diff options
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/correlations.py | 27 |
1 files changed, 16 insertions, 11 deletions
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py index 1e95800..8410995 100644 --- a/gn3/computations/correlations.py +++ b/gn3/computations/correlations.py @@ -70,8 +70,8 @@ pearson,spearman and biweight mid correlation return value is rho and p_value return (corr_coeffient, p_val) -def compute_sample_r_correlation(corr_method, trait_vals, - target_samples_vals) -> Optional[Tuple[float, float, int]]: +def compute_sample_r_correlation(trait_name, corr_method, trait_vals, + target_samples_vals) -> Optional[Tuple[str, float, float, int]]: """Given a primary trait values and target trait values calculate the correlation coeff and p value @@ -89,7 +89,7 @@ def compute_sample_r_correlation(corr_method, trait_vals, # xtodo check if corr_coefficient is None # should use numpy.isNan scipy.isNan is deprecated if corr_coeffient is not None: - return (corr_coeffient, p_value, num_overlap) + return (trait_name, corr_coeffient, p_value, num_overlap) return None @@ -123,24 +123,26 @@ def compute_all_sample_correlation(this_trait, target__datasets compute all sample correlation """ # xtodo fix trait_name currently returning single one + # pylint: disable-msg=too-many-locals this_trait_samples = this_trait["trait_sample_data"] corr_results = [] processed_values = [] for target_trait in target_dataset: - # trait_name = target_trait.get("trait_id") + trait_name = target_trait.get("trait_id") target_trait_data = target_trait["trait_sample_data"] # this_vals, target_vals = filter_shared_sample_keys( # this_trait_samples, target_trait_data) - processed_values.append((corr_method, *filter_shared_sample_keys( + processed_values.append((trait_name, corr_method, *filter_shared_sample_keys( this_trait_samples, target_trait_data))) with multiprocessing.Pool() as pool: results = pool.starmap(compute_sample_r_correlation, processed_values) for sample_correlation in results: if sample_correlation is not None: - (corr_coeffient, p_value, num_overlap) = sample_correlation + (trait_name, corr_coeffient, p_value, + num_overlap) = sample_correlation corr_result = { "corr_coeffient": corr_coeffient, @@ -148,7 +150,7 @@ def compute_all_sample_correlation(this_trait, "num_overlap": num_overlap } - corr_results.append({"trait_name_key": corr_result}) + corr_results.append({trait_name: corr_result}) return sorted( corr_results, @@ -158,7 +160,9 @@ def compute_all_sample_correlation(this_trait, def benchmark_compute_all_sample(this_trait, target_dataset, corr_method="pearson") ->List: - """Temp function to benchmark with compute_all_sample_r + """Temp function to benchmark with compute_all_sample_r\ + alternative to compute_all_sample_r where we use \ + multiprocessing """ this_trait_samples = this_trait["trait_sample_data"] @@ -166,18 +170,19 @@ def benchmark_compute_all_sample(this_trait, corr_results = [] for target_trait in target_dataset: - trait_id = target_trait.get("trait_id") + trait_name = target_trait.get("trait_id") target_trait_data = target_trait["trait_sample_data"] this_vals, target_vals = filter_shared_sample_keys( this_trait_samples, target_trait_data) sample_correlation = compute_sample_r_correlation( + trait_name=trait_name, corr_method=corr_method, trait_vals=this_vals, target_samples_vals=target_vals) if sample_correlation is not None: - (corr_coeffient, p_value, num_overlap) = sample_correlation + (trait_name, corr_coeffient, p_value, num_overlap) = sample_correlation else: continue @@ -188,7 +193,7 @@ def benchmark_compute_all_sample(this_trait, "num_overlap": num_overlap } - corr_results.append({trait_id: corr_result}) + corr_results.append({trait_name: corr_result}) return corr_results |