coronawhy / task-ts Goto Github PK
View Code? Open in Web Editor NEWWork related to time series prediction and forecasting of Coronavirus
Work related to time series prediction and forecasting of Coronavirus
We would like to incorporate the BEAT-19 data into our models.
Acceptance Criteria
Based on the top models identified #53 we now want to identify how well the models perform on most recent data.
Depends on:
#53
Adding enhanced evaluation
Acceptance criteria:
From an epidemiological perspective it would be useful to see if weather data helps or hinders model performance. Also it would be useful to what features the model attends to.
We will need to prepare a one page PDF poster for our workshop on Global Health at ICML.
Acceptance Criteria
We want to gather static demographic data on the factors that may affect the number of cases/deaths for each county:
Population age groups
Disease prevalence
Income level
Population density
Mean distance to hospital
Primary industries
Race
etc
Acceptance criteria:
Our scraper currently depends on: covidatlas/li#619 . We may have to switch to JHU or another data source if this doesn't work.
@efawe
Current version of mobility data in master has data at region level.
Task-geo is update with latest mobility data at sub-region level.
Update the data crawler to reflect the latest change.
This issue only occurs when installing task-geo
from source which gives the latest but not stable version.
Need to refactor function to work at training/inference time for LSTM.
Using the results of #43 we need a formal report examining transfers.
Acceptance criteria
Continuation of #61 as google cloud functions was not practical financially.
Based on the results of #53 we want to compare results for our best models and their ability to accurately forecast versus California models. We also want to highlight our methodological differences and how our approach can enhance forecast versus California models
Acceptance criteria
In order to successfully examine pre-training we need to pre-train a large number of weights on river flow data.
Acceptance Criteria
Need to be able to utilize state dict and load partially for transfer learning
A central goal of our COVID-19 forecasting efforts is to deploy a that provides tangible value to public policy officials. In order to do this we need the following steps completed
Based on @wwymak comments we would like to try forecasting cases on a rolling weekly average. While this removes a degree of granularity it may ease problems with reporting issues.
Acceptance criteria:
The data frame should not be masked on county alone otherwise we get this problem. Many counties particularly in the US have the exact same name. Instead create a new column that concatenates county and state/province. This will ensure no counties wind up in same geo forecasting segment. This is causing really weird negative case numbers which LSTM does not understand.
Re-run experiment checklist to see how it alters confidence interval.
great code thanks
may you clarify :
will it work for multivariate time series?
1
where all values are continues values
2
or even will it work for multivariate time series where values are mixture of continues and categorical values
for example 2 dimensions have continues values and 3 dimensions are categorical values
color weight gender height age
1 black 56 m 160 34
2 white 77 f 170 54
3 yellow 87 m 167 43
4 white 55 m 198 72
5 white 88 f 176 32
As an organization we want to inform public policy makers and residents if their county could be at increased risk for an outbreak. In order to do this we need to have daily updated results on new data and a simple dashboard to display predicted cases along with the CI for a specific county. Targeted completion
Before integrating the BEAT-19 in #45 we need to explore the file structure
Acceptance Criteria:`
Public policy officials and epidemiologists could use information on how specific policies impact new cases. Specifically, we would like to be able to determine the casual impacts of masking and social distancing.
@aradhanacha has been having trouble running notebooks for experiments which blocks her progress on #43.
Based on the results of #43 I would like to write a formal report to examine the best models both in terms of test_loss and test_loss on the final week.
Acceptance criteria
/usr/local/lib/python3.6/dist-packages/pandas/core/strings.py in _validate(data)
2096
2097 if inferred_dtype not in allowed_types:
-> 2098 raise AttributeError("Can only use .str accessor with string values!")
2099 return inferred_dtype
2100
AttributeError: Can only use .str accessor with string values!
Acceptance criteria
Background: We need an objective way to compare our models to standard models such as IMHE, Yougang, etc. This poses difficulty as our current models operate with respect to forecasting new cases on a daily basis for counties. Many of these other models operate at the state level.
Acceptance criteria:
When looping through Wanda_sweeps there seems to be an error related to the validation loader.
Traceback (most recent call last):
File "/usr/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/usr/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.6/dist-packages/wandb/wandb_agent.py", line 64, in _start
function()
File "<ipython-input-14-43e09d654e98>", line 7, in <lambda>
wandb.agent(sweep_id, lambda: train_function("PyTorch", make_config_file(file_path2, len(region), weight_path=None)))
File "/content/github_aistream-peelout_flow-forecast/flood_forecast/trainer.py", line 33, in train_function
train_transformer_style(trained_model, params["training_params"], params["forward_params"])
File "/content/github_aistream-peelout_flow-forecast/flood_forecast/pytorch_training.py", line 65, in train_transformer_style
test = compute_validation(test_data_loader, model.model, epoch, model.params["dataset_params"]["forecast_length"], criterion, model.device, decoder_structure=True, use_wandb=use_wandb, val_or_test="test_loss")
File "/content/github_aistream-peelout_flow-forecast/flood_forecast/pytorch_training.py", line 108, in compute_validation
for src, targ in validation_loader:
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 345, in __next__
data = self._next_data()
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 384, in _next_data
index = self._next_index() # may raise StopIteration
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 339, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 200, in __iter__
for idx in self.sampler:
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/sampler.py", line 62, in __iter__
return iter(range(len(self.data_source)))
ValueError: __len__() should return >= 0
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