class VarianceThreshold(FeatureSelectionAlgorithm):
r"""Implementation of feature selection using variance threshold.
Date:
2020
Author:
Luka Pečnik
License:
MIT
Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
See Also:
* :class:`niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm`
"""
Name = 'Variance Threshold'
def __init__(self, **kwargs):
r"""Initialize VarianceThreshold feature selection algorithm.
"""
self._params = dict(
threshold=ParameterDefinition(MinMax(0, 0.1), np.float))
self.__variance_threshold = VarThr()
def set_parameters(self, **kwargs):
r"""Set the parameters/arguments of the algorithm.
"""
self.__variance_threshold.set_params(**kwargs)
def select_features(self, x, y, **kwargs):
r"""Perform the feature selection process.
Arguments:
x (pandas.core.frame.DataFrame): Array of original features.
y (pandas.core.series.Series) Expected classifier results.
Returns:
numpy.ndarray[bool]: Mask of selected features.
"""
self.__variance_threshold.fit(x)
return self.__variance_threshold.get_support()
def to_string(self):
r"""User friendly representation of the object.
Returns:
str: User friendly representation of the object.
"""
return FeatureSelectionAlgorithm.to_string(self).format(
name=self.Name,
args=self._parameters_to_string(
self.__variance_threshold.get_params()))
from sklearn.feature_selection import VarianceThreshold
print('need to transpose to time by features')
feature_vector = spike_counts_batch_sample_batch.T
target_vector = intended_kin_sample.T
print(f'the feature vector has the shape of {feature_vector.shape}')
print(f'the target vector has the shape of {target_vector.shape}\n')
#set up the selector
variance_selector = VarianceThreshold(threshold=0)
#calculate the empirical variances
feature_variances = variance_selector.fit(feature_vector)
variance_selector_params = variance_selector.get_params()
#fit the transform
transformed_feature_vec = variance_selector.fit_transform(feature_vector)
#this needs to be fitted before returning the selected features, eh.
selected_feature_indx = variance_selector.get_support()
print(f'we know the feature variances to be {feature_variances}')
print(f'we know the parameters are {variance_selector_params}')
print(
f'after transform, we know the dim of the feature vector {transformed_feature_vec.shape}\n'
)
print(f'we therefore, know the selected indices are {selected_feature_indx}')
# In[ ]:
# feature_list_of_all_instances = StandardScaler().fit_transform( feature_list_of_all_instances )
for i in range(0, len(feature_list_of_all_instances)):
index = 0
for j in range(0, len(feature_list_of_all_instances[i])):
if j < 1282:
temp_features[i][j] = feature_list_of_all_instances[i][j]
# feature_list_of_all_instances = temp_features
feature_list_of_all_instances = StandardScaler().fit_transform(
feature_list_of_all_instances)
# print("for threshold T ",t )
variance = VarianceThreshold()
print(variance.get_params())
feature_list_of_all_instances = variance.fit_transform(
feature_list_of_all_instances)
print("reduced feature number ", len(feature_list_of_all_instances[1]))
feature_list_of_all_instances = SelectKBest(f_classif, k=100).fit_transform(
feature_list_of_all_instances, class_list_of_all_instances)
# feature_list_of_all_instances = SelectKBest(f_classif,k=50).fit_transform(feature_list_of_all_instances,class_list_of_all_instances)
#
print("reduced feature number ",
len(feature_list_of_all_instances[1])) # count = 0
# count = 0
# redunrent_list = []
# for i in range(0,519):
# # print("checking ", i,"th column" )
