映射 - 特征重要性与标签分类
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【中文标题】映射 - 特征重要性与标签分类【英文标题】：Mapping - Feature Importance vs Label classification 【发布时间】：2021-07-18 20:34:29 【问题描述】：我有一组与香草磅蛋糕烘焙相关的数据（200 行），具有 27 个特征，如下所示。标签caketaste 是衡量烤蛋糕有多好的标准，由bad(0)、neutral(1)、good(2) 定义。
Features = cake_id, flour_g, butter_g, sugar_g, salt_g, eggs_count, bakingpowder_g, milk_ml, water_ml, vanillaextract_ml, lemonzest_g, mixingtime_min, bakingtime_min, preheattime_min, coolingtime_min, bakingtemp_c, preheattemp_c, color_red, color_green, color_blue, traysize_small, traysize_medium, traysize_large, milktype_lowfat, milktype_skim, milktype_whole, trayshape.
Label = caketaste ["bad", "neutral", "good"]
我的任务是找到： a) 影响标签结果的 5 个最重要的特征； b) 找出对标签中“良好”分类做出贡献的 5 个最重要特征的值。
我可以使用 sklearn (Python) 解决这个问题，使用 RandomForestClassifier() 拟合数据，然后使用 Feature_Importance() 识别 5 个最重要的特征，即 mixingtime_min、bakingtime_min、sugar_g、@987654336 @和preheattemp_c。
最小、完整且可验证的示例：
#################################################################
# a) Libraries
#################################################################
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.inspection import permutation_importance
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MaxAbsScaler
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
import time
#################################################################
# b) Data Loading Symlinks
#################################################################
df = pd.read_excel("poundcake.xlsx", sheet_name="Sheet0", engine='openpyxl')
#################################################################
# c) Analyzing Dataframe
#################################################################
#Getting dataframe details e.g columns, total entries, data types etc
print("\n<syntax>: df.info()")
df.info()
#Getting the 1st 5 lines in the dataframe
print("\n<syntax>: df.head()")
df.head()
#################################################################
# d) Data Visualization
#################################################################
#Scatterplot SiteID vs LTE - Spectral Efficiency
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
ax.scatter(df["cake_id"], df["caketaste"], color='r')
ax.set_xlabel('cake_id')
ax.set_ylabel('caketaste')
ax.set_title('scatter plot')
plt.show()
#################################################################
# e) Feature selection
#################################################################
#Note:
#Machine learning models cannot work well with categorical (string) data, specifically scikit-learn.
#Need to convert the categorical variables into numeric types before building a machine learning model.
categorical_columns = ["trayshape"]
numerical_columns = ["flour_g","butter_g","sugar_g","salt_g","eggs_count","bakingpowder_g","milk_ml","water_ml","vanillaextract_ml","lemonzest_g","mixingtime_min","bakingtime_min","preheattime_min","coolingtime_min","bakingtemp_c","preheattemp_c","color_red","color_green","color_blue","traysize_small","traysize_medium","traysize_large","milktype_lowfat","milktype_skim","milktype_whole"]
#################################################################
# f) Dataset (Train Test Split)
#
# (Dataset)
# ┌──────────────────────────────────────────┐
# ┌──────────────────────────┬────────────┐
# | Training │ Test │
# └──────────────────────────┴────────────┘
#################################################################
# Prediction target - Training data
X = df[categorical_columns + numerical_columns]
# Prediction target - Training data
y = df["caketaste"]
# Break off validation set from training data. Default: train_size=0.75, test_size=0.25
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=42)
#################################################################
# Pipeline
#################################################################
#######################
# g) Column Transformer
#######################
categorical_encoder = OneHotEncoder(handle_unknown='ignore')
#Mean might not be suitable, Remove rows?
numerical_pipe = Pipeline([
('imp', SimpleImputer(strategy='mean'))
])
preprocessing = ColumnTransformer(
[('cat', categorical_encoder, categorical_columns),
('num', numerical_pipe, numerical_columns)])
#####################
# b) Pipeline Printer
#####################
#RF: builds multiple decision trees and merges (bagging) them together
#to get a more accurate and stable prediction (averaging).
pipe_xxx_xxx_rfo = Pipeline([
('pre', preprocessing),
('scl', None),
('pca', None),
('clf', RandomForestClassifier(random_state=42))
])
pipe_abs_xxx_rfo = Pipeline([
('pre', preprocessing),
('scl', MaxAbsScaler()),
('pca', None),
('clf', RandomForestClassifier(random_state=42))
])
#################################################################
# h) Hyper-Parameter Tuning
#################################################################
parameters_rfo =
'clf__n_estimators':[100],
'clf__criterion':['gini'],
'clf__min_samples_split':[2,5],
'clf__min_samples_leaf':[1,2]
parameters_rfo_bk =
'clf__n_estimators':[10,20,30,40,50,60,70,80,90,100,1000],
'clf__criterion':['gini','entropy'],
'clf__min_samples_split':[5,10,15,20,25,30],
'clf__min_samples_leaf':[1,2,3,4,5]
#########################
# i) GridSearch Printer
#########################
# scoring can be used as 'accuracy' or for MAE use 'neg_mean_absolute_error'
scr='accuracy'
grid_xxx_xxx_rfo = GridSearchCV(pipe_xxx_xxx_rfo,
param_grid=parameters_rfo,
scoring=scr,
cv=5,
refit=True)
grid_abs_xxx_rfo = GridSearchCV(pipe_abs_xxx_rfo,
param_grid=parameters_rfo,
scoring=scr,
cv=5,
refit=True)
print("Pipeline setup.... Complete")
###################################################
# Machine Learning Models Evaluation Algorithm
###################################################
grids = [grid_xxx_xxx_rfo, grid_abs_xxx_rfo]
grid_dict = 0: 'RandomForestClassifier',
1: 'RandomForestClassifier with AbsMaxScaler',
# Fit the grid search objects
print('Performing model optimizations...\n')
best_test_scr = -999999999999999 #Python3 does not allow to use None anymore
best_clf = 0
best_gs = ''
for idx, grid in enumerate(grids):
start_time = time.time()
print('*' * 100)
print('\nEstimator: %s' % grid_dict[idx])
# Fit grid search
grid.fit(X_train, y_train)
#Calculate the score once and use when needed
test_scr = grid.score(X_test,y_test)
train_scr = grid.score(X_train,y_train)
# Track best (lowest grid.score) model
if test_scr > best_test_scr:
best_test_scr = test_scr
best_train_scr = train_scr
best_rf = grid
best_clf = idx
print("..........................this model is better. SELECTED")
print("Best params : %s" % grid.best_params_)
print("Training accuracy : %s" % best_train_scr)
print("Test accuracy : %s" % best_test_scr)
print("Modeling time : %s" % time.strftime("%H:%M:%S", time.gmtime(time.time() - start_time)))
print('\nClassifier with best test set score: %s' % grid_dict[best_clf])
#########################################################################################
# j) Feature Importance using Gini Importance or Mean Decrease in Impurity (MDI)
# Note:
# 1.Calculates each feature importance as the sum over the number of splits (accross
# all trees) that include the feature, proportionaly to the number of samples it splits.
# 2. Biased towards cardinality i.e numerical variables
########################################################################################
ohe = (best_rf.best_estimator_.named_steps['pre'].named_transformers_['cat'])
feature_names = ohe.get_feature_names(input_features=categorical_columns)
feature_names = np.r_[feature_names, numerical_columns]
tree_feature_importances = (best_rf.best_estimator_.named_steps['clf'].feature_importances_)
sorted_idx = tree_feature_importances.argsort()
# Figure: Top Features
count=-28
y_ticks = np.arange(0, abs(count))
fig, ax = plt.subplots()
ax.barh(y_ticks[count:], tree_feature_importances[sorted_idx][count:])
ax.set_yticklabels(feature_names[sorted_idx][count:], fontsize=7)
ax.set_yticks(y_ticks[count:])
ax.set_title("Random Forest Tree's Feature Importance from Mean Decrease in Impurity (MDI)")
fig.tight_layout()
plt.show()
可以使用什么方法来解决任务 b)？我正在尝试回答以下研究问题，
mixingtime_min、bakingtime_min、flour_g、sugar_g 和 preheattemp_c 在统计上对良好的caketaste 有什么贡献（良好：2）？
可能的预期结果：
mixingtime_min = [5,10,15] AND
bakingtime_min = [50,51,52,53,54,55] AND
flour_g = [150,160,170,180] AND
sugar_g = [200, 250] AND
preheattemp_c = [150,160,170]
上面的结果基本上可以得出结论，如果一个人想要一个好吃的蛋糕，他需要用 150-180g 面粉和 200-250g 糖烘烤他的蛋糕，并在 5-15 分钟之间混合面团，然后再烘烤 50在 150-170ºC 的预热烤箱中 -55 分钟。
希望大家指点一下。
问题
您能指导我如何着手解决这个研究问题吗？ sklearn 中是否有任何库或其他能够获取此信息的库？ 任何额外的信息，如置信区间、异常值等都是额外的。
数据（磅蛋糕.xlsx）：
cake_id flour_g butter_g sugar_g salt_g eggs_count bakingpowder_g milk_ml water_ml vanillaextract_ml lemonzest_g mixingtime_min bakingtime_min preheattime_min coolingtime_min bakingtemp_c preheattemp_c color_red color_green color_blue traysize_small traysize_medium traysize_large milktype_lowfat milktype_skim milktype_whole trayshape caketaste
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【问题讨论】：
这当然很有趣，因为您需要获取值范围而不是特定值。 您可以使用lime 进行敏感性分析。但如果您想进行敏感性分析，最好选择LinearRegression 作为分类器。特别是因为你的大部分特征都是数字的，除了 Meat_Freshness，Food_Taste 似乎是分类的（所以对于 LR，你需要一次性处理它们，不要将它们视为数字）。
相关：Lime vs TreeInterpreter for interpreting decision tree, Random Forests interpretability
这个问题目前contains no code which violates SO guidelines (and appears to be coursework)，所以为了使它合法，你需要用lime（或其他包）尝试它，编辑它以向我们展示你的代码和输出，以及你在哪里卡住了。
@DavidLee 你是对的。它必须是特定的值。以上已编辑。我是 ML 新手。你有什么建议如何从特征重要性中找到对标签的 Good:2 分类有贡献的 3 个最重要特征的值的组合
【参考方案1】：
非常简单的解决方案可以使用您的数据运行决策树分类器并使用 grapviz 库可视化树这里是文档https://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html , 得到代码生成的点文件后，还可以在 webgraphiz 中进行可视化。此练习的结果可能是您期望的范围值。
【讨论】：
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