Pushing the docs to dev/ for branch: master, commit ffc63c6508817112ebb5157fc36696d4bf00e53b

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\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, 200\nX = np.random.randn(n_samples, n_features)\ncoef = 3 * np.random.randn(n_features)\ninds = np.arange(n_features)\nnp.random.shuffle(inds)\ncoef[inds[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\nplt.plot(enet.coef_, color='lightgreen', linewidth=2,\n         label='Elastic net coefficients')\nplt.plot(lasso.coef_, color='gold', linewidth=2,\n         label='Lasso coefficients')\nplt.plot(coef, '--', color='navy', label='original coefficients')\nplt.legend(loc='best')\nplt.title(\"Lasso R^2: %f, Elastic Net R^2: %f\"\n          % (r2_score_lasso, r2_score_enet))\nplt.show()"
+        "print(__doc__)\n\nimport 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(np.where(enet.coef_)[0], enet.coef_[enet.coef_ != 0],\n                   markerfmt='x', label='Elastic net coefficients')\nplt.setp([m, s], color=\"#2ca02c\")\nm, s, _ = plt.stem(np.where(lasso.coef_)[0], lasso.coef_[lasso.coef_ != 0],\n                   markerfmt='x', label='Lasso coefficients')\nplt.setp([m, s], color='#ff7f0e')\nplt.stem(np.where(coef)[0], coef[coef != 0], label='true coefficients',\n         markerfmt='bx')\n\nplt.legend(loc='best')\nplt.title(\"Lasso $R^2$: %.3f, Elastic Net $R^2$: %.3f\"\n          % (r2_score_lasso, r2_score_enet))\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -19,15 +19,16 @@
 # Generate some sparse data to play with
 np.random.seed(42)
 
-n_samples, n_features = 50, 200
+n_samples, n_features = 50, 100
 X = np.random.randn(n_samples, n_features)
-coef = 3 * np.random.randn(n_features)
-inds = np.arange(n_features)
-np.random.shuffle(inds)
-coef[inds[10:]] = 0  # sparsify coef
+
+# Decreasing coef w. alternated signs for visualization
+idx = np.arange(n_features)
+coef = (-1) ** idx * np.exp(-idx / 10)
+coef[10:] = 0  # sparsify coef
 y = np.dot(X, coef)
 
-# add noise
+# Add noise
 y += 0.01 * np.random.normal(size=n_samples)
 
 # Split data in train set and test set
@@ -58,12 +59,16 @@
 print(enet)
 print("r^2 on test data : %f" % r2_score_enet)
 
-plt.plot(enet.coef_, color='lightgreen', linewidth=2,
-         label='Elastic net coefficients')
-plt.plot(lasso.coef_, color='gold', linewidth=2,
-         label='Lasso coefficients')
-plt.plot(coef, '--', color='navy', label='original coefficients')
+m, s, _ = plt.stem(np.where(enet.coef_)[0], enet.coef_[enet.coef_ != 0],
+                   markerfmt='x', label='Elastic net coefficients')
+plt.setp([m, s], color="#2ca02c")
+m, s, _ = plt.stem(np.where(lasso.coef_)[0], lasso.coef_[lasso.coef_ != 0],
+                   markerfmt='x', label='Lasso coefficients')
+plt.setp([m, s], color='#ff7f0e')
+plt.stem(np.where(coef)[0], coef[coef != 0], label='true coefficients',
+         markerfmt='bx')
+
 plt.legend(loc='best')
-plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
+plt.title("Lasso $R^2$: %.3f, Elastic Net $R^2$: %.3f"
           % (r2_score_lasso, r2_score_enet))
 plt.show()