def rank_features_using_chisquare(cls,
data_frame,
target_key,
cols_to_ignore=None):
X = data_frame.values
keys = list(data_frame.keys())
target_col_idx = keys.index(target_key)
# Removing the target column from keys
del keys[target_col_idx]
# Remove all columns that are asked to be ignored
if cols_to_ignore is not None:
for col in cols_to_ignore:
idx = keys.index(col)
del keys[idx]
Y = data_frame.loc[:, target_key].values
X = data_frame.loc[:, keys]
neg_test_result = np.any(X < 0, axis=0)
non_negative_value_columns = [
keys[i] for i, res in enumerate(neg_test_result) if not res
]
# Het data for only positive valued columns
X = data_frame.loc[:, non_negative_value_columns]
score = chi_square.chi_square(X, Y)
rank = chi_square.feature_ranking(score)
ranked_features = [non_negative_value_columns[i] for i in rank]
return score, ranked_features, non_negative_value_columns
def apply_impl(self, data):
X, y = data.Xy
# TODO: verify if is possible implement this with numpy
y = pd.Categorical(y).codes
self._score = chi_square.chi_square(X, y)
# Input X must be non-negative. <- This happens when some scaler
# generates negative values.
self._rank = chi_square.feature_ranking(self._score)
self._nro_features = math.ceil((self.ratio) * X.shape[1])
return self.use_impl(data)
def run_fold(trial,P,X,y,method,dataset,parttype):
print 'Obtaining features for %s %s %s fold: %2d' % (parttype,method,dataset,trial)
n_samples, n_features = X.shape
train = P[:,trial] == 1
trnX = X[train]
trnY = y[train]
start_time = time.time()
if method == 'fisher':
score = fisher_score.fisher_score(trnX,trnY)
features = fisher_score.feature_ranking(score)
elif method == 'chi2':
score = chi_square.chi_square(trnX,trnY)
features = chi_square.feature_ranking(score)
elif method == 'relieff':
score = reliefF.reliefF(trnX,trnY)
features = reliefF.feature_ranking(score)
elif method == 'jmi':
features = JMI.jmi(trnX,trnY, n_selected_features=n_features)
elif method == 'mrmr':
features = MRMR.mrmr(trnX,trnY,n_selected_features=n_features)
elif method == 'infogain':
features = MIM.mim(trnX,trnY,n_selected_features=n_features)
elif method == 'svmrfe':
features = svmrfe(trnX,trnY)
elif method == 'hdmr':
sobol_set_all = scipy.io.loadmat('sobol_set.mat')
sobol_set = sobol_set_all['sobol_set']
sobol_set = sobol_set.astype(float)
params = {'sobol_set':sobol_set,'k':1,'p':3,'M':1000,'b':'L'}
models = hdmrlearn(trnX,trnY,params)
features,w = hdmrselect(X,models)
elif method == 'hdmrhaar':
sobol_set_all = scipy.io.loadmat('sobol_set.mat')
sobol_set = sobol_set_all['sobol_set']
sobol_set = sobol_set.astype(float)
params = {'sobol_set':sobol_set,'k':1,'p':255,'M':1000,'b':'H'}
models = hdmrlearn(trnX,trnY,params)
features,w = hdmrselect(X,models)
else:
print(method + 'does no exist')
cputime = time.time() - start_time
print features
print 'cputime %f' % cputime
return {'features': features, 'cputime': cputime}
def test_chi_squared(self):
X, y = self.DATA
f = FilterChiSquare(ratio=0.5)
f.fit(X, y)
X_, y_ = f.transform(X, y)
score = chi_square.chi_square(X, y)
rank = chi_square.feature_ranking(score)
selected = rank[0:5]
assert f.fit(X, y) is f
assert np.array_equal(f.rank(), rank)
assert np.allclose(f.score(), score)
assert np.array_equal(f.selected(), selected)
assert np.allclose(X_, X[:, selected])
assert np.array_equal(y_, y)
def main():
# load data
mat = scipy.io.loadmat('../data/BASEHOCK.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
n_samples, n_features = X.shape # number of samples and number of features
print X.shape
# split data into 10 folds
ss = cross_validation.KFold(n_samples, n_folds=10, shuffle=True)
# perform evaluation on classification task
num_fea = 100 # number of selected features
clf = svm.LinearSVC() # linear SVM
correct = 0
for train, test in ss:
# obtain the chi-square score of each feature
score = chi_square.chi_square(X, y)
# rank features in descending order according to score
idx = chi_square.feature_ranking(score)
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# train a classification model with the selected features on the training dataset
clf.fit(selected_features[train], y[train])
# predict the class labels of test data
y_predict = clf.predict(selected_features[test])
# obtain the classification accuracy on the test data
acc = accuracy_score(y[test], y_predict)
correct = correct + acc
# output the average classification accuracy over all 10 folds
print 'Accuracy:', float(correct) / 10
def main():
# load data
mat = scipy.io.loadmat('../data/BASEHOCK.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
n_samples, n_features = X.shape # number of samples and number of features
# split data into 10 folds
ss = cross_validation.KFold(n_samples, n_folds=10, shuffle=True)
# perform evaluation on classification task
num_fea = 100 # number of selected features
clf = svm.LinearSVC() # linear SVM
correct = 0
for train, test in ss:
# obtain the chi-square score of each feature
score = chi_square.chi_square(X, y)
# rank features in descending order according to score
idx = chi_square.feature_ranking(score)
# obtain the dataset on the selected features
selected_features = X[:, idx[0:num_fea]]
# train a classification model with the selected features on the training dataset
clf.fit(selected_features[train], y[train])
# predict the class labels of test data
y_predict = clf.predict(selected_features[test])
# obtain the classification accuracy on the test data
acc = accuracy_score(y[test], y_predict)
correct = correct + acc
# output the average classification accuracy over all 10 folds
print ('Accuracy:', float(correct)/10)
X = dataset.iloc[:, 2:
32] # [all rows, col from index 2 to the last one excluding 'Unnamed: 32']
y = dataset.iloc[:,
1] # [all rows, col one only which contains the classes of cancer]
labelencoder_Y = LabelEncoder()
y = labelencoder_Y.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
X_train = X_train.values
X_test = X_test.values
# compute RFS scores
score = RFS(X_train, y_train)
idx = feature_ranking(score)
np.save('features/rfs.npy', idx)
print('Features saved')
#idx = np.load('features/rfs.npy')
# create copies of the data
X_train_copy = X_train
y_train_copy = y_train
X_test_copy = X_test
y_test_copy = y_test
# train and compute accuracy of final model trained on selected features
final_list = []
for num_fea in range(30, 0, -1):
# load the copies of the original data
X_train = X_train_copy
selected_fea_train = X_train[:, idx[0:num_features]]
selected_fea_test = X_test[:, idx[0:num_features]]
clf.fit(selected_fea_train, y_train)
acc.append(accuracy_score(y_test, clf.predict(selected_fea_test)))
# reliefF
score = reliefF.reliefF(X_train, y_train)
idx = reliefF.feature_ranking(score)
selected_fea_train = X_train[:, idx[0:num_features]]
selected_fea_test = X_test[:, idx[0:num_features]]
clf.fit(selected_fea_train, y_train)
acc.append(accuracy_score(y_test, clf.predict(selected_fea_test)))
# chi_square
score = chi_square.chi_square(np.abs(X_train), y_train)
idx = chi_square.feature_ranking(score)
selected_fea_train = X_train[:, idx[0:num_features]]
selected_fea_test = X_test[:, idx[0:num_features]]
clf.fit(selected_fea_train, y_train)
acc.append(accuracy_score(y_test, clf.predict(selected_fea_test)))
# pca
pca = PCA(n_components=num_features)
pca.fit(X_train)
selected_fea_train = pca.transform(X_train)
selected_fea_test = pca.transform(X_test)
clf.fit(selected_fea_train, y_train)
acc.append(accuracy_score(y_test, clf.predict(selected_fea_test)))
# rfe
estimator = LinearSVC(random_state=random_state)
def chi_square_FS(X, y):
score = chi_square.chi_square(X, y)
idx = chi_square.feature_ranking(score)
return (idx, score)
