def _learn_model(scenario_name):
'''
Learns a classifier model for the specified scenario if one does
not already exist.
'''
scenario = _scenarios[scenario_name]
if path.exists(scenario['model']):
return
print 'Training the model for scenario {}...'.format(scenario_name)
# Decide on classifier
classifier = 0
if scenario['classifier'] == 'rf':
classifier = RandomForest()
sys.stdout.write('TRAINING RANDOM FOREST\n')
cutoff = [c * 0.1 for c in range(1, 10)]
elif scenario['classifier'] == 'svm':
classifier = sklearn_SVC(kernel='rbf', C=10, gamma=0.01)
sys.stdout.write('TRAINING SVM\n')
cutoff = [0.0]
# Load the required dataset and train the model
X, y, _ = datasets.csv2numpy(scenario['training'])
classifier.fit(X, y)
# Evaluate the model on the training dataset
y_pred = classifier.decision_function(X)
sys.stdout.write('Performance on training data:\n')
utility.print_stats_cutoff(y, y_pred, cutoff)
# Save the model in the corresponding file
classifier.save_model(scenario['model'])
def _learn_model(scenario_name):
'''
Learns a classifier model for the specified scenario if one does
not already exist.
'''
scenario = _scenarios[scenario_name]
if path.exists(scenario['model']):
return
print 'Training the model for scenario {}...'.format(scenario_name)
# Decide on classifier
classifier = 0
if scenario['classifier'] == 'rf':
classifier = RandomForest()
sys.stdout.write('TRAINING RANDOM FOREST\n')
cutoff = [c * 0.1 for c in range(1, 10)]
elif scenario['classifier'] == 'svm':
classifier = sklearn_SVC(kernel='rbf', C=10, gamma=0.01)
sys.stdout.write('TRAINING SVM\n')
cutoff = [0.0]
# Load the required dataset and train the model
X, y, _ = datasets.csv2numpy(scenario['training'])
classifier.fit(X, y)
# Evaluate the model on the training dataset
y_pred = classifier.decision_function(X)
sys.stdout.write('Performance on training data:\n')
utility.print_stats_cutoff(y, y_pred, cutoff)
# Save the model in the corresponding file
classifier.save_model(scenario['model'])
def evaluate_classifier(data, labels, test_data, test_labels):
'''
Returns the classification accuracies of the RandomForest
classifier trained on (data, labels) and tested on a list of
(test_data, test_labels).
'''
rf = RandomForest()
rf.fit(data, labels)
accs = []
for ted, tel in zip(test_data, test_labels):
pred = rf.predict(ted)
accs.append(accuracy_score(tel, pred))
return accs
def evaluate_classifier(data, labels, test_data, test_labels):
'''
Returns the classification accuracies of the RandomForest
classifier trained on (data, labels) and tested on a list of
(test_data, test_labels).
'''
rf = RandomForest()
rf.fit(data, labels)
accs = []
for ted, tel in zip(test_data, test_labels):
pred = rf.predict(ted)
accs.append(accuracy_score(tel, pred))
return accs
