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wassname
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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import torch
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import pandas as pd
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import os
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import gpytorch
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import argparse
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import datetime
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from botorch.models import SingleTaskGP
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from botorch.optim.fit import fit_gpytorch_torch
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from gpytorch.likelihoods import GaussianLikelihood
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from gpytorch.mlls import ExactMarginalLogLikelihood
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from gpytorch.means import ConstantMean, LinearMean
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from gpytorch.kernels import SpectralMixtureKernel, MaternKernel, RBFKernel, ScaleKernel
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from voltron.means import EWMAMean, DEWMAMean, TEWMAMean
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from voltron.train_utils import LearnGPCV, TrainVolModel, TrainVoltMagpieModel, TrainBasicModel
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from voltron.models import VoltMagpie
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from voltron.means import LogLinearMean
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from voltron.rollout_utils import GeneratePrediction, Rollouts
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from voltron.data import make_ticker_list, DataGetter, GetStockHistory
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def BasicWindRollouts(train_x, train_y, test_x, kernel_name, mean_name='ewma', k=20,
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train_iters=600, nsample=1000):
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kernel_possibilities = {"sm": SpectralMixtureKernel,
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"matern": MaternKernel,
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"rbf": RBFKernel}
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kernel = kernel_possibilities[kernel_name.lower()]
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if kernel_name.lower() != "sm":
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kernel = ScaleKernel(kernel())
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else:
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kernel = kernel(num_mixtures=20)
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kernel.initialize_from_data_empspect(train_x, train_y.log())
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model = SingleTaskGP(
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train_x.view(-1,1),
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train_y.log().reshape(-1, 1),
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covar_module=kernel,
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likelihood=GaussianLikelihood()
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)
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mean_name = mean_name.lower()
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if mean_name == "loglinear":
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model.mean_module = LogLinearMean(1)
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model.mean_module.initialize_from_data(train_x, train_y.log())
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elif mean_name == 'linear':
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model.mean_module = LinearMean(1)
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elif mean_name == "constant":
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model.mean_module = ConstantMean()
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elif mean_name == "ewma":
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model.mean_module = EWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
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elif mean_name == "dewma":
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model.mean_module = DEWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
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elif mean_name == "tewma":
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model.mean_module = TEWMAMean(train_x, train_y.log(), k=k).to(train_x.device)
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model = model.to(train_x.device)
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mll = ExactMarginalLogLikelihood(model.likelihood, model)
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fit_gpytorch_torch(mll, options={'maxiter':train_iters, 'disp':False})
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if mean_name in ["loglinear", "constant", 'linear']:
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save_samples = model.posterior(test_x).sample(torch.Size((nsample,
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))).squeeze(-1).cpu().detach()
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else:
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save_samples = Rollouts(
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train_x, train_y, test_x, model, nsample=nsample, method = "nonvol"
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).cpu().detach()
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torch.cuda.empty_cache()
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del model
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return save_samples
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@@ -1,178 +0,0 @@
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import numpy as np
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import torch
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import pandas as pd
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import gpytorch
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import argparse
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import datetime
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import warnings
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import copy
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import os
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from voltron.data import make_ticker_list, GetStockHistory
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import sys
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sys.path.append("../calibration")
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from LSTMUtils import SequenceDataset, LSTM, TrainLSTM, LSTMRollouts, NLL
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from torch.utils.data import DataLoader
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from voltron.train_utils import LearnGPCV, TrainVolModel, TrainVoltMagpieModel, TrainBasicModel
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from voltron.rollout_utils import Rollouts
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from BasicWind import BasicWindRollouts
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import pickle as pkl
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def main(args):
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stn_names, stn_lonlat, full_data = pkl.load(open("./wind_data.p", 'rb'))
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use_cuda = False
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if torch.cuda.is_available():
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use_cuda = True
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stn = args.stn_idx
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ntest = args.forecast_horizon
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ntrain = args.ntrain
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n_test_times = args.n_test_times
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ntime = full_data[0].shape[0]
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test_idxs = torch.arange(ntrain, ntime-ntest,
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int((ntime-ntest-ntrain)/n_test_times))
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stn_idxs = list(stn_names.keys())
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if args.kernel == 'volt':
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train_x = torch.arange(ntrain-1).float()/365
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else:
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train_x = torch.arange(ntrain).float()/365
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test_x = torch.arange(ntrain, ntrain + ntest).float()/365
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if use_cuda:
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train_x, test_x = train_x.cuda(), test_x.cuda()
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savepath = "./saved-outputs/stn" + str(stn) + "/"
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stn_data = full_data[stn]
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stn_data[stn_data == -99.0] = 0.
