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+
+Sequence to Sequence Models for Timeseries Regression¶
In this notebook we are going to tackle a harder problem:
+-
+
- predicting the future on a timeseries +
- by outputing sequence of predictions +
- with rough uncertainty (uncalibrated) +
- using forecasted information (like weather report, week, or cycle of the moon) +
Not many papers benchmark movels for multivariate regression, much less seq prediction with uncertainty. So this notebook will try a range of models on a range of dataset.
+We do this using a sequence to sqequence interface
+
+
+
+
+
+-
+
- [ ] don't overfit
-
+
- bejing data has a problem +
- Current? +
+ - [ ] make overlap between past and future? +
- [ ] do n=5 runs +
+
+
+
+
+In [1]:
+
+
+
+
+
+
+# OPTIONAL: Load the "autoreload" extension so that code can change. But blacklist large modules
+%load_ext autoreload
+%autoreload 2
+%aimport -pandas
+%aimport -torch
+%aimport -numpy
+%aimport -matplotlib
+%aimport -dask
+%aimport -tqdm
+%matplotlib inline
+
+
+
+
+
+In [2]:
+
+
+
+
+
+
+# Imports
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.autograd import Variable
+import torch
+import torch.utils.data
+
+import xarray as xr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from pathlib import Path
+from tqdm.auto import tqdm
+
+import pytorch_lightning as pl
+
+
+
+
+
+In [3]:
+
+
+
+
+
+
+import holoviews as hv
+from holoviews import opts
+from holoviews.operation.datashader import datashade, dynspread
+hv.extension('bokeh', inline=True)
+from seq2seq_time.visualization.hv_ggplot import ggplot_theme
+hv.renderer('bokeh').theme = ggplot_theme
+
+# holoview datashader timeseries options
+%opts RGB [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Curve [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Scatter [width=800 height=200 show_grid=True active_tools=["xwheel_zoom"] default_tools=["xpan","xwheel_zoom", "reset", "hover"] toolbar="right"]
+%opts Layout [width=800 height=200]
+
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+ 
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+
+
+In [4]:
+
+
+
+
+
+
+from seq2seq_time.data.dataset import Seq2SeqDataSet, Seq2SeqDataSets
+from seq2seq_time.predict import predict, predict_multi
+from seq2seq_time.util import dset_to_nc
+
+
+
+
+
+In [5]:
+
+
+
+
+
+
+import logging
+import warnings
+import seq2seq_time.silence
+warnings.simplefilter('once')
+warnings.simplefilter(action='ignore', category=FutureWarning)
+warnings.simplefilter(action='ignore', category=DeprecationWarning)
+warnings.filterwarnings('ignore', 'Consider increasing the value of the `num_workers` argument', UserWarning)
+warnings.filterwarnings('ignore', 'Your val_dataloader has `shuffle=True`', UserWarning)
+
+from pytorch_lightning import _logger as log
+log.setLevel(logging.WARN)
+
+
+
+
+
+Parameters¶
+
+
+
+
+
+In [6]:
+
+
+
+
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f'using {device}')
+
+timestamp = pd.Timestamp.now().strftime("%Y%m%d-%H%M%S")
+print(timestamp)
+window_past = 48*2
+window_future = 48
+batch_size = 64
+num_workers = 5
+datasets_root = Path('../data/processed/')
+window_past
+
+
+
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+
+Out[6]:
+
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+In [ ]:
+
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+
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+
+
+
+Plot helpers¶
+
+
+
+
+
+In [7]:
+
+
+
+
+
+
+def hv_plot_std(d: xr.Dataset):
+ """Plot predictions 2 standard deviations."""
+ xf = d.t_target
+ yp = d.y_pred
+ s = d.y_pred_std
+ return hv.Spread((xf, yp, s * 2),
+ label='2*std').opts(alpha=0.5, line_width=0)
+
+def hv_plot_pred(d: xr.Dataset):
+ """Plot prediction mean"""
+ xf = d.t_target
+ yp = d.y_pred
+ return hv.Curve({'x': xf, 'y': yp})
+
+def hv_plot_true(d: xr.Dataset):
+ """Plot true past and future data seperated by red line."""
+ # Plot true
+ x = np.concatenate([d.t_past, d.t_target])
+ yt = np.concatenate([d.y_past, d.y_true])
+ p = hv.Scatter({
+ 'x': x,
+ 'y': yt
+ }, label='true').opts(color='black')
+
+
+ # plot a red line for now
+ now=pd.Timestamp(d.t_source.squeeze().values)
+ p *= hv.VLine(now, label='now').opts(color='red', framewise=True)
+
+ p = p.opts(
+ ylabel=str(ds_preds.attrs['targets']),
+ xlabel=f'{now}'
+ )
+
+ return p
+
+def hv_plot_prediction(d: xr.Dataset) -> hv.Layout:
+ """Plot a prediction into the future, at a single point in time."""
+ p = hv_plot_true(d)
+ p *= hv_plot_pred(d)
+ p *= hv_plot_std(d)
+ return p
+
+
+
+
+
+In [8]:
+
+
+
+
+
+
+def plot_performance(ds_preds, full=False):
+ """Multiple plots using xr_preds"""
+ p = hv_plot_prediction(ds_preds.isel(t_source=10))
+ display(p)
+
+ n = len(ds_preds.t_source)
+ d_ahead = ds_preds.mean(['t_source'])['nll'].groupby('t_ahead_hours').mean()
+ nll_vs_tahead = (hv.Curve(
+ (d_ahead.t_ahead_hours,
+ d_ahead)).redim(x='hours ahead',
+ y='nll').opts(
+ title=f'NLL vs time ahead (no. samples={n})'))
+ display(nll_vs_tahead)
+
+ # Make a plot of the NLL over time. Does this solution get worse with time?
+ if full:
+ d_source = ds_preds.mean(['t_ahead'])['nll'].groupby('t_source').mean()
+ nll_vs_time = (hv.Curve(d_source).opts(
+ title='Error vs time of prediction'))
+ display(nll_vs_time)
+
+ # A scatter plot is easy with xarray
+ if full:
+ tlim = (ds_preds.y_true.min().item(), ds_preds.y_true.max().item())
+ true_vs_pred = datashade(hv.Scatter(
+ (ds_preds.y_true,
+ ds_preds.y_pred))).redim(x='true', y='pred').opts(width=400,
+ height=400,
+ xlim=tlim,
+ ylim=tlim,
+ title='Scatter plot')
+ true_vs_pred = dynspread(true_vs_pred)
+ true_vs_pred
+ display(true_vs_pred)
+
+def read_hist(trainer: pl.Trainer):
+ metrics_file_path = Path(trainer.logger.experiment[-1].log_dir)/'..'/'metrics.csv'
+ try:
+ df_hist = pd.read_csv(metrics_file_path)
+ df_hist['epoch'] = df_hist['epoch'].ffill()
+ df_histe = df_hist.set_index('epoch').groupby('epoch').mean()
+ return df_histe
+ except Exception as e:
+ print(e)
+
+def plot_hist(trainer: pl.Trainer):
+ """If you used a CSVLogger you can load and plot history here"""
+ try:
+ df_histe = read_hist(trainer)
+ if len(df_histe)>1:
+ p = hv.Curve(df_histe, kdims=['epoch'], vdims=['loss/train']).relabel('train')
+ p *= hv.Curve(df_histe, kdims=['epoch'], vdims=['loss/val']).relabel('val')
+ display(p.opts(ylabel='loss'))
+ return df_histe
+ except Exception as e:
+ print(e)
+ pass
+
+
+
+
+
+In [9]:
+
+
+
+
+
+
+# helpers to display our results as a dataframe
+
+def df_bold_min(data):
+ '''
+ highlight the maximum in a Series or DataFrame
