first commit

芳林公司项目/pytest/.pydio

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+76b34777-9508-42cd-a8d4-f171bab31cab

芳林公司项目/pytest/config/.pydio

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+3dec9002-fee9-4cf3-8bdf-5146a9825e15

芳林公司项目/pytest/config/index.py

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+MySqlConfig = {
+    'host': '127.0.0.1',
+    'username': 'root',
+    'password': 'lb714500',
+    'dataBase': 'shop',
+    'port': 3306
+}

芳林公司项目/pytest/db/.pydio

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+cc3a78fe-06ea-426c-9cca-a81ac37a7140

芳林公司项目/pytest/db/index.py

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+import pymysql
+from config.index import MySqlConfig
+
+class DataBaseHandle(object):
+    ''' 定义一个 MySQL 操作类'''
+    def __init__(self):
+        self.db = pymysql.connect(MySqlConfig['host'], MySqlConfig['username'],
+                                  MySqlConfig['password'], MySqlConfig['dataBase'],
+                                  MySqlConfig['port'], charset='utf8')
+    def insertDB(self, sql):
+        '插入数据库操作'
+
+        self.cursor = self.db.cursor()
+
+        try:
+            # 执行sql
+            self.cursor.execute(sql)
+            # tt = self.cursor.execute(sql)  # 返回 插入数据 条数 可以根据 返回值 判定处理结果
+            # print(tt)
+            self.db.commit()
+        except:
+            # 发生错误时回滚
+            self.db.rollback()
+        finally:
+            self.cursor.close()
+
+    def deleteDB(self, sql):
+        '操作数据库数据删除'
+        self.cursor = self.db.cursor()
+
+        try:
+            # 执行sql
+            self.cursor.execute(sql)
+            # tt = self.cursor.execute(sql) # 返回 删除数据 条数 可以根据 返回值 判定处理结果
+            # print(tt)
+            self.db.commit()
+        except:
+            # 发生错误时回滚
+            self.db.rollback()
+        finally:
+            self.cursor.close()
+
+    def updateDb(self, sql):
+        '更新数据库操作'
+        self.cursor = self.db.cursor()
+
+        try:
+            # 执行sql
+            self.cursor.execute(sql)
+            # tt = self.cursor.execute(sql) # 返回 更新数据 条数 可以根据 返回值 判定处理结果
+            # print(tt)
+            self.db.commit()
+        except:
+            # 发生错误时回滚
+            self.db.rollback()
+        finally:
+            self.cursor.close()
+
+    def selectDb(self, sql):
+        '数据库查询'
+        self.cursor = self.db.cursor()
+        try:
+            self.cursor.execute(sql)  # 返回 查询数据 条数 可以根据 返回值 判定处理结果
+            data = self.cursor.fetchall()  # 返回所有记录列表
+            return data
+        except:
+            print('Error: unable to fecth data')
+        finally:
+            self.cursor.close()
+
+    def closeDb(self):
+        '数据库连接关闭'
+        self.db.close()

芳林公司项目/pytest/index.py

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+#!/usr/bin/python
+# -*- coding: UTF-8 -*-
+import pymysql
+from db.index import DataBaseHandle
+from utils.probability.model import Predictor2
+from utils.qq import P_get
+
+
+# mysql = DataBaseHandle()
+# allList = mysql.selectDb("select * from wares")
+# for row in allList:
+#     print(row)
+
+# P_get()
+
+model = Predictor2()
+print(model.predict(844, 2,
+                    [
+                        [2012, 22176], [2013, 22352], [2014, 21302], [2015, 20386],
+                        [2016, 20044], [2017, 20081], [2018, 21577]
+                    ],
+                    569, 2019, [22748]
+                    )
+      )
+
+
+# gl = Predictor1()
+# gl.train((936,844,2,1), [[2019, 496, 569]])
+# print(gl.predict( (936,844,2,1), [[2019, 496, 569]]  ) )
+

芳林公司项目/pytest/utils/.pydio

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+52e4bf20-5c17-49a5-8d9e-c3722a307912

芳林公司项目/pytest/utils/probability/.pydio

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+7f06b2b7-61c2-4f4b-a9c4-8d2203a18fc6

