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)):
