机器学习在学生成绩预测模型上的应用
2024-06-19 05:59:24
数据准备
独热编码（除了决策树和随机森林），至于决策树和随机森林的名词性变量采用labelencoder或者数据库处理。
import numpy as np
import pandas as pd
from pandas import read_csv
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
filename_mat='student-mat.csv'
filename_por='student-por.csv'
data_mat=pd.read_csv(filename_mat,sep=';')
data_por=pd.read_csv(filename_por,sep=';')
data_dummies_mat=pd.get_dummies(data_mat)
data_dummies_por=pd.get_dummies(data_por)
data_dummies_mat.to_csv('studentmat_dummies.csv')
data_dummies_por.to_csv('studentpor_dummies.csv')生成直方图
import numpy as np
from matplotlib import pyplot
from pandas import read_csv
filename_mat_binary='studentmat_binary.csv'
filename_mat_fivelevel='studentmat_five.csv'
filename_mat_numeric='studentmat_numeric.csv'
filename_por_binary='studentpor_binary.csv'
filename_por_fivelevel='studentpor_fivelevel.csv'
filename_por_numeric='studentpor_numeric.csv'
data_mat_binary=read_csv(filename_mat_binary,header=None)
data_mat_fivelevel=read_csv(filename_mat_fivelevel,header=None)
data_mat_numeric=read_csv(filename_mat_numeric,header=None)
data_por_binary=read_csv(filename_por_binary,header=None)
data_por_fivelevel=read_csv(filename_por_fivelevel,header=None)
data_por_numeric=read_csv(filename_por_numeric,header=None)
y_mat_binary=data_mat_binary.iloc[:,32]
y_mat_fivelevel=data_mat_fivelevel.iloc[:,32]
y_mat_numeric=data_mat_numeric.iloc[:,32]
y_por_binary=data_por_binary.iloc[:,32]
y_por_fivelevel=data_por_fivelevel.iloc[:,32]
y_por_numeric=data_por_numeric.iloc[:,32]
pyplot.figure()
y_mat_binary.hist()
pyplot.title('mat_binary')
pyplot.show()
pyplot.figure()
y_mat_fivelevel.hist()
pyplot.title('mat_fivelevel')
pyplot.show()
pyplot.figure()
y_mat_numeric.hist()
pyplot.title('mat_numeric')
pyplot.show()
pyplot.figure()
y_por_binary.hist()
pyplot.title('por_binary')
pyplot.show()
pyplot.figure()
y_por_fivelevel.hist()
pyplot.title('por_fivelevel')
pyplot.show()
pyplot.figure()
y_por_numeric.hist()
pyplot.title('por_numeric')
pyplot.show()A_Mat_Reg的随机森林散点图
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename='studentmat_numeric.csv'
data=pd.read_csv(filename)
X,y=data.iloc[:,:32].values.astype(float),data.iloc[:,32].values.astype(float)
seed=8
num_tree=500
test_size=0.2
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=test_size,random_state=seed)plt.scatter(y_test,y_predicted)
array_x=np.array([0,20])
array_y=np.array([0,20])
plt.plot(array_x,array_y,'k')
regressor_forest=RandomForestRegressor(n_estimators=num_tree,random_state=seed)
regressor_forest.fit(X_train,y_train)
y_predicted=regressor_forest.predict(X_test)
plt.xlabel('Desired')
plt.ylabel('Predicted')
plt.show()生成决策树的对应输入设置的流程图
import numpy as np
import pandas as pd
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.model_selection import train_test_split
filename='studentpor_binary.csv'
data=pd.read_csv(filename)
feature_names=['sex','age','school','address','Pstatus','Medu','Mjob','Fedu','Fjob','guardian','famsize','famrel','reason','traveltime','studytime','failures','schoolsup','famsup','activities','paidclass','internet','nursery','higher','romantic','freetime','goout','Walc','Dalc','health','absences','G1','G2']X,y=data.iloc[:,:32].values,data.iloc[:,32].values
X_train,X_test,y_train,y_test=train_test_split(X,y)
clf=tree.DecisionTreeClassifier(max_depth=3)
clf.fit(X_train,y_train)
class_names=[]
for i in range(20):class_names.append(str(i))
#class_names=['class_0','class_1','class_2','class_3','class_4']
export_graphviz(clf,out_file="por_binary.dot",class_names=class_names,feature_names=feature_names,impurity=False,filled=True)
with open("por_binary.dot") as f:dot_graph=f.read()dot=graphviz.Source(dot_graph)使用随机森林得出各种设置的特征重要性
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.feature_selection import SelectFromModel
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error,explained_variance_score
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier
filename1='studentmat_binary.csv'
filename2='studentmat_five.csv'
filename3='studentmat_numeric.csv'
filename4='studentpor_binary.csv'
filename5='studentpor_fivelevel.csv'
filename6='studentpor_numeric.csv'
data=pd.read_csv(filename4)
seed=0
X,y=data.iloc[:,:31].values,data.iloc[:,32].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,random_state=seed)
feature_names=['sex','age','school','address','Pstatus','Medu','Mjob','Fedu','Fjob','guardian','famsize','famrel','reason','traveltime','studytime','failures','schoolsup','famsup','activities','paidclass','internet','nursery','higher','romantic','freetime','goout','Walc','Dalc','health','absences','G1']
forest=RandomForestRegressor(n_estimators=500,random_state=seed,n_jobs=-1)
forest.fit(X_train, y_train)
feature_importances=forest.feature_importances_
