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mattiasostmar/xgboost_liwc_umap_4functions_typealyzer_survey.ipynb

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xgboost_liwc_umap_4functions_typealyzer_survey.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import xgboost as xgb\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import plot_confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.model_selection import RepeatedStratifiedKFold\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.feature_selection import RFE\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import umap\n",
"import umap.plot\n",
"\n",
"from imblearn.under_sampling import RandomUnderSampler"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>url</th>\n",
" <th>typealyzer</th>\n",
" <th>actual</th>\n",
" <th>e</th>\n",
" <th>s</th>\n",
" <th>t</th>\n",
" <th>sntf_s</th>\n",
" <th>sntf_n</th>\n",
" <th>sntf_t</th>\n",
" <th>sntf_f</th>\n",
" <th>...</th>\n",
" <th>sad</th>\n",
" <th>you</th>\n",
" <th>cogmech</th>\n",
" <th>auxverb</th>\n",
" <th>they</th>\n",
" <th>incl</th>\n",
" <th>money</th>\n",
" <th>feel</th>\n",
" <th>we</th>\n",
" <th>hear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>http://adropofcolour.tumblr.com</td>\n",
" <td>ISFP</td>\n",
" <td>INFJ</td>\n",
" <td>0.291281</td>\n",
" <td>0.787844</td>\n",
" <td>0.460961</td>\n",
" <td>0.663515</td>\n",
" <td>0.178565</td>\n",
" <td>0.069282</td>\n",
" <td>0.088638</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.019704</td>\n",
" <td>0.098522</td>\n",
" <td>0.147783</td>\n",
" <td>0.000000</td>\n",
" <td>0.039409</td>\n",
" <td>0.009852</td>\n",
" <td>0.019704</td>\n",
" <td>0.044335</td>\n",
" <td>0.009852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>http://godheadcomplex.tumblr.com</td>\n",
" <td>ESFP</td>\n",
" <td>INFP</td>\n",
" <td>0.883579</td>\n",
" <td>0.951693</td>\n",
" <td>0.238407</td>\n",
" <td>0.855921</td>\n",
" <td>0.046931</td>\n",
" <td>0.021850</td>\n",
" <td>0.075297</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.017513</td>\n",
" <td>0.201401</td>\n",
" <td>0.084063</td>\n",
" <td>0.001751</td>\n",
" <td>0.056042</td>\n",
" <td>0.007005</td>\n",
" <td>0.017513</td>\n",
" <td>0.047285</td>\n",
" <td>0.003503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>http://chaotikaeon2.tumblr.com</td>\n",
" <td>INTJ</td>\n",
" <td>INTP</td>\n",
" <td>0.332444</td>\n",
" <td>0.357863</td>\n",
" <td>0.591322</td>\n",
" <td>0.147668</td>\n",
" <td>0.252326</td>\n",
" <td>0.339831</td>\n",
" <td>0.260175</td>\n",
" <td>...</td>\n",
" <td>0.003283</td>\n",
" <td>0.014540</td>\n",
" <td>0.181989</td>\n",
" <td>0.114916</td>\n",
" <td>0.000938</td>\n",
" <td>0.071295</td>\n",
" <td>0.010319</td>\n",
" <td>0.008912</td>\n",
" <td>0.054409</td>\n",
" <td>0.014540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>http://perpetually-in-transit.blogspot.com</td>\n",
" <td>ESFP</td>\n",
" <td>ENFJ</td>\n",
" <td>0.944394</td>\n",
" <td>0.943192</td>\n",
" <td>0.105527</td>\n",
" <td>0.778825</td>\n",
" <td>0.051134</td>\n",
" <td>0.017299</td>\n",
" <td>0.152742</td>\n",
" <td>...</td>\n",
" <td>0.002497</td>\n",
" <td>0.018727</td>\n",
" <td>0.207241</td>\n",
" <td>0.104869</td>\n",
" <td>0.002497</td>\n",
" <td>0.049938</td>\n",
" <td>0.014981</td>\n",
" <td>0.011236</td>\n",
" <td>0.041199</td>\n",
" <td>0.017478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>http://museofmystery.wordpress.com/2012/08/29/...</td>\n",
" <td>ISTP</td>\n",
" <td>INFP</td>\n",
" <td>0.073352</td>\n",
" <td>0.850472</td>\n",
" <td>0.608812</td>\n",
" <td>0.628322</td>\n",
" <td>0.112762</td>\n",
" <td>0.149270</td>\n",
" <td>0.109646</td>\n",
" <td>...</td>\n",
" <td>0.001031</td>\n",
" <td>0.005155</td>\n",
" <td>0.215464</td>\n",
" <td>0.122680</td>\n",
" <td>0.005155</td>\n",
" <td>0.043299</td>\n",
" <td>0.019588</td>\n",
" <td>0.002062</td>\n",
" <td>0.021649</td>\n",
" <td>0.012371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25432</th>\n",
" <td>http://pistoche.tumblr.com</td>\n",
" <td>ESFP</td>\n",
" <td>INTJ</td>\n",
" <td>0.685653</td>\n",
" <td>0.969891</td>\n",
" <td>0.480241</td>\n",
" <td>0.960824</td>\n",
" <td>0.029758</td>\n",
" <td>0.004220</td>\n",
" <td>0.005199</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25433</th>\n",
" <td>http://lokh.tumblr.com</td>\n",
" <td>ISTP</td>\n",
" <td>INTP</td>\n",
" <td>0.201637</td>\n",
" <td>0.553602</td>\n",
" <td>0.662618</td>\n",
" <td>0.468074</td>\n",
" <td>0.374926</td>\n",
" <td>0.099968</td>\n",
" <td>0.057033</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25435</th>\n",
" <td>http://readerdye.tumblr.com</td>\n",
" <td>ISTP</td>\n",
" <td>INFP</td>\n",
" <td>0.375704</td>\n",
" <td>0.756593</td>\n",
" <td>0.740688</td>\n",
" <td>0.697536</td>\n",
" <td>0.229456</td>\n",
" <td>0.051684</td>\n",
" <td>0.021324</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25436</th>\n",
" <td>http://loveisart.tumblr.com</td>\n",
" <td>ISTP</td>\n",
" <td>ENFP</td>\n",
" <td>0.002516</td>\n",
" <td>0.848823</td>\n",
" <td>0.661502</td>\n",
