Kaggle赛题分析(二)
Indoor Location & Navigation
室内导航和定位
通过智能手机的信号日志，识别具体用户在购物中心的位置。当前的定位算法在多层建筑中精度较差，且大部分是基于GPS信息构建。
这是一个回归问题，使用MSE进行评价，对于测试数据集，需要预测具体的楼层和位置坐标。
赛题数据：手机WIFI信号、地磁场信号、蓝牙信号、真实的室内位置。我们先来看一下文件分布
数据下载地址：https://www.kaggle.com/c/indoor-location-navigation/data
这个train文件夹当然是训练数据集，按照场地和楼层构成的数据，test文件夹当然是测试数据集，给定场地信息的信号数据。
在train文件夹下的很长一串的字符串代表不同的场地，再下面的子文件夹B1、F1等等是楼层。
再点进楼层文件夹是日志信息。点开日志信息，我们可以看到这样一些信息
我们可以看到它是一些国内商场的日志，所有的格式都是以txt的形式给出。它记录的是一些手机信息。前面是一些注释，比如手机型号，产生的时间，下面的非#开始的就是记录的一些日志，第一列是时间戳1578482906560，第二列是信号类型TYPE_WAYPOINT，第三列和第四列相当于具体的信号的取值。通过对信号类型的理解，而我们的标签target——具体定位的位置就为TYPE_WAYPOINT。而下面行的信息是一些其他类型的信息，不同的数据，记录的列数可能是不一样的。现在我们来看一下test文件夹。
我们可以看到test文件夹下面只给定了场地，并没有分楼层。点击一个文本文件，我们可以看到
这里没有TYPE_WAYPOINT信息，我们需要使用其他这些信息，比如说传感信号，Wifi信号去预测TYPE_WAYPOINT的值(具体的位置)。现在我们来看一下metadata文件夹。
metadata里面包含了一些场地楼层的原始信息，点开floor_image.png
floor_info.json存储了这一层的长和宽
{"map_info": {"height": 204.53342955266643, "width": 270.34143433711995}}geojson_map.json里面是具体的建筑物的序列化信息
现在我们来进行一定的数据分析，首先我们先将部分数据给读取出来
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
print(df.head())运行结果
time type col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8
0 1578482906560 TYPE_WAYPOINT 154.68399 81.80792 NaN NaN NaN NaN NaN NaN
1 1578482906672 TYPE_ACCELEROMETER 0.056015015 4.2749786 8.278137 2.0 NaN NaN NaN NaN
2 1578482906672 TYPE_MAGNETIC_FIELD 4.663086 -44.032288 -7.371521 3.0 NaN NaN NaN NaN
3 1578482906672 TYPE_GYROSCOPE 0.046081543 0.10348511 -0.103652954 3.0 NaN NaN NaN NaN
4 1578482906672 TYPE_ROTATION_VECTOR 0.0012338709 0.24022351 0.9687757 3.0 NaN NaN NaN NaN现在我们来看一下各种类型信息的数量统计
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
print(df['type'].value_counts())运行结果
TYPE_ACCELEROMETER_UNCALIBRATED 1399
TYPE_GYROSCOPE_UNCALIBRATED 1399
TYPE_MAGNETIC_FIELD_UNCALIBRATED 1399
TYPE_ROTATION_VECTOR 1399
TYPE_MAGNETIC_FIELD 1399
TYPE_ACCELEROMETER 1399
TYPE_GYROSCOPE 1399
TYPE_WIFI 1279
TYPE_BEACON 97
TYPE_WAYPOINT 4
Name: type, dtype: int64通过结果，我们可以发现，我们的标签target——TYPE_WAYPOINT的数量信息是比较少的。我们可以看一下在日志中，位置信息TYPE_WAYPOINT是如何随着时间发生改变的。
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
# print(df['type'].value_counts())
print(df[df['type'] == 'TYPE_WAYPOINT'])运行结果
time type col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8
0 1578482906560 TYPE_WAYPOINT 154.68399 81.80792 NaN NaN NaN NaN NaN NaN
1946 1578482911664 TYPE_WAYPOINT 151.99818 81.02271 NaN NaN NaN NaN NaN NaN
5818 1578482921653 TYPE_WAYPOINT 151.31499 75.86274 NaN NaN NaN NaN NaN NaN
10035 1578482932281 TYPE_WAYPOINT 154.68399 81.80792 NaN NaN NaN NaN NaN NaN接下来，我们定义一些信息抽取的一些方法
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
# df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
# print(df['type'].value_counts())
# print(df[df['type'] == 'TYPE_WAYPOINT'])
def extract_feature_df(trace_df, feature_name, col_names=('x', 'y', 'z', 'accuracy')):
# 信息抽取
# 给定类型名称的情况下筛选出该类型的信息
feature_df = trace_df[trace_df['type'] == feature_name].copy()
for i, col in enumerate(col_names, start=1):
feature_df[col] = feature_df[f'col_{i}'].astype('float64')
feature_df.drop(columns=[f'col_{i}' for i in range(1, 9)], inplace=True)
feature_df.drop(columns=['type'], inplace=True)
feature_df.reset_index(drop=True, inplace=True)
