数据科学系统完成，初步实现机器学习系统

Data_Science/Learn.py

@@ -5,7 +5,7 @@ from pyecharts import options as opts
 from pyecharts.charts import *
 from pyecharts.globals import SymbolType
 from pyecharts.components import Table
-from pyecharts.globals import GeoType #地图推荐使用ChartType
+from pyecharts.globals import GeoType #地图推荐使用GeoType而不是str
 from random import randint
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import *

Learn_Numpy.py

@@ -0,0 +1,755 @@
+from pyecharts.components import Table #绘制表格
+from pyecharts import options as opts
+from pyecharts.charts import Tab,Page
+from pandas import DataFrame,read_csv
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import *
+from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor
+from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor
+from sklearn.metrics import accuracy_score
+from sklearn.feature_selection import *
+from sklearn.preprocessing import *
+from sklearn.impute import SimpleImputer
+from sklearn.decomposition import PCA, IncrementalPCA,KernelPCA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+# import sklearn as sk
+
+
+#设置
+np.set_printoptions(threshold=np.inf)
+
+class Learner:
+    def __init__(self,*args,**kwargs):
+        self.numpy_Dic = {}#name:numpy
+
+    def Add_Form(self,data:np.array,name):
+        name = f'{name}[{len(self.numpy_Dic)}]'
+        self.numpy_Dic[name] = data
+
+    def read_csv(self,Dic,name,encoding='utf-8',str_must=False,sep=','):
+        type_ = np.str if str_must else np.float
+        pf_data = read_csv(Dic,encoding=encoding,delimiter=sep,header=None)
+        try:
+            data = pf_data.to_numpy(dtype=type_)
+        except ValueError:
+            data = pf_data.to_numpy(dtype=np.str)
+        if data.ndim == 1: data = np.expand_dims(data, axis=1)
+        self.Add_Form(data,name)
+        return data
+
+    def Add_Python(self, Text, sheet_name):
+        name = {}
+        name.update(globals().copy())
+        name.update(locals().copy())
+        exec(Text, name)
+        exec('get = Creat()', name)
+        if isinstance(name['get'], np.array):  # 已经是DataFram
+            get = name['get']
+        else:
+            try:
+                get = np.array(name['get'])
+            except:
+                get = np.array([name['get']])
+        self.Add_Form(get, sheet_name)
+        return get
+
+    def get_Form(self) -> dict:
+        return self.numpy_Dic.copy()
+
+    def get_Sheet(self,name) -> np.array:
+        return self.numpy_Dic[name].copy()
+
+    def to_CSV(self,Dic:str,name,sep) -> str:
+        get = self.get_Sheet(name)
+        np.savetxt(Dic, get, delimiter=sep)
+        return Dic
+
+    def to_Html_One(self,name,Dic=''):
+        if Dic == '': Dic = f'{name}.html'
+        get = self.get_Sheet(name)
+        if get.ndim == 1: get = np.expand_dims(get, axis=1)
+        get = get.tolist()
+        for i in range(len(get)):
+            get[i] = [i+1] + get[i]
+        headers = [i for i in range(len(get[0]))]
+        table = Table()
+        table.add(headers, get).set_global_opts(
+            title_opts=opts.ComponentTitleOpts(title=f"表格:{name}", subtitle="CoTan~机器学习:查看数据"))
+        table.render(Dic)
+        return Dic
+
+    def to_Html(self, name, Dic='', type_=0):
+        if Dic == '': Dic = f'{name}.html'
+        # 把要画的sheet放到第一个
+        Sheet_Dic = self.get_Form()
+        del Sheet_Dic[name]
+        Sheet_list = [name] + list(Sheet_Dic.keys())
+
+        class TAB_F:
+            def __init__(self, q):
+                self.tab = q  # 一个Tab
+
+            def render(self, Dic):
+                return self.tab.render(Dic)
+
+        # 生成一个显示页面
+        if type_ == 0:
+            class TAB(TAB_F):
+                def add(self, table, k, *f):
+                    self.tab.add(table, k)
+
+            tab = TAB(Tab(page_title='CoTan:查看表格'))  # 一个Tab
+        elif type_ == 1:
+            class TAB(TAB_F):
+                def add(self, table, *k):
+                    self.tab.add(table)
+
+            tab = TAB(Page(page_title='CoTan:查看表格', layout=Page.DraggablePageLayout))
+        else:
+            class TAB(TAB_F):
+                def add(self, table, *k):
+                    self.tab.add(table)
+
+            tab = TAB(Page(page_title='CoTan:查看表格', layout=Page.SimplePageLayout))
+        # 迭代添加内容
+        for name in Sheet_list:
+            get = self.get_Sheet(name)
+            if get.ndim == 1: get = np.expand_dims(get, axis=1)
+            get = get.tolist()
+            for i in range(len(get)):
+                get[i] = [i+1] + get[i]
+            headers = [i for i in range(len(get[0]))]
+            table = Table()
+            table.add(headers, get).set_global_opts(
+                title_opts=opts.ComponentTitleOpts(title=f"表格:{name}", subtitle="CoTan~机器学习:查看数据"))
+            tab.add(table, f'表格:{name}')
+        tab.render(Dic)
+        return Dic
+
+
+class Study_MachineBase:
+    def __init__(self,*args,**kwargs):
+        self.Model = None
+        self.have_Fit = False
+        #记录这两个是为了克隆
+
+    def Accuracy(self,y_Predict,y_Really):
+        return accuracy_score(y_Predict, y_Really)
+
+    def Fit(self,x_data,y_data,split=0.3,**kwargs):
+        self.have_Fit = True
+        y_data = y_data.ravel()
+        x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,test_size=split)
+        self.Model.fit(x_data,y_data)
+        train_score = self.Model.score(x_train,y_train)
+        test_score = self.Model.score(x_test,y_test)
+        return train_score,test_score
+
+    def Score(self,x_data,y_data):
+        Score = self.Model.score(x_data,y_data)