" <td>0.584138</td>\n",
" <td>0.118812</td>\n",
" <td>0.192779</td>\n",
" <td>0.104271</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25437</th>\n",
" <td>http://angelalll.tumblr.com</td>\n",
" <td>ESTP</td>\n",
" <td>INFP</td>\n",
" <td>0.814616</td>\n",
" <td>0.652280</td>\n",
" <td>0.832608</td>\n",
" <td>0.518149</td>\n",
" <td>0.281291</td>\n",
" <td>0.163392</td>\n",
" <td>0.037168</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22919 rows × 115 columns</p>\n",
"</div>"
],
"text/plain": [
" url typealyzer actual \\\n",
"1 http://adropofcolour.tumblr.com ISFP INFJ \n",
"2 http://godheadcomplex.tumblr.com ESFP INFP \n",
"3 http://chaotikaeon2.tumblr.com INTJ INTP \n",
"5 http://perpetually-in-transit.blogspot.com ESFP ENFJ \n",
"10 http://museofmystery.wordpress.com/2012/08/29/... ISTP INFP \n",
"... ... ... ... \n",
"25432 http://pistoche.tumblr.com ESFP INTJ \n",
"25433 http://lokh.tumblr.com ISTP INTP \n",
"25435 http://readerdye.tumblr.com ISTP INFP \n",
"25436 http://loveisart.tumblr.com ISTP ENFP \n",
"25437 http://angelalll.tumblr.com ESTP INFP \n",
"\n",
" e s t sntf_s sntf_n sntf_t sntf_f \\\n",
"1 0.291281 0.787844 0.460961 0.663515 0.178565 0.069282 0.088638 \n",
"2 0.883579 0.951693 0.238407 0.855921 0.046931 0.021850 0.075297 \n",
"3 0.332444 0.357863 0.591322 0.147668 0.252326 0.339831 0.260175 \n",
"5 0.944394 0.943192 0.105527 0.778825 0.051134 0.017299 0.152742 \n",
"10 0.073352 0.850472 0.608812 0.628322 0.112762 0.149270 0.109646 \n",
"... ... ... ... ... ... ... ... \n",
"25432 0.685653 0.969891 0.480241 0.960824 0.029758 0.004220 0.005199 \n",
"25433 0.201637 0.553602 0.662618 0.468074 0.374926 0.099968 0.057033 \n",
"25435 0.375704 0.756593 0.740688 0.697536 0.229456 0.051684 0.021324 \n",
"25436 0.002516 0.848823 0.661502 0.584138 0.118812 0.192779 0.104271 \n",
"25437 0.814616 0.652280 0.832608 0.518149 0.281291 0.163392 0.037168 \n",
"\n",
" ... sad you cogmech auxverb they incl \\\n",
"1 ... 0.000000 0.019704 0.098522 0.147783 0.000000 0.039409 \n",
"2 ... 0.000000 0.017513 0.201401 0.084063 0.001751 0.056042 \n",
"3 ... 0.003283 0.014540 0.181989 0.114916 0.000938 0.071295 \n",
"5 ... 0.002497 0.018727 0.207241 0.104869 0.002497 0.049938 \n",
"10 ... 0.001031 0.005155 0.215464 0.122680 0.005155 0.043299 \n",
"... ... ... ... ... ... ... ... \n",
"25432 ... NaN NaN NaN NaN NaN NaN \n",
"25433 ... NaN NaN NaN NaN NaN NaN \n",
"25435 ... NaN NaN NaN NaN NaN NaN \n",
"25436 ... NaN NaN NaN NaN NaN NaN \n",
"25437 ... NaN NaN NaN NaN NaN NaN \n",
"\n",
" money feel we hear \n",
"1 0.009852 0.019704 0.044335 0.009852 \n",
"2 0.007005 0.017513 0.047285 0.003503 \n",
"3 0.010319 0.008912 0.054409 0.014540 \n",
"5 0.014981 0.011236 0.041199 0.017478 \n",
"10 0.019588 0.002062 0.021649 0.012371 \n",
"... ... ... ... ... \n",
"25432 NaN NaN NaN NaN \n",
"25433 NaN NaN NaN NaN \n",
"25435 NaN NaN NaN NaN \n",
"25436 NaN NaN NaN NaN \n",
"25437 NaN NaN NaN NaN \n",
"\n",
"[22919 rows x 115 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_pickle(\"dataframe_survey_2018-01-23_enriched.pickle\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 f\n",
"2 t\n",
"3 f\n",
"5 f\n",
"10 t\n",
" ..\n",
"25432 f\n",
"25433 f\n",
"25435 t\n",
"25436 f\n",
"25437 n\n",
"Name: func, Length: 22919, dtype: object"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.func # 4 cognitive functions without their attitudinal directions introversion/extraversion"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/envs/mindalyzer/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
},
{
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" <th></th>\n",
" <th>negate</th>\n",
" <th>ppron</th>\n",
" <th>nonfl</th>\n",
" <th>i</th>\n",
" <th>relativ</th>\n",
" <th>percept</th>\n",
" <th>quant</th>\n",
" <th>affect</th>\n",
" <th>shehe</th>\n",
" <th>achieve</th>\n",
" <th>...</th>\n",
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" <th>cogmech</th>\n",
" <th>auxverb</th>\n",
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" <th>incl</th>\n",
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" <th>feel</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.034483</td>\n",
" <td>0.428571</td>\n",
" <td>0.049261</td>\n",
" <td>0.334975</td>\n",
" <td>0.197044</td>\n",
" <td>0.039409</td>\n",
" <td>0.024631</td>\n",
" <td>0.088670</td>\n",
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" <td>...</td>\n",
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" <td>0.098522</td>\n",
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" <td>0.000000</td>\n",
" <td>0.039409</td>\n",
" <td>0.009852</td>\n",
" <td>0.019704</td>\n",
" <td>0.044335</td>\n",
" <td>0.009852</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.040280</td>\n",
" <td>0.416813</td>\n",
" <td>0.063047</td>\n",
" <td>0.285464</td>\n",
" <td>0.316988</td>\n",
" <td>0.031524</td>\n",
" <td>0.029772</td>\n",
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" <td>0.064799</td>\n",
" <td>0.012259</td>\n",
" <td>...</td>\n",
" <td>0.017513</td>\n",
" <td>0.201401</td>\n",
" <td>0.084063</td>\n",
" <td>0.001751</td>\n",
