接特征工程和Kaggle赛题分析
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。
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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
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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。
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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。
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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
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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。
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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
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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。
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metadata里面包含了一些场地楼层的原始信息，点开floor_image.png
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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
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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
{'5a0546857ecc773753327266': ['a52823c2ed57e18f81da316e5bcac8bd2754ce96', '61a14256d195624aadd9dfd55c8643505635edd7', 'c93c29d2173b811a18de34940ccc210a3064230e', 'a09ab3d8a7700fec7b83389c06088c91748be41c', '4cff1c8cfec27801ddc9a690ade87c57f1142ee0', 'c59f0226dcdf235d7cfaba662bbdb5d31b4c3f14', '5b225e187d0dec3110683a74d0c9a5a4cb2022f5', 'd84cce12fbfba61bf930123050f61a11e2a29310', 'b2546cae6e588d38618eacc557dd0385812197cf', 'b26914599f6d9ba16b43975394e1eeb9d82f4bab', '68616cd56e6c03f67852c7394ddde68b07d7a3c6', 'c703b0a9624cbb37e30e71f4dcfe9347013fcbde', 'b0b1a58ed86926fdd2a0c584a85834d1cc9d2e0c', 'd6fde84d77e0618496709be2037bc47d952d69ce', 'ca0e0bebf85f1348808b0979b74868e4e675e4d8', '6bc91b3951089c3a225396608b138ca178479924', 'f65804069553e141a96eae17f475584617a5324b', '77d933b398825a1c6a9054bde71d44d2d3cb17f7', '5cfff2a06e481233f2df41ef8adc590a0d779517', '1aebe03ff86398cd62ed5ec0da3650e9041baa31', '288f3c61d2f10cc122ff9cd09c27c5aed544e4e2', 'fb1ebbb8a61b4299bd83248e745335974fdfff59', '0c235b02dd008286ea5053621c7ce50df7f17e10', '86bd6179355aaeb54bcd541040f23808d6656442', '2ad2da7d639746b33d7a6118afad5a2964b58ce5', '9cc80ca7212ff8df8aa509760e6759bcb7015da8', 'c70e999e91f2381a2856197df91987412d6e049e', '32ad7772f626e6d531c1de47806b68af6f33c339', 'b58b9ee349cc63d5c92f6b1c834cdd75cc0392ab', 'e95b2b7db211a0b001ef5ef18ffbd08cbc4f4e09', '9780331db07b369d8a009eb89552004b88334dae', 'd0f0c9fa52edde81962135fcf5e5b11e53ddd252', 'a9535d6a53bc660bdc3f2540251e6289b8c75bfd', '2798955a957d1c8c01f6341133cd847016b2b742', '5a1b76adb81b4e42fe9955ebfa04cc331a0b2927', '02a1be3a5dab38320f879489d8a1e0f2a72768b3', '8065741d577aad26b6f1decd65e64aada3becbc7', '84815f02df37456c258ff6504bba7bda8a46ef52', 'c48db7f3ed1858bb4fc191230e3d79d5eb178604', '10cf16ad6534635699a05f3a6c344526b9d7d549', '93cfc00cb75026eea74e8bb316b3673b53d374a9', '4ec476a535783b1129828738a517f58f8c275ae0', 