ofs_aif.ofs_asc package¶
Submodules¶
ofs_aif.ofs_asc.asc module¶
- class asc(connect_with_default_workspace=True)¶
Bases:
aif
,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 dataframefeature (
str
) – name of the featuretail (
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=None, 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 featuresoutlier_method (
str
) – name of the outlier method. Default is robust_zscoretechnique (
dataframe
) – technique to find thresholds. Default is percentileoutliers_proportion (
float
) – propotion of data points to be considered as outliers. Default is 0.05robust_zscore_cut_off (
int
) – cut-off for robust zscore method. Default is 3iqr_cut_off (
int
) – cut-off for iqr method. Default is 3nstdev (
int
) – cut-off for zscore method. Default is 3anomaly_proportion (
float
) – proportion of anomalies marked as an ouliers. Default is 0.10perc_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 dataframefeature (
str
) – name of the featurerange_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 dataframefeature (
str
) – name of the featureperc_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 dataframefeature (
str
) – name of the featureperc_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
- delete_exec_status_from_db(run_date=None, run_flag=True)¶
- This API delete entry from table
asc_scenario_execution_status
for given run_date This API is for internal use and being called inside API
execute_scenario
This API is used internally withasc.run_flag=True
- Parameters:
run_date – run date of a given scenario
run_flag – rerun/update flag
- Returns:
None
- This API delete entry from table
- 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 thresholdsthreshold_set_id (
str
) – threshold_set_id for which thresholds are tunedtitle (
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=True)¶
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 across JOB_IDs and sequential across RUN Dates.
- Returns:
Display successful message.
- export_import_threshold_notebook(scenario_id=None, objective_dict=None, debug=True)¶
This API will export threshold notebook from AMLScenarios-Converted folder and import it to the scenario definition folder with other templates.
- Parameters:
scenario_id – id of the scenario. Ex: 116000031
objective_dict – hierarichal folders strcuture in dict format
debug – It should be True for debugging purposes. It also displays the objective_id and zip filename used for scenario imported.
- Returns:
API response flag
- Examples:
>>> objective_dict = { >>> "1": {"name": "ASC", >>> "desc": "Automatic Scenario Calibration"}, >>> "2": {"name": "Analysis", >>> "desc": "Prod and PreProd Analysis"}, >>> "3": {"name": "Sig Cash Prod", >>> "desc": "sig cash scenario for production analysis"}} >>> >>> asc.export_import_threshold_notebook(scenario_id=116000031, objective_dict=objective_dict) >>>
- 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 byRUN_DATE
orSEGMENT_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 includesegments_exclude (
list
) – list of segments to excludeselect_features (
list
) – list of features to be plottedfigsize (
tuple
) – size of plottitle (
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 includesegments_exclude (
list
) – list of segments to excludefeature_name (
str
) – name of the feature being analyzedbins (
numpy array or list
) – array specifying bins, values should be bucketed into; If none deciles are used as intervalsround_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 | operatorssegments_include (
list
) – segments to be includedsegments_exclude (
list
) – segments to be excludedis_pre_prod_analysis (
bool
) – True for preprod analysisn_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_mapped_scenario()¶
get scenario name from asc_definition_master table for which the definition was created
- Param:
None
- Returns:
scenario name
- Examples:
>>> asc.get_definition_mapped_scenario()
- 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 includesegments_exclude (
list
) – list of segments to excludeselect_features (
list
) – List of feature names(strings) to be plottedfigsize (
tuple
) – size of plottitle (
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 includesegments_exclude (
list
) – list of segments to excludefeature_name (
str
) – feature to be analyzedbins (
numpy array or list
) – array specifying bins, values should be bucketed into; If none deciles are used as intervalsfigsize (
tuple
) – size of plottitle (
str
) – title of chart**kwargs –
Keyword arguments:
- round_digits (
int
) – number of digits to which bin limits are to be rounded
- round_digits (
- ax (
int
) – axis on which the plot is to be placed. Used only when creating multi grid plots
- ax (
- 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 includesegments_exclude (
list
) – list of segments to excludeselect_features (
list
) – List of feature names(strings) to be plottedbins (
numpy array or list
) – array specifying bins, values should be bucketed into; If none deciles are used as intervalsfigsize (
tuple
) – size of plottitle (
str
) – title of chart**kwargs –
Keyword arguments:
- round_digits (
int
) – number of digits to which bin limits are to be rounded
- round_digits (
- 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 includesegments_exclude (
list
) – list of segments to excludefeature_name (
str
) – feature to be analyzedbins (
numpy array or list
) – array specifying bins, values should be bucketed into; If none deciles are used as intervalsplot (
bool
) – specifying whether to plot a heat mapfigsize (
tuple
) – size of plot**kwargs –
Keyword arguments:
- cmap (
str
) – any accepted python color map
- cmap (
- round_digits (
int
) – number of digits to which bin limits are to be rounded
- round_digits (
- ax (
int
) – axis on which to place the plot. Used only for multigrid plots
- ax (
- 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 includesegments_exclude (
list
) – list of segments to excludeselect_features (
list
) – List of feature names(strings) to be plottedbins (
