproducable \n",
        "print('imports done')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: hmmlearn in /usr/local/lib/python3.7/dist-packages (0.2.5)\n",
            "Requirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16->hmmlearn) (1.0.1)\n",
            "imports done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMgNA0XWAUCX"
      },
      "source": [
        "## Data Loading and Splitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Oty-zQzIjgh",
        "outputId": "b4160563-e063-4b01-e8dd-094eac8cb226"
      },
      "source": [
        "data = np.load('data.npy')\n",
        "n_sample = data.shape[0]\n",
        "flat_data = data.reshape(n_sample, -1)\n",
        "print('data shape after flattening', flat_data.shape)\n",
        "n_train = int(0.8 * n_sample )\n",
        "n_test = n_sample - n_train\n",
        "train_data = flat_data[:n_train]\n",
        "test_data = flat_data[n_train:]\n",
        "print('Training data split', train_data.shape)\n",
        "print('Testing data split', test_data.shape)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "data shape after flattening (4500, 9)\n",
            "Training data split (3600, 9)\n",
            "Testing data split (900, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pzz_Bav3Ac4u"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "--cizdnnI1a9",
        "outputId": "6fe1bbaa-87b2-4a45-8cc2-d129c68c0b73"
      },
      "source": [
        "# model creation and training\n",
        "model = hmm.GaussianHMM(n_components=32,\n",
        "                        covariance_type=\"full\",\n",
        "                        n_iter=100,\n",
        "                        verbose=True)\n",
        "model.fit(train_data)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         1       -9825.9681             +nan\n",
            "         2       50783.7443      +60609.7125\n",
            "         3      191823.7693     +141040.0250\n",
            "         4      198193.6123       +6369.8430\n",
            "         5      198193.6130          +0.0007\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianHMM(algorithm='viterbi', covariance_type='full', covars_prior=0.01,\n",
              "            covars_weight=1, init_params='stmc', means_prior=0, means_weight=0,\n",
              "            min_covar=0.001, n_components=32, n_iter=100, params='stmc',\n",
              "            random_state=None, startprob_prior=1.0, tol=0.01,\n",
              "            transmat_prior=1.0, verbose=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKLcViKpAiTl"
      },
      "source": [
        "## Prediction with model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkJxEZ1ppY_9"
      },
      "source": [
        "# Prediction function to predict next data point\n",
        "def predict_next(model, known_data):\n",
        "  state_sequence = model.predict(known_data)\n",
        "  prob_next_step = model.transmat_[state_sequence[-1], :]\n",
        "  t1 = model._generate_sample_from_state(np.argmax(prob_next_step))\n",
        "  t1 = 1/(1 + np.exp(-t1)) # sigmoid to get probablity score like resutls\n",
        "  return t1"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzlKQO6H7iHd"
      },
      "source": [
        "# Distance functions to measure distance between target and prediction\n",
        "def hamming_dist(a, b):\n",
        "  return np.count_nonzero(a!=b)\n",
        "def euclidian_dist(a, b):\n",
        "  return np.linalg.norm(a-b)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2xz49JvpbTo",
        "outputId": "f13ec8a8-fa9b-4e27-ea6c-368694dcdd30"
      },
      "source": [
        "t1 = (predict_next(model, train_data[:5]) > 0.5).astype(np.float)\n",
        "t1"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 1., 0., 0., 1., 1., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-uzJ6gOyjhQ",
        "outputId": "919610ae-008a-4032-ab2d-3dffe7f85824"
      },
      "source": [
        "label = train_data[5]\n",
        "label"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 1., 0., 0., 1., 1., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXHcisR_y7cE",
        "outputId": "83331142-128e-4e4e-e412-38efb5fcb2c2"
      },
      "source": [
        "euclidian_dist(t1, label)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atvkiLMj6Tdj",
        "outputId": "bf5053b3-edb6-480f-eb07-1d43a4431940"
      },
      "source": [
        "hamming_dist(t1, label)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wq0CSYoAMnt"
      },
      "source": [
        "## Test Case Generations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zg220iP7_wu"
      },
      "source": [
        "test_idx = np.random.randint(5, n_test, size=100)\n",
        "start_idx = test_idx-5"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6vHpqlK9YYP",
        "outputId": "5c8d8119-c005-4c9f-d7f4-3352ed08cab0"
      },
      "source": [
        "\n",
        "test_cases = [test_data[start:end] for start, end in zip(start_idx,test_idx)]\n",
        "test_labels = test_data[test_idx]\n",
        "print(len(test_cases))\n",
        "print(test_labels.shape)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n",
            "(100, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLEAoaNO9aWk",
        "outputId": "15259bf5-424e-4305-df9c-97403907ad8e"
      },
      "source": [
        "dists = []\n",
        "for label, a_case in zip(test_labels, test_cases):\n",
        "  t1 = (predict_next(model, a_case) > 0.5).astype(np.float)\n",
        "  e_dist = euclidian_dist(t1, label)\n",
        "  h_dist = hamming_dist(t1, label)\n",
        "  dists.append([e_dist, h_dist])\n",
        "\n",
        "print('prediction done')"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prediction done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQUDvfcV_T-3"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbDYSOGb_UxV",
        "outputId": "0f0f9aee-9aaa-4bff-8067-81c0aa6a1913"
      },
      "source": [
        "dists = np.array(dists)\n",
        "print('Average distances:')\n",
        "d_means = np.mean(dists, axis=0)\n",
        "print('Euclidian:', d_means[0])\n",
        "print('Hamming  :', d_means[1])"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average distances:\n",
            "Euclidian: 1.4873705325979736\n",
            "Hamming  : 3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "t_5kC6cbAvDZ",
        "outputId": "7284f347-9fc5-4ae5-fe82-73a445be04b2"
      },
      "source": [
        "plt.plot(dists)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fca54697fd0>,\n",
              " <matplotlib.lines.Line2D at 0x7fca546a4210>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}