" <td>0.056042</td>\n",
" <td>0.007005</td>\n",
" <td>0.017513</td>\n",
" <td>0.047285</td>\n",
" <td>0.003503</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.017824</td>\n",
" <td>0.439962</td>\n",
" <td>0.068949</td>\n",
" <td>0.277674</td>\n",
" <td>0.353189</td>\n",
" <td>0.040807</td>\n",
" <td>0.031895</td>\n",
" <td>0.090994</td>\n",
" <td>0.092402</td>\n",
" <td>0.018293</td>\n",
" <td>...</td>\n",
" <td>0.014540</td>\n",
" <td>0.181989</td>\n",
" <td>0.114916</td>\n",
" <td>0.000938</td>\n",
" <td>0.071295</td>\n",
" <td>0.010319</td>\n",
" <td>0.008912</td>\n",
" <td>0.054409</td>\n",
" <td>0.014540</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.038702</td>\n",
" <td>0.377029</td>\n",
" <td>0.049938</td>\n",
" <td>0.233458</td>\n",
" <td>0.223471</td>\n",
" <td>0.046192</td>\n",
" <td>0.041199</td>\n",
" <td>0.072409</td>\n",
" <td>0.081149</td>\n",
" <td>0.024969</td>\n",
" <td>...</td>\n",
" <td>0.018727</td>\n",
" <td>0.207241</td>\n",
" <td>0.104869</td>\n",
" <td>0.002497</td>\n",
" <td>0.049938</td>\n",
" <td>0.014981</td>\n",
" <td>0.011236</td>\n",
" <td>0.041199</td>\n",
" <td>0.017478</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.014433</td>\n",
" <td>0.479381</td>\n",
" <td>0.091753</td>\n",
" <td>0.364948</td>\n",
" <td>0.319588</td>\n",
" <td>0.026804</td>\n",
" <td>0.047423</td>\n",
" <td>0.102062</td>\n",
" <td>0.082474</td>\n",
" <td>0.030928</td>\n",
" <td>...</td>\n",
" <td>0.005155</td>\n",
" <td>0.215464</td>\n",
" <td>0.122680</td>\n",
" <td>0.005155</td>\n",
" <td>0.043299</td>\n",
" <td>0.019588</td>\n",
" <td>0.002062</td>\n",
" <td>0.021649</td>\n",
" <td>0.012371</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22914</th>\n",
" <td>0.091241</td>\n",
" <td>0.390511</td>\n",
" <td>0.040146</td>\n",
" <td>0.244526</td>\n",
" <td>0.306569</td>\n",
" <td>0.025547</td>\n",
" <td>0.021898</td>\n",
" <td>0.065693</td>\n",
" <td>0.025547</td>\n",
" <td>0.021898</td>\n",
" <td>...</td>\n",
" <td>0.040146</td>\n",
" <td>0.244526</td>\n",
" <td>0.069343</td>\n",
" <td>0.003650</td>\n",
" <td>0.091241</td>\n",
" <td>0.007299</td>\n",
" <td>0.000000</td>\n",
" <td>0.076642</td>\n",
" <td>0.007299</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22915</th>\n",
" <td>0.020309</td>\n",
" <td>0.508530</td>\n",
" <td>0.053615</td>\n",
" <td>0.351340</td>\n",
" <td>0.287571</td>\n",
" <td>0.049553</td>\n",
" <td>0.045491</td>\n",
" <td>0.119415</td>\n",
" <td>0.084890</td>\n",
" <td>0.017059</td>\n",
" <td>...</td>\n",
" <td>0.019090</td>\n",
" <td>0.211210</td>\n",
" <td>0.150690</td>\n",
" <td>0.012998</td>\n",
" <td>0.045085</td>\n",
" <td>0.016247</td>\n",
" <td>0.016653</td>\n",
" <td>0.040211</td>\n",
" <td>0.010154</td>\n",
" <td>n</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22916</th>\n",
" <td>0.029458</td>\n",
" <td>0.482904</td>\n",
" <td>0.061021</td>\n",
" <td>0.307207</td>\n",
" <td>0.262493</td>\n",
" <td>0.039979</td>\n",
" <td>0.028406</td>\n",
" <td>0.110994</td>\n",
" <td>0.124671</td>\n",
" <td>0.025776</td>\n",
" <td>...</td>\n",
" <td>0.017885</td>\n",
" <td>0.197791</td>\n",
" <td>0.162020</td>\n",
" <td>0.006312</td>\n",
" <td>0.042083</td>\n",
" <td>0.010521</td>\n",
" <td>0.013677</td>\n",
" <td>0.026828</td>\n",
" <td>0.010521</td>\n",
" <td>n</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22917</th>\n",
" <td>0.113971</td>\n",
" <td>0.139706</td>\n",
" <td>0.025735</td>\n",
" <td>0.084559</td>\n",
" <td>0.136029</td>\n",
" <td>0.022059</td>\n",
" <td>0.018382</td>\n",
" <td>0.040441</td>\n",
" <td>0.025735</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.014706</td>\n",
" <td>0.113971</td>\n",
" <td>0.069853</td>\n",
" <td>0.007353</td>\n",
" <td>0.011029</td>\n",
" <td>0.000000</td>\n",
" <td>0.007353</td>\n",
" <td>0.007353</td>\n",
" <td>0.000000</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22918</th>\n",
" <td>0.003861</td>\n",
" <td>0.455598</td>\n",
" <td>0.057915</td>\n",
" <td>0.333977</td>\n",
" <td>0.322394</td>\n",
" <td>0.019305</td>\n",
" <td>0.036680</td>\n",
" <td>0.073359</td>\n",
" <td>0.075290</td>\n",
" <td>0.017375</td>\n",
" <td>...</td>\n",
" <td>0.027027</td>\n",
" <td>0.133205</td>\n",
" <td>0.113900</td>\n",
" <td>0.003861</td>\n",
" <td>0.055985</td>\n",
" <td>0.003861</td>\n",
" <td>0.003861</td>\n",
" <td>0.015444</td>\n",
" <td>0.011583</td>\n",
" <td>n</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20819 rows × 65 columns</p>\n",
"</div>"
],
"text/plain": [
" negate ppron nonfl i relativ percept quant \\\n",
"1 0.034483 0.428571 0.049261 0.334975 0.197044 0.039409 0.024631 \n",
"2 0.040280 0.416813 0.063047 0.285464 0.316988 0.031524 0.029772 \n",
"3 0.017824 0.439962 0.068949 0.277674 0.353189 0.040807 0.031895 \n",
"5 0.038702 0.377029 0.049938 0.233458 0.223471 0.046192 0.041199 \n",
"10 0.014433 0.479381 0.091753 0.364948 0.319588 0.026804 0.047423 \n",
"... ... ... ... ... ... ... ... \n",
"22914 0.091241 0.390511 0.040146 0.244526 0.306569 0.025547 0.021898 \n",
"22915 0.020309 0.508530 0.053615 0.351340 0.287571 0.049553 0.045491 \n",
