<filename>mesostat/stat/performance.py
import numpy as np
import scipy


def accuracy(nHit, nMiss, nFA, nCR):
    return (nHit + nCR) / (nHit + nMiss + nFA + nCR)


def d_prime(nHit, nMiss, nFA, nCR):
    hitRate = nHit / (nHit + nMiss)
    faRate  = nFA  / (nFA + nCR)

    zHit = scipy.stats.norm.ppf(hitRate)
    zFA = scipy.stats.norm.ppf(faRate)

    #     hitRateAdjusted = (nHit + 0.5) /((nHit + 0.5) + nMiss + 1)
    #     faRateAdjusted  = (nFA  + 0.5) /((nFA  + 0.5) + nCR + 1)

    #     zHit = scipy.stats.norm.ppf(hitRateAdjusted)
    #     zFA = scipy.stats.norm.ppf(faRateAdjusted)

    return zHit - zFA


# Given number of elements in a subset and total set, return their ratio
# If both are zero, return zero
# Complain if values are inadequate
def rate_0_protected(nArrSub, nArrTot):
    assert ~np.any(nArrSub < 0),       "Only non-negative arrays allowed"
    assert ~np.any(nArrTot < 0),       "Only non-negative arrays allowed"
    assert ~np.any(nArrTot < nArrSub), "Subset can't have greater size than the whole set"

    nonZeroIdx = nArrTot != 0
    rez = np.copy(nArrSub)
    rez[nonZeroIdx] /= nArrTot[nonZeroIdx]
    return rez


def accuracy_indices(yHat, y):
    if yHat.shape == y.shape:
        yReal = y
    elif (yHat.ndim == 2) and (y.ndim == 1) and (yHat.shape[0] == y.shape[0]):
        # If correct output is the same for all tests, extrude it along the second dimensions
        yReal = np.repeat([y], yHat.shape[1], axis=0).T
    else:
        raise ValueError("Unexpected shapes", yHat.shape, y.shape)

    idxsDict = {
        'TP' :  yHat &  yReal,  # True Positives
        'FP' :  yHat & ~yReal,  # False Positives
        'FN' : ~yHat &  yReal,  # False Negatives
        'TN' : ~yHat & ~yReal   # True Negatives
    }

    return yReal, idxsDict


def accuracy_tests(yHat, y, axis=0):
    yReal, idxsDict = accuracy_indices(yHat, y)

    freqTrue     = np.mean(yReal, axis=axis)    # Frequency of true outcomes
    freqHatTrue  = np.mean(yHat, axis=axis)     # Frequency of true predictions
    freqFalse    = 1 - freqTrue                 # Frequency of false outcomes
    freqHatFalse = 1 - freqHatTrue              # Frequency of false predictions

    freqTP = np.mean(idxsDict['TP'], axis=axis)   # Frequency of True Positives
    freqFP = np.mean(idxsDict['FP'], axis=axis)   # Frequency of False Positives
    freqFN = np.mean(idxsDict['FN'], axis=axis)   # Frequency of False Negatives
    freqTN = np.mean(idxsDict['TN'], axis=axis)   # Frequency of True Negatives

    TPR = rate_0_protected(freqTP, freqTrue)   # True Positive Rate - number of TP out of all that were possible
    FPR = rate_0_protected(freqFP, freqFalse)  # False Positive Rate - number of FP out of all that were possible

    return {
        "TP frequency": freqTP,
        "FP frequency": freqFP,
        "FN frequency": freqFN,
        "TN frequency": freqTN,
        "TPR": TPR,
        "FPR": FPR
    }

<filename>mesostat/stat/performance.py
import numpy as np
import scipy


def accuracy(nHit, nMiss, nFA, nCR):
    return (nHit + nCR) / (nHit + nMiss + nFA + nCR)


def d_prime(nHit, nMiss, nFA, nCR):
    hitRate = nHit / (nHit + nMiss)
    faRate  = nFA  / (nFA + nCR)

    zHit = scipy.stats.norm.ppf(hitRate)
    zFA = scipy.stats.norm.ppf(faRate)

    #     hitRateAdjusted = (nHit + 0.5) /((nHit + 0.5) + nMiss + 1)
    #     faRateAdjusted  = (nFA  + 0.5) /((nFA  + 0.5) + nCR + 1)

    #     zHit = scipy.stats.norm.ppf(hitRateAdjusted)
    #     zFA = scipy.stats.norm.ppf(faRateAdjusted)

    return zHit - zFA


# Given number of elements in a subset and total set, return their ratio
# If both are zero, return zero
# Complain if values are inadequate
def rate_0_protected(nArrSub, nArrTot):
    assert ~np.any(nArrSub < 0),       "Only non-negative arrays allowed"
    assert ~np.any(nArrTot < 0),       "Only non-negative arrays allowed"
    assert ~np.any(nArrTot < nArrSub), "Subset can't have greater size than the whole set"

    nonZeroIdx = nArrTot != 0
    rez = np.copy(nArrSub)
    rez[nonZeroIdx] /= nArrTot[nonZeroIdx]
    return rez


def accuracy_indices(yHat, y):
    if yHat.shape == y.shape:
        yReal = y
    elif (yHat.ndim == 2) and (y.ndim == 1) and (yHat.shape[0] == y.shape[0]):
        # If correct output is the same for all tests, extrude it along the second dimensions
        yReal = np.repeat([y], yHat.shape[1], axis=0).T
    else:
        raise ValueError("Unexpected shapes", yHat.shape, y.shape)

    idxsDict = {
        'TP' :  yHat &  yReal,  # True Positives
        'FP' :  yHat & ~yReal,  # False Positives
        'FN' : ~yHat &  yReal,  # False Negatives
        'TN' : ~yHat & ~yReal   # True Negatives
    }

    return yReal, idxsDict


def accuracy_tests(yHat, y, axis=0):
    yReal, idxsDict = accuracy_indices(yHat, y)

    freqTrue     = np.mean(yReal, axis=axis)    # Frequency of true outcomes
    freqHatTrue  = np.mean(yHat, axis=axis)     # Frequency of true predictions
    freqFalse    = 1 - freqTrue                 # Frequency of false outcomes
    freqHatFalse = 1 - freqHatTrue              # Frequency of false predictions

    freqTP = np.mean(idxsDict['TP'], axis=axis)   # Frequency of True Positives
    freqFP = np.mean(idxsDict['FP'], axis=axis)   # Frequency of False Positives
    freqFN = np.mean(idxsDict['FN'], axis=axis)   # Frequency of False Negatives
    freqTN = np.mean(idxsDict['TN'], axis=axis)   # Frequency of True Negatives

    TPR = rate_0_protected(freqTP, freqTrue)   # True Positive Rate - number of TP out of all that were possible
    FPR = rate_0_protected(freqFP, freqFalse)  # False Positive Rate - number of FP out of all that were possible

    return {
        "TP frequency": freqTP,
        "FP frequency": freqFP,
        "FN frequency": freqFN,
        "TN frequency": freqTN,
        "TPR": TPR,
        "FPR": FPR
    }

#     hitRateAdjusted = (nHit + 0.5) /((nHit + 0.5) + nMiss + 1) #     faRateAdjusted  = (nFA  + 0.5) /((nFA  + 0.5) + nCR + 1) #     zHit = scipy.stats.norm.ppf(hitRateAdjusted) #     zFA = scipy.stats.norm.ppf(faRateAdjusted) # Given number of elements in a subset and total set, return their ratio # If both are zero, return zero # Complain if values are inadequate # If correct output is the same for all tests, extrude it along the second dimensions # True Positives # False Positives # False Negatives # True Negatives # Frequency of true outcomes # Frequency of true predictions # Frequency of false outcomes # Frequency of false predictions # Frequency of True Positives # Frequency of False Positives # Frequency of False Negatives # Frequency of True Negatives # True Positive Rate - number of TP out of all that were possible # False Positive Rate - number of FP out of all that were possible