Override

The module provides methods to perform override analytics. In general, it can be used to analyse changes in grades per date. For changes in grades between two dates, see the "migration" method. It provides the number of observations, the rating grade upgrades, the rating grade downgrades, the rating grade migration rate (rating grade changes of more than one grade to number of cohort), and the rating grade downgrade rate (rating grade downgrades of more than one grade to rating grade changes of more than one grade). It also provides a "heatmap" override matrix.

Accessor

Initialise the DataFrame with the override method. Minimal working example:

df.crm.override(grade_1="GRADE_1", grade_2="GRADE_2")

Parameters:

Name	Type	Description	Default
grade_1	str	Defines the first grade column, for example, grades before an override. Scores can be transformed to grades via the module "general.py" if necessary.	required
grade_2	str	Defines the second grade column, for example, grades after an override. Scores can be transformed to grades via the module "general.py" if necessary.	required

Returns:

Type	Description
Override	Returns a class called "Override" providing override analytics methods.

Methods

plot(x_axis_label='Grade After Override', y_axis_label='Grade Before Override', grade_range=None, annotation=True, font_size=7, fig_size=(7.5, 7.5), path=None, show=False)

Minimal working example:

df.crm.override(grade_1="GRADE_1", grade_2="GRADE_2").plot()

Parameters:

Name	Type	Description	Default
x_axis_label	str	Defines the x-axis label.	'Grade After Override'
y_axis_label	str	Defines the y-axis label.	'Grade Before Override'
grade_range	list	Extends the grade range to the specified range. Use "cfg.GRADES" for the complete range.	None
annotation	bool	Adds a "Downgrade" and "Upgrade" box to the plot.	True
font_size	int	Defines the font size of the plot.	7
fig_size	tuple	Defines the figure size.	(7.5, 7.5)
path	str	Defines the saving path including the filename of the figure and should be entered as r"C:\<path>\<filename>.<image_format>".	None
show	bool	If True, plot is shown.	False

Returns:

Type	Description
BytesIO	Returns a "heatmap" override matrix.

table()

Minimal working example:

df.crm.override(grade_1="GRADE_1", grade_2="GRADE_2").table()

Returns:

Type	Description
DataFrame	Returns a table containing the number of observations, the rating grade upgrades, and the rating grade downgrades.

Examples

data

>>> import credit_risk_modelling as crm
>>> data = crm.load_data.load_data()
>>> data

           DATE    ID GRADE  GRADE_PD OVERRIDE  OVERRIDE_PD  DEFAULT
0    2019-12-31    10     B    0.1000        B       0.1000        0
1    2019-12-31   100   BBB    0.0090       BB       0.0400        0
2    2019-12-31  1000   BBB    0.0090      BBB       0.0090        0
3    2019-12-31  1001   BBB    0.0090      BBB       0.0090        0
4    2019-12-31  1003   BBB    0.0090      BBB       0.0090        0
...         ...   ...   ...       ...      ...          ...      ...
4145 2023-12-31   994    AA    0.0010       AA       0.0010        0
4146 2023-12-31   995    AA    0.0010       AA       0.0010        0
4147 2023-12-31   996     A    0.0020        A       0.0020        0
4148 2023-12-31   998     B    0.1000        B       0.1000        0
4149 2023-12-31   999   AAA    0.0002      AAA       0.0002        0

[4150 rows x 7 columns]

.table()

>>> (
>>>     data
>>>     .loc[lambda df: df["DATE"].dt.year == 2023]
>>>     .crm.override(grade_1="GRADE", grade_2="OVERRIDE")
>>>     .table()
>>> )

   OBS_1  OUTFLOWS  OBS_1_COHORT  OBS_2  INFLOWS  OBS_2_COHORT  UNCHANGED  UP_1  UP_2  UP_3  UP_>3  UP_TOTAL  DOWN_1  DOWN_2  DOWN_3  DOWN_>3  DOWN_TOTAL     MR       DR
0   1000         0          1000   1000        0          1000        929     9     7     3     12        31      13       8       4       15          40  0.049  0.55102

.plot()

>>> (
>>>     data
>>>     .loc[lambda df: df["DATE"].dt.year == 2023]
>>>     .crm.override(grade_1="GRADE", grade_2="OVERRIDE")
>>>     .plot(show=True)
>>> )