return feature_df
def load_points(filepath):
# 提取TYPE_WAYPOINT信息
trace_df = load_trace_as_dataframe(filepath)
points_df = extract_feature_df(trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y'))
return points_df
df = load_points(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
print(df)运行结果
time x y
0 1578482906560 154.68399 81.80792
1 1578482911664 151.99818 81.02271
2 1578482921653 151.31499 75.86274
3 1578482932281 154.68399 81.80792现在我们将得到的这些坐标轨迹画在原始的楼层平面图上
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
# df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
# print(df['type'].value_counts())
# print(df[df['type'] == 'TYPE_WAYPOINT'])
def extract_feature_df(trace_df, feature_name, col_names=('x', 'y', 'z', 'accuracy')):
# 信息抽取
# 给定类型名称的情况下筛选出该类型的信息
feature_df = trace_df[trace_df['type'] == feature_name].copy()
for i, col in enumerate(col_names, start=1):
feature_df[col] = feature_df[f'col_{i}'].astype('float64')
feature_df.drop(columns=[f'col_{i}' for i in range(1, 9)], inplace=True)
feature_df.drop(columns=['type'], inplace=True)
feature_df.reset_index(drop=True, inplace=True)
return feature_df
def load_points(filepath):
# 提取TYPE_WAYPOINT信息
trace_df = load_trace_as_dataframe(filepath)
points_df = extract_feature_df(trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y'))
return points_df
# df = load_points(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df)
def visualize_many_traces_on_the_map(traces_dataframes, map_images, width, height,
traces_filenames=None, figsize=None):
# 在地图上绘制多个坐标
fig = plt.figure(figsize=figsize)
ax = plt.subplot(111)
plt.imshow(map_images, extent=[0, width, 0, height])
if traces_filenames:
for filename, points in zip(traces_filenames, traces_dataframes):
plt.scatter(points['x'], points['y'], label=filename)
plt.plot(points['x'], points['y'])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
else:
for points in traces_dataframes:
plt.scatter(points['x'], points['y'])
plt.plot(points['x'], points['y'])
plt.show()
data_path = INPUT_PATH
print(os.listdir(data_path))
floor = "5a0546857ecc773753327266/B1"
floor_metadata_dir = os.path.join(data_path, 'metadata', floor)
floor_train_dir = os.path.join(data_path, 'train', floor)
print(os.listdir(floor_metadata_dir))
paths = []
for filename in os.listdir(floor_train_dir):
if '.txt' not in filename:
continue
path = os.path.join(floor_train_dir, filename)
points = load_points(path)
paths.append((filename, points))
paths = sorted(paths, key=lambda path: len(path[1]), reverse=True)
paths = paths[:20]
traces_dataframes = [trace for filename, trace in paths]
traces_filenames = [filename for filename, trace in paths]
MAP_IMAGE = plt.imread(os.path.join(floor_metadata_dir, 'floor_image.png'))
with open(os.path.join(floor_metadata_dir, 'floor_info.json')) as f:
content = f.read()
floor_info = json.loads(content)
MAP_HEIGHT = float(floor_info['map_info']['height'])
MAP_WIDTH = float(floor_info['map_info']['width'])
print(floor_info)
visualize_many_traces_on_the_map(traces_dataframes, MAP_IMAGE, MAP_WIDTH, MAP_HEIGHT,
traces_filenames=traces_filenames, figsize=(15, 12))运行结果
['.DS_Store', 'test', 'train', 'metadata', 'sample_submission.csv']
