ofs_aif.ofs_asc package¶

Submodules¶

ofs_aif.ofs_asc.asc module¶

class asc(connect_with_default_workspace=True)¶

Bases: ofs_aif.aif.aif, ofs_aif.aif_utility.aif_utility

This class ofs_asc contains the python methods for automatic scenario calibration use cae

add_model_groups(model_group_name=None)¶

Create segmentation (model group) for AMLES

Parameters: model_group_name – Unique name for the model group. Only alphanumeric character set including underscore, hyphen and space are allowed
Returns: successful message on successfully creating the model groups in AIF system.

Examples:

>>> input_pdf = pd.DataFrame({'MODEL_GROUP_NAME'   : ["Sig Cash 1"],
>>>                         'ENTITY_NAME'         : ["ASC"],
>>>                         'ATTRIBUTE_NAME'      : ["ASC"],
>>>                         'ATTRIBUTE_VALUE'     : ["ASC"],
>>>                         'LABEL_FILTER'        : ["ASC"],
>>>                         'FEATURE_TYPE_FILTER' : ["ASC"]
>>>                         })
>>>
>>> supervised.add_model_groups(self, input_pdf )

adjusted_boxplot_outliers(df=None, feature=None, tail='RIGHT', n_samples=20000)¶

It is similar to IQR method with some changes in parameters. It works well for skewed population. It customizes the range of valid data for both side of tails differently. Exponential model is used for fitting the data

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return dataframe without outliers

Examples:

>>> asc.adjusted_boxplot_outliers(df=input_df, feature='TRANS_BASE_AMT')

asc_cleanup4rerun()¶: This API is used internally for cleaning up the failed or partial loaded run ids in asc_runid_lookup table and also cleans the corresponding data from asc_event_master table.

asc_runid_lookup()¶: This API is for internal use which maps the run_id with current definition Id and version.

calculate_sample_size(sample_size_method=<function asc.hyper_geometric>, **kwargs)¶

It calculates sample size for each of the strata using hypergeometric distribution as a default method.

Parameters

sample_size_method –
Method to get the sample size. The default method is hyper_geometric. Valid options are:
- sample_size_method=hyper_geometric
  - It takes a sample from hypergeometric distribution
- sample_size_method=function
  - The user defined method which is to be passed. Ex: sample_size_method = proportionate_sampling
  - The first parameter of the user defined method should always the stratified summary dataframe.
  - The user-defined method should always return stratified summary with new feature SAMPLE_SIZE.
kwargs –
dict of tunable parameters.
- hyper_params
  - It is only applicable when sample_size_method = asc.hyper_geometric
  - Keys of dict should be a strata number and values should be tunable parameters.
  - Examplehyper_params={1{‘Pt’0.005, ‘Pe’0}, 2{‘Pt’0.005, ‘Pe’0}, 3{‘Pt’0.005, ‘Pe’0}} )
    
    Pe: Expected interesting event rate.
    
    Pt: Tolerable suspiciuos event rate
    
    Pw: Power of the test. Default is 95%
- Any number of parameters can be passed for user defined sample_size_method

Returns

dataframe

Examples:

>>> asc.calculate_sample_size(sample_size_method=asc.hyper_geometric)
>>>
>>> asc.calculate_sample_size(sample_size_method=asc.hyper_geometric, hyper_params={1 : {'Pt' : 0.005, 'Pe' : 0}, 2 : {'Pt' : 0.005, 'Pe' : 0}, 3 : {'Pt' : 0.005, 'Pe' : 0}})
>>>
>>> asc.calculate_sample_size(hyper_params={1 : {'Pt' : 0.005, 'Pe' : 0}, 2 : {'Pt' : 0.005, 'Pe' : 0}, 3 : {'Pt' : 0.005, 'Pe' : 0}})
>>>
>>> #User defined method for computing sample size
>>>
>>> def proportionate_sampling(stratified_summary, sample_proportions=[0.45, 0.30, 0.10]):
>>>     stratified_summary['SAMPLE_SIZE'] = 0
>>>     for idx, row in stratified_summary.iterrows():
>>>
>>>         if row['STRATA'] == 1:
>>>             stratified_summary.loc[idx, 'SAMPLE_SIZE'] = int(row['POPULATION'] * sample_proportions[0])
>>>         elif row['STRATA'] == 2:
>>>             stratified_summary.loc[idx, 'SAMPLE_SIZE'] = int(row['POPULATION'] * sample_proportions[1])
>>>         elif row['STRATA'] == 3:
>>>             stratified_summary.loc[idx, 'SAMPLE_SIZE'] = int(row['POPULATION'] * sample_proportions[2])
>>>
>>>     return stratified_summary
>>>
>>> asc.calculate_sample_size(sample_size_method=proportionate_sampling, sample_proportions=[0.30, 0.20, 0.10])

compute_initial_thresholds(features=None, outlier_method='robust_zscore', technique='percentile', outliers_proportion=0.05, robust_zscore_cut_off=3, iqr_cut_off=3, nstdev=3, anomaly_proportion=0.1, perc_list=[0.85, 0.9, 0.95], search_range=None, risk_level_priority={1: 'HR', 2: 'MR', 3: 'RR'}, tail='RIGHT')¶

This API is wrapper around outlier methods and thresholds computing techniques. It takes multiple parameters from the user and computes the thresholds for the features passed by the user.

