1050 lines
36 KiB
Plaintext
1050 lines
36 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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||
"execution_count": 6,
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||
"id": "12dba44a",
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"metadata": {},
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||
"outputs": [],
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"source": [
|
||
"import numpy as np\n",
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"import pandas as pd"
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]
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||
},
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{
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"cell_type": "code",
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||
"execution_count": 7,
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"id": "1d480e94",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"zsh:1: command not found: kaggle\r\n"
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]
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}
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],
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"source": [
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"!kaggle datasets download -d gender_classification_v7.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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||
"id": "13a40d88",
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"metadata": {
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"scrolled": true
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{
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"<style scoped>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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||
" <th></th>\n",
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" <th>long_hair</th>\n",
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||
" <th>forehead_width_cm</th>\n",
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||
" <th>forehead_height_cm</th>\n",
|
||
" <th>nose_wide</th>\n",
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" <th>nose_long</th>\n",
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||
" <th>lips_thin</th>\n",
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||
" <th>distance_nose_to_lip_long</th>\n",
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" <th>gender</th>\n",
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" long_hair forehead_width_cm forehead_height_cm nose_wide nose_long \\\n",
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"0 1 11.8 6.1 1 0 \n",
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"\n",
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" lips_thin distance_nose_to_lip_long gender \n",
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"0 1 1 Male \n",
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"\n",
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"[5001 rows x 8 columns]"
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"execution_count": 41,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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||
],
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"source": [
|
||
"raw_data = pd.read_csv(\"gender_class.csv\")\n",
|
||
"raw_data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "51c05e9a",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wyczyści zbiór z artefaktów (np. puste linie, przykłady z niepoprawnymi wartościami)"
|
||
]
|
||
},
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||
{
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"cell_type": "code",
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||
"id": "c70571df",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"def clean_data(data):\n",
|
||
" data.dropna(inplace=True)\n",
|
||
" return data"
|
||
]
|
||
},
|
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{
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"cell_type": "code",
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||
"execution_count": 43,
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"id": "0481b0dd",
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|
||
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|
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|
||
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|
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|
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|
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|
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|
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
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|
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|
||
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|
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
||
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|
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|
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],
|
||
"text/plain": [
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide nose_long \\\n",
|
||
"0 1 11.8 6.1 1 0 \n",
|
||
"1 0 14.0 5.4 0 0 \n",
|
||
"2 0 11.8 6.3 1 1 \n",
|
||
"3 0 14.4 6.1 0 1 \n",
|
||
"4 1 13.5 5.9 0 0 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"4996 1 13.6 5.1 0 0 \n",
|
||
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|
||
"4998 1 12.9 5.7 0 0 \n",
|
||
"4999 1 13.2 6.2 0 0 \n",
|
||
"5000 1 15.4 5.4 1 1 \n",
|
||
"\n",
|
||
" lips_thin distance_nose_to_lip_long gender \n",
|
||
"0 1 1 Male \n",
|
||
"1 1 0 Female \n",
|
||
"2 1 1 Male \n",
|
||
"3 1 1 Male \n",
|
||
"4 0 0 Female \n",
|
||
"... ... ... ... \n",
|
||
"4996 0 0 Female \n",
|
||
"4997 0 0 Female \n",
|
||
"4998 0 0 Female \n",
|
||
"4999 0 0 Female \n",
|
||
"5000 1 1 Male \n",
|
||
"\n",
|
||
"[5001 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"raw_data = clean_data(raw_data)\n",
|
||
"raw_data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "717fab23",
|
||
"metadata": {},
|
||
"source": [
|
||
"Dokona normalizacji danych w zbiorze (np. normalizacja wartości float do zakresu 0.0 - 1.0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"id": "7fcacf03",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def normalize_data(data):\n",
|
||
" # znormalizuj wartości float do zakresu 0.0 - 1.0\n",
|
||
" for col in data.columns:\n",
|
||
" if data[col].dtype == float:\n",
|
||
" data[col] = (data[col] - data[col].min()) / (data[col].max() - data[col].min())\n",
|
||
"\n",
|
||
" return data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"id": "bfd844ad",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"normalized_data = normalize_data(raw_data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
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"id": "2d0b8499",
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|
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|
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" long_hair forehead_width_cm forehead_height_cm nose_wide nose_long \\\n",
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"0 1 0.097561 0.50 1 0 \n",
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|
||