'f6d3e531f5c08830bcd666c11a2926e24bd47bc1', '2bb807107c76d4339fb55c9da49ecfab1f9120ca', 'd2402d8ef8ed4ba4933a76cce532c8774f589d32', '11fa59b18892d70b03751701eff1707495ccf618', 'cee7bafc23017e9de7cc9f69a04a4e1348d29ace', '963b140eb4a6b676ed10c75f6f0d3b8203958b35', '13b7aeaf441f2161481481fe67eace721cff07ab', '2a8748d9e77634ce29b193eb6d3fed569755841e', '3ec9cf559a195a0d7f65be92f699d42b7b093646', 'd771612396c3e2e557e986fafd9fc2c56a99d3cd', 'cc015d2acede7ad8eb7839f747b43fd6a87e363b', '1d52ac70bfa8eae028e7b0d05ef5ca2bfcf513e9', '903387b11fbfeedf3408343673fd54ffa6efb970', '3f97662166de2a7c0efd3f556c48df9a389bb129', '000840e5c600de293cea57f13326f273c86c3988', '3a25cb6ba0c7bf52d8abf6a5279a659dd03aeea0', '76b8b1ae2dda171dbe5f1923d66f9ccc839ef32d', 'bd9bc0a2092c040bfe6ba12f8aafac24e83b312a', 'da6f98ad244984d9aa9061b7a4c5a23dfcfd9276', 'd3120ebc132280d9a4db66cf58895c8f22a8c90b', '6915ad24a2edf8047f749233e19e9853f5dc17fd', 'd5ef388e09524518ba9dcddc32e638c32c12c1c0', 'f64c13fd10a07bca1bf2b7bd7a80630632ce62c9', '15705627b6d4a0995188c4a97eb3bb604abcc401', '326e159e0dbace167726287290620e11367bb15a', '15d53b7189ffbd7c6010c388a9ccea417d4f28ee', '7969541b4a6e4aedb64983b046fc8bdae157dc97', '0c75aad30012f215ae9377700feb44ee1ce42b88', '1d1d62dcf72481cc9580fed3b724f0d27015aaf1', '39e2a4fb550a6b4a0feb5f0ff7b07754b1e27eaa', 'de53ffe7e3c71c9ed5c845fa50e0521efa5f3685', '6c06bc0eb391695a8df55e3106b829fccd887f10', '8b3c0b4adecdc3281c15a824690f5bdf8563ad82', 'defb1317debff80bbec8fcdb53132edf3988ba93', 'c79035b2f27644be5bd748bff59297f28803fae1', '0e5a262402b8c9d98a3b5a424863f6a6c655ab98', '16dafadc5697e59f75664e1ba248486eaef2a782', '688a2ecf1f543651ec563d2c397b137baf8d39ea', '9ea330be4787b838d931c30f76aa9eb4ab69367e', '6daa34883d9b44405f1db9e78e2a9618340bfe03', '14bc29d59e0046cc7d5ce53054f702da18f37c53', '5ded3c04560f13fd035124da9e887353e2038287', '0c9ac4089f6dcdd7933d6ddc7a3551a67f7549c4', 'c6d8196cc9905774ff0a0dd0956df2206eba14ca', 'a5e1764311f9cefac840d655421dd657cd230c42', '5731a1cd721379cb00ca17b7fcacef1252c003b8', '3ddbf6a9b2084721cd633523afa18ff69d03aed9', '44632b2065e713e44db3479c9cea158b0d7d7b02', '6b14a2044dbc040d8c5a1db2a984bf8312f7e1b0', '91f6bfbc72885ff91266257929f3e701ac8d9d92', 'fc08b98693eaf49070af0be567348e447ec8be9e', '3894fd2dc629a96e16049f951be6b018ff627940', '815627a3e3ef0cf88ae51cd0376df58080f09983', 'bb5eb71d81285e23bcf69f0e7a72af02eb356587', '1f2eb6a4fe21ce42ff5d8184f78bc0e97f6a121f', '69b30a314f33a8bafe26c9543bac3026b6171e18', '6cec3a2eb9d9bba5a48ab33bd040479ffda9d1ce', '50354dce4504e2e95b814a2680e8f35d70ad810f', '679c0df4f6248f3003ce0011779e5cca7903e668', '345c70f0084ce5d54046a6939257931f641edf65', '6316fc52678b869c908390f9bd12345bcf07052e', '7eb1b6f432993717eedf721ae6b24b18ace3a44c', 'd1671e9a665dfac958e3cfa8cf5c40fb4e7c9b4f', 'a85a1a11f0a55266e9d3da10dd44cd020eb6770e', '7fc586091374052f653d79dba9e5b10fdf5871f9', 'e85375d18b255f0b61986993c1c829203d470026', '6d30c48e55ab600a4e8590c4c2a68add35a4c472', '9395f3c51c3efbd27914a5738950d7f9c1de0888', 'ea4208f40a36b18be923fcd5adb59b250d9cef96', 'cd9ee63b982cd8b78437a26d9b2f832d6b642e20', 'd26d3ceadd71540201e65658a307db95295dee25', '8130c3c8e8c3225d872c2a4df99c7518067632eb', '9a26d1e00217c44f9cff5628043c9d77c8e75c90', '2f08d0bea553aef451c65b4de64cd36339fb098f', 'a580dc48670c1fa937880a119044c68e5292686a', '19165c986d0ae8c4b34590617cdbf6568d808930', '3f55f400dfbc4f9bc3528feeb15c7ad39208ccb2', '0e005683e100c08b4b74c76e6392c3dd86490690', 'ef8edb79e602536229e73a67abad10d72382ce19', 'd8862ba8e2b0988b0cd4fc371c7d68f32e391720', '2ab3f38e73b53269d6883b8be80da9d38a86ef9b', '0681eb49559b2047783aa8b5cadc8501954f3b68', '486b1a9d061fe44d5a1c3f28e922baad454e3f02', '3fea4a01d89276bb6aba87c553e06441e4fc9095', '869f76279cbaf6f0520f880bef65cc1c20d36e53', '2de7db775eca5a709a31e77db60fd76e9e46911e', '6b6c054161e0f561d43041fbda8284626c95dd25', '48b7ba19fe5a3fea731fae64658b620005de0823', '291c30876ff92945236fc448dc48fb628c5108c1', 'd9345dbbcdfbe20405a86fa08bf4e39cad0c93cd', '2f69e5977adbebbb76a33cf75ba5dcf793eaf835', 'bdaef0e39a4be1fddc32bf6766b4ec4a4e1df725', '4570a3b13d469da6e23fb35763e1307401b5ff3e', '28732583b77da2d5f2847ff928947d42cdedf7d5', '110ddea0853350606bc1e744fda00bfc8f8c5437', 'f9c28549a90c103c64a6ac83d8e3714b7f3e521a', 'c55c9a0ed49b5fd4be47a865f70945690139cd8e', '78fb204176676997e44ba0e3852b5de735854342', '08f142fccbf96e34e95e8c17442a459f429a3310', '088641ea2e5e631fc12f049655218de9b04b98e5', '5e68e16875fe15e105884521db31b823a28219b9', '63eb11031a958d95443425759ebe5805dcea5e3d', '5e78b3ceece619fc9e8660c2a78f41f0c2beec7f', '16b00a9a9b2a6eba2df366a2404eb0d0f3d112a1', '0874d30f54b9b73fc02e8dc90f6b24c20ddb0132', '4c855e83713c01e6a46a1669db047dca05320474', '17abef1a35c19c0506e685cdb6c61d904c654c61', '6e2e49f898f360b818869ecd5c2d8298d0360763', '5d38a298f926cd7336b2f86a9e1be1ad06fa869d', 'a1f7714ed8ee129ff9196087b971929ff9cdd07d', 'c38d58b8e3250841dbc5f34ae98431e7402add93', 'f06d8a77df90309662c6a62b4c92bf0f624f9580', 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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)
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这里每一行代表的是每个手机用户连接各个Wifi接入点的信号强度以及该手机用户个人的位置。
我们需要使用同样的方法来构建测试数据集的特征
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")
# 分成训练数据集场地，测试数据集场地和时间戳
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
# # 对于一条记录，x、y、f，最近时刻商场热门wifi对应的强度
# 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
# print(dfs[0])
# building_df = pd.concat(dfs)
# building_dfs[building] = df
# building_df.to_csv(INPUT_PATH + building + "_1000_train.csv")
# 对提交数据集中按训练数据集场地进行分组
ssubm_building_g = ssubm_df.groupby(0)
feature_dict = {}
for gid0, g0 in ssubm_building_g:
# 对分组后的每一个热点Wifi进行排序
index = sorted(bssid[g0.iloc[0, 0]])
feats = []
# 对提交数据集中按测试数据集场地进行分组
for gid, g in g0.groupby(1):
# 对每一个测试数据集中的场地文件进行读取
with open(os.path.join(INPUT_PATH, 'test/' + g.iloc[0, 1] + '.txt')) as f:
txt = f.readlines()
wifi = []
for line in txt:
line = line.strip().split()
if line[1] == "TYPE_WIFI":
