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@@ -47,8 +47,8 @@ def generate_data(n_samples, n_features):
     for _ in range(n_averages):
         X, y = generate_data(n_train, n_features)
 
-        clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
-        clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
+        clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
+        clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
         oa = OAS(store_precision=False, assume_centered=False)
         clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(
             X, y
@@ -69,36 +69,37 @@ def generate_data(n_samples, n_features):
     features_samples_ratio,
     acc_clf1,
     linewidth=2,
-    label="Linear Discriminant Analysis with Ledoit Wolf",
-    color="navy",
-    linestyle="dashed",
+    label="LDA",
+    color="gold",
+    linestyle="solid",
 )
 plt.plot(
     features_samples_ratio,
     acc_clf2,
     linewidth=2,
-    label="Linear Discriminant Analysis",
-    color="gold",
-    linestyle="solid",
+    label="LDA with Ledoit Wolf",
+    color="navy",
+    linestyle="dashed",
 )
 plt.plot(
     features_samples_ratio,
     acc_clf3,
     linewidth=2,
-    label="Linear Discriminant Analysis with OAS",
+    label="LDA with OAS",
     color="red",
     linestyle="dotted",
 )
 
 plt.xlabel("n_features / n_samples")
 plt.ylabel("Classification accuracy")
 
-plt.legend(loc=3, prop={"size": 12})
+plt.legend(loc="lower left")
+plt.ylim((0.65, 1.0))
 plt.suptitle(
-    "Linear Discriminant Analysis vs. "
+    "LDA (Linear Discriminant Analysis) vs. "
     + "\n"
-    + "Shrinkage Linear Discriminant Analysis vs. "
+    + "LDA with Ledoit Wolf vs. "
     + "\n"
-    + "OAS Linear Discriminant Analysis (1 discriminative feature)"
+    + "LDA with OAS (1 discriminative feature)"
 )
 plt.show()
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.covariance import OAS\n\n\nn_train = 20  # samples for training\nn_test = 200  # samples for testing\nn_averages = 50  # how often to repeat classification\nn_features_max = 75  # maximum number of features\nstep = 4  # step size for the calculation\n\n\ndef generate_data(n_samples, n_features):\n    \"\"\"Generate random blob-ish data with noisy features.\n\n    This returns an array of input data with shape `(n_samples, n_features)`\n    and an array of `n_samples` target labels.\n\n    Only one feature contains discriminative information, the other features\n    contain only noise.\n    \"\"\"\n    X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])\n\n    # add non-discriminative features\n    if n_features > 1:\n        X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])\n    return X, y\n\n\nacc_clf1, acc_clf2, acc_clf3 = [], [], []\nn_features_range = range(1, n_features_max + 1, step)\nfor n_features in n_features_range:\n    score_clf1, score_clf2, score_clf3 = 0, 0, 0\n    for _ in range(n_averages):\n        X, y = generate_data(n_train, n_features)\n\n        clf1 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\").fit(X, y)\n        clf2 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=None).fit(X, y)\n        oa = OAS(store_precision=False, assume_centered=False)\n        clf3 = LinearDiscriminantAnalysis(solver=\"lsqr\", covariance_estimator=oa).fit(\n            X, y\n        )\n\n        X, y = generate_data(n_test, n_features)\n        score_clf1 += clf1.score(X, y)\n        score_clf2 += clf2.score(X, y)\n        score_clf3 += clf3.score(X, y)\n\n    acc_clf1.append(score_clf1 / n_averages)\n    acc_clf2.append(score_clf2 / n_averages)\n    acc_clf3.append(score_clf3 / n_averages)\n\nfeatures_samples_ratio = np.array(n_features_range) / n_train\n\nplt.plot(\n    features_samples_ratio,\n    acc_clf1,\n    linewidth=2,\n    label=\"Linear Discriminant Analysis with Ledoit Wolf\",\n    color=\"navy\",\n    linestyle=\"dashed\",\n)\nplt.plot(\n    features_samples_ratio,\n    acc_clf2,\n    linewidth=2,\n    label=\"Linear Discriminant Analysis\",\n    color=\"gold\",\n    linestyle=\"solid\",\n)\nplt.plot(\n    features_samples_ratio,\n    acc_clf3,\n    linewidth=2,\n    label=\"Linear Discriminant