+
+
+ Usage:
+ `df.style.apply(df_bold_min)`
+ '''
+ attr = 'font-weight: bold'
+ #remove % and cast to float
+ data = data.replace('%','', regex=True).astype(float)
+ if data.ndim == 1: # Series from .apply(axis=0) or axis=1
+ is_min = data == data.min()
+ return [attr if v else '' for v in is_min]
+ else: # from .apply(axis=None)
+ is_min = data == data.min().min()
+ return pd.DataFrame(np.where(is_min, attr, ''),
+ index=data.index, columns=data.columns)
+
+def format_results(results, metric=None, sort=True):
+ df_results = pd.concat({k:pd.DataFrame(v) for k,v in results.items()}).T
+ if metric:
+ df_results = df_results.xs(metric, axis=1, level=1).rename_axis(columns=metric)
+ if sort is True:
+ df_results['mean(e-e_baseline)'] = (df_results - df_results.T.BaselineMean).mean(1)
+ df_results = df_results.sort_values('mean(e-e_baseline)')
+ return df_results
+
+def display_results(results, metric='nll', strformat="{:.2f}", sort=True):
+ df_results = format_results(results, metric=metric, sort=sort)
+
+ # display metric
+ display(df_results
+ .style.format(strformat)
+ .apply(df_bold_min)
+ )
+
+
+
+
+
+Datasets¶
From easy to hard, these dataset show different challenges, all of them with more than 20k datapoints and with a regression output. See the 00.01 notebook for more details, and the code for more information.
+Some such as MetroInterstateTraffic are easier, some are periodic such as BejingPM25, some are conditional on inputs such as GasSensor, and some are noisy and periodic like IMOSCurrentsVel
+ +
+
+
+
+
+In [10]:
+
+
+
+
+
+
+from seq2seq_time.data.data import IMOSCurrentsVel, AppliancesEnergyPrediction, BejingPM25, GasSensor, MetroInterstateTraffic
+datasets = [GasSensor, IMOSCurrentsVel, AppliancesEnergyPrediction, BejingPM25, MetroInterstateTraffic]
+datasets
+
+
+
+
+
+
+
+
+
+
+
+Out[10]:
+
+
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+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
+
+
+
+
+In [11]:
+
+
+
+
+
+
+# # View train, test, val splits
+# l = hv.Layout()
+# for dataset in datasets:
+# d = dataset(datasets_root)
+
+# p = dynspread(
+# datashade(hv.Scatter(d.df_train[d.columns_target[0]]),
+# cmap='red'))
+# p *= dynspread(
+# datashade(hv.Scatter(d.df_val[d.columns_target[0]]),
+# cmap='green'))
+# p *= dynspread(
+# datashade(hv.Scatter(d.df_test[d.columns_target[0]]),
+# cmap='blue'))
+# p = p.opts(title=f"{dataset.__name__}, n={len(d)}, freq={d.df.index.freq.freqstr}")
+# display(p)
+
+
+
+
+
+Lightning¶
We will use pytorch lightning to handle all the training scaffolding. We have a common pytorch lightning class that takes in the model and defines training steps and logging.
