rapids/rules/models.snakefile

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ruleorder: nan_cells_ratio_of_cleaned_features > merge_features_and_targets
rule days_to_analyse:
input:
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
params:
days_before_surgery = "{days_before_surgery}",
days_in_hospital = "{days_in_hospital}",
days_after_discharge= "{days_after_discharge}"
output:
"data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv"
script:
"../src/models/select_days_to_analyse.py"
rule targets:
input:
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["TARGET_TABLE"] + "_raw.csv"
params:
pid = "{pid}",
summarised = "{summarised}",
targets_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_RATIO_THRESHOLD"],
targets_value_threshold = config["PARAMS_FOR_ANALYSIS"]["TARGETS_VALUE_THRESHOLD"]
output:
"data/processed/{pid}/targets_{summarised}.csv"
script:
"../src/models/targets.py"
rule demographic_features:
input:
participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
params:
pid = "{pid}",
features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"]
output:
"data/processed/{pid}/demographic_features.csv"
script:
"../src/features/demographic_features.py"
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_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.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/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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/{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"])
output:
"data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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:
features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"],
cols_nan_threshold = "{cols_nan_threshold}",
cols_var_threshold = "{cols_var_threshold}",
days_before_threshold = "{days_before_threshold}",
days_after_threshold = "{days_after_threshold}",
rows_nan_threshold = "{rows_nan_threshold}",
output:
"data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{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:
features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"],
cols_nan_threshold = "{cols_nan_threshold}",
cols_var_threshold = "{cols_var_threshold}",
days_before_threshold = "{days_before_threshold}",
days_after_threshold = "{days_after_threshold}",
rows_nan_threshold = "{rows_nan_threshold}",
output:
"data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv"
script:
"../src/models/clean_features_for_model.R"
rule nan_cells_ratio_of_cleaned_features:
input:
cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv"
output:
"data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv"
script:
"../src/models/nan_cells_ratio_of_cleaned_features.py"
rule merge_features_and_targets:
input:
cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{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:
summarised = "{summarised}",
cols_var_threshold = "{cols_var_threshold}",
numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"],
categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"],
features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"],
output:
"data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv"
script:
"../src/models/merge_features_and_targets.py"
rule baseline:
input:
"data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv"
params:
cv_method = "{cv_method}",
rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}",
demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"]
output:
"data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv"
log:
"data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}_notes.log"
script:
"../src/models/baseline.py"
rule modeling:
input:
data = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv"
params:
model = "{model}",
cv_method = "{cv_method}",
source = "{source}",
day_segment = "{day_segment}",
summarised = "{summarised}",
scaler = "{scaler}",
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/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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"
log:
"data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/notes.log"
script:
"../src/models/modeling.py"
rule merge_population_model_results:
input:
overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{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",
nan_cells_ratio = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv",
baseline = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv"
output:
"data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/merged_population_model_results.csv"
script:
"../src/models/merge_population_model_results.py"