芳林公司项目/pytest/utils/probability/model.py

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+import math
+import numpy as np
+from sklearn.svm import SVR
+from sklearn.linear_model import Ridge
+
+import requests
+
+
+class Predictor1:
+    def __init__(self,
+                 borderline=50,  # 当考生分数与录取线相差borderline时，即录取率大于99%
+                 sigma=0.0,  # 当考生分数等于录取线时，概率为50%，增加simga则录取概率增加，反之减小
+                 mode='svr',  # 'diff':差分法， 'svr'：机器学习
+                 remove_edges=False,  # 是否除去最大最小值
+                 sample_range=4,  # 使用最近几年的数据
+                 keep_latest_data=True,  # 是否始终使用上一年的数据
+                 # 以下是svr模型的参数
+                 kernel='rbf',
+                 degree=3,
+                 gamma='scale',
+                 coef0=0.0,
+                 tol=1e-3,
+                 C=10.0,
+                 epsilon=0.1,
+                 shrinking=True,
+                 cache_size=100,
+                 verbose=False,
+                 max_iter=-1):
+        '''
+        :param borderline: the probability of a score which is lager than borderline will be lager than 99%
+        :param sigma: control the middle line of probability. when the score equals to the passing score, the
+                      probability will be 50%, if increasing sigma, the probability will larger than 50% otherwise
+                      smaller than 50%
+        :param mode: 'diff' or 'svr
+        :param remove_edges: whether remove the max value and min value
+        :param sample_range: the number of data which is used to train
+        :param keep_latest_data: whether keep the data of the latest year
+        :param C: Regularization parameter
+        :param cache_size: Specify the size of the kernel cache (in MB)
+        '''
+
+        assert sample_range > 0 and borderline > 0
+
+        self.scale = 5. / borderline
+        self.sigma = sigma
+        self.mode = mode
+
+        self.remove_edges = remove_edges
+        self.sample_range = sample_range
+        self.keep_latest_data = keep_latest_data
+
+        self.kernel = kernel
+        self.degree = degree
+        self.gamma = gamma
+        self.coef0 = coef0
+        self.tol = tol
+        self.C = C
+        self.epsilon = epsilon
+        self.shrinking = shrinking
+        self.cache_size = cache_size
+        self.verbose = verbose
+        self.max_iter = max_iter
+
+        self.model_dict = {}
+        self.diff_dict = {}
+
+    def create_model(self):
+        return SVR(kernel=self.kernel,
+                   degree=self.degree,
+                   gamma=self.gamma,
+                   coef0=self.coef0,
+                   tol=self.tol,
+                   C=self.C,
+                   epsilon=self.epsilon,
+                   shrinking=self.shrinking,
+                   cache_size=self.cache_size,
+                   verbose=self.verbose,
+                   max_iter=self.max_iter)
+
+    def reset_models(self):
+        self.model_dict = {}
+
+    def modified_sigmoid(self, x):
+        return 1. / (1. + math.exp(-((x + self.sigma) * self.scale)))
+
+    def train(self, infos, data):
+        '''
+        :param infos: tuple (school, province, wenli, batch)
+        :param data: list[list] [[year, pro_cro, min_score]]
+        '''
+
+        assert isinstance(infos, tuple) and isinstance(data, list) and len(data) > 0
+
+        data.sort(key=lambda x: x[0], reverse=True)
+
+        if self.mode == 'svr':
+            if infos not in self.model_dict:
+                model = self.create_model()
+                self.model_dict[infos] = model
+            else:
+                model = self.model_dict[infos]
+
+            data = data[:self.sample_range]
+            train_x, train_y = [[d[0], d[1]] for d in data], [d[2] for d in data]
+            model.fit(train_x, train_y)
+
+        elif self.mode == 'diff':
+            N = self.sample_range + 2 if self.remove_edges else self.sample_range
+            diffs = [data[i][2] - data[i][1] for i in range(min(N, len(data)))]
+
+            if len(data) < N or not self.remove_edges:
+                self.diff_dict[infos] = sum(diffs) / len(diffs)
+            else:
+                if self.keep_latest_data:
+                    self.diff_dict[infos] = (sum(diffs) - max(diffs[1:]) - min(diffs[1:])) / (len(diffs) - 2)
+                else:
+                    self.diff_dict[infos] = (sum(diffs) - max(diffs) - min(diffs)) / (len(diffs) - 2)
+
+        else:
+            raise ValueError('invalid mode')
+
+    def predict(self, infos, data):
+        '''
+        :param infos: tuple [school, province, wenli, batch]
+        :param data: list[list] [[year, pro_cro, score]]
+        '''
+        assert isinstance(infos, tuple) and isinstance(data, list)
+
+        if self.mode == 'svr':
+            if infos not in self.model_dict:
+                raise ValueError("the model hasn't been trained")
+            else:
+                model = self.model_dict[infos]
+
+            pred_x, scores = [[d[0], d[1]] for d in data], [d[2] for d in data]
+
+            lines = model.predict(pred_x)
+            probabilities = [self.modified_sigmoid(scores[i] - lines[i]) for i in range(len(lines))]
+
+        elif self.mode == 'diff':
+            if infos not in self.diff_dict:
+                raise ValueError("the difference hasn't been calculated")
+            else:
+                diff = self.diff_dict[infos]
+
+            probabilities = [self.modified_sigmoid(data[i][2] - (data[i][1] + diff)) for i in range(len(data))]
+
+        else:
+            raise ValueError('invalid mode')
+
+        return probabilities
+
+
+class Predictor2:
+    def __init__(self, borderline=20, previour_years_num=3, alpha=100, sigma=3.1 + 0.09):
+        self.borderline = borderline  # 当考生分数与录取线相差borderline时，即录取率大于99%
+        self.previour_years_num = previour_years_num  # 使用过去几年的数据
+        self.alpha = alpha
+        self.scale = sigma / borderline
+        self.norm_arr = np.load('/Applications/DeskTop/公司项目/芳林公司项目/pytest/utils/probability/norm_arr.npy')
+
+    def __predict_lowest_rank(self, previous_data, year):
+        '''
+        :param previous_data: list[list] [[year, low_sort]]
+        :param year: int
+        :return:
+        '''
+        model = Ridge()
+        train_x, train_y = [[(x[0] % 100) * self.alpha] for x in previous_data], [x[1] for x in previous_data]
+        model.fit(train_x, train_y)
+
+        return model.predict([[(year % 100) * self.alpha]])[0]
+
+    def __query_score(self, province, wenli, weici, base_url='http://www.zhiyuanhelp.com/index.php/api/getweici?'):
+        url = base_url + 'province=%d&wenli=%d&weici=%d' % (province, wenli, weici)
+        try:
+            score = int(requests.get(url).text)
+        except:
+            raise ValueError('query_score: fail to query the score')
+
+        return score
+
+    def __calculate_probability(self, difference):
+        table = {-1: 37, 0: 45, 1: 55, 2: 63, 3: 73, 4: 80, 5: 96, 12: 96, 19: 99}
+        r=np.random.randn()
+
+        if difference < 0:
+            difference = -difference
+            positive = False
+        else:
+            positive = True
+
+        if difference > self.borderline:
+            return 1 if positive else 0
+        else:
+            difference *= self.scale
+
+            h, w = np.shape(self.norm_arr)
+            i, j = 1, 1
+
+            while i < h:
+                if (i == h - 1) or (self.norm_arr[i][0] <= difference and difference < self.norm_arr[i + 1][0]):
+                    break
+                i += 1
+            difference -= self.norm_arr[i][0]
+
+            while j < w:
+                if (j == w - 1) or (self.norm_arr[0][j] <= difference and difference < self.norm_arr[0][j + 1]):
+                    break
+                j += 1
+
+            return self.norm_arr[i][j] if positive else 1 - self.norm_arr[i][j]
+
+    def predict(self, province, wenli, previous_data, previous_lowest_score, current_year, current_ranks):
+        '''
+        :param province: int 省份代码
+        :param wenli: int 文理代码
+        :param previous_data: list[list] [[year, low_sort]] 要预测的学校前几年的年份和最低录取位次
+        :param previous_lowest_score: int 要预测的学校上一年的最低录取分数
+        :param current_year: int 要预测的年份（今年）
+        :param current_ranks: list[int] 要预测的学生位次（今年）,可输入多个
+        '''
+        previous_data = previous_data[:self.previour_years_num]
+        previous_data.sort(key=lambda x: x[0], reverse=True)
+
+        current_lowest_rank = self.__predict_lowest_rank(previous_data, current_year)
+
+        previous_lowest_rank = previous_data[0][1]
+        previous_ranks = [current_rank - current_lowest_rank + previous_lowest_rank for current_rank in current_ranks]
+        previous_scores = [self.__query_score(province, wenli, previous_rank) for previous_rank in previous_ranks]
+
+        probabilities = [self.__calculate_probability(previous_score - previous_lowest_score) for previous_score in
+                         previous_scores]
+
+        return probabilities
+
+
+# model = Predictor2()
+# print(model.predict(844, 2, [[2016, 11231], [2017, 12310], [2018, 12111]], 561, 2019, [14056]))

