diff --git a/zad1.ipynb b/zad1.ipynb
index d89f3d6..7937734 100644
--- a/zad1.ipynb
+++ b/zad1.ipynb
@@ -2,77 +2,44 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\Users\\macty\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import sklearn.model_selection\n",
- "from datasets import load_dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "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, 49.24it/s]\n"
- ]
- }
- ],
- "source": [
- "dataset = load_dataset(\"mstz/wine\", \"wine\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "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": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dataset[\"train\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
- "wine_dataset = pd.DataFrame(dataset[\"train\"])"
+ "import pandas as pd\n",
+ "import sklearn.model_selection"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "\n",
+ "url = \"https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv\"\n",
+ "save_path = \"Wine_Quality_Data.csv\"\n",
+ "\n",
+ "response = requests.get(url)\n",
+ "response.raise_for_status()\n",
+ "\n",
+ "with open(save_path, \"wb\") as f:\n",
+ " f.write(response.content)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wine_dataset = pd.read_csv(\"Wine_Quality_Data.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
"metadata": {},
"outputs": [
{
@@ -108,7 +75,7 @@
" | sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
" \n",
" \n",
" \n",
@@ -126,7 +93,7 @@
" 0.56 | \n",
" 9.4 | \n",
" 5 | \n",
- " 0 | \n",
+ " red | \n",
" \n",
" \n",
" | 1 | \n",
@@ -142,7 +109,7 @@
" 0.68 | \n",
" 9.8 | \n",
" 5 | \n",
- " 0 | \n",
+ " red | \n",
"
\n",
" \n",
" | 2 | \n",
@@ -158,7 +125,7 @@
" 0.65 | \n",
" 9.8 | \n",
" 5 | \n",
- " 0 | \n",
+ " red | \n",
"
\n",
" \n",
" | 3 | \n",
@@ -174,7 +141,7 @@
" 0.58 | \n",
" 9.8 | \n",
" 6 | \n",
- " 0 | \n",
+ " red | \n",
"
\n",
" \n",
" | 4 | \n",
@@ -190,7 +157,7 @@
" 0.56 | \n",
" 9.4 | \n",
" 5 | \n",
- " 0 | \n",
+ " red | \n",
"
\n",
" \n",
"\n",
@@ -211,15 +178,15 @@
"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 "
+ " alcohol quality color \n",
+ "0 9.4 5 red \n",
+ "1 9.8 5 red \n",
+ "2 9.8 5 red \n",
+ "3 9.8 6 red \n",
+ "4 9.4 5 red "
]
},
- "execution_count": 6,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -230,7 +197,16 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wine_dataset['color'] = wine_dataset['color'].replace({'red': 1, 'white': 0})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
"metadata": {},
"outputs": [
{
@@ -266,7 +242,7 @@
" sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
" \n",
" \n",
" \n",
@@ -300,7 +276,7 @@
" 0.531268 | \n",
" 10.491801 | \n",
" 5.818378 | \n",
- " 0.753886 | \n",
+ " 0.246114 | \n",
" \n",
" \n",
" | std | \n",
@@ -348,7 +324,7 @@
" 0.430000 | \n",
" 9.500000 | \n",
" 5.000000 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
@@ -364,7 +340,7 @@
" 0.510000 | \n",
" 10.300000 | \n",
" 6.000000 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
@@ -380,7 +356,7 @@
" 0.600000 | \n",
" 11.300000 | \n",
" 6.000000 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | max | \n",
@@ -423,18 +399,18 @@
"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",
+ " pH sulphates alcohol quality color \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",
+ "mean 3.218501 0.531268 10.491801 5.818378 0.246114 \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",
+ "25% 3.110000 0.430000 9.500000 5.000000 0.000000 \n",
+ "50% 3.210000 0.510000 10.300000 6.000000 0.000000 \n",
+ "75% 3.320000 0.600000 11.300000 6.000000 0.000000 \n",
