diff options
Diffstat (limited to 'gn3/computations/partial_correlations.py')
| -rw-r--r-- | gn3/computations/partial_correlations.py | 29 |
1 files changed, 10 insertions, 19 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 9b15bcb..2720316 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -8,7 +8,7 @@ GeneNetwork1. import math import warnings from functools import reduce, partial -from typing import Any, Tuple, Union, Sequence +from typing import Any, Tuple, Union, Sequence, Generator import numpy import pandas @@ -202,8 +202,7 @@ def good_dataset_samples_indexes( def partial_correlations_fast(# pylint: disable=[R0913, R0914] samples, primary_vals, control_vals, database_filename, - fetched_correlations, method: str, correlation_type: str) -> Tuple[ - int, Tuple[float, ...]]: + fetched_correlations, method: str, correlation_type: str) -> Generator: """ Computes partial correlation coefficients using data from a CSV file. @@ -237,7 +236,7 @@ def partial_correlations_fast(# pylint: disable=[R0913, R0914] ## return below. Once the surrounding code is successfully migrated and ## reworked, this complexity might go away, by getting rid of the ## `correlation_type` parameter - return len(all_correlations), tuple( + return ( corr + ( (fetched_correlations[corr[0]],) # type: ignore[index] if correlation_type == "literature" @@ -305,10 +304,7 @@ def compute_trait_info(primary_vals, control_vals, target, method): def compute_partial( primary_vals, control_vals, targets, data_start_pos, - method: str) -> Tuple[ - Union[ - Tuple[str, int, float, float, float, float], None], - ...]: + method: str) -> Generator: """ Compute the partial correlations. @@ -319,7 +315,7 @@ def compute_partial( This implementation reworks the child function `compute_partial` which will then be used in the place of `determinPartialsByR`. """ - return tuple( + return ( result for result in ( compute_trait_info( primary_vals, control_vals, (target[data_start_pos:], target[0]), method) @@ -328,10 +324,7 @@ def compute_partial( def partial_correlations_normal(# pylint: disable=R0913 primary_vals, control_vals, input_trait_gene_id, trait_database, - data_start_pos: int, db_type: str, method: str) -> Tuple[ - int, Tuple[Union[ - Tuple[str, int, float, float, float, float], None], - ...]]:#Tuple[float, ...] + data_start_pos: int, db_type: str, method: str) -> Generator: """ Computes the correlation coefficients. @@ -360,12 +353,10 @@ def partial_correlations_normal(# pylint: disable=R0913 "sgo literature correlation", "tissue correlation, pearson's r", "tissue correlation, spearman's rho")): return ( - len(trait_database), - tuple( - __add_lit_and_tiss_corr__(item) - for idx, item in enumerate(all_correlations))) + __add_lit_and_tiss_corr__(item) + for idx, item in enumerate(all_correlations)) - return len(trait_database), all_correlations + return all_correlations def partial_corrs(# pylint: disable=[R0913] conn, samples, primary_vals, control_vals, return_number, species, @@ -744,7 +735,7 @@ def partial_correlations_with_target_db(# pylint: disable=[R0913, R0914, R0911] conn) database_filename = get_filename(conn, target_db_name, TEXTDIR) - _total_traits, all_correlations = partial_corrs( + all_correlations = partial_corrs( conn, check_res["common_primary_control_samples"], check_res["fixed_primary_values"], check_res["fixed_control_values"], len(check_res["fixed_primary_values"]), check_res["species"], |