就读杭州电子科技大学自动化专业，大四。即将为杭州电子科技大学电子信息学院研究生。
喜欢的游戏：明日方舟、csgo……
想联系我也可以加QQ：2114496089，加好友都请备注来意。
圣光机联合学院毕业论文Latex参考
因为字体文件较大，win和mac没必要使用下载的字体，所以推荐使用no-fonts分支
包含字体文件：
git clone -b main –depth=1 http://git.pancake2021.work/pancake/GLT.git
或
http://git.pancake2021.work/pancake/GLT/archive/main.zip
不含字体文件：
git clone -b no-fonts –depth=1 http://git.pancake2021.work/pancake/GLT.git
或
http://git.pancake2021.work/pancake/GLT/archive/no-fonts.zip
LSTM简易通用版本
因为在数学建模中用的很麻烦，写了个简易易使用版本。
以下代码为最终成品代码 demo 版本，不推荐使用，最新版本可点下载链接或可见 csdn。
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import torch import torch.nn as nn import numpy as np import matplotlib.pyplot as plt import torch.nn.functional as F import warnings import pandas as pd import scipy.stats as st # 设定随机种子，以确保实验的可重复性 torch.manual_seed(1) np.random.seed(1) # 定义LSTM模型 class LSTM(nn.Module): hidden = None def __init__(self, input_size, hidden_size, output_size, num_layers): super().__init__() self.hidden_size = hidden_size self.num_layers = num_layers self.train_on_gpu = torch.cuda.is_available() # As batch_first=True, input: (batch_size, sequence_length, input_size) self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True) self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, hidden_size) self.fcOut = nn.Linear(hidden_size, output_size) self.drop = nn.Dropout(0.5) # 可选操作，可以把下一行注释 self.apply(LSTM.init_weights) def forward(self, x): # 防止 loss.backward 报错 hidden = [each.data for each in self.hidden] x, hidden = self.lstm(x, hidden) x, self.hidden = self.lstm2(x, hidden) x = x[:, -1, :] x = F.relu(self.fc(x)) x = self.drop(x) x = self.fcOut(x) return x def init_hidden(self, batch_size): weight = next(self.parameters()).data if self.train_on_gpu: self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda(), weight.new(self.num_layers, batch_size, self.hidden_size).zero_().cuda()) else: self.hidden = (weight.new(self.num_layers, batch_size, self.hidden_size).zero_(), weight.new(self.num_layers, batch_size, self.hidden_size).zero_()) @staticmethod def init_weights(m): if type(m) == nn.LSTM: for name, param in m.named_parameters(): if 'weight_ih' in name: torch.nn.init.orthogonal_(param.data) elif 'weight_hh' in name: torch.nn.init.orthogonal_(param.data) elif 'bias' in name: param.data.fill_(0) elif type(m) == nn.Conv1d or type(m) == nn.Linear: torch.nn.init.orthogonal_(m.weight) m.bias.data.fill_(0) class CustomLSTM: X, Y, data, model, optimizer, criterion, result, result_, window, losses = [None] * 10 def __init__(self, data: np.array, window): self.data = data self.input_dim = self.init_data() self.output_dim = self.input_dim self.slice(window) # data 可为一维数组或二维数组，强制把一维数组转化为二维数组 def init_data(self): assert (length := len(self.data.shape)) in [1, 2] if length == 1: self.data = self.data[:, np.newaxis] return len(self.data[0]) # 检查总数据大小是否能整除 batch def check_batch(self, batch_size): length = self.X.shape[0] # 保证 batch_size 比总长度小 assert length >= batch_size if batch_size * (length // batch_size) != length: warnings.warn(f'数据大小为{length}, batch大小为{batch_size},无法整除,会损失{(length % batch_size) / length * 100}%数据', DeprecationWarning) # 以 window 为窗口大小切片形成整个batch def slice(self, window): self.window = window X, Y = [], [] for i in range(len(self.data) - window): X.append(self.data[i:i + window]) Y.append(self.data[i + window]) X = np.array(X) Y = np.array(Y) X = torch.from_numpy(X).float() # (batch_size, sequence_length, input_size) Y = torch.from_numpy(Y).float() print(f"数据格式:X = {X.shape}, Y = {Y.shape}") self.X = X self.Y = Y def re_slice(self, window): self.X, self.Y, self.model, self.optimizer, self.criterion, self.result, self.result_, self.losses = [None] * 9 self.slice(window) # 初始化 LSTM model def init_lstm(self, hidden=64, lr=0.001, num_layers=1): self.model = LSTM(self.input_dim, hidden, self.output_dim, num_layers) self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr) self.criterion = nn.MSELoss() if torch.cuda.is_available(): self.model.cuda() self.X.cuda() self.Y.cuda() # 总数据产生 batch 并可以进行 shuffle @staticmethod def iterate_batches(inputs, targets, batchsize, shuffle=True): assert len(inputs) == len(targets) if shuffle: indices = np.arange(len(inputs)) np.random.shuffle(indices) for start_idx in range(0, len(inputs) - batchsize + 1, batchsize): if shuffle: excerpt = indices[start_idx:start_idx + batchsize] else: excerpt = slice(start_idx, start_idx + batchsize) yield inputs[excerpt], targets[excerpt] # 开始训练 def train(self, num_epochs=100, batch_size=128, max_batch=False): losses = [] if self.model is None: raise ValueError("请先使用CustomLSTM.init_lstm初始化网络") if max_batch: batch_size = self.X.shape[0] else: self.check_batch(batch_size) for epoch in range(num_epochs): loss_all = 0 self.model.init_hidden(batch_size) for index, (batch_x, batch_y) in enumerate(CustomLSTM.iterate_batches(self.X, self.Y, batch_size, shuffle=True)): self.optimizer.zero_grad() outputs = self.model(batch_x) loss = self.criterion(outputs, batch_y) loss.backward() self.optimizer.step() loss_all += loss.detach().cpu() losses.append(loss_all / (index + 1)) if epoch % 20 == 0: print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss_all / (index + 1))) self.losses = losses self.predicted() def plot_loss(self): if self.losses is None: raise ValueError("loss 不存在，请先进行训练") plt.figure(figsize=(12, 6)) plt.plot(self.losses) plt.xlabel("Epoch") plt.ylabel("Loss") plt.show() # 预测之后 n 天的值 def predict(self, n): # self.predicted() self.model.init_hidden(1) data = self.data[-self.window:, :].tolist() y = [] for i in range(n): x = torch.tensor(data).float().unsqueeze(0) result = self.model.cpu()(x).tolist() y.append(result[0]) data.append(result[0]) data.pop(0) self.result = np.array(y) return self.result # 预测从 window_size 到结束数据的预测值，以便与真实值做比较 def predicted(self): self.model.eval() self.model.init_hidden(len(self.X)) with torch.no_grad(): predicted = self.model(self.X) predicted = predicted.detach().cpu().numpy() self.result_ = predicted return self.result_ # 因为可以为二维数组即对多个变量进行预测，names即为每个变量的名字 def plot(self, names=None): if self.result is None: raise ValueError("请先使用CustomLSTM.predict") if names is None: names = [None] * len(self.data[0]) # further = self.predict(n) x = np.arange(len(self.data)) x_further = np.arange(len(self.data), len(self.data) + len(self.result)) plt.figure(figsize=(12, 6)) for i in range(len(self.data[0])): plt.plot(x, self.data, label=f'{names[0]} True Values') plt.plot(x[self.window:], self.result_[:, i], label=f'{names[0]} Predictions') plt.plot(x_further, self.result[:, i], label=f"{names[0]} Further Predictions") plt.show() # 画置信区间，demo 版本 # ToDo def plot_confidence(self, index=0, alpha=0.05): if self.result is None: raise ValueError("请先使用CustomLSTM.predict") plt.figure(figsize=(12, 6)) x = np.arange(len(self.data)) x_further = np.arange(len(self.data), len(self.data) + len(self.result)) y_true = self.data[self.window:, index].tolist() plt.plot(y_true, label='True Values') plt.plot(x_further, self.result[:, index], label="Further Predictions") plt.plot(x[self.window:], self.result_[:, index], label='Predictions') y_pred = self.result[:, index] lower, upper = Utils.ci(y_true, y_pred, alpha=alpha) plt.plot(y_pred, label='Predictions') plt.fill_between(np.arange(), lower, upper, alpha=0.2, label='Confidence interval') plt.legend() plt.show() # 打印 summary， 即 r^2 评价函数等 def summary(self): if self.model is None: raise ValueError("请先进行训练") print("==========Summary Begin===========") print("R2 =", score_r2 := Utils.r2(self.result_, self.data[self.window:, :])) print("MSE =", score_mse := Utils.mse(self.result_, self.data[self.window:, :])) print("RMSE =", score_rmse := np.sqrt(score_mse)) print("MAE =", score_mae := Utils.mae(self.result_, self.data[self.window:, :])) print("===========Summary end============") return score_r2, score_mse, score_rmse, score_mae # 工具 class Utils: # 置信区间，demo # ToDo @staticmethod def ci(y_true, y_pred, alpha=0.05): residuals = y_true - y_pred n = len(residuals) df = n - 1 t_value = st.norm.ppf(1 - alpha / 