deepecho_hello.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "deepecho_hello.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMkhbeVDxsIslV6zm7NwHuB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/microprediction/timeseries-notebooks/blob/main/deepecho_hello.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XfqYwIQQni35"
      },
      "source": [
        "!pip install deepecho\n",
        "from deepecho import PARModel\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "!pip install realdata\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "73Tpo2a2wtfr"
      },
      "source": [
        "# Demonstration of DeepEcho synthetic time series generation\n",
        "Follows https://pypi.org/project/deepecho/\n",
        "For more \"hello world\" examples of time series packages see\n",
        "[listing by downloads](https://www.microprediction.com/blog/popular-timeseries-packages)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "frbuFXVztnS9"
      },
      "source": [
        "## Get some real world time series\n",
        "Pulls live wriggly time series from [here](https://www.microprediction.org/browse_streams.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "e-Wsi3IBtNlY",
        "outputId": "39d9bcad-bf04-4897-c750-b86470341852"
      },
      "source": [
        "from realdata import get_values\n",
        "num = 0\n",
        "while num<500:\n",
        "    y1 = get_values()\n",
        "    y2 = get_values()\n",
        "    num = min(len(y1),len(y2))\n",
        "    y1 = y1[-num:]\n",
        "    y2 = y2[-num:]\n",
        "\n",
        "plt.plot(range(num),y1,range(num),y2)\n",
        "# Don't like? just run again"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f62be8d2358>,\n",
              " <matplotlib.lines.Line2D at 0x7f62be8d2470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86BwjsMswoDP"
      },
      "source": [
        "## Massage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "KLEfK4QVuBIN",
        "outputId": "f16cb9c9-496f-4f29-faac-ca977ced3b68"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame(columns=['day','price','category'])\n",
        "df['price'] = y1 + y2\n",
        "df['category'] = ['dog']*num+['cat']*num\n",
        "df['day'] = list(range(num)) + list(range(num))\n",
        "df[:2]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>day</th>\n",
              "      <th>price</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>911.3140</td>\n",
              "      <td>dog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>930.9901</td>\n",
              "      <td>dog</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   day     price category\n",
              "0    0  911.3140      dog\n",
              "1    1  930.9901      dog"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIS-Ln8_x8e-"
      },
      "source": [
        "## Fit with deepecho"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbNWjFuWn_L2",
        "outputId": "a582833b-1468-42be-e105-b5746066e415"
      },
      "source": [
        "data_types = {\n",
        "    'price': 'continuous',\n",
        "}\n",
        "model = PARModel(cuda=False)\n",
        "model.fit(\n",
        "    data=df,\n",
        "    entity_columns=['category'],\n",
        "    data_types=data_types,\n",
        "    sequence_index='day'\n",
        ")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/128 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "PARModel(epochs=128, sample_size=1, cuda='cpu', verbose=True) instance created\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 128 | Loss -28.850357055664062: 100%|██████████| 128/128 [01:18<00:00,  1.62it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2O4bbqpgyBob"
      },
      "source": [
        "## Then simulate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "9JyVTP_Nwbjc",
        "outputId": "3fb34afc-7a1e-4246-81ad-9ab0e12a46e9"
      },
      "source": [
        "fake_data = model.sample(num_entities=3)\n",
        "fake_data[:3]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 3/3 [00:55<00:00, 18.46s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "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>price</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25.454000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.017825</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.989103</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.442291</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.623879</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.235216</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3.190608</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.218730</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.594444</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>7.315282</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3.797633</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.768616</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3.166081</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-0.356114</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-4.260834</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2.745102</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>6.149025</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>9.807524</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>18.976707</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>16.762539</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        price  category\n",
              "0   25.454000       0.0\n",
              "1   10.017825       0.0\n",
              "2   -0.989103       0.0\n",
              "3    4.442291       0.0\n",
              "4    3.623879       0.0\n",
              "5    1.235216       0.0\n",
              "6    3.190608       0.0\n",
              "7    0.218730       0.0\n",
              "8    0.594444       0.0\n",
              "9    7.315282       0.0\n",
              "10   3.797633       0.0\n",
              "11   1.768616       0.0\n",
              "12   3.166081       0.0\n",
              "13  -0.356114       0.0\n",
              "14  -4.260834       0.0\n",
              "15   2.745102       0.0\n",
              "16   6.149025       0.0\n",
              "17   9.807524       0.0\n",
              "18  18.976707       0.0\n",
              "19  16.762539       0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "yFUVxM4RyiI7",
        "outputId": "6e34a47d-7dff-414f-9174-8bab8946d4d6"
      },
      "source": [
        "fake1 = fake_data[fake_data.category==0].price\n",
        "fake2 = fake_data[fake_data.category==1].price\n",
        "days = list(range(len(fake1)))\n",
        "plt.plot(days,fake1,days,fake2)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f62be1e2780>,\n",
              " <matplotlib.lines.Line2D at 0x7f62be1e2908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}