platform-demo-scripts/notebooks/Check model performance depending on station-random window.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "05600e0a-8b4b-42ab-96d5-9d6eb2c72102",
"metadata": {},
"source": [
"# Check model performance depending on station\n",
"\n",
"#### Samples generated with random window"
]
},
{
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"id": "1732f721-6b13-4fb3-8755-dc63cb255285",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkmilian\u001b[0m (\u001b[33mepos\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /Users/krystynamilian/.netrc\n"
]
}
],
"source": [
"import pandas as pd\n",
"from obspy.core.event import read_events\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import seisbench.models as sbm\n",
"import torch\n",
"import torch.nn as nn\n",
"\n",
"import seisbench.data as sbd\n",
"import seisbench.generate as sbg\n",
"import seisbench.models as sbm\n",
"from seisbench.util import worker_seeding\n",
"import numpy as np\n",
"from torch.utils.data import DataLoader\n",
"from pathlib import Path\n",
"import wandb\n",
"import os\n",
"import sys\n",
"\n",
"from pathlib import Path\n",
"cwd = str(Path.cwd().parent)\n",
"sys.path.append(cwd)\n",
"from scripts import train\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1a322214-ba56-4651-966d-f9afdcfecbc5",
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"wandb version 0.15.4 is available! To upgrade, please run:\n",
" $ pip install wandb --upgrade"
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"Syncing run <strong><a href='https://wandb.ai/epos/demo_scripts-notebooks/runs/8fry08nf' target=\"_blank\">fiery-sky-8</a></strong> to <a href='https://wandb.ai/epos/demo_scripts-notebooks' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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" View project at <a href='https://wandb.ai/epos/demo_scripts-notebooks' target=\"_blank\">https://wandb.ai/epos/demo_scripts-notebooks</a>"
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" View run at <a href='https://wandb.ai/epos/demo_scripts-notebooks/runs/8fry08nf' target=\"_blank\">https://wandb.ai/epos/demo_scripts-notebooks/runs/8fry08nf</a>"
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"\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n"
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"PhaseNet(\n",
" (inc): Conv1d(3, 8, kernel_size=(7,), stride=(1,), padding=same)\n",
" (in_bn): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" (down_branch): ModuleList(\n",
" (0): ModuleList(\n",
" (0): Conv1d(8, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
" (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): Conv1d(8, 8, kernel_size=(7,), stride=(4,), padding=(3,), bias=False)\n",
" (3): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
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" (2): Conv1d(16, 16, kernel_size=(7,), stride=(4,), bias=False)\n",
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" )\n",
" (2): ModuleList(\n",
" (0): Conv1d(16, 32, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (2): Conv1d(32, 32, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (2-3): 2 x None\n",
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" (2): Conv1d(32, 16, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" )\n",
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" (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): Conv1d(16, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" )\n",
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")"
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}
],
"source": [
"model = train.load_model()\n",
"\n",
"run = wandb.init()\n",
"artifact = run.use_artifact('epos/training_seisbench_models_on_igf_data/model:v113', type='model')\n",
"artifact_dir = artifact.download()\n",
"fname = artifact_dir + \"/\" + os.listdir(artifact_dir)[0]\n",
"\n",
"model.load_state_dict(torch.load(fname))\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "75cadd4b-7a0e-44cd-acc6-fef26f2cf551",
"metadata": {},
"outputs": [],
"source": [
"data_path = '../../../data/igf/seisbench_format'\n",
"sampling_rate = 100\n",
"data = sbd.WaveformDataset(data_path, sampling_rate=sampling_rate)\n",
"data.filter(data.metadata.trace_Pg_arrival_sample.notna())\n",
"\n",
"pick_mae = train.PickMAE(sampling_rate)\n",
"splits = ['train', 'dev', 'test']\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9cdb971d-a08e-49d8-97be-b6aaa53b083a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All samples: 18002\n",
"Training examples: 12444 69.1%\n",
"Development examples: 2773 15.4%\n",
"Test examples: 2785 15.5 %\n"
]
}
],
"source": [
"all_samples = len(data.train()) + len(data.dev()) + len(data.test())\n",
"print(f\"All samples: {all_samples}\")\n",
"print(f\"Training examples: {len(data.train())} {len(data.train())/all_samples * 100:.1f}%\" )\n",
"print(f\"Development examples: {len(data.dev())} {len(data.dev())/all_samples * 100:.1f}%\")\n",
"print(f\"Test examples: {len(data.test())} {len(data.test())/all_samples * 100:.1f} %\")"
]
},
{
"cell_type": "markdown",
"id": "ccf454fe-e55a-47bc-882a-3e39395147a5",
"metadata": {},
"source": [
"### Calculate overall model performance"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "4c7b4137-4599-4599-9e3e-064145bfccb4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Model resutls for train set\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025075\n",
"Test avg mae: 0.046459\n",
"\n",
"\n",
"\n",
"Model resutls for dev set\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025158\n",
"Test avg mae: 0.049454\n",
"\n",
"\n",
"\n",
"Model resutls for test set\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025469\n",
"Test avg mae: 0.047190\n",
"\n"
]
}
],
"source": [
"for split in splits: \n",
" print(f\"\\n\\nModel resutls for {split} set\")\n",
"\n",
" gen = train.get_data_generator(split=split, station=None, sampling_rate=sampling_rate, path=data_path, window='random')\n",
"\n",
" data_loader = DataLoader(gen, batch_size=256, shuffle=False, num_workers=0,\n",
" worker_init_fn=worker_seeding)\n",
" \n",
" test_loss, test_mae = train.test_one_epoch(model, data_loader, pick_mae, wandb_log=False)\n",
"\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"id": "a361bc6a-2dac-4e27-9417-3abe482161a5",
"metadata": {},
"source": [
"## Check # frames per station in each set"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b23b71b7-ba71-4e86-8978-5a70d4fbbdfb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: title={'center': 'Frames per station'}, xlabel='station_code'>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1500x300 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"frames_per_station = []\n",
"for split in splits: \n",
" frames_per_station.append(data.get_split(split).metadata.groupby('station_code').count()['index'])\n",
" \n",
"frames_per_station = pd.DataFrame(frames_per_station, index=splits).transpose()\n",
"frames_per_station.plot(kind='bar', figsize=(15,3), title='Frames per station')\n"
]
},
{
"cell_type": "markdown",
"id": "acd81c2b-318f-45ea-b639-bf1ed26b0c10",
"metadata": {},
"source": [
"## Calculate MAE per station for train/dev/test set"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "66992bf3-c49a-4885-8ab1-5b372ad65202",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train\n",
"BRDW\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029197\n",
"Test avg mae: 0.101062\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028921\n",
"Test avg mae: 0.080500\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029244\n",
"Test avg mae: 0.086313\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029136\n",
"Test avg mae: 0.099563\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029101\n",
"Test avg mae: 0.100688\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028700\n",
"Test avg mae: 0.079062\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029309\n",
"Test avg mae: 0.098625\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029184\n",
"Test avg mae: 0.084937\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029323\n",
"Test avg mae: 0.086125\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029393\n",
"Test avg mae: 0.103312\n",
"\n",
"GROD\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025653\n",
"Test avg mae: 0.062547\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025550\n",
"Test avg mae: 0.056665\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025562\n",
"Test avg mae: 0.055145\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025711\n",
"Test avg mae: 0.059011\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025634\n",
"Test avg mae: 0.057455\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025523\n",
"Test avg mae: 0.054818\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025585\n",
"Test avg mae: 0.056393\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025612\n",
"Test avg mae: 0.055862\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025611\n",
"Test avg mae: 0.058877\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025614\n",
"Test avg mae: 0.058422\n",
"\n",
"GUZI\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024391\n",
"Test avg mae: 0.035747\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024418\n",
"Test avg mae: 0.036143\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024409\n",
"Test avg mae: 0.035239\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024416\n",
"Test avg mae: 0.035553\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024445\n",
"Test avg mae: 0.035810\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024413\n",
"Test avg mae: 0.037195\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024443\n",
"Test avg mae: 0.035182\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024415\n",
"Test avg mae: 0.036012\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024411\n",
"Test avg mae: 0.036120\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024401\n",
"Test avg mae: 0.036362\n",
"\n",
"JEDR\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024772\n",
"Test avg mae: 0.036505\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024748\n",
"Test avg mae: 0.034873\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024829\n",
"Test avg mae: 0.036079\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024805\n",
"Test avg mae: 0.035852\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024755\n",
"Test avg mae: 0.034627\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024716\n",
"Test avg mae: 0.035023\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024677\n",
"Test avg mae: 0.033938\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024704\n",
"Test avg mae: 0.033226\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024675\n",
"Test avg mae: 0.034878\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024956\n",
"Test avg mae: 0.038911\n",
"\n",
"MOSK2\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024905\n",
"Test avg mae: 0.041251\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024831\n",
"Test avg mae: 0.040558\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024893\n",
"Test avg mae: 0.041713\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024854\n",
"Test avg mae: 0.039448\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024832\n",
"Test avg mae: 0.040370\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024837\n",
"Test avg mae: 0.040740\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024842\n",
"Test avg mae: 0.040198\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024900\n",
"Test avg mae: 0.040395\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024862\n",
"Test avg mae: 0.039904\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024800\n",
"Test avg mae: 0.040857\n",
"\n",
"NWLU\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024434\n",
"Test avg mae: 0.034428\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024436\n",
"Test avg mae: 0.034107\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024587\n",
"Test avg mae: 0.034464\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024417\n",
"Test avg mae: 0.034171\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024523\n",
"Test avg mae: 0.033232\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024620\n",
"Test avg mae: 0.034845\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024388\n",
"Test avg mae: 0.033914\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024565\n",
"Test avg mae: 0.034201\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024580\n",
"Test avg mae: 0.034112\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024502\n",
"Test avg mae: 0.033727\n",
"\n",
"PCHB\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024624\n",
"Test avg mae: 0.041649\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024515\n",
