fixed missing distl

made scikit-learn a soft dependency as well

ligeor/ebai/ebai.py

@@ -1,18 +1,24 @@
 import numpy as np
 import warnings
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.neural_network import MLPRegressor
 import matplotlib.pyplot as plt
-from sklearn.preprocessing import MinMaxScaler
 plt.style.use('science')
 from scipy.interpolate import interp1d
-from sklearn.model_selection import GridSearchCV
+
 try:
     import cmasher as cmr
     _use_cmr = True
 except:
     _use_cmr = False
 
+try:
+    from sklearn.preprocessing import MinMaxScaler
+    from sklearn.neighbors import KNeighborsRegressor
+    from sklearn.neural_network import MLPRegressor
+    from sklearn.model_selection import GridSearchCV
+except:
+    raise ImportError('scikit-learn needs to be installed to run EBAI.')
+
+
 class Ebai():
 
     def __init__(self, model=None, model_type='knn', 

ligeor/utils/processing.py

@@ -1,5 +1,8 @@
 import numpy as np
-import distl
+try:
+    import distl
+except:
+    raise ImportError('distl needs to be installed for sampling distributions.')
 
 
 def sample_skewed_gaussian(mean, sigma_low, sigma_high, size=1000):