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@@ -0,0 +1 @@ +/veritastool-2.0.2.tar.gz diff --git a/python-veritastool.spec b/python-veritastool.spec new file mode 100644 index 0000000..9565ba4 --- /dev/null +++ b/python-veritastool.spec @@ -0,0 +1,727 @@ +%global _empty_manifest_terminate_build 0 +Name: python-veritastool +Version: 2.0.2 +Release: 1 +Summary: Veritas Diagnosis tool for fairness & transparency assessment. +License: Apache 2.0 +URL: https://pypi.org/project/veritastool/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/9c/1f/eb84cfd6e3eff54e3caed29ad0bbac18fb19866812d4e497e6248cb94567/veritastool-2.0.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-pandas +Requires: python3-ipywidgets +Requires: python3-ipython +Requires: python3-matplotlib +Requires: python3-tqdm +Requires: python3-phik +Requires: python3-shap +Requires: python3-matplotlib-inline +Requires: python3-pytest +Requires: python3-Jinja2 + +%description + + +# Veritas Toolkit +[](https://codecov.io/gh/mas-veritas2/veritastool) +[](https://badge.fury.io/py/veritastool)[](https://www.python.org/downloads/release/python-3110/) +[](https://www.python.org/downloads/release/python-3916/) +[](https://www.python.org/downloads/release/python-3816/) +[](https://github.com/mas-veritas2/veritastool/blob/master/license.txt) +[](https://github.com/mas-veritas2/veritastool/actions/workflows/python-package.yml) + + + + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/main/icon/veritas_logo_new.png" ></p> + + +The purpose of this toolkit is to facilitate the adoption of Veritas Methodology on Fairness & Transparency Assessment and spur industry development. It will also +benefit customers by improving the fairness and transparency of financial services delivered by AIDA systems. + + +## Installation + +The easiest way to install veritastool is to download it from [`PyPI`](https://pypi.org/project/veritastool/). It's going to install the library itself and its prerequisites as well. It is suggested to create virtual environment with requirements.txt file first. + +```python +pip install veritastool +``` + +Then, you will be able to import the library and use its functionalities. Before we do that, we can run a test function on our sample datasets to see if our codes are performing as expected. + +```python +from veritastool.util.utility import test_function_cs +test_function_cs() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/test_evaluate_cs.png" width="800" height="100"></p> + +### Initialization ## + +You can now import the custom library that you would to use for diagnosis. In this example we will use the Credit Scoring custom library. + +```python +from veritastool.model.modelwrapper import ModelWrapper +from veritastool.model.model_container import ModelContainer +from veritastool.usecases.credit_scoring import CreditScoring +``` + +Once the relevant use case object (CreditScoring) and model container (ModelContainer) has been imported, you can upload your contents into the container and initialize the object for diagnosis. + +```python + +import pickle +import numpy as np + +#Load Credit Scoring Test Data +# NOTE: Assume current working directory is the root folder of the cloned veritastool repository +file = "./veritastool/examples/data/credit_score_dict.pickle" +input_file = open(file, "rb") +cs = pickle.load(input_file) + +#Model Contariner Parameters +y_true = np.array(cs["y_test"]) +y_pred = np.array(cs["y_pred"]) +y_train = np.array(cs["y_train"]) +p_grp = {'SEX': [1], 'MARRIAGE':[1]} +up_grp = {'SEX': [2], 'MARRIAGE':[2]} +x_train = cs["X_train"] +x_test = cs["X_test"] +model_name = "credit_scoring" +model_type = "classification" +y_prob = cs["y_prob"] +model_obj = LogisticRegression(C=0.1) +model_obj.fit(x_train, y_train) #fit the model as required for transparency analysis + +#Create Model Container +container = ModelContainer(y_true, p_grp, model_type, model_name, y_pred, y_prob, y_train, x_train=x_train, \ + x_test=x_test, model_object=model_obj, up_grp=up_grp) + +#Create Use Case Object +cre_sco_obj= CreditScoring(model_params = [container], fair_threshold = 80, fair_concern = "eligible", \ + fair_priority = "benefit", fair_impact = "normal", perf_metric_name="accuracy", \ + tran_row_num = [20,40], tran_max_sample = 1000, tran_pdp_feature = ['LIMIT_BAL'], tran_max_display = 10) + +``` +### API functions ### + +Below are the API functions that the user can execute to obtain the fairness and transparency diagnosis of their use cases. + +**Evaluate** + +The evaluate API function computes all performance and fairness metrics and renders it in a table format (default). It +also highlights the primary performance and fairness metrics (automatic if not specified by user). + +```python +cre_sco_obj.evaluate() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2.png" width="608" height="596"></p> + +You can also toggle the widget to view your results in a interactive visualization format. + +```python +cre_sco_obj.evaluate(visualize = True) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2_visualize.png" width="858" height="530"></p> + +**Tradeoff** + +Computes