leaspy.models.utilities

Functions

`is_array_like`(v)
`tensor_to_list`(x)	Convert input tensor to list.
`compute_std_from_variance`(variance, varname[, tol])	Check that variance is strictly positive and return its square root, otherwise fail with a convergence error.
`compute_patient_slopes_distribution`(df, *[, max_inds])	Compute linear regression slopes and their standard deviations for each feature.
`compute_linear_regression_subjects`(df, *[, max_inds])	Linear Regression on each feature to get intercept & slopes
`compute_patient_values_distribution`(df)	Returns means and standard deviations for the features of the given dataset values.
`compute_patient_time_distribution`(df)	Returns mu / sigma of given dataset times.
`get_log_velocities`(velocities, features, *[, min])	Get the log of the velocities, clamping them to min if negative.
`torch_round`(t, *[, tol])	Multiplies the tensor by tol, applies standard rounding, then scales back.

Module Contents

is_array_like(v)

Parameters:: v (Any)
Return type:: bool

tensor_to_list(x)

Convert input tensor to list.

Parameters:

xlist or torch.Tensor: Input tensor or list to be converted.

Returns:

list: List converted from tensor input, or original list if input was not a tensor.

Raises:

NotImplementedError: If the input is a WeightedTensor, as this functionality is not yet implemented.

Parameters:

x (Union[list, Tensor])

Return type:

list

compute_std_from_variance(variance, varname, tol=1e-05)

Check that variance is strictly positive and return its square root, otherwise fail with a convergence error. If variance is multivariate check that all components are strictly positive. TODO? a full Bayesian setting with good priors on all variables should prevent such convergence issues.

Parameters:

variancetorch.Tensor: The variance we would like to convert to a std-dev.
varnamestr: The name of the variable.
tolfloat, optional: The lower bound on variance, under which the converge error is raised. Default=1e-5.

Returns:

obj:: torch.Tensor The standard deviation from the variance.

Raises:

LeaspyConvergenceError: If the variance is less than the specified tolerance, indicating a convergence issue.

Parameters:

variance (Tensor)
varname (str)
tol (float)

Return type:

Tensor

compute_patient_slopes_distribution(df, *, max_inds=None)

Compute linear regression slopes and their standard deviations for each feature.

Parameters:

dfpd.DataFrame: DataFrame containing individual scores.
max_indsint, optional: Restrict computation to first max_inds individuals. Default=”None”

Returns:

Tuple`[:obj:`torch.Tensor, torch.Tensor]:: Tuple with : - [0] : torch.Tensor of shape (n_features,) - Regression slopes - [1] : torch.Tensor of shape (n_features,) - Standard deviation of the slopes

Parameters:

df (DataFrame)
max_inds (Optional[int])

Return type:

Tuple[Tensor, Tensor]

compute_linear_regression_subjects(df, *, max_inds=None)

Linear Regression on each feature to get intercept & slopes

Parameters:

dfpd.DataFrame: Contains the individual scores.
max_indsint, optional: Restrict computation to first max_inds individuals. Default=”None”

Returns:

obj:

Dict`[:obj:`str, pd.DataFrame]: Dictionary with : - keys : feature names - values : DataFrame with :

index : Individual IDs

columns : ‘intercept’, ‘slope’

Parameters:

df (DataFrame)
max_inds (Optional[int])

Return type:

Dict[str, DataFrame]

compute_patient_values_distribution(df)

Returns means and standard deviations for the features of the given dataset values.

Parameters:

dfpd.DataFrame: Contains the individual scores.

Returns:

obj:: Tuple[torch.Tensor, torch.Tensor]: Tuple with: - [0] : torch.Tensor of shape (n_features,) - Means of the features - [1] : torch.Tensor of shape (n_features,) - Standard deviations of the features

Parameters:

df (DataFrame)

Return type:

Tuple[Tensor, Tensor]

compute_patient_time_distribution(df)

Returns mu / sigma of given dataset times.

Parameters:

dfpd.DataFrame: Contains the individual scores.

Returns:

Tuple`[:obj:`torch.Tensor, torch.Tensor]:: Tuple with: - [0] : torch.Tensor - Mean of the times - [1] : torch.Tensor - Standard deviation of the times

Parameters:

df (DataFrame)

Return type:

Tuple[Tensor, Tensor]

get_log_velocities(velocities, features, *, min=0.01)

Get the log of the velocities, clamping them to min if negative.

Parameters:

velocitiestorch.Tensor: The velocities to be clamped and logged.
featuresList`[:obj:`str]: The names of the features corresponding to the velocities.
minfloat, optional: The minimum value to clamp the velocities to. Default=1e-2

Returns:

torch.Tensor: The log of the clamped velocities.

Raises:

Warning: If some negative velocities are provided.

Parameters:

velocities (Tensor)
features (List[str])
min (float)

Return type:

Tensor

torch_round(t, *, tol=1 << 16)

Multiplies the tensor by tol, applies standard rounding, then scales back. This effectively rounds values to the nearest multiple of 1.0 / tol.

Parameters:

ttorch.FloatTensor: The tensor to be rounded.
tolfloat, optional: The tolerance factor controlling rounding precision (higher = finer rounding). Default=1 << 16 (65536). This corresponds to rounding to ~ 10**-4.8.

Returns:

torch.FloatTensor: The rounded tensor with the same shape as input t.

Parameters:

t (torch.FloatTensor)
tol (float)

Return type:

torch.FloatTensor