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authorAlexander Kabui2021-04-16 02:37:25 +0300
committerAlexander Kabui2021-04-16 02:37:25 +0300
commit6c14eccb7a10cc598d4fa7ee4036cb44bddd9627 (patch)
treeea60c5a67453edfbf50e049d8874036fff037043 /gn3
parentf3f68f8eb92c7ec9c42bc20bc8e94c435cc745e2 (diff)
downloadgenenetwork3-6c14eccb7a10cc598d4fa7ee4036cb44bddd9627.tar.gz
benchmark normal function for sample r
Diffstat (limited to 'gn3')
-rw-r--r--gn3/api/correlation.py4
-rw-r--r--gn3/computations/correlations.py39
2 files changed, 38 insertions, 5 deletions
diff --git a/gn3/api/correlation.py b/gn3/api/correlation.py
index 7be8e30..e7e89cf 100644
--- a/gn3/api/correlation.py
+++ b/gn3/api/correlation.py
@@ -16,8 +16,6 @@ correlation = Blueprint("correlation", __name__)
def compute_sample_integration(corr_method="pearson"):
"""temporary api to help integrate genenetwork2 to genenetwork3 """
- # for debug
- print("Calling this endpoint")
correlation_input = request.get_json()
target_samplelist = correlation_input.get("target_samplelist")
@@ -76,8 +74,6 @@ def compute_lit_corr(species=None, gene_id=None):
@correlation.route("/tissue_corr/<string:corr_method>", methods=["POST"])
def compute_tissue_corr(corr_method="pearson"):
"""Api endpoint fr doing tissue correlation"""
- # for debug
- print("The request has been received")
tissue_input_data = request.get_json()
primary_tissue_dict = tissue_input_data["primary_tissue"]
target_tissues_dict = tissue_input_data["target_tissues_dict"]
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index fb62b56..90b6c8c 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -128,7 +128,7 @@ def compute_all_sample_correlation(this_trait,
corr_results = []
processed_values = []
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)
@@ -152,6 +152,43 @@ def compute_all_sample_correlation(this_trait,
return corr_results
+ def benchmark_compute_all_sample(this_trait,
+ target_datasets,
+ corr_method="pearson") ->List:
+ """Temp function to benchmark with compute_all_sample_r
+ """
+
+ this_trait_samples = this_trait["trait_sample_data"]
+
+ corr_results = []
+
+ for target_trait in target_dataset:
+ trait_id = 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(
+ 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
+
+ else:
+ continue
+
+ corr_result = {
+ "corr_coeffient": corr_coeffient,
+ "p_value": p_value,
+ "num_overlap": num_overlap
+ }
+
+ corr_results.append({trait_id: corr_result})
+
+ return corr_results
+
def tissue_lit_corr_for_probe_type(corr_type: str, top_corr_results):
"""Function that does either lit_corr_for_trait_list or tissue_corr _for_trait