https://github.com/freakwill/thomas
My Bayes algorithm, for the name of Thomas Bayes 👨🔬
https://github.com/freakwill/thomas
bayes-classifier naive-bayes-classifier python
Last synced: about 2 months ago
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My Bayes algorithm, for the name of Thomas Bayes 👨🔬
- Host: GitHub
- URL: https://github.com/freakwill/thomas
- Owner: Freakwill
- License: mit
- Created: 2018-10-06T13:10:28.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-22T12:57:33.000Z (about 7 years ago)
- Last Synced: 2025-06-21T15:04:14.374Z (about 1 year ago)
- Topics: bayes-classifier, naive-bayes-classifier, python
- Language: Python
- Homepage:
- Size: 2.62 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Thomas
My Bayes algorithm, for the name of Thomas Bayes
## Features
* Cope with continuous random varaibles intellegently.
* integer random varaibles (e.g. the mass of things with integer gram) will be treated as continuous ones in some case.
## Requirement
* numpy
* pandas
* scikit-learn (in examples)
* neupy
## Install
`pip install tomas`
not `thomas` which had been registered.
## Why
For the Honor of T. Bayes
## Grammar
Just see the example file.
## Examples
```python
import pandas as pd
from mystat import *
from scipy.stats import chi2_contingency
from sklearn.model_selection import train_test_split
train = pd.read_excel('modeling_data.xls', encoding='utf-8')
train['批次时间'] = [_.total_seconds() for _ in train['批次完成时间'] - train['批次开始时间']]
train['G-W-T'] = list(zip(train['克重'], train['重量'], train['批次时间']))
train['L-M'] = list(zip(train['长度'], train['门幅']))
# train = train.drop(columns=['批次开始时间', '批次完成时间', '配方ID', '流程卡号', '缸号', '批次时间'])
y_train = train['质量问题']
x_train, x_test, y_train, y_test = train_test_split(train, y_train, test_size=0.2)
# seperate the data in x_train to 1 + 3 groups as x_train and z_trains
x_train = x_train[['机器', '弹力', '氨纶', '织物', '纱线', '颜色', '客户', '月份', 'L-M', 'G-W-T']]
z_trains = x_train[[s for s in train.columns if s.startswith('助')]], x_train[[s for s in train.columns if s.startswith('染')]], x_train[[s for s in train.columns if s.startswith('光')]]
from tomas import *
def nb():
models = None # use GRNN to fit data (z_trains, y_train)
nbc = ZeroOneHemiNaiveBayesClassifier.fromDataFrame(x_train, z_trains, y_train, models)
y_pred = nbc.predictdf(x_test)
scores = check(y_test, y_pred)
print(report(scores))
nb()
# =>
| -> C1 | -> C0|
C1 | 128 | 43 |
C0 | 217 | 431 |
----------------
f-score(p) 0.4961
f-socre(n) 0.7683
mcc 0.3405
```
## Is it easy?
Yes
## Principle
