معرفی شرکت ها
ROCFunctions-0.1.2
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Receiver Operating Characteristic (ROC) functions package.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | ROCFunctions-0.1.2 |
| نام | ROCFunctions |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Anton Antonov |
| ایمیل نویسنده | antononcube@posteo.net |
| آدرس صفحه اصلی | https://github.com/antononcube/Python-packages/tree/main/ROCFunctions |
| آدرس اینترنتی | https://pypi.org/project/ROCFunctions/ |
| مجوز | - |
# ROCFunctions basic usage
This repository has the code of a Python package for
[Receiver Operating Characteristic (ROC)](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)
functions.
The ROC framework is used for analysis and tuning of binary classifiers, [Wk1].
(The classifiers are assumed to classify into a positive/true label or a negative/false label. )
For computational introduction to ROC utilization (in Mathematica) see the article
["Basic example of using ROC with Linear regression"](https://mathematicaforprediction.wordpress.com/2016/10/12/basic-example-of-using-roc-with-linear-regression/)
,
[AA1].
The examples below use the package
["RandomDataGenerators"](https://pypi.org/project/RandomDataGenerators/),
[AA2].
-------
## Installation
From PyPI.org:
```shell
python3 -m pip install ROCFunctions
```
------
## Usage examples
### Properties
Here are some retrieval functions:
```python
import pandas
from ROCFunctions import *
print(roc_functions("properties"))
```
['FunctionInterpretations', 'FunctionNames', 'Functions', 'Methods', 'Properties']
```python
print(roc_functions("FunctionInterpretations"))
```
{'TPR': 'true positive rate', 'TNR': 'true negative rate', 'SPC': 'specificity', 'PPV': 'positive predictive value', 'NPV': 'negative predictive value', 'FPR': 'false positive rate', 'FDR': 'false discovery rate', 'FNR': 'false negative rate', 'ACC': 'accuracy', 'AUROC': 'area under the ROC curve', 'FOR': 'false omission rate', 'F1': 'F1 score', 'MCC': 'Matthews correlation coefficient', 'Recall': 'same as TPR', 'Precision': 'same as PPV', 'Accuracy': 'same as ACC', 'Sensitivity': 'same as TPR'}
```python
print(roc_functions("FPR"))
```
<function FPR at 0x7f7612f48050>
### Single ROC record
**Definition:** A
ROC record (ROC-dictionary, or ROC-hash, or ROC-hash-map) is an associative object that has the keys:
"FalseNegative", "FalsePositive", "TrueNegative", "TruePositive".Here is an example:
```python
{"FalseNegative": 50, "FalsePositive": 51, "TrueNegative": 60, "TruePositive": 39}
```
{'FalseNegative': 50,
'FalsePositive': 51,
'TrueNegative': 60,
'TruePositive': 39}
Here we generate a random "dataset" with columns "Actual" and "Predicted" that have the values
"true" and "false"and show the summary:
```python
from RandomDataGenerators import *
dfRandomLabels = random_data_frame(200, ["Actual", "Predicted"],
generators={"Actual": ["true", "false"],
"Predicted": ["true", "false"]})
dfRandomLabels.shape
```
(200, 2)
Here is a sample of the dataset:
```python
print(dfRandomLabels[:4])
```
Actual Predicted
0 false false
1 false false
2 false false
3 true false
Here we make the corresponding ROC dictionary:
```python
to_roc_dict('true', 'false',
list(dfRandomLabels.Actual.values),
list(dfRandomLabels.Predicted.values))
```
{'TruePositive': 52,
'FalsePositive': 48,
'TrueNegative': 50,
'FalseNegative': 50}
### Multiple ROC records
Here we make random dataset with entries that associated with a certain threshold parameter with three unique values:
```python
dfRandomLabels2 = random_data_frame(200, ["Threshold", "Actual", "Predicted"],
generators={"Threshold": [0.2, 0.4, 0.6],
"Actual": ["true", "false"],
"Predicted": ["true", "false"]})
```
**Remark:** Threshold parameters are typically used while tuning Machine Learning (ML) classifiers. Here we find and print the ROC records(dictionaries) for each unique threshold value:
```python
thresholds = list(dfRandomLabels2.Threshold.drop_duplicates())
rocGroups = {}
for x in thresholds:
dfLocal = dfRandomLabels2[dfRandomLabels2["Threshold"] == x]
rocGroups[x] = to_roc_dict('true', 'false',
list(dfLocal.Actual.values),
list(dfLocal.Predicted.values))
rocGroups
```
{0.4: {'TruePositive': 13,
'FalsePositive': 23,
'TrueNegative': 24,
'FalseNegative': 12},
0.2: {'TruePositive': 18,
'FalsePositive': 11,
'TrueNegative': 19,
'FalseNegative': 18},
0.6: {'TruePositive': 23,
'FalsePositive': 9,
'TrueNegative': 16,
'FalseNegative': 14}}
### Application of ROC functions
Here we define a list of ROC functions:
```python
funcs = ["PPV", "NPV", "TPR", "ACC", "SPC", "MCC"]
```
Here we apply each ROC function to each of the ROC records obtained above:
```python
import pandas
rocRes = { k : {f: roc_functions(f)(v) for f in funcs} for (k, v) in rocGroups.items()}
print(pandas.DataFrame(rocRes))
```
0.4 0.2 0.6
PPV 0.361111 0.620690 0.718750
NPV 0.666667 0.513514 0.533333
TPR 0.520000 0.500000 0.621622
ACC 0.513889 0.560606 0.629032
SPC 0.510638 0.633333 0.640000
MCC 0.030640 0.134535 0.261666
-------
## References
### Articles
[Wk1] Wikipedia entry,
["Receiver operating characteristic"](https://en.wikipedia.org/wiki/Receiver_operating_characteristic).
[AA1] Anton Antonov,
["Basic example of using ROC with Linear regression"](https://mathematicaforprediction.wordpress.com/2016/10/12/basic-example-of-using-roc-with-linear-regression/)
,
(2016),
[MathematicaForPrediction at WordPress](https://mathematicaforprediction.wordpress.com).
[AA2] Anton Antonov,
["Introduction to data wrangling with Raku"](https://rakuforprediction.wordpress.com/2021/12/31/introduction-to-data-wrangling-with-raku/)
,
(2021),
[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).
### Packages
[AAp1] Anton Antonov,
[ROCFunctions Mathematica package](https://github.com/antononcube/MathematicaForPrediction/blob/master/ROCFunctions.m),
(2016-2022),
[MathematicaForPrediction at GitHub/antononcube](https://github.com/antononcube/MathematicaForPrediction/).
[AAp2] Anton Antonov,
[ROCFunctions R package](https://github.com/antononcube/R-packages/tree/master/ROCFunctions),
(2021),
[R-packages at GitHub/antononcube](https://github.com/antononcube/R-packages).
[AAp3] Anton Antonov,
[ML::ROCFunctions Raku package](https://github.com/antononcube/Raku-ML-ROCFunctions),
(2022),
[GitHub/antononcube](https://github.com/antononcube).
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl ROCFunctions-0.1.2:
pip install ROCFunctions-0.1.2.whl
نصب پکیج tar.gz ROCFunctions-0.1.2:
pip install ROCFunctions-0.1.2.tar.gz