Pushing the docs to dev/ for branch: main, commit 1e3c7be5ff7017550438036c42f0af502b9a2331

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -6,41 +6,44 @@
 Demonstrate the resolution of a regression problem
 using a k-Nearest Neighbor and the interpolation of the
 target using both barycenter and constant weights.
-
 """
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
-
 # %%
 # Generate sample data
 # --------------------
+# Here we generate a few data points to use to train the model. We also generate
+# data in the whole range of the training data to visualize how the model would
+# react in that whole region.
 import matplotlib.pyplot as plt
 import numpy as np
 
 from sklearn import neighbors
 
-np.random.seed(0)
-X = np.sort(5 * np.random.rand(40, 1), axis=0)
-T = np.linspace(0, 5, 500)[:, np.newaxis]
-y = np.sin(X).ravel()
+rng = np.random.RandomState(0)
+X_train = np.sort(5 * rng.rand(40, 1), axis=0)
+X_test = np.linspace(0, 5, 500)[:, np.newaxis]
+y = np.sin(X_train).ravel()
 
 # Add noise to targets
 y[::5] += 1 * (0.5 - np.random.rand(8))
 
 # %%
 # Fit regression model
 # --------------------
+# Here we train a model and visualize how `uniform` and `distance`
+# weights in prediction effect predicted values.
 n_neighbors = 5
 
 for i, weights in enumerate(["uniform", "distance"]):
     knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
-    y_ = knn.fit(X, y).predict(T)
+    y_ = knn.fit(X_train, y).predict(X_test)
 
     plt.subplot(2, 1, i + 1)
-    plt.scatter(X, y, color="darkorange", label="data")
-    plt.plot(T, y_, color="navy", label="prediction")
+    plt.scatter(X_train, y, color="darkorange", label="data")
+    plt.plot(X_test, y_, color="navy", label="prediction")
     plt.axis("tight")
     plt.legend()
     plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))

dev/_downloads/437df39fcde24ead7b91917f2133a53c/plot_regression.py

@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Generate sample data\n\n"
+        "## Generate sample data\nHere we generate a few data points to use to train the model. We also generate\ndata in the whole range of the training data to visualize how the model would\nreact in that whole region.\n\n"
       ]
     },
     {
@@ -33,14 +33,14 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import neighbors\n\nnp.random.seed(0)\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\nT = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import neighbors\n\nrng = np.random.RandomState(0)\nX_train = np.sort(5 * rng.rand(40, 1), axis=0)\nX_test = np.linspace(0, 5, 500)[:, np.newaxis]\ny = np.sin(X_train).ravel()\n\n# Add noise to targets\ny[::5] += 1 * (0.5 - np.random.rand(8))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Fit regression model\n\n"
+        "## Fit regression model\nHere we train a model and visualize how `uniform` and `distance`\nweights in prediction effect predicted values.\n\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "n_neighbors = 5\n\nfor i, weights in enumerate([\"uniform\", \"distance\"]):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X, y).predict(T)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X, y, color=\"darkorange\", label=\"data\")\n    plt.plot(T, y_, color=\"navy\", label=\"prediction\")\n    plt.axis(\"tight\")\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors, weights))\n\nplt.tight_layout()\nplt.show()"
+        "n_neighbors = 5\n\nfor i, weights in enumerate([\"uniform\", \"distance\"]):\n    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)\n    y_ = knn.fit(X_train, y).predict(X_test)\n\n    plt.subplot(2, 1, i + 1)\n    plt.scatter(X_train, y, color=\"darkorange\", label=\"data\")\n    plt.plot(X_test, y_, color=\"navy\", label=\"prediction\")\n    plt.axis(\"tight\")\n    plt.legend()\n    plt.title(\"KNeighborsRegressor (k = %i, weights = '%s')\" % (n_neighbors, weights))\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],