def var():
#特征选择，去掉方差值小的特征,去掉特征方差值小于threshold的那些特征
data = [[0,2,0,3],
[0,1,4,3],
[0,1,1,3]]
va = VarianceThreshold(threshold=0.5)
data = va.fit_transform(data)
print(data)
data = va.inverse_transform(data)
print(data)
def test_VarianceThreshold():
'''
test the method of VarianceThreshold
:return: None
'''
X = [[100, 1, 2, 3], [100, 4, 5, 6], [100, 7, 8, 9], [101, 11, 12, 13]]
selector = VarianceThreshold(1)
selector.fit(X)
print("Variances is %s" % selector.variances_)
print("After transform is %s" % selector.transform(X))
print("The surport is %s" % selector.get_support(True))
print("After reverse transform is %s" %
selector.inverse_transform(selector.transform(X)))
def varianceFilter(train_data, train_classes, threshold):
#if True:
# return frequencyFilter(train_data, train_classes, threshold)
'''
Variance filter
'''
vectorizer = DictVectorizer()
# Fit and transform the train data.
x_train = vectorizer.fit_transform(train_data)
#y_train = train_classes
sel = VarianceThreshold(threshold=(threshold * (1 - threshold)))
x_new = sel.fit_transform(x_train)
return vectorizer.inverse_transform(sel.inverse_transform(x_new))
def test_varianceThreshold():
from sklearn.feature_selection import VarianceThreshold
x = [
[100, 1, 2, 3],
[100, 4, 5, 6],
[100, 7, 8, 9],
[101, 11, 12, 13]
]
selector = VarianceThreshold(1)
selector.fit(x)
print("Variances is %s" % selector.variances_)
print("After transform is %s" % selector.transform(x))
print("The support is %s" % selector.get_support(True))
print("After reverse transform is %s" % selector.inverse_transform(selector.transform(x)))
def test_VarianceThreshold():
'''
测试 VarianceThreshold 的用法
:return: None
'''
X = [[100, 1, 2, 3],
[100, 4, 5, 6],
[100, 7, 8, 9],
[101, 11, 12, 13]]
selector = VarianceThreshold(threshold=1) #方差低于threshold的属性将被剔除
selector.fit(X)
print("Variances is %s" % selector.variances_)
print("After transform is %s" % selector.transform(X))
print("The surport is %s" % selector.get_support(True))
print("After reverse transform is %s" %
selector.inverse_transform(selector.transform(X)))
def test_VarianceThreshold():
'''
测试 VarianceThreshold 的用法
:return: None
'''
X=[[100,1,2,3],
[100,4,5,6],
[100,7,8,9],
[101,11,12,13]] #共四个特征
selector=VarianceThreshold(1)
selector.fit(X)
print("Variances is %s"%selector.variances_) #Variances is [ 0.1875 13.6875 13.6875 13.6875]
print("After transform is %s"%selector.transform(X)) #第1个特征被剔除了
print("The surport is %s"%selector.get_support(True)) #保留特征的索引下标
print("After reverse transform is %s"%
selector.inverse_transform(selector.transform(X))) #被剔除的特征填充为0
def variance_threshold():
x = [[1, -2, 3, 4, 5.], [3, 4, -5, 6, 7], [1, 7, 2, -6, 2],
[3, 8, 6, 2, -8]]
print(x)
selector = VarianceThreshold(threshold=2)
selector.fit(x)
print()
print(selector.variances_)
print()
print(selector.transform(x))
print()
print(selector.get_support(True))
print()
print(selector.inverse_transform(selector.transform(x)))
pass
def variance(train, validate, test):
'''
test the method of VarianceThreshold
:return: None
'''
selector=VarianceThreshold(1)
selector.fit(train)
train1 = selector.transform(train)
print("Variances is %s"%selector.variances_)
print("The support is %s"%selector.get_support(True))
print("After transform is %s"%train1)
print("After reverse transform is %s" %selector.inverse_transform(selector.transform(train)))
validate = selector.transform(validate)
test = selector.transform(test)
return train1, validate, test
oasis_dataset.white_matter_maps[0]) # 3D data
#############################################################################
# Preprocess data
# ----------------
nifti_masker = NiftiMasker(standardize=False,
smoothing_fwhm=2,
memory='nilearn_cache') # cache options
gm_maps_masked = nifti_masker.fit_transform(gm_imgs_train)
