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Commit f6029975 authored by Jean-Marie Lepioufle's avatar Jean-Marie Lepioufle
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typo

parent f2f5d696
The MIT License (MIT)
Copyright © 2020 NILU
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# mtsaq
A package for testing AQ forecasting with different DL models
Package under dev: changes might occur at anytime.
# jupyter with mtsaq on docker
```bash
docker pull jmll/jupyter.aqdl:0.1
docker run -p 8888:8888 jmll/jupyter.aqdl:0.1
```
from typing import Dict
from datetime import datetime
from precx.utils import get_random_alphanumeric_string
from mtsaq.utils import get_random_alphanumeric_string
class class_model():
def __init__(self, params: dict):
......
#from precx.models.linear_regression import simple_decode
#from precx.models.transformer_basic import greedy_decode
from mtsaq.models.linear_regression import simple_decode
from mtsaq.models.transformer_basic import greedy_decode
decoder_dict = {"greedy_decode": greedy_decode, "simple_decode": simple_decode}
#from precx.models.transformer.multi_head_base import MultiAttnHeadSimple
#from precx.models.transformer.transformer_basic import SimpleTransformer, CustomTransformerDecoder
#from precx.models.transformer.transformer_xl import TransformerXL
#from precx.models.transformer.dummy_torch import DummyTorchModel
#from precx.models.lstm.lstm import LSTM_mts
#from precx.models.linear_regression.linear_regression import SimpleLinearModel
#from precx.models.da_rnn.model import DARNN
#from precx.models.autoencoder.basic_ae import AE
#from mtsaq.models.transformer.multi_head_base import MultiAttnHeadSimple
from mtsaq.models.transformer.transformer_basic import SimpleTransformer, CustomTransformerDecoder
#from mtsaq.models.transformer.transformer_xl import TransformerXL
#from mtsaq.models.transformer.dummy_torch import DummyTorchModel
from mtsaq.models.lstm.lstm import LSTM_mts
from mtsaq.models.linear_regression.linear_regression import SimpleLinearModel
from mtsaq.models.da_rnn.model import DARNN
from mtsaq.models.autoencoder.basic_ae import AE
import torch
......@@ -14,11 +14,11 @@ Utility dictionaries to map a string to a class
"""
dictionnary_model = {
#"MultiAttnHeadSimple": MultiAttnHeadSimple,
#"SimpleTransformer": SimpleTransformer,
"SimpleTransformer": SimpleTransformer,
#"TransformerXL": TransformerXL,
#"DummyTorchModel": DummyTorchModel,
"LSTM_mts": LSTM_mts #,
#"SimpleLinearModel": SimpleLinearModel,
"SimpleLinearModel": SimpleLinearModel,
#"CustomTransformerDecoder": CustomTransformerDecoder,
#"DARNN": DARNN,
#"BasicAE": AE
......
from torch.optim import Adam, SGD
from precx.optim.optim import BertAdam
from mtsaq.optim.optim import BertAdam
from torch.nn import MSELoss, SmoothL1Loss, PoissonNLLLoss, L1Loss
from precx.optim.optim import RMSELoss, MAPELoss
from precx.optim.dilate_loss import DilateLoss
from mtsaq.optim.optim import RMSELoss, MAPELoss
from mtsaq.optim.dilate_loss import DilateLoss
optim_dict = {"Adam": Adam, "SGD": SGD, "BertAdam": BertAdam}
......
from setuptools import setup
import os
with open("README.md", "r") as fh:
long_description = fh.read()
library_folder = os.path.dirname(os.path.realpath(__file__))
requirementPath = f'{library_folder}/requirements.txt'
install_requires = []
if os.path.isfile(requirementPath):
with open(requirementPath) as f:
install_requires = f.read().splitlines()
dev_requirements = []
setup(
name='mtsaq',
version='0.01dev',
author="Jean-Marie Lepioufle",
author_email="jml@nilu.no",
packages=[
'mtsaq',
'mtsaq.class',
'mtsaq.criterion',
"mtsaq.dictionnary",
"mtsaq.models",
"mtsaq.optim",
"mtsaq.utils"],
license='MIT + Copyright NILU',
description='A package for testing AQ forecasting with different DL models',
long_description = long_description,
url="https://git.nilu.no/aqdl/mtsaq",
python_requires='>=3.6',
install_requires=install_requires,
extras_require={
'dev': dev_requirements})
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