index_sorted=np.flipud(np.argsort(feature_importances))
index_sorted1=np.array(feature_names)[index_sorted]
print(index_sorted1)
print(feature_importances[index_sorted])
pos=np.arange(index_sorted.shape[0])+0.5#标签居中
plt.title('Feature Importances(B_Por_Bin)')
plt.bar(pos,feature_importances[index_sorted],align='center')
plt.xticks(pos,np.array(feature_names)[index_sorted],rotation=90)
plt.show()DT.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 13:34:50 2019@author: zyf
"""
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename0='studentmat_binary.csv'
filename1='studentmat_five.csv'
filename2='studentmat_numeric.csv'
filename3='studentpor_binary.csv'
filename4='studentpor_fivelevel.csv'
filename5='studentpor_numeric.csv'
filenames=[filename0,filename1,filename2,filename3,filename4,filename5]
runs=20
num_folds=10
seed=8
num_tree=500
feature_names=['sex','age','school','address','Pstatus','Medu','Mjob','Fedu','Fjob','guardian','famsize','famrel','reason','traveltime','studytime','failures','schoolsup','famsup','activities','paidclass','internet','nursery','higher','romantic','freetime','goout','Walc','Dalc','health','absences']#主义修改特征数
for i in range(6):filename=filenames[i]if filename==filename0 or filename==filename1 or filename==filename3 or filename==filename4:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:32].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)classifier_tree=DecisionTreeClassifier(max_depth=5)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_tree,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('DT_Classifier_A模型交叉验证得分: %.3f' %np.mean(results))elif j==1:X,y=data.iloc[:,:31].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)classifier_tree=DecisionTreeClassifier(max_depth=5)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_tree,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('DT_Classifier_B模型交叉验证得分: %.3f' %np.mean(results))elif j==2:X,y=data.iloc[:,:30].values.astype(float),data.iloc[:,32].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)classifier_tree=DecisionTreeClassifier(max_depth=5)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_tree,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('DT_Classifier_C模型交叉验证得分: %.3f' %np.mean(results))elif filename==filename2 or filename==filename5:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:32].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)regressor_tree=DecisionTreeRegressor(max_depth=5)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_tree,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('DT_Regressor_A模型交叉验证得分: %.3f' % np.mean(results))elif j==1:X,y=data.iloc[:,:31].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)regressor_tree=DecisionTreeRegressor(max_depth=5)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_tree,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('DT_Regressor_B模型交叉验证得分: %.3f' % np.mean(results))elif j==2:X,y=data.iloc[:,:30].values.astype(float),data.iloc[:,32].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)regressor_tree=DecisionTreeRegressor(max_depth=5)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_tree,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('DT_Regressor_C模型交叉验证得分: %.3f' % np.mean(results))RF.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 13:56:18 2019@author: zyf
"""
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename0='studentmat_binary.csv'
filename1='studentmat_five.csv'
filename2='studentmat_numeric.csv'
filename3='studentpor_binary.csv'
filename4='studentpor_fivelevel.csv'
filename5='studentpor_numeric.csv'
filenames=[filename0,filename1,filename2,filename3,filename4,filename5]
runs=20
num_folds=10
seed=8
num_tree=500
feature_names=['sex','age','school','address','Pstatus','Medu','Mjob','Fedu','Fjob','guardian','famsize','famrel','reason','traveltime','studytime','failures','schoolsup','famsup','activities','paidclass','internet','nursery','higher','romantic','freetime','goout','Walc','Dalc','health','absences']#主义修改特征数
for i in range(6):filename=filenames[i]if filename==filename0 or filename==filename1 or filename==filename3 or filename==filename4:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:32].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)classifier_forest=RandomForestClassifier(n_estimators=500,random_state=seed)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_forest,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('RF_Classifier_A模型交叉验证得分: %.3f' %np.mean(results))elif j==1:X,y=data.iloc[:,:31].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)classifier_forest=RandomForestClassifier(n_estimators=500,random_state=seed)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_forest,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('RF_Classifier_B模型交叉验证得分: %.3f' %np.mean(results))elif j==2:X,y=data.iloc[:,:30].values.astype(float),data.iloc[:,32].