小编Chr*_*ano的帖子

我试图显示.png我使用以下构建的文件.

import pydot, StringIO
dot_data = StringIO.StringIO() 
tree.export_graphviz( clf, out_file = dot_data,    
feature_names =['age', 'sex', 'first_class', 'second_class', 'third_class'])
graph = pydot.graph_from_dot_data( dot_data.getvalue())
graph.write_png('titanic.png') 
from IPython.core.display import Image
Image( filename ='titanic.png')

我从未这样做过,非常感谢你的帮助.

我尝试了以下但没有错误也没有.png显示.

from PIL import Image
image = Image.open("titanic.png")
image.show()

以下代码读取清理的泰坦尼克数据,打印出所有功能和分数

import csv 
import numpy as np

data = np.genfromtxt('titanic.csv',dtype=float, delimiter=',', names=True)

feature_names = np.array(data.dtype.names)
feature_names = feature_names[[ 0,1,2,3,4]] 

data = np.genfromtxt('plants.csv',dtype=float, delimiter=',', skip_header=1)

_X = data[:, [0,1,2,3,4]] 
#Return a flattened array required by scikit-learn fit for 2nd argument
_y = np.ravel(data[:,[5]])

from sklearn import feature_selection
fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=20)
X_train_fs = fs.fit_transform(_X, _y)

print feature_names, '\n', fs.scores_

结果:

['A'  'B' 'C' 'D' 'E']
[  4.7324711   89.1428574   70.23474577   7.02447375  52.42447817]

我想要做的是捕获前20%的功能,并将名称和分数存储在一个数组中,然后我可以按分数排序.这将有助于我在更大的功能集减少尺寸.为什么我会获得所有5个功能,如何解决这个问题,以及如何存储和打印前20%的功能名称和分数？

我试图从scikit-learn ensemble打印出森林的决策树:例如对于DecisionTreeClassifier,我会使用:

from sklearn import tree  
clf = tree.DecisionTreeClassifier( criterion ='entropy', max_depth = 3,    
min_samples_leaf = 
clf = clf.fit( X_train, y_train) #Input this to analyze the training set.

import pydot, StringIO
dot_data = StringIO.StringIO() 
tree.export_graphviz( clf, out_file = dot_data,    
feature_names =[' age', 'sex', 'first_class', 'second_class', 'third_class'])
graph = pydot.graph_from_dot_data( dot_data.getvalue())
graph.write_png('visualtree.png') 
from IPython.core.display import Image
Image( filename =visualtree.png')

我为Random Forest Regressor尝试了类似的方法(见下文并得到错误)

# Fit regression model
from sklearn.ensemble import RandomForestRegressor
rfr_1 = RandomForestRegressor(n_estimators=10, max_depth=5)
rfr_1.fit(X, y)

from sklearn.ensemble import*
import pydot, …

如何计算python中的左侧特征向量？

    >>> import from numpy as np
    >>> from scipy.linalg import eig
    >>> np.set_printoptions(precision=4)
    >>> T = np.mat("0.2 0.4 0.4;0.8 0.2 0.0;0.8 0.0 0.2")
    >>> print "T\n", T
    T
    [[ 0.2  0.4  0.4]
     [ 0.8  0.2  0. ]
     [ 0.8  0.   0.2]]
    >>> w, vl, vr = eig(T, left=True)
    >>> vl
    array([[ 0.8165,  0.8165,  0.    ],
           [ 0.4082, -0.4082, -0.7071],
           [ 0.4082, -0.4082,  0.7071]])

这似乎不正确，谷歌对此并不友好！