def usLRtoPredict():
examId_List = ["e1", "e2", "e3", "e4"]
target_List = ["programStateRecord"]
print('|数据名称|预测|r^2|spearman|')
print('|-|-|-|')
for eid in examId_List:
for target in target_List:
dataFileName = eid + "-" + target
_data_matrix, _data_header, _score_array = getData("npsm//" +
dataFileName)
_feature_matrix = getSerevalColumn(
_data_matrix, [i for i in range(1, _data_header.__len__())])
# _feature_matrix = getSerevalColumn(_data_matrix,[1,8]);
# _feature_matrix = getSerevalColumn(_data_matrix,[1,4,8]);
# _feature_matrix = getSerevalColumn(_data_matrix,[1,2,3,9]);
_score_array = np.array(_score_array).reshape(
_score_array.__len__(), 1)
_lr = LinearRegression()
_lr.fit(_feature_matrix, _score_array)
y_predicted = _lr.predict(_feature_matrix)
print("|", dataFileName, "|",
getprecisionWithTorlerate(_score_array, y_predicted,
0.5), "|",
getprecisionWithTorlerate(_score_array, y_predicted,
1.5), "|",
getprecisionWithTorlerate(_score_array, y_predicted, 2.5),
"|", r2_score(_score_array, y_predicted), "|",
spearmanr(_score_array, y_predicted), "|",
getprecisionWithTorlerate(_score_array, y_predicted, 5))
def useLRtoPredictScore(targetFileName, exam_mark=None, needNorm=True):
if exam_mark is None:
exam_mark = DATA_MARK
_file_Relative_Path = os.path.join(exam_mark, targetFileName)
student_data, headerArray = load_data_from_file(_file_Relative_Path)
_score_map = get_final_score_map()
_score_array = []
for _student_record in student_data:
_score_array.append(_score_map[_student_record[0]])
targetFeatureIndexArray = [i for i in range(1, headerArray.__len__())]
featureMatrix = getSerevalColumn(student_data, targetFeatureIndexArray)
if needNorm:
featureMatrix = normizeMatrix(featureMatrix)
_lr = LinearRegression(fit_intercept=True)
_lr.fit(featureMatrix, _score_array)
y_predicted = _lr.predict(featureMatrix)
# y_predicted.astype(int)
print()
# print(headerArray);
# print(_lr.coef_)
# print(_lr.intercept_)
print(getprecisionWithTorlerate(y_predicted, _score_array, 0.5),
getprecisionWithTorlerate(y_predicted, _score_array, 1.5),
getprecisionWithTorlerate(y_predicted, _score_array, 2.5),
spearmanr(y_predicted, _score_array),
r2_score(_score_array, y_predicted))
def getDataAndScore(featureFileName, exam_mark=None, needHeader=False):
if exam_mark is None:
exam_mark = DATA_MARK
_file_Relative_Path = os.path.join(exam_mark, featureFileName)
student_data, headerArray = load_data_from_file(_file_Relative_Path)
_feature_matrix = getSerevalColumn(
student_data, [i for i in range(1, headerArray.__len__())])
# index_array = [1, 1, 1, 1, 1, 2, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 1, 1, 4];
# target_index_array = [];
# for _index,_value in enumerate(index_array):
# if _value == 1:
# target_index_array.append(_index+1);
# _feature_matrix = getSerevalColumn(student_data, target_index_array)
# feature_array = ["saveInterval", "programTime", "totalLength", "pasteCount",
# "codeIntervalCount","saveCount","buildInterval",
# "score","codeTime","successCount","testCount",
# "scoreRemainMiddle","avgRemoveErrorTime",
# ];
# _feature_matrix = getSerevalColumn(student_data,getTargetColumnList(headerArray,feature_array))
# _score_map = get_final_score_map(None);
_score_map = get_final_score_map()
_score_array = []
for record in student_data:
_score_array.append(_score_map[record[0]])
# sorted_score_array = sorted(_score_array);
# gap = _score_array.__len__() / 10;
# pre_score = -1;
# score_to_index_map = {};
# for index, score in enumerate(sorted_score_array):
# if score == pre_score:
# continue;
# else:
# fold_index = int(index / gap);
# score_to_index_map[score] = fold_index;
# pre_score = score;
#
# for index,value in enumerate(_score_array):
# _score_array[index] = score_to_index_map[value];
if needHeader:
return _feature_matrix, _score_array, headerArray
else:
return _feature_matrix, _score_array
def tryAllFeatureCompositeWithSVM():
data_matrix, scoreArray, header = load_routine_data(True)
clf = SVC(kernel="poly", decision_function_shape="ovr", degree=2)
compositeGenerator = GetColCombination(header)
for _n in range(1, header.__len__() + 1):
compositeGenerator.setCompositeNum(_n)
canFind, colArray = compositeGenerator.getNextComposite()
while canFind:
print(colArray, end=" : ")
colIndexArray = getTargetColumnList(header, colArray)
data = getSerevalColumn(data_matrix, colIndexArray)
precision_array = []
for _index in range(10):
scores = cross_val_score(clf,
data,
scoreArray,
cv=StratifiedKFold(5, shuffle=True))
precision_array.append(scores.mean())
print(np.array(precision_array).mean())
canFind, colArray = compositeGenerator.getNextComposite()
def usLRtoPredictWithKFold():
examId_List = ["e1", "e2", "e3", "e4"]
target_List = ["programStateRecord"]
print('|数据名称|预测|5分|10分|')
print('|-|-|-|')
for eid in examId_List:
for target in target_List:
dataFileName = eid + "-" + target
_data_matrix, _data_header, _score_array = getData("npsm//" +
dataFileName)
# _feature_matrix = getSerevalColumn(_data_matrix,[i for i in range(1,_data_header.__len__())]);
_feature_matrix = getSerevalColumn(_data_matrix, [8])
_score_array = np.array(_score_array).reshape(
_score_array.__len__(), 1)
# kf = KFold(n_splits=5, shuffle=True);
#
# accurate_array = [];
# within_5_array = [];
# within_10_array = [];
# r_2_array = [];
#
# for train_index_array, test_index_array in kf.split(_feature_matrix):
# X_train = [];
# X_test = [];
# y_train = [];
# y_test = [];
# for train_index in train_index_array:
# X_train.append(_feature_matrix[train_index]);
# y_train.append(_score_array[train_index]);
#
# for test_index in test_index_array:
# X_test.append(_feature_matrix[test_index]);
# y_test.append(_score_array[test_index])
#
# _lr = LinearRegression(fit_intercept=True);
# _lr.fit(X_train, y_train);
# y_predicted = _lr.predict(X_test);
#
# accurate_array.append(getprecisionWithTorlerate(y_test, y_predicted, 0.5));
# within_5_array.append(getprecisionWithTorlerate(y_test, y_predicted, 1.5));
# within_10_array.append(getprecisionWithTorlerate(y_test, y_predicted, 2.5));
#
# y_total_predict = _lr.predict(_feature_matrix)
# r_2_array.append(r2_score(_score_array,y_total_predict));
#
#
# 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());
_lr = LinearRegression(fit_intercept=True)
precision_array = []
for _index in range(10):
_scores = model_selection.cross_val_score(
_lr,
_feature_matrix,
_score_array,
cv=model_selection.StratifiedKFold(5, shuffle=True),
scoring=lr_precision)
precision_array.append(_scores.mean())
print("|", dataFileName, "|",
np.array(precision_array).mean(), "|")
def searchAddOneFeatureOneTime(mark):
# #"firstCodeTimeFromStart","saveInterval","pasteCount","buildInterval","codeBU","codeBS","scoreUp","successCount","debugTime","debugCount","debugErrorCount",
# #"failCount","codeBE", "keepError","scoreRemainHigh","useDebug","codeTime","scoreRemainZero","hasBuildError",
# feature_array = ["codeIntervalCount","totalLength","programTime","avgRemoveErrorTime",
# "testCount","saveCount","longDeleteCount","score",
# "scoreRemainMiddle","generateError","scoreDown","totalCount",
# ];
# feature_array = ["saveInterval","programTime","totalLength","codeTime","firstCodeTimeFromStart",
# "pasteCount","codeIntervalCount","saveCount","longDeleteCount","buildInterval",
# "codeBU","score","codeBS","testCount","successCount",
# "scoreUp","totalCount","scoreRemainZero","scoreRemainMiddle","avgRemoveErrorTime",
# "debugCount","debugTime","debugErrorCount","failCount","codeBE",
# "scoreDown","keepError","generateError","useDebug","hasBuildError",
# "scoreRemainHigh",
# ];
#5"firstCodeTimeFromStart", 16"totalCount",7"longDeleteCount",10"codeBS", 12 "scoreUp",
#12 "codeBU",13"scoreRemainZero",14"debugCount",14"debugTime",14"debugErrorCount",18"useDebug",19"hasBuildError"
#"totalLength","pasteCount", 提前移除
feature_array = [
"saveInterval",
"programTime",
"codeIntervalCount",
"saveCount",
"buildInterval",
"score",
"codeTime",
"successCount",
"testCount",
"scoreRemainMiddle",
"avgRemoveErrorTime",
"failCount",
"scoreDown",
"keepError",
"generateError",
"codeBE",
"scoreRemainHigh",
]
# feature_array = ["buildInterval","saveInterval","codeIntervalCount","totalLength","programTime","codeTime",
# "avgRemoveErrorTime","testCount",
# "saveCount","scoreRemainMiddle",
# "score","successCount","pasteCount",
# ];
# feature_array = ["codeIntervalCount","totalLength", "programTime","longDeleteCount",
# "avgRemoveErrorTime","testCount","saveCount","scoreRemainMiddle","score","scoreDown","generateError","totalCount",
# ];
dataArray, scoreArray, headerArray = getDataAndScore("concatfeature",
mark,
needHeader=True)
del headerArray[0]
x_array = []
y_array = []
#逐步添加特征直至完成
for _count in range(feature_array.__len__()):
target_feature_name_array = feature_array[:_count + 1]
# print(target_feature_name_array);
indexList = getTargetColumnList(headerArray, target_feature_name_array)
# print(indexList)
featureMatrix = getSerevalColumn(dataArray, indexList)
precision = useSVMtoPredictScore(featureMatrix, scoreArray)
print("%d : %.4f" % (_count + 1, precision))
x_array.append(_count + 1)
y_array.append(precision)
plt.figure()
plt.plot(x_array, y_array)
plt.show()