path: root/python-churneval.spec

%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 <<soumi.de@res.christuniversity.in>>

#### **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
<br>

___


### **Function:**
    get_performance_metrics               Function that returns evaluation metrics

___
<br>

### **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

<br>


___

### **Function:**
    top_decile_lift               Function that returns top decile lift of a sample

___
<br>

### **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

<br>


___

### **Function:**
    lift_curve               Function that plots lift curve of a model

___
<br>

### **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

<br>


___

### **Function:**
    gain_curve               Function that plots gain curve of a model

___
<br>

### **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 <<soumi.de@res.christuniversity.in>>

#### **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
<br>

___


### **Function:**
    get_performance_metrics               Function that returns evaluation metrics

___
<br>

### **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

<br>


___

### **Function:**
    top_decile_lift               Function that returns top decile lift of a sample

___
<br>

### **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

<br>


___

### **Function:**
    lift_curve               Function that plots lift curve of a model

___
<br>

### **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

<br>


___

### **Function:**
    gain_curve               Function that plots gain curve of a model

___
<br>

### **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 <<soumi.de@res.christuniversity.in>>

#### **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
<br>

___


### **Function:**
    get_performance_metrics               Function that returns evaluation metrics

___
<br>

### **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

<br>


___

### **Function:**
    top_decile_lift               Function that returns top decile lift of a sample

___
<br>

### **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

<br>


___

### **Function:**
    lift_curve               Function that plots lift curve of a model

___
<br>

### **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

<br>


___

### **Function:**
    gain_curve               Function that plots gain curve of a model

___
<br>

### **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 <Python_Bot@openeuler.org> - 1.3-1
- Package Spec generated