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'''
calc_performance_metrics_for_binary_predictions
Provides implementation of common metrics for assessing a binary classifier's
hard decisions against true binary labels, including:
* accuracy
* true positive rate and true negative rate (TPR and TNR)
* positive predictive value and negative predictive value (PPV and NPV)
'''
import numpy as np
def calc_TP_TN_FP_FN(ytrue_N, yhat_N):
''' Count the four possible states of true and predicted binary values.
Args
----
ytrue_N : 1D array, shape (n_examples,) = (N,)
All values must be either 0 or 1. Will be cast to int dtype.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array, shape (n_examples,) = (N,)
All values must be either 0 or 1. Will be cast to int dtype.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
TP : int
Number of true positives
TN : int
Number of true negatives
FP : int
Number of false positives
FN : int
Number of false negatives
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> TP, TN, FP, FN = calc_TP_TN_FP_FN(ytrue_N, yhat_N)
>>> TP
2
>>> TN
3
>>> FP
1
>>> FN
2
>>> np.allclose(TP + TN + FP + FN, N)
True
'''
# Cast input to integer just to be sure we're getting what's expected
ytrue_N = np.asarray(ytrue_N, dtype=np.int32)
yhat_N = np.asarray(yhat_N, dtype=np.int32)
# TODO fix by calculating the number of true pos, true neg, etc.
TP = 0
TN = 0
FP = 0
FN = 0
return TP, TN, FP, FN
def calc_ACC(ytrue_N, yhat_N):
''' Compute the accuracy of provided predicted binary values.
Args
----
ytrue_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
acc : float
Accuracy = ratio of number correct over total number of examples
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> acc = calc_ACC(ytrue_N, yhat_N)
>>> print("%.3f" % acc)
0.625
'''
# TODO compute accuracy
# You should *use* your calc_TP_TN_FP_FN function from above
# Hint: make sure denominator will never be exactly zero
# by adding a small value like 1e-10
return 0.0
def calc_TPR(ytrue_N, yhat_N):
''' Compute the true positive rate of provided predicted binary values.
Also known as the recall.
Args
----
ytrue_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
tpr : float
TPR = ratio of true positives over total labeled positive
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> tpr = calc_TPR(ytrue_N, yhat_N)
>>> print("%.3f" % tpr)
0.500
# Verify what happens with empty input
>>> empty_val = calc_TPR([], [])
>>> print("%.3f" % empty_val)
0.000
'''
# TODO compute TPR
# You should *use* your calc_TP_TN_FP_FN function from above
# Hint: make sure denominator will never be exactly zero
# by adding a small value like 1e-10
return 0.0
def calc_TNR(ytrue_N, yhat_N):
''' Compute the true negative rate of provided predicted binary values.
Args
----
ytrue_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
tnr : float
TNR = ratio of true negatives over total labeled negative.
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> tnr = calc_TNR(ytrue_N, yhat_N)
>>> print("%.3f" % tnr)
0.750
# Verify what happens with empty input
>>> empty_val = calc_TNR([], [])
>>> print("%.3f" % empty_val)
0.000
'''
# TODO compute TNR
# You should *use* your calc_TP_TN_FP_FN function from above
# Hint: make sure denominator will never be exactly zero
# by adding a small value like 1e-10
return 0.0
def calc_PPV(ytrue_N, yhat_N):
''' Compute positive predictive value of provided predicted binary values.
Also known as the precision.
Args
----
ytrue_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
ppv : float
PPV = ratio of true positives over total predicted positive.
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> ppv = calc_PPV(ytrue_N, yhat_N)
>>> print("%.3f" % ppv)
0.667
# Verify what happens with empty input
>>> empty_val = calc_PPV([], [])
>>> print("%.3f" % empty_val)
0.000
'''
# TODO compute PPV
# You should *use* your calc_TP_TN_FP_FN function from above
# Hint: make sure denominator will never be exactly zero
# by adding a small value like 1e-10
return 0.0
def calc_NPV(ytrue_N, yhat_N):
''' Compute negative predictive value of provided predicted binary values.
Args
----
ytrue_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents the binary 'true' label of one example
One entry per example in current dataset
yhat_N : 1D array of floats, shape (n_examples,) = (N,)
All values must be either 0.0 or 1.0.
Each entry represents a predicted label for one example
One entry per example in current dataset.
Needs to be same size as ytrue_N.
Returns
-------
npv : float
NPV = ratio of true negative over total predicted negative.
Examples
--------
>>> N = 8
>>> ytrue_N = np.asarray([0., 0., 0., 0., 1., 1., 1., 1.])
>>> yhat_N = np.asarray([0., 0., 1., 0., 1., 1., 0., 0.])
>>> npv = calc_NPV(ytrue_N, yhat_N)
>>> print("%.3f" % npv)
0.600
# Verify what happens with empty input
>>> empty_val = calc_NPV([], [])
>>> print("%.3f" % empty_val)
0.000
'''
# TODO compute NPV
# You should *use* your calc_TP_TN_FP_FN function from above
# Hint: make sure denominator will never be exactly zero
# by adding a small value like 1e-10
return 0.0
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