{
"cells": [
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import sklearn.model_selection\n",
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset wine (C:/Users/macty/.cache/huggingface/datasets/mstz___wine/wine/1.0.0/7c3844cac7ac7a22d5fbbaf60fc1d4e9c9deb1b9b9c4dbae6a7b1a962dbc96d8)\n",
"100%|██████████| 1/1 [00:00<00:00, 500.10it/s]\n"
]
}
],
"source": [
"dataset = load_dataset(\"mstz/wine\", \"wine\")"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['fixed_acidity', 'volatile_acidity', 'citric_acid', 'residual_sugar', 'chlorides', 'free_sulfur_dioxide', 'total_sulfur_dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality', 'is_red'],\n",
" num_rows: 6497\n",
"})"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset[\"train\"]"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [],
"source": [
"wine_dataset = pd.DataFrame(dataset[\"train\"])"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 7.4 | \n",
" 0.70 | \n",
" 0.00 | \n",
" 1.9 | \n",
" 0.076 | \n",
" 11.0 | \n",
" 34.0 | \n",
" 0.9978 | \n",
" 3.51 | \n",
" 0.56 | \n",
" 9.4 | \n",
" 5 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" 7.8 | \n",
" 0.88 | \n",
" 0.00 | \n",
" 2.6 | \n",
" 0.098 | \n",
" 25.0 | \n",
" 67.0 | \n",
" 0.9968 | \n",
" 3.20 | \n",
" 0.68 | \n",
" 9.8 | \n",
" 5 | \n",
" 0 | \n",
"
\n",
" \n",
" 2 | \n",
" 7.8 | \n",
" 0.76 | \n",
" 0.04 | \n",
" 2.3 | \n",
" 0.092 | \n",
" 15.0 | \n",
" 54.0 | \n",
" 0.9970 | \n",
" 3.26 | \n",
" 0.65 | \n",
" 9.8 | \n",
" 5 | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" 11.2 | \n",
" 0.28 | \n",
" 0.56 | \n",
" 1.9 | \n",
" 0.075 | \n",
" 17.0 | \n",
" 60.0 | \n",
" 0.9980 | \n",
" 3.16 | \n",
" 0.58 | \n",
" 9.8 | \n",
" 6 | \n",
" 0 | \n",
"
\n",
" \n",
" 4 | \n",
" 7.4 | \n",
" 0.70 | \n",
" 0.00 | \n",
" 1.9 | \n",
" 0.076 | \n",
" 11.0 | \n",
" 34.0 | \n",
" 0.9978 | \n",
" 3.51 | \n",
" 0.56 | \n",
" 9.4 | \n",
" 5 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n",
"0 7.4 0.70 0.00 1.9 0.076 \n",
"1 7.8 0.88 0.00 2.6 0.098 \n",
"2 7.8 0.76 0.04 2.3 0.092 \n",
"3 11.2 0.28 0.56 1.9 0.075 \n",
"4 7.4 0.70 0.00 1.9 0.076 \n",
"\n",
" free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n",
"0 11.0 34.0 0.9978 3.51 0.56 \n",
"1 25.0 67.0 0.9968 3.20 0.68 \n",
"2 15.0 54.0 0.9970 3.26 0.65 \n",
"3 17.0 60.0 0.9980 3.16 0.58 \n",
"4 11.0 34.0 0.9978 3.51 0.56 \n",
"\n",
" alcohol quality is_red \n",
"0 9.4 5 0 \n",
"1 9.8 5 0 \n",
"2 9.8 5 0 \n",
"3 9.8 6 0 \n",
"4 9.4 5 0 "
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_dataset.head()# podgląd danych"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 7.215307 | \n",
" 0.339666 | \n",
" 0.318633 | \n",
" 5.443235 | \n",
" 0.056034 | \n",
" 30.525319 | \n",
" 115.744574 | \n",
" 0.994697 | \n",
" 3.218501 | \n",
" 0.531268 | \n",
" 10.491801 | \n",
" 5.818378 | \n",
" 0.753886 | \n",
"
\n",
" \n",
" std | \n",
" 1.296434 | \n",
" 0.164636 | \n",
" 0.145318 | \n",
" 4.757804 | \n",
" 0.035034 | \n",
" 17.749400 | \n",
" 56.521855 | \n",
" 0.002999 | \n",
" 0.160787 | \n",
" 0.148806 | \n",
" 1.192712 | \n",
" 0.873255 | \n",
" 0.430779 | \n",
"
\n",
" \n",
" min | \n",
" 3.800000 | \n",
" 0.080000 | \n",
" 0.000000 | \n",
" 0.600000 | \n",
" 0.009000 | \n",
" 1.000000 | \n",
" 6.000000 | \n",
" 0.987110 | \n",
" 2.720000 | \n",
" 0.220000 | \n",
" 8.000000 | \n",
" 3.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 6.400000 | \n",
" 0.230000 | \n",
" 0.250000 | \n",
" 1.800000 | \n",
" 0.038000 | \n",
" 17.000000 | \n",
" 77.000000 | \n",
" 0.992340 | \n",
" 3.110000 | \n",
" 0.430000 | \n",
" 9.500000 | \n",
" 5.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 7.000000 | \n",
" 0.290000 | \n",
" 0.310000 | \n",
" 3.000000 | \n",
" 0.047000 | \n",
" 29.000000 | \n",
" 118.000000 | \n",
" 0.994890 | \n",
" 3.210000 | \n",
" 0.510000 | \n",
" 10.300000 | \n",
" 6.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 7.700000 | \n",
" 0.400000 | \n",
" 0.390000 | \n",
" 8.100000 | \n",
" 0.065000 | \n",
" 41.000000 | \n",
" 156.000000 | \n",
" 0.996990 | \n",
" 3.320000 | \n",
" 0.600000 | \n",
" 11.300000 | \n",
" 6.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 15.900000 | \n",
" 1.580000 | \n",
" 1.660000 | \n",
" 65.800000 | \n",
" 0.611000 | \n",
" 289.000000 | \n",
" 440.000000 | \n",
" 1.038980 | \n",
" 4.010000 | \n",
" 2.000000 | \n",
" 14.900000 | \n",
" 9.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 7.215307 0.339666 0.318633 5.443235 \n",
"std 1.296434 0.164636 0.145318 4.757804 \n",
"min 3.800000 0.080000 0.000000 0.600000 \n",
"25% 6.400000 0.230000 0.250000 1.800000 \n",
"50% 7.000000 0.290000 0.310000 3.000000 \n",
"75% 7.700000 0.400000 0.390000 8.100000 \n",
"max 15.900000 1.580000 1.660000 65.800000 \n",
"\n",
" chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 0.056034 30.525319 115.744574 0.994697 \n",
"std 0.035034 17.749400 56.521855 0.002999 \n",
"min 0.009000 1.000000 6.000000 0.987110 \n",
"25% 0.038000 17.000000 77.000000 0.992340 \n",
"50% 0.047000 29.000000 118.000000 0.994890 \n",
"75% 0.065000 41.000000 156.000000 0.996990 \n",
"max 0.611000 289.000000 440.000000 1.038980 \n",
"\n",
" pH sulphates alcohol quality is_red \n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 3.218501 0.531268 10.491801 5.818378 0.753886 \n",
"std 0.160787 0.148806 1.192712 0.873255 0.430779 \n",
"min 2.720000 0.220000 8.000000 3.000000 0.000000 \n",
"25% 3.110000 0.430000 9.500000 5.000000 1.000000 \n",
"50% 3.210000 0.510000 10.300000 6.000000 1.000000 \n",
"75% 3.320000 0.600000 11.300000 6.000000 1.000000 \n",
"max 4.010000 2.000000 14.900000 9.000000 1.000000 "
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_dataset.describe(include='all')"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wine_dataset[\"is_red\"].value_counts().plot(kind=\"bar\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.2964337577998153"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_dataset[\"fixed_acidity\"].std()"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([], dtype=int64), array([], dtype=int64))"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.where(pd.isnull(wine_dataset))## sprawdzanie czy istnieją puste wartości"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"for column in wine_dataset.columns:\n",
" wine_dataset[column] = wine_dataset[column] / wine_dataset[column].abs().max() # normalizacja"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
