From af9d6bc0446f479a7c7cff870de4da5df19fddd1 Mon Sep 17 00:00:00 2001
From: downes <downes>
Date: Wed, 12 Jul 2017 14:50:40 -0700
Subject: [PATCH 1/2] update sklearn calls to use latest API

Version 0.18 moved cross-validation to sklearn.model_selection - see http://scikit-learn.org/stable/whats_new.html#version-0-18
Version 0.17 deprecated class_weight="auto" in favor of class_weight="balanced"
---
 examples/brewing-logreg.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
index c053b73b39f..4b4cd6a3f02 100644
--- a/examples/brewing-logreg.ipynb
+++ b/examples/brewing-logreg.ipynb
@@ -73,7 +73,7 @@
     ")\n",
     "\n",
     "# Split into train and test\n",
-    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "X, Xt, y, yt = sklearn.model_selection.train_test_split(X, y)\n",
     "\n",
     "# Visualize sample of the data\n",
     "ind = np.random.permutation(X.shape[0])[:1000]\n",
@@ -111,7 +111,7 @@
     "%%timeit\n",
     "# Train and test the scikit-learn SGD logistic regression.\n",
     "clf = sklearn.linear_model.SGDClassifier(\n",
-    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='balanced')\n",
     "\n",
     "clf.fit(X, y)\n",
     "yt_pred = clf.predict(Xt)\n",

From 4b98f06c03c7cb84163ba7f681dbe9185fdcc5f9 Mon Sep 17 00:00:00 2001
From: downes <downes>
Date: Wed, 12 Jul 2017 14:52:53 -0700
Subject: [PATCH 2/2] update deprecated pandas call

pd.scatter_matrix -> pd.plotting.scatter_matrix
---
 examples/brewing-logreg.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
index 4b4cd6a3f02..0f87185a35b 100644
--- a/examples/brewing-logreg.ipynb
+++ b/examples/brewing-logreg.ipynb
@@ -78,7 +78,7 @@
     "# Visualize sample of the data\n",
     "ind = np.random.permutation(X.shape[0])[:1000]\n",
     "df = pd.DataFrame(X[ind])\n",
-    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+    "_ = pd.plotting.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
    ]
   },
   {