numpy array or list
) – array specifying bins, values should be bucketed into; If none deciles are used as intervalsplot (
bool
) – specifying whether to plot a heat mapfigsize (
tuple
) – size of plot**kwargs –
Keyword arguments:
- cmap (
str
) – any accepted python color map
- cmap (
- round_digits (
int
) – number of digits to which bin limits are to be rounded
- round_digits (
- 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 includesegments_exclude (
list
) – list of segments to excludeselect_features (
list
) – features to be analyzedbins (
list of lists or arrays
) – list of arrays specifying bins, values should be bucketed into; If none deciles are used as intervalsfigsize (
tuple
) – size of plotplot (
bool
) – specifying whether to plot a heat maptitle (
str
) – title for plot**kwargs –
Keyword arguments:
- cmap (
str
) – any accepted python color map
- cmap (
- round_digits (
int
) – number of digits to which bin limits are to be rounded
- round_digits (
- 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 purposesoracle_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 includesegments_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_scenario_job_ids(scenario_id=None)¶
This API returns the available JOB_IDs in KDD_JOB table for a given scenario
- Parameters:
scenario_id – scenario id. Ex: ‘116000031’
- Returns:
list of scenarios job ids
- Example:
>>> get_scenario_job_ids(scenario_id='116000031')
- 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 includefeatures_exclude (
list
) – list of segments to excludeplot (
bool
) – specifying whether to plot or notfigsize (
tuple
) – size of plotsample_frac (
float
) – fraction of sample to plot
- get_threshold_names(scenario_id='116000031', threshold_set_id=None)¶
This API loads the threshold names from KDD* table
- Parameters:
scenario_id – scenario id. Ex: ‘116000031’
threshold_set_id – (optional) threshold set id Ex: ‘116000048’
- Returns:
return dataframe
- Example:
>>> data_pdf = asc.get_threshold_names(scenario='116000031', threshold_set_id='116000048')
- get_threshold_set_ids(scenario_id=None, threshold_set_id=None)¶
This API loads the threshold values for tunable parameters from KDD threshold table
- Parameters:
scenario_id – scenario id. Ex: ‘116000031’
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_id='116000031', 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 retrievedsummary_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(scenario=None, definition_name=None, analysis='prod', debug=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:
scenario – name of the scenario
definition_name – folder name which will be created under Home/ASC/Analysis. Ex: ‘Sig Cash 2’
analysis – type of analysis whether prod or preprod. Default is prod
- Debug:
It should be True for debugging purposes. it also displays objective_id and zip filename used for scenario imported.
- Returns:
API response in json format.
- Examples:
>>> aif.import_analysis_templates(definition_name='Sig Cash 2') >>> >>> aif.import_analysis_templates(definition_name='RMF', analysis='preprod')
- insert_exec_status_to_db(execution_status_df=None, threshold_set_id=None, job_id=None)¶
- This API insert entry for each scenario being executed through execute_scenario API to track the triggered scenarios information
into table
asc_scenario_execution_status
This API is for internal use and being called inside APIexecute_scenario
- Parameters:
execution_status_df – dataframe contains notebook instance related information like Run Dates, scenario_code, objectiveId, modelid, notebookid, instanceid, model_status, api_status
threshold_set_id – Threshold set id for which scenario gets executed.
job_id – job id for which scenario gets executed.
- Returns:
None
- investigate_samples(strata_include=None, strata_exclude=None)¶
It pushes the selected samples to DB table
ASC_EVENT_SAMPLE
andASC_INVESTIGATED_ENTITIES
- Parameters:
strata_include (
list
) – strata to be includedstrata_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 dataframefeature (
str
) – name of the featureiqr_cut_off (
int
) – number of standard deviation. Default is 3tail (
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
and1
for non-outliers.- Parameters:
df (
dataframe
) – input dataframefeature (
str
) – name of the featureanomaly_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 dataframefeature (
str
) – name of the featureoutliers_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 dataframefeature (
str
) – name of the featurenstdev (
int
) – number of standard deviation. Default is 3tail (
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 tableload_asc_investigated_entities
: load ATL alerts for given run_id in ASC_INVESTIGATED_ENTITIESload_asc_event_master()
: load the events into ASC_EVENT_MASTER table for given run_ididentify_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 fetchedsegment (
str
) – select segment to displaystrata (
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(debug=False, show_status='current')¶
It shows the execution status of the scenarios. The status can be one of these:
RUNNING
FAILED
COMPLETED
- Parameters:
debug – True for debugging the error for particular job_id
show_status – Show scenario’s status for current executions or scenario’s status for current and all previous executions for given definition id and version.
- 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 featuressegments (
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 fetchedgroup_by (
str
) – aggregate alerts either byRUN_DATE
orSEGMENT_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 includedstrata_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 includedstrata_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_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 datesoracle_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 dataframefeature (
str
) – name of the featurenstdev (
int
) – number of standard deviation. Default is 3tail (
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