"max 4.010000 2.000000 14.900000 9.000000 1.000000 "
]
},
- "execution_count": 7,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@@ -445,7 +421,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 62,
"metadata": {},
"outputs": [
{
@@ -454,13 +430,13 @@
""
]
},
- "execution_count": 8,
+ "execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": "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",
"text/plain": [
""
]
@@ -470,14 +446,14 @@
}
],
"source": [
- "wine_dataset[\"is_red\"].value_counts().plot(kind=\"bar\")\n",
+ "wine_dataset[\"color\"].value_counts().plot(kind=\"bar\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 63,
"metadata": {},
"outputs": [
{
@@ -486,7 +462,7 @@
"1.2964337577998153"
]
},
- "execution_count": 9,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -497,7 +473,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 64,
"metadata": {},
"outputs": [
{
@@ -506,7 +482,7 @@
"(array([], dtype=int64), array([], dtype=int64))"
]
},
- "execution_count": 10,
+ "execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
@@ -518,7 +494,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
@@ -528,7 +504,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 66,
"metadata": {},
"outputs": [
{
@@ -564,7 +540,7 @@
" sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
"
\n",
" \n",
" \n",
@@ -598,7 +574,7 @@
" 0.265634 | \n",
" 0.704148 | \n",
" 0.646486 | \n",
- " 0.753886 | \n",
+ " 0.246114 | \n",
" \n",
" \n",
" | std | \n",
@@ -646,7 +622,7 @@
" 0.215000 | \n",
" 0.637584 | \n",
" 0.555556 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
@@ -662,7 +638,7 @@
" 0.255000 | \n",
" 0.691275 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
@@ -678,7 +654,7 @@
" 0.300000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | max | \n",
@@ -721,18 +697,18 @@
"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",
+ " pH sulphates alcohol quality color \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",
+ "mean 0.802619 0.265634 0.704148 0.646486 0.246114 \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",
+ "25% 0.775561 0.215000 0.637584 0.555556 0.000000 \n",
+ "50% 0.800499 0.255000 0.691275 0.666667 0.000000 \n",
+ "75% 0.827930 0.300000 0.758389 0.666667 0.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 "
]
},
- "execution_count": 12,
+ "execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
@@ -743,7 +719,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 67,
"metadata": {},
"outputs": [
{
@@ -762,7 +738,7 @@
"Name: fixed_acidity, dtype: float64"
]
},
- "execution_count": 13,
+ "execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
@@ -773,107 +749,107 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "1.0 4408\n",
- "0.0 1439\n",
- "Name: is_red, dtype: int64"
+ "0.0 4408\n",
+ "1.0 1439\n",
+ "Name: color, dtype: int64"
]
},
- "execution_count": 14,
+ "execution_count": 68,
"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",
+ "wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset[\"color\"])\n",
+ "wine_train[\"color\"].value_counts() \n",
"# podzielenie na train i test"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "1.0 490\n",
- "0.0 160\n",
- "Name: is_red, dtype: int64"
+ "0.0 490\n",
+ "1.0 160\n",
+ "Name: color, dtype: int64"
]
},
- "execution_count": 15,
+ "execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "wine_test[\"is_red\"].value_counts()"
+ "wine_test[\"color\"].value_counts()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 70,
"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"