"Test avg mae: 0.039478\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024490\n",
"Test avg mae: 0.040745\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024513\n",
"Test avg mae: 0.039800\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024597\n",
"Test avg mae: 0.042827\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024525\n",
"Test avg mae: 0.039057\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024588\n",
"Test avg mae: 0.042966\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024519\n",
"Test avg mae: 0.039862\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024510\n",
"Test avg mae: 0.040924\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024571\n",
"Test avg mae: 0.040463\n",
"\n",
"PPOL\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025924\n",
"Test avg mae: 0.061875\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026050\n",
"Test avg mae: 0.066563\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025983\n",
"Test avg mae: 0.067147\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025980\n",
"Test avg mae: 0.065153\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026014\n",
"Test avg mae: 0.061698\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026035\n",
"Test avg mae: 0.061963\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025978\n",
"Test avg mae: 0.063233\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025947\n",
"Test avg mae: 0.063183\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025983\n",
"Test avg mae: 0.065201\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026024\n",
"Test avg mae: 0.064024\n",
"\n",
"RUDN\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025199\n",
"Test avg mae: 0.044886\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025190\n",
"Test avg mae: 0.045990\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025130\n",
"Test avg mae: 0.045036\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025101\n",
"Test avg mae: 0.043167\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025226\n",
"Test avg mae: 0.047723\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025385\n",
"Test avg mae: 0.061125\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025306\n",
"Test avg mae: 0.046457\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025231\n",
"Test avg mae: 0.047450\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025327\n",
"Test avg mae: 0.047645\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025388\n",
"Test avg mae: 0.047236\n",
"\n",
"RYNR\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025864\n",
"Test avg mae: 0.066633\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025840\n",
"Test avg mae: 0.065376\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025626\n",
"Test avg mae: 0.062124\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025880\n",
"Test avg mae: 0.066312\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025988\n",
"Test avg mae: 0.067691\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025817\n",
"Test avg mae: 0.067727\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025806\n",
"Test avg mae: 0.065661\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025949\n",
"Test avg mae: 0.063175\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025792\n",
"Test avg mae: 0.066198\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025799\n",
"Test avg mae: 0.066651\n",
"\n",
"RZEC\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023818\n",
"Test avg mae: 0.031034\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023787\n",
"Test avg mae: 0.028276\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023800\n",
"Test avg mae: 0.024483\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023806\n",
"Test avg mae: 0.025862\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023858\n",
"Test avg mae: 0.028966\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023822\n",
"Test avg mae: 0.024828\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023817\n",
"Test avg mae: 0.027586\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023799\n",
"Test avg mae: 0.031379\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023909\n",
"Test avg mae: 0.031724\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023884\n",
"Test avg mae: 0.030345\n",
"\n",
"SGOR\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024467\n",
"Test avg mae: 0.037522\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024469\n",
"Test avg mae: 0.036812\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024430\n",
"Test avg mae: 0.035673\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024438\n",
"Test avg mae: 0.034225\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024428\n",
"Test avg mae: 0.034473\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024397\n",
"Test avg mae: 0.036998\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024478\n",
"Test avg mae: 0.036346\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024488\n",
"Test avg mae: 0.035342\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024540\n",
"Test avg mae: 0.038125\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024425\n",
"Test avg mae: 0.035026\n",
"\n",
"TRBC2\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024715\n",
"Test avg mae: 0.044793\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024758\n",
"Test avg mae: 0.046050\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024701\n",
"Test avg mae: 0.042903\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024757\n",
"Test avg mae: 0.045986\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024681\n",
"Test avg mae: 0.042041\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024607\n",
"Test avg mae: 0.044536\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024924\n",
"Test avg mae: 0.045319\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024695\n",
"Test avg mae: 0.044265\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024710\n",
"Test avg mae: 0.045154\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024677\n",
"Test avg mae: 0.045740\n",
"\n",
"TRN2\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025530\n",
"Test avg mae: 0.054768\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025488\n",
"Test avg mae: 0.050913\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025473\n",
"Test avg mae: 0.054110\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025458\n",
"Test avg mae: 0.057963\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025466\n",
"Test avg mae: 0.054473\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025534\n",
"Test avg mae: 0.054216\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025420\n",
"Test avg mae: 0.049408\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025467\n",
"Test avg mae: 0.053733\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025430\n",
"Test avg mae: 0.051889\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025404\n",
"Test avg mae: 0.054043\n",
"\n",
"TRZS\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024975\n",
"Test avg mae: 0.044880\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025067\n",
"Test avg mae: 0.043206\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025030\n",
"Test avg mae: 0.043493\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024915\n",
"Test avg mae: 0.044593\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025043\n",
"Test avg mae: 0.044641\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025157\n",
"Test avg mae: 0.045072\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024961\n",
"Test avg mae: 0.042823\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025094\n",
"Test avg mae: 0.044450\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025002\n",
"Test avg mae: 0.043589\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025029\n",
"Test avg mae: 0.044689\n",
"\n",
"ZMST\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025080\n",
"Test avg mae: 0.049731\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025050\n",
"Test avg mae: 0.048796\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025179\n",
"Test avg mae: 0.048982\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025070\n",
"Test avg mae: 0.049723\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025061\n",
"Test avg mae: 0.048439\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025102\n",
"Test avg mae: 0.049314\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025127\n",
"Test avg mae: 0.050508\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025169\n",
"Test avg mae: 0.049843\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025097\n",
"Test avg mae: 0.051237\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025070\n",
"Test avg mae: 0.048802\n",
"\n",
"LUBW\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028350\n",
"Test avg mae: 0.090000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027360\n",
"Test avg mae: 0.064848\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027597\n",
"Test avg mae: 0.074545\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028823\n",
"Test avg mae: 0.088182\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028706\n",
"Test avg mae: 0.110000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028709\n",
"Test avg mae: 0.093333\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028397\n",
"Test avg mae: 0.078182\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028619\n",
"Test avg mae: 0.095455\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028573\n",
"Test avg mae: 0.093333\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028804\n",
"Test avg mae: 0.119091\n",
"\n",
"DWOL\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024134\n",
"Test avg mae: 0.027203\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024156\n",
"Test avg mae: 0.027302\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024178\n",
"Test avg mae: 0.027137\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024149\n",
"Test avg mae: 0.027665\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024316\n",
"Test avg mae: 0.036156\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024161\n",
"Test avg mae: 0.027512\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024177\n",
"Test avg mae: 0.035867\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024151\n",
"Test avg mae: 0.027882\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024269\n",
"Test avg mae: 0.035224\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024150\n",
"Test avg mae: 0.026653\n",
"\n",
"LUBZ\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.030519\n",
"Test avg mae: 0.160000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.032446\n",
"Test avg mae: 0.180000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031204\n",
"Test avg mae: 0.160000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031441\n",
"Test avg mae: 0.170000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.032243\n",
"Test avg mae: 0.180000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.033196\n",
"Test avg mae: 0.175000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031445\n",
"Test avg mae: 0.170000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031896\n",
"Test avg mae: 0.150000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031475\n",
"Test avg mae: 0.160000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.031954\n",
"Test avg mae: 0.180000\n",
"\n",
"ZUKW2\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024746\n",
"Test avg mae: 0.035442\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024587\n",
"Test avg mae: 0.033472\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024649\n",
"Test avg mae: 0.032509\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024574\n",
"Test avg mae: 0.033508\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024625\n",
"Test avg mae: 0.033792\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024838\n",
"Test avg mae: 0.034519\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024788\n",
"Test avg mae: 0.036248\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024626\n",
"Test avg mae: 0.033304\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024571\n",
"Test avg mae: 0.032324\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024572\n",
"Test avg mae: 0.031848\n",
"\n",
"DABR\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024302\n",
"Test avg mae: 0.031127\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024232\n",
"Test avg mae: 0.061970\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024185\n",
"Test avg mae: 0.032058\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024129\n",
"Test avg mae: 0.031252\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024226\n",
"Test avg mae: 0.031566\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024113\n",
