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alessiot/Using Shapley Method with Text Classification.ipynb

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Using Shapley Method with Text Classification
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Using Shapley Method with Text Classification.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# https://docs.anaconda.com/anaconda/packages/r-language-pkg-docs/\n",
"# conda install rpy2\n",
"# conda install -c r r-essentials\n",
"# conda install -c r r-base\n",
"# conda search -f r-EXACTNAME to search for existing packages and conda install EXACTNAME\n",
"# If any issue happens, remove /anaconda3/lib/R and install everything again\n",
"\n",
"import rpy2.ipython\n",
"\n",
"from rpy2.robjects import pandas2ri \n",
"\n",
"pandas2ri.activate()\n",
"\n",
"%reload_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import model_selection, preprocessing, metrics, svm\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn import decomposition, ensemble\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import string\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"import xgboost\n",
"#from keras.preprocessing import text, sequence\n",
"#from keras import layers, models, optimizers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## YouTube Spam Comments Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"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>COMMENT_ID</th>\n",
" <th>AUTHOR</th>\n",
" <th>DATE</th>\n",
" <th>CONTENT</th>\n",
" <th>CLASS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU</td>\n",
" <td>Julius NM</td>\n",
" <td>2013-11-07T06:20:48</td>\n",
" <td>Huh, anyway check out this you[tube] channel: ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A</td>\n",
" <td>adam riyati</td>\n",
" <td>2013-11-07T12:37:15</td>\n",
" <td>Hey guys check out my new channel and our firs...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8</td>\n",
" <td>Evgeny Murashkin</td>\n",
" <td>2013-11-08T17:34:21</td>\n",
" <td>just for test I have to say murdev.com</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>z13jhp0bxqncu512g22wvzkasxmvvzjaz04</td>\n",
" <td>ElNino Melendez</td>\n",
" <td>2013-11-09T08:28:43</td>\n",
" <td>me shaking my sexy ass on my channel enjoy ^_^</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>z13fwbwp1oujthgqj04chlngpvzmtt3r3dw</td>\n",
" <td>GsMega</td>\n",
" <td>2013-11-10T16:05:38</td>\n",
" <td>watch?v=vtaRGgvGtWQ Check this out .</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" COMMENT_ID AUTHOR \\\n",
"0 LZQPQhLyRh80UYxNuaDWhIGQYNQ96IuCg-AYWqNPjpU Julius NM \n",
"1 LZQPQhLyRh_C2cTtd9MvFRJedxydaVW-2sNg5Diuo4A adam riyati \n",
"2 LZQPQhLyRh9MSZYnf8djyk0gEF9BHDPYrrK-qCczIY8 Evgeny Murashkin \n",
"3 z13jhp0bxqncu512g22wvzkasxmvvzjaz04 ElNino Melendez \n",
"4 z13fwbwp1oujthgqj04chlngpvzmtt3r3dw GsMega \n",
"\n",
" DATE CONTENT \\\n",
"0 2013-11-07T06:20:48 Huh, anyway check out this you[tube] channel: ... \n",
"1 2013-11-07T12:37:15 Hey guys check out my new channel and our firs... \n",
"2 2013-11-08T17:34:21 just for test I have to say murdev.com \n",
"3 2013-11-09T08:28:43 me shaking my sexy ass on my channel enjoy ^_^ \n",
"4 2013-11-10T16:05:38 watch?v=vtaRGgvGtWQ Check this out . \n",
"\n",
" CLASS \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Snippet to download the data from\n",
"# https://github.com/christophM/interpretable-ml-book/blob/master/R/get-SpamTube-dataset.R\n",
"# or https://gist.github.com/alessiot/57c9858e1e19b5e01869f67ad6d2a818\n",
"\n",
"# Data from\n",
"# http://www.dt.fee.unicamp.br/~tiago//youtubespamcollection/\n",
"\n",
"youtube_data = pd.read_csv(\"youtube_data/TubeSpam.csv\")\n",
"youtube_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"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>text</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Huh, anyway check out this you[tube] channel: ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Hey guys check out my new channel and our firs...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>just for test I have to say murdev.com</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>me shaking my sexy ass on my channel enjoy ^_^</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>watch?v=vtaRGgvGtWQ Check this out .</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text label\n",
"0 Huh, anyway check out this you[tube] channel: ... 1\n",
"1 Hey guys check out my new channel and our firs... 1\n",
"2 just for test I have to say murdev.com 1\n",
"3 me shaking my sexy ass on my channel enjoy ^_^ 1\n",
"4 watch?v=vtaRGgvGtWQ Check this out . 1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a dataframe using text and label\n",
"youtube_df = pd.DataFrame()\n",
"youtube_df['text'] = youtube_data['CONTENT']\n",
"youtube_df['label'] = youtube_data['CLASS']\n",
"youtube_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1956, 2)\n",
"(1954, 2)\n"
]
}
],
"source": [
"print(youtube_df.shape)\n",
"youtube_df.dropna(inplace=True)\n",
"print(youtube_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 1954 entries, 0 to 1955\n",
"Data columns (total 2 columns):\n",
"text 1954 non-null object\n",
"label 1954 non-null int64\n",
"dtypes: int64(1), object(1)\n",
"memory usage: 45.8+ KB\n"
]
}
],
"source": [
"youtube_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">text</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>unique</th>\n",
" <th>top</th>\n",
" <th>freq</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>949</td>\n",
" <td>903</td>\n",
" <td>Like</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1005</td>\n",
" <td>832</td>\n",
" <td>Check out this video on YouTube:</td>\n",
" <td>101</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text \n",
" count unique top freq\n",
"label \n",
"0 949 903 Like 4\n",
"1 1005 832 Check out this video on YouTube: 101"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df.groupby('label').describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">text</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>unique</th>\n",
" <th>top</th>\n",
" <th>freq</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>903</td>\n",
" <td>903</td>\n",
" <td>LOL this shit never gets old</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>832</td>\n",
" <td>832</td>\n",
" <td>You guys should check out this EXTRAORDINARY w...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text \n",
" count unique top freq\n",
"label \n",
"0 903 903 LOL this shit never gets old 1\n",
"1 832 832 You guys should check out this EXTRAORDINARY w... 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df.drop_duplicates(inplace=True)\n",
"youtube_df.groupby('label').describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"\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>text</th>\n",
" <th>label</th>\n",
" <th>length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Huh, anyway check out this you[tube] channel: ...</td>\n",
" <td>1</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Hey guys check out my new channel and our firs...</td>\n",
" <td>1</td>\n",
" <td>166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>just for test I have to say murdev.com</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>me shaking my sexy ass on my channel enjoy ^_^</td>\n",
" <td>1</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>watch?v=vtaRGgvGtWQ Check this out .</td>\n",
" <td>1</td>\n",
" <td>38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text label length\n",
"0 Huh, anyway check out this you[tube] channel: ... 1 56\n",
"1 Hey guys check out my new channel and our firs... 1 166\n",
"2 just for test I have to say murdev.com 1 38\n",
"3 me shaking my sexy ass on my channel enjoy ^_^ 1 47\n",
"4 watch?v=vtaRGgvGtWQ Check this out . 1 38"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df['length'] = youtube_df['text'].apply(len)\n",
"youtube_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a271d5240>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"youtube_df['length'].plot(bins=50,kind = 'hist')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 1735.000000\n",
"mean 93.136023\n",
"std 123.994623\n",
"min 2.000000\n",
"25% 27.000000\n",
"50% 50.000000\n",
"75% 100.000000\n",
"max 1199.000000\n",
"Name: length, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df['length'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 56, 166, 38, 47, 82, 24, 77, 46, 66, 19, 178,\n",
" 35, 22, 71, 316, 27, 93, 43, 314, 92, 33, 69,\n",
" 23, 20, 68, 228, 74, 81, 32, 110, 126, 61, 182,\n",
" 336, 160, 60, 215, 73, 124, 29, 94, 115, 156, 54,\n",
" 64, 13, 59, 42, 40, 10, 109, 214, 37, 18, 89,\n",
" 15, 95, 114, 41, 242, 144, 555, 16, 55, 85, 26,\n",
" 52, 25, 252, 63, 58, 105, 118, 49, 44, 122, 238,\n",
" 141, 12, 17, 80, 388, 108, 148, 4, 21, 34, 84,\n",
" 90, 48, 75, 62, 78, 36, 7, 39, 11, 72, 103,\n",
" 162, 30, 57, 101, 96, 270, 220, 335, 50, 196, 28,\n",
" 14, 100, 251, 65, 91, 76, 120, 51, 185, 233, 361,\n",
" 135, 83, 226, 53, 131, 752, 3, 111, 147, 99, 119,\n",
" 102, 151, 169, 168, 410, 45, 1077, 31, 181, 133, 107,\n",
" 865, 146, 130, 180, 167, 127, 149, 125, 158, 339, 79,\n",
" 202, 234, 201, 306, 264, 384, 274, 98, 1199, 417, 206,\n",
" 129, 286, 88, 204, 295, 212, 207, 112, 356, 200, 170,\n",
" 86, 372, 189, 67, 183, 157, 9, 590, 193, 279, 104,\n",
" 281, 1088, 213, 292, 222, 143, 184, 211, 517, 301, 70,\n",
" 519, 352, 152, 117, 163, 128, 113, 145, 241, 322, 325,\n",
" 346, 261, 175, 574, 421, 139, 154, 5, 437, 194, 355,\n",
" 6, 8, 580, 2, 377, 198, 375, 297, 97, 387, 331,\n",
" 250, 136, 87, 137, 308, 704, 277, 153, 116, 218, 121,\n",
" 199, 237, 235, 244, 227, 460, 332, 293, 466, 501, 248,\n",
" 498, 446, 453, 462, 438, 447, 451, 171, 393, 385, 359,\n",
" 368, 383, 380, 340, 503, 494, 369, 373, 527, 416, 187,\n",
" 351, 357, 360, 378, 515, 347, 476, 520, 489, 1012, 492,\n",
" 257, 239, 512, 216, 140, 504, 456, 507, 192, 480, 254,\n",
" 497, 179, 330, 282, 191, 229, 329, 500, 491, 195, 955,\n",
" 230, 636, 134, 106, 496, 484, 350, 479, 444, 471, 364,\n",
" 477, 174, 358, 349, 542, 344, 506, 483, 435, 190, 258,\n",
