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github.com/Victorletzelter/timeMCL
/ functions
Functions
448 in github.com/Victorletzelter/timeMCL
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Functions
448
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Types & classes
73
Method
analyze_assets
(self)
tsExperiments/stock_market_data/data_processors/creating_dataset.py:58
Method
base_dist
(self)
tsExperiments/data_and_transformation/flows.py:306
Method
base_dist
(self)
tsExperiments/data_and_transformation/flows.py:344
Method
calc_nonstandard_time_interval
(self)
tsExperiments/stock_market_data/data_processors/_base.py:161
Function
calculate_duration
(row)
tsExperiments/download/scripts_download_csv.py:82
Function
check_date
(d: str)
tsExperiments/stock_market_data/data_processors/_base.py:339
Method
clean_data
(self)
tsExperiments/stock_market_data/data_processors/_base.py:65
Method
clean_data
(self)
tsExperiments/stock_market_data/data_processors/yahoofinance.py:184
Method
compute_scale
( self, data: torch.Tensor, observed_indicator: torch.Tensor )
tsExperiments/data_and_transformation/scaler.py:80
Method
compute_scale
( self, data: torch.Tensor, observed_indicator: torch.Tensor )
tsExperiments/data_and_transformation/scaler.py:123
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/tMCL/lighting_grad.py:145
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/tactis2/lighting.py:145
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/timeGrad/lighting_grad.py:47
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/transformerTempFlow/lighting_grad.py:50
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/tempflow/lighting_grad.py:49
Method
configure_optimizers
Returns the optimizer to use.
tsExperiments/models/project_models/deepAR/lighting_grad.py:47
Function
convert_list_dates
(dates)
tsExperiments/download/scripts_download_csv.py:77
Method
create_embedding
(c: int, d: int)
tsExperiments/data_and_transformation/feature.py:19
Method
create_lightning_module
(self)
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:379
Method
create_lightning_module
(self)
tsExperiments/models/project_models/timeGrad/timeGradEstimator.py:275
Method
create_lightning_module
(self)
tsExperiments/models/project_models/transformerTempFlow/transformerTempFlow_estimator.py:254
Method
create_lightning_module
(self)
tsExperiments/models/project_models/tempflow/tempFlow_estimator.py:267
Method
create_lightning_module
(self)
tsExperiments/models/project_models/deepAR/estimator.py:275
Method
create_predictor
( self, transformation: Transformation, module )
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:414
Method
create_predictor
( self, transformation: Transformation, module )
tsExperiments/models/project_models/timeGrad/timeGradEstimator.py:310
Method
create_predictor
( self, transformation: Transformation, module )
tsExperiments/models/project_models/transformerTempFlow/transformerTempFlow_estimator.py:323
Method
create_predictor
( self, transformation: Transformation, module )
tsExperiments/models/project_models/tempflow/tempFlow_estimator.py:300
Method
create_predictor
( self, transformation: Transformation, module )
tsExperiments/models/project_models/deepAR/estimator.py:303
Method
create_training_data_loader
(self, data: Dataset, module, **kwargs)
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:365
Method
create_training_data_loader
(self, data: Dataset, module, **kwargs)
tsExperiments/models/project_models/timeGrad/timeGradEstimator.py:261
Method
create_training_data_loader
(self, data: Dataset, module, **kwargs)
tsExperiments/models/project_models/transformerTempFlow/transformerTempFlow_estimator.py:307
Method
create_training_data_loader
(self, data: Dataset, module, **kwargs)
tsExperiments/models/project_models/tempflow/tempFlow_estimator.py:253
Method
create_training_data_loader
(self, data: Dataset, module, **kwargs)
tsExperiments/models/project_models/deepAR/estimator.py:261
Method
