X_train = X_train_origin[features] X_test = X_test_origin[features] i = 0 result = Results() #getting test results for each classifier while (i < len(clfs)): clfs[i].fit(X_train, Y_train) preds = clfs[i].predict(X_test) result.accuracy = metrics.accuracy_score(Y_test, preds) result.precision = metrics.precision_score(Y_test, preds) result.recall = metrics.recall_score(Y_test, preds) result.k_cohen = metrics.cohen_kappa_score(Y_test, preds) result.f1_measure = metrics.f1_score(Y_test, preds) result.log_loss = metrics.log_loss(Y_test, clfs[i].predict_proba(X_test)) #write results into file printResults(result, clfNames[i], len(features)) i += 1 featureSize -= 5 #plotting test and train results dirPath = "Classification/Test/" plotter = Plotter(clfNames, dirPath) metricNames = ["Accuracy", "Precision", "Recall", "K_cohen", "F1_measure", "Log-loss"] i = 0 while (i < len(metricNames)): plotter.plotMetric(dirPath + metricNames[i] + ".png", i + 1) i += 1
#starting on the evaluation set X_train = X_train_origin[features] X_test = X_test_origin[features] i = 0 result = Results() while(i < len(clfs)): clfs[i].fit(X_train, Y_train) preds = clfs[i].predict(X_test) result.accuracy = metrics.accuracy_score(Y_test, preds) result.precision = metrics.precision_score(Y_test, preds, average="macro") result.recall = metrics.recall_score(Y_test, preds, average="macro") result.k_cohen = metrics.cohen_kappa_score(Y_test, preds) result.f1_measure = metrics.f1_score(Y_test, preds, average="macro") result.log_loss = metrics.log_loss(Y_test, clfs[i].predict_proba(X_test)) printResults(result, clfNames[i], len(features)) i += 1 featureSize -= 5 dirPath = "MultiClassification/Test/" plotter = Plotter(clfNames, dirPath) metricNames = ["Accuracy", "Precision", "Recall", "K_cohen", "F1_measure", "Log-loss"] i = 0 while( i < len(metricNames)): plotter.plotMetric( dirPath + metricNames[i]+".png", i + 1) i += 1 dirPath = "MultiClassification/Train/"
X_train = X_train_origin[features] X_test = X_test_origin[features] i = 0 result = Results() #getting test results for each classifier while (i < len(clfs)): clfs[i].fit(X_train, Y_train) preds = clfs[i].predict(X_test) result.accuracy = metrics.accuracy_score(Y_test, preds) result.precision = metrics.precision_score(Y_test, preds) result.recall = metrics.recall_score(Y_test, preds) result.k_cohen = metrics.cohen_kappa_score(Y_test, preds) result.f1_measure = metrics.f1_score(Y_test, preds) result.log_loss = metrics.log_loss(Y_test, clfs[i].predict_proba(X_test)) #write results into file printResults(result, clfNames[i], len(features)) i += 1 featureSize -= 5 #plotting test and train results dirPath = "Classification/Test/" plotter = Plotter(clfNames, dirPath) metricNames = [ "Accuracy", "Precision", "Recall", "K_cohen", "F1_measure", "Log-loss" ] i = 0
i = 0 result = Results() while (i < len(clfs)): clfs[i].fit(X_train, Y_train) preds = clfs[i].predict(X_test) result.accuracy = metrics.accuracy_score(Y_test, preds) result.precision = metrics.precision_score(Y_test, preds, average="macro") result.recall = metrics.recall_score(Y_test, preds, average="macro") result.k_cohen = metrics.cohen_kappa_score(Y_test, preds) result.f1_measure = metrics.f1_score(Y_test, preds, average="macro") result.log_loss = metrics.log_loss(Y_test, clfs[i].predict_proba(X_test)) printResults(result, clfNames[i], len(features)) i += 1 featureSize -= 5 dirPath = "MultiClassification/Test/" plotter = Plotter(clfNames, dirPath) metricNames = [ "Accuracy", "Precision", "Recall", "K_cohen", "F1_measure", "Log-loss" ] i = 0 while (i < len(metricNames)):