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xadupre committed Sep 7, 2024
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1 change: 0 additions & 1 deletion .local.jenkins.lin.yml
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Expand Up @@ -10,7 +10,6 @@ virtualenv:
install:
- $PYINT -m pip install --upgrade pip
- $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ scikit-learn>=0.24 --extra-index-url=https://pypi.python.org/simple/
- $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ jyquickhelper pyquickhelper pyensae pymmails pymyinstall pyrsslocal --extra-index-url=https://pypi.python.org/simple/
- $PYINT -m pip install --upgrade --no-cache-dir --no-deps --index http://localhost:8067/simple/ mlinsights>=0.3 --extra-index-url=https://pypi.python.org/simple/
- $PYINT -m pip install -r requirements.txt
- $PYINT -m pip install -r requirements-dev.txt
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1 change: 0 additions & 1 deletion _doc/conf.py
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@@ -1,4 +1,3 @@
# -*- coding: utf-8 -*-
import sys
import os
from sphinx_runpython.github_link import make_linkcode_resolve
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2 changes: 1 addition & 1 deletion _doc/examples/plot_logistic_decision.py
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@@ -1,4 +1,3 @@
# coding: utf-8
"""
.. _l-example-logistic-decision:
Expand All @@ -21,6 +20,7 @@
Il faut choisir un seuil sur l'axe des abscisses qui
permette de classer le jeu de données.
"""

import numpy
import matplotlib.pyplot as plt
from pandas import DataFrame
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143 changes: 0 additions & 143 deletions _doc/notebooks/dsgarden/classification_multiple.ipynb
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Expand Up @@ -9,149 +9,6 @@
"Explorations autour d'un problème de classification multiple."
]
},
{
"cell_type": "code",
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},
"execution_count": 2,
"metadata": {},
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"source": [
"from jyquickhelper import add_notebook_menu\n",
"\n",
"add_notebook_menu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
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177 changes: 15 additions & 162 deletions _doc/notebooks/dsgarden/correlation_non_lineaire.ipynb
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160 changes: 8 additions & 152 deletions _doc/notebooks/dsgarden/discret_gradient.ipynb
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Expand Up @@ -9,150 +9,6 @@
"Les méthodes d'optimisation à base de gradient s'appuie sur une fonction d'erreur dérivable qu'on devrait appliquer de préférence sur des variables aléatoires réelles. Ce notebook explore quelques idées."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"};\n",
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" begin_format, end_format);\n",
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"};\n",
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"<IPython.core.display.HTML object>"
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from jyquickhelper import add_notebook_menu\n",
"\n",
"add_notebook_menu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -349,7 +205,7 @@
" for i in classes:\n",
" X2 = numpy.zeros((X.shape[0], 3))\n",
" X2[:, i] = 1\n",
" Yb = Y == i\n",
" Yb = i == Y\n",
" XS.append(numpy.hstack([X, X2]))\n",
" Yb = Yb.reshape((len(Yb), 1))\n",
" YS.append(Yb)\n",
Expand Down Expand Up @@ -678,7 +534,7 @@
" # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n",
" X2 = numpy.random.random((X.shape[0], 3)) * 0.2\n",
" X2[:, i] += 1\n",
" Yb = Y == i\n",
" Yb = i == Y\n",
" XS.append(numpy.hstack([X, X2]))\n",
" Yb = Yb.reshape((len(Yb), 1))\n",
" YS.append(Yb)\n",
Expand Down Expand Up @@ -1084,7 +940,7 @@
")\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from xgboost import XGBClassifier\n",
"\n",
"models = [\n",
Expand Down Expand Up @@ -1806,7 +1662,7 @@
" # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.1\n",
" X2 = numpy.random.random((X.shape[0], 3)) * 0.2 - 0.1\n",
" X2[:, i] += 1\n",
" Yb = Y == i\n",
" Yb = i == Y\n",
" XS.append(numpy.hstack([X, X2]))\n",
" Yb = Yb.reshape((len(Yb), 1))\n",
" YS.append(Yb)\n",
Expand Down Expand Up @@ -2017,11 +1873,11 @@
" XS = []\n",
" YS = []\n",
" for i in classes:\n",
" for k in range(0, 5):\n",
" for k in range(5):\n",
" # X2 = numpy.random.randn((X.shape[0]* 3)).reshape(X.shape[0], 3) * 0.3\n",
" X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n",
" X2[:, i] += 1\n",
" Yb = Y == i\n",
" Yb = i == Y\n",
" XS.append(numpy.hstack([X, X2]))\n",
" Yb = Yb.reshape((len(Yb), 1))\n",
" YS.append(Yb)\n",
Expand Down Expand Up @@ -2236,7 +2092,7 @@
" for i in classes:\n",
" X2 = numpy.random.random((X.shape[0], 3)) * 0.8 - 0.4\n",
" X2[:, i] += 1 # * ((i % 2) * 2 - 1)\n",
" Yb = Y == i\n",
" Yb = i == Y\n",
" XS.append(numpy.hstack([X, X2]))\n",
" Yb = Yb.reshape((len(Yb), 1))\n",
" YS.append(Yb)\n",
Expand Down Expand Up @@ -2296,7 +2152,7 @@
"source": [
"M = numpy.zeros((9, 6))\n",
"Y = numpy.zeros((9, 1))\n",
"for i in range(0, 9):\n",
"for i in range(9):\n",
" M[i, i // 3] = 1\n",
" M[i, i % 3 + 3] = 1\n",
" Y[i] = 1 if i // 3 == i % 3 else 0\n",
Expand Down
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