{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dummy dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Find this notebook at `EpyNN/epynnlive/dummy_time/prepare_dataset.ipynb`. \n", "* Regular python code at `EpyNN/epynnlive/dummy_time/prepare_dataset.py`.\n", "\n", "Run the notebook online with [Google Colab](https://colab.research.google.com/github/Synthaze/EpyNN/blob/main/epynnlive/dummy_time/prepare_dataset.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is part of the series on preparing data for Neural Network regression with EpyNN.\n", "\n", "In addition to the topic-specific content, it contains several explanations about basics or general concepts in programming that are important in the context.\n", "\n", "Note that elements developed in the following notebooks may not be reviewed herein:\n", "\n", "* [Boolean dataset](../dummy_boolean/prepare_dataset.ipynb)\n", "* [String dataset](../dummy_string/prepare_dataset.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What is a time series?\n", "\n", "A time series is not a data type. It represents sequential data with respect to the dimension of time. Herein, we will work with **sequential** data containing **numerical** elements of the **float** data type." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Why preparing a dummy dataset with time features?\n", "\n", "The general interest of dummy dataset is explained in [dummy dataset with Boolean sample features](../dummy_boolean/prepare_dataset.ipynb#Why-preparing-a-dummy-dataset-with-Boolean-features)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare a set of time sample features and related label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# EpyNN/epynnlive/dummy_time/prepare_dataset.ipynb\n", "# Standard library imports\n", "import random\n", "\n", "# Related third party imports\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Seeding" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "random.seed(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to define a function which will generate pseudo-random time features." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def features_time(TIME=1, SAMPLING_RATE=128):\n", " \"\"\"Generate dummy time features.\n", "\n", " Time features may be white noise or a sum with a pure sine-wave.\n", "\n", " The pure sin-wave has random frequency lower than SAMPLING_RATE // 4.\n", "\n", " :param SAMPLING_RATE: Sampling rate (Hz), defaults to 128.\n", " :type SAMPLING_RATE: int\n", "\n", " :param TIME: Sampling time (s), defaults to 1.\n", " :type TIME: int\n", "\n", " :return: Time features of shape (N_FEATURES,).\n", " :rtype: :class:`numpy.ndarray`\n", "\n", " :return: White noise of shape (N_FEATURES,).\n", " :rtype: :class:`numpy.ndarray`\n", " \"\"\"\n", " # Number of features describing a sample\n", " N_FEATURES = TIME * SAMPLING_RATE\n", "\n", " # Initialize features array\n", " features = np.linspace(0, TIME, N_FEATURES, endpoint=False)\n", "\n", " # Random choice of true signal frequency\n", " signal_frequency = random.uniform(0, SAMPLING_RATE // 4)\n", "\n", " # Generate pure sine wave of N_FEATURES points\n", " features = np.sin(2 * np.pi * signal_frequency * features)\n", "\n", " # Generate white noise\n", " white_noise = np.random.normal(0, scale=0.5, size=N_FEATURES)\n", "\n", " # Random choice between noisy signal or white noise\n", " features = random.choice([features + white_noise, white_noise])\n", "\n", " return features, white_noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code is commented and quite self-explaining, let's proceed with a call." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "features, white_noise = features_time()\n", "\n", "plt.plot(features, label=\"features\")\n", "plt.plot(white_noise, label=\"white_noise\")\n", "plt.plot(features - white_noise, label=\"features - white_noise\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have noisy features from the random choice because when subtracting white_noise from features we recover a pure sine-wave of random frequency, as stated in the comment of the ``features_time()`` function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "features, white_noise = features_time()\n", "\n", "plt.plot(features, label=\"features\")\n", "plt.plot(white_noise, label=\"white_noise\")\n", "plt.plot(features - white_noise, label=\"features - white_noise\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By contrast, here we retrieved features which only contain ``white_noise``, since the subtract operation returns a flat line.\n", "\n", "Therefore, sample features may consist of noise containing a true signal, or just pure noise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As already mentioned, in this dummy example we have the *a priori* knowledge of the law we want to model.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def label_features(features, white_noise):\n", " \"\"\"Prepare label associated with features.\n", "\n", " The dummy law is:\n", "\n", " True signal in features = positive.\n", " No true signal in features = negative.\n", "\n", " :return: Time features of shape (N_FEATURES,).\n", " :rtype: :class:`numpy.ndarray`\n", "\n", " :return: White noise of shape (N_FEATURES,).\n", " :rtype: :class:`numpy.ndarray`\n", "\n", " :return: Single-digit label with respect to features.\n", " :rtype: int\n", " \"\"\"\n", " # Single-digit positive and negative labels\n", " p_label = 0\n", " n_label = 1\n", "\n", " # Test if features contains signal (0)\n", " if any(features != white_noise):\n", " label = p_label\n", "\n", " # Test if features is equal to white noise (1)\n", " elif all(features == white_noise):\n", " label = n_label\n", "\n", " return label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code above is commented and self explaining.\n", "\n", "Let's check the function we made for a few iterations." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 -6.533357433395695 -6.533357433395695\n", "0 5.060565673758379 4.295314530677464\n", "0 -7.430319493962538 -7.420946917171596\n", "1 8.245856832163877 8.245856832163877\n", "0 -0.048133388166399005 -1.0486463249958615\n" ] } ], "source": [ "for i in range(5):\n", " features, white_noise = features_time()\n", " label = label_features(features, white_noise)\n", "\n", " print(label, np.sum(features), np.sum(white_noise))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s go on and write the function which will iterate to generate a set of sample time features and label." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def prepare_dataset(N_SAMPLES=100):\n", " \"\"\"Prepare a set of dummy time sample features and label.\n", "\n", " :param N_SAMPLES: Number of samples to generate, defaults to 100.\n", " :type N_SAMPLES: int\n", "\n", " :return: Set of sample features.\n", " :rtype: tuple[:class:`numpy.ndarray`]\n", "\n", " :return: Set of single-digit sample label.\n", " :rtype: tuple[int]\n", " \"\"\"\n", " # Initialize X and Y datasets\n", " X_features = []\n", " Y_label = []\n", "\n", " # Iterate over N_SAMPLES\n", " for i in range(N_SAMPLES):\n", "\n", " # Compute random time features\n", " features, white_noise = features_time()\n", "\n", " # Retrieve label associated with features\n", " label = label_features(features, white_noise)\n", "\n", " # From n measurements to n steps with 1 measurements\n", " features = np.expand_dims(features, 1)\n", "\n", " # Append sample features to X_features\n", " X_features.append(features)\n", "\n", " # Append sample label to Y_label\n", " Y_label.append(label)\n", "\n", " # Prepare X-Y pairwise dataset\n", " dataset = list(zip(X_features, Y_label))\n", "\n", " # Shuffle dataset\n", " random.shuffle(dataset)\n", "\n", " # Separate X-Y pairs\n", " X_features, Y_label = zip(*dataset)\n", "\n", " return X_features, Y_label" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This simply wraps the example-specific elements which are the functions ``features_time()`` and ``label_features()``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Live examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function ``prepare_dataset()`` presented herein is used in the following live examples:\n", "\n", "* Notebook at`EpyNN/epynnlive/dummy_time/train.ipynb` or following [this link](train.ipynb). \n", "* Regular python code at `EpyNN/epynnlive/dummy_time/train.py`." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }