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Automation Toolkit for Machine Learning

Project description

atml

atml tries to address the following problem: Given a new data set, is there any toolkit that could help me quickly and conveniently come up with a good baseline model?

try it out

  • Install from PyPi
pip install atml
  • Load the data set or use your own one
import pandas as pd

df = pd.read_csv("./test/data/binary_data.csv")
X = df.drop('Survived', axis=1)

label_columns = ['Survived']
y = df[label_columns]
  • Instantiate and run with AtmlController with default
from atml import AtmlController

atml_c = AtmlController(with_default=True)
atml_c.run(X, y)
  • Register a new Model and Hyper parameter space for tuning
from atml import AtmlOrchestrator

atml_o = AtmlOrchestrator(with_default=True)

from sklearn.svm import SVC
sp = [
    {"property": "kernel", "type": "choice", "value": ["linear", "rbf"]},
    {"property": "gamma", "type": "choice", "value": ["scale", "auto"]}     
]
atml_o.auto_learning_socket.register(SVC(), sp)

atml_o.run(X, y)
'''

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