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sitc/ml21/preprocessing/11_3_autoclean.ipynb

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Added preprocessing notebooks
2024-04-03 20:50:36 +00:00
{
"cells": [
{
"cell_type": "markdown",
"id": "849ad57e-6adb-4c2e-afd6-73db37eef572",
"metadata": {},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"id": "179cc802-9f1d-40b0-bf0c-9d4fb7ea1262",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"id": "9858d815-0390-4e77-a5ff-a8d2a1960981",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"id": "238bab60-75f0-4d29-ab05-66afc463b506",
"metadata": {},
"source": [
"# Autoclean\n",
"A simple library to clean data. [Autoclean](https://github.com/elisemercury/AutoClean) supports:\n",
"AutoClean supports:\n",
"\n",
"* Handling of duplicates\n",
"* Various imputation methods for missing values\n",
"* Handling of outliers\n",
"* Encoding of categorical data (OneHot, Label)\n",
"* Extraction of data time values\n",
"\n",
"Install the package: **pip install py-AutoClean**.\n",
"\n",
"Parameters:\n",
"\n",
"* **duplicates**\n",
" * default: False,\n",
" * other values: 'auto', True\n",
"* **missing_num**\n",
" * default:False,\n",
" * other values:\t'auto', 'linreg', 'knn', 'mean', 'median', 'most_frequent', 'delete', False\n",
"* **missing_categ**\n",
" * default: False,\n",
" * other values:\t'auto', 'logreg', 'knn', 'most_frequent', 'delete', False\n",
"* **encode_categ**\n",
" * default: False,\n",
" * other values:\t'auto', ['onehot'], ['label'], False ; to encode only specific columns add a list of column names or indexes: ['auto', ['col1', 2]]\n",
"* **extract_datetime**\n",
" * default:\tFalse,\n",
" * other values:\t'auto', 'D', 'M', 'Y', 'h', 'm', 's'\n",
"* **outliers**\n",
" * default:\tFalse,\n",
" * other values:\t'auto', 'winz', 'delete'\n",
"* **outlier_param**\tdefault:\t1.5, other values:\tany int or float, False\n",
"* **logfile**\n",
" * default: True,\n",
" * other values:\tFalse\n",
"* **verbose**\n",
" * default: False,\n",
" * other values:\tTrue"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "491b034b-994e-4f06-b4bc-df0590a62aab",
"metadata": {},
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" PassengerId Survived Pclass \\\n",
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".. ... ... ... \n",
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"887 888 1 1 \n",
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" Name Sex Age SibSp \\\n",
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"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
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"890 Dooley, Mr. Patrick male 32.0 0 \n",
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"889 0 111369 30.0000 C148 C \n",
"890 0 370376 7.7500 NaN Q \n",
"\n",
"[891 rows x 12 columns]"
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},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from AutoClean import AutoClean\n",
"\n",
"df = pd.read_csv('https://raw.githubusercontent.com/gsi-upm/sitc/master/ml2/data-titanic/train.csv')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "d842eedf-3971-4966-a8b4-543bb56dd60d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AutoClean process completed in 0.289385 seconds\n",
"Logfile saved to: /home/cif/GoogleDrive/cursos/summer-school-romania/2019/notebooks/preprocessing/autoclean.log\n"
]
}
],
"source": [
"autoclean = AutoClean(df, mode='auto')\n",
"\n",
"# We can control the preprocessing\n",
"#autoclean = AutoClean(df, mode='auto', duplicates=False, missing_num=False, missing_categ=False, encode_categ=False, extract_datetime=False, outliers=False, outlier_param=1.5, logfile=True, verbose=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "4ede7c55-475a-4748-8cc4-788f46c88b26",
"metadata": {},
"outputs": [
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" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
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" Parch Ticket Fare Cabin Embarked Sex_female Sex_male \\\n",
"0 0 A/5 21171 7.2500 C128 S False True \n",
"1 0 PC 17599 65.6344 C85 C True False \n",
"2 0 STON/O2. 3101282 7.9250 C128 S True False \n",
"3 0 113803 53.1000 C123 S True False \n",
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},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_clean = autoclean.output\n",
"df_clean[0:5]"
]
}
],
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