trade-off between performance and fairness. + +```python +cre_sco_obj.tradeoff() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/tradeoff_2.png" width="625" height="516"></p> + +** Note: Replace {Balanced Accuracy} with the respective given metrics. + +**Feature Importance** + +Computes feature importance of protected features using leave one out analysis. + +```python +cre_sco_obj.feature_importance() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/faeture_imp_2.png" width="828" height="653"></p> + +**Root Cause** + +Computes the importance of variables contributing to the bias. + +```python +cre_sco_obj.root_cause() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/rootcause_2.png" width="581" height="530"></p> + +**Mitigate** + +User can choose methods to mitigate the bias. + +```python +mitigated = cre_sco_obj.mitigate(p_var=[], method=['reweigh', 'correlation', 'threshold']) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/mitigate_2.png" width="576" height="662"></p> + +**Explain** + +Runs the transparency analysis - global & local interpretability, partial dependence analysis and permutation importance + +```python +#run the entire transparency analysis +cre_sco_obj.explain() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/explain_2.png" width="624" height="1034"></p> + +```python +#get the local interpretability plot for specific row index and model +cre_sco_obj.explain(local_row_num = 20) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/local_2.png" width="514" height="464"></p> + +**Compile** + +Generates model artifact file in JSON format. This function also runs all the API functions if it hasn't already been run. + +```python +cre_sco_obj.compile() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/compile_2.png" width="529" height="209"></p> + +**Model Artifact** + +A JSON file that stores all the results from all the APIs. + +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/json_2.png" width="456" height="494"></p> + +## Examples + +You may refer to our example notebooks below to see how the toolkit can be applied: + +| Filename | Description | +| -----------------------| ------------- | +| [`CS_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/CS_demo.ipynb)| Tutorial notebook to diagnose a credit scoring model for predicting customers' loan repayment. | +| [`CM_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/customer_marketing_example/CM_demo.ipynb) | Tutorial notebook to diagnose a customer marketing uplift model for selecting existing customers for a marketing call to increase the sales of loan product. | +| [`BaseClassification_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseClassification_demo.ipynb) | Tutorial notebook for a multi-class propensity model | +| [`BaseRegression_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseRegression_demo.ipynb) | Tutorial notebook for a prediciton of a continuous target variable | +| [`PUW_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/PUW_demo.ipynb) | Tutorial notebook for a binary classification model to predict whether to award insurance policy by assessing risk | +| [`NewUseCaseCreation_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/NewUseCaseCreation_demo.ipynb) | Tutorial notebook to create a new use case note-book and add custom metrics | +| [`nonPythonModel_customMetric_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/nonPythonModel_customMetric_demo.ipynb) | Tutorial notebook to diagnose a credit scoring model by LibSVM (non-Python) with custom metric. | + +## License + +Veritas Toolkit is licensed under the Apache License, Version 2.0 - see [`LICENSE`](https://raw.githubusercontent.com/mas-veritas2/veritastool/master/license.txt) for more details. + + + + +%package -n python3-veritastool +Summary: Veritas Diagnosis tool for fairness & transparency assessment. +Provides: python-veritastool +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-veritastool + + +# Veritas Toolkit +[](https://codecov.io/gh/mas-veritas2/veritastool) +[](https://badge.fury.io/py/veritastool)[](https://www.python.org/downloads/release/python-3110/) +[](https://www.python.org/downloads/release/python-3916/) +[](https://www.python.org/downloads/release/python-3816/) +[](https://github.com/mas-veritas2/veritastool/blob/master/license.txt) +[](https://github.com/mas-veritas2/veritastool/actions/workflows/python-package.yml) + + + + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/main/icon/veritas_logo_new.png" ></p> + + +The purpose of this toolkit is to facilitate the adoption of Veritas Methodology on Fairness & Transparency Assessment and spur industry development. It will also +benefit customers by improving the fairness and transparency of financial services delivered by AIDA systems. + + +## Installation + +The easiest way to install veritastool is to download it from [`PyPI`](https://pypi.org/project/veritastool/). It's going to install the library itself and its prerequisites as well. It is suggested to create virtual environment with requirements.txt