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Project description
A Python framework for processing wearable data in the health domain.
Tasrif is a library for processing of eHealth data. It provides:
- A pipeline DSL for chaining together commonly used processing operations on time-series eHealth data, such as resampling, normalization, etc.
- DataReaders for reading eHealth datasets such as MyHeartCounts, SleepHealth and data from FitBit devices.
Installation
To use Tasrif, you will need to have the package installed. Please follow the bellow steps to install Tasrif:
First, create a virtual environment using venv with a linux operating system machine, or with Windows Subsystem for Linux
# Create a virtual environment
python3 -m venv tasrif-env
# Activate the virtual environment
source tasrif-env/bin/activate
# Upgrade pip
(tasrif-env) pip install --upgrade pip
Then, install Tasrif either from PyPI
(tasrif-env) pip install tasrif
or install from source
(tasrif-env) git clone https://github.com/qcri/tasrif
(tasrif-env) cd tasrif
(tasrif-env) pip install -e .
If no installation errors occur, see Quick start by usecase section to use Tasrif.
Note on feature extraction using Tasrif
Due to some outdated internal Tasrif dependancies on Pypi, we have decided to place those dependancies in requirements.txt. Once those packages are updated in Pypi, we will move them back to setup.py. The current requirements.txt specifies the dependancies links directly from Github. If you plan to use the following two operators: TSFreshFeatureExtractorOperator or CalculateTimeseriesPropertiesOperator, you will need TSFresh and Kats packages installed, which can be done by running the following command
(tasrif-env) MINIMAL_KATS=1 pip install -r requirements.txt
Note that MINIMAL_KATS=1 is passed in the installation script to minimally install Kats. See requirements.txt for details.
Features
Pipeline DSL
Tasrif provies a variety of processing operators that can be chained together in a pipeline. The operators themselves take as input and output Pandas DataFrames.
For example, consider the AggregateOperator:
>>> import pandas as pd
>>> from tasrif.processing_pipeline.custom import AggregateOperator
>>> from tasrif.processing_pipeline.pandas import DropNAOperator
>>> df0 = pd.DataFrame([
['Doha', 25, 30],
['Doha', 17, 50],
['Dubai', 20, 40],
['Dubai', 21, 42]],
columns=['city', 'min_temp', 'max_temp'])
>>> operator = AggregateOperator(
groupby_feature_names="city",
aggregation_definition={"min_temp": ["mean", "std"]})
>>> df0 = operator.process(df0)
>>> df0
[ city min_temp_mean min_temp_std
0 Doha 21.0 5.656854
1 Dubai 20.5 0.707107]
Operators are meant to be used as part of a pipeline, where they can be chained together for sequential processing of data:
>>> import pandas as pd
>>> from tasrif.processing_pipeline import SequenceOperator
>>> from tasrif.processing_pipeline.custom import AggregateOperator, CreateFeatureOperator
>>> from tasrif.processing_pipeline.pandas import ConvertToDatetimeOperator, SortOperator
>>> df0 = pd.DataFrame([
['15-07-2021', 'Doha', 25, 30],
['16-07-2021', 'Doha', 17, 50],
['15-07-2021', 'Dubai', 20, 40],
['16-07-2021', 'Dubai', 21, 42]],
columns=['date', 'city', 'min_temp', 'max_temp'])
>>> pipeline = SequenceOperator([
ConvertToDatetimeOperator(feature_names=["date"]),
CreateFeatureOperator(
feature_name='avg_temp',
feature_creator=lambda df: (df['min_temp'] + df['max_temp'])/2),
SortOperator(by='avg_temp')
])
>>> pipeline.process(df0)
[ date city min_temp max_temp avg_temp
0 2021-07-15 Doha 25 30 27.5
2 2021-07-15 Dubai 20 40 30.0
3 2021-07-16 Dubai 21 42 31.5
1 2021-07-16 Doha 17 50 33.5]
DataReaders
Tasrif also comes with DataReader classes for importing various eHealth datasets into pipelines. These readers preprocess the raw data and convert them into a DataFrame for downstream processing in a pipeline.
