{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_context('paper', font_scale=1.6)\n", "import keras.backend as K\n", "from natsort import natsorted\n", "\n", "SEED = 0\n", "SAVE_PLOTS = False" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob\n", "import os\n", "\n", "def parse_logs(log_files):\n", " max_epoch = 250\n", " logs = [pd.read_csv(f, index_col='epoch', parse_dates=[1]) for f in log_files]\n", " for log, f in zip(logs, log_files):\n", " run = f.split('/')[-2]\n", " log.drop(log.index[log.index > max_epoch], axis=0, inplace=True)\n", " if 'time' not in log:\n", " raise ValueError(\"Missing times from {}\".format(f))\n", " log.columns = ['time', run + ' Train', run + ' Valid']\n", " log['time'] = (log['time'] - log['time'].min()) / np.timedelta64(1, 's')\n", " step_logs = pd.concat([l.drop('time', axis=1, inplace=False) for l in logs], axis=1)\n", " time_logs = pd.concat([l.set_index('time') for l in logs], axis=1)\n", " \n", " return step_logs, time_logs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LABEL_MAP = {'epoch': 'Epoch', 'time': 'Time (s)'}\n", "\n", "def run_to_args(run):\n", " values = run.split(' ')[0].split('_')\n", " args = {'model_type': values[0].upper(), 'size': int(values[1]), \n", " 'num_layers': int(values[2].strip('x')),\n", " 'drop_frac': float(values[4].strip('drop')),\n", " 'bidirectional': 'bidir' in run}\n", " if 'emb' in run:\n", " args['embedding'] = int([v for v in values if 'emb' in v][0][3:])\n", " return args\n", " \n", "\n", "def training_plot(logs, loss_type='Valid', ylim=(1e-2, 1e0)):\n", " fig = plt.figure()\n", " colors = sns.color_palette(n_colors=int(len(logs.columns) / 2))\n", " for i, c in enumerate(step_logs.columns):\n", " if loss_type is not None and loss_type not in c:\n", " continue\n", " to_plot = logs[c]\n", " to_plot.dropna(inplace=True)\n", " args = run_to_args(c)\n", " to_plot.name = \"{model_type}({size}) x {num_layers}\".format(**args)\n", " if 'embedding' in args:\n", " to_plot.name += \", Embedding={}\".format(args['embedding'])\n", " to_plot.plot(color=colors[int(i / 2)], legend=True, logy=True)\n", " plt.xlabel(LABEL_MAP[logs.index.name])\n", " plt.ylabel('Loss')\n", " plt.ylim(ylim)\n", " return fig" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "from sklearn.metrics import confusion_matrix\n", "\n", "def plot_confusion_matrix(y, y_pred, classnames, filename=None):\n", " classnames = sorted(classnames)\n", " sns.set_style(\"whitegrid\", {'axes.grid' : False})\n", " cm = confusion_matrix(y, y_pred, classnames)\n", " plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues, vmin=0,\n", " vmax=np.unique(y, return_counts=True)[1].max())\n", " #plt.title(title)\n", " #plt.colorbar()\n", " tick_marks = np.arange(len(classnames))\n", " plt.xticks(tick_marks, classnames, rotation=45)\n", " plt.yticks(tick_marks, classnames)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, cm[i, j],\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label');\n", " if filename:\n", " plt.savefig(filename)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from copy import deepcopy\n", "from sklearn.model_selection import GridSearchCV, ParameterGrid\n", "\n", "RF_PARAM_GRID = {'n_estimators': [50, 100, 250], 'criterion': ['gini', 'entropy'],\n", " 'max_features': [0.05, 0.1, 0.2, 0.3], 'min_samples_leaf': [1, 2, 3]}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Period estimator training plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log_files = glob.glob('keras_logs/period/uneven/noise0.5/gru_*x2*_bidir/training.csv')\n", "\n", "step_logs, time_logs = parse_logs(log_files)\n", "step_plot = training_plot(step_logs)\n", "time_plot = training_plot(time_logs)\n", "if SAVE_PLOTS:\n", " step_plot.savefig('figures/gru_period_step.pdf')\n", " time_plot.savefig('figures/gru_period_time.