我运行了机器学习算法。现在我有一个系列,其索引=结果模型和列的特征是相应的权重。
我想将这些特征及其权重显示为热图,我想要显示比重量更轻的特征更重的高重量特征。是否有可能显示不同颜色的正重量而不是负重量?像所有具有正重量的特征(如绿色和正重量)可以具有基于重量值的暗,光,而所有负重量具有红色,并且具有负重量,颜色的强度再次相应于绝对值。
以下是典型特征权重矩阵的外观。它是一个以索引为特征的系列。
adm_hr_ls_7 [-0.0151751599842]
admittype_elective [-0.0767214648205]
admission_age_inyears [0.629567909855]
patient_race_caucasian [-0.0543069188]
gender_female [-0.0831126807492]
marital_status_married [-0.0219135568879]
religion_none [-0.0629291312093]
employmentstatus_retired [0.0620868529898]
employmentstatus_not_employed [0.0195733078954]
编辑:
你的代码给了我这样的东西
我正在寻找一个网格,其中显示所有顶部正面特征,颜色强度由权重的abs值引导。所有正重量将具有一种具有不同强度的颜色。类似的所有顶部负重量(在abs术语中再次为顶部)将具有对应于abs重量大小的具有变化强度的一种颜色。您的代码首先无法正确对齐标签。其次它提供了很多颜色。
让我们说这是数据。
admission_age_inyears [3.86703690989]
emergencydepartmentlengthofstayminutes [3.84708584711]
current_los_from_admissions [3.83956976064]
total_time_in_progressive_inpatient [3.63955027973]
total_time_spent_inpatient [2.59339330312]
nbr_of_hosp_last_90_days [2.44570139977]
total_time_spent_in_er [2.37914969651]
prior_admittype_emergency [2.18467109815]
nbr_inpatient_visits [2.09615621507]
curr_rx_gen_atorvastatin_calcium [2.08752966479]
substanceusehistory [1.91340885366]
timetofirstnurseminutes
to_be_discharged_to_hospice [-0.323042070071]
tot_est_median_age_years [-0.33548236033]
total_current_pharma_laxatives [-0.348768315972]
curr_rx_gen_rivaroxaban [-0.359848868739]
dis_notes_contact_info [-0.360264143656]
total_speak_indo_european [-0.373310297224]
patient_race_african_american [-0.391335453176]
financialclass_commercial [-0.427463083689]
curr_rx_gen_epinephrine_hcl [-0.44205667523]
tot_est_age_55_to_64_years [-0.451699358283]
percent_high_school_grad_or_higher [-0.461380248502]
tot_est_age_65_to_74_years
我想要的是前10-15个正权重应该用一种常见颜色(比如绿色)表示,使得每个特征的颜色强度由相应特征权重的abs值定义。类似的所有负权重特征(前10-15)应该由一个常见颜色表示,如红色,颜色强度由相应特征权重的绝对值定义
EDIT3:
我运行了这段代码。发出错误
n_features = 50
feature_names = ["feature_"+str(i) for i in range(n_features)]
weights = coef_lren.values
# select top 15 high and low features
indices = np.argsort(np.abs(weights))
n_top = 15
top = np.hstack((indices[:n_top], indices[-n_top:]))[::-1]
vmax = np.abs(weights).max()
plt.clf()
plt.imshow(weights[top].reshape((-1,1)),interpolation='nearest', cmap="seismic", vmin=-vmax, vmax=vmax)
plt.axes().xaxis.set_visible(False)
plt.colorbar()
tick_marks = np.arange(2 * n_top)
plt.yticks(tick_marks, [feature_names[i] for i in top])
433 not np.can_cast(self._A.dtype, np.float)):
--> 434 raise TypeError("Image data can not convert to float")
TypeError: Image data can not convert to float
答案 0 :(得分:1)
实际上还有一些工作要做,这应该会给你带来很好的结果:
# define the range for the color mapping
# make sure the color map is centered on 0
# >> use maximum absolute value and not the real min and max (default behaviou)
vmax = np.abs(my_weights).max()
plt.imshow(my_weights.reshape((-1,1)), cmap="seismic", vmin=-vmax, vmax=vmax)
# add feature names
feature_names = ['foo', 'bar', ...]
tick_marks = np.arange(len(feature_names))
plt.yticks(tick_marks, feature_names)
编辑:
import numpy as np
from matplotlib import pyplot as plt
n_features = 50
feature_names = ["feature_"+str(i) for i in range(n_features)]
weights = np.random.randn(n_features)
# select top 15 high and low features
indeces = np.argsort(weights)
n_top = 15
top = np.hstack((indeces[:n_top], indeces[-n_top:]))[::-1]
vmax = np.abs(weights).max()
plt.clf()
plt.imshow(weights[top].reshape((-1,1)),interpolation='nearest', cmap="seismic", vmin=-vmax, vmax=vmax)
plt.axes().xaxis.set_visible(False)
plt.colorbar()
tick_marks = np.arange(2 * n_top)
plt.yticks(tick_marks, [feature_names[i] for i in top])