手机行为预测 Baseline

本次赛事要求基于智能手机传感器数据预测人体6项活动。训练集8千条、测试集2千条，含561个特征，以准确率评分。提供含数据预处理和1D卷积神经网络的Baseline，可通过残差结构、数据扩增优化。

赛事介绍

如今的智能机已经很智能了，如果手机可以觉察到我们在生活中的一举一动，知道我们行动的意图，你觉得会如何？智能手机不仅搭载了多种惯性传感器，这使得基于智能手机的人体行为识别研究越来越受关注。

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在本次赛题由志愿者使用智能手机时，通过基本活动的行为构建而成。希望选手能够构建模型对活动行为进行预测。

赛事任务

实验是在 19-48 岁年龄段的 30 名志愿者中进行的。每个人在腰部佩戴智能手机（三星 Galaxy S II）进行六项活动（步行、楼上步行、楼下步行、坐、站、躺）。实验以 50Hz 的恒定速率捕获 3 轴线性加速度和 3 轴角速度。

赛题训练集案例如下：

训练集8千条数据；测试集共2000条数据；

数据总共100MB，赛题数据均为csv格式，列使用逗号分割。若使用Pandas读取数据，可参考如下代码：

import pandas as pdimport numpy as nptrain = pd.read_csv('train.csv.zip')

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对于数据集中的每一条记录，都提供了以下内容，来自加速度计的三轴加速度（总加速度）和估计的身体加速度、和来自陀螺仪的三轴角速度。总共是具有时域和频域变量的561个特征向量。

测试集中label字段Activity为空，需要选手预测。

评审规则

数据说明：选手需要提交测试集队伍排名预测，具体的提交格式如下：

ActivitySTANDINGLAYINGWALKINGSITTINGWALKINGWALKING_DOWNSTAIRSSTANDING

登录后复制评估指标：本次竞赛的使用准确率进行评分，数值越高精度越高，评估代码参考：

from sklearn.metrics import accuracy_scorey_pred = [0, 2, 1, 3]y_true = [0, 1, 2, 3]accuracy_score(y_true, y_pred)

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Baseline使用指导

1、点击‘fork按钮’，出现‘fork项目’弹窗
2、点击‘创建按钮’ ，出现‘运行项目’弹窗
3、点击‘运行项目’，自动跳转至新页面
4、点击‘启动环境’ ，出现‘选择运行环境’弹窗
5、选择运行环境（启动项目需要时间，请耐心等待），出现‘环境启动成功’弹窗，点击确定
6、点击进入环境，即可进入notebook环境
7、鼠标移至下方每个代码块内（代码块左侧边框会变成浅蓝色），再依次点击每个代码块左上角的‘三角形运行按钮’，待一个模块运行完以后再运行下一个模块，直至全部运行完成

8、下载页面左侧submission.zip压缩包

9、在比赛页提交submission.zip压缩包，等待系统评测结束后，即可登榜！

10、点击页面左侧‘版本-生成新版本’

11、填写‘版本名称’，点击‘生成版本按钮’，即可在个人主页查看到该项目（可选择公开此项目哦）

数据分析

In [1]

import pandas as pdimport paddleimport numpy as np%pylab inlineimport seaborn as snstrain_df = pd.read_csv('data/data137267/train.csv.zip')test_df = pd.read_csv('data/data137267/test.csv.zip')

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import Sized

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Populating the interactive namespace from numpy and matplotlib

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train_df.shape

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(8000, 562)

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train_df.columns

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Index(['tBodyAcc-mean()-X', 'tBodyAcc-mean()-Y', 'tBodyAcc-mean()-Z',       'tBodyAcc-std()-X', 'tBodyAcc-std()-Y', 'tBodyAcc-std()-Z',       'tBodyAcc-mad()-X', 'tBodyAcc-mad()-Y', 'tBodyAcc-mad()-Z',       'tBodyAcc-max()-X',       ...       'fBodyBodyGyroJerkMag-skewness()', 'fBodyBodyGyroJerkMag-kurtosis()',       'angle(tBodyAccMean,gravity)', 'angle(tBodyAccJerkMean),gravityMean)',       'angle(tBodyGyroMean,gravityMean)',       'angle(tBodyGyroJerkMean,gravityMean)', 'angle(X,gravityMean)',       'angle(Y,gravityMean)', 'angle(Z,gravityMean)', 'Activity'],      dtype='object', length=562)

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train_df['Activity'].value_counts().plot(kind='bar')

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  if isinstance(obj, collections.Iterator):/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  return list(data) if isinstance(data, collections.MappingView) else data

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plt.figure(figsize=(10, 5))sns.boxplot(y='tBodyAcc-mean()-X', x='Activity', data=train_df)plt.tight_layout()

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/seaborn/categorical.py:340: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  np.asarray(s, dtype=np.float)/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/seaborn/utils.py:538: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  np.asarray(values).astype(np.float)

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train_df['Activity'] = train_df['Activity'].map({    'LAYING': 0,    'STANDING': 1,    'SITTING': 2,    'WALKING': 3,    'WALKING_UPSTAIRS': 4,    'WALKING_DOWNSTAIRS': 5})