+ +
+
+
+
+
+In [12]:
+
+
+
+
+
+
+import pytorch_lightning as pl
+
+class PL_MODEL(pl.LightningModule):
+ def __init__(self, model, lr=3e-4, patience=None, weight_decay=0):
+ super().__init__()
+ self._model = model
+ self.lr = lr
+ self.patience = patience
+ self.weight_decay = weight_decay
+
+ def forward(self, x_past, y_past, x_future, y_future=None):
+ """Eval/Predict"""
+ y_dist, extra = self._model(x_past, y_past, x_future, y_future)
+ return y_dist, extra
+
+ def training_step(self, batch, batch_idx, phase='train'):
+ x_past, y_past, x_future, y_future = batch
+ y_dist, extra = self.forward(*batch)
+ loss = -y_dist.log_prob(y_future).mean()
+ self.log_dict({f'loss/{phase}':loss})
+ if ('loss' in extra) and (phase=='train'):
+ # some models have a special loss
+ loss = extra['loss']
+ self.log_dict({f'model_loss/{phase}':loss})
+ return loss
+
+ def validation_step(self, batch, batch_idx):
+ return self.training_step(batch, batch_idx, phase='val')
+
+ def configure_optimizers(self):
+ optim = torch.optim.AdamW(self.parameters(), lr=self.lr, weight_decay=self.weight_decay)
+ scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+ optim,
+ patience=self.patience,
+ verbose=True,
+ min_lr=1e-7,
+ ) if self.patience else None
+ return {'optimizer': optim, 'lr_scheduler': scheduler, 'monitor': 'loss/val'}
+
+
+
+
+
+In [13]:
+
+
+
+
+
+
+from torch.utils.data import DataLoader
+from pytorch_lightning.loggers import CSVLogger, WandbLogger, TensorBoardLogger, TestTubeLogger
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+from pytorch_lightning.callbacks import LearningRateMonitor
+
+
+
+
+
+Models¶
+
+
+
+
+
+In [14]:
+
+
+
+
+
+
+from seq2seq_time.models.baseline import BaselineLast, BaselineMean
+from seq2seq_time.models.lstm_seq2seq import LSTMSeq2Seq
+from seq2seq_time.models.lstm import LSTM
+from seq2seq_time.models.transformer import Transformer
+from seq2seq_time.models.transformer_seq2seq import TransformerSeq2Seq
+from seq2seq_time.models.neural_process import RANP
+from seq2seq_time.models.transformer_process import TransformerProcess
+from seq2seq_time.models.tcn import TCNSeq
+from seq2seq_time.models.inceptiontime import InceptionTimeSeq
+from seq2seq_time.models.xattention import CrossAttention
+
+
+
+
+
+In [15]:
+
+
+
+
+
+
+import gc
+
+def free_mem():
+ gc.collect()
+ torch.cuda.empty_cache()
+ gc.collect()
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
+
+
+
+
+In [16]:
+
+
+
+
+
+
+# PARAMS: model
+## Some datasets are easier, so we will vary the hidden size to predict overfitting
+hidden_size={'IMOSCurrentsVel': 8, #?
+ 'AppliancesEnergyPrediction': 8, # ?
+ 'BejingPM25': 8, # OK
+ 'GasSensor': 8, # OK
+ 'MetroInterstateTraffic': 16 # OK
+ }
+dropout=0.0
+layers=6
+nhead=4
+
+models = [
+# lambda xs, ys: BaselineLast(),
+ lambda xs, ys, hidden_size: BaselineMean(),
+ lambda xs, ys, hidden_size: Transformer(xs,
+ ys,
+ attention_dropout=dropout,
+ nhead=nhead,
+ nlayers=layers,
+ hidden_size=hidden_size),
+
+ lambda xs, ys, hidden_size:TransformerProcess(xs,
+ ys, hidden_size=hidden_size, nhead=nhead,
+ latent_dim=hidden_size//2, dropout=dropout,
+ nlayers=layers),
+ lambda xs, ys, hidden_size:TCNSeq(xs, ys, hidden_size=hidden_size, nlayers=layers, dropout=dropout, kernel_size=2),
+ lambda xs, ys, hidden_size: RANP(xs,
+ ys, hidden_dim=hidden_size, dropout=dropout,
+ latent_dim=hidden_size//2, n_decoder_layers=layers, n_latent_encoder_layers=layers, n_det_encoder_layers=layers),
+ lambda xs, ys, hidden_size: TransformerSeq2Seq(xs,
+ ys,
+ hidden_size=hidden_size,
+ nhead=nhead,
+ nlayers=layers,
+ attention_dropout=dropout
+ ),
+ lambda xs, ys, hidden_size: LSTM(xs,
+ ys,
+ hidden_size=hidden_size,
+ lstm_layers=layers//2,
+ lstm_dropout=dropout),
+ lambda xs, ys, hidden_size: LSTMSeq2Seq(xs,
+ ys,
+ hidden_size=hidden_size,
+ lstm_layers=layers//2,
+ lstm_dropout=dropout),
+ lambda xs, ys, hidden_size: CrossAttention(xs,
+ ys,
+ hidden_size=hidden_size,),
+ lambda xs, ys, hidden_size: InceptionTimeSeq(xs,
+ ys,
+ kernel_size=96,
+ layers=layers//2,
+ hidden_size=hidden_size,
+ bottleneck=hidden_size//4)
+
+]
+
+
+
+
+
+Lets summarize all models, and make sure they have a similar number of parameters
+ +
+
+
+
+
+In [18]:
+
+
+
+
+
+
+# Summarize each models shape and weights
+from seq2seq_time.torchsummaryX import summary
+
+# Get a batch
+Dataset = datasets[0]
+dataset = Dataset(datasets_root)
+ds_train, ds_val, ds_test = dataset.to_datasets(window_past=window_past,
+ window_future=window_future)
+dl_val = DataLoader(ds_val, batch_size=batch_size)
+batch = next(iter(dl_val))
+batch = [x.to(device).float() for x in batch]
+x_past, y_past, x_future, y_future = batch
+xs = x_past.shape[-1]
+ys = y_future.shape[-1]
+
+# summary of each model
+sizes=[]
+for m_fn in models:
+ pt_model = m_fn(xs, ys, 16).eval().to(device)
+ model_name = type(pt_model).__name__
+ with torch.no_grad():
+ df_summary, df_total = summary(pt_model, x_past, y_past, x_future, y_future, print_summary=False)
+ sizes.append(df_total.rename(columns={'Totals':model_name}))
+df_model_sizes = pd.concat(sizes, 1).T
+
+# Human readable numbers
+fmt=pd.io.formats.format.EngFormatter(use_eng_prefix=True)
+df_model_sizes_hr = df_model_sizes.apply(lambda x:x.apply(fmt))
+
+df_model_sizes_hr.to_markdown(open(f'../outputs/{timestamp}_models.md', 'w'))
+df_model_sizes_hr
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[18]:
+
+
+
+
+
+
+
+
+
+
+Train¶
+
+
+
+
+
+In [19]:
+
+
+
+
+
+
+from collections import defaultdict
+from seq2seq_time.metrics import rmse, smape
+
+
+
+
+
+In [20]:
+
+
+
+
+
+
+max_iters=20000
+
+
+
+
+
+In [21]:
+
+
+
+
+
+
+tensorboard_dir = Path(f"../outputs/{timestamp}").resolve()
+print(f'For tensorboard run:\ntensorboard --logdir="{tensorboard_dir}"')
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [22]:
+
+
+
+
+
+
+# DEBUG: sanity check
+
+for Dataset in datasets:
+ dataset_name = Dataset.__name__
+ dataset = Dataset(datasets_root)
+ ds_train, ds_val, ds_test = dataset.to_datasets(window_past=window_past,
+ window_future=window_future)
+
+ # Init data
+ x_past, y_past, x_future, y_future = ds_train.get_rows(10)
+ xs = x_past.shape[-1]
+ ys = y_future.shape[-1]
+
+ # Loaders
+ dl_train = DataLoader(ds_train,
+ batch_size=batch_size,
+ shuffle=True,
+ pin_memory=num_workers == 0,
+ num_workers=num_workers)
+ dl_val = DataLoader(ds_val,
+ shuffle=True,
+ batch_size=batch_size,
+ num_workers=num_workers)
+
+ for m_fn in models:
+ free_mem()
