Commit 96f0e9ff authored by Tantet Alexis (M.)'s avatar Tantet Alexis (M.)
Browse files

minor

parent 50917347
Pipeline #146911 passed with stages
in 3 minutes and 40 seconds
......@@ -26,7 +26,7 @@
},
{
"cell_type": "markdown",
"id": "66b8a569",
"id": "ec7c7ddc",
"metadata": {},
"source": [
"## Getting ready\n",
......@@ -95,7 +95,7 @@
},
{
"cell_type": "markdown",
"id": "55e16cd0",
"id": "71f72deb",
"metadata": {},
"source": [
"### Loading the climate data\n",
......@@ -146,7 +146,7 @@
},
{
"cell_type": "markdown",
"id": "368a91b4",
"id": "225a9e32",
"metadata": {},
"source": [
"### Preprocessing the inputs\n",
......@@ -189,7 +189,7 @@
},
{
"cell_type": "markdown",
"id": "fdc514cf",
"id": "6b3ed0fb",
"metadata": {},
"source": [
"### Analyzing the relationship between the climate variables and the demand\n",
......@@ -208,7 +208,7 @@
{
"cell_type": "code",
"execution_count": 4,
"id": "ac1913b1",
"id": "f99d9eb4",
"metadata": {},
"outputs": [
{
......@@ -319,7 +319,7 @@
{
"cell_type": "code",
"execution_count": 5,
"id": "30eb2415",
"id": "17b29c61",
"metadata": {},
"outputs": [
{
......@@ -361,7 +361,7 @@
},
{
"cell_type": "markdown",
"id": "8a2f3b58",
"id": "38be099a",
"metadata": {},
"source": [
"Answer:"
......@@ -369,7 +369,7 @@
},
{
"cell_type": "markdown",
"id": "88958a78",
"id": "c5b1cb82",
"metadata": {},
"source": [
"### Preparing feature extraction\n",
......@@ -380,7 +380,7 @@
{
"cell_type": "code",
"execution_count": 6,
"id": "881092ad",
"id": "0e6ad35c",
"metadata": {},
"outputs": [],
"source": [
......@@ -399,7 +399,7 @@
},
{
"cell_type": "markdown",
"id": "3bae9e4b",
"id": "8f7f6c44",
"metadata": {},
"source": [
"We also define a factorization by month.\n",
......@@ -426,7 +426,7 @@
},
{
"cell_type": "markdown",
"id": "39ca463b",
"id": "8b78db36",
"metadata": {},
"source": [
"> ***Question***\n",
......@@ -437,7 +437,7 @@
},
{
"cell_type": "markdown",
"id": "da33117e",
"id": "c27372d8",
"metadata": {},
"source": [
"### Regressions evaluation function\n",
......@@ -556,7 +556,7 @@
},
{
"cell_type": "markdown",
"id": "4f0622c9",
"id": "3920360a",
"metadata": {},
"source": [
"> ***Question***\n",
......@@ -571,7 +571,7 @@
},
{
"cell_type": "markdown",
"id": "f6c99ea1",
"id": "3d136c99",
"metadata": {},
"source": [
"## Feature selection\n",
......@@ -663,7 +663,7 @@
},
{
"cell_type": "markdown",
"id": "58675685",
"id": "73517e4c",
"metadata": {},
"source": [
"Answer: "
......@@ -671,7 +671,7 @@
},
{
"cell_type": "markdown",
"id": "99190d1a",
"id": "8edbec3c",
"metadata": {},
"source": [
"## Individual models\n",
......@@ -795,7 +795,7 @@
},
{
"cell_type": "markdown",
"id": "134747d8",
"id": "bced7728",
"metadata": {},
"source": [
"Answer: "
......@@ -803,7 +803,7 @@
},
{
"cell_type": "markdown",
"id": "82f05832",
"id": "783fa822",
"metadata": {},
"source": [
"### Decision-tree regression\n",
......@@ -885,7 +885,7 @@
},
{
"cell_type": "markdown",
"id": "4ed65265",
"id": "f3f29f55",
"metadata": {},
"source": [
"Answer:"
......@@ -893,7 +893,7 @@
},
{
"cell_type": "markdown",
"id": "1e7f00fc",
"id": "2b5a1b7a",
"metadata": {},
"source": [
"## Ensemble models\n",
......@@ -917,7 +917,7 @@
{
"cell_type": "code",
"execution_count": 12,
"id": "79e0a8a5",
"id": "f563ca2c",
"metadata": {},
"outputs": [
{
......@@ -983,7 +983,7 @@
},
{
"cell_type": "markdown",
"id": "432513b3",
"id": "9bc7038d",
"metadata": {},
"source": [
"Answer: "
......@@ -991,7 +991,7 @@
},
{
"cell_type": "markdown",
"id": "ae49d9ba",
"id": "1efb17c3",
"metadata": {},
"source": [
"### Random-forest regressor\n",
......@@ -1076,7 +1076,7 @@
},
{
"cell_type": "markdown",
"id": "907e2199",
"id": "271f5354",
"metadata": {},
"source": [
"Answer: "
......@@ -1084,7 +1084,7 @@
},
{
"cell_type": "markdown",
"id": "e596166f",
"id": "43a46464",
"metadata": {},
"source": [
"The following plot represents the mean and standard deviation of the importance given to the features by the trees in the random forest (see [Feature importance with a forest of trees in Scikit-learn User guide](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#feature-importances-with-a-forest-of-trees)).\n",
......@@ -1137,7 +1137,7 @@
},
{
"cell_type": "markdown",
"id": "ad997929",
"id": "e5ba0266",
"metadata": {},
"source": [
"Answer: "
......@@ -1145,7 +1145,7 @@
},
{
"cell_type": "markdown",
"id": "4e38454d",
"id": "2911d926",
"metadata": {},
"source": [
"### Voting regressor\n",
......@@ -1216,7 +1216,7 @@
},
{
"cell_type": "markdown",
"id": "439a0169",
"id": "9c317e88",
"metadata": {},
"source": [
"Answer: "
......@@ -1224,7 +1224,7 @@
},
{
"cell_type": "markdown",
"id": "613da9c1",
"id": "e491ff1a",
"metadata": {},
"source": [
"### Stacking regressor\n",
......@@ -1243,7 +1243,7 @@
{
"cell_type": "code",
"execution_count": 16,
"id": "0a1da2ae",
"id": "9691bad3",
"metadata": {},
"outputs": [
{
......@@ -1307,7 +1307,7 @@
},
{
"cell_type": "markdown",
"id": "8bcd0cd6",
"id": "ca367cf8",
"metadata": {},
"source": [
"Answer:"
......@@ -1315,7 +1315,7 @@
},
{
"cell_type": "markdown",
"id": "3859733d",
"id": "db9e9cda",
"metadata": {},
"source": [
"### AdaBoost regressor\n",
......@@ -1400,7 +1400,7 @@
},
{
"cell_type": "markdown",
"id": "80bf1b8c",
"id": "fc43fa30",
"metadata": {},
"source": [
"Answer: "
......@@ -1408,7 +1408,7 @@
},
{
"cell_type": "markdown",
"id": "8adfcdc5",
"id": "04c4771f",
"metadata": {},
"source": [
"> ***Question (optional)***\n",
......@@ -1422,7 +1422,7 @@
},
{
"cell_type": "markdown",
"id": "7900cd55",
"id": "05a7ee4b",
"metadata": {},
"source": [
"> ***Question (Optional)***\n",
......@@ -1433,7 +1433,7 @@
},
{
"cell_type": "markdown",
"id": "7d2970be",
"id": "f2f039c7",
"metadata": {},
"source": [
"> ***Question (Optional)***\n",
......@@ -8,7 +8,7 @@ channels:
dependencies:
- bokeh>=2.2.3
- hvplot
- matplotlib>=3.2.2
- matplotlib
- netcdf4
- numpy
- pandas>=1.2.4
......
from setuptools import setup
name = 'machine_learning_for_climate_and_energy'
reqs = ['bokeh >=2.2.3', 'hvplot', 'matplotlib >=3.2.2', 'netcdf4', 'numpy',
reqs = ['bokeh >=2.2.3', 'hvplot', 'matplotlib', 'netcdf4', 'numpy',
'pandas >=1.2.4', 'panel >=0.10.3', 'scipy', 'scikit-learn >=1.0',
'xarray']
......
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