['floor_image.png', 'floor_info.json', 'geojson_map.json']
{'map_info': {'height': 204.53342955266643, 'width': 270.34143433711995}}
当然我们也可以绘制单个手机用户的位置轨迹
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
# df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
# print(df['type'].value_counts())
# print(df[df['type'] == 'TYPE_WAYPOINT'])
def extract_feature_df(trace_df, feature_name, col_names=('x', 'y', 'z', 'accuracy')):
# 信息抽取
# 给定类型名称的情况下筛选出该类型的信息
feature_df = trace_df[trace_df['type'] == feature_name].copy()
for i, col in enumerate(col_names, start=1):
feature_df[col] = feature_df[f'col_{i}'].astype('float64')
feature_df.drop(columns=[f'col_{i}' for i in range(1, 9)], inplace=True)
feature_df.drop(columns=['type'], inplace=True)
feature_df.reset_index(drop=True, inplace=True)
return feature_df
def load_points(filepath):
# 提取TYPE_WAYPOINT信息
trace_df = load_trace_as_dataframe(filepath)
points_df = extract_feature_df(trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y'))
return points_df
# df = load_points(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df)
def visualize_many_traces_on_the_map(traces_dataframes, map_images, width, height,
traces_filenames=None, figsize=None):
# 在地图上绘制多个手机用户坐标轨迹
fig = plt.figure(figsize=figsize)
ax = plt.subplot(111)
plt.imshow(map_images, extent=[0, width, 0, height])
if traces_filenames:
for filename, points in zip(traces_filenames, traces_dataframes):
plt.scatter(points['x'], points['y'], label=filename)
plt.plot(points['x'], points['y'])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
else:
for points in traces_dataframes:
plt.scatter(points['x'], points['y'])
plt.plot(points['x'], points['y'])
plt.show()
data_path = INPUT_PATH
# print(os.listdir(data_path))
floor = "5a0546857ecc773753327266/B1"
floor_metadata_dir = os.path.join(data_path, 'metadata', floor)
floor_train_dir = os.path.join(data_path, 'train', floor)
# print(os.listdir(floor_metadata_dir))
paths = []
for filename in os.listdir(floor_train_dir):
if '.txt' not in filename:
continue
path = os.path.join(floor_train_dir, filename)
points = load_points(path)
paths.append((filename, points))
paths = sorted(paths, key=lambda path: len(path[1]), reverse=True)
# 只绘制前20个轨迹
paths = paths[:20]
traces_dataframes = [trace for filename, trace in paths]
traces_filenames = [filename for filename, trace in paths]
MAP_IMAGE = plt.imread(os.path.join(floor_metadata_dir, 'floor_image.png'))
with open(os.path.join(floor_metadata_dir, 'floor_info.json')) as f:
content = f.read()
floor_info = json.loads(content)
MAP_HEIGHT = float(floor_info['map_info']['height'])
MAP_WIDTH = float(floor_info['map_info']['width'])
# print(floor_info)
# visualize_many_traces_on_the_map(traces_dataframes, MAP_IMAGE, MAP_WIDTH, MAP_HEIGHT,
# traces_filenames=traces_filenames, figsize=(15, 12))
def visualize_single_trace_on_the_map(points_df, map_image, width, height,
scaling_coef=0.3, figsize=None):
# 在地图上绘制单个手机用户坐标轨迹
fig = plt.figure(figsize=figsize)
ax = plt.subplot(111)
plt.imshow(map_image, extent=[0, width, 0, height])
plt.plot(points_df['x'], points_df['y'], linewidth=5, linestyle='-', color='blue')
for i in range(len(points_df)):
ax.text(points_df.loc[i, 'x'], points_df.loc[i, 'y'], i,
