RGCN workflows

Snakefile_insal_rgcn_pytorch.smk

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+import os
+import numpy as np
+import torch
+import torch.optim as optim
+
+#river-dl
+code_dir = config['code_dir']
+# if using river_dl installed with pip this is not needed
+import sys
+sys.path.insert(1, code_dir)
+
+from river_dl.preproc_utils import asRunConfig
+from river_dl.preproc_utils import prep_all_data
+from river_dl.torch_utils import train_torch
+from river_dl.torch_utils import rmse_masked
+from river_dl.evaluate import combined_metrics
+from river_dl.torch_models import RGCN_v1
+from river_dl.predict import predict_from_io_data
+
+#user-defined functions
+from utils import *
+
+out_dir = config['out_dir']
+
+#spatial holdout info
+train_segs = check_spatial_segs(config['train_segs_f'])
+val_segs = check_spatial_segs(config['val_segs_f'])
+test_segs = check_spatial_segs(config['test_segs_f'])
+
+rule all:
+	input:
+		f"{out_dir}/finetuned_weights.pth",
+		f"{out_dir}/finetune_log.csv",
+		expand("{outdir}/{metric_type}_metrics.csv",
+				outdir=out_dir,
+				metric_type=['overall', 'month', 'reach', 'month_reach', 'monthly_all_sites', 'monthly_site_based', 'monthly_reach', 'biweekly_all_sites', 'biweekly_site_based', 'biweekly_reach', 'year', 'year_reach', 'yearly_all_sites', 'yearly_site_based', 'yearly_reach'],
+		),
+		expand("{outdir}/asRunConfig.yml",  outdir = out_dir),
+		expand("{outdir}/Snakefile", outdir = out_dir),
+		f"{out_dir}/trn_preds_obs.csv",
+		f"{out_dir}/val_preds_obs.csv",
+		f"{out_dir}/tst_preds_obs.csv"
+
+#save the as-run config settings to a text file
+rule as_run_config:
+	group: "prep"
+	output:
+		"{outdir}/asRunConfig.yml"
+	run:
+		asRunConfig(config, code_dir, output[0])
+
+#save the as-run snakefile to the output
+rule copy_snakefile:
+	group: "prep"
+	output:
+		"{outdir}/Snakefile"
+	shell:
+		"""
+		scp Snakefile_insal_rgcn_pytorch.smk {output[0]}
+		"""
+
+rule prep_io_data:
+	group: "prep"
+	input:
+		config['attrs_file'],
+		config['obs_file'],
+		config['dist_matrix_file']
+	output:
+		"{outdir}/prepped.npz"
+	threads: 3
+	run:
+		prep_all_data(
+				x_data_file = input[0],
+				y_data_file = input[1],
+				distfile = input[2],
+				x_vars = txt_to_list(config['x_vars_file']),
+				spatial_idx_name = "PRMS_segid",
+				time_idx_name = "Date",
+				y_vars_finetune = config['y_vars'],
+				train_start_date = txt_to_list(config['train_start_date_f']),
+				train_end_date = txt_to_list(config['train_end_date_f']),
+				val_start_date = txt_to_list(config['val_start_date_f']),
+				val_end_date = txt_to_list(config['val_end_date_f']),
+				test_start_date = txt_to_list(config['test_start_date_f']),
+				test_end_date = txt_to_list(config['test_end_date_f']),
+				val_sites = val_segs,
+				test_sites = test_segs,
+				explicit_spatial_partition = True,
+				earliest_time = config['earliest_date'],
+				latest_time = config['latest_date'],
+				out_file = output[0],
+				trn_offset = config['trn_offset'],
+				tst_val_offset = config['tst_val_offset'],
+				seq_len = config['seq_len'],
+				log_y_vars = config['log_y_vars']
+				)
+
+
+rule finetune_train:
+	group: "train"
+	input:
+		"{outdir}/prepped.npz"
+	output:
+		"{outdir}/finetuned_weights.pth",
+		"{outdir}/finetune_log.csv"
+	threads: 3
+	run:
+		data = np.load(input[0], allow_pickle=True)
+		num_segs = len(np.unique(data['ids_trn']))
+		adj_mx = data['dist_matrix']
+		in_dim = len(data['x_vars'])
+		device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+		model = RGCN_v1(input_dim = in_dim, 
+						hidden_dim = config['hidden_size'], 
+						adj_matrix = adj_mx, 
+						recur_dropout = config['recurrent_dropout'], 
+						dropout = config['dropout'], 
+						device = device, 
+						seed = config['seed'])
+		opt = optim.Adam(model.parameters(), lr = config['learning_rate'])
+		train_torch(model,
+					loss_function = rmse_masked,
+					optimizer = opt,
+					x_train = data['x_trn'],
+					y_train = data['y_obs_trn'],
+					x_val = data['x_val'],
+					y_val = data['y_obs_val'],
+					max_epochs = config['epochs'],
+					early_stopping_patience = config['early_stopping'],
+					batch_size = num_segs,
+					weights_file = output[0],
+					log_file = output[1],
+					device = device)
+
+
+rule make_predictions:
+	input:
+		"{outdir}/finetuned_weights.pth",
+		"{outdir}/prepped.npz"
+	output:
+		"{outdir}/{partition}_preds.feather"
+	group: "train_predict_evaluate"