wifi.append(line)
# 记录WIFI信号信息
wifi_df = pd.DataFrame(wifi)
# 获取WIFI信号中所有的时间戳列表
wifi_points = pd.DataFrame(wifi_df.groupby(0).count().index.tolist())
for timepoint in g.iloc[:, 2].tolist():
deltas = (wifi_points.astype(int) - int(timepoint)).abs()
# 获取WIFI信号中跟提交数据集中的时间戳时间间隔最小的索引
min_delta_idx = deltas.values.argmin()
# 获取WIFI信号中的该时间戳
wifi_block_timestamp = wifi_points.iloc[min_delta_idx].values[0]
# 对该时间戳的WIFI信号中的信号强度进行去重
wifi_block = wifi_df[wifi_df[0] == wifi_block_timestamp].drop_duplicates(subset=3)
# print(wifi_block)
# 将WIFI信号中的WIFI接入点转成索引，将信号强度作为唯一数据，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
# 最终得到的feat为某场地所有的Wifi接入点与信号强度以及训练数据集的场地_测试数据集场地_时间戳
feat = wifi_block.set_index(3)[4].reindex(index).fillna(-999)
feat['site_path_timestamp'] = g.iloc[0, 0] + "_" + g.iloc[0, 1] + "_" + timepoint
feats.append(feat)
# break
# break
# break
feature_df = pd.concat(feats, axis=1).T
feature_df.to_csv(INPUT_PATH + gid0 + "_1000_test.csv")
feature_dict[gid0] = feature_df
运行结果(csv中的数据)
000840e5c600de293cea57f13326f273c86c3988 00ad587dcb9c7ce3788b92e22777a22ee0efea31 00af060fc145ee6a6a50475efa57b91cbf54237f 00bcc61bdea4d52d050822d66952dd707c2fcdf3 00f0904087c01d922d6ebf3005607dfdeaf6687b 011e20ebf721a1c6dfec42e8ed1e2ac566073a2a 01d2f676abab6ec03ec5dc696bfd49d66e392ea1 01e25e4a25acd32baf5137b3031151f751fadbb4 026c2f057932da75680b21ecdbd23bf9cb9350f3 028a310e23177c3747d37971678dd964ee28ce17 02a1be3a5dab38320f879489d8a1e0f2a72768b3 043907be508d7eed391e1218cadd52308c4046e1 045ef6205f933119d7e186aa4e55fd6737cffe48 0482b38bf4daa9243fff612a2594d8206fde8fd5 0489782c0949a75a43da32cf6defd66e9ffd4f59 04af33baa87957b484fdf8d5f17a64f972af7f2b 04b820b511e2ad7b23e75c34413a1de404d4ccfc 050bf0c3db9a1017be0fc746de7b5c1a68a918a7 054639db76db4b40ae6ae4dcc615a661b3c1f532 059ea3d13de011f91587f1d176599605274f8ee8 05a83a7c1aeb1f8b9786599a98fda851c649f972 0681eb49559b2047783aa8b5cadc8501954f3b68 0697d08b2741a320bcdcbb9624caca0e3a415356 069d1a6cd62e727b197abd1c5efb2f678157150a 06aed0ed92794f30f58f2991e09e62bc01434362 06e02065793833706e3045b5c5a0b7c645ec76b2 0746024a3e1bc32db07864be99f31da059a871cf 07d2809389f6bcb2c232bc28f7457f12d76561a5 0874d30f54b9b73fc02e8dc90f6b24c20ddb0132 087be6452a54ea53874070a1b07f1479f06e1a78 088641ea2e5e631fc12f049655218de9b04b98e5 08f142fccbf96e34e95e8c17442a459f429a3310 093daa800aa8dd3a4543b51cdac53ae71fac9dcf 097599fa8b0d2bb4426ba0dae5ca9e578cdb8149 0981ea46343ddc36e67af51d3832ee20055e31ed 09e7a3ace3201397480073d7fde070df500d5804 0a08023cf58fd597ddf23b45187d9ac255fb8ff2 0a23758a5622e59426bd94da6702cf3262acb80d 0a32c7b628a7a216a79e552b9162822f0b66c348 0a670fddca113cc7a87dbadd903190dd6d22c75d 0a741c9b887c6f7793cc6a3d4c8fa221de5988ca 0a82da00c486894c4b08810661bd2a835898448f 0a92e69e85c964ee03c2d7f290b977d05a2b3444 0acd5446bf9e351dc039c99e653c4e398171a9b2 0ae129da9fb6e90b624d53a7e34a3edbb0a7244b 0af23ecb287b87d4d0528c23181e8242ecba1582 0b3ef761fe9c3069e744c6d2c2dfcba9d9b1f9b4 0b5f69e5bbfd1cd46da3b29afd1d674c0925c2ab 