try with stock data

experiments/base.py

@@ -62,9 +62,12 @@ class Experiment(ABC):
             # write command file
             command_file = os.path.join(instance_path, 'command')
             with open(command_file, 'w') as cmd:
+                # cmd.write(f'python -m {module} '
+                #           f'--config_path={instance_config_path} '
+                #           f'run >> {instance_path}/instance.log 2>&1')
                 cmd.write(f'python -m {module} '
-                          f'--config_path={instance_config_path} '
-                          f'run >> {instance_path}/instance.log 2>&1')
+                                        f'--config_path={instance_config_path} '
+                                        f'run 2>&1 | tee -a {instance_path}/instance.log')
 
     @abstractmethod
     def instance(self):
@@ -102,4 +105,4 @@ class Experiment(ABC):
     def build_experiment(self):
         if EXPERIMENTS_PATH in str(self.root):
             raise Exception('Cannot build ensemble from ensemble member configuration.')
-        self.build()
+        self.build()

experiments/configs/Stocks/96M.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96M'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime.layer_size = 256
+deeptime.inr_layers = 5
+deeptime.n_fourier_feats = 4096
+deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'M'
+ForecastDataset.horizon_len = 96
+ForecastDataset.lookback_mult = 1

experiments/configs/Stocks/96S.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96S'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime.layer_size = 256
+deeptime.inr_layers = 5
+deeptime.n_fourier_feats = 4096
+deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'S'
+ForecastDataset.horizon_len = 96
+ForecastDataset.lookback_mult = 3

experiments/configs/Stocks/96Splus.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96Splus'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime2'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime2.layer_size = 256
+deeptime2.inr_layers = 5
+deeptime2.n_fourier_feats = 4096
+deeptime2.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'S'
+ForecastDataset.horizon_len = 96
+ForecastDataset.lookback_mult = 3

experiments/configs/Stocks/96Splusshort.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96Splusshort'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime2'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime2.layer_size = 256
+deeptime2.inr_layers = 5
+deeptime2.n_fourier_feats = 4096
+deeptime2.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'S'
+ForecastDataset.horizon_len = 6
+ForecastDataset.lookback_mult = 8

experiments/configs/Stocks/96Sshort.gin

@@ -0,0 +1,37 @@
+build.experiment_name = 'Stocks/96Sshort'
+build.module = 'experiments.forecast'
+build.repeat = 1
+build.variables_dict = {
+}
+
+instance.model_type = 'deeptime'
+instance.save_vals = False
+
+get_optimizer.lr = 1e-3
+get_optimizer.lambda_lr = 1.
+get_optimizer.weight_decay = 0.
+
+get_scheduler.warmup_epochs = 5
+
+get_data.batch_size = 256
+
+train.loss_name = 'mse'
+train.epochs = 50
+train.clip = 10.
+
+Checkpoint.patience = 7
+
+deeptime.layer_size = 256
+deeptime.inr_layers = 5
+deeptime.n_fourier_feats = 4096
+deeptime.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
+
+ForecastDataset.data_path = 'stocks/OXY_2019.csv.gz'
+ForecastDataset.target = 'RSMKs_18_144_72'
+ForecastDataset.scale = True
+ForecastDataset.cross_learn = False
+ForecastDataset.time_features = []
+ForecastDataset.normalise_time_features = True
+ForecastDataset.features = 'S'
+ForecastDataset.horizon_len = 6
+ForecastDataset.lookback_mult = 8