statistics_table

menpofit.visualize.statistics_table(errors, method_names, auc_max_error, auc_error_step, auc_min_error=0.0, stats_types=None, stats_names=None, sort_by=None, precision=4)

Function that generates a table with statistical measures on the fitting results of various methods using pandas. It supports multiple types of statistical measures.

Note that the returned object is a pandas table which can be further converted to Latex tabular or simply a string. See the examples for more details.

Parameters

• errors (list of list of float) – A list that contains lists of float with the errors per method.

• method_names (list of str) – The list with the names that will appear for each method. Note that it must have the same length as errors.

• auc_max_error (float) – The maximum error value for computing the area under the curve.

• auc_error_step (float) – The sampling step of the error bins for computing the area under the curve.

• auc_min_error (float, optional) – The minimum error value for computing the area under the curve.

• stats_types (list of str or None, optional) –

  The types of statistical measures to compute. Possible options are:

  Value	Description
  mean	The mean value of the errors.
  std	The standard deviation of the errors.
  median	The median value of the errors.
  mad	The median absolute deviation of the errors.
  max	The max value of the errors.
  auc	The area under the curve based on the CED of the errors.
  fr	The failure rate (percentage of images that failed).

  If None, then all of them will be used with the above order.

• stats_names (list of str, optional) – The list with the names that will appear for each statistical measure type selected in stats_types. Note that it must have the same length as stats_types.

• sort_by (str or None, optional) –

  The column to use for sorting the methods. If None, then no sorting is performed and the methods will appear in the provided order of method_names. Possible options are:

  Value	Description
  mean	The mean value of the errors.
  std	The standard deviation of the errors.
  median	The median value of the errors.
  mad	The median absolute deviation of the errors.
  max	The max value of the errors.
  auc	The area under the curve based on the CED of the errors.
  fr	The failure rate (percentage of images that failed).

• precision (int, optional) – The precision of the reported values, i.e. the number of decimals.

Raises

• ValueError – stat_type must be selected from [mean, std, median, mad, max, auc, fr]

• ValueError – sort_by must be selected from [mean, std, median, mad, max, auc, fr]

• ValueError – stats_types and stats_names must have the same length

Returns

table (pandas.DataFrame) – The pandas table. It can be further converted to various format, such as Latex tabular or str.

Examples

Let us create some errors for 3 methods sampled from Normal distributions with different mean and standard deviations:

import numpy as np
from menpofit.visualize import statistics_table

method_names = ['Method_1', 'Method_2', 'Method_3']
errors = [list(np.random.normal(0.07, 0.02, 400)),
          list(np.random.normal(0.06, 0.03, 400)),
          list(np.random.normal(0.08, 0.04, 400))]

We can create a pandas DataFrame as:

tab = statistics_table(errors, method_names, auc_max_error=0.1,
                       auc_error_step=0.001, sort_by='auc')
tab

Pandas offers excellent functionalities. For example, the table can be converted to an str as:

print(tab.to_string())

or to a Latex tabular as:

print(tab.to_latex())