2e1e771b3d
Correct errors in RAPIDS config.
2021-12-09 17:34:37 +01:00
54c712b1cd
Further adapts RAPIDS and add graphical examples.
2021-12-08 19:20:53 +01:00
375cb10c1e
Adapt RAPIDS for our use case.
2021-12-08 18:39:10 +01:00
689535e444
Add rules for usernames and timezones.
2021-12-01 18:22:56 +01:00
f5c17aa7ce
Incorporate DB query for usernames into snakemake workflow.
2021-12-01 18:14:27 +01:00
7d0355d095
Gather usernames from DB to create CSV.
2021-12-01 17:22:22 +01:00
6eca98962f
Export timezone info, too.
2021-12-01 17:08:24 +01:00
0e6a18a660
Prepare data for export to CSV.
2021-12-01 16:50:43 +01:00
cf3801b120
Import participants' wristband info.
2021-12-01 11:55:58 +01:00
578f5ed5f3
Start preparing the true usernames CSV file.
2021-12-01 11:29:22 +01:00
6f9b513ba0
Make the participants rule run.
2021-11-29 18:37:50 +01:00
c2b27aef45
Add yaml files to submodule.
2021-11-24 19:12:36 +01:00
4bb01ba871
Add functions to prepare participants files.
2021-11-24 19:09:06 +01:00
080dc1d1fe
Add instructions for RAPIDS.
2021-11-18 17:22:08 +01:00
4e87664fd6
Force UNIX eol characters.
2021-11-17 19:20:19 +01:00
c0ea17dbe3
Update rapids.
2021-11-17 18:58:12 +01:00
1d65a31d8c
Update rapids submodule.
2021-11-17 12:59:12 +01:00
d4ba701202
Add the rapids fork submodule.
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Reproducible Analysis Pipeline for Data Streams
2021-11-17 12:47:42 +01:00
e33a49c9fc
Add a demo of pipeline.
2021-11-17 10:44:49 +01:00
d34c2ec5e9
Merge branch 'ambient' into ml_pipeline
2021-11-17 10:39:55 +01:00
005b09cfdf
[WIP] Fix tests to use pyprojroot.
2021-10-29 12:07:12 +02:00
6fc0d962ae
Remove low values of pressure.
2021-10-22 18:09:17 +02:00
92fbda242b
Explore barometer and temperature data.
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Add docstrings to models.
2021-10-14 17:59:33 +02:00
6302a0f0d9
Merge ambient sensors into one file.
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Explore barometer sensor data for one phone.
2021-10-13 16:57:38 +02:00
a63a7eac99
[WIP] Add a test for SensorFeatures.
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Additional analysis for adherence.
Small corrections.
2021-10-13 13:39:58 +02:00
b8c7606664
Add an option to read cached labels from a file.
2021-09-15 15:45:49 +02:00
ed062d25ee
Add export capabilities to labels.py.
2021-09-15 15:36:36 +02:00
20748890a8
Further refactor by moving helper functions.
2021-09-15 15:14:54 +02:00
28699a0fdf
Enable reading features from csv files.
2021-09-14 17:42:34 +02:00
af9e81fe40
Document the SensorFeatures class and its __init__ method.
2021-09-13 17:43:47 +02:00
b19eebbb92
Refactor machine_learning/pipeline.py by defining one class by file.
2021-09-13 11:41:57 +02:00
c1bb4ddf0f
Save calculated features to csv files.
2021-08-23 16:36:26 +02:00
0152fbe4ac
Delete the leftover class.
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Add more prints.
2021-08-23 16:09:23 +02:00
3611fc76f7
Fill NaNs after merging all features.
2021-08-21 19:48:57 +02:00
ee30c042ea
Fill NaNs introduced in merge for proximity.
2021-08-21 19:40:42 +02:00
a71e132edf
Prepare the first full pipeline.
2021-08-21 19:04:09 +02:00
24c4bef7e2
Print some more messages.
2021-08-21 19:03:44 +02:00
11381d6447
Add some print statements for monitoring progress.
2021-08-21 18:54:02 +02:00
d19995385d
Account for the case when there is no data for days with labels.
2021-08-21 18:49:57 +02:00
f73f86486a
Fill communication features with appropriate values.
2021-08-21 18:28:22 +02:00
aed73bb7ed
Add fill values for communication for rows with no calls/smses.
2021-08-21 18:17:58 +02:00
8507ff5761
Check for NaNs in the data, since sklearn.LinearRegression cannot handle them.
2021-08-21 17:46:00 +02:00
0b85ee8fdc
Merge branch 'master' into ml_pipeline
2021-08-21 17:37:45 +02:00
e2e268148d
Fill in 0.5 for undefined ratio.
...
When there are no calls and no smses (of a particular type), the ratio is undefined. But since their number is the same, I argue that the ratio can represent that with a 0.5, similarly to the case where no_calls_all = no_sms_all != 0.
2021-08-21 17:33:31 +02:00
00015a3b8d
Fill in zeroes when joining or unstacking.
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If there are no calls or smses for a particular day, there is no corresponding row in the features dataframe. When joining these, however, NaNs were introduced. Since a value of 0 is meaningful for all of these features, replace NaNs with 0's.
2021-08-21 17:31:15 +02:00
065cd4347e
[WIP] Add a class for model validation.
2021-08-20 19:44:50 +02:00
0b98d59aad
Aggregate labels using grouping_variable.
2021-08-20 19:17:22 +02:00
08fdec34f1
Merge features into a common df.
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But first, group communication by the grouping_variable.
2021-08-20 17:59:00 +02:00
72b16af75c
Make group_by consistent with communication.
2021-08-20 17:52:31 +02:00
d6337e82ac
Merge branch 'master' into ml_pipeline
2021-08-20 17:43:53 +02:00
junos