+        return Score
+
+    def Predict(self,x_data):
+        y_Predict = self.Model.predict(x_data)
+        return y_Predict,'预测'
+
+class prep_Base(Study_MachineBase):
+    def __init__(self,*args,**kwargs):
+        super(prep_Base, self).__init__(*args,**kwargs)
+        self.Model = None
+
+    def Fit(self, x_data,y_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data,y_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'特征工程'
+
+    def Score(self, x_data, y_data):
+        return 'None'  # 没有score
+
+class Line_Model(Study_MachineBase):
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(Line_Model, self).__init__(*args,**kwargs)
+        Model = {'Line':LinearRegression,'Ridge':Ridge,'Lasso':Lasso}[
+            model]
+        if model == 'Line':
+            self.Model = Model()
+            self.k = {}
+        else:
+            self.Model = Model(alpha=args_use['alpha'],max_iter=args_use['max_iter'])
+            self.k = {'alpha':args_use['alpha'],'max_iter':args_use['max_iter']}
+        #记录这两个是为了克隆
+        self.Alpha = args_use['alpha']
+        self.max_iter = args_use['max_iter']
+        self.Model_Name = model
+
+class LogisticRegression_Model(Study_MachineBase):
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(LogisticRegression_Model, self).__init__(*args,**kwargs)
+        self.Model = LogisticRegression(C=args_use['C'],max_iter=args_use['max_iter'])
+        #记录这两个是为了克隆
+        self.C = args_use['C']
+        self.max_iter = args_use['max_iter']
+        self.k = {'C':args_use['C'],'max_iter':args_use['max_iter']}
+        self.Model_Name = model
+
+class Knn_Model(Study_MachineBase):
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(Knn_Model, self).__init__(*args,**kwargs)
+        Model = {'Knn_class':KNeighborsClassifier,'Knn':KNeighborsRegressor}[model]
+        self.Model = Model(p=args_use['p'],n_neighbors=args_use['n_neighbors'])
+        #记录这两个是为了克隆
+        self.n_neighbors = args_use['n_neighbors']
+        self.p = args_use['p']
+        self.k = {'n_neighbors':args_use['n_neighbors'],'p':args_use['p']}
+        self.Model_Name = model
+
+class Tree_Model(Study_MachineBase):
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(Tree_Model, self).__init__(*args,**kwargs)
+        Model = {'Tree_class':DecisionTreeClassifier,'Tree':DecisionTreeRegressor}[model]
+        self.Model = Model(criterion=args_use['criterion'],splitter=args_use['splitter'],max_features=args_use['max_features']
+                           ,max_depth=args_use['max_depth'],min_samples_split=args_use['min_samples_split'])
+        #记录这两个是为了克隆
+        self.criterion = args_use['criterion']
+        self.splitter = args_use['splitter']
+        self.max_features = args_use['max_features']
+        self.max_depth = args_use['max_depth']
+        self.min_samples_split = args_use['min_samples_split']
+        self.k = {'criterion':args_use['criterion'],'splitter':args_use['splitter'],'max_features':args_use['max_features'],
+                  'max_depth':args_use['max_depth'],'min_samples_split':args_use['min_samples_split']}
+        self.Model_Name = model
+
+class Variance_Model(prep_Base):
+    def __init__(self,args_use,model,*args,**kwargs):#model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(Variance_Model, self).__init__(*args,**kwargs)
+        self.Model = VarianceThreshold(threshold=(args_use['P'] * (1 - args_use['P'])))
+        #记录这两个是为了克隆
+        self.threshold = args_use['P']
+        self.k = {'threshold':args_use['P']}
+        self.Model_Name = model
+
+class SelectKBest_Model(prep_Base):
+    def __init__(self, args_use, model, *args, **kwargs):  # model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(SelectKBest_Model, self).__init__(*args, **kwargs)
+        self.Model = SelectKBest(k=args_use['k'],score_func=args_use['score_func'])
+        # 记录这两个是为了克隆
+        self.k_ = args_use['k']
+        self.score_func=args_use['score_func']
+        self.k = {'k':args_use['k'],'score_func':args_use['score_func']}
+        self.Model_Name = model
+
+class SelectFrom_Model(prep_Base):
+    def __init__(self, args_use, Learner, *args, **kwargs):  # model表示当前选用的模型类型,Alpha针对正则化的参数
+        super(SelectFrom_Model, self).__init__(*args, **kwargs)
+
+        self.Model = Learner.Model
+        self.Select_Model = SelectFromModel(estimator=Learner.Model,max_features=args_use['k'],prefit=Learner.have_Fit)
+
+        self.max_features = args_use['k']
+        self.estimator=Learner.Model
+        self.k = {'max_features':args_use['k'],'estimator':Learner.Model}
+        self.Model_Name = 'SelectFrom_Model'
+
+    def Fit(self, x_data,y_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Select_Model.fit(x_data,y_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        try:
+            x_Predict = self.Select_Model.transform(x_data)
+            return x_Predict,'模型特征工程'
+        except:
+            return np.array([]),'无结果工程'
+
+class Standardization_Model(prep_Base):#z-score标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Standardization_Model, self).__init__(*args, **kwargs)
+        self.Model = StandardScaler()
+
+        self.k = {}
+        self.Model_Name = 'StandardScaler'
+
+class MinMaxScaler_Model(prep_Base):#离差标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(MinMaxScaler_Model, self).__init__(*args, **kwargs)
+        self.Model = MinMaxScaler(feature_range=args_use['feature_range'])
+
+        self.k = {}
+        self.Model_Name = 'MinMaxScaler'
+
+class LogScaler_Model(prep_Base):#对数标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(LogScaler_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.k = {}