" 6497.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.453793 | \n",
" 0.214978 | \n",
" 0.191948 | \n",
" 0.082724 | \n",
" 0.091708 | \n",
" 0.105624 | \n",
" 0.263056 | \n",
" 0.957378 | \n",
" 0.802619 | \n",
" 0.265634 | \n",
" 0.704148 | \n",
" 0.646486 | \n",
" 0.753886 | \n",
"
\n",
" \n",
" std | \n",
" 0.081537 | \n",
" 0.104200 | \n",
" 0.087541 | \n",
" 0.072307 | \n",
" 0.057338 | \n",
" 0.061417 | \n",
" 0.128459 | \n",
" 0.002886 | \n",
" 0.040097 | \n",
" 0.074403 | \n",
" 0.080048 | \n",
" 0.097028 | \n",
" 0.430779 | \n",
"
\n",
" \n",
" min | \n",
" 0.238994 | \n",
" 0.050633 | \n",
" 0.000000 | \n",
" 0.009119 | \n",
" 0.014730 | \n",
" 0.003460 | \n",
" 0.013636 | \n",
" 0.950076 | \n",
" 0.678304 | \n",
" 0.110000 | \n",
" 0.536913 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.402516 | \n",
" 0.145570 | \n",
" 0.150602 | \n",
" 0.027356 | \n",
" 0.062193 | \n",
" 0.058824 | \n",
" 0.175000 | \n",
" 0.955110 | \n",
" 0.775561 | \n",
" 0.215000 | \n",
" 0.637584 | \n",
" 0.555556 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.440252 | \n",
" 0.183544 | \n",
" 0.186747 | \n",
" 0.045593 | \n",
" 0.076923 | \n",
" 0.100346 | \n",
" 0.268182 | \n",
" 0.957564 | \n",
" 0.800499 | \n",
" 0.255000 | \n",
" 0.691275 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.484277 | \n",
" 0.253165 | \n",
" 0.234940 | \n",
" 0.123100 | \n",
" 0.106383 | \n",
" 0.141869 | \n",
" 0.354545 | \n",
" 0.959585 | \n",
" 0.827930 | \n",
" 0.300000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 0.453793 0.214978 0.191948 0.082724 \n",
"std 0.081537 0.104200 0.087541 0.072307 \n",
"min 0.238994 0.050633 0.000000 0.009119 \n",
"25% 0.402516 0.145570 0.150602 0.027356 \n",
"50% 0.440252 0.183544 0.186747 0.045593 \n",
"75% 0.484277 0.253165 0.234940 0.123100 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 0.091708 0.105624 0.263056 0.957378 \n",
"std 0.057338 0.061417 0.128459 0.002886 \n",
"min 0.014730 0.003460 0.013636 0.950076 \n",
"25% 0.062193 0.058824 0.175000 0.955110 \n",
"50% 0.076923 0.100346 0.268182 0.957564 \n",
"75% 0.106383 0.141869 0.354545 0.959585 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" pH sulphates alcohol quality is_red \n",
"count 6497.000000 6497.000000 6497.000000 6497.000000 6497.000000 \n",
"mean 0.802619 0.265634 0.704148 0.646486 0.753886 \n",
"std 0.040097 0.074403 0.080048 0.097028 0.430779 \n",
"min 0.678304 0.110000 0.536913 0.333333 0.000000 \n",
"25% 0.775561 0.215000 0.637584 0.555556 1.000000 \n",
"50% 0.800499 0.255000 0.691275 0.666667 1.000000 \n",
"75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 "
]
},
"execution_count": 192,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_dataset.describe(include='all') # sprawdzanie wartości po znormalizowaniu"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"652 1.000000\n",
"442 0.981132\n",
"557 0.981132\n",
"554 0.974843\n",
"555 0.974843\n",
"243 0.943396\n",
"244 0.943396\n",
"544 0.899371\n",
"3125 0.893082\n",
"374 0.880503\n",
"Name: fixed_acidity, dtype: float64"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_dataset[\"fixed_acidity\"].nlargest(10) #sprawdza czy najwyższe wartości mają sens"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 4408\n",
