diff --git a/notebooks/105_gct_to_xid.ipynb b/notebooks/105_gct_to_xid.ipynb new file mode 100644 index 0000000..60e5d2c --- /dev/null +++ b/notebooks/105_gct_to_xid.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GCT to X-ID\n", + "\n", + "What is the relationship between accuracy on the galaxy classification task (GCT) and accuracy on the cross-identification task?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle, h5py, astropy.io.ascii as asc\n", + "\n", + "with open('/Users/alger/data/Crowdastro/sets_atlas.pkl', 'rb') as f:\n", + " atlas_sets = pickle.load(f)['RGZ & Norris']\n", + "with open('/Users/alger/data/Crowdastro/sets_swire.pkl', 'rb') as f:\n", + " swire_sets = pickle.load(f)['RGZ & Norris']\n", + "with h5py.File('/Users/alger/data/Crowdastro/swire.h5') as f:\n", + " swire_features = f['features'].value\n", + "with h5py.File('/Users/alger/data/Crowdastro/crowdastro-swire.h5') as f:\n", + " swire_names = [i.decode('ascii') for i in f['/swire/cdfs/string'].value]\n", + " swire_coords = f['/swire/cdfs/numeric'][:, :2]\n", + "swire_labels = {i['swire']: i['norris_label'] for i in asc.read('/Users/alger/data/SWIRE/all_labels.csv')}\n", + "table = asc.read('/Users/alger/data/Crowdastro/one-table-to-rule-them-all.tbl')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import scipy.spatial\n", + "\n", + "swire_tree = scipy.spatial.KDTree(swire_coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "swire_name_to_index = {j:i for i, j in enumerate(swire_names)}" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/site-packages/sklearn/linear_model/base.py:352: RuntimeWarning: overflow encountered in exp\n", + " np.exp(prob, prob)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50\n", + "50\n", + "50\n", + "50\n", + "50\n", + "50\n", + "100\n", + "100\n", + "100\n", + "100\n", + "100\n", + "200\n", + "200\n", + "200\n", + "400\n", + "600\n", + "800\n", + "1200\n" + ] + } + ], + "source": [ + "import sklearn.linear_model, random, crowdastro.crowd.util, numpy\n", + "\n", + "# Generate some classifiers and test them.\n", + "\n", + "accs_gct = []\n", + "accs_xid = []\n", + "distances = []\n", + "for size in [50, 50, 50, 50, 50, 50, 50, 100, 100, 100, 100, 100, 200, 200, 200, 400, 600, 800, 1200]:\n", + " print(size)\n", + " for (train, test), (atlas_train, atlas_test) in zip(swire_sets, atlas_sets):\n", + " lr = sklearn.linear_model.LogisticRegression(C=1e10, class_weight='balanced')\n", + " # Choose a subset.\n", + " train = list(train)\n", + " random.shuffle(train)\n", + " while True not in {'True' == swire_labels[swire_names[n]] for n in train[:size]\n", + " if swire_labels[swire_names[n]]}:\n", + " random.shuffle(train)\n", + " train = train[:size]\n", + " train_features = [swire_features[n] for n in train if swire_labels[swire_names[n]]]\n", + " train_labels = ['True' == swire_labels[swire_names[n]] for n in train if swire_labels[swire_names[n]]]\n", + " test_features = [swire_features[n] for n in test if swire_labels[swire_names[n]]]\n", + " test_labels = ['True' == swire_labels[swire_names[n]] for n in test if swire_labels[swire_names[n]]]\n", + " lr.fit(train_features, train_labels)\n", + " \n", + " # Test on SWIRE (GCT).\n", + " pred_labels = lr.predict(test_features)\n", + " ba = crowdastro.crowd.util.balanced_accuracy(test_labels, pred_labels)\n", + " accs_gct.append(ba)\n", + " \n", + " # Test on ATLAS (X-ID).\n", + " n_correct = 0\n", + " n_total = 0\n", + " distances_ = []\n", + " for atlas in atlas_test:\n", + " row = table[table['Key'] == atlas][0]\n", + " ra = row['Component RA (Franzen)']\n", + " dec = row['Component DEC (Franzen)']\n", + " swire = row['Source SWIRE (Norris)']\n", + " if not swire.startswith('SWIRE'):\n", + " continue\n", + " nearby = swire_tree.query_ball_point(numpy.array([ra, dec]), 1 / 60)\n", + " nearby_features = swire_features[nearby]\n", + " if not nearby:\n", + " continue\n", + " atpreds = lr.predict_proba(nearby_features)[:, 1]\n", + " names = [swire_names[n] for n in nearby]\n", + " name = names[numpy.argmax(atpreds)]\n", + " n_correct += name == swire\n", + " n_total += 1\n", + " true_coords = swire_coords[swire_name_to_index[swire]]\n", + " pred_coords = swire_coords[swire_name_to_index[name]]\n", + " distance = numpy.linalg.norm(true_coords - pred_coords)\n", + " distances_.append(distance)\n", + " distances.append(distances_)\n", + " accs_xid.append(n_correct / n_total)" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(numpy.array(accs_gct) * 100, numpy.array(accs_xid) * 100, marker='o', alpha=0.7)\n", + "plt.xlabel('GCT BA')\n", + "plt.ylabel('X-ID Accuracy')\n", + "plt.grid(color='lightgrey', axis='y')\n", + "plt.savefig('/Users/alger/repos/crowdastro-projects/ATLAS-CDFS/gct-to-xid.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xs = []\n", + "ys = []\n", + "# yerr = []\n", + "for acc, distances_ in zip(accs_gct, distances):\n", + " xs.append(acc)\n", + " ys.append(numpy.mean(distances_) * 60 * 60)\n", + "# yerr.append(numpy.std(distances_))\n", + "plt.grid(axis='y', color='lightgrey')\n", + "plt.scatter(xs, ys, marker='o', linestyle='None', linewidth=0.5, alpha=0.7)\n", + "plt.ylim((0, 0.01 * 60 * 60))\n", + "plt.ylabel('Mean error (arcsec)')\n", + "plt.xlabel('GCT BA')\n", + "plt.savefig('/Users/alger/repos/crowdastro-projects/ATLAS-CDFS/gct-to-arcsec-error.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}