From 26d79edcda0e542153afd4487b3e6d04c8f666fc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien=20Geron?= <ageron@users.noreply.github.com>
Date: Fri, 15 Sep 2017 18:12:27 +0200
Subject: [PATCH] Scikit-Learn 0.19 Pipelines expect a list of tuples, not a
 tuple of tuples

---
 05_support_vector_machines.ipynb | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/05_support_vector_machines.ipynb b/05_support_vector_machines.ipynb
index bcf2ff1..24f9a5b 100644
--- a/05_support_vector_machines.ipynb
+++ b/05_support_vector_machines.ipynb
@@ -333,10 +333,10 @@
     "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
     "y = (iris[\"target\"] == 2).astype(np.float64)  # Iris-Virginica\n",
     "\n",
-    "svm_clf = Pipeline((\n",
+    "svm_clf = Pipeline([\n",
     "        (\"scaler\", StandardScaler()),\n",
     "        (\"linear_svc\", LinearSVC(C=1, loss=\"hinge\", random_state=42)),\n",
-    "    ))\n",
+    "    ])\n",
     "\n",
     "svm_clf.fit(X, y)"
    ]
@@ -378,14 +378,14 @@
     "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
     "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
     "\n",
-    "scaled_svm_clf1 = Pipeline((\n",
+    "scaled_svm_clf1 = Pipeline([\n",
     "        (\"scaler\", scaler),\n",
     "        (\"linear_svc\", svm_clf1),\n",
-    "    ))\n",
-    "scaled_svm_clf2 = Pipeline((\n",
+    "    ])\n",
+    "scaled_svm_clf2 = Pipeline([\n",
     "        (\"scaler\", scaler),\n",
     "        (\"linear_svc\", svm_clf2),\n",
-    "    ))\n",
+    "    ])\n",
     "\n",
     "scaled_svm_clf1.fit(X, y)\n",
     "scaled_svm_clf2.fit(X, y)"
@@ -544,11 +544,11 @@
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
-    "polynomial_svm_clf = Pipeline((\n",
+    "polynomial_svm_clf = Pipeline([\n",
     "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
     "        (\"scaler\", StandardScaler()),\n",
     "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\", random_state=42))\n",
-    "    ))\n",
+    "    ])\n",
     "\n",
     "polynomial_svm_clf.fit(X, y)"
    ]
@@ -592,10 +592,10 @@
    "source": [
     "from sklearn.svm import SVC\n",
     "\n",
-    "poly_kernel_svm_clf = Pipeline((\n",
+    "poly_kernel_svm_clf = Pipeline([\n",
     "        (\"scaler\", StandardScaler()),\n",
     "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
-    "    ))\n",
+    "    ])\n",
     "poly_kernel_svm_clf.fit(X, y)"
    ]
   },
@@ -609,10 +609,10 @@
    },
    "outputs": [],
    "source": [
-    "poly100_kernel_svm_clf = Pipeline((\n",
+    "poly100_kernel_svm_clf = Pipeline([\n",
     "        (\"scaler\", StandardScaler()),\n",
     "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
-    "    ))\n",
+    "    ])\n",
     "poly100_kernel_svm_clf.fit(X, y)"
    ]
   },
@@ -739,10 +739,10 @@
    },
    "outputs": [],
    "source": [
-    "rbf_kernel_svm_clf = Pipeline((\n",
+    "rbf_kernel_svm_clf = Pipeline([\n",
     "        (\"scaler\", StandardScaler()),\n",
     "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
-    "    ))\n",
+    "    ])\n",
     "rbf_kernel_svm_clf.fit(X, y)"
    ]
   },
@@ -765,10 +765,10 @@
     "\n",
     "svm_clfs = []\n",
     "for gamma, C in hyperparams:\n",
-    "    rbf_kernel_svm_clf = Pipeline((\n",
+    "    rbf_kernel_svm_clf = Pipeline([\n",
     "            (\"scaler\", StandardScaler()),\n",
     "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
-    "        ))\n",
+    "        ])\n",
     "    rbf_kernel_svm_clf.fit(X, y)\n",
     "    svm_clfs.append(rbf_kernel_svm_clf)\n",
     "\n",