"22916 0.029458 0.482904 0.061021 0.307207 0.262493 0.039979 0.028406 \n",
"22917 0.113971 0.139706 0.025735 0.084559 0.136029 0.022059 0.018382 \n",
"22918 0.003861 0.455598 0.057915 0.333977 0.322394 0.019305 0.036680 \n",
"\n",
" affect shehe achieve ... you cogmech auxverb \\\n",
"1 0.088670 0.029557 0.024631 ... 0.019704 0.098522 0.147783 \n",
"2 0.082312 0.064799 0.012259 ... 0.017513 0.201401 0.084063 \n",
"3 0.090994 0.092402 0.018293 ... 0.014540 0.181989 0.114916 \n",
"5 0.072409 0.081149 0.024969 ... 0.018727 0.207241 0.104869 \n",
"10 0.102062 0.082474 0.030928 ... 0.005155 0.215464 0.122680 \n",
"... ... ... ... ... ... ... ... \n",
"22914 0.065693 0.025547 0.021898 ... 0.040146 0.244526 0.069343 \n",
"22915 0.119415 0.084890 0.017059 ... 0.019090 0.211210 0.150690 \n",
"22916 0.110994 0.124671 0.025776 ... 0.017885 0.197791 0.162020 \n",
"22917 0.040441 0.025735 0.000000 ... 0.014706 0.113971 0.069853 \n",
"22918 0.073359 0.075290 0.017375 ... 0.027027 0.133205 0.113900 \n",
"\n",
" they incl money feel we hear y \n",
"1 0.000000 0.039409 0.009852 0.019704 0.044335 0.009852 f \n",
"2 0.001751 0.056042 0.007005 0.017513 0.047285 0.003503 t \n",
"3 0.000938 0.071295 0.010319 0.008912 0.054409 0.014540 f \n",
"5 0.002497 0.049938 0.014981 0.011236 0.041199 0.017478 f \n",
"10 0.005155 0.043299 0.019588 0.002062 0.021649 0.012371 t \n",
"... ... ... ... ... ... ... .. \n",
"22914 0.003650 0.091241 0.007299 0.000000 0.076642 0.007299 t \n",
"22915 0.012998 0.045085 0.016247 0.016653 0.040211 0.010154 n \n",
"22916 0.006312 0.042083 0.010521 0.013677 0.026828 0.010521 n \n",
"22917 0.007353 0.011029 0.000000 0.007353 0.007353 0.000000 f \n",
"22918 0.003861 0.055985 0.003861 0.003861 0.015444 0.011583 n \n",
"\n",
"[20819 rows x 65 columns]"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liwc_cols = [\"negate\",\"ppron\",\"nonfl\",\"i\",\"relativ\",\"percept\",\"quant\",\"affect\",\"shehe\",\"achieve\",\"bio\",\"leisure\",\"conj\",\"motion\",\"posemo\",\"adverb\",\"home\",\"future\",\"negemo\",\"number\",\"inhib\",\"humans\",\"pronoun\",\"excl\",\"space\",\"tentat\",\"see\",\"past\",\"anx\",\"family\",\"present\",\"health\",\"verb\",\"certain\",\"anger\",\"preps\",\"swear\",\"ingest\",\"discrep\",\"friend\",\"relig\",\"time\",\"cause\",\"article\",\"body\",\"social\",\"assent\",\"work\",\"sexual\",\"insight\",\"ipron\",\"filler\",\"death\",\"funct\",\"sad\",\"you\",\"cogmech\",\"auxverb\",\"they\",\"incl\",\"money\",\"feel\",\"we\",\"hear\"]\n",
"data = df[liwc_cols]\n",
"data[\"y\"] = df.func\n",
"data = data.dropna()\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65\n"
]
}
],
"source": [
"print(len(data.columns))\n",
"y = data.iloc[:,[64]]\n",
"X = data.iloc[:,0:63]"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2045)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4164, 63)"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.values.shape"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4164,)"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.values.ravel().shape"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"n 6883\n",
"f 4489\n",
"t 3278\n",
"s 2005\n",
"Name: y, dtype: int64"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(y_train.iloc[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
" importance_type='gain', interaction_constraints='',\n",
" learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
" min_child_weight=1, missing=nan, monotone_constraints='()',\n",
" n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
" objective='multi:softprob', random_state=0, reg_alpha=0,\n",
" reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
" tree_method='exact', validate_parameters=1, verbosity=None)"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = xgb.XGBClassifier()\n",
"model.fit(X_train, y_train.iloc[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.3547070124879923"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy = accuracy_score(y_test, y_pred)\n",
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 12))\n",
"plot_confusion_matrix(model, X_test, y_test, ax=ax)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# With balanced classes"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [],
"source": [
"rus = RandomUnderSampler(sampling_strategy='not minority', random_state=1)\n",
"data_balanced, balanced_y = rus.fit_resample(data, data['y'])"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>negate</th>\n",
" <th>ppron</th>\n",
" <th>nonfl</th>\n",
" <th>i</th>\n",
" <th>relativ</th>\n",
" <th>percept</th>\n",
" <th>quant</th>\n",
" <th>affect</th>\n",
" <th>shehe</th>\n",
" <th>achieve</th>\n",
" <th>...</th>\n",
" <th>you</th>\n",
" <th>cogmech</th>\n",
" <th>auxverb</th>\n",
" <th>they</th>\n",
" <th>incl</th>\n",
" <th>money</th>\n",
" <th>feel</th>\n",
" <th>we</th>\n",
" <th>hear</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.050562</td>\n",
" <td>0.252809</td>\n",
" <td>0.033708</td>\n",
" <td>0.188202</td>\n",
" <td>0.160112</td>\n",
" <td>0.025281</td>\n",
" <td>0.016854</td>\n",
" <td>0.070225</td>\n",
" <td>0.030899</td>\n",