Parameters

feature (list) – list of the features
outlier_method (str) – name of the outlier method. Default is robust_zscore
technique (dataframe) – technique to find thresholds. Default is percentile
outliers_proportion (float) – propotion of data points to be considered as outliers. Default is 0.05
robust_zscore_cut_off (int) – cut-off for robust zscore method. Default is 3
iqr_cut_off (int) – cut-off for iqr method. Default is 3
nstdev (int) – cut-off for zscore method. Default is 3
anomaly_proportion (float) – proportion of anomalies marked as an ouliers. Default is 0.10
perc_list (list) – percentile for risk level thresholds. Default is [0.85,0.90, 0.95]
search_range (tuple) – It works similar like python arange method.
risk_level_priority (dict) – risk level priorities. Ex: {1: 'HR', 2: 'MR', 3: 'RR'}
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return thresholds

Examples:

>>> asc.compute_initial_thresholds(feature='TRANS_BASE_AMT', features=['TRANS_BASE_AMT','TRANS_CT'], outlier_method='robust_zscore', technique='percentile',
>>>                       robust_zscore_cut_off=3, perc_list=[0.85,0.90,0.95])

compute_jump_thresholds(df=None, feature=None, risk_level_priority=None, range_limit=None)¶

This API computes the thresholds for a given feature. The thresholds for Jurisdictions are computed based on the highest peaks between the range defined by the user. If range_limit is None, then thresholds for risk levels will be the highest peaks from 0.85-0.90,0.90-0.95 and 0.95-0.99 for HR, MR and RR respectively

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
range_limit (tuple) – It works similar like python arange method. Default is (85,100,0.1).
risk_level_priority (dict) – risk level priorities. Ex: {1: 'HR', 2: 'MR', 3: 'RR'}

Returns

return thresholds

Examples:

>>> asc.compute_jump_thresholds(df=input_df, feature='TRANS_BASE_AMT', range_limit=(85,100,0.1))

compute_jumps(df=None, feature=None, perc_range_values=None)¶

It is an internal API to calculate the jumps for amount and count features.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
perc_range_values (list) – values of feature at different percentile.

Returns

return list of slope values

Examples:

>>> asc.compute_jumps(df=df, feature='TRANS_BASE_AMT', perc_range_values=[342424,5454646,5463737,6545454])

compute_percentile_thresholds(df=None, feature=None, perc_list=None, risk_level_priority=None)¶

This API computes the thresholds for a given feature. The thresholds for Jurisdictions are computed based on the perc_list passed by the user. If None is passed, then thresholds for risk levels will be set at 0.85,0.90,0.95 for HR, MR and RR respectively

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
perc_list (list) – percentile for risk level thresholds. Default is [0.85,0.90, 0.95]
risk_level_priority (dict) – risk level priorities. Ex: {1: 'HR', 2: 'MR', 3: 'RR'}

Returns

return dataframe without outliers

Examples:

>>> asc.compute_percentile_thresholds(df=input_df, feature='TRANS_BASE_AMT', perc_list=[0.85,0.90, 0.95])

configure_new_scenario(scenario_id=None)¶

This API will create the new scenario folder as a placeholder for the user for placing scenario focus and threshold set id notebooks. It also put an entry of new scenario in table ASC_SCENARIO_MASTER

Parameters: scenario_id – unique id of the scenario. Ex: ‘23413145’
Returns: API response in json format.

Examples:

>>> aif.configure_new_scenario(scenario_id='116000052')

create_definition(save_with_new_version=False, cleanup_results=False, version=None)¶

API creates unique definition using Model Group for a given Notebook. Internally called AIF create_definition method.

Parameters

save_with_new_version – Boolean flag with options True/False. It helps creating history of models/outputs for a given definition. Any version can be chosen at later point in time.
cleanup_results – Boolean flag with options True/False. When set to True, deletes all the outputs due to previous executions.
version – when multiple versions of the definitions are created, version is supplied to pick the required version of the definition. Default value is None means MAX version of the definition.

Returns

Return successful message on completion, and proper error message on failure.

Examples:

>>> ofs_asc.create_definition( save_with_new_version = False,
>>>                        cleanup_results = False,
>>>                        version = None )
Definition creation successful...
True

download_csv(user_thresholds=None, threshold_set_id=None, title='Download', filename='scenario_thresholds.csv')¶

This API allows the user to download the recommended thresholds to CSV file using HTML button.

Parameters

user_thresholds (dic) – key-value pair for feature and thresholds
threshold_set_id (str) – threshold_set_id for which thresholds are tuned
title (str) – title of HTML button

param filename: csv filename which will hold the recommended thresholds :type filename: str

Returns: diplay download button

Examples:

>>> tshld={'HR_Min_Trans_Ct': 2,
>>>     'HR_Min_Trans_Amt': 250088.11,
>>>     'MR_Min_Trans_Ct': 2,
>>>     'MR_Min_Trans_Amt': 250880.41,
>>>     'RR_Min_Trans_Ct': 2,
>>>     'RR_Min_Trans_Amt': 255344.37}
>>> asc.download_csv(user_thresholds=tshld, threshold_set_id=11600048)

execute_scenario(parallel=False)¶

This API executes multiple instances of scenario notebook in parallel for different fic_mis_date. The scenario data gets stored into table fcc_am_event_details

Parameters: parallel – if True, execute notebooks in parallel, otherwise sequential
Returns: Display successful message.

generate_expression(tunable_parameters=None)¶

It converts the tunable parameters passed by a user into a format accepted by the operator_overloading class.

Parameters: tunable_parameters (str) – tunable parameters passed by a user. Logical & and | operators should be used to create an expression. Ex: ‘Large_Cash_Transaction_Amt & (Large_Cash_Transaction_Cnt | (Large_Cash_Transaction_Amt & Large_Cash_Transaction_Cnt))’
Returns: expression

Examples:

>>> ofs_asc.generate_expression(tunable_parameters = 'Large_Cash_Transaction_Amt & (Large_Cash_Transaction_Cnt | (Large_Cash_Transaction_Amt & Large_Cash_Transaction_Cnt))')
'LARGE_CASH_TRANSACTION_AMT & (LARGE_CASH_TRANSACTION_CNT | (LARGE_CASH_TRANSACTION_AMT & LARGE_CASH_TRANSACTION_CNT))'

generate_threshold_id(base_threshold_set_id=None)¶

It calls the AML procedure which creates a new threshold set id from existing base threshold set id. The cloned threshold set id and original threshold set id mapping is stored in asc_runid_lookup table

Parameters: base_threshold_set_id – threshold set id which need to be cloned
Returns: new threshold set id

get_bar_plot(group_by='RUN_DATE', figsize=(14, 8))¶

It shows the bar plot comparison for prod alerts vs recommended alerts

Parameters

group_by (str) – aggregate alerts either by RUN_DATE or SEGMENT_ID
figsize (tuple) – figure size

Returns

display bar plot

Examples:

>>> asc.get_bar_plot(group_by='RUN_DATE')

get_cross_segment_parameter_analysis(segments_include=None, segments_exclude=None, select_features=None, figsize=(14, 6), title=None)¶

Function to compare distribution of alerts and effective alerts for specified parameters across specified segments.

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
select_features (list) – list of features to be plotted
figsize (tuple) – size of plot
title (str) – title of chart

Returns

plot

get_cross_tab(segments_include=None, segments_exclude=None, feature_name=None, bins=None, round_digits=0)¶

Utility function to get a cross tab of parameter and effective flag

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
feature_name (str) – name of the feature being analyzed
bins (numpy array or list) – array specifying bins, values should be bucketed into; If none deciles are used as intervals
round_digits (int) – number specifying how intervals should be rounded. To be used when deciles are used to bin the parameter values

Returns

dataframe

get_data(tag='BTL', tunable_parameters=None, segments_include=None, segments_exclude=None, is_pre_prod_analysis=False, n_samples=1000)¶

It retrieves the actual data for the tunable parameters passed by the user. Depending on the tag, either BTL or ATL data will be retrieved for analysis.

Parameters

tag (str) – ‘BTL’ tag for BTL analysis and ‘ATL’ tag for ATL analysis. Default is ‘BTL’
tunable_parameters (str) – (mandatory) Parmeteres to be tuned passed using logical & and | operators
segments_include (list) – segments to be included
segments_exclude (list) – segments to be excluded
is_pre_prod_analysis (bool) – True for preprod analysis
n_samples (int) – number of samples for finding out parameters datatypes. It is suggested to use when volume is huge, so dataypes could be tested on few records.

Returns

dataframe

Examples:

>>> tunable_parameters='Large_Cash_Transaction_Amt & (Large_Cash_Transaction_Cnt | (Large_Cash_Transaction_Amt & Large_Cash_Transaction_Cnt))'
>>> ofs_asc.get_data(tunable_parameters=tunable_parameters, segments_include=['AMEA_HR','AMEA_MR'])

get_definition_versions(is_scenario_execution=False)¶

Get available versions for the current definition.

Param: None
Returns: list of available versions for current definition.

Example:

>>> data_pdf = asc.get_definition_versions()

get_density_plots(segments_include=None, segments_exclude=None, select_features=None, figsize=(14, 6), title=None)¶

Function to get density plots for multiple parameters

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
select_features (list) – List of feature names(strings) to be plotted
figsize (tuple) – size of plot
title (str) – title of chart

Returns

plot

get_effectiveness_trend(segments_include=None, segments_exclude=None, feature_name=None, bins=None, figsize=(14, 6), title=None, **kwargs)¶

Function to get percentage effective alerts trend plot for a parameter

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
feature_name (str) – feature to be analyzed
bins (numpy array or list) – array specifying bins, values should be bucketed into; If none deciles are used as intervals
figsize (tuple) – size of plot
title (str) – title of chart
**kwargs –
Keyword arguments:
- round_digits (int) –
  number of digits to which bin limits are to be rounded
- ax (int) –
  axis on which the plot is to be placed. Used only when creating multi grid plots

Returns

Plot

get_effectiveness_trends(segments_include=None, segments_exclude=None, select_features=None, bins=None, figsize=(8, 6), title=None, **kwargs)¶

Function to get percentage effective alerts trend plots for multiple parameters

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
select_features (list) – List of feature names(strings) to be plotted
bins (numpy array or list) – array specifying bins, values should be bucketed into; If none deciles are used as intervals
figsize (tuple) – size of plot
title (str) – title of chart
**kwargs –
Keyword arguments:
- round_digits (int) –
  number of digits to which bin limits are to be rounded

Returns

Plot

get_event_trxns(trxn_type=None, trxn_identifier=None, temp_table_name=None)¶

This API retrieves the raw transactions for the selected transaction type and transaction identifier. User can pass multiple trxn types and pass trxn identifer for which the transactions dataframe will be retrieved. For each transaction type, a separate dataframe will be retrieved.

Parameters

trxn_type (list or str) – str or list of trxn types. Valid trxn types are ‘MI’,’CASH’,’WIRE’,’BACK_OFFICE’,’FOCAL_ENTITY’
trxn_identifier (str) – set of event ids for which transaction data is required. Possible inputs are ‘Threshold Set 1’, ‘Threshold Set 2’, ‘Intersection’, ‘Union’
temp_table_name (str) – intermediate table name which will store the sql results. If None, It will be created internally.

Returns

dict of dataframes with key as trxn_type

Examples:

>>> asc.get_event_trxns_all(trxn_type='CASH', trxn_identifier='Intersection')
>>>
>>> asc.get_event_trxns_all(trxn_type=['CASH','MI'], trxn_identifier='Threshold Set 1')
output:
{'CASH_TRXN' : df1, 'MI_TRXN' : df2}

get_event_trxns_all(trxn_type=None, trxn_identifier=None, parallel=False)¶

This API retrieves the raw transactions for the selected transaction type and transaction identifier. User can pass multiple trxn types and pass trxn identifer for which the transactions dataframe will be retrieved. For each transaction type, a separate dataframe will be retrieved.

Parameters

trxn_type (list or str) – str or list of trxn types. Valid trxn types are ‘MI’,’CASH’,’WIRE’,’BACK_OFFICE’,’FOCAL_ENTITY’
trxn_identifier (str) – set of event ids for which transaction data is required. Possible inputs are ‘Threshold Set 1’, ‘Threshold Set 2’, ‘Intersection’, ‘Union’
parallel (bool) – Use of multithreading for multiple values of trxn_identifier. Default is False.

Returns

dict of dataframes with key as trxn_type

Examples:

>>> asc.get_event_trxns_all(trxn_type='CASH', trxn_identifier='Intersection')
output:
{'CASH_TRXN_INTERSECTION' : df1, 'MI_TRXN_TRANSACTION' : df2}
>>> asc.get_event_trxns_all(trxn_type=['CASH','MI'], trxn_identifier=['Threshold Set 1 only','Threshold Set 2 only'])
output:
{'CASH_TRXN_THRESHOLD SET 1 ONLY' : df1, 'MI_TRXN_THRESHOLD SET 1 ONLY' : df2, 'CASH_TRXN_THRESHOLD SET 2 ONLY' : df3, 'MI_TRXN_THRESHOLD SET 2 ONLY' : df4}

get_frequency_table_1D(segments_include=None, segments_exclude=None, feature_name=None, bins=None, plot=False, figsize=(8, 6), **kwargs)¶

Function to return a frequency table and optionally convert to a heat map

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
feature_name (str) – feature to be analyzed
bins (numpy array or list) – array specifying bins, values should be bucketed into; If none deciles are used as intervals
plot (bool) – specifying whether to plot a heat map
figsize (tuple) – size of plot
**kwargs –
Keyword arguments:
- cmap (str) –
  any accepted python color map
- round_digits (int) –
  number of digits to which bin limits are to be rounded
- ax (int) –
  axis on which to place the plot. Used only for multigrid plots

Returns

dataframe and Plot(optional)

get_frequency_table_2D(segments_include=None, segments_exclude=None, select_features=None, bins=None, plot=False, figsize=(14, 8), **kwargs)¶