2, df) std_err = np.std(residuals, ddof=1) / np.sqrt(n) upper = residuals + t_value * std_err lower = residuals - t_value * std_err return lower, upper # r2 评价函数 @staticmethod def r2(y_pred, y_true): return 1 - ((y_pred - y_true) ** 2).sum(axis=0) / ((y_true.mean(axis=0) - y_true) ** 2).sum(axis=0) @staticmethod def mse(y_pred, y_true): return ((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred) @staticmethod def rmse(y_pred, y_true): return np.sqrt(((y_true - y_pred) ** 2).sum(axis=0) / len(y_pred)) @staticmethod def mae(y_pred, y_true): return (np.absolute(y_true - y_pred)).sum(axis=0) / len(y_true) # 添加快捷打开文件操作 @staticmethod def openfile(name): file_type = name.split(".")[-1] if file_type == "csv": df = pd.read_csv(name, encoding='GBK') elif file_type == "xlsx" or file_type == "xls": df = pd.read_excel(name) else: raise TypeError(f"{name} 类型不是 csv, xls, xlsx") # df = df[["列名字1", "列名字2"]] print(df) return np.array(df) def load_data(): # 以下注释代码为快捷打开操作 # return Utils.openfile(file_location) data = np.sin(np.arange(100) * np.pi / 50) + np.random.randn(100) * 0.1 return data if __name__ == "__main__": # 加载数据 data = load_data() # 初始化网络 window_size = 10 batch = 100 lstm = CustomLSTM(data, window_size) lstm.init_lstm(hidden=64, lr=0.001, num_layers=1) # 训练网络 # max_batch 表示是否以整一个数据作为 batch 不做分割 lstm.train(num_epochs=1000, max_batch=True) # lstm.train(num_epochs=50, batch_size=30, max_batch=False) # 调整窗口大小重新训练 # lstm.re_slice(20) # lstm.init_lstm(hidden=64, lr=0.001, num_layers=1) # lstm.train(num_epochs=50, batch_size=40, max_batch=False) # 打印 summary r2, mse, rmse, mae = lstm.summary() # 预测之后 100 步数据 lstm.predict(100) # 画图 lstm.plot_loss() lstm.plot(['data']) # lstm.plot_confidence(index=0) |
2022小美赛C题
主要讲解团队对2022小美赛C题的讲解，包含讲解与代码，赛题data。全部为python代码，基本不会提供代码注释及逻辑。
该题主要为时间序列分类，团队尝试的方法如下：
- 决策树、随机森林、极限森林分类（DecisionTreeClassifier、 RandomForestClassifier、 ExtraTreesClassifier）在文章中将简写为DTC、RFC、ETC
- 带有时间序列的决策树分类（MultivariateClassifier、 TimeSeriesForest）在文章中TimeSeriesForestClassifier将简写为TSFC
- LSTM 团队采用 DeepConvLSTM在文章中将简写为DCLSTM
- NSGA2剪枝优化
讲解顺序：python库→数据预处理：3\sigma与滤波→数据处理与导入（不含前一步的处理）→决策树等分类→TSFC→DCLSTM→GA决策树剪枝优化。总体结论：极限森林在赛题数据分类效果最好。
文章思路：数据处理→决策树分类→DCLSTM→算法对比→NSGA2对极限森林过拟合优化。
代码有前后关联性，后面代码没有的函数请到前面找。代码非常庞大，请耐心观看。
1.python库
python库
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pip3 install numpy matplotlib pandas pip3 isntall sklearn pip3 install torch-对应gpu版本 pip3 install pyts pip3 install geatpy |
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# math import math # plot import matplotlib.pyplot as plt # warning import warnings # system process import os # numpy import numpy as np # pandas import pandas as pd # data loader import pickle # csv import csv |
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# cross score from sklearn.model_selection import cross_val_score # classifier for trees from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier # train test split from sklearn.model_selection import train_test_split # nomolize from sklearn.preprocessing import normalize # scores from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score, roc_auc_score, roc_curve, auc, confusion_matrix |
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# draw tree from sklearn.tree import plot_tree # classifiers from pyts.classification import BOSSVS, TimeSeriesForest # multivarible from pyts.multivariate.classification import MultivariateClassifier |
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import torch from torch import nn import torch.nn.functional as F |
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import geatpy as ea from multiprocessing import Pool as ProcessPool |
2. 3\sigma and filter
Preprocess
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def three_sigma(col): rule = (col.mean() - 3 * col.std() > col) | (col.mean() + 3 * col.std() < col) index = np.arange(col.shape[0])[rule] return index |
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""" https://www.bilibili.com/read/cv17553128 """ result = [] for action in range(19): filename = "./data/a{}/p1".format(str(action + 1).zfill(2)) now = None for i in range(60): f = np.loadtxt(os.path.join(filename, "s.txt".format(str(i + 1).zfill(2))), delimiter=",").T if now is None: now = f else: now = np.c_[now, f] data = now[:, :300] for i in [2]: AccX_Value = data[i] outs = three_sigma(AccX_Value) for out in outs: AccX_Value[out] = AccX_Value[out - 5: out + 5].mean() AccX_Value = AccX_Value[:, np.newaxis] Time = np.linspace(0, 5 * 2.4, int(5 * 2.4 * 25)) AccX_Variance = 0.01 # time step dt = 1 / 25 # transition_matrix F = [[1, dt, 0.5 * dt ** 2], [0, 1, dt], [0, 0, 1]] # observation_matrix H = [0, 0, 1] # transition_covariance Q = [[0.2, 0, 0], [0, 0.1, 0], [0, 0, 10e-4]] # observation_covariance R = AccX_Variance # initial_state_mean X0 = [0, 0, AccX_Value[0, 0]] # initial_state_covariance P0 = [[0, 0, 0], [0, 0, 0], [0, 0, AccX_Variance]] n_timesteps = AccX_Value.shape[0] n_dim_state = 3 filtered_state_means = np.zeros((n_timesteps, n_dim_state)) filtered_state_covariances = np.zeros((n_timesteps, n_dim_state, n_dim_state)) kf = KalmanFilter(transition_matrices=F, observation_matrices=H, transition_covariance=Q, observation_covariance=R, initial_state_mean=X0, initial_state_covariance=P0) # iterative estimation for each new measurement for t in range(n_timesteps): if t == 0: filtered_state_means[t] = X0 filtered_state_covariances[t] = P0 else: filtered_state_means[t], filtered_state_covariances[t] = ( kf.filter_update( filtered_state_means[t - 1], filtered_state_covariances[t - 1], AccX_Value[t, 0] ) ) result.append(list(filtered_state_means[:, 2])) # plt.plot(Time, AccX_Value) # plt.plot(Time, filtered_state_means[:, 2], "r-") # plt.title('Acceleration X') # plt.ylim(7.5, 8.5) # plt.grid() # plt.legend() # plt.savefig("original.png") # plt.show() f = csv.writer(open("result filter.csv", "w", newline="")) f.writerows(result) |
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# n is the window size def sliding_window_filter(data_filter, n): n -= 1 length = len(data_filter) assert 1 <= n <= length filtered_data = np.copy(data_filter).astype(np.float32) for i in range(n): filtered_data[n:] += data_filter[i: length - n + i] filtered_data[n:] *= 1 / (n + 1) return filtered_data |
3. 数据处理
其中：num为选择哪些传感器，n_class为选择哪些活动进行分类
Data process
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# 标准data类，用于dataloder class _Data: def __init__(self, data, label, length=None): self.x = data self.y = label if length: self.len = length else: self.len = len(self.y) def __len__(self): return self.len def __getitem__(self, item): return self.x[item], self.y[item] # 提取特征 def process(data): arange = np.arange(0, len(data))[:, np.newaxis] varange = np.sum(arange ** 2) mean = data.mean(axis=0) std = np.std(data, axis=0) k = np.sum((data - mean) * arange / varange, axis=0) return np.r_[mean, std, k] # 以多少step（5s）提取数据 def load_data(step=1, process=None): num = [i for i in range(45)] dirname = "./data/" n_class = np.array(os.listdir(dirname)) now = [] label = [] for y, class_name in enumerate(n_class): print(f"now process {y + 1} ") dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name)) for person in range(len(dir_a)): dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person])) for segment in range(0, (len(dir_b) + 1) // step * step - 1, step): temp = None for i in range(step): if temp is None: temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num] else: temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num]] temp = normalize(temp, axis=0) now.append(process(temp) if process is not None else temp) label.append(y) data = _Data(np.array(now), np.array(label)) return data # 切分数据把5s数据进行切分 def load_data_cut(cut=3, process=None): num = [i for i in range(45)] dirname = "./data/" n_class = os.listdir(dirname) now = [] label = [] for y, class_name in enumerate(n_class[:]): print(f"now process {y + 1} ") dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name)) for person in