create_transformation
(self)
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:281
Method
create_transformation
(self)
tsExperiments/models/project_models/timeGrad/timeGradEstimator.py:176
Method
create_transformation
(self)
tsExperiments/models/project_models/transformerTempFlow/transformerTempFlow_estimator.py:177
Method
create_transformation
(self)
tsExperiments/models/project_models/tempflow/tempFlow_estimator.py:170
Method
create_transformation
(self)
tsExperiments/models/project_models/deepAR/estimator.py:176
Method
create_validation_data_loader
( self, data: Dataset, module, **kwargs )
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:348
Method
create_validation_data_loader
( self, data: Dataset, module, **kwargs )
tsExperiments/models/project_models/timeGrad/timeGradEstimator.py:243
Method
create_validation_data_loader
( self, data: Dataset, module, **kwargs )
tsExperiments/models/project_models/transformerTempFlow/transformerTempFlow_estimator.py:291
Method
create_validation_data_loader
( self, data: Dataset, module, **kwargs )
tsExperiments/models/project_models/tempflow/tempFlow_estimator.py:237
Method
create_validation_data_loader
( self, data: Dataset, module, **kwargs )
tsExperiments/models/project_models/deepAR/estimator.py:243
Method
denormalize
Undo the normalization done in the normalize() function. Parameters: ----------- norm_value: Tensor [batch, series,
tsExperiments/models/project_models/tactis2/model/tactis.py:191
Function
distorsion
r""" Distortion, also referred to as the Oracle or Quantization error in the literature (Pages & Printems, 2009; Lee et al., 2016; Perera et a
tsExperiments/models/project_models/tMCL/timeMCL_estimator.py:431
Method
distr_output
(self)
tsExperiments/models/project_models/deepAR/network.py:126
Method
distribution
(self, distr_args, scale=None)
tsExperiments/data_and_transformation/flows.py:26
Method
distribution
we overrided the distribution method! The goal was to retun a distribution objet. We return a DIFFUSION OBJECT
tsExperiments/models/project_models/timeGrad/utils.py:446
Method
domain_map
(self, loc, cov_factor, cov_diag)
tsExperiments/distribution_output/utils.py:47
Method
domain_map
(cls, cond)
tsExperiments/data_and_transformation/flows.py:23
Method
domain_map
It is used to convert arguments to the right shape and domain! Depends on the type of distribution
tsExperiments/models/project_models/tMCL/utils.py:32
Method
domain_map
It is used to convert arguments to the right shape and domain! Depends on the type of distribution
tsExperiments/models/project_models/timeGrad/utils.py:440
Method
download_data
( self, ticker_list: List[str], save_path: str = "./data/dataset.csv", proxy=N
tsExperiments/stock_market_data/data_processors/yahoofinance.py:93
Method
embedding_dim
Returns: -------- dim: int The expected dimensionality of the input embedding, and the dimensionality of the outp
tsExperiments/models/project_models/tactis2/model/encoder.py:77
Method
embedding_dim
Returns: -------- dim: int The expected dimensionality of the input embedding, and the dimensionality of the outp
tsExperiments/models/project_models/tactis2/model/encoder.py:208
Function
enforce_tags
Prompts user to input tags from command line if no tags are provided in config. :param cfg: A DictConfig composed by Hydra. :param save_to_fi
tsExperiments/utils/rich_utils.py:82
Method
event_shape
(self)
tsExperiments/distribution_output/utils.py:65
Method
event_shape
(self)
tsExperiments/data_and_transformation/flows.py:35
Method
event_shape
(self)
tsExperiments/models/project_models/tMCL/utils.py:51
Method
event_shape
(self)
tsExperiments/models/project_models/timeGrad/utils.py:457
Function
extras
Applies optional utilities before the task is started. Utilities: - Ignoring python warnings - Setting tags from command line
tsExperiments/utils/utils.py:100
Method
fillna
(self)
tsExperiments/stock_market_data/data_processors/_base.py:102
Method
fit
Train the model using the data in the DataFrame df.