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log_files = (glob.glob('keras_logs/period/uneven/noise0.5/gru*64*bidir/training.csv')\n", " + glob.glob('keras_logs/period/uneven/noise0.5/lstm*64*bidir/training.csv'))\n", "\n", "step_logs, time_logs = parse_logs(log_files)\n", "step_plot = training_plot(step_logs)\n", "time_plot = training_plot(time_logs)\n", "if SAVE_PLOTS:\n", " step_plot.savefig('figures/gru_lstm_period_step.pdf')\n", " time_plot.savefig('figures/gru_lstm_period_time.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "K.set_learning_phase(0)\n", "%run period.py --size 96 --num_layers 2 --drop_frac 0.25 --no_train --model_type gru --sigma 0.5 --lr 5e-4 --sim_type period/uneven/noise0.5 --bidirectional\n", "scaler.inverse_transform(Y, copy=False);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "train = np.arange(args.N_train)\n", "test = args.N_train + np.arange(args.N_test)\n", "pred_gru = model.predict(X)\n", "scaler.inverse_transform(pred_gru)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "import gatspy\n", "from distutils.version import LooseVersion\n", "assert LooseVersion(gatspy.__version__) < '0.3.0'\n", "\n", "pred_gat = np.zeros(pred_gru.shape)\n", "for i in range(pred_gat.shape[0]):\n", " t = np.cumsum(X[i, :, 0])\n", " x = X[i, :, 1]\n", " opt_args = {'period_range': (1.0, 10.0), 'quiet': True}\n", "# opt_args = {'period_range': (0.05, 0.95 * (t.max() - t.min())), 'quiet': True}\n", " model_gat = gatspy.periodic.LombScargleFast(fit_period=True, optimizer_kwds=opt_args)#, silence_warnings=True)\n", " model_gat.fit(t, x)\n", " omega = 2 * np.pi / model_gat.best_period\n", " off, A2, A1 = model_gat._best_params(omega)\n", "# pred_gat[i] = np.array([model_gat.best_period, np.sqrt(A1 ** 2 + A2 ** 2), np.arctan2(A2, A1), off + model_gat.ymean_])\n", " pred_gat[i] = np.array([model_gat.best_period, A1, A2, off + model_gat.ymean_])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "inds = test\n", "fig = sns.jointplot(pred_gru[inds, 0] - Y[inds, 0], pred_gat[inds, 0] - Y[inds, 0], xlim=(-1., 1.), ylim=(-1., 1.), stat_func=None)\n", "fig.ax_joint.annotate(\"GRU MSE: {:1.4f}\\nLomb-Scargle MSE: {:1.4f}\".format(np.mean((pred_gru[inds, 0] - Y[inds, 0]) ** 2), np.mean((pred_gat[inds, 0] - Y[inds, 0]) ** 2)), (-0.95, 0.78))\n", "fig.ax_joint.set_xlabel('GRU error')\n", "fig.ax_joint.set_ylabel('Lomb-Scargle error');\n", "#plt.title(\"GRU MAE: {}\\nGatspy MAE: {}\".format(np.median(np.abs(pred_gru[:, 0] - Y[inds, 0])), np.median(np.abs(pred_gat[:, 0] - Y[inds, 0]))))\n", "#sns.jointplot(pred_gru[inds, 0], Y[inds, 0])\n", "#sns.jointplot(pred_gat[inds, 0], Y[inds, 0])\n", "if SAVE_PLOTS:\n", " plt.savefig('figures/ls_period.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autoencoder training plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log_files = natsorted(glob.glob('keras_logs/autoencoder/uneven/noise0.5/gru_064_x2*/training.csv'))\n", "log_files = [l for l in log_files if 'emb64' not in l]\n", "\n", "step_logs, time_logs = parse_logs(log_files)\n", "step_plot = training_plot(step_logs, ylim=(0.01, 2.0))\n", "time_plot = training_plot(time_logs, ylim=(0.01, 2.0))\n", "if SAVE_PLOTS:\n", " step_plot.savefig('figures/gru_autoencoder_step.pdf')\n", " time_plot.savefig('figures/gru_autoencoder_time.