"Test avg mae: 0.030397\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024186\n",
"Test avg mae: 0.030980\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024138\n",
"Test avg mae: 0.031460\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024194\n",
"Test avg mae: 0.031061\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024163\n",
"Test avg mae: 0.031359\n",
"\n",
"PEKW2\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024472\n",
"Test avg mae: 0.043610\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024489\n",
"Test avg mae: 0.044439\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024524\n",
"Test avg mae: 0.043707\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024507\n",
"Test avg mae: 0.042732\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024472\n",
"Test avg mae: 0.042488\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024517\n",
"Test avg mae: 0.042537\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024527\n",
"Test avg mae: 0.043707\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024483\n",
"Test avg mae: 0.042585\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024475\n",
"Test avg mae: 0.044293\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024497\n",
"Test avg mae: 0.041854\n",
"\n",
"KRZY\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025522\n",
"Test avg mae: 0.054286\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025226\n",
"Test avg mae: 0.057143\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025248\n",
"Test avg mae: 0.070000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025417\n",
"Test avg mae: 0.047143\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025459\n",
"Test avg mae: 0.051429\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025202\n",
"Test avg mae: 0.058571\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025341\n",
"Test avg mae: 0.060000\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025273\n",
"Test avg mae: 0.054286\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025228\n",
"Test avg mae: 0.058571\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025349\n",
"Test avg mae: 0.048571\n",
"\n",
"OBIS\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023926\n",
"Test avg mae: 0.025517\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023971\n",
"Test avg mae: 0.026552\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023946\n",
"Test avg mae: 0.027793\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023967\n",
"Test avg mae: 0.024828\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023990\n",
"Test avg mae: 0.027586\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023950\n",
"Test avg mae: 0.026276\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023965\n",
"Test avg mae: 0.026345\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023990\n",
"Test avg mae: 0.025724\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023987\n",
"Test avg mae: 0.027034\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023955\n",
"Test avg mae: 0.027241\n",
"\n",
"KAZI\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023951\n",
"Test avg mae: 0.027810\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023934\n",
"Test avg mae: 0.026762\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023937\n",
"Test avg mae: 0.027619\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024004\n",
"Test avg mae: 0.027429\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023971\n",
"Test avg mae: 0.025429\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023979\n",
"Test avg mae: 0.027810\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023944\n",
"Test avg mae: 0.026286\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023980\n",
"Test avg mae: 0.026762\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023900\n",
"Test avg mae: 0.026095\n",
"\n",
"train (12444, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023933\n",
"Test avg mae: 0.027048\n",
"\n",
"KWLC\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"train (12444, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev\n",
"BRDW\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028971\n",
"Test avg mae: 0.086500\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029175\n",
"Test avg mae: 0.086000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027710\n",
"Test avg mae: 0.085000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027695\n",
"Test avg mae: 0.084000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028312\n",
"Test avg mae: 0.087000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027334\n",
"Test avg mae: 0.083000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027557\n",
"Test avg mae: 0.088000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028396\n",
"Test avg mae: 0.093500\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027768\n",
"Test avg mae: 0.085500\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026457\n",
"Test avg mae: 0.089000\n",
"\n",
"GROD\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025171\n",
"Test avg mae: 0.048325\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025280\n",
"Test avg mae: 0.048629\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025265\n",
"Test avg mae: 0.050152\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025124\n",
"Test avg mae: 0.044518\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025599\n",
"Test avg mae: 0.056294\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025292\n",
"Test avg mae: 0.046548\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025199\n",
"Test avg mae: 0.048629\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025107\n",
"Test avg mae: 0.048477\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025218\n",
"Test avg mae: 0.048782\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025200\n",
"Test avg mae: 0.050964\n",
"\n",
"GUZI\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024414\n",
"Test avg mae: 0.038017\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024456\n",
"Test avg mae: 0.037025\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024583\n",
"Test avg mae: 0.039917\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024552\n",
"Test avg mae: 0.040496\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024419\n",
"Test avg mae: 0.037190\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024548\n",
"Test avg mae: 0.040992\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024619\n",
"Test avg mae: 0.040413\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024461\n",
"Test avg mae: 0.038843\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024484\n",
"Test avg mae: 0.040248\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024423\n",
"Test avg mae: 0.034628\n",
"\n",
"JEDR\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025750\n",
"Test avg mae: 0.008889\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025724\n",
"Test avg mae: 0.012222\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024942\n",
"Test avg mae: 0.015556\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024687\n",
"Test avg mae: 0.013333\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025583\n",
"Test avg mae: 0.016667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026014\n",
"Test avg mae: 0.018889\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024612\n",
"Test avg mae: 0.015556\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024265\n",
"Test avg mae: 0.013333\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024791\n",
"Test avg mae: 0.008889\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024083\n",
"Test avg mae: 0.006667\n",
"\n",
"MOSK2\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024982\n",
"Test avg mae: 0.043147\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025065\n",
"Test avg mae: 0.039442\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025059\n",
"Test avg mae: 0.045685\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024984\n",
"Test avg mae: 0.046650\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025198\n",
"Test avg mae: 0.053655\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024918\n",
"Test avg mae: 0.049695\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024984\n",
"Test avg mae: 0.044924\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025159\n",
"Test avg mae: 0.039645\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025128\n",
"Test avg mae: 0.045076\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024987\n",
"Test avg mae: 0.045685\n",
"\n",
"NWLU\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025525\n",
"Test avg mae: 0.036667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024986\n",
"Test avg mae: 0.033913\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024918\n",
"Test avg mae: 0.037609\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025009\n",
"Test avg mae: 0.035507\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024886\n",
"Test avg mae: 0.036304\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025350\n",
"Test avg mae: 0.036159\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025268\n",
"Test avg mae: 0.037174\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025467\n",
"Test avg mae: 0.044203\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025363\n",
"Test avg mae: 0.037101\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025164\n",
"Test avg mae: 0.035072\n",
"\n",
"PCHB\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024568\n",
"Test avg mae: 0.041707\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024671\n",
"Test avg mae: 0.043171\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024638\n",
"Test avg mae: 0.040488\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024669\n",
"Test avg mae: 0.043537\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024710\n",
"Test avg mae: 0.042317\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024639\n",
"Test avg mae: 0.039024\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024660\n",
"Test avg mae: 0.042439\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024627\n",
"Test avg mae: 0.041951\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024666\n",
"Test avg mae: 0.040732\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024709\n",
"Test avg mae: 0.044878\n",
"\n",
"PPOL\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025905\n",
"Test avg mae: 0.055522\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025925\n",
"Test avg mae: 0.074627\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026258\n",
"Test avg mae: 0.071493\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025296\n",
"Test avg mae: 0.052836\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025339\n",
"Test avg mae: 0.050000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026370\n",
"Test avg mae: 0.091791\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025980\n",
"Test avg mae: 0.073881\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026007\n",
"Test avg mae: 0.074328\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025985\n",
"Test avg mae: 0.087910\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025757\n",
"Test avg mae: 0.058507\n",
"\n",
"RUDN\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025465\n",
"Test avg mae: 0.047500\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025528\n",
"Test avg mae: 0.049360\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025330\n",
"Test avg mae: 0.048256\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025654\n",
"Test avg mae: 0.050872\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025423\n",
"Test avg mae: 0.049360\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025523\n",
"Test avg mae: 0.051860\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025558\n",
"Test avg mae: 0.047791\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025529\n",
"Test avg mae: 0.048721\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025503\n",
"Test avg mae: 0.050930\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025489\n",
"Test avg mae: 0.047965\n",
"\n",
"RYNR\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026632\n",
"Test avg mae: 0.070596\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026046\n",
"Test avg mae: 0.059868\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026075\n",
"Test avg mae: 0.061192\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026108\n",
"Test avg mae: 0.060728\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026853\n",
"Test avg mae: 0.082980\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026386\n",
"Test avg mae: 0.061523\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027056\n",
"Test avg mae: 0.089735\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026100\n",
"Test avg mae: 0.068874\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026171\n",