"0.0 1439\n",
"Name: is_red, dtype: int64"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset[\"is_red\"])\n",
"wine_train[\"is_red\"].value_counts() \n",
"# podzielenie na train i test"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 490\n",
"0.0 160\n",
"Name: is_red, dtype: int64"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_test[\"is_red\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [],
"source": [
"wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test[\"is_red\"]) # podzielenie na test i validation"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 245\n",
"0.0 80\n",
"Name: is_red, dtype: int64"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_test[\"is_red\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0 245\n",
"0.0 80\n",
"Name: is_red, dtype: int64"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_val[\"is_red\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set_theme()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(wine_dataset.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [],
"source": [
"#sns.pairplot(data=wine_dataset, hue=\"is_red\")"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.448244 | \n",
" 0.217069 | \n",
" 0.180630 | \n",
" 0.078990 | \n",
" 0.088742 | \n",
" 0.103024 | \n",
" 0.257462 | \n",
" 0.957255 | \n",
" 0.803553 | \n",
" 0.263877 | \n",
" 0.703930 | \n",
" 0.646154 | \n",
" 0.753846 | \n",
"
\n",
" \n",
" std | \n",
" 0.074301 | \n",
" 0.107627 | \n",
" 0.078046 | \n",
" 0.070045 | \n",
" 0.051400 | \n",
" 0.054750 | \n",
" 0.125165 | \n",
" 0.002786 | \n",
" 0.039808 | \n",
" 0.072275 | \n",
" 0.078704 | \n",
" 0.095014 | \n",
" 0.431433 | \n",
"
\n",
" \n",
" min | \n",
" 0.314465 | \n",
" 0.063291 | \n",
" 0.000000 | \n",
" 0.012158 | \n",
" 0.031097 | \n",
" 0.010381 | \n",
" 0.020455 | \n",
" 0.951116 | \n",
" 0.713217 | \n",
" 0.130000 | \n",
" 0.570470 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.402516 | \n",
" 0.145570 | \n",
" 0.144578 | \n",
" 0.027356 | \n",
" 0.060556 | \n",
" 0.058824 | \n",
" 0.168182 | \n",
" 0.955168 | \n",
" 0.775561 | \n",
" 0.210000 | \n",
" 0.637584 | \n",
" 0.555556 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.433962 | \n",
" 0.177215 | \n",
" 0.180723 | \n",
" 0.042553 | \n",
" 0.078560 | \n",
" 0.100346 | \n",
" 0.261364 | \n",
" 0.957478 | \n",
" 0.800499 | \n",
" 0.250000 | \n",
" 0.691275 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.471698 | \n",
" 0.253165 | \n",
" 0.222892 | \n",
" 0.113982 | \n",
" 0.101473 | \n",
" 0.141869 | \n",
" 0.343182 | \n",
" 0.959354 | \n",
" 0.827930 | \n",
" 0.300000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 0.817610 | \n",
" 0.569620 | \n",
" 0.445783 | \n",
" 0.334347 | \n",
" 0.679214 | \n",
" 0.231834 | \n",
" 0.575000 | \n",
" 0.965264 | \n",
" 0.917706 | \n",
" 0.585000 | \n",
" 0.939597 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar \\\n",
"count 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.448244 0.217069 0.180630 0.078990 \n",
"std 0.074301 0.107627 0.078046 0.070045 \n",
"min 0.314465 0.063291 0.000000 0.012158 \n",
"25% 0.402516 0.145570 0.144578 0.027356 \n",
"50% 0.433962 0.177215 0.180723 0.042553 \n",
"75% 0.471698 0.253165 0.222892 0.113982 \n",
