def test_multisurfstar_pandas_inputs():
"""Check: Data (pandas DataFrame/Series): MultiSURF* works with pandas DataFrame and Series inputs"""
np.random.seed(320931)
clf = make_pipeline(MultiSURFstar(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_multisurfstar_pipeline_parallel():
"""Check: Data (Binary Endpoint, Discrete Features): MultiSURF* works in a sklearn pipeline when MultiSURF* is parallelized"""
np.random.seed(320931)
clf = make_pipeline(MultiSURFstar(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_multisurfstar_pipeline():
"""Ensure that MultiSURF* works in a sklearn pipeline when it is parallelized"""
np.random.seed(320931)
clf = make_pipeline(MultiSURFstar(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_multisurfstar_pipeline_mixed_attributes():
"""Check: Data (Mixed Attributes): MultiSURF* works in a sklearn pipeline"""
np.random.seed(320931)
clf = make_pipeline(MultiSURFstar(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_multisurfstar_pipeline_cont_endpoint():
"""Check: Data (Continuous Endpoint): MultiSURF* works in a sklearn pipeline"""
np.random.seed(320931)
clf = make_pipeline(MultiSURFstar(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 rebate(df, target, n_features):
"""
Run the MultiSURF* algorithm on a dataframe, returning the reduced df.
Args:
df (pandas.DataFrame): A dataframe
target (str): The target key (must be present in df)
n_features (int): The number of features desired to be returned.
Returns:
pd.DataFrame The dataframe with fewer features, and no target
"""
X = df.drop(target, axis=1)
y = df[target]
rf = MultiSURFstar(n_features_to_select=n_features, n_jobs=-1)
matrix = rf.fit_transform(X.values, y.values)
feats = []
for c in matrix.T:
for f in X.columns.values:
if np.array_equal(c, X[f].values) and f not in feats:
feats.append(f)
return df[feats]
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
