def test_surfstar_pandas_inputs():
"""Check: Data (pandas DataFrame/Series): SURF* works with pandas DataFrame and Series inputs"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(cross_val_score(clf, features_df, labels_s, cv=3,
n_jobs=-1)) > 0.7
def test_surfstar_pipeline_parallel():
"""Check: Data (Binary Endpoint, Discrete Features): SURF* works in a sklearn pipeline when SURF* is parallelized"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2, n_jobs=-1),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(cross_val_score(clf, features, labels, cv=3)) > 0.7
def test_surfstar_pipeline():
"""Ensure that SURF* works in a sklearn pipeline when it is parallelized"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2, n_jobs=-1),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(cross_val_score(clf, features, labels, cv=3)) > 0.7
def test_surfstar_pipeline_mixed_attributes():
"""Check: Data (Mixed Attributes): SURF* works in a sklearn pipeline"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(cross_val_score(clf, features_mixed_attributes,
labels_mixed_attributes, cv=3, n_jobs=-1)) > 0.7
def test_surfstar_pipeline_cont_endpoint():
"""Check: Data (Continuous Endpoint): SURF* works in a sklearn pipeline"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2),
RandomForestRegressor(n_estimators=100, n_jobs=-1))
assert abs(np.mean(cross_val_score(clf, features_cont_endpoint,
labels_cont_endpoint, cv=3, n_jobs=-1))) < 0.5
def test_surfstar_pipeline_missing_values():
"""Ensure that SURF* works in a sklearn pipeline with missing values"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2, n_jobs=-1), Imputer(),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(
cross_val_score(
clf, features_missing_values, labels_missing_values, cv=3)) > 0.7
def test_surfstar_pipeline_multiclass():
"""Check: Data (Multiclass Endpoint): SURF* works in a sklearn pipeline"""
np.random.seed(9238745)
clf = make_pipeline(SURFstar(n_features_to_select=2), SimpleImputer(),
RandomForestClassifier(n_estimators=100, n_jobs=-1))
assert np.mean(
cross_val_score(
clf, features_multiclass, labels_multiclass, cv=3,
n_jobs=-1)) > 0.7
RandomForestClassifier(n_jobs=-1, random_state=args.randomseed),
'ext':
ExtraTreesClassifier(n_estimators=100,
n_jobs=-1,
random_state=args.randomseed),
'svm':
SVC(kernel='sigmoid', random_state=args.randomseed)
}
# 初始化 fs 字典
fs = {
'ReliefF': ReliefF(n_features_to_select=100, verbose=False, n_jobs=-1),
# 'TuRF': TuRF(core_algorithm="ReliefF", n_features_to_select=100, verbose=False, n_jobs=-1),
'SURF': SURF(n_features_to_select=100, verbose=False, n_jobs=-1),
'SURFstar': SURFstar(n_features_to_select=100,
verbose=False,
n_jobs=-1)
}
print('\nClassifier parameters:', clf[args.classifier])
print('\nStarting cross validating without feature selection...\n')
# 特征排序前的增量特征预测，根据名称调用字典中指定分类器
y_pred_list = [
cross_val_predict(clf[args.classifier],
X[:, 0:i + 1],
y,
cv=args.kfolds,
n_jobs=-1) for i in trange(0, X.shape[1])
]
def rank_features_by_rebate_methods(data_split_list,
fs_method,
iterate,
remove_percent=0.1,
verbose=False):
## 0. Input arguments:
# data_split_list: data frame that contains the learning data
# fs_method: feature ranking methods to be used: 'SURF', 'SURFstar', 'MultiSURF', or 'MultiSURFstar'
# iterate: whether to implement TURF: True or False (TURF will remove low-ranking features after each iteration, effective when #features is large)
# remove_percent: percentage of features removed at each iteration (only applied when iterate = True)
# verbose: whether to show progress by each fold: True of False
## 1. Define function for feature ranking method
# SURF
if fs_method == 'SURF':
# Implement TURF extension when 'iterate == True'
if iterate == True:
fs = TuRF(core_algorithm='SURF', pct=remove_percent)
else:
fs = SURF()
# SURFstar
if fs_method == 'SURFstar':
if iterate == True:
fs = TuRF(core_algorithm='SURFstar', pct=remove_percent)
else:
fs = SURFstar()
# MultiSURF
if fs_method == 'MultiSURF':
if iterate == True:
fs = TuRF(core_algorithm='MultiSURF', pct=remove_percent)
else:
fs = MultiSURF()
# MultiSURFstar
if fs_method == 'MultiSURFstar':
if iterate == True:
fs = TuRF(core_algorithm='MultiSURFstar', pct=remove_percent)
else:
fs = MultiSURFstar()
## 2. Perform feature ranking on each fold of training data
# iterate by folds
feat_impt_dict = {}
for i in range(0, len(data_split_list)):
# intermediate output
if verbose == True:
print('Computing feature importance scores using data from fold ' +
str(i) + '\n')
# obtain training feature matrix and response vector
feat_train, label_train, _, _ = data_split_list[i]
# fit feature ranking model using the specified method
if iterate == True:
fs.fit(feat_train.values, label_train.values, list(feat_train))
else:
fs.fit(feat_train.values, label_train.values)
# output feature importance scores in a data frame
fold_name = 'Fold_' + str(i)
feat_impt_dict[fold_name] = fs.feature_importances_
# aggregate results from muliple folds into one data frame
feat_impt_df = pd.DataFrame(feat_impt_dict)
feat_impt_df.index = feat_train.columns
return feat_impt_df