Function to return a frequency table and optionally convert to a heat map

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
select_features (list) – List of feature names(strings) to be plotted
bins (numpy array or list) – array specifying bins, values should be bucketed into; If none deciles are used as intervals
plot (bool) – specifying whether to plot a heat map
figsize (tuple) – size of plot
**kwargs –
Keyword arguments:
- cmap (str) –
  any accepted python color map
- round_digits (int) –
  number of digits to which bin limits are to be rounded

Returns

dataframe and Plot(optional)

get_frequency_tables_1D(segments_include=None, segments_exclude=None, select_features=None, bins=None, figsize=(8, 6), plot=True, title=None, **kwargs)¶

Function to get frequency tables and optionally heatmaps for multiple parameters

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude
select_features (list) – features to be analyzed
bins (list of lists or arrays) – list of arrays specifying bins, values should be bucketed into; If none deciles are used as intervals
figsize (tuple) – size of plot
plot (bool) – specifying whether to plot a heat map
title (str) – title for plot
**kwargs –
Keyword arguments:
- cmap (str) –
  any accepted python color map
- round_digits (int) –
  number of digits to which bin limits are to be rounded

Returns

list of dataframes and Plot(optional)

get_investigation_status(oracle_ecm=True, test_ecm_conn=None)¶

Function to return the Investigation status for the Samples collected and Samples reviewed at ECM. This API connects to ECM to get the disposition status for the samples and update events dispositions in table ASC_INVESTIGATED_ENTITIES.

Parameters

test_ecm_conn (str) – ECM connection string for debug purposes
oracle_ecm (str) – Use oracle ECM to get the dispositions status for samples. Default is True

Returns

dataframe

get_overall_summary(segments_include=None, segments_exclude=None)¶

Function to get segment wise summary of ATL Data

Parameters

segments_include (list) – list of segments to include
segments_exclude (list) – list of segments to exclude

Returns

Dataframe with summary of alerts

get_samples(strata_include=None, strata_exclude=None)¶

It takes the random samples from each strata equal to the number of sample_size calculated.

::param strata_include: strata to be included :type strata_include: list :param strata_exclude: strata to be excluded :type strata_exclude: list

Returns: Returns successful message

Examples:

>>> ofs_asc.get_samples(strata_include = ['AMEA_HR_1','AMEA_MR_1'], strata_exclude = None)

get_skewness_kurtosis(features_include=None, features_exclude=None, plot=True, figsize=(12, 10), sample_frac=None)¶

Function to return a data distribution for features and also shows the skewness and kurtosis.

Parameters

features_include (list) – list of segments to include
features_exclude (list) – list of segments to exclude
plot (bool) – specifying whether to plot or not
figsize (tuple) – size of plot
sample_frac (float) – fraction of sample to plot

get_threshold_names(scenario='Sig Cash', threshold_set_id=None)¶

This API loads the threshold names from KDD* table

Parameters

scenario – scenario name. Ex: ‘Sig Cash’
threshold_set_id – (optional) threshold set id Ex: ‘116000048’

Returns

return dataframe

Example:

>>> data_pdf = asc.get_threshold_names(scenario='Sig Cash', threshold_set_id='116000048')

get_threshold_set_ids(scenario='Sig Cash', threshold_set_id=None)¶

This API loads the threshold values for tunable parameters from KDD threshold table

Parameters

scenario – scenario name. Ex: ‘Sig Cash’
focus – focus name. Ex: ‘CUSTOMER’
threshold_set_id – (optional) threshold set id Ex: ‘116000048’

Returns

return thresholds for tunable parameters along with threshold set ids.

Example:

>>> data_pdf = asc.get_threshold_set_ids(scenario='Sig Cash', threshold_set_id='116000048')

get_trxns_summary(versions_list=None, summary_of=None)¶

It shows the transaction’s summary by transaction type for versions passed by the user.

Parameters

versions_list (list) – list of versions for which trxn summary to be retrieved
summary_of (str) – get trxn summary of either Transaction or focal_entity. Default is Transaction

Returns

dict of dataframes for different trxn types

Examples:

>>> ofs_asc.get_trxns_summary(versions_list=[0,3], summary_of='TRANSACTION'])
>>>
>>> ofs_asc.get_trxns_summary(versions_list=[0,3], summary_of='FOCAL_ENTITY'])
Output :
Transactions summary is successfully fetched.....
Fetched data for trans_type ['CASH_TRXN']

hyper_geometric(N, Pt=0.005, Pe=0, Pw=0.95)¶

sample size calculation from Hyper geometric distribution

Parameters

N – Strata size
Pt – Tolerable suspiciuos event rate
Pe – Expected interesting event rate
Pw – Power or reliability

Returns

sample_size

identify_atl_btl_population()¶

This API calls a SQL procedure p_asc_insert_atl_data. It inserts all ATL alerts from KDD tables for given scenario and run dates.