"Test avg mae: 0.069868\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026221\n",
"Test avg mae: 0.063046\n",
"\n",
"RZEC\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"dev (2773, 17) 100\n",
"using random window\n",
"division by zero\n",
"SGOR\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024412\n",
"Test avg mae: 0.029419\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025008\n",
"Test avg mae: 0.084194\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025264\n",
"Test avg mae: 0.085161\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024350\n",
"Test avg mae: 0.031032\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024544\n",
"Test avg mae: 0.083613\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024492\n",
"Test avg mae: 0.083226\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024589\n",
"Test avg mae: 0.084387\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025363\n",
"Test avg mae: 0.084452\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024272\n",
"Test avg mae: 0.031806\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025422\n",
"Test avg mae: 0.083548\n",
"\n",
"TRBC2\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024320\n",
"Test avg mae: 0.031864\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024446\n",
"Test avg mae: 0.031864\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024385\n",
"Test avg mae: 0.031864\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024495\n",
"Test avg mae: 0.034068\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024545\n",
"Test avg mae: 0.032712\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024661\n",
"Test avg mae: 0.034746\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024446\n",
"Test avg mae: 0.030339\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024298\n",
"Test avg mae: 0.031356\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024805\n",
"Test avg mae: 0.033390\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024428\n",
"Test avg mae: 0.033390\n",
"\n",
"TRN2\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024707\n",
"Test avg mae: 0.039503\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024751\n",
"Test avg mae: 0.040442\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024843\n",
"Test avg mae: 0.039613\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024716\n",
"Test avg mae: 0.041713\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024761\n",
"Test avg mae: 0.039171\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024794\n",
"Test avg mae: 0.041160\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024743\n",
"Test avg mae: 0.040497\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024749\n",
"Test avg mae: 0.040994\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024759\n",
"Test avg mae: 0.040166\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024745\n",
"Test avg mae: 0.040331\n",
"\n",
"TRZS\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026324\n",
"Test avg mae: 0.089741\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025970\n",
"Test avg mae: 0.041897\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026548\n",
"Test avg mae: 0.179310\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026197\n",
"Test avg mae: 0.038534\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026940\n",
"Test avg mae: 0.093362\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026428\n",
"Test avg mae: 0.177241\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026037\n",
"Test avg mae: 0.177414\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026034\n",
"Test avg mae: 0.090948\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026646\n",
"Test avg mae: 0.176897\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026593\n",
"Test avg mae: 0.090776\n",
"\n",
"ZMST\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024322\n",
"Test avg mae: 0.029791\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024131\n",
"Test avg mae: 0.028063\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024258\n",
"Test avg mae: 0.029215\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024214\n",
"Test avg mae: 0.030157\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024274\n",
"Test avg mae: 0.031518\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024378\n",
"Test avg mae: 0.034660\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024129\n",
"Test avg mae: 0.027958\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024210\n",
"Test avg mae: 0.029581\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024291\n",
"Test avg mae: 0.034136\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024350\n",
"Test avg mae: 0.030052\n",
"\n",
"LUBW\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029929\n",
"Test avg mae: 0.117917\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.029545\n",
"Test avg mae: 0.113333\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.030242\n",
"Test avg mae: 0.134583\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028370\n",
"Test avg mae: 0.089167\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028703\n",
"Test avg mae: 0.119583\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.030046\n",
"Test avg mae: 0.122083\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.030243\n",
"Test avg mae: 0.130833\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.030052\n",
"Test avg mae: 0.123750\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027626\n",
"Test avg mae: 0.077917\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028850\n",
"Test avg mae: 0.120833\n",
"\n",
"DWOL\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024480\n",
"Test avg mae: 0.048835\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024443\n",
"Test avg mae: 0.031650\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024452\n",
"Test avg mae: 0.033495\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024461\n",
"Test avg mae: 0.032136\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024591\n",
"Test avg mae: 0.048641\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024491\n",
"Test avg mae: 0.032282\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024380\n",
"Test avg mae: 0.032476\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024482\n",
"Test avg mae: 0.049563\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024437\n",
"Test avg mae: 0.032913\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024607\n",
"Test avg mae: 0.049029\n",
"\n",
"LUBZ\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023679\n",
"Test avg mae: 0.020000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023553\n",
"Test avg mae: 0.006667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023672\n",
"Test avg mae: 0.026667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023809\n",
"Test avg mae: 0.013333\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023596\n",
"Test avg mae: 0.006667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023617\n",
"Test avg mae: 0.010000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023756\n",
"Test avg mae: 0.020000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023778\n",
"Test avg mae: 0.016667\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023575\n",
"Test avg mae: 0.010000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023789\n",
"Test avg mae: 0.020000\n",
"\n",
"ZUKW2\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024717\n",
"Test avg mae: 0.036456\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024627\n",
"Test avg mae: 0.035534\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024642\n",
"Test avg mae: 0.035194\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024675\n",
"Test avg mae: 0.038835\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024625\n",
"Test avg mae: 0.036359\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024703\n",
"Test avg mae: 0.037282\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024708\n",
"Test avg mae: 0.037961\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024797\n",
"Test avg mae: 0.036650\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024798\n",
"Test avg mae: 0.053786\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024559\n",
"Test avg mae: 0.036650\n",
"\n",
"DABR\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024410\n",
"Test avg mae: 0.036832\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024548\n",
"Test avg mae: 0.036460\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024549\n",
"Test avg mae: 0.034596\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024566\n",
"Test avg mae: 0.036708\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024553\n",
"Test avg mae: 0.036273\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024526\n",
"Test avg mae: 0.037143\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024570\n",
"Test avg mae: 0.037143\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024582\n",
"Test avg mae: 0.036708\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024457\n",
"Test avg mae: 0.036708\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024530\n",
"Test avg mae: 0.036584\n",
"\n",
"PEKW2\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026632\n",
"Test avg mae: 0.045361\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026724\n",
"Test avg mae: 0.047938\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026484\n",
"Test avg mae: 0.046392\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026670\n",
"Test avg mae: 0.048247\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026608\n",
"Test avg mae: 0.045464\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026605\n",
"Test avg mae: 0.046804\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026637\n",
"Test avg mae: 0.047629\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026723\n",
"Test avg mae: 0.046804\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026733\n",
"Test avg mae: 0.047010\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026889\n",
"Test avg mae: 0.058454\n",
"\n",
"KRZY\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038278\n",
"Test avg mae: 0.110000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038771\n",
"Test avg mae: 0.097273\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038392\n",
"Test avg mae: 0.099091\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038522\n",
"Test avg mae: 0.104545\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038693\n",
"Test avg mae: 0.104545\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038756\n",
"Test avg mae: 0.096364\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038586\n",
"Test avg mae: 0.099091\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038925\n",
"Test avg mae: 0.103636\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038687\n",
"Test avg mae: 0.098182\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.038287\n",
"Test avg mae: 0.106364\n",
"\n",
"OBIS\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024280\n",
"Test avg mae: 0.027320\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024778\n",
"Test avg mae: 0.115155\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024274\n",
"Test avg mae: 0.027938\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024968\n",
"Test avg mae: 0.113505\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024193\n",
"Test avg mae: 0.027938\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024322\n",
"Test avg mae: 0.027526\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024552\n",
"Test avg mae: 0.113608\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024237\n",
"Test avg mae: 0.028041\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024345\n",
"Test avg mae: 0.027732\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024254\n",
"Test avg mae: 0.028763\n",
"\n",
"KAZI\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024294\n",
"Test avg mae: 0.029000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024415\n",
"Test avg mae: 0.029909\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024394\n",
"Test avg mae: 0.030636\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024499\n",
"Test avg mae: 0.028727\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024466\n",
"Test avg mae: 0.030636\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024289\n",
"Test avg mae: 0.029364\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024452\n",
"Test avg mae: 0.028545\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024398\n",
"Test avg mae: 0.030091\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024537\n",
"Test avg mae: 0.029364\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024418\n",
"Test avg mae: 0.031091\n",
"\n",
"KWLC\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023724\n",
"Test avg mae: 0.030000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023606\n",
