Plotting and typing bugfixes.

.vscode/settings.json

@@ -0,0 +1,4 @@
+{
+    "python.analysis.typeCheckingMode": "basic",
+    "python.analysis.autoImportCompletions": false
+}

flexibleSubsetSelection/algorithm.py

@@ -13,7 +13,7 @@
 # --- Utility ------------------------------------------------------------------
 
 def randomSample(datasetSize: tuple, subsetSize: int, 
-                 seed: int | np.random.Generator = None):
+                 seed: (int | np.random.Generator | None) = None):
     """
     Randomly sample from dataset by generating random indices to create subset
 
@@ -46,7 +46,8 @@ def createEnvironment(outputFlag: int = 0):
 
     return environment
 
-def optimize(objective, constraints, environment, solver, log_file='gurobi_log.txt', verbose=False):
+def optimize(objective, constraints, environment, solver, 
+             log_file='gurobi_log.txt', verbose=False):
     """
     Sets up a cvxpy problem with given objective and constraints and solves it 
     using the specified solver.
@@ -99,7 +100,8 @@ def bestOfRandom(dataset, lossFunction, subsetSize, minLoss=0,
 
 
 def averageOfRandom(dataset, lossFunction, subsetSize, minLoss=0, 
-                    maxIterations=None, seed=None, verbose=False, selectBy="row"):
+                    maxIterations=None, seed=None, verbose=False, 
+                    selectBy="row"):
 
     if maxIterations is None:
         maxIterations = dataset.size[0]

flexibleSubsetSelection/generate.py

@@ -9,8 +9,8 @@
 
 # --- Random Dataset Generation ------------------------------------------------
 
-def randomData(randType: str | list, size: tuple, interval: tuple, 
-               seed: int | np.random.Generator = None) -> pd.DataFrame:
+def randomData(randType: str, size: tuple, interval: tuple, 
+               seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random data based on the specified random generation method.
 
@@ -31,7 +31,7 @@ def randomData(randType: str | list, size: tuple, interval: tuple,
     generators = {
         "uniform": uniform,
         "binary": binary,
-        "categorical": categorical,
+        "categories": categories,
         "normal": normal,
         "multimodal": multimodal,
         "skew": skew,
@@ -48,7 +48,7 @@ def randomData(randType: str | list, size: tuple, interval: tuple,
         raise ValueError(f"unknown random generation method: {randType}")
 
 def uniform(size: tuple, interval: tuple, 
-            seed: int | np.random.Generator = None) -> pd.DataFrame:
+            seed: (int | np.random.Generator| None) = None) -> pd.DataFrame:
     """
     Generate random data from a uniform distribution using numpy.
 
@@ -63,7 +63,8 @@ def uniform(size: tuple, interval: tuple,
     data = rng.uniform(interval[0], interval[1], size=size)
     return pd.DataFrame(data)
 
-def binary(size: tuple, seed: int | np.random.Generator = None) -> pd.DataFrame:
+def binary(size: tuple, 
+           seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random binary data points of bernoulli trials using numpy where 
     each feature has a random probability p.
@@ -79,8 +80,8 @@ def binary(size: tuple, seed: int | np.random.Generator = None) -> pd.DataFrame:
     data = rng.binomial(1, probabilities, size=size)
     return pd.DataFrame(data)
 
-def categorical(size: tuple, interval: tuple, 
-                seed: int | np.random.Generator = None) -> pd.DataFrame:
+def categories(size: tuple, interval: tuple, 
+               seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random categorical data points using numpy with a random number of 
     categories and a random probability p in interval.
@@ -101,7 +102,7 @@ def categorical(size: tuple, interval: tuple,
     return pd.DataFrame(data)
 
 def normal(size: tuple, interval: tuple, 
-           seed: int | np.random.Generator = None) -> pd.DataFrame:
+           seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random data from a normal distribution using numpy centered on 
     random mean and with random standard deviation.
@@ -121,7 +122,7 @@ def normal(size: tuple, interval: tuple,
     return pd.DataFrame(data)
 
 def multimodal(size: tuple, interval: tuple, sigmaInterval: tuple = (0.1, 3), 
-               seed: int | np.random.Generator = None) -> pd.DataFrame:
+               seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random data from multimodal distributions using numpy with a random
     number of normal distributions centered on random means and standard 
@@ -157,7 +158,7 @@ def multimodal(size: tuple, interval: tuple, sigmaInterval: tuple = (0.1, 3),
     return pd.DataFrame(data)
 
 def skew(size: tuple, interval: tuple = (-5, 5), 
-         seed: int | np.random.Generator = None) -> pd.DataFrame:
+         seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random data from skewed distributions using scipy with random 
     skewness parameter.
@@ -182,7 +183,7 @@ def skew(size: tuple, interval: tuple = (-5, 5),
 
 def blobs(size: tuple, interval: tuple, numClusters: int = 6, 
           sigmaInterval: tuple = (0.1, 3), 
-          seed: int | np.random.Generator = None) -> pd.DataFrame:
+          seed: (int | np.random.Generator | None) = None) -> pd.DataFrame:
     """
     Generate random data points using sklearn with numClusters blobs and with 
     random means and standard deviations.

flexibleSubsetSelection/loss.py

@@ -22,7 +22,7 @@ class MultiCriterion():
 
     def __init__(self, objectives: List[Callable], 
                  parameters: List[Dict[str, Any]], 
-                 weights: ArrayLike = None) -> None:
+                 weights: (np.ndarray | None) = None) -> None:
         """
         Define a multi-criterion loss function with a set of objectives, 
         weights, and parameters