" 430, 505, 370, 508, 522, 523])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df['length'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"\"im sorry for the spam but My name is Jenny. I go to high school where everyone dresses fashionable but for me I don't because i need money to buy cute clothes. I have low self esteem . I live with my dad. my mom passed away when i was 6 so i don't really have a mother figure. I have 2 brothers who is older than me. Since they are boys they get the attention while i just be alone. I really want to wear pretty clothes like the girls in my school and get a boyfriend. i just can't be my self. im very quite and shy at school because i don't have the confidence in myself to talk to someone. i did have one friend name Caroline but she moved away so now im alone. if you could donate some money to me it would be great. i don't care about expensive brand ill just shop at walmart because they have pretty clothes. also i wanna get my nails done at a salon . i see alot of girls have these french tips. i never had my nail did at a salon before i will really appreciate if i can and get my hair curled too. http://www.gofundme.com/dressprettyonce thanks omg please.\""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df[youtube_df['length'] == 1077]['text'].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a284e7e48>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x1a28612358>],\n",
" dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"youtube_df.hist(column='length',by ='label',bins=50,figsize = (10,4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comment Data Pre-Processing"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords # nltk.download('stopwords')\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.stem import WordNetLemmatizer #nltk.download('wordnet')\n",
"\n",
"import string, re\n",
"\n",
"stop_words = list(set(stopwords.words('english')))\n",
"stemmer= PorterStemmer()\n",
"lemmatizer=WordNetLemmatizer()\n",
"\n",
"translate_table = dict((ord(char), None) for char in string.punctuation) \n",
"\n",
"def nltk_text_preproc(text_in):\n",
" text_out = re.sub(r'\\d+', '', text_in) # rm numbers\n",
" text_out = text_out.translate(translate_table) # rm punct\n",
" text_out = text_out.strip() # rm white spaces\n",
"\n",
" return text_out\n",
"\n",
"def nltk_token_processing(tokens):\n",
" tokens = [i.lower() for i in tokens]\n",
" tokens = [i for i in tokens if not i in stop_words]\n",
" tokens = [stemmer.stem(i) for i in tokens]\n",
" tokens = [lemmatizer.lemmatize(i) for i in tokens]\n",
" \n",
" return tokens"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Huh, anyway check out this you[tube] channel: kobyoshi02',\n",
" \"Hey guys check out my new channel and our first vid THIS IS US THE MONKEYS!!! I'm the monkey in the white shirt,please leave a like comment and please subscribe!!!!\",\n",
" 'just for test I have to say murdev.com',\n",
" 'me shaking my sexy ass on my channel enjoy ^_^ ',\n",
" 'watch?v=vtaRGgvGtWQ Check this out .',\n",
" 'Hey, check out my new website!! This site is about kids stuff. kidsmediausa . com',\n",
" 'Subscribe to my channel ',\n",
" 'i turned it on mute as soon is i came on i just wanted to check the views...',\n",
" 'You should check my channel for Funny VIDEOS!!',\n",
" 'and u should.d check my channel and tell me what I should do next!']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_colwidth = 1000\n",
"youtube_df['text'].head(10).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['huh anyway check youtub channel kobyoshi',\n",
" 'hey guy check new channel first vid u monkey im monkey white shirtpleas leav like comment plea subscrib',\n",
" 'test say murdevcom',\n",
" 'shake sexi as channel enjoy',\n",
" 'watchvvtarggvgtwq check',\n",
" 'hey check new websit site kid stuff kidsmediausa com',\n",
" 'subscrib channel',\n",
" 'turn mute soon came want check view',\n",
" 'check channel funni video',\n",
" 'u shouldd check channel tell next']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df['text'].head(10).map(lambda x: nltk_text_preproc(x)).map(lambda x: nltk_token_processing(word_tokenize(''.join(x)))).map(lambda x: ' '.join(x)).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"youtube_df['text'] = youtube_df['text'].map(lambda x: nltk_text_preproc(x))\n",
"youtube_df['text'] = youtube_df['text'].map(lambda x: nltk_token_processing(word_tokenize(''.join(x))))\n",
"youtube_df['text'] = youtube_df['text'].map(lambda x: ' '.join(x))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1735, 3)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"youtube_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Extraction from Text"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=0.01,\n",
" ngram_range=(1, 3), preprocessor=None, stop_words=None,\n",
" strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=None, vocabulary=None)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a count vectorizer object \n",
"count_vect = CountVectorizer(min_df=0.01, max_df=1.0, ngram_range=(1,3)) #notice float and integer for doc proportion and number\n",
"count_vect.fit(youtube_df['text'])\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"youtube_df_text = count_vect.transform(youtube_df['text'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hey guy check new channel first vid u monkey im monkey white shirtpleas leav like comment plea subscrib\n"
]
}
],
"source": [
"text_example_orig = youtube_df['text'][1]\n",
"print(text_example_orig)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 12)\t1\n",
" (0, 13)\t1\n",
" (0, 18)\t1\n",
" (0, 22)\t1\n",
" (0, 39)\t1\n",
" (0, 52)\t1\n",
" (0, 53)\t1\n",
" (0, 58)\t1\n",
" (0, 59)\t1\n",
" (0, 66)\t1\n",
" (0, 71)\t1\n",
" (0, 72)\t1\n",
" (0, 100)\t1\n",
" (0, 113)\t1\n",
" (0, 115)\t1\n",
" (0, 136)\t1\n"
]
}
],
"source": [
"text_example_transf = youtube_df_text[1]\n",
"print(text_example_transf)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"channel\n",
"check\n",
"check new\n",
"comment\n",
"first\n",
"guy\n",
"guy check\n",
"hey\n",
"hey guy\n",
"im\n",
"like\n",
"like comment\n",
"new\n",
"plea\n",
"plea subscrib\n",
"subscrib\n"
]
}
],
"source": [
"#[count_vect.get_feature_names()[j] for i,j in text_example.items()]\n",
"for row, col in zip(*text_example_transf.nonzero()):\n",
" val = text_example_transf[row, col]\n",
" #print((row, col), val)\n",
" print(count_vect.get_feature_names()[col])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"also\n"
]
}
],
"source": [
"print (count_vect.get_feature_names()[0])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of Sparse Matrix: (1735, 166)\n",
"Amount of Non-Zero occurences: 7774\n",
"sparsity: 2.70%\n"
]
}
],
"source": [
"print ('Shape of Sparse Matrix: ', youtube_df_text.shape)\n",
"print ('Amount of Non-Zero occurences: ', youtube_df_text.nnz)\n",
"print ('sparsity: %.2f%%' % (100.0 * youtube_df_text.nnz /\n",
" (youtube_df_text.shape[0] * youtube_df_text.shape[1])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modeling\n",
"\n",
"https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# split the dataset into training and validation datasets \n",
"train_x, valid_x, train_y, valid_y, index_train, index_val = model_selection.train_test_split(youtube_df_text, youtube_df['label'], range(len(youtube_df['label'])), stratify=youtube_df['label'], random_state=1, train_size=0.8)\n",
"\n",
"# label encode the target variable \n",
"#encoder = preprocessing.LabelEncoder()\n",
"#train_y = encoder.fit_transform(train_y)\n",
"#valid_y = encoder.fit_transform(valid_y)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0, 1]), array([722, 666]))"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.unique(train_y, return_counts=True)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def train_model(classifier, feature_vector_train, label, feature_vector_valid, valid_label):\n",
" # fit the training dataset on the classifier\n",
" classifier.fit(feature_vector_train, label)\n",
" \n",
" # predict the labels on validation dataset\n",
" predictions = classifier.predict(feature_vector_valid)\n",
" \n",
" return classifier, metrics.accuracy_score(predictions, valid_label), metrics.classification_report(predictions, valid_label)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Xgb, Accuracy: 0.8789625360230547\n",
" precision recall f1-score support\n",
"\n",
" 0 0.98 0.82 0.89 215\n",
" 1 0.77 0.97 0.86 132\n",
"\n",
" accuracy 0.88 347\n",
" macro avg 0.87 0.90 0.88 347\n",
"weighted avg 0.90 0.88 0.88 347\n",
"\n"
]
}
],
"source": [
"# Extereme Gradient Boosting on Count Vectors\n",
"classifier, accuracy, confusion_matrix = train_model(xgboost.XGBClassifier(), train_x, train_y, valid_x, valid_y)\n",
"print(\"Xgb, Accuracy: \", accuracy)\n",
"print(confusion_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing Shapley Values with SHAP"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time to compute Shapley values (s): 0.111083984375\n"
]
}
],
"source": [
"import shap, time\n",
"\n",
"# Create the shap explainers\n",
"t0 = time.time()\n",
"explainer = shap.TreeExplainer(classifier)\n",
"shap_values_train = explainer.shap_values(youtube_df_text)\n",
"t1 = time.time()\n",
"timeit=t1-t0\n",
"print('time to compute Shapley values (s):', timeit)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# From sparse matrix to data frame\n",
"txt_dense_df = pd.DataFrame(youtube_df_text.todense(), columns=count_vect.get_feature_names())\n",
"shap_values_train_df = pd.DataFrame(shap_values_train, columns=txt_dense_df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 0, ..., 0, 0, 0])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = classifier.predict(youtube_df_text.tocsc())\n",
"predictions"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>would</th>\n",
" <th>wow</th>\n",
" <th>year</th>\n",
" <th>year old</th>\n",
" <th>youtub</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00187</td>\n",
" <td>0.0</td>\n",
" <td>-0.009025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.00187</td>\n",
" <td>0.0</td>\n",
" <td>0.001261</td>\n",
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" <td>0.0</td>\n",
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" <td>0.00187</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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"<p>5 rows × 166 columns</p>\n",
"</div>"
],
"text/plain": [
" also amp appreci awesom back beauti best best song billion \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.024080 0.0 0.055482 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.027162 0.0 0.062180 \n",
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"4 0.0 0.0 0.0 0.0 0.0 0.0 0.024080 0.0 0.055482 \n",
"\n",
" billion view ... websit call work work home work home today \\\n",
"0 0.0 ... 0.0 -0.004247 0.0 0.0 \n",
"1 0.0 ... 0.0 -0.002201 0.0 0.0 \n",
"2 0.0 ... 0.0 -0.004247 0.0 0.0 \n",
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"4 0.0 0.0 0.0 0.00187 0.0 0.001931 \n",
"\n",
"[5 rows x 166 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_values_train_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"shap_sum = np.abs(shap_values_train_df).mean(axis=0)\n",
"importance_df = pd.DataFrame([txt_dense_df.columns.tolist(), shap_sum.tolist()]).T\n",
"importance_df.columns = ['column_name', 'shap_importance (log-odds)']\n",
"importance_df = importance_df.sort_values('shap_importance (log-odds)', ascending=False)\n",
"importance_df['shap_importance (%)'] = importance_df['shap_importance (log-odds)'].apply(lambda x: 100*x/np.sum(importance_df['shap_importance (log-odds)']))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cumulative Importance 93.84924549264606\n"
]
},
{
"data": {
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" <tbody>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>check</td>\n",
" <td>0.833133</td>\n",
" <td>24.073396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>subscrib</td>\n",
" <td>0.695432</td>\n",
" <td>20.094540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>plea</td>\n",
" <td>0.271599</td>\n",
" <td>7.847871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>channel</td>\n",
" <td>0.261591</td>\n",
" <td>7.558681</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>song</td>\n",
" <td>0.164821</td>\n",
" <td>4.762496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>share</td>\n",
" <td>0.120465</td>\n",
" <td>3.480838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>view</td>\n",
" <td>0.11852</td>\n",
" <td>3.424650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>billion</td>\n",
" <td>0.104261</td>\n",
" <td>3.012629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>love</td>\n",
" <td>0.091123</td>\n",
" <td>2.633003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>new</td>\n",
" <td>0.0907167</td>\n",
" <td>2.621263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>money</td>\n",
" <td>0.0832313</td>\n",
" <td>2.404972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>help</td>\n",
" <td>0.066325</td>\n",
" <td>1.916462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>free</td>\n",
" <td>0.0612497</td>\n",
" <td>1.769812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>like comment</td>\n",
" <td>0.0592422</td>\n",
" <td>1.711805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>follow</td>\n",
" <td>0.0582572</td>\n",
" <td>1.683343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>hey</td>\n",
" <td>0.0506112</td>\n",
" <td>1.462412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>best</td>\n",
" <td>0.0416103</td>\n",
" <td>1.202330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>visit</td>\n",
" <td>0.0337998</td>\n",
" <td>0.976646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>onlin</td>\n",
" <td>0.021595</td>\n",
" <td>0.623989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>good</td>\n",
" <td>0.0203532</td>\n",
" <td>0.588105</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" column_name shap_importance (log-odds) shap_importance (%)\n",
"13 check 0.833133 24.073396\n",
"136 subscrib 0.695432 20.094540\n",
"113 plea 0.271599 7.847871\n",
"12 channel 0.261591 7.558681\n",
"129 song 0.164821 4.762496\n",
"126 share 0.120465 3.480838\n",
"150 view 0.11852 3.424650\n",
"8 billion 0.104261 3.012629\n",
"78 love 0.091123 2.633003\n",
"100 new 0.0907167 2.621263\n",
"91 money 0.0832313 2.404972\n",
"57 help 0.066325 1.916462\n",
"41 free 0.0612497 1.769812\n",
"72 like comment 0.0592422 1.711805\n",
"40 follow 0.0582572 1.683343\n",
"58 hey 0.0506112 1.462412\n",
"6 best 0.0416103 1.202330\n",
"151 visit 0.0337998 0.976646\n",
"104 onlin 0.021595 0.623989\n",
"49 good 0.0203532 0.588105"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"topN = 20\n",
"top20 = importance_df.iloc[0:topN][\"column_name\"]\n",
"\n",
"print('Cumulative Importance', np.sum(importance_df.iloc[0:topN][\"shap_importance (%)\"]))\n",
"\n",
"importance_df.iloc[0:topN]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x684 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Variable importance plot\n",
"shap.summary_plot(shap_values_train_df, txt_dense_df, plot_type=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"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>check</th>\n",
" <th>subscrib</th>\n",
" <th>plea</th>\n",
" <th>channel</th>\n",
" <th>song</th>\n",
" <th>share</th>\n",
" <th>view</th>\n",
" <th>billion</th>\n",
" <th>love</th>\n",
" <th>new</th>\n",
" <th>money</th>\n",
" <th>help</th>\n",
" <th>free</th>\n",
" <th>like comment</th>\n",
" <th>follow</th>\n",
" <th>hey</th>\n",
" <th>best</th>\n",
" <th>visit</th>\n",
" <th>onlin</th>\n",
" <th>good</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.824345</td>\n",
" <td>-0.198576</td>\n",
" <td>-0.084255</td>\n",
" <td>1.965496</td>\n",
" <td>0.061483</td>\n",
" <td>-0.044300</td>\n",
" <td>0.196018</td>\n",
" <td>0.055482</td>\n",
" <td>0.053910</td>\n",
" <td>-0.032171</td>\n",
" <td>-0.015307</td>\n",
" <td>-0.020225</td>\n",
" <td>-0.029916</td>\n",
" <td>-0.024696</td>\n",
" <td>-0.028492</td>\n",
" <td>-0.022651</td>\n",
" <td>0.024080</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.006785</td>\n",
" <td>0.010336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.525985</td>\n",
" <td>1.496080</td>\n",
" <td>0.809206</td>\n",
" <td>0.828458</td>\n",
" <td>0.067073</td>\n",
" <td>-0.033727</td>\n",
" <td>0.141687</td>\n",
" <td>0.062180</td>\n",
" <td>0.048936</td>\n",
" <td>0.886804</td>\n",
" <td>-0.014578</td>\n",
" <td>-0.019019</td>\n",
" <td>-0.014258</td>\n",
" <td>1.048241</td>\n",
" <td>-0.016627</td>\n",
" <td>0.455953</td>\n",
" <td>0.027162</td>\n",
" <td>-0.009353</td>\n",
" <td>-0.005190</td>\n",
" <td>0.010336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.487089</td>\n",
" <td>-0.377440</td>\n",
" <td>-0.132151</td>\n",
" <td>-0.097204</td>\n",
" <td>0.100908</td>\n",
" <td>-0.069717</td>\n",
" <td>0.045656</td>\n",
" <td>0.052477</td>\n",
" <td>0.053910</td>\n",
" <td>-0.036958</td>\n",
" <td>-0.044189</td>\n",
" <td>-0.029860</td>\n",
" <td>-0.034293</td>\n",
" <td>-0.030869</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.022072</td>\n",
" <td>0.022725</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.008364</td>\n",
" <td>0.010336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.461900</td>\n",
" <td>-0.355742</td>\n",
" <td>-0.121386</td>\n",
" <td>2.133291</td>\n",
" <td>0.093927</td>\n",
" <td>-0.062687</td>\n",
" <td>0.045333</td>\n",
" <td>0.052477</td>\n",
" <td>0.053910</td>\n",
" <td>-0.032171</td>\n",
" <td>-0.029274</td>\n",
" <td>-0.027708</td>\n",
" <td>-0.032070</td>\n",
" <td>-0.024696</td>\n",
" <td>-0.028492</td>\n",
" <td>-0.022072</td>\n",
" <td>0.022725</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.007092</td>\n",
" <td>0.010336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.976567</td>\n",
" <td>-0.208125</td>\n",
" <td>-0.094932</td>\n",
" <td>-0.090171</td>\n",
" <td>0.068464</td>\n",
" <td>-0.051330</td>\n",
" <td>0.201998</td>\n",
" <td>0.055482</td>\n",
" <td>0.053910</td>\n",
" <td>-0.036958</td>\n",
" <td>-0.038328</td>\n",
" <td>-0.022377</td>\n",
" <td>-0.032139</td>\n",
" <td>-0.030869</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.022651</td>\n",
" <td>0.024080</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.008057</td>\n",
" <td>0.010336</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" check subscrib plea channel song share view \\\n",
"0 2.824345 -0.198576 -0.084255 1.965496 0.061483 -0.044300 0.196018 \n",
"1 1.525985 1.496080 0.809206 0.828458 0.067073 -0.033727 0.141687 \n",
"2 -0.487089 -0.377440 -0.132151 -0.097204 0.100908 -0.069717 0.045656 \n",
"3 -0.461900 -0.355742 -0.121386 2.133291 0.093927 -0.062687 0.045333 \n",
"4 2.976567 -0.208125 -0.094932 -0.090171 0.068464 -0.051330 0.201998 \n",
"\n",
" billion love new money help free like comment \\\n",
"0 0.055482 0.053910 -0.032171 -0.015307 -0.020225 -0.029916 -0.024696 \n",
"1 0.062180 0.048936 0.886804 -0.014578 -0.019019 -0.014258 1.048241 \n",
"2 0.052477 0.053910 -0.036958 -0.044189 -0.029860 -0.034293 -0.030869 \n",
"3 0.052477 0.053910 -0.032171 -0.029274 -0.027708 -0.032070 -0.024696 \n",
"4 0.055482 0.053910 -0.036958 -0.038328 -0.022377 -0.032139 -0.030869 \n",
"\n",
" follow hey best visit onlin good \n",
"0 -0.028492 -0.022651 0.024080 -0.012927 -0.006785 0.010336 \n",
"1 -0.016627 0.455953 0.027162 -0.009353 -0.005190 0.010336 \n",
"2 -0.031113 -0.022072 0.022725 -0.012927 -0.008364 0.010336 \n",
"3 -0.028492 -0.022072 0.022725 -0.012927 -0.007092 0.010336 \n",
"4 -0.031113 -0.022651 0.024080 -0.012927 -0.008057 0.010336 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_values_imp = shap_values_train_df[top20]\n",
"shap_values_imp.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"text best funni viral video histori youtub true\n",
"label 0\n",
"length 69\n",
"Name: 180, dtype: object"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"j = 179\n",
"youtube_df.iloc[j]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" (0, 6)\t1\n",
" (0, 44)\t1\n",
" (0, 148)\t1\n",
" (0, 165)\t1\n"
]
},
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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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" Have you run `initjs()` in this notebook? If this notebook was from another\n",
" user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
" this notebook on github the Javascript has been stripped for security. If you are using\n",
" JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
"</div></div>\n",
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"source": [
"j = 179\n",
"\n",
"#print(shap_values_imp.iloc[j])\n",
"print(youtube_df_text[j])\n",
"\n",
"shap.initjs()\n",
"\n",
"# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)\n",
"shap.force_plot(explainer.expected_value, shap_values_imp.iloc[j].to_numpy(), \n",
" txt_dense_df.iloc[j][top20])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id='iWTYHR1LFN8I59VGGTLJP'>\n",
"<div style='color: #900; text-align: center;'>\n",
" <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
" Have you run `initjs()` in this notebook? If this notebook was from another\n",
" user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
" this notebook on github the Javascript has been stripped for security. If you are using\n",
" JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
"</div></div>\n",
" <script>\n",
" if (window.SHAP) SHAP.ReactDom.render(\n",
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" document.getElementById('iWTYHR1LFN8I59VGGTLJP')\n",
" );\n",
"</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# visualize the training set predictions\n",
"shap.force_plot(explainer.expected_value, shap_values_imp.to_numpy()[0:100,], txt_dense_df.iloc[0:100][top20])"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x684 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Shap values for each data instance\n",
"# Feature value: value of feature (from low to high) for each data instance\n",
"# SHAP value: impact negative or positive) on the output. \n",
"# Indicates how much is the change in log-odds. From this number we can extract the probability of success. \n",
"# offer_nm shows a blob at zero, i.e. no impact for most of the instances, then depending on the offer_nm type\n",
"# shows either a positive or negative impact on the output (predict 0 or 1)\n",
"shap.summary_plot(shap_values_train, txt_dense_df)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13 check\n",
"136 subscrib\n",
"113 plea\n",
"12 channel\n",
"129 song\n",
"126 share\n",
"150 view\n",
"8 billion\n",
"78 love\n",
"100 new\n",
"91 money\n",
"57 help\n",
"41 free\n",
"72 like comment\n",
"40 follow\n",
"58 hey\n",
"6 best\n",
"151 visit\n",
"104 onlin\n",
"49 good\n",
"Name: column_name, dtype: object"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"importance_df.iloc[0:topN]['column_name']"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sum of Shaphley values (log-odds)for j-instance: 6.5931883 prob: 0.9986322072613398\n",
"Average model output: 0.4795389048991354\n",
"Predicted probability value for j-instance: 0.9985234 Predicted value: 1\n",
"log_odds: 6.5931883 is expected to be equal to pred-expected: 6.598435139541432\n",
"pred-expected (prob): 0.5189845092358438\n"
]
}
],
"source": [
"j = 1\n",
"\n",
"# log odds margin to prob\n",
"# odds = exp(log odds), p = odds/(1+odds)\n",
"\n",
"log_odds = np.sum(shap_values_train_df.iloc[j].to_numpy())\n",
"avg_model_output = np.mean(youtube_df['label']) # prob\n",
"log_odds_avg_model_output = np.log(avg_model_output/(1-avg_model_output))\n",
"predicted_prob = classifier.predict_proba(youtube_df_text.tocsc())[j][1] #target=1\n",
"predicted_log_odds = np.log(predicted_prob/(1-predicted_prob))\n",
"\n",
"print(\"Sum of Shaphley values (log-odds)for j-instance:\", log_odds, \n",
" 'prob:', np.exp(log_odds)/(1.0+np.exp(log_odds)))\n",
"print(\"Average model output:\", avg_model_output)\n",
"print(\"Predicted probability value for j-instance:\", predicted_prob,\n",
" \"Predicted value:\", classifier.predict(youtube_df_text.tocsc())[j])\n",
"\n",
"print('log_odds:', log_odds, 'is expected to be equal to pred-expected:', predicted_log_odds-log_odds_avg_model_output)\n",
"print('pred-expected (prob):', predicted_prob-avg_model_output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering SHAP Values to Identify Outcome Reason"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"# Group keyword based on meaning\n",
"top20_dict = {'ACTION': ['check','subscrib','view','share','follow','help', 'visit'],\n",
" 'REQUEST': ['plea','hey'],\n",
" 'VALUE': ['money','free','billion', 'new', \"good\", 'best'],\n",
" 'YOUTUBE': ['channel', 'song', 'onlin'],\n",
" 'COMMENT': ['love', 'like comment'] \n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"## Data frame to pass to R\n",
"from itertools import chain\n",
"\n",
"ntopreason = 1#change this to allow more reasons to be captured\n",
"\n",
"top20_dict_values = list(top20_dict.values())\n",
"top20_dict_keys = list(top20_dict.keys())\n",
"\n",
"shap_values_imp_r = shap_values_imp.copy()\n",
"target_values_r = pd.Series(predictions)\n",
"\n",
"# Create summarizing labels\n",
"top_reasons_all = []\n",
"for i in range(shap_values_imp_r.shape[0]):\n",
" \n",
" shap_feat = shap_values_imp_r.iloc[i]\n",
" shap_feat = shap_feat.iloc[np.lexsort([shap_feat.index, shap_feat.values])]\n",
" \n",
" topN = shap_feat.index.to_list()[-ntopreason:] \n",
" topN_value = shap_feat.values[-ntopreason:]\n",
" \n",
" topN_idx = []\n",
" for topn in topN:\n",
" for idx in range(len(top20_dict_values)):\n",
" if topn in top20_dict_values[idx] and idx not in topN_idx:\n",
" topN_idx.append(idx)\n",
" \n",
" #topN_idx = [idx for idx in range(len(top20_dict_values)) for topn in topN if topn in top20_dict_values[idx]]\n",
" \n",
" #Ordered by increasing importance\n",
" top_reasons = [top20_dict_keys[x] for x in topN_idx]\n",
"\n",
" #print(i, topN, topN_idx, top_reasons)\n",
"\n",
" top_reasons_all.append(','.join(top_reasons))\n",
" \n",
"shap_values_imp_r['target'] = target_values_r\n",
"shap_values_imp_r['top_reasons'] = top_reasons_all"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"data": {
"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>check</th>\n",
" <th>subscrib</th>\n",
" <th>plea</th>\n",
" <th>channel</th>\n",
" <th>song</th>\n",
" <th>share</th>\n",
" <th>view</th>\n",
" <th>billion</th>\n",
" <th>love</th>\n",
" <th>new</th>\n",
" <th>...</th>\n",
" <th>free</th>\n",
" <th>like comment</th>\n",
" <th>follow</th>\n",
" <th>hey</th>\n",
" <th>best</th>\n",
" <th>visit</th>\n",
" <th>onlin</th>\n",
" <th>good</th>\n",
" <th>target</th>\n",
" <th>top_reasons</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.824345</td>\n",
" <td>-0.198576</td>\n",
" <td>-0.084255</td>\n",
" <td>1.965496</td>\n",
" <td>0.061483</td>\n",
" <td>-0.044300</td>\n",
" <td>0.196018</td>\n",
" <td>0.055482</td>\n",
" <td>0.053910</td>\n",
" <td>-0.032171</td>\n",
" <td>...</td>\n",
" <td>-0.029916</td>\n",
" <td>-0.024696</td>\n",
" <td>-0.028492</td>\n",
" <td>-0.022651</td>\n",
" <td>0.024080</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.006785</td>\n",
" <td>0.010336</td>\n",
" <td>1</td>\n",
" <td>ACTION</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.525985</td>\n",
" <td>1.496080</td>\n",
" <td>0.809206</td>\n",
" <td>0.828458</td>\n",
" <td>0.067073</td>\n",
" <td>-0.033727</td>\n",
" <td>0.141687</td>\n",
" <td>0.062180</td>\n",
" <td>0.048936</td>\n",
" <td>0.886804</td>\n",
" <td>...</td>\n",
" <td>-0.014258</td>\n",
" <td>1.048241</td>\n",
" <td>-0.016627</td>\n",
" <td>0.455953</td>\n",
" <td>0.027162</td>\n",
" <td>-0.009353</td>\n",
" <td>-0.005190</td>\n",
" <td>0.010336</td>\n",
" <td>1</td>\n",
" <td>ACTION</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.487089</td>\n",
" <td>-0.377440</td>\n",
" <td>-0.132151</td>\n",
" <td>-0.097204</td>\n",
" <td>0.100908</td>\n",
" <td>-0.069717</td>\n",
" <td>0.045656</td>\n",
" <td>0.052477</td>\n",
" <td>0.053910</td>\n",
" <td>-0.036958</td>\n",
" <td>...</td>\n",
" <td>-0.034293</td>\n",
" <td>-0.030869</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.022072</td>\n",
" <td>0.022725</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.008364</td>\n",
" <td>0.010336</td>\n",
" <td>0</td>\n",
" <td>YOUTUBE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.461900</td>\n",
" <td>-0.355742</td>\n",
" <td>-0.121386</td>\n",
" <td>2.133291</td>\n",
" <td>0.093927</td>\n",
" <td>-0.062687</td>\n",
" <td>0.045333</td>\n",
" <td>0.052477</td>\n",
" <td>0.053910</td>\n",
" <td>-0.032171</td>\n",
" <td>...</td>\n",
" <td>-0.032070</td>\n",
" <td>-0.024696</td>\n",
" <td>-0.028492</td>\n",
" <td>-0.022072</td>\n",
" <td>0.022725</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.007092</td>\n",
" <td>0.010336</td>\n",
" <td>1</td>\n",
" <td>YOUTUBE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.976567</td>\n",
" <td>-0.208125</td>\n",
" <td>-0.094932</td>\n",
" <td>-0.090171</td>\n",
" <td>0.068464</td>\n",
" <td>-0.051330</td>\n",
" <td>0.201998</td>\n",
" <td>0.055482</td>\n",
" <td>0.053910</td>\n",
" <td>-0.036958</td>\n",
" <td>...</td>\n",
" <td>-0.032139</td>\n",
" <td>-0.030869</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.022651</td>\n",
" <td>0.024080</td>\n",
" <td>-0.012927</td>\n",
" <td>-0.008057</td>\n",
" <td>0.010336</td>\n",
" <td>1</td>\n",
" <td>ACTION</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" check subscrib plea channel song share view \\\n",
"0 2.824345 -0.198576 -0.084255 1.965496 0.061483 -0.044300 0.196018 \n",
"1 1.525985 1.496080 0.809206 0.828458 0.067073 -0.033727 0.141687 \n",
"2 -0.487089 -0.377440 -0.132151 -0.097204 0.100908 -0.069717 0.045656 \n",
"3 -0.461900 -0.355742 -0.121386 2.133291 0.093927 -0.062687 0.045333 \n",
"4 2.976567 -0.208125 -0.094932 -0.090171 0.068464 -0.051330 0.201998 \n",
"\n",
" billion love new ... free like comment follow \\\n",
"0 0.055482 0.053910 -0.032171 ... -0.029916 -0.024696 -0.028492 \n",
"1 0.062180 0.048936 0.886804 ... -0.014258 1.048241 -0.016627 \n",
"2 0.052477 0.053910 -0.036958 ... -0.034293 -0.030869 -0.031113 \n",
"3 0.052477 0.053910 -0.032171 ... -0.032070 -0.024696 -0.028492 \n",
"4 0.055482 0.053910 -0.036958 ... -0.032139 -0.030869 -0.031113 \n",
"\n",
" hey best visit onlin good target top_reasons \n",
"0 -0.022651 0.024080 -0.012927 -0.006785 0.010336 1 ACTION \n",
"1 0.455953 0.027162 -0.009353 -0.005190 0.010336 1 ACTION \n",
"2 -0.022072 0.022725 -0.012927 -0.008364 0.010336 0 YOUTUBE \n",
"3 -0.022072 0.022725 -0.012927 -0.007092 0.010336 1 YOUTUBE \n",
"4 -0.022651 0.024080 -0.012927 -0.008057 0.010336 1 ACTION \n",
"\n",
"[5 rows x 22 columns]"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shap_values_imp_r.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"######## ONLY RUN ONCE ############\n",
"\n",
"# import rpy2's package module\n",
"import rpy2.robjects.packages as rpackages\n",
"\n",
"# import R's utility package\n",
"utils = rpackages.importr('utils')\n",
"\n",
"# select a mirror for R packages\n",
"utils.chooseCRANmirror(ind=1) # select the first mirror in the list\n",
"\n",
"# R package names\n",
"packnames = 'dendextend'\n",
"\n",
"if len(packnames) > 0:\n",
" print(\"Installing\", packnames)\n",
" utils.install_packages(packnames)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%R -i shap_values_imp_r -w 800 -h 800\n",
"\n",
"library(gplots)\n",
"\n",
"d_shap <- dist(shap_values_imp_r[1:(ncol(shap_values_imp_r)-2)], 'euclidean') \n",
"hc <- hclust(d_shap, method = \"ward.D2\")\n",
"dend <- as.dendrogram(hc)\n",
"\n",
"library(dendextend)\n",
"library(colorspace)\n",
"\n",
"## Find optimal number of clusters\n",
"clusters_test = list()\n",
"for (ncl in 2:50){\n",
" clusters_test[[ncl]] <- cutree(hc, k=ncl)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"h_clusters = rpy2.robjects.r['clusters_test']\n",
"\n",
"h_clusters_sil = []\n",
"cl_id = 0\n",
"for cl in h_clusters:\n",
" if cl is not rpy2.rinterface.NULL:\n",
" sil = metrics.silhouette_score(shap_values_imp_r.drop(shap_values_imp_r.columns[len(shap_values_imp_r.columns)-2:], axis=1, inplace=False), \n",
" cl, metric='euclidean')\n",
" h_clusters_sil.append(sil)\n",
" #print(cl_id, sil)\n",
" cl_id += 1\n",
" else:\n",
" cl_id += 1\n",
"\n",
"plt.plot(range(2, 51), h_clusters_sil)\n",
"plt.title('Silhouette')\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('Silhouette Index') #within cluster sum of squares\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%%R -i shap_values_imp_r -w 800 -h 800\n",
"\n",
"library(gplots)\n",
"\n",
"d_shap <- dist(shap_values_imp_r[1:(ncol(shap_values_imp_r)-2)], 'euclidean') \n",
"hc <- hclust(d_shap, method = \"ward.D2\")\n",
"dend <- as.dendrogram(hc)\n",
"\n",
"library(dendextend)\n",
"library(colorspace)\n",
"\n",
"n_clust <- 30\n",
"\n",
"dend <- color_branches(dend, k=n_clust) #, groupLabels=iris_species)\n",
"\n",
"clusters <- cutree(hc, k=n_clust)\n",
"#print(head(clusters))\n",
"\n",
"print(format(prop.table(table(clusters,shap_values_imp_r$top_reasons, shap_values_imp_r$target), margin=1), \n",
" digit=2, scientific = F))\n",
"print(format(prop.table(table(clusters,shap_values_imp_r$top_reasons), margin=1), \n",
" digit=2, scientific = F))\n",
"\n",
"heatmap.2(as.matrix(shap_values_imp_r[1:(ncol(shap_values_imp_r)-2)]), \n",
" dendrogram = \"row\",\n",
" Rowv = dend,\n",
" Colv = \"NA\", # this to make sure the columns are not ordered\n",
" key.xlab = \"Predicted - Average log-odds\",\n",
" #hclustfun=function(d) hclust(d, method=\"complete\"), \n",
" srtCol=45, adjCol = c(1,1))"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"cluster_model = KNeighborsClassifier(n_neighbors=1)\n",
"cluster_model.fit(shap_values_imp,rpy2.robjects.r['clusters'])"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30\n"
]
},
{
"data": {
"text/plain": [
"array([ 1, 2, 3, ..., 21, 3, 24], dtype=int32)"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Predict Output\n",
"predicted = cluster_model.predict(shap_values_imp_r[shap_values_imp_r.columns[:-2]]) \n",
"print(max(predicted))\n",
"predicted"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1735,)"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted.shape"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"data": {
"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>check</th>\n",
" <th>subscrib</th>\n",
" <th>plea</th>\n",
" <th>channel</th>\n",
" <th>song</th>\n",
" <th>share</th>\n",
" <th>view</th>\n",
" <th>billion</th>\n",
" <th>love</th>\n",
" <th>new</th>\n",
" <th>...</th>\n",
" <th>help</th>\n",
" <th>free</th>\n",
" <th>like comment</th>\n",
" <th>follow</th>\n",
" <th>hey</th>\n",
" <th>best</th>\n",
" <th>visit</th>\n",
" <th>onlin</th>\n",
" <th>good</th>\n",
" <th>avg_tgt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.835847</td>\n",
" <td>-0.196429</td>\n",
" <td>-0.083872</td>\n",
" <td>1.953783</td>\n",
" <td>0.029915</td>\n",
" <td>-0.044129</td>\n",
" <td>0.193826</td>\n",
" <td>0.054928</td>\n",
" <td>0.014252</td>\n",
" <td>-0.031791</td>\n",
" <td>...</td>\n",
" <td>-0.020023</td>\n",
" <td>-0.029561</td>\n",
" <td>-0.024367</td>\n",
" <td>-0.028410</td>\n",
" <td>0.042219</td>\n",
" <td>0.023472</td>\n",
" <td>-0.013020</td>\n",
" <td>-0.006785</td>\n",
" <td>0.004514</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.559111</td>\n",
" <td>1.834720</td>\n",
" <td>0.724109</td>\n",
" <td>1.554189</td>\n",
" <td>-0.090896</td>\n",
" <td>0.133334</td>\n",
" <td>0.156791</td>\n",
" <td>0.052437</td>\n",
" <td>0.004763</td>\n",
" <td>0.186725</td>\n",
" <td>...</td>\n",
" <td>0.147757</td>\n",
" <td>0.040038</td>\n",
" <td>0.124548</td>\n",
" <td>-0.019169</td>\n",
" <td>0.175500</td>\n",
" <td>0.025480</td>\n",
" <td>-0.010438</td>\n",
" <td>-0.005676</td>\n",
" <td>-0.006646</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.471949</td>\n",
" <td>-0.374756</td>\n",
" <td>-0.131913</td>\n",
" <td>-0.096920</td>\n",
" <td>0.097571</td>\n",
" <td>-0.069704</td>\n",
" <td>0.011779</td>\n",
" <td>0.052453</td>\n",
" <td>0.050129</td>\n",
" <td>-0.036996</td>\n",
" <td>...</td>\n",
" <td>-0.029760</td>\n",
" <td>-0.034258</td>\n",
" <td>-0.030822</td>\n",
" <td>-0.031086</td>\n",
" <td>-0.017665</td>\n",
" <td>0.022585</td>\n",
" <td>-0.010410</td>\n",
" <td>-0.006063</td>\n",
" <td>-0.002590</td>\n",
" <td>0.215892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.463012</td>\n",
" <td>-0.347725</td>\n",
" <td>-0.119100</td>\n",
" <td>2.107778</td>\n",
" <td>0.054802</td>\n",
" <td>-0.062288</td>\n",
" <td>0.044929</td>\n",
" <td>0.051856</td>\n",
" <td>0.034789</td>\n",
" <td>-0.031665</td>\n",
" <td>...</td>\n",
" <td>-0.027576</td>\n",
" <td>-0.031501</td>\n",
" <td>-0.024275</td>\n",
" <td>0.082793</td>\n",
" <td>0.020246</td>\n",
" <td>0.021760</td>\n",
" <td>0.118372</td>\n",
" <td>-0.007063</td>\n",
" <td>0.010228</td>\n",
" <td>0.888889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.979905</td>\n",
" <td>-0.204717</td>\n",
" <td>-0.082555</td>\n",
" <td>-0.073737</td>\n",
" <td>0.023594</td>\n",
" <td>-0.050982</td>\n",
" <td>0.199729</td>\n",
" <td>0.021398</td>\n",
" <td>0.027829</td>\n",
" <td>-0.036435</td>\n",
" <td>...</td>\n",
" <td>-0.005209</td>\n",
" <td>-0.017672</td>\n",
" <td>0.000926</td>\n",
" <td>0.019962</td>\n",
" <td>0.036778</td>\n",
" <td>0.018915</td>\n",
" <td>-0.006507</td>\n",
" <td>-0.008007</td>\n",
" <td>-0.006178</td>\n",
" <td>0.959677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2.950474</td>\n",
" <td>-0.191597</td>\n",
" <td>0.114343</td>\n",
" <td>-0.079831</td>\n",
" <td>-0.065293</td>\n",
" <td>-0.050489</td>\n",
" <td>0.194182</td>\n",
" <td>0.052955</td>\n",
" <td>0.034001</td>\n",
" <td>1.656466</td>\n",
" <td>...</td>\n",
" <td>-0.021851</td>\n",
" <td>-0.028409</td>\n",
" <td>-0.025393</td>\n",
" <td>-0.026720</td>\n",
" <td>0.084034</td>\n",
" <td>0.024072</td>\n",
" <td>-0.012936</td>\n",
" <td>-0.007864</td>\n",
" <td>-0.011350</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.239717</td>\n",
" <td>3.363768</td>\n",
" <td>-0.088712</td>\n",
" <td>1.865050</td>\n",
" <td>0.114055</td>\n",
" <td>-0.042633</td>\n",
" <td>0.024519</td>\n",
" <td>0.052750</td>\n",
" <td>0.046114</td>\n",
" <td>-0.029035</td>\n",
" <td>...</td>\n",
" <td>0.017375</td>\n",
" <td>-0.030457</td>\n",
" <td>-0.020838</td>\n",
" <td>-0.023250</td>\n",
" <td>0.058522</td>\n",
" <td>0.004653</td>\n",
" <td>0.033153</td>\n",
" <td>-0.007101</td>\n",
" <td>0.000160</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.439643</td>\n",
" <td>-0.214195</td>\n",
" <td>-0.006077</td>\n",
" <td>-0.089314</td>\n",
" <td>0.057104</td>\n",
" <td>-0.051789</td>\n",
" <td>-2.094527</td>\n",
" <td>0.055899</td>\n",
" <td>0.046993</td>\n",
" <td>0.054683</td>\n",
" <td>...</td>\n",
" <td>0.042362</td>\n",
" <td>0.063039</td>\n",
" <td>-0.029791</td>\n",
" <td>-0.030036</td>\n",
" <td>0.043582</td>\n",
" <td>0.019431</td>\n",
" <td>0.033384</td>\n",
" <td>0.037224</td>\n",
" <td>0.009408</td>\n",
" <td>0.176471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.247828</td>\n",
" <td>3.563774</td>\n",
" <td>-0.099949</td>\n",
" <td>-0.086063</td>\n",
" <td>0.066934</td>\n",
" <td>-0.050063</td>\n",
" <td>0.020211</td>\n",
" <td>0.051670</td>\n",
" <td>0.006079</td>\n",
" <td>-0.034030</td>\n",
" <td>...</td>\n",
" <td>-0.028080</td>\n",
" <td>-0.032644</td>\n",
" <td>-0.027312</td>\n",
" <td>-0.026106</td>\n",
" <td>0.009429</td>\n",
" <td>0.021991</td>\n",
" <td>-0.010566</td>\n",
" <td>-0.008351</td>\n",
" <td>0.006980</td>\n",
" <td>0.978022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.198357</td>\n",
" <td>3.303905</td>\n",
" <td>0.001408</td>\n",
" <td>0.244400</td>\n",
" <td>0.086842</td>\n",
" <td>-0.043802</td>\n",
" <td>-0.063549</td>\n",
" <td>0.048437</td>\n",
" <td>0.047200</td>\n",
" <td>0.330936</td>\n",
" <td>...</td>\n",
" <td>0.714012</td>\n",
" <td>0.277791</td>\n",
" <td>0.388260</td>\n",
" <td>-0.023326</td>\n",
" <td>0.030183</td>\n",
" <td>0.022917</td>\n",
" <td>-0.010109</td>\n",
" <td>-0.007623</td>\n",
" <td>-0.006395</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.361129</td>\n",
" <td>-0.288536</td>\n",
" <td>2.032145</td>\n",
" <td>-0.084442</td>\n",
" <td>0.042352</td>\n",
" <td>-0.067260</td>\n",
" <td>0.033126</td>\n",
" <td>0.050051</td>\n",
" <td>0.048127</td>\n",
" <td>0.071401</td>\n",
" <td>...</td>\n",
" <td>0.279119</td>\n",
" <td>0.030914</td>\n",
" <td>-0.028423</td>\n",
" <td>0.057055</td>\n",
" <td>0.028888</td>\n",
" <td>0.000064</td>\n",
" <td>0.063974</td>\n",
" <td>-0.006943</td>\n",
" <td>0.010953</td>\n",
" <td>0.926829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-0.478187</td>\n",
" <td>-0.352706</td>\n",
" <td>-0.102359</td>\n",
" <td>-0.087053</td>\n",
" <td>0.102253</td>\n",
" <td>-0.063679</td>\n",
" <td>0.045331</td>\n",
" <td>0.052588</td>\n",
" <td>0.019861</td>\n",
" <td>-0.030473</td>\n",
" <td>...</td>\n",
" <td>0.064717</td>\n",
" <td>0.174144</td>\n",
" <td>-0.029058</td>\n",
" <td>2.076134</td>\n",
" <td>0.028228</td>\n",
" <td>0.022161</td>\n",
" <td>0.030242</td>\n",
" <td>-0.008112</td>\n",
" <td>0.010672</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.452993</td>\n",
" <td>-0.344179</td>\n",
" <td>-0.125048</td>\n",
" <td>-0.078326</td>\n",
" <td>0.099880</td>\n",
" <td>-0.065921</td>\n",
" <td>0.044462</td>\n",
" <td>0.052684</td>\n",
" <td>0.051818</td>\n",
" <td>-0.036663</td>\n",
" <td>...</td>\n",
" <td>-0.029860</td>\n",
" <td>-0.033790</td>\n",
" <td>-0.026540</td>\n",
" <td>-0.030551</td>\n",
" <td>0.022394</td>\n",
" <td>-0.005239</td>\n",
" <td>0.084327</td>\n",
" <td>0.125073</td>\n",
" <td>0.010603</td>\n",
" <td>0.882353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2.393249</td>\n",
" <td>-0.175907</td>\n",
" <td>1.421754</td>\n",
" <td>-0.010884</td>\n",
" <td>-0.072200</td>\n",
" <td>0.284312</td>\n",
" <td>0.182213</td>\n",
" <td>0.052332</td>\n",
" <td>-0.048878</td>\n",
" <td>-0.032297</td>\n",
" <td>...</td>\n",
" <td>0.012558</td>\n",
" <td>-0.015194</td>\n",
" <td>-0.028598</td>\n",
" <td>-0.025524</td>\n",
" <td>0.115543</td>\n",
" <td>0.023767</td>\n",
" <td>-0.013030</td>\n",
" <td>-0.006391</td>\n",
" <td>0.010075</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2.282876</td>\n",
" <td>-0.170439</td>\n",
" <td>1.132359</td>\n",
" <td>1.719297</td>\n",
" <td>0.034156</td>\n",
" <td>-0.043699</td>\n",
" <td>0.179078</td>\n",
" <td>0.054619</td>\n",
" <td>0.003529</td>\n",
" <td>0.253884</td>\n",
" <td>...</td>\n",
" <td>0.045806</td>\n",
" <td>0.117118</td>\n",
" <td>-0.022039</td>\n",
" <td>-0.023220</td>\n",
" <td>0.130805</td>\n",
" <td>-0.060945</td>\n",
" <td>-0.013146</td>\n",
" <td>-0.005448</td>\n",
" <td>-0.009019</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-0.298709</td>\n",
" <td>-0.374963</td>\n",
" <td>-0.132241</td>\n",
" <td>-0.097162</td>\n",
" <td>0.029496</td>\n",
" <td>-0.065673</td>\n",
" <td>-0.355643</td>\n",
" <td>-1.845858</td>\n",
" <td>0.023289</td>\n",
" <td>-0.037468</td>\n",
" <td>...</td>\n",
" <td>-0.037661</td>\n",
" <td>-0.030981</td>\n",
" <td>-0.030729</td>\n",
" <td>-0.030969</td>\n",
" <td>-0.016924</td>\n",
" <td>0.017047</td>\n",
" <td>0.021891</td>\n",
" <td>-0.008489</td>\n",
" <td>0.009653</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1.529796</td>\n",
" <td>2.149595</td>\n",
" <td>0.548541</td>\n",
" <td>-0.076057</td>\n",
" <td>0.078399</td>\n",
" <td>0.143752</td>\n",
" <td>0.161078</td>\n",
" <td>0.054287</td>\n",
" <td>-0.034519</td>\n",
" <td>0.038061</td>\n",
" <td>...</td>\n",
" <td>0.078408</td>\n",
" <td>0.027316</td>\n",
" <td>-0.026050</td>\n",
" <td>0.194455</td>\n",
" <td>0.043963</td>\n",
" <td>-0.029779</td>\n",
" <td>-0.010375</td>\n",
" <td>-0.007210</td>\n",
" <td>-0.011201</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-0.460701</td>\n",
" <td>-0.360949</td>\n",
" <td>-0.080724</td>\n",
" <td>-0.080991</td>\n",
" <td>0.121244</td>\n",
" <td>-0.068051</td>\n",
" <td>0.043236</td>\n",
" <td>0.056840</td>\n",
" <td>-0.010488</td>\n",
" <td>0.143528</td>\n",
" <td>...</td>\n",
" <td>0.042183</td>\n",
" <td>1.785054</td>\n",
" <td>-0.028488</td>\n",
" <td>-0.027169</td>\n",
" <td>0.099730</td>\n",
" <td>-0.007896</td>\n",
" <td>-0.013157</td>\n",
" <td>-0.006726</td>\n",
" <td>0.010071</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.317404</td>\n",
" <td>-0.269273</td>\n",
" <td>1.857508</td>\n",
" <td>1.847346</td>\n",
" <td>0.094322</td>\n",
" <td>-0.061229</td>\n",
" <td>-0.018997</td>\n",
" <td>0.052916</td>\n",
" <td>0.021056</td>\n",
" <td>0.099530</td>\n",
" <td>...</td>\n",
" <td>0.120560</td>\n",
" <td>0.099761</td>\n",
" <td>-0.021937</td>\n",
" <td>-0.022206</td>\n",
" <td>0.126281</td>\n",
" <td>0.024365</td>\n",
" <td>0.242858</td>\n",
" <td>-0.005687</td>\n",
" <td>-0.015198</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-0.216817</td>\n",
" <td>3.033668</td>\n",
" <td>1.553303</td>\n",
" <td>-0.075489</td>\n",
" <td>0.081951</td>\n",
" <td>-0.048974</td>\n",
" <td>0.042838</td>\n",
" <td>0.051315</td>\n",
" <td>-0.001912</td>\n",
" <td>0.025740</td>\n",
" <td>...</td>\n",
" <td>0.126505</td>\n",
" <td>-0.014645</td>\n",
" <td>-0.025661</td>\n",
" <td>-0.021085</td>\n",
" <td>0.065575</td>\n",
" <td>0.025889</td>\n",
" <td>-0.010571</td>\n",
" <td>-0.006997</td>\n",
" <td>0.010600</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-0.490318</td>\n",
" <td>-0.358026</td>\n",
" <td>-0.125517</td>\n",
" <td>-0.099789</td>\n",
" <td>-0.607904</td>\n",
" <td>-0.069696</td>\n",
" <td>0.020092</td>\n",
" <td>0.038904</td>\n",
" <td>-0.098043</td>\n",
" <td>-0.038322</td>\n",
" <td>...</td>\n",
" <td>-0.029785</td>\n",
" <td>-0.030908</td>\n",
" <td>-0.030825</td>\n",
" <td>-0.031100</td>\n",
" <td>-0.012219</td>\n",
" <td>0.020347</td>\n",
" <td>-0.013343</td>\n",
" <td>-0.008374</td>\n",
" <td>-0.012459</td>\n",
" <td>0.041667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.156056</td>\n",
" <td>2.693915</td>\n",
" <td>1.312097</td>\n",
" <td>1.613869</td>\n",
" <td>0.058214</td>\n",
" <td>-0.041217</td>\n",
" <td>-0.092901</td>\n",
" <td>0.052278</td>\n",
" <td>0.024717</td>\n",
" <td>0.090199</td>\n",
" <td>...</td>\n",
" <td>0.142819</td>\n",
" <td>-0.014072</td>\n",
" <td>0.018496</td>\n",
" <td>0.124705</td>\n",
" <td>0.164467</td>\n",
" <td>0.025804</td>\n",
" <td>0.032488</td>\n",
" <td>-0.005804</td>\n",
" <td>-0.014849</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.385083</td>\n",
" <td>-0.366586</td>\n",
" <td>-0.130685</td>\n",
" <td>-0.090871</td>\n",
" <td>0.071501</td>\n",
" <td>-0.060977</td>\n",
" <td>-0.088423</td>\n",
" <td>-0.057244</td>\n",
" <td>0.046274</td>\n",
" <td>-0.037050</td>\n",
" <td>...</td>\n",
" <td>1.416863</td>\n",
" <td>-0.033481</td>\n",
" <td>-0.028949</td>\n",
" <td>-0.031113</td>\n",
" <td>0.047505</td>\n",
" <td>0.022203</td>\n",
" <td>-0.012920</td>\n",
" <td>0.060335</td>\n",
" <td>0.010083</td>\n",
" <td>0.818182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.492217</td>\n",
" <td>-0.365841</td>\n",
" <td>-0.125246</td>\n",
" <td>-0.098766</td>\n",
" <td>-0.288405</td>\n",
" <td>-0.069717</td>\n",
" <td>0.042832</td>\n",
" <td>0.042373</td>\n",
" <td>0.032915</td>\n",
" <td>-0.037920</td>\n",
" <td>...</td>\n",
" <td>-0.029776</td>\n",
" <td>-0.031638</td>\n",
" <td>-0.030843</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.018505</td>\n",
" <td>-0.522901</td>\n",
" <td>-0.014241</td>\n",
" <td>-0.008363</td>\n",
" <td>0.004505</td>\n",
" <td>0.053571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-0.487387</td>\n",
" <td>-0.376590</td>\n",
" <td>-0.131776</td>\n",
" <td>-0.097048</td>\n",
" <td>0.090895</td>\n",
" <td>-0.069708</td>\n",
" <td>0.042722</td>\n",
" <td>0.047966</td>\n",
" <td>-0.479514</td>\n",
" <td>-0.035965</td>\n",
" <td>...</td>\n",
" <td>-0.029797</td>\n",
" <td>-0.034151</td>\n",
" <td>-0.030869</td>\n",
" <td>-0.031113</td>\n",
" <td>-0.021970</td>\n",
" <td>0.015118</td>\n",
" <td>-0.014435</td>\n",
" <td>-0.008363</td>\n",
" <td>0.000765</td>\n",
" <td>0.075269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.368894</td>\n",
" <td>-0.301834</td>\n",
" <td>-0.126124</td>\n",
" <td>-0.082367</td>\n",
" <td>-0.017104</td>\n",
" <td>2.571590</td>\n",
" <td>-0.013770</td>\n",
" <td>-0.108719</td>\n",
" <td>0.022923</td>\n",
" <td>0.092464</td>\n",
" <td>...</td>\n",
" <td>-0.023018</td>\n",
" <td>0.036404</td>\n",
" <td>0.129310</td>\n",
" <td>-0.027667</td>\n",
" <td>0.009028</td>\n",
" <td>0.000954</td>\n",
" <td>-0.012913</td>\n",
" <td>-0.006958</td>\n",
" <td>0.010062</td>\n",
" <td>0.923077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-0.458762</td>\n",
" <td>-0.357213</td>\n",
" <td>-0.116387</td>\n",
" <td>-0.084237</td>\n",
" <td>-0.073804</td>\n",
" <td>-0.066680</td>\n",
" <td>-0.006032</td>\n",
" <td>-0.075260</td>\n",
" <td>0.036813</td>\n",
" <td>1.701985</td>\n",
" <td>...</td>\n",
" <td>0.206110</td>\n",
" <td>-0.032688</td>\n",
" <td>-0.024841</td>\n",
" <td>-0.026923</td>\n",
" <td>0.010734</td>\n",
" <td>0.021565</td>\n",
" <td>0.052554</td>\n",
" <td>0.054626</td>\n",
" <td>0.010096</td>\n",
" <td>0.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-0.283101</td>\n",
" <td>-0.229892</td>\n",
" <td>2.091926</td>\n",
" <td>0.091534</td>\n",
" <td>0.024631</td>\n",
" <td>2.489310</td>\n",
" <td>0.044257</td>\n",
" <td>0.051918</td>\n",
" <td>-0.015016</td>\n",
" <td>-0.031739</td>\n",
" <td>...</td>\n",
" <td>0.215815</td>\n",
" <td>-0.015854</td>\n",
" <td>-0.027897</td>\n",
" <td>-0.022939</td>\n",
" <td>-0.021348</td>\n",
" <td>0.025570</td>\n",
" <td>-0.013092</td>\n",
" <td>-0.005656</td>\n",
" <td>0.011382</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2.720262</td>\n",
" <td>-0.180835</td>\n",
" <td>-0.082362</td>\n",
" <td>-0.068109</td>\n",
" <td>0.049984</td>\n",
" <td>-0.044985</td>\n",
" <td>0.190233</td>\n",
" <td>0.055797</td>\n",
" <td>0.043070</td>\n",
" <td>-0.036389</td>\n",
" <td>...</td>\n",
" <td>-0.022263</td>\n",
" <td>-0.029789</td>\n",
" <td>-0.024726</td>\n",
" <td>-0.027344</td>\n",
" <td>0.074144</td>\n",
" <td>0.021314</td>\n",
" <td>0.621360</td>\n",
" <td>0.574500</td>\n",
" <td>0.011069</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-0.452327</td>\n",
" <td>-0.341061</td>\n",
" <td>0.226645</td>\n",
" <td>-0.058599</td>\n",
" <td>0.046782</td>\n",
" <td>-0.068066</td>\n",
" <td>0.009522</td>\n",
" <td>-0.061267</td>\n",
" <td>0.048912</td>\n",
" <td>-0.029057</td>\n",
" <td>...</td>\n",
" <td>-0.028069</td>\n",
" <td>-0.030853</td>\n",
" <td>2.070289</td>\n",
" <td>-0.029239</td>\n",
" <td>-0.016656</td>\n",
" <td>0.022832</td>\n",
" <td>-0.011816</td>\n",
" <td>-0.007659</td>\n",
" <td>0.010615</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>30 rows × 21 columns</p>\n",
"</div>"
],
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" check subscrib plea channel song share view \\\n",
"1 2.835847 -0.196429 -0.083872 1.953783 0.029915 -0.044129 0.193826 \n",
"2 1.559111 1.834720 0.724109 1.554189 -0.090896 0.133334 0.156791 \n",
"3 -0.471949 -0.374756 -0.131913 -0.096920 0.097571 -0.069704 0.011779 \n",
"4 -0.463012 -0.347725 -0.119100 2.107778 0.054802 -0.062288 0.044929 \n",
"5 2.979905 -0.204717 -0.082555 -0.073737 0.023594 -0.050982 0.199729 \n",
"6 2.950474 -0.191597 0.114343 -0.079831 -0.065293 -0.050489 0.194182 \n",
"7 -0.239717 3.363768 -0.088712 1.865050 0.114055 -0.042633 0.024519 \n",
"8 1.439643 -0.214195 -0.006077 -0.089314 0.057104 -0.051789 -2.094527 \n",
"9 -0.247828 3.563774 -0.099949 -0.086063 0.066934 -0.050063 0.020211 \n",
"10 -0.198357 3.303905 0.001408 0.244400 0.086842 -0.043802 -0.063549 \n",
"11 -0.361129 -0.288536 2.032145 -0.084442 0.042352 -0.067260 0.033126 \n",
"12 -0.478187 -0.352706 -0.102359 -0.087053 0.102253 -0.063679 0.045331 \n",
"13 -0.452993 -0.344179 -0.125048 -0.078326 0.099880 -0.065921 0.044462 \n",
"14 2.393249 -0.175907 1.421754 -0.010884 -0.072200 0.284312 0.182213 \n",
"15 2.282876 -0.170439 1.132359 1.719297 0.034156 -0.043699 0.179078 \n",
"16 -0.298709 -0.374963 -0.132241 -0.097162 0.029496 -0.065673 -0.355643 \n",
"17 1.529796 2.149595 0.548541 -0.076057 0.078399 0.143752 0.161078 \n",
"18 -0.460701 -0.360949 -0.080724 -0.080991 0.121244 -0.068051 0.043236 \n",
"19 -0.317404 -0.269273 1.857508 1.847346 0.094322 -0.061229 -0.018997 \n",
"20 -0.216817 3.033668 1.553303 -0.075489 0.081951 -0.048974 0.042838 \n",
"21 -0.490318 -0.358026 -0.125517 -0.099789 -0.607904 -0.069696 0.020092 \n",
"22 -0.156056 2.693915 1.312097 1.613869 0.058214 -0.041217 -0.092901 \n",
"23 -0.385083 -0.366586 -0.130685 -0.090871 0.071501 -0.060977 -0.088423 \n",
"24 -0.492217 -0.365841 -0.125246 -0.098766 -0.288405 -0.069717 0.042832 \n",
"25 -0.487387 -0.376590 -0.131776 -0.097048 0.090895 -0.069708 0.042722 \n",
"26 -0.368894 -0.301834 -0.126124 -0.082367 -0.017104 2.571590 -0.013770 \n",
"27 -0.458762 -0.357213 -0.116387 -0.084237 -0.073804 -0.066680 -0.006032 \n",
"28 -0.283101 -0.229892 2.091926 0.091534 0.024631 2.489310 0.044257 \n",
"29 2.720262 -0.180835 -0.082362 -0.068109 0.049984 -0.044985 0.190233 \n",
"30 -0.452327 -0.341061 0.226645 -0.058599 0.046782 -0.068066 0.009522 \n",
"\n",
" billion love new ... help free like comment \\\n",
"1 0.054928 0.014252 -0.031791 ... -0.020023 -0.029561 -0.024367 \n",
"2 0.052437 0.004763 0.186725 ... 0.147757 0.040038 0.124548 \n",
"3 0.052453 0.050129 -0.036996 ... -0.029760 -0.034258 -0.030822 \n",
"4 0.051856 0.034789 -0.031665 ... -0.027576 -0.031501 -0.024275 \n",
"5 0.021398 0.027829 -0.036435 ... -0.005209 -0.017672 0.000926 \n",
"6 0.052955 0.034001 1.656466 ... -0.021851 -0.028409 -0.025393 \n",
"7 0.052750 0.046114 -0.029035 ... 0.017375 -0.030457 -0.020838 \n",
"8 0.055899 0.046993 0.054683 ... 0.042362 0.063039 -0.029791 \n",
"9 0.051670 0.006079 -0.034030 ... -0.028080 -0.032644 -0.027312 \n",
"10 0.048437 0.047200 0.330936 ... 0.714012 0.277791 0.388260 \n",
"11 0.050051 0.048127 0.071401 ... 0.279119 0.030914 -0.028423 \n",
"12 0.052588 0.019861 -0.030473 ... 0.064717 0.174144 -0.029058 \n",
"13 0.052684 0.051818 -0.036663 ... -0.029860 -0.033790 -0.026540 \n",
"14 0.052332 -0.048878 -0.032297 ... 0.012558 -0.015194 -0.028598 \n",
"15 0.054619 0.003529 0.253884 ... 0.045806 0.117118 -0.022039 \n",
"16 -1.845858 0.023289 -0.037468 ... -0.037661 -0.030981 -0.030729 \n",
"17 0.054287 -0.034519 0.038061 ... 0.078408 0.027316 -0.026050 \n",
"18 0.056840 -0.010488 0.143528 ... 0.042183 1.785054 -0.028488 \n",
"19 0.052916 0.021056 0.099530 ... 0.120560 0.099761 -0.021937 \n",
"20 0.051315 -0.001912 0.025740 ... 0.126505 -0.014645 -0.025661 \n",
"21 0.038904 -0.098043 -0.038322 ... -0.029785 -0.030908 -0.030825 \n",
"22 0.052278 0.024717 0.090199 ... 0.142819 -0.014072 0.018496 \n",
"23 -0.057244 0.046274 -0.037050 ... 1.416863 -0.033481 -0.028949 \n",
"24 0.042373 0.032915 -0.037920 ... -0.029776 -0.031638 -0.030843 \n",
"25 0.047966 -0.479514 -0.035965 ... -0.029797 -0.034151 -0.030869 \n",
"26 -0.108719 0.022923 0.092464 ... -0.023018 0.036404 0.129310 \n",
"27 -0.075260 0.036813 1.701985 ... 0.206110 -0.032688 -0.024841 \n",
"28 0.051918 -0.015016 -0.031739 ... 0.215815 -0.015854 -0.027897 \n",
"29 0.055797 0.043070 -0.036389 ... -0.022263 -0.029789 -0.024726 \n",
"30 -0.061267 0.048912 -0.029057 ... -0.028069 -0.030853 2.070289 \n",
"\n",
" follow hey best visit onlin good avg_tgt \n",
"1 -0.028410 0.042219 0.023472 -0.013020 -0.006785 0.004514 1.000000 \n",
"2 -0.019169 0.175500 0.025480 -0.010438 -0.005676 -0.006646 1.000000 \n",
"3 -0.031086 -0.017665 0.022585 -0.010410 -0.006063 -0.002590 0.215892 \n",
"4 0.082793 0.020246 0.021760 0.118372 -0.007063 0.010228 0.888889 \n",
"5 0.019962 0.036778 0.018915 -0.006507 -0.008007 -0.006178 0.959677 \n",
"6 -0.026720 0.084034 0.024072 -0.012936 -0.007864 -0.011350 1.000000 \n",
"7 -0.023250 0.058522 0.004653 0.033153 -0.007101 0.000160 1.000000 \n",
"8 -0.030036 0.043582 0.019431 0.033384 0.037224 0.009408 0.176471 \n",
"9 -0.026106 0.009429 0.021991 -0.010566 -0.008351 0.006980 0.978022 \n",
"10 -0.023326 0.030183 0.022917 -0.010109 -0.007623 -0.006395 1.000000 \n",
"11 0.057055 0.028888 0.000064 0.063974 -0.006943 0.010953 0.926829 \n",
"12 2.076134 0.028228 0.022161 0.030242 -0.008112 0.010672 1.000000 \n",
"13 -0.030551 0.022394 -0.005239 0.084327 0.125073 0.010603 0.882353 \n",
"14 -0.025524 0.115543 0.023767 -0.013030 -0.006391 0.010075 1.000000 \n",
"15 -0.023220 0.130805 -0.060945 -0.013146 -0.005448 -0.009019 1.000000 \n",
"16 -0.030969 -0.016924 0.017047 0.021891 -0.008489 0.009653 0.000000 \n",
"17 0.194455 0.043963 -0.029779 -0.010375 -0.007210 -0.011201 1.000000 \n",
"18 -0.027169 0.099730 -0.007896 -0.013157 -0.006726 0.010071 1.000000 \n",
"19 -0.022206 0.126281 0.024365 0.242858 -0.005687 -0.015198 1.000000 \n",
"20 -0.021085 0.065575 0.025889 -0.010571 -0.006997 0.010600 1.000000 \n",
"21 -0.031100 -0.012219 0.020347 -0.013343 -0.008374 -0.012459 0.041667 \n",
"22 0.124705 0.164467 0.025804 0.032488 -0.005804 -0.014849 1.000000 \n",
"23 -0.031113 0.047505 0.022203 -0.012920 0.060335 0.010083 0.818182 \n",
"24 -0.031113 -0.018505 -0.522901 -0.014241 -0.008363 0.004505 0.053571 \n",
"25 -0.031113 -0.021970 0.015118 -0.014435 -0.008363 0.000765 0.075269 \n",
"26 -0.027667 0.009028 0.000954 -0.012913 -0.006958 0.010062 0.923077 \n",
"27 -0.026923 0.010734 0.021565 0.052554 0.054626 0.010096 0.750000 \n",
"28 -0.022939 -0.021348 0.025570 -0.013092 -0.005656 0.011382 1.000000 \n",
"29 -0.027344 0.074144 0.021314 0.621360 0.574500 0.011069 1.000000 \n",
"30 -0.029239 -0.016656 0.022832 -0.011816 -0.007659 0.010615 1.000000 \n",
"\n",
"[30 rows x 21 columns]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# split df into cluster groups\n",
"grouped = pd.concat([shap_values_imp, pd.Series(youtube_df['label'].tolist(), name='avg_tgt')], axis=1).groupby(predicted)\n",
"\n",
"# compute average impact to model prediction output. \n",
"sums = grouped.apply(lambda x: np.mean(x))\n",
"\n",
"sums"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
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if(\"le\"===r)for(i=0,a=0;i<t.length;i+=3)o=t[i]|t[i+1]<<8|t[i+2]<<16,this.words[a]|=o<<s&67108863,this.words[a+1]=o>>>26-s&67108863,(s+=24)>=26&&(s-=26,a++);return this.strip()},a.prototype._parseHex=function(t,e){this.length=Math.ceil((t.length-e)/6),this.words=new Array(this.length);for(var r=0;r<this.length;r++)this.words[r]=0;var n,i,a=0;for(r=t.length-6,n=0;r>=e;r-=6)i=s(t,r,r+6),this.words[n]|=i<<a&67108863,this.words[n+1]|=i>>>26-a&4194303,(a+=24)>=26&&(a-=26,n++);r+6!==e&&(i=s(t,e,r+6),this.words[n]|=i<<a&67108863,this.words[n+1]|=i>>>26-a&4194303),this.strip()},a.prototype._parseBase=function(t,e,r){this.words=[0],this.length=1;for(var n=0,i=1;i<=67108863;i*=e)n++;n--,i=i/e|0;for(var a=t.length-r,o=a%n,s=Math.min(a,a-o)+r,c=0,u=r;u<s;u+=n)c=l(t,u,u+n,e),this.imuln(i),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c);if(0!==o){var h=1;for(c=l(t,u,t.length,e),u=0;u<o;u++)h*=e;this.imuln(h),this.words[0]+c<67108864?this.words[0]+=c:this._iaddn(c)}},a.prototype.copy=function(t){t.words=new Array(this.length);for(var e=0;e<this.length;e++)t.words[e]=this.words[e];t.length=this.length,t.negative=this.negative,t.red=this.red},a.prototype.clone=function(){var t=new a(null);return this.copy(t),t},a.prototype._expand=function(t){for(;this.length<t;)this.words[this.length++]=0;return this},a.prototype.strip=function(){for(;this.length>1&&0===this.words[this.length-1];)this.length--;return this._normSign()},a.prototype._normSign=function(){return 1===this.length&&0===this.words[0]&&(this.negative=0),this},a.prototype.inspect=function(){return(this.red?\"<BN-R: \":\"<BN: \")+this.toString(16)+\">\"};var c=[\"\",\"0\",\"00\",\"000\",\"0000\",\"00000\",\"000000\",\"0000000\",\"00000000\",\"000000000\",\"0000000000\",\"00000000000\",\"000000000000\",\"0000000000000\",\"00000000000000\",\"000000000000000\",\"0000000000000000\",\"00000000000000000\",\"000000000000000000\",\"0000000000000000000\",\"00000000000000000000\",\"000000000000000000000\",\"0000000000000000000000\",\"00000000000000000000000\",\"000000000000000000000000\",\"0000000000000000000000000\"],u=[0,0,25,16,12,11,10,9,8,8,7,7,7,7,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5],h=[0,0,33554432,43046721,16777216,48828125,60466176,40353607,16777216,43046721,1e7,19487171,35831808,62748517,7529536,11390625,16777216,24137569,34012224,47045881,64e6,4084101,5153632,6436343,7962624,9765625,11881376,14348907,17210368,20511149,243e5,28629151,33554432,39135393,45435424,52521875,60466176];function f(t,e,r){r.negative=e.negative^t.negative;var n=t.length+e.length|0;r.length=n,n=n-1|0;var i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,h=67108863&l,f=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=f;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+h)/67108864|0,h=67108863&o}r.words[c]=0|h,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?c[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var f=u[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var g=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?g+r:c[f-g.length]+g+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var p=function(t,e,r){var 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mt=(c+(n=n+Math.imul(h,H)|0)|0)+((8191&(i=(i=i+Math.imul(h,G)|0)+Math.imul(f,H)|0))<<13)|0;c=((a=a+Math.imul(f,G)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(m,U),i=(i=Math.imul(m,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,H)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(g,H)|0,a=a+Math.imul(g,G)|0;var yt=(c+(n=n+Math.imul(h,W)|0)|0)+((8191&(i=(i=i+Math.imul(h,X)|0)+Math.imul(f,W)|0))<<13)|0;c=((a=a+Math.imul(f,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(m,H)|0,i=(i=i+Math.imul(m,G)|0)+Math.imul(y,H)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(g,W)|0,a=a+Math.imul(g,X)|0;var xt=(c+(n=n+Math.imul(h,J)|0)|0)+((8191&(i=(i=i+Math.imul(h,K)|0)+Math.imul(f,J)|0))<<13)|0;c=((a=a+Math.imul(f,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(k,U),i=(i=Math.imul(k,V))+Math.imul(T,U)|0,a=Math.imul(T,V),n=n+Math.imul(b,H)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,H)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(m,W)|0,i=(i=i+Math.imul(m,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(g,J)|0,a=a+Math.imul(g,K)|0;var bt=(c+(n=n+Math.imul(h,Q)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,Q)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(k,H)|0,i=(i=i+Math.imul(k,G)|0)+Math.imul(T,H)|0,a=a+Math.imul(T,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(m,J)|0,i=(i=i+Math.imul(m,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(g,Q)|0,a=a+Math.imul(g,tt)|0;var _t=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(k,W)|0,i=(i=i+Math.imul(k,X)|0)+Math.imul(T,W)|0,a=a+Math.imul(T,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(m,Q)|0,i=(i=i+Math.imul(m,tt)|0)+Math.imul(y,Q)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(g,rt)|0,a=a+Math.imul(g,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(k,J)|0,i=(i=i+Math.imul(k,K)|0)+Math.imul(T,J)|0,a=a+Math.imul(T,K)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(m,rt)|0,i=(i=i+Math.imul(m,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(g,at)|0,a=a+Math.imul(g,ot)|0;var kt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,H)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,X)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(k,Q)|0,i=(i=i+Math.imul(k,tt)|0)+Math.imul(T,Q)|0,a=a+Math.imul(T,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(m,at)|0,i=(i=i+Math.imul(m,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(g,lt)|0,a=a+Math.imul(g,ct)|0;var Tt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,W)|0,i=(i=i+Math.imul(O,X)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,X)|0,n=n+Math.imul(C,J)|0,i=(i=i+Math.imul(C,K)|0)+Math.imul(L,J)|0,a=a+Math.imul(L,K)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(k,rt)|0,i=(i=i+Math.imul(k,nt)|0)+Math.imul(T,rt)|0,a=a+Math.imul(T,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(m,lt)|0,i=(i=i+Math.imul(m,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(g,ht)|0,a=a+Math.imul(g,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,gt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,gt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(O,J)|0,i=(i=i+Math.imul(O,K)|0)+Math.imul(z,J)|0,a=a+Math.imul(z,K)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(k,at)|0,i=(i=i+Math.imul(k,ot)|0)+Math.imul(T,at)|0,a=a+Math.imul(T,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(m,ht)|0,i=(i=i+Math.imul(m,ft)|0)+Math.imul(y,ht)|0,a=a+Math.imul(y,ft)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,gt)|0)+Math.imul(g,dt)|0))<<13)|0;c=((a=a+Math.imul(g,gt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(z,Q)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(k,lt)|0,i=(i=i+Math.imul(k,ct)|0)+Math.imul(T,lt)|0,a=a+Math.imul(T,ct)|0,n=n+Math.imul(b,ht)|0,i=(i=i+Math.imul(b,ft)|0)+Math.imul(_,ht)|0,a=a+Math.imul(_,ft)|0;var St=(c+(n=n+Math.imul(m,dt)|0)|0)+((8191&(i=(i=i+Math.imul(m,gt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,gt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(k,ht)|0,i=(i=i+Math.imul(k,ft)|0)+Math.imul(T,ht)|0,a=a+Math.imul(T,ft)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,gt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,gt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(M,ht)|0,i=(i=i+Math.imul(M,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(k,dt)|0)|0)+((8191&(i=(i=i+Math.imul(k,gt)|0)+Math.imul(T,dt)|0))<<13)|0;c=((a=a+Math.imul(T,gt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,gt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,gt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(O,ht)|0,i=(i=i+Math.imul(O,ft)|0)+Math.imul(z,ht)|0,a=a+Math.imul(z,ft)|0;var Pt=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,gt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,gt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Ot=(c+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,gt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,gt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ht),i=(i=Math.imul(B,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,gt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,gt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var It=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,gt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,gt))+(i>>>13)|0)+(It>>>26)|0,It&=67108863,l[0]=vt,l[1]=mt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=kt,l[8]=Tt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=Pt,l[16]=Ot,l[17]=zt,l[18]=It,0!==c&&(l[19]=c,r.length++),r};function d(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(p=f),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?p(this,t,e):r<63?f(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,h=(0|t.words[u])*(0|e.words[c]),f=67108863&h;s=67108863&(f=f+s|0),i+=(o=(o=o+(h/67108864|0)|0)+(f>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):d(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var h=l,f=c,p=0;p<o;p++){var d=r[u+p],g=n[u+p],v=r[u+p+o],m=n[u+p+o],y=h*v-f*m;m=h*m+f*v,v=y,r[u+p]=d+v,n[u+p]=g+m,r[u+p+o]=d-v,n[u+p+o]=g-m,p!==s&&(y=l*h-c*f,f=l*f+c*h,h=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),h=new Array(n),f=r.words;f.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,h,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*h[p];l[p]=s[p]*h[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,f,a,n,i),this.conjugate(f,a,n),this.normalize13b(f,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),d(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var h=l-1;h>=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,g=1;0==(r.words[0]&g)&&d<26;++d,g<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new w(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function y(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function x(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function _(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function w(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){w.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(y,m),y.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},y.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(x,m),i(b,m),i(_,m),_.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new y;else if(\"p224\"===t)e=new x;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new _}return v[t]=e,e},w.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},w.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},w.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},w.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},w.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},w.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},w.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},w.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},w.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},w.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},w.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},w.prototype.isqr=function(t){return this.imul(t,t.clone())},w.prototype.sqr=function(t){return this.mul(t,t)},w.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,i),f=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var g=p,v=0;0!==g.cmp(s);v++)g=g.redSqr();n(v<d);var m=this.pow(h,new a(1).iushln(d-v-1));f=f.redMul(m),h=m.redSqr(),p=p.redMul(h),d=v}return f},w.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},w.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var h=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==h||0!==o?(o<<=1,o|=h,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},w.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},w.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,w),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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n,i=t(\"typedarray-pool\"),a=t(\"./lib/sweep\"),o=t(\"./lib/intersect\");function s(t,e){for(var r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var h,f=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,f,p))>0){if(1===u&&n)a.init(s),h=a.sweepComplete(u,r,0,s,f,p,0,s,f,p);else{var d=i.mallocDouble(2*u*c),g=i.mallocInt32(c);(c=l(e,u,d,g))>0&&(a.init(s+c),h=1===u?a.sweepBipartite(u,r,0,s,f,p,0,c,d,g):o(u,r,n,s,f,p,c,d,g),i.free(d),i.free(g))}i.free(f),i.free(p)}return h}}}function u(t,e){n.push([t,e])}},{\"./lib/intersect\":98,\"./lib/sweep\":102,\"typedarray-pool\":545}],97:[function(t,e,r){\"use strict\";var 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0!==(P=H?l.sweepBipartite(t,e,B,N,X,Z,R,$,Y,W):l.sweepBipartite(t,e,R,$,Y,W,B,N,X,Z)))return P}else T(O++,D+1,R,$,B,N,H,-1/0,1/0),T(O++,D+1,B,N,R,$,1^H,-1/0,1/0);if($<F){var Q=c(t,D,B,N,X,Z),tt=X[z*Q+D],et=g(t,D,Q,N,X,Z,tt);if(et<N&&T(O++,D,$,F,et,N,(4|H)+(G?16:0),tt,q),B<Q&&T(O++,D,$,F,B,Q,(2|H)+(G?16:0),V,tt),Q+1===et){if(void 0!==(P=G?M(t,D,e,$,F,Y,W,Q,X,Z[Q]):A(t,D,e,H,$,F,Y,W,Q,X,Z[Q])))return P}else if(Q<et){var rt;if(G){if(rt=y(t,D,$,F,Y,W,tt),$<rt){var nt=g(t,D,$,rt,Y,W,tt);if(D===t-2){if($<nt&&void 0!==(P=l.sweepComplete(t,e,$,nt,Y,W,Q,et,X,Z)))return P;if(nt<rt&&void 0!==(P=l.sweepBipartite(t,e,nt,rt,Y,W,Q,et,X,Z)))return P}else $<nt&&T(O++,D+1,$,nt,Q,et,16,-1/0,1/0),nt<rt&&(T(O++,D+1,nt,rt,Q,et,0,-1/0,1/0),T(O++,D+1,Q,et,nt,rt,1,-1/0,1/0))}}else rt=H?x(t,D,$,F,Y,W,tt):y(t,D,$,F,Y,W,tt),$<rt&&(D===t-2?P=H?l.sweepBipartite(t,e,Q,et,X,Z,$,rt,Y,W):l.sweepBipartite(t,e,$,rt,Y,W,Q,et,X,Z):(T(O++,D+1,$,rt,Q,et,H,-1/0,1/0),T(O++,D+1,Q,et,$,rt,1^H,-1/0,1/0)))}}}}};var n=t(\"typedarray-pool\"),i=t(\"bit-twiddle\"),a=t(\"./brute\"),o=a.partial,s=a.full,l=t(\"./sweep\"),c=t(\"./median\"),u=t(\"./partition\"),h=128,f=1<<22,p=1<<22,d=u(\"!(lo>=p0)&&!(p1>=hi)\",[\"p0\",\"p1\"]),g=u(\"lo===p0\",[\"p0\"]),v=u(\"lo<p0\",[\"p0\"]),m=u(\"hi<=p0\",[\"p0\"]),y=u(\"lo<=p0&&p0<=hi\",[\"p0\"]),x=u(\"lo<p0&&p0<=hi\",[\"p0\"]),b=6,_=2,w=n.mallocInt32(1024),k=n.mallocDouble(1024);function T(t,e,r,n,i,a,o,s,l){var c=b*t;w[c]=e,w[c+1]=r,w[c+2]=n,w[c+3]=i,w[c+4]=a,w[c+5]=o;var u=_*t;k[u]=s,k[u+1]=l}function A(t,e,r,n,i,a,o,s,l,c,u){var h=2*t,f=l*h,p=c[f+e];t:for(var d=i,g=i*h;d<a;++d,g+=h){var v=o[g+e],m=o[g+e+t];if(!(p<v||m<p)&&(!n||p!==v)){for(var y,x=s[d],b=e+1;b<t;++b){v=o[g+b],m=o[g+b+t];var _=c[f+b],w=c[f+b+t];if(m<_||w<v)continue t}if(void 0!==(y=n?r(u,x):r(x,u)))return y}}}function M(t,e,r,n,i,a,o,s,l,c){var u=2*t,h=s*u,f=l[h+e];t:for(var p=n,d=n*u;p<i;++p,d+=u){var g=o[p];if(g!==c){var v=a[d+e],m=a[d+e+t];if(!(f<v||m<f)){for(var y=e+1;y<t;++y){v=a[d+y],m=a[d+y+t];var x=l[h+y],b=l[h+y+t];if(m<x||b<v)continue t}var _=r(g,c);if(void 0!==_)return _}}}}},{\"./brute\":97,\"./median\":99,\"./partition\":100,\"./sweep\":102,\"bit-twiddle\":92,\"typedarray-pool\":545}],99:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,o,s,l){if(o<=r+1)return r;var c=r,u=o,h=o+r>>>1,f=2*t,p=h,d=s[f*h+e];for(;c<u;){if(u-c<i){a(t,e,c,u,s,l),d=s[f*h+e];break}var g=u-c,v=Math.random()*g+c|0,m=s[f*v+e],y=Math.random()*g+c|0,x=s[f*y+e],b=Math.random()*g+c|0,_=s[f*b+e];m<=x?_>=x?(p=y,d=x):m>=_?(p=v,d=m):(p=b,d=_):x>=_?(p=y,d=x):_>=m?(p=v,d=m):(p=b,d=_);for(var w=f*(u-1),k=f*p,T=0;T<f;++T,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var M=l[u-1];l[u-1]=l[p],l[p]=M,p=n(t,e,c,u-1,s,l,d);for(var w=f*(u-1),k=f*p,T=0;T<f;++T,++w,++k){var A=s[w];s[w]=s[k],s[k]=A}var M=l[u-1];if(l[u-1]=l[p],l[p]=M,h<p){for(u=p-1;c<u&&s[f*(u-1)+e]===d;)u-=1;u+=1}else{if(!(p<h))break;for(c=p+1;c<u&&s[f*c+e]===d;)c+=1}}return n(t,e,r,h,s,l,s[f*h+e])};var n=t(\"./partition\")(\"lo<p0\",[\"p0\"]),i=8;function a(t,e,r,n,i,a){for(var o=2*t,s=o*(r+1)+e,l=r+1;l<n;++l,s+=o)for(var c=i[s],u=l,h=o*(l-1);u>r&&i[h+e]>c;--u,h-=o){for(var f=h,p=h+o,d=0;d<o;++d,++f,++p){var g=i[f];i[f]=i[p],i[p]=g}var v=a[u];a[u]=a[u-1],a[u-1]=v}}},{\"./partition\":100}],100:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=\"abcdef\".split(\"\").concat(e),i=[];t.indexOf(\"lo\")>=0&&i.push(\"lo=e[k+n]\");t.indexOf(\"hi\")>=0&&i.push(\"hi=e[k+o]\");return r.push(n.replace(\"_\",i.join()).replace(\"$\",t)),Function.apply(void 0,r)};var n=\"for(var j=2*a,k=j*c,l=k,m=c,n=b,o=a+b,p=c;d>p;++p,k+=j){var _;if($)if(m===p)m+=1,l+=j;else{for(var s=0;j>s;++s){var t=e[k+s];e[k+s]=e[l],e[l++]=t}var u=f[p];f[p]=f[m],f[m++]=u}}return m\"},{}],101:[function(t,e,r){\"use strict\";e.exports=function(t,e){e<=4*n?i(0,e-1,t):function t(e,r,h){var 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