+ "wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test[\"color\"]) # podzielenie na test i validation"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "1.0 245\n",
- "0.0 80\n",
- "Name: is_red, dtype: int64"
+ "0.0 245\n",
+ "1.0 80\n",
+ "Name: color, dtype: int64"
]
},
- "execution_count": 17,
+ "execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "wine_test[\"is_red\"].value_counts()"
+ "wine_test[\"color\"].value_counts()"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "1.0 245\n",
- "0.0 80\n",
- "Name: is_red, dtype: int64"
+ "0.0 245\n",
+ "1.0 80\n",
+ "Name: color, dtype: int64"
]
},
- "execution_count": 18,
+ "execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "wine_val[\"is_red\"].value_counts()"
+ "wine_val[\"color\"].value_counts()"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
@@ -883,7 +859,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 74,
"metadata": {},
"outputs": [
{
@@ -892,7 +868,7 @@
"13"
]
},
- "execution_count": 20,
+ "execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
@@ -910,16 +886,16 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
- "#sns.pairplot(data=wine_dataset, hue=\"is_red\")"
+ "#sns.pairplot(data=wine_dataset, hue=\"color\")"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 76,
"metadata": {},
"outputs": [
{
@@ -955,7 +931,7 @@
" sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
"
\n",
" \n",
" \n",
@@ -977,114 +953,114 @@
" \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",
+ " 0.460126 | \n",
+ " 0.209883 | \n",
+ " 0.197294 | \n",
+ " 0.083839 | \n",
+ " 0.096352 | \n",
+ " 0.105307 | \n",
+ " 0.272028 | \n",
+ " 0.957685 | \n",
+ " 0.799770 | \n",
+ " 0.266477 | \n",
+ " 0.691389 | \n",
+ " 0.636239 | \n",
+ " 0.246154 | \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",
+ " 0.087321 | \n",
+ " 0.100971 | \n",
+ " 0.086532 | \n",
+ " 0.072172 | \n",
+ " 0.066017 | \n",
+ " 0.061895 | \n",
+ " 0.131981 | \n",
+ " 0.002780 | \n",
+ " 0.038640 | \n",
+ " 0.082243 | \n",
+ " 0.073293 | \n",
+ " 0.088732 | \n",
" 0.431433 | \n",
"
\n",
" \n",
" | min | \n",
- " 0.314465 | \n",
- " 0.063291 | \n",
+ " 0.308176 | \n",
+ " 0.066456 | \n",
" 0.000000 | \n",
- " 0.012158 | \n",
- " 0.031097 | \n",
- " 0.010381 | \n",
+ " 0.010638 | \n",
+ " 0.026187 | \n",
+ " 0.003460 | \n",
" 0.020455 | \n",
- " 0.951116 | \n",
- " 0.713217 | \n",
- " 0.130000 | \n",
- " 0.570470 | \n",
+ " 0.952030 | \n",
+ " 0.698254 | \n",
+ " 0.115000 | \n",
+ " 0.577181 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
- " 0.402516 | \n",
- " 0.145570 | \n",
- " 0.144578 | \n",
+ " 0.408805 | \n",
+ " 0.139241 | \n",
+ " 0.156627 | \n",
" 0.027356 | \n",
- " 0.060556 | \n",
+ " 0.062193 | \n",
" 0.058824 | \n",
- " 0.168182 | \n",
- " 0.955168 | \n",
- " 0.775561 | \n",
- " 0.210000 | \n",
- " 0.637584 | \n",
+ " 0.188636 | \n",
+ " 0.955322 | \n",
+ " 0.773067 | \n",
+ " 0.215000 | \n",
+ " 0.630872 | \n",
" 0.555556 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
- " 0.433962 | \n",
- " 0.177215 | \n",
- " 0.180723 | \n",
- " 0.042553 | \n",
+ " 0.440252 | \n",
+ " 0.189873 | \n",
+ " 0.186747 | \n",
+ " 0.048632 | \n",
" 0.078560 | \n",
" 0.100346 | \n",
- " 0.261364 | \n",
- " 0.957478 | \n",
- " 0.800499 | \n",
+ " 0.275000 | \n",
+ " 0.957978 | \n",
+ " 0.795511 | \n",
" 0.250000 | \n",
- " 0.691275 | \n",
+ " 0.671141 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \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",
+ " 0.484277 | \n",
+ " 0.240506 | \n",
+ " 0.246988 | \n",
+ " 0.121581 | \n",
+ " 0.116203 | \n",
+ " 0.145329 | \n",
+ " 0.356818 | \n",
