chore: add forecasting example (#10)

* chore: add forecasting example

* chore: add example for signal reconstruction

Author: JulesBelveze

examples/forecasting/config_forecasting.py

@@ -0,0 +1,60 @@
+import torch
+
+class Config(dict):
+    def __init__(self, **kwargs):
+        """
+        Initialize an instance of this class.
+
+        Args:
+
+        """
+        super().__init__(**kwargs)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def set(self, key, value):
+        """
+        Sets the value to the value.
+
+        Args:
+            key: (str):
+            value:
+        """
+        self[key] = value
+        setattr(self, key, value)
+
+
+config = Config(
+    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
+
+    categorical_cols=[],  # name of columns containing categorical variables
+    label_col=["AH"],  # name of target column
+    index_col="Date_Time",
+
+    output_size=1,  # for forecasting
+
+    num_epochs=100,
+    batch_size=16,
+    lr=1e-5,
+    reg1=True,
+    reg2=False,
+    reg_factor1=1e-4,
+    reg_factor2=1e-4,
+    seq_len=3,  # previous timestamps to use
+    prediction_window=1,  # number of timestamps to forecast
+    hidden_size_encoder=64,
+    hidden_size_decoder=64,
+    input_att=True,
+    temporal_att=True,
+    denoising=False,
+    directions=1,
+
+    max_grad_norm=0.1,
+    gradient_accumulation_steps=1,
+    logging_steps=100,
+    lrs_step_size=5000,
+
+    output_dir="output",
+    save_steps=5000,
+    eval_during_training=True
+)

examples/forecasting/run_forecasting.py

@@ -0,0 +1,67 @@
+import torch
+import argparse
+import pandas as pd
+import torch.nn as nn
+from tsa import TimeSeriesDataset, AutoEncForecast, train, evaluate
+from .config_forecasting import config
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def parse_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"])
+
+    ts = TimeSeriesDataset(
+        data=df,
+        categorical_cols=config["categorical_cols"],
+        target_col=config["label_col"],
+        seq_length=config["seq_len"],
+        prediction_window=config["prediction_window"]
+    )
+    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=config["batch_size"])
+
+    model = AutoEncForecast(config, input_size=nb_features).to(config["device"])
+    criterion = nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
+
+    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)

examples/reconstruction/config_reconstruction.py

@@ -0,0 +1,60 @@
+import torch
+
+class Config(dict):
+    def __init__(self, **kwargs):
+        """
+        Initialize an instance of this class.
+
+        Args:
+
+        """
+        super().__init__(**kwargs)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def set(self, key, value):
+        """
+        Sets the value to the value.
+
+        Args:
+            key: (str):
+            value:
+        """
+        self[key] = value
+        setattr(self, key, value)
+
+
+config = Config(
+    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
+
+    categorical_cols=[],  # name of columns containing categorical variables
+    label_col=[],  # name of target column
+    index_col="Date_Time",
+
+    output_size=1,  # for forecasting
+
+    num_epochs=100,
+    batch_size=16,
+    lr=1e-5,
+    reg1=True,
+    reg2=False,
+    reg_factor1=1e-4,
+    reg_factor2=1e-4,
+    seq_len=3,  # previous timestamps to use
+    prediction_window=1,  # number of timestamps to forecast
+    hidden_size_encoder=64,
+    hidden_size_decoder=64,
+    input_att=True,
+    temporal_att=True,
+    denoising=False,
+    directions=1,
+
+    max_grad_norm=0.1,
+    gradient_accumulation_steps=1,
+    logging_steps=100,
+    lrs_step_size=5000,
+
+    output_dir="output",
+    save_steps=5000,
+    eval_during_training=True
+)

examples/reconstruction/run_reconstruction.py

@@ -0,0 +1,67 @@
+import torch
+import argparse
+import pandas as pd
+import torch.nn as nn
+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():
+    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"])
+
+    ts = TimeSeriesDataset(
+        data=df,
+        categorical_cols=config["categorical_cols"],
+        target_col=config["label_col"],
+        seq_length=config["seq_len"],
+        prediction_window=config["prediction_window"]
+    )
+    train_iter, test_iter, nb_features = ts.get_loaders(batch_size=config["batch_size"])
+
+    model = AutoEncForecast(config, input_size=nb_features).to(config["device"])
+    criterion = nn.MSELoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
+
+    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)