"Test avg mae: 0.015000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023589\n",
"Test avg mae: 0.010000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023717\n",
"Test avg mae: 0.020000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023802\n",
"Test avg mae: 0.025000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023751\n",
"Test avg mae: 0.025000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023677\n",
"Test avg mae: 0.010000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024050\n",
"Test avg mae: 0.040000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023581\n",
"Test avg mae: 0.020000\n",
"\n",
"dev (2773, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023595\n",
"Test avg mae: 0.015000\n",
"\n",
"test\n",
"BRDW\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025934\n",
"Test avg mae: 0.060896\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026379\n",
"Test avg mae: 0.073284\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026053\n",
"Test avg mae: 0.069552\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026029\n",
"Test avg mae: 0.060448\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026420\n",
"Test avg mae: 0.058358\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026025\n",
"Test avg mae: 0.064627\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025910\n",
"Test avg mae: 0.060448\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026421\n",
"Test avg mae: 0.086119\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026229\n",
"Test avg mae: 0.059552\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026069\n",
"Test avg mae: 0.061045\n",
"\n",
"GROD\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025325\n",
"Test avg mae: 0.044286\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025248\n",
"Test avg mae: 0.044286\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025428\n",
"Test avg mae: 0.047029\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025281\n",
"Test avg mae: 0.042686\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025347\n",
"Test avg mae: 0.047200\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025475\n",
"Test avg mae: 0.048343\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025273\n",
"Test avg mae: 0.060286\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025299\n",
"Test avg mae: 0.046743\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025243\n",
"Test avg mae: 0.045829\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025070\n",
"Test avg mae: 0.043600\n",
"\n",
"GUZI\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025129\n",
"Test avg mae: 0.040556\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025515\n",
"Test avg mae: 0.050556\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025496\n",
"Test avg mae: 0.049841\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025842\n",
"Test avg mae: 0.050238\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025786\n",
"Test avg mae: 0.058254\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025247\n",
"Test avg mae: 0.042222\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025569\n",
"Test avg mae: 0.049841\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025710\n",
"Test avg mae: 0.049048\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025632\n",
"Test avg mae: 0.050476\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025607\n",
"Test avg mae: 0.059206\n",
"\n",
"JEDR\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027847\n",
"Test avg mae: 0.201290\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026951\n",
"Test avg mae: 0.043387\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027874\n",
"Test avg mae: 0.197258\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027162\n",
"Test avg mae: 0.042581\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026982\n",
"Test avg mae: 0.040161\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027034\n",
"Test avg mae: 0.042097\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027053\n",
"Test avg mae: 0.043710\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027056\n",
"Test avg mae: 0.041774\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027195\n",
"Test avg mae: 0.042903\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026985\n",
"Test avg mae: 0.043548\n",
"\n",
"MOSK2\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024563\n",
"Test avg mae: 0.036432\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024515\n",
"Test avg mae: 0.035276\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024500\n",
"Test avg mae: 0.035276\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024548\n",
"Test avg mae: 0.034724\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024531\n",
"Test avg mae: 0.035678\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024597\n",
"Test avg mae: 0.035779\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024430\n",
"Test avg mae: 0.035879\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024420\n",
"Test avg mae: 0.033618\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024539\n",
"Test avg mae: 0.035377\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024518\n",
"Test avg mae: 0.035327\n",
"\n",
"NWLU\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024630\n",
"Test avg mae: 0.031074\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024590\n",
"Test avg mae: 0.030872\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024608\n",
"Test avg mae: 0.030268\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024637\n",
"Test avg mae: 0.028591\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024670\n",
"Test avg mae: 0.029866\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024449\n",
"Test avg mae: 0.029597\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024694\n",
"Test avg mae: 0.030201\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024591\n",
"Test avg mae: 0.031544\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024664\n",
"Test avg mae: 0.029396\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024568\n",
"Test avg mae: 0.030470\n",
"\n",
"PCHB\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024545\n",
"Test avg mae: 0.037215\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024679\n",
"Test avg mae: 0.035443\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024926\n",
"Test avg mae: 0.045190\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024639\n",
"Test avg mae: 0.035696\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024640\n",
"Test avg mae: 0.036582\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024553\n",
"Test avg mae: 0.033797\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024715\n",
"Test avg mae: 0.044557\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024856\n",
"Test avg mae: 0.045949\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024775\n",
"Test avg mae: 0.046076\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024738\n",
"Test avg mae: 0.035570\n",
"\n",
"PPOL\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025437\n",
"Test avg mae: 0.048475\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025318\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025423\n",
"Test avg mae: 0.049661\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025431\n",
"Test avg mae: 0.049153\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025138\n",
"Test avg mae: 0.048305\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025164\n",
"Test avg mae: 0.048475\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025158\n",
"Test avg mae: 0.041695\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025334\n",
"Test avg mae: 0.050847\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025233\n",
"Test avg mae: 0.048983\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025176\n",
"Test avg mae: 0.048644\n",
"\n",
"RUDN\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025884\n",
"Test avg mae: 0.054605\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025868\n",
"Test avg mae: 0.059211\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026080\n",
"Test avg mae: 0.057566\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025783\n",
"Test avg mae: 0.058158\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025573\n",
"Test avg mae: 0.044605\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026530\n",
"Test avg mae: 0.055329\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026172\n",
"Test avg mae: 0.057434\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026277\n",
"Test avg mae: 0.057303\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026563\n",
"Test avg mae: 0.058487\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026182\n",
"Test avg mae: 0.055592\n",
"\n",
"RYNR\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027648\n",
"Test avg mae: 0.086963\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027588\n",
"Test avg mae: 0.082815\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027658\n",
"Test avg mae: 0.089778\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027625\n",
"Test avg mae: 0.108296\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026857\n",
"Test avg mae: 0.079926\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027323\n",
"Test avg mae: 0.098370\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026899\n",
"Test avg mae: 0.076296\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027445\n",
"Test avg mae: 0.077481\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027287\n",
"Test avg mae: 0.086963\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026892\n",
"Test avg mae: 0.084148\n",
"\n",
"RZEC\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"test (2785, 17) 100\n",
"using random window\n",
"division by zero\n",
"SGOR\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025765\n",
"Test avg mae: 0.042595\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025846\n",
"Test avg mae: 0.042848\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025771\n",
"Test avg mae: 0.043861\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026033\n",
"Test avg mae: 0.042532\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025628\n",
"Test avg mae: 0.043734\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025968\n",
"Test avg mae: 0.043987\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025798\n",
"Test avg mae: 0.042848\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026101\n",
"Test avg mae: 0.045063\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025852\n",
"Test avg mae: 0.042468\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025887\n",
"Test avg mae: 0.044241\n",
"\n",
"TRBC2\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024363\n",
"Test avg mae: 0.033091\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024329\n",
"Test avg mae: 0.034000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024381\n",
"Test avg mae: 0.032000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024374\n",
"Test avg mae: 0.032545\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024416\n",
"Test avg mae: 0.036182\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024293\n",
"Test avg mae: 0.031273\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024474\n",
"Test avg mae: 0.036727\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024390\n",
"Test avg mae: 0.036182\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024293\n",
"Test avg mae: 0.031818\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024265\n",
"Test avg mae: 0.033273\n",
"\n",
"TRN2\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027654\n",
"Test avg mae: 0.063333\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027777\n",
"Test avg mae: 0.060864\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027985\n",
"Test avg mae: 0.072531\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.028154\n",
"Test avg mae: 0.072840\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027799\n",
"Test avg mae: 0.070802\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027817\n",
"Test avg mae: 0.064198\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027730\n",
"Test avg mae: 0.064506\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027682\n",
"Test avg mae: 0.068889\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027775\n",
"Test avg mae: 0.064444\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.027888\n",
"Test avg mae: 0.071111\n",
"\n",
"TRZS\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024738\n",
"Test avg mae: 0.037113\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024839\n",
"Test avg mae: 0.036701\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024709\n",
"Test avg mae: 0.036082\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024755\n",
