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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"from sklearn.preprocessing import MultiLabelBinarizer\n",
"from sklearn.svm import OneClassSVM \n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import f1_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_pickle('../data/processed/features.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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],
"text/plain": [
" verified_email verified_primary_email n_works n_doi n_arxiv \\\n",
"0 False False 0 0 0 \n",
"1 True True 0 0 0 \n",
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"3 True True 0 0 0 \n",
"4 True True 0 0 0 \n",
"... ... ... ... ... ... \n",
"10989644 True True 0 0 0 \n",
"10989645 True True 7 7 0 \n",
"10989646 True True 0 0 0 \n",
"10989647 True True 0 0 0 \n",
"10989648 True True 0 0 0 \n",
"\n",
" n_pmc n_other_pids label n_emails n_urls ... n_employment \\\n",
"0 0 0 False 0 0 ... 0 \n",
"1 0 0 False 0 0 ... 1 \n",
"2 0 0 False 0 0 ... 0 \n",
"3 0 0 False 0 0 ... 1 \n",
"4 0 0 False 0 0 ... 2 \n",
"... ... ... ... ... ... ... ... \n",
"10989644 0 0 False 0 0 ... 1 \n",
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"10989646 0 0 False 0 0 ... 0 \n",
"10989647 0 0 False 0 0 ... 1 \n",
"10989648 0 0 False 0 0 ... 0 \n",
"\n",
" n_ext_work_source n_valid_education n_valid_employment \\\n",
"0 0 0.0 0.0 \n",
"1 0 0.0 1.0 \n",
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"... ... ... ... \n",
"10989644 0 1.0 1.0 \n",
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"10989646 0 0.0 0.0 \n",
"10989647 0 2.0 1.0 \n",
"10989648 0 0.0 0.0 \n",
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" biography_length biography_n_sentences biography_n_words \\\n",
"0 0 0.0 0.0 \n",
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"... ... ... ... \n",
"10989644 118 2.0 23.0 \n",
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"10989646 0 0.0 0.0 \n",
"10989647 0 0.0 0.0 \n",
"10989648 0 0.0 0.0 \n",
"\n",
" date_diff ref_year date_stale \n",
"0 0.009618 2018 1153.980551 \n",
"1 715.078025 2018 406.980815 \n",
"2 48.001631 2019 456.736688 \n",
"3 1863.042464 2015 217.817512 \n",
"4 827.372135 2014 1779.456397 \n",
"... ... ... ... \n",
"10989644 0.051116 2020 139.242812 \n",
"10989645 2131.978000 2015 158.560081 \n",
"10989646 0.000597 2020 139.226289 \n",
"10989647 1433.222830 2016 150.839463 \n",
"10989648 1591.542558 2016 152.263413 \n",
"\n",
"[10989649 rows x 23 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2075872, 23)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.label == 1].shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(8913777, 23)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.label == 0].shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# split into train/test sets\n",
"x = df.loc[:, df.columns != 'label']\n",
"y = df['label']\n",
added michele notebook
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"train_x, test_x, train_y, test_y = train_test_split(x, y, train_size=200000, test_size=1000000, random_state=2, stratify=y)"
new try with supervisioned ML
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]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
added michele notebook
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"(37779, 22)"
new try with supervisioned ML
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]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_x[train_y==1].shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(188893, 22)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_x[test_y==1].shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OneClassSVM()"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define outlier detection model\n",
"model = OneClassSVM(gamma='scale', nu=0.5)\n",
"\n",
"# fit on majority class\n",
"train_x = train_x[train_y==1]\n",
"model.fit(train_x)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F1 Score: 0.872\n"
]
}
],
"source": [
"# detect outliers in the test set\n",
"y_hat = model.predict(test_x)\n",
"\n",
"# mark inliers 1, outliers -1\n",
"test_y[test_y == 0] = -1\n",
"test_y[test_y == 1] = 1\n",
"\n",
"# calculate score\n",
"score = f1_score(test_y, y_hat, pos_label=-1)\n",
"print('F1 Score: %.3f' % score)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/andrea/.virtualenvs/data-science/lib/python3.8/site-packages/pandas/core/indexing.py:1597: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self.obj[key] = value\n",
"/Users/andrea/.virtualenvs/data-science/lib/python3.8/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" self._setitem_single_column(ilocs[0], value, pi)\n"
]
}
],
"source": [
"test_x.loc[:, 'label'] = test_y.values"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"test_x.loc[:, 'prediction'] = y_hat"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>verified_email</th>\n",
" <th>verified_primary_email</th>\n",
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" <th>biography_n_words</th>\n",
" <th>date_diff</th>\n",
" <th>ref_year</th>\n",
" <th>date_stale</th>\n",
" <th>label</th>\n",
" <th>prediction</th>\n",
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added michele notebook
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" <th>4867967</th>\n",
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" <td>True</td>\n",
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added michele notebook
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" <td>189</td>\n",
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added michele notebook
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" <td>12</td>\n",
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" <td>0</td>\n",
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added michele notebook
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" <td>1.0</td>\n",
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" <td>2.0</td>\n",
added michele notebook
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" <td>1099</td>\n",
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" <td>2071.017713</td>\n",
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" <td>136.067404</td>\n",
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" <td>1.0</td>\n",
added michele notebook
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" <td>-1</td>\n",
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" </tr>\n",
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added michele notebook
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" <th>8751870</th>\n",
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" <td>True</td>\n",
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added michele notebook
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added michele notebook
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" <td>0.0</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>722.965509</td>\n",
" <td>2018</td>\n",
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" <td>-1.0</td>\n",
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new try with supervisioned ML
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" </tr>\n",
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added michele notebook
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" <th>10041539</th>\n",
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" <td>True</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>2376.608882</td>\n",
" <td>2014</td>\n",
" <td>59.669744</td>\n",
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" <td>-1</td>\n",
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" </tr>\n",
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added michele notebook
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" <th>184408</th>\n",
new try with supervisioned ML
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" <td>True</td>\n",
" <td>True</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>21</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>1</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
" <td>...</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>4.0</td>\n",
" <td>1.0</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>461.839456</td>\n",
new try with supervisioned ML
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" <td>2017</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>1072.829123</td>\n",
" <td>1.0</td>\n",
" <td>-1</td>\n",
new try with supervisioned ML
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added michele notebook
2021-07-20 12:15:17 +02:00
" <th>8166189</th>\n",
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new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>1.0</td>\n",
" <td>1.0</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
" <td>395.328151</td>\n",
" <td>2019</td>\n",
" <td>92.050538</td>\n",
new try with supervisioned ML
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" <td>1.0</td>\n",
" <td>-1</td>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <tr>\n",
added michele notebook
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" <th>7680437</th>\n",
new try with supervisioned ML
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" <td>True</td>\n",
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added michele notebook
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" <td>34</td>\n",
" <td>25</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
added michele notebook
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" <td>35</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
added michele notebook
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" <td>1</td>\n",
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" <td>...</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>1.0</td>\n",
" <td>1.0</td>\n",
new try with supervisioned ML
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" <td>0</td>\n",
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added michele notebook
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" <td>1854.980732</td>\n",
" <td>2016</td>\n",
" <td>58.626353</td>\n",
new try with supervisioned ML
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" <td>-1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
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" <th>3679223</th>\n",
new try with supervisioned ML
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" <td>True</td>\n",
" <td>True</td>\n",
added michele notebook
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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new try with supervisioned ML
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" <td>0</td>\n",
" <td>0</td>\n",
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added michele notebook
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" <td>1.0</td>\n",
new try with supervisioned ML
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
"[205134 rows x 24 columns]"
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
]
},
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"metadata": {},
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added michele notebook
2021-07-20 12:15:17 +02:00
"execution_count": 16,
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"metadata": {},
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"source": [
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]
},
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
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2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
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added michele notebook
2021-07-20 12:15:17 +02:00
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new try with supervisioned ML
2021-04-29 18:50:02 +02:00
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" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.005271</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>169.293431</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>801178</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.008069</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>131.828965</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>10581997</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>445</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>4.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>85.0</td>\n",
" <td>0.020527</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>129.861984</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>9831120</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.004996</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>167.149854</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>7689620</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.003671</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>203.189280</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>10215555</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.000964</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>192.363398</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>10985986</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.003662</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>195.359312</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>8567972</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.005248</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>196.358010</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>1041978</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>445</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>4.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>85.0</td>\n",
" <td>0.006524</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>167.270642</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>2749172</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>540</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>4.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>103.0</td>\n",
" <td>0.001172</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>142.989201</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>3274872</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.017478</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>126.967926</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>7963350</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.003572</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>190.312686</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>4392500</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.004414</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>206.150937</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>3243302</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>1</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.003391</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>140.230023</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>6263478</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>552</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>4.0</td>\n",
" <td>107.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.005174</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>140.365511</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>4230883</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.001681</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>153.354072</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>3659063</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.010618</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>131.658167</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>5659388</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.011685</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>140.250630</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>8567973</th>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>0.003278</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>184.270401</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>9724190</th>\n",
new try with supervisioned ML
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" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>543</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>4.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>107.0</td>\n",
" <td>6.993352</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>135.124112</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <th>880090</th>\n",
new try with supervisioned ML
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" <td>True</td>\n",
" <td>True</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>445</td>\n",
" <td>4.0</td>\n",
" <td>85.0</td>\n",
" <td>0.001572</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>2020</td>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
" <td>143.298327</td>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
" <td>-1.0</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
added michele notebook
2021-07-20 12:15:17 +02:00
"<p>29 rows × 24 columns</p>\n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"</div>"
],
"text/plain": [
" verified_email verified_primary_email n_works n_doi n_arxiv \\\n",
"7579770 True True 0 0 0 \n",
"4173344 True True 0 0 0 \n",
"6370669 True True 0 0 0 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6099073 True True 0 0 0 \n",
"8341750 True True 0 0 0 \n",
"10546308 True True 0 0 0 \n",
new try with supervisioned ML
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"5109458 True True 0 0 0 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"989919 True True 0 0 0 \n",
"9173011 True True 0 0 0 \n",
"801178 True True 0 0 0 \n",
new try with supervisioned ML
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"10581997 True True 0 0 0 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"9831120 True True 0 0 0 \n",
"7689620 True True 0 0 0 \n",
"10215555 True True 0 0 0 \n",
"10985986 True True 0 0 0 \n",
"8567972 True True 0 0 0 \n",
"1041978 True True 0 0 0 \n",
"2749172 True True 0 0 0 \n",
"3274872 True True 0 0 0 \n",
"7963350 True True 0 0 0 \n",
"4392500 True True 0 0 0 \n",
new try with supervisioned ML
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"3243302 True True 0 0 0 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6263478 True True 0 0 0 \n",
"4230883 True True 0 0 0 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3659063 True True 0 0 0 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"5659388 True True 0 0 0 \n",
"8567973 True True 0 0 0 \n",
new try with supervisioned ML
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"9724190 True True 0 0 0 \n",
"880090 True True 0 0 0 \n",
"\n",
" n_pmc n_other_pids n_emails n_urls n_ids ... \\\n",
