Generated By PyCM Version {version}
''' HTML_DATASET_TYPE_TEMPLATE = ( "{message1}
\n" "{message2}
\n" ) TIMINGS_TEMPLATE = """ Matrix Creation: {matrix_creation:.6f} s Class Statistics: {class_statistics:.6f} s Overall Statistics: {overall_statistics:.6f} s Total: {total:.6f} s """ PROBABILITY_SIZE_ERROR = "All elements of the probability vector must have the same length and match the number of classes." PROBABILITY_TYPE_ERROR = "Probability vector elements must be numeric." PROBABILITY_SUM_ERROR = "The sum of the probability values must equal 1." THRESHOLDS_NUMBER_ERROR = "The number of thresholds must be at least 2." THRESHOLDS_TYPE_ERROR = "`thresholds` must be provided as a list or a NumPy array." THRESHOLDS_NUMERIC_ERROR = "`thresholds` must contain only numeric values." CLASSES_TYPE_ERROR = "`classes` must be provided as a list." CLASSES_MATCH_ERROR = "`classes` does not match the actual vector." MATRIX_CLASS_TYPE_ERROR = "All input matrix classes must be of the same type." MATRIX_FORMAT_ERROR = "Invalid input confusion matrix format." MAPPING_FORMAT_ERROR = "Invalid mapping format." MAPPING_CLASS_NAME_ERROR = "Invalid mapping class names." SEABORN_PLOT_LIBRARY_ERROR = "Failed to import seaborn module. Please install it using: `pip install seaborn`." MATPLOTLIB_PLOT_LIBRARY_ERROR = "Failed to import matplotlib module. Please install it using: `pip install matplotlib`." PLOT_COLORS_CLASS_MISMATCH_ERROR = "The number of colors does not match the number of classes." PLOT_MARKERS_CLASS_MISMATCH_ERROR = "The number of markers does not match the number of classes." VECTOR_TYPE_ERROR = "Input vectors must be provided as a list or a NumPy array." VECTOR_SIZE_ERROR = "Input vectors must have the same length." VECTOR_EMPTY_ERROR = "Input vectors must not be empty." VECTOR_ONLY_ERROR = "This option is only available in vector mode." VECTOR_UNIQUE_CLASS_ERROR = "`classes` must contain unique labels with no duplicates." NOT_ALL_SET_VECTOR_ERROR = "Failed to extract classes from input. Input vectors should be a list of sets with unified types." CLASS_NUMBER_ERROR = "The number of classes must be at least 2." METRICS_OFF_ERROR = "This method cannot be executed when `metrics_off=True`." CLASSES_ERROR = "The specified classes are not a subset of the matrix's classes." COMPARE_FORMAT_ERROR = "Input must be provided as a dictionary." CLASSES_LENGTH_ERROR = "The length of the classes does not match the length of the array." AREA_METHOD_ERROR = "The integral method must be either 'trapezoidal' or 'midpoint'." VECTOR_INDEX_ERROR = "Index is out of range for the given vector." INVALID_CLASS_NAME_ERROR = "The specified class name is not among the confusion matrix's classes." COMPARE_TYPE_ERROR = "Input must be a dictionary containing pycm.ConfusionMatrix objects." COMPARE_DOMAIN_ERROR = "All ConfusionMatrix objects must have the same domain (same sample size and number of classes)." COMPARE_NUMBER_ERROR = "At least 2 confusion matrices are required for comparison." COMPARE_CLASS_WEIGHT_ERROR = "`class_weight` must be a dictionary and specified for all classes." COMPARE_CLASS_BENCHMARK_WEIGHT_ERROR = "`class_benchmark_weight` must be a dictionary and specified for all class benchmarks." COMPARE_OVERALL_BENCHMARK_WEIGHT_ERROR = "`overall_benchmark_weight` must be a dictionary and specified for all overall benchmarks." COMPARE_CLASS_WEIGHT_WARNING = "Invalid `class_weight` format; the result is for unweighted mode." COMPARE_CLASS_BENCHMARK_WEIGHT_WARNING = "Invalid `class_benchmark_weight` format; the result is for unweighted mode." COMPARE_OVERALL_BENCHMARK_WEIGHT_WARNING = "Invalid `overall_benchmark_weight` format; the result is for unweighted mode." COMPARE_METRICS_OFF_ERROR = "Comparison cannot be performed when `metrics_off=True` in any matrix." COMBINE_TYPE_ERROR = "Input must be an instance of pycm.ConfusionMatrix." COMPARE_RESULT_WARNING = "Confusion matrices are too similar to identify a clear best option." WEIGHTED_KAPPA_WARNING = "Invalid weight format; the result is for unweighted kappa." WEIGHTED_ALPHA_WARNING = "Invalid weight format; the result is for unweighted alpha." AVERAGE_WEIGHT_ERROR = "`weight` must be a dictionary and specified for all classes." AVERAGE_INVALID_ERROR = "Invalid parameter!" BRIER_LOG_LOSS_CLASS_ERROR = "Actual vector contains strings; `pos_class` must be explicitly specified." BRIER_LOG_LOSS_PROB_ERROR = "This option is only available in binary probability mode." CLASS_NUMBER_WARNING = "Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization." CLASSES_WARNING = "Specified classes are not a subset of the classes in the actual and predicted vectors." CLASSES_TYPE_WARNING = "Classes is neither a list nor None, so it will be ignored." CURVE_NONE_WARNING = "The curve contains non-numerical value(s)." DEPRECATION_WARNING = "`{name}` is deprecated and may be removed in future releases." DISTANCE_METRIC_TYPE_ERROR = "`metric` type must be DistanceType." CLASS_NUMBER_THRESHOLD = 10 BALANCE_RATIO_THRESHOLD = 3 CLASS_PARAMS = { "TPR": "TPR", "TNR": "TNR", "PPV": "PPV", "NPV": "NPV", "FNR": "FNR", "FPR": "FPR", "FDR": "FDR", "FOR": "FOR", "ACC": "ACC", "F1": "F1", "MCC": "MCC", "BM": "BM", "MK": "MK", "PLR": "PLR", "NLR": "NLR", "DOR": "DOR", "TP": "TP", "TN": "TN", "FP": "FP", "FN": "FN", "POP": "POP", "P": "P", "PR": "PR", "N": "N", "TOP": "TOP", "TOPR": "TOPR", "TON": "TON", "PRE": "PRE", "G": "G", "RACC": "RACC", "F0.5": "F05", "F2": "F2", "ERR": "ERR", "RACCU": "RACCU", "J": "J", "IS": "IS", "CEN": "CEN", "MCEN": "MCEN", "AUC": "AUC", "sInd": "sInd", "dInd": "dInd", "DP": "DP", "Y": "Y", "PLRI": "PLRI", "DPI": "DPI", "AUCI": "AUCI", "GI": "GI", "LS": "LS", "AM": "AM", "BCD": "BCD", "OP": "OP", "IBA": "IBA", "GM": "GM", "Q": "Q", "AGM": "AGM", "NLRI": "NLRI", "MCCI": "MCCI", "AGF": "AGF", "OC": "OC", "OOC": "OOC", "AUPR": "AUPR", "ICSI": "ICSI", "QI": "QI", "HD": "HD", "BB": "BB", } OVERALL_PARAMS = { 'Overall ACC': 'Overall_ACC', 'Overall RACCU': 'Overall_RACCU', 'Overall RACC': 'Overall_RACC', 'Kappa': 'Kappa', 'Gwet AC1': 'AC1', 'Bennett S': 'S', 'Kappa Standard Error': 'Kappa_SE', 'Kappa Unbiased': 'KappaUnbiased', 'Scott PI': 'PI', 'Kappa No Prevalence': 'KappaNoPrevalence', 'Kappa 95% CI': 'Kappa_CI', 'Standard Error': 'SE', '95% CI': 'CI95', 'Chi-Squared': 'Chi_Squared', 'Phi-Squared': 'Phi_Squared', 'Cramer V': 'V', 'Response Entropy': 'ResponseEntropy', 'Reference Entropy': 'ReferenceEntropy', 'Cross Entropy': 'CrossEntropy', 'Joint Entropy': 'JointEntropy', 'Conditional Entropy': 'ConditionalEntropy', 'Mutual Information': 'MutualInformation', 'KL Divergence': 'KL', 'Lambda B': 'LambdaB', 'Lambda A': 'LambdaA', 'Chi-Squared DF': 'DF', 'Overall J': 'Overall_J', 'Hamming Loss': 'HammingLoss', 'Zero-one Loss': 'ZeroOneLoss', 'NIR': 'NIR', 'P-Value': 'PValue', 'Overall CEN': 'Overall_CEN', 'Overall MCEN': 'Overall_MCEN', 'Overall MCC': 'Overall_MCC', 'RR': 'RR', 'CBA': 'CBA', 'AUNU': 'AUNU', 'AUNP': 'AUNP', 'RCI': 'RCI', 