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我有一组与 Vanilla 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是衡量烤蛋糕的好坏程度，由 bad(0) 定义, neutral(1) , good(2) .
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"]
mixingtime_min ,
bakingtime_min ,
sugar_g ,
flour_g和
preheattemp_c .
#################################################################
# 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()
mixingtime_min 的值是多少,
bakingtime_min ,
flour_g ,
sugar_g和
preheattemp_c从统计上来说，这对良好的贡献
caketaste (好:2)？
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]
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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169 200 60 300 4 5 3 20 90 3 2 12 30 25 15 155 160 0 0 1 1 0 0 0 0 1 rectangle 0
170 180 70 250 6 4 3 30 50 1 2 12 35 25 10 155 150 0 0 0 1 0 0 1 0 0 rectangle 1
171 175 70 200 4 6 4 30 60 2 2 5 25 30 15 150 160 0 0 1 1 0 0 0 1 0 square 0
172 165 90 400 2 5 1 30 90 3 2 6 70 30 15 170 170 0 1 0 1 0 0 0 0 1 rectangle 2
173 165 70 200 6 6 4 20 70 1 1 5 65 20 20 175 155 0 0 0 1 0 0 0 1 0 round 0
174 180 50 650 2 3 3 20 70 3 2 12 40 30 15 155 170 0 0 0 1 0 0 0 0 1 rectangle 1
175 180 40 200 6 3 2 30 80 3 3 7 60 30 10 175 150 0 1 0 1 0 0 1 0 0 rectangle 2
176 180 60 400 2 5 3 20 50 1 3 5 20 30 15 175 150 0 1 0 1 0 0 0 1 0 rectangle 1
177 200 50 400 4 6 4 23 60 2 2 7 55 20 15 160 170 0 1 0 1 0 0 0 0 1 round 0
178 180 50 550 6 4 3 20 50 2 2 8 20 25 20 170 170 1 0 0 0 0 1 1 0 0 rectangle 0
179 175 70 250 8 4 1 20 50 2 3 6 60 30 15 170 170 0 0 0 0 1 0 0 0 1 square 0
180 195 70 400 6 4 4 23 60 3 1 7 65 25 15 170 150 1 0 0 1 0 0 0 1 0 rectangle 1
181 160 50 500 6 4 3 25 50 1 1 11 55 10 15 170 170 0 0 0 0 0 1 0 0 1 rectangle 1
182 180 90 500 6 3 3 23 60 2 1 8 20 30 15 170 170 0 0 0 0 0 1 0 1 0 rectangle 1
183 170 70 650 2 3 3 25 80 1 3 8 45 20 10 170 170 0 1 0 1 0 0 0 1 0 round 2
184 195 70 600 6 4 2 25 60 1 2 6 40 30 15 155 170 1 0 0 1 0 0 0 0 1 rectangle 1
185 165 70 200 6 4 1 20 60 1 2 8 45 15 15 170 150 0 1 0 1 0 0 0 0 1 round 1
186 165 80 200 4 4 3 30 60 1 1 8 25 30 10 160 170 0 1 0 1 0 0 1 0 0 round 0
187 175 60 600 4 2 3 20 60 1 2 6 25 20 15 170 155 0 0 0 1 0 0 1 0 0 rectangle 2
188 180 70 500 6 4 3 30 70 2 2 7 55 30 15 170 150 1 0 0 1 0 0 1 0 0 square 1
189 180 50 600 2 4 4 30 60 3 1 9 40 25 15 170 170 1 0 0 0 0 1 0 0 1 rectangle 0
190 160 50 600 8 3 2 20 60 3 2 12 30 30 15 165 150 0 0 0 0 1 0 1 0 0 rectangle 2
191 180 60 200 6 2 1 30 60 3 2 7 20 30 15 175 160 1 0 0 1 0 0 1 0 0 rectangle 2
192 195 70 600 6 4 3 23 80 2 2 12 50 25 10 170 170 0 0 0 0 0 1 1 0 0 rectangle 1
193 180 60 250 6 3 1 15 60 2 3 5 60 30 20 175 165 1 0 0 0 0 1 0 1 0 rectangle 1
194 170 70 250 6 4 1 20 90 2 2 10 25 20 20 175 170 0 0 0 1 0 0 0 1 0 round 1
195 180 90 250 6 3 1 25 50 1 2 9 55 30 15 170 175 1 0 0 0 0 1 1 0 0 rectangle 1
196 160 70 550 6 3 4 30 90 3 2 10 60 20 15 165 165 0 1 0 1 0 0 0 1 0 round 0
197 175 60 200 8 2 3 15 60 1 2 11 50 30 15 165 175 0 1 0 1 0 0 0 0 1 rectangle 1
198 170 80 500 6 3 2 25 50 1 1 5 60 20 15 175 150 1 0 0 1 0 0 0 1 0 square 2
199 180 50 600 4 4 4 15 80 1 1 5 50 20 15 170 170 1 0 0 1 0 0 0 1 0 rectangle 2
最佳答案
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 库将树可视化，这里是文档 https://scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html
, 你也可以在得到代码生成的dot文件后，在webgraphiz中进行可视化。此练习的结果可能是您期望的范围值。
关于python - 映射 - 特征重要性与标签分类，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/67250208/
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