file first. + +```python +pip install veritastool +``` + +Then, you will be able to import the library and use its functionalities. Before we do that, we can run a test function on our sample datasets to see if our codes are performing as expected. + +```python +from veritastool.util.utility import test_function_cs +test_function_cs() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/test_evaluate_cs.png" width="800" height="100"></p> + +### Initialization ## + +You can now import the custom library that you would to use for diagnosis. In this example we will use the Credit Scoring custom library. + +```python +from veritastool.model.modelwrapper import ModelWrapper +from veritastool.model.model_container import ModelContainer +from veritastool.usecases.credit_scoring import CreditScoring +``` + +Once the relevant use case object (CreditScoring) and model container (ModelContainer) has been imported, you can upload your contents into the container and initialize the object for diagnosis. + +```python + +import pickle +import numpy as np + +#Load Credit Scoring Test Data +# NOTE: Assume current working directory is the root folder of the cloned veritastool repository +file = "./veritastool/examples/data/credit_score_dict.pickle" +input_file = open(file, "rb") +cs = pickle.load(input_file) + +#Model Contariner Parameters +y_true = np.array(cs["y_test"]) +y_pred = np.array(cs["y_pred"]) +y_train = np.array(cs["y_train"]) +p_grp = {'SEX': [1], 'MARRIAGE':[1]} +up_grp = {'SEX': [2], 'MARRIAGE':[2]} +x_train = cs["X_train"] +x_test = cs["X_test"] +model_name = "credit_scoring" +model_type = "classification" +y_prob = cs["y_prob"] +model_obj = LogisticRegression(C=0.1) +model_obj.fit(x_train, y_train) #fit the model as required for transparency analysis + +#Create Model Container +container = ModelContainer(y_true, p_grp, model_type, model_name, y_pred, y_prob, y_train, x_train=x_train, \ + x_test=x_test, model_object=model_obj, up_grp=up_grp) + +#Create Use Case Object +cre_sco_obj= CreditScoring(model_params = [container], fair_threshold = 80, fair_concern = "eligible", \ + fair_priority = "benefit", fair_impact = "normal", perf_metric_name="accuracy", \ + tran_row_num = [20,40], tran_max_sample = 1000, tran_pdp_feature = ['LIMIT_BAL'], tran_max_display = 10) + +``` +### API functions ### + +Below are the API functions that the user can execute to obtain the fairness and transparency diagnosis of their use cases. + +**Evaluate** + +The evaluate API function computes all performance and fairness metrics and renders it in a table format (default). It +also highlights the primary performance and fairness metrics (automatic if not specified by user). + +```python +cre_sco_obj.evaluate() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2.png" width="608" height="596"></p> + +You can also toggle the widget to view your results in a interactive visualization format. + +```python +cre_sco_obj.evaluate(visualize = True) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2_visualize.png" width="858" height="530"></p> + +**Tradeoff** + +Computes trade-off between performance and fairness. + +```python +cre_sco_obj.tradeoff() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/tradeoff_2.png" width="625" height="516"></p> + +** Note: Replace {Balanced Accuracy} with the respective given metrics. + +**Feature Importance** + +Computes feature importance of protected features using leave one out analysis. + +```python +cre_sco_obj.feature_importance() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/faeture_imp_2.png" width="828" height="653"></p> + +**Root Cause** + +Computes the importance of variables contributing to the bias. + +```python +cre_sco_obj.root_cause() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/rootcause_2.png" width="581" height="530"></p> + +**Mitigate** + +User can choose methods to mitigate the bias. + +```python +mitigated = cre_sco_obj.mitigate(p_var=[], method=['reweigh', 'correlation', 'threshold']) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/mitigate_2.png" width="576" height="662"></p> + +**Explain** + +Runs the transparency analysis - global & local interpretability, partial dependence analysis and permutation importance + +```python +#run the entire transparency analysis +cre_sco_obj.explain() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/explain_2.png" width="624" height="1034"></p> + +```python +#get the local interpretability plot for specific row index and model +cre_sco_obj.explain(local_row_num = 20) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/local_2.png" width="514" height="464"></p> + +**Compile** + +Generates model artifact file in JSON format. This function also runs all the API functions if it hasn't already been run. + +```python +cre_sco_obj.compile() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/compile_2.png" width="529" height="209"></p> + +**Model Artifact** + +A JSON file that stores all the results from all the APIs. + +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/json_2.png" width="456" height="494"></p> + +## Examples + +You may refer to our example notebooks below to see how the toolkit can be applied: + +| Filename | Description | +| -----------------------| ------------- | +| [`CS_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/CS_demo.ipynb)| Tutorial notebook to diagnose a credit scoring model for predicting customers' loan repayment. | +| [`CM_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/customer_marketing_example/CM_demo.ipynb) | Tutorial notebook to diagnose a customer marketing uplift model for selecting existing customers for a marketing call to increase the sales of loan product. | +| [`BaseClassification_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseClassification_demo.ipynb) | Tutorial notebook for a multi-class propensity model | +| [`BaseRegression_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseRegression_demo.ipynb) | Tutorial notebook for a prediciton of a continuous target variable | +| [`PUW_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/PUW_demo.ipynb) | Tutorial notebook for a binary classification model to predict whether to award insurance policy by assessing risk | +| [`NewUseCaseCreation_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/NewUseCaseCreation_demo.ipynb) | Tutorial notebook to create a new use case note-book and add custom metrics | +| [`nonPythonModel_customMetric_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/nonPythonModel_customMetric_demo.ipynb) | Tutorial notebook to diagnose a credit scoring model by LibSVM (non-Python) with custom metric. | + +## License + +Veritas Toolkit is licensed under the Apache License, Version 2.0 - see [`LICENSE`](https://raw.githubusercontent.com/mas-veritas2/veritastool/master/license.txt) for more details. + + + + +%package help +Summary: Development documents and examples for veritastool +Provides: python3-veritastool-doc +%description help + + +# Veritas Toolkit +[](https://codecov.io/gh/mas-veritas2/veritastool) +[](https://badge.fury.io/py/veritastool)[](https://www.python.org/downloads/release/python-3110/) +[](https://www.python.org/downloads/release/python-3916/) +[](https://www.python.org/downloads/release/python-3816/) +[](https://github.com/mas-veritas2/veritastool/blob/master/license.txt) +[](https://github.com/mas-veritas2/veritastool/actions/workflows/python-package.yml) + + + + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/main/icon/veritas_logo_new.png" ></p> + + +The purpose of this toolkit is to facilitate the adoption of Veritas Methodology on Fairness & Transparency Assessment and spur industry development. It will also +benefit customers by improving the fairness and transparency of financial services delivered by AIDA systems. + + +## Installation + +The easiest way to install veritastool is to download it from [`PyPI`](https://pypi.org/project/veritastool/). It's going to install the library itself and its prerequisites as well. It is suggested to create virtual environment with requirements.txt file first. + +```python +pip install veritastool +``` + +Then, you will be able to import the library and use its functionalities. Before we do that, we can run a test function on our sample datasets to see if our codes are performing as expected. + +```python +from veritastool.util.utility import test_function_cs +test_function_cs() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/test_evaluate_cs.png" width="800" height="100"></p> + +### Initialization ## + +You can now import the custom library that you would to use for diagnosis. In this example we will use the Credit Scoring custom library. + +```python +from veritastool.model.modelwrapper import ModelWrapper +from veritastool.model.model_container import ModelContainer +from veritastool.usecases.credit_scoring import CreditScoring +``` + +Once the relevant use case object (CreditScoring) and model container (ModelContainer) has been imported, you can upload your contents into the container and initialize the object for diagnosis. + +```python + +import pickle +import numpy as np + +#Load Credit Scoring Test Data +# NOTE: Assume current working directory is the root folder of the cloned veritastool repository +file = "./veritastool/examples/data/credit_score_dict.pickle" +input_file = open(file, "rb") +cs = pickle.load(input_file) + +#Model Contariner Parameters +y_true = np.array(cs["y_test"]) +y_pred = np.array(cs["y_pred"]) +y_train = np.array(cs["y_train"]) +p_grp = {'SEX': [1], 'MARRIAGE':[1]} +up_grp = {'SEX': [2], 'MARRIAGE':[2]} +x_train = cs["X_train"] +x_test = cs["X_test"] +model_name = "credit_scoring" +model_type = "classification" +y_prob = cs["y_prob"] +model_obj = LogisticRegression(C=0.1) +model_obj.fit(x_train, y_train) #fit the model as required for transparency analysis + +#Create Model Container +container = ModelContainer(y_true, p_grp, model_type, model_name, y_pred, y_prob, y_train, x_train=x_train, \ + x_test=x_test, model_object=model_obj, up_grp=up_grp) + +#Create Use Case Object +cre_sco_obj= CreditScoring(model_params = [container], fair_threshold = 80, fair_concern = "eligible", \ + fair_priority = "benefit", fair_impact = "normal", perf_metric_name="accuracy", \ + tran_row_num = [20,40], tran_max_sample = 1000, tran_pdp_feature = ['LIMIT_BAL'], tran_max_display = 10) + +``` +### API functions ### + +Below are the API functions that the user can execute to obtain the fairness and transparency diagnosis of their use cases. + +**Evaluate** + +The evaluate API function computes all performance and fairness metrics and renders it in a table format (default). It +also highlights the primary performance and fairness metrics (automatic if not specified by user). + +```python +cre_sco_obj.evaluate() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2.png" width="608" height="596"></p> + +You can also toggle the widget to view your results in a interactive visualization format. + +```python +cre_sco_obj.evaluate(visualize = True) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/evaluate_2_visualize.png" width="858" height="530"></p> + +**Tradeoff** + +Computes trade-off between performance and fairness. + +```python +cre_sco_obj.tradeoff() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/tradeoff_2.png" width="625" height="516"></p> + +** Note: Replace {Balanced Accuracy} with the respective given metrics. + +**Feature Importance** + +Computes feature importance of protected features using leave one out analysis. + +```python +cre_sco_obj.feature_importance() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/faeture_imp_2.png" width="828" height="653"></p> + +**Root Cause** + +Computes the importance of variables contributing to the bias. + +```python +cre_sco_obj.root_cause() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/rootcause_2.png" width="581" height="530"></p> + +**Mitigate** + +User can choose methods to mitigate the bias. + +```python +mitigated = cre_sco_obj.mitigate(p_var=[], method=['reweigh', 'correlation', 'threshold']) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/mitigate_2.png" width="576" height="662"></p> + +**Explain** + +Runs the transparency analysis - global & local interpretability, partial dependence analysis and permutation importance + +```python +#run the entire transparency analysis +cre_sco_obj.explain() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/explain_2.png" width="624" height="1034"></p> + +```python +#get the local interpretability plot for specific row index and model +cre_sco_obj.explain(local_row_num = 20) +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/local_2.png" width="514" height="464"></p> + +**Compile** + +Generates model artifact file in JSON format. This function also runs all the API functions if it hasn't already been run. + +```python +cre_sco_obj.compile() +``` +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/compile_2.png" width="529" height="209"></p> + +**Model Artifact** + +A JSON file that stores all the results from all the APIs. + +Output: + +<p align="center"><img src="https://raw.githubusercontent.com/mas-veritas2/veritastool/master/icon/json_2.png" width="456" height="494"></p> + +## Examples + +You may refer to our example notebooks below to see how the toolkit can be applied: + +| Filename | Description | +| -----------------------| ------------- | +| [`CS_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/CS_demo.ipynb)| Tutorial notebook to diagnose a credit scoring model for predicting customers' loan repayment. | +| [`CM_Demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/customer_marketing_example/CM_demo.ipynb) | Tutorial notebook to diagnose a customer marketing uplift model for selecting existing customers for a marketing call to increase the sales of loan product. | +| [`BaseClassification_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseClassification_demo.ipynb) | Tutorial notebook for a multi-class propensity model | +| [`BaseRegression_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/BaseRegression_demo.ipynb) | Tutorial notebook for a prediciton of a continuous target variable | +| [`PUW_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/PUW_demo.ipynb) | Tutorial notebook for a binary classification model to predict whether to award insurance policy by assessing risk | +| [`NewUseCaseCreation_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/NewUseCaseCreation_demo.ipynb) | Tutorial notebook to create a new use case note-book and add custom metrics | +| [`nonPythonModel_customMetric_demo.ipynb`](https://github.com/mas-veritas2/veritastool/blob/master/veritastool/examples/nonPythonModel_customMetric_demo.ipynb) | Tutorial notebook to diagnose a credit scoring model by LibSVM (non-Python) with custom metric. | + +## License + +Veritas Toolkit is licensed under the Apache License, Version 2.0 - see [`LICENSE`](https://raw.githubusercontent.com/mas-veritas2/veritastool/master/license.txt) for more details. + + + + +%prep +%autosetup -n veritastool-2.0.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-veritastool -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.2-1 +- Package Spec generated @@ -0,0 +1 @@ +e34547a451d782aae913ae76f432bccf veritastool-2.0.2.tar.gz |