'Pearson C': 'C', 'TPR Micro': 'TPR_Micro', 'TPR Macro': 'TPR_Macro', 'CSI': 'CSI', 'ARI': 'ARI', 'TNR Micro': 'TNR_Micro', 'TNR Macro': 'TNR_Macro', 'Bangdiwala B': 'B', 'Krippendorff Alpha': 'Alpha', 'SOA1(Landis & Koch)': 'SOA1', 'SOA2(Fleiss)': 'SOA2', 'SOA3(Altman)': 'SOA3', 'SOA4(Cicchetti)': 'SOA4', 'SOA5(Cramer)': 'SOA5', 'SOA6(Matthews)': 'SOA6', 'SOA7(Lambda A)': 'SOA7', 'SOA8(Lambda B)': 'SOA8', 'SOA9(Krippendorff Alpha)': 'SOA9', 'SOA10(Pearson C)': 'SOA10', 'FPR Macro': 'FPR_Macro', 'FNR Macro': 'FNR_Macro', 'PPV Macro': 'PPV_Macro', 'NPV Macro': 'NPV_Macro', 'ACC Macro': 'ACC_Macro', 'F1 Macro': 'F1_Macro', 'FPR Micro': 'FPR_Micro', 'FNR Micro': 'FNR_Micro', 'PPV Micro': 'PPV_Micro', 'NPV Micro': 'NPV_Micro', 'F1 Micro': 'F1_Micro', } SUMMARY_OVERALL = [ "ACC Macro", "Kappa", "Overall ACC", "SOA1(Landis & Koch)", "Zero-one Loss", "F1 Macro", "TPR Macro", "PPV Macro", "NPV Macro", "FPR Macro"] SUMMARY_CLASS = [ "ACC", "AUC", "AUCI", "F1", "TPR", "FPR", "PPV", "TP", "FP", "FN", "TN", "N", "P", "POP", "TOP", "TON"] BINARY_RECOMMEND = ["ACC", "TPR", "PPV", "AUC", "AUCI", "TNR", "F1"] CI_CLASS_LIST = [ "TPR", "TNR", "PPV", "NPV", "ACC", "PLR", "NLR", "FPR", "FNR", "AUC", "PRE"] CI_OVERALL_LIST = ["Kappa", "Overall ACC"] CURVE_PARAMS = ["TPR", "FPR", "TNR", "PPV"] ALPHA_TWO_SIDE_TABLE = { 0.2: 1.28, 0.1: 1.645, 0.05: 1.96, 0.02: 2.326, 0.01: 2.576, 0.002: 3.09, 0.001: 3.29} ALPHA_ONE_SIDE_TABLE = { 0.1: 1.28, 0.05: 1.645, 0.01: 2.326, 0.005: 2.576, 0.001: 3.09, 0.0005: 3.29} CI_ALPHA_TWO_SIDE_WARNING = ("Invalid alpha value; automatically set to 0.05. Supported two-sided values are: " + ", ".join(map(str, sorted(ALPHA_TWO_SIDE_TABLE)))) CI_ALPHA_ONE_SIDE_WARNING = ("Invalid alpha value; automatically set to 0.05. Supported one-sided values are: " + ", ".join(map(str, sorted(ALPHA_ONE_SIDE_TABLE)))) CI_FORMAT_ERROR = "Input must be provided as a string." CI_SUPPORT_ERROR = ("Confidence interval calculation for this parameter is not supported in this version of pycm.\n" " Supported parameters are: ") + ", ".join(CI_CLASS_LIST) + ", " + ", ".join(CI_OVERALL_LIST) MULTICLASS_RECOMMEND = [ "ERR", "TPR Micro", "TPR Macro", "F1 Macro", "PPV Macro", "NPV Macro", "ACC", "Overall ACC", "MCC", "MCCI", "Overall MCC", "SOA6(Matthews)", "BCD", "Hamming Loss", "Zero-one Loss"] IMBALANCED_RECOMMEND = [ "Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "MCCI", "J", "Overall J", "Overall MCC", "SOA6(Matthews)", "Overall CEN", "Overall MCEN", "OP", "G", "GI", "DP", "DPI", "GI"] PLRI_SCORE = {"Good": 4, "Fair": 3, "Poor": 2, "Negligible": 1, "None": "None"} NLRI_SCORE = {"Good": 4, "Fair": 3, "Poor": 2, "Negligible": 1, "None": "None"} DPI_SCORE = {"Good": 4, "Fair": 3, "Limited": 2, "Poor": 1, "None": "None"} QI_SCORE = { "Strong": 4, "Moderate": 3, "Weak": 2, "Negligible": 1, "None": "None"} AUCI_SCORE = { "Excellent": 5, "Very Good": 4, "Good": 3, "Fair": 2, "Poor": 1, "None": "None"} SOA1_SCORE = { "Almost Perfect": 6, "Substantial": 5, "Moderate": 4, "Fair": 3, "Slight": 2, "Poor": 1, "None": "None"} SOA2_SCORE = { "Excellent": 3, "Intermediate to Good": 2, "Poor": 1, "None": "None"} SOA3_SCORE = { "Very Good": 5, "Good": 4, "Moderate": 3, "Fair": 2, "Poor": 1, "None": "None"} SOA4_SCORE = {"Excellent": 4, "Good": 3, "Fair": 2, "Poor": 1, "None": "None"} SOA5_SCORE = { "Very Strong": 6, "Strong": 5, "Relatively Strong": 4, "Moderate": 3, "Weak": 2, "Negligible": 1, "None": "None"} SOA6_SCORE = { "Very Strong": 5, "Strong": 