glmnet AV

class glmnet_av_regression.csvAV

Functor that handles glmnet regression for data supplied as a raw .csv ( Comma Seperated Value ) file.

__call__(file_contents, regression_var)

Run the regression and return the results.

Args:

file_contents: The raw contents of an HTTP POST request where form-data corresponds to a .csv file containing the design matrix, vector of predictors and any headers. regression_var: Index of the column that contains the vector of predictors.

Returns:

A pandas.DataFrame containing the regression coefficients corresponding to the support ( that is non-zero and signigiant coefficients ) along with the intercept term.

class glmnet_av_regression.jsonAV

Functor that handles glmnet regression for data supplied as a JSON string.

__call__(json_blob, regression_var)

Run the regression and return the results.

Args:

file_contents: The raw contents of an HTTP POST request where form-data corresponds to a JSON string containing the design matrix, vector of predictors and any headers. regression_var: Index of the column that contains the vector of predictors.

Returns:

A pandas.DataFrame containing the regression coefficients corresponding to the support ( that is non-zero and signigiant coefficients ) along with the intercept term.

class glmnet_av_regression.multiAV

Base class that handles some of data preparation for regressions performed via glmnet.

This class is not responsible for processing raw POST request form-data. It is assumed that the data has already been converted into a pandas DataFrame.

__weakref__

list of weak references to the object (if defined)

class glmnet_av_regression.xlsxAV

Functor that handles glmnet regression for data supplied as a raw .xlsx ( Excel ) file.

__call__(file_contents, regression_var)

Run the regression and return the results.

Args:

file_contents: The raw contents of an HTTP POST request where form-data corresponds to a .xlsx file containing the design matrix, vector of predictors and any headers. regression_var: Index of the column that contains the vector of predictors.

Returns:

A pandas.DataFrame containing the regression coefficients corresponding to the support ( that is non-zero and signigiant coefficients ) along with the intercept term.