+	run:
+		data = np.load(input[1])
+		adj_mx = data['dist_matrix']
+		in_dim = len(data['x_vars'])
+		device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+		model = RGCN_v1(input_dim = in_dim, 
+						hidden_dim = config['hidden_size'], 
+						adj_matrix = adj_mx, 
+						recur_dropout = config['recurrent_dropout'], 
+						dropout = config['dropout'], 
+						device = device, 
+						seed = config['seed'])
+		opt = optim.Adam(model.parameters(), lr = config['learning_rate'])
+		model.load_state_dict(torch.load(input[0]))
+		predict_from_io_data(model = model, 
+							io_data = input[1],
+							partition = wildcards.partition,
+							outfile = output[0],
+							trn_offset = config['trn_offset'],
+							tst_val_offset = config['tst_val_offset'],
+							spatial_idx_name = "PRMS_segid",
+							time_idx_name = "Date",
+							log_vars = config['log_y_vars'])
+
+
+#Order in the list is: 
+# spatial (bool), temporal (False or timestep to use), sum_aggregation (bool), site_based (bool)
+def get_grp_arg(wildcards):
+	if wildcards.metric_type == 'overall':
+		return [False, False, False, False]
+	elif wildcards.metric_type == 'month':
+		return [False, 'M', False, False]
+	elif wildcards.metric_type == 'reach':
+		return [True, False, False, False]
+	elif wildcards.metric_type == 'month_reach':
+		return [True, 'M', False, False]
+	elif wildcards.metric_type == 'monthly_site_based':
+		return [False, 'M', True, True]
+	elif wildcards.metric_type == 'monthly_all_sites':
+		return [False, 'M', True, False]
+	elif wildcards.metric_type == 'monthly_reach':
+		return [True, 'M', True, False]
+	elif wildcards.metric_type == 'year':
+		return [False, 'Y', False, False]
+	elif wildcards.metric_type == 'year_reach':
+		return [True, 'Y', False, False]
+	elif wildcards.metric_type == 'yearly_site_based':
+		return [False, 'Y', True, True]
+	elif wildcards.metric_type == 'yearly_all_sites':
+		return [False, 'Y', True, False]
+	elif wildcards.metric_type == 'yearly_reach':
+		return [True, 'Y', True, False]
+	elif wildcards.metric_type == 'biweekly_site_based':
+		return [False, '2W', True, True]
+	elif wildcards.metric_type == 'biweekly_all_sites':
+		return [False, '2W', True, False]
+	elif wildcards.metric_type == 'biweekly_reach':
+		return [True, '2W', True, False]
+
+#compute performance metrics
+rule combine_metrics:
+	group: 'train_predict_evaluate'
+	input:
+		config['obs_file'],
+		"{outdir}/trn_preds.feather",
+		"{outdir}/val_preds.feather",
+		"{outdir}/tst_preds.feather"
+	output:
+		"{outdir}/{metric_type}_metrics.csv"
+	params:
+		grp_arg = get_grp_arg
+	run:
+		combined_metrics(obs_file = input[0],
+						pred_trn = input[1],
+						pred_val = input[2],
+						pred_tst = input[3],
+						train_sites = train_segs,
+						val_sites = val_segs,
+						test_sites = test_segs,
+						group_spatially = params.grp_arg[0],
+						group_temporally = params.grp_arg[1],
+						sum_aggregation = params.grp_arg[2],
+						site_based = params.grp_arg[3],
+						outfile = output[0],
+						spatial_idx_name = "PRMS_segid",
+						time_idx_name = "Date")
+
+#write prediction+obs files to use in R plot targets
+rule write_preds_obs:
+	group: 'train_predict_evaluate'
+	input:
+		config['obs_file'],
+		"{outdir}/trn_preds.feather",
+		"{outdir}/val_preds.feather",
+		"{outdir}/tst_preds.feather"
+	output:
+		"{outdir}/trn_preds_obs.csv",
+		"{outdir}/val_preds_obs.csv",
+		"{outdir}/tst_preds_obs.csv"
+	run:
+		write_preds_obs(pred_file = input[1], 
+						obs_file = input[0],
+						partition = 'trn',
+						spatial_idx_name = "PRMS_segid", 
+						time_idx_name = "Date",
+						filepath = output[0],
+						spatial = config['spatial_write'],
+						train_sites = train_segs, 
+						val_sites = val_segs,
+						test_sites = test_segs)
+		write_preds_obs(pred_file = input[2], 
+						obs_file = input[0], 
+						partition = 'val',
+						spatial_idx_name = "PRMS_segid", 
+						time_idx_name = "Date",
+						filepath = output[1],
+						spatial = config['spatial_write'],
+						train_sites = train_segs, 
+						val_sites = val_segs,
+						test_sites = test_segs)
+		write_preds_obs(pred_file = input[3],
+						obs_file = input[0],
+						partition = 'tst',
+						spatial_idx_name = "PRMS_segid", 
+						time_idx_name = "Date",
+						filepath = output[2],
+						spatial = config['spatial_write'],
+						train_sites = train_segs, 
+						val_sites = val_segs,
+						test_sites = test_segs)

