DeepEnsemble 0.0.3

An Wrapper for performing ensembling techniques on deep learning models

DEEP ENSEMBLE

Description

In the context of deep learning, This library is a wrapper, It allows one to perform ensembling techniques such as Stacked Ensembling, Weighted Ensembling, Ensembling based on Votes. This library get in an array of predictions made by different models on which the user wants to perform ensembling and it performs either of the three ensembling technique based on user choice.

• What this does?

  • Gets array of predictions(probabilities)
  • Performs ensembling
  • Returns Score of the metrics(user prefers)
  • Returns Y_predicted resulting due to ensembling

• Ensembling Techniques included

  • Weighted Ensembling
  • Ensembling by Voting
  • Stacked Ensembling

Deep Ensemble

Deep Ensemble is a python package for ensembling the prediciton results.

How to use:

Step 1: Install the libaray

pip install DeepEnsemble

Step 2:

Import the library, and specify the path of the csv file.

from DeepEnsemble.DeepEnsemble import DeepEnsembler

Ensembler = DeepEnsembler(Y_pred, Y_actual, type=None, predThreshold=0.5, metrics="accuracy_score")
score,Y_pred_ensembled = Ensembler.WeightedEnsembling()

Note:

• Y_pred - Array of predictions made by models
• Y_actual - Array of actual Class

There are some optional parameters that you can specify as listed below,

Usage:

from DeepEnsemble.DeepEnsemble import DeepEnsembler
Y_Pred = np.array([results of model1],
                  [results of model2],
                  [results of model3],
                   ....)
Y_actual = np.array([actual class])

---Example1:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Weighted", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.WeightedClassifier()

---Example2:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Voting", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.VotingClassifier()

---Example3:
Ensembler = DeepEnsembler(Y_Pred, Y_actual, type="Stacking", predThreshold=0.6, metrics="cohen_kappa_score")
score,Y_pred_ensembled = Ensembler.StackingClassifier()

Parameters

---

Parameter	Default Value	Limit	Example
Y_pred	none	Provide a array with results of different models.	np.array([results of model1],[results of model2],[results of model3],....)
Y_actual	,	Provide a array with actual class	np.array([actual class])
type	Weighted	Weighted, Voted, Stacked
predThreshold	0.5	0 to 1	0.5
metrics	Sklearn Classification metrics	specify any valid classificaiton metrics from sklearn	"accuracy_score"

--- THANK YOU, CHEERS ---