Python Evaluation.f_measure方法代码示例

# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import f_measure [as 别名]

#.........这里部分代码省略.........
        classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
    evaluation = Evaluation(diabetes_data)
    evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
    print(evaluation.summary())
    print(evaluation.class_details())
    print(evaluation.matrix())
    print("areaUnderPRC/0: " + str(evaluation.area_under_prc(0)))
    print("weightedAreaUnderPRC: " + str(evaluation.weighted_area_under_prc))
    print("areaUnderROC/1: " + str(evaluation.area_under_roc(1)))
    print("weightedAreaUnderROC: " + str(evaluation.weighted_area_under_roc))
    print("avgCost: " + str(evaluation.avg_cost))
    print("totalCost: " + str(evaluation.total_cost))
    print("confusionMatrix: " + str(evaluation.confusion_matrix))
    print("correct: " + str(evaluation.correct))
    print("pctCorrect: " + str(evaluation.percent_correct))
    print("incorrect: " + str(evaluation.incorrect))
    print("pctIncorrect: " + str(evaluation.percent_incorrect))
    print("unclassified: " + str(evaluation.unclassified))
    print("pctUnclassified: " + str(evaluation.percent_unclassified))
    print("coverageOfTestCasesByPredictedRegions: " + str(evaluation.coverage_of_test_cases_by_predicted_regions))
    print("sizeOfPredictedRegions: " + str(evaluation.size_of_predicted_regions))
    print("falseNegativeRate: " + str(evaluation.false_negative_rate(1)))
    print("weightedFalseNegativeRate: " + str(evaluation.weighted_false_negative_rate))
    print("numFalseNegatives: " + str(evaluation.num_false_negatives(1)))
    print("trueNegativeRate: " + str(evaluation.true_negative_rate(1)))
    print("weightedTrueNegativeRate: " + str(evaluation.weighted_true_negative_rate))
    print("numTrueNegatives: " + str(evaluation.num_true_negatives(1)))
    print("falsePositiveRate: " + str(evaluation.false_positive_rate(1)))
    print("weightedFalsePositiveRate: " + str(evaluation.weighted_false_positive_rate))
    print("numFalsePositives: " + str(evaluation.num_false_positives(1)))
    print("truePositiveRate: " + str(evaluation.true_positive_rate(1)))
    print("weightedTruePositiveRate: " + str(evaluation.weighted_true_positive_rate))
    print("numTruePositives: " + str(evaluation.num_true_positives(1)))
    print("fMeasure: " + str(evaluation.f_measure(1)))
    print("weightedFMeasure: " + str(evaluation.weighted_f_measure))
    print("unweightedMacroFmeasure: " + str(evaluation.unweighted_macro_f_measure))
    print("unweightedMicroFmeasure: " + str(evaluation.unweighted_micro_f_measure))
    print("precision: " + str(evaluation.precision(1)))
    print("weightedPrecision: " + str(evaluation.weighted_precision))
    print("recall: " + str(evaluation.recall(1)))
    print("weightedRecall: " + str(evaluation.weighted_recall))
    print("kappa: " + str(evaluation.kappa))
    print("KBInformation: " + str(evaluation.kb_information))
    print("KBMeanInformation: " + str(evaluation.kb_mean_information))
    print("KBRelativeInformation: " + str(evaluation.kb_relative_information))
    print("SFEntropyGain: " + str(evaluation.sf_entropy_gain))
    print("SFMeanEntropyGain: " + str(evaluation.sf_mean_entropy_gain))
    print("SFMeanPriorEntropy: " + str(evaluation.sf_mean_prior_entropy))
    print("SFMeanSchemeEntropy: " + str(evaluation.sf_mean_scheme_entropy))
    print("matthewsCorrelationCoefficient: " + str(evaluation.matthews_correlation_coefficient(1)))
    print("weightedMatthewsCorrelation: " + str(evaluation.weighted_matthews_correlation))
    print("class priors: " + str(evaluation.class_priors))
    print("numInstances: " + str(evaluation.num_instances))
    print("meanAbsoluteError: " + str(evaluation.mean_absolute_error))
    print("meanPriorAbsoluteError: " + str(evaluation.mean_prior_absolute_error))
    print("relativeAbsoluteError: " + str(evaluation.relative_absolute_error))
    print("rootMeanSquaredError: " + str(evaluation.root_mean_squared_error))
    print("rootMeanPriorSquaredError: " + str(evaluation.root_mean_prior_squared_error))
    print("rootRelativeSquaredError: " + str(evaluation.root_relative_squared_error))
    print("prediction output:\n" + str(pred_output))
    plot_cls.plot_roc(
        evaluation, title="ROC diabetes",
        class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)
    plot_cls.plot_prc(
        evaluation, title="PRC diabetes",
        class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)

# 需要导入模块: from weka.classifiers import Evaluation [as 别名]
# 或者: from weka.classifiers.Evaluation import f_measure [as 别名]
def classify_and_save(classifier, name, outfile):
    random.seed("ML349")

    csv_header = [
                    "Game Name",
                    "SteamID",
                    "Algorithm",
                    "Number Players",
                    "%Players of Training Set",
                    "Accuracy",
                    "Precision (0)",
                    "Recall (0)",
                    "F1 (0)",
                    "Precision (1)",
                    "Recall (1)",
                    "F1 (1)"
    ]
    game_results = []

    with open("data/games_by_username_all.csv", "r") as f:
        game_list = f.next().rstrip().split(",")

    loader = Loader(classname="weka.core.converters.ArffLoader")
    train = loader.load_file("data/final_train.arff")
    test = loader.load_file("data/final_test.arff")

    count = 0
    for i in itertools.chain(xrange(0, 50), random.sample(xrange(50, len(game_list)), 450)):
        train.class_index = i
        test.class_index = i
        count += 1

        classifier.build_classifier(train)

        evaluation = Evaluation(train)
        evaluation.test_model(classifier, test)

        confusion = evaluation.confusion_matrix
        num_players = sum(confusion[1])
        steam_id = repr(train.class_attribute).split(" ")[1]
        result = [
                    game_list[i],
                    steam_id,
                    name,
                    int(num_players),
                    num_players/1955,
                    evaluation.percent_correct,
                    evaluation.precision(0),
                    evaluation.recall(0),
                    evaluation.f_measure(0),
                    evaluation.precision(1),
                    evaluation.recall(1),
                    evaluation.f_measure(1)
        ]

        game_results.append(result)
        print "\nResult #{2}/500 for {0} (SteamID {1}):".format(game_list[i], steam_id, count),
        print evaluation.summary()

    with open(outfile, "wb") as f:
        csv_writer = csv.writer(f, delimiter=",")
        csv_writer.writerow(csv_header)
        for r in game_results:
            csv_writer.writerow(r)