芳林公司项目/pytest/utils/qq.py

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+import math
+import numpy as np
+from scipy import integrate
+
+#历史数据处理
+def handle_data(all_marks=[569,
+570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,
+595,596,597,598,599
+]):#以列表形式传入省份所有考生的成绩[1,2,3]
+    a = np.mean(all_marks)#平均值
+    sigma = np.std(all_marks)#方差
+    print('a=',a,'sigma=',sigma)
+    return a,sigma
+
+#等效分计算
+def mark(a=584.0,sigma=8.94427190999916,per_mark=600):#a和sigma由上一个函数得出，per_mark为需要转化成等效成绩的高考分数
+    re_mark = 100 * ((per_mark - a) / sigma) + 500
+    if re_mark > 900:
+        re_mark = 900
+    elif re_mark < 100:
+        re_mark = 100
+    return re_mark
+
+#录取概率
+def P_get(uni_marks=[580,581,582,583,
+584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599],per_mark=589): #以列表形式学校录取的所有考生的成绩
+    per_mark = mark(per_mark=per_mark)
+    re_uni_mark = [] #学校录取学生的等效分
+    for i in uni_marks:
+        re_uni_mark.append(mark(per_mark=i))
+    E = np.mean(re_uni_mark) #学校录取考生的等效分平均值即期望
+    S = np.std(re_uni_mark)
+    D = S ** 2 #样本方差
+    def p(x): #录取分数的概率密度函数
+        return (1/(D*(2*math.pi)**0.5))*((math.e)**(-((x-E)**2)/(2*D)))
+    P,err = integrate.quad(p,100,per_mark)
+    P = P*100
+    print('录取概率为',P)
+    return P
+# num = 570
+# while num < 600:
+#     print('分数为',num,'时','录取概率为',P_get(per_mark=num))
+#     num += 1