ha='center', size=15, bbox=dict(boxstyle='circle, pad=0.3', fc='cyan', lw=2))
x_min, x_max = points_df['x'].min(), points_df['x'].max()
y_min, y_max = points_df['y'].min(), points_df['y'].max()
ax.set_xlim(x_min - scaling_coef * (x_max - x_min), x_max + scaling_coef * (x_max - x_min))
ax.set_ylim(y_min - scaling_coef * (y_max - y_min), y_max + scaling_coef * (y_max - y_min))
plt.show()
filename = "5e15bda71506f2000638feb5.txt"
trace_filepath = os.path.join(floor_train_dir, filename)
trace_df = load_trace_as_dataframe(trace_filepath)
points_df = extract_feature_df(trace_df, 'TYPE_WAYPOINT', col_names=('x', 'y'))
visualize_single_trace_on_the_map(points_df, MAP_IMAGE, MAP_WIDTH, MAP_HEIGHT, figsize=(10, 8))运行结果
这是一个典型的非结构化文本数据，现在我们来看看如何进一步分析和建模。
现在我们来看一下我们需要提交的数据集的样子。
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
ssubm = pd.read_csv(INPUT_PATH + "sample_submission.csv")
ssubm_df = ssubm['site_path_timestamp'].apply(lambda x: pd.Series(x.split('_')))
used_buildings = sorted(ssubm_df[0].value_counts().index.tolist())
# 对楼层编码
floor_map = {"B2": -2, "B1": -1, "F1": 0, "F2": 1, "F3": 2, "F4": 3, "F5": 4, "F6": 5, "F7": 6, "F8": 7, "F9": 8,
"1F": 0, "2F": 1, "3F": 2, "4F": 3, "5F": 4, "6F": 5, "7F": 6, "8F": 7, "9F": 8}
print(ssubm)
print(ssubm_df)
print(used_buildings)运行结果
site_path_timestamp floor x y
0 5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000000009 0 75.0 75.0
1 5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000009017 0 75.0 75.0
2 5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000015326 0 75.0 75.0
3 5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000018763 0 75.0 75.0
4 5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000022328 0 75.0 75.0
... ... ... ... ...
10128 5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f412_0000000082589 0 75.0 75.0
10129 5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f412_0000000085758 0 75.0 75.0
10130 5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f412_0000000090895 0 75.0 75.0
10131 5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f412_0000000096899 0 75.0 75.0
10132 5dc8cea7659e181adb076a3f_fd64de8c4a2fc5ebb0e9f412_0000000100447 0 75.0 75.0
[10133 rows x 4 columns]
0 1 2
0 5a0546857ecc773753327266 046cfa46be49fc10834815c6 0000000000009
1 5a0546857ecc773753327266 046cfa46be49fc10834815c6 0000000009017
2 5a0546857ecc773753327266 046cfa46be49fc10834815c6 0000000015326
3 5a0546857ecc773753327266 046cfa46be49fc10834815c6 0000000018763
4 5a0546857ecc773753327266 046cfa46be49fc10834815c6 0000000022328
... ... ... ...
10128 5dc8cea7659e181adb076a3f fd64de8c4a2fc5ebb0e9f412 0000000082589
10129 5dc8cea7659e181adb076a3f fd64de8c4a2fc5ebb0e9f412 0000000085758
10130 5dc8cea7659e181adb076a3f fd64de8c4a2fc5ebb0e9f412 0000000090895
10131 5dc8cea7659e181adb076a3f fd64de8c4a2fc5ebb0e9f412 0000000096899
10132 5dc8cea7659e181adb076a3f fd64de8c4a2fc5ebb0e9f412 0000000100447
[10133 rows x 3 columns]
