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Move baselines into a folder; rename column of "num_of_participants" with "num_of_rows" in modeling.py

pull/95/head
Meng Li 2020-08-05 07:51:35 -04:00
parent 639e54a247
commit 973e1669fa
3 changed files with 6 additions and 6 deletions
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2
Snakefile
View File

@ -230,7 +230,7 @@ if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]:
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"]))
files_to_compute.extend(expand(
expand("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}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv",
expand("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",
min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"],
min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"],
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],

6
rules/models.snakefile
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@ -157,9 +157,9 @@ rule baseline:
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}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv"
"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}/{source}_{day_segment}_{summarised}_{cv_method}_notes.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"
@ -192,7 +192,7 @@ 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}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.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:

4
src/models/modeling.py
View File

@ -104,7 +104,7 @@ for train_index, test_index in outer_cv.split(data_x):
# Compute number of participants and features
# values do not change between folds
if fold_count == 1:
num_of_participants = train_x.shape[0] + test_x.shape[0]
num_of_rows = train_x.shape[0] + test_x.shape[0]
num_of_features = train_x.shape[1]
targets_value_counts = train_y["target"].value_counts()
@ -150,7 +150,7 @@ else:
# Step 4. Save results, parameters, and metrics to CSV files
fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_prob": pred_y_prob})
fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []})
overall_results = pd.DataFrame({"num_of_participants": [num_of_participants], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]})
overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]})
feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id)
feature_importances_all_folds.insert(loc=1, column='pid', value=pid)