"max 0.817610 0.569620 0.445783 0.334347 \n",
"\n",
" chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n",
"count 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.088742 0.103024 0.257462 0.957255 \n",
"std 0.051400 0.054750 0.125165 0.002786 \n",
"min 0.031097 0.010381 0.020455 0.951116 \n",
"25% 0.060556 0.058824 0.168182 0.955168 \n",
"50% 0.078560 0.100346 0.261364 0.957478 \n",
"75% 0.101473 0.141869 0.343182 0.959354 \n",
"max 0.679214 0.231834 0.575000 0.965264 \n",
"\n",
" pH sulphates alcohol quality is_red \n",
"count 325.000000 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.803553 0.263877 0.703930 0.646154 0.753846 \n",
"std 0.039808 0.072275 0.078704 0.095014 0.431433 \n",
"min 0.713217 0.130000 0.570470 0.333333 0.000000 \n",
"25% 0.775561 0.210000 0.637584 0.555556 1.000000 \n",
"50% 0.800499 0.250000 0.691275 0.666667 1.000000 \n",
"75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n",
"max 0.917706 0.585000 0.939597 1.000000 1.000000 "
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_test.describe()"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
" 5847.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.453848 | \n",
" 0.215061 | \n",
" 0.192235 | \n",
" 0.082331 | \n",
" 0.092161 | \n",
" 0.105659 | \n",
" 0.262894 | \n",
" 0.957364 | \n",
" 0.802569 | \n",
" 0.265798 | \n",
" 0.704326 | \n",
" 0.646732 | \n",
" 0.753891 | \n",
"
\n",
" \n",
" std | \n",
" 0.081742 | \n",
" 0.104315 | \n",
" 0.088036 | \n",
" 0.071982 | \n",
" 0.058619 | \n",
" 0.061749 | \n",
" 0.128256 | \n",
" 0.002882 | \n",
" 0.039880 | \n",
" 0.074864 | \n",
" 0.079852 | \n",
" 0.096928 | \n",
" 0.430780 | \n",
"
\n",
" \n",
" min | \n",
" 0.238994 | \n",
" 0.050633 | \n",
" 0.000000 | \n",
" 0.009119 | \n",
" 0.014730 | \n",
" 0.003460 | \n",
" 0.013636 | \n",
" 0.950076 | \n",
" 0.678304 | \n",
" 0.110000 | \n",
" 0.536913 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.402516 | \n",
" 0.145570 | \n",
" 0.150602 | \n",
" 0.027356 | \n",
" 0.062193 | \n",
" 0.058824 | \n",
" 0.176136 | \n",
" 0.955071 | \n",
" 0.775561 | \n",
" 0.215000 | \n",
" 0.637584 | \n",
" 0.555556 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.440252 | \n",
" 0.183544 | \n",
" 0.186747 | \n",
" 0.045593 | \n",
" 0.076923 | \n",
" 0.100346 | \n",
" 0.268182 | \n",
" 0.957516 | \n",
" 0.800499 | \n",
" 0.255000 | \n",
" 0.691275 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.484277 | \n",
" 0.253165 | \n",
" 0.234940 | \n",
" 0.123100 | \n",
" 0.106383 | \n",
" 0.141869 | \n",
" 0.353409 | \n",
" 0.959581 | \n",
" 0.827930 | \n",
" 0.300000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar \\\n",
"count 5847.000000 5847.000000 5847.000000 5847.000000 \n",
"mean 0.453848 0.215061 0.192235 0.082331 \n",
"std 0.081742 0.104315 0.088036 0.071982 \n",
"min 0.238994 0.050633 0.000000 0.009119 \n",
"25% 0.402516 0.145570 0.150602 0.027356 \n",
"50% 0.440252 0.183544 0.186747 0.045593 \n",
"75% 0.484277 0.253165 0.234940 0.123100 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n",
"count 5847.000000 5847.000000 5847.000000 5847.000000 \n",
"mean 0.092161 0.105659 0.262894 0.957364 \n",
"std 0.058619 0.061749 0.128256 0.002882 \n",
"min 0.014730 0.003460 0.013636 0.950076 \n",
