5608 lines
2.4 MiB
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5608 lines
2.4 MiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "05600e0a-8b4b-42ab-96d5-9d6eb2c72102",
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"metadata": {},
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"source": [
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"# Check model performance depending on station\n",
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"\n",
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"#### Samples generated with random window"
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]
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},
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"cell_type": "code",
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"execution_count": 1,
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"id": "1732f721-6b13-4fb3-8755-dc63cb255285",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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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",
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"\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",
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"\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",
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"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /Users/krystynamilian/.netrc\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from obspy.core.event import read_events\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import seisbench.models as sbm\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"import seisbench.data as sbd\n",
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"import seisbench.generate as sbg\n",
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"import seisbench.models as sbm\n",
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"from seisbench.util import worker_seeding\n",
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"import numpy as np\n",
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"from torch.utils.data import DataLoader\n",
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"from pathlib import Path\n",
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"import wandb\n",
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"import os\n",
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"import sys\n",
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"\n",
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"from pathlib import Path\n",
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"cwd = str(Path.cwd().parent)\n",
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"sys.path.append(cwd)\n",
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"from scripts import train\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "1a322214-ba56-4651-966d-f9afdcfecbc5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"wandb version 0.15.4 is available! To upgrade, please run:\n",
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" $ pip install wandb --upgrade"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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"Tracking run with wandb version 0.15.3"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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"Run data is saved locally in <code>/Users/krystynamilian/Documents/praca/Cyfronet/epos/ai/repo/demo_scripts/notebooks/wandb/run-20230704_110544-8fry08nf</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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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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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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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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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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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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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n"
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]
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},
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{
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"data": {
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"text/plain": [
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"PhaseNet(\n",
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" (inc): Conv1d(3, 8, kernel_size=(7,), stride=(1,), padding=same)\n",
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" (in_bn): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (down_branch): ModuleList(\n",
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" (0): ModuleList(\n",
