Drop exclude_file parameters and functions

river_dl/preproc_utils.py

@@ -276,102 +276,6 @@ def reshape_for_training(data):
     return np.reshape(data, [n_batch * n_seg, seq_len, n_feat])
 
 
-def get_exclude_start_end(exclude_grp):
-    """
-    get the start and end dates for the exclude group
-    :param exclude_grp: [dict] dictionary representing the exclude group from
-    the exclude yml file
-    :return: [tuple of datetime objects] start date, end date
-    """
-    start = exclude_grp.get("start_date")
-    if start:
-        start = datetime.datetime.strptime(start, "%Y-%m-%d")
-
-    end = exclude_grp.get("end_date")
-    if end:
-        end = datetime.datetime.strptime(end, "%Y-%m-%d")
-    return start, end
-
-
-def get_exclude_vars(exclude_grp):
-    """
-    get the variables_to_log to exclude for the exclude group
-    :param exclude_grp: [dict] dictionary representing the exclude group from
-    the exclude yml file
-    :return: [list] variables_to_log to exclude
-    """
-    variable = exclude_grp.get("variable")
-    if not variable or variable == "both":
-        return ["seg_tave_water", "seg_outflow"]
-    elif variable == "temp":
-        return ["seg_tave_water"]
-    elif variable == "flow":
-        return ["seg_outflow"]
-    else:
-        raise ValueError("exclude variable must be flow, temp, or both")
-
-
-def get_exclude_seg_ids(exclude_grp, all_segs):
-    """
-    get the segments to exclude
-    :param exclude_grp: [dict] dictionary representing the exclude group from
-    the exclude yml file
-    :param all_segs: [array] all of the segments. this is needed if we are doing
-    a reverse exclusion
-    :return: [list like] the segments to exclude
-    """
-    # ex_segs are the sites to exclude
-    if "seg_id_nats_ex" in exclude_grp.keys():
-        ex_segs = exclude_grp["seg_id_nats_ex"]
-    # exclude all *but* the "seg_id_nats_in"
-    elif "seg_id_nats_in" in exclude_grp.keys():
-        ex_mask = ~all_segs.isin(exclude_grp["seg_id_nats_in"])
-        ex_segs = all_segs[ex_mask]
-    else:
-        ex_segs = all_segs
-    return ex_segs
-
-
-def exclude_segments(y_data, exclude_segs):
-    """
-    exclude segments from being trained on by setting their weights as zero
-    :param y_data:[xr dataset] y_dataset data. this is used to get the dimensions
-    :param exclude_segs: [list] list of segments to exclude in the loss
-    calculation
-    :return:
-    """
-    weights = initialize_weights(y_data, 1)
-    for seg_grp in exclude_segs:
-        # get the start and end dates is present
-        start, end = get_exclude_start_end(seg_grp)
-        exclude_vars = get_exclude_vars(seg_grp)
-        segs_to_exclude = get_exclude_seg_ids(seg_grp, weights.seg_id_nat)
-
-        # loop through the data_vars
-        for v in exclude_vars:
-            # set those weights to zero
-            weights[v].load()
-            weights[v].loc[
-                dict(date=slice(start, end), seg_id_nat=segs_to_exclude)
-            ] = 0
-    return weights
-
-
-def initialize_weights(y_data, initial_val=1):
-    """
-    initialize all weights with a value.
-    :param y_data:[xr dataset] y_dataset data. this is used to get the dimensions
-    :param initial_val: [num] a number to initialize the weights with. should
-    be between 0 and 1 (inclusive)
-    :return: [xr dataset] dataset weights initialized with a uniform value
-    """
-    weights = y_data.copy(deep=True)
-    for v in y_data.data_vars:
-        weights[v].load()
-        weights[v].loc[:, :] = initial_val
-    return weights
-
-
 def reduce_training_data_random(
     data_file,
     train_start_date="1980-10-01",
@@ -600,7 +504,6 @@ def prep_y_data(
     time_idx_name="date",
     seq_len=365,
     log_vars=None,
-    exclude_file=None,
     normalize_y=True,
     y_type="obs",
     y_std=None,
@@ -637,7 +540,6 @@ def prep_y_data(
     sites will be witheld from training and validation
     :param seq_len: [int] length of sequences (e.g., 365)
     :param log_vars: [list-like] which variables_to_log (if any) to take log of
-    :param exclude_file: [str] path to exclude file
     :param normalize_y: [bool] whether or not to normalize the y_dataset values
     :param y_type: [str] "obs" if observations or "pre" if pretraining
     :param y_std: [array-like] standard deviations of y_dataset variables_to_log
@@ -683,12 +585,6 @@ def prep_y_data(
     if log_vars:
         y_trn = log_variables(y_trn, log_vars)
 