+ " 0.959787 | \n",
+ " 0.822943 | \n",
+ " 0.305000 | \n",
+ " 0.738255 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \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",
+ " 0.943396 | \n",
+ " 0.715190 | \n",
+ " 0.469880 | \n",
+ " 0.303191 | \n",
+ " 0.764321 | \n",
+ " 0.479239 | \n",
+ " 0.781818 | \n",
+ " 0.966034 | \n",
+ " 0.895262 | \n",
+ " 0.975000 | \n",
+ " 0.906040 | \n",
+ " 0.888889 | \n",
" 1.000000 | \n",
"
\n",
" \n",
@@ -1094,36 +1070,36 @@
"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",
+ "mean 0.460126 0.209883 0.197294 0.083839 \n",
+ "std 0.087321 0.100971 0.086532 0.072172 \n",
+ "min 0.308176 0.066456 0.000000 0.010638 \n",
+ "25% 0.408805 0.139241 0.156627 0.027356 \n",
+ "50% 0.440252 0.189873 0.186747 0.048632 \n",
+ "75% 0.484277 0.240506 0.246988 0.121581 \n",
+ "max 0.943396 0.715190 0.469880 0.303191 \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",
+ "mean 0.096352 0.105307 0.272028 0.957685 \n",
+ "std 0.066017 0.061895 0.131981 0.002780 \n",
+ "min 0.026187 0.003460 0.020455 0.952030 \n",
+ "25% 0.062193 0.058824 0.188636 0.955322 \n",
+ "50% 0.078560 0.100346 0.275000 0.957978 \n",
+ "75% 0.116203 0.145329 0.356818 0.959787 \n",
+ "max 0.764321 0.479239 0.781818 0.966034 \n",
"\n",
- " pH sulphates alcohol quality is_red \n",
+ " pH sulphates alcohol quality color \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 "
+ "mean 0.799770 0.266477 0.691389 0.636239 0.246154 \n",
+ "std 0.038640 0.082243 0.073293 0.088732 0.431433 \n",
+ "min 0.698254 0.115000 0.577181 0.333333 0.000000 \n",
+ "25% 0.773067 0.215000 0.630872 0.555556 0.000000 \n",
+ "50% 0.795511 0.250000 0.671141 0.666667 0.000000 \n",
+ "75% 0.822943 0.305000 0.738255 0.666667 0.000000 \n",
+ "max 0.895262 0.975000 0.906040 0.888889 1.000000 "
]
},
- "execution_count": 22,
+ "execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
@@ -1134,7 +1110,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 77,
"metadata": {},
"outputs": [
{
@@ -1170,7 +1146,7 @@
" sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
" \n",
" \n",
" \n",
@@ -1192,34 +1168,34 @@
" \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",
+ " 0.453724 | \n",
+ " 0.215128 | \n",
+ " 0.192091 | \n",
+ " 0.082877 | \n",
+ " 0.091656 | \n",
+ " 0.105899 | \n",
+ " 0.262834 | \n",
+ " 0.957374 | \n",
+ " 0.802637 | \n",
+ " 0.265601 | \n",
+ " 0.704572 | \n",
+ " 0.646846 | \n",
+ " 0.246109 | \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",
+ " 0.081597 | \n",
+ " 0.104319 | \n",
+ " 0.087166 | \n",
+ " 0.072487 | \n",
+ " 0.057502 | \n",
+ " 0.061908 | \n",
+ " 0.128388 | \n",
+ " 0.002899 | \n",
+ " 0.040030 | \n",
+ " 0.074400 | \n",
+ " 0.080399 | \n",
+ " 0.097212 | \n",
" 0.430780 | \n",
"
\n",
" \n",
@@ -1246,13 +1222,13 @@
" | 0.027356 | \n",
" 0.062193 | \n",
" 0.058824 | \n",
- " 0.176136 | \n",
- " 0.955071 | \n",
+ " 0.175000 | \n",
+ " 0.955110 | \n",
" 0.775561 | \n",
" 0.215000 | \n",
" 0.637584 | \n",
" 0.555556 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
@@ -1263,28 +1239,28 @@
" 0.076923 | \n",
" 0.100346 | \n",
" 0.268182 | \n",
- " 0.957516 | \n",
+ " 0.957555 | \n",
" 0.800499 | \n",
" 0.255000 | \n",
" 0.691275 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 0.484277 | \n",
- " 0.253165 | \n",
+ " 0.259494 | \n",
" 0.234940 | \n",
" 0.123100 | \n",
" 0.106383 | \n",
" 0.141869 | \n",
- " 0.353409 | \n",
- " 0.959581 | \n",
+ " 0.354545 | \n",
+ " 0.959585 | \n",
" 0.827930 | \n",
" 0.300000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | max | \n",
@@ -1309,36 +1285,36 @@
"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",
+ "mean 0.453724 0.215128 0.192091 0.082877 \n",
+ "std 0.081597 0.104319 0.087166 0.072487 \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",
+ "75% 0.484277 0.259494 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",
+ "mean 0.091656 0.105899 0.262834 0.957374 \n",
+ "std 0.057502 0.061908 0.128388 0.002899 \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",
+ "25% 0.062193 0.058824 0.175000 0.955110 \n",
+ "50% 0.076923 0.100346 0.268182 0.957555 \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",
+ " pH sulphates alcohol quality color \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",
+ "mean 0.802637 0.265601 0.704572 0.646846 0.246109 \n",
+ "std 0.040030 0.074400 0.080399 0.097212 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",
+ "25% 0.775561 0.215000 0.637584 0.555556 0.000000 \n",
+ "50% 0.800499 0.255000 0.691275 0.666667 0.000000 \n",
+ "75% 0.827930 0.300000 0.758389 0.666667 0.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 "
]
},
- "execution_count": 23,
+ "execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
@@ -1349,7 +1325,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 78,
"metadata": {},
"outputs": [
{
@@ -1385,7 +1361,7 @@
" sulphates | \n",
" alcohol | \n",
" quality | \n",
- " is_red | \n",
+ " color | \n",
"
\n",
" \n",
" \n",
@@ -1407,49 +1383,49 @@
" \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",
+ " 0.448708 | \n",
+ " 0.217381 | \n",
+ " 0.184022 | \n",
+ " 0.078864 | \n",
+ " 0.088017 | \n",
+ " 0.100985 | \n",
+ " 0.258073 | \n",
+ " 0.957147 | \n",
+ " 0.805141 | \n",
+ " 0.265385 | \n",
+ " 0.709269 | \n",
+ " 0.650256 | \n",
+ " 0.246154 | \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",
+ " 0.073960 | \n",
+ " 0.105388 | \n",
+ " 0.094736 | \n",
+ " 0.069232 | \n",
+ " 0.043159 | \n",
+ " 0.051174 | \n",
+ " 0.126120 | \n",
+ " 0.002746 | \n",
+ " 0.042584 | \n",
+ " 0.065946 | \n",
+ " 0.079198 | \n",
+ " 0.101225 | \n",
" 0.431433 | \n",
"
\n",
" \n",
" | min | \n",
- " 0.295597 | \n",
- " 0.056962 | \n",
+ " 0.301887 | \n",
+ " 0.075949 | \n",
" 0.000000 | \n",
" 0.012158 | \n",
- " 0.019640 | \n",
- " 0.010381 | \n",
+ " 0.026187 | \n",
+ " 0.006920 | \n",
" 0.018182 | \n",
- " 0.950413 | \n",
- " 0.715711 | \n",
- " 0.140000 | \n",
- " 0.563758 | \n",
+ " 0.950731 | \n",
+ " 0.683292 | \n",
+ " 0.150000 | \n",
+ " 0.570470 | \n",
" 0.333333 | \n",
" 0.000000 | \n",
"
\n",
@@ -1457,63 +1433,63 @@
" 25% | \n",
" 0.402516 | \n",
" 0.145570 | \n",
- " 0.156627 | \n",
- " 0.030395 | \n",
- " 0.063830 | \n",
- " 0.055363 | \n",
+ " 0.138554 | \n",
+ " 0.028875 | \n",
+ " 0.062193 | \n",
+ " 0.058824 | \n",
" 0.179545 | \n",
- " 0.955456 | \n",
- " 0.773067 | \n",
+ " 0.954879 | \n",
+ " 0.775561 | \n",
" 0.215000 | \n",
- " 0.630872 | \n",
+ " 0.637584 | \n",
" 0.555556 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
" \n",
" \n",
" | 50% | \n",
- " 0.446541 | \n",
- " 0.183544 | \n",
+ " 0.433962 | \n",
+ " 0.177215 | \n",
" 0.186747 | \n",
- " 0.069149 | \n",
- " 0.078560 | \n",
+ " 0.042553 | \n",
+ " 0.076923 | \n",
" 0.100346 | \n",
- " 0.284091 | \n",
- " 0.957978 | \n",
- " 0.800499 | \n",
- " 0.250000 | \n",
- " 0.684564 | \n",
+ " 0.256818 | \n",
+ " 0.957189 | \n",
+ " 0.805486 | \n",
+ " 0.260000 | \n",
+ " 0.697987 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
- " 0.490566 | \n",
+ " 0.484277 | \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",
+ " 0.234940 | \n",
+ " 0.117021 | \n",
+ " 0.101473 | \n",
+ " 0.138408 | \n",
+ " 0.356818 | \n",
+ " 0.959306 | \n",
