Search_result/srr3.py
def quadratic_weighted_kappa(y, y_pred):
    """
    Calculates the quadratic weighted kappa
    axquadratic_weighted_kappa calculates the quadratic weighted kappa
    value, which is a measure of inter-rater agreement between two raters
    that provide discrete numeric ratings.  Potential values range from -1
    (representing complete disagreement) to 1 (representing complete
    agreement).  A kappa value of 0 is expected if all agreement is due to
    chance.
    quadratic_weighted_kappa(rater_a, rater_b), where rater_a and rater_b
    each correspond to a list of integer ratings.  These lists must have the
    same length.
    The ratings should be integers, and it is assumed that they contain
    the complete range of possible ratings.
    quadratic_weighted_kappa(X, min_rating, max_rating), where min_rating
    is the minimum possible rating, and max_rating is the maximum possible
    rating
    """
    rater_a = y
    rater_b = y_pred
    min_rating=None
    max_rating=None
    rater_a = np.array(rater_a, dtype=int)
    rater_b = np.array(rater_b, dtype=int)
    assert(len(rater_a) == len(rater_b))
    if min_rating is None:
        min_rating = min(min(rater_a), min(rater_b))
    if max_rating is None:
        max_rating = max(max(rater_a), max(rater_b))
    conf_mat = confusion_matrix(rater_a, rater_b,
                                min_rating, max_rating)
    num_ratings = len(conf_mat)
    num_scored_items = float(len(rater_a))

    hist_rater_a = histogram(rater_a, min_rating, max_rating)
    hist_rater_b = histogram(rater_b, min_rating, max_rating)

    numerator = 0.0
    denominator = 0.0

    for i in range(num_ratings):
        for j in range(num_ratings):
            expected_count = (hist_rater_a[i] * hist_rater_b[j]
                              / num_scored_items)
            d = pow(i - j, 2.0) / pow(num_ratings - 1, 2.0)
            numerator += d * conf_mat[i][j] / num_scored_items
            denominator += d * expected_count / num_scored_items

    return (1.0 - numerator / denominator)

    Calculates the quadratic weighted kappa
    axquadratic_weighted_kappa calculates the quadratic weighted kappa
    value, which is a measure of inter-rater agreement between two raters
    that provide discrete numeric ratings.  Potential values range from -1
    (representing complete disagreement) to 1 (representing complete
    agreement).  A kappa value of 0 is expected if all agreement is due to
    chance.
    quadratic_weighted_kappa(rater_a, rater_b), where rater_a and rater_b
    each correspond to a list of integer ratings.  These lists must have the
    same length.
    The ratings should be integers, and it is assumed that they contain
    the complete range of possible ratings.
    quadratic_weighted_kappa(X, min_rating, max_rating), where min_rating
    is the minimum possible rating, and max_rating is the maximum possible
    rating
    
Calculates the quadratic weighted kappa
axquadratic_weighted_kappa calculates the quadratic weighted kappa
value, which is a measure of inter-rater agreement between two raters
that provide discrete numeric ratings.  Potential values range from -1
(representing complete disagreement) to 1 (representing complete
agreement).  A kappa value of 0 is expected if all agreement is due to
chance.
Calculates the quadratic weighted kappa axquadratic_weighted_kappa calculates the quadratic weighted kappa value which is a measure of inter - rater agreement between two raters that provide discrete numeric ratings . Potential values range from - 1 ( representing complete disagreement ) to 1 ( representing complete agreement ) . A kappa value of 0 is expected if all agreement is due to chance .
def quadratic_weighted_kappa(y, y_pred):
    rater_a = y
    rater_b = y_pred
    min_rating=None
    max_rating=None
    rater_a = np.array(rater_a, dtype=int)
    rater_b = np.array(rater_b, dtype=int)
    assert(len(rater_a) == len(rater_b))
    if min_rating is None:
        min_rating = min(min(rater_a), min(rater_b))
    if max_rating is None:
        max_rating = max(max(rater_a), max(rater_b))
    conf_mat = confusion_matrix(rater_a, rater_b,
                                min_rating, max_rating)
    num_ratings = len(conf_mat)
    num_scored_items = float(len(rater_a))
    hist_rater_a = histogram(rater_a, min_rating, max_rating)
    hist_rater_b = histogram(rater_b, min_rating, max_rating)
    numerator = 0.0
    denominator = 0.0
    for i in range(num_ratings):
        for j in range(num_ratings):
            expected_count = (hist_rater_a[i] * hist_rater_b[j]
                              / num_scored_items)
            d = pow(i - j, 2.0) / pow(num_ratings - 1, 2.0)
            numerator += d * conf_mat[i][j] / num_scored_items
            denominator += d * expected_count / num_scored_items
    return (1.0 - numerator / denominator)
def quadratic_weighted_kappa ( y , y_pred ) : rater_a = y rater_b = y_pred min_rating = None max_rating = None rater_a = np . array ( rater_a , dtype = int ) rater_b = np . array ( rater_b , dtype = int ) assert ( len ( rater_a ) == len ( rater_b ) ) if min_rating is None : min_rating = min ( min ( rater_a ) , min ( rater_b ) ) if max_rating is None : max_rating = max ( max ( rater_a ) , max ( rater_b ) ) conf_mat = confusion_matrix ( rater_a , rater_b , min_rating , max_rating ) num_ratings = len ( conf_mat ) num_scored_items = float ( len ( rater_a ) ) hist_rater_a = histogram ( rater_a , min_rating , max_rating ) hist_rater_b = histogram ( rater_b , min_rating , max_rating ) numerator = 0.0 denominator = 0.0 for i in range ( num_ratings ) : for j in range ( num_ratings ) : expected_count = ( hist_rater_a [ i ] * hist_rater_b [ j ] / num_scored_items ) d = pow ( i - j , 2.0 ) / pow ( num_ratings - 1 , 2.0 ) numerator += d * conf_mat [ i ] [ j ] / num_scored_items denominator += d * expected_count / num_scored_items return ( 1.0 - numerator / denominator )
Calculates the quadratic weighted kappa
axquadratic_weighted_kappa calculates the quadratic weighted kappa
value, which is a measure of inter-rater agreement between two raters
that provide discrete numeric ratings.
Calculates the quadratic weighted kappa axquadratic_weighted_kappa calculates the quadratic weighted kappa value which is a measure of inter - rater agreement between two raters that provide discrete numeric ratings .
"""
    Calculates the quadratic weighted kappa
    axquadratic_weighted_kappa calculates the quadratic weighted kappa
    value, which is a measure of inter-rater agreement between two raters
    that provide discrete numeric ratings.  Potential values range from -1
    (representing complete disagreement) to 1 (representing complete
    agreement).  A kappa value of 0 is expected if all agreement is due to
    chance.
    quadratic_weighted_kappa(rater_a, rater_b), where rater_a and rater_b
    each correspond to a list of integer ratings.  These lists must have the
    same length.
    The ratings should be integers, and it is assumed that they contain
    the complete range of possible ratings.
    quadratic_weighted_kappa(X, min_rating, max_rating), where min_rating
    is the minimum possible rating, and max_rating is the maximum possible
    rating
    """
y
y_pred