# The features with too low between-subject variance are removed using
# :class:`sklearn.feature_selection.VarianceThreshold`.
from sklearn.feature_selection import VarianceThreshold
variance_threshold = VarianceThreshold(threshold=.01)
gm_maps_thresholded = variance_threshold.fit_transform(gm_maps_masked)
gm_maps_masked = variance_threshold.inverse_transform(gm_maps_thresholded)
# Then we convert the data back to the mask image in order to use it for
# decoding process
mask = nifti_masker.inverse_transform(variance_threshold.get_support())
############################################################################
# Prediction pipeline with ANOVA and SVR using
# :class:`nilearn.decoding.DecoderRegressor` Object
# In nilearn we can benefit from the built-in DecoderRegressor object to
# do ANOVA with SVR instead of manually defining the whole pipeline.
# This estimator also uses Cross Validation to select best models and ensemble
# them. Furthermore, you can pass n_jobs=<some_high_value> to the
# DecoderRegressor class to take advantage of a multi-core system.
# To save time (because these are anat images with many voxels), we include
('anova', feature_selection),
('svr', svr)])
### Fit and predict
anova_svr.fit(gm_maps_masked, age)
age_pred = anova_svr.predict(gm_maps_masked)
#############################################################################
# Visualization
# --------------
# Look at the SVR's discriminating weights
coef = svr.coef_
# reverse feature selection
coef = feature_selection.inverse_transform(coef)
# reverse variance threshold
coef = variance_threshold.inverse_transform(coef)
# reverse masking
weight_img = nifti_masker.inverse_transform(coef)
# Create the figure
from nilearn.plotting import plot_stat_map, show
bg_filename = gray_matter_map_filenames[0]
z_slice = 0
fig = plt.figure(figsize=(5.5, 7.5), facecolor='k')
# Hard setting vmax to highlight weights more
display = plot_stat_map(weight_img, bg_img=bg_filename,
display_mode='z', cut_coords=[z_slice],
figure=fig, vmax=1)
display.title('SVM weights', y=1.2)
gm_maps_masked = NiftiMasker().fit_transform(gray_matter_map_filenames)
data = variance_threshold.fit_transform(gm_maps_masked)
# Statistical inference
from nilearn.mass_univariate import permuted_ols
neg_log_pvals, t_scores_original_data, _ = permuted_ols(
age,
data, # + intercept as a covariate by default
n_perm=2000, # 1,000 in the interest of time; 10000 would be better
verbose=1, # display progress bar
n_jobs=1) # can be changed to use more CPUs
signed_neg_log_pvals = neg_log_pvals * np.sign(t_scores_original_data)
signed_neg_log_pvals_unmasked = nifti_masker.inverse_transform(
variance_threshold.inverse_transform(signed_neg_log_pvals))
# Show results
threshold = -np.log10(0.1) # 10% corrected
fig = plt.figure(figsize=(5.5, 7.5), facecolor='k')
display = plot_stat_map(signed_neg_log_pvals_unmasked,
bg_img=bg_filename,
threshold=threshold,
cmap=plt.cm.RdBu_r,
display_mode='z',
cut_coords=[z_slice],
figure=fig)
title = ('Negative $\\log_{10}$ p-values'
'\n(Non-parametric + max-type correction)')
print(df.head())
print(df.shape)
print(df.columns)
array = df.values
X = array[:, :13]
Y = array[:, 13]
#VarianceThreshold is Feature selector that removes all low-variance features.
#This feature selection algorithm looks only at the features (X), not the desired outputs (y),
#and can thus be used for unsupervised learning.
from sklearn.feature_selection import VarianceThreshold
sel = VarianceThreshold(threshold=(.9 * (1 - .9))) # for 90% threshhold
varTh = sel.fit(X, Y)
numpy.set_printoptions(precision=3)
print(sel.variances_)
for i in range(len(sel.variances_)):
print(sel.variances_[i], end=" ")
print(X.shape)
featTransformed = sel.transform(X)
print(df.columns)
print(featTransformed.shape)
#print(featTransformed[0:5,:])
featBack = sel.inverse_transform(featTransformed)
#print(featBack[0:5,:])
# -*-coding:utf-8-*-
# @auth ivan
# @time 20200611
# @goal test 054.Test_Feature_selection
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest, f_classif
X = [[100, 1, 2, 3], [100, 4, 5, 6], [100, 7, 8, 9], [101, 11, 12, 13]]
selector = VarianceThreshold(1)
selector.fit(X)
print('Variances is %s' % selector.variances_)
print('After transform is \n%s' % selector.transform(X))
print('The surport is %s' % selector.get_support(True))
print('The surport is %s' % selector.get_support(False))
print('After reverse transform is \n%s' %
selector.inverse_transform(selector.transform(X)))
# Variances is [ 0.1875 13.6875 13.6875 13.6875]
# After transform is
# [[ 1 2 3]
# [ 4 5 6]
# [ 7 8 9]
# [11 12 13]]
# The surport is [1 2 3]
# The surport is [False True True True]
# After reverse transform is
# [[ 0 1 2 3]
# [ 0 4 5 6]
# [ 0 7 8 9]
# [ 0 11 12 13]]
X = [[1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [3, 3, 3, 3, 3], [1, 1, 1, 1, 1]]
__author__ = 'ctiwary' import numpy as np from sklearn.feature_selection import VarianceThreshold # http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection # user guide http://scikit-learn.org/stable/modules/feature_selection.html#feature-selection # feature selection based on VarianceThreshold from sklearn.feature_selection import VarianceThreshold X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]] print np.shape(X) sel = VarianceThreshold(threshold=(.8 * (1 - .8))) features_list = sel.fit_transform(X) print features_list print sel.inverse_transform(features_list) print sel.get_support() print dir(sel) # Univariate feature selection from sklearn.datasets import load_iris from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 iris = load_iris() X, y = iris.data, iris.target print X.shape X_new = SelectKBest(chi2, k=2).fit_transform(X, y) print X_new.shape
from sklearn.feature_selection import VarianceThreshold
import DataReader
import pandas as pd
control_data = DataReader.DataShow("./data/control_rawdata.npy")
control_data.load_data()
features = ['C', 'I', 'O', 'F', 'M', 'LTXE', 'CIOFM', 'CIOFMLTXE', 'Scorr', 'Var', 'EyeMvt']
data = pd.DataFrame(control_data.data[0][0][17:, 3:14], columns=features)
selector = VarianceThreshold(1)
X = data.to_numpy()
selector.fit(X)
print('Variances is %s'%selector.variances_)
print('After transform is \n%s'%selector.transform(X))
print('The surport is %s'%selector.get_support(True)) # 如果为True那么返回的是被选中的特征的下标
print('The surport is %s'%selector.get_support(False)) # 如果为FALSE那么返回的是布尔类型的列表，反应是否选中这列特征
print('The feature is %s' % [x for (index, x) in enumerate(features) if index in selector.get_support(True)])
print('After reverse transform is \n%s'%selector.inverse_transform(selector.transform(X)))
y = boston.target
from sklearn.linear_model import LinearRegression
p = df['CHAS'].sum() / df.shape[0]
p * (1 - p)
get_ipython().run_line_magic('pinfo', 'VarianceThreshold')
vt = VarianceThreshold(50)
x_lt = vt.fit_transform(X)
x_lt
x_lt.shape
lr = LinearRegression()
lr.fit(x_lt, y)
get_ipython().run_line_magic('pinfo', 'lr.score')
lr.score(x_lt, y)
get_ipython().run_line_magic('pinfo', 'VarianceThreshold')
vt.variances_
x_lv = vt.inverse_transform(X)
X.shape
vt.fit_transform(X)
x_lv = vt.inverse_transform(X)
get_ipython().run_line_magic('pinfo', 'vt.inverse_transform')
get_ipython().run_line_magic('ls', '')
get_ipython().run_line_magic('pinfo', 'VarianceThreshold')
vt
vt.get_support
vt.get_support(np.arange(X.shape[1]))
import numpy as np
vt.get_support(np.arange(X.shape[1]))
vt.get_support()
x_lv = X[:, _]
x_lv.shape
get_ipython().run_line_magic('whos', '')