4, "Moderate": 3, "Weak": 2, "Negligible": 1, "None": "None"} SOA7_SCORE = { "Perfect": 6, "Very Strong": 5, "Strong": 4, "Moderate": 3, "Weak": 2, "Very Weak": 1, "None": "None" } SOA8_SCORE = { "Perfect": 6, "Very Strong": 5, "Strong": 4, "Moderate": 3, "Weak": 2, "Very Weak": 1, "None": "None" } SOA9_SCORE = { "High": 3, "Tentative": 2, "Low": 1, "None": "None" } SOA10_SCORE = { "Strong": 4, "Medium": 3, "Weak": 2, "Not Appreciable": 1, "None": "None" } CLASS_BENCHMARK_SCORE_DICT = { "PLRI": PLRI_SCORE, "NLRI": NLRI_SCORE, "DPI": DPI_SCORE, "AUCI": AUCI_SCORE, "MCCI": SOA6_SCORE, "QI": QI_SCORE} CLASS_BENCHMARK_LIST = sorted(CLASS_BENCHMARK_SCORE_DICT) OVERALL_BENCHMARK_SCORE_DICT = { "SOA1": SOA1_SCORE, "SOA2": SOA2_SCORE, "SOA3": SOA3_SCORE, "SOA4": SOA4_SCORE, "SOA5": SOA5_SCORE, "SOA6": SOA6_SCORE, "SOA7": SOA7_SCORE, "SOA8": SOA8_SCORE, "SOA9": SOA9_SCORE, "SOA10": SOA10_SCORE} OVERALL_BENCHMARK_LIST = sorted(OVERALL_BENCHMARK_SCORE_DICT) KAPPA_BENCHMARK_LIST = ["SOA1", "SOA2", "SOA3", "SOA4"] OVERALL_BENCHMARK_MAP = { "SOA1": "SOA1(Landis & Koch)", "SOA2": "SOA2(Fleiss)", "SOA3": "SOA3(Altman)", "SOA4": "SOA4(Cicchetti)", "SOA5": "SOA5(Cramer)", "SOA6": "SOA6(Matthews)", "SOA7": "SOA7(Lambda A)", "SOA8": "SOA8(Lambda B)", "SOA9": "SOA9(Krippendorff Alpha)", "SOA10": "SOA10(Pearson C)" } RECOMMEND_BACKGROUND_COLOR = "aqua" DEFAULT_BACKGROUND_COLOR = "transparent" RECOMMEND_HTML_MESSAGE = 'Note 1 : Recommended statistics for this type of classification highlighted in {color}'.format( color=RECOMMEND_BACKGROUND_COLOR) RECOMMEND_WARNING = ("The recommendation system assumes the input is the result of classification over the entire" " dataset, not just a subset. If the confusion matrix is based on test data classification," " the recommendation may not be valid.") RECOMMEND_HTML_MESSAGE2 = 'Note 2 : {message}'.format( message=RECOMMEND_WARNING) DOCUMENT_ADR = "http://www.pycm.io/doc/index.html#" DOCUMENT_ADR_ALT = "https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Document.ipynb#" PARAMS_DESCRIPTION = { "TPR": "sensitivity, recall, hit rate, or true positive rate", "TNR": "specificity or true negative rate", "PPV": "precision or positive predictive value", "NPV": "negative predictive value", "FNR": "miss rate or false negative rate", "FPR": "fall-out or false positive rate", "FDR": "false discovery rate", "FOR": "false omission rate", "ACC": "accuracy", "F1": "F1 Score - harmonic mean of precision and sensitivity", "MCC": "Matthews correlation coefficient", "MCCI": "Matthews correlation coefficient interpretation", "BM": "Informedness or Bookmaker Informedness", "MK": "Markedness", "PLR": "Positive likelihood ratio", "NLR": "Negative likelihood ratio", "DOR": "Diagnostic odds ratio", "TP": "true positive/hit", "TN": "true negative/correct rejection", "FP": "false positive/Type 1 error/false alarm", "FN": "false negative/miss/Type 2 error", "P": "Condition positive or Support", "N": "Condition negative", "TOP": "Test outcome positive", "TON": "Test outcome negative", "POP": "Population", "PRE": "Prevalence", "G": "G-measure geometric mean of precision and sensitivity", "RACC": "Random Accuracy", "F0.5": "F0.5 Score", "F2": "F2 Score", "ERR": "Error Rate", "RACCU": "Random Accuracy Unbiased", "J": "Jaccard index", "NIR": "No Information Rate", "IS": "Information Score", "CEN": "Confusion Entropy", "MCEN": "Modified Confusion Entropy", "AUC": "Area under the ROC curve", "dInd": "Distance index", "sInd": "Similarity