From cd07243f2eb372ebb94c76334376aaa58f9260cd Mon Sep 17 00:00:00 2001 From: JiaYanhao <2474840061@qq.com> Date: Fri, 26 Aug 2022 11:26:53 +0800 Subject: [PATCH] 0.0.6 --- BaseML/Classification.py | 77 ++++++++++++++++++++++++++++++++++++++++ BaseML/Regression.py | 57 +++++++++++++++++++++++++++++ 2 files changed, 134 insertions(+) create mode 100644 BaseML/Classification.py create mode 100644 BaseML/Regression.py diff --git a/BaseML/Classification.py b/BaseML/Classification.py new file mode 100644 index 0000000..31977bd --- /dev/null +++ b/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 diff --git a/BaseML/Regression.py b/BaseML/Regression.py new file mode 100644 index 0000000..14c13c4 --- /dev/null +++ b/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