Refactoring

.gitignore

@@ -109,6 +109,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+*.lock
 
 # Spyder project settings
 .spyderproject

pyproject.toml

@@ -0,0 +1,26 @@
+[project]
+name = "adastop"
+version = "0.1.3"
+description = "AdaStop implements a *statistical test to adaptively choose the number of runs of stochastic algorithms* necessary to compare these algorithms and be able to rank them with a theoretically controlled family-wise error rate"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "click>=7.0",
+    "matplotlib>=3.9",
+    "numpy>=1.23.2",
+    "pandas>=1.5.0",
+    "scipy>=1.9.2",
+    "tabulate>=0.8.9",
+]
+
+[project.scripts]
+adastop = "adastop.cli:adastop"
+
+[build-system]
+requires = ["uv_build>=0.13.6,<0.14.0"]
+build-backend = "uv_build"
+
+[dependency-groups]
+dev = [
+    "pytest>6.2.5",
+]

src/adastop/_version.py

@@ -0,0 +1,3 @@
+from importlib.metadata import version
+
+__version__ = version("adastop")

adastop/cli.py → src/adastop/cli.py

@@ -81,7 +81,7 @@ def compare(ctx, input_file, n_groups, size_group, n_permutations, alpha,  seed,
                 data[agent]=comparator.eval_values[agent]
 
     comparator.partial_compare(data, False)
-    if not("continue" in list(comparator.decisions.values())):
+    if "continue" not in list(comparator.decisions.values()):
         click.echo('')
         click.echo("Test is finished, decisions are")
         click.echo(comparator.get_results().to_markdown(index=False))
@@ -131,7 +131,7 @@ def plot(ctx, folder, target_file,width, height ):
     if os.path.isfile(path_lf):
         with open(path_lf, 'rb') as fp:
             comparator = pickle.load(fp)
-        assert not("continue" in list(comparator.decisions.values())), "Testing process not finished yet, cannot plot yet."
+        assert "continue" not in list(comparator.decisions.values()), "Testing process not finished yet, cannot plot yet."
     else:
         raise ValueError('Comparator save file not found.')
 

adastop/compare_agents.py → src/adastop/compare_agents.py

@@ -1,9 +1,6 @@
 import logging
 import numpy as np
 from copy import copy
-import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
-import matplotlib.patches as mpatches
 import math
 import pandas as pd
 import itertools
@@ -134,7 +131,7 @@ def compute_mean_diffs(self, k, Z):
         else:
             # Eliminate for conditional
             mean_diffs = {str(comp): [] for comp in self.mean_diffs}
-            to_remove = []
+
             for i in range(len(self.mean_diffs[str(comparisons[0])])):
                 zval = []
                 for comp in self.mean_diffs:
@@ -254,7 +251,6 @@ def partial_compare(self, eval_values, verbose=True):
             print("Step {}".format(k))
 
         current_decisions = np.array(["continue"] * len(self.id_tracked))
-        current_sign = np.zeros(len(current_decisions))
 
         for j in range(len(current_decisions)):
             current_comparisons = self.current_comparisons[
@@ -293,7 +289,6 @@ def partial_compare(self, eval_values, verbose=True):
             Tmax = 0
             Tmin = np.inf
             Tmaxsigned = 0
-            Tminsigned = 0
             for i, comp in enumerate(
                 self.current_comparisons[current_decisions == "continue"]
             ):
@@ -311,11 +306,6 @@ def partial_compare(self, eval_values, verbose=True):
 
                 if Ti < Tmin:
                     Tmin = Ti
-                    imin = i
-                    Tminsigned = (k + 1) * (
-                        np.mean(Z[comp[0]][: ((k + 1) * self.n[comp[0]])])
-                        - np.mean(Z[comp[1]][: ((k + 1) * self.n[comp[1]])])
-                    )
 
             if Tmax > bk_sup:
                 id_reject = np.arange(len(current_decisions))[
@@ -348,7 +338,7 @@ def partial_compare(self, eval_values, verbose=True):
 
         id_decided = np.array(current_decisions) != "continue"
 
-        if not ("continue" in self.decisions.values()):
+        if "continue" not in self.decisions.values():
             self.is_finished = True
 
         self.id_tracked = self.id_tracked[~id_decided]

adastop/plotting.py → src/adastop/plotting.py

@@ -58,24 +58,21 @@ def plot_results(comparator, agent_names=None, axes = None):
     else:
         (ax1, ax2) = axes
 
-    n_iterations = [comparator.n_iters[comparator.agent_names[i]] for i in id_sort]
-    the_table = ax1.table(
-        cellText=[n_iterations], rowLabels=["n_iter"], loc="top", cellLoc="center"
-    )
+
 
     # Draw the heatmap with the mask and correct aspect ratio
     colors = mpl.colormaps["Pastel1"].colors
     colors = [colors[0], "lightgray", colors[1], "white"]
     cmap = ListedColormap(colors, name="my_cmap")
 
-    im = ax1.imshow(links, cmap=cmap, vmin=-1, vmax=2, aspect='auto')
+    ax1.imshow(links, cmap=cmap, vmin=-1, vmax=2, aspect='auto')
 
     ax1.set_yticks(np.arange(len(id_sort)), labels=np.array(agent_names)[id_sort])
     ax1.set_xticks([], labels=[])
     # Loop over data dimensions and create text annotations.
     for i in range(len(annot)):
         for j in range(len(annot[0])):
-            text = ax1.text(j, i, annot[i][j],
+            ax1.text(j, i, annot[i][j],
                            ha="center", va="center", color="k")
 