Analysis with OAS\",\n    color=\"red\",\n    linestyle=\"dotted\",\n)\n\nplt.xlabel(\"n_features / n_samples\")\nplt.ylabel(\"Classification accuracy\")\n\nplt.legend(loc=3, prop={\"size\": 12})\nplt.suptitle(\n    \"Linear Discriminant Analysis vs. \"\n    + \"\\n\"\n    + \"Shrinkage Linear Discriminant Analysis vs. \"\n    + \"\\n\"\n    + \"OAS Linear Discriminant Analysis (1 discriminative feature)\"\n)\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.covariance import OAS\n\n\nn_train = 20  # samples for training\nn_test = 200  # samples for testing\nn_averages = 50  # how often to repeat classification\nn_features_max = 75  # maximum number of features\nstep = 4  # step size for the calculation\n\n\ndef generate_data(n_samples, n_features):\n    \"\"\"Generate random blob-ish data with noisy features.\n\n    This returns an array of input data with shape `(n_samples, n_features)`\n    and an array of `n_samples` target labels.\n\n    Only one feature contains discriminative information, the other features\n    contain only noise.\n    \"\"\"\n    X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])\n\n    # add non-discriminative features\n    if n_features > 1:\n        X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])\n    return X, y\n\n\nacc_clf1, acc_clf2, acc_clf3 = [], [], []\nn_features_range = range(1, n_features_max + 1, step)\nfor n_features in n_features_range:\n    score_clf1, score_clf2, score_clf3 = 0, 0, 0\n    for _ in range(n_averages):\n        X, y = generate_data(n_train, n_features)\n\n        clf1 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=None).fit(X, y)\n        clf2 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\").fit(X, y)\n        oa = OAS(store_precision=False, assume_centered=False)\n        clf3 = LinearDiscriminantAnalysis(solver=\"lsqr\", covariance_estimator=oa).fit(\n            X, y\n        )\n\n        X, y = generate_data(n_test, n_features)\n        score_clf1 += clf1.score(X, y)\n        score_clf2 += clf2.score(X, y)\n        score_clf3 += clf3.score(X, y)\n\n    acc_clf1.append(score_clf1 / n_averages)\n    acc_clf2.append(score_clf2 / n_averages)\n    acc_clf3.append(score_clf3 / n_averages)\n\nfeatures_samples_ratio = np.array(n_features_range) / n_train\n\nplt.plot(\n    features_samples_ratio,\n    acc_clf1,\n    linewidth=2,\n    label=\"LDA\",\n    color=\"gold\",\n    linestyle=\"solid\",\n)\nplt.plot(\n    features_samples_ratio,\n    acc_clf2,\n    linewidth=2,\n    label=\"LDA with Ledoit Wolf\",\n    color=\"navy\",\n    linestyle=\"dashed\",\n)\nplt.plot(\n    features_samples_ratio,\n    acc_clf3,\n    linewidth=2,\n    label=\"LDA with OAS\",\n    color=\"red\",\n    linestyle=\"dotted\",\n)\n\nplt.xlabel(\"n_features / n_samples\")\nplt.ylabel(\"Classification accuracy\")\n\nplt.legend(loc=\"lower left\")\nplt.ylim((0.65, 1.0))\nplt.suptitle(\n    \"LDA (Linear Discriminant Analysis) vs. \"\n    + \"\\n\"\n    + \"LDA with Ledoit Wolf vs. \"\n    + \"\\n\"\n    + \"LDA with OAS (1 discriminative feature)\"\n)\nplt.show()"
       ]
     }
   ],
Original file line number	Diff line number	Diff line change
`@@ -26,7 +26,7 @@`
`26`	`26`	`},`
`27`	`27`	`"outputs": [],`
`28`	`28`	`"source": [`
`29`		- "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.covariance import OAS\n\n\nn_train = 20 # samples for training\nn_test = 200 # samples for testing\nn_averages = 50 # how often to repeat classification\nn_features_max = 75 # maximum number of features\nstep = 4 # step size for the calculation\n\n\ndef generate_data(n_samples, n_features):\n \"\"\"Generate random blob-ish data with noisy features.\n\n This returns an array of input data with shape `(n_samples, n_features)`\n and an array of `n_samples` target labels.\n\n Only one feature contains discriminative information, the other features\n contain only noise.\n \"\"\"\n X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])\n\n # add non-discriminative features\n if n_features > 1:\n X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])\n return X, y\n\n\nacc_clf1, acc_clf2, acc_clf3 = [], [], []\nn_features_range = range(1, n_features_max + 1, step)\nfor n_features in n_features_range:\n score_clf1, score_clf2, score_clf3 = 0, 0, 0\n for _ in range(n_averages):\n X, y = generate_data(n_train, n_features)\n\n clf1 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\").fit(X, y)\n clf2 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=None).fit(X, y)\n oa = OAS(store_precision=False, assume_centered=False)\n clf3 = LinearDiscriminantAnalysis(solver=\"lsqr\", covariance_estimator=oa).fit(\n X, y\n )\n\n X, y = generate_data(n_test, n_features)\n score_clf1 += clf1.score(X, y)\n score_clf2 += clf2.score(X, y)\n score_clf3 += clf3.score(X, y)\n\n acc_clf1.append(score_clf1 / n_averages)\n acc_clf2.append(score_clf2 / n_averages)\n acc_clf3.append(score_clf3 / n_averages)\n\nfeatures_samples_ratio = np.array(n_features_range) / n_train\n\nplt.plot(\n features_samples_ratio,\n acc_clf1,\n linewidth=2,\n label=\"Linear Discriminant Analysis with Ledoit Wolf\",\n color=\"navy\",\n linestyle=\"dashed\",\n)\nplt.plot(\n features_samples_ratio,\n acc_clf2,\n linewidth=2,\n label=\"Linear Discriminant Analysis\",\n color=\"gold\",\n linestyle=\"solid\",\n)\nplt.plot(\n features_samples_ratio,\n acc_clf3,\n linewidth=2,\n label=\"Linear Discriminant Analysis with OAS\",\n color=\"red\",\n linestyle=\"dotted\",\n)\n\nplt.xlabel(\"n_features / n_samples\")\nplt.ylabel(\"Classification accuracy\")\n\nplt.legend(loc=3, prop={\"size\": 12})\nplt.suptitle(\n \"Linear Discriminant Analysis vs. \"\n + \"\\n\"\n + \"Shrinkage Linear Discriminant Analysis vs. \"\n + \"\\n\"\n + \"OAS Linear Discriminant Analysis (1 discriminative feature)\"\n)\nplt.show()"
	`29`	+ "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_blobs\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.covariance import OAS\n\n\nn_train = 20 # samples for training\nn_test = 200 # samples for testing\nn_averages = 50 # how often to repeat classification\nn_features_max = 75 # maximum number of features\nstep = 4 # step size for the calculation\n\n\ndef generate_data(n_samples, n_features):\n \"\"\"Generate random blob-ish data with noisy features.\n\n This returns an array of input data with shape `(n_samples, n_features)`\n and an array of `n_samples` target labels.\n\n Only one feature contains discriminative information, the other features\n contain only noise.\n \"\"\"\n X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])\n\n # add non-discriminative features\n if n_features > 1:\n X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])\n return X, y\n\n\nacc_clf1, acc_clf2, acc_clf3 = [], [], []\nn_features_range = range(1, n_features_max + 1, step)\nfor n_features in n_features_range:\n score_clf1, score_clf2, score_clf3 = 0, 0, 0\n for _ in range(n_averages):\n X, y = generate_data(n_train, n_features)\n\n clf1 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=None).fit(X, y)\n clf2 = LinearDiscriminantAnalysis(solver=\"lsqr\", shrinkage=\"auto\").fit(X, y)\n oa = OAS(store_precision=False, assume_centered=False)\n clf3 = LinearDiscriminantAnalysis(solver=\"lsqr\", covariance_estimator=oa).fit(\n X, y\n )\n\n X, y = generate_data(n_test, n_features)\n score_clf1 += clf1.score(X, y)\n score_clf2 += clf2.score(X, y)\n score_clf3 += clf3.score(X, y)\n\n acc_clf1.append(score_clf1 / n_averages)\n acc_clf2.append(score_clf2 / n_averages)\n acc_clf3.append(score_clf3 / n_averages)\n\nfeatures_samples_ratio = np.array(n_features_range) / n_train\n\nplt.plot(\n features_samples_ratio,\n acc_clf1,\n linewidth=2,\n label=\"LDA\",\n color=\"gold\",\n linestyle=\"solid\",\n)\nplt.plot(\n features_samples_ratio,\n acc_clf2,\n linewidth=2,\n label=\"LDA with Ledoit Wolf\",\n color=\"navy\",\n linestyle=\"dashed\",\n)\nplt.plot(\n features_samples_ratio,\n acc_clf3,\n linewidth=2,\n label=\"LDA with OAS\",\n color=\"red\",\n linestyle=\"dotted\",\n)\n\nplt.xlabel(\"n_features / n_samples\")\nplt.ylabel(\"Classification accuracy\")\n\nplt.legend(loc=\"lower left\")\nplt.ylim((0.65, 1.0))\nplt.suptitle(\n \"LDA (Linear Discriminant Analysis) vs. \"\n + \"\\n\"\n + \"LDA with Ledoit Wolf vs. \"\n + \"\\n\"\n + \"LDA with OAS (1 discriminative feature)\"\n)\nplt.show()"
`30`	`30`	`]`
`31`	`31`	`}`
`32`	`32`	`],`