+ pt_model = m_fn(xs, ys, hidden_size[dataset_name])
+ model_name = type(pt_model).__name__
+ print(timestamp, dataset_name, model_name)
+
+ # Wrap in lightning
+ model = PL_MODEL(pt_model,
+ lr=3e-4
+ ).to(device)
+ trainer = pl.Trainer(
+ fast_dev_run=True,
+ # GPU
+ gpus=1,
+ amp_level='O1',
+ precision=16,
+ )
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+In [23]:
+
+
+
+
+
+
+results = defaultdict(dict)
+for Dataset in tqdm(datasets, desc='datasets'):
+ dataset_name = Dataset.__name__
+ dataset = Dataset(datasets_root)
+ ds_train, ds_val, ds_test = dataset.to_datasets(window_past=window_past,
+ window_future=window_future)
+
+ # Init data
+ x_past, y_past, x_future, y_future = ds_train.get_rows(10)
+ xs = x_past.shape[-1]
+ ys = y_future.shape[-1]
+
+ # Loaders
+ dl_train = DataLoader(ds_train,
+ batch_size=batch_size,
+ shuffle=True,
+ pin_memory=num_workers == 0,
+ num_workers=num_workers)
+ dl_val = DataLoader(ds_val,
+ shuffle=True,
+ batch_size=batch_size,
+ num_workers=num_workers)
+
+ for m_fn in tqdm(models, desc=f'models ({dataset_name})'):
+ try:
+ free_mem()
+ pt_model = m_fn(xs, ys, hidden_size[dataset_name])
+ model_name = type(pt_model).__name__
+ print(timestamp, dataset_name, model_name)
+
+ # Wrap in lightning
+ patience = 2
+ model = PL_MODEL(pt_model,
+ lr=3e-4, patience=patience,
+# weight_decay=4e-5
+ ).to(device)
+
+ # Trainer
+ save_dir = f"../outputs/{timestamp}/{dataset_name}"
+ name =f'{model_name}'
+ trainer = pl.Trainer(
+ # Training length
+ min_epochs=2,
+ max_epochs=100,
+ limit_train_batches=max_iters//batch_size,
+ limit_val_batches=max_iters//batch_size//5,
+ # Misc
+ gradient_clip_val=20,
+ terminate_on_nan=True,
+ # GPU
+ gpus=1,
+ amp_level='O1',
+ precision=16,
+ # Logging
+ default_root_dir=save_dir,
+ logger=[
+ TestTubeLogger(name=name, save_dir=save_dir)
+ ],
+ # Callbacks
+ callbacks=[
+ EarlyStopping(monitor='loss/val', patience=patience * 2),
+ LearningRateMonitor(logging_interval='epoch')
+ ],
+ )
+
+ # Train
+ trainer.fit(model, dl_train, dl_val)
+
+ ds_preds = predict(model.to(device),
+ ds_test,
+ batch_size * 2,
+ device=device,
+ scaler=dataset.output_scaler)
+
+# display(read_hist(trainer))
+
+ metrics = dict(
+ rmse=rmse(ds_preds.y_true, ds_preds.y_pred).item(),
+ smape=smape(ds_preds.y_true, ds_preds.y_pred).item(),
+ nll=ds_preds.nll.mean().item()
+ )
+ results[dataset_name][model_name] = metrics
+ display_results(results, 'nll', sort=False)
+
+ pred_path = Path(trainer.logger.experiment[-1].log_dir)/'..'/'ds_preds.nc'
+ dset_to_nc(ds_preds, pred_path)
+ model.cpu()
+ except Exception as e:
+ logging.exception('failed to run model')
+
+df_results = pd.concat({k:pd.DataFrame(v) for k,v in results.items()})
+display(df_results)
+
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+
+Leaderboard¶
+
+
+
+
+
+In [24]:
+
+
+
+
+
+
+print(f'Negative Log-Likelihood (NLL).\nover {window_future} steps')
+df_results = pd.concat({k:pd.DataFrame(v) for k,v in results.items()})
+display_results(results, 'nll')
+
+
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+In [25]:
+
+
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+
+
+
+def results_html(results, metric='nll', strformat="{:.2f}"):
+ df_results = format_results(results, metric=metric)