Supported datasets include:
- MyHeartCounts
- SleepHealth
- Zenodo FitBit
- Export data from FitBit devices
- Export data from Withings devices
- ...and more
DataReaders can be used by treating them as source operators in a pipeline:
from tasrif.processing_pipeline import SequenceOperator
from tasrif.data_readers.my_heart_counts import DayOneSurveyDataset
from tasrif.processing_pipeline import DropNAOperator
day_one_survey_path = <path to MyHeartCounts DayOneSurvey file>
pipeline = Pipeline([
DayOneSurveyDataset(day_one_survey_path),
DropNAOperator,
SetIndexOperator('healthCode'),
])
pipeline.process()
Quick start by usecase
- Reading data
- Compute statistics
- Extract features from existing columns
- Filter data
- Wrangle data
- Test prepared data
- Create a pipeline to link the operators
- Debug your pipeline
- Define a custom operator
- Other references
Reading data
Reading a single csv file
from tasrif.processing_pipeline.pandas import ReadCsvOperator
operator = ReadCsvOperator('examples/quick_start/csvs/participant1.csv')
df = operator.process()[0]
Reading multiple csvs in a folder
from tasrif.processing_pipeline.custom import ReadCsvFolderOperator
operator = ReadCsvFolderOperator(name_pattern='examples/quick_start/csvs/*.csv')
df = operator.process()[0]
by default, ReadCsvFolderOperator concatenates the csvs into one dataframe. if you would like to work on the csvs separately, you can pass the argument concatenate=False to ReadCsvFolderOperator, which returns a python generator that iterates the csvs.
Reading csvs referenced by a column in dataframe df
import pandas as pd
from tasrif.processing_pipeline.custom import ReadNestedCsvOperator
df = pd.DataFrame({"name": ['Alfred', 'Roy'],
"age": [43, 32],
"csv_files_column": ['participant1.csv', 'participant2.csv']})
operator = ReadNestedCsvOperator(folder_path='examples/quick_start/csvs/',
field='csv_files_column')
generator = operator.process(df)[0]
for record, details in generator:
print(record)
print(details)
Reading json files referenced by a column in dataframe df
import pandas as pd
from tasrif.processing_pipeline.custom import IterateJsonOperator
df = pd.DataFrame({"name": ['Alfred', 'Roy'],
"age": [43, 32],
"json_files_column": ['participant1.json', 'participant2.json']})
operator = IterateJsonOperator(folder_path='examples/quick_start/jsons/',
field='json_files_column',
pipeline=None)
generator = operator.process(df)[0]
for record, details in generator:
print(record)
print(details)
Compute statistics
Compute quick statistics using StatisticsOperator. StatisticsOperator includes counts of rows, missing data, duplicate rows, and others.
import pandas as pd
from tasrif.processing_pipeline.custom import StatisticsOperator
df = pd.DataFrame( [
['2020-02-20', 1000, 1800, 1], ['2020-02-21', 5000, 2100, 1], ['2020-02-22', 10000, 2400, 1],
['2020-02-20', 1000, 1800, 1], ['2020-02-21', 5000, 2100, 1], ['2020-02-22', 10000, 2400, 1],
['2020-02-20', 0, 1600, 2], ['2020-02-21', 4000, 2000, 2], ['2020-02-22', 11000, 2400, 2],
['2020-02-20', None, 2000, 3], ['2020-02-21', 0, 2700, 3], ['2020-02-22', 15000, 3100, 3]],
columns=['Day', 'Steps', 'Calories', 'PersonId'])
filter_features = {
'Steps': lambda x : x > 0
}
sop = StatisticsOperator(participant_identifier='PersonId',
date_feature_name='Day',
filter_features=filter_features)
sop.process(df)[0]
Or use ParticipationOverviewOperator to see statistics per participant. Pass the argument overview_type="date_vs_features" to compute statistics per date. See below
from tasrif.processing_pipeline.custom import ParticipationOverviewOperator
sop = ParticipationOverviewOperator(participant_identifier='PersonId',
date_feature_name='Day',
overview_type='participant_vs_features')
sop.process(df)[0]
Use AggregateOperator if you require specific statistics for some columns
from tasrif.processing_pipeline.custom import AggregateOperator
operator = AggregateOperator(groupby_feature_names ="PersonId",
aggregation_definition= {"Steps": ["mean", "std"],
"Calories": ["sum"]
})
operator.process(df)[0]
Extract features from existing columns
Convert time columns into cyclical features, which are more efficiently grasped by machine learning models
from tasrif.processing_pipeline.custom import EncodeCyclicalFeaturesOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
df = ReadCsvOperator('examples/quick_start/steps_per_day.csv',
parse_dates=['Date']).process()[0]
operator = EncodeCyclicalFeaturesOperator(date_feature_name="Date",
category_definition="day")
operator.process(df)[0]
Extract timeseries features using CalculateTimeseriesPropertiesOperator which internally calls kats package
from tasrif.processing_pipeline.kats import CalculateTimeseriesPropertiesOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
df = ReadCsvOperator('examples/quick_start/long_ts.csv',
parse_dates=['Date']).process()[0]
operator = CalculateTimeseriesPropertiesOperator(date_feature_name="Date", value_column='Steps')
operator.process(df)[0]
Extract using features using tsfresh package
from tasrif.processing_pipeline.custom import SlidingWindowOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
from tasrif.processing_pipeline.tsfresh import TSFreshFeatureExtractorOperator
df = ReadCsvOperator('examples/quick_start/cgm.csv',
parse_dates=['dateTime']).process()[0]
op = SlidingWindowOperator(winsize="1h15t",
time_col="dateTime",
label_col="CGM",
participant_identifier="patientID")
df_timeseries, df_labels, df_label_time, df_pids = op.process(df)[0]
op = TSFreshFeatureExtractorOperator(seq_id_col="seq_id", date_feature_name='dateTime', value_col='CGM')
features = op.process(df_timeseries)[0]
features.dropna(axis=1)
Note that TSFreshFeatureExtractorOperator requires a column seq_id. This column indicates which entities the time series belong to. Features will be extracted individually for each entity (id). The resulting feature matrix will contain one row per id. The column can be created manually or be created via SlidingWindowOperator.
Filter data
filter rows, days, or participants with a custom condition using FilterOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
from tasrif.processing_pipeline.custom import FilterOperator
df = ReadCsvOperator('examples/quick_start/filter_example.csv',
parse_dates=['Hours']).process()[0]
operator = FilterOperator(participant_identifier="Id",
date_feature_name="Hours",
epoch_filter=lambda df: df['Steps'] > 10,
day_filter={
"column": "Hours",
"filter": lambda x: x.count() < 10,
"consecutive_days": (7, 12) # 7 minimum consecutive days, and 12 max
},
filter_type="include")
operator.process(df)[0]
Wrangle data
Add a column using CreateFeatureOperator
import pandas as pd
from pandas import Timestamp
df = pd.DataFrame([
[Timestamp('2016-12-31 00:00:00'), Timestamp('2017-01-01 09:03:00'), 5470, 2968, 1],
[Timestamp('2017-01-01 00:00:00'), Timestamp('2017-01-01 23:44:00'), 9769, 2073, 1],
[Timestamp('2017-01-02 00:00:00'), Timestamp('2017-01-02 16:54:00'), 9444, 2883, 1],
[Timestamp('2017-01-03 00:00:00'), Timestamp('2017-01-05 22:49:00'), 20064, 2287, 1],
[Timestamp('2017-01-04 00:00:00'), Timestamp('2017-01-06 07:27:00'),16771, 2716, 1]],
columns = ['startTime', 'endTime', 'steps', 'calories', 'personId']
)
operator = CreateFeatureOperator(
feature_name="duration",
feature_creator=lambda df: df['endTime'] - df['startTime'])
operator.process(df)[0]
Upsample or downsample date features using ResampleOperator. The first argument rule can be minutes min, hours H, days D, and more. See details of resampling here
from tasrif.processing_pipeline.pandas import ReadCsvOperator
from tasrif.processing_pipeline.custom import ResampleOperator
df = ReadCsvOperator('examples/quick_start/sleep.csv',
parse_dates=['timestamp'],
index_col=['timestamp']).process()[0]
op = ResampleOperator('D', {'sleep_level': 'mean'})
op.process(df)
Note that, currently, the index of the dataframe has to be of type DatetimeIndex so that ResampleOperator can be called correctly.