range(len(dir_a)): dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person])) for segment in range(0, len(dir_b)): file = np.loadtxt(os.path.join(now_file_name, dir_b[segment]), delimiter=",")[:, num] number = len(file) // cut for i in range(cut): file_cut = normalize(file[number * i: number * (i + 1), :], axis=0) now.append(process(file_cut) if process is not None else file_cut) label.append(y) data = _Data(np.array(now), np.array(label)) return data # shuffle def shuffleData(X, y, seed=None): import random random.seed(seed) index = [i for i in range(len(X))] random.shuffle(index) X = X[index] y = y[index] return X, y |
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# 标准方式如下（5s） data = load_data(step:int, process) # 数据切分的更小 data = load_data_cut(cut:int, process) # 保存数据 f = open("data.data", 'wb') pickle.dump(data, f) # 加载数据 f = open("data.data", 'rb') data = pickle.load(f) data = _Data(*shuffleData(data.x, data.y, 1)) |
4. 决策树等分类
说明：代码包含k-fold cross-validation，其中注释代码可用作别的用途，具体你要画什么图就用什么代码。
Classifier
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f = open("data.data", 'rb') data = pickle.load(f) data = _Data(*shuffleData(data.x, data.y, 1)) result = [] K = 10 size = (n := len(data)) // K # K-fold cross validation for i in range(10): now_silence = [i for i in range(i * size)] + [i for i in range((i + 1) * size, n)] now_silence_test = [i for i in range(i * size, (i + 1) * size)] X_train, X_test, y_train, y_test = data.x[now_silence], data.x[now_silence_test], data.y[now_silence], data.y[now_silence_test] clf3 = ExtraTreesClassifier(max_depth=10, max_leaf_nodes=100, min_samples_split=2, random_state=0) clf3.fit(X_train, y_train) out = clf3.predict(X_test) result.append([f1_score(y_test, out, average='weighted'), precision_score(y_test, out, average='weighted'), recall_score(y_test, out, average='weighted'), accuracy_score(y_test, out)]) # clf1 = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=0) # clf2 = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=2, bootstrap=True) # clf1.fit(x_train, y_train) # clf2.fit(x_train, y_train) # print(clf1.feature_importances_) # print(clf2.feature_importances_) # print(clf3.feature_importances_) # result.append([clf1.score(x_test, y_test), clf2.score(x_test, y_test), clf3.score(x_test, y_test)]) # confusion_matrix(y_test, clf3.predict(y_test)) # scores1 = cross_val_score(clf1, x_train, y_train) # scores2 = cross_val_score(clf2, x_train, y_train) # scores3 = cross_val_score(clf3, x_train, y_train) # print('DecisionTreeClassifier交叉验证准确率为:' + str(scores1.mean())) # print('RandomForestClassifier交叉验证准确率为:' + str(scores2.mean())) # print('ExtraTreesClassifier交叉验证准确率为:' + str(scores3.mean())) print(result) print(np.array(result).mean(axis=0)) f = open("result tree k fold.txt", 'w') f.writelines(str(result)) |
5. TFSC
TFSC
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dirname = "./data/" # 具体让那几个东西进行分类 n_class = os.listdir(dirname) # n_class = [n_class[0], n_class[17]] # 具体使用那些传感器 num = [i for i in range(45)] now = [] label = [] for y, class_name in enumerate(n_class): print(f"now process {y + 1} ") for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]): for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]): now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T) label.append(y) data = _Data(np.array(now), np.array(label)) print("data process finished, start TSFC") X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.1) clf = MultivariateClassifier(TimeSeriesForest(n_jobs=6)) # print(clf.estimators_[0]._pipeline['rfc'].estimators_) clf.fit(X_train, y_train) print(clf.score(X_test, y_test)) with open("clf.data", 'wb') as f: pickle.dump(clf, f) f.close() |