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" (0): Conv1d(8, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(8, 8, kernel_size=(7,), stride=(4,), padding=(3,), bias=False)\n",
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" (3): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (1): ModuleList(\n",
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" (0): Conv1d(8, 16, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (1): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(16, 16, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (3): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (2): ModuleList(\n",
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" (0): Conv1d(16, 32, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (1): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(32, 32, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (3): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (3): ModuleList(\n",
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" (0): Conv1d(32, 64, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (1): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(64, 64, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (3): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (4): ModuleList(\n",
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" (0): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (1): BatchNorm1d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2-3): 2 x None\n",
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" )\n",
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" )\n",
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" (up_branch): ModuleList(\n",
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" (0): ModuleList(\n",
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" (0): ConvTranspose1d(128, 64, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (1): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(128, 64, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (3): BatchNorm1d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (1): ModuleList(\n",
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" (0): ConvTranspose1d(64, 32, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (1): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(64, 32, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (3): BatchNorm1d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (2): ModuleList(\n",
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" (0): ConvTranspose1d(32, 16, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (1): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(32, 16, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (3): BatchNorm1d(16, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" (3): ModuleList(\n",
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" (0): ConvTranspose1d(16, 8, kernel_size=(7,), stride=(4,), bias=False)\n",
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" (1): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (2): Conv1d(16, 8, kernel_size=(7,), stride=(1,), padding=same, bias=False)\n",
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" (3): BatchNorm1d(8, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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" )\n",
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" )\n",
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" (out): Conv1d(8, 2, kernel_size=(1,), stride=(1,), padding=same)\n",
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" (softmax): Softmax(dim=1)\n",
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")"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model = train.load_model()\n",
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"\n",
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"run = wandb.init()\n",
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"artifact = run.use_artifact('epos/training_seisbench_models_on_igf_data/model:v113', type='model')\n",
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"artifact_dir = artifact.download()\n",
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"fname = artifact_dir + \"/\" + os.listdir(artifact_dir)[0]\n",
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"\n",
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"model.load_state_dict(torch.load(fname))\n",
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"model.eval()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "75cadd4b-7a0e-44cd-acc6-fef26f2cf551",
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = '../../../data/igf/seisbench_format'\n",
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"sampling_rate = 100\n",
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"data = sbd.WaveformDataset(data_path, sampling_rate=sampling_rate)\n",
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"data.filter(data.metadata.trace_Pg_arrival_sample.notna())\n",