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "K.set_learning_phase(0)\n", "%run autoencoder.py --size 96 --num_layers 2 --drop_frac 0.25 --no_train --model_type gru --sigma 0.5 --lr 5e-4 --sim_type autoencoder/uneven/noise0.5 --embedding 32 --bidirectional" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sinusoid(p, A1, A2, b):\n", " return lambda t: A1 * np.cos(2 * np.pi / p * t) + A2 * np.sin(2 * np.pi / p * t) + b\n", "\n", "N = 3\n", "inds = [16, 10, 25]\n", "colors = sns.color_palette(n_colors=len(inds))\n", "for j, i in enumerate(inds):\n", " t = X_raw[i, :, 0]\n", " m = X[i, :, 1]\n", " T = np.linspace(0, t.max(), 501)\n", " plt.figure()\n", " plt.plot(T, sinusoid(*sample_data.phase_to_sin_cos(Y)[i])(T), color=colors[j])\n", " plt.plot(t, m, 'o', color=colors[j])\n", " plt.xlabel('t')\n", " plt.ylabel('f(t)')\n", " plt.title(f'ω={Y[i, 0]:1.3}, A={Y[i, 1]:1.3}, ϕ={Y[i, 2]:1.3}, b={Y[i, 3]:1.3}')\n", " if SAVE_PLOTS:\n", " plt.savefig(f'figures/sinusoid_{j}.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "train = np.arange(args.N_train)\n", "test = np.arange(args.N_train, args.N_train + args.N_test)\n", "\n", "# TODO re-run without this; why so slow...?\n", "#train = train[:5000]\n", "#X = np.r_[X[train], X[test]]; Y = np.r_[Y[train], Y[test]]; test = np.arange(len(train), len(train) + args.N_test)\n", "\n", "encode_model = Model(input=model.input, output=model.get_layer('encoding').output)\n", "encoding = encode_model.predict({'main_input': X, 'aux_input': np.delete(X, 1, axis=2)})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "df = pd.DataFrame(np.c_[Y[train], encoding[train]], \n", " columns=[f'Y_{i}' for i in range(4)] + [f'Embedding {i}' for i in range(encoding.shape[1])])\n", "sns.pairplot(df, kind='reg', x_vars=df.columns[:4], y_vars=df.columns[4:])\n", "\n", "df['Y_0'] **= -1\n", "df.rename(columns={'Y_0': 'Period', 'Y_2': 'Phase (radian)'}, inplace=True)\n", "R = df.corr(method='spearman')\n", "\n", "emb_period = np.argmax(np.abs(R.iloc[Y.shape[1]:, 0]))\n", "sns.jointplot(df['Period'], df[emb_period], kind='hex', stat_func=None)\n", "if SAVE_PLOTS:\n", " plt.savefig('figures/period_hex.pdf')\n", "\n", "emb_phase = np.argmax(np.abs(R.iloc[Y.shape[1]:, 2]))\n", "sns.jointplot(df['Phase (radian)'], df[emb_phase], kind='hex', stat_func=None)\n", "if SAVE_PLOTS:\n", " plt.savefig('figures/phase_hex.pdf')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.svm import SVR\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "model = RandomForestRegressor(n_estimators=1000)\n", "model.fit(encoding[train], 1. / Y[train, 0])\n", "sns.regplot(1. / Y[test, 0], model.predict(encoding[test]), line_kws={'color': 'black', 'linestyle': 'dotted'})\n", "plt.annotate(f\"Mean squared error: {np.mean((1. / Y[test, 0] - model.predict(encoding[test])) ** 2):1.4f}\", (0.8, 9.8))\n", "plt.xlabel('True period')\n", "plt.ylabel('Estimated period')\n", "if SAVE_PLOTS:\n", " plt.savefig('figures/period_rf.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ASAS reconstructions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log_files = natsorted(glob.glob('keras_logs/asas_full/n200_ls0.2/gru_064_x2_5m04_drop25_emb*_bidir/training.csv'))\n", "\n", "step_logs, time_logs = parse_logs(log_files)\n", "step_plot = training_plot(step_logs, ylim=(3e-2, 1e-1))\n", "time_plot = training_plot(time_logs, ylim=(3e-2, 1e-1))\n", "if SAVE_PLOTS:\n", " step_plot.savefig('figures/asas_autoencoder_step.pdf')\n", " time_plot.savefig('figures/asas_autoencoder_time.pdf')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "Loading /Users/brettnaul/Dropbox/Documents/timeflow/keras_logs/asas_full/n200_ss0.7/gru_064_x1_1m03_drop25_emb64_bidir/weights.h5...