Format comments.
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@ -13,7 +13,7 @@
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# name: straw2analysis
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# name: straw2analysis
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# ---
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# ---
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# %matplotlib inline
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# %matplotlib inline
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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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@ -43,19 +43,20 @@ UNDERSAMPLING = False
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# (bool) If True this will train and test data on balanced dataset
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# (bool) If True this will train and test data on balanced dataset
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# (using undersampling method)
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# (using undersampling method)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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model_input = pd.read_csv(
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model_input = pd.read_csv(
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"E:/STRAWresults/20230415/daily/input_PANAS_negative_affect_mean.csv"
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"E:/STRAWresults/20230415/stress_event/input_appraisal_stressfulness_event_mean.csv"
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)
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)
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# model_input =
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# model_input =
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# model_input[model_input.columns.drop(
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# model_input[model_input.columns.drop(
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# list(model_input.filter(regex='empatica_temperature'))
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# list(model_input.filter(regex='empatica_temperature'))
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# )]
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# )]
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# model_input = model_input[model_input['local_segment'].str.contains("daily")]
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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model_input["target"].value_counts()
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# bins = [-10, 0, 10] # bins for z-scored targets
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# bins = [-10, 0, 10] # bins for z-scored targets
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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model_input["target"], edges = pd.cut(
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model_input["target"], edges = pd.cut(
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@ -69,7 +70,7 @@ model_input["target"] = (
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model_input["target"].value_counts()
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model_input["target"].value_counts()
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# UnderSampling
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# UnderSampling
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if UNDERSAMPLING:
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if UNDERSAMPLING:
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no_stress = model_input[model_input["target"] == 0]
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no_stress = model_input[model_input["target"] == 0]
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@ -79,7 +80,7 @@ if UNDERSAMPLING:
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model_input = pd.concat([stress, no_stress], axis=0)
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model_input = pd.concat([stress, no_stress], axis=0)
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# %% jupyter={"source_hidden": false, "outputs_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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model_input_encoded = impute_encode_categorical_features(model_input)
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model_input_encoded = impute_encode_categorical_features(model_input)
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# %%
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# %%
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data_x, data_y, data_groups = prepare_sklearn_data_format(
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data_x, data_y, data_groups = prepare_sklearn_data_format(
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@ -98,6 +99,8 @@ data_y.shape
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scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
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scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
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# %%
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# %%
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scores.to_csv(
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scores.to_csv(
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"../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
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"../presentation/appraisal_stressfulness_event_classification_"
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+ CV_METHOD
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+ ".csv",
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index=False,
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index=False,
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)
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)
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