From 258f3ca89f41078d3d8a7c2f53744cef5ede980e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=A2=81=E4=BC=8A=E9=9B=AF?= Date: Mon, 24 Oct 2022 17:53:54 +0800 Subject: [PATCH 1/3] start START2 --- BaseML/Classification.py | 136 +++++-- BaseML/Regression.py | 64 +++- BaseML/__pycache__/AdaBoost.cpython-37.pyc | Bin 0 -> 1929 bytes BaseML/__pycache__/CART.cpython-37.pyc | Bin 0 -> 1186 bytes .../__pycache__/Classification.cpython-37.pyc | Bin 0 -> 5247 bytes BaseML/__pycache__/GaussianNB.cpython-37.pyc | Bin 0 -> 2258 bytes .../__pycache__/KNNClassifier.cpython-37.pyc | Bin 0 -> 1552 bytes BaseML/__pycache__/PCA.cpython-37.pyc | Bin 0 -> 1170 bytes BaseML/__pycache__/Perceptron.cpython-37.pyc | Bin 0 -> 1211 bytes BaseML/__pycache__/Regression.cpython-37.pyc | Bin 0 -> 2680 bytes BaseML/__pycache__/SVM.cpython-37.pyc | Bin 0 -> 1833 bytes BaseML/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 545 bytes Downloads/China_cities.csv | 352 ++++++++++++++++++ checkpoint.pkl | Bin 0 -> 10267 bytes demo/__init__.py | 0 demo/iris_cls_demo.py | 26 ++ demo/kemans.py | 12 - demo/kmeans.py | 45 +++ test/test_datasets.py | 17 + 19 files changed, 596 insertions(+), 56 deletions(-) create mode 100644 BaseML/__pycache__/AdaBoost.cpython-37.pyc create mode 100644 BaseML/__pycache__/CART.cpython-37.pyc create mode 100644 BaseML/__pycache__/Classification.cpython-37.pyc create mode 100644 BaseML/__pycache__/GaussianNB.cpython-37.pyc create mode 100644 BaseML/__pycache__/KNNClassifier.cpython-37.pyc create mode 100644 BaseML/__pycache__/PCA.cpython-37.pyc create mode 100644 BaseML/__pycache__/Perceptron.cpython-37.pyc create mode 100644 BaseML/__pycache__/Regression.cpython-37.pyc create mode 100644 BaseML/__pycache__/SVM.cpython-37.pyc create mode 100644 BaseML/__pycache__/__init__.cpython-37.pyc create mode 100644 Downloads/China_cities.csv create mode 100644 checkpoint.pkl create mode 100644 demo/__init__.py create mode 100644 demo/iris_cls_demo.py delete mode 100644 demo/kemans.py create mode 100644 demo/kmeans.py create mode 100644 test/test_datasets.py diff --git a/BaseML/Classification.py b/BaseML/Classification.py index 1137280..ac89b44 100644 --- a/BaseML/Classification.py +++ b/BaseML/Classification.py @@ -6,11 +6,13 @@ from sklearn.ensemble import AdaBoostClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier +from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score, silhouette_samples import joblib +import random class cls: def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=5): @@ -18,7 +20,7 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS= self.cwd = os.path.dirname(os.getcwd()) # 获取当前文件的绝对路径 self.file_dirname = os.path.dirname(os.path.abspath(__file__)) self.dataset_path = ' ' - self.test_size = ' ' + self.test_size = 0.2 self.test_set = ' ' self.x_train, self.x_test, self.y_train, self.y_test = 0, 0, 0, 0 if self.algorithm == 'KNN': @@ -34,22 +36,17 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS= elif self.algorithm == 'Kmeans': self.n = N_CLUSTERS self.model = KMeans(self.n) + elif self.algorithm == 'MLP': + self.model = MLPClassifier(solver='lbfgs') def train(self, seed=0, data_type='csv'): - if self.algorithm == 'AdaBoost' or 'SVM' or 'NaiveBayes': + if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP']: np.random.seed(seed) - if data_type == 'csv': - dataset = pd.read_csv(self.dataset_path, sep=',', header=None).values - np.random.shuffle(dataset) + np.random.shuffle(self.dataset) - data, label = dataset[:, :-1], dataset[:, -1] - train_index = int((1 - self.test_size) * len(dataset)) - train_data, train_label = data[:train_index, ], label[:train_index] - self.test_set = { - 'data': data[train_index:, ], - 'label': label[train_index:] - } - self.model.fit(train_data, train_label) + self.x_train, self.x_test, self.y_train, self.y_test = \ + train_test_split(self.x_train, self.y_train, test_size=0.2, random_state=0) + self.model.fit(self.x_train, self.y_train) elif self.algorithm == 'CART': self.model.fit(self.dataset) @@ -58,51 +55,110 @@ def train(self, seed=0, data_type='csv'): elif self.algorithm == 'KNN': self.x_train, self.x_test, self.y_train, self.y_test = \ - train_test_split(self.dataset['data'], self.dataset['target'], test_size=0.2, random_state=0) + train_test_split(self.x_train, self.y_train, test_size=0.2, random_state=0) self.model.fit(self.x_train, self.y_train) acc = self.model.score(self.x_test, self.y_test) print('准确率为：{}%'.format(acc * 100)) elif self.algorithm == 'Kmeans': - self.dataset = pd.read_csv(self.dataset_path) - self.x_train = self.dataset.drop('省级行政区', axis=1) - self.x_train = self.x_train.drop("城市", axis=1) - self.x_np = np.array(self.x_train) - self.model.fit(self.x_np) + # 对列数据进行文本过滤，只抽取有数据的列 + delete_list = [] + if self.x_train.ndim >= 2: + for col_idx in range(self.x_train.shape[1]): + col = self.x_train[:,col_idx] + # 随机取一个元素，查看其type + if isinstance(random.choice(col),str): + delete_list.append(col_idx) - def inference(self, data): - if self.algorithm == 'AdaBoost' or 'SVM' or 'NaiveBayes': - pred = self.model.predict(self.test_set['data']) - acc = accuracy_score(self.test_set['label'], pred) - print('准确率为：{}%'.format(acc * 100)) + self.x_train = np.delete(self.x_train, delete_list, axis=1) + self.model.fit(self.x_train) - elif self.algorithm == 'KNN': - result = self.model.predict(data) - print(result) - print("分类结果：{}".format(self.dataset['target_names'][result])) + def inference(self, data = np.nan): + if data is not np.nan: # 对data进行了指定 + self.x_test = data - elif self.algorithm == 'CART': - self.model.fit_transform(data) - print(self.model.n_features_) - print(self.model.n_samples_) + if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP','KNN','CART']: + pred = self.model.predict(self.x_test) + return pred elif self.algorithm == 'Kmeans': labels = self.model.labels_ # 获取聚类标签 - print(silhouette_score(self.x_np, labels)) # 获取聚类结果总的轮廓系数 + print(silhouette_score(self.x_train, labels)) # 获取聚类结果总的轮廓系数 print(self.model.cluster_centers_) # 输出类簇中心 for i in range(self.n): print(f" CLUSTER-{i+1} ".center(60, '=')) print(self.dataset[labels == i]) - def load_dataset(self, path, test_size=0.2, dataset=''): - self.dataset_path = path - self.test_size = test_size - self.dataset=dataset + if data is not np.nan: + pred = self.model.predict(data) + return pred + + # 从文件加载数据集，支持csv文件和txt文件 + def load_dataset_from_file(self, path, x_column = [], y_column = []): + if type == 'csv': + self.dataset = pd.read_csv(path).values # .values就转成numpy格式了 + self.get_data(X,y,x_column,y_column) + elif type == 'txt': + self.dataset = np.loadtxt(path) + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) - def save(self): + # 从数据加载数据集，支持['numpy','list','DataFrame'] + def load_dataset_from_data(self, X, y = None, x_column = [], y_column = []): + if type(X) != type(y): + raise TypeError("数据格式不同，无法加载") + if isinstance(X,list): + X = np.array(X) + y = np.array(y) + self.get_data(X,y,x_column,y_column) + elif isinstance(X,np.ndarray): + self.get_data(X,y,x_column,y_column) + elif isinstance(X,pd.DataFrame): + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + + + + # 支持的type有['csv', 'numpy','pandas','list','txt]，后面一律转为numpy格式 + def load_dataset(self, X, y = None, type = None, x_column = [], y_column = []): + if len(x_column) == 0: + raise ValueError("请传入数据列号") + if type == 'csv': + self.dataset = pd.read_csv(X).values # .values就转成numpy格式了 + self.get_data(X,y,x_column,y_column) + elif type == 'numpy': # 统一转成numpy格式 + self.get_data(X,y,x_column,y_column) + elif type == 'pandas': + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + elif type == 'list': + X = np.array(X) + y = np.array(y) + self.get_data(X,y,x_column,y_column) + elif type == 'txt': + self.dataset = np.loadtxt(X) + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + + + def save(self,path="checkpoint.pkl"): print("Saving model checkpoints...") - joblib.dump(self.model, '../checkpoint.pkl', compress=3) + joblib.dump(self.model, path, compress=3) def load(self, path): - joblib.load(path) \ No newline at end of file + self.model = joblib.load(path) + + def get_data(self,X,y,x_column,y_column): + if len(X): + self.x_train = X[:,x_column] + if len(y): # + if y.ndim == 1: + y = y.reshape(-1,1) + self.y_train = y[:,y_column] + if self.y_train.shape[0]: + self.dataset = np.concatenate((self.x_train,self.y_train),axis=1) # 按列进行拼接 diff --git a/BaseML/Regression.py b/BaseML/Regression.py index ef918ad..5fb9496 100644 --- a/BaseML/Regression.py +++ b/BaseML/Regression.py @@ -52,10 +52,66 @@ def inference(self, data): print(self.model.n_features_) print(self.model.n_samples_) - def load_dataset(self,path,test_size=0.2, dataset=''): - self.dataset_path = path - self.test_size = test_size - self.dataset = dataset + # 从文件加载数据集，支持csv文件和txt文件 + def load_dataset_from_file(self, path, x_column = [], y_column = []): + if type == 'csv': + self.dataset = pd.read_csv(path).values # .values就转成numpy格式了 + self.get_data(X,y,x_column,y_column) + elif type == 'txt': + self.dataset = np.loadtxt(path) + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + + # 从数据加载数据集，支持['numpy','list','DataFrame'] + def load_dataset_from_data(self, X, y = None, x_column = [], y_column = []): + if type(X) != type(y): + raise TypeError("数据格式不同，无法加载") + if isinstance(X,list): + X = np.array(X) + y = np.array(y) + self.get_data(X,y,x_column,y_column) + elif isinstance(X,np.ndarray): + self.get_data(X,y,x_column,y_column) + elif isinstance(X,pd.DataFrame): + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + + + + # 支持的type有['csv', 'numpy','pandas','list','txt]，后面一律转为numpy格式 + def load_dataset(self, X, y = None, type = None, x_column = [], y_column = []): + if len(x_column) == 0: + raise ValueError("请传入数据列号") + if type == 'csv': + self.dataset = pd.read_csv(X).values # .values就转成numpy格式了 + self.get_data(X,y,x_column,y_column) + elif type == 'numpy': # 统一转成numpy格式 + self.get_data(X,y,x_column,y_column) + elif type == 'pandas': + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + elif type == 'list': + X = np.array(X) + y = np.array(y) + self.get_data(X,y,x_column,y_column) + elif type == 'txt': + self.dataset = np.loadtxt(X) + X = X.values + y = y.values + self.get_data(X,y,x_column,y_column) + + def get_data(self,X,y,x_column,y_column): + if len(X): + self.x_train = X[:,x_column] + if len(y): # + if y.ndim == 1: + y = y.reshape(-1,1) + self.y_train = y[:,y_column] + if self.y_train.shape[0]: + self.dataset = 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4126 bytes Downloads/lenses.csv | 24 ++++++++++++++++++ checkpoint.pkl | Bin 10267 -> 827 bytes demo/.vscode/launch.json | 16 ++++++++++++ demo/cart_demo.py | 13 ++++++++++ demo/iris_cls_demo.py | 4 ++- demo/lenses.csv | 24 ++++++++++++++++++ demo/mymodel.pkl | Bin 0 -> 1061 bytes mymodel.pkl | Bin 0 -> 1084 bytes test/test_datasets.py | 17 ------------- 12 files changed, 93 insertions(+), 27 deletions(-) create mode 100644 Downloads/lenses.csv create mode 100644 demo/.vscode/launch.json create mode 100644 demo/cart_demo.py create mode 100644 demo/lenses.csv create mode 100644 demo/mymodel.pkl create mode 100644 mymodel.pkl delete mode 100644 test/test_datasets.py diff --git a/BaseML/Classification.py b/BaseML/Classification.py index ac89b44..75fa2b5 100644 --- a/BaseML/Classification.py +++ b/BaseML/Classification.py @@ -49,8 +49,8 @@ def train(self, seed=0, data_type='csv'): self.model.fit(self.x_train, self.y_train) elif self.algorithm == 'CART': - self.model.fit(self.dataset) - print(self.model.explained_variance_ratio_) + self.model.fit(self.x_train, self.y_train) + # print(self.model.explained_variance_ratio_) # 返回所保留的n个成分各自的方差百分比,这里可以理解为单个变量方差贡献率。 