\n", "CPU times: user 30.3 s, sys: 2.71 s, total: 33.1 s\n", "Wall time: 33.4 s\n" ] } ], "source": [ "%%time\n", "import os\n", "import joblib\n", "from keras_util import get_run_id\n", "from survey_autoencoder import main as survey_autoencoder\n", "\n", "arg_dict = {'size': 64, 'embedding': 64, 'num_layers': 1, 'model_type': 'gru',\n", " 'sim_type': 'asas_full/n200_ss0.7', 'n_min': 200, 'n_max': 200,\n", " 'lr': 1e-3, 'bidirectional': True, 'ss_resid': 0.7,\n", " 'drop_frac': 0.25, 'survey_files': ['data/asas/full.pkl'],\n", " 'period_fold': False, 'no_train': True}\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X, X_raw, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)\n", "full = joblib.load('data/asas/full.pkl')\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "if args.ss_resid:\n", " split = [el for el in split if el.ss_resid <= args.ss_resid]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(33103, 26482.4, 6620.6)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(len(X), 0.8 * len(X), 0.2 * len(X))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27594\n", "7797\n", "CPU times: user 642 ms, sys: 426 ms, total: 1.07 s\n", "Wall time: 1.41 s\n" ] }, { "data": { "image/png": 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MnTsX9+7dg6OjY4P1SUhIgKWlJYyMjERtrVu3hoWFBTIyMnDy5EmUlJRg5syZou3l5eVg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\norBixQoMGjQI7u7uiI2Nlb63j48Pnn32WRw7dgxAW3tXcXExJk+ebID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ViboH9D7384tx9Oxd9A5piQcPVRHxD/JL0EhumhmzeltNRV14Duh79c8jBbal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\nx6Nr164ICwtDTU0NwsLCoFarMWDAAPTr1w85OTkA7u6JvPvuuxgxYgT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%time\n", "pred = model.predict({'main_input': X[:], 'aux_input': X[:, :, [0,]]})\n", "ords = np.argsort(np.sum((pred.squeeze() - X[:, :, 1]) ** 2, axis=1))\n", "inds, labels = zip(*[(8320, '25'), (23994, '75')])\n", "for ord_i, label in zip(inds, labels):\n", " i = ords[ord_i]\n", " print(i)\n", " t = X_raw[~wrong_units][i, :, 0]\n", " m = X_raw[~wrong_units][i, :, 1]\n", " e = X_raw[~wrong_units][i, :, 2]\n", " m_est = pred[i] * scales[i, 0] + means[i]\n", " plt.figure()\n", " plt.errorbar(t, m, e, None, 'o');\n", "# plt.errorbar(t, m_est, None, None, 'o');\n", " plt.xlabel('Time [day]')\n", " plt.ylabel('Magnitude')\n", " plt.legend(['Original', 'Reconstructed'])\n", " plt.title(f'{split[i].survey} {split[i].name} ({split[i].label})')\n", " plt.gca().invert_yaxis()\n", " if SAVE_PLOTS:\n", " plt.savefig(f'paper/figures/asas_reconstruct_{label}.pdf')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "Loading /Users/brettnaul/Dropbox/Documents/timeflow/keras_logs/asas_fold/n200/gru_064_x1_1m03_drop25_emb64_bidir/weights.h5...\n", "CPU times: user 18.6 s, sys: 3.01 s, total: 21.6 s\n", "Wall time: 23.1 s\n" ] } ], "source": [ "%%time\n", "import os\n", "from keras_util import get_run_id\n", "from survey_autoencoder import main as survey_autoencoder\n", "\n", "arg_dict = {'size': 64, 'embedding': 64, 'num_layers': 1, 'model_type': 'gru',\n", " 'sim_type': 'asas_fold/n200', 'n_min': 200, 'n_max': 200,\n", " 'lr': 1e-3, 'bidirectional': True, 'ss_resid': 0.7,\n", " 'drop_frac': 0.25, 'survey_files': ['data/asas/full.pkl'],\n", " 'period_fold': True, 'no_train': True}\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X_fold, X_raw_fold, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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CpUsXyMrKeqtzExHJDV9PBwb4Oumt6968mtZ3QYC9Zx4VyZ5tYSIu04kUGeam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RUYoqo5VGVHhRpduA27XaBSBIrGjgg9h9Nsos5qA7UNBw73hAbREOi+zXk5//+V5gZKNnxEXAvz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\nKljg6svf5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Sdo9ueSLTplZtroGuzdkHPFHz6Fy5ePJbO9W2Tr8WQYCpI7t5vBJ3hgwU+oRj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vaxxx7T/v+FF14Y9Lbp6Oicm6g1t9S/OulhyMk9q0ZI5ay76c5lWjt9vP7xYVo79WxwHZ2zTUWx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\nGRkZfPLJJ9SsWRMdH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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred_fold = model.predict({'main_input': X_fold[:], 'aux_input': X_fold[:, :, [0,]]})\n", "ords_fold = np.argsort(np.sum((pred_fold.squeeze() - X_fold[:, :, 1]) ** 2, axis=1))\n", "for ord_i, label in zip(inds, labels):\n", " i = ords[ord_i]\n", " t = X_raw_fold[~wrong_units][i, :, 0]\n", " m = X_raw_fold[~wrong_units][i, :, 1]\n", " e = X_raw_fold[~wrong_units][i, :, 2]\n", " m_est = (pred[i] * scales[i, 0] + means[i])[np.argsort(split[i].times % split[i].p)]\n", " m_fold = pred_fold[i] * scales[i, 0] + means[i]\n", " plt.figure()\n", " plt.errorbar(t, m, e, None, 'o');\n", " plt.errorbar(t, m_est, None, None, 'o', alpha=0.6);\n", " plt.errorbar(t, m_fold, None, None, 'o');\n", " plt.xlabel('Phase [day]')\n", " plt.ylabel('Magnitude')\n", " plt.title(f'{split[i].survey} {split[i].name} ({split[i].label})')\n", " plt.legend(['Original', 'Reconstructed (non-folded)', 'Reconstructed (folded)'])\n", " plt.gca().invert_yaxis()\n", " if SAVE_PLOTS:\n", " plt.savefig(f'figures/asas_folded_reconstruct_{label}.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ASAS autoencoder random forests" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "import os\n", "import joblib\n", "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n", "from sklearn.ensemble import RandomForestClassifier\n", "from light_curve import LightCurve\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.models import Model\n", "from keras_util import get_run_id\n", "from survey_autoencoder import main as survey_autoencoder, preprocess\n", "\n", "arg_dict = {'size': 64, 'embedding': 64, 'num_layers': 1, 'model_type': 'gru',\n", " 'sim_type': 'asas_fold/n200_noise', 'n_min': 200, 'n_max': 200,\n", " 'lr': 1e-3, 'bidirectional': True, 'ss_resid': 0.7,\n", " 'drop_frac': 0.25, 'survey_files': ['data/asas/full.pkl'],\n", " 'period_fold': True, 'no_train': True}\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X, X_raw, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)\n", "\n", "top_classes = ['RR_Lyrae_FM', 'W_Ursae_Maj', 'Classical_Cepheid', 'Beta_Persei', 'Semireg_PV']\n", "full = joblib.load('data/asas/full.pkl')\n", "full = [lc for lc in full if lc.label in top_classes]\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "#if args.ss_resid:\n", "# split = [el for el in split if el.ss_resid <= args.ss_resid]\n", "if args.period_fold:\n", " for lc in split:\n", " lc.period_fold()\n", "X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split]\n", "classnames, indices = np.unique([lc.label for lc in split], return_inverse=True)\n", "periods = [lc.p for lc in split]\n", "y = classnames[indices]\n", "train, valid = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED).split(X_list, y))[0]\n", "\n", "X_raw = pad_sequences(X_list, value=0., dtype='float', padding='post')\n", "X, means, scales, wrong_units = preprocess(X_raw, args.m_max)\n", "encode_model = Model(input=model.input, output=model.get_layer('encoding').output)\n", "encoding = encode_model.predict({'main_input': X, 'aux_input': np.delete(X, 1, axis=2)})\n", "encoding = np.c_[encoding, means, scales, periods]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "asas_model = GridSearchCV(RandomForestClassifier(random_state=0), RF_PARAM_GRID)\n", "asas_model.fit(encoding[train], y[train])\n", "print(\"Training score: {}%\".format(100 * asas_model.score(encoding[train], y[train])))\n", "print(\"Validation score: {}%\".format(100 * asas_model.score(encoding[valid], y[valid])))\n", "plot_confusion_matrix(y[valid], asas_model.predict(encoding[valid]), classnames,\n", " 'figures/asas_confusion.pdf' if SAVE_PLOTS else None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ASAS Richards random forests" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from sklearn.model_selection import StratifiedKFold\n", "from light_curve import LightCurve\n", "import joblib\n", "from cesium.features import LOMB_SCARGLE_FEATS, CADENCE_FEATS, GENERAL_FEATS\n", "from cesium.featurize import featurize_time_series\n", "\n", "from argparse import Namespace; args = Namespace(n_min=200, n_max=200)\n", "\n", "top_classes = ['RR_Lyrae_FM', 'W_Ursae_Maj', 'Classical_Cepheid', 'Beta_Persei', 'Semireg_PV']\n", "full = joblib.load('data/asas/full.pkl')\n", "full = [lc for lc in full if lc.label in top_classes]\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split]\n", "classnames, indices = np.unique([lc.label for lc in split], return_inverse=True)\n", "y = classnames[indices]\n", "train, valid = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED).split(X_list, y))[0]\n", "\n", "times, measurements, errors = zip(*[x.T for x in X_list])\n", "\n", "features_to_use = LOMB_SCARGLE_FEATS + GENERAL_FEATS# + CADENCE_FEATS\n", "\n", "fset = featurize_time_series(times, measurements, errors, features_to_use)\n", "#joblib.dump(fset, 'asas_richards.pkl', compress=True)\n", "#fset = joblib.load('asas_richards.pkl')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "cs_model = GridSearchCV(RandomForestClassifier(random_state=0), RF_PARAM_GRID)\n", "cs_model.fit(fset.iloc[train], y[train])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cs_pred = cs_model.predict(fset)\n", "print(f\"Training score: {100 * np.mean(cs_pred[train] == y[train])}%\")\n", "print(f\"Validation score: {100 * np.mean(cs_pred[valid] == y[valid])}%\")\n", "plot_confusion_matrix(y[valid], cs_model.predict(fset.iloc[valid]), classnames,\n", " 'figures/asas_richards_confusion.pdf' if SAVE_PLOTS else None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LINEAR reconstructions" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "Loading /Users/brettnaul/Dropbox/Documents/timeflow/keras_logs/linear/n200/gru_096_x2_1m03_drop25_emb64_bidir/weights.h5...\n" ] } ], "source": [ "import os\n", "import joblib\n", "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n", "from sklearn.ensemble import RandomForestClassifier\n", "from light_curve import LightCurve\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.models import Model\n", "from keras_util import parse_model_args, get_run_id\n", "from survey_autoencoder import main as survey_autoencoder\n", "\n", "arg_dict = {'size': 96, 'embedding': 64, 'num_layers': 2, 'model_type': 'gru',\n", " 'sim_type': 'linear/n200', 'n_min': 200, 'n_max': 200,\n", " 'lr': 1e-3, 'bidirectional': True, 'drop_frac': 0.25, 'period_fold': False,\n", " 'survey_files': ['data/linear/full.pkl'], 'no_train': True}\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X, X_raw, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred = model.predict({'main_input': X[:], 'aux_input': X[:, :, 0:1]})\n", "ords = np.argsort(np.mean((pred.squeeze() - X[:, :, 1]) ** 2 / (X_raw[:, :, 2] / scales), axis=1))\n", "inds, labels = zip(*[(1142, '25'), (3425, '75')])\n", "#for i, label in zip(inds, labels):\n", "# t = X_raw[~wrong_units][i, :, 0]\n", "# m = X_raw[~wrong_units][i, :, 1]\n", "# e = X_raw[~wrong_units][i, :, 2]\n", "# m_est = pred[i] * scales[i, 0] + means[i]\n", "# plt.figure()\n", "# plt.errorbar(t, m, e, None, 'o');\n", "# plt.errorbar(t, m_est, None, None, 'o');\n", "# plt.xlabel('Time [day]')\n", "# plt.ylabel('Magnitude')\n", "# plt.legend(['Original', 'Reconstructed'])\n", "# plt.gca().invert_yaxis()\n", "# if SAVE_PLOTS:\n", "# plt.savefig(f'figures/linear_reconstruct_{label}.pdf')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n", "Loading /Users/brettnaul/Dropbox/Documents/timeflow/keras_logs/asas_linear_fold/n200_ss0.7/gru_096_x2_5m04_drop25_emb64_bidir/weights.h5...