+ " 0.830424 | \n",
" 0.305000 | \n",
" 0.758389 | \n",
" 0.666667 | \n",
- " 1.000000 | \n",
+ " 0.000000 | \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",
+ " 0.798742 | \n",
+ " 0.696203 | \n",
+ " 0.602410 | \n",
+ " 0.303191 | \n",
+ " 0.436989 | \n",
+ " 0.280277 | \n",
+ " 0.575000 | \n",
+ " 0.962935 | \n",
+ " 0.935162 | \n",
+ " 0.490000 | \n",
+ " 0.953020 | \n",
" 0.888889 | \n",
" 1.000000 | \n",
"
\n",
@@ -1524,36 +1500,36 @@
"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",
+ "mean 0.448708 0.217381 0.184022 0.078864 \n",
+ "std 0.073960 0.105388 0.094736 0.069232 \n",
+ "min 0.301887 0.075949 0.000000 0.012158 \n",
+ "25% 0.402516 0.145570 0.138554 0.028875 \n",
+ "50% 0.433962 0.177215 0.186747 0.042553 \n",
+ "75% 0.484277 0.253165 0.234940 0.117021 \n",
+ "max 0.798742 0.696203 0.602410 0.303191 \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",
+ "mean 0.088017 0.100985 0.258073 0.957147 \n",
+ "std 0.043159 0.051174 0.126120 0.002746 \n",
+ "min 0.026187 0.006920 0.018182 0.950731 \n",
+ "25% 0.062193 0.058824 0.179545 0.954879 \n",
+ "50% 0.076923 0.100346 0.256818 0.957189 \n",
+ "75% 0.101473 0.138408 0.356818 0.959306 \n",
+ "max 0.436989 0.280277 0.575000 0.962935 \n",
"\n",
- " pH sulphates alcohol quality is_red \n",
+ " pH sulphates alcohol quality color \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 "
+ "mean 0.805141 0.265385 0.709269 0.650256 0.246154 \n",
+ "std 0.042584 0.065946 0.079198 0.101225 0.431433 \n",
+ "min 0.683292 0.150000 0.570470 0.333333 0.000000 \n",
+ "25% 0.775561 0.215000 0.637584 0.555556 0.000000 \n",
+ "50% 0.805486 0.260000 0.697987 0.666667 0.000000 \n",
+ "75% 0.830424 0.305000 0.758389 0.666667 0.000000 \n",
+ "max 0.935162 0.490000 0.953020 0.888889 1.000000 "
]
},
- "execution_count": 24,
+ "execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
@@ -1564,7 +1540,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
@@ -1575,7 +1551,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
@@ -1594,7 +1570,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
@@ -1614,7 +1590,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
@@ -1636,7 +1612,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
@@ -1650,7 +1626,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
@@ -1661,18 +1637,18 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch 1, loss: 0.5358\n",
- "Epoch 3, loss: 0.3417\n",
- "Epoch 5, loss: 0.3344\n",
- "Epoch 7, loss: 0.3338\n",
- "Epoch 9, loss: 0.3318\n",
+ "Epoch 1, loss: 0.4864\n",
+ "Epoch 3, loss: 0.3413\n",
+ "Epoch 5, loss: 0.3345\n",
+ "Epoch 7, loss: 0.3337\n",
+ "Epoch 9, loss: 0.3331\n",
"Finished Training\n"
]
}
@@ -1700,7 +1676,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 86,
"metadata": {},
"outputs": [
{
diff --git a/zad1.py b/zad1.py
index ac14eb8..a95332b 100644
--- a/zad1.py
+++ b/zad1.py
@@ -1,171 +1,61 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[2]:
-
-
import pandas as pd
import sklearn.model_selection
from datasets import load_dataset
-
-
-# In[3]:
-
-
-dataset = load_dataset("mstz/wine", "wine")
-
-
-# In[4]:
-
-
-dataset["train"]
-
-
-# In[5]:
-
-
-wine_dataset = pd.DataFrame(dataset["train"])
-
-
-# In[6]:
-
-
-wine_dataset.head()# podgląd danych
-
-
-# In[7]:
-
-
-wine_dataset.describe(include='all')
-
-
-# In[8]:
-
-
-wine_dataset["is_red"].value_counts().plot(kind="bar")
-
-
-
-
-# In[9]:
-
-
-wine_dataset["fixed_acidity"].std()
-
-
-# In[10]:
-
-
+import mlflow
+import mlflow.sklearn
import numpy as np
-np.where(pd.isnull(wine_dataset))## sprawdzanie czy istnieją puste wartości
