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adapted some calls to new scikit version

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pull/3/head
Oscar Araque 6 years ago
parent a8355e1ee6
commit 6d38d96f16
5 changed files with 629 additions and 736 deletions
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32
ml1/2_4_Preprocessing.ipynb
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@ -56,9 +56,7 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
@ -83,13 +81,11 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"execution_count": 2,
"metadata": {},
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"source": [
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.model_selection import train_test_split\n",
"x_iris, y_iris = iris.data, iris.target\n",
"# Test set will be the 25% taken randomly\n",
"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)"
@ -98,9 +94,7 @@
{
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{
"name": "stdout",
@ -118,9 +112,7 @@
{
"cell_type": "code",
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{
"name": "stdout",
@ -191,9 +183,7 @@
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# Standardize the features\n",
@ -206,9 +196,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"metadata": {},
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{
"name": "stdout",
@ -306,9 +294,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}

77
ml1/2_5_1_kNN_Model.ipynb
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@ -71,10 +71,8 @@
},
{
"cell_type": "code",
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"metadata": {
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"source": [
"# library for displaying plots\n",
@ -86,10 +84,8 @@
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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"execution_count": 2,
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"source": [
"## First, we repeat the load and preprocessing steps\n",
@ -99,7 +95,7 @@
"iris = datasets.load_iris()\n",
"\n",
"# Training and test spliting\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"x_iris, y_iris = iris.data, iris.target\n",
"\n",
@ -139,10 +135,8 @@
},
{
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"execution_count": 3,
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{
"data": {
@ -152,7 +146,7 @@
" weights='uniform')"
]
},
"execution_count": 5,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@ -170,10 +164,8 @@
},
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@ -198,9 +190,7 @@
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@ -221,9 +211,7 @@
{
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@ -251,9 +239,7 @@
{
"cell_type": "code",
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@ -309,9 +295,7 @@
{
"cell_type": "code",
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@ -349,9 +333,7 @@
{
"cell_type": "code",
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{
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@ -369,9 +351,7 @@
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"We see we classify well all the 'setosa' and 'versicolor' samples. "
]
@ -392,10 +372,8 @@
},
{
"cell_type": "code",
"execution_count": 16,
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"execution_count": 8,
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{
"name": "stdout",
@ -407,7 +385,7 @@
}
],
"source": [
"from sklearn.cross_validation import cross_val_score, KFold\n",
"from sklearn.model_selection import cross_val_score, KFold\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
@ -418,8 +396,7 @@
"])\n",
"\n",
"# create a k-fold cross validation iterator of k=10 folds\n",
"\n",
"cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n",
"cv = KFold(10, shuffle=True, random_state=33)\n",
"\n",
"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
@ -437,10 +414,8 @@
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
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"execution_count": 9,
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{
"name": "stdout",
@ -481,9 +456,7 @@
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
@ -568,9 +541,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}

153
ml1/2_5_2_Decision_Tree_Model.ipynb
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1089
ml1/2_6_Model_Tuning.ipynb
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14
ml1/2_7_Model_Persistence.ipynb
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@ -56,9 +56,7 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
@ -79,7 +77,7 @@
"iris = datasets.load_iris()\n",
"\n",
"# Training and test spliting\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.model_selection import train_test_split\n",
"x_iris, y_iris = iris.data, iris.target\n",
"# Test set will be the 25% taken randomly\n",
"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
@ -109,9 +107,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [
{
"data": {
@ -196,9 +192,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1+"
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}

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