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with open("clf.data", 'rb') as f: clf = pickle.load(f) print(clf) f.close() print(len(clf.estimators_[0].feature_importances_)) # now = [] # label = [] # for y, class_name in enumerate(n_class): # print(f"now process {y + 1} ") # for person, person_name in enumerate(os.listdir(now_dir_name := os.path.join(dirname, class_name))[:]): # for segment, segment_name in enumerate(os.listdir(now_file_name := os.path.join(now_dir_name, person_name))[:]): # now.append(np.loadtxt(os.path.join(now_file_name, segment_name), delimiter=",")[:, num].T) # label.append(y) # data = _Data(np.array(now), np.array(label)) # print(clf.score(data.x, data.y)) |
6. DCLSTM
DCLSTM
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""" https://github.com/dspanah/Sensor-Based-Human-Activity-Recognition-DeepConvLSTM-Pytorch """ def load_dataset(filename, num, step=None): dirname = filename now = [] label = [] for y, class_name in enumerate(os.listdir(dirname)[:]): print(f"now process {y + 1} ") dir_a = os.listdir(now_dir_name := os.path.join(dirname, class_name)) for person in range(len(dir_a)): dir_b = os.listdir(now_file_name := os.path.join(now_dir_name, dir_a[person])) for segment in range(0, (len(dir_b) + 1) // step * step - 1, step): temp = None for i in range(step): if temp is None: temp = np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T else: temp = np.r_[temp, np.loadtxt(os.path.join(now_file_name, dir_b[i + segment]), delimiter=",")[:, num].T] now.append(temp) label.append(y) x = torch.nn.functional.normalize(torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32), dim=-1) # x = torch.tensor(torch.from_numpy(np.array(now)), dtype=torch.float32) data = _Data(x, torch.from_numpy(np.array(label)).long()) return data # data1 无滤波归一化 data2滤波无归一化 data3滤波归一化 datafile = "./data.data" if os.path.exists(datafile): with open(datafile, 'rb') as f: data = pickle.load(f) f.close() else: print("Loading data...") num = [i for i in range(45)] data = load_dataset('./data/', num, 1) print("Done") with open(datafile, 'wb') as f: pickle.dump(data, f) f.close() X_train, X_test, y_train, y_test = train_test_split(data.x, data.y, test_size=0.3, random_state=1) # for i in range(len(X_test)): # for j in range(-6, 0): # X_test[i][j][:] = torch.tensor([0]*125) print(X_train.shape) _, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS = X_train.shape class HARModel(nn.Module): def __init__(self, n_hidden=128, n_layers=1, n_filters=100, n_classes=19, filter_size=1, drop_prob=0.5): super(HARModel, self).__init__() self.drop_prob = drop_prob self.n_layers = n_layers self.n_hidden = n_hidden self.n_filters = n_filters self.n_classes = n_classes self.filter_size = (filter_size,) self.conv1 = nn.Conv1d(NB_SENSOR_CHANNELS, n_filters, self.filter_size) self.conv2 = nn.Conv1d(n_filters, n_filters, self.filter_size) self.conv3 = nn.Conv1d(n_filters, n_filters, self.filter_size) self.conv4 = nn.Conv1d(n_filters, n_filters, self.filter_size) self.lstm1 = nn.LSTM(n_filters, n_hidden, n_layers) self.lstm2 = nn.LSTM(n_hidden, n_hidden, n_layers) self.fc = nn.Linear(n_hidden, n_classes) self.dropout = nn.Dropout(drop_prob) def forward(self, x, hidden, batch_size): x = x.view(-1, NB_SENSOR_CHANNELS, SLIDING_WINDOW_LENGTH) x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = F.relu(self.conv4(x)) x = x.view(-1, batch_size, self.n_filters) # x = x.view(SLIDING_WINDOW_LENGTH, -1, NB_SENSOR_CHANNELS) x, hidden = self.lstm1(x, hidden) x, hidden = self.lstm2(x, hidden) x = x.contiguous().view(-1, self.n_hidden) x = self.dropout(x) x = self.fc(x) out = x.view(batch_size, -1, self.n_classes)[:, -1, :] return out, hidden def init_hidden(self, batch_size): ''' Initializes hidden state ''' # Create two new tensors with sizes n_layers x batch_size x n_hidden, # initialized to zero, for hidden state and cell state of LSTM weight = next(self.parameters()).data if (train_on_gpu): hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda(), weight.new(self.n_layers, batch_size, self.n_hidden).zero_().cuda()) else: hidden = (weight.new(self.n_layers, batch_size, self.n_hidden).zero_(), weight.new(self.n_layers, batch_size, self.n_hidden).zero_()) return hidden net = HARModel() def init_weights(m): if type(m) == nn.LSTM: for name, param in m.named_parameters(): if 