0be2972e802a68de6066e692b402d75c69b94c33 0c235b02dd008286ea5053621c7ce50df7f17e10 0c4130e1e769677a016612b48d96480a8cdd1dc4 0c75aad30012f215ae9377700feb44ee1ce42b88 0c9ac4089f6dcdd7933d6ddc7a3551a67f7549c4 0d0221202a9b42c2561704448ef04840b18ae8a8 0d22d098d33a2b3ba059f1d9809e45661ef24739 0d65bccbb61c2aaff137673e2e3e401d305a2621 0e005683e100c08b4b74c76e6392c3dd86490690 0e2250a84319bd6fb557786aa36b3c329f9b4fd1 0e5814f651b6a398edeaaa76c4a6d3d2920be890 0e5a262402b8c9d98a3b5a424863f6a6c655ab98 0eafcd2f8e1fbad0cc7b0ee227d18c772d562e6d 105d017be5036876409c1f48c31f7f5684e61906 107b4cda60752a916e9719ed0bbb177951144855 10bae54946b5b77e8dd6b95fe42eeca3fdb5e0cf 10cf16ad6534635699a05f3a6c344526b9d7d549 11035847ca0854c99104df042eb0c28912b47855 110ddea0853350606bc1e744fda00bfc8f8c5437 112de1223847bd845f0f6f2a6ddcfce1d0290f03 113b2ccf3a21d62269e24c8a0b2907871ac0548e 11a92d7b4a838ff9119cf928c55f91ca353e2c8b 11b29720f56e05203439593fc1d09d068c370362 11ee5a7bcc33630821f55783947909ad6f64a3c0 11fa59b18892d70b03751701eff1707495ccf618 12255ad2dc5e24065c29137e415fdcc743b03b09 1275fa852bd0c68febf1d8ca38b527c9b2404548 1290ce7d7e17eed000ac5928f4f32f681c660d12 12b84c152a88b83c6ebbfa5a427101d14a3ed941 130cf54fc1d3d3c2de2febdf405087bfc0af07f9 132b2b96189f2ec6d52bdf5ef1350b0cc32c3b39 13658398bbef2ab3a4d9eefcbd92fa5c6ca83152 13ab407e5ea93d8d63431f9b41fc644b7c739eda 13b7aeaf441f2161481481fe67eace721cff07ab 13cd1e6a6af504e46baff3e4ebf4084566a55272 1477e2804baeafbd9bee7bb329bd44359af70b35 149c09a117b9851201c75f97b4a7cc94b75fdcb4 14bc29d59e0046cc7d5ce53054f702da18f37c53 15705627b6d4a0995188c4a97eb3bb604abcc401 15b5928c3944942e322366d6548d690de9a6380f 15d4a31cd3d067065469f1e9e969cbcf02d0a479 15d53b7189ffbd7c6010c388a9ccea417d4f28ee 164cb3a5b9bf5adcf986462d0bfa9b14a2ea0a25 164d78c002a490c56bb036db273c707e311dedc5 1665601c2eda5ed92cad8dd4879e46af793bdf8e 169479ef75a2db149091129bc2e009b0c3f26f9f 16b00a9a9b2a6eba2df366a2404eb0d0f3d112a1 16dafadc5697e59f75664e1ba248486eaef2a782 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1b51d55a893a54033a8bc88278c2d0ad7ee87b35 1b66000d0d96cc0a1edeab4593a1e1e217c162b5 1b8286ce49240e893317288ec4c13ad463a63829 1bb0e992cff45a54d29e97f47a7d1281435a5e3b 1bc27f89396ed1c36aa496f448551620bb4e1f20 1c229d4aeecc898b349b60d9cc554b0ed00aec0f 1c8e3ea2ec9808edfe25e8c3eb6fb2ee4bfd7c16 1ccfad270acf0c810365000a5b7b1202b946a80c 1cdf73f6cdd794c3695bc868cf2742fc50d3174f 1d067d90fd180e2de69d53c8f42bf26176fcd0f8 1d14b137ab03637ad1bb542d4acd1c515c2fc195 1d1d62dcf72481cc9580fed3b724f0d27015aaf1 1d46c19cfbaacc249aa686a9b4d78834a4748318 1d52ac70bfa8eae028e7b0d05ef5ca2bfcf513e9 1e03d9b22fdd3e0a9141525c2772fe1c82327124 1e10f0386854e58a5d81c13c76409217721d494f 1ec5e4ca2c856b82dd0b298e8c4382d79de95e21 1edd1dad481ddbfb7c7735767a48eb1854ac5ef5 1efaee6f4a0ee182fa8e0bb31069f2d774694553 1f2eb6a4fe21ce42ff5d8184f78bc0e97f6a121f 1f38cc3d16cf43dfdbea382785f717bc3a30dcce 1f6187b5a484f10cf8ee49b0483e1aefe6b2ada8 1fd4e31d82de1a11ddeb8016b9a8d0d888ab7680 21f3a763336708abc51d71bcbe48b925d260d308 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这里表示的是某商场某个时刻的Wifi信号强度信息。现在训练数据集的特征和测试数据集的特征都提取完成了，现在就剩下了建模。这里依然采用LightGBM模型进行训练。
