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Create image directory and check for sklearn >= 0.20

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This commit is contained in:
Aurélien Geron
2019-01-21 18:42:31 +08:00
parent b546b743be
commit 1a6bb0b199
5 changed files with 89 additions and 121 deletions
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91
01_the_machine_learning_landscape.ipynb
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@@ -38,6 +38,17 @@
"assert sys.version_info >= (3, 5)" "assert sys.version_info >= (3, 5)"
] ]
}, },
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Scikit-Learn ≥0.20 is required\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@@ -47,7 +58,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -73,7 +84,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -83,7 +94,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -97,7 +108,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -190,20 +201,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"fundamentals\"\n", "CHAPTER_ID = \"fundamentals\"\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)" " plt.savefig(path, format=fig_extension, dpi=resolution)"
] ]
}, },
{ {
@@ -215,7 +228,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -240,7 +253,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -252,7 +265,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -275,7 +288,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -288,7 +301,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -299,7 +312,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -308,7 +321,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -321,7 +334,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -346,7 +359,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -355,7 +368,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -364,7 +377,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -388,7 +401,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -403,7 +416,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -419,7 +432,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -431,7 +444,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -450,7 +463,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 23,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -459,7 +472,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 24,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -468,7 +481,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 25,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -489,7 +502,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 26,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -526,7 +539,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -535,7 +548,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -544,7 +557,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -561,7 +574,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 30,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -592,7 +605,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 31,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -616,7 +629,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -625,7 +638,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -634,7 +647,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -665,7 +678,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -677,7 +690,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -688,7 +701,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -699,7 +712,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 38,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [

24
02_end_to_end_machine_learning_project.ipynb
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@@ -22,7 +22,7 @@
"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."
] ]
}, },
{ {
@@ -35,6 +35,10 @@
"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",
@@ -54,6 +58,7 @@
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"end_to_end_project\"\n", "CHAPTER_ID = \"end_to_end_project\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\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, fig_extension=\"png\", resolution=300):\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
@@ -67,23 +72,6 @@
"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": {},

33
03_classification.ipynb
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@@ -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",
@@ -51,30 +55,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"classification\"\n", "CHAPTER_ID = \"classification\"\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)" " 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": null,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"assert sklearn.__version__ >= \"0.20\""
] ]
}, },
{ {
@@ -120,7 +109,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"28*28" "28 * 28"
] ]
}, },
{ {

31
04_training_linear_models.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",
@@ -56,36 +60,21 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"training_linear_models\"\n", "CHAPTER_ID = \"training_linear_models\"\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": {},

31
05_support_vector_machines.ipynb
View File

@@ -27,7 +27,7 @@
"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."
] ]
}, },
{ {
@@ -40,6 +40,10 @@
"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",
@@ -58,30 +62,15 @@
"# Where to save the figures\n", "# Where to save the figures\n",
"PROJECT_ROOT_DIR = \".\"\n", "PROJECT_ROOT_DIR = \".\"\n",
"CHAPTER_ID = \"svm\"\n", "CHAPTER_ID = \"svm\"\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)" " 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\""
] ]
}, },
{ {