elif self.algorithm == 'KNN': @@ -89,17 +89,21 @@ def inference(self, data = np.nan): print(f" CLUSTER-{i+1} ".center(60, '=')) print(self.dataset[labels == i]) - if data is not np.nan: - pred = self.model.predict(data) - return pred + pred = self.model.predict(self.x_test) + return pred + # 从文件加载数据集，支持csv文件和txt文件 def load_dataset_from_file(self, path, x_column = [], y_column = []): if type == 'csv': self.dataset = pd.read_csv(path).values # .values就转成numpy格式了 + X = self.dataset[:,x_column] + y = self.dataset[:,y_column] self.get_data(X,y,x_column,y_column) elif type == 'txt': self.dataset = np.loadtxt(path) + X = self.dataset[:,x_column] + y = self.dataset[:,y_column] X = X.values y = y.values self.get_data(X,y,x_column,y_column) @@ -127,7 +131,7 @@ def load_dataset(self, X, y = None, type = None, x_column = [], y_column = []): raise ValueError("请传入数据列号") if type == 'csv': self.dataset = pd.read_csv(X).values # .values就转成numpy格式了 - self.get_data(X,y,x_column,y_column) + self.get_data(self.dataset,self.dataset,x_column,y_column) elif type == 'numpy': # 统一转成numpy格式 self.get_data(X,y,x_column,y_column) elif type == 'pandas': @@ -140,14 +144,14 @@ def load_dataset(self, X, y = None, type = None, x_column = [], y_column = []): self.get_data(X,y,x_column,y_column) elif type == 'txt': self.dataset = np.loadtxt(X) - X = X.values - y = y.values - self.get_data(X,y,x_column,y_column) + self.dataset = self.dataset.values + self.get_data(self.dataset,self.dataset,x_column,y_column) def save(self,path="checkpoint.pkl"): print("Saving model checkpoints...") joblib.dump(self.model, path, compress=3) + print("Saved successfully!") def load(self, path): diff --git a/BaseML/__pycache__/Classification.cpython-37.pyc b/BaseML/__pycache__/Classification.cpython-37.pyc index 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znR%8PdbIoimLL4t+A}3w+;yJQbys!eM+}Z2wVx`_^!%$e%`faap6Yw*=W%Pd{mk2m zQ5Gb2;CXqqb}w4rZte0oAOYZA%S-s{a$CMGcZPt* zuM6Lb(iVJ^y)f(J<*4T4rIl6n^gNWEr?OMktF*RjpKn1=Z25hIvyU%)mqg-g3%-@F{^@$7eg?iv?D|tJ;;Xc1 CF(~u^ literal 0 HcmV?d00001 diff --git a/test/test_datasets.py b/test/test_datasets.py deleted file mode 100644 index c2d38ae..0000000 --- a/test/test_datasets.py +++ /dev/null @@ -1,17 +0,0 @@ -from BaseML import Classification -import numpy as np - -DATA_PATH = 'Downloads/China_cities.csv' # 数据集路径 - -if __name__ == '__main__': - - model = Classification.cls(algorithm = 'Kmeans', N_CLUSTERS=5) - # 指定数据集的路径 - model.load_dataset_from_file(path="Downloads/China_cities.csv",x_column=[0,1], y_column=[0]) - - # 开始训练 - model.train() - # kmeans输出聚类结果，不需要输入数据 - model.inference() - # 模型保留 - model.save() \ No newline at end of file From 1d41f324900c55586171c9c3e14950386599125e Mon Sep 17 00:00:00 2001 From: Even-ok <1284915396@qq.com> Date: Wed, 26 Oct 2022 14:02:55 +0800 Subject: [PATCH 3/3] 1026 --- demo/iris_cls_demo.py | 3 +-- demo/lenses.csv | 24 ------------------------ 2 files changed, 1 insertion(+), 26 deletions(-) delete mode 100644 demo/lenses.csv diff --git a/demo/iris_cls_demo.py b/demo/iris_cls_demo.py index 17f3943..354ec42 100644 --- a/demo/iris_cls_demo.py +++ b/demo/iris_cls_demo.py @@ -1,5 +1,4 @@ -import sys -sys.path.append("/home/PJLAB/liangyiwen/Even/code/OpenBaseLab-Edu") + from BaseML import Classification import numpy as np from sklearn import datasets diff --git a/demo/lenses.csv b/demo/lenses.csv deleted file mode 100644 index c8b324d..0000000 --- a/demo/lenses.csv +++ /dev/null @@ -1,24 +0,0 @@ -1,1,1,1,1,3 -2,1,1,1,2,2 -3,1,1,2,1,3 -4,1,1,2,2,1 -5,1,2,1,1,3 -6,1,2,1,2,2 -7,1,2,2,1,3 -8,1,2,2,2,1 -9,2,1,1,1,3 -10,2,1,1,2,2 -11,2,1,2,1,3 -12,2,1,2,2,1 -13,2,2,1,1,3 -14,2,2,1,2,2 -15,2,2,2,1,3 -16,2,2,2,2,3 -17,3,1,1,1,3 -18,3,1,1,2,3 -19,3,1,2,1,3 -20,3,1,2,2,1 -21,3,2,1,1,3 -22,3,2,1,2,2 -23,3,2,2,1,3 -24,3,2,2,2,3