Pushing the docs to dev/ for branch: main, commit 2dc35b97b0824cb8bc3f17d2382700a5ebf3ab74

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,72 @@
       },
       "outputs": [],
       "source": [
-        "# author: Gael Varoquaux <[email protected]>\n# License: BSD 3 clause\n# Copyright: INRIA\n\nimport numpy as np\nfrom scipy import linalg\nfrom sklearn.datasets import make_sparse_spd_matrix\nfrom sklearn.covariance import GraphicalLassoCV, ledoit_wolf\nimport matplotlib.pyplot as plt\n\n# #############################################################################\n# Generate the data\nn_samples = 60\nn_features = 20\n\nprng = np.random.RandomState(1)\nprec = make_sparse_spd_matrix(\n    n_features, alpha=0.98, smallest_coef=0.4, largest_coef=0.7, random_state=prng\n)\ncov = linalg.inv(prec)\nd = np.sqrt(np.diag(cov))\ncov /= d\ncov /= d[:, np.newaxis]\nprec *= d\nprec *= d[:, np.newaxis]\nX = prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples)\nX -= X.mean(axis=0)\nX /= X.std(axis=0)\n\n# #############################################################################\n# Estimate the covariance\nemp_cov = np.dot(X.T, X) / n_samples\n\nmodel = GraphicalLassoCV()\nmodel.fit(X)\ncov_ = model.covariance_\nprec_ = model.precision_\n\nlw_cov_, _ = ledoit_wolf(X)\nlw_prec_ = linalg.inv(lw_cov_)\n\n# #############################################################################\n# Plot the results\nplt.figure(figsize=(10, 6))\nplt.subplots_adjust(left=0.02, right=0.98)\n\n# plot the covariances\ncovs = [\n    (\"Empirical\", emp_cov),\n    (\"Ledoit-Wolf\", lw_cov_),\n    (\"GraphicalLassoCV\", cov_),\n    (\"True\", cov),\n]\nvmax = cov_.max()\nfor i, (name, this_cov) in enumerate(covs):\n    plt.subplot(2, 4, i + 1)\n    plt.imshow(\n        this_cov, interpolation=\"nearest\", vmin=-vmax, vmax=vmax, cmap=plt.cm.RdBu_r\n    )\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(\"%s covariance\" % name)\n\n\n# plot the precisions\nprecs = [\n    (\"Empirical\", linalg.inv(emp_cov)),\n    (\"Ledoit-Wolf\", lw_prec_),\n    (\"GraphicalLasso\", prec_),\n    (\"True\", prec),\n]\nvmax = 0.9 * prec_.max()\nfor i, (name, this_prec) in enumerate(precs):\n    ax = plt.subplot(2, 4, i + 5)\n    plt.imshow(\n        np.ma.masked_equal(this_prec, 0),\n        interpolation=\"nearest\",\n        vmin=-vmax,\n        vmax=vmax,\n        cmap=plt.cm.RdBu_r,\n    )\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(\"%s precision\" % name)\n    if hasattr(ax, \"set_facecolor\"):\n        ax.set_facecolor(\".7\")\n    else:\n        ax.set_axis_bgcolor(\".7\")\n\n# plot the model selection metric\nplt.figure(figsize=(4, 3))\nplt.axes([0.2, 0.15, 0.75, 0.7])\nplt.plot(model.cv_results_[\"alphas\"], model.cv_results_[\"mean_score\"], \"o-\")\nplt.axvline(model.alpha_, color=\".5\")\nplt.title(\"Model selection\")\nplt.ylabel(\"Cross-validation score\")\nplt.xlabel(\"alpha\")\n\nplt.show()"
+        "# author: Gael Varoquaux <[email protected]>\n# License: BSD 3 clause\n# Copyright: INRIA"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generate the data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom scipy import linalg\nfrom sklearn.datasets import make_sparse_spd_matrix\n\nn_samples = 60\nn_features = 20\n\nprng = np.random.RandomState(1)\nprec = make_sparse_spd_matrix(\n    n_features, alpha=0.98, smallest_coef=0.4, largest_coef=0.7, random_state=prng\n)\ncov = linalg.inv(prec)\nd = np.sqrt(np.diag(cov))\ncov /= d\ncov /= d[:, np.newaxis]\nprec *= d\nprec *= d[:, np.newaxis]\nX = prng.multivariate_normal(np.zeros(n_features), cov, size=n_samples)\nX -= X.mean(axis=0)\nX /= X.std(axis=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Estimate the covariance\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.covariance import GraphicalLassoCV, ledoit_wolf\n\nemp_cov = np.dot(X.T, X) / n_samples\n\nmodel = GraphicalLassoCV()\nmodel.fit(X)\ncov_ = model.covariance_\nprec_ = model.precision_\n\nlw_cov_, _ = ledoit_wolf(X)\nlw_prec_ = linalg.inv(lw_cov_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot the results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nplt.figure(figsize=(10, 6))\nplt.subplots_adjust(left=0.02, right=0.98)\n\n# plot the covariances\ncovs = [\n    (\"Empirical\", emp_cov),\n    (\"Ledoit-Wolf\", lw_cov_),\n    (\"GraphicalLassoCV\", cov_),\n    (\"True\", cov),\n]\nvmax = cov_.max()\nfor i, (name, this_cov) in enumerate(covs):\n    plt.subplot(2, 4, i + 1)\n    plt.imshow(\n        this_cov, interpolation=\"nearest\", vmin=-vmax, vmax=vmax, cmap=plt.cm.RdBu_r\n    )\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(\"%s covariance\" % name)\n\n\n# plot the precisions\nprecs = [\n    (\"Empirical\", linalg.inv(emp_cov)),\n    (\"Ledoit-Wolf\", lw_prec_),\n    (\"GraphicalLasso\", prec_),\n    (\"True\", prec),\n]\nvmax = 0.9 * prec_.max()\nfor i, (name, this_prec) in enumerate(precs):\n    ax = plt.subplot(2, 4, i + 5)\n    plt.imshow(\n        np.ma.masked_equal(this_prec, 0),\n        interpolation=\"nearest\",\n        vmin=-vmax,\n        vmax=vmax,\n        cmap=plt.cm.RdBu_r,\n    )\n    plt.xticks(())\n    plt.yticks(())\n    plt.title(\"%s precision\" % name)\n    if hasattr(ax, \"set_facecolor\"):\n        ax.set_facecolor(\".7\")\n    else:\n        ax.set_axis_bgcolor(\".7\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# plot the model selection metric\nplt.figure(figsize=(4, 3))\nplt.axes([0.2, 0.15, 0.75, 0.7])\nplt.plot(model.cv_results_[\"alphas\"], model.cv_results_[\"mean_test_score\"], \"o-\")\nplt.axvline(model.alpha_, color=\".5\")\nplt.title(\"Model selection\")\nplt.ylabel(\"Cross-validation score\")\nplt.xlabel(\"alpha\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/493307eb257cfb3d4e056ee73a41842e/plot_sparse_cov.ipynb

