class f_regressionFDRPrim(primitive):
def __init__(self, random_state=0):
super(f_regressionFDRPrim, self).__init__(name='f_regressionFDR')
self.id = 34
self.PCA_LAPACK_Prim = []
self.type = 'feature selection'
self.description = "Filter: Select the p-values for an estimated false discovery rate with F-value between label/feature for regression tasks. This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate."
self.hyperparams_run = {'default': True}
self.selector = None
self.accept_type = 'c_r'
def can_accept(self, data):
return self.can_accept_c(data, 'Regression')
def is_needed(self, data):
if data['X'].shape[1] < 3:
return False
return True
def fit(self, data):
data = handle_data(data)
self.selector = SelectFdr(f_regression)
self.selector.fit(data['X'], data['Y'])
def produce(self, data):
output = handle_data(data)
cols = list(output['X'].columns)
mask = self.selector.get_support(indices=False)
final_cols = list(compress(cols, mask))
output['X'] = pd.DataFrame(self.selector.transform(output['X']), columns=final_cols)
final_output = {0: output}
return final_output
class UnivariateSelectChiFDRPrim(primitive):
def __init__(self, random_state=0):
super(UnivariateSelectChiFDRPrim, self).__init__(name='UnivariateSelectChiFDR')
self.id = 31
self.PCA_LAPACK_Prim = []
self.type = 'feature selection'
self.description = "Filter: Select the p-values for an estimated false discovery rate with Chi-square. This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate."
self.hyperparams_run = {'default': True}
self.selector = None
self.accept_type = 'd'
def can_accept(self, data):
return self.can_accept_d(data, 'Classification')
def is_needed(self, data):
if data['X'].shape[1] < 3:
return False
return True
def fit(self, data):
data = handle_data(data)
self.selector = SelectFdr(chi2, alpha=0.05)
self.selector.fit(data['X'], data['Y'])
def produce(self, data):
output = handle_data(data)
cols = list(output['X'].columns)
try:
mask = self.selector.get_support(indices=False)
final_cols = list(compress(cols, mask))
output['X'] = pd.DataFrame(self.selector.transform(output['X']), columns=final_cols)
except Exception as e:
print(e)
final_output = {0: output}
return final_output
def svm_cv(data, data_target):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, data_target)
print "*" * 79
print "Training..."
# selector = SelectFdr(chi2)
selector = SelectFdr(f_classif)
selector.fit(X_train, y_train)
clf = svm.SVC(kernel='linear', probability=True)
clf.fit(selector.transform(X_train), y_train)
print "Testing..."
pred = clf.predict(selector.transform(X_test))
probs = pred.predict_proba(selector.transfrom(X_test))
accuracy_score = metrics.accuracy_score(y_test, pred)
classification_report = metrics.classification_report(y_test, pred)
support = selector.get_support()
print support
print accuracy_score
print classification_report
precision, recall, thresholds = precision_recall_curve(y_test, probs[:, 1])
def select_fdr(input_data,
feature_names=None,
score_func=f_classif,
alpha=0.05):
if score_func == f_classif:
input_data, feature_names, _ = remove_constant(input_data,
feature_names)
x_train = input_data[0]
y_train = input_data[1]
x_test = input_data[2]
y_test = input_data[3]
dims = len(x_train.shape)
if dims == 3:
x_train = flatten(x_train)
x_test = flatten(x_test)
done = False
increment = alpha
while not done:
feature_selector = SelectFdr(score_func=score_func, alpha=alpha)
temp_x_train = feature_selector.fit_transform(x_train, y_train)
temp_x_test = feature_selector.transform(x_test)
if temp_x_train.shape[1] > 1 and temp_x_test.shape[1] > 1:
done = True
x_train = temp_x_train
x_test = temp_x_test
else:
msg = 'Feature selection was too aggresive, '
msg += 'increasing alpha from {} to {}'.format(
alpha, alpha + increment)
alpha += increment
logging.warning(msg)
if dims == 3:
x_train = make3D(x_train)
x_test = make3D(x_test)
output_data = (x_train, y_train, x_test, y_test)
if feature_names is not None:
mask = feature_selector.get_support()
feature_names = feature_names[mask]
logging.info('Selected {} features'.format(x_train.shape[1]))
final_args = {'score_func': score_func, 'alpha': alpha}
return output_data, feature_names, final_args
def select_fdr(df, target_col):
y = df[target_col]
X = df.drop(target_col, axis=1)
selector = SelectFdr(chi2, alpha=0.01).fit(X, y)
true_list = list(selector.get_support())
index = [i for i in range(len(true_list)) if true_list[i] == True]
if len(index) == 0:
print(
'No features were selected: either the data is too noisy or the selection Test_data too strict.'
)
return df
else:
saved_columns = [list(X.columns)[i] for i in index]
result = pd.DataFrame(selector.transform(X), columns=saved_columns)
result[target_col] = y
return result
###### MASKING FOR SELECTED STIMS targetNames = ['bottle', 'face', 'scissors'] # the ttims of interest stimMask = targetData.labels.isin(targetNames) # indices for the stim of interest X_fMRI_selected = X_fMRI[stimMask] # features (for selected stimuli only) y = np.array(targetData.labelInd)[stimMask] # labels ###### FEATURE SELECTION # FDR feature selector selector = SelectFdr(f_classif, alpha=0.01) # FDR selector object selector.fit(X_fMRI_selected, y) # learning from the data X = selector.transform(X_fMRI_selected) # Selected features only indVoxels = selector.get_support(indices=True) # indices of surviving voxels ###### VISUALIZING FEATURE LOCATIONS # binary vector with 1s indicating selected voxels bROI = np.zeros(X_fMRI.shape[-1]) bROI[indVoxels] = 1 # reverse masking bROI_img = masker.inverse_transform(bROI) # Create the figure plot_stat_map(bROI_img, imgAnat, title='Voxels surviving FDR')