-    # filter pretrain/finetune y_dataset
-    if exclude_file:
-        exclude_segs = read_exclude_segs_file(exclude_file)
-        y_wgts = exclude_segments(y_trn, exclude_segs=exclude_segs)
-    else:
-        y_wgts = initialize_weights(y_trn)
     # scale y_dataset training data and get the mean and std
     # scale the validation partition to benchmark epoch performance
     if normalize_y:
@@ -713,9 +609,6 @@ def prep_y_data(
             "y_obs_trn": convert_batch_reshape(
                 y_trn, spatial_idx_name, time_idx_name, offset=trn_offset, seq_len=seq_len
             ),
-            "y_obs_wgts": convert_batch_reshape(
-                y_wgts, spatial_idx_name, time_idx_name, offset=trn_offset, seq_len=seq_len
-            ),
             "y_obs_val": convert_batch_reshape(
                 y_val, spatial_idx_name, time_idx_name, offset=tst_val_offset, seq_len=seq_len
             ),
@@ -768,7 +661,6 @@ def prep_all_data(
     dist_type="updown",
     catch_prop_file=None,
     catch_prop_vars=None,
-    exclude_file=None,
     log_y_vars=False,
     out_file=None,
     segs=None,
@@ -823,7 +715,6 @@ def prep_all_data(
     left unfilled, the catchment properties will not be included as predictors
     :param catch_prop_vars: [list of str] list of catchment properties to use. If
     left unfilled and a catchment property file is supplied all variables will be used.
-    :param exclude_file: [str] path to exclude file
     :param log_y_vars: [bool] whether or not to take the log of discharge in
     training
     :param segs: [list-like] which segments to prepare the data for
@@ -1005,7 +896,6 @@ def prep_all_data(
             time_idx_name=time_idx_name,
             seq_len=seq_len,
             log_vars=log_y_vars,
-            exclude_file=exclude_file,
             normalize_y=normalize_y,
             y_type="obs",
             trn_offset = trn_offset,
@@ -1028,7 +918,6 @@ def prep_all_data(
                 time_idx_name=time_idx_name,
                 seq_len=seq_len,
                 log_vars=log_y_vars,
-                exclude_file=exclude_file,
                 normalize_y=normalize_y,
                 y_type="pre",
                 y_std=y_obs_data["y_std"],
@@ -1053,7 +942,6 @@ def prep_all_data(
             time_idx_name=time_idx_name,
             seq_len=seq_len,
             log_vars=log_y_vars,
-            exclude_file=exclude_file,
             normalize_y=normalize_y,
             y_type="pre",
             trn_offset = trn_offset,
@@ -1118,35 +1006,3 @@ def prep_adj_matrix(infile, dist_type, dist_idx_name, segs=None, out_file=None):
         np.savez_compressed(out_file, dist_matrix=A_hat)
     return A_hat
 
-
-def read_exclude_segs_file(exclude_file):
-    """
-    read the exclude segs file. should be a yml file with start_date and list of
-    segments to exclude
-    --
-    example exclude file:
-
-    group_after_2017:
-        start_date: "2017-10-01"
-        variable: "temp"
-        seg_id_nats_ex:
-            - 1556
-            - 1569
-    group_2018_water_year:
-        start_date: "2017-10-01"
-        end_date: "2018-10-01"
-        seg_id_nats_ex:
-            - 1653
-    group_all_time:
-        seg_id_nats_in:
-            - 1806
-            - 2030
-
-    --
-    :param exclude_file: [str] exclude segs file
-    :return: [list] list of dictionaries of segments to exclude. dict keys must
-    have 'seg_id_nats' and may also have 'start_date' and 'end_date'
-    """
-    with open(exclude_file, "r") as s:
-        d = yaml.safe_load(s)
-    return [val for key, val in d.items()]

workflow_examples/Snakefile_basic.smk

@@ -49,7 +49,6 @@ rule prep_io_data:
                   spatial_idx_name='segs_test',
                   time_idx_name='times_test',
                   catch_prop_file=None,
-                  exclude_file=None,
                   train_start_date=config['train_start_date'],
                   train_end_date=config['train_end_date'],
                   val_start_date=config['val_start_date'],

workflow_examples/Snakefile_gwn.smk

@@ -61,7 +61,6 @@ rule prep_io_data:
             y_vars_pretrain=config['y_vars_pretrain'],
             y_vars_finetune=config['y_vars_finetune'],
             catch_prop_file=None,
-            exclude_file=None,
             train_start_date=config['train_start_date'],
             train_end_date=config['train_end_date'],
             val_start_date=config['val_start_date'],

workflow_examples/Snakefile_pretrain_LSTM.smk

@@ -54,7 +54,6 @@ rule prep_io_data:
                   spatial_idx_name='segs_test',
                   time_idx_name='times_test',
                   catch_prop_file=None,
-                  exclude_file=None,
                   train_start_date=config['train_start_date'],
                   train_end_date=config['train_end_date'],
                   val_start_date=config['val_start_date'],

workflow_examples/Snakefile_rgcn.smk

@@ -63,7 +63,6 @@ rule prep_io_data:
                   y_vars_pretrain=config['y_vars_pretrain'],
                   y_vars_finetune=config['y_vars_finetune'],
                   catch_prop_file=None,
-                  exclude_file=None,
                   train_start_date=config['train_start_date'],
                   train_end_date=config['train_end_date'],
                   val_start_date=config['val_start_date'],

workflow_examples/Snakefile_rgcn_hypertune.smk

@@ -67,7 +67,6 @@ rule prep_io_data:
             y_vars_pretrain=config['y_vars_pretrain'],
             y_vars_finetune=config['y_vars_finetune'],
             catch_prop_file=None,
-            exclude_file=None,
             train_start_date=config['train_start_date'],
             train_end_date=config['train_end_date'],
             val_start_date=config['val_start_date'],

workflow_examples/Snakefile_rgcn_pytorch.smk

@@ -61,7 +61,6 @@ rule prep_io_data:
                   y_vars_pretrain=config['y_vars_pretrain'],
                   y_vars_finetune=config['y_vars_finetune'],
                   catch_prop_file=None,
-                  exclude_file=None,
                   train_start_date=config['train_start_date'],
                   train_end_date=config['train_end_date'],
                   val_start_date=config['val_start_date'],