Bases: Metalearner_Base
A Weighted Mean based Metalearner.
This class should be passed to an ensemble function/class like Stacking for combining predictions.
This Metalearner computes the Area Under the Receiver Operating Characteristic Curve (ROC AUC)
for each model, and utilizes these scores for a weighted Mean to average predictions.
Info
Can be utilized for binary, multi-class and multi-label tasks.
Source code in aucmedi/ensemble/metalearner/averaging_mean_weighted.py
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98 | class AveragingWeightedMean(Metalearner_Base):
""" A Weighted Mean based Metalearner.
This class should be passed to an ensemble function/class like Stacking for combining predictions.
This Metalearner computes the Area Under the Receiver Operating Characteristic Curve (ROC AUC)
for each model, and utilizes these scores for a weighted Mean to average predictions.
!!! info
Can be utilized for binary, multi-class and multi-label tasks.
"""
#---------------------------------------------#
# Initialization #
#---------------------------------------------#
def __init__(self):
self.model = {}
#---------------------------------------------#
# Training #
#---------------------------------------------#
def train(self, x, y):
# Identify number of models and classes
n_classes = y.shape[1]
n_models = int(x.shape[1] / n_classes)
# Preprocess data input
data = np.reshape(x, (x.shape[0], n_models, n_classes))
# Compute AUC scores and store them to cache
weights = []
for m in range(n_models):
pred = data[:,m,:]
score = roc_auc_score(y, pred, average="macro")
weights.append(score)
# Store results to cache
self.model["weights"] = weights
self.model["n_classes"] = n_classes
self.model["n_models"] = n_models
#---------------------------------------------#
# Prediction #
#---------------------------------------------#
def predict(self, data):
# Preprocess data input
preds = np.reshape(data, (data.shape[0],
self.model["n_models"],
self.model["n_classes"]))
# Compute weighted mean
pred = np.average(preds, axis=1, weights=self.model["weights"])
# Return results
return pred
#---------------------------------------------#
# Dump Model to Disk #
#---------------------------------------------#
def dump(self, path):
# Dump model to disk via pickle
with open(path, "wb") as pickle_writer:
pickle.dump(self.model, pickle_writer)
#---------------------------------------------#
# Load Model from Disk #
#---------------------------------------------#
def load(self, path):
# Load model from disk via pickle
with open(path, "rb") as pickle_reader:
self.model = pickle.load(pickle_reader)
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