{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CEVAE vs. Meta-Learners Benchmark with IHDP + Synthetic Datasets" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:06.716322Z", "start_time": "2021-02-01T21:16:00.790891Z" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "import torch\n", "\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from xgboost import XGBRegressor\n", "from lightgbm import LGBMRegressor\n", "from sklearn.metrics import mean_absolute_error\n", "from sklearn.metrics import mean_squared_error as mse\n", "from scipy.stats import entropy\n", "import warnings\n", "import logging\n", "\n", "from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor\n", "from causalml.inference.torch import CEVAE\n", "from causalml.propensity import ElasticNetPropensityModel\n", "from causalml.metrics import *\n", "from causalml.dataset import simulate_hidden_confounder\n", "\n", "%matplotlib inline\n", "\n", "warnings.filterwarnings('ignore')\n", "logger = logging.getLogger('causalml')\n", "logger.setLevel(logging.DEBUG)\n", "\n", "plt.style.use('fivethirtyeight')\n", "sns.set_palette('Paired')\n", "plt.rcParams['figure.figsize'] = (12,8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IHDP semi-synthetic dataset\n", "\n", "Hill introduced a semi-synthetic dataset constructed from the Infant Health\n", "and Development Program (IHDP). This dataset is based on a randomized experiment\n", "investigating the effect of home visits by specialists on future cognitive scores. The IHDP simulation is considered the de-facto standard benchmark for neural network treatment effect\n", "estimation methods." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:07.130301Z", "start_time": "2021-02-01T21:16:06.722641Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6723, 30)\n", "(672300, 30)\n" ] } ], "source": [ "# load all ihadp data\n", "df = pd.DataFrame()\n", "for i in range(1, 10):\n", " data = pd.read_csv('./data/ihdp_npci_' + str(i) + '.csv', header=None)\n", " df = pd.concat([data, df])\n", "cols = [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\"] + [i for i in range(25)]\n", "df.columns = cols\n", "print(df.shape)\n", "\n", "# replicate the data 100 times\n", "replications = 100\n", "df = pd.concat([df]*replications, ignore_index=True)\n", "print(df.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:07.144511Z", "start_time": "2021-02-01T21:16:07.139182Z" } }, "outputs": [], "source": [ "# set which features are binary\n", "binfeats = [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n", "# set which features are continuous\n", "contfeats = [i for i in range(25) if i not in binfeats]\n", "\n", "# reorder features with binary first and continuous after\n", "perm = binfeats + contfeats" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:07.366309Z", "start_time": "2021-02-01T21:16:07.152398Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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treatmenty_factualy_cfactualmu0mu101234...15161718192021222324
0149.64792134.95076237.17329150.383798-0.528603-0.3434551.1285540.161703-0.316603...1111000000
1016.07341249.43531316.08724949.546234-1.736945-1.8020020.3838282.244320-0.629189...1111000000
2019.64300748.59821018.04485549.661068-0.807451-0.202946-0.360898-0.8796060.808706...1011000000
3026.36832249.71520424.60596449.9711960.3900830.596582-1.850350-0.879606-0.004017...1011000000
4020.25889351.14741820.61281649.794120-1.045229-0.6027100.0114650.1617030.683672...1111000000
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5 rows × 30 columns

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" ], "text/plain": [ " treatment y_factual y_cfactual mu0 mu1 0 1 \\\n", "0 1 49.647921 34.950762 37.173291 50.383798 -0.528603 -0.343455 \n", "1 0 16.073412 49.435313 16.087249 49.546234 -1.736945 -1.802002 \n", "2 0 19.643007 48.598210 18.044855 49.661068 -0.807451 -0.202946 \n", "3 0 26.368322 49.715204 24.605964 49.971196 0.390083 0.596582 \n", "4 0 20.258893 51.147418 20.612816 49.794120 -1.045229 -0.602710 \n", "\n", " 2 3 4 ... 15 16 17 18 19 20 21 22 23 24 \n", "0 1.128554 0.161703 -0.316603 ... 1 1 1 1 0 0 0 0 0 0 \n", "1 0.383828 2.244320 -0.629189 ... 1 1 1 1 0 0 0 0 0 0 \n", "2 -0.360898 -0.879606 0.808706 ... 1 0 1 1 0 0 0 0 0 0 \n", "3 -1.850350 -0.879606 -0.004017 ... 1 0 1 1 0 0 0 0 0 0 \n", "4 0.011465 0.161703 0.683672 ... 1 1 1 1 0 0 0 0 0 0 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.reset_index(drop=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:34.604702Z", "start_time": "2021-02-01T21:16:07.370970Z" } }, "outputs": [], "source": [ "X = df[perm].values\n", "treatment = df['treatment'].values\n", "y = df['y_factual'].values\n", "y_cf = df['y_cfactual'].values\n", "tau = df.apply(lambda d: d['y_factual'] - d['y_cfactual'] if d['treatment']==1\n", " else d['y_cfactual'] - d['y_factual'],\n", " axis=1)\n", "mu_0 = df['mu0'].values\n", "mu_1 = df['mu1'].values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:35.018237Z", "start_time": "2021-02-01T21:16:34.606960Z" } }, "outputs": [], "source": [ "# seperate for train and test\n", "itr, ite = train_test_split(np.arange(X.shape[0]), test_size=0.2, random_state=1)\n", "X_train, treatment_train, y_train, y_cf_train, tau_train, mu_0_train, mu_1_train = X[itr], treatment[itr], y[itr], y_cf[itr], tau[itr], mu_0[itr], mu_1[itr]\n", "X_val, treatment_val, y_val, y_cf_val, tau_val, mu_0_val, mu_1_val = X[ite], treatment[ite], y[ite], y_cf[ite], tau[ite], mu_0[ite], mu_1[ite]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CEVAE Model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:35.024352Z", "start_time": "2021-02-01T21:16:35.020848Z" } }, "outputs": [], "source": [ "# cevae model settings\n", "outcome_dist = \"normal\"\n", "latent_dim = 20\n", "hidden_dim = 200\n", "num_epochs = 5\n", "batch_size = 1000\n", "learning_rate = 0.001\n", "learning_rate_decay = 0.01\n", "num_layers = 2" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:16:35.032884Z", "start_time": "2021-02-01T21:16:35.029438Z" } }, "outputs": [], "source": [ "cevae = CEVAE(outcome_dist=outcome_dist,\n", " latent_dim=latent_dim,\n", " hidden_dim=hidden_dim,\n", " num_epochs=num_epochs,\n", " batch_size=batch_size,\n", " learning_rate=learning_rate,\n", " learning_rate_decay=learning_rate_decay,\n", " num_layers=num_layers)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T21:18:39.930593Z", "start_time": "2021-02-01T21:16:35.037013Z" }, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO \t Training with 538 minibatches per epoch\n", "DEBUG \t step 0 loss = 1021.35\n", "DEBUG \t step 1 loss = 421.484\n", "DEBUG \t step 2 loss = 338.296\n", "DEBUG \t step 3 loss = 319.514\n", "DEBUG \t step 4 loss = 217.484\n", "DEBUG \t step 5 loss = 237.474\n", "DEBUG \t step 6 loss = 242.367\n", "DEBUG \t step 7 loss = 236.713\n", "DEBUG \t step 8 loss = 200.399\n", "DEBUG \t step 9 loss = 201.788\n", "DEBUG \t step 10 loss = 220.049\n", "DEBUG \t step 11 loss = 213.79\n", "DEBUG \t step 12 loss = 190.921\n", "DEBUG \t step 13 loss = 196.359\n", "DEBUG \t step 14 loss = 189.747\n", "DEBUG \t step 15 loss = 167.321\n", "DEBUG \t step 16 loss = 159.207\n", "DEBUG \t step 17 loss = 154.599\n", "DEBUG \t step 18 loss = 150.961\n", "DEBUG \t step 19 loss = 149.938\n", "DEBUG \t step 20 loss = 134.768\n", "DEBUG \t step 21 loss = 140.833\n", "DEBUG \t step 22 loss = 146.769\n", "DEBUG \t step 23 loss = 132.524\n", "DEBUG \t step 24 loss = 134.194\n", "DEBUG \t step 25 loss = 130.618\n", "DEBUG \t step 26 loss = 136.787\n", "DEBUG \t step 27 loss = 126.727\n", "DEBUG \t step 28 loss = 120.942\n", "DEBUG \t step 29 loss = 118.619\n", "DEBUG \t step 30 loss = 120.946\n", "DEBUG \t step 31 loss = 110.782\n", "DEBUG \t step 32 loss = 120.907\n", "DEBUG \t step 33 loss = 106.87\n", "DEBUG \t step 34 loss = 95.3908\n", "DEBUG \t step 35 loss = 104.229\n", "DEBUG \t step 36 loss = 100.688\n", "DEBUG \t step 37 loss = 102.31\n", "DEBUG \t step 38 loss = 96.3181\n", "DEBUG \t step 39 loss = 92.0119\n", "DEBUG \t step 40 loss = 101.374\n", "DEBUG \t step 41 loss = 95.1874\n", "DEBUG \t step 42 loss = 91.693\n", "DEBUG \t step 43 loss = 83.7838\n", "DEBUG \t step 44 loss = 76.9446\n", "DEBUG \t step 45 loss = 77.8403\n", "DEBUG \t step 46 loss = 81.372\n", "DEBUG \t step 47 loss = 82.7198\n", "DEBUG \t step 48 loss = 72.8519\n", "DEBUG \t step 49 loss = 76.6569\n", "DEBUG \t step 50 loss = 75.7397\n", "DEBUG \t step 51 loss = 79.6319\n", "DEBUG \t step 52 loss = 79.2719\n", "DEBUG \t step 53 loss = 74.6354\n", "DEBUG \t step 54 loss = 68.5501\n", "DEBUG \t step 55 loss = 72.5121\n", "DEBUG \t step 56 loss = 65.3819\n", "DEBUG \t step 57 loss = 68.0494\n", "DEBUG \t step 58 loss = 69.0703\n", "DEBUG \t step 59 loss = 67.7917\n", "DEBUG \t step 60 loss = 66.9287\n", "DEBUG \t step 61 loss = 58.5794\n", "DEBUG \t step 62 loss = 59.4718\n", "DEBUG \t step 63 loss = 62.9541\n", "DEBUG \t step 64 loss = 60.0412\n", "DEBUG \t step 65 loss = 57.8926\n", "DEBUG \t step 66 loss = 57.5324\n", "DEBUG \t step 67 loss = 56.5494\n", "DEBUG \t step 68 loss = 52.2587\n", "DEBUG \t step 69 loss = 55.7073\n", "DEBUG \t step 70 loss = 54.979\n", "DEBUG \t step 71 loss = 55.4208\n", "DEBUG \t step 72 loss = 54.7927\n", "DEBUG \t step 73 loss = 49.0343\n", "DEBUG \t step 74 loss = 53.8712\n", "DEBUG \t step 75 loss = 50.4505\n", "DEBUG \t step 76 loss = 49.2015\n", "DEBUG \t step 77 loss = 49.1161\n", "DEBUG \t step 78 loss = 51.0351\n", "DEBUG \t step 79 loss = 47.8925\n", "DEBUG \t step 80 loss = 48.4682\n", "DEBUG \t step 81 loss = 47.0941\n", "DEBUG \t step 82 loss = 44.807\n", "DEBUG \t step 83 loss = 43.6143\n", "DEBUG \t step 84 loss = 48.9903\n", "DEBUG \t step 85 loss = 46.6454\n", "DEBUG \t step 86 loss = 46.2746\n", "DEBUG \t step 87 loss = 47.5599\n", "DEBUG \t step 88 loss = 45.7764\n", "DEBUG \t step 89 loss = 42.9916\n", "DEBUG \t step 90 loss = 43.2444\n", "DEBUG \t step 91 loss = 43.616\n", "DEBUG \t step 92 loss = 41.0364\n", "DEBUG \t step 93 loss = 40.7751\n", "DEBUG \t step 94 loss = 39.693\n", "DEBUG \t step 95 loss = 41.2092\n", "DEBUG \t step 96 loss = 41.3535\n", "DEBUG \t step 97 loss = 39.0969\n", "DEBUG \t step 98 loss = 39.176\n", "DEBUG \t step 99 loss = 41.4575\n", "DEBUG \t step 100 loss = 40.5371\n", "DEBUG \t step 101 loss = 39.4805\n", "DEBUG \t step 102 loss = 37.7776\n", "DEBUG \t step 103 loss = 36.5425\n", "DEBUG \t step 104 loss = 37.3177\n", "DEBUG \t step 105 loss = 37.9773\n", "DEBUG \t step 106 loss = 36.8961\n", "DEBUG \t step 107 loss = 36.6936\n", "DEBUG \t step 108 loss = 35.1503\n", "DEBUG \t step 109 loss = 37.8622\n", "DEBUG \t step 110 loss = 36.6135\n", "DEBUG \t step 111 loss = 34.6556\n", "DEBUG \t step 112 loss = 32.9034\n", "DEBUG \t step 113 loss = 35.928\n", "DEBUG \t step 114 loss = 35.6375\n", "DEBUG \t step 115 loss = 34.8875\n", "DEBUG \t step 116 loss = 32.4369\n", "DEBUG \t step 117 loss = 35.5889\n", "DEBUG \t step 118 loss = 33.3445\n", "DEBUG \t step 119 loss = 35.3891\n", "DEBUG \t step 120 loss = 32.7132\n", "DEBUG \t step 121 loss = 32.4759\n", "DEBUG \t step 122 loss = 33.143\n", "DEBUG \t step 123 loss = 31.3498\n", "DEBUG \t step 124 loss = 31.6331\n", "DEBUG \t step 125 loss = 33.2434\n", "DEBUG \t step 126 loss = 31.1028\n", "DEBUG \t step 127 loss = 32.8674\n", "DEBUG \t step 128 loss = 32.8578\n", "DEBUG \t step 129 loss = 32.625\n", "DEBUG \t step 130 loss = 31.8448\n", "DEBUG \t step 131 loss = 30.8554\n", "DEBUG \t step 132 loss = 31.9763\n", "DEBUG \t step 133 loss = 29.6616\n", "DEBUG \t step 134 loss = 30.0425\n", "DEBUG \t step 135 loss = 30.836\n", "DEBUG \t step 136 loss = 31.0736\n", "DEBUG \t step 137 loss = 30.8878\n", "DEBUG \t step 138 loss = 30.43\n", "DEBUG \t step 139 loss = 30.6093\n", "DEBUG \t step 140 loss = 30.7339\n", "DEBUG \t step 141 loss = 30.0207\n", "DEBUG \t step 142 loss = 29.3626\n", "DEBUG \t step 143 loss = 29.7463\n", "DEBUG \t step 144 loss = 29.4184\n", "DEBUG \t step 145 loss = 29.2421\n", "DEBUG \t step 146 loss = 29.7529\n", "DEBUG \t step 147 loss = 29.3111\n", "DEBUG \t step 148 loss = 28.7811\n", "DEBUG \t step 149 loss = 29.3185\n", "DEBUG \t step 150 loss = 28.3709\n", "DEBUG \t step 151 loss = 30.2563\n", "DEBUG \t step 152 loss = 29.5989\n", "DEBUG \t step 153 loss = 28.8563\n", "DEBUG \t step 154 loss = 27.3948\n", "DEBUG \t step 155 loss = 28.3484\n", "DEBUG \t step 156 loss = 29.0616\n", "DEBUG \t step 157 loss = 28.8883\n", "DEBUG \t step 158 loss = 27.0463\n", "DEBUG \t step 159 loss = 27.3796\n", "DEBUG \t step 160 loss = 29.0732\n", "DEBUG \t step 161 loss = 26.8263\n", "DEBUG \t step 162 loss = 27.2883\n", "DEBUG \t step 163 loss = 28.6272\n", "DEBUG \t step 164 loss = 26.7478\n", "DEBUG \t step 165 loss = 27.6244\n", "DEBUG \t step 166 loss = 26.3508\n", "DEBUG \t step 167 loss = 26.1734\n", "DEBUG \t step 168 loss = 26.4877\n", "DEBUG \t step 169 loss = 26.9542\n", "DEBUG \t step 170 loss = 27.5395\n", "DEBUG \t step 171 loss = 26.4924\n", "DEBUG \t step 172 loss = 26.2203\n", "DEBUG \t step 173 loss = 26.039\n", "DEBUG \t step 174 loss = 25.7883\n", "DEBUG \t step 175 loss = 25.7104\n", "DEBUG \t step 176 loss = 25.9135\n", "DEBUG \t step 177 loss = 25.8419\n", "DEBUG \t step 178 loss = 26.897\n", "DEBUG \t step 179 loss = 24.8235\n", "DEBUG \t step 180 loss = 25.8669\n", "DEBUG \t step 181 loss = 26.442\n", "DEBUG \t step 182 loss = 24.7512\n", "DEBUG \t step 183 loss = 25.4444\n", "DEBUG \t step 184 loss = 25.7225\n", "DEBUG \t step 185 loss = 24.9703\n", "DEBUG \t step 186 loss = 25.5197\n", "DEBUG \t step 187 loss = 25.3311\n", "DEBUG \t step 188 loss = 25.0711\n", "DEBUG \t step 189 loss = 25.5542\n", "DEBUG \t step 190 loss = 25.2289\n", "DEBUG \t step 191 loss = 24.9589\n", "DEBUG \t step 192 loss = 24.5436\n", "DEBUG \t step 193 loss = 24.4451\n", "DEBUG \t step 194 loss = 23.3428\n", "DEBUG \t step 195 loss = 24.6046\n", "DEBUG \t step 196 loss = 25.1871\n", "DEBUG \t step 197 loss = 24.1005\n", "DEBUG \t step 198 loss = 24.287\n", "DEBUG \t step 199 loss = 24.4165\n", "DEBUG \t step 200 loss = 24.5855\n", "DEBUG \t step 201 loss = 23.2874\n", "DEBUG \t step 202 loss = 23.8787\n", "DEBUG \t step 203 loss = 24.5806\n", "DEBUG \t step 204 loss = 24.0906\n", "DEBUG \t step 205 loss = 25.0818\n", "DEBUG \t step 206 loss = 23.9177\n", "DEBUG \t step 207 loss = 25.0566\n", "DEBUG \t step 208 loss = 23.0722\n", "DEBUG \t step 209 loss = 23.8822\n", "DEBUG \t step 210 loss = 24.3339\n", "DEBUG \t step 211 loss = 24.7321\n", "DEBUG \t step 212 loss = 22.9672\n", "DEBUG \t step 213 loss = 23.6966\n", "DEBUG \t step 214 loss = 23.0869\n", "DEBUG \t step 215 loss = 23.5599\n", "DEBUG \t step 216 loss = 23.6307\n", "DEBUG \t step 217 loss = 23.1928\n", "DEBUG \t step 218 loss = 23.9375\n", "DEBUG \t step 219 loss = 23.65\n", "DEBUG \t step 220 loss = 22.5324\n", "DEBUG \t step 221 loss = 23.7082\n", "DEBUG \t step 222 loss = 22.854\n", "DEBUG \t step 223 loss = 21.8886\n", "DEBUG \t step 224 loss = 23.4573\n", "DEBUG \t step 225 loss = 22.4752\n", "DEBUG \t step 226 loss = 22.2281\n", "DEBUG \t step 227 loss = 22.6597\n", "DEBUG \t step 228 loss = 22.8313\n", "DEBUG \t step 229 loss = 22.8756\n", "DEBUG \t step 230 loss = 22.1289\n", "DEBUG \t step 231 loss = 22.6235\n", "DEBUG \t step 232 loss = 22.0739\n", "DEBUG \t step 233 loss = 22.7643\n", "DEBUG \t step 234 loss = 21.5396\n", "DEBUG \t step 235 loss = 21.5537\n", "DEBUG \t step 236 loss = 21.8743\n", "DEBUG \t step 237 loss = 22.6117\n", "DEBUG \t step 238 loss = 22.8206\n", "DEBUG \t step 239 loss = 22.8641\n", "DEBUG \t step 240 loss = 22.5666\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 241 loss = 22.3578\n", "DEBUG \t step 242 loss = 23.3638\n", "DEBUG \t step 243 loss = 22.1094\n", "DEBUG \t step 244 loss = 22.1056\n", "DEBUG \t step 245 loss = 22.1651\n", "DEBUG \t step 246 loss = 21.4072\n", "DEBUG \t step 247 loss = 21.4627\n", "DEBUG \t step 248 loss = 21.2096\n", "DEBUG \t step 249 loss = 21.3499\n", "DEBUG \t step 250 loss = 21.4386\n", "DEBUG \t step 251 loss = 21.3385\n", "DEBUG \t step 252 loss = 21.3782\n", "DEBUG \t step 253 loss = 20.7455\n", "DEBUG \t step 254 loss = 22.3244\n", "DEBUG \t step 255 loss = 21.1068\n", "DEBUG \t step 256 loss = 21.5648\n", "DEBUG \t step 257 loss = 21.5746\n", "DEBUG \t step 258 loss = 21.6169\n", "DEBUG \t step 259 loss = 21.2303\n", "DEBUG \t step 260 loss = 21.8207\n", "DEBUG \t step 261 loss = 21.2217\n", "DEBUG \t step 262 loss = 22.4259\n", "DEBUG \t step 263 loss = 21.2911\n", "DEBUG \t step 264 loss = 21.9783\n", "DEBUG \t step 265 loss = 120.585\n", "DEBUG \t step 266 loss = 22.3958\n", "DEBUG \t step 267 loss = 21.1204\n", "DEBUG \t step 268 loss = 20.3405\n", "DEBUG \t step 269 loss = 19.9695\n", "DEBUG \t step 270 loss = 21.6718\n", "DEBUG \t step 271 loss = 20.8654\n", "DEBUG \t step 272 loss = 20.4101\n", "DEBUG \t step 273 loss = 20.769\n", "DEBUG \t step 274 loss = 20.5526\n", "DEBUG \t step 275 loss = 20.026\n", "DEBUG \t step 276 loss = 20.2413\n", "DEBUG \t step 277 loss = 20.0747\n", "DEBUG \t step 278 loss = 20.6848\n", "DEBUG \t step 279 loss = 20.0956\n", "DEBUG \t step 280 loss = 20.667\n", "DEBUG \t step 281 loss = 19.8283\n", "DEBUG \t step 282 loss = 19.8651\n", "DEBUG \t step 283 loss = 19.4686\n", "DEBUG \t step 284 loss = 19.7195\n", "DEBUG \t step 285 loss = 20.1469\n", "DEBUG \t step 286 loss = 19.8956\n", "DEBUG \t step 287 loss = 20.3657\n", "DEBUG \t step 288 loss = 20.1624\n", "DEBUG \t step 289 loss = 20.8871\n", "DEBUG \t step 290 loss = 19.7327\n", "DEBUG \t step 291 loss = 19.3476\n", "DEBUG \t step 292 loss = 