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Drop Python 2 (woohoo!) and import matplotlib as mpl

main
Aurélien Geron 2019-01-16 23:42:00 +08:00
parent ca6eb8c147
commit d2518a679b
8 changed files with 223 additions and 259 deletions
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178
01_the_machine_learning_landscape.ipynb
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@ -13,14 +13,14 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Setup" "# Code example 1-1"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "Although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead."
] ]
}, },
{ {
@ -33,45 +33,9 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"\n", "assert sys.version_info >= (3, 5)"
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"np.random.seed(42)\n",
"\n",
"# To plot pretty figures\n",
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n",
"plt.rcParams['xtick.labelsize'] = 12\n",
"plt.rcParams['ytick.labelsize'] = 12\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"fundamentals\"\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format='png', dpi=300)\n",
"\n",
"# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n",
"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code example 1-1"
] ]
}, },
{ {
@ -122,9 +86,23 @@
"execution_count": 4, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [
"# To plot pretty figures directly within Jupyter\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"mpl.rc('axes', labelsize=14)\n",
"mpl.rc('xtick', labelsize=12)\n",
"mpl.rc('ytick', labelsize=12)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [ "source": [
"# Code example\n", "# Code example\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
@ -204,6 +182,47 @@
"outputs": [], "outputs": [],
"source": [] "source": []
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a function to save the figures."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"fundamentals\"\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format='png', dpi=300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make this notebook's output stable across runs:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -222,7 +241,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -234,7 +253,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -257,7 +276,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -270,7 +289,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -281,7 +300,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -290,7 +309,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -303,7 +322,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -328,7 +347,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -337,7 +356,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -346,7 +365,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -370,7 +389,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -385,7 +404,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -401,7 +420,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -413,7 +432,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -432,7 +451,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -441,7 +460,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -450,7 +469,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -471,16 +490,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Code example\n", "# Code example\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import sklearn\n", "import sklearn.linear_model\n",
"\n", "\n",
"# Load the data\n", "# Load the data\n",
"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n", "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
@ -509,7 +528,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -518,7 +537,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -527,7 +546,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -544,7 +563,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -575,7 +594,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -599,7 +618,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -608,7 +627,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -617,7 +636,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -648,7 +667,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -660,7 +679,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -670,7 +689,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -680,7 +699,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -694,18 +713,11 @@
"X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n", "X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n",
"print(model.predict(X_new)) # outputs [[ 5.76666667]]" "print(model.predict(X_new)) # outputs [[ 5.76666667]]"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -719,7 +731,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

26
02_end_to_end_machine_learning_project.ipynb
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@ -11,13 +11,6 @@
"*This notebook contains all the sample code and solutions to the exercices in chapter 2.*" "*This notebook contains all the sample code and solutions to the exercices in chapter 2.*"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book."
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -29,7 +22,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -38,8 +31,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -50,11 +44,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -2177,7 +2171,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -2191,7 +2185,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": { "nav_menu": {
"height": "279px", "height": "279px",

179
03_classification.ipynb
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@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -29,8 +29,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -41,11 +42,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -79,13 +80,10 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def sort_by_target(mnist):\n", "from sklearn.datasets import fetch_openml\n",
" reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]\n", "mnist = fetch_openml('mnist_784', version=1)\n",
" reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]\n", "mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
" mnist.data[:60000] = mnist.data[reorder_train]\n", "mnist[\"data\"], mnist[\"target\"]"
" mnist.target[:60000] = mnist.target[reorder_train]\n",
" mnist.data[60000:] = mnist.data[reorder_test + 60000]\n",
" mnist.target[60000:] = mnist.target[reorder_test + 60000]"
] ]
}, },
{ {
@ -93,30 +91,13 @@
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [
"try:\n",
" from sklearn.datasets import fetch_openml\n",
" mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
" mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
" sort_by_target(mnist) # fetch_openml() returns an unsorted dataset\n",
"except ImportError:\n",
" from sklearn.datasets import fetch_mldata\n",
" mnist = fetch_mldata('MNIST original')\n",
"mnist[\"data\"], mnist[\"target\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [ "source": [
"mnist.data.shape" "mnist.data.shape"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -126,7 +107,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -135,7 +116,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -144,18 +125,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"some_digit = X[36000]\n", "some_digit = X[36000]\n",
"some_digit_image = some_digit.reshape(28, 28)\n", "some_digit_image = some_digit.reshape(28, 28)\n",
"plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,\n", "plt.imshow(some_digit_image, cmap = mpl.cm.binary, interpolation=\"nearest\")\n",
" interpolation=\"nearest\")\n",
"plt.axis(\"off\")\n", "plt.axis(\"off\")\n",
"\n", "\n",
"save_fig(\"some_digit_plot\")\n", "save_fig(\"some_digit_plot\")\n",
@ -164,20 +144,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def plot_digit(data):\n", "def plot_digit(data):\n",
" image = data.reshape(28, 28)\n", " image = data.reshape(28, 28)\n",
" plt.imshow(image, cmap = matplotlib.cm.binary,\n", " plt.imshow(image, cmap = mpl.cm.binary,\n",
" interpolation=\"nearest\")\n", " interpolation=\"nearest\")\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -194,13 +174,13 @@
" rimages = images[row * images_per_row : (row + 1) * images_per_row]\n", " rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
" row_images.append(np.concatenate(rimages, axis=1))\n", " row_images.append(np.concatenate(rimages, axis=1))\n",
" image = np.concatenate(row_images, axis=0)\n", " image = np.concatenate(row_images, axis=0)\n",
" plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n", " plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -213,7 +193,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -222,7 +202,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -231,7 +211,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -250,7 +230,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -267,7 +247,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -279,7 +259,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -288,7 +268,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -298,7 +278,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -322,7 +302,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -336,7 +316,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -346,7 +326,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -357,7 +337,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -368,7 +348,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -377,7 +357,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -386,7 +366,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -397,16 +377,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"4344 / (4344 + 1307)" "4432 / (4432 + 1607)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -415,16 +395,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"4344 / (4344 + 1077)" "4432 / (4432 + 989)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -434,16 +414,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"4344 / (4344 + (1077 + 1307)/2)" "4432 / (4432 + (1607 + 989)/2)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -453,7 +433,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -463,7 +443,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -472,18 +452,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 40,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"threshold = 200000\n", "threshold = -200000\n",
"y_some_digit_pred = (y_scores > threshold)\n", "y_some_digit_pred = (y_scores > threshold)\n",
"y_some_digit_pred" "y_some_digit_pred"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -491,36 +471,9 @@
" method=\"decision_function\")" " method=\"decision_function\")"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: there was an [issue](https://github.com/scikit-learn/scikit-learn/issues/9589) in Scikit-Learn 0.19.0 (fixed in 0.19.1) where the result of `cross_val_predict()` was incorrect in the binary classification case when using `method=\"decision_function\"`, as in the code above. The resulting array had an extra first dimension full of 0s. Just in case you are using 0.19.0, we need to add this small hack to work around this issue:"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"y_scores.shape"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# hack to work around issue #9589 in Scikit-Learn 0.19.0\n",
"if y_scores.ndim == 2:\n",
" y_scores = y_scores[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -531,7 +484,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -551,7 +504,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -560,7 +513,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 45,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -569,7 +522,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 46,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -578,7 +531,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 47,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -587,7 +540,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 48,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -612,7 +565,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 46, "execution_count": 49,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -623,7 +576,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 47, "execution_count": 50,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -642,7 +595,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 51,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -660,7 +613,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 52,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -672,7 +625,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 50, "execution_count": 53,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -2531,7 +2484,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -2545,7 +2498,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

19
04_training_linear_models.ipynb
View File

@ -25,7 +25,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -34,8 +34,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -46,11 +47,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -1729,7 +1730,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -1743,7 +1744,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

