def scorefinalScoreWithExpDef():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["finalscore"]
scoreMap = getGradeMap()
print('|数据名称|r^2|')
print('|-|-|')
model = LinearRegression()
for al in algorithm_List:
for target in target_List:
for eid in examId_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
scoreArray = getOneColumn(student_data, 2)
for index in range(scoreArray.__len__()):
scoreArray[index] = scoreMap[scoreArray[index]]
# print(scoreArray);
scoreArray = np.array(scoreArray).reshape(
scoreArray.__len__(), 1)
watwinArray = getOneColumn(student_data, 1)
watwinArray = np.array(watwinArray).reshape(
watwinArray.__len__(), 1)
score = model.fit(watwinArray,
scoreArray).score(watwinArray, scoreArray)
print("|", dataFileName, "|", score, "|")
def scoreAll():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["score", "finalscore"]
print('|数据名称|r^2|')
print('|-|-|')
model = LinearRegression()
for al in algorithm_List:
for target in target_List:
for eid in examId_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
scoreArray = []
final_score_map = get_final_score_map()
for _line in student_data:
scoreArray.append(final_score_map[str(_line[0])])
scoreArray = np.array(scoreArray).reshape(
scoreArray.__len__(), 1)
watwinArray = getOneColumn(student_data, 1)
watwinArray = np.array(watwinArray).reshape(
watwinArray.__len__(), 1)
score = model.fit(watwinArray,
scoreArray).score(watwinArray, scoreArray)
print("|", dataFileName, "|", score, "|")
def getGradeMap():
scoreData = getStudentData("finalscore")
scoreArray = getOneColumn(scoreData, 1)
scoreArray.sort()
gradeArray = [40, 50, 70]
for index in range(gradeArray.__len__()):
gradeArray[index] = float(
gradeArray[index]) * scoreArray.__len__() / 100
scoreMap = {}
for index, value in enumerate(scoreArray):
if value not in scoreMap:
grade = -1
if index < gradeArray[0]:
grade = 0
elif index < gradeArray[1]:
grade = 1
elif index < gradeArray[2]:
grade = 2
else:
grade = 3
scoreMap[value] = grade
return scoreMap
def usLRtoPredictWithExpDef():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["finalscore"]
scoreMap = getGradeMap()
print('|数据名称|预测准确率|')
print('|-|-|')
for al in algorithm_List:
for eid in examId_List:
for target in target_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
# scoreArray = getOneColumn(student_data, 2);
scoreArray = []
final_score_map = get_final_score_map()
scoreArray = getOneColumn(student_data, 2)
for index in range(scoreArray.__len__()):
scoreArray[index] = scoreMap[scoreArray[index]]
scoreArray = np.array(scoreArray).reshape(
scoreArray.__len__(), 1)
watwinArray = getOneColumn(student_data, 1)
watwinArray = np.array(watwinArray).reshape(
watwinArray.__len__(), 1)
_lr = LinearRegression(fit_intercept=True)
_lr.fit(watwinArray, scoreArray)
y_predicted = _lr.predict(watwinArray)
print("|", dataFileName, "|",
getprecisionWithTorlerate(y_predicted, scoreArray, 0.5),
"|")
def usePearsonrCalAll():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["score", "finalscore"]
print('|数据名称|相关系数|p|')
print('|-|-|-|')
for al in algorithm_List:
for target in target_List:
for eid in examId_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
# scoreArray = getOneColumn(student_data, 2);
scoreArray = []
final_score_map = get_final_score_map()
for _line in student_data:
scoreArray.append(final_score_map[str(_line[0])])
watwinArray = getOneColumn(student_data, 1)
