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Add 18 hour daily data and slightly modify jupyter script.

ml_pipeline
Primoz 2022-10-18 10:29:59 +02:00
parent cdff4da930
commit 9f7fa0c8e0
13 changed files with 4002 additions and 1 deletions
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data/daily_18_hours_all_targets/input_JCQ_coworker_support_mean.csv 100644
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data/daily_18_hours_all_targets/input_JCQ_job_control_mean.csv 100644
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data/daily_18_hours_all_targets/input_JCQ_job_demand_mean.csv 100644
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data/daily_18_hours_all_targets/input_JCQ_supervisor_support_mean.csv 100644
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data/daily_18_hours_all_targets/input_PANAS_negative_affect_mean.csv 100644
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data/daily_18_hours_all_targets/input_PANAS_positive_affect_mean.csv 100644
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data/24_hours_all_targets/input_JCQ_coworker_support_mean.csv → data/daily_24_hours_all_targets/input_JCQ_coworker_support_mean.csv
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data/24_hours_all_targets/input_JCQ_job_control_mean.csv → data/daily_24_hours_all_targets/input_JCQ_job_control_mean.csv
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data/24_hours_all_targets/input_JCQ_job_demand_mean.csv → data/daily_24_hours_all_targets/input_JCQ_job_demand_mean.csv
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data/24_hours_all_targets/input_JCQ_supervisor_support_mean.csv → data/daily_24_hours_all_targets/input_JCQ_supervisor_support_mean.csv
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data/24_hours_all_targets/input_PANAS_negative_affect_mean.csv → data/daily_24_hours_all_targets/input_PANAS_negative_affect_mean.csv
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data/24_hours_all_targets/input_PANAS_positive_affect_mean.csv → data/daily_24_hours_all_targets/input_PANAS_positive_affect_mean.csv
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exploration/ml_pipeline_daily_cleaned.py
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@ -50,7 +50,7 @@ import machine_learning.model
# ## PANAS negative affect # ## PANAS negative affect
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": true}
model_input = pd.read_csv("../data/input_PANAS_negative_affect_mean.csv") model_input = pd.read_csv("../data/daily_18_hours_all_targets/input_PANAS_negative_affect_mean.csv")
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": true}
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
@ -105,6 +105,9 @@ sum(data_y.isna())
# ### Baseline: Dummy Regression (mean) # ### Baseline: Dummy Regression (mean)
dummy_regr = DummyRegressor(strategy="mean") dummy_regr = DummyRegressor(strategy="mean")
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true} # %% jupyter={"source_hidden": true}
lin_reg_scores = cross_validate( lin_reg_scores = cross_validate(
dummy_regr, dummy_regr,