Merge pull request XiaoLabJHU#4 from XiaoLabJHU/dev

Updating scripts to align with PEP8, added docstrings, flake8 linting and reduced loop/functions complexity

.gitignore

@@ -102,11 +102,11 @@ celerybeat.pid
 *.sage.py
 
 # Environments
-.env
-.venv
-env/
-venv/
-ENV/
+.env*
+.venv*
+env*/
+venv*/
+ENV*/
 env.bak/
 venv.bak/
 

CODE/Computation_time_vs_data_size.py

@@ -0,0 +1,78 @@
+"""
+Computation time vs. data size with an expression matrix (genes X conditions)
+"""
+import math
+import os
+import time
+import numpy as np
+import Building_MI_matrices as Building_MI_matrices_mod
+
+
+def time_array_per_MIestimator_and_MIquantity(mi_est, mi_quantity_to_calc, rand_seed=13):
+    """
+    Measure computation time for different MI estimators and quantities.
+
+    Parameters:
+    mi_est (str): Mutual information estimator ("Shannon", "KSG", "KL").
+    mi_quantity_to_calc (str): Quantity to calculate (e.g., "MI", "TC").
+    rand_seed (int, optional): Random seed for reproducibility. Default is 13.
+
+    Returns:
+    np.ndarray: Array of computation times for different data sizes.
+    """
+    np.random.seed(rand_seed)
+
+    # constants initialization
+    MI_matrix_fname = "MI_matrix.dat"
+    # list of vector sizes equivalent to a single gene expression profile with Ntot conditions/perturbations
+    Ntot_list = [100, 250, 500, 1000]
+    # Ntot_list = [50, 100]  # debug
+    time_array = np.zeros(len(Ntot_list), dtype=float)
+
+    # We make a list with number of bins to be used corresponding to the different Ntot size
+    if mi_est == "Shannon":
+        bins_or_neighbors_list = [math.floor(Ntot ** (1/2)) for Ntot in Ntot_list]
+    elif mi_est in ["KSG", "KL"]:
+        bins_or_neighbors_list = [1] * len(Ntot_list)
+
+    # Generate "gene expression" matrix for 50 gemes amd upto 1000 conditions/perturbations
+    m = np.random.normal(8, 1.5, size=(50, 1000))
+
+    # Build MI matrix and save time to build matrix in time_array
+    for n, Ntot in enumerate(Ntot_list):
+        input1_data_array = m[:, :Ntot]
+        bins_or_neighbors = bins_or_neighbors_list[n]
+
+        start_time = time.time()
+
+        if mi_quantity_to_calc == "MI2":
+            Building_MI_matrices_mod.mi2_matrix_build(MI_matrix_fname, input1_data_array, bins_or_neighbors, mi_est)
+
+        elif mi_quantity_to_calc == "TC":
+            if mi_est == "KSG":
+                Building_MI_matrices_mod.tc_matrix_build(MI_matrix_fname, input1_data_array, bins_or_neighbors, mi_est)
+            elif mi_est in ["KL", "Shannon"]:
+                Building_MI_matrices_mod.tc_matrix_build_from_entropies(MI_matrix_fname, input1_data_array,
+                                                                        bins_or_neighbors, mi_est)
+
+        end_time = time.time()
+        time_array[n] = end_time - start_time
+
+    print(mi_est, time_array)
+    output_fname = f"Time_array_{mi_quantity_to_calc}_{mi_est}{bins_or_neighbors}_50genes_100to1k_perturb_fast.txt"
+    np.savetxt(output_fname, time_array)
+
+
+# Change to folder where data will be saved
+path_to_data = os.path.expanduser('../DATA/MI_comparison_FB_vs_KNN/')
+os.chdir(path_to_data)
+
+# Calculating time arrays for MI2 for the MI estimators: Shannon, KL, KSG
+time_array_per_MIestimator_and_MIquantity("Shannon", "MI2")
+time_array_per_MIestimator_and_MIquantity("KL", "MI2")
+time_array_per_MIestimator_and_MIquantity("KSG", "MI2")
+
+# Calculating time arrays for TC for the MI estimators: Shannon, KL, KSG
+time_array_per_MIestimator_and_MIquantity("Shannon", "TC")
+time_array_per_MIestimator_and_MIquantity("KL", "TC")
+time_array_per_MIestimator_and_MIquantity("KSG", "TC")