Pushing the docs to dev/ for branch: main, commit ca0862a9dbc5dadab2ccd30828de6de0c6f1f69d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -80,7 +80,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Uncalibrated :class:`~sklearn.naive_bayes.GaussianNB` is poorly calibrated\nbecause of\nthe redundant features which violate the assumption of feature-independence\nand result in an overly confident classifier, which is indicated by the\ntypical transposed-sigmoid curve. Calibration of the probabilities of\n:class:`~sklearn.naive_bayes.GaussianNB` with `isotonic` can fix\nthis issue as can be seen from the nearly diagonal calibration curve.\n`Sigmoid regression <sigmoid_regressor>` also improves calibration\nslightly,\nalbeit not as strongly as the non-parametric isotonic regression. This can be\nattributed to the fact that we have plenty of calibration data such that the\ngreater flexibility of the non-parametric model can be exploited.\n\nBelow we will make a quantitative analysis considering several classification\nmetrics: `brier_score_loss`, `log_loss`,\n`precision, recall, F1 score <precision_recall_f_measure_metrics>` and\n`ROC AUC <roc_metrics>`.\n\n"
+        "Uncalibrated :class:`~sklearn.naive_bayes.GaussianNB` is poorly calibrated\nbecause of\nthe redundant features which violate the assumption of feature-independence\nand result in an overly confident classifier, which is indicated by the\ntypical transposed-sigmoid curve. Calibration of the probabilities of\n:class:`~sklearn.naive_bayes.GaussianNB` with `isotonic` can fix\nthis issue as can be seen from the nearly diagonal calibration curve.\n:ref:sigmoid regression `<sigmoid_regressor>` also improves calibration\nslightly,\nalbeit not as strongly as the non-parametric isotonic regression. This can be\nattributed to the fact that we have plenty of calibration data such that the\ngreater flexibility of the non-parametric model can be exploited.\n\nBelow we will make a quantitative analysis considering several classification\nmetrics: `brier_score_loss`, `log_loss`,\n`precision, recall, F1 score <precision_recall_f_measure_metrics>` and\n`ROC AUC <roc_metrics>`.\n\n"
       ]
     },
     {

dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb

@@ -124,7 +124,7 @@
 # typical transposed-sigmoid curve. Calibration of the probabilities of
 # :class:`~sklearn.naive_bayes.GaussianNB` with :ref:`isotonic` can fix
 # this issue as can be seen from the nearly diagonal calibration curve.
-# :ref:`Sigmoid regression <sigmoid_regressor>` also improves calibration
+# :ref:sigmoid regression `<sigmoid_regressor>` also improves calibration
 # slightly,
 # albeit not as strongly as the non-parametric isotonic regression. This can be
 # attributed to the fact that we have plenty of calibration data such that the

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -41,7 +41,7 @@
 plt.subplot(4, 1, 1)
 plt.xlim(0, 512)
 plt.title("Sparse signal")
-plt.stem(idx, w[idx])
+plt.stem(idx, w[idx], use_line_collection=True)
 
 # plot the noise-free reconstruction
 omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
@@ -51,7 +51,7 @@
 plt.subplot(4, 1, 2)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noise-free measurements")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 # plot the noisy reconstruction
 omp.fit(X, y_noisy)
@@ -60,7 +60,7 @@
 plt.subplot(4, 1, 3)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 # plot the noisy reconstruction with number of non-zeros set by CV
 omp_cv = OrthogonalMatchingPursuitCV()
@@ -70,7 +70,7 @@
 plt.subplot(4, 1, 4)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements with CV")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
 plt.suptitle("Sparse signal recovery with Orthogonal Matching Pursuit", fontsize=16)

dev/_downloads/85db957603c93bd3e0a4265ea6565b13/plot_calibration_curve.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(\n    n_samples=1,\n    n_components=n_components,\n    n_features=n_features,\n    n_nonzero_coefs=n_nonzero_coefs,\n    random_state=0,\n    data_transposed=True,\n)\n\n(idx,) = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx])\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r])\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV()\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r])\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle(\"Sparse signal recovery with Orthogonal Matching Pursuit\", fontsize=16)\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(\n    n_samples=1,\n    n_components=n_components,\n    n_features=n_features,\n    n_nonzero_coefs=n_nonzero_coefs,\n    random_state=0,\n    data_transposed=True,\n)\n\n(idx,) = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx], use_line_collection=True)\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV()\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle(\"Sparse signal recovery with Orthogonal Matching Pursuit\", fontsize=16)\nplt.show()"
       ]
     }
   ],