index", "DP": "Discriminant power", "Y": "Youden index", "PLRI": "Positive likelihood ratio interpretation", "NLRI": "Negative likelihood ratio interpretation", "DPI": "Discriminant power interpretation", "AUCI": "AUC value interpretation", "GI": "Gini index", "LS": "Lift score", "AM": "Difference between automatic and manual classification", "BCD": "Bray-Curtis dissimilarity", "OP": "Optimized precision", "IBA": "Index of balanced accuracy", "GM": "G-mean geometric mean of specificity and sensitivity", "Q": "Yule Q - coefficient of colligation", "QI": "Yule Q interpretation", "AGM": "Adjusted geometric mean", "AGF": "Adjusted F-score", "OC": "Overlap coefficient", "OOC": "Otsuka-Ochiai coefficient", "AUPR": "Area under the PR curve", "ICSI": "Individual classification success index", "HD": "Hamming distance", "BB": "Braun-Blanquet similarity", "PR": "Positive rate", "TOPR": "Test outcome positive rate", } PARAMS_LINK = { "TPR": "TPR-(True-positive-rate)", "TNR": "TNR-(True-negative-rate)", "PPV": "PPV-(Positive-predictive-value)", "NPV": "NPV-(Negative-predictive-value)", "FNR": "FNR-(False-negative-rate)", "FPR": "FPR-(False-positive-rate)", "FDR": "FDR-(False-discovery-rate)", "FOR": "FOR-(False-omission-rate)", "ACC": "ACC-(Accuracy)", "F1": "FBeta-Score", "F0.5": "FBeta-Score", "F2": "FBeta-Score", "MCC": "MCC-(Matthews-correlation-coefficient)", "BM": "BM-(Bookmaker-informedness)", "MK": "MK-(Markedness)", "PLR": "PLR-(Positive-likelihood-ratio)", "NLR": "NLR-(Negative-likelihood-ratio)", "DOR": "DOR-(Diagnostic-odds-ratio)", "TP": "TP-(True-positive)", "TN": "TN-(True-negative)", "FP": "FP-(False-positive)", "FN": "FN-(False-negative)", "P": "P-(Condition-positive)", "N": "N-(Condition-negative)", "POP": "POP-(Population)", "TOP": "TOP-(Test-outcome-positive)", "TON": "TON-(Test-outcome-negative)", "G": "G-(G-measure)", "ERR": "ERR-(Error-rate)", "RACC": "RACC-(Random-accuracy)", "RACCU": "RACCU-(Random-accuracy-unbiased)", "PRE": "PRE-(Prevalence)", "Overall ACC": "Overall_ACC", "Kappa": "Kappa", "Overall RACC": "Overall_RACC", "SOA1(Landis & Koch)": "SOA1-(Landis-&-Koch's-benchmark)", "SOA2(Fleiss)": "SOA2-(Fleiss'-benchmark)", "SOA3(Altman)": "SOA3-(Altman's-benchmark)", "SOA4(Cicchetti)": "SOA4-(Cicchetti's-benchmark)", "TPR Macro": "TPR_Macro", "FNR Macro": "FNR_Macro", "TNR Macro": "TNR_Macro", "FPR Macro": "FPR_Macro", "PPV Macro": "PPV_Macro", "NPV Macro": "NPV_Macro", "F1 Macro": "F1_Macro", "F1 Micro": "F1_Micro", "ACC Macro": "ACC_Macro", "TPR Micro": "TPR_Micro", "FNR Micro": "FNR_Micro", "TNR Micro": "TNR_Micro", "FPR Micro": "FPR_Micro", "PPV Micro": "PPV_Micro", "NPV Micro": "NPV_Micro", "Scott PI": "Scott's-Pi", "Gwet AC1": "Gwet's-AC1", "Bennett S": "Bennett's-S", "Kappa 95% CI": "Kappa-95%25-CI", "Kappa Standard Error": "Kappa-standard-error", "Chi-Squared": "Chi-squared", "Phi-Squared": "Phi-squared", "Cramer V": "Cramer's-V", "Chi-Squared DF": "Chi-squared-DF", "95% CI": "95%25-CI", "Standard Error": "Standard-error", "Response Entropy": "Response-entropy", "Reference Entropy": "Reference-entropy", "Cross Entropy": "Cross-entropy", "Joint Entropy": "Joint-entropy", "Conditional Entropy": "Conditional-entropy", "KL Divergence": "Kullback-Leibler-divergence", "Lambda B": "Goodman-&-Kruskal's-lambda-B", "Lambda A": "Goodman-&-Kruskal's-lambda-A", "Kappa Unbiased": "Kappa-unbiased", "Overall RACCU": "Overall_RACCU", "Kappa No Prevalence": "Kappa-no-prevalence", "Mutual Information": "Mutual-information", "J": "J-(Jaccard-index)", "Overall