diff options
| -rw-r--r-- | gn3/computations/correlations.py | 27 | ||||
| -rw-r--r-- | tests/unit/computations/test_correlation.py | 15 |
2 files changed, 25 insertions, 17 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 diff --git a/tests/unit/computations/test_correlation.py b/tests/unit/computations/test_correlation.py index 9f3feab..8bb5cd1 100644 --- a/tests/unit/computations/test_correlation.py +++ b/tests/unit/computations/test_correlation.py @@ -120,21 +120,24 @@ class TestCorrelation(TestCase): [3.4, 6.2, 4, 1.1, 8, 1.1], 6) compute_corr.side_effect = [(0.7, 0.3), (-1.0, 0.9), (1, 0.21)] - pearson_results = compute_sample_r_correlation(corr_method="pearson", + pearson_results = compute_sample_r_correlation(trait_name="1412_at", + corr_method="pearson", trait_vals=primary_values, target_samples_vals=target_values) - spearman_results = compute_sample_r_correlation(corr_method="spearman", + spearman_results = compute_sample_r_correlation(trait_name="1412_at", + corr_method="spearman", trait_vals=primary_values, target_samples_vals=target_values) - bicor_results = compute_sample_r_correlation(corr_method="bicor", + bicor_results = compute_sample_r_correlation(trait_name="1412_at", + corr_method="bicor", trait_vals=primary_values, target_samples_vals=target_values) - self.assertEqual(bicor_results, (1, 0.21, 6)) - self.assertEqual(pearson_results, (0.7, 0.3, 6)) - self.assertEqual(spearman_results, (-1.0, 0.9, 6)) + self.assertEqual(bicor_results, ("1412_at", 1, 0.21, 6)) + self.assertEqual(pearson_results, ("1412_at", 0.7, 0.3, 6)) + self.assertEqual(spearman_results, ("1412_at", -1.0, 0.9, 6)) self.assertIsInstance( pearson_results, tuple, "message") |