Create image directory and check for sklearn >= 0.20 and TensorFlow >= 2.0-preview
Aurélien Geron
parent
6b8dff91d0
commit
b546b743be
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@ -25,7 +25,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's 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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"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), as well as Scikit-Learn ≥0.20."
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]
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},
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{
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@ -38,6 +38,10 @@
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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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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\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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@ -56,32 +60,15 @@
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"decision_trees\"\n",
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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
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"\n",
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"def image_path(fig_id):\n",
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" return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\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(image_path(fig_id) + \".png\", 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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"This notebook assumes you have installed Scikit-Learn ≥0.20."
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\""
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" plt.savefig(path, format=fig_extension, dpi=resolution)"
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]
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},
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{
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@ -93,7 +80,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -110,25 +97,28 @@
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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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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from graphviz import Source\n",
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"from sklearn.tree import export_graphviz\n",
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"\n",
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"export_graphviz(\n",
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" tree_clf,\n",
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" out_file=image_path(\"iris_tree.dot\"),\n",
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" out_file=os.path.join(IMAGES_PATH, \"iris_tree.dot\"),\n",
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" feature_names=iris.feature_names[2:],\n",
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" class_names=iris.target_names,\n",
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" rounded=True,\n",
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" filled=True\n",
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" )"
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" )\n",
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"\n",
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"Source.from_file(os.path.join(IMAGES_PATH, \"iris_tree.dot\"))"
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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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@ -182,7 +172,7 @@
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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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@ -191,7 +181,7 @@
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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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@ -207,7 +197,7 @@
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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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@ -216,7 +206,7 @@
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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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@ -230,7 +220,7 @@
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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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@ -247,7 +237,7 @@
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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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@ -273,7 +263,7 @@
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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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@ -292,7 +282,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -328,7 +318,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -342,7 +332,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -354,7 +344,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -401,19 +391,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"export_graphviz(\n",
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" tree_reg1,\n",
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" out_file=image_path(\"regression_tree.dot\"),\n",
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" out_file=os.path.join(IMAGES_PATH, \"regression_tree.dot\"),\n",
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" feature_names=[\"x1\"],\n",
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" rounded=True,\n",
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" filled=True\n",
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" )"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"Source.from_file(os.path.join(IMAGES_PATH, \"regression_tree.dot\"))"
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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": 18,
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@ -25,7 +25,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's 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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"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), as well as Scikit-Learn ≥0.20."
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]
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},
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{
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@ -38,6 +38,10 @@
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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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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\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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@ -56,32 +60,15 @@
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"ensembles\"\n",
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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
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"\n",
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"def image_path(fig_id):\n",
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" return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\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(image_path(fig_id) + \".png\", 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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"This notebook assumes you have installed Scikit-Learn ≥0.20."
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\""
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" plt.savefig(path, format=fig_extension, dpi=resolution)"
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]
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},
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{
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@ -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 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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"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), as well as Scikit-Learn ≥0.20."
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]
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},
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{
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@ -33,6 +33,10 @@
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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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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\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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@ -51,36 +55,21 @@
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"dim_reduction\"\n",
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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\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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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\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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" plt.savefig(path, format=fig_extension, dpi=resolution)\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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"This notebook assumes you have installed Scikit-Learn ≥0.20."
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\""
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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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@ -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 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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"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), as well as Scikit-Learn ≥0.20."
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]
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},
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{
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@ -33,6 +33,10 @@
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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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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\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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@ -51,13 +55,15 @@
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"unsupervised_learning\"\n",
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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\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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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\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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" plt.savefig(path, format=fig_extension, dpi=resolution)\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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@ -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 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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"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), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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]
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},
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{
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@ -33,6 +33,14 @@
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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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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"import tensorflow as tf\n",
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"assert hasattr(tf.compat, \"v1\")\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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@ -51,13 +59,15 @@
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"ann\"\n",
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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\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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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
|
||||
" print(\"Saving figure\", fig_id)\n",
|
||||
" if tight_layout:\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.savefig(path, format='png', dpi=300)\n",
|
||||
" plt.savefig(path, format=fig_extension, dpi=resolution)\n",
|
||||
"\n",
|
||||
"# Ignore useless warnings (see SciPy issue #5998)\n",
|
||||
"import warnings\n",
|
||||
|
|
|
|||
Loading…
Reference in New Issue