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"\n",
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"pick_mae = train.PickMAE(sampling_rate)\n",
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"splits = ['train', 'dev', 'test']\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "9cdb971d-a08e-49d8-97be-b6aaa53b083a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"All samples: 18002\n",
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"Training examples: 12444 69.1%\n",
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"Development examples: 2773 15.4%\n",
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"Test examples: 2785 15.5 %\n"
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]
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}
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],
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"source": [
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"all_samples = len(data.train()) + len(data.dev()) + len(data.test())\n",
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"print(f\"All samples: {all_samples}\")\n",
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"print(f\"Training examples: {len(data.train())} {len(data.train())/all_samples * 100:.1f}%\" )\n",
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"print(f\"Development examples: {len(data.dev())} {len(data.dev())/all_samples * 100:.1f}%\")\n",
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"print(f\"Test examples: {len(data.test())} {len(data.test())/all_samples * 100:.1f} %\")"
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]
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},
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{
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"cell_type": "markdown",
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||
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"id": "ccf454fe-e55a-47bc-882a-3e39395147a5",
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"metadata": {},
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"source": [
|
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"### Calculate overall model performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4c7b4137-4599-4599-9e3e-064145bfccb4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"Model resutls for train set\n",
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"train (12444, 17) 100\n",
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"using random window\n",
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"Test avg loss: 0.025075\n",
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"Test avg mae: 0.046459\n",
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"\n",
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"\n",
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"\n",
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"Model resutls for dev set\n",
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"dev (2773, 17) 100\n",
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"using random window\n",
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"Test avg loss: 0.025158\n",
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"Test avg mae: 0.049454\n",
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"\n",
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"\n",
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"\n",
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"Model resutls for test set\n",
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"test (2785, 17) 100\n",
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"using random window\n",
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"Test avg loss: 0.025469\n",
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|
|
"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": {
|
||
|
|
"image/png": "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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": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"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"
|
||
|
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]
|
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},
|
||
|
|
{
|
||
|
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"data": {
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"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": [
|
||
|
|
"53 0.45\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": [
|
||
|
|
"110 0.26\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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|