Set the start hour of the day to some hour using SetStartHourOfDayOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
from tasrif.processing_pipeline.custom import SetStartHourOfDayOperator
df = ReadCsvOperator('examples/quick_start/filter_example.csv',
parse_dates=['Hours']).process()[0]
operator = SetStartHourOfDayOperator(date_feature_name='Hours',
participant_identifier='Id',
shift=6)
operator.process(df)[0]
a new column shifted_time_col will be created. This can be useful if the user wants to calculate statistics at a redefined times of the day instead of midnight-to-midnight (e.g. 8:00 AM - 8:00 AM).
Concatenate multiple dataframes or a generator using ConcatOperator
import pandas as pd
from tasrif.processing_pipeline.pandas import ConcatOperator
df = pd.DataFrame([
[Timestamp('2016-12-31 00:00:00'), Timestamp('2017-01-01 09:03:00'), 5470, 2968, 1],
[Timestamp('2017-01-01 00:00:00'), Timestamp('2017-01-01 23:44:00'), 9769, 2073, 1],
[Timestamp('2017-01-02 00:00:00'), Timestamp('2017-01-02 16:54:00'), 9444, 2883, 1],
[Timestamp('2017-01-03 00:00:00'), Timestamp('2017-01-05 22:49:00'), 20064, 2287, 1],
[Timestamp('2017-01-04 00:00:00'), Timestamp('2017-01-06 07:27:00'),16771, 2716, 1]],
columns = ['startTime', 'endTime', 'steps', 'calories', 'personId']
)
df1 = df.copy()
df2 = df.copy()
concatenated_df = ConcatOperator().process(df1, df2)[0]
Normalize selected columns
import pandas as pd
from tasrif.processing_pipeline.custom import NormalizeOperator
from tasrif.processing_pipeline.custom import NormalizeTransformOperator
df = pd.DataFrame([
[1, "2020-05-01 00:00:00", 10],
[1, "2020-05-01 01:00:00", 15],
[1, "2020-05-01 03:00:00", 23],
[2, "2020-05-02 00:00:00", 17],
[2, "2020-05-02 01:00:00", 11]],
columns=['logId', 'timestamp', 'sleep_level'])
op = NormalizeOperator('all', 'minmax', {'feature_range': (0, 2)})
output = op.process(df)
Use the fit normalizer on different data using NormalizeTransformOperator
trained_model = output[0][1]
op = NormalizeTransformOperator('all', trained_model)
output = op.process(df)
output
Use AggregateActivityDatesOperator to view the start date and end date of a
dataframe that has a date column per row per participant.