+        self.Model_Name = 'LogScaler'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.max_logx = np.log(x_data.max())
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        try:
+            max_logx = self.max_logx
+        except:
+            self.have_Fit = False
+            self.Fit(x_data)
+            max_logx = self.max_logx
+        x_Predict = (np.log(x_data)/max_logx)
+        return x_Predict,'对数变换'
+
+class atanScaler_Model(prep_Base):#对数标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(atanScaler_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.k = {}
+        self.Model_Name = 'atanScaler'
+
+    def Fit(self, x_data, *args, **kwargs):
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = (np.arctan(x_data)*(2/np.pi))
+        return x_Predict,'atan变换'
+
+class decimalScaler_Model(prep_Base):#小数定标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(decimalScaler_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.k = {}
+        self.Model_Name = 'Decimal_normalization'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.j = max([judging_Digits(x_data.max()),judging_Digits(x_data.min())])
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        try:
+            j = self.j
+        except:
+            self.have_Fit = False
+            self.Fit(x_data)
+            j = self.j
+        x_Predict = (x_data/(10**j))
+        return x_Predict,'小数定标标准化'
+
+class Mapzoom_Model(prep_Base):#映射标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Mapzoom_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.feature_range = args_use['feature_range']
+        self.k = {}
+        self.Model_Name = 'Decimal_normalization'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.max = x_data.max()
+            self.min = x_data.min()
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        try:
+            max = self.max
+            min = self.min
+        except:
+            self.have_Fit = False
+            self.Fit(x_data)
+            max = self.max
+            min = self.min
+        x_Predict = (x_data * (self.feature_range[1] - self.feature_range[0])) / (max - min)
+        return x_Predict,'映射标准化'
+
+class sigmodScaler_Model(prep_Base):#sigmod变换
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(sigmodScaler_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.k = {}
+        self.Model_Name = 'sigmodScaler_Model'
+
+    def Fit(self, x_data, *args, **kwargs):
+        return 'None', 'None'
+
+    def Predict(self, x_data:np.array):
+        x_Predict = (1/(1+np.exp(-x_data)))
+        return x_Predict,'Sigmod变换'
+
+class Fuzzy_quantization_Model(prep_Base):#模糊量化标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Fuzzy_quantization_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        self.feature_range = args_use['feature_range']
+        self.k = {}
+        self.Model_Name = 'Fuzzy_quantization'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.max = x_data.max()
+            self.min = x_data.min()
+        return 'None', 'None'
+
+    def Predict(self, x_data,*args,**kwargs):
+        try:
+            max = self.max
+            min = self.min
+        except:
+            self.have_Fit = False
+            self.Fit(x_data)
+            max = self.max
+            min = self.min
+        x_Predict = 1 / 2 + (1 / 2) * np.sin(np.pi / (max - min) * (x_data - (max-min) / 2))
+        return x_Predict,'映射标准化'
+
+class Regularization_Model(prep_Base):#离差标准化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Regularization_Model, self).__init__(*args, **kwargs)
+        self.Model = Normalizer(norm=args_use['norm'])
+
+        self.k = {'norm':args_use['norm']}
+        self.Model_Name = 'Regularization'
+
+class Binarizer_Model(prep_Base):#二值化
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Binarizer_Model, self).__init__(*args, **kwargs)
+        self.Model = Binarizer(threshold=args_use['threshold'])
+
+        self.k = {}
+        self.Model_Name = 'Binarizer'
+
+class Discretization_Model(prep_Base):#n值离散
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Discretization_Model, self).__init__(*args, **kwargs)
+        self.Model = None
+
+        range_ = args_use['split_range']
+        if range_ == []:raise Exception
+        elif len(range_) == 1:range_.append(range_[0])
+        self.range = range_
+        self.k = {}
+        self.Model_Name = 'Discretization'
+
+    def Fit(self,*args,**kwargs):
+        return 'None','None'
+
+    def Predict(self,x_data):
+        x_Predict = x_data.copy()#复制
+        range_ = self.range
+        bool_list = []
+        max_ = len(range_) - 1
+        o_t = None
+        for i in range(len(range_)):
+            try:
+                t = float(range_[i])
+            except:continue
+            if o_t == None:#第一个参数
+                bool_list.append(x_Predict <= t)
+            else:
+                bool_list.append((o_t <= x_Predict) == (x_Predict < t))
+                if i == max_:
+                    bool_list.append(t <= x_Predict)
+            o_t = t
+        for i in range(len(bool_list)):
+            x_Predict[bool_list[i]] = i
+        return x_Predict,f'{len(bool_list)}值离散化'
+
+class Label_Model(prep_Base):#数字编码
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Label_Model, self).__init__(*args, **kwargs)
+        self.Model = []
+        self.k = {}
+        self.Model_Name = 'LabelEncoder'
+
+    def Fit(self,x_data,*args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            if x_data.ndim == 1:x_data = np.array([x_data])
+            for i in range(x_data.shape[1]):
+                self.Model.append(LabelEncoder().fit(np.ravel(x_data[:,i])))#训练机器
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = x_data.copy()
+        if x_data.ndim == 1: x_data = np.array([x_data])
+        for i in range(x_data.shape[1]):
+            x_Predict[:,i] = self.Model[i].transform(x_data[:,i])
+        return x_Predict,'数字编码'
+
+class OneHotEncoder_Model(prep_Base):#独热编码
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(OneHotEncoder_Model, self).__init__(*args, **kwargs)
+        self.Model = []
+
+        self.ndim_up = args_use['ndim_up']
+        self.k = {}
+        self.Model_Name = 'OneHotEncoder'
+
+    def Fit(self,x_data,*args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            if x_data.ndim == 1:x_data = [x_data]
+            for i in range(x_data.shape[1]):
+                data = np.expand_dims(x_data[:,i], axis=1)#独热编码需要升维
+                self.Model.append(OneHotEncoder().fit(data))#训练机器
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_new = []
+        for i in range(x_data.shape[1]):
+            data = np.expand_dims(x_data[:, i], axis=1)  # 独热编码需要升维
+            oneHot = self.Model[i].transform(data).toarray().tolist()
+            print(len(oneHot),oneHot)
+            x_new.append(oneHot)#添加到列表中
+        x_new = DataFrame(x_new).to_numpy()#新列表的行数据是原data列数据的独热码(只需要ndim=2，暂时没想到numpy的做法)
+        x_Predict = []
+        for i in range(x_new.shape[1]):
+            x_Predict.append(x_new[:,i])
+        x_Predict = np.array(x_Predict)#转换回array
+        if not self.ndim_up:#需要降维操作
+            print('Q')
+            new_xPredict = []
+            for i in x_Predict:
+                new_list = []
+                list_ = i.tolist()
+                for a in list_:
+                    new_list += a
+                new = np.array(new_list)
+                new_xPredict.append(new)
+            return np.array(new_xPredict),'独热编码'
+        return x_Predict,'独热编码'#不需要降维
+
+class Missed_Model(prep_Base):#缺失数据补充
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(Missed_Model, self).__init__(*args, **kwargs)
+        self.Model = SimpleImputer(missing_values=args_use['miss_value'], strategy=args_use['fill_method'],
+                                   fill_value=args_use['fill_value'])
+
+        self.k = {}
+        self.Model_Name = 'Missed'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'填充缺失'
+
+class PCA_Model(prep_Base):
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(PCA_Model, self).__init__(*args, **kwargs)
+        self.Model = PCA(n_components=args_use['n_components'])
+
+        self.n_components = args_use['n_components']
+        self.k = {'n_components':args_use['n_components']}
+        self.Model_Name = 'PCA'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'PCA'
+
+class RPCA_Model(prep_Base):
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(RPCA_Model, self).__init__(*args, **kwargs)
+        self.Model = IncrementalPCA(n_components=args_use['n_components'])
+
+        self.n_components = args_use['n_components']
+        self.k = {'n_components': args_use['n_components']}
+        self.Model_Name = 'RPCA'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'RPCA'
+
+class KPCA_Model(prep_Base):
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(KPCA_Model, self).__init__(*args, **kwargs)
+        self.Model = KernelPCA(n_components=args_use['n_components'], kernel=args_use['kernel'])
+        self.n_components = args_use['n_components']
+        self.kernel = args_use['kernel']
+        self.k = {'n_components': args_use['n_components'],'kernel':args_use['kernel']}
+        self.Model_Name = 'KPCA'
+
+    def Fit(self, x_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'KPCA'
+
+class LDA_Model(prep_Base):
+    def __init__(self, args_use, model, *args, **kwargs):
+        super(LDA_Model, self).__init__(*args, **kwargs)
+        self.Model = LDA(n_components=args_use['n_components'])
+        self.n_components = args_use['n_components']
+        self.k = {'n_components': args_use['n_components']}
+        self.Model_Name = 'LDA'
+
+    def Fit(self, x_data,y_data, *args, **kwargs):
+        if not self.have_Fit:  # 不允许第二次训练
+            self.Model.fit(x_data,y_data)
+        return 'None', 'None'
+
+    def Predict(self, x_data):
+        x_Predict = self.Model.transform(x_data)
+        return x_Predict,'LDA'
+
+class Machine_Learner(Learner):#数据处理者
+    def __init__(self,*args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.Learner = {}#记录机器
+        self.Learn_Dic = {'Line':Line_Model,
+                          'Ridge':Line_Model,
+                          'Lasso':Line_Model,
+                          'LogisticRegression':LogisticRegression_Model,
+                          'Knn_class':Knn_Model,
+                          'Knn': Knn_Model,
+                          'Tree_class': Tree_Model,
+                          'Tree': Tree_Model,
+                          'Variance':Variance_Model,
+                          'SelectKBest':SelectKBest_Model,
+                          'Z-Score':Standardization_Model,
+                          'MinMaxScaler':MinMaxScaler_Model,
+                          'LogScaler':LogScaler_Model,
+                          'atanScaler':atanScaler_Model,
+                          'decimalScaler':decimalScaler_Model,
+                          'sigmodScaler':sigmodScaler_Model,
+                          'Mapzoom':Mapzoom_Model,
+                          'Fuzzy_quantization':Fuzzy_quantization_Model,
+                          'Regularization':Regularization_Model,
+                          'Binarizer':Binarizer_Model,
+                          'Discretization':Discretization_Model,
+                          'Label':Label_Model,
+                          'OneHotEncoder':OneHotEncoder_Model,
+                          'Missed':Missed_Model,
+                          'PCA':PCA_Model,
+                          'RPCA':RPCA_Model,
+                          'KPCA':KPCA_Model,
+                          'LDA':LDA_Model,
+                          }
+        self.Learner_Type = {}#记录机器的类型
+
+    def p_Args(self,Text,Type):#解析参数
+        args = {}
+        args_use = {}
+        #输入数据
+        exec(Text,args)
+        #处理数据
+        args_use['alpha'] = float(args.get('alpha',1.0))#L1和L2正则化用
+        args_use['C'] = float(args.get('C', 1.0))  # L1和L2正则化用
+        args_use['max_iter'] = int(args.get('max_iter', 1000))  # L1和L2正则化用
+        args_use['n_neighbors'] = int(args.get('K_knn', 5))#knn邻居数 (命名不同)
+        args_use['p'] = int(args.get('p', 2))  # 距离计算方式
+        args_use['nDim_2'] = bool(args.get('nDim_2', True))  # 数据是否降维
+
+        if Type == 'Tree':
+            args_use['criterion'] = 'mse' if bool(args.get('is_MSE', True)) else 'mae'  # 是否使用基尼不纯度
+        else:
+            args_use['criterion'] = 'gini' if bool(args.get('is_Gini', True)) else 'entropy'  # 是否使用基尼不纯度
+        args_use['splitter'] = 'random' if bool(args.get('is_random', False)) else 'best' # 决策树节点是否随机选用最优
+        args_use['max_features'] = args.get('max_features', None) # 选用最多特征数
+        args_use['max_depth'] = args.get('max_depth', None)  # 最大深度
+        args_use['min_samples_split'] = int(args.get('min_samples_split', 2))  # 是否继续划分（容易造成过拟合）
+
+        args_use['P'] = float(args.get('min_samples_split', 0.8))
+        args_use['k'] = args.get('k',1)
+        args_use['score_func'] = ({'chi2':chi2,'f_classif':f_classif,'mutual_info_classif':mutual_info_classif,
+                                   'f_regression':f_regression,'mutual_info_regression':mutual_info_regression}.
+                                  get(args.get('score_func','f_classif'),f_classif))
+
+        args_use['feature_range'] = tuple(args.get('feature_range',(0,1)))
+        args_use['norm'] = args.get('norm','l2')#正则化的方式L1或者L2
+
+        args_use['threshold'] = float(args.get('threshold', 0.0))  # 二值化特征
+
+        args_use['split_range'] = list(args.get('split_range', [0]))  # 二值化特征
+
+        args_use['ndim_up'] = bool(args.get('ndim_up', True))
+        args_use['miss_value'] = args.get('miss_value',np.nan)
+        args_use['fill_method'] = args.get('fill_method','mean')
+        args_use['fill_value'] = args.get('fill_value',None)
+
+        args_use['n_components'] = args.get('n_components',1)
+        args_use['kernel'] = args.get('kernel','linear')
+        return args_use
+
+    def Add_Learner(self,Learner,Text=''):
+        get = self.Learn_Dic[Learner]
+        name = f'Le[{len(self.Learner)}]{Learner}'
+        #参数调节
+        args_use = self.p_Args(Text,Learner)
+        #生成学习器
+        self.Learner[name] = get(model=Learner,args_use=args_use)
+        self.Learner_Type[name] = Learner
+
+    def Add_SelectFrom_Model(self,Learner,Text=''):#Learner代表选中的学习器
+        model = self.get_Learner(Learner)
+        name = f'Le[{len(self.Learner)}]SelectFrom_Model'
+        #参数调节
+        args_use = self.p_Args(Text,'SelectFrom_Model')
+        #生成学习器
+        self.Learner[name] = SelectFrom_Model(Learner=model,args_use=args_use,Dic=self.Learn_Dic)
+        self.Learner_Type[name] = 'SelectFrom_Model'
+
+    def Return_Learner(self):
+        return self.Learner.copy()
+
+    def get_Learner(self,name):
+        return self.Learner[name]
+
+    def get_Learner_Type(self,name):
+        return self.Learner_Type[name]
+
+    def Fit(self,x_name,y_name,Learner,split=0.3,*args,**kwargs):
+        x_data = self.get_Sheet(x_name)
+        y_data = self.get_Sheet(y_name)
+        model = self.get_Learner(Learner)
+        return model.Fit(x_data,y_data,split)
+
+    def Predict(self,x_name,Learner,Text='',**kwargs):
+        x_data = self.get_Sheet(x_name)
+        model = self.get_Learner(Learner)
+        y_data,name = model.Predict(x_data)
+        self.Add_Form(y_data,f'{x_name}:{name}')
+        return y_data
+
+    def Score(self,name_x,name_y,Learner):#Score_Only表示仅评分 Fit_Simp 是普遍类操作
+        model = self.get_Learner(Learner)
+        x = self.get_Sheet(name_x)
+        y = self.get_Sheet(name_y)
+        return model.Score(x,y)
+
+    def Show_Args(self,Learner,Dic):#显示参数
+        pass
+
+    def Del_Leaner(self,Leaner):
+        del self.Learner[Leaner]
+        del self.Learner_Type[Leaner]
+
+def judging_Digits(num:(int,float)):
+    a = str(abs(num)).split('.')[0]
+    if a == '':raise ValueError
+    return len(a)

Machine_learning.py

@@ -0,0 +1,582 @@
+import tkinter
+import webbrowser
+from tkinter.filedialog import askopenfilename, asksaveasfilename
+import tkinter.messagebox
+import os
+import chardet
+from tkinter.scrolledtext import ScrolledText
+import Learn_Numpy
+
+def Main():
+    global top,ML,Form_List,PATH,bg,bbg,fg
+    PATH = os.getcwd()
+    Form_List = []
+    ML = Learn_Numpy.Machine_Learner()
+
+    top = tkinter.Tk()
+    bg = '#FFFAFA'  # 主颜色
+    bbg = '#FFFAFA'  # 按钮颜色
+    fg = '#000000'  # 文字颜色
+    top["bg"] = bg
+    FONT = ('黑体', 11)  # 设置字体
+    top.title('CoTan机器学习')
+    top.resizable(width=False, height=False)
+    top.geometry('+10+10')  # 设置所在位置
+
+    width_B = 13  # 标准宽度
+    height_B = 2
+    a_y = 0
+    a_x = 0
+
