معرفی شرکت ها
boexplain-0.1.1
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
BOExplain
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | boexplain-0.1.1 |
| نام | boexplain |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Brandon Lockhart |
| ایمیل نویسنده | brandon_lockhart@sfu.ca |
| آدرس صفحه اصلی | https://github.com/sfu-db/BOExplain |
| آدرس اینترنتی | https://pypi.org/project/boexplain/ |
| مجوز | MIT |
# BOExplain, Explaining Inference Queries with Bayesian Optimization
BOExplain is a library for explaining inference queries with Bayesian optimization. The corresponding paper can be found at https://arxiv.org/abs/2102.05308.
## Installation
```
pip install boexplain
```
## Documentation
The documentation is available at [https://sfu-db.github.io/BOExplain/](https://sfu-db.github.io/BOExplain/). (shortcut to [fmin](https://sfu-db.github.io/BOExplain/api_reference/boexplain.files.search.html#boexplain.files.search.fmin), [fmax](https://sfu-db.github.io/BOExplain/api_reference/boexplain.files.search.html#boexplain.files.search.fmax))
## Getting Started
Derive an explanation for why the predicted rate of having an income over $50K is higher for men compared to women in the UCI ML [Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult).
1. Load the data and prepare it for ML.
``` python
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
df = pd.read_csv("adult.data",
names=[
"Age", "Workclass", "fnlwgt", "Education",
"Education-Num", "Marital Status", "Occupation",
"Relationship", "Race", "Gender", "Capital Gain",
"Capital Loss", "Hours per week", "Country", "Income"
],
na_values=" ?")
df['Income'].replace({" <=50K": 0, ' >50K': 1}, inplace=True)
df['Gender'].replace({" Male": 0, ' Female': 1}, inplace=True)
df = pd.get_dummies(df)
train, test = train_test_split(df, test_size=0.2)
test = test.drop(columns='Income')
```
2. Define the objective function that trains a random forest classifier and queries the ratio of predicted rates of having an income over $50K between men and women.
``` python
def obj(train_filtered):
rf = RandomForestClassifier(n_estimators=13, random_state=0)
rf.fit(train_filtered.drop(columns='Income'), train_filtered['Income'])
test["prediction"] = rf.predict(test)
rates = test.groupby("Gender")["prediction"].sum() / test.groupby("Gender")["prediction"].size()
test.drop(columns='prediction', inplace=True)
return rates[0] / rates[1]
```
3. Use the function `fmin` to minimize the objective function.
``` python
from boexplain import fmin
train_filtered = fmin(
data=train,
f=obj,
columns=["Age", "Education-Num"],
runtime=30,
)
```
<!-- which returns a predicate 28 <= Age <= 59 and 6 <= Education-Num <= 16. Removing the tuples satisfying the returned predicate reduces the ratio from 3.54 to 2.7. -->
## Reproduce the Experiments
To reproduce the experiments, you can clone the repo and create a poetry environment (install [Poetry](https://python-poetry.org/docs/#installation)). Run
```bash
poetry install
```
To setup the poetry environment a for jupyter notebook, run
```bash
poetry run ipython kernel install --name=boexplain
```
An ipython kernel has been created for this environemnt.
### Adult Experiment
To reproduce the results of the Adult experiment and recreate Figure 6, follow the instruction in [adult.ipynb](https://github.com/sfu-db/BOExplain/blob/main/adult.ipynb).
### Credit Experiment
To reproduce the results of the Credit experiment and recreate Figure 8, follow the instruction in [credit.ipynb](https://github.com/sfu-db/BOExplain/blob/main/credit.ipynb).
### House Experiment
To reproduce the results of the House experiment and recreate Figure 7, follow the instruction in [house.ipynb](https://github.com/sfu-db/BOExplain/blob/main/house.ipynb).
### Scorpion Synthetic Data Experiment
To reproduce the results of the experiment with Scorpion's synthetic data and corresponding query, and recreate Figure 4, follow the instruction in [scorpion.ipynb](https://github.com/sfu-db/BOExplain/blob/main/scorpion.ipynb).
نیازمندی
| مقدار | نام |
|---|---|
| ==1.2.1 | pandas |
| ==1.20.0 | numpy |
| ==1.6.0 | scipy |
| ==0.24.1 | scikit-learn |
| ==4.1.0 | altair |
| ==0.0 | imblearn |
| ==4.51.0 | tqdm |
| ==4.4.0 | colorlog |
| ==0.3.0 | numpyencoder |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9,<4.0 | Python |
نحوه نصب
نصب پکیج whl boexplain-0.1.1:
pip install boexplain-0.1.1.whl
نصب پکیج tar.gz boexplain-0.1.1:
pip install boexplain-0.1.1.tar.gz