import pandas as pd
from tasrif.processing_pipeline.custom import AggregateActivityDatesOperator
from tasrif.processing_pipeline.pandas import ReadCsvOperator
reader = ReadCsvOperator('examples/quick_start/activity_long.csv')
df = reader.process()[0]
operator = AggregateActivityDatesOperator(date_feature_name="date",
participant_identifier=['Id', 'logId'])
df = operator.process(df)[0]
df
You can use jqOperator to process JSON data
import pandas as pd
from tasrif.processing_pipeline.custom import JqOperator
df = [
{
"date": "2020-01-01",
"sleep": [
{
"sleep_data": [
{
"level": "rem",
"minutes": 180
},
{
"level": "deep",
"minutes": 80
},
{
"level": "light",
"minutes": 300
}
]
}
]
},
{
"date": "2020-01-02",
"sleep": [
{
"sleep_data": [
{
"level": "rem",
"minutes": 280
},
{
"level": "deep",
"minutes": 60
},
{
"level": "light",
"minutes": 200
}
]
}
]
}
]
op = JqOperator("map({date, sleep: .sleep[].sleep_data})")
op.process(df)
Test prepared data
See if your prepared data can act as an input to a machine learning model
from tasrif.processing_pipeline.custom import LinearFitOperator
df = pd.DataFrame([
[1, "2020-05-01 00:00:00", 10, 'poor'],
[1, "2020-05-01 01:00:00", 15, 'poor'],
[1, "2020-05-01 03:00:00", 23, 'good'],
[2, "2020-05-02 00:00:00", 17, 'good'],
[2, "2020-05-02 01:00:00", 11, 'poor']],
columns=['logId', 'timestamp', 'sleep_level', 'sleep_quality'])
op = LinearFitOperator(feature_names='sleep_level',
target='sleep_quality',
target_type='categorical')
op.process(df)
Create a pipeline to link the operators
Chain operators using SequenceOperator
import pandas as pd
from tasrif.processing_pipeline import SequenceOperator
from tasrif.processing_pipeline.custom import AggregateOperator, CreateFeatureOperator, SetStartHourOfDayOperator
from tasrif.processing_pipeline.pandas import ConvertToDatetimeOperator, SortOperator, ReadCsvOperator
df = ReadCsvOperator('examples/quick_start/cgm.csv').process()[0]
df
pipeline = SequenceOperator([
ConvertToDatetimeOperator(feature_names=["dateTime"]),
SetStartHourOfDayOperator(date_feature_name='dateTime',
participant_identifier='patientID',
shift=6),
SortOperator(by='dateTime'),
AggregateOperator(groupby_feature_names ="patientID",
aggregation_definition= {"CGM": ["mean", "std"]})
])
pipeline.process(df)
Debug your pipeline
Tasrif contains observers under tasrif/processing_pipeline/observers/ that are useful for seeing how the operators change your data. For instance, you can print the head of processed dataframe after every operator. You can do so by passing an observer to the observers argument in SequenceOperator.
import pandas as pd
from tasrif.processing_pipeline.pandas import RenameOperator
from tasrif.processing_pipeline.observers import FunctionalObserver, LoggingObserver, GroupbyLoggingObserver
from tasrif.processing_pipeline import SequenceOperator, Observer
df = pd.DataFrame([
[1, "2020-05-01 00:00:00", 1],
[1, "2020-05-01 01:00:00", 1],
[1, "2020-05-01 03:00:00", 2],
[2, "2020-05-02 00:00:00", 1],
[2, "2020-05-02 01:00:00", 1]],
columns=['logId', 'timestamp', 'sleep_level'])
pipeline = SequenceOperator([RenameOperator(columns={"timestamp": "time"}),
RenameOperator(columns={"time": "time_difference"})],
observers=[LoggingObserver("head,tail")])
result = pipeline.process(df[0])
result
Define a custom operator
Users can inherit from MapProcessingOperator to quickly build their own custom operators that perform map-like operations.
from tasrif.processing_pipeline.map_processing_operator import MapProcessingOperator
class SizeOperator(MapProcessingOperator):
def _processing_function(self, df):
return df.size
Other references
- You may examine
tasrif/processing_pipeline/test_scripts/for other minimal examples of Tasrif's operators. - Common Pandas functions can be found under
tasrif/processing_pipeline/pandas/
Documentation
Tasrif's official documentation is hosted here: https://tasrif.qcri.org
You can build the docs locally after installing the dependencies in setup.py and
requirements.txt by:
cd docs
make html
You can then browse through them by opening docs/build/html/index.html in a browser.
Contributing
This project is much stronger with your collaboration. Be part of it!
Thank you all amazing contributors!
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