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入CSV', command=Add_CSV, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入Py', command=Add_Python, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 1, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='导出CSV', command=to_CSV, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    global name_Input
+    a_y += 1
+    tkinter.Label(top, text='表格名称:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
+                                                                                                   row=a_y)  # 设置说明
+    name_Input = tkinter.Entry(top, width=width_B)
+    name_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除表格', font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看表格', command=Show, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 1, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看单一表格', command=Show_One, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    global Form_BOX, Index_BOX, Column_BOX, to_HTML_Type, Seq_Input, Code_Input,str_must
+    a_y += 1
+    to_HTML_Type = tkinter.IntVar()  # 正，负，0
+    lable = ['选项卡型', '可移动型', '自适应型']  # 复选框
+    for i in range(3):
+        tkinter.Radiobutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg, text=lable[i],
+                            variable=to_HTML_Type,
+                            value=i).grid(column=a_x + i, row=a_y, sticky=tkinter.W)
+
+    str_must = tkinter.IntVar()
+    a_y += 1
+    tkinter.Label(top, text='编码方式:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
+                                                                                                   row=a_y)  # 设置说明
+    Code_Input = tkinter.Entry(top, width=width_B)
+    Code_Input.grid(column=a_x + 1, row=a_y, sticky=tkinter.E + tkinter.W)
+    buttom = tkinter.Checkbutton(top, bg=bg, fg=fg, activebackground=bg, activeforeground=fg, selectcolor=bg,
+                                 text='字符串类型',
+                                 variable=str_must)
+    buttom.grid(column=a_x + 2, row=a_y, sticky=tkinter.W)
+
+    a_y += 1
+    tkinter.Label(top, text='CSV分隔符:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
+                                                                                                     row=a_y)  # 设置说明
+    Seq_Input = tkinter.Entry(top, width=width_B)
+    Seq_Input.grid(column=a_x + 1,columnspan=2, row=a_y, sticky=tkinter.E + tkinter.W)
+
+    a_y += 1
+    Form_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 10)  # 显示符号
+    Form_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=10, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
+
+
+    a_x += 3
+    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
+    a_x += 1
+    a_y = 0
+
+    tkinter.Label(top, text='【机器学习】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
+                                                                                                        columnspan=3,
+                                                                                                        row=a_y,
+                                                                                                        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
+
+    global ML_BOX, ML_OUT,X_OUT,Y_OUT
+    a_y += 1
+    X_OUT = tkinter.StringVar()
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=X_OUT)
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
+    Put['state'] = 'readonly'
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用特征集', command=set_Feature, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
+
+    Y_OUT = tkinter.StringVar()
+    a_y += 1
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=Y_OUT)
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
+    Put['state'] = 'readonly'
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用标签集', command=set_Label, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
+
+    ML_OUT = tkinter.StringVar()
+    a_y += 1
+    Put = tkinter.Entry(top, width=width_B * 2, textvariable=ML_OUT)
+    Put.grid(column=a_x, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
+    Put['state'] = 'readonly'
+    tkinter.Button(top, bg=bbg, fg=fg, text='选用学习器', command=set_Learner, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y, sticky=tkinter.E + tkinter.W)
+
+    global Split_Input
+    a_y += 1
+    tkinter.Label(top, text='测试数据分割:', bg=bg, fg=fg, font=FONT, width=width_B, height=height_B).grid(column=a_x,
+                                                                                                     row=a_y)
+    Split_Input = tkinter.Entry(top, width=width_B * 2)
+    Split_Input.grid(column=a_x + 1, row=a_y, columnspan=2, sticky=tkinter.E + tkinter.W)
+
+    a_y += 1
+    ML_BOX = tkinter.Listbox(top, width=width_B * 3, height=height_B * 5)
+    ML_BOX.grid(column=a_x, row=a_y, columnspan=3, rowspan=5, sticky=tkinter.E + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 5