Param: None
Returns: None

import_analysis_templates(definition_name=None, is_pre_prod_analysis=False)¶

This API will create the objectives in MMG by taking scenario folder name as an input and also imports below analysis drafts to respective objectives.

For Prod Scenarios

BTL Analysis

ATL Analysis

ASC Scenario Execution

Impact Analysis

For Pre-Prod Scenario

ASC Scenario Execution

Pre-Prod Analysis

Parameters

definition_name – folder name which will be created under Home/ASC/Analysis. Ex: ‘Sig Cash 2’
is_pre_prod_analysis – Boolean Flag (True/False) - True for pre-prod analysis

Returns

API response in json format.

Examples:

>>> aif.import_analysis_templates(definition_name='Sig Cash 2')
>>>
>>> aif.import_analysis_templates(definition_name='RMF', is_pre_prod_analysis=True)

investigate_samples(strata_include=None, strata_exclude=None)¶

It pushes the selected samples to DB table ASC_EVENT_SAMPLE and ASC_INVESTIGATED_ENTITIES

Parameters

strata_include (list) – strata to be included
strata_exclude (list) – strata to be excluded

Returns

Returns successful message

Examples:

>>> ofs_asc.investigate_samples(strata_include = None, strata_exclude = ['AMEA_HR_0','AMEA_MR_0','AMEA_RR_0'])

iqr_outliers(df=None, feature=None, iqr_cut_off=3, tail='RIGHT')¶

It removes outliers based on the inter-quantile-range.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
iqr_cut_off (int) – number of standard deviation. Default is 3
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return dataframe without outliers

Examples:

>>> asc.iqr_outliers(df=input_df, feature='TRANS_BASE_AMT', iqr_cut_off=3)

isolation_forest_outliers(df=None, feature=None, anomaly_proportion=0.1)¶

It is based on random forest technique and identify anomalies in data. It removes the outliers by marking them -1 and 1 for non-outliers.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
anomaly_proportion (float) – proportion of outliers marked as anomalies

Returns

return dataframe without outliers

Examples:

>>> asc.isolation_forest_outliers(df=input_df, feature='TRANS_BASE_AMT')

load_asc_event_master()¶

It calls a SQL procedure p_asc_load_event_master which loads data into ASC_EVENT_MASTER table for a list of fic_mis_date given by a class variable self.run_dates

Param: None
Returns: status message for each fic_mis_date

load_asc_investigated_entities()¶: Load ATL alerts in asc_investigation_entities

load_object(version=None)¶

Loads the object saved using self.save_object()

Parameters: version – when multiple versions of the definitions are created, version is supplied to pick the required version of the definition. Default value is None means MAX version of the definition.
Returns: valid python object on successful execution.

Example:

>>> data_pdf = ofs_asc.load_object()

percent_outliers(df=None, feature=None, outliers_proportion=0.05, tail='RIGHT')¶

It removes the outliers above the percentile cut-off for univariate.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
outliers_proportion (float) – threshold value. Default is 5%
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return dataframe without outliers

Examples:

>>> asc.percent_outliers(df=input_df, feature='TRANS_BASE_AMT', outliers_proportion=0.05)

percentile_method(df_stratification=None, perc_list=None)¶

sample size calculation from Hyper geometric distribution

Parameters

df_stratification – input dataframe
perc_list – percentile list on which data will be split

Returns

stratified dataframe

perform_stratification(stratification_method=<function asc.percentile_method>, **kwargs)¶

It stratifies the input population (group by JURISDICTION and RISK_LEVEL) using stratified sampling and assigns strata number to each group within chosen segment. The default method is percentile used for creating stratas where each tunable parameter is converted to percentile feature and then based on the cutt-off percentile passed by perc_list, the segments gets splited into number of stratas equal to the splited values passed in perc_list.

Parameters

stratification_method – It takes function as an input. strata will be created on percentile method. Default method is percentile_method. It converts each tunable parameter into percentile and then pass to the expression. The expression evaluates the final percentile which is then used to split the population into different strata. - User defined method for stratification is also acceptable. Ex: stratification_method=stratification_on_amount. - In user-defined parameter, the first parameter should always be input dataframe. - User-defined method should always return STRATA as one more column with input dataframe.
Kwargs –
Keyword arguments: - perc_list :
- It is only applicable when startification_method=percentile_method
- List of cut-off percentiles based on which strata will be created.
- Strata number will start from left to right of the list.
- The order of strata will be from strata 1, strata 2, strata 3 and so on..
- Strata 1 will be closer to ATL line and last strata will be at the bottom of BTL data

Returns

successful message

Examples:

>>> #inbuit method
>>> asc.perform_stratification(stratification_method=asc.percentile_method, perc_list=[0.8,0.6,0.4,0.2])
>>>
>>> #User-defined method
>>> def stratification_on_amount(evented_data_pdf, split_on_amount=[200000, 100000, 50000]):
>>>     evented_data_pdf['STRATA'] = len(split_on_amount)+1
>>>     df_filter=evented_data_pdf.copy()
>>>     strata_no = 1
>>>     for i in split_on_amount:
>>>         df = df_filter[df_filter['STRATA'] == len(split_on_amount) + 1]
>>>         ls_event_ids = df[df['TRANS_BASE_AMT'] >= i]['EVENT_ID'].to_list()
>>>         df_filter.loc[df_filter['EVENT_ID'].isin(ls_event_ids), 'STRATA'] = strata_no
>>>         evented_data_pdf.loc[evented_data_pdf['EVENT_ID'].isin(ls_event_ids), 'STRATA'] = strata_no
>>>         strata_no = strata_no + 1
>>> return evented_data_pdf
>>>
>>> #Calling the user defined method
>>> asc.perform_stratification(stratification_method=stratification_on_amount, split_on_amount=[200000, 100000, 50000])

robust_zscore_outliers(df=None, feature=None, nstdev=3, tail='RIGHT')¶

It is similar like zscore with some changes in parameters. It works well for skewed population. Since mean and standard deviations are heavily influenced by outliers, instead of them it uses median and absolute deviation from median. Also called Median Absolute Deviation (MAD) method.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
nstdev (int) – number of standard deviation. Default is 3
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return dataframe without outliers

Examples:

>>> asc.robust_zscore_outliers(df=input_df, feature='TRANS_BASE_AMT', nstdev=2.5)

save_object(description=None)¶

save python objects permanently. Load them whenever needed.

Parameters: description – description for the object to be saved
Returns: Paragrapgh execution will show success else failure.

Example:

>>> data_pdf = data.frame({'COL1':[1,2,3], 'COL2':[4,5,6]})
>>> ofs_asc.save_object( value = data_pdf)

scenario_post_processing()¶

It calls multiple methods in sequence:

asc_runid_lookup() : insert run_id and map it to defintion id and version in ASC_RUNID_LOOKUP table
load_asc_investigated_entities : load ATL alerts for given run_id in ASC_INVESTIGATED_ENTITIES
load_asc_event_master() : load the events into ASC_EVENT_MASTER table for given run_id
identify_atl_btl_population() : Segregate ATL alerts from BTL in ASC_EVENT_MASTER table

Param: None
Returns: None

show_btl_thresholds(df=None, segment=None, strata=None)¶

It displays the stored thresholds from BTL analysis.

Parameters

df (dataframe) – run dates for which alerts to be fetched
segment (str) – select segment to display
strata (str) – select strata number to display

Returns

Returns BTL thresholds

Examples:

>>> asc.show_btl_thresholds(df = df_summary, segment='AMEA_HR', strata='1')

show_event_volume(group_by='ALL', tag=None)¶

This API displays the alerts volume for different group_by input. The table ASC_EVENT_MASTER will be used as an input for aggregating the data.

Parameters

group_by – input value on which alerts volume will be aggregated. It takes following inputs: - RUN_DATE : Aggregate alerts on fic_mis_date - SEGMENT_ID : Aggregate alerts on Jurisdiction and Risk_Level - EVENT_TAG : Aggregate alerts by event tag i.e. ATL or BTL - JURISDICTION : Aggregate alerts by jurisdiction - JURISDICTION_AND_RUN_DATE : Aggregate alerts by jurisdiction and run date - ALL : Aggregate alerts by all inputs together i.e by SEGMENT_ID, RUN_DATE, EVENT_TAG
tag – Filter ‘ATL’ or ‘BTL’. If None, It will show both ATL and BTL alerts.

Returns

dataframe showing alerts volume by different inputs.

show_event_volume_comparison(versions_list=None, group_by='RUN_DATE', tag=None)¶

This API is a wrapper around show_event_volume by taking an additional parameter versions_list which must be supplied by user for comparison of alerts volume

Parameters

versions_list – list of versions
group_by – input value on which alerts volume will be aggregated. It takes following inputs: - RUN_DATE : Aggregate alerts on fic_mis_date. Default - SEGMENT_ID : Aggregate alerts on Jurisdiction and Risk_Level - EVENT_TAG : Aggregate alerts by event tag i.e. ATL or BTL - JURISDICTION : Aggregate alerts by jurisdiction - JURISDICTION_AND_RUN_DATE : Aggregate alerts by jurisdiction and run date - ALL : Aggregate alerts by all inputs together i.e. by SEGMENT_ID, RUN_DATE, EVENT_TAG
tag – Filter ‘ATL’ or ‘BTL’. If None, It will show both ATL and BTL alerts.

Returns

list of dataframes for supplied versions

Examples:

>>>
>>> asc.show_event_volume_comparison(versions_list=[0,3], group_by='RUN_DATE')
>>>

show_execution_status()¶

It shows the execution status of the scenarios. The status can be one of these:

RUNNING

FAILED

COMPLETED

Param: None
Returns: dataframe

show_initial_thresholds(features=None, segments=None)¶

It shows the computed thrsholds in dataframe and also provided an options to user for filtering thresholds based on selected segments and features

Parameters

features (list) – name of the features
segments (list) – name of the segments

Returns

return dataframe

Examples:

>>> asc.show_initial_thresholds(features=['TRANS_BASE_AMT','TRANS_CT'], segments=['AMEA'])

show_investigation_summary()¶

This API returns the summary of percentage effective alerts for each segment and strata.