19.841\n", "DEBUG \t step 293 loss = 20.0052\n", "DEBUG \t step 294 loss = 19.7133\n", "DEBUG \t step 295 loss = 19.7911\n", "DEBUG \t step 296 loss = 18.6917\n", "DEBUG \t step 297 loss = 19.795\n", "DEBUG \t step 298 loss = 19.1175\n", "DEBUG \t step 299 loss = 20.1492\n", "DEBUG \t step 300 loss = 19.7831\n", "DEBUG \t step 301 loss = 19.7247\n", "DEBUG \t step 302 loss = 19.5755\n", "DEBUG \t step 303 loss = 19.9661\n", "DEBUG \t step 304 loss = 18.2884\n", "DEBUG \t step 305 loss = 19.6565\n", "DEBUG \t step 306 loss = 19.6213\n", "DEBUG \t step 307 loss = 19.2026\n", "DEBUG \t step 308 loss = 19.8699\n", "DEBUG \t step 309 loss = 18.7806\n", "DEBUG \t step 310 loss = 18.8876\n", "DEBUG \t step 311 loss = 19.3982\n", "DEBUG \t step 312 loss = 19.1813\n", "DEBUG \t step 313 loss = 18.9337\n", "DEBUG \t step 314 loss = 18.2574\n", "DEBUG \t step 315 loss = 19.0662\n", "DEBUG \t step 316 loss = 19.1584\n", "DEBUG \t step 317 loss = 18.1926\n", "DEBUG \t step 318 loss = 18.7658\n", "DEBUG \t step 319 loss = 18.2249\n", "DEBUG \t step 320 loss = 19.003\n", "DEBUG \t step 321 loss = 19.0593\n", "DEBUG \t step 322 loss = 18.9254\n", "DEBUG \t step 323 loss = 19.0602\n", "DEBUG \t step 324 loss = 18.5273\n", "DEBUG \t step 325 loss = 18.2321\n", "DEBUG \t step 326 loss = 18.354\n", "DEBUG \t step 327 loss = 18.2741\n", "DEBUG \t step 328 loss = 18.544\n", "DEBUG \t step 329 loss = 18.3197\n", "DEBUG \t step 330 loss = 18.8422\n", "DEBUG \t step 331 loss = 18.4199\n", "DEBUG \t step 332 loss = 17.7382\n", "DEBUG \t step 333 loss = 18.1209\n", "DEBUG \t step 334 loss = 18.4557\n", "DEBUG \t step 335 loss = 18.5937\n", "DEBUG \t step 336 loss = 17.7678\n", "DEBUG \t step 337 loss = 19.1363\n", "DEBUG \t step 338 loss = 18.0725\n", "DEBUG \t step 339 loss = 18.3309\n", "DEBUG \t step 340 loss = 17.9822\n", "DEBUG \t step 341 loss = 17.7317\n", "DEBUG \t step 342 loss = 18.1821\n", "DEBUG \t step 343 loss = 18.1704\n", "DEBUG \t step 344 loss = 18.0436\n", "DEBUG \t step 345 loss = 17.3161\n", "DEBUG \t step 346 loss = 17.1744\n", "DEBUG \t step 347 loss = 18.4531\n", "DEBUG \t step 348 loss = 17.097\n", "DEBUG \t step 349 loss = 17.2031\n", "DEBUG \t step 350 loss = 17.7855\n", "DEBUG \t step 351 loss = 17.3887\n", "DEBUG \t step 352 loss = 18.1904\n", "DEBUG \t step 353 loss = 16.9673\n", "DEBUG \t step 354 loss = 17.6665\n", "DEBUG \t step 355 loss = 17.9181\n", "DEBUG \t step 356 loss = 17.3892\n", "DEBUG \t step 357 loss = 18.6147\n", "DEBUG \t step 358 loss = 17.0139\n", "DEBUG \t step 359 loss = 17.4958\n", "DEBUG \t step 360 loss = 16.8143\n", "DEBUG \t step 361 loss = 16.8076\n", "DEBUG \t step 362 loss = 17.2509\n", "DEBUG \t step 363 loss = 16.6091\n", "DEBUG \t step 364 loss = 16.5105\n", "DEBUG \t step 365 loss = 16.8734\n", "DEBUG \t step 366 loss = 16.7367\n", "DEBUG \t step 367 loss = 16.3754\n", "DEBUG \t step 368 loss = 16.7072\n", "DEBUG \t step 369 loss = 16.6687\n", "DEBUG \t step 370 loss = 16.4918\n", "DEBUG \t step 371 loss = 17.4622\n", "DEBUG \t step 372 loss = 16.5902\n", "DEBUG \t step 373 loss = 17.0211\n", "DEBUG \t step 374 loss = 16.1971\n", "DEBUG \t step 375 loss = 17.1127\n", "DEBUG \t step 376 loss = 17.0151\n", "DEBUG \t step 377 loss = 16.5271\n", "DEBUG \t step 378 loss = 15.7553\n", "DEBUG \t step 379 loss = 17.5206\n", "DEBUG \t step 380 loss = 16.1141\n", "DEBUG \t step 381 loss = 16.0002\n", "DEBUG \t step 382 loss = 16.7775\n", "DEBUG \t step 383 loss = 16.0455\n", "DEBUG \t step 384 loss = 16.4851\n", "DEBUG \t step 385 loss = 15.9572\n", "DEBUG \t step 386 loss = 16.045\n", "DEBUG \t step 387 loss = 16.3194\n", "DEBUG \t step 388 loss = 16.827\n", "DEBUG \t step 389 loss = 16.818\n", "DEBUG \t step 390 loss = 16.5154\n", "DEBUG \t step 391 loss = 16.4575\n", "DEBUG \t step 392 loss = 16.3866\n", "DEBUG \t step 393 loss = 16.7649\n", "DEBUG \t step 394 loss = 16.3661\n", "DEBUG \t step 395 loss = 16.0388\n", "DEBUG \t step 396 loss = 16.3603\n", "DEBUG \t step 397 loss = 15.9295\n", "DEBUG \t step 398 loss = 16.2829\n", "DEBUG \t step 399 loss = 15.7255\n", "DEBUG \t step 400 loss = 15.9625\n", "DEBUG \t step 401 loss = 16.2877\n", "DEBUG \t step 402 loss = 15.9384\n", "DEBUG \t step 403 loss = 15.7691\n", "DEBUG \t step 404 loss = 15.3813\n", "DEBUG \t step 405 loss = 16.3497\n", "DEBUG \t step 406 loss = 15.6471\n", "DEBUG \t step 407 loss = 15.7245\n", "DEBUG \t step 408 loss = 15.5237\n", "DEBUG \t step 409 loss = 15.4977\n", "DEBUG \t step 410 loss = 15.7544\n", "DEBUG \t step 411 loss = 16.4454\n", "DEBUG \t step 412 loss = 15.8385\n", "DEBUG \t step 413 loss = 15.8783\n", "DEBUG \t step 414 loss = 14.5518\n", "DEBUG \t step 415 loss = 15.248\n", "DEBUG \t step 416 loss = 15.4766\n", "DEBUG \t step 417 loss = 15.1702\n", "DEBUG \t step 418 loss = 15.0027\n", "DEBUG \t step 419 loss = 14.7798\n", "DEBUG \t step 420 loss = 14.2242\n", "DEBUG \t step 421 loss = 14.7344\n", "DEBUG \t step 422 loss = 15.3192\n", "DEBUG \t step 423 loss = 14.5862\n", "DEBUG \t step 424 loss = 14.8549\n", "DEBUG \t step 425 loss = 15.1208\n", "DEBUG \t step 426 loss = 15.6343\n", "DEBUG \t step 427 loss = 14.9648\n", "DEBUG \t step 428 loss = 15.8638\n", "DEBUG \t step 429 loss = 14.7795\n", "DEBUG \t step 430 loss = 15.1229\n", "DEBUG \t step 431 loss = 14.9709\n", "DEBUG \t step 432 loss = 15.3807\n", "DEBUG \t step 433 loss = 14.2497\n", "DEBUG \t step 434 loss = 15.0741\n", "DEBUG \t step 435 loss = 13.8058\n", "DEBUG \t step 436 loss = 15.0915\n", "DEBUG \t step 437 loss = 15.2831\n", "DEBUG \t step 438 loss = 15.0772\n", "DEBUG \t step 439 loss = 15.8433\n", "DEBUG \t step 440 loss = 15.3281\n", "DEBUG \t step 441 loss = 14.7288\n", "DEBUG \t step 442 loss = 15.1505\n", "DEBUG \t step 443 loss = 15.3472\n", "DEBUG \t step 444 loss = 13.545\n", "DEBUG \t step 445 loss = 14.6441\n", "DEBUG \t step 446 loss = 14.0351\n", "DEBUG \t step 447 loss = 14.0212\n", "DEBUG \t step 448 loss = 14.1237\n", "DEBUG \t step 449 loss = 14.4073\n", "DEBUG \t step 450 loss = 14.4118\n", "DEBUG \t step 451 loss = 13.9406\n", "DEBUG \t step 452 loss = 15.0758\n", "DEBUG \t step 453 loss = 14.9103\n", "DEBUG \t step 454 loss = 14.3315\n", "DEBUG \t step 455 loss = 13.8796\n", "DEBUG \t step 456 loss = 13.9354\n", "DEBUG \t step 457 loss = 13.8283\n", "DEBUG \t step 458 loss = 14.8273\n", "DEBUG \t step 459 loss = 14.4759\n", "DEBUG \t step 460 loss = 14.5714\n", "DEBUG \t step 461 loss = 14.0121\n", "DEBUG \t step 462 loss = 14.393\n", "DEBUG \t step 463 loss = 14.4324\n", "DEBUG \t step 464 loss = 14.0807\n", "DEBUG \t step 465 loss = 14.3522\n", "DEBUG \t step 466 loss = 14.4154\n", "DEBUG \t step 467 loss = 13.1898\n", "DEBUG \t step 468 loss = 14.06\n", "DEBUG \t step 469 loss = 20.7401\n", "DEBUG \t step 470 loss = 14.2803\n", "DEBUG \t step 471 loss = 14.287\n", "DEBUG \t step 472 loss = 14.0215\n", "DEBUG \t step 473 loss = 13.4496\n", "DEBUG \t step 474 loss = 14.033\n", "DEBUG \t step 475 loss = 14.4732\n", "DEBUG \t step 476 loss = 13.7291\n", "DEBUG \t step 477 loss = 13.0513\n", "DEBUG \t step 478 loss = 13.6051\n", "DEBUG \t step 479 loss = 13.5316\n", "DEBUG \t step 480 loss = 13.5474\n", "DEBUG \t step 481 loss = 13.7794\n", "DEBUG \t step 482 loss = 13.8363\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 483 loss = 13.2939\n", "DEBUG \t step 484 loss = 13.3987\n", "DEBUG \t step 485 loss = 13.4694\n", "DEBUG \t step 486 loss = 13.0736\n", "DEBUG \t step 487 loss = 12.9663\n", "DEBUG \t step 488 loss = 13.4017\n", "DEBUG \t step 489 loss = 13.1387\n", "DEBUG \t step 490 loss = 12.8554\n", "DEBUG \t step 491 loss = 13.7535\n", "DEBUG \t step 492 loss = 13.0516\n", "DEBUG \t step 493 loss = 12.9229\n", "DEBUG \t step 494 loss = 13.0794\n", "DEBUG \t step 495 loss = 12.6742\n", "DEBUG \t step 496 loss = 12.5159\n", "DEBUG \t step 497 loss = 13.8863\n", "DEBUG \t step 498 loss = 13.275\n", "DEBUG \t step 499 loss = 13.8195\n", "DEBUG \t step 500 loss = 14.2111\n", "DEBUG \t step 501 loss = 12.8113\n", "DEBUG \t step 502 loss = 13.5611\n", "DEBUG \t step 503 loss = 13.1597\n", "DEBUG \t step 504 loss = 12.7698\n", "DEBUG \t step 505 loss = 12.655\n", "DEBUG \t step 506 loss = 13.3424\n", "DEBUG \t step 507 loss = 13.0807\n", "DEBUG \t step 508 loss = 13.4257\n", "DEBUG \t step 509 loss = 12.769\n", "DEBUG \t step 510 loss = 13.2426\n", "DEBUG \t step 511 loss = 13.7624\n", "DEBUG \t step 512 loss = 13.4707\n", "DEBUG \t step 513 loss = 12.6719\n", "DEBUG \t step 514 loss = 12.7837\n", "DEBUG \t step 515 loss = 12.3574\n", "DEBUG \t step 516 loss = 12.4319\n", "DEBUG \t step 517 loss = 12.2339\n", "DEBUG \t step 518 loss = 12.5959\n", "DEBUG \t step 519 loss = 12.9824\n", "DEBUG \t step 520 loss = 12.7877\n", "DEBUG \t step 521 loss = 13.0799\n", "DEBUG \t step 522 loss = 12.6134\n", "DEBUG \t step 523 loss = 12.0151\n", "DEBUG \t step 524 loss = 13.6236\n", "DEBUG \t step 525 loss = 13.0926\n", "DEBUG \t step 526 loss = 12.7921\n", "DEBUG \t step 527 loss = 12.3066\n", "DEBUG \t step 528 loss = 12.657\n", "DEBUG \t step 529 loss = 12.1989\n", "DEBUG \t step 530 loss = 12.6969\n", "DEBUG \t step 531 loss = 12.205\n", "DEBUG \t step 532 loss = 12.7905\n", "DEBUG \t step 533 loss = 12.6645\n", "DEBUG \t step 534 loss = 11.9637\n", "DEBUG \t step 535 loss = 12.3953\n", "DEBUG \t step 536 loss = 12.326\n", "DEBUG \t step 537 loss = 12.3011\n", "DEBUG \t step 538 loss = 12.3628\n", "DEBUG \t step 539 loss = 13.1567\n", "DEBUG \t step 540 loss = 12.5927\n", "DEBUG \t step 541 loss = 12.5462\n", "DEBUG \t step 542 loss = 12.2117\n", "DEBUG \t step 543 loss = 11.9447\n", "DEBUG \t step 544 loss = 12.5186\n", "DEBUG \t step 545 loss = 11.6064\n", "DEBUG \t step 546 loss = 12.1038\n", "DEBUG \t step 547 loss = 12.4013\n", "DEBUG \t step 548 loss = 12.1646\n", "DEBUG \t step 549 loss = 11.6217\n", "DEBUG \t step 550 loss = 11.7608\n", "DEBUG \t step 551 loss = 12.044\n", "DEBUG \t step 552 loss = 11.5987\n", "DEBUG \t step 553 loss = 12.2336\n", "DEBUG \t step 554 loss = 11.6134\n", "DEBUG \t step 555 loss = 12.212\n", "DEBUG \t step 556 loss = 11.7942\n", "DEBUG \t step 557 loss = 11.8134\n", "DEBUG \t step 558 loss = 11.8879\n", "DEBUG \t step 559 loss = 11.5601\n", "DEBUG \t step 560 loss = 11.8819\n", "DEBUG \t step 561 loss = 11.2771\n", "DEBUG \t step 562 loss = 12.6852\n", "DEBUG \t step 563 loss = 11.8853\n", "DEBUG \t step 564 loss = 11.8232\n", "DEBUG \t step 565 loss = 12.2208\n", "DEBUG \t step 566 loss = 11.8434\n", "DEBUG \t step 567 loss = 10.8617\n", "DEBUG \t step 568 loss = 11.9089\n", "DEBUG \t step 569 loss = 12.8768\n", "DEBUG \t step 570 loss = 11.7326\n", "DEBUG \t step 571 loss = 11.6924\n", "DEBUG \t step 572 loss = 12.071\n", "DEBUG \t step 573 loss = 11.4507\n", "DEBUG \t step 574 loss = 11.9765\n", "DEBUG \t step 575 loss = 12.3481\n", "DEBUG \t step 576 loss = 10.7076\n", "DEBUG \t step 577 loss = 11.2173\n", "DEBUG \t step 578 loss = 11.6225\n", "DEBUG \t step 579 loss = 11.7975\n", "DEBUG \t step 580 loss = 11.4295\n", "DEBUG \t step 581 loss = 11.7824\n", "DEBUG \t step 582 loss = 12.1286\n", "DEBUG \t step 583 loss = 10.932\n", "DEBUG \t step 584 loss = 11.9352\n", "DEBUG \t step 585 loss = 11.4005\n", "DEBUG \t step 586 loss = 11.1264\n", "DEBUG \t step 587 loss = 10.3828\n", "DEBUG \t step 588 loss = 10.6477\n", "DEBUG \t step 589 loss = 11.2266\n", "DEBUG \t step 590 loss = 11.7988\n", "DEBUG \t step 591 loss = 11.1602\n", "DEBUG \t step 592 loss = 11.2809\n", "DEBUG \t step 593 loss = 11.0131\n", "DEBUG \t step 594 loss = 11.3859\n", "DEBUG \t step 595 loss = 11.1015\n", "DEBUG \t step 596 loss = 11.4198\n", "DEBUG \t step 597 loss = 10.501\n", "DEBUG \t step 598 loss = 11.206\n", "DEBUG \t step 599 loss = 11.2975\n", "DEBUG \t step 600 loss = 10.0333\n", "DEBUG \t step 601 loss = 9.98137\n", "DEBUG \t step 602 loss = 12.6949\n", "DEBUG \t step 603 loss = 11.1914\n", "DEBUG \t step 604 loss = 10.2179\n", "DEBUG \t step 605 loss = 10.8835\n", "DEBUG \t step 606 loss = 10.3426\n", "DEBUG \t step 607 loss = 10.9994\n", "DEBUG \t step 608 loss = 10.4913\n", "DEBUG \t step 609 loss = 10.5934\n", "DEBUG \t step 610 loss = 11.2756\n", "DEBUG \t step 611 loss = 10.6515\n", "DEBUG \t step 612 loss = 10.634\n", "DEBUG \t step 613 loss = 10.6894\n", "DEBUG \t step 614 loss = 10.4173\n", "DEBUG \t step 615 loss = 10.3444\n", "DEBUG \t step 616 loss = 16.9274\n", "DEBUG \t step 617 loss = 10.6686\n", "DEBUG \t step 618 loss = 10.6302\n", "DEBUG \t step 619 loss = 11.4147\n", "DEBUG \t step 620 loss = 10.4305\n", "DEBUG \t step 621 loss = 9.93963\n", "DEBUG \t step 622 loss = 10.2567\n", "DEBUG \t step 623 loss = 10.4703\n", "DEBUG \t step 624 loss = 10.5793\n", "DEBUG \t step 625 loss = 10.7117\n", "DEBUG \t step 626 loss = 10.6469\n", "DEBUG \t step 627 loss = 10.6067\n", "DEBUG \t step 628 loss = 10.2047\n", "DEBUG \t step 629 loss = 10.7753\n", "DEBUG \t step 630 loss = 9.84085\n", "DEBUG \t step 631 loss = 9.8512\n", "DEBUG \t step 632 loss = 9.90551\n", "DEBUG \t step 633 loss = 10.2306\n", "DEBUG \t step 634 loss = 10.4\n", "DEBUG \t step 635 loss = 9.96456\n", "DEBUG \t step 636 loss = 10.0543\n", "DEBUG \t step 637 loss = 10.4722\n", "DEBUG \t step 638 loss = 10.2624\n", "DEBUG \t step 639 loss = 9.8927\n", "DEBUG \t step 640 loss = 9.74269\n", "DEBUG \t step 641 loss = 10.0714\n", "DEBUG \t step 642 loss = 9.4886\n", "DEBUG \t step 643 loss = 11.2356\n", "DEBUG \t step 644 loss = 10.4613\n", "DEBUG \t step 645 loss = 9.92244\n", "DEBUG \t step 646 loss = 10.5003\n", "DEBUG \t step 647 loss = 9.28321\n", "DEBUG \t step 648 loss = 10.0217\n", "DEBUG \t step 649 loss = 9.95832\n", "DEBUG \t step 650 loss = 9.89816\n", "DEBUG \t step 651 loss = 9.97542\n", "DEBUG \t step 652 loss = 9.11257\n", "DEBUG \t step 653 loss = 9.9837\n", "DEBUG \t step 654 loss = 10.1827\n", "DEBUG \t step 655 loss = 10.101\n", "DEBUG \t step 656 loss = 9.23931\n", "DEBUG \t step 657 loss = 8.75782\n", "DEBUG \t step 658 loss = 9.40421\n", "DEBUG \t step 659 loss = 9.13174\n", "DEBUG \t step 660 loss = 9.68286\n", "DEBUG \t step 661 loss = 10.4162\n", "DEBUG \t step 662 loss = 8.75674\n", "DEBUG \t step 663 loss = 10.001\n", "DEBUG \t step 664 loss = 9.40904\n", "DEBUG \t step 665 loss = 10.1505\n", "DEBUG \t step 666 loss = 10.1748\n", "DEBUG \t step 667 loss = 10.2148\n", "DEBUG \t step 668 loss = 10.2481\n", "DEBUG \t step 669 loss = 9.96609\n", "DEBUG \t step 670 loss = 9.65714\n", "DEBUG \t step 671 loss = 9.60848\n", "DEBUG \t step 672 loss = 9.84922\n", "DEBUG \t step 673 loss = 10.0371\n", "DEBUG \t step 674 loss = 9.28353\n", "DEBUG \t step 675 loss = 9.06586\n", "DEBUG \t step 676 loss = 9.44504\n", "DEBUG \t step 677 loss = 9.66529\n", "DEBUG \t step 678 loss = 9.7542\n", "DEBUG \t step 679 loss = 9.10189\n", "DEBUG \t step 680 loss = 9.36793\n", "DEBUG \t step 681 loss = 9.47525\n", "DEBUG \t step 682 loss = 9.98902\n", "DEBUG \t step 683 loss = 9.58746\n", "DEBUG \t step 684 loss = 8.77309\n", "DEBUG \t step 685 loss = 9.58264\n", "DEBUG \t step 686 loss = 9.774\n", "DEBUG \t step 687 loss = 10.1397\n", "DEBUG \t step 688 loss = 10.2031\n", "DEBUG \t step 689 loss = 8.85642\n", "DEBUG \t step 690 loss = 8.65729\n", "DEBUG \t step 691 loss = 9.30864\n", "DEBUG \t step 692 loss = 9.08819\n", "DEBUG \t step 693 loss = 8.79863\n", "DEBUG \t step 694 loss = 9.54987\n", "DEBUG \t step 695 loss = 8.96493\n", "DEBUG \t step 696 loss = 8.57488\n", "DEBUG \t step 697 loss = 9.37986\n", "DEBUG \t step 698 loss = 9.12005\n", "DEBUG \t step 699 loss = 9.55977\n", "DEBUG \t step 700 loss = 9.71548\n", "DEBUG \t step 701 loss = 8.66767\n", "DEBUG \t step 702 loss = 9.24891\n", "DEBUG \t step 703 loss = 8.96681\n", "DEBUG \t step 704 loss = 8.50462\n", "DEBUG \t step 705 loss = 8.97093\n", "DEBUG \t step 706 loss = 8.42754\n", "DEBUG \t step 707 loss = 8.31459\n", "DEBUG \t step 708 loss = 8.92468\n", "DEBUG \t step 709 loss = 8.62381\n", "DEBUG \t step 710 loss = 8.99014\n", "DEBUG \t step 711 loss = 9.12061\n", "DEBUG \t step 712 loss = 9.1673\n", "DEBUG \t step 713 loss = 8.71748\n", "DEBUG \t step 714 loss = 9.10944\n", "DEBUG \t step 715 loss = 8.2948\n", "DEBUG \t step 716 loss = 9.03157\n", "DEBUG \t step 717 loss = 8.86918\n", "DEBUG \t step 718 loss = 8.4948\n", "DEBUG \t step 719 loss = 8.20143\n", "DEBUG \t step 720 loss = 9.02752\n", "DEBUG \t step 721 loss = 9.07482\n", "DEBUG \t step 722 loss = 8.47549\n", "DEBUG \t step 723 loss = 8.6139\n", "DEBUG \t step 724 loss = 8.71389\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 725 loss = 8.71019\n", "DEBUG \t step 726 loss = 9.34067\n", "DEBUG \t step 727 loss = 8.33531\n", "DEBUG \t step 728 loss = 8.50657\n", "DEBUG \t step 729 loss = 7.92335\n", "DEBUG \t step 730 loss = 8.73418\n", "DEBUG \t step 731 loss = 7.50367\n", "DEBUG \t step 732 loss = 8.30074\n", "DEBUG \t step 733 loss = 8.10457\n", "DEBUG \t step 734 loss = 8.57933\n", "DEBUG \t step 735 loss = 8.29648\n", "DEBUG \t step 736 loss = 9.08495\n", "DEBUG \t step 737 loss = 9.19558\n", "DEBUG \t step 738 loss = 7.57463\n", "DEBUG \t step 739 loss = 8.25734\n", "DEBUG \t step 740 loss = 8.1562\n", "DEBUG \t step 741 loss = 8.13552\n", "DEBUG \t step 742 loss = 8.61787\n", "DEBUG \t step 743 loss = 7.84507\n", "DEBUG \t step 744 loss = 8.50339\n", "DEBUG \t step 745 loss = 9.99432\n", "DEBUG \t step 746 loss = 8.67392\n", "DEBUG \t step 747 loss = 7.62062\n", "DEBUG \t step 748 loss = 8.47083\n", "DEBUG \t step 749 loss = 7.59856\n", "DEBUG \t step 750 loss = 8.73944\n", "DEBUG \t step 751 loss = 7.82123\n", "DEBUG \t step 752 loss = 8.3673\n", "DEBUG \t step 753 loss = 8.05969\n", "DEBUG \t step 754 loss = 7.67401\n", "DEBUG \t step 755 