Delete analysis section of config.yaml
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config.yaml
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config.yaml
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@ -241,79 +241,3 @@ OVERALL_COMPLIANCE_HEATMAP:
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour
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### Example Analysis ################################################################
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PARAMS_FOR_ANALYSIS:
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COMPUTE: False
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GROUNDTRUTH_TABLE: participant_info
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TARGET_TABLE: participant_target
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SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"]
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DAY_SEGMENTS: *day_segments
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PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen]
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FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep]
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PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile
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DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays]
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CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"]
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FEATURES_EXCLUDE_DAY_IDX: False
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# Whether or not to include only days with enough valid sensed hours
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# logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile
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DROP_VALID_SENSED_DAYS:
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ENABLED: True
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# Whether or not to include certain days in the analysis, logic can be found in rule days_to_analyse of rules/mystudy.snakefile
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# If you want to include all days downloaded for each participant, set ENABLED to False
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DAYS_TO_ANALYSE:
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ENABLED: True
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DAYS_BEFORE_SURGERY: 15
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DAYS_IN_HOSPITAL: F # T or F
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DAYS_AFTER_DISCHARGE: 7
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# Cleaning Parameters
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COLS_NAN_THRESHOLD: [0.1, 0.3, 0.5]
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COLS_VAR_THRESHOLD: True
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ROWS_NAN_THRESHOLD: [0.1, 0.3, 0.5]
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PARTICIPANT_DAYS_BEFORE_THRESHOLD: 7
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PARTICIPANT_DAYS_AFTER_THRESHOLD: 4
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# Extract summarised features from daily features with any of the following substrings
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NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"]
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CATEGORICAL_OPERATORS: ["mostcommon"]
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MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"]
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CV_METHODS: ["LeaveOneOut"]
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SUMMARISED: ["summarised"] # "summarised" or "notsummarised"
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RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"]
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MODEL_SCALER:
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LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"]
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kNN: ["minmaxscaler", "standardscaler", "robustscaler"]
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SVM: ["minmaxscaler", "standardscaler", "robustscaler"]
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DT: ["notnormalized"]
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RF: ["notnormalized"]
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GB: ["notnormalized"]
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XGBoost: ["notnormalized"]
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LightGBM: ["notnormalized"]
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MODEL_HYPERPARAMS:
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LogReg:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]}
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kNN:
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{"clf__n_neighbors": [1, 3, 5], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]}
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SVM:
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{"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]}
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DT:
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{"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "auto", "sqrt", "log2"]}
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RF:
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{"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 3, 5, 7, 9]}
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GB:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [3, 5, 7, 9]}
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XGBoost:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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LightGBM:
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{"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]}
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# Target Settings:
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# 1 => TARGETS_RATIO_THRESHOLD (ceiling) or more of available CESD scores were TARGETS_VALUE_THRESHOLD or higher; 0 => otherwise
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TARGETS_RATIO_THRESHOLD: 0.5
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TARGETS_VALUE_THRESHOLD: 16
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