Delete analysis section of config.yaml

config.yaml

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