Use 'np.random' rather than 'import numpy.random as rnd', and add random_state to make notebook's output constant

07_ensemble_learning_and_random_forests.ipynb

@@ -55,11 +55,10 @@
     "\n",
     "# Common imports\n",
     "import numpy as np\n",
-    "import numpy.random as rnd\n",
     "import os\n",
     "\n",
     "# to make this notebook's output stable across runs\n",
-    "rnd.seed(42)\n",
+    "np.random.seed(42)\n",
     "\n",
     "# To plot pretty figures\n",
     "%matplotlib inline\n",
@@ -104,7 +103,7 @@
    "outputs": [],
    "source": [
     "heads_proba = 0.51\n",
-    "coin_tosses = (rnd.rand(10000, 10) < heads_proba).astype(np.int32)\n",
+    "coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
     "cumulative_heads_ratio = np.cumsum(coin_tosses, axis=0) / np.arange(1, 10001).reshape(-1, 1)"
    ]
   },
@@ -134,7 +133,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -174,7 +175,9 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -190,7 +193,9 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -208,7 +213,9 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -234,7 +241,9 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -358,7 +367,9 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -442,7 +453,7 @@
     "\n",
     "for i in range(15):\n",
     "    tree_clf = DecisionTreeClassifier(max_leaf_nodes=16, random_state=42 + i)\n",
-    "    indices_with_replacement = rnd.randint(0, len(X_train), len(X_train))\n",
+    "    indices_with_replacement = np.random.randint(0, len(X_train), len(X_train))\n",
     "    tree_clf.fit(X[indices_with_replacement], y[indices_with_replacement])\n",
     "    plot_decision_boundary(tree_clf, X, y, axes=[-1.5, 2.5, -1, 1.5], alpha=0.02, contour=False)\n",
     "\n",
@@ -592,7 +603,9 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -608,7 +621,9 @@
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -632,7 +647,7 @@
     "    sample_weights = np.ones(m)\n",
     "    for i in range(5):\n",
     "        plt.subplot(subplot)\n",
-    "        svm_clf = SVC(kernel=\"rbf\", C=0.05)\n",
+    "        svm_clf = SVC(kernel=\"rbf\", C=0.05, random_state=42)\n",
     "        svm_clf.fit(X_train, y_train, sample_weight=sample_weights)\n",
     "        y_pred = svm_clf.predict(X_train)\n",
     "        sample_weights[y_pred != y_train] *= (1 + learning_rate)\n",
@@ -676,13 +691,15 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
-    "rnd.seed(42)\n",
-    "X = rnd.rand(100, 1) - 0.5\n",
-    "y = 3*X[:, 0]**2 + 0.05 * rnd.randn(100)"
+    "np.random.seed(42)\n",
+    "X = np.random.rand(100, 1) - 0.5\n",
+    "y = 3*X[:, 0]**2 + 0.05 * np.random.randn(100)"
    ]
   },
   {
@@ -705,7 +722,9 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -718,7 +737,9 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -731,7 +752,9 @@
    "cell_type": "code",
    "execution_count": 36,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -742,7 +765,9 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -753,7 +778,9 @@
    "cell_type": "code",
    "execution_count": 38,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -833,7 +860,9 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -845,7 +874,9 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -904,7 +935,9 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [