1537 lines
72 KiB
Plaintext
1537 lines
72 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Processing and transform features"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"#### Encode categorical features"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [
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{
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"data": {
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"text/plain": " id gender\n0 1 male\n1 2 female",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>male</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>female</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"import pandas as pd\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"\n",
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"data = pd.DataFrame(dict(\n",
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" id=[1, 2],\n",
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" gender=[\"male\", \"female\"],\n",
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"))\n",
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"data.head()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [
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{
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"data": {
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"text/plain": "array(['female', 'male'], dtype=object)"
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"target = data[\"gender\"]\n",
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"\n",
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"encoder = LabelEncoder()\n",
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"encoder.fit(target)\n",
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"encoder.classes_"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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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"outputs": [
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{
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"data": {
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"text/plain": "array([1, 0])"
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},
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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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],
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"source": [
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"encoder.transform(target)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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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"outputs": [
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{
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"data": {
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"text/plain": "(array([0, 1]), Index(['male', 'female'], dtype='object'))"
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pd.factorize(data[\"gender\"])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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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"outputs": [
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{
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"data": {
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"text/plain": " id is_female is_male\n0 1 0 1\n1 2 1 0",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>is_female</th>\n <th>is_male</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pd.get_dummies(data, prefix=\"is\")"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[['male']\n",
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" ['female']]\n",
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"[array(['female', 'male'], dtype=object)]\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import OneHotEncoder\n",
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"\n",
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"array = target.values.reshape(-1, 1)\n",
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"print(array)\n",
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"\n",
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"encoder = OneHotEncoder(sparse=False)\n",
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"encoder.fit(array)\n",
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"print(encoder.categories_)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[0., 1.],\n [1., 0.]])"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"encoder.transform(array)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"#### Scaling numerical features"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"raw data mean: [-0.25 0.5 0.5 ]\n",
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"raw data std: [1.47901995 0.5 1.11803399]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"\n",
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"data = np.array([\n",
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" [0, 0, 1],\n",
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" [-1, 1, 0],\n",
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" [-2, 0, 2],\n",
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" [2, 1, -1],\n",
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"])\n",
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"print(f\"raw data mean: {data.mean(axis=0)}\")\n",
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"print(f\"raw data std: {data.std(axis=0)}\")"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[ 0.16903085, -1. , 0.4472136 ],\n [-0.50709255, 1. , -0.4472136 ],\n [-1.18321596, -1. , 1.34164079],\n [ 1.52127766, 1. , -1.34164079]])"
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scaler = StandardScaler()\n",
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"scaler.fit(data)\n",
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"scaler.transform(data)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Mean = [0. 0. 0.]\n",
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"Std = [1. 1. 1.]\n"
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]
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}
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],
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"source": [
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"scaled = scaler.fit_transform(data)\n",
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"print(f\"Mean = {scaled.mean(axis=0)}\")\n",
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"print(f\"Std = {scaled.std(axis=0)}\")"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[0.5 , 0. , 0.66666667],\n [0.25 , 1. , 0.33333333],\n [0. , 0. , 1. ],\n [1. , 1. , 0. ]])"
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},
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"execution_count": 11,
|
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"from sklearn.preprocessing import MinMaxScaler\n",
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"\n",
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"scaler = MinMaxScaler()\n",
|
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"scaler.fit_transform(data)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
|
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[ 0. , 0. , 1. ],\n [-0.5 , 0.5 , 0. ],\n [-0.5 , 0. , 0.5 ],\n [ 0.5 , 0.25, -0.25]])"
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"from sklearn.preprocessing import Normalizer\n",
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"\n",
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"scaler = Normalizer(norm=\"l1\")\n",
|
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"scaler.fit_transform(data)"
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],
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"metadata": {
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"collapsed": false,
|
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
|
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"### Split data"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"outputs": [
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{
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"data": {
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"text/plain": " sepal.length sepal.width petal.length petal.width variety\n0 5.1 3.5 1.4 0.2 Setosa\n1 4.9 3.0 1.4 0.2 Setosa\n2 4.7 3.2 1.3 0.2 Setosa\n3 4.6 3.1 1.5 0.2 Setosa\n4 5.0 3.6 1.4 0.2 Setosa",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sepal.length</th>\n <th>sepal.width</th>\n <th>petal.length</th>\n <th>petal.width</th>\n <th>variety</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>5.1</td>\n <td>3.5</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Setosa</td>\n </tr>\n <tr>\n <th>1</th>\n <td>4.9</td>\n <td>3.0</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Setosa</td>\n </tr>\n <tr>\n <th>2</th>\n <td>4.7</td>\n <td>3.2</td>\n <td>1.3</td>\n <td>0.2</td>\n <td>Setosa</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4.6</td>\n <td>3.1</td>\n <td>1.5</td>\n <td>0.2</td>\n <td>Setosa</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5.0</td>\n <td>3.6</td>\n <td>1.4</td>\n <td>0.2</td>\n <td>Setosa</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"import pathlib\n",
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"\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"csvfile = pathlib.Path(\"../../data/iris.csv\")\n",
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"iris = pd.read_csv(csvfile)\n",
|
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"# iris[\"variety\"] = pd.factorize(iris[\"variety\"])[0]\n",
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"iris.head()"
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],
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"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
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"name": "#%%\n"
|
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}
|
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}
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 14,
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"outputs": [
|
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{
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"data": {
|
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"text/plain": " sepal.length sepal.width petal.length petal.width\n0 5.1 3.5 1.4 0.2\n1 4.9 3.0 1.4 0.2\n2 4.7 3.2 1.3 0.2\n3 4.6 3.1 1.5 0.2\n4 5.0 3.6 1.4 0.2",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>sepal.length</th>\n <th>sepal.width</th>\n <th>petal.length</th>\n <th>petal.width</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>5.1</td>\n <td>3.5</td>\n <td>1.4</td>\n <td>0.2</td>\n </tr>\n <tr>\n <th>1</th>\n <td>4.9</td>\n <td>3.0</td>\n <td>1.4</td>\n <td>0.2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>4.7</td>\n <td>3.2</td>\n <td>1.3</td>\n <td>0.2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4.6</td>\n <td>3.1</td>\n <td>1.5</td>\n <td>0.2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5.0</td>\n <td>3.6</td>\n <td>1.4</td>\n <td>0.2</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"X = iris.drop(\"variety\", axis=1)\n",
|
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"X.head()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"outputs": [],
|
|
"source": [
|
|
"y = iris[\"variety\"]"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"X_train shape: (105, 4)\n",
|
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"X_test shape: (45, 4)\n",
|
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"y_train shape: (105,)\n",
|
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"y_test shape: (45,)\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"# train:test = 7:3\n",
|
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"\n",
|
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
|
|
"print(f\"X_train shape: {X_train.shape}\")\n",
|
|
"print(f\"X_test shape: {X_test.shape}\")\n",
|
|
"print(f\"y_train shape: {y_train.shape}\")\n",
|
|
"print(f\"y_test shape: {y_test.shape}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"X_train shape: (120, 4)\n",
|
|
"X_test shape: (30, 4)\n",
|
|
"y_train shape: (120,)\n",
|
|
"y_test shape: (30,)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# train:test = 0.8:0.2\n",
|
|
"\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)\n",
|
|
"print(f\"X_train shape: {X_train.shape}\")\n",
|
|
"print(f\"X_test shape: {X_test.shape}\")\n",
|
|
"print(f\"y_train shape: {y_train.shape}\")\n",
|
|
"print(f\"y_test shape: {y_test.shape}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X,\n",
|
|
" y,\n",
|
|
" test_size=0.3,\n",
|
|
" random_state=233,\n",
|
|
")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"outputs": [],
|
|
"source": [
|
|
"# train, validation, test\n",
|
|
"\n",
|
|
"X_train, X_, y_train, y_ = train_test_split(\n",
|
|
" X, y, test_size=0.3, random_state=233\n",
|
|
")\n",
|
|
"\n",
|
|
"X_val, X_test, y_val, y_test = train_test_split(\n",
|
|
" X_, y_, test_size=0.5, random_state=233\n",
|
|
")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"X_train shape: (105, 4)\n",
|
|
"X_val shape: (22, 4)\n",
|
|
"X_test shape: (23, 4)\n",
|
|
"y_train shape: (105,)\n",
|
|
"y_val shape: (22,)\n",
|
|
"y_test shape: (23,)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"X_train shape: {X_train.shape}\")\n",
|
|
"print(f\"X_val shape: {X_val.shape}\")\n",
|
|
"print(f\"X_test shape: {X_test.shape}\")\n",
|
|
"print(f\"y_train shape: {y_train.shape}\")\n",
|
|
"print(f\"y_val shape: {y_val.shape}\")\n",
|
|
"print(f\"y_test shape: {y_test.shape}\")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Grid Search\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[ -63.76056336 182.20484712 32.45346758 70.87751298 351.51266014\n",
|
|
" -7.01893893 59.89221748 33.89847802 75.65454954 47.92124999]\n",
|
|
" [ 0.69419195 3.35562957 5.69461738 -152.93603272 187.21731051\n",
|
|
" -3.10092616 -2.3676087 -236.78609838 -22.06231977 -159.77767906]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.datasets import make_classification\n",
|
|
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"\n",
|
|
"X, y = make_classification(\n",
|
|
" n_samples=500,\n",
|
|
" n_classes=3,\n",
|
|
" n_features=10,\n",
|
|
" n_informative=6,\n",
|
|
" scale=None,\n",
|
|
" random_state=233,\n",
|
|
")\n",
|
|
"\n",
|
|
"print(X[:2])"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[0 0 1 0 2]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(y[:5])\n"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"outputs": [],
|
|
"source": [
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
" X, y, test_size=0.25, random_state=233\n",
|
|
")"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Fitting 5 folds for each of 72 candidates, totalling 360 fits\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=10, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.3s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.2s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 0.4s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
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"[CV] END model__C=100, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 1.7s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 1.3s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 2.3s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 1.7s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=linear; total time= 2.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
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"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 1.5s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 1.2s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 2.2s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 2.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=linear; total time= 2.5s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=3, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 1.7s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 1.3s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 2.3s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 1.8s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=linear; total time= 2.3s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 1.7s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 1.3s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 2.2s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 1.7s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=linear; total time= 2.1s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=4, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 1.6s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 1.3s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 2.2s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 1.7s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=linear; total time= 2.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 1.6s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 1.3s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 2.3s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 1.7s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=linear; total time= 2.1s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=sigmoid; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n",
|
|
"[CV] END model__C=1000, model__degree=5, model__gamma=0.0001, model__kernel=rbf; total time= 0.0s\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": "GridSearchCV(cv=5,\n estimator=Pipeline(steps=[('scaler', StandardScaler()),\n ('model', SVC())]),\n param_grid={'model__C': [1, 10, 100, 1000],\n 'model__degree': [3, 4, 5],\n 'model__gamma': [0.001, 0.0001],\n 'model__kernel': ['linear', 'sigmoid', 'rbf']},\n verbose=2)"
|
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},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"pipe = Pipeline(steps=[(\"scaler\", StandardScaler()), (\"model\", SVC())])\n",
|
|
"\n",
|
|
"param_grid = dict(\n",
|
|
" model__C=[1, 10, 100, 1000],\n",
|
|
" model__kernel=[\"linear\", \"sigmoid\", \"rbf\"],\n",
|
|
" model__gamma=[0.001, 0.0001],\n",
|
|
" model__degree=[3, 4, 5],\n",
|
|
")\n",
|
|
"\n",
|
|
"gscv = GridSearchCV(\n",
|
|
" pipe,\n",
|
|
" param_grid=param_grid,\n",
|
|
" cv=5,\n",
|
|
" verbose=2,\n",
|
|
")\n",
|
|
"gscv.fit(X_train, y_train)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
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}
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 25,
|
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"outputs": [
|
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{
|
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"data": {
|
|
"text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n ('model', SVC(C=1000, gamma=0.001))])"
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gscv.best_estimator_"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 26,
|
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"outputs": [
|
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{
|
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"data": {
|
|
"text/plain": "0.768"
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gscv.best_score_"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 27,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "{'model__C': 1000,\n 'model__degree': 3,\n 'model__gamma': 0.001,\n 'model__kernel': 'rbf'}"
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gscv.best_params_"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n ('model', SVC(C=1000, gamma=0.001))])"
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pipe.set_params(**gscv.best_params_)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "Pipeline(steps=[('scaler', StandardScaler()),\n ('model', SVC(C=1000, gamma=0.001))])"
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pipe.fit(X_train, y_train)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "0.792"
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pipe.score(X_test, y_test)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Pipeline"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "Pipeline(steps=[('scaler', StandardScaler()), ('model', SVC())])"
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"from sklearn.pipeline import make_pipeline\n",
|
|
"\n",
|
|
"pipe = Pipeline(steps=[(\"scaler\", StandardScaler()), (\"model\", SVC())])\n",
|
|
"pipe"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "Pipeline(steps=[('standardscaler', StandardScaler()), ('svc', SVC())])"
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pipe = make_pipeline(StandardScaler(), SVC())\n",
|
|
"pipe"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": " feature1 feature2 feature3 feature4 feature5 feature6 \\\n0 -63.760563 182.204847 32.453468 70.877513 351.512660 -7.018939 \n1 0.694192 3.355630 5.694617 -152.936033 187.217311 -3.100926 \n2 -5.393549 184.011971 -38.685624 -119.911350 75.420933 -3.545307 \n3 -6.519019 39.696925 33.622070 -103.923978 97.519965 -0.220483 \n4 -29.057685 65.604430 72.474747 34.571492 155.318598 -2.501579 \n\n feature7 feature8 feature9 feature10 y \n0 59.892217 33.898478 75.654550 47.921250 0 \n1 -2.367609 -236.786098 -22.062320 -159.777679 0 \n2 78.284389 71.452933 -46.133634 -23.066554 1 \n3 28.255805 -90.692126 -51.072717 6.297472 0 \n4 -98.117314 209.305727 -113.667228 24.212523 2 ",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>feature1</th>\n <th>feature2</th>\n <th>feature3</th>\n <th>feature4</th>\n <th>feature5</th>\n <th>feature6</th>\n <th>feature7</th>\n <th>feature8</th>\n <th>feature9</th>\n <th>feature10</th>\n <th>y</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>-63.760563</td>\n <td>182.204847</td>\n <td>32.453468</td>\n <td>70.877513</td>\n <td>351.512660</td>\n <td>-7.018939</td>\n <td>59.892217</td>\n <td>33.898478</td>\n <td>75.654550</td>\n <td>47.921250</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.694192</td>\n <td>3.355630</td>\n <td>5.694617</td>\n <td>-152.936033</td>\n <td>187.217311</td>\n <td>-3.100926</td>\n <td>-2.367609</td>\n <td>-236.786098</td>\n <td>-22.062320</td>\n <td>-159.777679</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>-5.393549</td>\n <td>184.011971</td>\n <td>-38.685624</td>\n <td>-119.911350</td>\n <td>75.420933</td>\n <td>-3.545307</td>\n <td>78.284389</td>\n <td>71.452933</td>\n <td>-46.133634</td>\n <td>-23.066554</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-6.519019</td>\n <td>39.696925</td>\n <td>33.622070</td>\n <td>-103.923978</td>\n <td>97.519965</td>\n <td>-0.220483</td>\n <td>28.255805</td>\n <td>-90.692126</td>\n <td>-51.072717</td>\n <td>6.297472</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>-29.057685</td>\n <td>65.604430</td>\n <td>72.474747</td>\n <td>34.571492</td>\n <td>155.318598</td>\n <td>-2.501579</td>\n <td>-98.117314</td>\n <td>209.305727</td>\n <td>-113.667228</td>\n <td>24.212523</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.datasets import make_classification\n",
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"\n",
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"X, y = make_classification(\n",
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" n_samples=500,\n",
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" n_classes=3,\n",
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" n_features=10,\n",
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" n_informative=6,\n",
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" scale=None,\n",
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" random_state=233,\n",
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")\n",
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"\n",
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"data = pd.DataFrame(\n",
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" X,\n",
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" columns=[f\"feature{n + 1}\" for n in range(0, 10)],\n",
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")\n",
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"data[\"y\"] = y\n",
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"data.head()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"outputs": [
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{
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"data": {
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"text/plain": "Pipeline(steps=[('preprocessor',\n ColumnTransformer(transformers=[('odd_features_with_standardscaler',\n StandardScaler(),\n ['feature3', 'feature5',\n 'feature7', 'feature9']),\n ('even_features_with_mimaxscaler',\n MinMaxScaler(),\n ['feature2', 'feature4',\n 'feature6', 'feature8']),\n ('passthrough_feature',\n 'passthrough',\n ['feature1', 'feature10'])])),\n ('model', SVC())])"
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
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"\n",
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"preprocessor = ColumnTransformer(\n",
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" transformers=[\n",
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" (\"odd_features_with_standardscaler\", StandardScaler(), [f\"feature{n}\" for n in [3, 5, 7, 9]]),\n",
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" (\"even_features_with_mimaxscaler\", MinMaxScaler(), [f\"feature{n}\" for n in [2, 4, 6, 8]]),\n",
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" (\"passthrough_feature\", \"passthrough\", [\"feature1\", \"feature10\"]),\n",
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" ],\n",
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" remainder=\"drop\",\n",
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")\n",
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"\n",
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"\n",
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"pipe = Pipeline(\n",
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" steps=[\n",
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" (\"preprocessor\", preprocessor),\n",
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" (\"model\", SVC()),\n",
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" ]\n",
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")\n",
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"pipe.fit(data, data[\"y\"])"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Persistence"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"outputs": [
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{
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"data": {
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"text/plain": "[PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/LinearRegression.pkl')]"
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"import pickle\n",
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"import pathlib\n",
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"import tempfile\n",
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"\n",
|
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"from sklearn.linear_model import LinearRegression\n",
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"\n",
|
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"root = pathlib.Path(tempfile.mkdtemp())\n",
|
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"fpath = root.joinpath(\"LinearRegression.pkl\")\n",
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"\n",
|
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"regressor = LinearRegression()\n",
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"\n",
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"\n",
|
|
"with open(fpath, \"wb\") as file:\n",
|
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" pickle.dump(regressor, file)\n",
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"\n",
|
|
"files = list(root.iterdir())\n",
|
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"files"
|
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],
|
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
|
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}
|
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}
|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "{'copy_X': True,\n 'fit_intercept': True,\n 'n_jobs': None,\n 'normalize': 'deprecated',\n 'positive': False}"
|
|
},
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"file = open(fpath, \"rb\")\n",
|
|
"model = pickle.load(file)\n",
|
|
"file.close()\n",
|
|
"\n",
|
|
"model.get_params()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
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"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/SVC.joblib'), PosixPath('/var/folders/0t/s0c95rbs6ds7w_b0d471p0kc0000gn/T/tmpeuwitk1w/LinearRegression.pkl')]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import joblib\n",
|
|
"from sklearn.svm import SVC\n",
|
|
"\n",
|
|
"classifier = SVC()\n",
|
|
"fpath = root.joinpath(\"SVC.joblib\")\n",
|
|
"\n",
|
|
"joblib.dump(classifier, fpath)\n",
|
|
"files = list(root.iterdir())\n",
|
|
"print(files)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "{'C': 1.0,\n 'break_ties': False,\n 'cache_size': 200,\n 'class_weight': None,\n 'coef0': 0.0,\n 'decision_function_shape': 'ovr',\n 'degree': 3,\n 'gamma': 'scale',\n 'kernel': 'rbf',\n 'max_iter': -1,\n 'probability': False,\n 'random_state': None,\n 'shrinking': True,\n 'tol': 0.001,\n 'verbose': False}"
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model = joblib.load(fpath)\n",
|
|
"model.get_params()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |