def main():
hsic_lasso = HSICLasso()
#out_list = ['c'+str(i) for i in range(1,51)]
#print (out_list)
hsic_lasso.input("./user_data_new.csv",
output_list=[
'c1', 'c2', 'c3', 'c4', 'c5,', 'c6', 'c7', 'c8', 'c9',
'c10'
])
# ,'c11', 'c12', 'c13', 'c14', 'c15,', 'c16', 'c17', 'c18', 'c19', 'c20','c21', 'c22', 'c23', 'c24', 'c25,', 'c26', 'c27', 'c28', 'c29', 'c30'])
hsic_lasso.regression(100, B=50)
hsic_lasso.dump()
select_index = hsic_lasso.get_index()
print(select_index)
print(hsic_lasso.get_index_score())
#hsic_lasso.plot_path()
print(hsic_lasso.get_features())
X_select = hsic_lasso.X_in[select_index, :]
np.savetxt('X_select.txt', X_select, fmt=str('%.5f'), encoding='utf-8')
def HSICLasso(self):
df_ = self.data.copy()
cols = list(df_.columns)[:-1] + ['class']
df_.columns = cols
hsic_lasso = HSICLasso()
hsic_lasso.input(self.X_train.values, self.Y_train.values)
if self.type == CLASSIFICATION:
hsic_lasso.classification(self.num_top_features)
elif self.type == REGRESSION:
hsic_lasso.regression(self.num_top_features)
feats = [
df_.columns[int(val) - 1] for val in hsic_lasso.get_features()
]
for feat, imp in zip(feats, hsic_lasso.get_index_score()):
features_[feat] = imp
self.report_feature_importance(features_,
self.num_top_features,
label="HSICLasso")
def hsic_lasso_matric(self,
data,
n_jobs=2,
n_sample=False,
frac_sample=False):
'''Calculate hsic lasso (subtract correlation between explanatory variables).
Since the correlation coefficient matrix is not symmetric, it is viewed in the row direction.
The correlation between variable 0 and the other variable is stored as the component on the 0th row,
and the correlation between variable 1 and the other variable is stored as the component on the first row.
n_jobs : (int) Indicates the number of cores to be calculated. -1 for GPU.
data: (numpy or pandas) A data frame that contains all explanatory and objective variables
n_sample : (int) How much random sampling to do. False if not.
If a numerical value is entered, sampling is performed using that number of rows.
frac_sample: [0 ~ 1] (float) Sampled as a percentage of the number of rows. Not used at the same time as n_sample.
'''
data = copy(data)
data = pd.DataFrame(data).dropna()
# Sampling when n_sample contains a numerical value
if not n_sample:
if not frac_sample:
# n_sample=False, frac_sample=False
pass
else:
# n_sample=False, frac_sample=int
data = data.sample(frac=frac_sample, replace=True)
else:
if not frac_sample:
# n_sample=int, frac_sample=False
data = data.sample(n=n_sample, replace=True)
else:
# n_sample=int, frac_sample=int
raise ValueError(
'Please enter a value for `frac` OR `n`, not both')
data = check_array(data, accept_sparse="csc",
dtype=float) # Convert to numpy.ndarray
n_col = data.shape[1]
hsic_array = np.empty((0, n_col - 1), float)
for i in range(n_col):
X = np.delete(data, obj=i, axis=1)
y = data[:, i]
# Calculation of hsic_lasso
hsic_lasso = HSICLasso()
hsic_lasso.input(X, y)
hsic_lasso.regression(num_feat=X.shape[1],
discrete_x=False,
n_jobs=n_jobs)
# hsic_lasso only appears in descending order of score, so sort
hsic_ = np.array(
[hsic_lasso.get_index(),
hsic_lasso.get_index_score()])
hsic_ = hsic_.T # Transpose because it is difficult to use
# Since there are not enough scores that came out, add 0.0 to the index to complement
lack_set = set([x for x in range(X.shape[1])]) - set(hsic_[:, 0])
for lack in lack_set:
lack_list = np.array([[lack, 0.0]])
hsic_ = np.append(hsic_, lack_list, axis=0)
hsic_ = hsic_[np.argsort(hsic_[:, 0])] # Sort by index
hsic_array = np.append(hsic_array,
hsic_[:, 1].reshape(1, -1),
axis=0)
# Since it does not include the correlation component with itself, add 1.0
n_row = hsic_array.shape[0]
for i in range(n_row):
insert_i = (n_row + 1) * i
hsic_array = np.insert(hsic_array, insert_i, 1.0)
self.hsic_lasso = hsic_array.reshape(n_row, -1)
return self.hsic_lasso
