%global _empty_manifest_terminate_build 0 Name: python-churneval Version: 1.3 Release: 1 Summary: churneval is a python package for evaluating churn models License: LICENSE.txt URL: https://pypi.org/project/churneval/ Source0: https://mirrors.aliyun.com/pypi/web/packages/0d/b7/3df790382621d9510eb905cb0e6625913e94a027a08a33b9b1cfa7d0c135/churneval-1.3.tar.gz BuildArch: noarch Requires: python3-churneval Requires: python3-pandas Requires: python3-sklearn Requires: python3-matplotlib %description # Package "churneval" #### **Version:** 1.3 #### **Author:** *Soumi De* #### **Maintained by:** Soumi De <> #### **Description:** churneval is a package to evaluate models used in churn classification. The evaluation metrics include accuracy, sensitivity, specificity, precision, F1-score and top decile lift. The package also contains functions to plot lift curve and gain curve of a model. #### **License:** GPL-3 #### **Date:** 12th November, 2021
___ ### **Function:** get_performance_metrics Function that returns evaluation metrics ___
### **Usage:** from churneval import get_performance_metrics get_performance_metrics(model_name, true_class, predicted_class, predicted_probs) ### **Arguments:** * model_name: Abbreviated name of the churn model (in text) * true_class: A dataframe of true class labels with shape (n,1) * predicted_class: An array of binary predicted class with shape (n,) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** A dataframe consisting of elements given below: * Model_Name: Abbreviated name of the churn model * Accuracy: Accuracy of churn model * Confusion Matrix: A 2X2 array representing confusion matrix * Precision: Precision value * Sensitivity: Sensitivity value * Specificity: Specificity value * F1-score: F1-score * ROC_score: Area under the curve * top_dec_lift: Top decile lift value
___ ### **Function:** top_decile_lift Function that returns top decile lift of a sample ___
### **Usage:** from churneval import top_decile_lift top_decile_lift(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A float object with top decile lift value
___ ### **Function:** lift_curve Function that plots lift curve of a model ___
### **Usage:** from churneval import lift_curve lift_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Lift of the model
___ ### **Function:** gain_curve Function that plots gain curve of a model ___
### **Usage:** from churneval import gain_curve gain_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Gain of the model %package -n python3-churneval Summary: churneval is a python package for evaluating churn models Provides: python-churneval BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-churneval # Package "churneval" #### **Version:** 1.3 #### **Author:** *Soumi De* #### **Maintained by:** Soumi De <> #### **Description:** churneval is a package to evaluate models used in churn classification. The evaluation metrics include accuracy, sensitivity, specificity, precision, F1-score and top decile lift. The package also contains functions to plot lift curve and gain curve of a model. #### **License:** GPL-3 #### **Date:** 12th November, 2021
___ ### **Function:** get_performance_metrics Function that returns evaluation metrics ___
### **Usage:** from churneval import get_performance_metrics get_performance_metrics(model_name, true_class, predicted_class, predicted_probs) ### **Arguments:** * model_name: Abbreviated name of the churn model (in text) * true_class: A dataframe of true class labels with shape (n,1) * predicted_class: An array of binary predicted class with shape (n,) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** A dataframe consisting of elements given below: * Model_Name: Abbreviated name of the churn model * Accuracy: Accuracy of churn model * Confusion Matrix: A 2X2 array representing confusion matrix * Precision: Precision value * Sensitivity: Sensitivity value * Specificity: Specificity value * F1-score: F1-score * ROC_score: Area under the curve * top_dec_lift: Top decile lift value
___ ### **Function:** top_decile_lift Function that returns top decile lift of a sample ___
### **Usage:** from churneval import top_decile_lift top_decile_lift(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A float object with top decile lift value
___ ### **Function:** lift_curve Function that plots lift curve of a model ___
### **Usage:** from churneval import lift_curve lift_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Lift of the model
___ ### **Function:** gain_curve Function that plots gain curve of a model ___
### **Usage:** from churneval import gain_curve gain_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Gain of the model %package help Summary: Development documents and examples for churneval Provides: python3-churneval-doc %description help # Package "churneval" #### **Version:** 1.3 #### **Author:** *Soumi De* #### **Maintained by:** Soumi De <> #### **Description:** churneval is a package to evaluate models used in churn classification. The evaluation metrics include accuracy, sensitivity, specificity, precision, F1-score and top decile lift. The package also contains functions to plot lift curve and gain curve of a model. #### **License:** GPL-3 #### **Date:** 12th November, 2021
___ ### **Function:** get_performance_metrics Function that returns evaluation metrics ___
### **Usage:** from churneval import get_performance_metrics get_performance_metrics(model_name, true_class, predicted_class, predicted_probs) ### **Arguments:** * model_name: Abbreviated name of the churn model (in text) * true_class: A dataframe of true class labels with shape (n,1) * predicted_class: An array of binary predicted class with shape (n,) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** A dataframe consisting of elements given below: * Model_Name: Abbreviated name of the churn model * Accuracy: Accuracy of churn model * Confusion Matrix: A 2X2 array representing confusion matrix * Precision: Precision value * Sensitivity: Sensitivity value * Specificity: Specificity value * F1-score: F1-score * ROC_score: Area under the curve * top_dec_lift: Top decile lift value
___ ### **Function:** top_decile_lift Function that returns top decile lift of a sample ___
### **Usage:** from churneval import top_decile_lift top_decile_lift(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A float object with top decile lift value
___ ### **Function:** lift_curve Function that plots lift curve of a model ___
### **Usage:** from churneval import lift_curve lift_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Lift of the model
___ ### **Function:** gain_curve Function that plots gain curve of a model ___
### **Usage:** from churneval import gain_curve gain_curve(true_class, predicted_probs) ### **Arguments:** * true_class: A dataframe of true class labels with shape (n,1) * predicted_probs: An array of predicted class probabilities with shape (n,) ### **Returned Values:** * A plot that shows lift curve * x-axis: Proportion of data * y-axis: Gain of the model %prep %autosetup -n churneval-1.3 %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-churneval -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 1.3-1 - Package Spec generated