# Quickstart¶

The best way is get started is to quickly jump into an example: Here is a Google Colab notebook to train a simple Logistic Regression model on the UCI Adult dataset.
And here is a step-by-step tutorial.

## Model class creation¶

Create a subclass of seldonian.algorithm.SeldonianAlgorithm class.

from seldonian.algorithm import *
class ExampleSeldonianModel(SeldonianAlgorithm):
def __init__(self, *params, **kwargs ):
example_model = Model()
#initialize all the model parameters
pass


Now that we have a basic model setup, we need to implement the abstract method of SeldonianAlgorithm class.

• predict - This is a basic prediction method that uses the current model parameters to predict the output targets.

from seldonian.algorithm import *
class ExampleSeldonianModel(SeldonianAlgorithm):
def __init__(self, *params, **kwargs ):
self.example_model = Model()
#initialize all the model parameters
pass
def predict(self, X, **kwargs):
# prediction based on teh model
return self.example_model.predict(X)

• data returns the complete data and targets as a tuple back. This includes the safety as well as the candidate data.

from seldonian.algorithm import *
class ExampleSeldonianModel(SeldonianAlgorithm):
def __init__(self, *params, **kwargs ):
self.example_model = Model()
#initialize all the model parameters
pass
def predict(self, X, **kwargs):
# prediction based on teh model
return self.example_model.predict(X)
def data(self):
return X, y

• fit trains the model with the constraints.

from seldonian.algorithm import *
class ExampleSeldonianModel(SeldonianAlgorithm):
def __init__(self, *params, **kwargs ):
self.example_model = Model()
#initialize all the model parameters
pass
def predict(self, X, **kwargs):
# prediction based on teh model
return self.example_model.predict(X)
def data(self):
return self.X, self.y
def fit(self, *args, **kwargs):
# fit model based under the constraint that g >0.
pass


There are various examples of such constraint optimization problems implemented like the Lagrangian 2 player game as implemented in the VanillaNN class.

Or using a barrier when optimizing using a Black box optimization technique like CMA-ES or scipy.optimize.minimize class. You can find them under the seldonian.seldonian package.

• _safetyTest performs a the safety test using the safety set, or predicts the upper bound of the constraint g(\theta) during candidate selection (or in this case, fit).

from seldonian.algorithm import *
class ExampleSeldonianModel(SeldonianAlgorithm):
def __init__(self, *params, **kwargs ):
self.example_model = Model()
#initialize all the model parameters
pass
def predict(self, X, **kwargs):
# prediction based on teh model
return self.example_model.predict(X)
def data(self):
return self.X, self.y
def fit(self, *args, **kwargs):
# fit model based under the constraint that g >0.
pass
def _safetyTest(self, predict, **kwargs):
if predict:
# predict the upper bound during candidate selection
return 1 if passed_is_predicted else 0
pass
else:
# run the actual safety test
return 1 if passed else 0
pass
pass


## Training¶

This is all you need to implement a Seldonian model. You also need some constraints that are basically function callables. Some examples of such constraints is present in the seldonian.objectives package. A sample run would look something like this -


constraints = [constraint1, constraint2,...] #list of function callables
seldonian_model = ExampleSeldonianModel(constriants, data, other_args)
X, y = data
seldonian_model.fit(X, y)
return seldonian_model if seldonian_model._safetyTest() else NSF # No solution found
# we now have a trained model you can now do your predictions on this model