Pushing the docs to dev/ for branch: master, commit 28ad450a43665cd459f9dd6336a17bc9218d834f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import linalg\n\nfrom sklearn.covariance import LedoitWolf, OAS, ShrunkCovariance, \\\n    log_likelihood, empirical_covariance\nfrom sklearn.model_selection import GridSearchCV\n\n\n# #############################################################################\n# Generate sample data\nn_features, n_samples = 40, 20\nnp.random.seed(42)\nbase_X_train = np.random.normal(size=(n_samples, n_features))\nbase_X_test = np.random.normal(size=(n_samples, n_features))\n\n# Color samples\ncoloring_matrix = np.random.normal(size=(n_features, n_features))\nX_train = np.dot(base_X_train, coloring_matrix)\nX_test = np.dot(base_X_test, coloring_matrix)\n\n# #############################################################################\n# Compute the likelihood on test data\n\n# spanning a range of possible shrinkage coefficient values\nshrinkages = np.logspace(-2, 0, 30)\nnegative_logliks = [-ShrunkCovariance(shrinkage=s).fit(X_train).score(X_test)\n                    for s in shrinkages]\n\n# under the ground-truth model, which we would not have access to in real\n# settings\nreal_cov = np.dot(coloring_matrix.T, coloring_matrix)\nemp_cov = empirical_covariance(X_train)\nloglik_real = -log_likelihood(emp_cov, linalg.inv(real_cov))\n\n# #############################################################################\n# Compare different approaches to setting the parameter\n\n# GridSearch for an optimal shrinkage coefficient\ntuned_parameters = [{'shrinkage': shrinkages}]\ncv = GridSearchCV(ShrunkCovariance(), tuned_parameters)\ncv.fit(X_train)\n\n# Ledoit-Wolf optimal shrinkage coefficient estimate\nlw = LedoitWolf()\nloglik_lw = lw.fit(X_train).score(X_test)\n\n# OAS coefficient estimate\noa = OAS()\nloglik_oa = oa.fit(X_train).score(X_test)\n\n# #############################################################################\n# Plot results\nfig = plt.figure()\nplt.title(\"Regularized covariance: likelihood and shrinkage coefficient\")\nplt.xlabel('Regularizaton parameter: shrinkage coefficient')\nplt.ylabel('Error: negative log-likelihood on test data')\n# range shrinkage curve\nplt.loglog(shrinkages, negative_logliks, label=\"Negative log-likelihood\")\n\nplt.plot(plt.xlim(), 2 * [loglik_real], '--r',\n         label=\"Real covariance likelihood\")\n\n# adjust view\nlik_max = np.amax(negative_logliks)\nlik_min = np.amin(negative_logliks)\nymin = lik_min - 6. * np.log((plt.ylim()[1] - plt.ylim()[0]))\nymax = lik_max + 10. * np.log(lik_max - lik_min)\nxmin = shrinkages[0]\nxmax = shrinkages[-1]\n# LW likelihood\nplt.vlines(lw.shrinkage_, ymin, -loglik_lw, color='magenta',\n           linewidth=3, label='Ledoit-Wolf estimate')\n# OAS likelihood\nplt.vlines(oa.shrinkage_, ymin, -loglik_oa, color='purple',\n           linewidth=3, label='OAS estimate')\n# best CV estimator likelihood\nplt.vlines(cv.best_estimator_.shrinkage, ymin,\n           -cv.best_estimator_.score(X_test), color='cyan',\n           linewidth=3, label='Cross-validation best estimate')\n\nplt.ylim(ymin, ymax)\nplt.xlim(xmin, xmax)\nplt.legend()\n\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import linalg\n\nfrom sklearn.covariance import LedoitWolf, OAS, ShrunkCovariance, \\\n    log_likelihood, empirical_covariance\nfrom sklearn.model_selection import GridSearchCV\n\n\n# #############################################################################\n# Generate sample data\nn_features, n_samples = 40, 20\nnp.random.seed(42)\nbase_X_train = np.random.normal(size=(n_samples, n_features))\nbase_X_test = np.random.normal(size=(n_samples, n_features))\n\n# Color samples\ncoloring_matrix = np.random.normal(size=(n_features, n_features))\nX_train = np.dot(base_X_train, coloring_matrix)\nX_test = np.dot(base_X_test, coloring_matrix)\n\n# #############################################################################\n# Compute the likelihood on test data\n\n# spanning a range of possible shrinkage coefficient values\nshrinkages = np.logspace(-2, 0, 30)\nnegative_logliks = [-ShrunkCovariance(shrinkage=s).fit(X_train).score(X_test)\n                    for s in shrinkages]\n\n# under the ground-truth model, which we would not have access to in real\n# settings\nreal_cov = np.dot(coloring_matrix.T, coloring_matrix)\nemp_cov = empirical_covariance(X_train)\nloglik_real = -log_likelihood(emp_cov, linalg.inv(real_cov))\n\n# #############################################################################\n# Compare different approaches to setting the parameter\n\n# GridSearch for an optimal shrinkage coefficient\ntuned_parameters = [{'shrinkage': shrinkages}]\ncv = GridSearchCV(ShrunkCovariance(), tuned_parameters)\ncv.fit(X_train)\n\n# Ledoit-Wolf optimal shrinkage coefficient estimate\nlw = LedoitWolf()\nloglik_lw = lw.fit(X_train).score(X_test)\n\n# OAS coefficient estimate\noa = OAS()\nloglik_oa = oa.fit(X_train).score(X_test)\n\n# #############################################################################\n# Plot results\nfig = plt.figure()\nplt.title(\"Regularized covariance: likelihood and shrinkage coefficient\")\nplt.xlabel('Regularization parameter: shrinkage coefficient')\nplt.ylabel('Error: negative log-likelihood on test data')\n# range shrinkage curve\nplt.loglog(shrinkages, negative_logliks, label=\"Negative log-likelihood\")\n\nplt.plot(plt.xlim(), 2 * [loglik_real], '--r',\n         label=\"Real covariance likelihood\")\n\n# adjust view\nlik_max = np.amax(negative_logliks)\nlik_min = np.amin(negative_logliks)\nymin = lik_min - 6. * np.log((plt.ylim()[1] - plt.ylim()[0]))\nymax = lik_max + 10. * np.log(lik_max - lik_min)\nxmin = shrinkages[0]\nxmax = shrinkages[-1]\n# LW likelihood\nplt.vlines(lw.shrinkage_, ymin, -loglik_lw, color='magenta',\n           linewidth=3, label='Ledoit-Wolf estimate')\n# OAS likelihood\nplt.vlines(oa.shrinkage_, ymin, -loglik_oa, color='purple',\n           linewidth=3, label='OAS estimate')\n# best CV estimator likelihood\nplt.vlines(cv.best_estimator_.shrinkage, ymin,\n           -cv.best_estimator_.score(X_test), color='cyan',\n           linewidth=3, label='Cross-validation best estimate')\n\nplt.ylim(ymin, ymax)\nplt.xlim(xmin, xmax)\nplt.legend()\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -98,7 +98,7 @@
 # Plot results
 fig = plt.figure()
 plt.title("Regularized covariance: likelihood and shrinkage coefficient")
-plt.xlabel('Regularizaton parameter: shrinkage coefficient')
+plt.xlabel('Regularization parameter: shrinkage coefficient')
 plt.ylabel('Error: negative log-likelihood on test data')
 # range shrinkage curve
 plt.loglog(shrinkages, negative_logliks, label="Negative log-likelihood")