 
@@ -149,13 +146,13 @@ def plot_results_sota(comparator, agent_names=None, axes = None):
     colors = mpl.colormaps["Pastel1"].colors
     colors = [colors[0], "lightgray", colors[1], "white"]
     cmap = ListedColormap(colors, name="my_cmap")
-    im = ax1.imshow([links], cmap=cmap, vmin=-1, vmax=2, aspect='auto')
+    ax1.imshow([links], cmap=cmap, vmin=-1, vmax=2, aspect='auto')
 
     ax1.set_yticks([], labels=[])
     ax1.set_xticks([], labels=[])
     # Loop over data dimensions and create text annotations.
     for i in range(len(annot)):
-        text = ax1.text(i, 0, annot[i],
+        ax1.text(i, 0, annot[i],
                            ha="center", va="center", color="k")
     ax1.autoscale(False)
 

tests/test_cli.py

@@ -30,7 +30,7 @@ def test_cli():
     assert result.exit_code == 0
     result = runner.invoke(adastop, ['status', 'examples'])
     assert result.exit_code == 0
-    assert os.path.exists(test_pdf_path) == True
+    assert os.path.exists(test_pdf_path)
 
 
     result = runner.invoke(adastop, ['reset', 'examples'])

tests/test_compare_agents.py

@@ -9,7 +9,6 @@
 
 def test_partial_compare():
     rng = np.random.RandomState(seed)
-    idxs = []
     comparator = MultipleAgentsComparator(n=3, K=3, B=B,  alpha=alpha, seed=42)
     evals = {"Agent "+str(k): rng.normal(size=3) for k in range(3)}
     comparator.partial_compare(evals)
@@ -38,7 +37,7 @@ def test_type1(K,n):
             else:
                 evals = {"Agent "+str(k): rng.normal(size=n) for k in range(n_agents)}
             comparator.partial_compare(evals)
-        idxs.append(not("equal" in comparator.decisions.values()))
+        idxs.append("equal" not in comparator.decisions.values())
         print(comparator.get_results().to_string(index=False))
     assert np.mean(idxs) < 2*alpha + 1/4/(np.sqrt(n_runs)), "type 1 error seems to be too large."
 
@@ -59,7 +58,7 @@ def test_type2(K,n):
             else:
                 evals = {"Agent "+str(k): rng.normal(size=n)+2*k for k in range(n_agents)}
             comparator.partial_compare(evals)
-        idxs.append(not("equal" in comparator.decisions.values()))
+        idxs.append("equal" not in comparator.decisions.values())
     assert np.mean(idxs) > 0.3, "type 2 error seems to be too large."
 
 

tests/test_plot.py

@@ -1,4 +1,3 @@
-import pytest
 from adastop import MultipleAgentsComparator
 import numpy as np
 import matplotlib.pyplot as plt
@@ -23,7 +22,6 @@ def test_plot():
         comparator.partial_compare(evals)
     comparator.plot_results()
 
-    # plt.savefig('fig.pdf')
     fig, axes= plt.subplots(1,2)
     comparator.plot_results(axes=axes)
 
@@ -44,7 +42,7 @@ def test_plot_sota():
             evals = {"Agent "+str(k): rng.normal(size=n) for k in range(n_agents)}
         comparator.partial_compare(evals)
     comparator.plot_results_sota()
-    # plt.savefig('fig2.pdf')
+
     fig, axes= plt.subplots(1,2)
     comparator.plot_results_sota(axes=axes)
 
@@ -61,7 +59,7 @@ def test_plot_noteq():
         else:
             evals = {"Agent "+str(k): rng.normal(size=10)+k for k in range(n_agents)}
         comparator.partial_compare(evals)
-    # plt.savefig('fig2.pdf')
+
     fig, axes= plt.subplots(1,2)
     comparator.plot_results(axes=axes)
 
@@ -81,7 +79,7 @@ def test_plot_sota_noteq():
             evals = {"Agent "+str(k): rng.normal(size=10) for k in range(n_agents)}
         comparator.partial_compare(evals)
     comparator.plot_results_sota()
-    # plt.savefig('fig2.pdf')
+
     fig, axes= plt.subplots(1,2)
     comparator.plot_results_sota(axes=axes)
 
@@ -100,7 +98,7 @@ def test_plot_noteq2():
         else:
             evals = {"Agent "+str(k): rng.normal(size=10)+np.abs(2*K-k) for k in range(n_agents)}
         comparator.partial_compare(evals)
-    # plt.savefig('fig2.pdf')
+
     fig, axes= plt.subplots(1,2)
     comparator.plot_results(axes=axes)
 
@@ -120,7 +118,7 @@ def test_plot_sota_noteq2():
             evals = {"Agent "+str(k): rng.normal(size=10)+np.abs(2*K-k) for k in range(n_agents)}
         comparator.partial_compare(evals)
     comparator.plot_results_sota()
-    # plt.savefig('fig2.pdf')
+
     fig, axes= plt.subplots(1,2)
     comparator.plot_results_sota(axes=axes)