mirror of
https://github.com/ArthurDanjou/handson-ml3.git
synced 2026-01-14 12:14:36 +01:00
Drop Python 2 (woohoo!) and import matplotlib as mpl
This commit is contained in:
Aurélien Geron
@@ -13,14 +13,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup"
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"# Code example 1-1"
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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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"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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"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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@@ -33,45 +33,9 @@
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},
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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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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"np.random.seed(42)\n",
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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.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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"\n",
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"fundamentals\"\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
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" print(\"Saving figure\", fig_id)\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(path, format='png', dpi=300)\n",
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"\n",
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"# Ignore useless warnings (see SciPy issue #5998)\n",
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"import warnings\n",
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"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
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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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"# Code example 1-1"
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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)"
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]
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},
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{
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@@ -122,9 +86,23 @@
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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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"# To plot pretty figures directly within Jupyter\n",
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"%matplotlib inline\n",
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"import matplotlib as mpl\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)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Code example\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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"import numpy as np\n",
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"import pandas as pd\n",
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@@ -204,6 +182,47 @@
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"outputs": [],
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"source": []
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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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"Create a function to save the figures."
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"fundamentals\"\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
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" print(\"Saving figure\", fig_id)\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(path, format='png', dpi=300)"
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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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"Make this notebook's output stable across runs:"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)"
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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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@@ -222,7 +241,7 @@
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -234,7 +253,7 @@
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -257,7 +276,7 @@
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -270,7 +289,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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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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@@ -281,7 +300,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [],
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"source": [
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@@ -290,7 +309,7 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"cell_type": "code",
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@@ -328,7 +347,7 @@
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"cell_type": "code",
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@@ -337,7 +356,7 @@
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@@ -370,7 +389,7 @@
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@@ -432,7 +451,7 @@
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"cell_type": "code",
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{
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"cell_type": "code",
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@@ -450,7 +469,7 @@
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{
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"cell_type": "code",
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@@ -471,16 +490,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code example\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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"import numpy as np\n",
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"import pandas as pd\n",
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"import sklearn\n",
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"import sklearn.linear_model\n",
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"\n",
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"# Load the data\n",
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"oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
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},
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{
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@@ -575,7 +594,7 @@
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@@ -599,7 +618,7 @@
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{
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@@ -608,7 +627,7 @@
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@@ -617,7 +636,7 @@
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},
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@@ -648,7 +667,7 @@
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@@ -660,7 +679,7 @@
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},
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{
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"metadata": {},
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@@ -670,7 +689,7 @@
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},
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"cell_type": "code",
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@@ -680,7 +699,7 @@
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -694,18 +713,11 @@
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"X_new = np.array([[22587.0]]) # Cyprus' GDP per capita\n",
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"print(model.predict(X_new)) # outputs [[ 5.76666667]]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 - tf2",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.6.8"
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},
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"nav_menu": {},
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"toc": {
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