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.ensemble import (RandomForestClassifier,RandomForestRegressor,GradientBoostingClassifier, GradientBoostingRegressor) 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,NMF from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA from sklearn.svm import SVC,SVR#SVC是svm分类，SVR是svm回归 from sklearn.neural_network import MLPClassifier,MLPRegressor from sklearn.manifold import TSNE from sklearn.cluster import KMeans,AgglomerativeClustering,DBSCAN # 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 Unsupervised(prep_Base): 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' class UnsupervisedModel(prep_Base): def Fit(self, x_data, *args, **kwargs): self.Model.fit(x_data) return 'None', 'None' 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 Forest_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(Forest_Model, self).__init__(*args,**kwargs) Model = {'Forest_class':RandomForestClassifier,'Forest':RandomForestRegressor}[model] self.Model = Model(n_estimators=args_use['n_Tree'],criterion=args_use['criterion'],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.n_estimators = args_use['n_Tree'] self.criterion = args_use['criterion'] 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 = {'n_estimators':args_use['n_Tree'],'criterion':args_use['criterion'],'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 GradientTree_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(GradientTree_Model, self).__init__(*args,**kwargs) Model = {'GradientTree_class':GradientBoostingClassifier,'GradientTree':GradientBoostingRegressor}[model] self.Model = Model(n_estimators=args_use['n_Tree'],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 SVC_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(SVC_Model, self).__init__(*args,**kwargs) self.Model = SVC(C=args_use['C'],gamma=args_use['gamma'],kernel=args_use['kernel']) #记录这两个是为了克隆 self.C = args_use['C'] self.gamma = args_use['gamma'] self.kernel = args_use['kernel'] self.k = {'C':args_use['C'],'gamma':args_use['gamma'],'kernel':args_use['kernel']} self.Model_Name = model class SVR_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(SVR_Model, self).__init__(*args,**kwargs) self.Model = SVR(C=args_use['C'],gamma=args_use['gamma'],kernel=args_use['kernel']) #记录这两个是为了克隆 self.C = args_use['C'] self.gamma = args_use['gamma'] self.kernel = args_use['kernel'] self.k = {'C':args_use['C'],'gamma':args_use['gamma'],'kernel':args_use['kernel']} self.Model_Name = model class Variance_Model(Unsupervised):#无监督 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):#有监督 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):#有监督 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 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(Unsupervised):#z-score标准化 无监督 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(Unsupervised):#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):#atan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(Unsupervised):#正则化 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(Unsupervised):#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(Unsupervised):#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 Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'填充缺失' class PCA_Model(Unsupervised): 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 Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'PCA' class RPCA_Model(Unsupervised): 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 Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'RPCA' class KPCA_Model(Unsupervised): 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 Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'KPCA' class LDA_Model(Unsupervised): 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 Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'LDA' class NMF_Model(Unsupervised): def __init__(self, args_use, model, *args, **kwargs): super(NMF_Model, self).__init__(*args, **kwargs) self.Model = NMF(n_components=args_use['n_components']) self.n_components = args_use['n_components'] self.k = {'n_components':args_use['n_components']} self.Model_Name = 'NFM' def Predict(self, x_data): x_Predict = self.Model.transform(x_data) return x_Predict,'NMF' class TSNE_Model(Unsupervised): def __init__(self, args_use, model, *args, **kwargs): super(TSNE_Model, self).