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Add the rule to merge population model results

pull/95/head
Meng Li 2020-05-15 18:49:14 -04:00
parent 8df8a5c2b3
commit 34ffe4abaf
3 changed files with 40 additions and 0 deletions
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14
Snakefile
View File

@ -150,6 +150,20 @@ rule all:
zip,
model = models,
scaler = scalers),
expand(
expand("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}}/merged_population_model_results.csv",
rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"],
cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"],
source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"],
day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"],
summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]),
zip,
model = models,
scaler = scalers),
# Vizualisations
expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),

10
rules/models.snakefile
View File

@ -148,3 +148,13 @@ rule modeling:
"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}/notes.log"
script:
"../src/models/modeling.py"
rule merge_population_model_results:
input:
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",
nan_cells_ratio = "data/processed/data_for_population_model/{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/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}_{cv_method}_baseline.csv"
output:
"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}/merged_population_model_results.csv"
script:
"../src/models/merge_population_model_results.py"

16
src/models/merge_population_model_results.py 100644
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@ -0,0 +1,16 @@
import pandas as pd
overall_results = pd.read_csv(snakemake.input["overall_results"])
nan_cells_ratio = pd.read_csv(snakemake.input["nan_cells_ratio"])
baseline = pd.read_csv(snakemake.input["baseline"], index_col=["method"])
# add nan cells ratio
overall_results.insert(3, "nan_cells_ratio", nan_cells_ratio["nan_cells_ratio"])
# add baseline
baseline = baseline.stack().to_frame().T
baseline.columns = ['{}_{}'.format(*col) for col in baseline.columns]
baseline = baseline.add_prefix('b_')
results = pd.concat([overall_results, baseline], axis=1)
results.to_csv(snakemake.output[0], index=False)