\n", "CPU times: user 4min 33s, sys: 57.6 s, total: 5min 30s\n", "Wall time: 1min\n" ] } ], "source": [ "%%time\n", "import os\n", "import joblib\n", "from keras_util import get_run_id\n", "from survey_autoencoder import main as survey_autoencoder\n", "\n", "arg_dict = {'size': 96, 'embedding': 64, 'num_layers': 2, 'model_type': 'gru',\n", " 'sim_type': 'asas_linear_fold/n200_ss0.7', 'n_min': 200, 'n_max': 200,\n", " 'lr': 5e-4, 'bidirectional': True,\n", " 'drop_frac': 0.25, 'survey_files': ['data/linear/full.pkl'],\n", " 'period_fold': True, 'no_train': True}\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X, X_raw, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)\n", "pred_fold = model.predict({'main_input': X[:], 'aux_input': X[:, :, [0,]]}, batch_size=2000)\n", "ords_fold = np.argsort(np.sum((pred_fold.squeeze() - X[:, :, 1]) ** 2, axis=1))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2761\n", "39\n" ] }, { "data": { "image/png": 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q5ZVXXuObb5Zx/34Mdnb23L8fw+LFC7hy5RIuLi6MGzfB8LCLjX3IwoVfcOnS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\nVqxgcrnckc02y9r32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DhwK25+TkEBkZyZIlSwJsRsXFxZw4cYITJ05csj3JYrEwdepUfv/737N48WLKyso4duwY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\nnplFo7CXa7nuW4XsrN6KZG3wbK9UKCjKHI8xybeceCzEOv1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PtgaGWzsd1J8s4+Otp0OG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\nnpGOjg6Rk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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "full = joblib.load('data/linear/full.pkl')\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "for ord_i, label in zip(inds, labels):\n", " i = ords[ord_i]\n", " print(i)\n", " t = X_raw[~wrong_units][i, :, 0]\n", " m = X_raw[~wrong_units][i, :, 1]\n", " e = X_raw[~wrong_units][i, :, 2]\n", " m_est = (pred[i] * scales[i, 0] + means[i])[np.argsort(split[i].times % split[i].p)]\n", " m_fold = pred_fold[i] * scales[i, 0] + means[i]\n", " plt.figure()\n", " plt.errorbar(t, m, e, None, 'o');\n", " plt.errorbar(t, m_est, None, None, 'o', alpha=0.6);\n", " plt.errorbar(t, m_fold, None, None, 'o');\n", " plt.xlabel('Phase [day]')\n", " plt.ylabel('Magnitude')\n", " plt.legend(['Original', 'Reconstructed (non-folded)', 'Reconstructed (folded)'])\n", " plt.title(f'{split[i].survey} {split[i].name} ({split[i].label})')\n", " plt.gca().invert_yaxis()\n", " if SAVE_PLOTS:\n", " plt.savefig(f'figures/linear_folded_reconstruct_{label}.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LINEAR autoencoder random forests" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "import os\n", "import joblib\n", "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n", "from sklearn.ensemble import RandomForestClassifier\n", "from light_curve import LightCurve\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.models import Model\n", "from keras_util import get_run_id\n", "from survey_autoencoder import main as survey_autoencoder, preprocess\n", "\n", "#arg_dict = {'size': 96, 'embedding': 64, 'num_layers': 2, 'model_type': 'gru',\n", "# 'sim_type': 'asas_linear_fold/n200_ss0.7', 'n_min': 200, 'n_max': 200,\n", "# 'lr': 5e-4, 'bidirectional': True, 'drop_frac': 0.25, 'period_fold': True,\n", "# 'survey_files': ['data/linear/full.pkl'], 'no_train': True}\n", "arg_dict = args.