573 lines
131 KiB
Plaintext
573 lines
131 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Feature Engineering\n",
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"## Einfuehrung\n",
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"## Exploration\n",
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"## Transformation\n",
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"## Konstruktion"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-02-20T10:49:38.133741Z",
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"start_time": "2020-02-20T10:49:36.231993Z"
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}
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},
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"outputs": [],
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"source": [
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"## preparation: import libraries\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns; sns.set()\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Ableiten aus bestehenden"
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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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"metadata": {},
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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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" direction speed\n",
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"0 68 21\n",
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"1 223 30\n",
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"2 157 41\n",
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"3 282 42\n",
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"4 280 22\n",
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"5 98 33\n",
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"6 99 30\n",
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"7 288 0\n",
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"8 344 46\n",
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"9 315 52\n",
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" direction speed x y\n",
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"0 68 21 19.470861 7.866738\n",
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"1 223 30 -20.459951 -21.940611\n",
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"2 157 41 16.019976 -37.740699\n",
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"3 282 42 -41.082199 8.732291\n",
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"4 280 22 -21.665771 3.820260\n",
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"5 98 33 32.678846 -4.592712\n",
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"6 99 30 29.630650 -4.693034\n",
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"7 288 0 -0.000000 0.000000\n",
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"8 344 46 -12.679318 44.218038\n",
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"9 315 52 -36.769553 36.769553\n"
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]
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}
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],
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"source": [
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"## example 1\n",
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"\n",
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"## a simulated data example: calculate x and y components of wind direction and speed\n",
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"np.random.seed(1234)\n",
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"direction = (np.random.rand(10) * 360).astype(int)\n",
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"speed = (np.random.rand(10) * 60).astype(int)\n",
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"data = pd.DataFrame(data={\n",
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" 'direction': direction, \n",
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" 'speed': speed}\n",
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")\n",
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"print(data)\n",
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"\n",
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"## transform\n",
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"data['x'] = np.sin(data.direction * np.pi / 180) * data.speed\n",
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"data['y'] = np.cos(data.direction * np.pi / 180) * data.speed\n",
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"\n",
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"## check\n",
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"print(data)"
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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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"metadata": {},
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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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"0 3/12/2016\n",
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"1 4/02/2016\n",
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"2 4/03/2017\n",
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"3 4/03/2017\n",
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"4 4/06/2016\n",
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"Name: Date, dtype: object\n",
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" Date date_dt day month year daydiff\n",
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"0 3/12/2016 2016-12-03 3 12 2016 337\n",
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"1 4/02/2016 2016-02-04 4 2 2016 34\n",
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"2 4/03/2017 2017-03-04 4 3 2017 428\n",
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"3 4/03/2017 2017-03-04 4 3 2017 428\n",
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"4 4/06/2016 2016-06-04 4 6 2016 155\n"
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]