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)classifier_forest=RandomForestClassifier(n_estimators=500,random_state=seed)results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(classifier_forest,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('RF_Classifier_C模型交叉验证得分: %.3f' %np.mean(results))elif filename==filename2 or filename==filename5:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:32].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)regressor_forest=RandomForestRegressor(n_estimators=500,random_state=seed)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_forest,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('RF_Regressor_A模型交叉验证得分: %.3f' % np.mean(results))elif j==1:X,y=data.iloc[:,:31].values.astype(float),data.iloc[:,32].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)regressor_forest=RandomForestRegressor(n_estimators=500,random_state=seed)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_forest,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('RF_Regressor_B模型交叉验证得分: %.3f' % np.mean(results))elif j==2:X,y=data.iloc[:,:30].values.astype(float),data.iloc[:,32].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)regressor_forest=RandomForestRegressor(n_estimators=500,random_state=seed)results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(regressor_forest,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('RF_Regressor_C模型交叉验证得分: %.3f' % np.mean(results))
SVM.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 14:49:02 2019@author: zyf
"""# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 21:22:37 2019@author: zyf
"""from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.feature_selection import SelectFromModel
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPRegressor
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename0='studentmat_dummies_binary.csv'
filename1='studentmat_dummies_five.csv'
filename2='studentmat_dummies_numeric.csv'
filename3='studentpor_dummies_binary.csv'
filename4='studentpor_dummies_five.csv'
filename5='studentpor_dummies_numeric.csv'
feature_names=['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel','freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences', 'school_GP', 'school_MS', 'sex_F', 'sex_M', 'address_R','address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T','Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services','Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other','Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home','reason_other', 'reason_reputation', 'guardian_father','guardian_mother', 'guardian_other', 'schoolsup_no', 'schoolsup_yes','famsup_no', 'famsup_yes', 'paid_no', 'paid_yes', 'activities_no','activities_yes', 'nursery_no', 'nursery_yes', 'higher_no','higher_yes', 'internet_no', 'internet_yes', 'romantic_no','romantic_yes']
filenames=[filename0,filename1,filename2,filename3,filename4,filename5]
runs=20
num_folds=10
seed=8
for i in range(6):filename=filenames[i]if filename==filename0 or filename==filename1 or filename==filename3 or filename==filename4:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')pipe_svc=Pipeline([('scl',StandardScaler()),('clf',SVC(random_state=seed))])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'clf__C': param_range,'clf__kernel': ['linear']},{'clf__C': param_range,'clf__gamma': param_range,'clf__kernel': ['rbf']}]scoring='accuracy'grid=GridSearchCV(estimator=pipe_svc,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Classifier_A模型的最佳得分:{:.3f}'.format(grid.best_score_))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')pipe_svc=Pipeline([('scl',StandardScaler()),('clf',SVC(random_state=seed))])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'clf__C': param_range,'clf__kernel': ['linear']},{'clf__C': param_range,'clf__gamma': param_range,'clf__kernel': ['rbf']}]scoring='accuracy'grid=GridSearchCV(estimator=pipe_svc,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Classifier_B模型的最佳得分:{:.3f}'.format(grid.best_score_))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_namespipe_svc=Pipeline([('scl',StandardScaler()),('clf',SVC(random_state=seed))])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'clf__C': param_range,'clf__kernel': ['linear']},{'clf__C': param_range,'clf__gamma': param_range,'clf__kernel': ['rbf']}]scoring='accuracy'grid=GridSearchCV(estimator=pipe_svc,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Classifier_C模型的最佳得分:{:.3f}'.format(grid.best_score_))elif filename==filename2 or