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In [1]:
%matplotlib inline
In [2]:
# from google.colab import drive
# drive.mount('/content/drive/')
# %cd /content/drive/MyDrive/Udem/Sem2/Representation_Learning/IFT6135_Programming/Pyraformer/transformer
Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount("/content/drive/", force_remount=True).
/content/drive/MyDrive/Udem/Sem2/Representation_Learning/IFT6135_Programming/Pyraformer/transformer
In [3]:
# !pip install black
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In [4]:
# !black estimator.py
All done! ✨ 🍰 ✨
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In [4]:
!pip install pytorch-lightning
!pip install gluonts
!pip install datasets
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In [5]:
%matplotlib inline
from matplotlib import pyplot as plt
import matplotlib.dates as mdates

from itertools import islice
In [7]:
from gluonts.evaluation import make_evaluation_predictions, Evaluator
from gluonts.dataset.repository.datasets import get_dataset

from estimator import PyraformerEstimator
/usr/local/lib/python3.7/dist-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.
  "Using `json`-module for json-handling. "
In [8]:
dataset = get_dataset("electricity")
In [11]:
estimator = PyraformerEstimator(
    freq=dataset.metadata.freq,
    prediction_length=dataset.metadata.prediction_length,
    num_feat_static_cat=1,
    cardinality=[321],
    single_step= True,
    batch_size=1,
    num_batches_per_epoch=100,
    trainer_kwargs=dict(max_epochs=1, accelerator='gpu', gpus=1
))
In [12]:
predictor = estimator.train(training_data=dataset.train,num_workers=2)
axis=-1 min_past=0 min_future=24 num_instances=1.0 total_length=0 n=0

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.
  f"Attribute {k!r} is an instance of `nn.Module` and is already saved during checkpointing."
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/parsing.py:262: UserWarning: Attribute 'loss' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss'])`.
  f"Attribute {k!r} is an instance of `nn.Module` and is already saved during checkpointing."
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/configuration_validator.py:133: UserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.
  rank_zero_warn("You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.")
Missing logger folder: /content/drive/MyDrive/Udem/Sem2/Representation_Learning/IFT6135_Programming/Pyraformer/transformer/lightning_logs
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:324: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
  timestamp = pd.Timestamp(timestamp_input, freq=freq)
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:327: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  if isinstance(timestamp.freq, Tick):
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:329: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  timestamp.floor(timestamp.freq), timestamp.freq
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:329: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
  timestamp.floor(timestamp.freq), timestamp.freq
/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:343: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  self._freq_base = start.freq.base

  | Name  | Type              | Params
--------------------------------------------
0 | model | PyraformerSSModel | 6.6 M 
1 | loss  | SingleStepLoss    | 0     
--------------------------------------------
6.6 M     Trainable params
0         Non-trainable params
6.6 M     Total params
26.580    Total estimated model params size (MB)
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:324: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
  timestamp = pd.Timestamp(timestamp_input, freq=freq)
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:327: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  if isinstance(timestamp.freq, Tick):
/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  return _shift_timestamp_helper(ts, ts.freq, offset)
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:329: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  timestamp.floor(timestamp.freq), timestamp.freq
/usr/local/lib/python3.7/dist-packages/gluonts/dataset/common.py:329: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
  timestamp.floor(timestamp.freq), timestamp.freq
/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:386: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  if self._full_range_date_features is not None
/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:343: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  self._freq_base = start.freq.base
/usr/local/lib/python3.7/dist-packages/gluonts/transform/split.py:36: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  return _shift_timestamp_helper(ts, ts.freq, offset)
/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:340: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  self._freq_base is None or self._freq_base == start.freq.base
/usr/local/lib/python3.7/dist-packages/gluonts/transform/feature.py:386: FutureWarning: Timestamp.freq is deprecated and will be removed in a future version
  if self._full_range_date_features is not None

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-12-9895b8213728> in <module>()
----> 1 predictor = estimator.train(training_data=dataset.train,num_workers=2)

/usr/local/lib/python3.7/dist-packages/gluonts/torch/model/estimator.py in train(self, training_data, validation_data, num_workers, shuffle_buffer_length, cache_data, ckpt_path, **kwargs)
    195             cache_data=cache_data,
    196             ckpt_path=ckpt_path,
--> 197             **kwargs,
    198         ).predictor

/usr/local/lib/python3.7/dist-packages/gluonts/torch/model/estimator.py in train_model(self, training_data, validation_data, num_workers, shuffle_buffer_length, cache_data, ckpt_path, **kwargs)
    159             train_dataloaders=training_data_loader,
    160             val_dataloaders=validation_data_loader,
--> 161             ckpt_path=ckpt_path,
    162         )
    163 

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    767         self.strategy.model = model
    768         self._call_and_handle_interrupt(
--> 769             self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
    770         )
    771 

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _call_and_handle_interrupt(self, trainer_fn, *args, **kwargs)
    719                 return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
    720             else:
--> 721                 return trainer_fn(*args, **kwargs)
    722         # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7
    723         except KeyboardInterrupt as exception:

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    807             ckpt_path, model_provided=True, model_connected=self.lightning_module is not None
    808         )
--> 809         results = self._run(model, ckpt_path=self.ckpt_path)
    810 
    811         assert self.state.stopped

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _run(self, model, ckpt_path)
   1220             self._call_lightning_module_hook("on_fit_start")
   1221 
-> 1222         self._log_hyperparams()
   1223 
   1224         if self.strategy.restore_checkpoint_after_setup:

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/trainer.py in _log_hyperparams(self)
   1288         for logger in self.loggers:
   1289             if hparams_initial is not None:
-> 1290                 logger.log_hyperparams(hparams_initial)
   1291             logger.log_graph(self.lightning_module)
   1292             logger.save()

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/rank_zero.py in wrapped_fn(*args, **kwargs)
     30     def wrapped_fn(*args: Any, **kwargs: Any) -> Optional[Any]:
     31         if rank_zero_only.rank == 0:
---> 32             return fn(*args, **kwargs)
     33         return None
     34 

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loggers/tensorboard.py in log_hyperparams(self, params, metrics)
    200         # format params into the suitable for tensorboard
    201         params = _flatten_dict(params)
--> 202         params = self._sanitize_params(params)
    203 
    204         if metrics is None:

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/loggers/tensorboard.py in _sanitize_params(params)
    315     @staticmethod
    316     def _sanitize_params(params: Dict[str, Any]) -> Dict[str, Any]:
--> 317         params = _utils_sanitize_params(params)
    318         # logging of arrays with dimension > 1 is not supported, sanitize as string
    319         return {k: str(v) if isinstance(v, (torch.Tensor, np.ndarray)) and v.ndim > 1 else v for k, v in params.items()}

/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/logger.py in _sanitize_params(params)
    128             params[k] = params[k].item()
    129         elif type(params[k]) not in [bool, int, float, str, torch.Tensor]:
--> 130             params[k] = str(params[k])
    131     return params
    132 

/usr/local/lib/python3.7/dist-packages/gluonts/core/component.py in validated_repr(self)
    307 
    308         def validated_repr(self) -> str:
--> 309             return dump_code(self)
    310 
    311         def validated_getnewargs_ex(self):

/usr/local/lib/python3.7/dist-packages/gluonts/core/serde/_repr.py in dump_code(o)
    113     """
    114 
--> 115     return as_repr(encode(o))
    116 
    117 

/usr/lib/python3.7/functools.py in wrapper(*args, **kw)
    838                             '1 positional argument')
    839 
--> 840         return dispatch(args[0].__class__)(*args, **kw)
    841 
    842     funcname = getattr(func, '__name__', 'singledispatch function')

/usr/local/lib/python3.7/dist-packages/gluonts/core/serde/_base.py in encode(v)
    218             # args need to be a list, since we encode tuples explicitly
    219             "args": encode(list(args)),
--> 220             "kwargs": encode(kwargs),
    221         }
    222 

/usr/lib/python3.7/functools.py in wrapper(*args, **kw)
    838                             '1 positional argument')
    839 
--> 840         return dispatch(args[0].__class__)(*args, **kw)
    841 
    842     funcname = getattr(func, '__name__', 'singledispatch function')

/usr/local/lib/python3.7/dist-packages/gluonts/core/serde/_base.py in encode(v)
    205 
    206     if isinstance(v, dict):
--> 207         return valmap(encode, v)
    208 
    209     if isinstance(v, type):

/usr/local/lib/python3.7/dist-packages/toolz/dicttoolz.py in valmap(func, d, factory)
     81     """
     82     rv = factory()
---> 83     rv.update(zip(d.keys(), map(func, d.values())))
     84     return rv
     85 

/usr/lib/python3.7/functools.py in wrapper(*args, **kw)
    838                             '1 positional argument')
    839 
--> 840         return dispatch(args[0].__class__)(*args, **kw)
    841 
    842     funcname = getattr(func, '__name__', 'singledispatch function')

/usr/local/lib/python3.7/dist-packages/gluonts/core/serde/_base.py in encode(v)
    241         pass
    242 
--> 243     raise RuntimeError(bad_type_msg.format(fqname_for(v.__class__)))
    244 
    245 

RuntimeError: Cannot serialize type torch.device. See the documentation of the `encode` and
`validate` functions at

    http://gluon-ts.mxnet.io/api/gluonts/gluonts.html

and the Python documentation of the `__getnewargs_ex__` magic method at

    https://docs.python.org/3/library/pickle.html#object.__getnewargs_ex__

for more information how to make this type serializable.
In [ ]:
# plt.figure(figsize=(20, 15))
# date_formater = mdates.DateFormatter('%b, %d')
# plt.rcParams.update({'font.size': 15})

# for idx, (forecast, ts) in islice(enumerate(zip(forecasts, tss)), 9):
#     ax = plt.subplot(3, 3, idx+1)

#     plt.plot(ts[-4 * dataset.metadata.prediction_length:], label="target", )
#     forecast.plot( color='g')
#     plt.xticks(rotation=60)
#     ax.xaxis.set_major_formatter(date_formater)

# plt.gcf().tight_layout()
# plt.legend()
# plt.show()
In [ ]:
# def plot_prob_forecasts(ts_entry, forecast_entry):
#     plot_length = 70
#     prediction_intervals = (50.0, 90.0)
#     legend = ["observations", "median prediction"] + [f"{k}% prediction interval" for k in prediction_intervals][::-1]

#     fig, ax = plt.subplots(1, 1, figsize=(10, 7))
#     ts_entry[-plot_length:].plot(ax=ax)  # plot the time series
#     forecast_entry.plot(prediction_intervals=prediction_intervals, color='g')
#     plt.grid(which="both")
#     plt.legend(legend, loc="best")
#     plt.show()
In [ ]:
# index = 123
# plot_prob_forecasts(tss[index], forecasts[index])
In [ ]:
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