__dict__\n", "run = get_run_id(**arg_dict)\n", "log_dir = os.path.join(os.getcwd(), 'keras_logs', arg_dict['sim_type'], run)\n", "weights_path = os.path.join(log_dir, 'weights.h5')\n", "if not os.path.exists(weights_path):\n", " raise FileNotFoundError(weights_path)\n", "X, X_raw, model, means, scales, wrong_units, args = survey_autoencoder(arg_dict)\n", "\n", "full = joblib.load('data/linear/full.pkl')\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "if args.period_fold:\n", " for lc in split:\n", " lc.period_fold()\n", "X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split]\n", "classnames, indices = np.unique([lc.label for lc in split], return_inverse=True)\n", "periods = [lc.p for lc in split]\n", "y = classnames[indices]\n", "train, valid = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED).split(X_list, y))[0]\n", "\n", "encode_model = Model(input=model.input, output=model.get_layer('encoding').output)\n", "encoding = encode_model.predict({'main_input': X, 'aux_input': np.delete(X, 1, axis=2)})\n", "encoding = np.c_[encoding, means, scales, periods]\n", "linear_model = GridSearchCV(RandomForestClassifier(random_state=0), RF_PARAM_GRID)\n", "linear_model.fit(encoding[train], y[train])\n", "print(\"Training score: {}%\".format(100 * linear_model.score(encoding[train], y[train])))\n", "print(\"Validation score: {}%\".format(100 * linear_model.score(encoding[valid], y[valid])))\n", "plot_confusion_matrix(y[valid], cs_model.predict(encoding[valid]), classnames,\n", " 'figures/linear_confusion.pdf' if SAVE_PLOTS else None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LINEAR Richards random forests" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from sklearn.model_selection import StratifiedKFold\n", "from light_curve import LightCurve\n", "import joblib\n", "from cesium.features import LOMB_SCARGLE_FEATS, CADENCE_FEATS, GENERAL_FEATS\n", "from cesium.featurize import featurize_time_series\n", "\n", "from argparse import Namespace; args = Namespace(n_min=200, n_max=200)\n", "\n", "full = joblib.load('data/linear/full.pkl')\n", "split = [el for lc in full for el in lc.split(args.n_min, args.n_max)]\n", "X_list = [np.c_[lc.times, lc.measurements, lc.errors] for lc in split]\n", "classnames, indices = np.unique([lc.label for lc in split], return_inverse=True)\n", "y = classnames[indices]\n", "train, valid = list(StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED).split(X_list, y))[0]\n", "\n", "times, measurements, errors = zip(*[x.T for x in X_list])\n", "\n", "features_to_use = LOMB_SCARGLE_FEATS + GENERAL_FEATS# + CADENCE_FEATS\n", "\n", "fset = featurize_time_series(times, measurements, errors, features_to_use)\n", "#joblib.dump(fset, 'linear_richards.pkl', compress=True)\n", "#fset = joblib.load('linear_richards.pkl')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "cs_model = GridSearchCV(RandomForestClassifier(random_state=0), RF_PARAM_GRID)\n", "cs_model.fit(fset.iloc[train], y[train])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cs_pred = cs_model.predict(fset)\n", "print(f\"Training score: {100 * np.mean(cs_pred[train] == y[train])}%\")\n", "print(f\"Validation score: {100 * np.mean(cs_pred[valid] == y[valid])}%\")\n", "plot_confusion_matrix(y[valid], cs_model.predict(fset.iloc[valid]), classnames,\n", " 'figures/linear_richards_confusion.pdf' if SAVE_PLOTS else None)" ] } ], "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }