Pushing the docs to dev/ for branch: main, commit e41753ebd57c44ae91b389f190c43ddc0b384a75

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LassoLarsIC\nfrom sklearn.pipeline import make_pipeline\n\nlasso_lars_ic = make_pipeline(\n    StandardScaler(), LassoLarsIC(criterion=\"aic\", normalize=False)\n).fit(X, y)"
+        "from sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LassoLarsIC\nfrom sklearn.pipeline import make_pipeline\n\nlasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion=\"aic\")).fit(X, y)"
       ]
     },
     {

dev/_downloads/51833337bfc73d152b44902e5baa50ff/plot_lasso_lars_ic.ipynb

@@ -64,9 +64,7 @@
 from sklearn.pipeline import make_pipeline
 
 start_time = time.time()
-lasso_lars_ic = make_pipeline(
-    StandardScaler(), LassoLarsIC(criterion="aic", normalize=False)
-).fit(X, y)
+lasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion="aic")).fit(X, y)
 fit_time = time.time() - start_time
 
 # %%
@@ -193,7 +191,7 @@ def highlight_min(x):
 from sklearn.linear_model import LassoLarsCV
 
 start_time = time.time()
-model = make_pipeline(StandardScaler(), LassoLarsCV(cv=20, normalize=False)).fit(X, y)
+model = make_pipeline(StandardScaler(), LassoLarsCV(cv=20)).fit(X, y)
 fit_time = time.time() - start_time
 
 # %%

dev/_downloads/58580795dd881384f33e7e6492e154e2/plot_lasso_model_selection.py

@@ -49,9 +49,7 @@
 from sklearn.linear_model import LassoLarsIC
 from sklearn.pipeline import make_pipeline
 
-lasso_lars_ic = make_pipeline(
-    StandardScaler(), LassoLarsIC(criterion="aic", normalize=False)
-).fit(X, y)
+lasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion="aic")).fit(X, y)
 
 
 # %%

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LassoLarsIC\nfrom sklearn.pipeline import make_pipeline\n\nstart_time = time.time()\nlasso_lars_ic = make_pipeline(\n    StandardScaler(), LassoLarsIC(criterion=\"aic\", normalize=False)\n).fit(X, y)\nfit_time = time.time() - start_time"
+        "import time\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LassoLarsIC\nfrom sklearn.pipeline import make_pipeline\n\nstart_time = time.time()\nlasso_lars_ic = make_pipeline(StandardScaler(), LassoLarsIC(criterion=\"aic\")).fit(X, y)\nfit_time = time.time() - start_time"
       ]
     },
     {
@@ -199,7 +199,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.linear_model import LassoLarsCV\n\nstart_time = time.time()\nmodel = make_pipeline(StandardScaler(), LassoLarsCV(cv=20, normalize=False)).fit(X, y)\nfit_time = time.time() - start_time"
+        "from sklearn.linear_model import LassoLarsCV\n\nstart_time = time.time()\nmodel = make_pipeline(StandardScaler(), LassoLarsCV(cv=20)).fit(X, y)\nfit_time = time.time() - start_time"
       ]
     },
     {

dev/_downloads/8c96d910ed5b614924f36c896b1934a6/plot_lasso_lars_ic.py

@@ -44,7 +44,7 @@
 plt.stem(idx, w[idx], use_line_collection=True)
 
 # plot the noise-free reconstruction
-omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs, normalize=False)
+omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
 omp.fit(X, y)
 coef = omp.coef_
 (idx_r,) = coef.nonzero()
@@ -63,7 +63,7 @@
 plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 # plot the noisy reconstruction with number of non-zeros set by CV
-omp_cv = OrthogonalMatchingPursuitCV(normalize=False)
+omp_cv = OrthogonalMatchingPursuitCV()
 omp_cv.fit(X, y_noisy)
 coef = omp_cv.coef_
 (idx_r,) = coef.nonzero()

dev/_downloads/901ad159d788e1122a5616f537985e74/plot_lasso_model_selection.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(\n    n_samples=1,\n    n_components=n_components,\n    n_features=n_features,\n    n_nonzero_coefs=n_nonzero_coefs,\n    random_state=0,\n    data_transposed=True,\n)\n\n(idx,) = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx], use_line_collection=True)\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs, normalize=False)\nomp.fit(X, y)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV(normalize=False)\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle(\"Sparse signal recovery with Orthogonal Matching Pursuit\", fontsize=16)\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.linear_model import OrthogonalMatchingPursuit\nfrom sklearn.linear_model import OrthogonalMatchingPursuitCV\nfrom sklearn.datasets import make_sparse_coded_signal\n\nn_components, n_features = 512, 100\nn_nonzero_coefs = 17\n\n# generate the data\n\n# y = Xw\n# |x|_0 = n_nonzero_coefs\n\ny, X, w = make_sparse_coded_signal(\n    n_samples=1,\n    n_components=n_components,\n    n_features=n_features,\n    n_nonzero_coefs=n_nonzero_coefs,\n    random_state=0,\n    data_transposed=True,\n)\n\n(idx,) = w.nonzero()\n\n# distort the clean signal\ny_noisy = y + 0.05 * np.random.randn(len(y))\n\n# plot the sparse signal\nplt.figure(figsize=(7, 7))\nplt.subplot(4, 1, 1)\nplt.xlim(0, 512)\nplt.title(\"Sparse signal\")\nplt.stem(idx, w[idx], use_line_collection=True)\n\n# plot the noise-free reconstruction\nomp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)\nomp.fit(X, y)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 2)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noise-free measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction\nomp.fit(X, y_noisy)\ncoef = omp.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 3)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\n# plot the noisy reconstruction with number of non-zeros set by CV\nomp_cv = OrthogonalMatchingPursuitCV()\nomp_cv.fit(X, y_noisy)\ncoef = omp_cv.coef_\n(idx_r,) = coef.nonzero()\nplt.subplot(4, 1, 4)\nplt.xlim(0, 512)\nplt.title(\"Recovered signal from noisy measurements with CV\")\nplt.stem(idx_r, coef[idx_r], use_line_collection=True)\n\nplt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)\nplt.suptitle(\"Sparse signal recovery with Orthogonal Matching Pursuit\", fontsize=16)\nplt.show()"
       ]
     }
   ],