19
05_support_vector_machines.ipynb
View File

@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -29,8 +29,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -41,11 +42,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -1792,7 +1793,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -1806,7 +1807,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": {}, "nav_menu": {},
"toc": { "toc": {

19
06_decision_trees.ipynb
View File

@ -25,7 +25,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -34,8 +34,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -46,11 +47,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -696,7 +697,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -710,7 +711,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": { "nav_menu": {
"height": "309px", "height": "309px",

21
07_ensemble_learning_and_random_forests.ipynb
View File

@ -25,7 +25,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -34,8 +34,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -46,11 +47,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -454,7 +455,7 @@
"source": [ "source": [
"def plot_digit(data):\n", "def plot_digit(data):\n",
" image = data.reshape(28, 28)\n", " image = data.reshape(28, 28)\n",
" plt.imshow(image, cmap = matplotlib.cm.hot,\n", " plt.imshow(image, cmap = mpl.cm.hot,\n",
" interpolation=\"nearest\")\n", " interpolation=\"nearest\")\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
@ -1336,7 +1337,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 - tf2",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -1350,7 +1351,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.6" "version": "3.6.8"
}, },
"nav_menu": { "nav_menu": {
"height": "252px", "height": "252px",

21
08_dimensionality_reduction.ipynb
View File

@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" "First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead)."
] ]
}, },
{ {
@ -29,8 +29,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# Python ≥3.5 is required\n",
"from __future__ import division, print_function, unicode_literals\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
@ -41,11 +42,11 @@
"\n", "\n",
"# To plot pretty figures\n", "# To plot pretty figures\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib\n", "import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"plt.rcParams['axes.labelsize'] = 14\n", "mpl.rc('axes', labelsize=14)\n",
"plt.rcParams['xtick.labelsize'] = 12\n", "mpl.rc('xtick', labelsize=12)\n",
"plt.rcParams['ytick.labelsize'] = 12\n", "mpl.rc('ytick', labelsize=12)\n",
"\n", "\n",
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
@ -873,7 +874,7 @@
" rimages = images[row * images_per_row : (row + 1) * images_per_row]\n", " rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
" row_images.append(np.concatenate(rimages, axis=1))\n", " row_images.append(np.concatenate(rimages, axis=1))\n",
" image = np.concatenate(row_images, axis=0)\n", " image = np.concatenate(row_images, axis=0)\n",
" plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n", " plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
" plt.axis(\"off\")" " plt.axis(\"off\")"
] ]
}, },
@ -1848,7 +1849,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"plt.figure(figsize=(9,9))\n", "plt.figure(figsize=(9,9))\n",
"cmap = matplotlib.cm.get_cmap(\"jet\")\n", "cmap = mpl.cm.get_cmap(\"jet\")\n",
"for digit in (2, 3, 5):\n", "for digit in (2, 3, 5):\n",
" plt.scatter(X_reduced[y == digit, 0], X_reduced[y == digit, 1], c=[cmap(digit / 9)])\n", " plt.scatter(X_reduced[y == digit, 0], X_reduced[y == digit, 1], c=[cmap(digit / 9)])\n",
"plt.axis('off')\n", "plt.axis('off')\n",
@ -1930,7 +1931,7 @@
" neighbors = np.array([[10., 10.]])\n", " neighbors = np.array([[10., 10.]])\n",
" # The rest should be self-explanatory\n", " # The rest should be self-explanatory\n",
" plt.figure(figsize=figsize)\n", " plt.figure(figsize=figsize)\n",
" cmap = matplotlib.cm.get_cmap(\"jet\")\n", " cmap = mpl.cm.get_cmap(\"jet\")\n",
" digits = np.unique(y)\n", " digits = np.unique(y)\n",
" for digit in digits:\n", " for digit in digits:\n",
" plt.scatter(X_normalized[y == digit, 0], X_normalized[y == digit, 1], c=[cmap(digit / 9)])\n", " plt.scatter(X_normalized[y == digit, 0], X_normalized[y == digit, 1], c=[cmap(digit / 9)])\n",