/
pycm_handler.py
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/
pycm_handler.py
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# -*- coding: utf-8 -*-
"""ConfusionMatrix handlers."""
from __future__ import division
from .pycm_class_func import class_statistics
from .pycm_error import pycmVectorError, pycmMatrixError
from .pycm_overall_func import overall_statistics
from .pycm_util import *
from .pycm_param import *
import json
import types
import numpy
def __class_stat_init__(cm):
"""
Init individual class stat.
:param cm : ConfusionMatrix
:type cm : pycm.ConfusionMatrix object
:return: None
"""
cm.TPR = cm.class_stat["TPR"]
cm.TNR = cm.class_stat["TNR"]
cm.PPV = cm.class_stat["PPV"]
cm.NPV = cm.class_stat["NPV"]
cm.FNR = cm.class_stat["FNR"]
cm.FPR = cm.class_stat["FPR"]
cm.FDR = cm.class_stat["FDR"]
cm.FOR = cm.class_stat["FOR"]
cm.ACC = cm.class_stat["ACC"]
cm.F1 = cm.class_stat["F1"]
cm.MCC = cm.class_stat["MCC"]
cm.BM = cm.class_stat["BM"]
cm.MK = cm.class_stat["MK"]
cm.DOR = cm.class_stat["DOR"]
cm.PLR = cm.class_stat["PLR"]
cm.NLR = cm.class_stat["NLR"]
cm.POP = cm.class_stat["POP"]
cm.P = cm.class_stat["P"]
cm.N = cm.class_stat["N"]
cm.TOP = cm.class_stat["TOP"]
cm.TON = cm.class_stat["TON"]
cm.PRE = cm.class_stat["PRE"]
cm.G = cm.class_stat["G"]
cm.RACC = cm.class_stat["RACC"]
cm.RACCU = cm.class_stat["RACCU"]
cm.F2 = cm.class_stat["F2"]
cm.F05 = cm.class_stat["F0.5"]
cm.ERR = cm.class_stat["ERR"]
cm.J = cm.class_stat["J"]
cm.IS = cm.class_stat["IS"]
cm.CEN = cm.class_stat["CEN"]
cm.MCEN = cm.class_stat["MCEN"]
cm.AUC = cm.class_stat["AUC"]
cm.dInd = cm.class_stat["dInd"]
cm.sInd = cm.class_stat["sInd"]
cm.DP = cm.class_stat["DP"]
cm.Y = cm.class_stat["Y"]
cm.PLRI = cm.class_stat["PLRI"]
cm.DPI = cm.class_stat["DPI"]
cm.AUCI = cm.class_stat["AUCI"]
cm.GI = cm.class_stat["GI"]
cm.LS = cm.class_stat["LS"]
cm.AM = cm.class_stat["AM"]
cm.BCD = cm.class_stat["BCD"]
cm.OP = cm.class_stat["OP"]
cm.IBA = cm.class_stat["IBA"]
cm.GM = cm.class_stat["GM"]
cm.Q = cm.class_stat["Q"]
cm.QI = cm.class_stat["QI"]
cm.AGM = cm.class_stat["AGM"]
cm.NLRI = cm.class_stat["NLRI"]
cm.MCCI = cm.class_stat["MCCI"]
cm.AGF = cm.class_stat["AGF"]
cm.OC = cm.class_stat["OC"]
cm.OOC = cm.class_stat["OOC"]
cm.AUPR = cm.class_stat["AUPR"]
cm.ICSI = cm.class_stat["ICSI"]
def __overall_stat_init__(cm):
"""
Init individual overall stat.
:param cm: ConfusionMatrix
:type cm : pycm.ConfusionMatrix object
:return: None
"""
cm.Overall_J = cm.overall_stat["Overall J"]
cm.SOA1 = cm.overall_stat["SOA1(Landis & Koch)"]
cm.SOA2 = cm.overall_stat["SOA2(Fleiss)"]
cm.SOA3 = cm.overall_stat["SOA3(Altman)"]
cm.SOA4 = cm.overall_stat["SOA4(Cicchetti)"]
cm.Kappa = cm.overall_stat["Kappa"]
cm.Overall_ACC = cm.overall_stat["Overall ACC"]
cm.TNR_Macro = cm.overall_stat["TNR Macro"]
cm.TPR_Macro = cm.overall_stat["TPR Macro"]
cm.FNR_Macro = cm.overall_stat["FNR Macro"]
cm.FPR_Macro = cm.overall_stat["FPR Macro"]
cm.PPV_Macro = cm.overall_stat["PPV Macro"]
cm.ACC_Macro = cm.overall_stat["ACC Macro"]
cm.TNR_Micro = cm.overall_stat["TNR Micro"]
cm.FPR_Micro = cm.overall_stat["FPR Micro"]
cm.TPR_Micro = cm.overall_stat["TPR Micro"]
cm.FNR_Micro = cm.overall_stat["FNR Micro"]
cm.PPV_Micro = cm.overall_stat["PPV Micro"]
cm.F1_Macro = cm.overall_stat["F1 Macro"]
cm.F1_Micro = cm.overall_stat["F1 Micro"]
cm.Overall_RACC = cm.overall_stat["Overall RACC"]
cm.Overall_RACCU = cm.overall_stat["Overall RACCU"]
cm.PI = cm.overall_stat["Scott PI"]
cm.AC1 = cm.overall_stat["Gwet AC1"]
cm.S = cm.overall_stat["Bennett S"]
cm.Kappa_SE = cm.overall_stat["Kappa Standard Error"]
cm.Kappa_CI = cm.overall_stat["Kappa 95% CI"]
cm.Chi_Squared = cm.overall_stat["Chi-Squared"]
cm.Phi_Squared = cm.overall_stat["Phi-Squared"]
cm.KappaUnbiased = cm.overall_stat["Kappa Unbiased"]
cm.KappaNoPrevalence = cm.overall_stat["Kappa No Prevalence"]
cm.V = cm.overall_stat["Cramer V"]
cm.DF = cm.overall_stat["Chi-Squared DF"]
cm.CI95 = cm.overall_stat["95% CI"]
cm.SE = cm.overall_stat["Standard Error"]
cm.ReferenceEntropy = cm.overall_stat["Reference Entropy"]
cm.ResponseEntropy = cm.overall_stat["Response Entropy"]
cm.CrossEntropy = cm.overall_stat["Cross Entropy"]
cm.JointEntropy = cm.overall_stat["Joint Entropy"]
cm.ConditionalEntropy = cm.overall_stat["Conditional Entropy"]
cm.MutualInformation = cm.overall_stat["Mutual Information"]
cm.KL = cm.overall_stat["KL Divergence"]
cm.LambdaB = cm.overall_stat["Lambda B"]
cm.LambdaA = cm.overall_stat["Lambda A"]
cm.HammingLoss = cm.overall_stat["Hamming Loss"]
cm.ZeroOneLoss = cm.overall_stat["Zero-one Loss"]
cm.NIR = cm.overall_stat["NIR"]
cm.PValue = cm.overall_stat["P-Value"]
cm.Overall_CEN = cm.overall_stat["Overall CEN"]
cm.Overall_MCEN = cm.overall_stat["Overall MCEN"]
cm.Overall_MCC = cm.overall_stat["Overall MCC"]
cm.RR = cm.overall_stat["RR"]
cm.CBA = cm.overall_stat["CBA"]
cm.AUNU = cm.overall_stat["AUNU"]
cm.AUNP = cm.overall_stat["AUNP"]
cm.RCI = cm.overall_stat["RCI"]
cm.C = cm.overall_stat["Pearson C"]
cm.SOA5 = cm.overall_stat["SOA5(Cramer)"]
cm.SOA6 = cm.overall_stat["SOA6(Matthews)"]
cm.CSI = cm.overall_stat["CSI"]
cm.ARI = cm.overall_stat["ARI"]
cm.B = cm.overall_stat["Bangdiwala B"]
cm.Alpha = cm.overall_stat["Krippendorff Alpha"]
def __obj_assign_handler__(cm, matrix_param):
"""
Assign basic parameters to ConfusionMatrix.
:param cm: ConfusionMatrix
:type cm : pycm.ConfusionMatrix object
:param matrix_param: matrix parameters
:type matrix_param: dict
:return: None
"""
cm.classes = matrix_param[0]
cm.table = matrix_param[1]
cm.matrix = cm.table
cm.normalized_table = normalized_table_calc(cm.classes, cm.table)
cm.normalized_matrix = cm.normalized_table
cm.TP = matrix_param[2]
cm.TN = matrix_param[3]
cm.FP = matrix_param[4]
cm.FN = matrix_param[5]
statistic_result = class_statistics(
TP=matrix_param[2],
TN=matrix_param[3],
FP=matrix_param[4],
FN=matrix_param[5],
classes=matrix_param[0],
table=matrix_param[1])
cm.class_stat = statistic_result
cm.overall_stat = overall_statistics(
RACC=statistic_result["RACC"],
RACCU=statistic_result["RACCU"],
TPR=statistic_result["TPR"],
PPV=statistic_result["PPV"],
F1=statistic_result["F1"],
TP=statistic_result["TP"],
FN=statistic_result["FN"],
ACC=statistic_result["ACC"],
POP=statistic_result["POP"],
P=statistic_result["P"],
TOP=statistic_result["TOP"],
jaccard_list=statistic_result["J"],
classes=cm.classes,
table=cm.table,
CEN_dict=statistic_result["CEN"],
MCEN_dict=statistic_result["MCEN"],
AUC_dict=statistic_result["AUC"],
ICSI_dict=statistic_result["ICSI"],
TNR=statistic_result["TNR"],
TN=statistic_result["TN"],
FP=statistic_result["FP"])
def __obj_file_handler__(cm, file):
"""
Handle object conditions for file.
:param cm: ConfusionMatrix
:type cm : pycm.ConfusionMatrix object
:param file : saved confusion matrix file object
:type file : (io.IOBase & file)
:return: matrix parameters as list
"""
obj_data = json.load(file)
if obj_data["Actual-Vector"] is not None and obj_data[
"Predict-Vector"] is not None:
try:
loaded_weights = obj_data["Sample-Weight"]
except Exception:
loaded_weights = None
matrix_param = matrix_params_calc(obj_data[
"Actual-Vector"],
obj_data[
"Predict-Vector"], loaded_weights)
cm.actual_vector = obj_data["Actual-Vector"]
cm.predict_vector = obj_data["Predict-Vector"]
cm.weights = loaded_weights
else:
try:
loaded_transpose = obj_data["Transpose"]
except Exception:
loaded_transpose = False
cm.transpose = loaded_transpose
loaded_matrix = dict(obj_data["Matrix"])
for i in loaded_matrix.keys():
loaded_matrix[i] = dict(loaded_matrix[i])
matrix_param = matrix_params_from_table(loaded_matrix)
cm.digit = obj_data["Digit"]
try:
cm.imbalance = obj_data["Imbalanced"]
except Exception:
cm.imbalance = None
return matrix_param
def __obj_matrix_handler__(matrix, transpose):
"""
Handle object conditions for matrix.
:param matrix: direct matrix
:type matrix: dict
:param transpose : transpose flag
:type transpose : bool
:return: matrix parameters as list
"""
if matrix_check(matrix):
if class_check(list(matrix.keys())) is False:
raise pycmMatrixError(MATRIX_CLASS_TYPE_ERROR)
matrix_param = matrix_params_from_table(matrix, transpose)
else:
raise pycmMatrixError(MATRIX_FORMAT_ERROR)
return matrix_param
def __obj_vector_handler__(
cm,
actual_vector,
predict_vector,
threshold,
sample_weight,
classes):
"""
Handle object conditions for vectors.
:param cm: ConfusionMatrix
:type cm : pycm.ConfusionMatrix object
:param actual_vector: Actual Vector
:type actual_vector: python list or numpy array of any stringable objects
:param predict_vector: Predicted Vector
:type predict_vector: python list or numpy array of any stringable objects
:param threshold : activation threshold function
:type threshold : FunctionType (function or lambda)
:param sample_weight : sample weights list
:type sample_weight : list
:param classes: ordered labels of classes
:type classes: list
:return: matrix parameters as list
"""
if isinstance(threshold, types.FunctionType):
predict_vector = list(map(threshold, predict_vector))
cm.predict_vector = predict_vector
if not isinstance(actual_vector, (list, numpy.ndarray)) or not \
isinstance(predict_vector, (list, numpy.ndarray)):
raise pycmVectorError(VECTOR_TYPE_ERROR)
if len(actual_vector) != len(predict_vector):
raise pycmVectorError(VECTOR_SIZE_ERROR)
if len(actual_vector) == 0 or len(predict_vector) == 0:
raise pycmVectorError(VECTOR_EMPTY_ERROR)
if classes is not None and len(set(classes)) != len(classes):
raise pycmVectorError(VECTOR_UNIQUE_CLASS_ERROR)
matrix_param = matrix_params_calc(
actual_vector, predict_vector, sample_weight, classes)
if isinstance(sample_weight, (list, numpy.ndarray)):
cm.weights = sample_weight
return matrix_param