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zzrSip72K$n>hh`LwsAO6!a+&V9jOiL!C_Q^Du4CnD5-vzr6$_J4)&G$ldXinbi-8+)FZBp&8CBcJX z0il`J+hc@d12Sl8^Vo_yY0_`|*xeh9{AtIn6RiYicK*?|M>!bnVt-qKPOk%7MUoQ{ zxySq@yl0OFO*7I(1;1_5Wg+Fxe$Z=cdmQ%`GWFNhHqu_I9l7fKfY(DeHe^1=l5FWD zyz$U_7CN>dRfRuwvvpw#DWB^$Aq`3?cd@#tpV|5~1gQnNE;wWvl>|jYou0`rouv9w z&ol?`_%?vt8mi-rU;8y(X|=rD@ie<=o7t^v!N)`T+P75`e}e=+*yaA`p8CA8_kIKS Vc5 Date: Mon, 8 Mar 2021 19:40:33 +0100 Subject: [PATCH 2/5] Ok --- .../lab_02-checkpoint.ipynb | 770 ++++++++++++++++++ Materialy/Grupa3/lab_02.ipynb | 27 +- 2 files changed, 793 insertions(+), 4 deletions(-) create mode 100644 Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb diff --git a/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb b/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb new file mode 100644 index 000000000..678b3e268 --- /dev/null +++ b/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pakiety" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import sklearn \n", + "from sklearn.datasets import load_boston\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import warnings\n", + "#warnings.filterwarnings('ignore')\n", + "np.random.seed(23)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zbiór danych nt. butów męskich\n", + "## naszym celem jest przewidzenie ceny - prices_amountmin" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'menshoes.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# wczytujemy dane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mshoes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'menshoes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# w tym momencie następuje odruch bezwarunkowy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mshoes_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 821\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1048\u001b[0m )\n\u001b[0;32m 1049\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1869\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1361\u001b[0m \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1364\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'menshoes.csv'" + ] + } + ], + "source": [ + "# wczytujemy dane \n", + "shoes_df = pd.read_csv('menshoes.csv')\n", + "\n", + "# w tym momencie następuje odruch bezwarunkowy\n", + "shoes_df.head()\n", + "\n", + "# jakie wnioski?\n", + "# @conclusions\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Korzystając z przyjaciół przyjrzyjmy się bliżej\n", + "shoes_df.info()\n", + "# @conclusions\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "threshold = shoes_df.shape[0] * 0.7\n", + "shoes_df_drop = shoes_df.loc[:, shoes_df.apply(lambda x: x.isna().sum(), axis=0) < threshold]\n", + "shoes_df_drop.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "które kolumny jeszcze wydają się niepotrzebne? \n", + "które kolumny trzeba przekształcić?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# @conclusions\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# popatrzmy na kolory butów\n", + "shoes_df_drop['colors'].value_counts()\n", + "\n", + "# jakie jest ograniczenie metody .value_counts()?\n", + "# @conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# wartości NA\n", + "pd.DataFrame(shoes_df_drop['colors']).info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "jest sporo braków \n", + "pomówimy jak z tym żyć za chwilę \n", + "najpierw wizualizacja \n", + "jakieś pomysły na wizualizację zmienna ciągła vs. kategoryczna?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "Black_Brown = shoes_df_drop.loc[shoes_df_drop['colors'].isin(['Black', 'Brown'])]\n", + "violin_plot = sns.violinplot(Black_Brown['colors'], Black_Brown['prices_amountmin'])\n", + "violin_plot.set_title('Rozkład ceny względem koloru butów')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "popular_colors=shoes_df_drop.loc[shoes_df_drop['colors'].isin(shoes_df_drop['colors'].value_counts().index[:5])]\n", + "mean_price_popular_colors=popular_colors.groupby('colors')['prices_amountmin'].mean()\n", + "mean_price_popular_colors.plot(kind='bar', title='Średnia cena')\n", + "plt.\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imputacja zmiennych kategorycznych" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kolumna colors miała dużo nieuzupełnionych wartości \n", + "jak można je uzupełnić?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# możnaby najczęściej występującym kolorem, ale czy to nie wprowadza fałszywej informacji?\n", + "# lepsze podejście - nowa klasa 'Other'\n", + "shoes_df_drop['colors'].fillna('Missing_color') #działa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# czy można lepiej?\n", + "# można!\n", + "shoes_df_drop['missing' + 'colors'] = shoes_df_drop['colors'].isna()*1\n", + "shoes_df_drop['colors'].fillna('Missing_color', inplace=True)\n", + "\n", + "# po co ta informacja, skoro w kolumnie już jest napisane, że to brakujący kolor?\n", + "# @conclusion\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Uwaga, to co jest wyżej to wcale nie jest prawda \n", + "### przekonamy się o tym przy one hot encoding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imputacja zmiennych ciągłych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# w naszym zbiorze nie ma nic do imputacji ciągłej\n", + "# zerknijmy na szutczny zbiór\n", + "fake_data=pd.DataFrame({'num':np.random.choice([None, 3,4], 100), \n", + " 'cat': np.random.choice([None, 'Puma','Nike','Adidas'], 100, p=[0.92, 0.03, 0.03, 0.02])})\n", + "fake_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# średnia czy mediana?\n", + "fake_data.num.fillna(fake_data.num.median(), inplace=True) #fake_data.num.mean()\n", + "fake_data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Outliery" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# dane jedynie z przedziału (średnia +- 3 sigma) - ma to sens gdy rozkład normalny\n", + "data=pd.DataFrame({'num':np.random.normal(2,0.4,1000)})\n", + "factor = 3\n", + "upper_lim = data['num'].mean () + data['num'].std () * factor\n", + "lower_lim = data['num'].mean () - data['num'].std () * factor\n", + "\n", + "data = data[(data['num'] < upper_lim) & (data['num'] > lower_lim)]\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# usuwanie na podstawie skrajnych percentyli\n", + "# to już zadziała \"dobrze\" dla każdego rozkładu\n", + "# załadujmy nasz ulubiony Boston\n", + "boston_dict = load_boston()\n", + "boston_df = pd.DataFrame(boston_dict['data'], columns=boston_dict['feature_names'])\n", + "\n", + "dis_data = boston_df['DIS']\n", + "print('Wejściowy rozmiar: ', dis_data.shape[0])\n", + "\n", + "upper_lim = dis_data.quantile(.95)\n", + "lower_lim = dis_data.quantile(.05)\n", + "\n", + "data_percentile = dis_data[(dis_data < upper_lim) & (dis_data > lower_lim)]\n", + "print('Wyjściowy rozmiar: ', data_percentile.shape[0])\n", + "print('Pozostało %: ', round(data_percentile.shape[0]/dis_data.shape[0], 2))\n", + "\n", + "# co tu jest nie halo?\n", + "# @conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# to może boxplot?\n", + "# faktycznie, dołu nie powinniśmy obcinać\n", + "# ale na górze są outliery!\n", + "plot_box = sns.boxplot(boston_df['DIS'])\n", + "plot_box.set_title('Rozkład zmiennej DIS')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# nope\n", + "# ten rozkład taki jest, nie można go za to winić\n", + "# tu nie ma outlierów\n", + "plot_dens=sns.histplot(boston_df['DIS'])\n", + "plot_dens.set_title('Rozkład zmiennej DIS')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Grouping & Binning\n", + "### Agregujemy klasy do wyższego poziomu lub tniemy zmienną ciągłą na klasy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "czasami potrzebujemy zrobić ze zmiennej ciągłej kategoryczną \n", + "albo mamy zmienną kategoryczną o bardzo dużej liczbie klas \n", + "albo dużo klas mało licznych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# zobaczmy jak wygląda kolumna brand \n", + "shoes_df_drop['brand'].value_counts()\n", + "# co można z tym zrobić?\n", + "# @conclusions\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# jest aż 560 marek, które występują raz\n", + "shoes_df_drop['brand'].value_counts()[shoes_df_drop['brand'].value_counts() == 1].shape[0]\n", + "\n", + "# czyli grupujemy w kategorię Other?