@@ -54,14 +54,13 @@
 # License: BSD 3 clause
 # Copyright: INRIA
 
+# %%
+# Generate the data
+# -----------------
 import numpy as np
 from scipy import linalg
 from sklearn.datasets import make_sparse_spd_matrix
-from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
-import matplotlib.pyplot as plt
 
-# #############################################################################
-# Generate the data
 n_samples = 60
 n_features = 20
 
@@ -79,8 +78,11 @@
 X -= X.mean(axis=0)
 X /= X.std(axis=0)
 
-# #############################################################################
+# %%
 # Estimate the covariance
+# -----------------------
+from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
+
 emp_cov = np.dot(X.T, X) / n_samples
 
 model = GraphicalLassoCV()
@@ -91,8 +93,11 @@
 lw_cov_, _ = ledoit_wolf(X)
 lw_prec_ = linalg.inv(lw_cov_)
 
-# #############################################################################
+# %%
 # Plot the results
+# ----------------
+import matplotlib.pyplot as plt
+
 plt.figure(figsize=(10, 6))
 plt.subplots_adjust(left=0.02, right=0.98)
 
@@ -139,10 +144,12 @@
     else:
         ax.set_axis_bgcolor(".7")
 
+# %%
+
 # plot the model selection metric
 plt.figure(figsize=(4, 3))
 plt.axes([0.2, 0.15, 0.75, 0.7])
-plt.plot(model.cv_results_["alphas"], model.cv_results_["mean_score"], "o-")
+plt.plot(model.cv_results_["alphas"], model.cv_results_["mean_test_score"], "o-")
 plt.axvline(model.alpha_, color=".5")
 plt.title("Model selection")
 plt.ylabel("Cross-validation score")