J": "Overall_J", "Hamming Loss": "Hamming-loss", "Zero-one Loss": "Zero-one-loss", "NIR": "NIR-(No-information-rate)", "P-Value": "P-Value", "IS": "IS-(Information-score)", "CEN": "CEN-(Confusion-entropy)", "Overall CEN": "Overall_CEN", "MCEN": "MCEN-(Modified-confusion-entropy)", "Overall MCEN": "Overall_MCEN", "Overall MCC": "Overall_MCC", "RR": "RR-(Global-performance-index)", "CBA": "CBA-(Class-balance-accuracy)", "AUC": "AUC-(Area-under-the-ROC-curve)", "AUNU": "AUNU", "AUNP": "AUNP", "sInd": "sInd-(Similarity-index)", "dInd": "dInd-(Distance-index)", "RCI": "RCI-(Relative-classifier-information)", "DP": "DP-(Discriminant-power)", "Y": "Y-(Youden-index)", "PLRI": "PLRI-(Positive-likelihood-ratio-interpretation)", "DPI": "DPI-(Discriminant-power-interpretation)", "AUCI": "AUCI-(AUC-value-interpretation)", "GI": "GI-(Gini-index)", "LS": "LS-(Lift-score)", "AM": "AM-(Automatic/Manual)", "BCD": "BCD-(Bray-Curtis-dissimilarity)", "OP": "OP-(Optimized-precision)", "IBA": "IBA-(Index-of-balanced-accuracy)", "GM": "GM-(G-mean)", "Pearson C": "Pearson's-C", "Q": "Q-(Yule's-Q)", "AGM": "AGM-(Adjusted-G-mean)", "SOA5(Cramer)": "SOA5-(Cramer's-benchmark)", "NLRI": "NLRI-(Negative-likelihood-ratio-interpretation)", "MCCI": "MCCI-(Matthews-correlation-coefficient-interpretation)", "SOA6(Matthews)": "SOA6-(Matthews's-benchmark)", "AGF": "AGF-(Adjusted-F-score)", "OC": "OC-(Overlap-coefficient)", "OOC": "OOC-(Otsuka-Ochiai-coefficient)", "AUPR": "AUPR-(Area-under-the-PR-curve)", "ICSI": "ICSI-(Individual-classification-success-index)", "CSI": "CSI-(Classification-success-index)", "QI": "QI-(Yule's-Q-interpretation)", "ARI": "ARI-(Adjusted-Rand-index)", "Bangdiwala B": "Bangdiwala's-B", "Krippendorff Alpha": "Krippendorff's-alpha", "HD": "HD-(Hamming-distance)", "BB": "BB-(Braun-Blanquet-similarity)", "PR": "PR-(Positive-rate)", "TOPR": "TOPR-(Test-outcome-positive-rate)", "SOA7(Lambda A)": "SOA7-(Goodman-&-Kruskal's-lambda-A-benchmark)", "SOA8(Lambda B)": "SOA8-(Goodman-&-Kruskal's-lambda-B-benchmark)", "SOA9(Krippendorff Alpha)": "SOA9-(Krippendorff's-alpha-benchmark)", "SOA10(Pearson C)": "SOA10-(Pearson's-C-benchmark)" } CAPITALIZE_FILTER = [ "BCD", "AUCI", "Q", "AGF", "OOC", "AUPR", "AUC", "QI", "BB"] BENCHMARK_COLOR = { "PLRI": { "Negligible": "Red", "Poor": "Orange", "Fair": "Yellow", "Good": "Green", "None": "White"}, "QI": { "Negligible": "Red", "Weak": "Orange", "Moderate": "Yellow", "Strong": "Green", "None": "White"}, "NLRI": { "Negligible": "Red", "Poor": "Orange", "Fair": "Yellow", "Good": "Green", "None": "White"}, "DPI": { "Poor": "Red", "Limited": "Orange", "Fair": "Yellow", "Good": "Green", "None": "White"}, "AUCI": { "Poor": "Red", "Fair": "Orange", "Good": "YellowGreen", "Very Good": "LawnGreen", "Excellent": "Green", "None": "White"}, "MCCI": { "Negligible": "Red", "Weak": "Orange", "Moderate": "Yellow", "Strong": "LawnGreen", "Very Strong": "Green", "None": "White"}, "SOA1(Landis & Koch)": { "Poor": "Red", "Slight": "OrangeRed", "Fair": "Orange", "Moderate": "Yellow", "Substantial": "LawnGreen", "Almost Perfect": "Green", "None": "White"}, "SOA2(Fleiss)": { "Poor": "Red", "Intermediate to Good": "LawnGreen", "Excellent": "Green", "None": "White"}, "SOA3(Altman)": { "Poor": "Red", "Fair": "Orange", "Moderate": "Yellow", "Good": "LawnGreen", "Very Good": "Green", "None": "White"}, "SOA4(Cicchetti)": { "Poor": "Red", "Fair": "Orange", "Good": "LawnGreen", "Excellent": "Green", "None": "White"}, "SOA5(Cramer)": { "Negligible": "Red", "Weak": "Orange", "Moderate": "Yellow", "Relatively