Pushing the docs to dev/ for branch: main, commit c4be686fad50aa19b7c72e52d717d5b7ca40d123

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -9,13 +9,15 @@
 
 """
 
+# %%
+# Data Generation
+# ---------------------------------------------------
+
 import numpy as np
 import matplotlib.pyplot as plt
 
 from sklearn.metrics import r2_score
 
-# #############################################################################
-# Generate some sparse data to play with
 np.random.seed(42)
 
 n_samples, n_features = 50, 100
@@ -35,8 +37,10 @@
 X_train, y_train = X[: n_samples // 2], y[: n_samples // 2]
 X_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]
 
-# #############################################################################
+# %%
 # Lasso
+# ---------------------------------------------------
+
 from sklearn.linear_model import Lasso
 
 alpha = 0.1
@@ -47,8 +51,10 @@
 print(lasso)
 print("r^2 on test data : %f" % r2_score_lasso)
 
-# #############################################################################
+# %%
 # ElasticNet
+# ---------------------------------------------------
+
 from sklearn.linear_model import ElasticNet
 
 enet = ElasticNet(alpha=alpha, l1_ratio=0.7)
@@ -58,6 +64,11 @@
 print(enet)
 print("r^2 on test data : %f" % r2_score_enet)
 
+
+# %%
+# Plot
+# ---------------------------------------------------
+
 m, s, _ = plt.stem(
     np.where(enet.coef_)[0],
     enet.coef_[enet.coef_ != 0],

dev/_downloads/3d58721191491072eecc520f0a45cdb3/plot_lasso_and_elasticnet.py

@@ -18,6 +18,67 @@
         "\n# Lasso and Elastic Net for Sparse Signals\n\nEstimates Lasso and Elastic-Net regression models on a manually generated\nsparse signal corrupted with an additive noise. Estimated coefficients are\ncompared with the ground-truth.\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data Generation\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import r2_score\n\nnp.random.seed(42)\n\nn_samples, n_features = 50, 100\nX = np.random.randn(n_samples, n_features)\n\n# Decreasing coef w. alternated signs for visualization\nidx = np.arange(n_features)\ncoef = (-1) ** idx * np.exp(-idx / 10)\ncoef[10:] = 0  # sparsify coef\ny = np.dot(X, coef)\n\n# Add noise\ny += 0.01 * np.random.normal(size=n_samples)\n\n# Split data in train set and test set\nn_samples = X.shape[0]\nX_train, y_train = X[: n_samples // 2], y[: n_samples // 2]\nX_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Lasso\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Lasso\n\nalpha = 0.1\nlasso = Lasso(alpha=alpha)\n\ny_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)\nr2_score_lasso = r2_score(y_test, y_pred_lasso)\nprint(lasso)\nprint(\"r^2 on test data : %f\" % r2_score_lasso)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## ElasticNet\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import ElasticNet\n\nenet = ElasticNet(alpha=alpha, l1_ratio=0.7)\n\ny_pred_enet = enet.fit(X_train, y_train).predict(X_test)\nr2_score_enet = r2_score(y_test, y_pred_enet)\nprint(enet)\nprint(\"r^2 on test data : %f\" % r2_score_enet)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot\n\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -26,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import r2_score\n\n# #############################################################################\n# Generate some sparse data to play with\nnp.random.seed(42)\n\nn_samples, n_features = 50, 100\nX = np.random.randn(n_samples, n_features)\n\n# Decreasing coef w. alternated signs for visualization\nidx = np.arange(n_features)\ncoef = (-1) ** idx * np.exp(-idx / 10)\ncoef[10:] = 0  # sparsify coef\ny = np.dot(X, coef)\n\n# Add noise\ny += 0.01 * np.random.normal(size=n_samples)\n\n# Split data in train set and test set\nn_samples = X.shape[0]\nX_train, y_train = X[: n_samples // 2], y[: n_samples // 2]\nX_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]\n\n# #############################################################################\n# Lasso\nfrom sklearn.linear_model import Lasso\n\nalpha = 0.1\nlasso = Lasso(alpha=alpha)\n\ny_pred_lasso = lasso.fit(X_train, y_train).predict(X_test)\nr2_score_lasso = r2_score(y_test, y_pred_lasso)\nprint(lasso)\nprint(\"r^2 on test data : %f\" % r2_score_lasso)\n\n# #############################################################################\n# ElasticNet\nfrom sklearn.linear_model import ElasticNet\n\nenet = ElasticNet(alpha=alpha, l1_ratio=0.7)\n\ny_pred_enet = enet.fit(X_train, y_train).predict(X_test)\nr2_score_enet = r2_score(y_test, y_pred_enet)\nprint(enet)\nprint(\"r^2 on test data : %f\" % r2_score_enet)\n\nm, s, _ = plt.stem(\n    np.where(enet.coef_)[0],\n    enet.coef_[enet.coef_ != 0],\n    markerfmt=\"x\",\n    label=\"Elastic net coefficients\",\n    use_line_collection=True,\n)\nplt.setp([m, s], color=\"#2ca02c\")\nm, s, _ = plt.stem(\n    np.where(lasso.coef_)[0],\n    lasso.coef_[lasso.coef_ != 0],\n    markerfmt=\"x\",\n    label=\"Lasso coefficients\",\n    use_line_collection=True,\n)\nplt.setp([m, s], color=\"#ff7f0e\")\nplt.stem(\n    np.where(coef)[0],\n    coef[coef != 0],\n    label=\"true coefficients\",\n    markerfmt=\"bx\",\n    use_line_collection=True,\n)\n\nplt.legend(loc=\"best\")\nplt.title(\n    \"Lasso $R^2$: %.3f, Elastic Net $R^2$: %.3f\" % (r2_score_lasso, r2_score_enet)\n)\nplt.show()"
+        "m, s, _ = plt.stem(\n    np.where(enet.coef_)[0],\n    enet.coef_[enet.coef_ != 0],\n    markerfmt=\"x\",\n    label=\"Elastic net coefficients\",\n    use_line_collection=True,\n)\nplt.setp([m, s], color=\"#2ca02c\")\nm, s, _ = plt.stem(\n    np.where(lasso.coef_)[0],\n    lasso.coef_[lasso.coef_ != 0],\n    markerfmt=\"x\",\n    label=\"Lasso coefficients\",\n    use_line_collection=True,\n)\nplt.setp([m, s], color=\"#ff7f0e\")\nplt.stem(\n    np.where(coef)[0],\n    coef[coef != 0],\n    label=\"true coefficients\",\n    markerfmt=\"bx\",\n    use_line_collection=True,\n)\n\nplt.legend(loc=\"best\")\nplt.title(\n    \"Lasso $R^2$: %.3f, Elastic Net $R^2$: %.3f\" % (r2_score_lasso, r2_score_enet)\n)\nplt.show()"
       ]
     }
   ],