diff --git a/IUM_1_434788.ipynb b/IUM_1_434788.ipynb new file mode 100644 index 0000000..aa6d173 --- /dev/null +++ b/IUM_1_434788.ipynb @@ -0,0 +1,2688 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "IUM_1_434788.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "shaFKPEixPn4" + }, + "source": [ + "# 1. Pobranie zbioru danych z Repozytorium" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-03GDjWtxD7W", + "outputId": "35c4ed64-62c4-47f9-a407-571b072bf831" + }, + "source": [ + "!curl -OL https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 98k 0 98k 0 0 282k 0 --:--:-- --:--:-- --:--:-- 281k\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "sAUNi0ylxWUm", + "outputId": "27072275-680f-4154-bdf2-e952a63ab25e" + }, + "source": [ + "import pandas as pd\n", + "wine=pd.read_csv('winequality-red.csv')\n", + "wine" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
07.40.7000.001.90.07611.034.00.997803.510.569.45
17.80.8800.002.60.09825.067.00.996803.200.689.85
27.80.7600.042.30.09215.054.00.997003.260.659.85
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47.40.7000.001.90.07611.034.00.997803.510.569.45
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" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid ... sulphates alcohol quality\n", + "0 7.4 0.700 0.00 ... 0.56 9.4 5\n", + "1 7.8 0.880 0.00 ... 0.68 9.8 5\n", + "2 7.8 0.760 0.04 ... 0.65 9.8 5\n", + "3 11.2 0.280 0.56 ... 0.58 9.8 6\n", + "4 7.4 0.700 0.00 ... 0.56 9.4 5\n", + "... ... ... ... ... ... ... ...\n", + "1594 6.2 0.600 0.08 ... 0.58 10.5 5\n", + "1595 5.9 0.550 0.10 ... 0.76 11.2 6\n", + "1596 6.3 0.510 0.13 ... 0.75 11.0 6\n", + "1597 5.9 0.645 0.12 ... 0.71 10.2 5\n", + "1598 6.0 0.310 0.47 ... 0.66 11.0 6\n", + "\n", + "[1599 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4H-i6DJlxduP" + }, + "source": [ + "# 2. Podział na zbiory test/train przy pomocy SciKit" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nZO_naLatT0o" + }, + "source": [ + "Próbowałem również podzielić na podzbiory Train:Dev:Test 6:2:2 Przy pomocy basha ale uznałem, że wygodniejsze jest korzystanie z \"train_test_split()\". Docelowo podział będzie dokonywany na 4 zmienne ` X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)`, jednak chciałem zachować konwencje z przykładu, z ćwiczeń." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ebHl5Aw1uuK1" + }, + "source": [ + "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X88VMhb0x3gJ" + }, + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "wine_train, wine_test = train_test_split(wine, test_size=360,train_size=959, random_state=1)" + ], + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OzjEfgNOyAWs", + "outputId": "0035e197-98c9-4a15-c1f9-23742d6a0595" + }, + "source": [ + "wine_test[\"quality\"].value_counts()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5 155\n", + "6 149\n", + "7 37\n", + "4 16\n", + "8 2\n", + "3 1\n", + "Name: quality, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SpQZIuSxyAd0", + "outputId": "6302469b-8853-45ea-b4d4-eae6078e96cf" + }, + "source": [ + "wine_train[\"quality\"].value_counts()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5 400\n", + "6 388\n", + "7 125\n", + "4 30\n", + "8 11\n", + "3 5\n", + "Name: quality, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wAq8KmNdyNOm" + }, + "source": [ + "# 3. Statystyki dla zbior" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Wcq9YSTfXbs1" + }, + "source": [ + "from matplotlib import pyplot as plt\n", + "import seaborn as sns" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EjDFpgdPy_of" + }, + "source": [ + "## 3.1. Zbiór Train" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "SscUak3AydG0", + "outputId": "edcf5523-066f-4c75-bc9c-1628a413edf7" + }, + "source": [ + "wine_train" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
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11068.20.2300.421.90.0699.017.00.993763.210.5412.36
65010.70.4300.392.20.1068.032.00.998602.890.509.65
.......................................