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from sklearn.preprocessing import StandardScalerscaler = StandardScaler()scaler.fit(train_df.values[:, :-1])train_df.iloc[:, :-1] = scaler.transform(train_df.values[:, :-1])test_df.iloc[:, :] = scaler.transform(test_df.values)

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搭建模型

In [8]

class Classifier(paddle.nn.Layer):    # self代表类的实例自身    def __init__(self):        # 初始化父类中的一些参数        super(Classifier, self).__init__()                self.conv1 = paddle.nn.Conv1D(in_channels=1, out_channels=16, kernel_size=3)        self.conv2 = paddle.nn.Conv1D(in_channels=16, out_channels=32, kernel_size=3)        self.conv3 = paddle.nn.Conv1D(in_channels=32, out_channels=64, kernel_size=3)        self.flatten = paddle.nn.Flatten()        self.dropout = paddle.nn.Dropout()        self.fc = paddle.nn.Linear(in_features=128, out_features=6)        self.relu = paddle.nn.ReLU()        self.pool = paddle.nn.MaxPool1D(6)        self.softmax = paddle.nn.Softmax()    # 网络的前向计算    def forward(self, inputs):        x = self.pool(self.relu(self.conv1(inputs)))        x = self.pool(self.relu(self.conv2(x)))        x = self.dropout(x)        x = self.pool(self.relu(self.conv3(x)))        x = self.dropout(x)        x = self.flatten(x)        x = self.relu(self.fc(x))        x = self.softmax(x)        return x

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model = Classifier()model.train()opt = paddle.optimizer.SGD(learning_rate=0.005, parameters=model.parameters())loss_fn = paddle.nn.CrossEntropyLoss()

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EPOCH_NUM = 40   # 设置外层循环次数BATCH_SIZE = 16  # 设置batch大小training_data = train_df.iloc[:-1000].values.astype(np.float32)val_data = train_df.iloc[-1000:].values.astype(np.float32)training_data = training_data.reshape(-1, 1, 562)val_data = val_data.reshape(-1, 1, 562)

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# 定义外层循环for epoch_id in range(EPOCH_NUM):    # 在每轮迭代开始之前，将训练数据的顺序随机的打乱    np.random.shuffle(training_data)        # 将训练数据进行拆分，每个batch包含10条数据    mini_batches = [training_data[k:k+BATCH_SIZE] for k in range(0, len(training_data), BATCH_SIZE)]        # 定义内层循环    for iter_id, mini_batch in enumerate(mini_batches):        model.train()        x = np.array(mini_batch[:,:, :-1]) # 获得当前批次训练数据        y = np.array(mini_batch[:,:, -1:]) # 获得当前批次训练标签                # 将numpy数据转为飞桨动态图tensor的格式        features = paddle.to_tensor(x)        y = paddle.to_tensor(y)                # 前向计算        predicts = model(features)                # 计算损失        loss = loss_fn(predicts, y.flatten().astype(int))        avg_loss = paddle.mean(loss)        # 反向传播，计算每层参数的梯度值        avg_loss.backward()        # 更新参数，根据设置好的学习率迭代一步        opt.step()        # 清空梯度变量，以备下一轮计算        opt.clear_grad()        # 训练与验证        if iter_id%2000==0 and epoch_id % 10 == 0:            acc = predicts.argmax(1) == y.flatten().astype(int)            acc = acc.astype(float).mean()            model.eval()            val_predict = model(paddle.to_tensor(val_data[:, :, :-1])).argmax(1)            val_label = val_data[:, :, -1]            val_acc = np.mean(val_predict.numpy() == val_label.flatten())            print("epoch: {}, iter: {}, loss is: {}, acc is {} / {}".format(                epoch_id, iter_id, avg_loss.numpy(), acc.numpy(), val_acc))

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epoch: 0, iter: 0, loss is: [1.9004316], acc is [0.125] / 0.182epoch: 10, iter: 0, loss is: [1.6665952], acc is [0.375] / 0.368epoch: 20, iter: 0, loss is: [1.5449493], acc is [0.5] / 0.478epoch: 30, iter: 0, loss is: [1.5502174], acc is [0.5] / 0.525

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model.eval()test_data = paddle.to_tensor(test_df.values.reshape(-1, 1, 561).astype(np.float32))test_predict = model(test_data)test_predict = test_predict.argmax(1).numpy()

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test_predict = pd.DataFrame({'Activity': test_predict})test_predict['Activity'] = test_predict['Activity'].map({    0:'LAYING',    1:'STANDING',    2:'SITTING',    3:'WALKING',    4:'WALKING_UPSTAIRS',    5:'WALKING_DOWNSTAIRS'})

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test_predict.to_csv('submission.csv', index=None)!zip submission.zip submission.csv

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updating: submission.csv (deflated 94%)

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未来上分点

模型可以加入残差结构，参考resnet。数据可以加入数据扩增，比如加噪音。In [ ]

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