mirror of
https://github.com/gsi-upm/sitc
synced 2024-11-22 06:22:29 +00:00
adapted some calls to new scikit version
This commit is contained in:
Oscar Araque
parent
a8355e1ee6
commit
6d38d96f16
@ -56,9 +56,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import datasets\n",
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@ -83,13 +81,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.cross_validation import train_test_split\n",
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"from sklearn.model_selection import train_test_split\n",
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"x_iris, y_iris = iris.data, iris.target\n",
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"# Test set will be the 25% taken randomly\n",
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"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)"
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@ -98,9 +94,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -118,9 +112,7 @@
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -191,9 +183,7 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Standardize the features\n",
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@ -206,9 +196,7 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -306,9 +294,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1+"
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}
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@ -71,10 +71,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# library for displaying plots\n",
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@ -86,10 +84,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"## First, we repeat the load and preprocessing steps\n",
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@ -99,7 +95,7 @@
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"iris = datasets.load_iris()\n",
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"\n",
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"# Training and test spliting\n",
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"from sklearn.cross_validation import train_test_split\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"x_iris, y_iris = iris.data, iris.target\n",
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"\n",
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@ -139,10 +135,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -152,7 +146,7 @@
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" weights='uniform')"
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]
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},
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"execution_count": 5,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -170,10 +164,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -198,9 +190,7 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -221,9 +211,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -251,9 +239,7 @@
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -309,9 +295,7 @@
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -349,9 +333,7 @@
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -369,9 +351,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"source": [
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"We see we classify well all the 'setosa' and 'versicolor' samples. "
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]
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@ -392,10 +372,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -407,7 +385,7 @@
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}
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],
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"source": [
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"from sklearn.cross_validation import cross_val_score, KFold\n",
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"from sklearn.model_selection import cross_val_score, KFold\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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@ -418,8 +396,7 @@
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"])\n",
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"\n",
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"# create a k-fold cross validation iterator of k=10 folds\n",
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"\n",
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"cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n",
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"cv = KFold(10, shuffle=True, random_state=33)\n",
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"\n",
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"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
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"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
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@ -437,10 +414,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false
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},
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -481,9 +456,7 @@
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -568,9 +541,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1+"
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}
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File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
@ -56,9 +56,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -79,7 +77,7 @@
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"iris = datasets.load_iris()\n",
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"\n",
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"# Training and test spliting\n",
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"from sklearn.cross_validation import train_test_split\n",
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"from sklearn.model_selection import train_test_split\n",
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"x_iris, y_iris = iris.data, iris.target\n",
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"# Test set will be the 25% taken randomly\n",
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"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
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@ -109,9 +107,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [
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{
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"data": {
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@ -196,9 +192,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1+"
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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}
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