5267.30.3650.492.50.08839.0106.00.996603.360.7811.05
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count959.000000959.000000959.000000959.000000959.000000959.000000959.000000959.000000959.00000959.000000959.000000959.000000
mean8.3290930.5268090.2698642.4937430.08823015.88373345.7387900.9967363.310480.66148110.4331605.657977
std1.8083940.1752210.1983771.2623290.05055510.48573931.8970950.0019250.154620.1716391.0843490.805654
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25%7.1000000.4000000.0900001.9000000.0700007.00000022.0000000.9955403.210000.5500009.5000005.000000
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75%9.3000000.6350000.4300002.6000000.09000022.00000061.0000000.9978703.400000.73000011.1000006.000000
max15.9000001.3300001.00000015.4000000.61000072.000000278.0000001.0036904.010002.00000014.9000008.000000
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" + ], + "text/plain": [ + " fixed acidity volatile acidity ... alcohol quality\n", + "count 959.000000 959.000000 ... 959.000000 959.000000\n", + "mean 8.329093 0.526809 ... 10.433160 5.657977\n", + "std 1.808394 0.175221 ... 1.084349 0.805654\n", + "min 4.600000 0.120000 ... 8.400000 3.000000\n", + "25% 7.100000 0.400000 ... 9.500000 5.000000\n", + "50% 7.900000 0.520000 ... 10.100000 6.000000\n", + "75% 9.300000 0.635000 ... 11.100000 6.000000\n", + "max 15.900000 1.330000 ... 14.900000 8.000000\n", + "\n", + "[8 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JWXJ2CZQuylE" + }, + "source": [ + "Testowy Wykres (quality, volatile acidity)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "HbsfwCL7XpNe", + "outputId": "3e95f7ba-b73f-4861-e3a5-c3ec3029e3a5" + }, + "source": [ + "fig = plt.figure(figsize = (10,6))\n", + "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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360 rows × 12 columns

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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count360.000000360.000000360.000000360.000000360.000000360.000000360.000000360.000000360.000000360.000000360.000000360.000000
mean8.3486110.5187640.2754442.5422220.08611416.09305648.7777780.9967473.3010830.65383310.3688895.586111
std1.5805740.1825540.1825081.5284650.04344510.42109735.0057780.0017920.1453790.1683061.0417290.767245
min5.0000000.1200000.0000000.9000000.0420003.0000006.0000000.9900702.8700000.3700008.7000003.000000
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75%9.2000000.6400000.4200002.6000000.09000021.00000065.7500000.9976833.3900000.72000011.0000006.000000
max15.6000001.1150000.79000015.5000000.61100068.000000289.0000001.0036903.7500001.95000014.0000008.000000
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" + ], + "text/plain": [ + " fixed acidity volatile acidity ... alcohol quality\n", + "count 360.000000 360.000000 ... 360.000000 360.000000\n", + "mean 8.348611 0.518764 ... 10.368889 5.586111\n", + "std 1.580574 0.182554 ... 1.041729 0.767245\n", + "min 5.000000 0.120000 ... 8.700000 3.000000\n", + "25% 7.200000 0.380000 ... 9.500000 5.000000\n", + "50% 8.000000 0.500000 ... 10.100000 6.000000\n", + "75% 9.200000 0.640000 ... 11.000000 6.000000\n", + "max 15.600000 1.115000 ... 14.000000 8.000000\n", + "\n", + "[8 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wzaUXARnu824" + }, + "source": [ + "Testowy Wykres (quality, volatile acidity)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "3GksWzExaHV7", + "outputId": "e7076b4b-79b9-4c9b-a1e8-44b5897175ce" + }, + "source": [ + "fig = plt.figure(figsize = (10,6))\n", + "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid ... sulphates alcohol quality\n", + "0 7.4 0.700 0.00 ... 0.56 9.4 5\n", + "1 7.8 0.880 0.00 ... 0.68 9.8 5\n", + "2 7.8 0.760 0.04 ... 0.65 9.8 5\n", + "3 11.2 0.280 0.56 ... 0.58 9.8 6\n", + "4 7.4 0.700 0.00 ... 0.56 9.4 5\n", + "... ... ... ... ... ... ... ...\n", + "1594 6.2 0.600 0.08 ... 0.58 10.5 5\n", + "1595 5.9 0.550 0.10 ... 0.76 11.2 6\n", + "1596 6.3 0.510 0.13 ... 0.75 11.0 6\n", + "1597 5.9 0.645 0.12 ... 0.71 10.2 5\n", + "1598 6.0 0.310 0.47 ... 0.66 11.0 6\n", + "\n", + "[1599 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ua_ctPpVzeKJ", + "outputId": "358ff4d0-bc4b-489e-dd00-b3cf31b4ccfd" + }, + "source": [ + "wine[\"quality\"].value_counts()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5 681\n", + "6 638\n", + "7 199\n", + "4 53\n", + "8 18\n", + "3 10\n", + "Name: quality, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "-06v1i7XzeOz", + "outputId": "54a6e104-8137-41a5-a65c-41bf2ff5203f" + }, + "source": [ + "wine.describe(include='all')" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