+    tkinter.Button(top, bg=bbg, fg=fg, text='导入学习器', font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='查看数据', command=Show_Args, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='删除学习器', command=Del_Leaner, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='训练机器', command=Fit_Learner, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='测试机器', command=Score_Learner, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 1, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='数据预测', command=Predict_Learner, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='单一变量特征选择', command=Add_SelectKBest, font=FONT, width=width_B, height=height_B).grid(column=a_x, row=a_y,columnspan=2,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    tkinter.Button(top, bg=bbg, fg=fg, text='映射标准化', command=Add_Mapzoom, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x+2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='方差特征选择',command=Add_Variance, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='使用学习器筛选', command=Add_SelectFrom_Model, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 1, row=a_y,sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='模糊量化标准化', command=Add_Fuzzy_quantization, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 2, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='Z-score',command=Add_Z_Score, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='离差标准化', command=Add_MinMaxScaler, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='Log变换', command=Add_LogScaler, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='atan变换',command=Add_atanScaler, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='小数定标准化', command=Add_decimalScaler, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='Sigmod变换', command=Add_sigmodScaler, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='正则化',command=Add_Regularization, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='二值离散', command=Add_Binarizer, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='多值离散', command=Add_Discretization, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='独热编码',command=Add_OneHotEncoder, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='数字编码', command=Add_Label, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='缺失填充', command=Add_Missed, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='PCA降维',command=Add_PCA, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='RPCA降维', command=Add_RPCA, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='KPCA升维', command=Add_KPCA, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='LDA', command=Add_LDA, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,columnspan=3,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='线性回归', command=Add_Line, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='岭回归', command=Add_Ridge, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='Lasso', command=Add_Lasso, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 2, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='逻辑回归', command=Add_LogisticRegression, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='决策树回归',command=Add_Tree, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='决策树分类',command=Add_Tree_Class, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 2, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_y += 1
+    tkinter.Button(top, bg=bbg, fg=fg, text='朴素贝叶斯', font=FONT, width=width_B, height=height_B).grid(
+        column=a_x, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近预测', command=Add_Knn, font=FONT, width=width_B, height=height_B).grid(
+        column=a_x + 1, row=a_y,
+        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+    tkinter.Button(top, bg=bbg, fg=fg, text='K邻近分类', command=Add_Knn_Class, font=FONT, width=width_B,
+                   height=height_B).grid(column=a_x + 2, row=a_y,
+                                         sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)
+
+    a_x += 3
+    tkinter.Label(top, text='', bg=bg, fg=fg, font=FONT, width=1).grid(column=a_x, row=a_y)  # 设置说明
+    a_x += 1
+    a_y = 0
+
+    tkinter.Label(top, text='【学习器配置】', bg=bg, fg=fg, font=FONT, width=width_B * 3, height=height_B).grid(column=a_x,
+                                                                                                        columnspan=3,
+                                                                                                        row=a_y,
+                                                                                                        sticky=tkinter.E + tkinter.W + tkinter.W + tkinter.S + tkinter.N)  # 设置说明
+
+    global Args_Learner
+    a_y += 1
+    Args_Learner = tkinter.Text(top, width=width_B * 3, height=height_B * 11)
+    Args_Learner.grid(column=a_x, row=a_y, columnspan=3, rowspan=11,
+                      sticky=tkinter.E + tkinter.W + tkinter.N + tkinter.S)
+
+    top.mainloop()
+
+
+def Del_Leaner():
+    Learn = get_Learner(True)
+    set_Learne = get_Learner(False)  # 获取学习器Learner
+    if set_Learne != Learn:
+        ML.Del_Leaner(Learn)
+    Update_Leaner()
+
+
+def Show_Args():
+    learner = get_Learner(True)
+    if tkinter.messagebox.askokcancel('提示', f'是否将数据生成表格。\n(可绘制成散点图对比数据)'):
+        Dic = asksaveasfilename(title='选择保存的CSV', filetypes=[("CSV", ".csv")])
+    else:
+        Dic = ''
+    Data = ML.Show_Args(learner, Dic)
+    title = f'CoTan数据处理 查看数据:{learner}'
+    Creat_TextSheet(f'对象:{learner}\n\n{Data[0]}\n\n\n{Data[1]}', title)
+    Update_BOX()
+
+
+def get_Args_Learner():
+    global Args_Learner
+    return Args_Learner.get('0.0', tkinter.END)
+
+
+def Score_Learner():
+    learner = get_Learner()
+    socore = ML.Score(get_Name(False,True),get_Name(False,False), learner)
+    tkinter.messagebox.showinfo('测试完成', f'针对测试数据评分结果为:{socore}')
+
+
+def Predict_Learner():
+    learner = get_Learner()
+    Data = ML.Predict(get_Name(False,True),learner)
+    title = f'CoTan数据处理 学习器:{learner}'
+    Creat_TextSheet(Data, title)
+    Update_BOX()
+
+
+def Fit_Learner():
+    learner = get_Learner()
+    try:
+        split = float(Split_Input.get())
+        if split < 0 or 1 < split: raise Exception
+    except:
+        split = 0.3
+    socore = ML.Fit(get_Name(False,True),get_Name(False,False), learner, Text=get_Args_Learner(), split=split)
