Metadata-Version: 2.1
Name: classification-reportzr
Version: 0.0.1b4
Summary: Automate machine learning classification task report for Pak Zuherman
Home-page: https://github.com/khalidm31415/classification-reportzr
License: UNKNOWN
Keywords: classification report,laporan klasifikasi,zuherman,zr
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: Cython (<1.0.0,>=0.28.5)
Requires-Dist: pandas (<2.0.0,>=1.0.0)
Requires-Dist: pytest (<7.0.0,>=6.2.1)
Requires-Dist: scikit-learn (<1.0.0,>=0.18.0)
Requires-Dist: typing-extensions (<4.0.0.0,>=3.10.0.0)
Requires-Dist: wheel (<1.0.0,>=0.36.2)

# Classification Reportzr

Automate machine learning classification task report for Pak Zuherman

## Install

```bash
pip install -U classification-reportzr
```

## Test

```bash
pytest -v
```

## Usage

### Setting-up the experiment

```python
from sklearn import datasets
from sklearn.svm import SVC

from reporterzr import Reporterzr

iris = datasets.load_iris()
samples, labels = iris.data[:-1], iris.target[:-1]

param_grid = {
    'C': [10,50,100],
    'gamma': [0.005,0.05,0.5]
}
svc_reporter = Reporterzr(SVC, param_grid)
```

### Run The Experiment

```python
# `test_sizes` defaults to [0.1, ..., 0.9]
# `repetition` defaults to 10
svc_reporter.run_experiment(samples, labels, test_sizes=[0.1, 0.2], repetition=3)
```

prints

```
    Test Size    C  gamma       Train Accuracies  Max Train  Mean Train  Stdev Train        Test Accuracies  Max Test  Mean Test  Stdev Test
0         0.1   10  0.005     [0.97, 0.97, 0.97]      0.970       0.970          0.0  [0.933, 0.933, 0.933]     0.933      0.933         0.0
1         0.1   10  0.050  [0.993, 0.993, 0.993]      0.993       0.993          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
2         0.1   10  0.500  [0.985, 0.985, 0.985]      0.985       0.985          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
3         0.1   50  0.005  [0.993, 0.993, 0.993]      0.993       0.993          0.0  [0.933, 0.933, 0.933]     0.933      0.933         0.0
4         0.1   50  0.050  [0.985, 0.985, 0.985]      0.985       0.985          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
5         0.1   50  0.500  [0.993, 0.993, 0.993]      0.993       0.993          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
6         0.1  100  0.005  [0.993, 0.993, 0.993]      0.993       0.993          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
7         0.1  100  0.050  [0.985, 0.985, 0.985]      0.985       0.985          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
8         0.1  100  0.500  [0.985, 0.985, 0.985]      0.985       0.985          0.0  [0.867, 0.867, 0.867]     0.867      0.867         0.0
9         0.2   10  0.005  [0.958, 0.958, 0.958]      0.958       0.958          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
10        0.2   10  0.050  [0.992, 0.992, 0.992]      0.992       0.992          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
11        0.2   10  0.500  [0.983, 0.983, 0.983]      0.983       0.983          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
12        0.2   50  0.005  [0.983, 0.983, 0.983]      0.983       0.983          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
13        0.2   50  0.050  [0.966, 0.966, 0.966]      0.966       0.966          0.0  [0.967, 0.967, 0.967]     0.967      0.967         0.0
14        0.2   50  0.500  [0.975, 0.975, 0.975]      0.975       0.975          0.0  [0.967, 0.967, 0.967]     0.967      0.967         0.0
15        0.2  100  0.005  [0.992, 0.992, 0.992]      0.992       0.992          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
16        0.2  100  0.050  [0.975, 0.975, 0.975]      0.975       0.975          0.0        [1.0, 1.0, 1.0]     1.000      1.000         0.0
17        0.2  100  0.500  [0.992, 0.992, 0.992]      0.992       0.992          0.0  [0.967, 0.967, 0.967]     0.967      0.967         0.0
```