\n", + "# @conclusion\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# przyjrzyjmy się bliżej\n", + "brands = shoes_df_drop[['brand']].groupby(['brand']).size().sort_values(ascending=False).reset_index()\n", + "brands.columns = ['brand', 'count']\n", + "brands.loc[brands['brand'].apply(lambda x:'nike' in x.lower())]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "może warto najpierw zgrupować Najacze a potem dopiero Others? \n", + "Nike i NIKE to na pewno to samo, ale może NIKE - Kobe to dość niszowe obuwie i warto, żeby było Others? \n", + "to samodzielna decyzja" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nike_synonyms = brands.loc[brands['brand'].apply(lambda x:'nike' in x.lower()), 'brand'].values\n", + "\n", + "small_classes = shoes_df_drop['brand'].value_counts()[shoes_df_drop['brand'].value_counts() == 1].index\n", + "\n", + "shoes_df_drop['brands' + '_processed'] = np.where(shoes_df_drop['brand'].isin(nike_synonyms), 'nike', \n", + " np.where(shoes_df_drop['brand'].isin(small_classes), 'Other', shoes_df_drop['brand']))\n", + "\n", + "shoes_df_drop['brands' + '_processed'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# można to zrobić też przy pomocy słownika\n", + "geo=np.random.choice((\"Poland\",'Chile', 'France', 'Spain'), 100)\n", + "geo=pd.Series(geo)\n", + "geo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dict_geo={'Poland': \"Europe\", \"Chile\":\"South America\", \"France\":\"Europe\"}\n", + "from collections import defaultdict\n", + "countries_list = [('Poland','Europe'), ('France','Europe'), ('Chile','South America')]\n", + "geo.map(dict_geo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# metoda z użyciem dict/defaultdict\n", + "countries_dict = defaultdict(lambda:'Other')\n", + "for continent, country in countries_list:\n", + " countries_dict[continent]=country\n", + "geo.map(countries_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# naszym celem było przewidywanie cen butów\n", + "# ale może wystarczy jeśli przewidzimy to bardziej z grubsza? Tanie, średnie, drogie\n", + "prices_hist = sns.histplot(shoes_df_drop['prices_amountmin'])\n", + "prices_hist.set_title('Rozkład cen butów')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.cut(shoes_df_drop['prices_amountmin'], bins=[0, 100, 200, 250], labels=['cheap', 'affordable', 'expensive'])[17:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cutted = pd.cut(shoes_df_drop['prices_amountmin'], bins=[0, 100, 200, np.inf], labels=['cheap', 'affordable', 'expensive'])\n", + "cutted[18:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# zawsze warto sprawdzić\n", + "cutted[cutted.isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# i spróbować zrozumieć dlaczego\n", + "shoes_df_drop.loc[4176, 'prices_amountmin']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dla przypomnienia - Log Transform" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dis_dist = sns.distplot(boston_df['DIS'])\n", + "dis_dist.set_title('Rozkład zmiennej DIS')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dis_log_dist = sns.distplot(np.log1p(boston_df['DIS']))\n", + "dis_log_dist.set_title('Rozkład logarytmu zmiennej DIS')\n", + "plt.show()\n", + "# nie jest idealnie, ale na pewno mniej skośnie \\:)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Categorical variables encoding\n", + "### algorytmy często nie lubią zmiennych kategorycznych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']\n", + "values = np.array(data)\n", + "\n", + "# integer encode\n", + "le = LabelEncoder()\n", + "integer_encoded = le.fit_transform(values)\n", + "print(integer_encoded)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Warto zauważyć, że to ma sens, tylko dla zmiennych, które reprezentują jakieś poziomy/kolejność" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# uwaga! \n", + "# nie panujemy nad kolejnością -> check OrdinalEncoder\n", + "data = ['hot', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']\n", + "values = np.array(data)\n", + "le = LabelEncoder()\n", + "integer_encoded = le.fit_transform(values)\n", + "print(integer_encoded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# binary encode\n", + "onehot_encoder = OneHotEncoder(sparse=False)\n", + "integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n", + "onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n", + "print(onehot_encoded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# przykład z naszych danych\n", + "# kolumna categories wyglądała obiecująco\n", + "shoes_df_drop.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shoes_df_drop['categories'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# podrążmy temat\n", + "categories = {}\n", + "def split_and_count(x, categories):\n", + " cat_list = x.split(',')\n", + " for cat in cat_list:\n", + " categories.setdefault(cat, 0)\n", + " categories[cat] += 1\n", + " return categories\n", + "\n", + "for row in shoes_df_drop['categories'].iteritems():\n", + " split_and_count(row[1], categories)\n", + "categories_df = pd.DataFrame.from_dict(categories, orient='index').reset_index()\n", + "categories_df.columns = ['category', 'count']\n", + "categories_df.sort_values(by='count', ascending=False).head(20)\n", + "\n", + "# kategorie typu athletic, *sport, *outwear mogą się nadawać na one-hot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scaling\n", + "- min-max scaling\n", + "- standarization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "niektóre algorytmy nie lubią dużych skal zmiennych - regresja liniowa z poprzednich zajęć \n", + "jakiś parametr musi \"obsłużyć\" bardzo małe i bardzo duże liczby \n", + "inne potrzebują mieć zmienne w konkretnym przedziale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# min-max scaling\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "data = np.array([-1, 2, -0.5, 6, 0, 10, 1, 18]).reshape(-1, 1)\n", + "mm_scaler = MinMaxScaler()\n", + "\n", + "print(mm_scaler.fit_transform(data))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# standarization\n", + "from sklearn.preprocessing import StandardScaler\n", + "data = np.array([-1, 2, -0.5, 6, 0, 10, 1, 18]).reshape(-1, 1)\n", + "scaler = StandardScaler()\n", + "scaled_data = scaler.fit_transform(data)\n", + "print(scaled_data)\n", + "print(np.round(np.mean(scaled_data), 4), np.std(scaled_data))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Extracting info from date" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import date\n", + "\n", + "data = pd.DataFrame({'date':\n", + "['01-01-2017',\n", + "'04-12-2008',\n", + "'23-06-1988',\n", + "'25-08-1999',\n", + "'20-02-1993',\n", + "]})\n", + "\n", + "#Transform string to date\n", + "data['date'] = pd.to_datetime(data.date, format=\"%d-%m-%Y\")\n", + "\n", + "#Extracting Year\n", + "data['year'] = data['date'].dt.year\n", + "\n", + "#Extracting Month\n", + "data['month'] = data['date'].dt.month\n", + "\n", + "#Extracting passed years since the date\n", + "data['passed_years'] = date.today().year - data['date'].dt.year\n", + "\n", + "#Extracting passed months since the date\n", + "data['passed_months'] = (date.today().year - data['date'].dt.year) * 12 + date.today().month - data['date'].dt.month\n", + "\n", + "#Extracting the weekday name of the date\n", + "data['day_name'] = data['date'].dt.day_name()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# warto poczytać\n", + "pakiet category_encoders:\n", + "- https://kiwidamien.github.io/encoding-categorical-variables.html\n", + "- https://pbpython.com/categorical-encoding.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ciekawa strona z przykładami wizualizacji (wraz z kodem):\n", + " https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Materialy/Grupa3/lab_02.ipynb b/Materialy/Grupa3/lab_02.ipynb index 0550afbd2..678b3e268 100644 --- a/Materialy/Grupa3/lab_02.ipynb +++ b/Materialy/Grupa3/lab_02.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,9 +35,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'menshoes.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# wczytujemy dane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mshoes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'menshoes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# w tym momencie następuje odruch bezwarunkowy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mshoes_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 820\u001b[0m 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ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m 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+++++++- .../lab_03-checkpoint.ipynb | 762 +++++++++++++++++ Materialy/Grupa3/lab_02.ipynb | 771 +++++++++++++++++- Materialy/Grupa3/lab_03.ipynb | 2 +- 4 files changed, 1845 insertions(+), 52 deletions(-) create mode 100644 Materialy/Grupa3/.ipynb_checkpoints/lab_03-checkpoint.ipynb diff --git a/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb b/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb index 678b3e268..935a22143 100644 --- a/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb +++ b/Materialy/Grupa3/.ipynb_checkpoints/lab_02-checkpoint.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -35,26 +35,243 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'menshoes.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# wczytujemy dane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mshoes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'menshoes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# w tym momencie następuje odruch bezwarunkowy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m 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parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m 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\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1048\u001b[0m )\n\u001b[0;32m 1049\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1869\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1361\u001b[0m \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1364\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'menshoes.csv'" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Patryk\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3071: DtypeWarning: Columns (29) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n" ] + }, + { + "data": { + "text/html": [ + "

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idasinsbrandcategoriescolorscountdateaddeddateupdateddescriptionsdimension...prices_warrantyquantitiesreviewssizesskussourceurlsupcvinwebsiteidsweight
0AVpfHrJ6ilAPnD_xVXOINaNJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaNNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11NaNNaNNaN
1AVpfHrJ6ilAPnD_xVXOINaNJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaNNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11NaNNaNNaN
2AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
3AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
4AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
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" + ], + "text/plain": [ + " id asins brand \\\n", + "0 AVpfHrJ6ilAPnD_xVXOI NaN Josmo \n", + "1 AVpfHrJ6ilAPnD_xVXOI NaN Josmo \n", + "2 AVpfHsWP1cnluZ0-eVZ7 NaN SERVUS BY HONEYWELL \n", + "3 AVpfHsWP1cnluZ0-eVZ7 NaN SERVUS BY HONEYWELL \n", + "4 AVpfHsWP1cnluZ0-eVZ7 NaN SERVUS BY HONEYWELL \n", + "\n", + " categories colors count \\\n", + "0 Clothing,Shoes,Men's Shoes,All Men's Shoes NaN NaN \n", + "1 Clothing,Shoes,Men's Shoes,All Men's Shoes NaN NaN \n", + "2 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN NaN \n", + "3 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN NaN \n", + "4 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN NaN \n", + "\n", + " dateadded dateupdated \\\n", + "0 2016-11-07T00:45:12Z 2016-11-07T00:45:12Z \n", + "1 2016-11-07T00:45:12Z 2016-11-07T00:45:12Z \n", + "2 2016-06-14T04:29:57Z 2016-07-09T20:26:48Z \n", + "3 2016-06-14T04:29:57Z 2016-07-09T20:26:48Z \n", + "4 2016-06-14T04:29:57Z 2016-07-09T20:26:48Z \n", + "\n", + " descriptions dimension ... \\\n", + "0 [{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU... NaN ... \n", + "1 [{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU... NaN ... \n", + "2 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "3 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "4 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "\n", + " prices_warranty quantities reviews sizes skus \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " sourceurls upc vin \\\n", + "0 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 NaN \n", + "1 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 NaN \n", + "2 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "3 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "4 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "\n", + " websiteids weight \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -70,9 +287,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 18280 entries, 0 to 18279\n", + "Data columns (total 48 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 18280 non-null object \n", + " 1 asins 2161 non-null object \n", + " 2 brand 18263 non-null object \n", + " 3 categories 18280 non-null object \n", + " 4 colors 10344 non-null object \n", + " 5 count 0 non-null float64\n", + " 6 dateadded 18280 non-null object \n", + " 7 dateupdated 18280 non-null object \n", + " 8 descriptions 9704 non-null object \n", + " 9 dimension 2968 non-null object \n", + " 10 ean 9690 non-null float64\n", + " 11 features 13299 non-null object \n", + " 12 flavors 0 non-null float64\n", + " 13 imageurls 17255 non-null object \n", + " 14 isbn 0 non-null float64\n", + " 15 keys 18280 non-null object \n", + " 16 manufacturer 6296 non-null object \n", + " 17 manufacturernumber 14309 non-null object \n", + " 18 merchants 13018 non-null object \n", + " 19 name 18280 non-null object \n", + " 20 prices_amountmin 18280 non-null float64\n", + " 21 prices_amountmax 18280 non-null float64\n", + " 22 prices_availability 114 non-null object \n", + " 23 prices_color 558 non-null object \n", + " 24 prices_condition 11675 non-null object \n", + " 25 prices_count 0 non-null float64\n", + " 26 prices_currency 18280 non-null object \n", + " 27 prices_dateadded 18280 non-null object \n", + " 28 prices_dateseen 18280 non-null object \n", + " 29 prices_flavor 1 non-null object \n", + " 30 prices_issale 18280 non-null bool \n", + " 31 prices_merchant 13160 non-null object \n", + " 32 prices_offer 5874 non-null object \n", + " 33 prices_returnpolicy 798 non-null object \n", + " 34 prices_shipping 4906 non-null object \n", + " 35 prices_size 486 non-null object \n", + " 36 prices_source 0 non-null float64\n", + " 37 prices_sourceurls 18265 non-null object \n", + " 38 prices_warranty 42 non-null object \n", + " 39 quantities 0 non-null float64\n", + " 40 reviews 1592 non-null object \n", + " 41 sizes 5920 non-null object \n", + " 42 skus 8120 non-null object \n", + " 43 sourceurls 18246 non-null object \n", + " 44 upc 10488 non-null float64\n", + " 45 vin 0 non-null float64\n", + " 46 websiteids 0 non-null float64\n", + " 47 weight 570 non-null object \n", + "dtypes: bool(1), float64(12), object(35)\n", + "memory usage: 6.6+ MB\n" + ] + } + ], "source": [ "# Korzystając z przyjaciół przyjrzyjmy się bliżej\n", "shoes_df.info()\n", @@ -532,9 +811,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 2 0 1 1 2 0 2 1]\n" + ] + } + ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import OneHotEncoder\n", @@ -557,9 +844,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 2 0 1 1 2 0 2 1]\n" + ] + } + ], "source": [ "# uwaga! \n", "# nie panujemy nad kolejnością -> check OrdinalEncoder\n", @@ -572,9 +867,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [0. 1. 0.]]\n" + ] + } + ], "source": [ "# binary encode\n", "onehot_encoder = OneHotEncoder(sparse=False)\n", diff --git a/Materialy/Grupa3/.ipynb_checkpoints/lab_03-checkpoint.ipynb b/Materialy/Grupa3/.ipynb_checkpoints/lab_03-checkpoint.ipynb new file mode 100644 index 000000000..f40737fb6 --- /dev/null +++ b/Materialy/Grupa3/.ipynb_checkpoints/lab_03-checkpoint.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Klasyfikacja \n", + "Klasyfikacja to rodzaj algorytmu statystycznego, który przydziela obserwacje statystyczne do klas, bazując na atrybutach tych obserwacji.\n", + "\n", + "**Definicja:**\n", + "Dla danego zbioru danych trenujących $\\{(x_1,y_1),\\ldots,(x_n,y_n)\\}$ algorytm potrafi znaleźć funkcję klasyfikującją $h: X -> Y$, która przydziela obiektowi $x\\in X$ klasę $y \\in Y$.\n", + "\n", + "- prawdopodobieństwo aposteriori: $P(Y=i|X)$ *\n", + "- funkcja klasyfikacyjna przyjmuje postać: $h(X) = argmax_{1,\\ldots,y} P(Y=i|X)$\n", + "\n", + "*większość klasyfikatorów modeluje prawdopodobieństwa, wyjątek stanowi SVM\n", + "\n", + "Przykłady klasyfikacji:\n", + "- wykrywanie czy pacjent jest chory na daną chorobę na podstawie wyników badań\n", + "- klasyfikacja maili jako spam/nie-spam\n", + "- czy transakcja dokonana na koncie klienta banku to oszustwo/kradzież czy też normalna transakcja\n", + "- rozpoznawania na obrazu różnych rodzajów zwierząt\n", + "- rozpoznawanie czy pasażer przeżyje katastrofę na Titanicu\n", + "\n", + "**Na potrzeby uproszczenia wyjaśniania w dalszej części labów, skupimy się tylko na klasyfikacji binarnej.**\n", + "\n", + "Zajmiemy się zbiorem gdzie klasyfikujemy u pacjentów czy występuje choroba serca czy nie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T15:01:18.130629Z", + "start_time": "2020-03-07T15:01:18.127672Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "np.random.seed = 42" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T14:45:39.531710Z", + "start_time": "2020-03-07T14:45:39.518696Z" + } + }, + "outputs": [], + "source": [ + "data = pd.read_csv('heart.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T14:45:53.174538Z", + "start_time": "2020-03-07T14:45:53.166435Z" + } + }, + "outputs": [], + "source": [ + "# Szybko sprawdzamy podstawowe cechy danych\n", + "na_ratio_cols = data.isna().mean(axis=0)\n", + "na_ratio_cols" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y = np.array(data['chd'])\n", + "X = data.drop(['chd','famhist'],axis=1)\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T14:52:23.920291Z", + "start_time": "2020-03-07T14:52:23.911127Z" + } + }, + "outputs": [], + "source": [ + "# Szybkie ćwiczenie - wykonaj dowolne kodowanie zmiennej kategorycznej\n", + "\n", + "X.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sposoby podziału danych\n", + "- Jak radzić sobie z overfitingiem?\n", + "- Jakie znacie sposoby podziału danych na treningowe i testowe?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](https://media.geeksforgeeks.org/wp-content/cdn-uploads/20190523171258/overfitting_2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.geeksforgeeks.org/underfitting-and-overfitting-in-machine-learning/\n", + "\n", + "## Zbiór treningowy, walidacyjny i testowy¶\n", + "Prosty podział danych na część, na której uczymy model i na część która służy nam do sprawdzenia jego skuteczności." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T17:50:27.159896Z", + "start_time": "2020-03-07T17:50:27.154910Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_val, y_train, y_val = train_test_split(\n", + " X, y, stratify=y, test_size=0.3, random_state=42\n", + ")\n", + "X_val, X_test, y_val, y_test = train_test_split(\n", + " X_val, y_val, stratify=y_val, test_size=0.3, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(y).hist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(y_test).hist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.Series(y_test).hist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T17:50:52.333567Z", + "start_time": "2020-03-07T17:50:52.329929Z" + } + }, + "outputs": [], + "source": [ + "print(X.shape,X_train.shape, X_val.shape, X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jaki znacie najprostszy klasyfikator?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.dummy import DummyClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T16:35:17.078577Z", + "start_time": "2020-03-07T16:35:17.072606Z" + } + }, + "outputs": [], + "source": [ + "dc = DummyClassifier(strategy='uniform', random_state=42)\n", + "dc.fit(X_train,y_train)\n", + "y_proba = dc.predict_proba(X_val)\n", + "y_hat = dc.predict(X_val)\n", + "print(\"proba: \" + str(y_proba[0:10,0]) + '\\ny: ' + str(y_hat[0:10]) + '\\ny_hat: ' + str(y_val[0:10]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Przetestujcie jaki będzie wynik działania algorytmu gdy zmienimy parametr *strategy* (oraz porównać accuracy) - podpowiedź: skorzystaj z dokumentacji funkcji" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: policzyć accuracy dla baselinu (z inną strategią niż uniform) na train i validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Jakieś inne proste klasyfikatory?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regresja logistyczna - czemu by nie prognozować prawdopodobieństwa za pomocą regresji liniowej?\n", + "\n", + "**Przypomnienie:** uogólniony model liniowy: $y_{i}=\\beta _{0}1+\\beta _{1}x_{i1}+\\cdots +\\beta _{p}x_{ip} = x^T \\beta$\n", + "\n", + "- Jaki jest podstawowy problem z wykorzystaniem regresji do modelowania prawdopodobieństwa?\n", + "- Jakie macie propozycje rozwiązania tego problemu?\n", + "\n", + "$odds = \\frac{P(Y=1|X)}{P(Y=0|X)} = \\frac{p}{1-p}$ $\\in (0,\\infty)$\n", + "\n", + "$\\log({odds}) \\in (-\\infty, \\infty)$\n", + "\n", + "Co pozwala nam modelować powyższe równanie dzięki regresji liniowej, po przekształceniu równania, uzyskujemy prawdopodobieństwo sukcesu:\n", + "\n", + "$x^T \\beta = \\log({\\frac{p}{1-p}}) \\Rightarrow p = \\frac{1}{1+\\exp({-x^T \\beta})}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1534281070/linear_vs_logistic_regression_edxw03.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T15:17:06.286476Z", + "start_time": "2020-03-07T15:17:06.227772Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "lr = LogisticRegression(max_iter=1000)\n", + "\n", + "lr.fit(X_train,y_train)\n", + "y_hat = lr.predict(X_val)\n", + "print('y: ' + str(y_hat[0:10]) + '\\ny_hat: ' + str(y_val[0:10]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: policzyć accuracy dla logita z l1, l2, i bez regularyzacji na train i validation\n", + "#porównać z baselinem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lr.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lr.intercept_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Jak interpretować wyniki?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# jak się zmieni powyższy wynik gdy zwiększymy wartość czwartej cechy (tj. adiposity) dla pierwszej obserwacji o 1\n", + "\n", + "#solution\n", + "experiment=X_val.iloc[0,:]\n", + "experiment[3]=experiment[3]+1\n", + "np.log(lr.predict_proba(experiment.values.reshape(1,-1))[0,1]/lr.predict_proba(experiment.values.reshape(1,-1))[0,0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dlaczego można było się przewidzieć, że taki właśnie będzie wynik?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#solution\n", + "np.log(lr.predict_proba(X_val)[0,1]/lr.predict_proba(X_val)[0,0])+lr.coef_[0,3]\n", + "# otrzymano taki sam wynik - nie trzeba było wykonywać metody predict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO Jaki będzie wynik gdy wektor cech będzie miał tylko zerowe elementy?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#solution\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dlaczego można było się przewidzieć, że taki właśnie będzie wynik?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#solution\n", + "1/(1+np.exp(-lr.intercept_))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Jakie są zalety regresji logistycznej?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drzewo decyzyjne\n", + "- Jak wykorzystać model drzewa do predykcji klasyfikacji/regresji?\n", + "- jakie problemy może to generować?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T16:33:36.468292Z", + "start_time": "2020-03-07T16:33:36.240821Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier,plot_tree #export_graphviz\n", + "## biblioteka poniżej może być problematyczna na Windows\n", + "#import graphviz\n", + "\n", + "tree1 = DecisionTreeClassifier()\n", + "\n", + "tree1.fit(X_train,y_train)\n", + "y_hat = tree1.predict(X_val)\n", + "print('y: ' + str(y_hat[0:10]) + '\\ny_hat: ' + str(y_val[0:10]))\n", + "\n", + "#plt.figure(figsize=(20,20))\n", + "#splits=tree.plot_tree(tree1, filled=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tree1.get_params()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO spróbujcie wytrenować model ze zmienionymi parametrami" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SVM\n", + "Znalezienie równania hiperpłaszczyzny, która najlepiej dzieli nasz zbiór danych na klasy.\n", + "\n", + "**Uwaga: w przypadku SVM nie modelujemy prawdopodobieństwa przynależności do danej klasy - domyślnym wyjściem jest informacja o konkretnej klasie**\n", + "- Co jeżeli nie istnieje taka płaszczyzna?\n", + "- Co jeżeli nasze dane nie są separowalne liniowo, tylko np. radialnie?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/72/SVM_margin.png/300px-SVM_margin.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://en.wikipedia.org/wiki/Support-vector_machine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Kernel trick" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image](https://machine-learning-note.readthedocs.io/en/latest/_images/svm_kernel_trick.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://machine-learning-note.readthedocs.io/en/latest/algorithm/svm.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T16:57:59.319024Z", + "start_time": "2020-03-07T16:57:59.302833Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.svm import SVC\n", + "svm = SVC()\n", + "\n", + "svm.fit(X_train,y_train)\n", + "y_hat = svm.predict(X_val)\n", + "print('y: ' + str(y_hat[0:10]) + '\\ny_hat: ' + str(y_val[0:10]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jakie są wady?\n", + "- trudno dobrać optymalne parametry\n", + "- metoda wrażliwa na skalowanie danych\n", + "- długo się \"uczy\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Naiwny Klasyfikator Bayesowski\n", + "Jest oparty na założeniu o wzajemnej niezależności zmiennych. Często nie mają one żadnego związku z rzeczywistością i właśnie z tego powodu nazywa się je naiwnymi." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/cae70e6035d9ac52c547bc1c666e372063b85324)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mianownik nie zależy od C więc nie będziemy go dalej analizować - skupimy się na liczniku.\n", + "![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/2d0555690cd428cb6d6a52ea6b6391256125a45c) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rekurencyjnie obliczenia będą kontynuowane. Teraz pora zrozumieć dokładniej dlaczego występuje słowo \"naiwny\" w nazwie metody.\n", + " Zakładamy bowiem że cechy $F_i$ są niezależne czyli ![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/8898f2ee081f407669fdb7a4f60e390615513346)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ostatecznie wzór to: ![image](https://wikimedia.org/api/rest_v1/media/math/render/svg/a5978cc50b1c3d745ad304987a750aeb4a27df5b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://pl.wikipedia.org/wiki/Naiwny_klasyfikator_bayesowski" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T17:27:54.549246Z", + "start_time": "2020-03-07T17:27:54.541140Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "\n", + "nb = GaussianNB()\n", + "\n", + "nb.fit(X_train,y_train)\n", + "y_hat = nb.predict(X_val)\n", + "print('y: ' + str(y_hat[0:10]) + '\\ny_hat: ' + str(y_val[0:10]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lepszy sposób na podział danych na zbiory treningowe i testowe" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Crossvalidation\n", + "- Czy możemy stosować CV dzieląc zbiór, tak by w zbiorze walidacyjnym pozostała tylko jedna obserwacja danych?\n", + "- Czy sprawdzając performance modelu przez CV, możemy potem nauczyć model na całym zbiorze danych?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-07T17:58:33.491733Z", + "start_time": "2020-03-07T17:58:33.127377Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score\n", + "X_train_val=pd.concat((X_train,X_val))\n", + "y_train_val=np.concatenate((y_train,y_val), axis=0)\n", + "cross_val_score(lr, X_train_val, y_train_val, scoring='accuracy', cv = 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Miary ocen jakości klasyfikatorów\n", + "- Jakie znacie miary oceny klasyfikatorów?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accuracy\n", + "$ACC = \\frac{TP+TN}{ALL}$\n", + "\n", + "Bardzo intuicyjna miara - ile obserwacji zakwalifikowaliśmy poprawnie.\n", + "\n", + "- Jaki jest problem z *accuracy*?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision & Recall\n", + "$PRECISION = \\frac{TP}{TP+FP}= \\frac{TP}{\\text{TOTAL PREDICTED POSITIVE}}$\n", + "\n", + "$RECALL = \\frac{TP}{TP+FN}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### F1 Score\n", + "$F1\\_SCORE =\\frac{2*PRECISION*RECALL}{PRECISION+RECALL}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ROC AUC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Image](https://mathspace.pl/wp-content/uploads/2016/09/ROC-krzywa.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://mathspace.pl/matematyka/receiver-operating-characteristic-krzywa-roc-czyli-ocena-jakosci-klasyfikacji-czesc-7/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image](https://cdn.analyticsvidhya.com/wp-content/uploads/2020/06/data-1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Zadanie** - przetestować 3 modele przedstawione dziś na zajęciach i sprawdzić, który jest lepszy na podstawie wyżej wymienionych miar. Należy zastosować kroswalidację." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.8" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Materialy/Grupa3/lab_02.ipynb b/Materialy/Grupa3/lab_02.ipynb index 678b3e268..6170211e1 100644 --- a/Materialy/Grupa3/lab_02.ipynb +++ b/Materialy/Grupa3/lab_02.ipynb @@ -39,22 +39,239 @@ "metadata": {}, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'menshoes.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# wczytujemy dane\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mshoes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'menshoes.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# w tym momencie następuje odruch bezwarunkowy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mshoes_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 821\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1048\u001b[0m )\n\u001b[0;32m 1049\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1869\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1361\u001b[0m \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1364\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'menshoes.csv'" + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Patryk\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (29) have mixed types.Specify dtype option on import or set low_memory=False.\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n" ] + }, + { + "data": { + "text/html": [ + "
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idasinsbrandcategoriescolorscountdateaddeddateupdateddescriptionsdimension...prices_warrantyquantitiesreviewssizesskussourceurlsupcvinwebsiteidsweight
0AVpfHrJ6ilAPnD_xVXOINaNJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaNNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11NaNNaNNaN
1AVpfHrJ6ilAPnD_xVXOINaNJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaNNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11NaNNaNNaN
2AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
3AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
4AVpfHsWP1cnluZ0-eVZ7NaNSERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaNNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN...NaNNaNNaNNaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNNaNNaN
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5 rows × 48 columns

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NaN ... \n", + "1 [{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU... NaN ... \n", + "2 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "3 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "4 [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... NaN ... \n", + "\n", + " prices_warranty quantities reviews sizes skus \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN \n", + "\n", + " sourceurls upc vin \\\n", + "0 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 NaN \n", + "1 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 NaN \n", + "2 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "3 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "4 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "\n", + " websiteids weight \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + "[5 rows x 48 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -70,9 +287,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 18280 entries, 0 to 18279\n", + "Data columns (total 48 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 18280 non-null object \n", + " 1 asins 2161 non-null object \n", + " 2 brand 18263 non-null object \n", + " 3 categories 18280 non-null object \n", + " 4 colors 10344 non-null object \n", + " 5 count 0 non-null float64\n", + " 6 dateadded 18280 non-null object \n", + " 7 dateupdated 18280 non-null object \n", + " 8 descriptions 9704 non-null object \n", + " 9 dimension 2968 non-null object \n", + " 10 ean 9690 non-null float64\n", + " 11 features 13299 non-null object \n", + " 12 flavors 0 non-null float64\n", + " 13 imageurls 17255 non-null object \n", + " 14 isbn 0 non-null float64\n", + " 15 keys 18280 non-null object \n", + " 16 manufacturer 6296 non-null object \n", + " 17 manufacturernumber 14309 non-null object \n", + " 18 merchants 13018 non-null object \n", + " 19 name 18280 non-null object \n", + " 20 prices_amountmin 18280 non-null float64\n", + " 21 prices_amountmax 18280 non-null float64\n", + " 22 prices_availability 114 non-null object \n", + " 23 prices_color 558 non-null object \n", + " 24 prices_condition 11675 non-null object \n", + " 25 prices_count 0 non-null float64\n", + " 26 prices_currency 18280 non-null object \n", + " 27 prices_dateadded 18280 non-null object \n", + " 28 prices_dateseen 18280 non-null object \n", + " 29 prices_flavor 1 non-null object \n", + " 30 prices_issale 18280 non-null bool \n", + " 31 prices_merchant 13160 non-null object \n", + " 32 prices_offer 5874 non-null object \n", + " 33 prices_returnpolicy 798 non-null object \n", + " 34 prices_shipping 4906 non-null object \n", + " 35 prices_size 486 non-null object \n", + " 36 prices_source 0 non-null float64\n", + " 37 prices_sourceurls 18265 non-null object \n", + " 38 prices_warranty 42 non-null object \n", + " 39 quantities 0 non-null float64\n", + " 40 reviews 1592 non-null object \n", + " 41 sizes 5920 non-null object \n", + " 42 skus 8120 non-null object \n", + " 43 sourceurls 18246 non-null object \n", + " 44 upc 10488 non-null float64\n", + " 45 vin 0 non-null float64\n", + " 46 websiteids 0 non-null float64\n", + " 47 weight 570 non-null object \n", + "dtypes: bool(1), float64(12), object(35)\n", + "memory usage: 6.6+ MB\n" + ] + } + ], "source": [ "# Korzystając z przyjaciół przyjrzyjmy się bliżej\n", "shoes_df.info()\n", @@ -81,9 +360,416 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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idbrandcategoriescolorsdateaddeddateupdateddescriptionseanfeaturesimageurls...prices_dateaddedprices_dateseenprices_issaleprices_merchantprices_offerprices_sourceurlssizesskussourceurlsupc
0AVpfHrJ6ilAPnD_xVXOIJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...6.993020e+11[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Shoe...https://i5.walmartimages.com/asr/13ac3d61-003c......2016-11-07T00:45:12Z2016-11-05T00:00:00ZTrueNaNREDUCED USD 12.10https://www.walmart.com/ip/Josmo-8190-Plain-In...NaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11
1AVpfHrJ6ilAPnD_xVXOIJosmoClothing,Shoes,Men's Shoes,All Men's ShoesNaN2016-11-07T00:45:12Z2016-11-07T00:45:12Z[{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU...6.993020e+11[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Shoe...https://i5.walmartimages.com/asr/13ac3d61-003c......2016-11-07T00:45:12Z2016-11-05T00:00:00ZFalseUnbeatableSale - Walmart.comREDUCED USD 12.10https://www.walmart.com/ip/Josmo-8190-Plain-In...NaNNaNhttps://www.walmart.com/ip/Josmo-8190-Plain-In...6.993020e+11
2AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-03-08T00:00:00ZFalseSIM Supply Inc - Walmart.comNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
3AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2015-11-30T00:00:00ZFalseSIM Supply Inc - Walmart.comNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
4AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-04-29T00:00:00ZFalseSIM Supply Inc - Walmart.comNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
5AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-07-09T00:00:00ZFalseSIM Supply Inc - Walmart.comNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
6AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-03-10T00:00:00ZFalseSIM Supply Inc - Walmart.comNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
7AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-06-13T00:00:00ZTrueSIM Supply Inc - Walmart.comSAVINGS_AMT USD 9.80http://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
8AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-07-09T00:00:00ZTrueSIM Supply Inc - Walmart.comSAVINGS_AMT USD 34.59http://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
9AVpfHsWP1cnluZ0-eVZ7SERVUS BY HONEYWELLAll Men's Shoes,Shoes,Men's Shoes,ClothingNaN2016-06-14T04:29:57Z2016-07-09T20:26:48Z[{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU...NaN[{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo...http://i5.walmartimages.com/dfw/dce07b8c-5844/......2016-06-14T04:29:57Z2016-04-18T00:00:00ZFalseSIM Supply Inc - Walmart.comSAVINGS_AMT USD 34.59http://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaNNaNhttp://www.walmart.com/ip/Studs-Shoe-Large-Pr-...NaN
\n", + "

10 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " id brand \\\n", + "0 AVpfHrJ6ilAPnD_xVXOI Josmo \n", + "1 AVpfHrJ6ilAPnD_xVXOI Josmo \n", + "2 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "3 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "4 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "5 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "6 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "7 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "8 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "9 AVpfHsWP1cnluZ0-eVZ7 SERVUS BY HONEYWELL \n", + "\n", + " categories colors dateadded \\\n", + "0 Clothing,Shoes,Men's Shoes,All Men's Shoes NaN 2016-11-07T00:45:12Z \n", + "1 Clothing,Shoes,Men's Shoes,All Men's Shoes NaN 2016-11-07T00:45:12Z \n", + "2 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "3 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "4 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "5 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "6 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "7 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "8 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "9 All Men's Shoes,Shoes,Men's Shoes,Clothing NaN 2016-06-14T04:29:57Z \n", + "\n", + " dateupdated descriptions \\\n", + "0 2016-11-07T00:45:12Z [{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU... \n", + "1 2016-11-07T00:45:12Z [{\"dateSeen\":[\"2016-11-07T00:45:12Z\"],\"sourceU... \n", + "2 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "3 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "4 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "5 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "6 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "7 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "8 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "9 2016-07-09T20:26:48Z [{\"dateSeen\":[\"2016-07-09T20:26:48Z\"],\"sourceU... \n", + "\n", + " ean features \\\n", + "0 6.993020e+11 [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Shoe... \n", + "1 6.993020e+11 [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Shoe... \n", + "2 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "3 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "4 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "5 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "6 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "7 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "8 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "9 NaN [{\"key\":\"Gender\",\"value\":[\"Men\"]},{\"key\":\"Colo... \n", + "\n", + " imageurls ... \\\n", + "0 https://i5.walmartimages.com/asr/13ac3d61-003c... ... \n", + "1 https://i5.walmartimages.com/asr/13ac3d61-003c... ... \n", + "2 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "3 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "4 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "5 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "6 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "7 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "8 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "9 http://i5.walmartimages.com/dfw/dce07b8c-5844/... ... \n", + "\n", + " prices_dateadded prices_dateseen prices_issale \\\n", + "0 2016-11-07T00:45:12Z 2016-11-05T00:00:00Z True \n", + "1 2016-11-07T00:45:12Z 2016-11-05T00:00:00Z False \n", + "2 2016-06-14T04:29:57Z 2016-03-08T00:00:00Z False \n", + "3 2016-06-14T04:29:57Z 2015-11-30T00:00:00Z False \n", + "4 2016-06-14T04:29:57Z 2016-04-29T00:00:00Z False \n", + "5 2016-06-14T04:29:57Z 2016-07-09T00:00:00Z False \n", + "6 2016-06-14T04:29:57Z 2016-03-10T00:00:00Z False \n", + "7 2016-06-14T04:29:57Z 2016-06-13T00:00:00Z True \n", + "8 2016-06-14T04:29:57Z 2016-07-09T00:00:00Z True \n", + "9 2016-06-14T04:29:57Z 2016-04-18T00:00:00Z False \n", + "\n", + " prices_merchant prices_offer \\\n", + "0 NaN REDUCED USD 12.10 \n", + "1 UnbeatableSale - Walmart.com REDUCED USD 12.10 \n", + "2 SIM Supply Inc - Walmart.com NaN \n", + "3 SIM Supply Inc - Walmart.com NaN \n", + "4 SIM Supply Inc - Walmart.com NaN \n", + "5 SIM Supply Inc - Walmart.com NaN \n", + "6 SIM Supply Inc - Walmart.com NaN \n", + "7 SIM Supply Inc - Walmart.com SAVINGS_AMT USD 9.80 \n", + "8 SIM Supply Inc - Walmart.com SAVINGS_AMT USD 34.59 \n", + "9 SIM Supply Inc - Walmart.com SAVINGS_AMT USD 34.59 \n", + "\n", + " prices_sourceurls sizes skus \\\n", + "0 https://www.walmart.com/ip/Josmo-8190-Plain-In... NaN NaN \n", + "1 https://www.walmart.com/ip/Josmo-8190-Plain-In... NaN NaN \n", + "2 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "3 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "4 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "5 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "6 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "7 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "8 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "9 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN NaN \n", + "\n", + " sourceurls upc \n", + "0 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 \n", + "1 https://www.walmart.com/ip/Josmo-8190-Plain-In... 6.993020e+11 \n", + "2 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "3 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "4 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "5 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "6 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "7 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "8 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "9 http://www.walmart.com/ip/Studs-Shoe-Large-Pr-... NaN \n", + "\n", + "[10 rows x 29 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "threshold = shoes_df.shape[0] * 0.7\n", "shoes_df_drop = shoes_df.loc[:, shoes_df.apply(lambda x: x.isna().sum(), axis=0) < threshold]\n", @@ -532,9 +1218,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 0 2 0 1 1 2 0 2 1]\n" + ] + } + ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import OneHotEncoder\n", @@ -557,9 +1251,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0 2 0 1 1 2 0 2 1]\n" + ] + } + ], "source": [ "# uwaga! \n", "# nie panujemy nad kolejnością -> check OrdinalEncoder\n", @@ -572,9 +1274,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [0. 1. 0.]]\n" + ] + } + ], "source": [ "# binary encode\n", "onehot_encoder = OneHotEncoder(sparse=False)\n", @@ -762,7 +1481,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/Materialy/Grupa3/lab_03.ipynb b/Materialy/Grupa3/lab_03.ipynb index 7c4c914db..f40737fb6 100644 --- a/Materialy/Grupa3/lab_03.ipynb +++ b/Materialy/Grupa3/lab_03.ipynb @@ -741,7 +741,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.8" }, "toc": { "base_numbering": 1, From 40ce368a518284831ef3d8c2975f235cf77451fb Mon Sep 17 00:00:00 2001 From: "patryk.slowakiewicz.stud@pw.edu.pl" Date: Sun, 18 Apr 2021 01:05:20 +0200 Subject: [PATCH 4/5] [Grupa 3] - HW3_Slowakiewicz --- .../HW3 - Slowakiewicz.html | 14731 ++++++++++++++++ .../HW3 - Slowakiewicz.ipynb | 391 + 2 files changed, 15122 insertions(+) create mode 100644 Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.html create mode 100644 Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.ipynb diff --git a/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.html b/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.html new file mode 100644 index 000000000..b4abedd5c --- /dev/null +++ b/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.html @@ -0,0 +1,14731 @@ + + + + + +HW3 - Slowakiewicz + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.ipynb b/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.ipynb new file mode 100644 index 000000000..ce316d54a --- /dev/null +++ b/Prace_domowe/Praca_domowa3/Grupa3/Slowakiewicz Patryk/HW3 - Slowakiewicz.ipynb @@ -0,0 +1,391 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "id": "norwegian-concord", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.tree import DecisionTreeClassifier,plot_tree\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "import warnings\n", + "from sklearn.metrics import accuracy_score, recall_score, precision_score, classification_report, roc_auc_score\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "crucial-laundry", + "metadata": {}, + "source": [ + "## Wczytanie i prygotowanie danych\n", + "\n", + "Dane podzieliłem na 3 zbiory: treningowy, testowy i walidacyjny." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "endangered-concentration", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('australia.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "filled-enzyme", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine',\n", + " 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am',\n", + " 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm',\n", + " 'Temp9am', 'Temp3pm', 'RainToday', 'RainTomorrow'],\n", + " dtype='object')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "introductory-jewelry", + "metadata": {}, + "outputs": [], + "source": [ + "x = df.drop('RainTomorrow', axis = 1)\n", + "y = df.RainTomorrow\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y)\n", + "x_test, x_val, y_test, y_val = train_test_split(x_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "concrete-garden", + "metadata": {}, + "source": [ + "## Trenowanie modeli\n", + "\n", + "Postanowiłem sprawdzić jak działać będzie:\n", + "- pojedyńcze drzewo w którym narzuciłem minimalną liczbę danych w liściu na 300 aby zapobiec przeuczaniu się modelu\n", + "- regresja logistyczna której zmieniłem karę za liczbę zminnych, ponieważ i tak mamy dużo obserwacji\n", + "- las losowy, sprawdzę czy faktycznie jest lepszy od pojedyńczego drzewa, zwiększyłem tu liczbę dzrzew do 150 oraz minimalą wartośCI w liściu DO 300" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "unsigned-hydrogen", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8483645301569295" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeClassifier(min_samples_leaf=300)\n", + "tree.fit(x_train, y_train)\n", + "tree.score(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "wired-westminster", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8575345055776139" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr = LogisticRegression(penalty='none', max_iter=500)\n", + "lr.fit(x_train, y_train)\n", + "lr.score(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "failing-porcelain", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8548875023633957" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rf = RandomForestClassifier(n_estimators=150, min_samples_leaf=300)\n", + "rf.fit(x_train, y_train)\n", + "rf.score(x_test, y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "suited-louisiana", + "metadata": {}, + "source": [ + "## Badanie skuteczności\n", + "\n", + "Aby zbadać skuteczność modeli sprawdziłem metodą średniej z crossvalidacji każdy z nich dla 4 równych metryk. Ogólne accuracy jest podobne dla każdego z modeli, gdzie Regresja Logistyczna jest o ok 0.5 pp lepsza. \n", + "Aby upewnić się co do wyniku sprawdzam modele na zbiorze testowym. W tym wypadku róznica w Accuracy jest jeszcze mniejsza. Warto się zastanownić czy któraś z miar nie jest dla nas ważniejsza. Uważam, że wyłapywanie dni które są deszczowe jest dla nas jak najbardziej kluczowe, dlatego powinniśmy dużą uwagę przyłożyć do miary Recall. W tym wypadku Log Reg jest dużo lepsze od pozostałych modeli, choć nadal z dość słabym wynikiem, nieco ponad 52%. \n", + "\n", + "Biorąc pod uwagę ponniższe wyniki uważam, że Log Reg jest najlepszym modelem do naszego problemu." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "stock-albuquerque", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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accuracyprecisionrecallroc_auc