Strong": "YellowGreen", "Strong": "LawnGreen", "Very Strong": "Green", "None": "White"}, "SOA6(Matthews)": { "Negligible": "Red", "Weak": "Orange", "Moderate": "Yellow", "Strong": "LawnGreen", "Very Strong": "Green", "None": "White"}, "SOA7(Lambda A)": { "Very Weak": "Red", "Weak": "OrangeRed", "Moderate": "Orange", "Strong": "Yellow", "Very Strong": "LawnGreen", "Perfect": "Green", "None": "White"}, "SOA8(Lambda B)": { "Very Weak": "Red", "Weak": "OrangeRed", "Moderate": "Orange", "Strong": "Yellow", "Very Strong": "LawnGreen", "Perfect": "Green", "None": "White"}, "SOA9(Krippendorff Alpha)": { "Low": "Red", "Tentative": "LawnGreen", "High": "Green", "None": "White"}, "SOA10(Pearson C)": { "Not Appreciable": "Red", "Weak": "Orange", "Medium": "LawnGreen", "Strong": "Green", "None": "White"} } BENCHMARK_LIST = list(BENCHMARK_COLOR) BENCHMARK_CLASS_SIZES = [2, 5, 10, 100, 1000] BENCHMARK_POPULATION_SIZES = [10, 100, 1000, 10000] BENCHMARK_REPORT_TEMPLATE = """Number of classes: {num_classes} Population size: {total_population} Class distribution scenario: {scenario} Timing: + Matrix creation: {timing_matrix_creation}s + Class statistics: {timing_class_statistics}s + Overall statistics: {timing_overall_statistics}s + Total: {timing_total}s ====================================================== """ TABLE_COLOR = { # Pink Colors "pink": [255, 192, 203], "lightpink": [255, 182, 193], "hotpink": [255, 105, 180], "deeppink": [255, 20, 147], "palevioletred": [219, 112, 147], "mediumvioletred": [199, 21, 133], # Red Colors "lightsalmon": [255, 160, 122], "salmon": [250, 128, 114], "darksalmon": [233, 150, 122], "lightcoral": [240, 128, 128], "indianred": [205, 92, 92], "crimson": [220, 20, 60], "firebrick": [178, 34, 34], "darkred": [139, 0, 0], "red": [255, 0, 0], # Orange Colors "orangered": [255, 69, 0], "tomato": [255, 99, 71], "coral": [255, 127, 80], "darkorange": [255, 140, 0], "orange": [255, 165, 0], # Yellow Colors "yellow": [255, 255, 0], "lightyellow": [255, 255, 224], "lemonchiffon": [255, 250, 205], "lightgoldenrodyellow": [250, 250, 210], "papayawhip": [255, 239, 213], "moccasin": [255, 228, 181], "peachpuff": [255, 218, 185], "palegoldenrod": [238, 232, 170], "khaki": [240, 230, 140], "darkkhaki": [189, 183, 107], "gold": [255, 215, 0], # Brown Colors "cornsilk": [255, 248, 220], "blanchedalmond": [255, 235, 205], "bisque": [255, 228, 196], "navajowhite": [255, 222, 173], "wheat": [245, 222, 179], "burlywood": [222, 184, 135], "tan": [210, 180, 140], "rosybrown": [188, 143, 143], "sandybrown": [244, 164, 96], "goldenrod": [218, 165, 32], "darkgoldenrod": [184, 134, 11], "peru": [205, 133, 63], "chocolate": [210, 105, 30], "saddlebrown": [139, 69, 19], "sienna": [160, 82, 45], "brown": [165, 42, 42], "maroon": [128, 0, 0], # Green Colors "darkolivegreen": [85, 107, 47], "olive": [128, 128, 0], "olivedrab": [107, 142, 35], "yellowgreen": [154, 205, 50], "limegreen": [50, 205, 50], "lime": [0, 255, 0], "lawngreen": [124, 252, 0], "chartreuse": [127, 255, 0], "greenyellow": [173, 255, 47], "springgreen": [0, 255, 127], "mediumspringgreen": [0, 250, 154], "lightgreen": [144, 238, 144], "palegreen": [152, 251, 152], "darkseagreen": [143, 188, 143], "mediumaquamarine": [102, 205, 170], "mediumseagreen": [60, 179, 113], "seagreen": [46, 139, 87], "forestgreen": [34, 139, 34], "green": [0, 128, 0], "darkgreen": [0, 100, 0], # Cyan