['5a0546857ecc773753327266', '5c3c44b80379370013e0fd2b', '5d27075f03f801723c2e360f', '5d27096c03f801723c31e5e0', '5d27097f03f801723c320d97', '5d27099f03f801723c32511d', '5d2709a003f801723c3251bf', '5d2709b303f801723c327472', '5d2709bb03f801723c32852c', '5d2709c303f801723c3299ee', '5d2709d403f801723c32bd39', '5d2709e003f801723c32d896', '5da138274db8ce0c98bbd3d2', '5da1382d4db8ce0c98bbe92e', '5da138314db8ce0c98bbf3a0', '5da138364db8ce0c98bc00f1', '5da1383b4db8ce0c98bc11ab', '5da138754db8ce0c98bca82f', '5da138764db8ce0c98bcaa46', '5da1389e4db8ce0c98bd0547', '5da138b74db8ce0c98bd4774', '5da958dd46f8266d0737457b', '5dbc1d84c1eb61796cf7c010', '5dc8cea7659e181adb076a3f']其实在我们的数据集中，除了标签target——TYPE_WAYPOINT以外，最为关键的一个特征就是Wifi信息——TYPE_WIFI ，我们可以来看一下TYPE_WIFI的一些数据
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def load_trace_as_dataframe(filepath):
# 读取日志文件
# 设置读取的字段名为time,type以及后面的取值，最多为8个
names = ['time', 'type'] + [f'col_{i}' for i in range(1, 9)]
trace_df = pd.read_csv(filepath, sep='\t', comment='#', header=None, names=names)
trace_df.sort_values(by='time', inplace=True)
trace_df.reset_index(drop=True, inplace=True)
return trace_df
df = load_trace_as_dataframe(INPUT_PATH + "train/5a0546857ecc773753327266/B1/5e15bda71506f2000638feb5.txt")
# print(df.head())
# print(df['type'].value_counts())
print(df[df['type'] == 'TYPE_WIFI'])运行结果
time type col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8
256 1578482907398 TYPE_WIFI 66afd751525c9f51702fa1d2d906799c2e0c1d05 cefdb0c230cb725c985a620e6bbe9199f083518c -81 2457.0 1.578483e+12 NaN NaN NaN
257 1578482907398 TYPE_WIFI b6ffe5619e02871fcd04f61c9bb4b5c53a3f46b7 3a6b2bc5f25fcaf92ec3cdfe2cc40889ef2fd6ab -81 2472.0 1.578483e+12 NaN NaN NaN
258 1578482907398 TYPE_WIFI ea4a14e0d5bcdd20703fbe3bbc90f70b171ff140 591ea59cf88e3397db5d60eb00a5147edd69399a -82 5765.0 1.578483e+12 NaN NaN NaN
259 1578482907398 TYPE_WIFI b6ffe5619e02871fcd04f61c9bb4b5c53a3f46b7 c4ba813f5d699add984dc85cd5792f6d8a66d33a -82 2452.0 1.578483e+12 NaN NaN NaN
260 1578482907398 TYPE_WIFI 5731b8e08abc69d4c4d685c58164059207c93310 d20af782ef6c1db82795ae9b7ba39866dc298bd7 -82 2452.0 1.578483e+12 NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ...
11077 1578482934609 TYPE_WIFI 7182afc4e5c212133d5d7d76eb3df6c24618302b 9e716ffd84238082386afa4fd72cf4c8b68cbc93 -78 2472.0 1.578483e+12 NaN NaN NaN
11078 1578482934609 TYPE_WIFI d839a45ebe64ab48b60a407d837fb01d3c0dfef9 6e388d1db5ba8dd9de80522a4ddf50402cf443b3 -79 5785.0 1.578483e+12 NaN NaN NaN
11079 1578482934609 TYPE_WIFI 66afd751525c9f51702fa1d2d906799c2e0c1d05 cefdb0c230cb725c985a620e6bbe9199f083518c -79 2457.0 1.578483e+12 NaN NaN NaN
11080 1578482934609 TYPE_WIFI 090f27e83f72bc23d60baed6a73c55161f7ee127 d6a858af60bc6f39894eb9118b1aa44f8cfd6be2 -79 5765.0 1.578483e+12 NaN NaN NaN
11081 1578482934609 TYPE_WIFI b9f0208be00bd8b337be7f12e02e3a3ce846e22b f920a2e4cb52165850990d9d37d391b630f7de14 -78 5805.0 1.578483e+12 NaN NaN NaN
[1279 rows x 10 columns]这里col_1叫ssid，表示接入Wifi的设备的名称；col_2叫bssid，表示接入某个无线点的时候的mac地址，如果一个用户在商城中，如果手机不变的情况下，ssid是不变的，但是接入到不同的Wifi的情况下，bssid会发生变化；col_3叫RSSI，相当于是我们对在具体一个Wifi的强度的信息。我们主要要用到bssid和RSSI这两列来构造我们的特征。在原始的训练集里面，它给定了在一定时间内，用户手机所接收到的Wifi信号以及一定时间内用户手机的TYPE_WAYPOINT坐标。那我们就可以找到在时间最近的Wifi信号的强度RSSI是什么。相当于是如果对测试集进行预测，我们就可以直接去找测试集手机接收到的RSSI信号在训练集对应到的数据集里面跟它相隔最近的TYPE_WAYPOINT到底是什么。就相当于找到最近的Wifi信号，然后再通过Wifi信号找到它对应的TYPE_WAYPOINT坐标即可。
我们现在来看一下某场地内有多少个活跃的(超过1000个手机用户接入)WIFI接入点。
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import glob
import gc
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
ssubm = pd.read_csv(INPUT_PATH + "sample_submission.csv")
ssubm_df = ssubm['site_path_timestamp'].apply(lambda x: pd.Series(x.split('_')))
used_buildings = sorted(ssubm_df[0].value_counts().index.tolist())
# 对楼层编码
floor_map = {"B2": -2, "B1": -1, "F1": 0, "F2": 1, "F3": 2, "F4": 3, "F5": 4, "F6": 5, "F7": 6, "F8": 7, "F9": 8,
"1F": 0, "2F": 1, "3F": 2, "4F": 3, "5F": 4, "6F": 5, "7F": 6, "8F": 7, "9F": 8}
# print(ssubm)
# print(ssubm_df)
# print(used_buildings)
bssid = {}
for building in used_buildings:
folders = sorted(glob.glob(os.path.join(INPUT_PATH, 'train/' + building + '/*')))
wifi = []
for folder in folders:
floor = floor_map[folder.split('/')[-1]]
files = glob.glob(os.path.join(folder, "*.txt"))
for file in files:
with open(file) as f:
txt = f.readlines()
for e, line in enumerate(txt):
tmp = line.strip().split()
if tmp[1] == "TYPE_WIFI":
wifi.append(tmp)
df = pd.DataFrame(wifi)
# 对最近接入点WIFI地址bssid进行统计
value_counts = df[3].value_counts()
# 获取WIFI接入点超过1000个的WIFI接入点列表
top_bassid = value_counts[value_counts > 1000].index.tolist()
print(len(top_bassid))
# 将场地与该场地内的这些WIFI接入点列表进行映射
bssid[building] = top_bassid
print(bssid)
gc.collect()
break运行结果
941
/*
* 提示：该行代码过长，系统自动注释不进行高亮。一键复制会移除系统注释
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'130cf54fc1d3d3c2de2febdf405087bfc0af07f9', '132b2b96189f2ec6d52bdf5ef1350b0cc32c3b39', '29ff8189476530f9911ac91136e243c31ed708f3', 'ed8e8d2a28a421a676fcc831df3d54f5bdd73f4b', '3e8f00d2264d44a94eaf17b4c3912a69755f3f7e', '8b1a3d0c37552823c9ba241f42a4695b3490117a', '93c018b147c540cf86fab5b3313c726e6d834bb9', '555f3b025612c4e974002b44557de6f99d6dde10', '3db54c633a9e85ceb2be1fb42ce621b4d8ddae89', '4f80e58ed0dc492a636424eabdca54e7ac793865', '795d1626c77816f86ac1eb8d78c3101942072533', '2c790ae489e9ddfa4c3daa4650a81c7802017e83', '05a83a7c1aeb1f8b9786599a98fda851c649f972', '92fa127222645a92ea37393b7607e90945faf15d', 'c547eff4fbfa40a9c01f2245644bd62e12f114f7', '38baca34a3a4b71985364f63e7703dca4102c1ea', '99f977e2743d2837da2220a64e3e1cf670128ee8', '7dbbd3c8fbb16c74d85427d9e2a3af1448e85742', '1d46c19cfbaacc249aa686a9b4d78834a4748318', '2c1c9a4126880c99fec3e8e5dcbb081c7b80a75d', '172b8a530c6f1946a495576ea9e885874db2f288', '5df90113fd5cd4956e5135b4428e61b8ac8bb131', 'd73bd2355a2d42a4e56384a1ed451a10833e6e39', 'dde939787f5816414aa5d3433912063fe46271cf', 'c420057010e6ee7a7f310ea43fb563cf8b84403c', '957e5a532bef0002d98fcb30eaab5bb4b21328f8', '70d12fc0dd1de471525bdf503281de165f26e1e4', '21f3a763336708abc51d71bcbe48b925d260d308', '2aa08d092d0199c06d22684642ef1c79d9722adb', '8427be40e53c4ceedf57858018c1e79e65839586', '62d59ccd131a148d1446fff352b958f6ae3169b3', '39b659030076131ea5010f0264cb233c0e20757e', '059ea3d13de011f91587f1d176599605274f8ee8', 'b51737bd5018a4ff3020b5fc9c123f5b117c5947', '87fdeed43264d9e2fa9d56db007cde1a640a237b', 'ae0cdd92bc54930049e37c6fe795b85993966f61', '8916e5c6de1aa3702de8495f22892d325957704f', '33b89e718e692bd6779eb805a792de8c76d63ce6', '1efaee6f4a0ee182fa8e0bb31069f2d774694553']}
*/通过结果，我们可以看到在场地5a0546857ecc773753327266内有941个这样的WIFI接入点。现在我们依据之前的描述来完成特征提取。
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import os
import json