Param: None
Returns: dataframe

show_prod_alerts_volume(run_dates=None, group_by='RUN_DATE')¶

This API shows the statistics for production alerts

Parameters

run_dates (list) – run dates for which alerts to be fetched
group_by (str) – aggregate alerts either by RUN_DATE or SEGMENT_ID

Returns

Returns alerts statistics

Examples:

>>> asc.show_prod_alerts_volume(run_dates = ['20221007','20221014','20221022'], group_by='RUN_DATE')

show_sample_size(strata_include=None, strata_exclude=None)¶

It shows the sample size calculated for each of the segments.

Parameters

strata_include (list) – strata to be included
strata_exclude (list) – strata to be excluded

Returns

dataframe showing sample size for each strata

Examples:

>>> ofs_asc.show_sample_size(strata_include = ['AMEA_HR_1','AMEA_MR_1'], strata_exclude = None)

show_samples(strata_include=None, strata_exclude=None)¶

It shows the selected samples for each strata.

Parameters

strata_include (list) – strata to be included
strata_exclude (list) – strata to be excluded

Returns

Returns dataframe showing selected samples

Examples:

>>> ofs_asc.show_samples(strata_include = ['AMEA_HR_1','AMEA_MR_1'], strata_exclude = None)

show_scenario_bindings()¶

Returns the binding names for current selected scenario which user can choose for setting up the tunable parameters.

Param: None
Returns: list of scenario binding’s names

show_scenario_execution_parameters()¶

This API finds out all the possible run dates for date ranges provided by the user and display all parameters set by the user.

Param: None
Returns: Display parameters set by a user.

show_scenario_master()¶

Display available scenarios and focal entities for analysis

Param: None
Returns: dataframe

Examples:

>>> ofs_asc.show_scenario_master()

show_scenarios()¶

This API shows available scenarios along with unqiue scenario Id

Param: None
Returns: return dataframe

Example:

>>> asc.show_scenarios()

show_stratification_summary()¶

It displays strata number assigned to each group along with events population within each strata.

Param: None
Returns: dataframe showing strata number assigned to each segment

Examples:

>>> ofs_asc.show_stratification_summary()

show_venn_diagram(trxn_type=None, figsize=(16, 10), title=None)¶

It displays the venn diagram for transaction summary for different transaction types and different threshold set Ids. User can view the common thransactions between the different threshold set Ids

Parameters

trxn_type (list or str) – str or list of trxn types. Valid trxn types are ‘MI’,’CASH’,’WIRE’,’BACK_OFFICE’
figsize (tuple) – size of the figure. Default is (16,10)
title (str) – title to the figure. If None, title will be auto generated based on aggregate type was TRANSACTION or FOCUS

Returns

venn diagram for trxn types

Examples:

>>> asc.show_venn_diagram(trxn_type='CASH', figsize=(16,10))
>>>
>>> asc.show_venn_diagram(trxn_type=['CASH','MI'], title='Transactions overview')

show_version_summary()¶

Show available versions and threshold set ids for given definiton name

Param: None
Returns: version details along with threshold set id

Example:

>>> data_pdf = asc.show_version_summary()

update_dispositions_from_ecm(scenario_id=None, run_dates=None, oracle_ecm=True, test_ecm_conn=None)¶

This API connects with oracle ECM and update dispositions in ASC_INVESTIGATED_ENTITIES for a scenario and run dates. A connection with ECM with name ASC_ECM must have created in CS before calling this API

Parameters

scenario_id – valid scenario id
test_ecm_conn (str) – list of comma separated run dates
oracle_ecm (bool) – True for Oracle ECM. Dispositions are to be updated either manually or by some other way for third party ECM if flag is False.

Returns

successful message

update_kdd_cal(script_type='start', ficmisdate=None, run_batch_name='DLY')¶

zscore_outliers(df=None, feature=None, nstdev=3, tail='RIGHT')¶

It removes the outliers which is away from the mean by some number of standard deviation.

Parameters

df (dataframe) – input dataframe
feature (str) – name of the feature
nstdev (int) – number of standard deviation. Default is 3
tail (str) – tail to use for identifying outliers. Valid inputs are LEFT,`RIGHT` or BOTH. Default is RIGHT tail

Returns

return dataframe without outliers

Examples:

>>> asc.zscore_outliers(df=input_df, feature='TRANS_BASE_AMT', nstdev=2.5)

class operator_overloading(operator_value)¶: Bases: object

ofs_aif.ofs_asc package¶

Submodules¶

ofs_aif.ofs_asc.asc module¶

Module contents¶