PyTorch Loaders¶

class flood_forecast.preprocessing.pytorch_loaders.CSVDataLoader(file_path: str, forecast_history: int, forecast_length: int, target_col: List, relevant_cols: List, scaling=None, start_stamp: int = 0, end_stamp: Optional[int] = None, gcp_service_key: Optional[str] = None, interpolate_param: bool = False, sort_column=None, scaled_cols=None, feature_params=None, id_series_col=None, no_scale=False)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(file_path: str, forecast_history: int, forecast_length: int, target_col: List, relevant_cols: List, scaling=None, start_stamp: int = 0, end_stamp: Optional[int] = None, gcp_service_key: Optional[str] = None, interpolate_param: bool = False, sort_column=None, scaled_cols=None, feature_params=None, id_series_col=None, no_scale=False)[source]¶

A data loader that takes a CSV file and properly batches for use in training/eval a PyTorch model :param file_path: The path to the CSV file you wish to use. :param forecast_history: This is the length of the historical time series data you wish to

utilize for forecasting

Parameters

forecast_length – The number of time steps to forecast ahead (for transformer this must equal history_length)
relevant_cols – Supply column names you wish to predict in the forecast (others will not be used)
target_col – The target column or columns you to predict. If you only have one still use a list [‘cfs’]
scaling – (highly reccomended) If provided should be a subclass of sklearn.base.BaseEstimator

and sklearn.base.TransformerMixin) i.e StandardScaler, MaxAbsScaler, MinMaxScaler, etc) Note without a scaler the loss is likely to explode and cause infinite loss which will corrupt weights :param start_stamp int: Optional if you want to only use part of a CSV for training, validation

or testing supply these

Parameters

int (end_stamp) – Optional if you want to only use part of a CSV for training, validation, or testing supply these
str (sort_column) – The column to sort the time series on prior to forecast.
scaled_cols – The columns you want scaling applied to (if left blank will default to all columns)
feature_params – These are the datetime features you want to create.
no_scale – This means that the end labels will not be scaled when running

inverse_scale(result_data: Union[torch.Tensor, pandas.core.series.Series, numpy.ndarray]) → torch.Tensor[source]¶

Un-does the scaling of the data

Parameters: result_data (Union[torch.Tensor, pd.Series, np.ndarray]) – The data you want to unscale can handle multiple data types.
Returns: Returns the unscaled data as PyTorch tensor.
Return type: torch.Tensor

class flood_forecast.preprocessing.pytorch_loaders.CSVTestLoader(df_path: str, forecast_total: int, use_real_precip=True, use_real_temp=True, target_supplied=True, interpolate=False, sort_column_clone=None, **kwargs)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(df_path: str, forecast_total: int, use_real_precip=True, use_real_temp=True, target_supplied=True, interpolate=False, sort_column_clone=None, **kwargs)[source]¶

Parameters: df_path (str) –

A data loader for the test data.

get_from_start_date(forecast_start: datetime.datetime)[source]¶

convert_real_batches(the_col: str, rows_to_convert)[source]¶: A helper function to return properly divided precip and temp values to be stacked with forecasted cfs.

convert_history_batches(the_col: Union[str, List[str]], rows_to_convert: pandas.core.frame.DataFrame)[source]¶

A helper function to return dataframe in batches of size (history_len, num_features)

Args:: the_col (str): column names rows_to_convert (pd.Dataframe): rows in a dataframe to be converted into batches

inverse_scale(result_data: Union[torch.Tensor, pandas.core.series.Series, numpy.ndarray]) → torch.Tensor¶

Un-does the scaling of the data

Parameters: result_data (Union[torch.Tensor, pd.Series, np.ndarray]) – The data you want to unscale can handle multiple data types.
Returns: Returns the unscaled data as PyTorch tensor.
Return type: torch.Tensor

class flood_forecast.preprocessing.pytorch_loaders.AEDataloader(file_path: str, relevant_cols: List, scaling=None, start_stamp: int = 0, target_col: Optional[List] = None, end_stamp: Optional[int] = None, unsqueeze_dim: int = 1, interpolate_param=False, forecast_history=1, no_scale=True, sort_column=None)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(file_path: str, relevant_cols: List, scaling=None, start_stamp: int = 0, target_col: Optional[List] = None, end_stamp: Optional[int] = None, unsqueeze_dim: int = 1, interpolate_param=False, forecast_history=1, no_scale=True, sort_column=None)[source]¶: A data loader class for autoencoders. Overrides __len__ and __getitem__ from generic dataloader. Also defaults forecast_history and forecast_length to 1. Since AE will likely only use one row. Same parameters as before.

inverse_scale(result_data: Union[torch.Tensor, pandas.core.series.Series, numpy.ndarray]) → torch.Tensor¶

Un-does the scaling of the data

Parameters: result_data (Union[torch.Tensor, pd.Series, np.ndarray]) – The data you want to unscale can handle multiple data types.
Returns: Returns the unscaled data as PyTorch tensor.
Return type: torch.Tensor

class flood_forecast.preprocessing.pytorch_loaders.TemporalLoader(time_feats: List[str], kwargs)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(time_feats: List[str], kwargs)[source]¶

[summary]

Parameters

time_feats (List[str]) – [description]
kwargs ([type]) – [description]

static df_to_numpy(pandas_stuff: pandas.core.frame.DataFrame)[source]¶

inverse_scale(result_data: Union[torch.Tensor, pandas.core.series.Series, numpy.ndarray]) → torch.Tensor¶

Un-does the scaling of the data

Parameters: result_data (Union[torch.Tensor, pd.Series, np.ndarray]) – The data you want to unscale can handle multiple data types.
Returns: Returns the unscaled data as PyTorch tensor.
Return type: torch.Tensor

class flood_forecast.preprocessing.pytorch_loaders.TemporalTestLoader(time_feats, kwargs={}, decoder_step_len=None)[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

__init__(time_feats, kwargs={}, decoder_step_len=None)[source]¶

[summary]

Parameters

time_feats ([type]) – [description]
kwargs (dict, optional) – [description], defaults to {}
decoder_step_len ([type], optional) – [description], defaults to None

convert_history_batches(the_col: Union[str, List[str]], rows_to_convert: pandas.core.frame.DataFrame)¶

A helper function to return dataframe in batches of size (history_len, num_features)

Args:: the_col (str): column names rows_to_convert (pd.Dataframe): rows in a dataframe to be converted into batches

convert_real_batches(the_col: str, rows_to_convert)¶: A helper function to return properly divided precip and temp values to be stacked with forecasted cfs.

get_from_start_date(forecast_start: datetime.datetime)¶

inverse_scale(result_data: Union[torch.Tensor, pandas.core.series.Series, numpy.ndarray]) → torch.Tensor¶

Un-does the scaling of the data

Parameters: result_data (Union[torch.Tensor, pd.Series, np.ndarray]) – The data you want to unscale can handle multiple data types.
Returns: Returns the unscaled data as PyTorch tensor.
Return type: torch.Tensor

static df_to_numpy(pandas_stuff: pandas.core.frame.DataFrame)[source]¶