0.0.6

BaseML/Classification.py

@@ -0,0 +1,77 @@
+import pandas as pd
+import numpy as np
+import os
+from sklearn.metrics import accuracy_score, mean_squared_error
+from sklearn.naive_bayes import GaussianNB
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+
+class cls:
+    def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, ):
+        self.algorithm = algorithm
+        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_set = ' '
+        self.x_train, self.x_test, self.y_train, self.y_test = 0, 0, 0, 0
+        if self.algorithm == 'KNN':
+            self.model = KNeighborsClassifier(n_neighbors=n_neighbors)
+        elif self.algorithm == 'SVM':
+            self.model = SVC()
+        elif self.algorithm == 'NaiveBayes':
+            self.model = GaussianNB()
+        elif self.algorithm == 'CART':
+            self.model = DecisionTreeClassifier()
+        elif self.algorithm == 'AdaBoost':
+            self.model = AdaBoostClassifier(n_estimators=n_estimators, random_state=0)
+
+    def train(self, seed=0, data_type='csv'):
+        if self.algorithm == 'AdaBoost' or 'SVM' or 'NaiveBayes':
+            np.random.seed(seed)
+            if data_type == 'csv':
+                dataset = pd.read_csv(self.dataset_path, sep=',', header=None).values
+            np.random.shuffle(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)
+
+        elif self.algorithm == 'CART':
+            self.model.fit(self.dataset)
+            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':
+            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)
+            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))
+
+    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))
+        elif self.algorithm == 'KNN':
+            result = self.model.predict(data)
+            print(result)
+            print("分类结果：{}".format(self.dataset['target_names'][result]))
+        elif self.algorithm == 'CART':
+            self.model.fit_transform(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

BaseML/Regression.py

@@ -0,0 +1,57 @@
+import os
+from turtle import back
+import pandas as pd 
+import numpy as np
+from sklearn.linear_model import Perceptron as per
+from sklearn.metrics import accuracy_score, mean_squared_error, r2_score
+from sklearn import linear_model
+from sklearn.decomposition import PCA as pca_reduction
+
+class reg:
+    def __init__(self, algorithm='', n_components='mle'):
+        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.algorithm = algorithm
+        self.dataset_path = ' '
+        self.test_size = ' '
+        if self.algorithm ==  'LR':
+            self.model = linear_model.LinearRegression()
+        elif self.algorithm ==  'Perceptron':
+            self.model = per()
+        elif self.algorithm ==  'PCA':
+            self.model = pca_reduction(n_components=n_components)
+
+    def train(self, seed=0, data_type='csv'):
+        if self.algorithm ==  'LR':
+            np.random.seed(seed)
+            if data_type == 'csv':
+                dataset = pd.read_csv(self.dataset_path,sep=',',header=None).values
+            np.random.shuffle(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)
+        elif self.algorithm ==  'Perceptron' or 'PCA':
+            self.model.fit(self.dataset)
+            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。
+
+    def inference(self, data):
+        if self.algorithm ==  'LR':
+            pred = self.model.predict(self.test_set['data']) 
+            loss = mean_squared_error(self.test_set['label'],pred)
+            print('Loss: {}'.format(loss))
+        elif self.algorithm ==  'Perceptron' or 'PCA':
+            self.model.fit_transform(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