Pushing the docs to dev/ for branch: main, commit 780b393ad62ef63209f77aaae7051622134f0217

Author: sklearn-ci

dev/_downloads/02192f99342d6d1323161978b6c80bfc/plot_ols_ridge.zip

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nX = np.array(\n    [\n        [0.4, -0.7],\n        [-1.5, -1.0],\n        [-1.4, -0.9],\n        [-1.3, -1.2],\n        [-1.1, -0.2],\n        [-1.2, -0.4],\n        [-0.5, 1.2],\n        [-1.5, 2.1],\n        [1.0, 1.0],\n        [1.3, 0.8],\n        [1.2, 0.5],\n        [0.2, -2.0],\n        [0.5, -2.4],\n        [0.2, -2.3],\n        [0.0, -2.7],\n        [1.3, 2.1],\n    ]\n)\n\ny = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1])\n\n# Plotting settings\nfig, ax = plt.subplots(figsize=(4, 3))\nx_min, x_max, y_min, y_max = -3, 3, -3, 3\nax.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\n\n# Plot samples by color and add legend\nscatter = ax.scatter(X[:, 0], X[:, 1], s=150, c=y, label=y, edgecolors=\"k\")\nax.legend(*scatter.legend_elements(), loc=\"upper right\", title=\"Classes\")\nax.set_title(\"Samples in two-dimensional feature space\")\n_ = plt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nX = np.array(\n    [\n        [0.4, -0.7],\n        [-1.5, -1.0],\n        [-1.4, -0.9],\n        [-1.3, -1.2],\n        [-1.1, -0.2],\n        [-1.2, -0.4],\n        [-0.5, 1.2],\n        [-1.5, 2.1],\n        [1.0, 1.0],\n        [1.3, 0.8],\n        [1.2, 0.5],\n        [0.2, -2.0],\n        [0.5, -2.4],\n        [0.2, -2.3],\n        [0.0, -2.7],\n        [1.3, 2.1],\n    ]\n)\n\ny = np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1])\n\n# Plotting settings\nfig, ax = plt.subplots(figsize=(4, 3))\nx_min, x_max, y_min, y_max = -3, 3, -3, 3\nax.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\n\n# Plot samples by color and add legend\nscatter = ax.scatter(X[:, 0], X[:, 1], s=150, c=y, label=y, edgecolors=\"k\")\nax.legend(*scatter.legend_elements(), loc=\"upper right\", title=\"Classes\")\nax.set_title(\"Samples in two-dimensional feature space\")\nplt.show()"
       ]
     },
     {

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from matplotlib import pyplot as plt\n\nfrom sklearn.datasets import make_checkerboard\n\nn_clusters = (4, 3)\ndata, rows, columns = make_checkerboard(\n    shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42\n)\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Original dataset\")\n_ = plt.show()"
+        "from matplotlib import pyplot as plt\n\nfrom sklearn.datasets import make_checkerboard\n\nn_clusters = (4, 3)\ndata, rows, columns = make_checkerboard(\n    shape=(300, 300), n_clusters=n_clusters, noise=10, shuffle=False, random_state=42\n)\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Original dataset\")\nplt.show()"
       ]
     },
     {
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "data = data[row_idx_shuffled][:, col_idx_shuffled]\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Shuffled dataset\")\n_ = plt.show()"
+        "data = data[row_idx_shuffled][:, col_idx_shuffled]\n\nplt.matshow(data, cmap=plt.cm.Blues)\nplt.title(\"Shuffled dataset\")\nplt.show()"
       ]
     },
     {
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "# Reordering first the rows and then the columns.\nreordered_rows = data[np.argsort(model.row_labels_)]\nreordered_data = reordered_rows[:, np.argsort(model.column_labels_)]\n\nplt.matshow(reordered_data, cmap=plt.cm.Blues)\nplt.title(\"After biclustering; rearranged to show biclusters\")\n_ = plt.show()"
+        "# Reordering first the rows and then the columns.\nreordered_rows = data[np.argsort(model.row_labels_)]\nreordered_data = reordered_rows[:, np.argsort(model.column_labels_)]\n\nplt.matshow(reordered_data, cmap=plt.cm.Blues)\nplt.title(\"After biclustering; rearranged to show biclusters\")\nplt.show()"
       ]
     },
     {

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -79,7 +79,7 @@
 scatter = ax.scatter(X[:, 0], X[:, 1], s=150, c=y, label=y, edgecolors="k")
 ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes")
 ax.set_title("Samples in two-dimensional feature space")
-_ = plt.show()
+plt.show()
 
 # %%
 # We can see that the samples are not clearly separable by a straight line.

dev/_downloads/038d1885eb5f5ea53aca42da3031fe38/plot_document_clustering.zip

@@ -43,7 +43,7 @@
 
 plt.matshow(data, cmap=plt.cm.Blues)
 plt.title("Original dataset")
-_ = plt.show()
+plt.show()
 
 # %%
 # We shuffle the data and the goal is to reconstruct it afterwards using
@@ -62,7 +62,7 @@
 
 plt.matshow(data, cmap=plt.cm.Blues)
 plt.title("Shuffled dataset")
-_ = plt.show()
+plt.show()
 
 # %%
 # Fitting `SpectralBiclustering`
@@ -102,7 +102,7 @@
 
 plt.matshow(reordered_data, cmap=plt.cm.Blues)
 plt.title("After biclustering; rearranged to show biclusters")
-_ = plt.show()
+plt.show()
 
 # %%
 # As a last step, we want to demonstrate the relationships between the row