__init__(*args, **kwargs) self.Model = TSNE(n_components=args_use['n_components']) self.n_components = args_use['n_components'] self.k = {'n_components':args_use['n_components']} self.Model_Name = 't-SNE' def Fit(self,*args, **kwargs): return 'None', 'None' def Predict(self, x_data): x_Predict = self.Model.fit_transform(x_data) return x_Predict,'SNE' class MLP_Model(Study_MachineBase):#神经网络(多层感知机)，有监督学习 def __init__(self,args_use,model,*args,**kwargs): super(MLP_Model, self).__init__(*args,**kwargs) Model = {'MLP':MLPRegressor,'MLP_class':MLPClassifier}[model] self.Model = Model(hidden_layer_sizes=args_use['hidden_size'],activation=args_use['activation'], solver=args_use['solver'],alpha=args_use['alpha'],max_iter=args_use['max_iter']) #记录这两个是为了克隆 self.hidden_layer_sizes = args_use['hidden_size'] self.activation = args_use['activation'] self.max_iter = args_use['max_iter'] self.solver = args_use['solver'] self.alpha = args_use['alpha'] self.k = {'hidden_layer_sizes':args_use['hidden_size'],'activation':args_use['activation'],'max_iter':args_use['max_iter'], 'solver':args_use['solver'],'alpha':args_use['alpha']} self.Model_Name = model class kmeans_Model(UnsupervisedModel): def __init__(self, args_use, model, *args, **kwargs): super(kmeans_Model, self).__init__(*args, **kwargs) self.Model = KMeans(n_clusters=args_use['n_clusters']) self.n_clusters = args_use['n_clusters'] self.k = {'n_clusters':args_use['n_clusters']} self.Model_Name = 'k-means' def Predict(self, x_data): y_Predict = self.Model.predict(x_data) return y_Predict,'k-means' class Agglomerative_Model(UnsupervisedModel): def __init__(self, args_use, model, *args, **kwargs): super(Agglomerative_Model, self).__init__(*args, **kwargs) self.Model = AgglomerativeClustering(n_clusters=args_use['n_clusters'])#默认为2，不同于k-means self.n_clusters = args_use['n_clusters'] self.k = {'n_clusters':args_use['n_clusters']} self.Model_Name = 'Agglomerative' def Predict(self, x_data): y_Predict = self.Model.fit_predict(x_data) return y_Predict,'Agglomerative' class DBSCAN_Model(UnsupervisedModel): def __init__(self, args_use, model, *args, **kwargs): super(DBSCAN_Model, self).__init__(*args, **kwargs) self.Model = DBSCAN(eps = args_use['eps'], min_samples = args_use['min_samples']) #eps是距离(0.5)，min_samples(5)是簇与噪音分界线(每个簇最小元素数) # min_samples self.eps = args_use['eps'] self.min_samples = args_use['min_samples'] self.k = {'min_samples':args_use['min_samples'],'eps':args_use['eps']} self.Model_Name = 'DBSCAN' def Predict(self, x_data): y_Predict = self.Model.fit_predict(x_data) return y_Predict,'DBSCAN' 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, 'Forest':Forest_Model, 'Forest_class': Forest_Model, 'GradientTree_class':GradientTree_Model, 'GradientTree': GradientTree_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, 'SVC':SVC_Model, 'SVR':SVR_Model, 'MLP':MLP_Model, 'MLP_class': MLP_Model, 'NMF':NMF_Model, 't-SNE':TSNE_Model, 'k-means':kmeans_Model, 'Agglomerative':Agglomerative_Model, 'DBSCAN':DBSCAN_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 if Type in ('MLP','MLP_class'): args_use['alpha'] = float(args.get('alpha', 0.0001)) # MLP正则化用 else: 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 if Type in ('MLP','MLP_class'): args_use['max_iter'] = int(args.get('max_iter', 200)) # L1和L2正则化用 else: 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 in ('Tree','Forest','GradientTree'): 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','rbf' if Type in ('SVR','SVR') else 'linear') args_use['n_Tree'] = args.get('n_Tree',100) args_use['gamma'] = args.get('gamma',1) args_use['hidden_size'] = tuple(args.get('hidden_size',(100,))) args_use['activation'] = str(args.get('activation','relu')) args_use['solver'] = str(args.get('solver','adam')) if Type in ('k-means',): args_use['n_clusters'] = int(args.get('n_clusters',8)) else: args_use['n_clusters'] = int(args.get('n_clusters', 2)) 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)