"7579770 0 0 0 1 0 ... \n",
"4173344 0 0 0 1 0 ... \n",
"6370669 0 0 0 1 0 ... \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6099073 0 0 0 1 0 ... \n",
"8341750 0 0 0 10 0 ... \n",
"10546308 0 0 0 1 0 ... \n",
new try with supervisioned ML
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"5109458 0 0 0 1 0 ... \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"989919 0 0 0 1 0 ... \n",
"9173011 0 0 0 1 0 ... \n",
"801178 0 0 0 1 0 ... \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"10581997 0 0 0 1 0 ... \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"9831120 0 0 0 1 0 ... \n",
"7689620 0 0 0 1 0 ... \n",
"10215555 0 0 0 1 0 ... \n",
"10985986 0 0 0 1 0 ... \n",
"8567972 0 0 0 1 0 ... \n",
"1041978 0 0 0 1 0 ... \n",
"2749172 0 0 0 1 0 ... \n",
"3274872 0 0 0 1 0 ... \n",
"7963350 0 0 0 1 0 ... \n",
"4392500 0 0 0 1 0 ... \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3243302 0 0 0 1 0 ... \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6263478 0 0 0 1 0 ... \n",
"4230883 0 0 0 1 0 ... \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3659063 0 0 0 1 0 ... \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"5659388 0 0 0 1 0 ... \n",
"8567973 0 0 0 1 0 ... \n",
new try with supervisioned ML
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"9724190 0 0 0 1 0 ... \n",
"880090 0 0 0 1 0 ... \n",
"\n",
" n_valid_education n_valid_employment biography_length \\\n",
"7579770 0.0 0.0 445 \n",
"4173344 0.0 0.0 445 \n",
"6370669 0.0 0.0 445 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6099073 0.0 0.0 445 \n",
"8341750 0.0 0.0 392 \n",
"10546308 0.0 0.0 445 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"5109458 0.0 0.0 445 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"989919 0.0 0.0 561 \n",
"9173011 0.0 0.0 445 \n",
"801178 0.0 0.0 445 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"10581997 0.0 0.0 445 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"9831120 0.0 0.0 445 \n",
"7689620 0.0 0.0 445 \n",
"10215555 0.0 0.0 445 \n",
"10985986 0.0 0.0 445 \n",
"8567972 0.0 0.0 445 \n",
"1041978 0.0 0.0 445 \n",
"2749172 0.0 0.0 540 \n",
"3274872 0.0 0.0 445 \n",
"7963350 0.0 0.0 445 \n",
"4392500 0.0 0.0 445 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3243302 0.0 0.0 445 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6263478 0.0 0.0 552 \n",
"4230883 0.0 0.0 445 \n",
new try with supervisioned ML
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"3659063 0.0 0.0 445 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"5659388 0.0 0.0 445 \n",
"8567973 0.0 0.0 445 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"9724190 0.0 0.0 543 \n",
"880090 0.0 0.0 445 \n",
"\n",
" biography_n_sentences biography_n_words date_diff ref_year \\\n",
"7579770 4.0 85.0 0.002694 2020 \n",
"4173344 4.0 85.0 0.005405 2020 \n",
"6370669 4.0 85.0 0.006984 2020 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6099073 4.0 85.0 0.025714 2020 \n",
"8341750 3.0 74.0 0.015018 2021 \n",
"10546308 4.0 85.0 0.002717 2020 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"5109458 4.0 85.0 0.004633 2020 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"989919 5.0 110.0 0.002649 2020 \n",
"9173011 4.0 85.0 0.005271 2020 \n",
"801178 4.0 85.0 0.008069 2020 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"10581997 4.0 85.0 0.020527 2020 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"9831120 4.0 85.0 0.004996 2020 \n",
"7689620 4.0 85.0 0.003671 2020 \n",
"10215555 4.0 85.0 0.000964 2020 \n",
"10985986 4.0 85.0 0.003662 2020 \n",
"8567972 4.0 85.0 0.005248 2020 \n",
"1041978 4.0 85.0 0.006524 2020 \n",
"2749172 4.0 103.0 0.001172 2020 \n",
"3274872 4.0 85.0 0.017478 2020 \n",
"7963350 4.0 85.0 0.003572 2020 \n",
"4392500 4.0 85.0 0.004414 2020 \n",
new try with supervisioned ML
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"3243302 4.0 85.0 0.003391 2020 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6263478 4.0 107.0 0.005174 2020 \n",
"4230883 4.0 85.0 0.001681 2020 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3659063 4.0 85.0 0.010618 2020 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"5659388 4.0 85.0 0.011685 2020 \n",
"8567973 4.0 85.0 0.003278 2020 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"9724190 4.0 107.0 6.993352 2020 \n",
"880090 4.0 85.0 0.001572 2020 \n",
"\n",
" date_stale label prediction \n",
"7579770 184.261009 -1.0 -1 \n",
"4173344 191.303842 -1.0 -1 \n",
"6370669 178.167846 -1.0 -1 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6099073 142.184268 -1.0 -1 \n",
"8341750 46.549118 -1.0 -1 \n",
"10546308 203.149999 -1.0 -1 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"5109458 140.166676 -1.0 -1 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"989919 146.098129 -1.0 -1 \n",
"9173011 169.293431 -1.0 -1 \n",
"801178 131.828965 -1.0 -1 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"10581997 129.861984 -1.0 -1 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"9831120 167.149854 -1.0 -1 \n",
"7689620 203.189280 -1.0 -1 \n",
"10215555 192.363398 -1.0 -1 \n",
"10985986 195.359312 -1.0 -1 \n",
"8567972 196.358010 -1.0 -1 \n",
"1041978 167.270642 -1.0 -1 \n",
"2749172 142.989201 -1.0 -1 \n",
"3274872 126.967926 -1.0 -1 \n",
"7963350 190.312686 -1.0 -1 \n",
"4392500 206.150937 -1.0 -1 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3243302 140.230023 -1.0 -1 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"6263478 140.365511 -1.0 -1 \n",
"4230883 153.354072 -1.0 -1 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"3659063 131.658167 -1.0 -1 \n",
added michele notebook
2021-07-20 12:15:17 +02:00
"5659388 140.250630 -1.0 -1 \n",
"8567973 184.270401 -1.0 -1 \n",
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"9724190 135.124112 -1.0 -1 \n",
"880090 143.298327 -1.0 -1 \n",
"\n",
added michele notebook
2021-07-20 12:15:17 +02:00
"[29 rows x 24 columns]"
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
]
},
added michele notebook
2021-07-20 12:15:17 +02:00
"execution_count": 18,
new try with supervisioned ML
2021-04-29 18:50:02 +02:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_x[(test_x.index.isin(fake_df.index))]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}