Tree0.8453740.7196390.4964830.866734
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" + ], + "text/plain": [ + " accuracy precision recall roc_auc\n", + "Tree 0.845374 0.719639 0.496483 0.866734\n", + "Log Reg 0.851826 0.730565 0.525895 0.880104\n", + "Random Forest 0.849321 0.761191 0.467711 0.881172" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(columns=['accuracy','precision','recall', 'roc_auc'], index=['Tree', 'Log Reg', 'Random Forest'])\n", + "ls = [tree, lr, rf]\n", + "for i in range(0, len(ls)):\n", + " for metric in df.columns.values:\n", + " df.loc[df.index[i],metric] = np.mean(cross_val_score(ls[i], x_train, y_train, scoring=metric))\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "subsequent-classification", + "metadata": {}, + "outputs": [], + "source": [ + "tree_y_hat = tree.predict(x_test)\n", + "lr_y_hat = lr.predict(x_test)\n", + "rf_y_hat = rf.predict(x_test)\n", + "ls_hat = [tree_y_hat, lr_y_hat, rf_y_hat]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "magnetic-comment", + "metadata": {}, + "outputs": [], + "source": [ + "df1 = pd.DataFrame(columns=['accuracy','precision','recall', 'roc_auc'], index=['Tree', 'Log Reg', 'Random Forest'])\n", + "metric = df1.columns.values\n", + "for i in range(0, len(ls)):\n", + " df1.loc[df.index[i],metric[0]] = accuracy_score(y_test, ls_hat[i])\n", + " df1.loc[df.index[i],metric[1]] = precision_score(y_test, ls_hat[i])\n", + " df1.loc[df.index[i],metric[2]] = recall_score(y_test, ls_hat[i])\n", + " df1.loc[df.index[i],metric[3]] = roc_auc_score(y_test, ls_hat[i])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "functional-corpus", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " accuracy precision recall roc_auc\n", + "Tree 0.847797 0.699387 0.504425 0.722758\n", + "Log Reg 0.857535 0.731123 0.526991 0.737167\n", + "Random Forest 0.854888 0.76023 0.468584 0.714215" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "forward-immigration", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fa76f3d97a2a7d0ba4cc80281e2b9b6931ae835b Mon Sep 17 00:00:00 2001 From: PSlowakiewicz <56126526+PSlowakiewicz@users.noreply.github.com> Date: Fri, 7 May 2021 02:17:52 +0200 Subject: [PATCH 5/5] Add files via upload --- .../Patryk_Slowakiewicz/HW 4.html | 15658 ++++++++++++++++ .../Patryk_Slowakiewicz/HW 4.ipynb | 1099 ++ .../Patryk_Slowakiewicz/train.csv | 2001 ++ 3 files changed, 18758 insertions(+) create mode 100644 Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.html create mode 100644 Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.ipynb create mode 100644 Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/train.csv diff --git a/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.html b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.html new file mode 100644 index 000000000..0b2663651 --- /dev/null +++ b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.html @@ -0,0 +1,15658 @@ + + + + + +HW 4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.ipynb b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.ipynb new file mode 100644 index 000000000..0b9370cf4 --- /dev/null +++ b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/HW 4.ipynb @@ -0,0 +1,1099 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 126, + "id": "upper-special", + "metadata": {}, + "outputs": [], + "source": [ + "import dalex as dx\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import category_encoders as ce\n", + "import sklearn.pipeline\n", + "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n", + "from sklearn.svm import SVR, SVC\n", + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.model_selection import train_test_split, cross_val_score\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "weird-spending", + "metadata": {}, + "source": [ + "## Przygotowanie danych" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "packed-bennett", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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construction_yearsurfacefloorno_roomsdistrict
119532531Srodmiescie
2199214395Bielany
319375612Praga
419959373Ochota
5199214465Mokotow
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" + ], + "text/plain": [ + " construction_year surface floor no_rooms district\n", + "1 1953 25 3 1 Srodmiescie\n", + "2 1992 143 9 5 Bielany\n", + "3 1937 56 1 2 Praga\n", + "4 1995 93 7 3 Ochota\n", + "5 1992 144 6 5 Mokotow" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train = dx.datasets.load_apartments()\n", + "y_train = x_train['m2_price']\n", + "x_train.drop('m2_price', axis=1, inplace=True)\n", + "\n", + "x_test = dx.datasets.load_apartments_test()\n", + "y_test = x_test['m2_price']\n", + "x_test.drop('m2_price', axis= 1, inplace=True)\n", + "\n", + "x_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bottom-harassment", + "metadata": {}, + "outputs": [], + "source": [ + "dist = x_test['district'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "diverse-holly", + "metadata": {}, + "outputs": [], + "source": [ + "enc = ce.OneHotEncoder(cols=['district'], )\n", + "enc.fit(x_test)\n", + "x_test = enc.fit_transform(x_test)\n", + "x_train = enc.fit_transform(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "distributed-picking", + "metadata": {}, + "outputs": [], + "source": [ + "x_test.columns.values[-10:] = dist\n", + "x_train.columns.values[-10:] = dist" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "pressing-astrology", + "metadata": {}, + "outputs": [], + "source": [ + "def score_model(y, y_hat, func = (lambda x: x)):\n", + " l = len(y)\n", + " return round(sum((func(y)-func(y_hat))**2)/l, 2)" + ] + }, + { + "cell_type": "markdown", + "id": "surgical-missouri", + "metadata": {}, + "source": [ + "## Base line\n", + "\n", + "Błąd średniokwadratowy ze średniej" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "crude-eligibility", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "810804.49" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score_model(y_test, y_test.mean())" + ] + }, + { + "cell_type": "markdown", + "id": "extraordinary-following", + "metadata": {}, + "source": [ + "## Pierwszy model\n", + "\n", + "Okazuje się gorszy od baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "authentic-daughter", + "metadata": {}, + "outputs": [], + "source": [ + "svm = SVR()\n", + "svm.fit(x_train, y_train)\n", + "y_hat = svm.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "stable-storage", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "826320.67" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score_model(y_test, y_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cognitive-closure", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(y_test)" + ] + }, + { + "cell_type": "markdown", + "id": "sensitive-doctor", + "metadata": {}, + "source": [ + "## Skalowanie zmiennych\n", + "\n", + "Skaluję zmienne przy pomocy MinMaxScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "unique-projector", + "metadata": {}, + "outputs": [], + "source": [ + "mm = MinMaxScaler()\n", + "mm_x_train = mm.fit_transform(x_train)\n", + "mm_x_test = mm.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "changed-mounting", + "metadata": {}, + "outputs": [], + "source": [ + "mm_x_train = pd.DataFrame(mm_x_train, columns=x_train.columns.values)\n", + "mm_x_test = pd.DataFrame(mm_x_test, columns=x_train.columns.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "technological-valve", + "metadata": {}, + "outputs": [], + "source": [ + "m = min(y_train)\n", + "r = max(y_train)-min(y_train)\n", + "mm_y_train = (y_train-m)/r\n", + "mm_y_test = (y_test-m)/r" + ] + }, + { + "cell_type": "markdown", + "id": "described-success", + "metadata": {}, + "source": [ + "## Model ze skalowaniem\n", + "\n", + "Okazuje się, że przeskalowanie danych znacznie poprawia predykcyjność modelu, z zaznaczeniem, że przy mierzeniu błędu średniokwadratowego przeskalowuję spowrotem uzyskane pradykcję, aby były porównywalne." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "august-niger", + "metadata": {}, + "outputs": [], + "source": [ + "svm2 = SVR()\n", + "svm.fit(mm_x_train, mm_y_train)\n", + "mm_y_hat = svm.predict(mm_x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "diagnostic-property", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "360463.38" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score_model(mm_y_test, mm_y_hat, lambda x: x*r+m)" + ] + }, + { + "cell_type": "markdown", + "id": "academic-halloween", + "metadata": {}, + "source": [ + "## Grid Search\n", + "\n", + "Użyłem GridSearch aby sprawdzić czy jest możliwość polepszenia predykcyjności modelu. Okazuję się, że taki model ma nieznacznie większy błąd. A jedyny parametr jaki zmieniliśmy jest C (*cost*)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "ordinary-lloyd", + "metadata": {}, + "outputs": [], + "source": [ + "grid_search = {\n", + " 'C' : np.logspace(-4, 4, 10),\n", + " 'gamma' : ['scale', 'auto'],\n", + " 'degree' : range(1,5),\n", + " 'kernel' : ['linear', 'poly', 'rbf']\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "national-beaver", + "metadata": {}, + "outputs": [], + "source": [ + "svr3 = SVR()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "controlling-pearl", + "metadata": {}, + "outputs": [], + "source": [ + "gs = GridSearchCV(svr3, grid_search, cv=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "dangerous-continent", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=10, estimator=SVR(),\n", + " param_grid={'C': array([1.00000000e-04, 7.74263683e-04, 5.99484250e-03, 4.64158883e-02,\n", + " 3.59381366e-01, 2.78255940e+00, 2.15443469e+01, 1.66810054e+02,\n", + " 1.29154967e+03, 1.00000000e+04]),\n", + " 'degree': range(1, 5), 'gamma': ['scale', 'auto'],\n", + " 'kernel': ['linear', 'poly', 'rbf']})" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs.fit(mm_x_train, mm_y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "handy-consent", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.3593813663804626, 'degree': 1, 'gamma': 'scale', 'kernel': 'rbf'}" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "angry-influence", + "metadata": {}, + "outputs": [], + "source": [ + "svr4 = SVR(C = 0.3593813663804626, degree= 1, gamma='scale', kernel='rbf')\n", + "svr4.fit(mm_x_train, mm_y_train)\n", + "gs_y_hat = svr4.predict(mm_x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "focal-right", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "360518.27" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "score_model(mm_y_test, gs_y_hat, lambda x: x*r+m)" + ] + }, + { + "cell_type": "markdown", + "id": "representative-canyon", + "metadata": {}, + "source": [ + "# Zbiór nr. 2\n", + "\n", + "Zbiór pobrałem ze strony https://www.kaggle.com/iabhishekofficial/mobile-price-classification?select=train.csv.\n", + "Zawiera informację o parametrach telefonach komórkowych i ich przedziale cenowym. Aby zredukować problem z multi-label classification zmapuję przedziały cenowe odpowiednio:\n", + "- 0 -> 0\n", + "- 1 -> 0\n", + "- 2 -> 1\n", + "- 3 -> 1" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "molecular-bulgaria", + "metadata": {}, + "outputs": [], + "source": [ + "x = pd.read_csv('train.csv')\n", + "y = x['price_range']\n", + "x.drop('price_range', axis = 1, inplace=True)\n", + "y = y.map(lambda x: x//2)\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, test_size=0.2, random_state=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "id": "miniature-dollar", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifi
192276401.0191450.512481152539134119112100
1622105712.7031410.19741039789120331692110
192156912.5100410.31241138860526511774000
1499174210.5151430.91763735614071921923110
1082104412.8170330.61294134212621816171615101
\n", + "
" + ], + "text/plain": [ + " battery_power blue clock_speed dual_sim fc four_g int_memory \\\n", + "1922 764 0 1.0 1 9 1 45 \n", + "1622 1057 1 2.7 0 3 1 41 \n", + "1921 569 1 2.5 1 0 0 41 \n", + "1499 1742 1 0.5 1 5 1 43 \n", + "1082 1044 1 2.8 1 7 0 33 \n", + "\n", + " m_dep mobile_wt n_cores pc px_height px_width ram sc_h sc_w \\\n", + "1922 0.5 124 8 11 52 539 1341 19 1 \n", + "1622 0.1 97 4 10 397 891 2033 16 9 \n", + "1921 0.3 124 1 1 388 605 2651 17 7 \n", + "1499 0.9 176 3 7 356 1407 1921 9 2 \n", + "1082 0.6 129 4 13 42 1262 1816 17 16 \n", + "\n", + " talk_time three_g touch_screen wifi \n", + "1922 12 1 0 0 \n", + "1622 2 1 1 0 \n", + "1921 4 0 0 0 \n", + "1499 3 1 1 0 \n", + "1082 15 1 0 1 " + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "developmental-boulder", + "metadata": {}, + "source": [ + "## Baseline\n", + "\n", + "Ponieważ w naszych danych wszystkie przedziały cenowe mają taką samą ilość danych a przy podziale używamy parametru stratify to nasz baseline będzie wynosił 50%" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "equal-riding", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_train.hist()" + ] + }, + { + "cell_type": "markdown", + "id": "available-narrative", + "metadata": {}, + "source": [ + "## Prosty model SVC\n", + "\n", + "Nawet podstawowy model bardzo dobrze przewiduję ceny telefonów z 96,5 accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "brutal-steering", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.965" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc = SVC()\n", + "svc.fit(x_train, y_train)\n", + "svc.score(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "bearing-criminal", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.985625" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cross_val_score(svc, x_train, y_train).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "considerable-extreme", + "metadata": {}, + "source": [ + "## SVC ze skalowaniem\n", + "\n", + "Skaluję dane za pomocą MinMaxScaler. A poniweaż nasz target jest już przeskalowany to wystarczy użyć to na x_train, x_test.\n", + "\n", + "Uzyskaliśmy bardzo ciekway wynik, ponieważ skalowanie danych pogorszyło wyniki przewidywań, aby to sprawdzić użyłem równeż walidacji krzyżowej której wyniki również były lepsze na danych nieprzeskalowanych." + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "hydraulic-headset", + "metadata": {}, + "outputs": [], + "source": [ + "mm = MinMaxScaler()\n", + "mm_x_train = mm.fit_transform(x_train)\n", + "mm_x_test = mm.transform(x_test)\n", + "mm_x_train = pd.DataFrame(mm_x_train, columns=x_train.columns.values)\n", + "mm_x_test = pd.DataFrame(mm_x_test, columns=x_train.columns.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "id": "bizarre-whole", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9325" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc2 = SVC()\n", + "svc2.fit(mm_x_train, y_train)\n", + "svc2.score(mm_x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "endangered-affect", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.95375" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cross_val_score(svc, mm_x_train, y_train).mean()" + ] + }, + { + "cell_type": "markdown", + "id": "constant-insight", + "metadata": {}, + "source": [ + "## GridSearch na SVC\n", + "\n", + "W tym przypaku dzięki GridSearch udało się poprawić wyniki SVC dla nieprzeskalowanych danych do 98,25% accuracy. Aby upewnić się, że skalowanie faktycznie daje gorsze wyniki jego brak, wykonałem gridsearch również dla danych przeskalowanych ale wyniki nadal były gorsze 96%." + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "quantitative-metadata", + "metadata": {}, + "outputs": [], + "source": [ + "grid_search2 = {\n", + " 'C' : np.logspace(-2, 2, 5),\n", + " 'gamma' : ['scale', 'auto'],\n", + " 'degree' : range(1,3),\n", + " 'kernel' : ['rbf']\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "optical-walker", + "metadata": {}, + "outputs": [], + "source": [ + "svc3 = SVC()\n", + "gs_c = GridSearchCV(svc3, grid_search2)" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "id": "dramatic-patio", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(estimator=SVC(),\n", + " param_grid={'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02]),\n", + " 'degree': range(1, 3), 'gamma': ['scale', 'auto'],\n", + " 'kernel': ['rbf']})" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs_c.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "id": "requested-tracy", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy dla modelu nie skalowanego: 0.9825\n" + ] + } + ], + "source": [ + "print('Accuracy dla modelu nie skalowanego: ' + str(gs_c.best_estimator_.score(x_test, y_test)))" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "id": "herbal-termination", + "metadata": {}, + "outputs": [], + "source": [ + "svc3 = SVC()\n", + "gs_c2 = GridSearchCV(svc3, grid_search2)" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "dedicated-scratch", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(estimator=SVC(),\n", + " param_grid={'C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02]),\n", + " 'degree': range(1, 3), 'gamma': ['scale', 'auto'],\n", + " 'kernel': ['rbf']})" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gs_c2.fit(mm_x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "cubic-flush", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy dla modelu skalowanego: 0.96\n" + ] + } + ], + "source": [ + "print('Accuracy dla modelu skalowanego: ' + str(gs_c2.best_estimator_.score(mm_x_test, y_test)))" + ] + }, + { + "cell_type": "markdown", + "id": "elementary-crawford", + "metadata": {}, + "source": [ + "# Wnioski\n", + "\n", + "Należy zauważyć, że wyniki modelowań różniły się w obu przypadkach. Na pierwszym zbiorze apartments z paczki *dalex* skalowanie danych znacznie poprawiło wyniki modelu ale już GridSearch minimalnie je pogorszył. \n", + "W drugim przypadku skalowanie danych przyniosło znaczące pogorsznie wyników, a w takim wypadku GridSearch przyczynił się do ich małej poprawy. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/train.csv b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/train.csv new file mode 100644 index 000000000..c86e2fa16 --- /dev/null +++ b/Prace_domowe/Praca_domowa4/Patryk_Slowakiewicz/train.csv @@ -0,0 +1,2001 @@ +battery_power,blue,clock_speed,dual_sim,fc,four_g,int_memory,m_dep,mobile_wt,n_cores,pc,px_height,px_width,ram,sc_h,sc_w,talk_time,three_g,touch_screen,wifi,price_range +842,0,2.2,0,1,0,7,0.6,188,2,2,20,756,2549,9,7,19,0,0,1,1 +1021,1,0.5,1,0,1,53,0.7,136,3,6,905,1988,2631,17,3,7,1,1,0,2 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