+    tkinter.messagebox.showinfo('训练完成', f'针对训练数据({(1 - split) * 100}%)评分结果为:{socore[0]}\n'
+                                        f'针对测试数据评分({split * 100}%)结果为:{socore[1]}')
+
+
+def set_Feature():
+    global X_OUT
+    X_OUT.set(get_Name())
+
+
+def set_Label():
+    global Y_OUT
+    Y_OUT.set(get_Name())
+
+
+def set_Learner():
+    global ML_OUT
+    ML_OUT.set(get_Learner(True))
+
+
+def get_Learner(Type=False):
+    global Learn_Dic, ML_BOX, ML_OUT
+    if Type:
+        try:
+            return list(Learn_Dic.keys())[ML_BOX.curselection()[0]]
+        except:
+            # raise
+            try:
+                return list(Learn_Dic.keys)[0]
+            except:
+                return None
+    else:
+        try:
+            return ML_OUT.get()
+        except:
+            return None
+
+def Add_LDA():
+    Add_leaner('LDA')
+
+def Add_KPCA():
+    Add_leaner('KPCA')
+
+def Add_RPCA():
+    Add_leaner('RPCA')
+
+def Add_PCA():
+    Add_leaner('PCA')
+
+def Add_Missed():
+    Add_leaner('Missed')
+
+def Add_Label():
+    Add_leaner('Label')
+
+def Add_OneHotEncoder():
+    Add_leaner('OneHotEncoder')
+
+def Add_Discretization():
+    Add_leaner('Discretization')
+
+def Add_Binarizer():
+    Add_leaner('Binarizer')
+
+def Add_Regularization():
+    Add_leaner('Regularization')
+
+def Add_Fuzzy_quantization():
+    Add_leaner('Fuzzy_quantization')
+
+def Add_Mapzoom():
+    Add_leaner('Mapzoom')
+
+def Add_sigmodScaler():
+    Add_leaner('sigmodScaler')
+
+def Add_decimalScaler():
+    Add_leaner('decimalScaler')
+
+def Add_atanScaler():
+    Add_leaner('atanScaler')
+
+def Add_LogScaler():
+    Add_leaner('LogScaler')
+
+def Add_MinMaxScaler():
+    Add_leaner('MinMaxScaler')
+
+def Add_Z_Score():
+    Add_leaner('Z-Score')
+
+def Add_Tree_Class():
+    Add_leaner('Tree_class')
+
+def Add_Tree():
+    Add_leaner('Tree')
+
+
+def Add_SelectKBest():
+    Add_leaner('SelectKBest')
+
+
+def Add_Knn_Class():
+    Add_leaner('Knn_class')
+
+
+def Add_LogisticRegression():
+    Add_leaner('LogisticRegression')
+
+
+def Add_Lasso():
+    Add_leaner('Lasso')
+
+
+def Add_Variance():
+    Add_leaner('Variance')
+
+
+def Add_Knn():
+    Add_leaner('Knn')
+
+
+def Add_Ridge():
+    Add_leaner('Ridge')
+
+
+def Add_Line():
+    Add_leaner('Line')
+
+
+def Add_SelectFrom_Model():  # 添加Lenear的核心
+    ML.Add_SelectFrom_Model(get_Learner(), Text=get_Args_Learner())
+    Update_Leaner()
+
+
+def Add_leaner(Type):  # 添加Lenear的核心
+    ML.Add_Learner(Type, Text=get_Args_Learner())
+    Update_Leaner()
+
+
+def Update_Leaner():
+    global Learn_Dic, ML_BOX
+    Learn_Dic = ML.Return_Learner()
+    ML_BOX.delete(0, tkinter.END)
+    ML_BOX.insert(tkinter.END, *Learn_Dic.keys())
+
+
+def Show_One():
+    global PATH, to_HTML_Type
+    Dic = f'{PATH}/$Show_Sheet.html'
+    try:
+        name = get_Name()
+        if name == None: raise Exception
+        ML.to_Html_One(name, Dic)
+        webbrowser.open(Dic)
+    except:
+        # pass
+        raise
+
+
+def Show():
+    global PATH, to_HTML_Type
+    Dic = f'{PATH}/$Show_Sheet.html'
+    try:
+        name = get_Name()
+        if name == None: raise Exception
+        ML.to_Html(name, Dic, to_HTML_Type.get())
+        webbrowser.open(Dic)
+    except:
+        pass
+
+
+def to_CSV():
+    global top, Seq_Input, Code_Input, str_must, Index_must
+    Dic = asksaveasfilename(title='选择保存的CSV', filetypes=[("CSV", ".csv")])
+    Seq = Seq_Input.get()
+    name = get_Name()
+    ML.to_CSV(Dic, name, Seq)
+    Update_BOX()
+
+
+def Add_CSV():
+    global top, Seq_Input, Code_Input, str_must, name_Input
+    Dic = askopenfilename(title='选择载入的CSV', filetypes=[("CSV", ".csv")])
+    if Dic == '': return False
+    Seq = Seq_Input.get()
+    Codeing = Code_Input.get()
+    str_ = bool(str_must.get())
+    name = name_Input.get().replace(' ', '')
+    if name == '':
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
+        print(name)
+    if Codeing == '':
+        with open(Dic, 'rb') as f:
+            Codeing = chardet.detect(f.read())['encoding']
+    if Seq == '': Seq = ','
+    ML.read_csv(Dic, name, Codeing, str_, Seq,)
+    Update_BOX()
+
+
+def Add_Python():
+    global top, Seq_Input, Code_Input, str_must, Index_must
+    Dic = askopenfilename(title='选择载入的py', filetypes=[("Python", ".py"), ("Txt", ".txt")])
+    name = name_Input.get().replace(' ', '')
+    if name == '':
+        name = os.path.splitext(os.path.split(Dic)[1])[0]
+    with open(Dic, 'r') as f:
+        ML.Add_Python(f.read(), name)
+    Update_BOX()
+
+def get_Name(Type=True, x=True):  # 获得名字统一接口
+    global Form_List, Form_BOX, X_OUT
+    if Type:
+        try:
+            return Form_List[Form_BOX.curselection()[0]]
+        except:
+            try:
+                return Form_List[0]
+            except:
+                return None
+    else:
+        try:
+            if x:
+                return X_OUT.get()
+            else:
+                return Y_OUT.get()
+        except:
+            return None
+
+
+def Update_BOX():
+    global top, Form_BOX, Form_List
+    Form_List = list(ML.get_Form().keys())
+    Form_BOX.delete(0, tkinter.END)
+    Form_BOX.insert(tkinter.END, *Form_List)
+
+def Creat_TextSheet(data, name):
+    global bg
+    new_top = tkinter.Toplevel(bg=bg)
+    new_top.title(name)
+    new_top.geometry('+10+10')  # 设置所在位置
+    text = ScrolledText(new_top, font=('黑体', 13), height=50)
+    text.pack(fill=tkinter.BOTH)
+    text.insert('0.0', data)
+    text.config(state=tkinter.DISABLED)
+    new_top.resizable(width=False, height=False)
+
+if __name__ == '__main__':
+    Main()