"Test avg mae: 0.035773\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024726\n",
"Test avg mae: 0.035567\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024823\n",
"Test avg mae: 0.036701\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024761\n",
"Test avg mae: 0.035052\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024710\n",
"Test avg mae: 0.033918\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024731\n",
"Test avg mae: 0.038454\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024848\n",
"Test avg mae: 0.036186\n",
"\n",
"ZMST\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025525\n",
"Test avg mae: 0.040159\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025413\n",
"Test avg mae: 0.036667\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025533\n",
"Test avg mae: 0.038624\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025489\n",
"Test avg mae: 0.038254\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025375\n",
"Test avg mae: 0.037249\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025236\n",
"Test avg mae: 0.037513\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025373\n",
"Test avg mae: 0.037354\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025520\n",
"Test avg mae: 0.051640\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025388\n",
"Test avg mae: 0.037566\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025274\n",
"Test avg mae: 0.038201\n",
"\n",
"LUBW\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025608\n",
"Test avg mae: 0.063000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025088\n",
"Test avg mae: 0.042000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025370\n",
"Test avg mae: 0.044000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025287\n",
"Test avg mae: 0.055000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024473\n",
"Test avg mae: 0.033000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024490\n",
"Test avg mae: 0.039000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026141\n",
"Test avg mae: 0.061000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025247\n",
"Test avg mae: 0.048000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025590\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025214\n",
"Test avg mae: 0.046000\n",
"\n",
"DWOL\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024828\n",
"Test avg mae: 0.036150\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024786\n",
"Test avg mae: 0.037000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024687\n",
"Test avg mae: 0.035150\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024730\n",
"Test avg mae: 0.036050\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024863\n",
"Test avg mae: 0.036000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024617\n",
"Test avg mae: 0.037150\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024813\n",
"Test avg mae: 0.038150\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024747\n",
"Test avg mae: 0.035500\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024657\n",
"Test avg mae: 0.036600\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024653\n",
"Test avg mae: 0.036100\n",
"\n",
"LUBZ\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023514\n",
"Test avg mae: 0.020000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023465\n",
"Test avg mae: 0.010000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023750\n",
"Test avg mae: 0.030000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023617\n",
"Test avg mae: 0.000000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023518\n",
"Test avg mae: 0.000000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023535\n",
"Test avg mae: 0.010000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023519\n",
"Test avg mae: 0.000000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023594\n",
"Test avg mae: 0.010000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023488\n",
"Test avg mae: 0.010000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.023619\n",
"Test avg mae: 0.010000\n",
"\n",
"ZUKW2\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024833\n",
"Test avg mae: 0.034316\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024815\n",
"Test avg mae: 0.034789\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024881\n",
"Test avg mae: 0.034526\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024811\n",
"Test avg mae: 0.035632\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024757\n",
"Test avg mae: 0.032316\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024949\n",
"Test avg mae: 0.034000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024804\n",
"Test avg mae: 0.034474\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024805\n",
"Test avg mae: 0.034789\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024807\n",
"Test avg mae: 0.036105\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024812\n",
"Test avg mae: 0.035316\n",
"\n",
"DABR\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024772\n",
"Test avg mae: 0.039000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025039\n",
"Test avg mae: 0.040500\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025131\n",
"Test avg mae: 0.037250\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025016\n",
"Test avg mae: 0.038500\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025114\n",
"Test avg mae: 0.039125\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024994\n",
"Test avg mae: 0.038688\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025277\n",
"Test avg mae: 0.038375\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024936\n",
"Test avg mae: 0.038125\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024811\n",
"Test avg mae: 0.038500\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025205\n",
"Test avg mae: 0.039000\n",
"\n",
"PEKW2\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024234\n",
"Test avg mae: 0.036897\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024220\n",
"Test avg mae: 0.037816\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024231\n",
"Test avg mae: 0.037931\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024209\n",
"Test avg mae: 0.036207\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024214\n",
"Test avg mae: 0.037241\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024192\n",
"Test avg mae: 0.038966\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024206\n",
"Test avg mae: 0.036207\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024220\n",
"Test avg mae: 0.034828\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024178\n",
"Test avg mae: 0.037126\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024201\n",
"Test avg mae: 0.040460\n",
"\n",
"KRZY\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025639\n",
"Test avg mae: 0.070000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024752\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024743\n",
"Test avg mae: 0.060000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025550\n",
"Test avg mae: 0.070000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025862\n",
"Test avg mae: 0.060000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024733\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025121\n",
"Test avg mae: 0.060000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026550\n",
"Test avg mae: 0.080000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.024653\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025185\n",
"Test avg mae: 0.070000\n",
"\n",
"OBIS\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025883\n",
"Test avg mae: 0.054245\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026269\n",
"Test avg mae: 0.054717\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025488\n",
"Test avg mae: 0.054245\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025862\n",
"Test avg mae: 0.042830\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025587\n",
"Test avg mae: 0.056509\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.026170\n",
"Test avg mae: 0.055566\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025503\n",
"Test avg mae: 0.057925\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025792\n",
"Test avg mae: 0.055755\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025840\n",
"Test avg mae: 0.052736\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025825\n",
"Test avg mae: 0.056415\n",
"\n",
"KAZI\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025883\n",
"Test avg mae: 0.044649\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025872\n",
"Test avg mae: 0.042982\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025842\n",
"Test avg mae: 0.042281\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025850\n",
"Test avg mae: 0.040789\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025822\n",
"Test avg mae: 0.041930\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025798\n",
"Test avg mae: 0.041491\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025872\n",
"Test avg mae: 0.041404\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025900\n",
"Test avg mae: 0.041316\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025843\n",
"Test avg mae: 0.042632\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025867\n",
"Test avg mae: 0.042368\n",
"\n",
"KWLC\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025280\n",
"Test avg mae: 0.051923\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025302\n",
"Test avg mae: 0.050000\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025410\n",
"Test avg mae: 0.053462\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025195\n",
"Test avg mae: 0.048269\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025163\n",
"Test avg mae: 0.049423\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025168\n",
"Test avg mae: 0.048269\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025206\n",
"Test avg mae: 0.050385\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025152\n",
"Test avg mae: 0.048846\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025373\n",
"Test avg mae: 0.054615\n",
"\n",
"test (2785, 17) 100\n",
"using random window\n",
"Test avg loss: 0.025162\n",
"Test avg mae: 0.050192\n",
"\n"
]
}
],
"source": [
"\n",
"\n",
"stations = data.metadata.station_code.unique()\n",
"\n",
"results = []\n",
"\n",
"for split in splits: \n",
" split_results = {}\n",
" print(split)\n",
" for station in stations: \n",
" print(station)\n",
" split_results[station] = {'mae':[], 'loss':[]}\n",
" for i in range(10):\n",
" gen = train.get_data_generator(split=split, station=station, sampling_rate=sampling_rate, path=data_path)\n",
" data_loader = DataLoader(gen, batch_size=256, shuffle=False, num_workers=0,\n",
" worker_init_fn=worker_seeding)\n",
" \n",
" test_loss, test_mae = None, None\n",
" try: \n",
" test_loss, test_mae = train.test_one_epoch(model, data_loader, pick_mae, wandb_log=False)\n",
" test_mae = float(test_mae)\n",
" \n",
" except Exception as e: \n",
" print(e)\n",
" \n",
" split_results[station]['mae'].append(test_mae)\n",
" split_results[station]['loss'].append(test_loss)\n",
" results.append(split_results)\n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"id": "206e6973-61bb-4f84-95a5-f910c7c7dc2b",
"metadata": {},
"source": [
"#### Plot results"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f0366dc6-d1e1-44cb-b748-d0af44a77219",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
" return _methods._mean(a, axis=axis, dtype=dtype,\n",