"25% 0.062193 0.058824 0.176136 0.955071 \n",
"50% 0.076923 0.100346 0.268182 0.957516 \n",
"75% 0.106383 0.141869 0.353409 0.959581 \n",
"max 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" pH sulphates alcohol quality is_red \n",
"count 5847.000000 5847.000000 5847.000000 5847.000000 5847.000000 \n",
"mean 0.802569 0.265798 0.704326 0.646732 0.753891 \n",
"std 0.039880 0.074864 0.079852 0.096928 0.430780 \n",
"min 0.678304 0.110000 0.536913 0.333333 0.000000 \n",
"25% 0.775561 0.215000 0.637584 0.555556 1.000000 \n",
"50% 0.800499 0.255000 0.691275 0.666667 1.000000 \n",
"75% 0.827930 0.300000 0.758389 0.666667 1.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 "
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" fixed_acidity | \n",
" volatile_acidity | \n",
" citric_acid | \n",
" residual_sugar | \n",
" chlorides | \n",
" free_sulfur_dioxide | \n",
" total_sulfur_dioxide | \n",
" density | \n",
" pH | \n",
" sulphates | \n",
" alcohol | \n",
" quality | \n",
" is_red | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
" 325.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.458355 | \n",
" 0.211412 | \n",
" 0.198091 | \n",
" 0.093521 | \n",
" 0.086537 | \n",
" 0.107596 | \n",
" 0.271556 | \n",
" 0.957757 | \n",
" 0.802570 | \n",
" 0.264446 | \n",
" 0.701160 | \n",
" 0.642393 | \n",
" 0.753846 | \n",
"
\n",
" \n",
" std | \n",
" 0.084621 | \n",
" 0.098749 | \n",
" 0.086862 | \n",
" 0.079346 | \n",
" 0.035141 | \n",
" 0.061805 | \n",
" 0.135185 | \n",
" 0.003031 | \n",
" 0.044183 | \n",
" 0.068086 | \n",
" 0.084939 | \n",
" 0.100957 | \n",
" 0.431433 | \n",
"
\n",
" \n",
" min | \n",
" 0.295597 | \n",
" 0.056962 | \n",
" 0.000000 | \n",
" 0.012158 | \n",
" 0.019640 | \n",
" 0.010381 | \n",
" 0.018182 | \n",
" 0.950413 | \n",
" 0.715711 | \n",
" 0.140000 | \n",
" 0.563758 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.402516 | \n",
" 0.145570 | \n",
" 0.156627 | \n",
" 0.030395 | \n",
" 0.063830 | \n",
" 0.055363 | \n",
" 0.179545 | \n",
" 0.955456 | \n",
" 0.773067 | \n",
" 0.215000 | \n",
" 0.630872 | \n",
" 0.555556 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.446541 | \n",
" 0.183544 | \n",
" 0.186747 | \n",
" 0.069149 | \n",
" 0.078560 | \n",
" 0.100346 | \n",
" 0.284091 | \n",
" 0.957978 | \n",
" 0.800499 | \n",
" 0.250000 | \n",
" 0.684564 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.490566 | \n",
" 0.253165 | \n",
" 0.240964 | \n",
" 0.133739 | \n",
" 0.098200 | \n",
" 0.155709 | \n",
" 0.370455 | \n",
" 0.960028 | \n",
" 0.827930 | \n",
" 0.305000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 0.943396 | \n",
" 0.746835 | \n",
" 0.445783 | \n",
" 0.480243 | \n",
" 0.278232 | \n",
" 0.266436 | \n",
" 0.570455 | \n",
" 0.972396 | \n",
" 1.000000 | \n",
" 0.570000 | \n",
" 0.939597 | \n",
" 0.888889 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" fixed_acidity volatile_acidity citric_acid residual_sugar \\\n",
"count 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.458355 0.211412 0.198091 0.093521 \n",
"std 0.084621 0.098749 0.086862 0.079346 \n",
"min 0.295597 0.056962 0.000000 0.012158 \n",
"25% 0.402516 0.145570 0.156627 0.030395 \n",
"50% 0.446541 0.183544 0.186747 0.069149 \n",