loss = 8.23807\n", "DEBUG \t step 756 loss = 7.85361\n", "DEBUG \t step 757 loss = 8.29006\n", "DEBUG \t step 758 loss = 7.93663\n", "DEBUG \t step 759 loss = 7.14638\n", "DEBUG \t step 760 loss = 7.75548\n", "DEBUG \t step 761 loss = 7.23605\n", "DEBUG \t step 762 loss = 8.39854\n", "DEBUG \t step 763 loss = 8.36651\n", "DEBUG \t step 764 loss = 8.08217\n", "DEBUG \t step 765 loss = 8.51663\n", "DEBUG \t step 766 loss = 17.1032\n", "DEBUG \t step 767 loss = 8.11124\n", "DEBUG \t step 768 loss = 8.07747\n", "DEBUG \t step 769 loss = 7.82815\n", "DEBUG \t step 770 loss = 9.03203\n", "DEBUG \t step 771 loss = 8.53237\n", "DEBUG \t step 772 loss = 7.96279\n", "DEBUG \t step 773 loss = 8.05574\n", "DEBUG \t step 774 loss = 7.76004\n", "DEBUG \t step 775 loss = 7.35636\n", "DEBUG \t step 776 loss = 8.11715\n", "DEBUG \t step 777 loss = 8.26839\n", "DEBUG \t step 778 loss = 8.3788\n", "DEBUG \t step 779 loss = 8.4216\n", "DEBUG \t step 780 loss = 8.70143\n", "DEBUG \t step 781 loss = 7.68424\n", "DEBUG \t step 782 loss = 7.71564\n", "DEBUG \t step 783 loss = 8.99345\n", "DEBUG \t step 784 loss = 7.84072\n", "DEBUG \t step 785 loss = 7.97106\n", "DEBUG \t step 786 loss = 8.17313\n", "DEBUG \t step 787 loss = 8.43836\n", "DEBUG \t step 788 loss = 8.48604\n", "DEBUG \t step 789 loss = 7.89398\n", "DEBUG \t step 790 loss = 7.66896\n", "DEBUG \t step 791 loss = 7.93176\n", "DEBUG \t step 792 loss = 7.50743\n", "DEBUG \t step 793 loss = 7.35892\n", "DEBUG \t step 794 loss = 8.19966\n", "DEBUG \t step 795 loss = 8.04621\n", "DEBUG \t step 796 loss = 7.20783\n", "DEBUG \t step 797 loss = 7.82553\n", "DEBUG \t step 798 loss = 7.99542\n", "DEBUG \t step 799 loss = 7.39769\n", "DEBUG \t step 800 loss = 7.53701\n", "DEBUG \t step 801 loss = 7.24536\n", "DEBUG \t step 802 loss = 7.33658\n", "DEBUG \t step 803 loss = 7.342\n", "DEBUG \t step 804 loss = 7.75321\n", "DEBUG \t step 805 loss = 6.91357\n", "DEBUG \t step 806 loss = 7.52435\n", "DEBUG \t step 807 loss = 7.56103\n", "DEBUG \t step 808 loss = 7.79389\n", "DEBUG \t step 809 loss = 8.33436\n", "DEBUG \t step 810 loss = 7.46276\n", "DEBUG \t step 811 loss = 7.03648\n", "DEBUG \t step 812 loss = 7.09304\n", "DEBUG \t step 813 loss = 7.55697\n", "DEBUG \t step 814 loss = 7.74993\n", "DEBUG \t step 815 loss = 7.77072\n", "DEBUG \t step 816 loss = 7.57071\n", "DEBUG \t step 817 loss = 7.87914\n", "DEBUG \t step 818 loss = 7.59507\n", "DEBUG \t step 819 loss = 7.95819\n", "DEBUG \t step 820 loss = 7.26536\n", "DEBUG \t step 821 loss = 7.76702\n", "DEBUG \t step 822 loss = 6.81672\n", "DEBUG \t step 823 loss = 7.69591\n", "DEBUG \t step 824 loss = 7.49277\n", "DEBUG \t step 825 loss = 7.71589\n", "DEBUG \t step 826 loss = 7.54939\n", "DEBUG \t step 827 loss = 7.14454\n", "DEBUG \t step 828 loss = 6.54073\n", "DEBUG \t step 829 loss = 7.31939\n", "DEBUG \t step 830 loss = 8.24107\n", "DEBUG \t step 831 loss = 7.75897\n", "DEBUG \t step 832 loss = 7.0123\n", "DEBUG \t step 833 loss = 6.6658\n", "DEBUG \t step 834 loss = 7.17121\n", "DEBUG \t step 835 loss = 7.8772\n", "DEBUG \t step 836 loss = 6.91091\n", "DEBUG \t step 837 loss = 7.24767\n", "DEBUG \t step 838 loss = 7.3708\n", "DEBUG \t step 839 loss = 6.72671\n", "DEBUG \t step 840 loss = 6.91319\n", "DEBUG \t step 841 loss = 7.38147\n", "DEBUG \t step 842 loss = 6.73919\n", "DEBUG \t step 843 loss = 7.1541\n", "DEBUG \t step 844 loss = 7.09714\n", "DEBUG \t step 845 loss = 7.6505\n", "DEBUG \t step 846 loss = 6.37122\n", "DEBUG \t step 847 loss = 7.15714\n", "DEBUG \t step 848 loss = 6.78871\n", "DEBUG \t step 849 loss = 6.43234\n", "DEBUG \t step 850 loss = 6.64114\n", "DEBUG \t step 851 loss = 6.98987\n", "DEBUG \t step 852 loss = 7.51277\n", "DEBUG \t step 853 loss = 7.34095\n", "DEBUG \t step 854 loss = 7.5216\n", "DEBUG \t step 855 loss = 6.37953\n", "DEBUG \t step 856 loss = 7.08232\n", "DEBUG \t step 857 loss = 6.96187\n", "DEBUG \t step 858 loss = 6.12791\n", "DEBUG \t step 859 loss = 6.71254\n", "DEBUG \t step 860 loss = 6.15329\n", "DEBUG \t step 861 loss = 6.74574\n", "DEBUG \t step 862 loss = 7.24058\n", "DEBUG \t step 863 loss = 6.16476\n", "DEBUG \t step 864 loss = 7.61778\n", "DEBUG \t step 865 loss = 6.35608\n", "DEBUG \t step 866 loss = 6.53307\n", "DEBUG \t step 867 loss = 6.36949\n", "DEBUG \t step 868 loss = 6.71838\n", "DEBUG \t step 869 loss = 7.3967\n", "DEBUG \t step 870 loss = 6.65597\n", "DEBUG \t step 871 loss = 6.77125\n", "DEBUG \t step 872 loss = 6.67395\n", "DEBUG \t step 873 loss = 6.40736\n", "DEBUG \t step 874 loss = 6.35543\n", "DEBUG \t step 875 loss = 6.74703\n", "DEBUG \t step 876 loss = 6.58434\n", "DEBUG \t step 877 loss = 6.62172\n", "DEBUG \t step 878 loss = 6.65244\n", "DEBUG \t step 879 loss = 6.97937\n", "DEBUG \t step 880 loss = 6.42221\n", "DEBUG \t step 881 loss = 6.84026\n", "DEBUG \t step 882 loss = 6.72631\n", "DEBUG \t step 883 loss = 6.90398\n", "DEBUG \t step 884 loss = 6.6266\n", "DEBUG \t step 885 loss = 6.51678\n", "DEBUG \t step 886 loss = 6.65169\n", "DEBUG \t step 887 loss = 6.63095\n", "DEBUG \t step 888 loss = 6.24306\n", "DEBUG \t step 889 loss = 7.46224\n", "DEBUG \t step 890 loss = 6.84275\n", "DEBUG \t step 891 loss = 6.19764\n", "DEBUG \t step 892 loss = 7.16809\n", "DEBUG \t step 893 loss = 6.57301\n", "DEBUG \t step 894 loss = 6.72905\n", "DEBUG \t step 895 loss = 7.3967\n", "DEBUG \t step 896 loss = 6.78504\n", "DEBUG \t step 897 loss = 6.52102\n", "DEBUG \t step 898 loss = 6.07938\n", "DEBUG \t step 899 loss = 5.95618\n", "DEBUG \t step 900 loss = 6.14126\n", "DEBUG \t step 901 loss = 5.67246\n", "DEBUG \t step 902 loss = 5.59678\n", "DEBUG \t step 903 loss = 6.5394\n", "DEBUG \t step 904 loss = 6.4651\n", "DEBUG \t step 905 loss = 6.64771\n", "DEBUG \t step 906 loss = 6.44477\n", "DEBUG \t step 907 loss = 5.17112\n", "DEBUG \t step 908 loss = 5.80493\n", "DEBUG \t step 909 loss = 6.36914\n", "DEBUG \t step 910 loss = 6.68615\n", "DEBUG \t step 911 loss = 5.53628\n", "DEBUG \t step 912 loss = 6.51742\n", "DEBUG \t step 913 loss = 6.95286\n", "DEBUG \t step 914 loss = 7.2883\n", "DEBUG \t step 915 loss = 6.09494\n", "DEBUG \t step 916 loss = 6.74383\n", "DEBUG \t step 917 loss = 6.3917\n", "DEBUG \t step 918 loss = 6.25799\n", "DEBUG \t step 919 loss = 6.55483\n", "DEBUG \t step 920 loss = 6.44743\n", "DEBUG \t step 921 loss = 5.77905\n", "DEBUG \t step 922 loss = 5.98885\n", "DEBUG \t step 923 loss = 5.83527\n", "DEBUG \t step 924 loss = 5.93447\n", "DEBUG \t step 925 loss = 5.9199\n", "DEBUG \t step 926 loss = 6.01515\n", "DEBUG \t step 927 loss = 6.14634\n", "DEBUG \t step 928 loss = 5.77208\n", "DEBUG \t step 929 loss = 6.78369\n", "DEBUG \t step 930 loss = 6.21236\n", "DEBUG \t step 931 loss = 5.98394\n", "DEBUG \t step 932 loss = 6.51115\n", "DEBUG \t step 933 loss = 6.44652\n", "DEBUG \t step 934 loss = 5.83554\n", "DEBUG \t step 935 loss = 6.30905\n", "DEBUG \t step 936 loss = 5.93238\n", "DEBUG \t step 937 loss = 6.50758\n", "DEBUG \t step 938 loss = 5.93256\n", "DEBUG \t step 939 loss = 6.06647\n", "DEBUG \t step 940 loss = 6.03391\n", "DEBUG \t step 941 loss = 5.51953\n", "DEBUG \t step 942 loss = 6.03728\n", "DEBUG \t step 943 loss = 6.18949\n", "DEBUG \t step 944 loss = 6.10855\n", "DEBUG \t step 945 loss = 5.92263\n", "DEBUG \t step 946 loss = 6.72183\n", "DEBUG \t step 947 loss = 6.11911\n", "DEBUG \t step 948 loss = 5.84314\n", "DEBUG \t step 949 loss = 6.02928\n", "DEBUG \t step 950 loss = 5.82459\n", "DEBUG \t step 951 loss = 5.98588\n", "DEBUG \t step 952 loss = 5.75092\n", "DEBUG \t step 953 loss = 6.19303\n", "DEBUG \t step 954 loss = 5.78729\n", "DEBUG \t step 955 loss = 5.9059\n", "DEBUG \t step 956 loss = 5.31694\n", "DEBUG \t step 957 loss = 5.71936\n", "DEBUG \t step 958 loss = 6.06149\n", "DEBUG \t step 959 loss = 4.93583\n", "DEBUG \t step 960 loss = 5.8746\n", "DEBUG \t step 961 loss = 5.81154\n", "DEBUG \t step 962 loss = 6.22302\n", "DEBUG \t step 963 loss = 4.62915\n", "DEBUG \t step 964 loss = 6.26837\n", "DEBUG \t step 965 loss = 6.9227\n", "DEBUG \t step 966 loss = 5.69589\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 967 loss = 4.89925\n", "DEBUG \t step 968 loss = 5.95339\n", "DEBUG \t step 969 loss = 5.41167\n", "DEBUG \t step 970 loss = 5.61495\n", "DEBUG \t step 971 loss = 6.08719\n", "DEBUG \t step 972 loss = 5.70671\n", "DEBUG \t step 973 loss = 6.29176\n", "DEBUG \t step 974 loss = 5.96967\n", "DEBUG \t step 975 loss = 5.64207\n", "DEBUG \t step 976 loss = 6.11389\n", "DEBUG \t step 977 loss = 5.4677\n", "DEBUG \t step 978 loss = 5.26326\n", "DEBUG \t step 979 loss = 5.63665\n", "DEBUG \t step 980 loss = 5.47218\n", "DEBUG \t step 981 loss = 5.76207\n", "DEBUG \t step 982 loss = 5.25431\n", "DEBUG \t step 983 loss = 5.11318\n", "DEBUG \t step 984 loss = 5.23281\n", "DEBUG \t step 985 loss = 4.9322\n", "DEBUG \t step 986 loss = 5.19766\n", "DEBUG \t step 987 loss = 5.32089\n", "DEBUG \t step 988 loss = 5.56581\n", "DEBUG \t step 989 loss = 5.68178\n", "DEBUG \t step 990 loss = 4.37302\n", "DEBUG \t step 991 loss = 5.50948\n", "DEBUG \t step 992 loss = 5.3806\n", "DEBUG \t step 993 loss = 6.08309\n", "DEBUG \t step 994 loss = 5.74113\n", "DEBUG \t step 995 loss = 5.29156\n", "DEBUG \t step 996 loss = 6.09862\n", "DEBUG \t step 997 loss = 4.34491\n", "DEBUG \t step 998 loss = 4.74828\n", "DEBUG \t step 999 loss = 5.1352\n", "DEBUG \t step 1000 loss = 5.90098\n", "DEBUG \t step 1001 loss = 5.65187\n", "DEBUG \t step 1002 loss = 4.99241\n", "DEBUG \t step 1003 loss = 4.93651\n", "DEBUG \t step 1004 loss = 5.71697\n", "DEBUG \t step 1005 loss = 5.12284\n", "DEBUG \t step 1006 loss = 6.20878\n", "DEBUG \t step 1007 loss = 5.12986\n", "DEBUG \t step 1008 loss = 4.9672\n", "DEBUG \t step 1009 loss = 5.65217\n", "DEBUG \t step 1010 loss = 5.48825\n", "DEBUG \t step 1011 loss = 5.54487\n", "DEBUG \t step 1012 loss = 5.84657\n", "DEBUG \t step 1013 loss = 5.74514\n", "DEBUG \t step 1014 loss = 5.23785\n", "DEBUG \t step 1015 loss = 4.71362\n", "DEBUG \t step 1016 loss = 4.36813\n", "DEBUG \t step 1017 loss = 5.45256\n", "DEBUG \t step 1018 loss = 5.15537\n", "DEBUG \t step 1019 loss = 5.42831\n", "DEBUG \t step 1020 loss = 5.17\n", "DEBUG \t step 1021 loss = 4.94556\n", "DEBUG \t step 1022 loss = 5.84439\n", "DEBUG \t step 1023 loss = 5.11129\n", "DEBUG \t step 1024 loss = 4.68024\n", "DEBUG \t step 1025 loss = 4.6169\n", "DEBUG \t step 1026 loss = 4.95606\n", "DEBUG \t step 1027 loss = 4.74444\n", "DEBUG \t step 1028 loss = 4.27131\n", "DEBUG \t step 1029 loss = 4.88013\n", "DEBUG \t step 1030 loss = 4.77623\n", "DEBUG \t step 1031 loss = 5.86898\n", "DEBUG \t step 1032 loss = 5.16058\n", "DEBUG \t step 1033 loss = 4.97931\n", "DEBUG \t step 1034 loss = 5.05067\n", "DEBUG \t step 1035 loss = 5.13984\n", "DEBUG \t step 1036 loss = 5.39295\n", "DEBUG \t step 1037 loss = 4.95942\n", "DEBUG \t step 1038 loss = 5.33035\n", "DEBUG \t step 1039 loss = 4.99434\n", "DEBUG \t step 1040 loss = 4.98677\n", "DEBUG \t step 1041 loss = 4.65488\n", "DEBUG \t step 1042 loss = 4.61823\n", "DEBUG \t step 1043 loss = 4.68538\n", "DEBUG \t step 1044 loss = 4.55243\n", "DEBUG \t step 1045 loss = 4.72619\n", "DEBUG \t step 1046 loss = 4.88855\n", "DEBUG \t step 1047 loss = 4.91348\n", "DEBUG \t step 1048 loss = 4.14682\n", "DEBUG \t step 1049 loss = 5.40462\n", "DEBUG \t step 1050 loss = 4.9091\n", "DEBUG \t step 1051 loss = 4.81781\n", "DEBUG \t step 1052 loss = 4.87586\n", "DEBUG \t step 1053 loss = 5.02846\n", "DEBUG \t step 1054 loss = 5.07139\n", "DEBUG \t step 1055 loss = 4.59791\n", "DEBUG \t step 1056 loss = 4.63243\n", "DEBUG \t step 1057 loss = 5.06353\n", "DEBUG \t step 1058 loss = 3.85668\n", "DEBUG \t step 1059 loss = 5.28508\n", "DEBUG \t step 1060 loss = 5.2355\n", "DEBUG \t step 1061 loss = 4.07526\n", "DEBUG \t step 1062 loss = 4.13481\n", "DEBUG \t step 1063 loss = 5.15536\n", "DEBUG \t step 1064 loss = 4.30691\n", "DEBUG \t step 1065 loss = 4.27459\n", "DEBUG \t step 1066 loss = 4.41401\n", "DEBUG \t step 1067 loss = 4.55242\n", "DEBUG \t step 1068 loss = 5.11923\n", "DEBUG \t step 1069 loss = 4.62136\n", "DEBUG \t step 1070 loss = 4.88281\n", "DEBUG \t step 1071 loss = 6.58954\n", "DEBUG \t step 1072 loss = 4.35964\n", "DEBUG \t step 1073 loss = 4.70629\n", "DEBUG \t step 1074 loss = 4.33995\n", "DEBUG \t step 1075 loss = 4.68683\n", "DEBUG \t step 1076 loss = 4.2739\n", "DEBUG \t step 1077 loss = 3.67668\n", "DEBUG \t step 1078 loss = 4.68557\n", "DEBUG \t step 1079 loss = 4.38688\n", "DEBUG \t step 1080 loss = 4.37331\n", "DEBUG \t step 1081 loss = 4.81933\n", "DEBUG \t step 1082 loss = 4.4695\n", "DEBUG \t step 1083 loss = 4.97354\n", "DEBUG \t step 1084 loss = 4.51781\n", "DEBUG \t step 1085 loss = 4.12469\n", "DEBUG \t step 1086 loss = 6.42285\n", "DEBUG \t step 1087 loss = 5.01891\n", "DEBUG \t step 1088 loss = 4.62022\n", "DEBUG \t step 1089 loss = 4.87794\n", "DEBUG \t step 1090 loss = 4.91586\n", "DEBUG \t step 1091 loss = 4.10107\n", "DEBUG \t step 1092 loss = 4.64939\n", "DEBUG \t step 1093 loss = 5.02957\n", "DEBUG \t step 1094 loss = 4.41712\n", "DEBUG \t step 1095 loss = 4.42776\n", "DEBUG \t step 1096 loss = 4.28038\n", "DEBUG \t step 1097 loss = 4.93038\n", "DEBUG \t step 1098 loss = 4.39647\n", "DEBUG \t step 1099 loss = 4.14815\n", "DEBUG \t step 1100 loss = 4.47418\n", "DEBUG \t step 1101 loss = 4.53913\n", "DEBUG \t step 1102 loss = 4.18599\n", "DEBUG \t step 1103 loss = 4.42585\n", "DEBUG \t step 1104 loss = 4.52254\n", "DEBUG \t step 1105 loss = 3.73001\n", "DEBUG \t step 1106 loss = 3.80091\n", "DEBUG \t step 1107 loss = 4.65234\n", "DEBUG \t step 1108 loss = 4.22851\n", "DEBUG \t step 1109 loss = 3.80812\n", "DEBUG \t step 1110 loss = 4.85446\n", "DEBUG \t step 1111 loss = 3.86523\n", "DEBUG \t step 1112 loss = 4.18319\n", "DEBUG \t step 1113 loss = 4.21953\n", "DEBUG \t step 1114 loss = 5.04039\n", "DEBUG \t step 1115 loss = 4.80243\n", "DEBUG \t step 1116 loss = 4.30441\n", "DEBUG \t step 1117 loss = 5.39042\n", "DEBUG \t step 1118 loss = 4.25597\n", "DEBUG \t step 1119 loss = 5.07854\n", "DEBUG \t step 1120 loss = 4.12041\n", "DEBUG \t step 1121 loss = 3.47527\n", "DEBUG \t step 1122 loss = 4.13058\n", "DEBUG \t step 1123 loss = 3.55016\n", "DEBUG \t step 1124 loss = 4.84087\n", "DEBUG \t step 1125 loss = 4.22556\n", "DEBUG \t step 1126 loss = 4.61652\n", "DEBUG \t step 1127 loss = 4.38913\n", "DEBUG \t step 1128 loss = 4.1752\n", "DEBUG \t step 1129 loss = 4.35237\n", "DEBUG \t step 1130 loss = 4.11809\n", "DEBUG \t step 1131 loss = 4.52757\n", "DEBUG \t step 1132 loss = 3.64453\n", "DEBUG \t step 1133 loss = 3.92684\n", "DEBUG \t step 1134 loss = 4.419\n", "DEBUG \t step 1135 loss = 4.53101\n", "DEBUG \t step 1136 loss = 4.20247\n", "DEBUG \t step 1137 loss = 4.4274\n", "DEBUG \t step 1138 loss = 4.00318\n", "DEBUG \t step 1139 loss = 6.42864\n", "DEBUG \t step 1140 loss = 4.00687\n", "DEBUG \t step 1141 loss = 4.74919\n", "DEBUG \t step 1142 loss = 3.83376\n", "DEBUG \t step 1143 loss = 4.00634\n", "DEBUG \t step 1144 loss = 3.43185\n", "DEBUG \t step 1145 loss = 3.91977\n", "DEBUG \t step 1146 loss = 3.8136\n", "DEBUG \t step 1147 loss = 4.02812\n", "DEBUG \t step 1148 loss = 4.1181\n", "DEBUG \t step 1149 loss = 3.40067\n", "DEBUG \t step 1150 loss = 3.87853\n", "DEBUG \t step 1151 loss = 4.30686\n", "DEBUG \t step 1152 loss = 4.22774\n", "DEBUG \t step 1153 loss = 4.38618\n", "DEBUG \t step 1154 loss = 4.56262\n", "DEBUG \t step 1155 loss = 4.45982\n", "DEBUG \t step 1156 loss = 4.59891\n", "DEBUG \t step 1157 loss = 4.44961\n", "DEBUG \t step 1158 loss = 4.0087\n", "DEBUG \t step 1159 loss = 4.88411\n", "DEBUG \t step 1160 loss = 3.81384\n", "DEBUG \t step 1161 loss = 3.60741\n", "DEBUG \t step 1162 loss = 4.1445\n", "DEBUG \t step 1163 loss = 4.40349\n", "DEBUG \t step 1164 loss = 3.83159\n", "DEBUG \t step 1165 loss = 3.76538\n", "DEBUG \t step 1166 loss = 4.21465\n", "DEBUG \t step 1167 loss = 3.94987\n", "DEBUG \t step 1168 loss = 4.0818\n", "DEBUG \t step 1169 loss = 4.06183\n", "DEBUG \t step 1170 loss = 3.47987\n", "DEBUG \t step 1171 loss = 3.67692\n", "DEBUG \t step 1172 loss = 4.20745\n", "DEBUG \t step 1173 loss = 3.84148\n", "DEBUG \t step 1174 loss = 3.49437\n", "DEBUG \t step 1175 loss = 3.67877\n", "DEBUG \t step 1176 loss = 3.95581\n", "DEBUG \t step 1177 loss = 4.26368\n", "DEBUG \t step 1178 loss = 3.89446\n", "DEBUG \t step 1179 loss = 3.66383\n", "DEBUG \t step 1180 loss = 4.65264\n", "DEBUG \t step 1181 loss = 3.91674\n", "DEBUG \t step 1182 loss = 3.80197\n", "DEBUG \t step 1183 loss = 3.24795\n", "DEBUG \t step 1184 loss = 4.25066\n", "DEBUG \t step 1185 loss = 3.59737\n", "DEBUG \t step 1186 loss = 4.23543\n", "DEBUG \t step 1187 loss = 4.40551\n", "DEBUG \t step 1188 loss = 3.06393\n", "DEBUG \t step 1189 loss = 3.78871\n", "DEBUG \t step 1190 loss = 4.47356\n", "DEBUG \t step 1191 loss = 3.01607\n", "DEBUG \t step 1192 loss = 3.5921\n", "DEBUG \t step 1193 loss = 4.14678\n", "DEBUG \t step 1194 loss = 4.06156\n", "DEBUG \t step 1195 loss = 3.63912\n", "DEBUG \t step 1196 loss = 3.80904\n", "DEBUG \t step 1197 loss = 3.94498\n", "DEBUG \t step 1198 loss = 4.46766\n", "DEBUG \t step 1199 loss = 3.94135\n", "DEBUG \t step 1200 loss = 3.16809\n", "DEBUG \t step 1201 loss = 4.44084\n", "DEBUG \t step 1202 loss = 4.10566\n", "DEBUG \t step 1203 loss = 3.80488\n", "DEBUG \t step 1204 loss = 3.19777\n", "DEBUG \t step 1205 loss = 2.95526\n", "DEBUG \t step 1206 loss = 4.49641\n", "DEBUG \t step 1207 loss = 4.23787\n", "DEBUG \t step 1208 loss = 3.70975\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 1209 loss = 3.79127\n", "DEBUG \t step 1210 loss = 3.59221\n", "DEBUG \t step 1211 loss = 3.88194\n", "DEBUG \t step 1212 loss = 3.40576\n", "DEBUG \t step 1213 loss = 3.87329\n", "DEBUG \t step 1214 loss = 3.49796\n", "DEBUG \t step 1215 loss = 3.24266\n", "DEBUG \t step 1216 loss = 3.73337\n", "DEBUG \t step 1217 loss = 3.64298\n", "DEBUG \t step 1218 loss = 3.20159\n", "DEBUG \t step 1219 loss = 2.85318\n", "DEBUG \t step 1220 loss = 3.73986\n", "DEBUG \t step 1221 loss = 3.01543\n", "DEBUG \t step 1222 loss = 3.32277\n", "DEBUG \t step 1223 loss = 2.74171\n", "DEBUG \t step 1224 loss = 3.70805\n", "DEBUG \t step 1225 loss = 3.61112\n", "DEBUG \t step 1226 loss = 2.88479\n", "DEBUG \t step 1227 loss = 3.65801\n", "DEBUG \t step 1228 loss = 4.02943\n", "DEBUG \t step 1229 loss = 2.83562\n", "DEBUG \t step 1230 loss = 3.24228\n", "DEBUG \t step 1231 loss = 3.2782\n", "DEBUG \t step 1232 loss = 3.59486\n", "DEBUG \t step 1233 loss = 3.65803\n", "DEBUG \t step 1234 loss = 2.6809\n", "DEBUG \t step 1235 loss = 3.3619\n", "DEBUG \t step 1236 loss = 3.39297\n", "DEBUG \t step 1237 loss = 3.81023\n", "DEBUG \t step 1238 loss = 3.22556\n", "DEBUG \t step 1239 loss = 3.19648\n", "DEBUG \t step 1240 loss = 4.0888\n", "DEBUG \t step 1241 loss = 3.74848\n", "DEBUG \t step 1242 loss = 2.87371\n", "DEBUG \t step 1243 loss = 2.63874\n", "DEBUG \t step 1244 loss = 3.5867\n", "DEBUG \t step 1245 loss = 2.79683\n", "DEBUG \t step 1246 loss = 2.68036\n", "DEBUG \t step 1247 loss = 3.90314\n", "DEBUG \t step 1248 loss = 2.79271\n", "DEBUG \t step 1249 loss = 3.35704\n", "DEBUG \t step 1250 loss = 3.22364\n", "DEBUG \t step 1251 loss = 4.49007\n", "DEBUG \t step 1252 loss = 3.48859\n", "DEBUG \t step 1253 loss = 3.53123\n", "DEBUG \t step 1254 loss = 3.95726\n", "DEBUG \t step 1255 loss = 3.76191\n", "DEBUG \t step 1256 loss = 3.16396\n", "DEBUG \t step 1257 loss = 3.27892\n", "DEBUG \t step 1258 loss = 3.61666\n", "DEBUG \t step 1259 loss = 2.60104\n", "DEBUG \t step 1260 loss = 3.61282\n", "DEBUG \t step 1261 loss = 3.39698\n", "DEBUG \t step 1262 loss = 3.25254\n", "DEBUG \t step 1263 loss = 3.60338\n", "DEBUG \t step 1264 loss = 3.24701\n", "DEBUG \t step 1265 loss = 2.68532\n", "DEBUG \t step 1266 loss = 3.48767\n", "DEBUG \t step 1267 loss = 3.38295\n", "DEBUG \t step 1268 loss = 3.05102\n", "DEBUG \t step 1269 loss = 2.66065\n", "DEBUG \t step 1270 loss = 4.91023\n", "DEBUG \t step 1271 loss = 3.58709\n", "DEBUG \t step 1272 loss = 2.62444\n", "DEBUG \t step 1273 loss = 3.1492\n", "DEBUG \t step 1274 loss = 2.40123\n", "DEBUG \t step 1275 loss = 3.45261\n", "DEBUG \t step 1276 loss = 3.09002\n", "DEBUG \t step 1277 loss = 3.43325\n", "DEBUG \t step 1278 loss = 3.65285\n", "DEBUG \t step 1279 loss = 5.20928\n", "DEBUG \t step 1280 loss = 3.18166\n", "DEBUG \t step 1281 loss = 2.98796\n", "DEBUG \t step 1282 loss = 3.51501\n", "DEBUG \t step 1283 loss = 3.69819\n", "DEBUG \t step 1284 loss = 2.9171\n", "DEBUG \t step 1285 loss = 3.58279\n", "DEBUG \t step 1286 loss = 3.22799\n", "DEBUG \t step 1287 loss = 2.95054\n", "DEBUG \t step 1288 loss = 2.73463\n", "DEBUG \t step 1289 loss = 2.94937\n", "DEBUG \t step 1290 loss = 3.66875\n", "DEBUG \t step 1291 loss = 5.37338\n", "DEBUG \t step 1292 loss = 3.4862\n", "DEBUG \t step 1293 loss = 3.53109\n", "DEBUG \t step 1294 loss = 3.13318\n", "DEBUG \t step 1295 loss = 3.44508\n", "DEBUG \t step 1296 loss = 3.03238\n", "DEBUG \t step 1297 loss = 3.20079\n", "DEBUG \t step 1298 loss = 2.97329\n", "DEBUG \t step 1299 loss = 2.847\n", "DEBUG \t step 1300 loss = 2.9055\n", "DEBUG \t step 1301 loss = 2.11617\n", "DEBUG \t step 1302 loss = 3.67571\n", "DEBUG \t step 1303 loss = 3.05302\n", "DEBUG \t step 1304 loss = 2.67335\n", "DEBUG \t step 1305 loss = 3.19011\n", "DEBUG \t step 1306 loss = 2.28169\n", "DEBUG \t step 1307 loss = 3.15299\n", "DEBUG \t step 1308 loss = 2.48567\n", "DEBUG \t step 1309 loss = 3.02921\n", "DEBUG \t step 1310 loss = 2.74102\n", "DEBUG \t step 1311 loss = 2.92383\n", "DEBUG \t step 1312 loss = 3.50952\n", "DEBUG \t step 1313 loss = 3.4817\n", "DEBUG \t step 1314 loss = 2.90958\n", "DEBUG \t step 1315 loss = 3.17264\n", "DEBUG \t step 1316 loss = 3.00095\n", "DEBUG \t step 1317 loss = 3.28235\n", "DEBUG \t step 1318 loss = 3.1123\n", "DEBUG \t step 1319 loss = 3.19697\n", "DEBUG \t step 1320 loss = 3.23534\n", "DEBUG \t step 1321 loss = 2.62485\n", "DEBUG \t step 1322 loss = 2.39473\n", "DEBUG \t step 1323 loss = 2.65671\n", "DEBUG \t step 1324 loss = 2.6517\n", "DEBUG \t step 1325 loss = 2.83837\n", "DEBUG \t step 1326 loss = 2.96297\n", "DEBUG \t step 1327 loss = 3.27864\n", "DEBUG \t step 1328 loss = 2.8699\n", "DEBUG \t step 1329 loss = 2.41302\n", "DEBUG \t step 1330 loss = 2.75787\n", "DEBUG \t step 1331 loss = 2.02633\n", "DEBUG \t step 1332 loss = 2.64443\n", "DEBUG \t step 1333 loss = 3.00131\n", "DEBUG \t step 1334 loss = 2.90105\n", "DEBUG \t step 1335 loss = 2.53407\n", "DEBUG \t step 1336 loss = 2.69649\n", "DEBUG \t step 1337 loss = 3.10092\n", "DEBUG \t step 1338 loss = 2.40056\n", "DEBUG \t step 1339 loss = 2.89754\n", "DEBUG \t step 1340 loss = 3.58338\n", "DEBUG \t step 1341 loss = 2.91623\n", "DEBUG \t step 1342 loss = 3.01027\n", "DEBUG \t step 1343 loss = 2.88131\n", "DEBUG \t step 1344 loss = 2.61064\n", "DEBUG \t step 1345 loss = 3.21264\n", "DEBUG \t step 1346 loss = 3.68778\n", "DEBUG \t step 1347 loss = 3.20522\n", "DEBUG \t step 1348 loss = 3.02826\n", "DEBUG \t step 1349 loss = 2.26471\n", "DEBUG \t step 1350 loss = 1.86408\n", "DEBUG \t step 1351 loss = 2.38076\n", "DEBUG \t step 1352 loss = 3.04889\n", "DEBUG \t step 1353 loss = 2.88127\n", "DEBUG \t step 1354 loss = 2.29979\n", "DEBUG \t step 1355 loss = 2.32288\n", "DEBUG \t step 1356 loss = 2.58144\n", "DEBUG \t step 1357 loss = 3.13952\n", "DEBUG \t step 1358 loss = 2.64957\n", "DEBUG \t step 1359 loss = 2.66308\n", "DEBUG \t step 1360 loss = 2.4935\n", "DEBUG \t step 1361 loss = 2.44679\n", "DEBUG \t step 1362 loss = 2.35046\n", "DEBUG \t step 1363 loss = 2.68055\n", "DEBUG \t step 1364 loss = 2.70021\n", "DEBUG \t step 1365 loss = 2.92847\n", "DEBUG \t step 1366 loss = 2.65287\n", "DEBUG \t step 1367 loss = 3.36018\n", "DEBUG \t step 1368 loss = 3.14083\n", "DEBUG \t step 1369 loss = 3.2839\n", "DEBUG \t step 1370 loss = 2.87706\n", "DEBUG \t step 1371 loss = 2.28323\n", "DEBUG \t step 1372 loss = 2.71482\n", "DEBUG \t step 1373 loss = 3.14818\n", "DEBUG \t step 1374 loss = 1.91019\n", "DEBUG \t step 1375 loss = 3.26189\n", "DEBUG \t step 1376 loss = 2.32266\n", "DEBUG \t step 1377 loss = 2.58565\n", "DEBUG \t step 1378 loss = 2.78616\n", "DEBUG \t step 1379 loss = 2.61887\n", "DEBUG \t step 1380 loss = 1.77536\n", "DEBUG \t step 1381 loss = 2.46593\n", "DEBUG \t step 1382 loss = 2.03291\n", "DEBUG \t step 1383 loss = 2.25107\n", "DEBUG \t step 1384 loss = 2.02538\n", "DEBUG \t step 1385 loss = 2.64462\n", "DEBUG \t step 1386 loss = 2.52711\n", "DEBUG \t step 1387 loss = 2.82251\n", "DEBUG \t step 1388 loss = 1.84549\n", "DEBUG \t step 1389 loss = 2.80308\n", "DEBUG \t step 1390 loss = 2.50824\n", "DEBUG \t step 1391 loss = 2.32621\n", "DEBUG \t step 1392 loss = 2.47522\n", "DEBUG \t step 1393 loss = 2.25115\n", "DEBUG \t step 1394 loss = 2.13335\n", "DEBUG \t step 1395 loss = 2.34713\n", "DEBUG \t step 1396 loss = 2.70859\n", "DEBUG \t step 1397 loss = 2.40365\n", "DEBUG \t step 1398 loss = 1.77973\n", "DEBUG \t step 1399 loss = 2.20398\n", "DEBUG \t step 1400 loss = 2.03752\n", "DEBUG \t step 1401 loss = 2.92017\n", "DEBUG \t step 1402 loss = 2.30887\n", "DEBUG \t step 1403 loss = 2.55533\n", "DEBUG \t step 1404 loss = 3.27081\n", "DEBUG \t step 1405 loss = 2.00323\n", "DEBUG \t step 1406 loss = 2.58616\n", "DEBUG \t step 1407 loss = 2.32837\n", "DEBUG \t step 1408 loss = 2.62355\n", "DEBUG \t step 1409 loss = 2.55319\n", "DEBUG \t step 1410 loss = 2.91456\n", "DEBUG \t step 1411 loss = 2.51186\n", "DEBUG \t step 1412 loss = 2.58023\n", "DEBUG \t step 1413 loss = 2.11317\n", "DEBUG \t step 1414 loss = 2.72763\n", "DEBUG \t step 1415 loss = 2.46438\n", "DEBUG \t step 1416 loss = 2.66077\n", "DEBUG \t step 1417 loss = 3.45261\n", "DEBUG \t step 1418 loss = 1.30968\n", "DEBUG \t step 1419 loss = 2.02033\n", "DEBUG \t step 1420 loss = 1.66572\n", "DEBUG \t step 1421 loss = 2.63344\n", "DEBUG \t step 1422 loss = 2.79048\n", "DEBUG \t step 1423 loss = 2.36907\n", "DEBUG \t step 1424 loss = 2.09989\n", "DEBUG \t step 1425 loss = 1.90149\n", "DEBUG \t step 1426 loss = 1.62709\n", "DEBUG \t step 1427 loss = 1.95195\n", "DEBUG \t step 1428 loss = 1.51384\n", "DEBUG \t step 1429 loss = 2.89507\n", "DEBUG \t step 1430 loss = 2.15085\n", "DEBUG \t step 1431 loss = 3.11155\n", "DEBUG \t step 1432 loss = 2.44331\n", "DEBUG \t step 1433 loss = 2.20407\n", "DEBUG \t step 1434 loss = 2.08581\n", "DEBUG \t step 1435 loss = 2.42461\n", "DEBUG \t step 1436 loss = 1.99394\n", "DEBUG \t step 1437 loss = 2.04695\n", "DEBUG \t step 1438 loss = 2.82294\n", "DEBUG \t step 1439 loss = 2.33058\n", "DEBUG \t step 1440 loss = 2.10667\n", "DEBUG \t step 1441 loss = 2.3715\n", "DEBUG \t step 1442 loss = 2.13589\n", "DEBUG \t step 1443 loss = 2.0997\n", "DEBUG \t step 1444 loss = 2.40378\n", "DEBUG \t step 1445 loss = 2.69322\n", "DEBUG \t step 1446 loss = 2.3217\n", "DEBUG \t step 1447 loss = 3.06968\n", "DEBUG \t step 1448 loss = 2.19487\n", "DEBUG \t step 1449 loss = 2.62741\n", "DEBUG \t step 1450 loss = 1.93388\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 1451 loss = 2.23005\n", "DEBUG \t step 1452 loss = 2.05846\n", "DEBUG \t step 1453 loss = 2.37242\n", "DEBUG \t step 1454 loss = 1.70136\n", "DEBUG \t step 1455 loss = 2.47376\n", "DEBUG \t step 1456 loss = 2.62243\n", "DEBUG \t step 1457 loss = 2.22\n", "DEBUG \t step 1458 loss = 2.60625\n", "DEBUG \t step 1459 loss = 1.61209\n", "DEBUG \t step 1460 loss = 2.40373\n", "DEBUG \t step 1461 loss = 3.32855\n", "DEBUG \t step 1462 loss = 2.61678\n", "DEBUG \t step 1463 loss = 3.63504\n", "DEBUG \t step 1464 loss = 2.30637\n", "DEBUG \t step 1465 loss = 2.62554\n", "DEBUG \t step 1466 loss = 2.52577\n", "DEBUG \t step 1467 loss = 2.04929\n", "DEBUG \t step 1468 loss = 2.80166\n", "DEBUG \t step 1469 loss = 2.27281\n", "DEBUG \t step 1470 loss = 2.53645\n", "DEBUG \t step 1471 loss = 2.23338\n", "DEBUG \t step 1472 loss = 2.09672\n", "DEBUG \t step 1473 loss = 2.42459\n", "DEBUG \t step 1474 loss = 2.39755\n", "DEBUG \t step 1475 loss = 2.70626\n", "DEBUG \t step 1476 loss = 2.14803\n", "DEBUG \t step 1477 loss = 2.12395\n", "DEBUG \t step 1478 loss = 2.0754\n", "DEBUG \t step 1479 loss = 2.52702\n", "DEBUG \t step 1480 loss = 2.14769\n", "DEBUG \t step 1481 loss = 1.52042\n", "DEBUG \t step 1482 loss = 2.93158\n", "DEBUG \t step 1483 loss = 2.05924\n", "DEBUG \t step 1484 loss = 2.20132\n", "DEBUG \t step 1485 loss = 2.50342\n", "DEBUG \t step 1486 loss = 2.16502\n", "DEBUG \t step 1487 loss = 2.30084\n", "DEBUG \t step 1488 loss = 1.63317\n", "DEBUG \t step 1489 loss = 1.89554\n", "DEBUG \t step 1490 loss = 1.68024\n", "DEBUG \t step 1491 loss = 1.84459\n", "DEBUG \t step 1492 loss = 1.63598\n", "DEBUG \t step 1493 loss = 1.38678\n", "DEBUG \t step 1494 loss = 1.71994\n", "DEBUG \t step 1495 loss = 1.81303\n", "DEBUG \t step 1496 loss = 2.59038\n", "DEBUG \t step 1497 loss = 1.6169\n", "DEBUG \t step 1498 loss = 1.90588\n", "DEBUG \t step 1499 loss = 2.14643\n", "DEBUG \t step 1500 loss = 2.01967\n", "DEBUG \t step 1501 loss = 1.91788\n", "DEBUG \t step 1502 loss = 1.75204\n", "DEBUG \t step 1503 loss = 2.31053\n", "DEBUG \t step 1504 loss = 2.12471\n", "DEBUG \t step 1505 loss = 2.22645\n", "DEBUG \t step 1506 loss = 2.04981\n", "DEBUG \t step 1507 loss = 1.88154\n", "DEBUG \t step 1508 loss = 1.58932\n", "DEBUG \t step 1509 loss = 1.74206\n", "DEBUG \t step 1510 loss = 2.37344\n", "DEBUG \t step 1511 loss = 1.17495\n", "DEBUG \t step 1512 loss = 1.82669\n", "DEBUG \t step 1513 loss = 1.3465\n", "DEBUG \t step 1514 loss = 1.10967\n", "DEBUG \t step 1515 loss = 1.68837\n", "DEBUG \t step 1516 loss = 2.49356\n", "DEBUG \t step 1517 loss = 1.35455\n", "DEBUG \t step 1518 loss = 1.27578\n", "DEBUG \t step 1519 loss = 1.65972\n", "DEBUG \t step 1520 loss = 1.66863\n", "DEBUG \t step 1521 loss = 1.89212\n", "DEBUG \t step 1522 loss = 1.54516\n", "DEBUG \t step 1523 loss = 1.393\n", "DEBUG \t step 1524 loss = 1.88502\n", "DEBUG \t step 1525 loss = 2.90167\n", "DEBUG \t step 1526 loss = 1.52293\n", "DEBUG \t step 1527 loss = 1.99959\n", "DEBUG \t step 1528 loss = 1.23991\n", "DEBUG \t step 1529 loss = 2.5743\n", "DEBUG \t step 1530 loss = 1.36191\n", "DEBUG \t step 1531 loss = 1.72816\n", "DEBUG \t step 1532 loss = 1.58642\n", "DEBUG \t step 1533 loss = 1.48767\n", "DEBUG \t step 1534 loss = 1.89661\n", "DEBUG \t step 1535 loss = 2.36828\n", "DEBUG \t step 1536 loss = 1.07969\n", "DEBUG \t step 1537 loss = 1.76135\n", "DEBUG \t step 1538 loss = 1.71266\n", "DEBUG \t step 1539 loss = 1.89935\n", "DEBUG \t step 1540 loss = 1.46401\n", "DEBUG \t step 1541 loss = 0.630489\n", "DEBUG \t step 1542 loss = 1.97178\n", "DEBUG \t step 1543 loss = 1.54882\n", "DEBUG \t step 1544 loss = 1.59709\n", "DEBUG \t step 1545 loss = 1.05165\n", "DEBUG \t step 1546 loss = 1.80869\n", "DEBUG \t step 1547 loss = 2.13186\n", "DEBUG \t step 1548 loss = 2.48523\n", "DEBUG \t step 1549 loss = 1.36797\n", "DEBUG \t step 1550 loss = 2.11571\n", "DEBUG \t step 1551 loss = 1.90579\n", "DEBUG \t step 1552 loss = 1.53151\n", "DEBUG \t step 1553 loss = 1.99713\n", "DEBUG \t step 1554 loss = 2.22942\n", "DEBUG \t step 1555 loss = 2.03508\n", "DEBUG \t step 1556 loss = 1.91097\n", "DEBUG \t step 1557 loss = 1.64553\n", "DEBUG \t step 1558 loss = 2.31868\n", "DEBUG \t step 1559 loss = 1.88206\n", "DEBUG \t step 1560 loss = 1.84929\n", "DEBUG \t step 1561 loss = 1.74253\n", "DEBUG \t step 1562 loss = 1.55262\n", "DEBUG \t step 1563 loss = 1.24187\n", "DEBUG \t step 1564 loss = 2.21666\n", "DEBUG \t step 1565 loss = 1.54179\n", "DEBUG \t step 1566 loss = 1.18126\n", "DEBUG \t step 1567 loss = 1.60436\n", "DEBUG \t step 1568 loss = 1.62646\n", "DEBUG \t step 1569 loss = 1.13235\n", "DEBUG \t step 1570 loss = 1.73874\n", "DEBUG \t step 1571 loss = 2.98272\n", "DEBUG \t step 1572 loss = 1.97496\n", "DEBUG \t step 1573 loss = 1.40697\n", "DEBUG \t step 1574 loss = 1.75862\n", "DEBUG \t step 1575 loss = 2.24646\n", "DEBUG \t step 1576 loss = 1.71452\n", "DEBUG \t step 1577 loss = 2.13269\n", "DEBUG \t step 1578 loss = 1.87098\n", "DEBUG \t step 1579 loss = 0.903461\n", "DEBUG \t step 1580 loss = 1.25201\n", "DEBUG \t step 1581 loss = 1.8638\n", "DEBUG \t step 1582 loss = 1.8996\n", "DEBUG \t step 1583 loss = 1.43805\n", "DEBUG \t step 1584 loss = 1.15156\n", "DEBUG \t step 1585 loss = 1.41428\n", "DEBUG \t step 1586 loss = 1.13043\n", "DEBUG \t step 1587 loss = 0.838783\n", "DEBUG \t step 1588 loss = 0.782387\n", "DEBUG \t step 1589 loss = 1.6801\n", "DEBUG \t step 1590 loss = 2.16813\n", "DEBUG \t step 1591 loss = 2.3584\n", "DEBUG \t step 1592 loss = 2.03198\n", "DEBUG \t step 1593 loss = 1.6852\n", "DEBUG \t step 1594 loss = 1.6894\n", "DEBUG \t step 1595 loss = 2.05611\n", "DEBUG \t step 1596 loss = 2.04665\n", "DEBUG \t step 1597 loss = 1.44473\n", "DEBUG \t step 1598 loss = 2.35641\n", "DEBUG \t step 1599 loss = 1.77884\n", "DEBUG \t step 1600 loss = 1.29297\n", "DEBUG \t step 1601 loss = 1.44123\n", "DEBUG \t step 1602 loss = 1.03164\n", "DEBUG \t step 1603 loss = 1.97062\n", "DEBUG \t step 1604 loss = 1.84778\n", "DEBUG \t step 1605 loss = 1.97628\n", "DEBUG \t step 1606 loss = 1.80254\n", "DEBUG \t step 1607 loss = 1.53044\n", "DEBUG \t step 1608 loss = 1.69098\n", "DEBUG \t step 1609 loss = 1.92866\n", "DEBUG \t step 1610 loss = 1.70258\n", "DEBUG \t step 1611 loss = 1.76521\n", "DEBUG \t step 1612 loss = 1.52449\n", "DEBUG \t step 1613 loss = 1.15307\n", "DEBUG \t step 1614 loss = 1.88707\n", "DEBUG \t step 1615 loss = 1.61141\n", "DEBUG \t step 1616 loss = 1.23801\n", "DEBUG \t step 1617 loss = 1.51574\n", "DEBUG \t step 1618 loss = 1.26473\n", "DEBUG \t step 1619 loss = 1.24652\n", "DEBUG \t step 1620 loss = 1.06793\n", "DEBUG \t step 1621 loss = 1.89787\n", "DEBUG \t step 1622 loss = 1.49286\n", "DEBUG \t step 1623 loss = 0.830939\n", "DEBUG \t step 1624 loss = 1.66349\n", "DEBUG \t step 1625 loss = 1.17004\n", "DEBUG \t step 1626 loss = 1.24293\n", "DEBUG \t step 1627 loss = 1.90752\n", "DEBUG \t step 1628 loss = 2.46158\n", "DEBUG \t step 1629 loss = 1.45676\n", "DEBUG \t step 1630 loss = 1.70154\n", "DEBUG \t step 1631 loss = 1.18527\n", "DEBUG \t step 1632 loss = 1.32646\n", "DEBUG \t step 1633 loss = 1.34788\n", "DEBUG \t step 1634 loss = 1.57518\n", "DEBUG \t step 1635 loss = 1.92275\n", "DEBUG \t step 1636 loss = 1.85572\n", "DEBUG \t step 1637 loss = 1.18637\n", "DEBUG \t step 1638 loss = 0.775541\n", "DEBUG \t step 1639 loss = 1.3429\n", "DEBUG \t step 1640 loss = 1.74344\n", "DEBUG \t step 1641 loss = 1.40233\n", "DEBUG \t step 1642 loss = 1.9051\n", "DEBUG \t step 1643 loss = 1.16771\n", "DEBUG \t step 1644 loss = 1.1377\n", "DEBUG \t step 1645 loss = 1.73862\n", "DEBUG \t step 1646 loss = 0.958234\n", "DEBUG \t step 1647 loss = 1.11713\n", "DEBUG \t step 1648 loss = 0.944722\n", "DEBUG \t step 1649 loss = 3.08687\n", "DEBUG \t step 1650 loss = 1.27105\n", "DEBUG \t step 1651 loss = 0.857286\n", "DEBUG \t step 1652 loss = 1.52856\n", "DEBUG \t step 1653 loss = 1.96828\n", "DEBUG \t step 1654 loss = 0.92382\n", "DEBUG \t step 1655 loss = 2.05783\n", "DEBUG \t step 1656 loss = 1.16256\n", "DEBUG \t step 1657 loss = 1.42272\n", "DEBUG \t step 1658 loss = 1.07507\n", "DEBUG \t step 1659 loss = 1.64777\n", "DEBUG \t step 1660 loss = 0.919807\n", "DEBUG \t step 1661 loss = 0.726715\n", "DEBUG \t step 1662 loss = 1.57691\n", "DEBUG \t step 1663 loss = 1.38782\n", "DEBUG \t step 1664 loss = 1.26784\n", "DEBUG \t step 1665 loss = 1.64389\n", "DEBUG \t step 1666 loss = 0.984072\n", "DEBUG \t step 1667 loss = 1.65232\n", "DEBUG \t step 1668 loss = 1.8319\n", "DEBUG \t step 1669 loss = 1.46141\n", "DEBUG \t step 1670 loss = 0.989564\n", "DEBUG \t step 1671 loss = 1.60373\n", "DEBUG \t step 1672 loss = 1.79838\n", "DEBUG \t step 1673 loss = 1.0971\n", "DEBUG \t step 1674 loss = 1.6531\n", "DEBUG \t step 1675 loss = 0.569279\n", "DEBUG \t step 1676 loss = 1.1229\n", "DEBUG \t step 1677 loss = 2.09242\n", "DEBUG \t step 1678 loss = 1.25957\n", "DEBUG \t step 1679 loss = 1.20155\n", "DEBUG \t step 1680 loss = 0.445877\n", "DEBUG \t step 1681 loss = 1.06367\n", "DEBUG \t step 1682 loss = 1.53222\n", "DEBUG \t step 1683 loss = 1.46691\n", "DEBUG \t step 1684 loss = 1.33858\n", "DEBUG \t step 1685 loss = 1.34251\n", "DEBUG \t step 1686 loss = 1.41284\n", "DEBUG \t step 1687 loss = 1.13937\n", "DEBUG \t step 1688 loss = 2.37319\n", "DEBUG \t step 1689 loss = 0.934886\n", "DEBUG \t step 1690 loss = 0.989814\n", "DEBUG \t step 1691 loss = 1.37887\n", "DEBUG \t step 1692 loss = 1.40474\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 1693 loss = 1.73022\n", "DEBUG \t step 1694 loss = 0.660628\n", "DEBUG \t step 1695 loss = 1.47228\n", "DEBUG \t step 1696 loss = 1.16098\n", "DEBUG \t step 1697 loss = 1.3503\n", "DEBUG \t step 1698 loss = 1.31396\n", "DEBUG \t step 1699 loss = 2.02182\n", "DEBUG \t step 1700 loss = 0.960196\n", "DEBUG \t step 1701 loss = 1.45575\n", "DEBUG \t step 1702 loss = 1.09297\n", "DEBUG \t step 1703 loss = 1.27731\n", "DEBUG \t step 1704 loss = 1.63084\n", "DEBUG \t step 1705 loss = 1.46701\n", "DEBUG \t step 1706 loss = 1.58075\n", "DEBUG \t step 1707 loss = 2.77646\n", "DEBUG \t step 1708 loss = 1.66917\n", "DEBUG \t step 1709 loss = 1.53974\n", "DEBUG \t step 1710 loss = 0.746076\n", "DEBUG \t step 1711 loss = 0.787667\n", "DEBUG \t step 1712 loss = 1.48705\n", "DEBUG \t step 1713 loss = 1.15223\n", "DEBUG \t step 1714 loss = 0.74432\n", "DEBUG \t step 1715 loss = 1.20326\n", "DEBUG \t step 1716 loss = 1.05584\n", "DEBUG \t step 1717 loss = 1.25595\n", "DEBUG \t step 1718 loss = 1.63639\n", "DEBUG \t step 1719 loss = 1.18738\n", "DEBUG \t step 1720 loss = 0.997565\n", "DEBUG \t step 1721 loss = 1.59334\n", "DEBUG \t step 1722 loss = 1.18497\n", "DEBUG \t step 1723 loss = 1.39869\n", "DEBUG \t step 1724 loss = 1.13685\n", "DEBUG \t step 1725 loss = 0.477479\n", "DEBUG \t step 1726 loss = 1.42541\n", "DEBUG \t step 1727 loss = 1.47176\n", "DEBUG \t step 1728 loss = 2.13344\n", "DEBUG \t step 1729 loss = 0.989916\n", "DEBUG \t step 1730 loss = 1.00084\n", "DEBUG \t step 1731 loss = 1.31844\n", "DEBUG \t step 1732 loss = 1.44907\n", "DEBUG \t step 1733 loss = 1.14411\n", "DEBUG \t step 1734 loss = 0.997098\n", "DEBUG \t step 1735 loss = 1.22144\n", "DEBUG \t step 1736 loss = 1.65521\n", "DEBUG \t step 1737 loss = 1.04064\n", "DEBUG \t step 1738 loss = 1.40232\n", "DEBUG \t step 1739 loss = 1.21052\n", "DEBUG \t step 1740 loss = 0.52208\n", "DEBUG \t step 1741 loss = 0.96464\n", "DEBUG \t step 1742 loss = 0.922535\n", "DEBUG \t step 1743 loss = 0.57069\n", "DEBUG \t step 1744 loss = 1.29497\n", "DEBUG \t step 1745 loss = 0.764636\n", "DEBUG \t step 1746 loss = 0.596204\n", "DEBUG \t step 1747 loss = 1.47739\n", "DEBUG \t step 1748 loss = 0.704551\n", "DEBUG \t step 1749 loss = 1.13051\n", "DEBUG \t step 1750 loss = 1.81735\n", "DEBUG \t step 1751 loss = 1.15569\n", "DEBUG \t step 1752 loss = 0.62525\n", "DEBUG \t step 1753 loss = -0.14409\n", "DEBUG \t step 1754 loss = 0.819491\n", "DEBUG \t step 1755 loss = 0.584971\n", "DEBUG \t step 1756 loss = 1.50396\n", "DEBUG \t step 1757 loss = 1.12784\n", "DEBUG \t step 1758 loss = 1.37416\n", "DEBUG \t step 1759 loss = 0.944302\n", "DEBUG \t step 1760 loss = 0.708327\n", "DEBUG \t step 1761 loss = 1.51183\n", "DEBUG \t step 1762 loss = 0.951956\n", "DEBUG \t step 1763 loss = 1.13992\n", "DEBUG \t step 1764 loss = -0.0584559\n", "DEBUG \t step 1765 loss = 0.941625\n", "DEBUG \t step 1766 loss = 1.46371\n", "DEBUG \t step 1767 loss = 1.36433\n", "DEBUG \t step 1768 loss = 0.560516\n", "DEBUG \t step 1769 loss = 1.35952\n", "DEBUG \t step 1770 loss = 1.01687\n", "DEBUG \t step 1771 loss = 1.21911\n", "DEBUG \t step 1772 loss = 1.8578\n", "DEBUG \t step 1773 loss = 0.774448\n", "DEBUG \t step 1774 loss = 1.37295\n", "DEBUG \t step 1775 loss = 1.18173\n", "DEBUG \t step 1776 loss = 1.66936\n", "DEBUG \t step 1777 loss = 0.860755\n", "DEBUG \t step 1778 loss = 1.32138\n", "DEBUG \t step 1779 loss = 0.898082\n", "DEBUG \t step 1780 loss = 1.12301\n", "DEBUG \t step 1781 loss = 0.960121\n", "DEBUG \t step 1782 loss = 1.20348\n", "DEBUG \t step 1783 loss = 0.758963\n", "DEBUG \t step 1784 loss = 0.862989\n", "DEBUG \t step 1785 loss = 1.21436\n", "DEBUG \t step 1786 loss = 0.458139\n", "DEBUG \t step 1787 loss = 1.46172\n", "DEBUG \t step 1788 loss = 0.843393\n", "DEBUG \t step 1789 loss = 0.533864\n", "DEBUG \t step 1790 loss = 0.960291\n", "DEBUG \t step 1791 loss = 0.630529\n", "DEBUG \t step 1792 loss = 1.45164\n", "DEBUG \t step 1793 loss = 0.664835\n", "DEBUG \t step 1794 loss = 0.710118\n", "DEBUG \t step 1795 loss = 0.719209\n", "DEBUG \t step 1796 loss = 0.810381\n", "DEBUG \t step 1797 loss = 0.138259\n", "DEBUG \t step 1798 loss = 1.22091\n", "DEBUG \t step 1799 loss = 0.446191\n", "DEBUG \t step 1800 loss = 1.12451\n", "DEBUG \t step 1801 loss = 0.847999\n", "DEBUG \t step 1802 loss = 1.09745\n", "DEBUG \t step 1803 loss = 1.45925\n", "DEBUG \t step 1804 loss = 0.713525\n", "DEBUG \t step 1805 loss = 0.953999\n", "DEBUG \t step 1806 loss = 1.14265\n", "DEBUG \t step 1807 loss = 0.244373\n", "DEBUG \t step 1808 loss = 1.06263\n", "DEBUG \t step 1809 loss = 0.771337\n", "DEBUG \t step 1810 loss = 1.0411\n", "DEBUG \t step 1811 loss = 1.37541\n", "DEBUG \t step 1812 loss = 1.5398\n", "DEBUG \t step 1813 loss = 1.04689\n", "DEBUG \t step 1814 loss = 1.50583\n", "DEBUG \t step 1815 loss = 0.278969\n", "DEBUG \t step 1816 loss = 0.303059\n", "DEBUG \t step 1817 loss = 0.843962\n", "DEBUG \t step 1818 loss = 0.360989\n", "DEBUG \t step 1819 loss = 1.42488\n", "DEBUG \t step 1820 loss = 0.334529\n", "DEBUG \t step 1821 loss = 1.15429\n", "DEBUG \t step 1822 loss = 0.942839\n", "DEBUG \t step 1823 loss = -0.0623802\n", "DEBUG \t step 1824 loss = 1.2242\n", "DEBUG \t step 1825 loss = 0.110633\n", "DEBUG \t step 1826 loss = 1.04671\n", "DEBUG \t step 1827 loss = 0.814721\n", "DEBUG \t step 1828 loss = 0.981389\n", "DEBUG \t step 1829 loss = 0.374465\n", "DEBUG \t step 1830 loss = 0.682603\n", "DEBUG \t step 1831 loss = 0.888044\n", "DEBUG \t step 1832 loss = 1.00653\n", "DEBUG \t step 1833 loss = -0.192628\n", "DEBUG \t step 1834 loss = 1.33105\n", "DEBUG \t step 1835 loss = -0.292317\n", "DEBUG \t step 1836 loss = 1.40156\n", "DEBUG \t step 1837 loss = 0.548849\n", "DEBUG \t step 1838 loss = 0.733393\n", "DEBUG \t step 1839 loss = 0.737875\n", "DEBUG \t step 1840 loss = 0.953065\n", "DEBUG \t step 1841 loss = 1.35565\n", "DEBUG \t step 1842 loss = 0.334132\n", "DEBUG \t step 1843 loss = 0.527886\n", "DEBUG \t step 1844 loss = 0.728576\n", "DEBUG \t step 1845 loss = 0.971659\n", "DEBUG \t step 1846 loss = 1.0362\n", "DEBUG \t step 1847 loss = 1.1995\n", "DEBUG \t step 1848 loss = 0.74542\n", "DEBUG \t step 1849 loss = 0.822038\n", "DEBUG \t step 1850 loss = 0.14102\n", "DEBUG \t step 1851 loss = 0.351881\n", "DEBUG \t step 1852 loss = 0.718691\n", "DEBUG \t step 1853 loss = 0.454031\n", "DEBUG \t step 1854 loss = 1.34327\n", "DEBUG \t step 1855 loss = 1.12586\n", "DEBUG \t step 1856 loss = 0.794541\n", "DEBUG \t step 1857 loss = 0.881259\n", "DEBUG \t step 1858 loss = 0.402362\n", "DEBUG \t step 1859 loss = 0.490797\n", "DEBUG \t step 1860 loss = 0.12956\n", "DEBUG \t step 1861 loss = 1.00601\n", "DEBUG \t step 1862 loss = 0.0126683\n", "DEBUG \t step 1863 loss = 0.367983\n", "DEBUG \t step 1864 loss = 0.519085\n", "DEBUG \t step 1865 loss = 1.5708\n", "DEBUG \t step 1866 loss = 1.47664\n", "DEBUG \t step 1867 loss = 0.891001\n", "DEBUG \t step 1868 loss = 1.33164\n", "DEBUG \t step 1869 loss = 1.43242\n", "DEBUG \t step 1870 loss = 1.57703\n", "DEBUG \t step 1871 loss = 0.409759\n", "DEBUG \t step 1872 loss = 0.481442\n", "DEBUG \t step 1873 loss = 0.433702\n", "DEBUG \t step 1874 loss = 0.102985\n", "DEBUG \t step 1875 loss = 1.07597\n", "DEBUG \t step 1876 loss = 0.628031\n", "DEBUG \t step 1877 loss = -0.0152627\n", "DEBUG \t step 1878 loss = 0.482545\n", "DEBUG \t step 1879 loss = 1.55648\n", "DEBUG \t step 1880 loss = 0.844998\n", "DEBUG \t step 1881 loss = 0.42592\n", "DEBUG \t step 1882 loss = -0.0152035\n", "DEBUG \t step 1883 loss = -0.0997669\n", "DEBUG \t step 1884 loss = 1.01354\n", "DEBUG \t step 1885 loss = 0.490207\n", "DEBUG \t step 1886 loss = 0.736687\n", "DEBUG \t step 1887 loss = 0.433603\n", "DEBUG \t step 1888 loss = 1.07525\n", "DEBUG \t step 1889 loss = 0.678383\n", "DEBUG \t step 1890 loss = 0.980835\n", "DEBUG \t step 1891 loss = 0.470526\n", "DEBUG \t step 1892 loss = 0.591348\n", "DEBUG \t step 1893 loss = 0.496179\n", "DEBUG \t step 1894 loss = 0.164359\n", "DEBUG \t step 1895 loss = 0.505431\n", "DEBUG \t step 1896 loss = 0.848054\n", "DEBUG \t step 1897 loss = 1.22015\n", "DEBUG \t step 1898 loss = 0.21223\n", "DEBUG \t step 1899 loss = 0.804585\n", "DEBUG \t step 1900 loss = 0.337482\n", "DEBUG \t step 1901 loss = 0.380753\n", "DEBUG \t step 1902 loss = 1.09557\n", "DEBUG \t step 1903 loss = 0.452767\n", "DEBUG \t step 1904 loss = 0.505589\n", "DEBUG \t step 1905 loss = 0.533463\n", "DEBUG \t step 1906 loss = 0.732611\n", "DEBUG \t step 1907 loss = 0.457369\n", "DEBUG \t step 1908 loss = 0.397615\n", "DEBUG \t step 1909 loss = 0.304795\n", "DEBUG \t step 1910 loss = 0.832857\n", "DEBUG \t step 1911 loss = 0.776005\n", "DEBUG \t step 1912 loss = 0.0557357\n", "DEBUG \t step 1913 loss = 1.06473\n", "DEBUG \t step 1914 loss = 0.621938\n", "DEBUG \t step 1915 loss = 3.8174\n", "DEBUG \t step 1916 loss = 0.834741\n", "DEBUG \t step 1917 loss = 0.432647\n", "DEBUG \t step 1918 loss = 1.0107\n", "DEBUG \t step 1919 loss = 0.887171\n", "DEBUG \t step 1920 loss = 0.214395\n", "DEBUG \t step 1921 loss = 0.27015\n", "DEBUG \t step 1922 loss = 0.723923\n", "DEBUG \t step 1923 loss = 0.0225524\n", "DEBUG \t step 1924 loss = 0.311126\n", "DEBUG \t step 1925 loss = 0.163129\n", "DEBUG \t step 1926 loss = 1.0852\n", "DEBUG \t step 1927 loss = 0.845341\n", "DEBUG \t step 1928 loss = 0.067302\n", "DEBUG \t step 1929 loss = 1.81058\n", "DEBUG \t step 1930 loss = 0.711902\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 1931 loss = 0.544337\n", "DEBUG \t step 1932 loss = 0.729942\n", "DEBUG \t step 1933 loss = 0.281568\n", "DEBUG \t step 1934 loss = 0.746916\n", "DEBUG \t step 1935 loss = 0.731851\n", "DEBUG \t step 1936 loss = 0.861581\n", "DEBUG \t step 1937 loss = 0.587285\n", "DEBUG \t step 1938 loss = 0.375893\n", "DEBUG \t step 1939 loss = 0.52338\n", "DEBUG \t step 1940 loss = 0.0507239\n", "DEBUG \t step 1941 loss = 0.544204\n", "DEBUG \t step 1942 loss = 0.139653\n", "DEBUG \t step 1943 loss = 0.603852\n", "DEBUG \t step 1944 loss = 0.591492\n", "DEBUG \t step 1945 loss = 0.211932\n", "DEBUG \t step 1946 loss = 0.632158\n", "DEBUG \t step 1947 loss = 0.613739\n", "DEBUG \t step 1948 loss = 1.12637\n", "DEBUG \t step 1949 loss = 0.655486\n", "DEBUG \t step 1950 loss = 0.687108\n", "DEBUG \t step 1951 loss = 0.224532\n", "DEBUG \t step 1952 loss = 0.675569\n", "DEBUG \t step 1953 loss = 1.16836\n", "DEBUG \t step 1954 loss = 0.575642\n", "DEBUG \t step 1955 loss = 0.314398\n", "DEBUG \t step 1956 loss = 0.949717\n", "DEBUG \t step 1957 loss = 1.06026\n", "DEBUG \t step 1958 loss = 0.894075\n", "DEBUG \t step 1959 loss = 0.268737\n", "DEBUG \t step 1960 loss = -0.0684191\n", "DEBUG \t step 1961 loss = 0.301358\n", "DEBUG \t step 1962 loss = 0.670349\n", "DEBUG \t step 1963 loss = 0.631736\n", "DEBUG \t step 1964 loss = 1.17734\n", "DEBUG \t step 1965 loss = -0.0977912\n", "DEBUG \t step 1966 loss = 0.872278\n", "DEBUG \t step 1967 loss = 0.0835433\n", "DEBUG \t step 1968 loss = -0.0705985\n", "DEBUG \t step 1969 loss = 0.193565\n", "DEBUG \t step 1970 loss = 0.817641\n", "DEBUG \t step 1971 loss = 1.54214\n", "DEBUG \t step 1972 loss = -0.0112863\n", "DEBUG \t step 1973 loss = 0.170732\n", "DEBUG \t step 1974 loss = 0.437139\n", "DEBUG \t step 1975 loss = -0.0416076\n", "DEBUG \t step 1976 loss = 0.201051\n", "DEBUG \t step 1977 loss = 0.663106\n", "DEBUG \t step 1978 loss = 0.647153\n", "DEBUG \t step 1979 loss = 0.138818\n", "DEBUG \t step 1980 loss = 0.0719861\n", "DEBUG \t step 1981 loss = 1.12457\n", "DEBUG \t step 1982 loss = 0.123392\n", "DEBUG \t step 1983 loss = 0.35576\n", "DEBUG \t step 1984 loss = 0.187577\n", "DEBUG \t step 1985 loss = 0.158135\n", "DEBUG \t step 1986 loss = 0.172388\n", "DEBUG \t step 1987 loss = 0.864039\n", "DEBUG \t step 1988 loss = 0.522948\n", "DEBUG \t step 1989 loss = 0.218993\n", "DEBUG \t step 1990 loss = 0.958601\n", "DEBUG \t step 1991 loss = 0.0281422\n", "DEBUG \t step 1992 loss = 0.15538\n", "DEBUG \t step 1993 loss = 0.298106\n", "DEBUG \t step 1994 loss = 0.192198\n", "DEBUG \t step 1995 loss = -0.437914\n", "DEBUG \t step 1996 loss = 0.17182\n", "DEBUG \t step 1997 loss = 0.625345\n", "DEBUG \t step 1998 loss = 0.443585\n", "DEBUG \t step 1999 loss = -0.0372677\n", "DEBUG \t step 2000 loss = 0.0965499\n", "DEBUG \t step 2001 loss = 0.684757\n", "DEBUG \t step 2002 loss = 0.0434506\n", "DEBUG \t step 2003 loss = 0.179006\n", "DEBUG \t step 2004 loss = 0.585443\n", "DEBUG \t step 2005 loss = 0.75187\n", "DEBUG \t step 2006 loss = -0.19287\n", "DEBUG \t step 2007 loss = 0.753149\n", "DEBUG \t step 2008 loss = 0.524784\n", "DEBUG \t step 2009 loss = 0.500014\n", "DEBUG \t step 2010 loss = 0.68905\n", "DEBUG \t step 2011 loss = 0.508104\n", "DEBUG \t step 2012 loss = 1.12944\n", "DEBUG \t step 2013 loss = 0.636447\n", "DEBUG \t step 2014 loss = 1.07191\n", "DEBUG \t step 2015 loss = 0.620359\n", "DEBUG \t step 2016 loss = -0.0672604\n", "DEBUG \t step 2017 loss = 0.12611\n", "DEBUG \t step 2018 loss = -0.160067\n", "DEBUG \t step 2019 loss = 0.560006\n", "DEBUG \t step 2020 loss = -0.0938559\n", "DEBUG \t step 2021 loss = 0.2633\n", "DEBUG \t step 2022 loss = -0.24172\n", "DEBUG \t step 2023 loss = 0.23306\n", "DEBUG \t step 2024 loss = -0.119578\n", "DEBUG \t step 2025 loss = 0.304582\n", "DEBUG \t step 2026 loss = 0.222591\n", "DEBUG \t step 2027 loss = 0.47586\n", "DEBUG \t step 2028 loss = 0.504828\n", "DEBUG \t step 2029 loss = 0.422783\n", "DEBUG \t step 2030 loss = 0.346542\n", "DEBUG \t step 2031 loss = 0.22548\n", "DEBUG \t step 2032 loss = 0.0345138\n", "DEBUG \t step 2033 loss = 0.727085\n", "DEBUG \t step 2034 loss = 0.438053\n", "DEBUG \t step 2035 loss = -0.163181\n", "DEBUG \t step 2036 loss = 0.816675\n", "DEBUG \t step 2037 loss = 0.0115353\n", "DEBUG \t step 2038 loss = 0.768062\n", "DEBUG \t step 2039 loss = 0.24584\n", "DEBUG \t step 2040 loss = 0.290391\n", "DEBUG \t step 2041 loss = 0.955838\n", "DEBUG \t step 2042 loss = 0.185171\n", "DEBUG \t step 2043 loss = -0.360956\n", "DEBUG \t step 2044 loss = 0.12458\n", "DEBUG \t step 2045 loss = 0.00191054\n", "DEBUG \t step 2046 loss = 0.0451765\n", "DEBUG \t step 2047 loss = 0.215519\n", "DEBUG \t step 2048 loss = 0.159755\n", "DEBUG \t step 2049 loss = 0.917712\n", "DEBUG \t step 2050 loss = -0.26462\n", "DEBUG \t step 2051 loss = 0.310773\n", "DEBUG \t step 2052 loss = -0.0363671\n", "DEBUG \t step 2053 loss = 0.0293219\n", "DEBUG \t step 2054 loss = -0.00587582\n", "DEBUG \t step 2055 loss = 0.471752\n", "DEBUG \t step 2056 loss = 0.238597\n", "DEBUG \t step 2057 loss = 0.0422264\n", "DEBUG \t step 2058 loss = -0.543846\n", "DEBUG \t step 2059 loss = 0.777388\n", "DEBUG \t step 2060 loss = -0.693749\n", "DEBUG \t step 2061 loss = 0.0994059\n", "DEBUG \t step 2062 loss = -0.286047\n", "DEBUG \t step 2063 loss = 0.766898\n", "DEBUG \t step 2064 loss = -0.142116\n", "DEBUG \t step 2065 loss = 0.883171\n", "DEBUG \t step 2066 loss = 0.180947\n", "DEBUG \t step 2067 loss = 0.210857\n", "DEBUG \t step 2068 loss = 0.118777\n", "DEBUG \t step 2069 loss = -0.141074\n", "DEBUG \t step 2070 loss = 0.363284\n", "DEBUG \t step 2071 loss = 0.39178\n", "DEBUG \t step 2072 loss = 0.305299\n", "DEBUG \t step 2073 loss = 0.545026\n", "DEBUG 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loss = -0.296839\n", "DEBUG \t step 2464 loss = -0.213982\n", "DEBUG \t step 2465 loss = -0.139768\n", "DEBUG \t step 2466 loss = -0.554577\n", "DEBUG \t step 2467 loss = -1.29373\n", "DEBUG \t step 2468 loss = 0.168238\n", "DEBUG \t step 2469 loss = 0.134877\n", "DEBUG \t step 2470 loss = -0.255521\n", "DEBUG \t step 2471 loss = -0.750256\n", "DEBUG \t step 2472 loss = -0.0114451\n", "DEBUG \t step 2473 loss = -0.410735\n", "DEBUG \t step 2474 loss = 0.218873\n", "DEBUG \t step 2475 loss = -0.141217\n", "DEBUG \t step 2476 loss = -0.78113\n", "DEBUG \t step 2477 loss = -0.143108\n", "DEBUG \t step 2478 loss = -0.0878578\n", "DEBUG \t step 2479 loss = 0.498992\n", "DEBUG \t step 2480 loss = -0.385873\n", "DEBUG \t step 2481 loss = 0.697456\n", "DEBUG \t step 2482 loss = -0.330902\n", "DEBUG \t step 2483 loss = -0.416052\n", "DEBUG \t step 2484 loss = -0.0582824\n", "DEBUG \t step 2485 loss = -0.749726\n", "DEBUG \t step 2486 loss = -0.705093\n", "DEBUG \t step 2487 loss = -0.366732\n", "DEBUG \t step 2488 loss = 0.0636343\n", "DEBUG \t step 2489 loss = -0.428274\n", "DEBUG \t step 2490 loss = -0.97996\n", "DEBUG \t step 2491 loss = -0.721423\n", "DEBUG \t step 2492 loss = -0.901971\n", "DEBUG \t step 2493 loss = -0.821726\n", "DEBUG \t step 2494 loss = -0.48277\n", "DEBUG \t step 2495 loss = 0.159761\n", "DEBUG \t step 2496 loss = -0.802472\n", "DEBUG \t step 2497 loss = -0.687559\n", "DEBUG \t step 2498 loss = -0.256268\n", "DEBUG \t step 2499 loss = -0.571636\n", "DEBUG \t step 2500 loss = -0.184076\n", "DEBUG \t step 2501 loss = -0.0532485\n", "DEBUG \t step 2502 loss = 0.0489593\n", "DEBUG \t step 2503 loss = -0.699592\n", "DEBUG \t step 2504 loss = -0.964232\n", "DEBUG \t step 2505 loss = -0.33835\n", "DEBUG \t step 2506 loss = -0.425566\n", "DEBUG \t step 2507 loss = -0.0965802\n", "DEBUG \t step 2508 loss = -0.745661\n", "DEBUG \t step 2509 loss = -0.103916\n", "DEBUG \t step 2510 loss = -0.489986\n", "DEBUG \t step 2511 loss = -1.22721\n", "DEBUG \t step 2512 loss = -0.573065\n", "DEBUG \t step 2513 loss = -0.8967\n", "DEBUG \t step 2514 loss = -0.714046\n", "DEBUG \t step 2515 loss = -0.893781\n", "DEBUG \t step 2516 loss = 0.465743\n", "DEBUG \t step 2517 loss = -0.941392\n", "DEBUG \t step 2518 loss = -0.858442\n", "DEBUG \t step 2519 loss = -0.18183\n", "DEBUG \t step 2520 loss = -0.380441\n", "DEBUG \t step 2521 loss = -0.374258\n", "DEBUG \t step 2522 loss = -0.682367\n", "DEBUG \t step 2523 loss = -0.821137\n", "DEBUG \t step 2524 loss = -0.445525\n", "DEBUG \t step 2525 loss = -0.97567\n", "DEBUG \t step 2526 loss = -0.547556\n", "DEBUG \t step 2527 loss = -0.853315\n", "DEBUG \t step 2528 loss = 0.114161\n", "DEBUG \t step 2529 loss = -0.579036\n", "DEBUG \t step 2530 loss = 0.0135827\n", "DEBUG \t step 2531 loss = -0.0582753\n", "DEBUG \t step 2532 loss = -0.140801\n", "DEBUG \t step 2533 loss = -0.182517\n", "DEBUG \t step 2534 loss = -0.829945\n", "DEBUG \t step 2535 loss = -0.0669306\n", "DEBUG \t step 2536 loss = -0.467228\n", "DEBUG \t step 2537 loss = -0.584846\n", "DEBUG \t step 2538 loss = -0.273549\n", "DEBUG \t step 2539 loss = -0.00248221\n", "DEBUG \t step 2540 loss = -0.345479\n", "DEBUG \t step 2541 loss = -0.515946\n", "DEBUG \t step 2542 loss = -0.103854\n", "DEBUG \t step 2543 loss = 0.187452\n", "DEBUG \t step 2544 loss = -0.154338\n", "DEBUG \t step 2545 loss = -0.915668\n", "DEBUG \t step 2546 loss = -0.75074\n", "DEBUG \t step 2547 loss = -0.235062\n", "DEBUG \t step 2548 loss = -0.615748\n", "DEBUG \t step 2549 loss = 0.163511\n", "DEBUG \t step 2550 loss = -0.558204\n", "DEBUG \t step 2551 loss = -0.429658\n", "DEBUG \t step 2552 loss = -0.527625\n", "DEBUG \t step 2553 loss = -0.663658\n", "DEBUG \t step 2554 loss = -0.866039\n", "DEBUG \t step 2555 loss = -0.0667327\n", "DEBUG \t step 2556 loss = -1.14744\n", "DEBUG \t step 2557 loss = -0.599862\n", "DEBUG \t step 2558 loss = -0.628051\n", "DEBUG \t step 2559 loss = -1.02429\n", "DEBUG \t step 2560 loss = -0.812641\n", "DEBUG \t step 2561 loss = -0.207669\n", "DEBUG \t step 2562 loss = -0.346239\n", "DEBUG \t step 2563 loss = -0.42864\n", "DEBUG \t step 2564 loss = -0.769289\n", "DEBUG \t step 2565 loss = -0.442619\n", "DEBUG \t step 2566 loss = -0.551839\n", "DEBUG \t step 2567 loss = -0.434892\n", "DEBUG \t step 2568 loss = -0.822885\n", "DEBUG \t step 2569 loss = -0.0774252\n", "DEBUG \t step 2570 loss = -0.962704\n", "DEBUG \t step 2571 loss = 0.382489\n", "DEBUG \t step 2572 loss = -0.340682\n", "DEBUG \t step 2573 loss = -0.42353\n", "DEBUG \t step 2574 loss = -0.0114898\n", "DEBUG \t step 2575 loss = -0.210306\n", "DEBUG \t step 2576 loss = -0.625316\n", "DEBUG \t step 2577 loss = -0.61977\n", "DEBUG \t step 2578 loss = -0.641895\n", "DEBUG \t step 2579 loss = -0.158468\n", "DEBUG \t step 2580 loss = -0.376173\n", "DEBUG \t step 2581 loss = -0.562516\n", "DEBUG \t step 2582 loss = -0.606728\n", "DEBUG \t step 2583 loss = -0.486623\n", "DEBUG \t step 2584 loss = -0.253736\n", "DEBUG \t step 2585 loss = 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\t step 2610 loss = -0.414367\n", "DEBUG \t step 2611 loss = -0.63994\n", "DEBUG \t step 2612 loss = -0.993912\n", "DEBUG \t step 2613 loss = -1.01504\n", "DEBUG \t step 2614 loss = 0.596202\n", "DEBUG \t step 2615 loss = 0.482037\n", "DEBUG \t step 2616 loss = -0.301577\n", "DEBUG \t step 2617 loss = -1.49396\n", "DEBUG \t step 2618 loss = -0.392669\n", "DEBUG \t step 2619 loss = -0.324627\n", "DEBUG \t step 2620 loss = 0.619205\n", "DEBUG \t step 2621 loss = -0.269684\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 2622 loss = -0.661252\n", "DEBUG \t step 2623 loss = -0.774471\n", "DEBUG \t step 2624 loss = -1.18561\n", "DEBUG \t step 2625 loss = -0.275053\n", "DEBUG \t step 2626 loss = -0.887767\n", "DEBUG \t step 2627 loss = 0.287073\n", "DEBUG \t step 2628 loss = -0.905378\n", "DEBUG \t step 2629 loss = 0.0570901\n", "DEBUG \t step 2630 loss = -0.351999\n", "DEBUG \t step 2631 loss = -0.118707\n", "DEBUG \t step 2632 loss = -0.671623\n", "DEBUG \t step 2633 loss = -0.681996\n", "DEBUG \t step 2634 loss = -0.521377\n", "DEBUG \t step 2635 loss = -0.617793\n", "DEBUG \t step 2636 loss = -0.603524\n", "DEBUG \t step 2637 loss = -0.821486\n", "DEBUG \t step 2638 loss = -0.356088\n", "DEBUG \t step 2639 loss = -0.536534\n", "DEBUG \t step 2640 loss = -0.747998\n", "DEBUG \t step 2641 loss = -0.439992\n", "DEBUG \t step 2642 loss = -0.0627628\n", "DEBUG \t step 2643 loss = 0.331022\n", "DEBUG \t step 2644 loss = -0.441603\n", "DEBUG \t step 2645 loss = -0.515788\n", "DEBUG \t step 2646 loss = -0.475961\n", "DEBUG \t step 2647 loss = -0.401744\n", "DEBUG \t step 2648 loss = 0.262217\n", "DEBUG \t step 2649 loss = -0.831643\n", "DEBUG \t step 2650 loss = -1.12754\n", "DEBUG \t step 2651 loss = -0.829439\n", "DEBUG \t step 2652 loss = 0.0111126\n", "DEBUG \t step 2653 loss = 0.0545446\n", "DEBUG \t step 2654 loss = -0.34779\n", "DEBUG \t step 2655 loss = -0.239686\n", "DEBUG \t step 2656 loss = -0.0659961\n", "DEBUG \t step 2657 loss = -0.0800167\n", "DEBUG \t step 2658 loss = -0.56742\n", "DEBUG \t step 2659 loss = -1.12966\n", "DEBUG \t step 2660 loss = -0.735846\n", "DEBUG \t step 2661 loss = -0.857747\n", "DEBUG \t step 2662 loss = -0.626603\n", "DEBUG \t step 2663 loss = 0.501296\n", "DEBUG \t step 2664 loss = -0.345909\n", "DEBUG \t step 2665 loss = -0.48826\n", "DEBUG \t step 2666 loss = -0.425832\n", "DEBUG \t step 2667 loss = -0.622227\n", "DEBUG \t step 2668 loss = 0.0905803\n", "DEBUG \t step 2669 loss = -0.934806\n", "DEBUG \t step 2670 loss = -0.55195\n", "DEBUG \t step 2671 loss = 0.285835\n", "DEBUG \t step 2672 loss = -0.62289\n", "DEBUG \t step 2673 loss = -0.438078\n", "DEBUG \t step 2674 loss = -0.351686\n", "DEBUG \t step 2675 loss = -0.476577\n", "DEBUG \t step 2676 loss = -0.894385\n", "DEBUG \t step 2677 loss = -0.258823\n", "DEBUG \t step 2678 loss = -0.413825\n", "DEBUG \t step 2679 loss = -0.737152\n", "DEBUG \t step 2680 loss = -0.756135\n", "DEBUG \t step 2681 loss = -0.475365\n", "DEBUG \t step 2682 loss = -0.271527\n", "DEBUG \t step 2683 loss = -0.628242\n", "DEBUG \t step 2684 loss = -1.36686\n", "DEBUG \t step 2685 loss = -0.608447\n", "DEBUG \t step 2686 loss = -0.685795\n", "DEBUG \t step 2687 loss = -0.240269\n", "DEBUG \t step 2688 loss = 0.146378\n", "DEBUG \t step 2689 loss = -1.10885\n" ] } ], "source": [ "# fit\n", "losses = cevae.fit(X=torch.tensor(X_train, dtype=torch.float),\n", " treatment=torch.tensor(treatment_train, dtype=torch.float),\n", " y=torch.tensor(y_train, dtype=torch.float))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:41:35.070742Z", "start_time": "2021-02-01T21:18:39.932087Z" }, "jupyter": { "outputs_hidden": true } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO \t Evaluating 538 minibatches\n", "DEBUG \t batch ate = 0.62191\n", "DEBUG \t batch ate = 0.613137\n", "DEBUG \t batch ate = 0.688279\n", "DEBUG \t batch ate = 0.530233\n", "DEBUG \t batch ate = 0.814089\n", "DEBUG \t batch ate = 0.623182\n", "DEBUG \t batch ate = 0.657884\n", "DEBUG \t batch ate = 0.594205\n", "DEBUG \t batch ate = 0.319953\n", "DEBUG \t batch ate = 0.557599\n", "DEBUG \t batch ate = 0.718177\n", "DEBUG \t batch ate = 0.441256\n", "DEBUG \t batch ate = 0.654653\n", "DEBUG \t batch ate = 0.70725\n", "DEBUG \t batch ate = 0.715862\n", "DEBUG \t batch ate = 0.193786\n", "DEBUG \t batch ate = 0.557451\n", "DEBUG \t batch ate = 0.788378\n", "DEBUG \t batch ate = 0.605489\n", "DEBUG \t batch ate = 0.669786\n", "DEBUG \t batch ate = 0.852794\n", "DEBUG \t batch ate = 0.755987\n", "DEBUG \t batch ate = 0.510262\n", "DEBUG \t batch ate = 0.502153\n", "DEBUG \t batch ate = 0.254691\n", "DEBUG \t batch ate = 0.369999\n", "DEBUG \t batch ate = 0.59401\n", "DEBUG \t batch ate = 0.608015\n", "DEBUG \t batch ate = 0.661765\n", "DEBUG \t batch ate = 0.25462\n", "DEBUG \t batch ate = 0.771231\n", "DEBUG \t batch ate = 0.530303\n", "DEBUG \t batch ate = 0.566246\n", "DEBUG \t batch ate = 0.683882\n", "DEBUG \t batch ate = 0.616635\n", "DEBUG \t batch ate = 0.324804\n", "DEBUG \t batch ate = 0.383451\n", "DEBUG \t batch ate = 0.690402\n", "DEBUG \t batch ate = 0.558513\n", "DEBUG \t batch ate = 0.618007\n", "DEBUG \t batch ate = 0.551096\n", "DEBUG \t batch ate = 0.462644\n", "DEBUG \t batch ate = 0.615761\n", "DEBUG \t batch ate = 0.543891\n", "DEBUG \t batch ate = 0.432806\n", "DEBUG \t batch ate = 0.562174\n", "DEBUG \t batch ate = 0.654926\n", "DEBUG \t batch ate = 0.421796\n", "DEBUG \t batch ate = 0.719893\n", "DEBUG \t batch ate = 0.454017\n", "DEBUG \t batch ate = 0.699385\n", "DEBUG \t batch ate = 0.54048\n", "DEBUG \t batch ate = 0.333772\n", "DEBUG \t batch ate = 0.737522\n", "DEBUG \t batch ate = 0.5696\n", "DEBUG \t batch ate = 0.467629\n", "DEBUG \t batch ate = 0.601579\n", "DEBUG \t batch ate = 0.509313\n", "DEBUG \t batch ate = 0.385523\n", "DEBUG \t batch ate = 0.510085\n", "DEBUG \t batch ate = 0.661952\n", "DEBUG \t batch ate = 0.600664\n", "DEBUG \t batch ate = 0.066584\n", "DEBUG \t batch ate = 0.552528\n", "DEBUG \t batch ate = 0.467475\n", "DEBUG \t batch ate = 0.539326\n", "DEBUG \t batch ate = 0.694311\n", "DEBUG \t batch ate = 0.198014\n", "DEBUG \t batch ate = 0.61709\n", "DEBUG \t batch ate = 0.408558\n", "DEBUG \t batch ate = 0.684187\n", "DEBUG \t batch ate = 0.447501\n", "DEBUG \t batch ate = 0.347885\n", "DEBUG \t batch ate = 0.561035\n", "DEBUG \t batch ate = 0.617192\n", "DEBUG \t batch ate = 0.81278\n", "DEBUG \t batch ate = 0.61961\n", "DEBUG \t batch ate = 1.01213\n", "DEBUG \t batch ate = 0.345585\n", "DEBUG \t batch ate = 0.51818\n", "DEBUG \t batch ate = 0.436719\n", "DEBUG \t batch ate = 0.604546\n", "DEBUG \t batch ate = 0.706353\n", "DEBUG \t batch ate = 0.661419\n", "DEBUG \t batch ate = 0.787418\n", "DEBUG \t batch ate = 0.61231\n", "DEBUG \t batch ate = 0.629355\n", "DEBUG \t batch ate = 0.550861\n", "DEBUG \t batch ate = 0.472948\n", "DEBUG \t batch ate = 0.594738\n", "DEBUG \t batch ate = 0.844747\n", "DEBUG \t batch ate = 0.682486\n", "DEBUG \t batch ate = 0.607738\n", "DEBUG \t batch ate = 0.49322\n", "DEBUG \t batch ate = 0.547857\n", "DEBUG \t batch ate = 0.255665\n", "DEBUG \t batch ate = 0.564768\n", "DEBUG \t batch ate = 0.34345\n", "DEBUG \t batch ate = 0.40075\n", "DEBUG \t batch ate = 0.72982\n", "DEBUG \t batch ate = 0.878728\n", "DEBUG \t batch ate = 0.860621\n", "DEBUG \t batch ate = 0.544359\n", "DEBUG \t batch ate = 0.777127\n", "DEBUG \t batch ate = 0.590297\n", "DEBUG \t batch ate = 0.880415\n", "DEBUG \t batch ate = 0.67375\n", "DEBUG \t batch ate = 0.784914\n", "DEBUG \t batch ate = 0.511374\n", "DEBUG \t batch ate = 0.327954\n", "DEBUG \t batch ate = 0.628989\n", "DEBUG \t batch ate = 0.529468\n", "DEBUG \t batch ate = 0.688235\n", "DEBUG \t batch ate = 0.872871\n", "DEBUG \t batch ate = 0.3485\n", "DEBUG \t batch ate = 0.572016\n", "DEBUG \t batch ate = 0.565154\n", "DEBUG \t batch ate = 0.588927\n", "DEBUG \t batch ate = 0.520636\n", "DEBUG \t batch ate = 0.345301\n", "DEBUG \t batch ate = 0.611386\n", "DEBUG \t batch ate = 0.702772\n", "DEBUG \t batch ate = 0.764302\n", "DEBUG \t batch ate = 0.638517\n", "DEBUG \t batch ate = 0.498749\n", "DEBUG \t batch ate = 0.922372\n", "DEBUG \t batch ate = 0.648347\n", "DEBUG \t batch ate = 0.930839\n", "DEBUG \t batch ate = 0.841956\n", "DEBUG \t batch ate = 0.687886\n", "DEBUG \t batch ate = 0.804776\n", "DEBUG \t batch ate = 0.550305\n", "DEBUG \t batch ate = 0.625526\n", "DEBUG \t batch ate = 0.856957\n", "DEBUG \t batch ate = 0.470616\n", "DEBUG \t batch ate = 0.507122\n", "DEBUG \t batch ate = 0.358198\n", "DEBUG \t batch ate = 0.6335\n", "DEBUG \t batch ate = 0.473881\n", "DEBUG \t batch ate = 0.415356\n", "DEBUG \t batch ate = 0.309733\n", "DEBUG \t batch ate = 0.290068\n", "DEBUG \t batch ate = 0.470317\n", "DEBUG \t batch ate = 0.668486\n", "DEBUG \t batch ate = 0.580281\n", "DEBUG \t batch ate = 0.772137\n", "DEBUG \t batch ate = 0.490976\n", "DEBUG \t batch ate = 0.511012\n", "DEBUG \t batch ate = 0.441551\n", "DEBUG \t batch ate = 0.575225\n", "DEBUG \t batch ate = 0.591247\n", "DEBUG \t batch ate = 0.368313\n", "DEBUG \t batch ate = 0.350138\n", "DEBUG \t batch ate = 0.603038\n", "DEBUG \t batch ate = 0.241947\n", "DEBUG \t batch ate = 0.599275\n", "DEBUG \t batch ate = 0.41003\n", "DEBUG \t batch ate = 0.447525\n", "DEBUG \t batch ate = 0.79099\n", "DEBUG \t batch ate = 0.506499\n", "DEBUG \t batch ate = 0.61826\n", "DEBUG \t batch ate = 0.651964\n", "DEBUG \t batch ate = 0.52761\n", "DEBUG \t batch ate = 0.888067\n", "DEBUG \t batch ate = 0.367077\n", "DEBUG \t batch ate = 0.524761\n", "DEBUG \t batch ate = 0.6165\n", "DEBUG \t batch ate = 0.72863\n", "DEBUG \t batch ate = 0.516559\n", "DEBUG \t batch ate = 0.385291\n", "DEBUG \t batch ate = 0.660073\n", "DEBUG \t batch ate = 0.465947\n", "DEBUG \t batch ate = 0.586065\n", "DEBUG \t batch ate = 0.533599\n", "DEBUG \t batch ate = 0.916433\n", "DEBUG \t batch ate = 0.658235\n", "DEBUG \t batch ate = 0.770213\n", "DEBUG \t batch ate = 0.634768\n", "DEBUG \t batch ate = 0.887955\n", "DEBUG \t batch ate = 0.374664\n", "DEBUG \t batch ate = 0.649699\n", "DEBUG \t batch ate = 0.550386\n", "DEBUG \t batch ate = 0.516355\n", "DEBUG \t batch ate = 0.425265\n", "DEBUG \t batch ate = 0.264789\n", "DEBUG \t batch ate = 0.775339\n", "DEBUG \t batch ate = 0.636203\n", "DEBUG \t batch ate = 0.507562\n", "DEBUG \t batch ate = 0.885973\n", "DEBUG \t batch ate = 0.951861\n", "DEBUG \t batch ate = 0.370282\n", "DEBUG \t batch ate = 0.69922\n", "DEBUG \t batch ate = 0.956577\n", "DEBUG \t batch ate = 0.789856\n", "DEBUG \t batch ate = 0.726278\n", "DEBUG \t batch ate = 0.165073\n", "DEBUG \t batch ate = 0.530907\n", "DEBUG \t batch ate = 0.602567\n", "DEBUG \t batch ate = 0.682041\n", "DEBUG \t batch ate = 0.54427\n", "DEBUG \t batch ate = 0.787318\n", "DEBUG \t batch ate = 0.491623\n", "DEBUG \t batch ate = 0.794449\n", "DEBUG \t batch ate = 0.928849\n", "DEBUG \t batch ate = 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"DEBUG \t batch ate = 0.478516\n", "DEBUG \t batch ate = 0.925168\n", "DEBUG \t batch ate = 0.605732\n", "DEBUG \t batch ate = 0.321156\n", "DEBUG \t batch ate = 0.47538\n", "DEBUG \t batch ate = 0.750148\n", "DEBUG \t batch ate = 0.468002\n", "DEBUG \t batch ate = 0.483354\n", "DEBUG \t batch ate = 0.727932\n", "DEBUG \t batch ate = 0.499526\n", "DEBUG \t batch ate = 0.505064\n", "DEBUG \t batch ate = 1.03597\n", "DEBUG \t batch ate = 0.528672\n", "DEBUG \t batch ate = 0.713761\n", "DEBUG \t batch ate = 0.657063\n", "DEBUG \t batch ate = 0.677198\n", "DEBUG \t batch ate = 0.761366\n", "DEBUG \t batch ate = 0.569046\n", "DEBUG \t batch ate = 0.806944\n", "DEBUG \t batch ate = 0.512402\n", "DEBUG \t batch ate = 0.638473\n", "DEBUG \t batch ate = 0.594415\n", "DEBUG \t batch ate = 0.662585\n", "DEBUG \t batch ate = 0.815776\n", "DEBUG \t batch ate = 0.547243\n", "DEBUG \t batch ate = 0.446772\n", "DEBUG \t batch ate = 0.609724\n", "DEBUG \t batch ate = 0.672535\n", "DEBUG \t batch ate = 0.294262\n", "DEBUG \t batch ate = 0.650225\n", "DEBUG \t batch ate = 0.437027\n", "DEBUG \t batch ate = 0.395884\n", "DEBUG \t batch ate = 0.457884\n", "DEBUG \t batch ate = 0.381654\n", "DEBUG \t batch ate = 0.474322\n", "DEBUG \t batch ate = 0.636114\n", "DEBUG \t batch ate = 0.433205\n", "DEBUG \t batch ate = 0.340026\n", "DEBUG \t batch ate = 0.631428\n", "DEBUG \t batch ate = 0.465448\n", "DEBUG \t batch ate = 0.438805\n", "DEBUG \t batch ate = 0.50323\n", "DEBUG \t batch ate = 0.522954\n", "DEBUG \t batch ate = 0.58916\n" ] } ], "source": [ "# predict\n", "ite_train = cevae.predict(X_train)\n", "ite_val = cevae.predict(X_val)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:41:35.076150Z", "start_time": "2021-02-01T23:41:35.073086Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.58953923 0.5956359\n" ] } ], "source": [ "ate_train = ite_train.mean()\n", "ate_val = ite_val.mean()\n", "print(ate_train, ate_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Meta Learners" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:43:21.827553Z", "start_time": "2021-02-01T23:41:35.077523Z" } }, "outputs": [], "source": [ "# fit propensity model\n", "p_model = ElasticNetPropensityModel()\n", "p_train = p_model.fit_predict(X_train, treatment_train)\n", "p_val = p_model.fit_predict(X_val, treatment_val)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:43:57.203494Z", "start_time": "2021-02-01T23:43:21.829195Z" } }, "outputs": [], "source": [ "s_learner = BaseSRegressor(LGBMRegressor())\n", "s_ate = s_learner.estimate_ate(X_train, treatment_train, y_train)[0]\n", "s_ite_train = s_learner.fit_predict(X_train, treatment_train, y_train)\n", "s_ite_val = s_learner.predict(X_val)\n", "\n", "t_learner = BaseTRegressor(LGBMRegressor())\n", "t_ate = t_learner.estimate_ate(X_train, treatment_train, y_train)[0][0]\n", "t_ite_train = t_learner.fit_predict(X_train, treatment_train, y_train)\n", "t_ite_val = t_learner.predict(X_val, treatment_val, y_val)\n", "\n", "x_learner = BaseXRegressor(LGBMRegressor())\n", "x_ate = x_learner.estimate_ate(X_train, treatment_train, y_train, p_train)[0][0]\n", "x_ite_train = x_learner.fit_predict(X_train, treatment_train, y_train, p_train)\n", "x_ite_val = x_learner.predict(X_val, treatment_val, y_val, p_val)\n", "\n", "r_learner = BaseRRegressor(LGBMRegressor())\n", "r_ate = r_learner.estimate_ate(X_train, treatment_train, y_train, p_train)[0][0]\n", "r_ite_train = r_learner.fit_predict(X_train, treatment_train, y_train, p_train)\n", "r_ite_val = r_learner.predict(X_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Results Comparsion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:17.848932Z", "start_time": "2021-02-01T23:43:57.205878Z" } }, "outputs": [], "source": [ "df_preds_train = pd.DataFrame([s_ite_train.ravel(),\n", " t_ite_train.ravel(),\n", " x_ite_train.ravel(),\n", " r_ite_train.ravel(),\n", " ite_train.ravel(),\n", " tau_train.ravel(),\n", " treatment_train.ravel(),\n", " y_train.ravel()],\n", " index=['S','T','X','R','CEVAE','tau','w','y']).T\n", "\n", "df_cumgain_train = get_cumgain(df_preds_train)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:20.252428Z", "start_time": "2021-02-01T23:44:17.850639Z" } }, "outputs": [], "source": [ "df_result_train = pd.DataFrame([s_ate, t_ate, x_ate, r_ate, ate_train, tau_train.mean()],\n", " index=['S','T','X','R','CEVAE','actual'], columns=['ATE'])\n", "df_result_train['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite_train, t_ite_train, x_ite_train, r_ite_train, ite_train],\n", " [tau_train.values.reshape(-1,1)]*5 )\n", " ] + [None]\n", "df_result_train['AUUC'] = auuc_score(df_preds_train)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:20.261314Z", "start_time": "2021-02-01T23:44:20.253755Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " ATE MAE AUUC\n", "S 4.690540 4.581416 0.684130\n", "T 4.708557 4.715296 0.684878\n", "X 4.555315 4.549527 0.671956\n", "R 0.714936 5.991034 0.586835\n", "CEVAE 0.589539 6.238858 0.566627\n", "actual 4.755900 NaN NaN" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_result_train" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:22.760162Z", "start_time": "2021-02-01T23:44:20.262955Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_gain(df_preds_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Validation" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:27.616283Z", "start_time": "2021-02-01T23:44:22.761877Z" } }, "outputs": [], "source": [ "df_preds_val = pd.DataFrame([s_ite_val.ravel(),\n", " t_ite_val.ravel(),\n", " x_ite_val.ravel(),\n", " r_ite_val.ravel(),\n", " ite_val.ravel(),\n", " tau_val.ravel(),\n", " treatment_val.ravel(),\n", " y_val.ravel()],\n", " index=['S','T','X','R','CEVAE','tau','w','y']).T\n", "\n", "df_cumgain_val = get_cumgain(df_preds_val)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:28.162127Z", "start_time": "2021-02-01T23:44:27.617962Z" } }, "outputs": [], "source": [ "df_result_val = pd.DataFrame([s_ite_val.mean(), t_ite_val.mean(), x_ite_val.mean(), r_ite_val.mean(), ate_val, tau_val.mean()],\n", " index=['S','T','X','R','CEVAE','actual'], columns=['ATE'])\n", "df_result_val['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite_val, t_ite_val, x_ite_val, r_ite_val, ite_val],\n", " [tau_val.values.reshape(-1,1)]*5 )\n", " ] + [None]\n", "df_result_val['AUUC'] = auuc_score(df_preds_val)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:28.169322Z", "start_time": "2021-02-01T23:44:28.163676Z" } }, "outputs": [ { "data": { "text/html": [ "
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ATEMAEAUUC
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" ], "text/plain": [ " ATE MAE AUUC\n", "S 4.690676 4.582191 0.683782\n", "T 4.709923 4.717909 0.684032\n", "X 4.560680 4.544644 0.671907\n", "R 0.761550 5.997526 0.586110\n", "CEVAE 0.595636 6.241192 0.566356\n", "actual 4.774991 NaN NaN" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_result_val" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:44:28.889771Z", "start_time": "2021-02-01T23:44:28.170875Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_gain(df_preds_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Synthetic Data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:04.322003Z", "start_time": "2021-02-01T23:46:46.214260Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO \t Training with 80 minibatches per epoch\n", "DEBUG \t step 0 loss = 14.0534\n", "DEBUG \t step 1 loss = 13.2864\n", "DEBUG \t step 2 loss = 13.0712\n", "DEBUG \t step 3 loss = 12.4646\n", "DEBUG \t step 4 loss = 12.0247\n", "DEBUG \t step 5 loss = 11.5239\n", "DEBUG \t step 6 loss = 11.2934\n", "DEBUG \t step 7 loss = 11.3141\n", "DEBUG \t step 8 loss = 10.8347\n", "DEBUG \t step 9 loss = 10.7364\n", "DEBUG \t step 10 loss = 10.5978\n", "DEBUG \t step 11 loss = 10.2533\n", "DEBUG \t step 12 loss = 10.131\n", "DEBUG \t step 13 loss = 10.0307\n", "DEBUG \t step 14 loss = 9.57977\n", "DEBUG \t step 15 loss = 9.79295\n", "DEBUG \t step 16 loss = 9.46927\n", "DEBUG \t step 17 loss = 9.57581\n", "DEBUG \t step 18 loss = 9.24119\n", "DEBUG \t step 19 loss = 9.34084\n", "DEBUG \t step 20 loss = 9.32529\n", "DEBUG \t step 21 loss = 9.40313\n", "DEBUG \t step 22 loss = 9.27057\n", "DEBUG \t step 23 loss = 9.05239\n", "DEBUG \t step 24 loss = 9.17952\n", "DEBUG \t step 25 loss = 8.93083\n", "DEBUG \t step 26 loss = 8.88059\n", "DEBUG \t step 27 loss = 9.06328\n", "DEBUG \t step 28 loss = 8.97881\n", "DEBUG \t step 29 loss = 8.7639\n", "DEBUG \t step 30 loss = 8.80499\n", "DEBUG \t step 31 loss = 8.87173\n", "DEBUG \t step 32 loss = 8.56747\n", "DEBUG \t step 33 loss = 8.61066\n", "DEBUG \t step 34 loss = 8.79932\n", "DEBUG \t step 35 loss = 8.62871\n", "DEBUG \t step 36 loss = 8.54852\n", "DEBUG \t step 37 loss = 8.38022\n", "DEBUG \t step 38 loss = 8.31573\n", "DEBUG \t step 39 loss = 8.53857\n", "DEBUG \t step 40 loss = 8.57149\n", "DEBUG \t step 41 loss = 8.25793\n", "DEBUG \t step 42 loss = 8.54684\n", "DEBUG \t step 43 loss = 8.47699\n", "DEBUG \t step 44 loss = 8.3233\n", "DEBUG \t step 45 loss = 8.40228\n", "DEBUG \t step 46 loss = 8.14949\n", "DEBUG \t step 47 loss = 8.2015\n", "DEBUG \t step 48 loss = 8.07472\n", "DEBUG \t step 49 loss = 8.16795\n", "DEBUG \t step 50 loss = 8.34108\n", "DEBUG \t step 51 loss = 8.57682\n", "DEBUG \t step 52 loss = 8.24426\n", "DEBUG \t step 53 loss = 8.33251\n", "DEBUG \t step 54 loss = 8.10115\n", "DEBUG \t step 55 loss = 8.67902\n", "DEBUG \t step 56 loss = 8.14677\n", "DEBUG \t step 57 loss = 8.1041\n", "DEBUG \t step 58 loss = 8.15102\n", "DEBUG \t step 59 loss = 8.00679\n", "DEBUG \t step 60 loss = 8.0271\n", "DEBUG \t step 61 loss = 7.96041\n", "DEBUG \t step 62 loss = 7.82294\n", "DEBUG \t step 63 loss = 8.13456\n", "DEBUG \t step 64 loss = 8.23367\n", "DEBUG \t step 65 loss = 8.1886\n", "DEBUG \t step 66 loss = 8.11654\n", "DEBUG \t step 67 loss = 8.22645\n", "DEBUG \t step 68 loss = 8.29743\n", "DEBUG \t step 69 loss = 8.24127\n", "DEBUG \t step 70 loss = 7.86166\n", "DEBUG \t step 71 loss = 8.22115\n", "DEBUG \t step 72 loss = 7.8913\n", "DEBUG \t step 73 loss = 7.96265\n", "DEBUG \t step 74 loss = 7.96243\n", "DEBUG \t step 75 loss = 7.99336\n", "DEBUG \t step 76 loss = 7.97742\n", "DEBUG \t step 77 loss = 7.90728\n", "DEBUG \t step 78 loss = 7.79539\n", "DEBUG \t step 79 loss = 8.1732\n", "DEBUG \t step 80 loss = 8.05217\n", "DEBUG \t step 81 loss = 8.34642\n", "DEBUG \t step 82 loss = 8.03199\n", "DEBUG \t step 83 loss = 7.64226\n", "DEBUG \t step 84 loss = 7.60438\n", "DEBUG \t step 85 loss = 7.5962\n", "DEBUG \t step 86 loss = 7.85927\n", "DEBUG \t step 87 loss = 7.98567\n", "DEBUG \t step 88 loss = 7.82793\n", "DEBUG \t step 89 loss = 7.90716\n", "DEBUG \t step 90 loss = 7.71277\n", "DEBUG \t step 91 loss = 7.97724\n", "DEBUG \t step 92 loss = 7.84886\n", "DEBUG \t step 93 loss = 7.88323\n", "DEBUG \t step 94 loss = 7.58179\n", "DEBUG \t step 95 loss = 7.89912\n", "DEBUG \t step 96 loss = 7.67735\n", "DEBUG \t step 97 loss = 7.84808\n", "DEBUG \t step 98 loss = 7.66705\n", "DEBUG \t step 99 loss = 7.65615\n", "DEBUG \t step 100 loss = 7.73811\n", "DEBUG \t step 101 loss = 7.64997\n", "DEBUG \t step 102 loss = 8.36613\n", "DEBUG \t step 103 loss = 7.72687\n", "DEBUG \t step 104 loss = 7.68498\n", "DEBUG \t step 105 loss = 7.50849\n", "DEBUG \t step 106 loss = 7.63987\n", "DEBUG \t step 107 loss = 7.75501\n", "DEBUG \t step 108 loss = 7.62423\n", "DEBUG \t step 109 loss = 7.66921\n", "DEBUG \t step 110 loss = 7.50166\n", "DEBUG \t step 111 loss = 7.62314\n", "DEBUG \t step 112 loss = 7.80907\n", "DEBUG \t step 113 loss = 7.65659\n", "DEBUG \t step 114 loss = 7.55159\n", "DEBUG \t step 115 loss = 7.60577\n", "DEBUG \t step 116 loss = 7.36759\n", "DEBUG \t step 117 loss = 7.43037\n", "DEBUG \t step 118 loss = 7.41372\n", "DEBUG \t step 119 loss = 7.58245\n", "DEBUG \t step 120 loss = 7.75382\n", "DEBUG \t step 121 loss = 7.75345\n", "DEBUG \t step 122 loss = 7.71091\n", "DEBUG \t step 123 loss = 7.61762\n", "DEBUG \t step 124 loss = 7.5415\n", "DEBUG \t step 125 loss = 7.70995\n", "DEBUG \t step 126 loss = 7.43083\n", "DEBUG \t step 127 loss = 7.62284\n", "DEBUG \t step 128 loss = 7.57494\n", "DEBUG \t step 129 loss = 7.43229\n", "DEBUG \t step 130 loss = 7.417\n", "DEBUG \t step 131 loss = 7.36716\n", "DEBUG \t step 132 loss = 7.58527\n", "DEBUG \t step 133 loss = 7.61684\n", "DEBUG \t step 134 loss = 7.55247\n", "DEBUG \t step 135 loss = 7.54181\n", "DEBUG \t step 136 loss = 7.47493\n", "DEBUG \t step 137 loss = 7.65583\n", "DEBUG \t step 138 loss = 7.33769\n", "DEBUG \t step 139 loss = 7.36649\n", "DEBUG \t step 140 loss = 7.3634\n", "DEBUG \t step 141 loss = 7.50731\n", "DEBUG \t step 142 loss = 7.60657\n", "DEBUG \t step 143 loss = 7.38694\n", "DEBUG \t step 144 loss = 7.3596\n", "DEBUG \t step 145 loss = 7.42744\n", "DEBUG \t step 146 loss = 7.46609\n", "DEBUG \t step 147 loss = 7.44444\n", "DEBUG \t step 148 loss = 7.44656\n", "DEBUG \t step 149 loss = 7.32834\n", "DEBUG \t step 150 loss = 7.63049\n", "DEBUG \t step 151 loss = 7.43903\n", "DEBUG \t step 152 loss = 7.28372\n", "DEBUG \t step 153 loss = 7.28897\n", "DEBUG \t step 154 loss = 7.3515\n", "DEBUG \t step 155 loss = 7.29871\n", "DEBUG \t step 156 loss = 7.47948\n", "DEBUG \t step 157 loss = 7.56888\n", "DEBUG \t step 158 loss = 7.50302\n", "DEBUG \t step 159 loss = 7.14918\n", "DEBUG \t step 160 loss = 7.34611\n", "DEBUG \t step 161 loss = 7.04855\n", "DEBUG \t step 162 loss = 7.38615\n", "DEBUG \t step 163 loss = 7.39172\n", "DEBUG \t step 164 loss = 7.35778\n", "DEBUG \t step 165 loss = 7.39445\n", "DEBUG \t step 166 loss = 7.41489\n", "DEBUG \t step 167 loss = 7.36096\n", "DEBUG \t step 168 loss = 7.49107\n", "DEBUG \t step 169 loss = 7.31799\n", "DEBUG \t step 170 loss = 7.34851\n", "DEBUG \t step 171 loss = 7.17355\n", "DEBUG \t step 172 loss = 7.38851\n", "DEBUG \t step 173 loss = 7.35425\n", "DEBUG \t step 174 loss = 7.39068\n", "DEBUG \t step 175 loss = 7.08015\n", "DEBUG \t step 176 loss = 7.05245\n", "DEBUG \t step 177 loss = 7.43696\n", "DEBUG \t step 178 loss = 7.32325\n", "DEBUG \t step 179 loss = 7.31021\n", "DEBUG \t step 180 loss = 7.32132\n", "DEBUG \t step 181 loss = 7.34862\n", "DEBUG \t step 182 loss = 7.2863\n", "DEBUG \t step 183 loss = 7.04851\n", "DEBUG \t step 184 loss = 7.09608\n", "DEBUG \t step 185 loss = 7.30419\n", "DEBUG \t step 186 loss = 7.57377\n", "DEBUG \t step 187 loss = 7.17361\n", "DEBUG \t step 188 loss = 7.14099\n", "DEBUG \t step 189 loss = 7.0449\n", "DEBUG \t step 190 loss = 7.33529\n", "DEBUG \t step 191 loss = 8.26479\n", "DEBUG \t step 192 loss = 7.07407\n", "DEBUG \t step 193 loss = 7.17149\n", "DEBUG \t step 194 loss = 7.18364\n", "DEBUG \t step 195 loss = 7.27539\n", "DEBUG \t step 196 loss = 7.32838\n", "DEBUG \t step 197 loss = 7.26303\n", "DEBUG \t step 198 loss = 7.17846\n", "DEBUG \t step 199 loss = 7.43274\n", "DEBUG \t step 200 loss = 7.05834\n", "DEBUG \t step 201 loss = 7.06987\n", "DEBUG \t step 202 loss = 7.23815\n", "DEBUG \t step 203 loss = 7.2454\n", "DEBUG \t step 204 loss = 7.29509\n", "DEBUG \t step 205 loss = 7.13663\n", "DEBUG \t step 206 loss = 6.96725\n", "DEBUG \t step 207 loss = 7.11374\n", "DEBUG \t step 208 loss = 6.93604\n", "DEBUG \t step 209 loss = 7.14596\n", "DEBUG \t step 210 loss = 7.12832\n", "DEBUG \t step 211 loss = 7.16911\n", "DEBUG \t step 212 loss = 6.9426\n", "DEBUG \t step 213 loss = 7.18095\n", "DEBUG \t step 214 loss = 7.06178\n", "DEBUG \t step 215 loss = 7.10941\n", "DEBUG \t step 216 loss = 7.11186\n", "DEBUG \t step 217 loss = 7.20186\n", "DEBUG \t step 218 loss = 7.27586\n", "DEBUG \t step 219 loss = 7.1021\n", "DEBUG \t step 220 loss = 6.94478\n", "DEBUG \t step 221 loss = 7.09795\n", "DEBUG \t step 222 loss = 6.88571\n", "DEBUG \t step 223 loss = 7.03089\n", "DEBUG \t step 224 loss = 7.23866\n", "DEBUG \t step 225 loss = 7.10442\n", "DEBUG \t step 226 loss = 6.95982\n", "DEBUG \t step 227 loss = 8.71509\n", "DEBUG \t step 228 loss = 6.93005\n", "DEBUG \t step 229 loss = 7.2101\n", "DEBUG \t step 230 loss = 7.23326\n", "DEBUG \t step 231 loss = 6.94798\n", "DEBUG \t step 232 loss = 6.83511\n", "DEBUG \t step 233 loss = 6.99621\n", "DEBUG \t step 234 loss = 6.79696\n", "DEBUG \t step 235 loss = 7.21458\n", "DEBUG \t step 236 loss = 6.97841\n", "DEBUG \t step 237 loss = 7.12467\n", "DEBUG \t step 238 loss = 6.98927\n", "DEBUG \t step 239 loss = 7.13294\n", "DEBUG \t step 240 loss = 7.17033\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t step 241 loss = 7.09788\n", "DEBUG \t step 242 loss = 6.98868\n", "DEBUG \t step 243 loss = 7.0711\n", "DEBUG \t step 244 loss = 7.10628\n", "DEBUG \t step 245 loss = 7.12893\n", "DEBUG \t step 246 loss = 6.94537\n", "DEBUG \t step 247 loss = 6.98222\n", "DEBUG \t step 248 loss = 7.12801\n", "DEBUG \t step 249 loss = 6.94684\n", "DEBUG \t step 250 loss = 7.01901\n", "DEBUG \t step 251 loss = 7.03228\n", "DEBUG \t step 252 loss = 7.14612\n", "DEBUG \t step 253 loss = 7.04241\n", "DEBUG \t step 254 loss = 6.92232\n", "DEBUG \t step 255 loss = 7.02093\n", "DEBUG \t step 256 loss = 6.98689\n", "DEBUG \t step 257 loss = 6.97682\n", "DEBUG \t step 258 loss = 6.99232\n", "DEBUG \t step 259 loss = 7.01528\n", "DEBUG \t step 260 loss = 6.86835\n", "DEBUG \t step 261 loss = 7.00633\n", "DEBUG \t step 262 loss = 7.06246\n", "DEBUG \t step 263 loss = 6.90189\n", "DEBUG \t step 264 loss = 7.07629\n", "DEBUG \t step 265 loss = 6.88559\n", "DEBUG \t step 266 loss = 6.92606\n", "DEBUG \t step 267 loss = 6.8929\n", "DEBUG \t step 268 loss = 6.83142\n", "DEBUG \t step 269 loss = 6.73955\n", "DEBUG \t step 270 loss = 6.81085\n", "DEBUG \t step 271 loss = 6.87084\n", "DEBUG \t step 272 loss = 6.88125\n", "DEBUG \t step 273 loss = 6.94562\n", "DEBUG \t step 274 loss = 6.9711\n", "DEBUG \t step 275 loss = 7.01001\n", "DEBUG \t step 276 loss = 6.91986\n", "DEBUG \t step 277 loss = 6.92239\n", "DEBUG \t step 278 loss = 6.70706\n", "DEBUG \t step 279 loss = 6.84017\n", "DEBUG \t step 280 loss = 7.09178\n", "DEBUG \t step 281 loss = 6.7313\n", "DEBUG \t step 282 loss = 6.79816\n", "DEBUG \t step 283 loss = 6.86953\n", "DEBUG \t step 284 loss = 6.92598\n", "DEBUG \t step 285 loss = 7.0731\n", "DEBUG \t step 286 loss = 6.91421\n", "DEBUG \t step 287 loss = 6.76945\n", "DEBUG \t step 288 loss = 6.74834\n", "DEBUG \t step 289 loss = 6.84824\n", "DEBUG \t step 290 loss = 6.88344\n", "DEBUG \t step 291 loss = 6.85244\n", "DEBUG \t step 292 loss = 6.922\n", "DEBUG \t step 293 loss = 9.57555\n", "DEBUG \t step 294 loss = 6.83098\n", "DEBUG \t step 295 loss = 7.43121\n", "DEBUG \t step 296 loss = 6.95061\n", "DEBUG \t step 297 loss = 6.79967\n", "DEBUG \t step 298 loss = 6.7929\n", "DEBUG \t step 299 loss = 6.7355\n", "DEBUG \t step 300 loss = 7.01345\n", "DEBUG \t step 301 loss = 6.83328\n", "DEBUG \t step 302 loss = 6.62454\n", "DEBUG \t step 303 loss = 6.84473\n", "DEBUG \t step 304 loss = 9.05065\n", "DEBUG \t step 305 loss = 7.038\n", "DEBUG \t step 306 loss = 6.60419\n", "DEBUG \t step 307 loss = 6.80575\n", "DEBUG \t step 308 loss = 6.73912\n", "DEBUG \t step 309 loss = 6.47463\n", "DEBUG \t step 310 loss = 6.84484\n", "DEBUG \t step 311 loss = 6.73429\n", "DEBUG \t step 312 loss = 6.89219\n", "DEBUG \t step 313 loss = 7.05905\n", "DEBUG \t step 314 loss = 6.82365\n", "DEBUG \t step 315 loss = 6.72354\n", "DEBUG \t step 316 loss = 6.54532\n", "DEBUG \t step 317 loss = 6.95339\n", "DEBUG \t step 318 loss = 7.0503\n", "DEBUG \t step 319 loss = 6.78209\n", "DEBUG \t step 320 loss = 6.59514\n", "DEBUG \t step 321 loss = 6.89779\n", "DEBUG \t step 322 loss = 6.72151\n", "DEBUG \t step 323 loss = 6.90015\n", "DEBUG \t step 324 loss = 7.00599\n", "DEBUG \t step 325 loss = 6.85437\n", "DEBUG \t step 326 loss = 6.89033\n", "DEBUG \t step 327 loss = 6.7871\n", "DEBUG \t step 328 loss = 6.8493\n", "DEBUG \t step 329 loss = 6.80922\n", "DEBUG \t step 330 loss = 6.96322\n", "DEBUG \t step 331 loss = 6.84506\n", "DEBUG \t step 332 loss = 6.87015\n", "DEBUG \t step 333 loss = 6.88979\n", "DEBUG \t step 334 loss = 6.64982\n", "DEBUG \t step 335 loss = 6.86292\n", "DEBUG \t step 336 loss = 6.92489\n", "DEBUG \t step 337 loss = 6.62396\n", "DEBUG \t step 338 loss = 6.84564\n", "DEBUG \t step 339 loss = 6.62305\n", "DEBUG \t step 340 loss = 7.36375\n", "DEBUG \t step 341 loss = 6.73599\n", "DEBUG \t step 342 loss = 6.80353\n", "DEBUG \t step 343 loss = 6.96371\n", "DEBUG \t step 344 loss = 6.89915\n", "DEBUG \t