def main(): f = open("me.stdout", "r").read() print f (confusionMatrix, labels, ytrue, ypred, trueCount) = readConfusionMatrix.readText(f) for row in confusionMatrix: print row precisionMicro = np.float(metrics.precision_score(ytrue, ypred, average="micro")) recallMicro = np.float(metrics.recall_score(ytrue, ypred, average="micro")) f1Micro = np.float(metrics.f1_score(ytrue, ypred, average="micro")) f1Macro = np.float(metrics.f1_score(ytrue, ypred, pos_label=1, average="macro")) precisionMacro = np.float(metrics.precision_score(ytrue, ypred, average="macro")) recallMacro = np.float(metrics.recall_score(ytrue, ypred, average="macro")) mConf = metrics.confusion_matrix(ytrue, ypred) print mConf print labels print len(ytrue) print len(ypred) print trueCount print metrics.accuracy_score(ytrue, ypred) print precisionMicro print recallMicro print f1Micro print f1Macro print precisionMacro print recallMacro
def readAndUploadClassifierOutput(featureSet, diseaseFolder, disease, connection, source): text = open(diseaseFolder + "/me.stdout", "r").read() (confusionMatrix, confusionLabelDict, y_truth, y_pred, trueCount) = readConfusionMatrix.readText(text) precisionMicro = np.float(metrics.precision_score(y_truth, y_pred, average="micro")) recallMicro = np.float(metrics.recall_score(y_truth, y_pred, average="micro")) f1Micro = np.float(metrics.f1_score(y_truth, y_pred, average="micro")) f1Macro = np.float(metrics.f1_score(y_truth, y_pred, average="macro")) precisionMacro = np.float(metrics.precision_score(y_truth, y_pred, average="macro")) recallMacro = np.float(metrics.recall_score(y_truth, y_pred, average="macro")) accuracy = np.float(metrics.accuracy_score(y_truth, y_pred)) insertResult.insertClassifierResults(connection, (featureSet, "maxent", trueCount, disease, precisionMicro, recallMicro, f1Micro, recallMacro, precisionMacro, f1Macro, source, accuracy))
def readAndUploadClassifierOutput(featureSet, diseaseFolder, disease, connection, source): text = open(diseaseFolder + "/me.stdout", "r").read() (confusionMatrix, confusionLabelDict, y_truth, y_pred, trueCount) = readConfusionMatrix.readText(text) precisionMicro = np.float( metrics.precision_score(y_truth, y_pred, average="micro")) recallMicro = np.float( metrics.recall_score(y_truth, y_pred, average="micro")) f1Micro = np.float(metrics.f1_score(y_truth, y_pred, average="micro")) f1Macro = np.float(metrics.f1_score(y_truth, y_pred, average="macro")) precisionMacro = np.float( metrics.precision_score(y_truth, y_pred, average="macro")) recallMacro = np.float( metrics.recall_score(y_truth, y_pred, average="macro")) accuracy = np.float(metrics.accuracy_score(y_truth, y_pred)) insertResult.insertClassifierResults( connection, (featureSet, "maxent", trueCount, disease, precisionMicro, recallMicro, f1Micro, recallMacro, precisionMacro, f1Macro, source, accuracy))
def main(): f = open("me.stdout", "r").read() print f (confusionMatrix, labels, ytrue, ypred, trueCount) = readConfusionMatrix.readText(f) for row in confusionMatrix: print row precisionMicro = np.float( metrics.precision_score(ytrue, ypred, average="micro")) recallMicro = np.float(metrics.recall_score(ytrue, ypred, average="micro")) f1Micro = np.float(metrics.f1_score(ytrue, ypred, average="micro")) f1Macro = np.float( metrics.f1_score(ytrue, ypred, pos_label=1, average="macro")) precisionMacro = np.float( metrics.precision_score(ytrue, ypred, average="macro")) recallMacro = np.float(metrics.recall_score(ytrue, ypred, average="macro")) mConf = metrics.confusion_matrix(ytrue, ypred) print mConf print labels print len(ytrue) print len(ypred) print trueCount print metrics.accuracy_score(ytrue, ypred) print precisionMicro print recallMicro print f1Micro print f1Macro print precisionMacro print recallMacro