From 3fb300e3f9da778e2589e8993c77b8e9a91d370a Mon Sep 17 00:00:00 2001 From: isabela-pf Date: Tue, 28 Sep 2021 16:01:41 -0700 Subject: [PATCH 1/7] DOC add empty alt attributes --- doc/modules/biclustering.rst | 2 ++ doc/modules/calibration.rst | 1 + doc/modules/clustering.rst | 25 +++++++++++++++++++++++++ doc/modules/covariance.rst | 5 +++++ 4 files changed, 33 insertions(+) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 7ec175883d4cd..25ba16c1c7552 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -48,6 +48,7 @@ columns: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png :align: center :scale: 50 + :alt: An example of biclusters formed by partitioning rows and columns. @@ -60,6 +61,7 @@ small: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_003.png :align: center :scale: 50 + :alt: An example of checkerboard biclusters. diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index 1fcd1d501d100..f1bacab8a3a96 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -53,6 +53,7 @@ by showing the number of samples in each predicted probability bin. .. figure:: ../auto_examples/calibration/images/sphx_glr_plot_compare_calibration_001.png :target: ../auto_examples/calibration/plot_compare_calibration.html :align: center + :alt: .. currentmodule:: sklearn.linear_model diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 65f33fe1fbebb..ab2ac68680d05 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -33,6 +33,7 @@ Overview of clustering methods :target: ../auto_examples/cluster/plot_cluster_comparison.html :align: center :scale: 50 + :alt: A comparison of the clustering algorithms in scikit-learn @@ -166,6 +167,7 @@ It suffers from various drawbacks: :target: ../auto_examples/cluster/plot_kmeans_assumptions.html :align: center :scale: 50 + :alt: K-means is often referred to as Lloyd's algorithm. In basic terms, the algorithm has three steps. The first step chooses the initial centroids, with @@ -182,6 +184,7 @@ until the centroids do not move significantly. :target: ../auto_examples/cluster/plot_kmeans_digits.html :align: right :scale: 35 + :alt: K-means is equivalent to the expectation-maximization algorithm with a small, all-equal, diagonal covariance matrix. @@ -272,6 +275,7 @@ small, as shown in the example and cited reference. :target: ../auto_examples/cluster/plot_mini_batch_kmeans.html :align: center :scale: 100 + :alt: .. topic:: Examples: @@ -310,6 +314,7 @@ is given. :target: ../auto_examples/cluster/plot_affinity_propagation.html :align: center :scale: 50 + :alt: Affinity Propagation can be interesting as it chooses the number of @@ -419,6 +424,7 @@ given sample. :target: ../auto_examples/cluster/plot_mean_shift.html :align: center :scale: 50 + :alt: .. topic:: Examples: @@ -461,10 +467,12 @@ computed using a function of a gradient of the image. .. |noisy_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_001.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 + :alt: .. |segmented_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_002.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 + :alt: .. centered:: |noisy_img| |segmented_img| @@ -492,10 +500,12 @@ computed using a function of a gradient of the image. .. |coin_kmeans| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png :target: ../auto_examples/cluster/plot_coin_segmentation.html :scale: 65 + :alt: .. |coin_discretize| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png :target: ../auto_examples/cluster/plot_coin_segmentation.html :scale: 65 + :alt: Different label assignment strategies ------------------------------------- @@ -598,6 +608,7 @@ linkage strategies. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_linkage_comparison_001.png :target: ../auto_examples/cluster/plot_linkage_comparison.html :scale: 43 + :alt: Agglomerative cluster has a "rich get richer" behavior that leads to uneven cluster sizes. In this regard, single linkage is the worst @@ -623,6 +634,7 @@ of the data, though more so in the case of small sample sizes. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_dendrogram_001.png :target: ../auto_examples/cluster/plot_agglomerative_dendrogram.html :scale: 42 + :alt: @@ -641,10 +653,12 @@ the roll. .. |unstructured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_001.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 + :alt: .. |structured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_002.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 + :alt: .. centered:: |unstructured| |structured| @@ -694,18 +708,22 @@ enable only merging of neighboring pixels on an image, as in the .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_001.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 + :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_002.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 + :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_003.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 + :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_004.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 + :alt: Varying the metric @@ -730,14 +748,17 @@ each class. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_005.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 + :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_006.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 + :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_007.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 + :alt: .. topic:: Examples: @@ -798,6 +819,7 @@ by black points below. .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :target: ../auto_examples/cluster/plot_dbscan.html :scale: 50 + :alt: .. centered:: |dbscan_results| @@ -883,6 +905,7 @@ potential reachable points. .. |optics_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_optics_001.png :target: ../auto_examples/cluster/plot_optics.html :scale: 50 + :alt: .. centered:: |optics_results| @@ -1025,6 +1048,7 @@ the user is advised .. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png :target: ../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html + :alt: .. topic:: References: @@ -1516,6 +1540,7 @@ Drawbacks :target: ../auto_examples/cluster/plot_adjusted_for_chance_measures.html :align: center :scale: 100 + :alt: - These metrics **require the knowledge of the ground truth classes** while almost never available in practice or requires manual assignment by diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index c97676ea62108..170dd3788f8d1 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -154,6 +154,7 @@ object to the same sample. :target: ../auto_examples/covariance/plot_covariance_estimation.html :align: center :scale: 65% + :alt: Bias-variance trade-off when setting the shrinkage: comparing the choices of Ledoit-Wolf and OAS estimators @@ -178,6 +179,7 @@ object to the same sample. :target: ../auto_examples/covariance/plot_lw_vs_oas.html :align: center :scale: 75% + :alt: .. _sparse_inverse_covariance: @@ -210,6 +212,7 @@ cross-validation to automatically set the ``alpha`` parameter. :target: ../auto_examples/covariance/plot_sparse_cov.html :align: center :scale: 60% + :alt: *A comparison of maximum likelihood, shrinkage and sparse estimates of the covariance and precision matrix in the very small samples @@ -333,10 +336,12 @@ attributes of a :class:`MinCovDet` robust covariance estimator object. .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html :scale: 49% + :alt: .. |mahalanobis| image:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png :target: ../auto_examples/covariance/plot_mahalanobis_distances.html :scale: 49% + :alt: From 20b8de6b7e202a59bf3ee0f045ec1d27c4db45ea Mon Sep 17 00:00:00 2001 From: Isabela Presedo-Floyd <50221806+isabela-pf@users.noreply.github.com> Date: Thu, 30 Sep 2021 17:58:46 -0700 Subject: [PATCH 2/7] Update doc/modules/biclustering.rst Co-authored-by: Reshama Shaikh <2507232+reshamas@users.noreply.github.com> --- doc/modules/biclustering.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 25ba16c1c7552..3c438a91628f4 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -48,7 +48,7 @@ columns: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png :align: center :scale: 50 - :alt: + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The darkest 5 squares of heat map run diagonally from top left to bottom right. An example of biclusters formed by partitioning rows and columns. From 36c439f297e584d3c422f3b3a52f1335f029e024 Mon Sep 17 00:00:00 2001 From: Isabela Presedo-Floyd <50221806+isabela-pf@users.noreply.github.com> Date: Thu, 30 Sep 2021 17:58:46 -0700 Subject: [PATCH 3/7] DOC Add alt text to biclustering.rst Co-authored-by: Reshama Shaikh <2507232+reshamas@users.noreply.github.com> --- doc/modules/biclustering.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 25ba16c1c7552..3c438a91628f4 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -48,7 +48,7 @@ columns: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png :align: center :scale: 50 - :alt: + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The darkest 5 squares of heat map run diagonally from top left to bottom right. An example of biclusters formed by partitioning rows and columns. From 73f9fe30a86d702b6637eb5605ea432893683ca9 Mon Sep 17 00:00:00 2001 From: Isabela Presedo-Floyd <50221806+isabela-pf@users.noreply.github.com> Date: Sat, 9 Oct 2021 23:46:11 -0700 Subject: [PATCH 4/7] DOC Add alt text to documentation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Madelene Campos Co-authored-by: Brigitta Sipőcz Co-authored-by: InessaPawson Co-authored-by: sunsyray <33903999+sunsyray@users.noreply.github.com> Co-authored-by: Agustina Co-authored-by: MarsBarLee <46167686+MarsBarLee@users.noreply.github.com> Co-authored-by: Aerik Pawson <45904740+aerikpawson@users.noreply.github.com> --- doc/modules/biclustering.rst | 2 +- doc/modules/calibration.rst | 2 +- doc/modules/clustering.rst | 25 ++++++++++++++++++------- doc/modules/covariance.rst | 8 +++++--- 4 files changed, 25 insertions(+), 12 deletions(-) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 3c438a91628f4..8cb4898903e2a 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -61,7 +61,7 @@ small: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_003.png :align: center :scale: 50 - :alt: + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The variance of the values within each bicluster is small, causing a checkerboard effect. An example of checkerboard biclusters. diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index f1bacab8a3a96..14b05c806759d 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -53,7 +53,7 @@ by showing the number of samples in each predicted probability bin. .. figure:: ../auto_examples/calibration/images/sphx_glr_plot_compare_calibration_001.png :target: ../auto_examples/calibration/plot_compare_calibration.html :align: center - :alt: + :alt: Five plots comparing calibration classifiers. The first plot compares Logistic, Naive Bayes, SVC, and Random forest classifiers side by side with perfect calibration. The following four histograms isolate each classifier to describe their predicted probability on the x-axis and count on the y-axis. .. currentmodule:: sklearn.linear_model diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index ab2ac68680d05..788c8e9a9cdd1 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -275,7 +275,10 @@ small, as shown in the example and cited reference. :target: ../auto_examples/cluster/plot_mini_batch_kmeans.html :align: center :scale: 100 - :alt: + :alt: A figure of three panels with scatter plots for KMeans, MiniBatchKMeans, + and their difference. Both methods have very similar results, the left and + middle panels show the three identified clusters. The difference + panel highlights the <20 points that differ out of the 3000 points. .. topic:: Examples: @@ -314,7 +317,7 @@ is given. :target: ../auto_examples/cluster/plot_affinity_propagation.html :align: center :scale: 50 - :alt: + :alt: Three distinct clusters created using affinity propagation. Affinity Propagation can be interesting as it chooses the number of @@ -467,12 +470,12 @@ computed using a function of a gradient of the image. .. |noisy_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_001.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 - :alt: + :alt: An image of connected nearly equally sized circles. The circles are in shades of green, blue, and yellow. The entire image is pixelated, blurry. .. |segmented_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_002.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 - :alt: + :alt: An image of connected nearly equally sized circles. Each circle has a distinct color: dark green, light green, blue, and yellow. The clarity of the image is much better compared to the previous one. .. centered:: |noisy_img| |segmented_img| @@ -653,12 +656,20 @@ the roll. .. |unstructured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_001.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 - :alt: + :alt: A 3D scatter plot of 1500 data points. Points are located along a swirl + or swiss roll. The six clusters are highlighted in different colours. + The hierarchical clustering is performed without connectivity + constraints on the structure and is solely based on distance therefore + clusters extend to multiple layers of the roll. .. |structured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_002.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 - :alt: + :alt: A 3D scatter plot of 1500 data points. Points are located along a swirl + or swiss roll. The six clusters are highlighted in different colours. + The hierarchical clustering is performed with connectivity constraints + that respect the structure of the roll therefore the clusters form a nice + parcellation along the roll. .. centered:: |unstructured| |structured| @@ -819,7 +830,7 @@ by black points below. .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :target: ../auto_examples/cluster/plot_dbscan.html :scale: 50 - :alt: + :alt: A bubble chart that maps three clusters, with the x-axis from -2.5 to 2 and the y-axis from -2 to 2.5. Outliers are indicated separately and are evenly spaced around the clusters. .. centered:: |dbscan_results| diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index 170dd3788f8d1..d0983d12fd230 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -179,7 +179,8 @@ object to the same sample. :target: ../auto_examples/covariance/plot_lw_vs_oas.html :align: center :scale: 75% - :alt: + :alt: Two figures. One of them plots the Mean Squared Error difference between a LedoitWolf and an OAS estimator of the covariance with the y-axis from 0 to 60. The other one compares the Shrinkage covariance estimation between the LedoitWolf and OAS, with the y-axis from 0 to 1. In iboth figures the x-axis is the number of samples from 5 to 30. + .. _sparse_inverse_covariance: @@ -212,7 +213,8 @@ cross-validation to automatically set the ``alpha`` parameter. :target: ../auto_examples/covariance/plot_sparse_cov.html :align: center :scale: 60% - :alt: + :alt: Various matrix using different estimators: Empircal covariance, Empircal precision, Ledoit-Wolf covariance, Ledoit-Wolf precision, GraphicaLassoCV covariance, GraphicaLassoCV precision, True covariance and True Precision. + *A comparison of maximum likelihood, shrinkage and sparse estimates of the covariance and precision matrix in the very small samples @@ -336,7 +338,7 @@ attributes of a :class:`MinCovDet` robust covariance estimator object. .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html :scale: 49% - :alt: + :alt: Two line charts comparing estimation errors that are made when using various types of location and covariance estimates on contaminated Gaussian distributed data sets. .. |mahalanobis| image:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png :target: ../auto_examples/covariance/plot_mahalanobis_distances.html From a188cfaee448971dd207788e1a26afbb91708fcd Mon Sep 17 00:00:00 2001 From: isabela-pf Date: Sun, 10 Oct 2021 00:21:42 -0700 Subject: [PATCH 5/7] DOC Add alt text to covariance.rst --- doc/modules/covariance.rst | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index d0983d12fd230..4768957a53659 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -154,7 +154,9 @@ object to the same sample. :target: ../auto_examples/covariance/plot_covariance_estimation.html :align: center :scale: 65% - :alt: + :alt: A plot comparing shrinkage coefficients by regularization parameter: + shrinkage coefficient on the x-axis and error: negative log-likelihood on test data + on the y-axis. Bias-variance trade-off when setting the shrinkage: comparing the choices of Ledoit-Wolf and OAS estimators @@ -343,7 +345,7 @@ attributes of a :class:`MinCovDet` robust covariance estimator object. .. |mahalanobis| image:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png :target: ../auto_examples/covariance/plot_mahalanobis_distances.html :scale: 49% - :alt: + :alt: Scatterplot with plotted Mahalanobis distances. From b4903aa308e04fa327aa8e0f705cc02b53713ef5 Mon Sep 17 00:00:00 2001 From: isabela-pf Date: Sun, 10 Oct 2021 22:39:44 -0700 Subject: [PATCH 6/7] DOC Remove unused alt tags --- doc/modules/clustering.rst | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 788c8e9a9cdd1..683cb2b3c3e74 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -33,7 +33,6 @@ Overview of clustering methods :target: ../auto_examples/cluster/plot_cluster_comparison.html :align: center :scale: 50 - :alt: A comparison of the clustering algorithms in scikit-learn @@ -167,7 +166,6 @@ It suffers from various drawbacks: :target: ../auto_examples/cluster/plot_kmeans_assumptions.html :align: center :scale: 50 - :alt: K-means is often referred to as Lloyd's algorithm. In basic terms, the algorithm has three steps. The first step chooses the initial centroids, with @@ -184,7 +182,6 @@ until the centroids do not move significantly. :target: ../auto_examples/cluster/plot_kmeans_digits.html :align: right :scale: 35 - :alt: K-means is equivalent to the expectation-maximization algorithm with a small, all-equal, diagonal covariance matrix. @@ -427,7 +424,6 @@ given sample. :target: ../auto_examples/cluster/plot_mean_shift.html :align: center :scale: 50 - :alt: .. topic:: Examples: @@ -503,12 +499,10 @@ computed using a function of a gradient of the image. .. |coin_kmeans| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png :target: ../auto_examples/cluster/plot_coin_segmentation.html :scale: 65 - :alt: .. |coin_discretize| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png :target: ../auto_examples/cluster/plot_coin_segmentation.html :scale: 65 - :alt: Different label assignment strategies ------------------------------------- @@ -611,7 +605,6 @@ linkage strategies. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_linkage_comparison_001.png :target: ../auto_examples/cluster/plot_linkage_comparison.html :scale: 43 - :alt: Agglomerative cluster has a "rich get richer" behavior that leads to uneven cluster sizes. In this regard, single linkage is the worst @@ -637,7 +630,6 @@ of the data, though more so in the case of small sample sizes. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_dendrogram_001.png :target: ../auto_examples/cluster/plot_agglomerative_dendrogram.html :scale: 42 - :alt: @@ -719,22 +711,18 @@ enable only merging of neighboring pixels on an image, as in the .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_001.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 - :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_002.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 - :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_003.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 - :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_004.png :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 - :alt: Varying the metric @@ -759,17 +747,14 @@ each class. .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_005.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 - :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_006.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 - :alt: .. image:: ../auto_examples/cluster/images/sphx_glr_plot_agglomerative_clustering_metrics_007.png :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 - :alt: .. topic:: Examples: @@ -916,7 +901,6 @@ potential reachable points. .. |optics_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_optics_001.png :target: ../auto_examples/cluster/plot_optics.html :scale: 50 - :alt: .. centered:: |optics_results| @@ -1059,7 +1043,6 @@ the user is advised .. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png :target: ../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html - :alt: .. topic:: References: @@ -1551,7 +1534,6 @@ Drawbacks :target: ../auto_examples/cluster/plot_adjusted_for_chance_measures.html :align: center :scale: 100 - :alt: - These metrics **require the knowledge of the ground truth classes** while almost never available in practice or requires manual assignment by From 90952b59cf72f03d76ad73ba19ba7ac16b66f263 Mon Sep 17 00:00:00 2001 From: isabela-pf Date: Sun, 10 Oct 2021 23:56:54 -0700 Subject: [PATCH 7/7] DOC Edit alt text formatting --- doc/modules/biclustering.rst | 6 ++++-- doc/modules/calibration.rst | 5 ++++- doc/modules/clustering.rst | 11 ++++++++--- doc/modules/covariance.rst | 14 +++++++++++--- 4 files changed, 27 insertions(+), 9 deletions(-) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 8cb4898903e2a..14a7bc9f7e076 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -48,7 +48,8 @@ columns: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png :align: center :scale: 50 - :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The darkest 5 squares of heat map run diagonally from top left to bottom right. + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The darkest 5 + squares of heat map run diagonally from top left to bottom right. An example of biclusters formed by partitioning rows and columns. @@ -61,7 +62,8 @@ small: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_003.png :align: center :scale: 50 - :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The variance of the values within each bicluster is small, causing a checkerboard effect. + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The variance of + the values within each bicluster is small, causing a checkerboard effect. An example of checkerboard biclusters. diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index 14b05c806759d..096350c7fa85b 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -53,7 +53,10 @@ by showing the number of samples in each predicted probability bin. .. figure:: ../auto_examples/calibration/images/sphx_glr_plot_compare_calibration_001.png :target: ../auto_examples/calibration/plot_compare_calibration.html :align: center - :alt: Five plots comparing calibration classifiers. The first plot compares Logistic, Naive Bayes, SVC, and Random forest classifiers side by side with perfect calibration. The following four histograms isolate each classifier to describe their predicted probability on the x-axis and count on the y-axis. + :alt: Five plots comparing calibration classifiers. The first plot compares + Logistic, Naive Bayes, SVC, and Random forest classifiers side by side with perfect + calibration. The following four histograms isolate each classifier to describe + their predicted probability on the x-axis and count on the y-axis. .. currentmodule:: sklearn.linear_model diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 683cb2b3c3e74..0cca7a3c16219 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -466,12 +466,15 @@ computed using a function of a gradient of the image. .. |noisy_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_001.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 - :alt: An image of connected nearly equally sized circles. The circles are in shades of green, blue, and yellow. The entire image is pixelated, blurry. + :alt: An image of connected nearly equally sized circles. The circles are in + shades of green, blue, and yellow. The entire image is pixelated, blurry. .. |segmented_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_002.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 - :alt: An image of connected nearly equally sized circles. Each circle has a distinct color: dark green, light green, blue, and yellow. The clarity of the image is much better compared to the previous one. + :alt: An image of connected nearly equally sized circles. Each circle has a + distinct color: dark green, light green, blue, and yellow. The clarity of the image + is much better compared to the previous one. .. centered:: |noisy_img| |segmented_img| @@ -815,7 +818,9 @@ by black points below. .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :target: ../auto_examples/cluster/plot_dbscan.html :scale: 50 - :alt: A bubble chart that maps three clusters, with the x-axis from -2.5 to 2 and the y-axis from -2 to 2.5. Outliers are indicated separately and are evenly spaced around the clusters. + :alt: A bubble chart that maps three clusters, with the x-axis from -2.5 to 2 + and the y-axis from -2 to 2.5. Outliers are indicated separately and are + evenly spaced around the clusters. .. centered:: |dbscan_results| diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index 4768957a53659..23b65b40e427b 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -181,7 +181,11 @@ object to the same sample. :target: ../auto_examples/covariance/plot_lw_vs_oas.html :align: center :scale: 75% - :alt: Two figures. One of them plots the Mean Squared Error difference between a LedoitWolf and an OAS estimator of the covariance with the y-axis from 0 to 60. The other one compares the Shrinkage covariance estimation between the LedoitWolf and OAS, with the y-axis from 0 to 1. In iboth figures the x-axis is the number of samples from 5 to 30. + :alt: Two figures. One of them plots the Mean Squared Error difference between a + LedoitWolf and an OAS estimator of the covariance with the y-axis from 0 to 60. + The other one compares the Shrinkage covariance estimation between the LedoitWolf + and OAS, with the y-axis from 0 to 1. In iboth figures the x-axis is the number of + samples from 5 to 30. @@ -215,7 +219,9 @@ cross-validation to automatically set the ``alpha`` parameter. :target: ../auto_examples/covariance/plot_sparse_cov.html :align: center :scale: 60% - :alt: Various matrix using different estimators: Empircal covariance, Empircal precision, Ledoit-Wolf covariance, Ledoit-Wolf precision, GraphicaLassoCV covariance, GraphicaLassoCV precision, True covariance and True Precision. + :alt: Various matrix using different estimators: Empircal covariance, Empircal + precision, Ledoit-Wolf covariance, Ledoit-Wolf precision, GraphicaLassoCV + covariance, GraphicaLassoCV precision, True covariance and True Precision. *A comparison of maximum likelihood, shrinkage and sparse estimates of @@ -340,7 +346,9 @@ attributes of a :class:`MinCovDet` robust covariance estimator object. .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html :scale: 49% - :alt: Two line charts comparing estimation errors that are made when using various types of location and covariance estimates on contaminated Gaussian distributed data sets. + :alt: Two line charts comparing estimation errors that are made when using various + types of location and covariance estimates on contaminated Gaussian distributed + data sets. .. |mahalanobis| image:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png :target: ../auto_examples/covariance/plot_mahalanobis_distances.html