step 345 loss = 6.64238\n", "DEBUG \t step 346 loss = 6.51934\n", "DEBUG \t step 347 loss = 6.78445\n", "DEBUG \t step 348 loss = 6.94965\n", "DEBUG \t step 349 loss = 6.78796\n", "DEBUG \t step 350 loss = 6.77106\n", "DEBUG \t step 351 loss = 6.7466\n", "DEBUG \t step 352 loss = 6.77313\n", "DEBUG \t step 353 loss = 6.70463\n", "DEBUG \t step 354 loss = 6.96683\n", "DEBUG \t step 355 loss = 6.73415\n", "DEBUG \t step 356 loss = 6.73694\n", "DEBUG \t step 357 loss = 6.60738\n", "DEBUG \t step 358 loss = 9.84151\n", "DEBUG \t step 359 loss = 6.84548\n", "DEBUG \t step 360 loss = 6.57425\n", "DEBUG \t step 361 loss = 6.78442\n", "DEBUG \t step 362 loss = 6.68523\n", "DEBUG \t step 363 loss = 6.93113\n", "DEBUG \t step 364 loss = 9.26669\n", "DEBUG \t step 365 loss = 6.71749\n", "DEBUG \t step 366 loss = 6.60656\n", "DEBUG \t step 367 loss = 6.7795\n", "DEBUG \t step 368 loss = 6.55477\n", "DEBUG \t step 369 loss = 6.73777\n", "DEBUG \t step 370 loss = 6.80791\n", "DEBUG \t step 371 loss = 6.75802\n", "DEBUG \t step 372 loss = 6.80779\n", "DEBUG \t step 373 loss = 6.82983\n", "DEBUG \t step 374 loss = 6.5821\n", "DEBUG \t step 375 loss = 6.81309\n", "DEBUG \t step 376 loss = 6.58409\n", "DEBUG \t step 377 loss = 6.59094\n", "DEBUG \t step 378 loss = 6.59232\n", "DEBUG \t step 379 loss = 7.0035\n", "DEBUG \t step 380 loss = 6.65775\n", "DEBUG \t step 381 loss = 6.61621\n", "DEBUG \t step 382 loss = 6.6329\n", "DEBUG \t step 383 loss = 6.63025\n", "DEBUG \t step 384 loss = 6.61858\n", "DEBUG \t step 385 loss = 6.63814\n", "DEBUG \t step 386 loss = 6.50298\n", "DEBUG \t step 387 loss = 6.62591\n", "DEBUG \t step 388 loss = 6.56514\n", "DEBUG \t step 389 loss = 6.67944\n", "DEBUG \t step 390 loss = 6.80612\n", "DEBUG \t step 391 loss = 6.61369\n", "DEBUG \t step 392 loss = 6.85104\n", "DEBUG \t step 393 loss = 6.61612\n", "DEBUG \t step 394 loss = 6.55337\n", "DEBUG \t step 395 loss = 6.76919\n", "DEBUG \t step 396 loss = 6.66491\n", "DEBUG \t step 397 loss = 6.57224\n", "DEBUG \t step 398 loss = 6.54065\n", "DEBUG \t step 399 loss = 6.73794\n", "INFO \t Evaluating 80 minibatches\n", "DEBUG \t batch ate = 0.823513\n", "DEBUG \t batch ate = 0.824189\n", "DEBUG \t batch ate = 0.820978\n", "DEBUG \t batch ate = 0.822631\n", "DEBUG \t batch ate = 0.823555\n", "DEBUG \t batch ate = 0.822441\n", "DEBUG \t batch ate = 0.823683\n", "DEBUG \t batch ate = 0.822339\n", "DEBUG \t batch ate = 0.823964\n", "DEBUG \t batch ate = 0.823921\n", "DEBUG \t batch ate = 0.825266\n", "DEBUG \t batch ate = 0.822931\n", "DEBUG \t batch ate = 0.823049\n", "DEBUG \t batch ate = 0.824161\n", "DEBUG \t batch ate = 0.821918\n", "DEBUG \t batch ate = 0.824303\n", "DEBUG \t batch ate = 0.823845\n", "DEBUG \t batch ate = 0.822578\n", "DEBUG \t batch ate = 0.825122\n", "DEBUG \t batch ate = 0.823321\n", "DEBUG \t batch ate = 0.823198\n", "DEBUG \t batch ate = 0.823159\n", "DEBUG \t batch ate = 0.823571\n", "DEBUG \t batch ate = 0.822972\n", "DEBUG \t batch ate = 0.82311\n", "DEBUG \t batch ate = 0.821233\n", "DEBUG \t batch ate = 0.824326\n", "DEBUG \t batch ate = 0.823645\n", "DEBUG \t batch ate = 0.8233\n", "DEBUG \t batch ate = 0.821567\n", "DEBUG \t batch ate = 0.820404\n", "DEBUG \t batch ate = 0.821521\n", "DEBUG \t batch ate = 0.82027\n", "DEBUG \t batch ate = 0.824084\n", "DEBUG \t batch ate = 0.824593\n", "DEBUG \t batch ate = 0.823614\n", "DEBUG \t batch ate = 0.820698\n", "DEBUG \t batch ate = 0.824454\n", "DEBUG \t batch ate = 0.819246\n", "DEBUG \t batch ate = 0.823614\n", "DEBUG \t batch ate = 0.822471\n", "DEBUG \t batch ate = 0.822809\n", "DEBUG \t batch ate = 0.82155\n", "DEBUG \t batch ate = 0.822985\n", "DEBUG \t batch ate = 0.821966\n", "DEBUG \t batch ate = 0.822152\n", "DEBUG \t batch ate = 0.824818\n", "DEBUG \t batch ate = 0.821926\n", "DEBUG \t batch ate = 0.821183\n", "DEBUG \t batch ate = 0.821644\n", "DEBUG \t batch ate = 0.823652\n", "DEBUG \t batch ate = 0.822925\n", "DEBUG \t batch ate = 0.822612\n", "DEBUG \t batch ate = 0.824216\n", "DEBUG \t batch ate = 0.824456\n", "DEBUG \t batch ate = 0.822995\n", "DEBUG \t batch ate = 0.823972\n", "DEBUG \t batch ate = 0.821021\n", "DEBUG \t batch ate = 0.822201\n", "DEBUG \t batch ate = 0.821493\n", "DEBUG \t batch ate = 0.823859\n", "DEBUG \t batch ate = 0.819778\n", "DEBUG \t batch ate = 0.822789\n", "DEBUG \t batch ate = 0.825457\n", "DEBUG \t batch ate = 0.824181\n", "DEBUG \t batch ate = 0.821647\n", "DEBUG \t batch ate = 0.82509\n", "DEBUG \t batch ate = 0.821287\n", "DEBUG \t batch ate = 0.824007\n", "DEBUG \t batch ate = 0.821076\n", "DEBUG \t batch ate = 0.823777\n", "DEBUG \t batch ate = 0.822884\n", "DEBUG \t batch ate = 0.824057\n", "DEBUG \t batch ate = 0.820844\n", "DEBUG \t batch ate = 0.821426\n", "DEBUG \t batch ate = 0.82413\n", "DEBUG \t batch ate = 0.822516\n", "DEBUG \t batch ate = 0.823242\n", "DEBUG \t batch ate = 0.820823\n", "DEBUG \t batch ate = 0.822049\n", "INFO \t Evaluating 20 minibatches\n", "DEBUG \t batch ate = 0.823355\n", "DEBUG \t batch ate = 0.826493\n", "DEBUG \t batch ate = 0.825423\n", "DEBUG \t batch ate = 0.825241\n", "DEBUG \t batch ate = 0.823623\n", "DEBUG \t batch ate = 0.823627\n", "DEBUG \t batch ate = 0.821589\n", "DEBUG \t batch ate = 0.824463\n", "DEBUG \t batch ate = 0.821071\n", "DEBUG \t batch ate = 0.820596\n", "DEBUG \t batch ate = 0.823198\n", "DEBUG \t batch ate = 0.820816\n", "DEBUG \t batch ate = 0.823484\n", "DEBUG \t batch ate = 0.823282\n", "DEBUG \t batch ate = 0.825439\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "DEBUG \t batch ate = 0.822407\n", "DEBUG \t batch ate = 0.822365\n", "DEBUG \t batch ate = 0.825534\n", "DEBUG \t batch ate = 0.822151\n", "DEBUG \t batch ate = 0.823306\n" ] } ], "source": [ "y, X, w, tau, b, e = simulate_hidden_confounder(n=100000, p=5, sigma=1.0, adj=0.)\n", "\n", "X_train, X_val, y_train, y_val, w_train, w_val, tau_train, tau_val, b_train, b_val, e_train, e_val = \\\n", " train_test_split(X, y, w, tau, b, e, test_size=0.2, random_state=123, shuffle=True)\n", "\n", "preds_dict_train = {}\n", "preds_dict_valid = {}\n", "\n", "preds_dict_train['Actuals'] = tau_train\n", "preds_dict_valid['Actuals'] = tau_val\n", "\n", "preds_dict_train['generated_data'] = {\n", " 'y': y_train,\n", " 'X': X_train,\n", " 'w': w_train,\n", " 'tau': tau_train,\n", " 'b': b_train,\n", " 'e': e_train}\n", "preds_dict_valid['generated_data'] = {\n", " 'y': y_val,\n", " 'X': X_val,\n", " 'w': w_val,\n", " 'tau': tau_val,\n", " 'b': b_val,\n", " 'e': e_val}\n", "\n", "# Predict p_hat because e would not be directly observed in real-life\n", "p_model = ElasticNetPropensityModel()\n", "p_hat_train = p_model.fit_predict(X_train, w_train)\n", "p_hat_val = p_model.fit_predict(X_val, w_val)\n", "\n", "for base_learner, label_l in zip([BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],\n", " ['S', 'T', 'X', 'R']):\n", " for model, label_m in zip([LinearRegression, XGBRegressor], ['LR', 'XGB']):\n", " # RLearner will need to fit on the p_hat\n", " if label_l != 'R':\n", " learner = base_learner(model())\n", " # fit the model on training data only\n", " learner.fit(X=X_train, treatment=w_train, y=y_train)\n", " try:\n", " preds_dict_train['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_train, p=p_hat_train).flatten()\n", " preds_dict_valid['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_val, p=p_hat_val).flatten()\n", " except TypeError:\n", " preds_dict_train['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_train, treatment=w_train, y=y_train).flatten()\n", " preds_dict_valid['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_val, treatment=w_val, y=y_val).flatten()\n", " else:\n", " learner = base_learner(model())\n", " learner.fit(X=X_train, p=p_hat_train, treatment=w_train, y=y_train)\n", " preds_dict_train['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_train).flatten()\n", " preds_dict_valid['{} Learner ({})'.format(\n", " label_l, label_m)] = learner.predict(X=X_val).flatten()\n", "\n", "# cevae model settings\n", "outcome_dist = \"normal\"\n", "latent_dim = 20\n", "hidden_dim = 200\n", "num_epochs = 5\n", "batch_size = 1000\n", "learning_rate = 1e-3\n", "learning_rate_decay = 0.1\n", "num_layers = 3\n", "num_samples = 10\n", "\n", "cevae = CEVAE(outcome_dist=outcome_dist,\n", " latent_dim=latent_dim,\n", " hidden_dim=hidden_dim,\n", " num_epochs=num_epochs,\n", " batch_size=batch_size,\n", " learning_rate=learning_rate,\n", " learning_rate_decay=learning_rate_decay,\n", " num_layers=num_layers,\n", " num_samples=num_samples)\n", "\n", "# fit\n", "losses = cevae.fit(X=torch.tensor(X_train, dtype=torch.float),\n", " treatment=torch.tensor(w_train, dtype=torch.float),\n", " y=torch.tensor(y_train, dtype=torch.float))\n", "\n", "preds_dict_train['CEVAE'] = cevae.predict(X_train).flatten()\n", "preds_dict_valid['CEVAE'] = cevae.predict(X_val).flatten()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:04.460479Z", "start_time": "2021-02-01T23:48:04.323693Z" } }, "outputs": [], "source": [ "actuals_train = preds_dict_train['Actuals']\n", "actuals_validation = preds_dict_valid['Actuals']\n", "\n", "synthetic_summary_train = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_train)] for label, preds\n", " in preds_dict_train.items() if 'generated' not in label.lower()},\n", " index=['ATE', 'MSE']).T\n", "synthetic_summary_train['Abs % Error of ATE'] = np.abs(\n", " (synthetic_summary_train['ATE']/synthetic_summary_train.loc['Actuals', 'ATE']) - 1)\n", "\n", "synthetic_summary_validation = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_validation)]\n", " for label, preds in preds_dict_valid.items()\n", " if 'generated' not in label.lower()},\n", " index=['ATE', 'MSE']).T\n", "synthetic_summary_validation['Abs % Error of ATE'] = np.abs(\n", " (synthetic_summary_validation['ATE']/synthetic_summary_validation.loc['Actuals', 'ATE']) - 1)\n", "\n", "# calculate kl divergence for training\n", "for label in synthetic_summary_train.index:\n", " stacked_values = np.hstack((preds_dict_train[label], actuals_train))\n", " stacked_low = np.percentile(stacked_values, 0.1)\n", " stacked_high = np.percentile(stacked_values, 99.9)\n", " bins = np.linspace(stacked_low, stacked_high, 100)\n", "\n", " distr = np.histogram(preds_dict_train[label], bins=bins)[0]\n", " distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n", " true_distr = np.histogram(actuals_train, bins=bins)[0]\n", " true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n", "\n", " kl = entropy(distr, true_distr)\n", " synthetic_summary_train.loc[label, 'KL Divergence'] = kl\n", "\n", "# calculate kl divergence for validation\n", "for label in synthetic_summary_validation.index:\n", " stacked_values = np.hstack((preds_dict_valid[label], actuals_validation))\n", " stacked_low = np.percentile(stacked_values, 0.1)\n", " stacked_high = np.percentile(stacked_values, 99.9)\n", " bins = np.linspace(stacked_low, stacked_high, 100)\n", "\n", " distr = np.histogram(preds_dict_valid[label], bins=bins)[0]\n", " distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n", " true_distr = np.histogram(actuals_validation, bins=bins)[0]\n", " true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n", "\n", " kl = entropy(distr, true_distr)\n", " synthetic_summary_validation.loc[label, 'KL Divergence'] = kl" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:07.625291Z", "start_time": "2021-02-01T23:48:04.462870Z" } }, "outputs": [], "source": [ "df_preds_train = pd.DataFrame([preds_dict_train['S Learner (LR)'].ravel(),\n", " preds_dict_train['S Learner (XGB)'].ravel(),\n", " preds_dict_train['T Learner (LR)'].ravel(),\n", " preds_dict_train['T Learner (XGB)'].ravel(),\n", " preds_dict_train['X Learner (LR)'].ravel(),\n", " preds_dict_train['X Learner (XGB)'].ravel(),\n", " preds_dict_train['R Learner (LR)'].ravel(),\n", " preds_dict_train['R Learner (XGB)'].ravel(),\n", " preds_dict_train['CEVAE'].ravel(),\n", " preds_dict_train['generated_data']['tau'].ravel(),\n", " preds_dict_train['generated_data']['w'].ravel(),\n", " preds_dict_train['generated_data']['y'].ravel()],\n", " index=['S Learner (LR)','S Learner (XGB)',\n", " 'T Learner (LR)','T Learner (XGB)',\n", " 'X Learner (LR)','X Learner (XGB)',\n", " 'R Learner (LR)','R Learner (XGB)',\n", " 'CEVAE','tau','w','y']).T\n", "\n", "synthetic_summary_train['AUUC'] = auuc_score(df_preds_train).iloc[:-1]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:08.381588Z", "start_time": "2021-02-01T23:48:07.627371Z" } }, "outputs": [], "source": [ "df_preds_validation = pd.DataFrame([preds_dict_valid['S Learner (LR)'].ravel(),\n", " preds_dict_valid['S Learner (XGB)'].ravel(),\n", " preds_dict_valid['T Learner (LR)'].ravel(),\n", " preds_dict_valid['T Learner (XGB)'].ravel(),\n", " preds_dict_valid['X Learner (LR)'].ravel(),\n", " preds_dict_valid['X Learner (XGB)'].ravel(),\n", " preds_dict_valid['R Learner (LR)'].ravel(),\n", " preds_dict_valid['R Learner (XGB)'].ravel(),\n", " preds_dict_valid['CEVAE'].ravel(),\n", " preds_dict_valid['generated_data']['tau'].ravel(),\n", " preds_dict_valid['generated_data']['w'].ravel(),\n", " preds_dict_valid['generated_data']['y'].ravel()],\n", " index=['S Learner (LR)','S Learner (XGB)',\n", " 'T Learner (LR)','T Learner (XGB)',\n", " 'X Learner (LR)','X Learner (XGB)',\n", " 'R Learner (LR)','R Learner (XGB)',\n", " 'CEVAE','tau','w','y']).T\n", "\n", "synthetic_summary_validation['AUUC'] = auuc_score(df_preds_validation).iloc[:-1]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:08.392392Z", "start_time": "2021-02-01T23:48:08.383180Z" } }, "outputs": [ { "data": { "text/html": [ "
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ATEMSEAbs % Error of ATEKL DivergenceAUUC
Actuals0.7261150.0000000.0000000.000000NaN
S Learner (LR)0.8323360.0624620.1462876.2784130.499991
S Learner (XGB)0.8077430.0397350.1124172.5512970.554885
T Learner (LR)0.8333640.0596650.1477033.3126960.523272
T Learner (XGB)0.8035920.0405240.1067012.5657150.553197
X Learner (LR)0.8333640.0596650.1477033.3126960.523272
X Learner (XGB)0.8033490.0385800.1063672.5009470.555391
R Learner (LR)0.8338450.0602390.1483653.5111570.523214
R Learner (XGB)0.7354420.0468480.0128452.8361280.539213
CEVAE0.8228530.0581770.1332273.1570590.519150
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" ], "text/plain": [ " ATE MSE Abs % Error of ATE KL Divergence \\\n", "Actuals 0.726115 0.000000 0.000000 0.000000 \n", "S Learner (LR) 0.832336 0.062462 0.146287 6.278413 \n", "S Learner (XGB) 0.807743 0.039735 0.112417 2.551297 \n", "T Learner (LR) 0.833364 0.059665 0.147703 3.312696 \n", "T Learner (XGB) 0.803592 0.040524 0.106701 2.565715 \n", "X Learner (LR) 0.833364 0.059665 0.147703 3.312696 \n", "X Learner (XGB) 0.803349 0.038580 0.106367 2.500947 \n", "R Learner (LR) 0.833845 0.060239 0.148365 3.511157 \n", "R Learner (XGB) 0.735442 0.046848 0.012845 2.836128 \n", "CEVAE 0.822853 0.058177 0.133227 3.157059 \n", "\n", " AUUC \n", "Actuals NaN \n", "S Learner (LR) 0.499991 \n", "S Learner (XGB) 0.554885 \n", "T Learner (LR) 0.523272 \n", "T Learner (XGB) 0.553197 \n", "X Learner (LR) 0.523272 \n", "X Learner (XGB) 0.555391 \n", "R Learner (LR) 0.523214 \n", "R Learner (XGB) 0.539213 \n", "CEVAE 0.519150 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "synthetic_summary_train" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:08.401366Z", "start_time": "2021-02-01T23:48:08.393987Z" } }, "outputs": [ { "data": { "text/html": [ "
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ATEMSEAbs % Error of ATEKL DivergenceAUUC
Actuals0.7283710.0000000.0000000.000000NaN
S Learner (LR)0.8323360.0619830.1427366.2784130.499967
S Learner (XGB)0.8088440.0406380.1104832.5487140.553011
T Learner (LR)0.8338050.0593050.1447533.3168840.522972
T Learner (XGB)0.8037660.0424240.1035122.5616880.549279
X Learner (LR)0.8338050.0593050.1447533.3168840.522972
X Learner (XGB)0.8035300.0396990.1031872.4898220.553039
R Learner (LR)0.8341790.0598510.1452663.5127460.522887
R Learner (XGB)0.7361470.0466850.0106752.7475960.536579
CEVAE0.8233730.0576900.1304303.1521610.519573
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" ], "text/plain": [ " ATE MSE Abs % Error of ATE KL Divergence \\\n", "Actuals 0.728371 0.000000 0.000000 0.000000 \n", "S Learner (LR) 0.832336 0.061983 0.142736 6.278413 \n", "S Learner (XGB) 0.808844 0.040638 0.110483 2.548714 \n", "T Learner (LR) 0.833805 0.059305 0.144753 3.316884 \n", "T Learner (XGB) 0.803766 0.042424 0.103512 2.561688 \n", "X Learner (LR) 0.833805 0.059305 0.144753 3.316884 \n", "X Learner (XGB) 0.803530 0.039699 0.103187 2.489822 \n", "R Learner (LR) 0.834179 0.059851 0.145266 3.512746 \n", "R Learner (XGB) 0.736147 0.046685 0.010675 2.747596 \n", "CEVAE 0.823373 0.057690 0.130430 3.152161 \n", "\n", " AUUC \n", "Actuals NaN \n", "S Learner (LR) 0.499967 \n", "S Learner (XGB) 0.553011 \n", "T Learner (LR) 0.522972 \n", "T Learner (XGB) 0.549279 \n", "X Learner (LR) 0.522972 \n", "X Learner (XGB) 0.553039 \n", "R Learner (LR) 0.522887 \n", "R Learner (XGB) 0.536579 \n", "CEVAE 0.519573 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "synthetic_summary_validation" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:09.079086Z", "start_time": "2021-02-01T23:48:08.402848Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_gain(df_preds_train)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2021-02-01T23:48:09.487505Z", "start_time": "2021-02-01T23:48:09.083225Z" } }, "outputs": [ { "data": { "image/png": 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", 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