import numpy as np
import pandas as pd
import glob
import gc
import matplotlib.pyplot as plt
import seaborn as sns
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
ssubm = pd.read_csv(INPUT_PATH + "sample_submission.csv")
ssubm_df = ssubm['site_path_timestamp'].apply(lambda x: pd.Series(x.split('_')))
used_buildings = sorted(ssubm_df[0].value_counts().index.tolist())
# 对楼层编码
floor_map = {"B2": -2, "B1": -1, "F1": 0, "F2": 1, "F3": 2, "F4": 3, "F5": 4, "F6": 5, "F7": 6, "F8": 7, "F9": 8,
"1F": 0, "2F": 1, "3F": 2, "4F": 3, "5F": 4, "6F": 5, "7F": 6, "8F": 7, "9F": 8}
# print(ssubm)
# print(ssubm_df)
# print(used_buildings)
bssid = {}
for building in used_buildings:
folders = sorted(glob.glob(os.path.join(INPUT_PATH, 'train/' + building + '/*')))
wifi = []
for folder in folders:
floor = floor_map[folder.split('/')[-1]]
files = glob.glob(os.path.join(folder, "*.txt"))
for file in files:
with open(file) as f:
txt = f.readlines()
for e, line in enumerate(txt):
tmp = line.strip().split()
if tmp[1] == "TYPE_WIFI":
wifi.append(tmp)
df = pd.DataFrame(wifi)
# 对最近接入点WIFI地址bssid进行统计
value_counts = df[3].value_counts()
# 获取WIFI接入点超过1000个的WIFI接入点列表
top_bassid = value_counts[value_counts > 1000].index.tolist()
# print(len(top_bassid))
# 将场地与该场地内的这些热门WIFI接入点列表进行映射
bssid[building] = top_bassid
# print(bssid)
gc.collect()
# break
# 对时间戳进行分组，对WIFI接入点进行去重
for gid, g in df.groupby(0):
g = g.drop_duplicates(subset=3)
break
# print(g)
with open(INPUT_PATH + "bssid_1000.json", "w") as f:
json.dump(bssid, f)
with open(INPUT_PATH + "bssid_1000.json") as f:
bssid = json.load(f)
building_dfs = {}
for building in used_buildings:
folders = sorted(glob.glob(os.path.join(INPUT_PATH, 'train/' + building + '/*')))
dfs = []
index = sorted(bssid[building])
print(building)
for folder in folders:
floor = floor_map[folder.split('/')[-1]]
files = glob.glob(os.path.join(folder, "*.txt"))
# print(floor)
for file in files:
wifi = []
waypoint = []
with open(file) as f:
txt = f.readlines()
for line in txt:
line = line.strip().split()
if line[1] == "TYPE_WAYPOINT":
waypoint.append(line)
if line[1] == "TYPE_WIFI":
wifi.append(line)
df = pd.DataFrame(np.array(wifi))
# print(df)
for gid, g in df.groupby(0):
dists = []
# 获取WIFI信号日志和用户地点日志时间间隔最小的索引nearest_wp_index
for e, k in enumerate(waypoint):
dist = abs(int(gid) - int(k[0]))
dists.append(dist)
nearest_wp_index = np.argmin(dists)
g = g.drop_duplicates(subset=3)
# 拿取WIFI接入点和信号强度信息
tmp = g.iloc[:, 3:5]
# 将WIFI接入点转成索引，再将所有的热门WIFI当索引加入其索引，再将加入的热点索引值为空的替换成-999，再将矩阵转置
# 最终得到的feat为某场地所有的Wifi接入点与信号强度以及此时用户的位置(x,y坐标)，楼层以及场地
feat = tmp.set_index(3).reindex(index).replace(np.nan, -999).T
feat['x'] = float(waypoint[nearest_wp_index][2])
feat['y'] = float(waypoint[nearest_wp_index][3])
feat['f'] = floor
feat['path'] = file.split('/')[-1].split('.')[0]
dfs.append(feat)
# break
# break
# break
building_df = pd.concat(dfs)
building_dfs[building] = df
building_df.to_csv(INPUT_PATH + building + "_1000_train.csv")运行结果(在5a0546857ecc773753327266_1000_train.csv文件中)
/*
* 提示：该行代码过长，系统自动注释不进行高亮。一键复制会移除系统注释
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本文参与 腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。
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