"/Users/krystynamilian/virtualenvs/epos/lib/python3.9/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABMYAAAGaCAYAAADkeU/nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACGGklEQVR4nOzdd3gUVf/+8Xs3IQmkUaVDkN5ROgqhRIqABimCSFNBEETloaNUERRBUEAUqSpIEak+KAJButK7dFAgdAg1QHJ+f/DLflmyCQmPZCfs+3Vde8HOzO7eO5mdnf3MOWdsxhgjAAAAAAAAwMPY3R0AAAAAAAAAcAcKYwAAAAAAAPBIFMYAAAAAAADgkSiMAQAAAAAAwCNRGAMAAAAAAIBHojAGAAAAAAAAj0RhDAAAAAAAAB6JwhgAAAAAAAA8EoUxAAAAAAAAeCQKYwAAwKOEhISobdu27o7x2LPZbBo4cGCKv+7AgQNls9lS/HUBAEDqRGEMAAALmjp1qmw2m2w2m9asWRNvvjFGuXPnls1mU4MGDdyQ8PGxZ88eDRw4UEePHnV3lBR18uRJDRw4UNu2bXvo5/j555/dUvxKSf/GekqKGTNmaPTo0Y/0NRLy0Ucfaf78+W55bQAA3I3CGAAAFubn56cZM2bEm75q1Sr9888/8vX1dUOqx8uePXs0aNAgjyyMDRo06H8ujA0aNMjlvBs3buj9999/6Od+WO+//75u3Ljxrz3fv7GekoLCGAAA7kFhDAAAC3v++ec1Z84c3blzx2n6jBkzVLZsWWXLls1NyR5ebGysbt686e4Yqd6dO3d069Ytd8dIkJ+fn7y9vVP8db29veXn55firwsAAFInCmMAAFhYixYtdP78eS1btswx7datW5o7d65eeeUVl4+JjY3V6NGjVbx4cfn5+Slr1qx68803dfHiRaflFixYoPr16ytHjhzy9fVV/vz5NWTIEMXExDgtV716dZUoUUJ79uxRjRo1lC5dOuXMmVOffPJJkt6DzWZTly5d9P3336t48eLy9fXV0qVLJUknTpzQa6+9pqxZs8rX11fFixfX5MmT4z3HF198oeLFiytdunTKkCGDypUr59SSrm3btgoJCYn3uAeNNzV16lQ1bdpUklSjRg1H99WIiAhJ0qZNm1SnTh1lzpxZadOmVb58+fTaa6898D2HhISoQYMG+vXXX1WmTBn5+fmpWLFimjdvXrxlL126pHfffVe5c+eWr6+vChQooI8//lixsbGOZY4ePSqbzaZPP/1Uo0ePVv78+eXr66s9e/YkmGHZsmV69tlnlT59egUEBKhw4cLq27evJCkiIkLly5eXJLVr187xvqdOnSpJWr16tZo2bao8efLI19dXuXPn1nvvvefUEqtt27YaN26cJDkef++6djXG2NatW1WvXj0FBQUpICBAtWrV0oYNG5yWietGvHbtWnXr1k1ZsmSRv7+/GjVqpLNnzz5w3bv6m8dtg/Pnz1eJEiUc21rcdpiQB60nSdq4caPq1q2r4OBgpUuXTqGhoVq7dq3T81y5ckXvvvuuQkJC5OvrqyeeeELPPfectmzZIunuZ2zJkiU6duyY4zVcbc/3SuzvGyc6OloDBgxQgQIFHH/Hnj17Kjo62mndXLt2TdOmTXO8NmPwAQA8ScqfxgMAAEkWEhKiypUra+bMmapXr54k6b///a8uX76s5s2b6/PPP4/3mDfffFNTp05Vu3bt1LVrVx05ckRjx47V1q1btXbtWqVJk0bS3QJEQECAunXrpoCAAK1YsUL9+/dXVFSURowY4fScFy9eVN26dfXSSy+pWbNmmjt3rnr16qWSJUs6ciVmxYoVmj17trp06aLMmTMrJCREp0+fVqVKlRxFiyxZsui///2vXn/9dUVFRendd9+VJE2cOFFdu3ZVkyZN9M477+jmzZvasWOHNm7cmGBxMKmqVaumrl276vPPP1ffvn1VtGhRSVLRokV15swZ1a5dW1myZFHv3r2VPn16HT161GVxy5UDBw7o5ZdfVseOHdWmTRtNmTJFTZs21dKlS/Xcc89Jkq5fv67Q0FCdOHFCb775pvLkyaN169apT58+OnXqVLyudVOmTNHNmzfVoUMH+fr6KmPGjC5fe/fu3WrQoIFKlSqlwYMHy9fXVwcPHnQUbIoWLarBgwerf//+6tChg6pWrSpJqlKliiRpzpw5un79ujp16qRMmTLpjz/+0BdffKF//vlHc+bMkXR3Ozt58qSWLVumb7/99oHrY/fu3apataqCgoLUs2dPpUmTRl999ZWqV6+uVatWqWLFik7Lv/3228qQIYMGDBigo0ePavTo0erSpYtmzZqVpPV/vzVr1mjevHl66623FBgYqM8//1yNGzfW8ePHlSlTJpePedB6WrFiherVq6eyZctqwIABstvtmjJlimrWrKnVq1erQoUKkqSOHTtq7ty56tKli4oVK6bz589rzZo12rt3r55++mn169dPly9f1j///KPPPvtMkhQQEJDoukzs7yvdLZC/8MILWrNmjTp06KCiRYtq586d+uyzz7R//35H18lvv/1Wb7zxhipUqKAOHTpIkvLnz/9Q6xgAgFTJAAAAy5kyZYqRZP78808zduxYExgYaK5fv26MMaZp06amRo0axhhj8ubNa+rXr+943OrVq40k8/333zs939KlS+NNj3u+e7355psmXbp05ubNm45poaGhRpKZPn26Y1p0dLTJli2bady48QPfiyRjt9vN7t27naa//vrrJnv27ObcuXNO05s3b26Cg4Md+V588UVTvHjxRF+jTZs2Jm/evPGmDxgwwNx/uJM3b17Tpk0bx/05c+YYSWblypVOy/3000+Ov0Fy5c2b10gyP/74o2Pa5cuXTfbs2c1TTz3lmDZkyBDj7+9v9u/f7/T43r17Gy8vL3P8+HFjjDFHjhwxkkxQUJA5c+bMA1//s88+M5LM2bNnE1zmzz//NJLMlClT4s1ztW0MGzbM2Gw2c+zYMce0zp07x1u/cSSZAQMGOO6Hh4cbHx8fc+jQIce0kydPmsDAQFOtWjXHtLhtPywszMTGxjqmv/fee8bLy8tcunQpwfdkjOu/uSTj4+NjDh486Ji2fft2I8l88cUXiT5fQuspNjbWFCxY0NSpU8cp5/Xr102+fPnMc88955gWHBxsOnfunOjr1K9f3+U27EpS/r7ffvutsdvtZvXq1U7TJ0yYYCSZtWvXOqb5+/s7fSYAAPAkdKUEAMDimjVrphs3bmjx4sW6cuWKFi9enGBLqTlz5ig4OFjPPfeczp0757iVLVtWAQEBWrlypWPZtGnTOv5/5coVnTt3TlWrVtX169e1b98+p+cNCAjQq6++6rjv4+OjChUq6PDhw0l6D6GhoSpWrJjjvjFGP/74oxo2bChjjFPWOnXq6PLly45uZunTp9c///yjP//8M0mv9W9Jnz69JGnx4sW6fft2sh+fI0cONWrUyHE/KChIrVu31tatWxUZGSnp7t+ratWqypAhg9M6CAsLU0xMjH7//Xen52zcuLGyZMmS5OwLFixw6pKZVPduG9euXdO5c+dUpUoVGWO0devWZD9fTEyMfv31V4WHh+vJJ590TM+ePbteeeUVrVmzRlFRUU6P6dChg1OXyKpVqyomJkbHjh1L9utLUlhYmFNLqFKlSikoKCjJ2/D9tm3bpgMHDuiVV17R+fPnHX+7a9euqVatWvr9998d6z59+vTauHGjTp48+VCvdb+k/H3nzJmjokWLqkiRIk7bVs2aNSXJaV8AAIAnozAGAIDFZcmSRWFhYZoxY4bmzZunmJgYNWnSxOWyBw4c0OXLl/XEE08oS5YsTrerV6/qzJkzjmV3796tRo0aKTg4WEFBQcqSJYuj+HX58mWn582VK1e8cZsyZMgQb9yyhOTLl8/p/tmzZ3Xp0iV9/fXX8XK2a9dOkhxZe/XqpYCAAFWoUEEFCxZU586d443h9CiEhoaqcePGGjRokDJnzqwXX3xRU6ZMcRqfKTEFChSIt84KFSokSY4rYB44cEBLly6Ntw7CwsIkyenvJcVfjwl5+eW
"text/plain": [
"<Figure size 1500x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results_df = []\n",
"for i, split in enumerate(splits): \n",
" df = pd.DataFrame(results[i]).transpose()\n",
"\n",
" for station, values in df[['mae']].itertuples():\n",
" df.loc[station, 'mae'] = np.mean([v for v in values if v is not None]) \n",
" for station, values in df[['loss']].itertuples():\n",
" df.loc[station, 'loss'] = np.mean([v for v in values if v is not None]) \n",
" \n",
" df.plot(kind='bar', figsize=(15,4), title=f\"Mean results per station in {split} set\")\n",
"\n",
" results_df.append(df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "14171ea5-5438-4e57-8e1d-ed30df68340b",
"metadata": {},
"outputs": [],
"source": [
"### Check correlation between trainin data size and obtained results"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d2bec214-3058-4337-848b-035dc2395c76",
"metadata": {},
"outputs": [],
"source": [
"stats = frames_per_station.copy()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c8718b24-7257-4bc5-b6a4-da68012dafac",
"metadata": {},
"outputs": [],
"source": [
"stats['train_mae'] = results_df[0]['mae']\n",
"stats['dev_mae'] = results_df[1]['mae']\n",
"stats['test_mae'] = results_df[2]['mae']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "30cc267a-8b2c-4b72-9e55-19e87874ebcb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='train', ylabel='train_mae'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stats.plot(kind='scatter', x ='train', y='train_mae')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ebac708b-c3c2-4f08-9574-4c6daffa96bd",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>train</th>\n",
" <th>dev</th>\n",
" <th>test</th>\n",
" <th>train_mae</th>\n",
" <th>dev_mae</th>\n",
" <th>test_mae</th>\n",
" </tr>\n",
" <tr>\n",
" <th>station_code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BRDW</th>\n",
" <td>160.0</td>\n",
" <td>20.0</td>\n",
" <td>67.0</td>\n",
" <td>0.092019</td>\n",
" <td>0.08675</td>\n",
" <td>0.065433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DABR</th>\n",
" <td>359.0</td>\n",
" <td>161.0</td>\n",
" <td>160.0</td>\n",
" <td>0.034323</td>\n",
" <td>0.036516</td>\n",
" <td>0.038706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DWOL</th>\n",
" <td>479.0</td>\n",
" <td>206.0</td>\n",
" <td>200.0</td>\n",
" <td>0.02986</td>\n",
" <td>0.039102</td>\n",
" <td>0.036385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GROD</th>\n",
" <td>1052.0</td>\n",
" <td>197.0</td>\n",
" <td>175.0</td>\n",
" <td>0.05752</td>\n",
" <td>0.049132</td>\n",
" <td>0.047029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GUZI</th>\n",
" <td>740.0</td>\n",
" <td>121.0</td>\n",
" <td>126.0</td>\n",
" <td>0.035936</td>\n",
" <td>0.038777</td>\n",
" <td>0.050024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>JEDR</th>\n",
" <td>809.0</td>\n",
" <td>9.0</td>\n",
" <td>62.0</td>\n",
" <td>0.035391</td>\n",
" <td>0.013</td>\n",
" <td>0.073871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KAZI</th>\n",
" <td>105.0</td>\n",
" <td>110.0</td>\n",
" <td>114.0</td>\n",
" <td>0.026905</td>\n",
" <td>0.029736</td>\n",
" <td>0.042184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KRZY</th>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
" <td>0.056</td>\n",
" <td>0.101909</td>\n",
" <td>0.062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LUBW</th>\n",
" <td>33.0</td>\n",
" <td>24.0</td>\n",
" <td>10.0</td>\n",
" <td>0.090697</td>\n",
" <td>0.115</td>\n",
" <td>0.0481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LUBZ</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>0.1685</td>\n",
" <td>0.015</td>\n",
" <td>0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOSK2</th>\n",
" <td>958.0</td>\n",
" <td>197.0</td>\n",
" <td>199.0</td>\n",
" <td>0.040543</td>\n",
" <td>0.04536</td>\n",
" <td>0.035337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NWLU</th>\n",
" <td>902.0</td>\n",
" <td>138.0</td>\n",
" <td>149.0</td>\n",
" <td>0.03412</td>\n",
" <td>0.036971</td>\n",
" <td>0.030188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OBIS</th>\n",
" <td>145.0</td>\n",
" <td>97.0</td>\n",
" <td>106.0</td>\n",
" <td>0.02649</td>\n",
" <td>0.053753</td>\n",
" <td>0.054094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PCHB</th>\n",
" <td>420.0</td>\n",
" <td>82.0</td>\n",
" <td>79.0</td>\n",
" <td>0.040777</td>\n",
" <td>0.042024</td>\n",
" <td>0.039608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PEKW2</th>\n",
" <td>205.0</td>\n",
" <td>97.0</td>\n",
" <td>87.0</td>\n",
" <td>0.043195</td>\n",
" <td>0.04801</td>\n",
" <td>0.037368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PPOL</th>\n",
" <td>463.0</td>\n",
" <td>67.0</td>\n",
" <td>59.0</td>\n",
" <td>0.064004</td>\n",
" <td>0.06909</td>\n",
" <td>0.048424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RUDN</th>\n",
" <td>941.0</td>\n",
" <td>172.0</td>\n",
" <td>152.0</td>\n",
" <td>0.047671</td>\n",
" <td>0.049262</td>\n",
" <td>0.055829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RYNR</th>\n",
" <td>874.0</td>\n",
" <td>151.0</td>\n",
" <td>135.0</td>\n",
" <td>0.065755</td>\n",
" <td>0.068841</td>\n",
" <td>0.087104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RZEC</th>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.028448</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SGOR</th>\n",
" <td>845.0</td>\n",
" <td>155.0</td>\n",
" <td>158.0</td>\n",
" <td>0.036054</td>\n",
" <td>0.068084</td>\n",
" <td>0.043418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TRBC2</th>\n",
" <td>295.0</td>\n",
" <td>59.0</td>\n",
" <td>55.0</td>\n",
" <td>0.044679</td>\n",
" <td>0.032559</td>\n",
" <td>0.033709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TRN2</th>\n",
" <td>1020.0</td>\n",
" <td>181.0</td>\n",
" <td>162.0</td>\n",
" <td>0.053552</td>\n",
" <td>0.040359</td>\n",
" <td>0.067352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TRZS</th>\n",
" <td>209.0</td>\n",
" <td>116.0</td>\n",
" <td>97.0</td>\n",
" <td>0.044144</td>\n",
" <td>0.115612</td>\n",
" <td>0.036155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZMST</th>\n",
" <td>1084.0</td>\n",
" <td>191.0</td>\n",
" <td>189.0</td>\n",
" <td>0.049537</td>\n",
" <td>0.030513</td>\n",
" <td>0.039323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZUKW2</th>\n",
" <td>308.0</td>\n",
" <td>206.0</td>\n",
" <td>190.0</td>\n",
" <td>0.033697</td>\n",
" <td>0.038471</td>\n",
" <td>0.034626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KWLC</th>\n",
" <td>NaN</td>\n",
" <td>2.0</td>\n",
" <td>52.0</td>\n",
" <td>NaN</td>\n",
" <td>0.021</td>\n",
" <td>0.050538</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" train dev test train_mae dev_mae test_mae\n",
"station_code \n",
"BRDW 160.0 20.0 67.0 0.092019 0.08675 0.065433\n",
"DABR 359.0 161.0 160.0 0.034323 0.036516 0.038706\n",
"DWOL 479.0 206.0 200.0 0.02986 0.039102 0.036385\n",
"GROD 1052.0 197.0 175.0 0.05752 0.049132 0.047029\n",
"GUZI 740.0 121.0 126.0 0.035936 0.038777 0.050024\n",
"JEDR 809.0 9.0 62.0 0.035391 0.013 0.073871\n",
"KAZI 105.0 110.0 114.0 0.026905 0.029736 0.042184\n",
"KRZY 7.0 11.0 1.0 0.056 0.101909 0.062\n",
"LUBW 33.0 24.0 10.0 0.090697 0.115 0.0481\n",
"LUBZ 2.0 3.0 1.0 0.1685 0.015 0.01\n",
"MOSK2 958.0 197.0 199.0 0.040543 0.04536 0.035337\n",
"NWLU 902.0 138.0 149.0 0.03412 0.036971 0.030188\n",
"OBIS 145.0 97.0 106.0 0.02649 0.053753 0.054094\n",
"PCHB 420.0 82.0 79.0 0.040777 0.042024 0.039608\n",
"PEKW2 205.0 97.0 87.0 0.043195 0.04801 0.037368\n",
"PPOL 463.0 67.0 59.0 0.064004 0.06909 0.048424\n",
"RUDN 941.0 172.0 152.0 0.047671 0.049262 0.055829\n",
"RYNR 874.0 151.0 135.0 0.065755 0.068841 0.087104\n",
"RZEC 29.0 NaN NaN 0.028448 NaN NaN\n",
"SGOR 845.0 155.0 158.0 0.036054 0.068084 0.043418\n",
"TRBC2 295.0 59.0 55.0 0.044679 0.032559 0.033709\n",
"TRN2 1020.0 181.0 162.0 0.053552 0.040359 0.067352\n",
"TRZS 209.0 116.0 97.0 0.044144 0.115612 0.036155\n",
"ZMST 1084.0 191.0 189.0 0.049537 0.030513 0.039323\n",
"ZUKW2 308.0 206.0 190.0 0.033697 0.038471 0.034626\n",
"KWLC NaN 2.0 52.0 NaN 0.021 0.050538"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats"
]
},
{
"cell_type": "markdown",
"id": "f02c9173-986c-45d5-973c-a68406ff20df",
"metadata": {},
"source": [
"### Check predictions for stations with highest MAE"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "66a99163-04cd-4ecb-a3c8-fa8dc112c435",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"highest mean MAE in dev set: 0.12 for station: TRZS\n",
"highest mean MAE in test set: 0.09 for station: RYNR\n"
]
}
],
"source": [
"dev_res = results_df[1]\n",
"station_with_worst_res_dev_set = dev_res[dev_res.mae == dev_res.mae.max()].index[0]\n",
"highest_dev_mae = dev_res.loc[station_with_worst_res_dev_set, 'mae']\n",
"\n",
"test_res = results_df[2]\n",
"station_with_worst_res_test_set = test_res[test_res.mae == test_res.mae.max()].index[0]\n",
"highest_test_mae = test_res.loc[station_with_worst_res_test_set, 'mae']\n",
"print(f\"highest mean MAE in dev set: {highest_dev_mae:.2f} for station: {station_with_worst_res_dev_set}\")\n",
"print(f\"highest mean MAE in test set: {highest_test_mae:.2f} for station: {station_with_worst_res_test_set}\")\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "73a07433-1663-440c-bce3-d73988e9685b",
"metadata": {},
"outputs": [],
"source": [
"def plot_sample(sample, model, i, desc=None): \n",
" fig = plt.figure(figsize=(15, 10))\n",
" \n",
" axs = fig.subplots(2, 1, sharex=True, gridspec_kw={\"hspace\": 0, \"height_ratios\": [3, 2]})\n",
" axs[0].plot(sample[\"X\"][0].T, label='x')\n",
" plt.legend()\n",
" axs[1].plot(sample[\"y\"][0].T, label='y')\n",
" \n",
" model.eval() # close the model for evaluation\n",
" \n",
" with torch.no_grad():\n",
" pred = model(torch.tensor(sample[\"X\"], device=model.device).unsqueeze(0)) # Add a fake batch dimension\n",
" pred = pred[0].cpu().numpy()\n",
" \n",
" axs[1].plot(pred[0], label='pred', color='orange')\n",
" plt.legend()\n",
"\n",
" pred_pick_idx = np.argmax(pred[0])\n",
" true_pick_idx = np.argmax(sample['y'][0])\n",
"\n",
" \n",
" \n",
" mae_error = np.abs(pred_pick_idx - true_pick_idx) /100 #mae in seconds\n",