INPUT_PATH = "/Users/admin/Downloads/indoor-location-navigation/"
import pandas as pd
import numpy as np
import lightgbm as lgb
import glob
import os
if __name__ == "__main__":
pd.set_option('display.max_columns', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth', 1000)
def comp_metric(xhat, yhat, fhat, x, y, f):
# 评价指标
intermediate = np.sqrt(np.power(xhat - x, 2) + np.power(yhat - y, 2)) + 15 * np.abs(fhat - f)
return intermediate.sum() / xhat.shape[0]
train_files = sorted(glob.glob(os.path.join(INPUT_PATH, '*_train.csv')))
test_files = sorted(glob.glob(os.path.join(INPUT_PATH, '*_test.csv')))
ssubm = pd.read_csv(INPUT_PATH + "sample_submission.csv", index_col=0)
predictions = []
for e, file in enumerate(train_files):
data = pd.read_csv(file, index_col=0)
test_data = pd.read_csv(test_files[e], index_col=0)
np.random.shuffle(data.values)
cutoff = int(data.shape[0] * 0.95)
# 将95%的去掉标签(x,y,f,path)的数据作为训练数据
X_train = data.iloc[:cutoff, :-4]
# 提取标签y坐标
Y_trainy = data.iloc[:cutoff, -3]
# 提取标签x坐标
Y_trainx = data.iloc[:cutoff, -4]
# 提取标签f楼层
Y_trainf = data.iloc[:cutoff, -2]
# 将5%的去掉标签(x,y,f,path)的数据作为验证数据
X_test = data.iloc[cutoff:, :-4]
# 提取验证标签y坐标
Y_testy = data.iloc[cutoff:, -3]
# 提取验证标签x坐标
Y_testx = data.iloc[cutoff:, -4]
# 提取验证标签f楼层
Y_testf = data.iloc[cutoff:, -2]
# 创建一个150个子模型(树)，每个子模型(树)最多127个叶子节点的LightGBM的回归模型
modely = lgb.LGBMRegressor(n_estimators=150, num_leaves=127)
# 对y坐标为标签进行训练
modely.fit(X_train, Y_trainy)
modelx = lgb.LGBMRegressor(n_estimators=150, num_leaves=127)
modelx.fit(X_train, Y_trainx)
# 创建一个150个子模型(树)，每个子模型(树)最多127个叶子节点的LightGBM的分类模型
modelf = lgb.LGBMClassifier(n_estimators=150, num_leaves=127)
# 对f楼层为标签进行训练
modelf.fit(X_train, Y_trainf)
# 对验证数据集进行预测
predsx = modelx.predict(X_test)
predsy = modely.predict(X_test)
predsf = modelf.predict(X_test)
print(file)
print(data.shape)
# 打印三个模型的评价指标
print(comp_metric(predsx, predsy, predsf, Y_testx.values, Y_testy.values, Y_testf.values))
# 对测试数据集进行预测
test_predsx = modelx.predict(test_data.iloc[:, :-1])
test_predsy = modely.predict(test_data.iloc[:, :-1])
test_predsf = modelf.predict(test_data.iloc[:, :-1])
test_preds = pd.DataFrame(np.stack((test_predsf, test_predsx, test_predsy))).T
test_preds.columns = ssubm.columns
test_preds.index = test_data['site_path_timestamp']
test_preds['floor'] = test_preds['floor'].astype(int)
predictions.append(test_preds)
all_preds = pd.concat(predictions)
all_preds = all_preds.reindex(ssubm.index)
all_preds.to_csv(INPUT_PATH + "submission.csv")
运行结果
/Users/admin/Downloads/indoor-location-navigation/5a0546857ecc773753327266_1000_train.csv
(9296, 945)
11.847351310157741
这里我们可以看到评价指标(类似均方根误差)为11.847
另外生成的提交数据(csv文件中)为(截取部分数据)
site_path_timestamp floor x y
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000000009 0 91.35031809 96.44442141
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000009017 0 95.78423828 101.0603432
