Better lint (#132)

* Better lint

* fix one unit test

* fix wrong change

* update cp

* warning

* mac

* update mac

Author: xadupre

.gitignore

@@ -51,6 +51,7 @@ _doc/examples/plot_*.onnx
 _doc/examples/plot_*.xlsx
 _doc/_static/require.js
 _doc/_static/viz.js
+_doc/sg_execution_times.rst
 _unittests/ut__main/*.png
 _unittests/test_constants.h
 mlinsights/_config.py

CHANGELOGS.rst

@@ -1,8 +1,12 @@
-
 ===========
 Change Logs
 ===========
 
+0.5.1
+=====
+
+* :pr:`132` builds against scikit-learn==1.5.0, python 3.12
+
 0.5.0
 =====
 

_doc/examples/plot_constraint_kmeans.py

@@ -62,7 +62,7 @@
 
 colors = "brgy"
 fig, ax = plt.subplots(1, 1, figsize=(4, 4))
-for i in range(0, max(cl) + 1):
+for i in range(max(cl) + 1):
     ax.plot(X[cl == i, 0], X[cl == i, 1], colors[i] + ".", label="cl%d" % i)
     x = [km.cluster_centers_[i, 0], km.cluster_centers_[i, 0]]
     y = [km.cluster_centers_[i, 1], km.cluster_centers_[i, 1]]
@@ -97,7 +97,7 @@
 #
 
 fig, ax = plt.subplots(1, 2, figsize=(10, 4))
-for i in range(0, max(cl1) + 1):
+for i in range(max(cl1) + 1):
     ax[0].plot(X[cl1 == i, 0], X[cl1 == i, 1], colors[i] + ".", label="cl%d" % i)
     ax[1].plot(X[cl2 == i, 0], X[cl2 == i, 1], colors[i] + ".", label="cl%d" % i)
     x = [km1.cluster_centers_[i, 0], km1.cluster_centers_[i, 0]]
@@ -135,7 +135,7 @@ def plot_delaunay(ax, edges, points):
 
 
 fig, ax = plt.subplots(1, 2, figsize=(10, 4))
-for i in range(0, max(cl) + 1):
+for i in range(max(cl) + 1):
     ax[0].plot(X[cl == i, 0], X[cl == i, 1], colors[i] + ".", label="cl%d" % i)
     x = [km.cluster_centers_[i, 0], km.cluster_centers_[i, 0]]
     y = [km.cluster_centers_[i, 1], km.cluster_centers_[i, 1]]
@@ -145,7 +145,7 @@ def plot_delaunay(ax, edges, points):
 
 cls = km.cluster_centers_iter_
 ax[1].plot(X[:, 0], X[:, 1], ".", label="X", color="#AAAAAA", ms=3)
-for i in range(0, max(cl) + 1):
+for i in range(max(cl) + 1):
     ms = numpy.arange(cls.shape[-1]).astype(numpy.float64) / cls.shape[-1] * 50 + 1
     ax[1].scatter(cls[i, 0, :], cls[i, 1, :], color=colors[i], s=ms, label="cl%d" % i)
     plot_delaunay(ax[1], edges, km.cluster_centers_)

_doc/examples/plot_logistic_regression_clustering.py

@@ -23,8 +23,8 @@
 Xs = []
 Ys = []
 n = 20
-for i in range(0, 5):
-    for j in range(0, 4):
+for i in range(5):
+    for j in range(4):
         x1 = numpy.random.rand(n) + i * 1.1
         x2 = numpy.random.rand(n) + j * 1.1
         Xs.append(numpy.vstack([x1, x2]).T)
@@ -41,7 +41,7 @@
 fig, ax = plt.subplots(1, 1, figsize=(6, 4))
 for i in set(Y):
     ax.plot(
-        X[Y == i, 0], X[Y == i, 1], "o", label="cl%d" % i, color=plt.cm.tab20.colors[i]
+        X[i == Y, 0], X[i == Y, 1], "o", label="cl%d" % i, color=plt.cm.tab20.colors[i]
     )
 ax.legend()
 ax.set_title("Classification not convex")

_doc/examples/plot_piecewise_linear_regression_criterion.py

@@ -1,9 +1,9 @@
 """
 Custom DecisionTreeRegressor adapted to a linear regression
 ===========================================================
- 
+
 A :class:`sklearn.tree.DecisionTreeRegressor`
-can be trained with a couple of possible criterions but it is possible 
+can be trained with a couple of possible criterions but it is possible
 to implement a custom one (see `hellinger_distance_criterion
 <https://github.com/EvgeniDubov/hellinger-distance-criterion/blob/master/hellinger_distance_criterion.pyx>`_).
 See also tutorial

_doc/examples/plot_search_images_torch.py

@@ -147,7 +147,7 @@
 imgs = datasets.ImageFolder("simages", trans)
 dataloader = DataLoader(imgs, batch_size=1, shuffle=True, num_workers=1)
 img_seq = iter(dataloader)
-imgs = list(img[0] for i, img in zip(range(2), img_seq))
+imgs = [img[0] for i, img in zip(range(2), img_seq)]
 #######################################
 #
 
@@ -182,7 +182,7 @@
     ConcatDataset([imgs1, imgs2]), batch_size=1, shuffle=True, num_workers=1
 )
 img_seq = iter(dataloader)
-imgs = list(img[0] for i, img in zip(range(10), img_seq))
+imgs = [img[0] for i, img in zip(range(10), img_seq)]
 #######################################
 #
 
@@ -259,7 +259,7 @@
 imgs = datasets.ImageFolder("simages", trans)
 dataloader = DataLoader(imgs, batch_size=1, shuffle=False, num_workers=1)
 img_seq = iter(dataloader)
-imgs = list(img[0] for img in img_seq)
+imgs = [img[0] for img in img_seq]
 
 all_outputs = [model.forward(img).detach().numpy().ravel() for img in imgs]
 

_doc/examples/plot_sklearn_transformed_target.py

@@ -202,7 +202,7 @@ def evaluation():
     rnd = []
     perf_reg = []
     perf_clr = []
-    for rs in range(0, 200):
+    for rs in range(200):
         rnd.append(rs)
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rs)
         reg = LinearRegression()
@@ -247,7 +247,7 @@ def evaluation2():
     perf_clr = []
     acc_reg = []
     acc_clr = []
-    for rs in range(0, 50):
+    for rs in range(50):
         rnd.append(rs)
         X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rs)
         reg = LinearRegression()
@@ -368,7 +368,7 @@ def permute(y):
 
 
 rows = []
-for i in range(0, 10):
+for _i in range(10):
     regpt = TransformedTargetRegressor2(LinearRegression(), transformer="permute")
     regpt.fit(X_train, y_train)
     logpt = TransformedTargetClassifier2(

_doc/examples/plot_visualize_pipeline.py

@@ -238,6 +238,6 @@
 # Every piece behaves the same way.
 
 
-for coor, model, vars in enumerate_pipeline_models(model):
+for coor, m, _vars in enumerate_pipeline_models(model):
     print(coor)
-    print(model._debug)
+    print(m._debug)

_unittests/ut_helpers/test_debug.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 import numpy.random
 from sklearn.linear_model import LinearRegression, LogisticRegression

_unittests/ut_helpers/test_parameters.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 import unittest
 from mlinsights.ext_test_case import ExtTestCase
 from mlinsights.helpers.parameters import format_value