filename==filename5:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')pipe_svr=Pipeline([('scl',StandardScaler()),('reg',SVR())])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'reg__C': param_range,'reg__kernel': ['linear']},{'reg__C': param_range,'reg__gamma': param_range,'reg__kernel': ['rbf']}]scoring='neg_mean_squared_error'grid=GridSearchCV(estimator=pipe_svr,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Regressor_A模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')pipe_svr=Pipeline([('scl',StandardScaler()),('reg',SVR())])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'reg__C': param_range,'reg__kernel': ['linear']},{'reg__C': param_range,'reg__gamma': param_range,'reg__kernel': ['rbf']}]scoring='neg_mean_squared_error'grid=GridSearchCV(estimator=pipe_svr,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Regressor_B模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_namespipe_svr=Pipeline([('scl',StandardScaler()),('reg',SVR())])param_range=[0.0001,0.001,0.01,0.1,1.0,10.0,100.0,1000.0]param_grid=[{'reg__C': param_range,'reg__kernel': ['linear']},{'reg__C': param_range,'reg__gamma': param_range,'reg__kernel': ['rbf']}]scoring='neg_mean_squared_error'grid=GridSearchCV(estimator=pipe_svr,param_grid=param_grid,cv=num_folds,scoring=scoring)grid=grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('SVM_Regressor_C模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))NN.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 21:22:37 2019@author: zyf
"""from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.feature_selection import SelectFromModel
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
from sklearn.neural_network import MLPRegressor
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename0='studentmat_dummies_binary.csv'
filename1='studentmat_dummies_five.csv'
filename2='studentmat_dummies_numeric.csv'
filename3='studentpor_dummies_binary.csv'
filename4='studentpor_dummies_five.csv'
filename5='studentpor_dummies_numeric.csv'
feature_names=['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel','freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences', 'school_GP', 'school_MS', 'sex_F', 'sex_M', 'address_R','address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T','Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services','Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other','Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home','reason_other', 'reason_reputation', 'guardian_father','guardian_mother', 'guardian_other', 'schoolsup_no', 'schoolsup_yes','famsup_no', 'famsup_yes', 'paid_no', 'paid_yes', 'activities_no','activities_yes', 'nursery_no', 'nursery_yes', 'higher_no','higher_yes', 'internet_no', 'internet_yes', 'romantic_no','romantic_yes']
filenames=[filename0,filename1,filename2,filename3,filename4,filename5]
runs=20
num_folds=10
seed=8
for i in range(6):filename=filenames[i]if filename==filename0 or filename==filename1 or filename==filename3 or filename==filename4:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')params=[{'clf':[MLPClassifier(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'clf__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'clf':[RandomForestRegressor(random_state=seed)],'scaler':[None],'clf__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('clf',MLPClassifier())])grid=GridSearchCV(pipe,params,cv=num_folds)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Classifier_A模型的最佳得分:{:.3f}'.format(grid.best_score_))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')params=[{'clf':[MLPClassifier(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'clf__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'clf':[RandomForestRegressor(random_state=seed)],'scaler':[None],'clf__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('clf',MLPClassifier())])grid=GridSearchCV(pipe,params,cv=num_folds)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Classifier_B模型的最佳得分:{:.3f}'.format(grid.best_score_))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_namesparams=[{'clf':[MLPClassifier(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'clf__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'clf':[RandomForestRegressor(random_state=seed)],'scaler':[None],'clf__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('clf',MLPClassifier())])grid=GridSearchCV(pipe,params,cv=num_folds)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Classifier_C模型的最佳得分:{:.3f}'.format(grid.best_score_))elif filename==filename2 or filename==filename5:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')params=[{'reg':[MLPRegressor(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'reg__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'reg':[RandomForestRegressor(random_state=seed)],'scaler':[None],'reg__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())])scoring='neg_mean_squared_error'grid=GridSearchCV(pipe,params,cv=num_folds,scoring=scoring)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Regressor_A模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')params=[{'reg':[MLPRegressor(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'reg__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'reg':[RandomForestRegressor(random_state=seed)],'scaler':[None],'reg__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())])scoring='neg_mean_squared_error'grid=GridSearchCV(pipe,params,cv=num_folds,scoring=scoring)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Regressor_B模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_namesparams=[{'reg':[MLPRegressor(solver='lbfgs',random_state=seed)],'scaler':[StandardScaler(),None],'reg__hidden_layer_sizes':[(50,),(100,),(100,100)]},{'reg':[RandomForestRegressor(random_state=seed)],'scaler':[None],'reg__n_estimators':[50,100,500]}]pipe=Pipeline([('scaler',StandardScaler()),('reg',MLPRegressor())])scoring='neg_mean_squared_error'grid=GridSearchCV(pipe,params,cv=num_folds,scoring=scoring)grid.fit(X,y)print(filename)print('最佳模型:{}'.format(grid.best_params_))print('NN_Regressor_C模型的最佳得分:{:.3f}'.format(np.sqrt(abs(grid.best_score_))))NV.py
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 14:03:01 2019@author: zyf
"""
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
from sklearn.feature_selection import SelectFromModel
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
from matplotlib import pyplot
from sklearn.metrics import mean_squared_error,explained_variance_score
from pandas import read_csv
from pandas.plotting import scatter_matrix
from pandas import set_option
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesClassifier
import sklearn.naive_bayes
from sklearn.neural_network import MLPClassifier
import graphviz
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import tree,datasets
from sklearn.tree import DecisionTreeRegressor
import math
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression
filename0='studentmat_dummies_binary.csv'
filename1='studentmat_dummies_five.csv'
filename2='studentmat_dummies_numeric.csv'
filename3='studentpor_dummies_binary.csv'
filename4='studentpor_dummies_five.csv'
filename5='studentpor_dummies_numeric.csv'
feature_names=['age', 'Medu', 'Fedu', 'traveltime', 'studytime', 'failures', 'famrel','freetime', 'goout', 'Dalc', 'Walc', 'health', 'absences', 'school_GP', 'school_MS', 'sex_F', 'sex_M', 'address_R','address_U', 'famsize_GT3', 'famsize_LE3', 'Pstatus_A', 'Pstatus_T','Mjob_at_home', 'Mjob_health', 'Mjob_other', 'Mjob_services','Mjob_teacher', 'Fjob_at_home', 'Fjob_health', 'Fjob_other','Fjob_services', 'Fjob_teacher', 'reason_course', 'reason_home','reason_other', 'reason_reputation', 'guardian_father','guardian_mother', 'guardian_other', 'schoolsup_no', 'schoolsup_yes','famsup_no', 'famsup_yes', 'paid_no', 'paid_yes', 'activities_no','activities_yes', 'nursery_no', 'nursery_yes', 'higher_no','higher_yes', 'internet_no', 'internet_yes', 'romantic_no','romantic_yes']
filenames=[filename0,filename1,filename2,filename3,filename4,filename5]
runs=20
num_folds=10
seed=8
for i in range(6):filename=filenames[i]if filename==filename0 or filename==filename1 or filename==filename3 or filename==filename4:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('NV_Classifier_A模型交叉验证得分: %.3f' %np.mean(results))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('NV_Classifier_B模型交叉验证得分: %.3f' %np.mean(results))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='accuracy'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(scores.mean())print(filename)print('NV_Classifier_C模型交叉验证得分: %.3f' %np.mean(results))elif filename==filename2 or filename==filename5:data=pd.read_csv(filename)for j in range(3):if j==0:X,y=data.iloc[:,:58].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')feature_names.append('G2')kfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('NV_Regressor_A模型交叉验证得分: %.3f' % np.mean(results))elif j==1:X,y=data.iloc[:,:57].values.astype(float),data.iloc[:,58].values.astype(float)feature_names.append('G1')kfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('NV_Regressor_B模型交叉验证得分: %.3f' % np.mean(results))elif j==2:X,y=data.iloc[:,:56].values.astype(float),data.iloc[:,58].values.astype(float)feature_names=feature_nameskfold=KFold(n_splits=num_folds,random_state=seed)pipe=make_pipeline(StandardScaler(),SelectFromModel(RandomForestRegressor(random_state=seed)),GaussianNB())results=[]scoring='neg_mean_squared_error'for i in range(20):scores=cross_val_score(pipe,X,y,cv=kfold,scoring=scoring)results.append(np.sqrt(abs(scores.mean())))print(filename)print('NV_Regressor_C模型交叉验证得分: %.3f' % np.mean(results))机器学习在学生成绩预测模型上的应用相关推荐
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