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}
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],
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"source": [
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"## example 2\n",
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"\n",
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"## read melbourn housing data\n",
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"datapath = '../3_data'\n",
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"from os import chdir; chdir(datapath)\n",
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"data = pd.read_csv('melb_data.csv')\n",
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"\n",
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"## check before\n",
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"print(data.Date.head())\n",
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"\n",
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"## transform to datetime\n",
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"data['date_dt'] = pd.to_datetime(data.Date, format=\"%d/%m/%Y\")\n",
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"\n",
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"## extract day, month and year\n",
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"data['year'] = data.date_dt.dt.year\n",
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"data['month'] = data.date_dt.dt.month\n",
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"data['day'] = data.date_dt.dt.day\n",
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"\n",
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"## calc difference from a start date\n",
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"start_date = pd.to_datetime('1/1/2016', format=\"%d/%m/%Y\")\n",
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"data['daydiff'] = (data.date_dt - start_date).dt.days\n",
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"\n",
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"## check\n",
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"print(data[['Date', 'date_dt', 'day', 'month', 'year', 'daydiff']].head())"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"## convert to datetime on readeing file\n",
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"## pass names of variables to parse as list\n",
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"data = pd.read_csv('melb_data.csv', parse_dates=['Date'], dayfirst=True)\n",
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"print(data.Date.info())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Dimensionsreduktion mit PCA"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"## bank_data with minimal feature engineering\n",
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"\n",
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"## load data\n",
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"data = pd.read_csv('bank_data.csv', sep=';')\n",
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"\n",
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"## make target numerical\n",
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"data.y = np.where(data.y == 'yes', 1, 0)\n",
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"\n",
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"## select numerical variables\n",
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"data = data.select_dtypes(exclude='object')\n",
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"\n",
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"## drop na\n",
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"data = data.dropna()\n",
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"\n",
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"## features - target - split\n",
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"X = data.drop('y', axis=1)\n",
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"y = data['y']\n",
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"\n",
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"## scale features\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"scaler = StandardScaler().set_output(transform=\"pandas\")\n",
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"X = scaler.fit_transform(X)"
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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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"metadata": {},
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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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"[[-0.82454003 1.45559011 -1.0976675 -0.91524634 0.08531455 0.15409804\n",
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" -0.04340854 -0.21781905 -0.01493519 -0.01620926]\n",
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" [ 0.7627286 0.72786538 -0.32087404 0.13931963 -0.77314314 -0.13132969\n",
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" -0.59687245 -0.0447055 -0.04996993 0.14532455]\n",
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" [-1.01426275 1.69289619 -1.41943603 0.31810256 -0.21176908 -0.17164499\n",
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" -0.1649386 -0.17817061 0.08333184 -0.04625594]]\n"
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]
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}
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],
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"source": [
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"## rotate using PCA\n",
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"from sklearn.decomposition import PCA ## import trainer class\n",
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"model = PCA() ## instantiate trainer object\n",
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"pred = model.fit_transform(X) ## train and apply trainer on data\n",
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"print(pred[:3, :]) ## check prediction (result, optional)"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 500x500 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"## check\n",
|
|
"plt.figure(figsize=(5, 5))\n",
|
|
"ax = sns.scatterplot(x=pred[:, 0], y=pred[:, 1], hue=y, s=10, alpha=0.25, legend=False)\n",
|
|
"ax.set(xlabel='PC 1', ylabel='PC 2');"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" pc variance cumvar\n",
|
|
"0 1 0.382339 0.382339\n",
|
|
"1 2 0.140965 0.523304\n",
|
|
"2 3 0.115029 0.638333\n",
|
|
"3 4 0.100849 0.739182\n",
|
|
"4 5 0.093686 0.832868\n",
|
|
"5 6 0.087776 0.920644\n",
|
|
"6 7 0.047166 0.967810\n",
|
|
"7 8 0.028729 0.996538\n",
|
|
"8 9 0.002440 0.998978\n",
|
|
"9 10 0.001022 1.000000\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
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}
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],
|
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"source": [
|
|
"## show distribution of variances\n",
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|
"\n",
|
|
"results = pd.DataFrame({\n",
|
|
" 'pc' : range(1, model.n_components_ + 1),\n",
|
|
" 'variance': model.explained_variance_ratio_\n",
|
|
"})\n",
|
|
"results['cumvar'] = results.variance.cumsum()\n",
|
|
"print(results.head(10))\n",
|
|
"\n",
|
|
"## paretplot mit pc1\n",
|
|
"## pareto plot\n",
|
|
"sns.barplot(data=results, x='pc', y='variance', color='steelblue')\n",
|
|
"sns.lineplot(data=results, x=results.pc - 1, y='cumvar', color='red')\n",
|
|
"plt.axhline(y=0.9, color='black', linestyle='--') \n",
|
|
"plt.xticks(rotation=90);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "raw",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"## Filter out FutureWarnings\n",
|
|
"import warnings\n",
|
|
"warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<style type=\"text/css\">\n",
|
|
"#T_e6c26_row0_col0, #T_e6c26_row1_col1, #T_e6c26_row2_col2, #T_e6c26_row3_col3, #T_e6c26_row4_col4, #T_e6c26_row5_col5, #T_e6c26_row6_col6, #T_e6c26_row7_col7, #T_e6c26_row8_col8, #T_e6c26_row9_col9 {\n",
|
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" background-color: #023858;\n",
|
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" color: #f1f1f1;\n",
|
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"}\n",
|
|
"#T_e6c26_row0_col1, #T_e6c26_row0_col2, #T_e6c26_row0_col3, #T_e6c26_row0_col4, #T_e6c26_row0_col5, #T_e6c26_row0_col6, #T_e6c26_row0_col7, #T_e6c26_row0_col8, #T_e6c26_row0_col9, #T_e6c26_row1_col0, #T_e6c26_row1_col2, #T_e6c26_row1_col3, #T_e6c26_row1_col4, #T_e6c26_row1_col5, #T_e6c26_row1_col6, #T_e6c26_row1_col7, #T_e6c26_row1_col8, #T_e6c26_row1_col9, #T_e6c26_row2_col0, #T_e6c26_row2_col1, #T_e6c26_row2_col3, #T_e6c26_row2_col4, #T_e6c26_row2_col5, #T_e6c26_row2_col6, #T_e6c26_row2_col7, #T_e6c26_row2_col8, #T_e6c26_row2_col9, #T_e6c26_row3_col0, #T_e6c26_row3_col1, #T_e6c26_row3_col2, #T_e6c26_row3_col4, #T_e6c26_row3_col5, #T_e6c26_row3_col6, #T_e6c26_row3_col7, #T_e6c26_row3_col8, #T_e6c26_row3_col9, #T_e6c26_row4_col0, #T_e6c26_row4_col1, #T_e6c26_row4_col2, #T_e6c26_row4_col3, #T_e6c26_row4_col5, #T_e6c26_row4_col6, #T_e6c26_row4_col7, #T_e6c26_row4_col8, #T_e6c26_row4_col9, #T_e6c26_row5_col0, #T_e6c26_row5_col1, #T_e6c26_row5_col2, #T_e6c26_row5_col3, #T_e6c26_row5_col4, #T_e6c26_row5_col6, #T_e6c26_row5_col7, #T_e6c26_row5_col8, #T_e6c26_row5_col9, #T_e6c26_row6_col0, #T_e6c26_row6_col1, #T_e6c26_row6_col2, #T_e6c26_row6_col3, #T_e6c26_row6_col4, #T_e6c26_row6_col5, #T_e6c26_row6_col7, #T_e6c26_row6_col8, #T_e6c26_row6_col9, #T_e6c26_row7_col0, #T_e6c26_row7_col1, #T_e6c26_row7_col2, #T_e6c26_row7_col3, #T_e6c26_row7_col4, #T_e6c26_row7_col5, #T_e6c26_row7_col6, #T_e6c26_row7_col8, #T_e6c26_row7_col9, #T_e6c26_row8_col0, #T_e6c26_row8_col1, #T_e6c26_row8_col2, #T_e6c26_row8_col3, #T_e6c26_row8_col4, #T_e6c26_row8_col5, #T_e6c26_row8_col6, #T_e6c26_row8_col7, #T_e6c26_row8_col9, #T_e6c26_row9_col0, #T_e6c26_row9_col1, #T_e6c26_row9_col2, #T_e6c26_row9_col3, #T_e6c26_row9_col4, #T_e6c26_row9_col5, #T_e6c26_row9_col6, #T_e6c26_row9_col7, #T_e6c26_row9_col8 {\n",
|
|
" background-color: #fff7fb;\n",
|
|
" color: #000000;\n",
|
|
"}\n",
|
|
"</style>\n",
|
|
"<table id=\"T_e6c26\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th class=\"blank level0\" > </th>\n",
|
|
" <th id=\"T_e6c26_level0_col0\" class=\"col_heading level0 col0\" >PC 1</th>\n",
|
|
" <th id=\"T_e6c26_level0_col1\" class=\"col_heading level0 col1\" >PC 2</th>\n",
|
|
" <th id=\"T_e6c26_level0_col2\" class=\"col_heading level0 col2\" >PC 3</th>\n",
|
|
" <th id=\"T_e6c26_level0_col3\" class=\"col_heading level0 col3\" >PC 4</th>\n",
|
|
" <th id=\"T_e6c26_level0_col4\" class=\"col_heading level0 col4\" >PC 5</th>\n",
|
|
" <th id=\"T_e6c26_level0_col5\" class=\"col_heading level0 col5\" >PC 6</th>\n",
|
|
" <th id=\"T_e6c26_level0_col6\" class=\"col_heading level0 col6\" >PC 7</th>\n",
|
|
" <th id=\"T_e6c26_level0_col7\" class=\"col_heading level0 col7\" >PC 8</th>\n",
|
|
" <th id=\"T_e6c26_level0_col8\" class=\"col_heading level0 col8\" >PC 9</th>\n",
|
|
" <th id=\"T_e6c26_level0_col9\" class=\"col_heading level0 col9\" >PC 10</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row0\" class=\"row_heading level0 row0\" >PC 1</th>\n",
|
|
" <td id=\"T_e6c26_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col1\" class=\"data row0 col1\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col2\" class=\"data row0 col2\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col3\" class=\"data row0 col3\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col4\" class=\"data row0 col4\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col6\" class=\"data row0 col6\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col7\" class=\"data row0 col7\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col8\" class=\"data row0 col8\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row0_col9\" class=\"data row0 col9\" >-0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row1\" class=\"row_heading level0 row1\" >PC 2</th>\n",
|
|
" <td id=\"T_e6c26_row1_col0\" class=\"data row1 col0\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col2\" class=\"data row1 col2\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col3\" class=\"data row1 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col6\" class=\"data row1 col6\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col8\" class=\"data row1 col8\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row1_col9\" class=\"data row1 col9\" >0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row2\" class=\"row_heading level0 row2\" >PC 3</th>\n",
|
|
" <td id=\"T_e6c26_row2_col0\" class=\"data row2 col0\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col1\" class=\"data row2 col1\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col3\" class=\"data row2 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col4\" class=\"data row2 col4\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col6\" class=\"data row2 col6\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col8\" class=\"data row2 col8\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row3\" class=\"row_heading level0 row3\" >PC 4</th>\n",
|
|
" <td id=\"T_e6c26_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col1\" class=\"data row3 col1\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col2\" class=\"data row3 col2\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col5\" class=\"data row3 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col6\" class=\"data row3 col6\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col8\" class=\"data row3 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row3_col9\" class=\"data row3 col9\" >-0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row4\" class=\"row_heading level0 row4\" >PC 5</th>\n",
|
|
" <td id=\"T_e6c26_row4_col0\" class=\"data row4 col0\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col2\" class=\"data row4 col2\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col5\" class=\"data row4 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col7\" class=\"data row4 col7\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col8\" class=\"data row4 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row4_col9\" class=\"data row4 col9\" >0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row5\" class=\"row_heading level0 row5\" >PC 6</th>\n",
|
|
" <td id=\"T_e6c26_row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col1\" class=\"data row5 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col3\" class=\"data row5 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col4\" class=\"data row5 col4\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col6\" class=\"data row5 col6\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col7\" class=\"data row5 col7\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col8\" class=\"data row5 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row5_col9\" class=\"data row5 col9\" >-0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row6\" class=\"row_heading level0 row6\" >PC 7</th>\n",
|
|
" <td id=\"T_e6c26_row6_col0\" class=\"data row6 col0\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col1\" class=\"data row6 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col3\" class=\"data row6 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col5\" class=\"data row6 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col7\" class=\"data row6 col7\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col8\" class=\"data row6 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row6_col9\" class=\"data row6 col9\" >0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row7\" class=\"row_heading level0 row7\" >PC 8</th>\n",
|
|
" <td id=\"T_e6c26_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col4\" class=\"data row7 col4\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col5\" class=\"data row7 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col6\" class=\"data row7 col6\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col8\" class=\"data row7 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row7_col9\" class=\"data row7 col9\" >-0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row8\" class=\"row_heading level0 row8\" >PC 9</th>\n",
|
|
" <td id=\"T_e6c26_row8_col0\" class=\"data row8 col0\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col1\" class=\"data row8 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col3\" class=\"data row8 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col4\" class=\"data row8 col4\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col5\" class=\"data row8 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col6\" class=\"data row8 col6\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col7\" class=\"data row8 col7\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
|
|
" <td id=\"T_e6c26_row8_col9\" class=\"data row8 col9\" >-0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th id=\"T_e6c26_level0_row9\" class=\"row_heading level0 row9\" >PC 10</th>\n",
|
|
" <td id=\"T_e6c26_row9_col0\" class=\"data row9 col0\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col1\" class=\"data row9 col1\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col3\" class=\"data row9 col3\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col5\" class=\"data row9 col5\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col6\" class=\"data row9 col6\" >0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col7\" class=\"data row9 col7\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col8\" class=\"data row9 col8\" >-0.000000</td>\n",
|
|
" <td id=\"T_e6c26_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n"
|
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],
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],
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|
|
"## correlations of principal components\n",
|
|
"results = pd.DataFrame(pred, columns=['PC ' + str(x) for x in range(1, model.n_components_ + 1)])\n",
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"results.corr().style.background_gradient(axis=None)"
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