"5000 1 0.975610 0.15 1 1 \n",
|
||
"\n",
|
||
" lips_thin distance_nose_to_lip_long gender \n",
|
||
"0 1 1 Male \n",
|
||
"1 1 0 Female \n",
|
||
"2 1 1 Male \n",
|
||
"3 1 1 Male \n",
|
||
"4 0 0 Female \n",
|
||
"... ... ... ... \n",
|
||
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||
"4997 0 0 Female \n",
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||
"4998 0 0 Female \n",
|
||
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|
||
"5000 1 1 Male \n",
|
||
"\n",
|
||
"[5001 rows x 8 columns]"
|
||
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|
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|
||
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|
||
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|
||
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|
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|
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|
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"source": [
|
||
"normalized_data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "61fbcddc",
|
||
"metadata": {},
|
||
"source": [
|
||
"2. Jeśli brak w zbiorze gotowego podziału na podzbiory train/dev/test, to dokona takiego podziału"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"id": "dc386189",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"train, dev, test = np.split(normalized_data.sample(frac=1, random_state=42), [int(.7*len(normalized_data)), int(.85*len(normalized_data))])\n",
|
||
"\n",
|
||
"# zapisz dane w osobnych plikach csv\n",
|
||
"train.to_csv('train.csv', index=False)\n",
|
||
"dev.to_csv('dev.csv', index=False)\n",
|
||
"test.to_csv('test.csv', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"id": "9f888962",
|
||
"metadata": {},
|
||
"outputs": [
|
||
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|
||
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|
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|
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|
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
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|
||
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|
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||
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||
" <td>0.20</td>\n",
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||
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||
" <td>0</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <tr>\n",
|
||
" <th>117</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
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|
||
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|
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
"text/plain": [
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide nose_long \\\n",
|
||
"4432 1 0.512195 0.10 1 1 \n",
|
||
"2162 1 0.243902 0.70 1 1 \n",
|
||
"2396 1 0.512195 0.15 1 0 \n",
|
||
"4769 1 0.853659 0.10 1 1 \n",
|
||
"2271 1 0.292683 0.70 0 1 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"846 1 0.097561 0.45 1 1 \n",
|
||
"2551 0 0.243902 0.35 1 1 \n",
|
||
"2928 1 0.634146 0.20 0 0 \n",
|
||
"117 1 0.707317 0.50 0 0 \n",
|
||
"645 1 0.195122 0.05 1 0 \n",
|
||
"\n",
|
||
" lips_thin distance_nose_to_lip_long gender \n",
|
||
"4432 1 1 Male \n",
|
||
"2162 1 1 Male \n",
|
||
"2396 0 0 Female \n",
|
||
"4769 0 1 Male \n",
|
||
"2271 0 0 Female \n",
|
||
"... ... ... ... \n",
|
||
"846 1 1 Male \n",
|
||
"2551 1 1 Male \n",
|
||
"2928 0 0 Female \n",
|
||
"117 0 0 Female \n",
|
||
"645 0 0 Female \n",
|
||
"\n",
|
||
"[750 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dev"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"id": "4598cea1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide \\\n",
|
||
"count 5001.000000 5001.000000 5001.000000 5001.000000 \n",
|
||
"mean 0.869626 0.434508 0.423155 0.493901 \n",
|
||
"std 0.336748 0.270031 0.270634 0.500013 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 1.000000 0.195122 0.200000 0.000000 \n",
|
||
"50% 1.000000 0.414634 0.400000 0.000000 \n",
|
||
"75% 1.000000 0.634146 0.650000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" nose_long lips_thin distance_nose_to_lip_long \n",
|
||
"count 5001.000000 5001.000000 5001.000000 \n",
|
||
"mean 0.507898 0.493101 0.498900 \n",
|
||
"std 0.499988 0.500002 0.500049 \n",
|
||
"min 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 \n",
|
||
"50% 1.000000 0.000000 0.000000 \n",
|
||
"75% 1.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 \n",
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide \\\n",
|
||
"count 3500.000000 3500.000000 3500.000000 3500.000000 \n",
|
||
"mean 0.870000 0.436021 0.425900 0.505714 \n",
|
||
"std 0.336351 0.270492 0.271348 0.500039 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 1.000000 0.195122 0.200000 0.000000 \n",
|
||
"50% 1.000000 0.414634 0.400000 1.000000 \n",
|
||
"75% 1.000000 0.634146 0.650000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" nose_long lips_thin distance_nose_to_lip_long \n",
|
||
"count 3500.000000 3500.000000 3500.000000 \n",
|
||
"mean 0.522000 0.499429 0.507714 \n",
|
||
"std 0.499587 0.500071 0.500012 \n",
|
||
"min 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 \n",
|
||
"50% 1.000000 0.000000 1.000000 \n",
|
||
"75% 1.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 \n",
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide \\\n",
|
||
"count 750.000000 750.000000 750.000000 750.000000 \n",
|
||
"mean 0.870667 0.419285 0.416933 0.472000 \n",
|
||
"std 0.335792 0.264474 0.269500 0.499549 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 1.000000 0.195122 0.200000 0.000000 \n",
|
||
"50% 1.000000 0.414634 0.400000 0.000000 \n",
|
||
"75% 1.000000 0.634146 0.637500 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" nose_long lips_thin distance_nose_to_lip_long \n",
|
||
"count 750.000000 750.000000 750.000000 \n",
|
||
"mean 0.466667 0.481333 0.465333 \n",
|
||
"std 0.499221 0.499985 0.499130 \n",
|
||
"min 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 0.000000 \n",
|
||
"75% 1.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 \n",
|
||
" long_hair forehead_width_cm forehead_height_cm nose_wide \\\n",
|
||
"count 751.000000 751.000000 751.000000 751.000000 \n",
|
||
"mean 0.866844 0.442662 0.416578 0.460719 \n",
|
||
"std 0.339969 0.273141 0.268567 0.498787 \n",
|
||
"min 0.000000 0.000000 0.000000 0.000000 \n",
|
||
"25% 1.000000 0.195122 0.200000 0.000000 \n",
|
||
"50% 1.000000 0.439024 0.400000 0.000000 \n",
|
||
"75% 1.000000 0.658537 0.600000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 1.000000 \n",
|
||
"\n",
|
||
" nose_long lips_thin distance_nose_to_lip_long \n",
|
||
"count 751.000000 751.000000 751.000000 \n",
|
||
"mean 0.483356 0.475366 0.491345 \n",
|
||
"std 0.500056 0.499726 0.500258 \n",
|
||
"min 0.000000 0.000000 0.000000 \n",
|
||
"25% 0.000000 0.000000 0.000000 \n",
|
||
"50% 0.000000 0.000000 0.000000 \n",
|
||
"75% 1.000000 1.000000 1.000000 \n",
|
||
"max 1.000000 1.000000 1.000000 \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for d in [raw_data,train, dev, test]:\n",
|
||
" print( d.describe())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "8fa84a56",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.8.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|