Work still in progress. When first version is ready - the package will be published to PyPi
Ember is a project that aims to provide developer with easy to use interface to create machine learning pipelines. From preprocessing to final optimized model.
It is based on scikit-learn and gradient boosting frameworks like:
It also uses some other libraries like
- Category encoders - For more encoding options
- hyperopt - For automatic optimization of models
To your disposal are:
- Independent modules:
- Automatic Learner Module:
Currently you need to clone the repository or download it as zip and then include the code in project manually. It should change soon so you will be able to use PyPi package.
In most cases you would like to use independent modules for quickly assembling your model. This process is inspired by the way keras works.
from ember.preprocessing import Preprocessor
from ember.utils import DtypeSelector
from ember.optimize import GridSelector, BayesSelector
from sklearn.pipeline import make_pipeline
import pandas as pd
import numpy as np
from ember.impute import GeneralImputer
from ember.preprocessing import GeneralEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
dataset_classification = r'Ember\datasets\classification\autos.csv'
data = pd.read_csv(dataset_classification)
X, y = data.drop(columns = ['class']), data['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, random_state = 42)
preprocessor = Preprocessor()
preprocessor.add_branch("categorical")
preprocessor.add_branch('numerical')
preprocessor.add_transformer_to_branch("numerical", DtypeSelector(np.number))
preprocessor.add_transformer_to_branch("numerical", GeneralImputer('Simple'))
preprocessor.add_transformer_to_branch("categorical", DtypeSelector(np.object))
preprocessor.add_transformer_to_branch("categorical", GeneralImputer('Simple', strategy='most_frequent'))
preprocessor.add_transformer_to_branch("categorical", GeneralEncoder(kind = 'LE'))
final = preprocessor.merge()
model = GridSelector('classification')
clf_pipe = make_pipeline(final, model)
clf_pipe.fit(X_train, y_train)
print(accuracy_score(y_test, clf_pipe.predict(X_test)))As you can see creating whole pipeline is very simple. If you were to skip imports it would be many times shorter than equivalent written in standard way.
You also have an option to completely automate the process. In this case you can use Learner class from autolearn module.
from ember.autolearn import Learner
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, r2_score
dataset_regression = r'Ember\datasets\regression\auto-price.csv'
data = pd.read_csv(dataset_regression)
X, y = data.drop(columns = ['class']), data['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42, test_size = 20)
learner = Learner(objective='regression', X = X_train, y = y_train)
learner.fit(cv = 5, optimizer = 'bayes', cat=False, speed=100)
results = learner.predict(X_test)
print(r2_score(y_test, results))The way Learner creates and optimizes model can be controlled by providing different hyperparameters to its fit() method.
If you wish to use Embers AutoML functionality in your web browser, you can run command:
streamlit run streamlit_app.py
It will open server on localhost with simple application providing beforementioned functionality.
- More clearly comment modules - closer following sklearn docs convention
- Extend optimization module so it can be used in more general cases
- Build visualization module
- Build automatic dashboard
- Sphinx docs
- Publish as PyPi package