+ f = f'../outputs/{timestamp}_leaderboard.html'
+ print('saved to', f)
+ df_results.to_html(f, float_format=lambda n:strformat.format(n))
+
+ f = f'../outputs/{timestamp}_leaderboard.md'
+ print(f)
+ df_results.round(2).to_markdown(open(f, 'w'))
+results_html(results, 'nll')
+
+
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+
+
+
+Plots¶
-
+
- TODO make legends smaller +
- TODO either many batches, or plot of X steps ahead +
+
+
+
+
+In [26]:
+
+
+
+
+
+
+# Load saved preds
+ds_predss = defaultdict(dict)
+for Dataset in datasets:
+ dataset_name = Dataset.__name__
+ for m_fn in models:
+ pt_model = m_fn(xs, ys, hidden_size[dataset_name])
+ model_name = type(pt_model).__name__
+ save_dir = Path(f"../outputs")/timestamp/dataset_name/model_name
+
+ # Get latest checkpoint
+ fs = sorted(save_dir.glob("**/ds_preds.nc"))
+ if len(fs)>0:
+ ds_preds = xr.open_dataset(fs[-1])
+ ds_predss[dataset_name][model_name] = ds_preds
+
+
+
+
+
+In [27]:
+
+
+
+
+
+
+data_i = 300
+
+
+
+
+
+In [28]:
+
+
+
+
+
+
+# Plot mean of predictions
+n = hv.Layout()
+for dataset in ds_predss.keys():
+ d = next(iter(ds_predss[dataset].values())).isel(t_source=data_i)
+ p = hv_plot_true(d)
+ for model in ds_predss[dataset].keys():
+ ds_preds = ds_predss[dataset][model]
+ d = ds_preds.isel(t_source=data_i)
+ p *= hv_plot_pred(d).relabel(label=f"{model}")
+ n += p.opts(title=dataset, legend_position='top_left')
+n.cols(1).opts(shared_axes=False)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[28]:
+
+
+
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
+
+
+
+
+In [30]:
+
+
+
+
+
+
+dataset='IMOSCurrentsVel'
+data_i=844
+n = hv.Layout()
+for i, model in enumerate(ds_predss[dataset].keys()):
+ ds_preds = ds_predss[dataset][model]
+ d = ds_preds.isel(t_source=data_i)
+ p = hv_plot_true(d)
+ p *= hv_plot_pred(d).relabel('pred')
+ p *= hv_plot_std(d)
+ n += p.opts(title=f'{dataset} {model}', legend_position='top_left')
+n.cols(1)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[30]:
+
+
+
+
+
+
+
+
+
+
+In [39]:
+
+
+
+
+
+
+# 1/0
+
+
+
+
+
+In [33]:
+
+
+
+
+
+
+# Explore predictions with dynamic map
+
+def plot_predictions_ahead(dataset='IMOSCurrentsVel', t_ahead_i=6, start=0, window_steps=1800):
+ d = next(iter(ds_predss[dataset].values())).isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))
+
+ p = hv.Scatter({
+ 'x': d.t_target,
+ 'y': d.y_true
+ }, label='true').opts(color='black', framewise=True)
+ for model in results[dataset].keys():
+ ds_preds = ds_predss[dataset][model]
+ d = ds_preds.isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))
+ p *= hv.Curve({'x': d.t_target, 'y':d.y_pred}, label=model).relabel(label=f"{model}")
+
+ p = p.opts(title=f"Dataset: {dataset}, {d.freq}*{t_ahead_i} ahead", height=250, legend_position='top', ylabel=d.targets)
+ return p.opts(framewise=True)
+
+dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 't_ahead_i', 'start', 'window_steps'])
+dmap = dmap.redim.values(dataset=list(ds_predss.keys()))
+dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000))
+dmap = dmap.redim.default(t_ahead_i=10, window_steps=800)
+dmap
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[33]:
+
+
+
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+1/0
