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metrics.py
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metrics.py
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import numpy as np
class Metrics():
""" Metrics parent class.
Attributes
----------
None
Methods
-------
__init__()
Constuctor.
"""
def __init__(self, ):
""" Constructor.
Parameters
----------
None
Notes
-----
None
"""
pass
class AccuracyMetrics(Metrics):
""" Accuracy metrics class.
Attributes
----------
name : str
The name of the metric.
Methods
-------
__init__()
Constuctor.
compute(y, scores)
Computes the accuracy of inferred numerical labels when compared to their true counterparts.
"""
def __init__(self, ):
""" Constructor.
Parameters
----------
None
Notes
-----
None
"""
super().__init__()
self.name = "accuracy"
def compute(self, y, scores):
""" Computes the accuracy of inferred numerical labels when compared to their true counterparts.
Parameters
----------
y : numpy.ndarray
True labels.
Shape is (number of data points, )
scores : numpy.ndarray
Activation of last layer of the model - the scores of the network.
Shape is (batch_size, out_dim) where out_dim is the output
dimension of the last layer of the model - usually same as
the number of classes.
Returns
-------
float
The accuracy of inferred numerical labels when compared to their true counterparts.
Notes
-----
None
Raises
------
AssertionError
If y.shape is not the same as y_hat.shape
"""
y_hat = np.argmax(scores, axis=1)
assert y.shape == y_hat.shape
n = y.shape[0]
return np.where(y_hat == y)[0].size / n
class MeanSquaredErrorMetrics(Metrics):
""" MSE metrics class.
Attributes
----------
name : str
The name of the metric.
Methods
-------
__init__()
Constuctor.
compute(y, scores)
Computes the MSE of inferred numerical labels when compared to their true counterparts.
"""
def __init__(self, ):
""" Constructor.
Parameters
----------
None
Notes
-----
None
"""
super().__init__()
self.name = "mse metrics"
def compute(self, y, scores):
""" Computes the MSE of inferred numerical labels when compared to their true counterparts.
Parameters
----------
y : numpy.ndarray
True labels.
Shape is (number of data points, )
scores : numpy.ndarray
Activation of last layer of the model - the scores of the network.
Shape is (batch_size, out_dim) where out_dim is the output
dimension of the last layer of the model - usually same as
the number of classes.
Returns
-------
float
The accuracy of inferred numerical labels when compared to their true counterparts.
Notes
-----
None
Raises
------
AssertionError
If y.shape is not the same as y_hat.shape
"""
return np.mean(np.square((y - scores)))