'weight_ih' in name: torch.nn.init.orthogonal_(param.data) elif 'weight_hh' in name: torch.nn.init.orthogonal_(param.data) elif 'bias' in name: param.data.fill_(0) elif type(m) == nn.Conv1d or type(m) == nn.Linear: torch.nn.init.orthogonal_(m.weight) m.bias.data.fill_(0) net.apply(init_weights) def iterate_minibatches(inputs, targets, batchsize, shuffle=True): assert len(inputs) == len(targets) if shuffle: indices = np.arange(len(inputs)) np.random.shuffle(indices) for start_idx in range(0, len(inputs) - batchsize + 1, batchsize): if shuffle: excerpt = indices[start_idx:start_idx + batchsize] else: excerpt = slice(start_idx, start_idx + batchsize) yield inputs[excerpt], targets[excerpt] ## check if GPU is available train_on_gpu = torch.cuda.is_available() if (train_on_gpu): print('Training on GPU!') else: print('No GPU available, training on CPU; consider making n_epochs very small.') def train(net, epochs=1000, batch_size=400, lr=0.001): opt = torch.optim.Adam(net.parameters(), lr=lr) criterion = nn.CrossEntropyLoss() if (train_on_gpu): net.cuda() for e in range(epochs): # initialize hidden state h = net.init_hidden(batch_size) train_losses = [] net.train() for batch in iterate_minibatches(X_train, y_train, batch_size): inputs, targets = batch if (train_on_gpu): inputs, targets = inputs.cuda(), targets.cuda() # Creating new variables for the hidden state, otherwise # we'd backprop through the entire training history h = tuple([each.data for each in h]) # zero accumulated gradients opt.zero_grad() # get the output from the model output, h = net(inputs, h, batch_size) loss = criterion(output, targets.long()) train_losses.append(loss.item()) loss.backward() opt.step() val_h = net.init_hidden(batch_size) val_losses = [] accuracy = 0 f1score = 0 net.eval() with torch.no_grad(): for batch in iterate_minibatches(X_test, y_test, batch_size): inputs, targets = batch val_h = tuple([each.data for each in val_h]) if (train_on_gpu): inputs, targets = inputs.cuda(), targets.cuda() output, val_h = net(inputs, val_h, batch_size) # print(confusion_matrix(y_train, output)) val_loss = criterion(output, targets.long()) val_losses.append(val_loss.item()) top_p, top_class = output.topk(1, dim=1) equals = top_class == targets.view(*top_class.shape).long() accuracy += torch.mean(equals.type(torch.FloatTensor)) f1score += metrics.f1_score(top_class.cpu(), targets.view(*top_class.shape).long().cpu(), average='weighted') net.train() # reset to train mode after iterationg through validation data print("Epoch: {}/{}...".format(e + 1, epochs), "Train Loss: {:.4f}...".format(np.mean(train_losses)), "Val Loss: {:.4f}...".format(np.mean(val_losses)), "Val Acc: {:.4f}...".format(accuracy / (len(X_test) // batch_size)), "F1-Score: {:.4f}...".format(f1score / (len(X_test) // batch_size))) train(net) |
7. NSGA-ii剪枝优化
NSGA2
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# 自定义 GA class My_nsga(ea.Problem): def __init__(self, pools=10): # max_depth = 18-30 min_samples_leaf = 10-20 max_features=85-135 max_leaf_nodes = 50-200 ccp_alpha = 0.0001-0.002 max_depth = [18, 30] min_samples_leaf = [10, 20] max_feature = [85, 135] max_leaf_node = [50, 200] ccp_alpha = [0.0001, 0.002] name = 'Tree Classifier' M = 2 maxormins = [-1, 1] Dim = 5 varTypes = [1] * 4 + [0] lb, ub = list(zip(*[max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha])) lb = list(lb) ub = list(ub) lbin = [1] * Dim ubin = [1] * Dim self.ans = {} self.max_score = -float("inf") self.epoch = 0 self.pool = ProcessPool(pools) ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin) # 目标函数即神经网络返回值 # 多线程 # @cal_time() def evalVars(self, Vars): global data args = [] for i in range(len(Vars)): varibal = list(Vars[i]) args.append(varibal + [data.x, data.y]) result = self.pool.starmap_async(get_ans, args) result.wait() ans = result.get() ans = np.array(ans) print(f"Epoch: {self.epoch}, Epoch Max: {ans.max(axis=0)}") self.epoch += 1 return ans def get_ans(max_depth, min_samples_leaf, max_feature, max_leaf_node, ccp_alpha, x, y): score = [] max_depth, min_samples_leaf, max_feature, max_leaf_node = map(int, (max_depth, min_samples_leaf, max_feature, max_leaf_node)) for i in range(10): x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7) clf = ExtraTreesClassifier(max_depth=max_depth, min_samples_leaf=min_samples_leaf, max_features=max_feature, max_leaf_nodes=max_leaf_node, ccp_alpha=ccp_alpha, n_estimators=10, min_samples_split=2, bootstrap=False) clf.fit(x_train, y_train) score1 = float(clf.score(x_test, y_test)) score2 = float(clf.score(x_train, y_train)) score.append([score1, abs(score1 - score2)]) return np.array(score).mean(axis=0) # 运行 GA # @cal_time() def Run_nsga(ndind=10, maxgen=100, pools=10): problem = My_nsga(pools) encoding = "RI" # 查看染色体编码 # field = ea.crtfld(encoding, problem.varTypes, problem.ranges, problem.borders) # print(ea.crtpc(encoding, ndind, field)) myAlgorithm = ea.moea_NSGA2_templet(problem, ea.Population(Encoding=encoding, NIND=ndind), MAXGEN=maxgen, logTras=1, drawing=1) res = ea.optimize(myAlgorithm, seed=1, verbose=True, drawing=1, outputMsg=1, drawLog=1, saveFlag=1, dirName='result') print(res) if __name__ == "__main__": with open(r"./Datas/data_tree_{}".format(0), 'rb') as f: data = pickle.load(f) f.close() Run_nsga(30, 100, 14) |
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Python 遗传算法库 Geatpy 深入
为了应对更加复杂的问题，不同的编码方式的问题，对此进行代码讲解，个人觉得这个遗传算法库做的很好，上手比较快，适用性很强，强烈推荐，用的很顺手。
示例说明：
a = [0~30连续数] * _num
b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列
c = [0~30离散数（整数）] * _num
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import geatpy as ea import numpy as np # 示例说明： # a = [0~30连续数] * _num # b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列 # c = [0~30离散数（整数）] * _num def func(a, b, c): return np.sin(np.pi * a * b) * np.cos(np.pi * c * b) * np.tan(np.pi * a * b * c) # 初始化文件num大小 _num = 10 # 自定义 GA class My_nsga(ea.Problem): def __init__(self): global _num name = 'GEATPY Facilitate Learning' # 只优化func一个变量 M = 1 # 最大化变量 maxormins = [-1] * M # 一共有 a.size + b.size + c.size 个数 Dim = _num + _num + _num # a 连续， b and c 离散 varTypes = [0] * _num + [1] * _num + [1] * _num # a ∈ [0,30], b ∈ [1,_num], c ∈ [0,30] lb = [0] * _num + [1] * _num + [0] * _num ub = [30] * _num + [_num] * _num + [30] * _num # 下限可取 lbin = [1] * Dim # 上限可取 ubin = [1] * Dim # 保存最大数据 self.max_ans = 0 # epoch self.epoch = 0 # super ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin) # 目标函数即神经网络返回值 def evalVars(self, Vars): # init ans ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1) for i in range(len(Vars)): # 一定要确定区域选择是否正确 a = Vars[i][:_num] # 可以选择int类型防止特定错误，因为正常输出时为float，具体问题可能会出错 b = list(map(int, Vars[i][_num: _num + _num])) c = Vars[i][-_num:] score = func(a, b, c) ans[i] = score.max() self.max_ans = (now_max := ans.max()) print(f"Epoch: {self.epoch + 1}, Epoch Max: {now_max}, Global Max: {self.max_ans}") self.epoch += 1 return ans # 运行 GA def Run_nsga(ndind=10, maxgen=100, *args, **kwargs): global _num # init problem problem = My_nsga() # 染色体编码方式为[格雷编码，排列编码，格雷编码] encodings = ["RI", "P", "RI"] # 设置具体field，一定要多加这一部，geatpy还没有具体到能直接输入encoding: List[str]，需要进行转换才能使用 # 一定要注意数组范围，报错的话一定要仔细检查！！！ field1 = ea.crtfld(encodings[0], problem.varTypes[:_num], problem.ranges[:, :_num], problem.borders[:, :_num]) field2 = ea.crtfld(encodings[1], problem.varTypes[_num: _num + _num], problem.ranges[:, _num: _num + _num], problem.borders[:, _num: _num + _num]) field3 = ea.crtfld(encodings[2], problem.varTypes[-_num:], problem.ranges[:, -_num:], problem.borders[:, -_num:]) # 合并field fields = [field1, field2, field3] # 代入模板 myAlgorithm = ea.soea_psy_EGA_templet(problem, ea.PsyPopulation(Encodings=encodings, Fields=fields, NIND=ndind), MAXGEN=maxgen, logTras=0) # 是否画图 myAlgorithm.drawing = 0 # 进行优化 res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=0, outputMsg=True, drawLog=False, saveFlag=False, dirName='result') return res['Vars'][0] if __name__ == "__main__": Run_nsga(50, 1000) |
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