tsExperiments/models/project_models/ETS/model.py:21
Method
forward
(self, features: torch.Tensor)
tsExperiments/data_and_transformation/feature.py:27
Method
forward
Parameters ---------- data tensor of shape (N, T, C) if ``time_first == True`` or (N, C, T) if ``time
tsExperiments/data_and_transformation/scaler.py:24
Method
forward
( self, data: torch.Tensor, observed_indicator: torch.Tensor )
tsExperiments/data_and_transformation/scaler.py:181
Method
forward
(self, data: torch.Tensor, observed_indicator: torch.Tensor)
tsExperiments/data_and_transformation/scaler.py:290
Method
forward
(self, x, y)
tsExperiments/data_and_transformation/flows.py:85
Method
forward
(self, x, cond_y=None)
tsExperiments/data_and_transformation/flows.py:114
Method
forward
(self, x, y=None)
tsExperiments/data_and_transformation/flows.py:187
Method
forward
(self, x, y=None)
tsExperiments/data_and_transformation/flows.py:246
Method
forward
(self, x)
tsExperiments/models/project_models/tMCL/utils.py:62
Method
forward
Predicts samples given the trained DeepVAR model. All tensors should have NTC layout. Parameters ---------- t
tsExperiments/models/project_models/tMCL/timeMCL_network.py:719
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/tMCL/lighting_grad.py:46
Method
forward
Parameters: ----------- past_target_norm: torch.Tensor [batch, time steps, series] The historical data that are a
tsExperiments/models/project_models/tactis2/network.py:116
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/tactis2/lighting.py:34
Method
forward
Transform the given value according to the given parameters, computing the derivative of the transformation at the same time.
tsExperiments/models/project_models/tactis2/model/flow.py:98
Method
forward
Transform the given value according to the given parameters, computing the derivative of the transformation at the same time.
tsExperiments/models/project_models/tactis2/model/flow.py:214
Method
forward
Compute the embedding for each series and time step. Parameters: ----------- encoded: Tensor [batch, series, time st
tsExperiments/models/project_models/tactis2/model/encoder.py:217
Method
forward
(self, diffusion_step)
tsExperiments/models/project_models/timeGrad/utils.py:24
Method
forward
(self, x, conditioner, diffusion_step)
tsExperiments/models/project_models/timeGrad/utils.py:60
Method
forward
(self, x)
tsExperiments/models/project_models/timeGrad/utils.py:82
Method
forward
(self, inputs, time, cond)
tsExperiments/models/project_models/timeGrad/utils.py:128
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/timeGrad/lighting_grad.py:29
Method
forward
Predicts samples given the trained DeepVAR model. All tensors should have NTC layout. Parameters ---------- t
tsExperiments/models/project_models/timeGrad/timeGradNetwork.py:673
Method
forward
Predicts samples given the trained DeepVAR model. All tensors should have NTC layout. Parameters ---------- t
tsExperiments/models/project_models/transformerTempFlow/transTempFlow_network.py:652
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/transformerTempFlow/lighting_grad.py:32
Method
forward
Predicts samples given the trained DeepVAR model. All tensors should have NTC layout. Parameters ---------- t
tsExperiments/models/project_models/tempflow/tempflow_network.py:627
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/tempflow/lighting_grad.py:33
Method
forward
Predicts samples given the trained DeepVAR model. All tensors should have NTC layout. Parameters ---------- t
tsExperiments/models/project_models/deepAR/network.py:648
Method
forward
(self, *args, **kwargs)
tsExperiments/models/project_models/deepAR/lighting_grad.py:31
Function
get_metric_value
Safely retrieves value of the metric logged in LightningModule. :param metric_dict: A dict containing metric values. :param metric_name: If p
tsExperiments/utils/utils.py:186
Method
get_pos_encoding
(self, timesteps: torch.IntTensor)
tsExperiments/models/project_models/tactis2/model/tactis.py:98
Method
get_trading_days
(self, start: str, end: str)
tsExperiments/stock_market_data/data_processors/_base.py:146
Method
initialize_stage_1
(self, net, optimizer_name, ckpt=None)
tsExperiments/models/project_models/tactis2/lighting.py:44
Method
interpolate
(self, x1, x2, t=None, lam=0.5)
tsExperiments/models/project_models/timeGrad/utils.py:357
Method
inverse
(self, u, y)
tsExperiments/data_and_transformation/flows.py:92
Method
inverse
(self, y, cond_y=None)
tsExperiments/data_and_transformation/flows.py:144
Method
inverse
(self, u, y=None)
tsExperiments/data_and_transformation/flows.py:210
Method
inverse
Compute the inverse cumulative density function of a marginal conditioned using the given context, for the given value of u. This met
tsExperiments/models/project_models/tactis2/model/marginal.py:131
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