Merge pull request #1 from abeludc93/update-dependencies-and-tests

Update dependencies and tests

Author: JerryLife

example/SplitEvaluateExample.py

@@ -1,32 +1,32 @@
+#%% Generate a large dataset
 from sklearn.datasets import make_classification
-
-# Generate a large dataset
 X, y = make_classification(n_samples=10000, n_features=10)
 
-
+#%% Imports VertiBench
 from vertibench.Evaluator import ImportanceEvaluator, CorrelationEvaluator
 from vertibench.Splitter import ImportanceSplitter, CorrelationSplitter
 from sklearn.linear_model import LogisticRegression
 
-# Split by importance
+#%% Split by importance
 imp_splitter = ImportanceSplitter(num_parties=4, weights=[1, 1, 1, 3])
 Xs = imp_splitter.split(X)
 
-# Evaluate split by importance
+#%% Evaluate split by importance
 model = LogisticRegression()
 model.fit(X, y)
 imp_evaluator = ImportanceEvaluator()
 imp_scores = imp_evaluator.evaluate(Xs, model.predict)
 alpha = imp_evaluator.evaluate_alpha(scores=imp_scores)
 print(f"Importance scores: {imp_scores}, alpha: {alpha}")
 
-# Split by correlation
+#%% Split by correlation
 corr_splitter = CorrelationSplitter(num_parties=4)
 Xs = corr_splitter.fit_split(X)
 
-# Evaluate split by correlation
+#%% Evaluate split by correlation
 corr_evaluator = CorrelationEvaluator()
 corr_scores = corr_evaluator.fit_evaluate(Xs)
 beta = corr_evaluator.evaluate_beta()
 print(f"Correlation scores: {corr_scores}, beta: {beta}")
 
+#%%

pyproject.toml

@@ -31,13 +31,13 @@ urls = { "Homepage" = "https://github.com/Xtra-Computing/VertiBench" }
 # Define your console scripts under [project.scripts] if any in the future
 
 dependencies = [
-    "matplotlib>=3.7",
-    "numpy==1.24",
-    "pymoo==0.6.1.1",
-    "scikit-learn>=1.2",
-    "scipy>=1.10",
-    "shap==0.43",
-    "torch>=2.0",
+    "matplotlib>=3.7,<4",
+    "numpy>=2.1,<3",
+    "pymoo>=0.6.1,<0.6.1.6",
+    "scikit-learn>=1.6,<2",
+    "scipy>=1.10,<2",
+    "shap>=0.50.0,<1",
+    "torch>=2.0,<3",
 ]
 
 [project.optional-dependencies]

src/utils/__pycache__/__init__.cpython-310.pyc

@@ -1,13 +1,9 @@
-import warnings
-from typing import Iterable
-import time
 import os
-
 import numpy as np
 import pandas as pd
 import torch
 import torch.linalg
-from scipy.stats import spearmanr, hmean, gmean
+from scipy.stats import spearmanr
 from sklearn.utils.extmath import randomized_svd
 import shap
 import matplotlib.pyplot as plt
@@ -17,7 +13,6 @@
 from pymoo.core.duplicate import ElementwiseDuplicateElimination
 from pymoo.core.problem import ElementwiseProblem
 from pymoo.optimize import minimize
-from pymoo.termination.default import DefaultSingleObjectiveTermination
 from pymoo.core.problem import StarmapParallelization
 from multiprocessing.pool import ThreadPool
 

src/utils/__pycache__/logger.cpython-310.pyc

@@ -1,18 +1,15 @@
 from numbers import Real
 import warnings
 import abc
-
 import numpy as np
 import torch
-import torch.linalg
 from pymoo.algorithms.soo.nonconvex.brkga import BRKGA
 from pymoo.core.duplicate import ElementwiseDuplicateElimination
 from pymoo.core.problem import ElementwiseProblem
 from pymoo.optimize import minimize
 from pymoo.termination.default import DefaultSingleObjectiveTermination
 from pymoo.core.problem import StarmapParallelization
 from multiprocessing.pool import ThreadPool
-
 from .Evaluator import CorrelationEvaluator
 
 

src/utils/__pycache__/utils.cpython-310.pyc

@@ -1,12 +1,9 @@
 import unittest
-
-from collections import defaultdict
 import numpy as np
 import xgboost as xgb
 from sklearn.datasets import make_classification
 from sklearn.linear_model import LinearRegression
-
-from src.vertibench.Evaluator import ImportanceEvaluator, CorrelationEvaluator
+from vertibench.Evaluator import ImportanceEvaluator
 
 
 class TestAlphaEvaluator(unittest.TestCase):
@@ -58,3 +55,7 @@ def test_evaluate_alpha(self):
             lr = LinearRegression()
             lr.fit(np.array(split_ratios).reshape(-1, 1), np.array(alpha1s).reshape(-1, 1))
             self.assertGreater(lr.coef_[0][0], 0)
+
+
+if __name__ == '__main__':
+    unittest.main()

src/vertibench/Evaluator.py

@@ -1,11 +1,9 @@
 import unittest
-
 from collections import defaultdict
 import numpy as np
 import xgboost as xgb
 from sklearn.datasets import make_classification
-
-from src.vertibench.Evaluator import ImportanceEvaluator, CorrelationEvaluator
+from vertibench.Evaluator import ImportanceEvaluator, CorrelationEvaluator
 
 
 def generate_data():

src/vertibench/Splitter.py

@@ -1,14 +1,11 @@
 import unittest
-import random
 from itertools import product
 from collections import defaultdict
 from sklearn.datasets import make_classification
-
 import numpy as np
 from scipy.stats import spearmanr
 import xgboost as xgb
-from src.vertibench.Splitter import ImportanceSplitter, CorrelationSplitter, SimpleSplitter
-from src.vertibench.Evaluator import ImportanceEvaluator, CorrelationEvaluator
+from vertibench.Splitter import ImportanceSplitter, CorrelationSplitter
 
 
 def generate_data():
@@ -263,4 +260,5 @@ def test_fit_split(self):
                         self.assertAlmostEqual(beta, beta_eval, delta=0.1)
 
 
-
+if __name__ == '__main__':
+    unittest.main()