+import logging
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+# logging.basicConfig(level=logging.WARN)
+# logger = logging.getLogger(__name__)
-# In[11]:
+# mlflow.set_tracking_uri("http://localhost:5000")
+# mlflow.set_experiment("s123456")
+# def eval_metrics(actual, pred):
+# rmse = np.sqrt(mean_squared_error(actual, pred))
+# mae = mean_absolute_error(actual, pred)
+# r2 = r2_score(actual, pred)
+# return rmse, mae, r2
+import requests
+url = "https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv"
+save_path = "Wine_Quality_Data.csv"
+
+response = requests.get(url)
+response.raise_for_status()
+
+with open(save_path, "wb") as f:
+ f.write(response.content)
+wine_dataset = pd.read_csv("Wine_Quality_Data.csv")
+wine_dataset['color'] = wine_dataset['color'].replace({'red': 1, 'white': 0})
for column in wine_dataset.columns:
wine_dataset[column] = wine_dataset[column] / wine_dataset[column].abs().max() # normalizacja
-# In[12]:
-
-
-wine_dataset.describe(include='all') # sprawdzanie wartości po znormalizowaniu
-
-
-# In[13]:
-
-
-wine_dataset["fixed_acidity"].nlargest(10) #sprawdza czy najwyższe wartości mają sens
-
-
-# In[14]:
-
-
from sklearn.model_selection import train_test_split
-wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset["is_red"])
-wine_train["is_red"].value_counts()
+wine_train, wine_test = sklearn.model_selection.train_test_split(wine_dataset, test_size=0.1, random_state=1, stratify=wine_dataset["color"])
+wine_train["color"].value_counts()
# podzielenie na train i test
-
-# In[15]:
+wine_test["color"].value_counts()
-wine_test["is_red"].value_counts()
+wine_test, wine_val = sklearn.model_selection.train_test_split(wine_test, test_size=0.5, random_state=1, stratify=wine_test["color"]) # podzielenie na test i validation
+wine_test["color"].value_counts()
-# In[16]:
-
-
-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
-
-
-# In[17]:
-
-
-wine_test["is_red"].value_counts()
-
-
-# In[18]:
-
-
-wine_val["is_red"].value_counts()
-
-
-# In[19]:
-
+wine_val["color"].value_counts()
import seaborn as sns
sns.set_theme()
-
-# In[20]:
-
-
-len(wine_dataset.columns)
-
-
-# In[ ]:
-
-
-
-
-
-# In[21]:
-
-
-#sns.pairplot(data=wine_dataset, hue="is_red")
-
-
-# In[22]:
-
-
-wine_test.describe()
-
-
-# In[23]:
-
-
-wine_train.describe()
-
-
-# In[24]:
-
-
-wine_val.describe()
-
-
-# In[25]:
-
-
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
-# In[26]:
-
-
class TabularDataset(Dataset):
def __init__(self, data):
self.data = data.values.astype('float32')
@@ -179,9 +69,6 @@ class TabularDataset(Dataset):
return len(self.data)
-# In[27]:
-
-
batch_size = 64
train_dataset = TabularDataset(wine_train)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
@@ -189,15 +76,6 @@ test_dataset = TabularDataset(wine_test)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
-# In[ ]:
-
-
-
-
-
-# In[28]:
-
-
class TabularModel(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(TabularModel, self).__init__()
@@ -213,10 +91,6 @@ class TabularModel(nn.Module):
out = self.softmax(out)
return out
-
-# In[29]:
-
-
input_dim = wine_train.shape[1] - 1
hidden_dim = 32
output_dim = 2
@@ -225,17 +99,10 @@ criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
-# In[30]:
-
-
model = TabularModel(input_dim=len(wine_train.columns)-1, hidden_dim=32, output_dim=2)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
-
-# In[31]:
-
-
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
@@ -256,9 +123,6 @@ for epoch in range(num_epochs):
print('Finished Training')
-# In[32]:
-
-
correct = 0
total = 0
with torch.no_grad():