Colors "aqua": [0, 255, 255], "cyan": [0, 255, 255], "lightcyan": [224, 255, 255], "paleturquoise": [175, 238, 238], "aquamarine": [127, 255, 212], "turquoiseaq": [64, 224, 208], "mediumturquoise": [72, 209, 204], "darkturquoise": [0, 206, 209], "lightseaGreen": [32, 178, 170], "cadetblue": [95, 158, 160], "darkcyan": [0, 139, 139], "teal": [0, 128, 128], # Blue Colors "lightsteelblue": [176, 196, 222], "powderblue": [176, 224, 230], "lightblue": [173, 216, 230], "skyblue": [135, 206, 235], "lightskyblue": [135, 206, 250], "deepskyblue": [0, 191, 255], "dodgerblue": [30, 144, 237], "cornflowerblue": [100, 149, 237], "steelblue": [70, 130, 180], "royalblue": [65, 105, 225], "blue": [0, 0, 255], "mediumblue": [0, 0, 205], "darkblue": [0, 0, 139], "navy": [0, 0, 128], "midnightblue": [25, 25, 112], # Purple Colors "lavender": [230, 230, 250], "thistle": [216, 191, 216], "plum": [221, 160, 221], "violet": [238, 130, 238], "orchid": [218, 112, 214], "fuchsia": [255, 0, 255], "magenta": [255, 0, 255], "mediumorchid": [186, 85, 211], "mediumpurple": [147, 112, 219], "blueviolet": [138, 43, 226], "darkviolet": [148, 0, 211], "darkorchid": [153, 50, 204], "darkmagenta": [139, 0, 139], "purple": [128, 0, 128], "indigo": [75, 0, 130], "darkslateblue": [72, 61, 139], "slateblue": [106, 90, 205], "mediumslateblue": [123, 104, 238], # White Colors "white": [255, 255, 255], "snow": [255, 250, 250], "honeydew": [240, 255, 240], "mintcream": [245, 255, 250], "azure": [240, 255, 255], "aliceblue": [240, 248, 255], "ghostwhite": [248, 248, 255], "whitesmoke": [245, 245, 245], "seashell": [255, 245, 238], "beige": [245, 245, 220], "oldlace": [253, 245, 230], "floralwhite": [255, 250, 240], "ivory": [255, 255, 240], "antiquewhite": [250, 235, 215], "linen": [250, 240, 230], "lavenderblush": [255, 240, 245], "mistyrose": [255, 228, 225], # Gray Colors "gainsboro": [220, 220, 220], "lightgray": [211, 211, 211], "silver": [192, 192, 192], "darkgray": [169, 169, 169], "gray": [128, 128, 128], "dimgray": [105, 105, 105], "lightslategray": [119, 136, 153], "slategray": [112, 128, 144], "darkslategray": [47, 79, 79], "black": [0, 0, 0] } NDTRI_P0 = [ -5.99633501014107895267E1, 9.80010754185999661536E1, -5.66762857469070293439E1, 1.39312609387279679503E1, -1.23916583867381258016E0, ] NDTRI_Q0 = [ 1.95448858338141759834E0, 4.67627912898881538453E0, 8.63602421390890590575E1, -2.25462687854119370527E2, 2.00260212380060660359E2, -8.20372256168333339912E1, 1.59056225126211695515E1, -1.18331621121330003142E0, ] NDTRI_P1 = [ 4.05544892305962419923E0, 3.15251094599893866154E1, 5.71628192246421288162E1, 4.40805073893200834700E1, 1.46849561928858024014E1, 2.18663306850790267539E0, -1.40256079171354495875E-1, -3.50424626827848203418E-2, -8.57456785154685413611E-4, ] NDTRI_Q1 = [ 1.57799883256466749731E1, 4.53907635128879210584E1, 4.13172038254672030440E1, 1.50425385692907503408E1, 2.50464946208309415979E0, -1.42182922854787788574E-1, -3.80806407691578277194E-2, -9.33259480895457427372E-4, ] NDTRI_P2 = [ 3.23774891776946035970E0, 6.91522889068984211695E0, 3.93881025292474443415E0, 1.33303460815807542389E0, 2.01485389549179081538E-1, 1.23716634817820021358E-2, 3.01581553508235416007E-4, 2.65806974686737550832E-6, 6.23974539184983293730E-9, ] NDTRI_Q2 = [ 6.02427039364742014255E0, 3.67983563856160859403E0, 1.37702099489081330271E0, 2.16236993594496635890E-1, 1.34204006088543189037E-2, 3.28014464682127739104E-4, 2.89247864745380683936E-6, 6.79019408009981274425E-9, ]