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" + ], + "text/plain": [ + " fixed acidity volatile acidity ... alcohol quality\n", + "count 1599.000000 1599.000000 ... 1599.000000 1599.000000\n", + "mean 8.319637 0.527821 ... 10.422983 5.636023\n", + "std 1.741096 0.179060 ... 1.065668 0.807569\n", + "min 4.600000 0.120000 ... 8.400000 3.000000\n", + "25% 7.100000 0.390000 ... 9.500000 5.000000\n", + "50% 7.900000 0.520000 ... 10.200000 6.000000\n", + "75% 9.200000 0.640000 ... 11.100000 6.000000\n", + "max 15.900000 1.580000 ... 14.900000 8.000000\n", + "\n", + "[8 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t8Y53QPyu_fO" + }, + "source": [ + "Testowy Wykres (quality, volatile acidity)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "id": "hEe3BYcJaKnF", + "outputId": "ccaf5bc0-889b-453b-f50b-eae3f8c50ee6" + }, + "source": [ + "fig = plt.figure(figsize = (10,6))\n", + "sns.barplot(x = 'quality', y = 'volatile acidity', data = wine)" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid ... sulphates alcohol quality\n", + "0 7.4 0.700 0.00 ... 0.56 9.4 8.0\n", + "1 7.8 0.880 0.00 ... 0.68 9.8 8.0\n", + "2 7.8 0.760 0.04 ... 0.65 9.8 8.0\n", + "3 11.2 0.280 0.56 ... 0.58 9.8 12.0\n", + "4 7.4 0.700 0.00 ... 0.56 9.4 8.0\n", + "... ... ... ... ... ... ... ...\n", + "1594 6.2 0.600 0.08 ... 0.58 10.5 8.0\n", + "1595 5.9 0.550 0.10 ... 0.76 11.2 12.0\n", + "1596 6.3 0.510 0.13 ... 0.75 11.0 12.0\n", + "1597 5.9 0.645 0.12 ... 0.71 10.2 8.0\n", + "1598 6.0 0.310 0.47 ... 0.66 11.0 12.0\n", + "\n", + "[1599 rows x 12 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I1AwZoyN4RHs", + "outputId": "81a417a4-236b-41e1-8d26-4462b2e13711" + }, + "source": [ + "wine[\"quality\"].value_counts()" + ], + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "8.0 681\n", + "12.0 638\n", + "16.0 199\n", + "4.0 53\n", + "20.0 18\n", + "0.0 10\n", + "Name: quality, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XBU3z_of414w" + }, + "source": [ + "# 5. Usuwanie artefaktów" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KCstRwQp5-X1" + }, + "source": [ + "### Całe szczęscie nie ma w moim zbiorze ani pustych linijek, ani przykładów z niepoprawnymi wartościami" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EJqksTP545UV" + }, + "source": [ + "# Znajdźmy pustą linijkę:\n", + "! grep -P \"^$\" -n winequality-red.csv" + ], + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8DuoPn3Fa0kP" + }, + "source": [ + "Szukanie wartości \"NA\": https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "REYF2AWjz_lr", + "outputId": "148c1b42-d301-4208-e09a-2333ce73c5e1" + }, + "source": [ + "wine.isnull().sum()" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "fixed acidity 0\n", + "volatile acidity 0\n", + "citric acid 0\n", + "residual sugar 0\n", + "chlorides 0\n", + "free sulfur dioxide 0\n", + "total sulfur dioxide 0\n", + "density 0\n", + "pH 0\n", + "sulphates 0\n", + "alcohol 0\n", + "quality 0\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RbkqNj9_akcU" + }, + "source": [ + "wine.dropna(inplace=True) " + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "id": "4WylJo9malyG", + "outputId": "8858109b-c7e8-4ddc-de07-790bcb39c5a4" + }, + "source": [ + "wine" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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17.80.8800.002.60.09825.067.00.996803.200.689.88.0
27.80.7600.042.30.09215.054.00.997003.260.659.88.0
311.20.2800.561.90.07517.060.00.998003.160.589.812.0
47.40.7000.001.90.07611.034.00.997803.510.569.48.0
.......................................
15946.20.6000.082.00.09032.044.00.994903.450.5810.58.0
15955.90.5500.102.20.06239.051.00.995123.520.7611.212.0
15966.30.5100.132.30.07629.040.00.995743.420.7511.012.0
15975.90.6450.122.00.07532.044.00.995473.570.7110.28.0
15986.00.3100.473.60.06718.042.00.995493.390.6611.012.0
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