def experiment(dataset_directory):
train, validation, test = loading.load_dataset(dataset_directory)
features = train.feature_names
values_of_k = range(5, 50, 5)
metrics = [
selection.metric_chi2,
selection.metric_random,
selection.metric_infogain
]
results = []
for kfeatures in values_of_k:
for metric in metrics:
metric_name = metric.__name__
print
print "Testing k=%d, metric=%s" % (kfeatures, metric_name)
selector = selection.feature_selector(metric, kfeatures, train.data, train.target)
selected_indices = selection.get_selected_feature_indices(selector)
train_data_selected = selection.filter_features(selector, train.data)
test_data_selected = selection.filter_features(selector, test.data)
train_acc, train_f1, train_pr_auc = selection.train_test_eval(train_data_selected, train.target,
train_data_selected, train.target)
test_acc, test_f1, test_pr_auc = selection.train_test_eval(train_data_selected, train.target,
test_data_selected, test.target)
selection.print_metrics(train_acc, train_f1, train_pr_auc, test_acc, test_f1, test_pr_auc)
results.append(dict(
kfeatures=kfeatures,
metric=metric_name,
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
output_name = dataset_directory / "features_%s_%d.csv" % (metric_name, kfeatures)
with open(output_name, 'wb') as out:
selection.list_selected(features, selected_indices, out=out)
print "Features saved to %s" % output_name
print
print "Using all the features (Logistic Regression):"
train_acc, train_f1, train_pr_auc = selection.train_test_eval(train.data, train.target, train.data, train.target)
test_acc, test_f1, test_pr_auc = selection.train_test_eval(train.data, train.target, test.data, test.target)
selection.print_metrics(train_acc, train_f1, train_pr_auc, test_acc, test_f1, test_pr_auc)
results.append(dict(
kfeatures=len(features),
metric='logreg',
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
print
print "Using all the features (SVM):"
train_acc, train_f1, train_pr_auc = selection.train_test_eval(train.data, train.target, train.data, train.target,
model='svm')
test_acc, test_f1, test_pr_auc = selection.train_test_eval(train.data, train.target, test.data, test.target,
model='svm')
selection.print_metrics(train_acc, train_f1, train_pr_auc, test_acc, test_f1, test_pr_auc)
results.append(dict(
kfeatures=len(features),
metric='svm',
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
experiment_stats = dataset_directory / "feature_selection_experiment.csv"
with open(experiment_stats, 'wb') as stats:
writer = csv.DictWriter(stats, fieldnames=(
'metric', 'kfeatures',
'train_accuracy', 'train_f1', 'train_pr_auc',
'test_accuracy', 'test_f1', 'test_pr_auc'))
writer.writeheader()
writer.writerows(results)
print "Saved results in %s" % experiment_stats
from loading import load_dataset
import selection as s
print
print "Loading a test dataset"
train, validation, test = load_dataset('test_data')
print
print "With all features..."
acc, f1, auc = s.train_test_eval(train.data, train.target, test.data, test.target)
s.print_metrics(acc, f1, auc)
features = train.feature_names
kfeatures = 2
print
print "With Chi-squared..."
selected = s.select_and_eval(s.metric_chi2, kfeatures, train.data, train.target, test.data, test.target)
s.print_selected(features, selected)
print
print "With random..."
selected = s.select_and_eval(s.metric_random, kfeatures, train.data, train.target, test.data, test.target)
s.print_selected(features, selected)
print
print "With infogain..."
selected = s.select_and_eval(s.metric_infogain, kfeatures, train.data, train.target, test.data, test.target)
s.print_selected(features, selected)
def experiment(dataset_directory):
train, validation, test = loading.load_dataset(dataset_directory)
features = train.feature_names
values_of_k = range(5, 50, 5)
metrics = [
selection.metric_chi2, selection.metric_random,
selection.metric_infogain
]
results = []
for kfeatures in values_of_k:
for metric in metrics:
metric_name = metric.__name__
print
print "Testing k=%d, metric=%s" % (kfeatures, metric_name)
selector = selection.feature_selector(metric, kfeatures,
train.data, train.target)
selected_indices = selection.get_selected_feature_indices(selector)
train_data_selected = selection.filter_features(
selector, train.data)
test_data_selected = selection.filter_features(selector, test.data)
train_acc, train_f1, train_pr_auc = selection.train_test_eval(
train_data_selected, train.target, train_data_selected,
train.target)
test_acc, test_f1, test_pr_auc = selection.train_test_eval(
train_data_selected, train.target, test_data_selected,
test.target)
selection.print_metrics(train_acc, train_f1, train_pr_auc,
test_acc, test_f1, test_pr_auc)
results.append(
dict(
kfeatures=kfeatures,
metric=metric_name,
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
output_name = dataset_directory / "features_%s_%d.csv" % (
metric_name, kfeatures)
with open(output_name, 'wb') as out:
selection.list_selected(features, selected_indices, out=out)
print "Features saved to %s" % output_name
print
print "Using all the features (Logistic Regression):"
train_acc, train_f1, train_pr_auc = selection.train_test_eval(
train.data, train.target, train.data, train.target)
test_acc, test_f1, test_pr_auc = selection.train_test_eval(
train.data, train.target, test.data, test.target)
selection.print_metrics(train_acc, train_f1, train_pr_auc, test_acc,
test_f1, test_pr_auc)
results.append(
dict(
kfeatures=len(features),
metric='logreg',
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
print
print "Using all the features (SVM):"
train_acc, train_f1, train_pr_auc = selection.train_test_eval(train.data,
train.target,
train.data,
train.target,
model='svm')
test_acc, test_f1, test_pr_auc = selection.train_test_eval(train.data,
train.target,
test.data,
test.target,
model='svm')
selection.print_metrics(train_acc, train_f1, train_pr_auc, test_acc,
test_f1, test_pr_auc)
results.append(
dict(
kfeatures=len(features),
metric='svm',
train_accuracy=train_acc,
train_f1=train_f1,
train_pr_auc=train_pr_auc,
test_accuracy=test_acc,
test_f1=test_f1,
test_pr_auc=test_pr_auc,
))
experiment_stats = dataset_directory / "feature_selection_experiment.csv"
with open(experiment_stats, 'wb') as stats:
writer = csv.DictWriter(stats,
fieldnames=('metric', 'kfeatures',
'train_accuracy', 'train_f1',
'train_pr_auc', 'test_accuracy',
'test_f1', 'test_pr_auc'))
writer.writeheader()
writer.writerows(results)
print "Saved results in %s" % experiment_stats
