# cm2metrics
A lightweight package that analyzes multiple metrics directly from
confusion matrix.
## Features
- Get all classes parsing results in native _Dataframe_ format
- Print all classes or specified class parsing summary in friendly format
- Only requires _numpy_ and _pandas_, without relying on other machine learning packages.
- Easy to use with a few APIs
- Supports **16 metrics** for each class:
* **tp**: true positive
* **tn**: true negative
* **fp**: false positive
* **fn**: false negative
* **tpr**: true positive rate
* **tnr**: true negative rate
* **fpr**: false positive rate
* **fnr**: false negative rate
* **atc**: actual true count
* **afc**: actual false count
* **ptc**: predict true count
* **pfc**: predict false count
* **accruacy**
* **precision**
* **recall**
* **f1**
## General
- **Version**: 0.1
- **Dependency**: Python(3.6,3.7.3.8), numpy, pandas
## Install
```
pip install cm2metrics
```
## Use
#### General use
0. Generate a confusion matrix
<pre>
# use scikitlearn
from sklearn.metrics import confusion_matrix
#cm is ndarray, convert to dataframe
cm = confusion_matrix(true_target, pred_target)
df_cm = pd.DataFrame(cm, index=class_names, columns=class_names)
# or, use a randomly generated confusion matrix(for test)
# see details in cm_test.py
class_names = {0:"class0", 1:"class1", 2:"class2"}
df_cm = pd.DataFrame([(1,2,3),(4,5,6),(7,8,9)])
df_cm.rename(index=class_names, columns=class_names, inplace=True)
</pre>
1. Init a confusion matrix parser
<pre>
from cm2metrics.parse_cm import ConfusionMatrixParser
cm_parser = ConfusionMatrixParser(df_cm)
</pre>
2. Parse the confusion matrix
<pre>
# parsing result(cm_parsed) is a dataframe
cm_parsed = cm_parser.parse_confusion_matrix()
print(cm_parsed)
Sample output:
tp fp tn fn tpr fpr tnr fnr atc afc ptc pfc accuracy precision recall f1
class0 1 28 11 5 0.166667 0.717949 0.282051 0.833333 6 39 12 33 0.644444 0.083333 0.166667 0.111111
class1 5 20 10 10 0.333333 0.666667 0.333333 0.666667 15 30 15 30 0.555556 0.333333 0.333333 0.333333
class2 9 12 9 15 0.375000 0.571429 0.428571 0.625000 24 21 18 27 0.466667 0.500000 0.375000 0.428571
# get class0 true positive
tp = cm_parsed.loc["class0"].at["tp"]
</pre>
#### Print parsing summary in friendly format
<pre>
# print one class summary by name using class_name parameter
cm_parser.print_summary(class_name="class0")
# print one class summary by index in confusion matrix using class_index parameter
cm_parser.print_summary(class_index=0)
# print all classes summary by not specifying parameters
cm_parser.print_summary()
Sample output for class0 summary:
Summary for class0
TP: 1
TN: 28
FP: 11
FN: 5
TPR: 0.167
TNR: 0.718
FPR: 0.282
FNR: 0.833
Actual true count: 6
Actual false count: 39
Predict true count: 12
Predict false count: 33
Accuracy: 0.644
Precision: 0.083
Recall: 0.167
F1: 0.111
</pre>
# License
MIT license.