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" <td>0.115169</td>\n",
" <td>0.002809</td>\n",
" <td>0.025281</td>\n",
" <td>0.005618</td>\n",
" <td>0.016854</td>\n",
" <td>0.028090</td>\n",
" <td>0.002809</td>\n",
" <td>f</td>\n",
" </tr>\n",
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" <td>0.038462</td>\n",
" <td>0.378205</td>\n",
" <td>0.054487</td>\n",
" <td>0.272436</td>\n",
" <td>0.285256</td>\n",
" <td>0.054487</td>\n",
" <td>0.038462</td>\n",
" <td>0.108974</td>\n",
" <td>0.057692</td>\n",
" <td>0.006410</td>\n",
" <td>...</td>\n",
" <td>0.012821</td>\n",
" <td>0.227564</td>\n",
" <td>0.108974</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.003205</td>\n",
" <td>0.019231</td>\n",
" <td>0.035256</td>\n",
" <td>0.022436</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.018786</td>\n",
" <td>0.552023</td>\n",
" <td>0.046243</td>\n",
" <td>0.341040</td>\n",
" <td>0.339595</td>\n",
" <td>0.052023</td>\n",
" <td>0.066474</td>\n",
" <td>0.114162</td>\n",
" <td>0.135838</td>\n",
" <td>0.026012</td>\n",
" <td>...</td>\n",
" <td>0.027457</td>\n",
" <td>0.225434</td>\n",
" <td>0.167630</td>\n",
" <td>0.020231</td>\n",
" <td>0.049133</td>\n",
" <td>0.010116</td>\n",
" <td>0.018786</td>\n",
" <td>0.027457</td>\n",
" <td>0.020231</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.039113</td>\n",
" <td>0.413299</td>\n",
" <td>0.061278</td>\n",
" <td>0.320730</td>\n",
" <td>0.251630</td>\n",
" <td>0.053455</td>\n",
" <td>0.032595</td>\n",
" <td>0.067797</td>\n",
" <td>0.053455</td>\n",
" <td>0.002608</td>\n",
" <td>...</td>\n",
" <td>0.010430</td>\n",
" <td>0.170795</td>\n",
" <td>0.088657</td>\n",
" <td>0.001304</td>\n",
" <td>0.045632</td>\n",
" <td>0.005215</td>\n",
" <td>0.013038</td>\n",
" <td>0.027379</td>\n",
" <td>0.007823</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.026490</td>\n",
" <td>0.394702</td>\n",
" <td>0.079470</td>\n",
" <td>0.270199</td>\n",
" <td>0.241060</td>\n",
" <td>0.046358</td>\n",
" <td>0.027815</td>\n",
" <td>0.092715</td>\n",
" <td>0.070199</td>\n",
" <td>0.023841</td>\n",
" <td>...</td>\n",
" <td>0.027815</td>\n",
" <td>0.143046</td>\n",
" <td>0.092715</td>\n",
" <td>0.000000</td>\n",
" <td>0.018543</td>\n",
" <td>0.006623</td>\n",
" <td>0.010596</td>\n",
" <td>0.026490</td>\n",
" <td>0.025166</td>\n",
" <td>f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>0.274920</td>\n",
" <td>0.310289</td>\n",
" <td>0.032154</td>\n",
" <td>0.029475</td>\n",
" <td>0.088424</td>\n",
" <td>0.138800</td>\n",
" <td>0.012326</td>\n",
" <td>...</td>\n",
" <td>0.013398</td>\n",
" <td>0.190782</td>\n",
" <td>0.150054</td>\n",
" <td>0.003751</td>\n",
" <td>0.043944</td>\n",
" <td>0.008039</td>\n",
" <td>0.007503</td>\n",
" <td>0.024116</td>\n",
" <td>0.013934</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10032</th>\n",
" <td>0.052083</td>\n",
" <td>0.322917</td>\n",
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" <td>0.237500</td>\n",
" <td>0.018750</td>\n",
" <td>0.006250</td>\n",
" <td>0.093750</td>\n",
" <td>0.041667</td>\n",
" <td>0.010417</td>\n",
" <td>...</td>\n",
" <td>0.008333</td>\n",
" <td>0.129167</td>\n",
" <td>0.079167</td>\n",
" <td>0.002083</td>\n",
" <td>0.012500</td>\n",
" <td>0.002083</td>\n",
" <td>0.006250</td>\n",
" <td>0.041667</td>\n",
" <td>0.004167</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10033</th>\n",
" <td>0.128889</td>\n",
" <td>0.306667</td>\n",
" <td>0.017778</td>\n",
" <td>0.213333</td>\n",
" <td>0.284444</td>\n",
" <td>0.022222</td>\n",
" <td>0.013333</td>\n",
" <td>0.075556</td>\n",
" <td>0.022222</td>\n",
" <td>0.017778</td>\n",
" <td>...</td>\n",
" <td>0.026667</td>\n",
" <td>0.155556</td>\n",
" <td>0.053333</td>\n",
" <td>0.000000</td>\n",
" <td>0.031111</td>\n",
" <td>0.008889</td>\n",
" <td>0.008889</td>\n",
" <td>0.044444</td>\n",
" <td>0.000000</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10034</th>\n",
" <td>0.048276</td>\n",
" <td>0.439655</td>\n",
" <td>0.047414</td>\n",
" <td>0.289655</td>\n",
" <td>0.238793</td>\n",
" <td>0.046552</td>\n",
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" <td>0.076724</td>\n",
" <td>0.015517</td>\n",
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" <td>0.199138</td>\n",
" <td>0.129310</td>\n",
" <td>0.000862</td>\n",
" <td>0.052586</td>\n",
" <td>0.008621</td>\n",
" <td>0.012069</td>\n",
" <td>0.051724</td>\n",
" <td>0.022414</td>\n",
" <td>t</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10035</th>\n",
" <td>0.060134</td>\n",
" <td>0.443207</td>\n",
" <td>0.044543</td>\n",
" <td>0.293987</td>\n",
" <td>0.360802</td>\n",
" <td>0.020045</td>\n",
" <td>0.042316</td>\n",
" <td>0.122494</td>\n",
" <td>0.073497</td>\n",
" <td>0.026726</td>\n",
" <td>...</td>\n",
" <td>0.022272</td>\n",
" <td>0.200445</td>\n",
" <td>0.113586</td>\n",
" <td>0.006682</td>\n",
" <td>0.069042</td>\n",
" <td>0.002227</td>\n",
" <td>0.011136</td>\n",
" <td>0.046771</td>\n",
" <td>0.004454</td>\n",
" <td>t</td>\n",
" </tr>\n",
" </tbody>\n",
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"<p>10036 rows × 65 columns</p>\n",
"</div>"
],
"text/plain": [
" negate ppron nonfl i relativ percept quant \\\n",
"0 0.050562 0.252809 0.033708 0.188202 0.160112 0.025281 0.016854 \n",
"1 0.038462 0.378205 0.054487 0.272436 0.285256 0.054487 0.038462 \n",
"2 0.018786 0.552023 0.046243 0.341040 0.339595 0.052023 0.066474 \n",
"3 0.039113 0.413299 0.061278 0.320730 0.251630 0.053455 0.032595 \n",
"4 0.026490 0.394702 0.079470 0.270199 0.241060 0.046358 0.027815 \n",
"... ... ... ... ... ... ... ... \n",
"10031 0.027867 0.454984 0.065380 0.274920 0.310289 0.032154 0.029475 \n",
"10032 0.052083 0.322917 0.050000 0.229167 0.237500 0.018750 0.006250 \n",
"10033 0.128889 0.306667 0.017778 0.213333 0.284444 0.022222 0.013333 \n",
"10034 0.048276 0.439655 0.047414 0.289655 0.238793 0.046552 0.056034 \n",
"10035 0.060134 0.443207 0.044543 0.293987 0.360802 0.020045 0.042316 \n",
"\n",
" affect shehe achieve ... you cogmech auxverb \\\n",
"0 0.070225 0.030899 0.008427 ... 0.002809 0.115169 0.115169 \n",
"1 0.108974 0.057692 0.006410 ... 0.012821 0.227564 0.108974 \n",
"2 0.114162 0.135838 0.026012 ... 0.027457 0.225434 0.167630 \n",
"3 0.067797 0.053455 0.002608 ... 0.010430 0.170795 0.088657 \n",
"4 0.092715 0.070199 0.023841 ... 0.027815 0.143046 0.092715 \n",
"... ... ... ... ... ... ... ... \n",
"10031 0.088424 0.138800 0.012326 ... 0.013398 0.190782 0.150054 \n",
"10032 0.093750 0.041667 0.010417 ... 0.008333 0.129167 0.079167 \n",
"10033 0.075556 0.022222 0.017778 ... 0.026667 0.155556 0.053333 \n",
"10034 0.106034 0.076724 0.015517 ... 0.020690 0.199138 0.129310 \n",
"10035 0.122494 0.073497 0.026726 ... 0.022272 0.200445 0.113586 \n",
"\n",
" they incl money feel we hear y \n",
"0 0.002809 0.025281 0.005618 0.016854 0.028090 0.002809 f \n",
"1 0.000000 0.083333 0.003205 0.019231 0.035256 0.022436 f \n",
"2 0.020231 0.049133 0.010116 0.018786 0.027457 0.020231 f \n",
"3 0.001304 0.045632 0.005215 0.013038 0.027379 0.007823 f \n",
"4 0.000000 0.018543 0.006623 0.010596 0.026490 0.025166 f \n",
"... ... ... ... ... ... ... .. \n",
"10031 0.003751 0.043944 0.008039 0.007503 0.024116 0.013934 t \n",
"10032 0.002083 0.012500 0.002083 0.006250 0.041667 0.004167 t \n",
"10033 0.000000 0.031111 0.008889 0.008889 0.044444 0.000000 t \n",
"10034 0.000862 0.052586 0.008621 0.012069 0.051724 0.022414 t \n",
"10035 0.006682 0.069042 0.002227 0.011136 0.046771 0.004454 t \n",
"\n",
"[10036 rows x 65 columns]"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_balanced"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"t 2509\n",
"n 2509\n",
"f 2509\n",
"s 2509\n",
"Name: y, dtype: int64"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"balanced_y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"t 2509\n",
"n 2509\n",
"f 2509\n",
"s 2509\n",
"Name: y, dtype: int64"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_balanced.y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65\n"
]
}
],
"source": [
"print(len(data.columns))\n",
"y = data_balanced.iloc[:,[64]]\n",
"X = data_balanced.iloc[:,0:63]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2045)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"s 2033\n",
"t 2017\n",
"f 1994\n",
"n 1984\n",
"Name: y, dtype: int64"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"n 525\n",
"f 515\n",
"t 492\n",
"s 476\n",
"Name: y, dtype: int64"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test.y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
" importance_type='gain', interaction_constraints='',\n",
" learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
" min_child_weight=1, missing=nan, monotone_constraints='()',\n",
" n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
" objective='multi:softprob', random_state=0, reg_alpha=0,\n",
" reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
" tree_method='exact', validate_parameters=1, verbosity=None)"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = xgb.XGBClassifier()\n",
"model.fit(X_train.values, y_train.iloc[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
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" 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>negate</th>\n",
" <th>ppron</th>\n",
" <th>nonfl</th>\n",
" <th>i</th>\n",
" <th>relativ</th>\n",
" <th>percept</th>\n",
" <th>quant</th>\n",
" <th>affect</th>\n",
" <th>shehe</th>\n",
" <th>achieve</th>\n",
" <th>...</th>\n",
" <th>funct</th>\n",
" <th>sad</th>\n",
" <th>you</th>\n",
" <th>cogmech</th>\n",
" <th>auxverb</th>\n",
" <th>they</th>\n",
" <th>incl</th>\n",
" <th>money</th>\n",
" <th>feel</th>\n",
" <th>we</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7343</th>\n",
" <td>0.020151</td>\n",
" <td>0.458438</td>\n",
" <td>0.076826</td>\n",
" <td>0.332494</td>\n",
" <td>0.328715</td>\n",
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" <td>0.021411</td>\n",
" <td>0.152393</td>\n",
" <td>0.119647</td>\n",
" <td>0.010076</td>\n",
" <td>0.035264</td>\n",
" <td>0.013854</td>\n",
" <td>0.003778</td>\n",
" <td>0.050378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2127</th>\n",
" <td>0.034139</td>\n",
" <td>0.470128</td>\n",
" <td>0.034139</td>\n",
" <td>0.301565</td>\n",
" <td>0.281650</td>\n",
" <td>0.046230</td>\n",
" <td>0.044808</td>\n",
" <td>0.088193</td>\n",
" <td>0.076814</td>\n",
" <td>0.014936</td>\n",
" <td>...</td>\n",
" <td>1.494310</td>\n",
" <td>0.002845</td>\n",
" <td>0.013514</td>\n",
" <td>0.256046</td>\n",
" <td>0.148649</td>\n",
" <td>0.008535</td>\n",
" <td>0.047653</td>\n",
" <td>0.009957</td>\n",
" <td>0.014936</td>\n",
" <td>0.069701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2225</th>\n",
" <td>0.030955</td>\n",
" <td>0.484260</td>\n",
" <td>0.073977</td>\n",
" <td>0.351522</td>\n",
" <td>0.317419</td>\n",
" <td>0.049318</td>\n",
" <td>0.037775</td>\n",
" <td>0.107030</td>\n",
" <td>0.076600</td>\n",
" <td>0.031480</td>\n",
" <td>...</td>\n",
" <td>1.606506</td>\n",
" <td>0.003148</td>\n",
" <td>0.021511</td>\n",
" <td>0.178909</td>\n",
" <td>0.136411</td>\n",
" <td>0.006821</td>\n",
" <td>0.038300</td>\n",
" <td>0.013641</td>\n",
" <td>0.018363</td>\n",
" <td>0.027807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5473</th>\n",
" <td>0.067358</td>\n",
" <td>0.393782</td>\n",
" <td>0.058722</td>\n",
" <td>0.260794</td>\n",
" <td>0.303972</td>\n",
" <td>0.034542</td>\n",
" <td>0.058722</td>\n",
" <td>0.074266</td>\n",
" <td>0.072539</td>\n",
" <td>0.010363</td>\n",
" <td>...</td>\n",
" <td>1.298791</td>\n",
" <td>0.003454</td>\n",
" <td>0.005181</td>\n",
" <td>0.127807</td>\n",
" <td>0.100173</td>\n",
" <td>0.001727</td>\n",
" <td>0.032815</td>\n",
" <td>0.015544</td>\n",
" <td>0.013817</td>\n",
" <td>0.053541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1515</th>\n",
" <td>0.037975</td>\n",
" <td>0.379747</td>\n",
" <td>0.042194</td>\n",
" <td>0.299578</td>\n",
" <td>0.248945</td>\n",
" <td>0.059072</td>\n",
" <td>0.046414</td>\n",
" <td>0.033755</td>\n",
" <td>0.063291</td>\n",
" <td>0.016878</td>\n",
" <td>...</td>\n",
" <td>1.059072</td>\n",
" <td>0.000000</td>\n",
" <td>0.004219</td>\n",
" <td>0.168776</td>\n",
" <td>0.067511</td>\n",
" <td>0.008439</td>\n",
" <td>0.025316</td>\n",
" <td>0.021097</td>\n",
" <td>0.021097</td>\n",
" <td>0.004219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>0.053942</td>\n",
" <td>0.448133</td>\n",
" <td>0.049793</td>\n",
" <td>0.319502</td>\n",
" <td>0.242739</td>\n",
" <td>0.020747</td>\n",
" <td>0.024896</td>\n",
" <td>0.080913</td>\n",
" <td>0.068465</td>\n",
" <td>0.006224</td>\n",
" <td>...</td>\n",
" <td>1.307054</td>\n",
" <td>0.014523</td>\n",
" <td>0.020747</td>\n",
" <td>0.155602</td>\n",
" <td>0.105809</td>\n",
" <td>0.008299</td>\n",
" <td>0.037344</td>\n",
" <td>0.020747</td>\n",
" <td>0.006224</td>\n",
" <td>0.031120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3477</th>\n",
" <td>0.025516</td>\n",
" <td>0.520656</td>\n",
" <td>0.102066</td>\n",
" <td>0.375456</td>\n",
" <td>0.284933</td>\n",
" <td>0.060753</td>\n",
" <td>0.034629</td>\n",
" <td>0.116039</td>\n",
" <td>0.078372</td>\n",
" <td>0.027339</td>\n",
" <td>...</td>\n",
" <td>1.597813</td>\n",
" <td>0.007290</td>\n",
" <td>0.029162</td>\n",
" <td>0.157959</td>\n",
" <td>0.156744</td>\n",
" <td>0.002430</td>\n",
" <td>0.033414</td>\n",
" <td>0.019441</td>\n",
" <td>0.026124</td>\n",
" <td>0.035237</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3042</th>\n",
" <td>0.010163</td>\n",
" <td>0.443089</td>\n",
" <td>0.052846</td>\n",
" <td>0.239837</td>\n",
" <td>0.241870</td>\n",
" <td>0.034553</td>\n",
" <td>0.038618</td>\n",
" <td>0.083333</td>\n",
" <td>0.095528</td>\n",
" <td>0.010163</td>\n",
" <td>...</td>\n",
" <td>1.355691</td>\n",
" <td>0.008130</td>\n",
" <td>0.026423</td>\n",
" <td>0.172764</td>\n",
" <td>0.095528</td>\n",
" <td>0.012195</td>\n",
" <td>0.069106</td>\n",
" <td>0.000000</td>\n",
" <td>0.006098</td>\n",
" <td>0.069106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9734</th>\n",
" <td>0.045028</td>\n",
" <td>0.360225</td>\n",
" <td>0.061914</td>\n",
" <td>0.250469</td>\n",
" <td>0.275797</td>\n",
" <td>0.055347</td>\n",
" <td>0.034709</td>\n",
" <td>0.110694</td>\n",
" <td>0.059099</td>\n",
" <td>0.014071</td>\n",
" <td>...</td>\n",
" <td>1.252345</td>\n",
" <td>0.016886</td>\n",
" <td>0.009381</td>\n",
" <td>0.214822</td>\n",
" <td>0.119137</td>\n",
" <td>0.000938</td>\n",
" <td>0.045966</td>\n",
" <td>0.024390</td>\n",
" <td>0.037523</td>\n",
" <td>0.040338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9261</th>\n",
" <td>0.034934</td>\n",
" <td>0.425036</td>\n",
" <td>0.055313</td>\n",
" <td>0.216885</td>\n",
" <td>0.229985</td>\n",
" <td>0.039301</td>\n",
" <td>0.024745</td>\n",
" <td>0.090247</td>\n",
" <td>0.085881</td>\n",
" <td>0.011645</td>\n",
" <td>...</td>\n",
" <td>1.199418</td>\n",
" <td>0.001456</td>\n",
" <td>0.080058</td>\n",
" <td>0.177584</td>\n",
" <td>0.109170</td>\n",
" <td>0.001456</td>\n",
" <td>0.046579</td>\n",
" <td>0.008734</td>\n",
" <td>0.013100</td>\n",
" <td>0.040757</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2008 rows × 63 columns</p>\n",
"</div>"
],
"text/plain": [
" negate ppron nonfl i relativ percept quant \\\n",
"7343 0.020151 0.458438 0.076826 0.332494 0.328715 0.030227 0.036524 \n",
"2127 0.034139 0.470128 0.034139 0.301565 0.281650 0.046230 0.044808 \n",
"2225 0.030955 0.484260 0.073977 0.351522 0.317419 0.049318 0.037775 \n",
"5473 0.067358 0.393782 0.058722 0.260794 0.303972 0.034542 0.058722 \n",
"1515 0.037975 0.379747 0.042194 0.299578 0.248945 0.059072 0.046414 \n",
"... ... ... ... ... ... ... ... \n",
"550 0.053942 0.448133 0.049793 0.319502 0.242739 0.020747 0.024896 \n",
"3477 0.025516 0.520656 0.102066 0.375456 0.284933 0.060753 0.034629 \n",
"3042 0.010163 0.443089 0.052846 0.239837 0.241870 0.034553 0.038618 \n",
"9734 0.045028 0.360225 0.061914 0.250469 0.275797 0.055347 0.034709 \n",
"9261 0.034934 0.425036 0.055313 0.216885 0.229985 0.039301 0.024745 \n",
"\n",
" affect shehe achieve ... funct sad you \\\n",
"7343 0.095718 0.044081 0.028967 ... 1.358942 0.006297 0.021411 \n",
"2127 0.088193 0.076814 0.014936 ... 1.494310 0.002845 0.013514 \n",
"2225 0.107030 0.076600 0.031480 ... 1.606506 0.003148 0.021511 \n",
"5473 0.074266 0.072539 0.010363 ... 1.298791 0.003454 0.005181 \n",
"1515 0.033755 0.063291 0.016878 ... 1.059072 0.000000 0.004219 \n",
"... ... ... ... ... ... ... ... \n",
"550 0.080913 0.068465 0.006224 ... 1.307054 0.014523 0.020747 \n",
"3477 0.116039 0.078372 0.027339 ... 1.597813 0.007290 0.029162 \n",
"3042 0.083333 0.095528 0.010163 ... 1.355691 0.008130 0.026423 \n",
"9734 0.110694 0.059099 0.014071 ... 1.252345 0.016886 0.009381 \n",
"9261 0.090247 0.085881 0.011645 ... 1.199418 0.001456 0.080058 \n",
"\n",
" cogmech auxverb they incl money feel we \n",
"7343 0.152393 0.119647 0.010076 0.035264 0.013854 0.003778 0.050378 \n",
"2127 0.256046 0.148649 0.008535 0.047653 0.009957 0.014936 0.069701 \n",
"2225 0.178909 0.136411 0.006821 0.038300 0.013641 0.018363 0.027807 \n",
"5473 0.127807 0.100173 0.001727 0.032815 0.015544 0.013817 0.053541 \n",
"1515 0.168776 0.067511 0.008439 0.025316 0.021097 0.021097 0.004219 \n",
"... ... ... ... ... ... ... ... \n",
"550 0.155602 0.105809 0.008299 0.037344 0.020747 0.006224 0.031120 \n",
"3477 0.157959 0.156744 0.002430 0.033414 0.019441 0.026124 0.035237 \n",
"3042 0.172764 0.095528 0.012195 0.069106 0.000000 0.006098 0.069106 \n",
"9734 0.214822 0.119137 0.000938 0.045966 0.024390 0.037523 0.040338 \n",
"9261 0.177584 0.109170 0.001456 0.046579 0.008734 0.013100 0.040757 \n",
"\n",
"[2008 rows x 63 columns]"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(X_test.values)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.24651394422310757"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy = accuracy_score(y_test, y_pred)\n",
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 12))\n",
"plot_confusion_matrix(model, X_test.values, y_test, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" f 0.27 0.27 0.27 515\n",
" n 0.26 0.25 0.25 525\n",
" s 0.24 0.26 0.25 476\n",
" t 0.21 0.21 0.21 492\n",
"\n",
" accuracy 0.25 2008\n",
" macro avg 0.25 0.25 0.25 2008\n",
"weighted avg 0.25 0.25 0.25 2008\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# With reduced dimensionality"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [],
"source": [
"reducer = umap.UMAP()\n",
"mapper = umap.UMAP().fit(X) # for plotting\n",
"embedding = reducer.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10036, 2)"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"embedding.shape"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['f', 'f', 'f', ..., 't', 't', 't'], dtype=object)"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.values.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"umap.plot.points(mapper, labels=y.values.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(embedding, y, test_size=0.2, random_state=2045)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10036, 2)"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"embedding.shape"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10036, 1)"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.values.shape"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/envs/mindalyzer/lib/python3.6/site-packages/sklearn/utils/validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" return f(**kwargs)\n"
]
},
{
"data": {
"text/plain": [
"array(['n', 't', 'f', ..., 's', 's', 's'], dtype=object)"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = xgb.XGBClassifier()\n",
"model.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"y_pred"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8028, 2)"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2008, 2)"
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 12))\n",
"plot_confusion_matrix(model, X_test, y_test, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" f 0.26 0.23 0.24 515\n",
" n 0.23 0.23 0.23 525\n",
" s 0.21 0.24 0.23 476\n",
" t 0.24 0.24 0.24 492\n",
"\n",
" accuracy 0.23 2008\n",
" macro avg 0.23 0.23 0.23 2008\n",
"weighted avg 0.24 0.23 0.23 2008\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test, y_pred))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (mindalyzer)",
"language": "python",
"name": "mindalyzer"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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