:orphan:

:mod:`config`
=============

.. py:module:: config


Module Contents
---------------

.. function:: str2num(s)

   Converts parameter strings to appropriate types.

   :param s: Parameter value
   :type s: str
   :return: Appropriately converted value
   :rtype: Varying


.. function:: get_hparams(init_features: ('Architecture complexity [int]', 'option') = 16, in_days: ('Number of input days [int]', 'option') = 2, out_days: ('Number of output days [int]', 'option') = 1, epochs: ('Number of training epochs [int]', 'option') = 100, learning_rate: ('Maximum learning rate [float]', 'option') = 0.001, batch_size: ('Batch size of the input [int]', 'option') = 1, split: ('Test split fraction [float]', 'option') = 0.2, use_16bit: ('Use 16-bit precision for training (train only)', 'option') = True, gpus: ('Number of GPUs to use [int]', 'option') = 1, optim: ('Learning rate optimizer: one_cycle or cosine (train only) [str]', 'option') = 'one_cycle', dry_run: ('Use small amount of data for sanity check [Bool]', 'option') = False, find_lr: ('Automatically search for an ideal learning rate [Bool]', 'option') = False, search_bs: ('Scale the batch dynamically for full GPU usage [Bool]', 'option') = False, case_study: ('The case-study region to use for inference: australia, california, portugal, siberia, chile, uk [Bool/str]', 'option') = False, clip_output: ('Limit the inference to the datapoints within supplied range (e.g. 0.5,60) [Bool/list]', 'option') = False, boxcox: ('Apply boxcox transformation with specified lambda while training and the inverse boxcox transformation during the inference. [Bool/float]', 'option') = 0.1182, binned: ('Show the extended metrics for supplied comma separated binned FWI value range (e.g. 0,15,70) [Bool/list]', 'option') = '0,5.2,11.2,21.3,38.0,50', round_to_zero: ('Round off the target values below the specified threshold to zero [Bool/float]', 'option') = False, isolate_frp: ('Exclude the isolated datapoints with FRP > 0 [Bool]', 'option') = False, date_range: ('Filter the data with specified date range in YYYY-MM-DD format. E.g. 2019-04-01,2019-05-01 [Bool/str]', 'option') = False, cb_loss: ('Use Class-Balanced loss with the supplied beta parameter [Bool/float]', 'option') = False, chronological_split: ('Do chronological train-test split in the specified ratio [Bool/float]', 'option') = False, undersample: ('Undersample the datapoints with smaller than specified FWI [Bool/float]', 'option') = False, model: ('Model to use: unet, unet_downsampled, unet_snipped, unet_tapered, unet_interpolated [str]', 'option') = 'unet_tapered', out: ('Output data for training: fwi_reanalysis or gfas_frp [str]', 'option') = 'fwi_reanalysis', benchmark: ('Benchmark the FWI-Forecast data against FWI-Reanalysis [Bool]', 'option') = False, smos_input: ('Use soil-moisture input data [Bool]', 'option') = 'False', forecast_dir: ('Directory containing the forecast data. Alternatively set $FORECAST_DIR [str]', 'option') = os.environ.get('FORECAST_DIR'), forcings_dir: ('Directory containing the forcings data Alternatively set $FORCINGS_DIR [str]', 'option') = os.environ.get('FORCINGS_DIR'), smos_dir: ('Directory containing the soil-moisture data Alternatively set $SMOS_DIR [str]', 'option') = os.environ.get('SMOS_DIR'), reanalysis_dir: ('Directory containing the reanalysis data. Alternatively set $REANALYSIS_DIR. [str]', 'option') = os.environ.get('REANALYSIS_DIR'), frp_dir: ('Directory containing the FRP data. Alternatively set $FRP_DIR. [str]', 'option') = os.environ.get('FRP_DIR'), mask: ('File containing the mask stored as the numpy array [str]', 'option') = 'src/dataloader/mask/reanalysis_mask.npy', comment: ('Used for logging [str]', 'option') = False, checkpoint_file: ('Path to the test model checkpoint [Bool/str]', 'option') = False)

   Process and print the dictionary of project wide arguments.

   :return: Dictionary containing configuration options.
   :rtype: dict


.. function:: create_config()

   Generates config file in yaml format from parsed arguments and saves to src/config/ dir.

   :return: hparams in Namespace
   :rtype: Namespace