"75% 0.490566 0.253165 0.240964 0.133739 \n",
"max 0.943396 0.746835 0.445783 0.480243 \n",
"\n",
" chlorides free_sulfur_dioxide total_sulfur_dioxide density \\\n",
"count 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.086537 0.107596 0.271556 0.957757 \n",
"std 0.035141 0.061805 0.135185 0.003031 \n",
"min 0.019640 0.010381 0.018182 0.950413 \n",
"25% 0.063830 0.055363 0.179545 0.955456 \n",
"50% 0.078560 0.100346 0.284091 0.957978 \n",
"75% 0.098200 0.155709 0.370455 0.960028 \n",
"max 0.278232 0.266436 0.570455 0.972396 \n",
"\n",
" pH sulphates alcohol quality is_red \n",
"count 325.000000 325.000000 325.000000 325.000000 325.000000 \n",
"mean 0.802570 0.264446 0.701160 0.642393 0.753846 \n",
"std 0.044183 0.068086 0.084939 0.100957 0.431433 \n",
"min 0.715711 0.140000 0.563758 0.333333 0.000000 \n",
"25% 0.773067 0.215000 0.630872 0.555556 1.000000 \n",
"50% 0.800499 0.250000 0.684564 0.666667 1.000000 \n",
"75% 0.827930 0.305000 0.758389 0.666667 1.000000 \n",
"max 1.000000 0.570000 0.939597 0.888889 1.000000 "
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wine_val.describe()"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"from torch.utils.data import DataLoader, Dataset"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [],
"source": [
"class TabularDataset(Dataset):\n",
" def __init__(self, data):\n",
" self.data = data.values.astype('float32')\n",
"\n",
" def __getitem__(self, index):\n",
" x = torch.tensor(self.data[index, :-1])\n",
" y = torch.tensor(self.data[index, -1])\n",
" return x, y\n",
"\n",
" def __len__(self):\n",
" return len(self.data)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 64\n",
"train_dataset = TabularDataset(wine_train)\n",
"train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
"test_dataset = TabularDataset(wine_test)\n",
"test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [],
"source": [
"class TabularModel(nn.Module):\n",
" def __init__(self, input_dim, hidden_dim, output_dim):\n",
" super(TabularModel, self).__init__()\n",
" self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
" self.relu = nn.ReLU()\n",
" self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
" self.softmax = nn.Softmax(dim=1)\n",
" \n",
" def forward(self, x):\n",
" out = self.fc1(x)\n",
" out = self.relu(out)\n",
" out = self.fc2(out)\n",
" out = self.softmax(out)\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"input_dim = wine_train.shape[1] - 1\n",
"hidden_dim = 32\n",
"output_dim = 2\n",
"model = TabularModel(input_dim, hidden_dim, output_dim)\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(model.parameters())"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [],
"source": [
"model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=32, output_dim=2)\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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" [0.3483, 0.6517],\n",
" [0.3267, 0.6733]], grad_fn=)\n",
"tensor([[0.2725, 0.7275],\n",
" [0.2981, 0.7019],\n",
" [0.2908, 0.7092],\n",
" [0.2678, 0.7322],\n",
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"tensor([[0.1890, 0.8110],\n",
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"Epoch 1, loss: 0.4508\n",
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