config_insal_spatial-dyn_rgcn_pytorch.yml

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+runDescription: "DRB Inland Salinity RGCN prediction with spatial holdout - dynamic attributes"
+
+# Input files
+obs_file: "2_process/out/drb_SC_obs_PRMS.zarr"
+attrs_file: "2_process/out/drb_attrs_PRMS.zarr"
+dist_matrix_file: "1_fetch/out/drb_distance_matrix.npz"
+# File containing the attribute names to use within the attrs_file
+x_vars_file: "4_predict/out/dynamic_attrs.txt"
+
+#river-dl code directory
+code_dir: "../river-dl"
+
+# Attribute names within obs_file
+y_vars: ['mean_value']
+#Attributs to take the log of for model training.
+log_y_vars: False
+
+# output location
+out_dir: "4_predict/out/spatial/RGCN/dynamic_test1"
+
+#random seed for training False==No seed, otherwise specify the seed
+seed: 21023
+
+lambdas: [1]
+
+#Define number of epochs and the early stopping criteria (number of epochs) 
+epochs: 200
+#setting this to False will turn off early stopping rounds
+early_stopping: 10
+
+
+#Hyperparameter settings
+hidden_size: 20
+dropout: 0.2
+recurrent_dropout: 0.2
+learning_rate: 0.005
+trn_offset: 1.0
+tst_val_offset: 1.0
+seq_len: 365
+
+# Define the spatial test segments (to be loaded from a file) 
+# Use 'None' if all segments may be used
+# using f instead of file because file triggers preproc_utils to generate metadata that wouldn't exist for 'None'
+# None for training means that val and test segs are removed to create the training data.
+train_segs_f: 'None'
+val_segs_f: '4_predict/out/spatial_val_reaches1.txt'
+test_segs_f: '4_predict/out/spatial_test_reaches.txt'
+#when True, the final file of predictions and observations are trimmed to the reaches in each of the 3 above files
+spatial_write: True
+
+# Define the earliest date, latest date, training, validation, and testing time periods
+earliest_date: '1984-09-30'
+latest_date: '2021-12-31'
+train_start_date_f: "4_predict/out/train_start.txt"
+train_end_date_f: "4_predict/out/test_end.txt"
+val_start_date_f: "4_predict/out/train_start.txt"
+val_end_date_f: "4_predict/out/test_end.txt"
+test_start_date_f: "4_predict/out/train_start.txt"
+test_end_date_f: "4_predict/out/test_end.txt"

config_insal_temporal-minstatdyn_rgcn_pytorch.yml

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+runDescription: "DRB Inland Salinity RGCN prediction with temporal holdout - min static and dynamic attributes"
+
+# Input files
+obs_file: "2_process/out/drb_SC_obs_PRMS.zarr"
+attrs_file: "2_process/out/drb_attrs_PRMS.zarr"
+dist_matrix_file: "1_fetch/out/drb_distance_matrix.npz"
+# File containing the attribute names to use within the attrs_file
+x_vars_file: "4_predict/out/min_static_dynamic_attrs.txt"
+
+#river-dl code directory
+code_dir: "../river-dl"
+
+# Attribute names within obs_file
+y_vars: ['mean_value']
+#Attributs to take the log of for model training.
+log_y_vars: False
+
+# output location
+out_dir: "4_predict/out/temporal/RGCN/min_static_dynamic_test1"
+
+#random seed for training False==No seed, otherwise specify the seed
+seed: 21023
+
+lambdas: [1]
+
+#Define number of epochs and the early stopping criteria (number of epochs) 
+epochs: 200
+#setting this to False will turn off early stopping rounds
+early_stopping: 10
+
+
+#Hyperparameter settings
+hidden_size: 20
+dropout: 0.2
+recurrent_dropout: 0.2
+learning_rate: 0.005
+trn_offset: 1.0
+tst_val_offset: 1.0
+seq_len: 365
+
+# Define the spatial test segments (to be loaded from a file) 
+# Use 'None' if all segments may be used
+# using f instead of file because file triggers preproc_utils to generate metadata that wouldn't exist for 'None'
+train_segs_f: 'None'
+val_segs_f: 'None'
+test_segs_f: 'None'
+#when True, the final file of predictions and observations are trimmed to the reaches in each of the 3 above files
+spatial_write: False
+
+# Define the earliest date, latest date, training, validation, and testing time periods
+earliest_date: '1984-09-30'
+latest_date: '2021-12-31'
+train_start_date_f: "4_predict/out/train_start.txt"
+train_end_date_f: "4_predict/out/val_end4.txt"
+val_start_date_f: "4_predict/out/val_start5.txt"
+val_end_date_f: "4_predict/out/train_end.txt"
+test_start_date_f: "4_predict/out/test_start.txt"
+test_end_date_f: "4_predict/out/test_end.txt"