1. adding correction for the optimization autml and imputation, with …

…normalization and timing

env/default_values.toml

@@ -19,6 +19,26 @@ learning_rate = 0.01
 iterations = 50
 sequence_length = 7
 
+[greedy]
+n_calls = 250
+selected_metrics='RMSE'
+
+[bayesian]
+n_calls = 2
+n_random_starts = 50
+acq_func = 'gp_hedge'
+selected_metrics='RMSE'
+
+[pso]
+n_particles = 50
+c1 = 0.5
+c2 = 0.3
+w = 0.9
+iterations=10
+n_processes=1
+selected_metrics='RMSE'
+
+
 [explainer]
 splitter = 10
 nbr_series = 15

imputegap/algorithms/cdrec.py

@@ -1,6 +1,7 @@
 import ctypes
 import os
 import platform
+import time
 import ctypes as __native_c_types_import;
 import numpy as __numpy_import;
 
@@ -80,7 +81,7 @@ def native_cdrec(__py_matrix, __py_rank, __py_eps, __py_iters):
     return __py_recovered;
 
 
-def cdrec(contamination, truncation_rank, iterations, epsilon):
+def cdrec(contamination, truncation_rank, iterations, epsilon, logs=True):
     """
     CDREC algorithm for imputation of missing data
     @author : Quentin Nater
@@ -90,11 +91,19 @@ def cdrec(contamination, truncation_rank, iterations, epsilon):
     :param epsilon : learning rate
     :param iterations : number of iterations
 
+    :param logs: print logs of time execution
+
     :return: imputed_matrix, metrics : all time series with imputation data and their metrics
 
     """
+    start_time = time.time()  # Record start time
 
     # Call the C++ function to perform recovery
     imputed_matrix = native_cdrec(contamination, truncation_rank, epsilon, iterations)
 
+    end_time = time.time()
+
+    if logs:
+        print(f"\n\t\t> logs, imputation cdrec - Execution Time: {(end_time - start_time):.4f} seconds\n")
+
     return imputed_matrix

imputegap/algorithms/iim.py

@@ -1,9 +1,8 @@
-import numpy as np
-
+import time
 from imputegap.wrapper.AlgoPython.IIM.testerIIM import impute_with_algorithm
 
 
-def iim(contamination, number_neighbor, algo_code):
+def iim(contamination, number_neighbor, algo_code, logs=True):
     """
     Template zero impute for adding your own algorithms
     @author : Quentin Nater
@@ -12,10 +11,18 @@ def iim(contamination, number_neighbor, algo_code):
     :param adaptive_flag: The algorithm will run the non-adaptive version of the algorithm, as described in the paper
     :param number_neighbor : The number of neighbors to use for the KNN classifier, by default 10.
     :param algo_code : Action of the IIM output
+
+    :param logs: print logs of time execution
+
     :return: imputed_matrix, metrics : all time series with imputation data and their metrics
 
     """
-    #imputed_matrix = iim_recovery(matrix_nan=contamination, adaptive_flag=adaptive_flag, learning_neighbors=number_neighbor)
+    start_time = time.time()  # Record start time
+
     imputed_matrix = impute_with_algorithm(algo_code, contamination.copy(), number_neighbor)
 
+    end_time = time.time()
+    if logs:
+        print(f"\n\t\t> logs, imputation iim - Execution Time: {(end_time - start_time):.4f} seconds\n")
+
     return imputed_matrix

imputegap/algorithms/mrnn.py

@@ -1,7 +1,8 @@
+import time
 from imputegap.wrapper.AlgoPython.MRNN.testerMRNN import mrnn_recov
 
 
-def mrnn(contamination, hidden_dim, learning_rate, iterations, sequence_length):
+def mrnn(contamination, hidden_dim, learning_rate, iterations, sequence_length, logs=True):
 
     """
     Template zero impute for adding your own algorithms
@@ -12,9 +13,18 @@ def mrnn(contamination, hidden_dim, learning_rate, iterations, sequence_length):
     :param learning_rate : learning rate of the training
     :param iterations : iterations during the training
     :param seq_length : length of the sequences inside MRNN
+
+    :param logs: print logs of time execution
+
     :return: imputed_matrix, metrics : all time series with imputation data and their metrics
 
     """
+    start_time = time.time()  # Record start time
+
     imputed_matrix = mrnn_recov(matrix_in=contamination, hidden_dim=hidden_dim, learning_rate=learning_rate, iterations=iterations, seq_length=sequence_length)
 
+    end_time = time.time()
+    if logs:
+        print(f"\n\t\t> logs, imputation mrnn - Execution Time: {(end_time - start_time):.4f} seconds\n")
+
     return imputed_matrix

imputegap/algorithms/stmvl.py

@@ -1,4 +1,5 @@
 import ctypes
+import time
 import os
 import platform
 import ctypes as __native_c_types_import;
@@ -34,7 +35,7 @@ def load_share_lib(name = "lib_stmvl"):
         lib_path = os.path.join(local_path_win)
     else:
         lib_path = os.path.join(local_path_lin)
-    print("\n", lib_path, " has been loaded...")
+    #print("\n", lib_path, " has been loaded...")
 
     return ctypes.CDLL(lib_path)
 
@@ -86,7 +87,7 @@ def native_stmvl(__py_matrix, __py_window, __py_gamma, __py_alpha):
     return __py_recovered;
 
 
-def stmvl(contamination, window_size, gamma, alpha):
+def stmvl(contamination, window_size, gamma, alpha, logs=True):
     """
     CDREC algorithm for imputation of missing data
     @author : Quentin Nater
@@ -96,13 +97,20 @@ def stmvl(contamination, window_size, gamma, alpha):
     :param gamma: smoothing parameter for temporal weight
     :param alpha: power for spatial weight
 
+    :param logs: print logs of time execution
+
     :return: imputed_matrix, metrics : all time series with imputation data and their metrics
 
     """
+    start_time = time.time()  # Record start time
 
     # Call the C++ function to perform recovery
     imputed_matrix = native_stmvl(contamination, window_size, gamma, alpha)
 
+    end_time = time.time()
+    if logs:
+        print(f"\n\t\t> logs, imputation stvml - Execution Time: {(end_time - start_time):.4f} seconds\n")
+
     return imputed_matrix
 
 

imputegap/explainer/explainer.py

@@ -451,7 +451,7 @@ def shap_explainer(raw_data, algorithm="cdrec", params=None, contamination="mcar
             elif algorithm == "mrnn":
                 algo = Imputation.ML.MRNN(obfuscated_matrix)
 
-            algo.impute(params)
+            algo.impute(user_defined=True, params=params)
             algo.score(raw_data)
             imputation_results = algo.metrics