Winning solution of the Indoor Location & Navigation Kaggle competition

Tom Van de Wiele

Last update: Dec 28, 2022

Related tags

Deep Learning Indoor-Location-Navigation-Public

Overview

This repository contains the code to generate the winning solution of the Kaggle competition on indoor location and navigation organized by Microsoft Research.

Our team name: "Track me if you can".

Authors:

Are Haartveit
Dmitry Gordeev
Tom Van de Wiele

References

Steps to obtain the approximate winning submission

Clone the repository, it doesn't matter where you clone it to since the source code and data are disentangled.
Create a project folder on a disk with at least 150GB of free space. Create a "Data" subfolder in your project folder. This will be referred to as "your data folder" in what follows.
Download the raw text data from here and extract it into your data folder.
Download the cleaned raw data from here and extract it into the "reference_preprocessed" subfolder of your data folder.
Add your data folder to line 19 in src/utils.py.
Run main.py.

If all goes well, the pipeline should create a "final_submissions" subfolder in your data folder with two final submissions. Note that these are likely slightly different from our actual submissions due to inherent training stochasticity. When you make a late submit of these submissions to the leaderboard, you should obtain a private score around 1.5, which can be further reduced to about 1.3 after fixing the private test floor predictions (not part of this repository).

Main script parameters

Mode ("-m" or "--mode"). Default: 'test'. Select from ('valid', 'test').
Suppress multipricessing ("-s"). Default: no suppression of multiprocessing.
Fast (and bad) sensor models ("-f"). Default: no fast sensor models. Mostly useful for verifying that all dependencies are in place. Ignored when copying sensor models (next parameter).
Copy sensor predictions ("-c"). Default: no copying of pretrained sensor predictions. Useful if you want to speed up the pipeline since training sensor models is the slowest part.

Hardware requirements

Due to the size of the data set, you need at least 32 GB RAM to be able to run the pipeline successfully.

Known issues

If you run out of memory, try running the pipeline again. It should continue where it left it in the previous run.

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Comments

How to get the text file in Indoor-Location-Navigation-Public/data/ including leaderboard_type.csv?

Congrats 1st place in this competition! This is an amazing solution and thanks for sharing!

I'm wondering about how to get the text file in Indoor-Location-Navigation-Public/data/ including leaderboard_type.csv, non_parametric_wifi - valid - 2021-03-30 091444.csv, submission_cost_minimization.csv, test - 2021-05-15 051944.csv.

At the beginning of the whole program, I found that you copy the text file mentioned above and I dont know how to get these files and is there any code to build these files?

I am a beginner in indoor localization and looking forward to your reply!

Thanks!

opened by YoonaYaa 4
Issue while running the script

Thank you for your brilliant work in the indoor location navigation, I am so impressed of your achievement and congratulation on your champion! I am new in this field and I am trying to understand your code, attempting to run it at first. However I met an error as follows:

Traceback (most recent call last): File "main.py", line 61, in main(args.mode, args.s) File "main.py", line 36, in main sensor_errors_over_time_device.run() File "./src/sensor_errors_over_time_device.py", line 63, in run preds = pd.read_csv(absolute_fold_folder / f'preds_bag_fold_{i}.csv') File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 605, in read_csv return _read(filepath_or_buffer, kwds) File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 457, in _read parser = TextFileReader(filepath_or_buffer, **kwds) File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 814, in init self._engine = self._make_engine(self.engine) File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 1045, in _make_engine return mapping[engine](self.f, **self.options) # type: ignore[call-arg] File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 1862, in init self._open_handles(src, kwds) File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py", line 1357, in _open_handles self.handles = get_handle( File "/home/yangxj/anaconda3/lib/python3.8/site-packages/pandas/io/common.py", line 642, in get_handle handle = open( FileNotFoundError: [Errno 2] No such file or directory: '../Indoor-Location-Navigation-Public/Models/sensor_absolute_movement/errors cv/preds_bag_fold_0.csv'

I am not sure if I got all the necessary files, could you kindly help me with it?

opened by YangXiaojie1998 1
all_train.pickle not found in main procedural

I try to follow the sequence 'Steps to obtain the approximate winning submission' , but it's get stop at function extract_floor_start_end in infer_start_end_time_leak.py and error message is as show below: https://i.imgur.com/6DTPrBn.png Exception has occurred: FileNotFoundError [Errno 2] No such file or directory: '/mnt/d/Project/LocationAI/IndoolLocationAI/Github-Indoor-Location-Navigation-Public/MyData/train/5a0546857ecc773753327266/B1/all_train.pickle' File "/mnt/d/Project/LocationAI/IndoolLocationAI/Github-Indoor-Location-Navigation-Public/src/infer_start_end_time_leak.py", line 57, in extract_floor_start_end with open(floor_wifi_path, 'rb') as f: File "/mnt/d/Project/LocationAI/IndoolLocationAI/Github-Indoor-Location-Navigation-Public/src/infer_start_end_time_leak.py", line 442, in run extract_floor_start_end( File "/mnt/d/Project/LocationAI/IndoolLocationAI/Github-Indoor-Location-Navigation-Public/main.py", line 66, in main infer_start_end_time_leak.run(mode) File "/mnt/d/Project/LocationAI/IndoolLocationAI/Github-Indoor-Location-Navigation-Public/main.py", line 93, in main(args.mode, args.s, args.f, args.c)

It's seems that it doesn't find the file all_train.pickle. What is this file for?And how to create it?

opened by reinforce314 0