"\n",
" fig.suptitle(f\"Predictions for sample: {i} {desc}, mae: {mae_error}s\")\n",
" \n",
" plt.show()\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"id": "d28d456a-4034-4eea-ac28-ed29ddb98d75",
"metadata": {},
"source": [
"### Find random samples that reproduce obtained results \n",
"\n",
"Results are not deterministic, because samples generator used during training augments samples by introducing random padding, see https://seisbench.readthedocs.io/en/stable/pages/documentation/generate.html?highlight=generate#seisbench.generate.windows.RandomWindow"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "235ae13e-3250-49e6-8bb2-765c037b0870",
"metadata": {},
"outputs": [],
"source": [
"##### dev set"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "5e3829f5-360a-431f-9fdd-bdeff6a5a1e4",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n",
"dev (2773, 17) 100\n",
"using random window\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.594e+01 4.900e-01 4.500e-01 2.600e-01 2.000e-01 1.300e-01 1.200e-01\n",
" 1.200e-01 1.000e-01 9.000e-02 9.000e-02 8.000e-02 8.000e-02 6.000e-02\n",
" 6.000e-02 6.000e-02 6.000e-02 6.000e-02 6.000e-02 6.000e-02 5.000e-02\n",
" 5.000e-02 5.000e-02 5.000e-02 4.000e-02 4.000e-02 4.000e-02 4.000e-02\n",
" 4.000e-02 4.000e-02 4.000e-02 4.000e-02 4.000e-02 4.000e-02 3.000e-02\n",
" 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02\n",
" 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02 3.000e-02\n",
" 3.000e-02 3.000e-02 3.000e-02 3.000e-02 2.000e-02 2.000e-02 2.000e-02\n",
" 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02\n",
" 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02\n",
" 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 2.000e-02 1.000e-02\n",
" 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02\n",
" 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02\n",
" 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02 1.000e-02\n",
" 1.000e-02 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n",
" 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n",
" 0.000e+00 0.000e+00 0.000e+00 0.000e+00]\n",
"59 15.94\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.49\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"53 0.45\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"110 0.26\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"64 0.2\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"mean_mae = 0\n",
"samples = []\n",
"split = 'dev'\n",
"station = station_with_worst_res_dev_set\n",
"\n",
"while mean_mae < highest_dev_mae: \n",
"\n",
" gen = train.get_data_generator(split=split, station=station , sampling_rate=sampling_rate, path=data_path)\n",
" station_mae = []\n",
" with torch.no_grad():\n",
" for i in range(len(gen)): \n",
" # idx = np.random.randint(len(gen))\n",
" idx = i\n",
" sample = gen[idx]\n",
" samples.append(sample)\n",
" pred = model(torch.tensor(sample[\"X\"], device=model.device).unsqueeze(0)) \n",
" pred = pred[0].cpu().numpy()\n",
" \n",
" pred_pick_idx = np.argmax(pred[0])\n",
" true_pick_idx = np.argmax(sample['y'][0]) \n",
" \n",
" mae_error = np.abs(pred_pick_idx - true_pick_idx) /100 #mae in seconds\n",
" station_mae.append(mae_error)\n",
" \n",
" sorted = np.argsort(station_mae)[::-1]\n",
" mean_mae = np.mean(station_mae)\n",
"\n",
"print(np.array(station_mae)[sorted])\n",
"\n",
"\n",
"## plot samples with mae error at leas 0.2s\n",
"for idx in sorted:\n",
" if station_mae[idx] < 0.2: \n",
" break\n",
" print(idx, station_mae[idx])\n",
" plot_sample(samples[idx], model, idx, desc=f\" from station {station} {split} set\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "fc074059-8faf-4d7b-8b2c-49995df6fb37",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test (2785, 17) 100\n",
"using random window\n",
"test (2785, 17) 100\n",
"using random window\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 91 40 63 106 28 119 100 33 129 105 42 54 86 32 130 24 61 3\n",
" 79 73 67 89 128 124 76 50 46 36 71 31 5 83 98 126 18 21\n",
" 77 2 134 114 127 109 125 22 30 23 20 81 15 43 44 45 113 49\n",
" 96 51 74 94 117 52 118 95 122 57 123 14 13 12 9 8 7 6\n",
" 131 132 56 47 65 93 90 97 110 87 75 85 108 102 80 59 25 104\n",
" 19 58 17 82 84 11 10 4 120 0 68 55 48 69 112 53 38 39\n",
" 37 60 116 66 111 92 101 88 62 99 64 1 34 133 16 35 70 41\n",
" 78 103 115 26 27 121 107 72 29]\n",
"[3.12 2.56 1.23 0.5 0.37 0.23 0.22 0.14 0.13 0.12 0.11 0.1 0.1 0.09\n",
" 0.09 0.08 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.06 0.06 0.06 0.06 0.06\n",
" 0.05 0.05 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04\n",
" 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03\n",
" 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02\n",
" 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02\n",
" 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01\n",
" 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0. ]\n",
"91 3.12\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"40 2.56\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"63 1.23\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"106 0.5\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"28 0.37\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"119 0.23\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 0.22\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"mean_mae = 0\n",
"samples = []\n",
"split = 'test'\n",
"station = station_with_worst_res_test_set\n",
"\n",
"while mean_mae < highest_test_mae: \n",
"\n",
" gen = train.get_data_generator(split=split, station=station , sampling_rate=sampling_rate, path=data_path)\n",
" station_mae = []\n",
" with torch.no_grad():\n",
" for i in range(len(gen)): \n",
" # idx = np.random.randint(len(gen))\n",
" idx = i\n",
" sample = gen[idx]\n",
" samples.append(sample)\n",
" pred = model(torch.tensor(sample[\"X\"], device=model.device).unsqueeze(0)) \n",
" pred = pred[0].cpu().numpy()\n",
" \n",
" pred_pick_idx = np.argmax(pred[0])\n",
" true_pick_idx = np.argmax(sample['y'][0]) \n",
" \n",
" mae_error = np.abs(pred_pick_idx - true_pick_idx) /100 #mae in seconds\n",
" station_mae.append(mae_error)\n",
" \n",
" sorted = np.argsort(station_mae)[::-1]\n",
" mean_mae = np.mean(station_mae)\n",
"\n",
"print(sorted)\n",
"print(np.array(station_mae)[sorted])\n",
"\n",
"\n",
"## plot samples with mae error at leas 0.2s\n",
"for idx in sorted:\n",
" if station_mae[idx] < 0.2: \n",
" break\n",
" print(idx, station_mae[idx])\n",
" plot_sample(samples[idx], model, idx, desc=f\" from station {station} {split} set\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7e599f32-b888-4235-8d15-f9d2c98a59cf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"index 30312\n",
"source_origin_time 2021-08-24 03:01:23.500\n",
"source_latitude_deg 5714247.36402\n",
"source_longitude_deg 5578128.427708\n",
"source_depth_km 0.8\n",
"source_magnitude 1.522222\n",
"split test\n",
"station_network_code PL\n",
"station_code MOSK2\n",
"trace_channel EHE\n",
"trace_sampling_rate_hz 100.0\n",
"trace_start_time 2021-08-24T03:01:17.360000Z\n",
"trace_Pg_arrival_sample 727.0\n",
"trace_name bucket29$66,:3,:2001\n",
"trace_Sg_arrival_sample NaN\n",
"trace_chunk \n",
"trace_component_order ZNE\n",
"Name: 30312, dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.test().metadata.iloc[idx]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "b3d9f158-5e63-4cc1-8052-844749fe098f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "07561939-653f-441d-96c1-ac8046668838",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"test (2785, 17) 100\n",
"using random window\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABNIAAAORCAYAAAA3ZI+fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5xU1d0/8M/MbAUECwgYidijxhJJRBMTGxFrNI/RmPLYjTFi9CHV/BK7MXajEruIRqOxd6qAIghKExCU3ln6NtgyM/f3x+y995xzz7ll5s7OLn7eeRl2Z245986d2ZnPfM85CcuyLBAREREREREREZGvZKkbQERERERERERE1BkwSCMiIiIiIiIiIgqBQRoREREREREREVEIDNKIiIiIiIiIiIhCYJBGREREREREREQUAoM0IiIiIiIiIiKiEBikERERERERERERhcAgjYiIiIiIiIiIKAQGaURERERERERERCEwSCMiIuog+vfvj4suusj5fcKECUgkEpgwYUJs+0gkErjxxhtj217c7rrrLuyzzz5IpVI44ogjSt2cDqmjP4ZEREREOzIGaURERACefvppJBIJ57+qqioccMABGDJkCGpqakrdvEjefffdThm0jB49Gn/84x/xve99D8OHD8ff//73UjfpK+3hhx/Gueeei69//etIJBJSyCsaN24cLrnkEhxwwAHo0qUL9tlnH1x22WVYu3atZ9nRo0fj0ksvxTe/+U2kUin079/fuP/bbrsNP/rRj9C7d2+GhwGefPJJHHTQQaiqqsL++++PBx98MPS6zc3N+NOf/oQ99tgD1dXVGDhwIMaMGSMts2zZMun1Uf3v8ssvj/uQiIiIOqyyUjeAiIioI7n55pux9957o6mpCZMmTcLDDz+Md999F3PnzkWXLl3atS0/+MEPsH37dlRUVERa791338WwYcO0wcP27dtRVtYx//y///77SCaTePLJJyMfM8XvjjvuQH19PY466ihtKGb705/+hM2bN+Pcc8/F/vvvjyVLluChhx7C22+/jVmzZqFPnz7Oss8//zxefPFFHHnkkdhjjz189//Xv/4Vffr0wbe+9S2MGjUqtuPa0Tz66KP49a9/jXPOOQdDhw7Fhx9+iN/+9rfYtm0b/vSnPwWuf9FFF+Hll1/Gtddei/333x9PP/00TjvtNIwfPx7HHnssAKBXr1549tlnPeuOHDkSzz33HE4++eTYj4uIiKij6pjvpImIiErk1FNPxbe//W0AwGWXXYbddtsN9957L9544w387Gc/067T2NiIrl27xt6WZDKJqqqqWLcZ9/bitH79elRXV8cWolmWhaamJlRXV8eyva+aiRMnOtVo3bp1My5377334thjj0Uy6XZ0OOWUU3DcccfhoYcewq233urc/ve//x2PP/44ysvLccYZZ2Du3LnG7S5duhT9+/fHxo0b0atXr3gOagezfft2/L//9/9w+umn4+WXXwYAXH755chms7jlllvwq1/9Crvssotx/WnTpuGFF17AXXfdhd///vcAgAsuuADf/OY38cc//hGTJ08GAHTt2hW//OUvPes//fTT6N69O84888wiHB0REVHHxK6dREREPk488UQAuQ/1QK56o1u3bli8eDFOO+007LTTTvjFL34BAMhms7j//vtxyCGHoKqqCr1798YVV1yBLVu2SNu0LAu33nor9txzT3Tp0gUnnHAC5s2b59m3aYy0qVOn4rTTTsMuu+yCrl274rDDDsM///lPp33Dhg0DAKnrlU3XRW7mzJk49dRT0b17d3Tr1g0nnXQSPv74Y2kZu+vrRx99hKFDh6JXr17o2rUrfvzjH2PDhg3Ssp9++ikGDx6Mnj17orq6GnvvvTcuueQS3/OcSCQwfPhwNDY2Om1++umnAQDpdBq33HIL9t13X1RWVqJ///74y1/+gubmZmkb/fv3xxlnnIFRo0bh29/+Nqqrq/Hoo48a97lw4UKcc8456NOnD6qqqrDnnnvi/PPPR21trbPM8OHDceKJJ2L33XdHZWUlDj74YDz88MOebdn7njBhgrPvQw891HnsXn31VRx66KGoqqrCgAEDMHPmTGl9+7pasmQJBg8ejK5du2KPPfbAzTffDMuyfM8dAKxevRqXXHIJevfujcrKShxyyCF46qmnPMutWLECCxYsCNweAOy1117StWPygx/8QArR7Nt23XVXzJ8/X7p9jz32QHl5eaj9+3X7jFOhj91nn32Giy66CPvssw+qqqrQp08fXHLJJdi0aZNnX3E/TuPHj8emTZvwm9/8Rrr9qquuQmNjI9555x3f9V9++WWkUin86le/cm6rqqrCpZdeiilTpmDlypXGddeuXYvx48fjf/7nf6SAvr6+Htdeey369++PyspK7L777vjhD3+IGTNmBB4PERFRZ8CKNCIiIh+LFy8GAOy2227Obel0GoMHD8axxx6Lu+++2+nyecUVV+Dpp5/GxRdfjN/+9rdYunQpHnroIcycORMfffSREyBcf/31uPXWW3HaaafhtNNOw4wZM3DyySejpaUlsD1jxozBGWecgb59++Kaa65Bnz59MH/+fLz99tu45pprcMUVV2DNmjUYM2aMtiuWat68efj+97+P7t27449//CPKy8vx6KOP4vjjj8fEiRMxcOBAafmrr74au+yyC2644QYsW7YM999/P4YMGYIXX3wRQK6q7OSTT0avXr3w5z//GTvvvDOWLVuGV1991bcdzz77LB577DFMmzYNTzzxBADgu9/9LoBcZeCIESPwk5/8BL/73e8wdepU3H777Zg/fz5ee+01aTtffPEFfvazn+GKK67A5ZdfjgMPPFC7v5aWFgwePBjNzc24+uqr0adPH6xevRpvv/02tm7dih49egDIjRN2yCGH4Ec/+hHKysrw1ltv4Te/+Q2y2SyuuuoqaZuLFi3Cz3/+c1xxxRX45S9/ibvvvhtnnnkmHnnkEfzlL39xwo7bb78d5513Hr744gspgMpkMjjllFNw9NFH484778TIkSNxww03IJ1O4+abbzaeu5qaGhx99NFIJBIYMmQIevXqhffeew+XXnop6urqcO211zrLXnDBBZg4cWKocK4QDQ0NaGhoQM+ePYu6n7gU8tiNGTMGS5YswcUXX4w+ffpg3rx5eOyxxzBv3jx8/PHHThhZjMfJDvXsKlrbgAEDkEwmMXPmTG0lmbj+AQccgO7du0u3H3XUUQCAWbNmoV+/ftp1X3jhBWSzWeeLBNuvf/1rvPzyyxgyZAgOPvhgbNq0CZMmTcL8+fNx5JFH+h4PERFRp2ARERGRNXz4cAuANXbsWGvDhg3WypUrrRdeeMHabbfdrOrqamvVqlWWZVnWhRdeaAGw/vznP0vrf/jhhxYA67nnnpNuHzlypHT7+vXrrYqKCuv000+3stmss9xf/vIXC4B14YUXOreNHz/eAmCNHz/esizLSqfT1t57723ttdde1pYtW6T9iNu66qqrLNOfeADWDTfc4Px+9tlnWxUVFdbixYud29asWWPttNNO1g9+8APP+Rk0aJC0r//7v/+zUqmUtXXrVsuyLOu1116zAFiffPKJdv9+LrzwQqtr167SbbNmzbIAWJdddpl0++9//3sLgPX+++87t+21114WAGvkyJGB+5o5c6YFwHrppZd8l9u2bZvntsGDB1v77LOPdJu978mTJzu3jRo1ygJgVVdXW8uXL3duf/TRR6XH1bLc6+rqq692bstms9bpp59uVVRUWBs2bHBuVx/DSy+91Orbt6+1ceNGqU3nn3++1aNHD+kYjjvuOOO14adr167StRnklltusQBY48aNMy5z+umnW3vttVfgtjZs2OA55jgV+tjprpH//Oc/FgDrgw8+cG4rxuN01VVXWalUSntfr169rPPPP993/UMOOcQ68cQTPbfPmzfPAmA98sgjxnUHDBhg9e3b18pkMtLtPXr0sK666qrAthMREXVW7NpJREQkGDRoEHr16oV+/frh/PPPR7du3fDaa6/ha1/7mrTclVdeKf3+0ksvoUePHvjhD3+IjRs3Ov8NGDAA3bp
"text/plain": [
"<Figure size 1500x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gen = train.get_data_generator(split='test', station=None , sampling_rate=sampling_rate, path=data_path)\n",
"\n",
"for i in range(5): \n",
" idx = np.random.randint(len(gen))\n",
" sample = gen[idx]\n",
" \n",
" plot_sample(sample, model, idx, desc=\"\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b9a025cc-15a0-4ae0-a926-15d594a40200",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'X': array([[-2.9614418e-19, -2.9614418e-19, -2.9614418e-19, ...,\n",
" -2.9614418e-19, -2.9614418e-19, -2.9614418e-19],\n",
" [-6.4017395e-19, -6.4017395e-19, -6.4017395e-19, ...,\n",
" -6.4017395e-19, -6.4017395e-19, -6.4017395e-19],\n",
" [-1.0255816e-18, -1.0255816e-18, -1.0255816e-18, ...,\n",
" -1.0255816e-18, -1.0255816e-18, -1.0255816e-18]], dtype=float32),\n",
" 'y': array([[2.43596292e-226, 7.12942807e-226, 2.08428048e-225, ...,\n",
" 0.00000000e+000, 0.00000000e+000, 0.00000000e+000],\n",
" [1.00000000e+000, 1.00000000e+000, 1.00000000e+000, ...,\n",
" 1.00000000e+000, 1.00000000e+000, 1.00000000e+000]])}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "902426eb-6e4d-472f-b38a-bbc94761af83",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}