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"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": "iVBORw0KGgoAAAANSUhEUgAABNIAAAORCAYAAAA3ZI+fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeZgU1dk28Lu7Z2UZQNkFATUxmiC8EkUTFRcirtEkLpjkVTAu0eASEv3UVwWJiXGJ4o6iIm5xi0jcWAUVBRERkFX2fYZ1Fmbr6e7z/dFzqk6dOlVd3dMz48D9y+UF011dVb3MhLrnec4TEkIIEBERERERERERka9wc58AERERERERERFRS8AgjYiIiIiIiIiIKAAGaURERERERERERAEwSCMiIiIiIiIiIgqAQRoREREREREREVEADNKIiIiIiIiIiIgCYJBGREREREREREQUAIM0IiIiIiIiIiKiABikERERERERERERBcAgjYjoANS7d28MGzbM+nr27NkIhUKYPXt21o4RCoUwevTorO0v2x588EEcdthhiEQi6N+/f3OfzvfS9/09zJaXX34ZP/rRj5Cbm4v27ds39+l8r5166qk49dRTm/s0iIiIiJoNgzQioib24osvIhQKWf8VFBTghz/8IUaMGIGSkpLmPr20fPjhhy0yaJk2bRpuvfVW/PznP8eECRPwj3/8o7lPierNmTPH+t7YtWuX6/6tW7fikksuQfv27VFUVIQLLrgA69aty/h4K1euxLBhw3D44Ydj/PjxePbZZxty+t8L//jHP/Duu+9m/Pjly5dj9OjR2LBhQ9bOqaFk2C//i0Qi6Ny5My666CKsWLECAFBaWopu3brh5z//OYQQrn3MmzcP4XAYt9xyCwBg9OjRCIVC6NKlC6qqqlzb9+7dG+edd57jNvUcQqEQioqKMGjQIHzwwQeBnse2bdswevRoLFq0KM1XID2vvfYaxo4d26jH8NLQz9/+4Omnn8bFF1+MQw89FKFQyPGLq1RWrlyJW2+9Ff3790fbtm3RrVs3nHvuuViwYIFr20mTJmHIkCHo3r078vPz0aNHD1x00UVYunRpFp8NERHpcpr7BIiIDlRjxoxBnz59UFNTgzlz5uDpp5/Ghx9+iKVLl6JVq1ZNei6nnHIKqqurkZeXl9bjPvzwQzz55JPGMK26uho5Od/P/5v5+OOPEQ6H8fzzz6f9nKnxJBIJ3HDDDWjdujUqKytd9+/btw+nnXYaysrKcMcddyA3NxePPPIIBg0ahEWLFuHggw9O+5izZ89GIpHAo48+iiOOOCIbT6PZ/eMf/8BFF12ECy+8MKPHL1++HPfccw9OPfVU9O7d23HftGnTGn6CDXDjjTfiuOOOQ11dHZYsWYJx48Zh9uzZWLp0Kbp27YqxY8di6NChGD9+PK655hrrcbFYDH/84x/Rq1cv3HPPPY597tixA08//TT+8pe/BDqHX/ziF7j88sshhMDGjRvx9NNP4/zzz8dHH32EIUOG+D5227ZtuOeee9C7d+9GrYR97bXXsHTpUtx8882NdgwvDf387Q/uv/9+VFRU4Pjjj8f27dvTeuxzzz2H559/Hr/5zW9w/fXXo6ysDM888wxOOOEETJkyBYMHD7a2/fbbb9GhQwfcdNNN6NixI4qLi/HCCy/g+OOPx9y5c9GvX79sPzUiIgKDNCKiZnP22Wfjpz/9KQDgqquuwsEHH4yHH34YkydPxmWXXWZ8TGVlJVq3bp31cwmHwygoKMjqPrO9v2zasWMHCgsLsxaiCSFQU1ODwsLCrOzvQPXss89i8+bNuOqqq/Doo4+67n/qqaewevVqzJ8/H8cddxyA5PfRT37yE/zrX//KqLJwx44dAJCypZPvcVJzB88nn3wyLrroIuvrI488Etdddx1eeukl3Hrrrbj00ksxceJE3HbbbbjgggvQpUsXAMCjjz6KxYsX48MPP3T9oqJ///548MEHcf311wd6f3/4wx/i97//vfX1b37zGxx99NF49NFHUwZpdGD45JNPrGq0Nm3apPXYyy67DKNHj3Y87sorr8RRRx2F0aNHO4K0u+++2/X4q666Cj169MDTTz+NcePGZf4kiIjIE1s7iYi+J04//XQAwPr16wEAw4YNQ5s2bbB27Vqcc845aNu2LX73u98BSFbujB07Fj/+8Y9RUFCALl264Nprr8XevXsd+xRC4N5770WPHj3QqlUrnHbaaVi2bJnr2F5rpH355Zc455xz0KFDB7Ru3RrHHHOMFXAMGzYMTz75JABnu5NkWl/rm2++wdlnn42ioiK0adMGZ5xxBubNm+fYRra+fv755xg5ciQ6deqE1q1b41e/+hV27tzp2HbBggUYMmQIOnbsiMLCQvTp0wdXXnml7+scCoUwYcIEVFZWWuf84osvAkhWrfztb3/D4Ycfjvz8fPTu3Rt33HEHamtrHfuQLV9Tp07FT3/6UxQWFuKZZ57xPObq1avxm9/8Bl27dkVBQQF69OiBoUOHoqyszNpmwoQJOP3009G5c2fk5+fj6KOPxtNPP+3alzz27NmzrWP37dvXeu/eeecd9O3bFwUFBRgwYAC++eYbx+Pl52rdunUYMmQIWrduje7du2PMmDHGdjjd1q1bceWVV6JLly7Iz8/Hj3/8Y7zwwguu7TZt2oSVK1em3J+0Z88e3HnnnRgzZoxnqPX222/juOOOs0I0APjRj36EM844A2+++WbgY0m9e/fGqFGjAACdOnVyfGb93uN169bh4osvxkEHHYRWrVrhhBNOcLX2ye+pN998E/fccw8OOeQQtG3bFhdddBHKyspQW1uLm2++GZ07d0abNm0wfPhw1+fMJNVnKRQKobKyEhMnTrQ+37KtbOPGjbj++utx5JFHorCwEAcffDAuvvhiRwvniy++iIsvvhgAcNppp1n7kJ8v0xppO3bswB/+8Ad06dIFBQUF6NevHyZOnOjYZsOGDQiFQnjooYfw7LPPWt9jxx13HL766quUz9vLySefDABYu3atddtTTz2F2tpajBw5EgCwefNmjB49GpdeeinOPvts1z7uvvtulJSUGL/fgjjqqKPQsWNHxzmYzJ492/rsDh8+3PXzB0j+zD3rrLPQrl07tGrVCoMGDcLnn3/u2E9FRQVuvvlm9O7dG/n5+ejcuTN+8YtfYOHChQCS79EHH3yAjRs3WsfQKwt106dPx0knnYT27dujTZs2OPLII3HHHXc4tqmtrcWoUaNwxBFHID8/Hz179sStt97q+Nz6ff7S0dCfc0uWLMGwYcNw2GGHoaCgAF27dsWVV16J3bt3u47VGD/TevXq5fj/w3QMGDDAFb4dfPDBOPnkk602Zj+dO3dGq1atUFpa6rj99ddfx4ABA9C2bVsUFRWhb9++xl9YEBFRaqxIIyL6npAXYWp7WiwWw5AhQ3DSSSfhoYcesioprr32Wrz44osYPnw4brzxRqxfvx5PPPEEvvnmG3z++efIzc0FkLxAvPfee3HOOefgnHPOwcKFC3HmmWciGo2mPJ/p06fjvPPOQ7du3XDTTTeha9euWLFiBd5//33cdNNNuPbaa7Ft2zZMnz4dL7/8csr9LVu2DCeffDKKiopw6623Ijc3F8888wxOPfVUfPLJJxg4cKBj+xtuuAEdOnTAqFGjsGHDBowdOxYjRozAG2+8ASB58X7mmWeiU6dOuO2229C+fXts2LAB77zzju95vPzyy3j22Wcxf/58PPfccwCAn/3sZwCSv8mfOHEiLrroIvzlL3/Bl19+ifvuuw8rVqzApEmTHPtZtWoVLrvsMlx77bW4+uqrceSRRxqPF41GMWTIENTW1uKGG25A165dsXXrVrz//vsoLS1Fu3btACTX1Pnxj3+MX/7yl8jJycF7772H66+/HolEAn/6058c+1yzZg1++9vf4tprr8Xvf/97PPTQQzj//PMxbtw43HHHHbj++usBAPfddx8uueQSrFq1CuGw/buzeDyOs846CyeccAIeeOABTJkyBaNGjUIsFsOYMWM8X7uSkhKccMIJCIVCGDFiBDp16oSPPvoIf/jDH1B
|
||
|
|
"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": {
|
||
|
|
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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": {
|
||
|
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"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": {
|
||
|
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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": {
|
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|
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"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": {
|
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|
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"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": {
|
||
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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": {
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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": {
|
||
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"image/png": "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
|
||
|
|
"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
|
||
|
|
}
|