def select_features(pair_data, labels, features):
"""
进行特征选择
"""
print '特征选择...'
# 1. 过滤掉“低方差”的特征列
vt_sel = VarianceThreshold(threshold=(0.85 * (1 - 0.85)))
vt_sel.fit(pair_data)
# 本次实验中没有需要过滤的特征，在这里只是举例
sel_features1 = features[vt_sel.get_support()]
sel_pair_data1 = pair_data[:, vt_sel.get_support()]
print '“低方差”过滤掉%d个特征' % (features.shape[0] - sel_features1.shape[0])
# 2. 根据“单变量统计分析”选择特征\
# 保留重要的前90%的特征
sp_sel = SelectPercentile(percentile=95)
sp_sel.fit(sel_pair_data1, labels)
sel_features2 = sel_features1[sp_sel.get_support()]
sel_pair_data2 = sel_pair_data1[:, sp_sel.get_support()]
print '“单变量统计分析”过滤掉%d个特征' % (sel_features1.shape[0] -
sel_features2.shape[0])
# 根据特征的score绘制柱状图
feat_ser = pd.Series(data=sp_sel.scores_, index=features)
sorted_feat_ser = feat_ser.sort_values(ascending=False)
plt.figure(figsize=(18, 12))
sorted_feat_ser.plot(kind='bar')
plt.savefig('./feat_importance.png')
plt.show()
return sel_pair_data2, sel_features2
def selected_features(pair_data, labels, features):
#过滤掉低方差的特征值
vt_sel = VarianceThreshold(threshold=(0.85 * (1 - 0.85)))
vt_sel.fit(pair_data)
#本次试验中没有需要过滤的特征，在这里只是举例
print 'vt_sel.get_support()====', vt_sel.get_support()
sel_features1 = features[vt_sel.get_support()]
sel_pair_data1 = pair_data[:, vt_sel.get_support()]
print '低方差过滤掉%d个特征' % (features.shape[0] - sel_features1.shape[0])
print 'features.shape[0]====', features.shape[0], '======', features.shape
print 'sel_features1.shape[0]====', sel_features1.shape[
0], '=========', sel_features1.shape
#2 根据 单变量统计分析 选择特证
#保留重要的前90%的特征
sp_sel = SelectPercentile(percentile=95)
sp_sel.fit(sel_pair_data1, labels)
sel_features2 = sel_features1[sp_sel.get_support()]
sel_pair_data2 = sel_pair_data1[:, sp_sel.get_support()]
print '单变量统计分析过滤掉%d个特征' % (sel_features1.shape[0] - sel_features2.shape[0])
# 根据特征scroe绘制柱状图
feat_ser = pd.Series(data=sp_sel.scores_, index=features)
sort_feat_ser = feat_ser.sort_values(ascending=False)
plt.figure(figsize=(18, 12))
sort_feat_ser.plot(kind='bar')
plt.savefig('../feat_importance.png')
plt.show()
return sel_pair_data2, sel_features2
def test_select_percentile_classif():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple classification problem
with the percentile heuristic
"""
X, Y = make_classification(n_samples=200,
n_features=20,
n_informative=3,
n_redundant=2,
n_repeated=0,
n_classes=8,
n_clusters_per_class=1,
flip_y=0.0,
class_sep=10,
shuffle=False,
random_state=0)
univariate_filter = SelectPercentile(f_classif, percentile=25)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_classif, mode='percentile',
param=25).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def test_select_percentile_classif():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple classification problem
with the percentile heuristic
"""
X, Y = make_classification(
n_samples=200,
n_features=20,
n_informative=3,
n_redundant=2,
n_repeated=0,
n_classes=8,
n_clusters_per_class=1,
flip_y=0.0,
class_sep=10,
shuffle=False,
random_state=0,
)
univariate_filter = SelectPercentile(f_classif, percentile=25)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_classif, mode="percentile", param=25).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
def select_features(pair_data, labels, features):
# 1. 过滤掉“低方差”的特征列
vt_sel = VarianceThreshold(threshold=(0.9*(1-0.9)))
vt_sel.fit(pair_data)
# print(vt_sel.get_support())
#过滤掉噪声特征
features = features[vt_sel.get_support()]
pair_data = pair_data[:,vt_sel.get_support()]
# print(pair_data)
#得到最重要的95%的样本
sp_sel = SelectPercentile(percentile=95)
sp_sel.fit(pair_data, labels)
features = features[sp_sel.get_support()]
pair_data_1 = pair_data[:,sp_sel.get_support()]
# print(pair_data_1)
return pair_data_1,features
def test_select_percentile_regression_full():
"""
Test whether the relative univariate feature selection
selects all features when '100%' is asked.
"""
X, Y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectPercentile(f_regression, percentile=100)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode="percentile", param=100).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.ones(20)
assert_array_equal(support, gtruth)
def test_select_percentile_regression_full():
"""
Test whether the relative univariate feature selection
selects all features when '100%' is asked.
"""
X, Y = make_regression(n_samples=200, n_features=20,
n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectPercentile(f_regression, percentile=100)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode='percentile',
param=100).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.ones(20)
assert_array_equal(support, gtruth)
def test_select_percentile_regression():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple regression problem
with the percentile heuristic
"""
X, Y = make_regression(n_samples=200, n_features=20, n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectPercentile(f_regression, percentile=25)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode="percentile", param=25).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
X_2 = X.copy()
X_2[:, np.logical_not(support)] = 0
assert_array_equal(X_2, univariate_filter.inverse_transform(X_r))
def test_select_percentile_regression():
"""
Test whether the relative univariate feature selection
gets the correct items in a simple regression problem
with the percentile heuristic
"""
X, Y = make_regression(n_samples=200, n_features=20,
n_informative=5, shuffle=False, random_state=0)
univariate_filter = SelectPercentile(f_regression, percentile=25)
X_r = univariate_filter.fit(X, Y).transform(X)
X_r2 = GenericUnivariateSelect(f_regression, mode='percentile',
param=25).fit(X, Y).transform(X)
assert_array_equal(X_r, X_r2)
support = univariate_filter.get_support()
gtruth = np.zeros(20)
gtruth[:5] = 1
assert_array_equal(support, gtruth)
X_2 = X.copy()
X_2[:, np.logical_not(support)] = 0
assert_array_equal(X_2, univariate_filter.inverse_transform(X_r))
