mirror of
https://github.com/ArthurDanjou/handson-ml3.git
synced 2026-01-31 03:57:55 +01:00
Drop Python 2 (woohoo!) and import matplotlib as mpl
This commit is contained in:
Aurélien Geron
@@ -20,7 +20,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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:"
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"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)."
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]
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},
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{
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@@ -29,8 +29,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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"from __future__ import division, print_function, unicode_literals\n",
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"# Python ≥3.5 is required\n",
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"import sys\n",
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"assert sys.version_info >= (3, 5)\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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@@ -41,11 +42,11 @@
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"\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['axes.labelsize'] = 14\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"mpl.rc('axes', labelsize=14)\n",
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"mpl.rc('xtick', labelsize=12)\n",
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"mpl.rc('ytick', labelsize=12)\n",
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"\n",
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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@@ -79,13 +80,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def sort_by_target(mnist):\n",
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" reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]\n",
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" reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]\n",
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" mnist.data[:60000] = mnist.data[reorder_train]\n",
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" mnist.target[:60000] = mnist.target[reorder_train]\n",
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" mnist.data[60000:] = mnist.data[reorder_test + 60000]\n",
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" mnist.target[60000:] = mnist.target[reorder_test + 60000]"
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"from sklearn.datasets import fetch_openml\n",
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"mnist = fetch_openml('mnist_784', version=1)\n",
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"mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
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"mnist[\"data\"], mnist[\"target\"]"
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]
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},
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{
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@@ -93,30 +91,13 @@
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" from sklearn.datasets import fetch_openml\n",
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" mnist = fetch_openml('mnist_784', version=1, cache=True)\n",
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" mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings\n",
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" sort_by_target(mnist) # fetch_openml() returns an unsorted dataset\n",
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"except ImportError:\n",
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" from sklearn.datasets import fetch_mldata\n",
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" mnist = fetch_mldata('MNIST original')\n",
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"mnist[\"data\"], mnist[\"target\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"mnist.data.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -126,7 +107,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -135,7 +116,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -144,18 +125,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"some_digit = X[36000]\n",
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"some_digit_image = some_digit.reshape(28, 28)\n",
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"plt.imshow(some_digit_image, cmap = matplotlib.cm.binary,\n",
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" interpolation=\"nearest\")\n",
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"plt.imshow(some_digit_image, cmap = mpl.cm.binary, interpolation=\"nearest\")\n",
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"plt.axis(\"off\")\n",
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"\n",
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"save_fig(\"some_digit_plot\")\n",
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@@ -164,20 +144,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_digit(data):\n",
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" image = data.reshape(28, 28)\n",
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" plt.imshow(image, cmap = matplotlib.cm.binary,\n",
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" plt.imshow(image, cmap = mpl.cm.binary,\n",
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" interpolation=\"nearest\")\n",
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" plt.axis(\"off\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -194,13 +174,13 @@
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" rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
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" row_images.append(np.concatenate(rimages, axis=1))\n",
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" image = np.concatenate(row_images, axis=0)\n",
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" plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
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" plt.imshow(image, cmap = mpl.cm.binary, **options)\n",
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" plt.axis(\"off\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -213,7 +193,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -222,7 +202,7 @@
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{
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@@ -231,7 +211,7 @@
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@@ -267,7 +247,7 @@
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@@ -279,7 +259,7 @@
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@@ -288,7 +268,7 @@
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@@ -298,7 +278,7 @@
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"source": [
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"4344 / (4344 + 1307)"
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"4432 / (4432 + 1607)"
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"4344 / (4344 + 1077)"
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"4432 / (4432 + 989)"
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"source": [
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"4344 / (4344 + (1077 + 1307)/2)"
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"4432 / (4432 + (1607 + 989)/2)"
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]
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"source": [
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"threshold = 200000\n",
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"threshold = -200000\n",
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"y_some_digit_pred = (y_scores > threshold)\n",
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"y_some_digit_pred"
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]
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" method=\"decision_function\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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:"
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]
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},
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{
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"outputs": [],
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"source": [
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"# hack to work around issue #9589 in Scikit-Learn 0.19.0\n",
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"if y_scores.ndim == 2:\n",
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" y_scores = y_scores[:, 1]"
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|
||||
"execution_count": 47,
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -642,7 +595,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -660,7 +613,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 52,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -672,7 +625,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -2531,7 +2484,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 - tf2",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@@ -2545,7 +2498,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"nav_menu": {},
|
||||
"toc": {
|
||||
|
||||
Reference in New Issue
Block a user