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000015326 0 89.37644652 104.6010504
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000018763 0 87.68702242 105.8018905
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000022328 0 86.3411304 110.9726297
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000029946 0 85.89804258 111.791335
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000040283 0 86.05449233 111.2339216
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000051343 0 84.57550261 107.9793239
5a0546857ecc773753327266_046cfa46be49fc10834815c6_0000000064014 0 84.65512739 104.3153858
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000000012 0 86.85921388 13.43255085
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000005748 0 84.4265552 15.32324735
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000014654 0 83.09659829 13.61825885
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000019316 0 79.98820955 13.77209281
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000027660 0 83.21162871 19.75595642
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000034445 0 86.71747497 15.26747536
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000045583 0 84.20824969 13.80866684
5a0546857ecc773753327266_05d052dde78384b0c543d89c_0000000058977 0 83.33401223 17.8449886
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000000011 -1 157.0403826 171.0640444
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000011818 -1 159.3681662 159.8528629
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000019825 -1 159.4743927 154.3828182
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000027199 -1 162.1237298 164.5037938
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000033660 -1 167.542069 164.6778312
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000043214 -1 167.2290274 164.3748587
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000053320 -1 169.4816089 158.0309159
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000059671 -1 175.6675466 158.9716003
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000064822 -1 185.5745015 159.3967962
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000072018 -1 187.2667655 159.0520404
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000084546 -1 159.8953111 172.0090131
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000094486 -1 158.3883281 165.6197695
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000103491 -1 161.3073712 165.0953302
5a0546857ecc773753327266_0c06cc9f21d172618d74c6c8_0000000112216 -1 162.0058941 173.3711312
5a0546857ecc773753327266_146035943a1482883ed98570_0000000000011 -1 62.35331493 159.9370218
5a0546857ecc773753327266_146035943a1482883ed98570_0000000004535 -1 62.35331493 159.9370218
5a0546857ecc773753327266_146035943a1482883ed98570_0000000011498 -1 58.79308643 157.030047
5a0546857ecc773753327266_146035943a1482883ed98570_0000000019249 -1 58.39346368 157.1500315
