chore: use hydra config parser

Author: JulesBelveze

examples/forecasting/config.yaml

@@ -0,0 +1,34 @@
+data:
+  categorical_cols: [ ]
+  label_col: ["AH"]
+  index_col: "Date_Time"
+  prediction_window: 1
+  seq_len: 3
+
+training:
+  num_epochs: 100
+  batch_size: 16
+  lr: 1e-5
+  reg1: True
+  reg2: False
+  reg_factor1: 1e-4
+  reg_factor2: 1e-4
+  hidden_size_encoder: 64
+  hidden_size_decoder: 64
+  output_size: 1
+  input_att: True
+  temporal_att: True
+  denoising: False
+  directions: 1
+  max_grad_norm: 0.1
+  gradient_accumulation_steps: 1
+  lrs_step_size: 5000
+
+general:
+  do_eval: True
+  do_train: True
+  logging_steps: 100
+
+  output_dir: "output"
+  save_steps: 5000
+  eval_during_training: True

examples/forecasting/config_forecasting.py

@@ -1,72 +1,37 @@
-import torch
-import argparse
+import hydra
 import pandas as pd
+import torch
 import torch.nn as nn
+
 from tsa import TimeSeriesDataset, AutoEncForecast, train, evaluate
-from .config_forecasting import config
+from tsa.utils import load_checkpoint
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-def parse_args():
-    """
-    Parse command line arguments.
-
-    Args:
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--batch-size", default=config["batch_size"], type=int, help="batch size")
-    parser.add_argument("--output-size", default=config["output_size"], type=int,
-                        help="size of the ouput: default value to 1 for forecasting")
-    parser.add_argument("--label-col", default=config["label_col"], type=str, help="name of the target column")
-    parser.add_argument("--input-att", default=config["input_att"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not activate the input attention mechanism")
-    parser.add_argument("--temporal-att", default=config["temporal_att"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not activate the temporal attention mechanism")
-    parser.add_argument("--seq-len", default=config["seq_len"], type=int, help="window length to use for forecasting")
-    parser.add_argument("--hidden-size-encoder", default=config["hidden_size_encoder"], type=int,
-                        help="size of the encoder's hidden states")
-    parser.add_argument("--hidden-size-decoder", default=config["hidden_size_decoder"], type=int,
-                        help="size of the decoder's hidden states")
-    parser.add_argument("--reg-factor1", default=config["reg_factor1"], type=float,
-                        help="contribution factor of the L1 regularization if using a sparse autoencoder")
-    parser.add_argument("--reg-factor2", default=config["reg_factor2"], type=float,
-                        help="contribution factor of the L2 regularization if using a sparse autoencoder")
-    parser.add_argument("--reg1", default=config["reg1"], type=lambda x: (str(x).lower() == "true"),
-                        help="activate/deactivate L1 regularization")
-    parser.add_argument("--reg2", default=config["reg2"], type=lambda x: (str(x).lower() == "true"),
-                        help="activate/deactivate L2 regularization")
-    parser.add_argument("--denoising", default=config["denoising"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not to use a denoising autoencoder")
-    parser.add_argument("--do-train", default=True, type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not to train the model")
-    parser.add_argument("--do-eval", default=False, type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not evaluating the mode")
-    parser.add_argument("--output-dir", default=config["output_dir"], help="name of folder to output files")
-    parser.add_argument("--ckpt", default=None, help="checkpoint path for evaluation")
-    return parser.parse_args()
-
 
-if __name__ == "__main__":
-    args = vars(parse_args())
-    config.update(args)
-
-    df = pd.read_csv("data/AirQualityUCI.csv", index_col=config["index_col"])
+@hydra.main(config_path="./", config_name="config")
+def run(args):
+    df = pd.read_csv("data/AirQualityUCI.csv", index_col=args.data.index_col)
 
     ts = TimeSeriesDataset(
         data=df,
-        categorical_cols=config["categorical_cols"],
-        target_col=config["label_col"],
-        seq_length=config["seq_len"],
-        prediction_window=config["prediction_window"]
+        categorical_cols=args.data.categorical_cols,
+        target_col=args.data.label_col,
+        seq_length=args.data.seq_len,
+        prediction_window=args.data.prediction_window
     )
-    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=config["batch_size"])
+    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=args.data.batch_size)
 
-    model = AutoEncForecast(config, input_size=nb_features).to(config["device"])
+    model = AutoEncForecast(args.training, input_size=nb_features).to(device)
     criterion = nn.MSELoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.training.lr)
+
+    if args.general.do_eval and args.general.ckpt:
+        model, _, loss, epoch = load_checkpoint(args.general.ckpt, model, optimizer, device)
+        evaluate(test_iter, loss, model, args, ts)
+    elif args.general.do_train:
+        train(train_iter, test_iter, model, criterion, optimizer, args, ts)
 
-    if config["do_eval"] and config["ckpt"]:
-        model, _, loss, epoch = load_checkpoint(config["ckpt"], model, optimizer, config["device"])
-        evaluate(test_iter, loss, model, config)
-    elif config["do_train"]:
-        train(train_iter, test_iter, model, criterion, optimizer, config)
+
+if __name__ == "__main__":
+    run()

examples/forecasting/run_forecasting.py

@@ -0,0 +1,34 @@
+data:
+  categorical_cols: [ ]
+  label_col: []
+  index_col: "Date_Time"
+  prediction_window: 1
+  seq_len: 3
+  batch_size: 16
+
+training:
+  num_epochs: 100
+  lr: 1e-5
+  reg1: True
+  reg2: False
+  reg_factor1: 1e-4
+  reg_factor2: 1e-4
+  hidden_size_encoder: 64
+  hidden_size_decoder: 64
+  input_att: True
+  temporal_att: True
+  output_size: 1
+  denoising: False
+  max_grad_norm: 0.1
+  lrs_step_size: 5000
+  gradient_accumulation_steps: 1
+  directions: 1
+
+general:
+  do_eval: True
+  do_train: True
+  logging_steps: 100
+
+  output_dir: "output"
+  save_steps: 5000
+  eval_during_training: True

examples/reconstruction/config.yaml

@@ -1,72 +1,37 @@
-import torch
-import argparse
+import hydra
 import pandas as pd
+import torch
 import torch.nn as nn
+
+from tsa.utils import load_checkpoint
 from tsa import TimeSeriesDataset, AutoEncForecast, train, evaluate
-from .config_reconstruction import config
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-def parse_args():
-    """
-    Parse command line arguments.
-
-    Args:
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--batch-size", default=config["batch_size"], type=int, help="batch size")
-    parser.add_argument("--output-size", default=config["output_size"], type=int,
-                        help="size of the ouput: default value to 1 for forecasting")
-    parser.add_argument("--label-col", default=config["label_col"], type=str, help="name of the target column")
-    parser.add_argument("--input-att", default=config["input_att"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not activate the input attention mechanism")
-    parser.add_argument("--temporal-att", default=config["temporal_att"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not activate the temporal attention mechanism")
-    parser.add_argument("--seq-len", default=config["seq_len"], type=int, help="window length to use for forecasting")
-    parser.add_argument("--hidden-size-encoder", default=config["hidden_size_encoder"], type=int,
-                        help="size of the encoder's hidden states")
-    parser.add_argument("--hidden-size-decoder", default=config["hidden_size_decoder"], type=int,
-                        help="size of the decoder's hidden states")
-    parser.add_argument("--reg-factor1", default=config["reg_factor1"], type=float,
-                        help="contribution factor of the L1 regularization if using a sparse autoencoder")
-    parser.add_argument("--reg-factor2", default=config["reg_factor2"], type=float,
-                        help="contribution factor of the L2 regularization if using a sparse autoencoder")
-    parser.add_argument("--reg1", default=config["reg1"], type=lambda x: (str(x).lower() == "true"),
-                        help="activate/deactivate L1 regularization")
-    parser.add_argument("--reg2", default=config["reg2"], type=lambda x: (str(x).lower() == "true"),
-                        help="activate/deactivate L2 regularization")
-    parser.add_argument("--denoising", default=config["denoising"], type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not to use a denoising autoencoder")
-    parser.add_argument("--do-train", default=True, type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not to train the model")
-    parser.add_argument("--do-eval", default=False, type=lambda x: (str(x).lower() == "true"),
-                        help="whether or not evaluating the mode")
-    parser.add_argument("--output-dir", default=config["output_dir"], help="name of folder to output files")
-    parser.add_argument("--ckpt", default=None, help="checkpoint path for evaluation")
-    return parser.parse_args()
-
 
-if __name__ == "__main__":
-    args = vars(parse_args())
-    config.update(args)
-
-    df = pd.read_csv("data/AirQualityUCI.csv", index_col=config["index_col"])
+@hydra.main(config_path="./", config_name="config")
+def run(args):
+    df = pd.read_csv("data/AirQualityUCI.csv", index_col=args.data.index_col)
 
     ts = TimeSeriesDataset(
         data=df,
-        categorical_cols=config["categorical_cols"],
-        target_col=config["label_col"],
-        seq_length=config["seq_len"],
-        prediction_window=config["prediction_window"]
+        categorical_cols=args.data.categorical_cols,
+        target_col=args.data.label_col,
+        seq_length=args.data.seq_len,
+        prediction_window=args.data.prediction_window
     )
-    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=config["batch_size"])
+    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=args.data.batch_size)
 
-    model = AutoEncForecast(config, input_size=nb_features).to(config["device"])
+    model = AutoEncForecast(args, input_size=nb_features).to(device)
     criterion = nn.MSELoss()
-    optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.training.lr)
+
+    if args.general.do_eval and args.general.ckpt:
+        model, _, loss, epoch = load_checkpoint(args.general.ckpt, model, optimizer, device)
+        evaluate(test_iter, loss, model, args, ts)
+    elif args.general.do_train:
+        train(train_iter, test_iter, model, criterion, optimizer, args, ts)
 
-    if config["do_eval"] and config["ckpt"]:
-        model, _, loss, epoch = load_checkpoint(config["ckpt"], model, optimizer, config["device"])
-        evaluate(test_iter, loss, model, config)
-    elif config["do_train"]:
-        train(train_iter, test_iter, model, criterion, optimizer, config)
+
+if __name__ == "__main__":
+    run()