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+
+
+
+
+
+
+
+In [37]:
+
+
+
+
+
+
+# Explore predictions with dynamic map
+
+def plot_predictions_ahead(dataset='IMOSCurrentsVel', t_ahead_i=6, start=0, window_steps=1800):
+ ds_preds = ds_predss[dataset]
+ l = hv.Layout()
+ for model in ds_preds.keys():
+ d = ds_preds[model].isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))
+ x = d.t_target
+ y = d.y_pred
+ s = d.y_pred_std
+
+ p = hv.Scatter({
+ 'x': d.t_target,
+ 'y': d.y_true
+ }).opts(color='black')
+ p *= hv.Curve({'x': x, 'y':y})
+ p *= hv.Spread((x, y, s * 2)).opts(alpha=0.5, line_width=0)
+ l += p.opts(title=f"Dataset: {dataset}, model={model}, {d.freq}*{t_ahead_i} ahead", height=250, legend_position='top', ylabel=d.targets)
+ return l.cols(1)
+
+dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 't_ahead_i', 'start', 'window_steps'])
+dmap = dmap.redim.values(dataset=list(ds_predss.keys()))
+dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000))
+dmap = dmap.redim.default(t_ahead_i=10, window_steps=400, dataset='IMOSCurrentsVel')
+dmap
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Out[37]:
+
+
+
+
+
+
+
+
+
+
+In [36]:
+
+
+
+
+
+
+# def plot_at_i(time_i, dataset, model):
+# d = ds_predss[dataset][model].isel(t_source=time_i)
+# return hv_plot_prediction(d).relabel(label=f"{model}")
+
+# dmap = hv.DynamicMap(plot_at_i, kdims=['t_source', 'dataset', 'model'])
+# t = ds_preds.t_source.values
+# models = list(next(iter(ds_predss.values())).keys())
+# dmap = dmap.redim.values(
+# t_source=range(len(t)),
+# dataset=list(ds_predss.keys()),
+# model=models,
+# )
+# dmap.opts(framewise=True)
+
+
+
+
+
+In [31]:
+
+
+
+
+
+
+# plot_performance(ds_preds, full=True)
+
+
+
+
+
+In [38]:
+
+
+
+
+
+
+# # Explore predictions with dynamic map
+
+# def plot_predictions_ahead(dataset='IMOSCurrentsVel', model='', t_ahead_i=6, start=0, window_steps=1800):
+# d = next(iter(ds_predss[dataset].values())).isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))
+
+# p = hv.Scatter({
+# 'x': d.t_target,
+# 'y': d.y_true
+# }, label='true').opts(color='black', framewise=True)
+
+# ds_preds = ds_predss[dataset][model]
+# d = ds_preds.isel(t_ahead=t_ahead_i).isel(t_source=slice(start, start+window_steps))
+# x = d.t_target
+# y = d.y_pred
+# s = d.y_pred_std
+# p *= hv.Curve({'x': x, 'y':y}, label=model).relabel(label=f"{model}")
+# p *= hv.Spread((x, y, s * 2),
+# label='2*std').opts(alpha=0.5, line_width=0)
+
+# p = p.opts(title=f"Dataset: {dataset}, model={model}, {d.freq}*{t_ahead_i} ahead", height=250, legend_position='top', ylabel=d.targets)
+# return p.opts(framewise=True)
+
+# dmap = hv.DynamicMap(plot_predictions_ahead, kdims=['dataset', 'model', 't_ahead_i', 'start', 'window_steps'])
+# dmap = dmap.redim.values(dataset=list(ds_predss.keys()), model=models)
+# dmap = dmap.redim.range(t_ahead_i=(0, window_future), start=(0, 5000), window_steps=(10, 5000))
+# dmap = dmap.redim.default(t_ahead_i=10, window_steps=1000)
+# dmap
+
+
+
+
+
+In [ ]:
+
+
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+
+In [ ]:
+
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+
+In [ ]:
+
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+
+In [ ]:
+
+
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+
+
+