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if stn_data.mean() != 0:
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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for last_day in test_idxs:
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# try:
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raw_y = stn_data[last_day-ntrain:last_day] + 1
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train_y = torch.FloatTensor(raw_y)
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if use_cuda:
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train_y = train_y.cuda()
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if args.kernel == 'volt':
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with gpytorch.settings.max_cholesky_size(2000):
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vol = LearnGPCV(train_x, train_y, train_iters=200,
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printing=False)
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vmod, vlh = TrainVolModel(train_x, vol,
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train_iters=500, printing=False)
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if args.mean == 'constant':
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voltron, lh = TrainVoltMagpieModel(train_x, train_y[1:],
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vmod, vlh, vol,
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printing=False,
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train_iters=200, mean_func="constant")
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vmod.eval();
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voltron.eval();
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voltron.vol_model.eval();
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theta = 0.01
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# for theta in [0., 0.01, 0.025, 0.05, 0.1]:
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temp_model = copy.deepcopy(voltron)
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with torch.no_grad():
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save_samples = Rollouts(train_x, train_y, test_x, temp_model,
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nsample=args.nsample, theta=theta)
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torch.save(save_samples, savepath + args.kernel + "_theta" + str(theta) +\
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"_" + str(last_day.item()) + ".pt")
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del temp_model
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else:
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for k in [400]:
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voltron, lh = TrainVoltMagpieModel(train_x, train_y[1:],
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vmod, vlh, vol,
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printing=False,
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train_iters=0, mean_func="ewma", k=k)
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vmod.eval();
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voltron.eval();
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voltron.vol_model.eval();
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for theta in [0.01]:
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temp_model = copy.deepcopy(voltron)
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with torch.no_grad():
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save_samples = Rollouts(train_x, train_y,
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test_x, temp_model,
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nsample=args.nsample, theta=theta)
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torch.save(save_samples, savepath + args.kernel + "_ema" + str(k) +\
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"_theta" + str(theta) +\
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"_" + str(last_day.item()) + ".pt")
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del temp_model
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del voltron, vmod, vol, vlh
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else:
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k=200
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rollouts = BasicWindRollouts(train_x, train_y, test_x,
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train_iters=args.train_epochs,
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kernel_name=args.kernel,
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mean_name=args.mean, k=k,
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nsample=200)
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torch.save(rollouts, savepath + args.kernel + "_" +\
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args.mean + str(k) + "_" + str(last_day.item()) + ".pt")
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print("stn ", stn, " idx ", last_day.item())
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torch.cuda.empty_cache()
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# except:
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# print("### BROKEN stn", stn, " idx", last_day, " ###")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--stn_idx",
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type=int,
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default=0,
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)
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parser.add_argument(
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"--mean",
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type=str,
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default='constant',
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)
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parser.add_argument(
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"--n_test_times",
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type=int,
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default=10,
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)
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parser.add_argument(
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"--forecast_horizon",
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type=int,
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default=100,
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)
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parser.add_argument(
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"--kernel",
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type=str,
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default="matern",
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)
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parser.add_argument(
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"--ntrain",
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type=int,
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default=400,
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)
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parser.add_argument(
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"--nsample",
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type=int,
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default=1000,
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)
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parser.add_argument(
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"--printing",
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type=bool,
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default=False
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)
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parser.add_argument(
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"--train_epochs",
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type=int,
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default=500,
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)
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parser.add_argument(
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"--save",
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type=bool,
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default=False,
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)
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args = parser.parse_args()
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main(args)
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@@ -1,15 +0,0 @@
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This directory contains the code needed to run Volt+Magpie on wind speed data taken from the [U.S. Climate Reference Network](https://www.ncei.noaa.gov/access/crn/).
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To source the data first walk through the `make_wind_dataset` notebook.
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To generate forecasts for a station then run
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```{bash}
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python GPGenerator.py
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--kernel={volt, sm, matern} ## kernel choice
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--stn_idx=0 ## station index in the dataset
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--mean={ewma, constant} ## mean choice
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--ntrain=400 ## training window
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--n_test_times=100 ## number of test time points
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```
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