rapids/rules/models.snakefile

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def input_merge_features_of_single_participant(wildcards):
if wildcards.source == "phone_fitbit_features":
return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment)
else:
return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment)
def optional_input_days_to_include(wildcards):
if config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]:
# This input automatically trigers the rule days_to_analyse in mystudy.snakefile
return ["data/interim/{pid}/days_to_analyse" + \
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \
"_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"]
else:
return []
def optional_input_valid_sensed_days(wildcards):
if config["PARAMS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]:
# This input automatically trigers the rule phone_valid_sensed_days in preprocessing.snakefile
return ["data/interim/{pid}/phone_valid_sensed_days.csv"]
else:
return []
rule merge_features_for_individual_model:
input:
feature_files = input_merge_features_of_single_participant,
phone_valid_sensed_days = optional_input_valid_sensed_days,
days_to_include = optional_input_days_to_include
params:
source = "{source}"
output:
"data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_original.csv"
script:
"../src/models/merge_features_for_individual_model.R"
rule merge_features_for_population_model:
input:
feature_files = expand("data/processed/{pid}/data_for_individual_model/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"])
output:
"data/processed/data_for_population_model/{source}_{day_segment}_original.csv"
script:
"../src/models/merge_features_for_population_model.R"
rule merge_demographicfeatures_for_population_model:
input:
data_files = expand("data/processed/{pid}/demographic_features.csv", pid=config["PIDS"])
output:
"data/processed/data_for_population_model/demographic_features.csv"
script:
"../src/models/merge_data_for_population_model.py"
rule merge_targets_for_population_model:
input:
data_files = expand("data/processed/{pid}/targets_{{summarised}}.csv", pid=config["PIDS"])
output:
"data/processed/data_for_population_model/targets_{summarised}.csv"
script:
"../src/models/merge_data_for_population_model.py"
rule clean_features_for_individual_model:
input:
rules.merge_features_for_individual_model.output
params:
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"]
output:
"data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_clean.csv"
script:
"../src/models/clean_features_for_model.R"
rule clean_features_for_population_model:
input:
rules.merge_features_for_population_model.output
params:
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"]
output:
"data/processed/data_for_population_model/{source}_{day_segment}_clean.csv"
script:
"../src/models/clean_features_for_model.R"
2020-04-30 00:53:54 +02:00
rule modeling:
input:
cleaned_features = "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv",
demographic_features = "data/processed/data_for_population_model/demographic_features.csv",
targets = "data/processed/data_for_population_model/targets_{summarised}.csv",
params:
model = "{model}",
cv_method = "{cv_method}",
source = "{source}",
day_segment = "{day_segment}",
summarised = "{summarised}",
scaler = "{scaler}",
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"],
categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"],
categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"],
model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"],
rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}"
output:
fold_predictions = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_predictions.csv",
fold_metrics = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_metrics.csv",
overall_results = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv",
fold_feature_importances = "data/processed/output_population_model/{rows_nan_threshold}_{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_feature_importances.csv"
script:
"../src/models/modeling.py"