docs/source/content/examples/stream-based_sampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stream-based sampling\n",
    "===================\n",
    "\n",
    "In addition to pool-based sampling, the stream-based scenario can also be implemented easily with modAL. In this case, the labels are not queried from a pool of instances. Rather, they are given one-by-one for the learner, which queries for its label if it finds the example useful. For instance, an example can be marked as useful if the prediction is uncertain, because acquiring its label would remove this uncertainty.\n",
    "\n",
    "The executable script for this example can be [found here](https://github.com/cosmic-cortex/modAL/blob/master/examples/stream-based_sampling.py)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To enforce a reproducible result across runs, we set a random seed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset\n",
    "-----------\n",
    "\n",
    "In this example, we are going to learn a black square on a white background. We are going to use a random forest classifier in a stream-based setting. First, let's generate some data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# creating the image\n",
    "im_width = 500\n",
    "im_height = 500\n",
    "im = np.zeros((im_height, im_width))\n",
    "im[100:im_width - 1 - 100, 100:im_height - 1 - 100] = 1\n",
    "\n",
    "# create the data to stream from\n",
    "X_full = np.transpose(\n",
    "    [np.tile(np.asarray(range(im.shape[0])), im.shape[1]),\n",
    "     np.repeat(np.asarray(range(im.shape[1])), im.shape[0])]\n",
    ")\n",
    "# map the intensity values against the grid\n",
    "y_full = np.asarray([im[P[0], P[1]] for P in X_full])\n",
    "\n",
    "# create the data to stream from\n",
    "X_full = np.transpose(\n",
    "    [np.tile(np.asarray(range(im.shape[0])), im.shape[1]),\n",
    "     np.repeat(np.asarray(range(im.shape[1])), im.shape[0])]\n",
    ")\n",
    "# map the intensity values against the grid\n",
    "y_full = np.asarray([im[P[0], P[1]] for P in X_full])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you are wondering, here is how this looks like! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "with plt.style.context('seaborn-white'):\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    plt.imshow(im)\n",
    "    plt.title('The shape to learn')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Active learning with stream-based sampling\n",
    "-----------------------------------------------------------------\n",
    "\n",
    "For classification, we will use a random forest classifier. Initializing the learner is the same as always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial prediction accuracy: 0.500020\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/namazu/.local/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from modAL.models import ActiveLearner\n",
    "\n",
    "# assembling initial training set\n",
    "n_initial = 5\n",
    "initial_idx = np.random.choice(range(len(X_full)), size=n_initial, replace=False)\n",
    "X_train, y_train = X_full[initial_idx], y_full[initial_idx]\n",
    "\n",
    "# initialize the learner\n",
    "learner = ActiveLearner(\n",
    "    estimator=RandomForestClassifier(),\n",
    "    X_training=X_train, y_training=y_train\n",
    ")\n",
    "unqueried_score = learner.score(X_full, y_full)\n",
    "\n",
    "print('Initial prediction accuracy: %f' % unqueried_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how our classifier performs on the initial training set! This is how the class prediction probabilities look like for each pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing initial prediciton\n",
    "with plt.style.context('seaborn-white'):\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    prediction = learner.predict_proba(X_full)[:, 0]\n",
    "    plt.imshow(prediction.reshape(im_width, im_height))\n",
    "    plt.title('Initial prediction accuracy: %f' % unqueried_score)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to randomly sample pixels from the image. If the prediction of the pixel's value is uncertain, we query the true value and teach it to the classifier. We are going to do this until we reach at least 90% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel no. 192607 queried, new accuracy: 0.525604\n",
      "Pixel no. 249064 queried, new accuracy: 0.458388\n",
      "Pixel no. 235489 queried, new accuracy: 0.596292\n",
      "Pixel no. 194087 queried, new accuracy: 0.712372\n",
      "Pixel no. 91580 queried, new accuracy: 0.710612\n",
      "Pixel no. 199404 queried, new accuracy: 0.649388\n",
      "Pixel no. 11005 queried, new accuracy: 0.767728\n",
      "Pixel no. 151961 queried, new accuracy: 0.751044\n",
      "Pixel no. 17891 queried, new accuracy: 0.783008\n",
      "Pixel no. 117487 queried, new accuracy: 0.728964\n",
      "Pixel no. 206353 queried, new accuracy: 0.836684\n",
      "Pixel no. 154880 queried, new accuracy: 0.876088\n",
      "Pixel no. 27770 queried, new accuracy: 0.848776\n",
      "Pixel no. 194658 queried, new accuracy: 0.892492\n",
      "Pixel no. 178776 queried, new accuracy: 0.943476\n"
     ]
    }
   ],
   "source": [
    "from modAL.uncertainty import classifier_uncertainty\n",
    "\n",
    "performance_history = [unqueried_score]\n",
    "\n",
    "# learning until the accuracy reaches a given threshold\n",
    "while learner.score(X_full, y_full) < 0.90:\n",
    "    stream_idx = np.random.choice(range(len(X_full)))\n",
    "    if classifier_uncertainty(learner, X_full[stream_idx].reshape(1, -1)) >= 0.4:\n",
    "        learner.teach(X_full[stream_idx].reshape(1, -1), y_full[stream_idx].reshape(-1, ))\n",
    "        new_score = learner.score(X_full, y_full)\n",
    "        performance_history.append(new_score)\n",
    "        print('Pixel no. %d queried, new accuracy: %f' % (stream_idx, new_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1105x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our performance over time.\n",
    "fig, ax = plt.subplots(figsize=(8.5, 6), dpi=130)\n",
    "\n",
    "ax.plot(performance_history)\n",
    "ax.scatter(range(len(performance_history)), performance_history, s=13)\n",
    "\n",
    "ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=5, integer=True))\n",
    "ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=10))\n",
    "ax.yaxis.set_major_formatter(mpl.ticker.PercentFormatter(xmax=1))\n",
    "\n",
    "ax.set_ylim(bottom=0, top=1)\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_title('Incremental classification accuracy')\n",
    "ax.set_xlabel('Query iteration')\n",
    "ax.set_ylabel('Classification Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}