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Create image directory and check for sklearn >= 0.20 and TensorFlow >= 2.0-preview

main
Aurélien Geron 2019-01-21 18:13:10 +08:00
parent 6b8dff91d0
commit b546b743be
5 changed files with 84 additions and 93 deletions
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81
06_decision_trees.ipynb
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@ -25,7 +25,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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)." "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."
] ]
}, },
{ {
@ -38,6 +38,10 @@
"import sys\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n", "assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
@ -56,32 +60,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"decision_trees\"\n", "CHAPTER_ID = \"decision_trees\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n", "\n",
"def image_path(fig_id):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" print(\"Saving figure\", fig_id)\n", " print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n", " if tight_layout:\n",
" plt.tight_layout()\n", " plt.tight_layout()\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)" " plt.savefig(path, format=fig_extension, dpi=resolution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook assumes you have installed Scikit-Learn ≥0.20."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
] ]
}, },
{ {
@ -93,7 +80,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -110,25 +97,28 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from graphviz import Source\n",
"from sklearn.tree import export_graphviz\n", "from sklearn.tree import export_graphviz\n",
"\n", "\n",
"export_graphviz(\n", "export_graphviz(\n",
" tree_clf,\n", " tree_clf,\n",
" out_file=image_path(\"iris_tree.dot\"),\n", " out_file=os.path.join(IMAGES_PATH, \"iris_tree.dot\"),\n",
" feature_names=iris.feature_names[2:],\n", " feature_names=iris.feature_names[2:],\n",
" class_names=iris.target_names,\n", " class_names=iris.target_names,\n",
" rounded=True,\n", " rounded=True,\n",
" filled=True\n", " filled=True\n",
" )" " )\n",
"\n",
"Source.from_file(os.path.join(IMAGES_PATH, \"iris_tree.dot\"))"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -182,7 +172,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -191,7 +181,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -207,7 +197,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -216,7 +206,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -230,7 +220,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -247,7 +237,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -273,7 +263,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -292,7 +282,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -328,7 +318,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -342,7 +332,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -354,7 +344,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -401,19 +391,28 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"export_graphviz(\n", "export_graphviz(\n",
" tree_reg1,\n", " tree_reg1,\n",
" out_file=image_path(\"regression_tree.dot\"),\n", " out_file=os.path.join(IMAGES_PATH, \"regression_tree.dot\"),\n",
" feature_names=[\"x1\"],\n", " feature_names=[\"x1\"],\n",
" rounded=True,\n", " rounded=True,\n",
" filled=True\n", " filled=True\n",
" )" " )"
] ]
}, },
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"Source.from_file(os.path.join(IMAGES_PATH, \"regression_tree.dot\"))"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 18,

33
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 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)." "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."
] ]
}, },
{ {
@ -38,6 +38,10 @@
"import sys\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n", "assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
@ -56,32 +60,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"ensembles\"\n", "CHAPTER_ID = \"ensembles\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n", "\n",
"def image_path(fig_id):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
"\n",
"def save_fig(fig_id, tight_layout=True):\n",
" print(\"Saving figure\", fig_id)\n", " print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n", " if tight_layout:\n",
" plt.tight_layout()\n", " plt.tight_layout()\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)" " plt.savefig(path, format=fig_extension, dpi=resolution)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook assumes you have installed Scikit-Learn ≥0.20."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
] ]
}, },
{ {

31
08_dimensionality_reduction.ipynb
View File

@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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)." "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."
] ]
}, },
{ {
@ -33,6 +33,10 @@
"import sys\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n", "assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
@ -51,36 +55,21 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"dim_reduction\"\n", "CHAPTER_ID = \"dim_reduction\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n", "\n",
"def save_fig(fig_id, tight_layout=True):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n", " print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n", " if tight_layout:\n",
" plt.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", "\n",
"# Ignore useless warnings (see SciPy issue #5998)\n", "# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n", "import warnings\n",
"warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")" "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook assumes you have installed Scikit-Learn ≥0.20."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},

14
09_unsupervised_learning.ipynb
View File

@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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)." "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."
] ]
}, },
{ {
@ -33,6 +33,10 @@
"import sys\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n", "assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
@ -51,13 +55,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"unsupervised_learning\"\n", "CHAPTER_ID = \"unsupervised_learning\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n", "\n",
"def save_fig(fig_id, tight_layout=True):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n", " print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n", " if tight_layout:\n",
" plt.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", "\n",
"# Ignore useless warnings (see SciPy issue #5998)\n", "# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n", "import warnings\n",

18
10_neural_nets_with_keras.ipynb
View File

@ -20,7 +20,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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)." "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."
] ]
}, },
{ {
@ -33,6 +33,14 @@
"import sys\n", "import sys\n",
"assert sys.version_info >= (3, 5)\n", "assert sys.version_info >= (3, 5)\n",
"\n", "\n",
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\"\n",
"\n",
"# TensorFlow ≥2.0-preview is required\n",
"import tensorflow as tf\n",
"assert hasattr(tf.compat, \"v1\")\n",
"\n",
"# Common imports\n", "# Common imports\n",
"import numpy as np\n", "import numpy as np\n",
"import os\n", "import os\n",
@ -51,13 +59,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"ann\"\n", "CHAPTER_ID = \"ann\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n", "\n",
"def save_fig(fig_id, tight_layout=True):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n", " print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n", " if tight_layout:\n",
" plt.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", "\n",
"# Ignore useless warnings (see SciPy issue #5998)\n", "# Ignore useless warnings (see SciPy issue #5998)\n",
"import warnings\n", "import warnings\n",