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stress_at_work_analysis
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Add baseline features.

master
junos 2023-05-31 22:25:39 +02:00
parent 9cc6bf7c21
commit 112d968715
1 changed files with 23 additions and 7 deletions
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30
exploration/ml_pipeline_classification_composite.py
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@ -46,12 +46,7 @@ print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
PATH_FULL = PATH_BASE / SEGMENT_LENGTH / "features" / "all_sensor_features.csv"
model_input = pd.read_csv(PATH_FULL)
if SEGMENT_LENGTH == "daily":
DAY_LENGTH = "daily" # or "working"
print(DAY_LENGTH)
model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
all_features_with_baseline = pd.read_csv(PATH_FULL)
# %%
TARGETS = [
@ -129,8 +124,29 @@ if UNDERSAMPLING:
model_input = pd.concat([stress, no_stress], axis=0)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# %%
TARGET_VARIABLE = "PANAS_negative_affect"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
PATH_FULL_HELP = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
model_input_with_baseline = pd.read_csv(PATH_FULL_HELP, index_col="local_segment")
# %%
baseline_col_names = [
col for col in model_input_with_baseline.columns if col not in model_input.columns
]
print(baseline_col_names)
# %%
model_input = model_input.join(
model_input_with_baseline[baseline_col_names], how="left"
)
model_input.reset_index(inplace=True)
# %%
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
model_input_encoded, CV_METHOD