c, p = pearsonr(scoreArray, watwinArray)
print("|", dataFileName, "|", c, "|", p, "|")
def usLRtoPredict():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["finalscore"]
print('|数据名称|预测|5分|10分|')
print('|-|-|-|')
for al in algorithm_List:
for eid in examId_List:
for target in target_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
# scoreArray = getOneColumn(student_data, 2);
scoreArray = []
final_score_map = get_final_score_map()
for _line in student_data:
scoreArray.append(final_score_map[str(_line[0])])
scoreArray = np.array(scoreArray).reshape(
scoreArray.__len__(), 1)
watwinArray = getOneColumn(student_data, 1)
watwinArray = np.array(watwinArray).reshape(
watwinArray.__len__(), 1)
_lr = LinearRegression(fit_intercept=True)
_lr.fit(watwinArray, scoreArray)
y_predicted = _lr.predict(watwinArray)
print("|", dataFileName, "|",
getprecisionWithTorlerate(y_predicted, scoreArray,
0.5), "|",
getprecisionWithTorlerate(y_predicted, scoreArray,
1.5), "|",
getprecisionWithTorlerate(y_predicted, scoreArray, 2.5),
"|", r2_score(scoreArray, y_predicted), "|",
spearmanr(y_predicted, scoreArray))
def usLRtoPredictWithKFold():
examId_List = ["e1", "e2", "e3", "e4"]
algorithm_List = ["EQ", "Watwin"]
target_List = ["finalscore"]
print('|数据名称|预测|5分|10分|')
print('|-|-|-|')
for al in algorithm_List:
for eid in examId_List:
for target in target_List:
dataFileName = eid + "-" + al + "-" + target
student_data = getStudentData(al + "//" + dataFileName)
# scoreArray = getOneColumn(student_data, 2);
scoreArray = []
final_score_map = get_final_score_map()
for _line in student_data:
scoreArray.append(final_score_map[str(_line[0])])
scoreArray = np.array(scoreArray).reshape(
scoreArray.__len__(), 1)
watwinArray = getOneColumn(student_data, 1)
watwinArray = np.array(watwinArray).reshape(
watwinArray.__len__(), 1)
kf = KFold(n_splits=10, shuffle=True)
accurate_array = []
within_5_array = []
r_2_array = []
within_10_array = []
for train_index_array, test_index_array in kf.split(
watwinArray):
X_train = []
X_test = []
y_train = []
y_test = []
for train_index in train_index_array:
X_train.append(watwinArray[train_index])
y_train.append(scoreArray[train_index])
for test_index in test_index_array:
X_test.append(watwinArray[test_index])
y_test.append(scoreArray[test_index])
_lr = LinearRegression(fit_intercept=True)
_lr.fit(X_train, y_train)
y_predicted = _lr.predict(X_test)
accurate_array.append(
getprecisionWithTorlerate(y_predicted, y_test, 0.5))
within_5_array.append(
getprecisionWithTorlerate(y_test, y_predicted, 1.5))
within_10_array.append(
getprecisionWithTorlerate(y_test, y_predicted, 2.5))
r_2_array.append(r2_score(y_test, y_predicted))
print("|", dataFileName, "|",
np.array(accurate_array).mean(), "|",
np.array(within_5_array).mean(), "|",
np.array(within_10_array).mean(), "|",
np.array(r_2_array).mean())
from watwin.datapre import getStudentData
_student_data = getStudentData()
#采用撒点图展示
import numpy as np
import matplotlib.pyplot as plt
from common.DataHelper import getOneColumn
def showWatwinAndScore():
x = np.arange(1,
_student_data.__len__() + 1)
y1 = getOneColumn(_student_data, 1)
y2 = getOneColumn(_student_data, 2)
# for i in range(len(y2)):
# y2[i] = 10*(1-y2[i]);
fig = plt.figure()
#设置标题
#fig.set_title('Scatter Plot')
#设置X轴标签
#plt.xlabel('X')
#设置Y轴标签
#plt.ylabel('Y')
#画散点图
plt.scatter(x, y1, c='r', marker='x')
plt.scatter(x, y2, color='c', marker='+')