parent
baa94c4c4e
commit
ac86221662
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@ -415,11 +415,15 @@ for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
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# Baseline features
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if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/baseline_merged.csv"))
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files_to_compute.extend("data/raw/baseline_merged.csv")
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files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/baseline_questionnaires.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]))
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# Targets (labels)
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if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
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files_to_compute.extend(expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"]))
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rule all:
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input:
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files_to_compute
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@ -649,5 +649,6 @@ PARAMS_FOR_ANALYSIS:
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CATEGORICAL_FEATURES: [gender]
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TARGET:
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COMPUTE: True
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SCALE: [positive_affect, negative_affect]
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@ -27,3 +27,12 @@ rule baseline_features:
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features="data/processed/features/{pid}/baseline_features.csv"
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script:
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"../src/data/baseline_features.py"
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rule parse_targets:
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input:
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targets = "data/processed/features/{pid}/phone_esm.csv",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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output:
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"data/processed/targets/{pid}/parsed_targets.csv"
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script:
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"../src/models/parse_targets.py"
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@ -0,0 +1,6 @@
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import pandas as pd
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targets = pd.read_csv(snakemake.input["targets"])
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targets.to_csv(snakemake.output[0], index=False)
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