Convert the class into a YAML object.
Add an example config file and demonstrate its usage in ex_ml_pipeline.ipynb.rapids
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@ -27,7 +27,7 @@ To install:
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ipython kernel install --user --name=straw2analysis
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ipython kernel install --user --name=straw2analysis
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```
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```
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2. Provide an .env file to be used by `python-dotenv` which should be placed in the top folder of the application
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2. Provide a file called `.env` to be used by `python-dotenv` which should be placed in the top folder of the application
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and should have the form:
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and should have the form:
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```
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```
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@ -15,6 +15,7 @@ dependencies:
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- psycopg2
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- psycopg2
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- python-dotenv
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- python-dotenv
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- pytz
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- pytz
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- pyyaml
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- seaborn
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- seaborn
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- scikit-learn
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- scikit-learn
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- sqlalchemy
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- sqlalchemy
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@ -18,6 +18,7 @@
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import datetime
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import datetime
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import os
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import os
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import sys
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import sys
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import yaml
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import seaborn as sns
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import seaborn as sns
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from sklearn import linear_model
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from sklearn import linear_model
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@ -156,20 +157,17 @@ lin_reg_proximity.score(
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from machine_learning import pipeline
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from machine_learning import pipeline
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# %%
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# %%
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ml_pipeline = pipeline.MachineLearningPipeline(
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with open('../machine_learning/config/minimal_features.yaml', 'r') as file:
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labels_questionnaire="PANAS", data_types="proximity"
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sensor_features = yaml.full_load(file)
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)
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# %%
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# %%
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ml_pipeline.get_labels()
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sensor_features.set_sensor_data()
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# %% tags=[]
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ml_pipeline.get_sensor_data()
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# %%
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# %%
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ml_pipeline.aggregate_daily()
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sensor_features.get_sensor_data("proximity")
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# %%
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# %%
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ml_pipeline.df_full_data_daily_means
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sensor_features.calculate_features()
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# %%
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# %%
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sensor_features.get_features("proximity", "all")
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@ -0,0 +1,5 @@
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--- !SensorFeatures
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grouping_variable: date_lj
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data_types: [proximity]
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feature_names: all
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participants_usernames: [nokia_0000003]
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@ -1,6 +1,7 @@
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import datetime
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import datetime
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import pandas as pd
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import pandas as pd
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import yaml
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from sklearn.model_selection import cross_val_score
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from sklearn.model_selection import cross_val_score
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import participants.query_db
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import participants.query_db
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@ -8,7 +9,9 @@ from features import esm, helper, proximity
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from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
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from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
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class SensorFeatures:
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class SensorFeatures(yaml.YAMLObject):
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yaml_tag = u'!SensorFeatures'
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def __init__(
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def __init__(
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self,
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self,
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grouping_variable,
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grouping_variable,
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