def main():
subj_num, fwhm_mm = 1, 4
voxels_sorted_by_t_statistic = lm.VoxelExtractor(subj_num,
'int-ext').t_stat()
design_matrix = dm.DesignMatrix(dp.get_smoothed_2d_path(subj_num, fwhm_mm))
train_volume_indices = pv.get_train_indices()
cv_values = []
for num_features in NUM_FEATURES:
voxel_feature_indices = voxels_sorted_by_t_statistic[:num_features]
X_train = design_matrix.get_design_matrix(train_volume_indices,
voxel_feature_indices)
y_train = np.array(design_matrix.get_labels(train_volume_indices))
cv_accuracies = []
for train, test in KFold(len(X_train), 5):
X_cv_train = X_train[train, :]
y_cv_train = y_train[train]
X_cv_test = X_train[test, :]
y_cv_test = y_train[test]
model = rf.Classifier(X_cv_train, y_cv_train)
model.train()
y_predicted = model.predict(X_cv_test)
cv_accuracies.append(accuracy_score(y_predicted, y_cv_test))
print(np.mean(cv_accuracies))
cv_values.append(np.mean(cv_accuracies))
output_path = '{0}/figures/rf_cross_validated_accuracies.txt'.format(
REPO_HOME_PATH)
np.savetxt(output_path, cv_values)
print('Saved {0}'.format(output_path))
def main():
subj_num, fwhm_mm = 1, 4
voxels_sorted_by_t_statistic = lm.VoxelExtractor(subj_num,
'int-ext').t_stat()
design_matrix = dm.DesignMatrix(dp.get_smoothed_2d_path(subj_num, fwhm_mm))
train_volume_indices = pv.get_train_indices()
cv_values = []
for num_features in NUM_FEATURES:
voxel_feature_indices = voxels_sorted_by_t_statistic[:num_features]
X_train = design_matrix.get_design_matrix(train_volume_indices,
voxel_feature_indices)
y_train = np.array(design_matrix.get_labels(train_volume_indices))
cv_accuracies = []
for train, test in KFold(len(X_train), 5):
X_cv_train = X_train[train, :]
y_cv_train = y_train[train]
X_cv_test = X_train[test, :]
y_cv_test = y_train[test]
model = rf.Classifier(X_cv_train, y_cv_train)
model.train()
y_predicted = model.predict(X_cv_test)
cv_accuracies.append(accuracy_score(y_predicted, y_cv_test))
print(np.mean(cv_accuracies))
cv_values.append(np.mean(cv_accuracies))
output_path = '{0}/figures/rf_cross_validated_accuracies.txt'.format(
REPO_HOME_PATH)
np.savetxt(output_path, cv_values)
print('Saved {0}'.format(output_path))
def main():
subj_num, fwhm_mm = 1, 4
voxels_sorted_by_t_statistic = lm.VoxelExtractor(subj_num,
'int-ext').t_stat()
design_matrix = dm.DesignMatrix(dp.get_smoothed_2d_path(subj_num, fwhm_mm))
train_volume_indices = pv.get_train_indices()
test_volume_indices = pv.get_test_indices()
voxel_feature_indices = voxels_sorted_by_t_statistic[:NUM_FEATURES]
X_train = design_matrix.get_design_matrix(train_volume_indices,
voxel_feature_indices)
y_train = np.array(design_matrix.get_labels(train_volume_indices))
X_test = design_matrix.get_design_matrix(test_volume_indices,
voxel_feature_indices)
y_test = np.array(design_matrix.get_labels(test_volume_indices))
model = rf.Classifier(X_train, y_train)
model.train()
y_predicted = model.predict(X_test)
accuracy = accuracy_score(y_test, y_predicted)
print('Validation set accuracy: {0}'.format(accuracy))
output_path = '{0}/figures/validation_accuracy.txt'.format(REPO_HOME_PATH)
np.savetxt(output_path, np.array([accuracy]))
print('Saved {0}'.format(output_path))
def main():
subj_num, fwhm_mm = 1, 4
voxels_sorted_by_t_statistic = lm.VoxelExtractor(subj_num,
'int-ext').t_stat()
design_matrix = dm.DesignMatrix(dp.get_smoothed_2d_path(subj_num, fwhm_mm))
train_volume_indices = pv.get_train_indices()
test_volume_indices = pv.get_test_indices()
voxel_feature_indices = voxels_sorted_by_t_statistic[:NUM_FEATURES]
X_train = design_matrix.get_design_matrix(train_volume_indices,
voxel_feature_indices)
y_train = np.array(design_matrix.get_labels(train_volume_indices))
X_test = design_matrix.get_design_matrix(test_volume_indices,
voxel_feature_indices)
y_test = np.array(design_matrix.get_labels(test_volume_indices))
model = rf.Classifier(X_train, y_train)
model.train()
y_predicted = model.predict(X_test)
accuracy = accuracy_score(y_test, y_predicted)
print('Validation set accuracy: {0}'.format(accuracy))
output_path = '{0}/figures/validation_accuracy.txt'.format(
REPO_HOME_PATH)
np.savetxt(output_path, np.array([accuracy]))
print('Saved {0}'.format(output_path))
import numpy as np
from sklearn.cross_validation import KFold
from sklearn.metrics import accuracy_score
from stat159lambda.classification import design_matrix as dm
from stat159lambda.classification.random_forest import rf
from stat159lambda.classification import partition_volumes as pv
from stat159lambda.config import REPO_HOME_PATH
from stat159lambda.linear_modeling import linear_modeling as lm
design_matrix = dm.DesignMatrix(
'{0}/data/processed/sub1_rcds_2d.npy'.format(REPO_HOME_PATH),
pv.get_train_indices(), range(200))
X_train = design_matrix.get_design_matrix()
y_train = np.array(design_matrix.get_labels())
cv_accuracies = []
for train, test in KFold(len(X_train), 5):
X_cv_train = X_train[train, :]
y_cv_train = y_train[train]
X_cv_test = X_train[test, :]
y_cv_test = y_train[test]
model = rf.Classifier(X_cv_train, y_cv_train)
model.train()
y_predicted = model.predict(X_cv_test)
cv_accuracies.append(accuracy_score(y_predicted, y_cv_test))
print np.mean(cv_accuracies)