Create image directory and check for sklearn >= 0.20
Aurélien Geron
parent
b546b743be
commit
1a6bb0b199
|
|
@ -38,6 +38,17 @@
|
|||
"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",
|
||||
"metadata": {},
|
||||
|
|
@ -47,7 +58,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -73,7 +84,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -83,7 +94,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -97,7 +108,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -190,20 +201,22 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Where to save the figures\n",
|
||||
"PROJECT_ROOT_DIR = \".\"\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",
|
||||
"def save_fig(fig_id, tight_layout=True):\n",
|
||||
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\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",
|
||||
" print(\"Saving figure\", fig_id)\n",
|
||||
" if 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",
|
||||
"execution_count": 7,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -240,7 +253,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -252,7 +265,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -275,7 +288,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -288,7 +301,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -299,7 +312,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -308,7 +321,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -321,7 +334,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -346,7 +359,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -355,7 +368,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -364,7 +377,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -388,7 +401,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -403,7 +416,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -419,7 +432,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -431,7 +444,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -450,7 +463,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -459,7 +472,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -468,7 +481,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -489,7 +502,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -526,7 +539,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -535,7 +548,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -544,7 +557,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -561,7 +574,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -592,7 +605,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -616,7 +629,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -625,7 +638,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -634,7 +647,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -665,7 +678,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -677,7 +690,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -688,7 +701,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -699,7 +712,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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",
|
||||
"assert sys.version_info >= (3, 5)\n",
|
||||
"\n",
|
||||
"# Scikit-Learn ≥0.20 is required\n",
|
||||
"import sklearn\n",
|
||||
"assert sklearn.__version__ >= \"0.20\"\n",
|
||||
"\n",
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
|
|
@ -54,6 +58,7 @@
|
|||
"PROJECT_ROOT_DIR = \".\"\n",
|
||||
"CHAPTER_ID = \"end_to_end_project\"\n",
|
||||
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
|
||||
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
|
||||
"\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",
|
||||
|
|
@ -67,23 +72,6 @@
|
|||
"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",
|
||||
"metadata": {},
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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",
|
||||
"assert sys.version_info >= (3, 5)\n",
|
||||
"\n",
|
||||
"# Scikit-Learn ≥0.20 is required\n",
|
||||
"import sklearn\n",
|
||||
"assert sklearn.__version__ >= \"0.20\"\n",
|
||||
"\n",
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
|
|
@ -51,30 +55,15 @@
|
|||
"# Where to save the figures\n",
|
||||
"PROJECT_ROOT_DIR = \".\"\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",
|
||||
"def save_fig(fig_id, tight_layout=True):\n",
|
||||
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\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",
|
||||
" print(\"Saving figure\", fig_id)\n",
|
||||
" if tight_layout:\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.savefig(path, format='png', dpi=300)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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\""
|
||||
" plt.savefig(path, format=fig_extension, dpi=resolution)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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",
|
||||
"assert sys.version_info >= (3, 5)\n",
|
||||
"\n",
|
||||
"# Scikit-Learn ≥0.20 is required\n",
|
||||
"import sklearn\n",
|
||||
"assert sklearn.__version__ >= \"0.20\"\n",
|
||||
"\n",
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
|
|
@ -56,36 +60,21 @@
|
|||
"# Where to save the figures\n",
|
||||
"PROJECT_ROOT_DIR = \".\"\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",
|
||||
"def save_fig(fig_id, tight_layout=True):\n",
|
||||
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\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",
|
||||
" 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",
|
||||
"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",
|
||||
"metadata": {},
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"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",
|
||||
"assert sys.version_info >= (3, 5)\n",
|
||||
"\n",
|
||||
"# Scikit-Learn ≥0.20 is required\n",
|
||||
"import sklearn\n",
|
||||
"assert sklearn.__version__ >= \"0.20\"\n",
|
||||
"\n",
|
||||
"# Common imports\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
|
|
@ -58,30 +62,15 @@
|
|||
"# Where to save the figures\n",
|
||||
"PROJECT_ROOT_DIR = \".\"\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",
|
||||
"def save_fig(fig_id, tight_layout=True):\n",
|
||||
" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\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",
|
||||
" print(\"Saving figure\", fig_id)\n",
|
||||
" if tight_layout:\n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.savefig(path, format='png', dpi=300)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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\""
|
||||
" plt.savefig(path, format=fig_extension, dpi=resolution)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Reference in New Issue