commit 166cc7407a88e55891984f85a6c27d027bc956cd Author: bartosz.maslanka.consultant Date: Fri Apr 21 14:50:47 2023 +0200 Add zad2 diff --git a/.ipynb_checkpoints/zad_02-checkpoint.ipynb b/.ipynb_checkpoints/zad_02-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/zad_02-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dev.csv b/dev.csv new file mode 100644 index 0000000..3a16e22 --- /dev/null +++ b/dev.csv @@ -0,0 +1,751 @@ +long_hair,forehead_width_cm,forehead_height_cm,nose_wide,nose_long,lips_thin,distance_nose_to_lip_long,gender +1,0.5121951219512195,0.10000000000000009,1,1,1,1,Male +1,0.24390243902439027,0.7000000000000002,1,1,1,1,Male +1,0.5121951219512195,0.15000000000000036,1,0,0,0,Female +1,0.853658536585366,0.10000000000000009,1,1,0,1,Male +1,0.29268292682926816,0.7000000000000002,0,1,0,0,Female +0,0.26829268292682923,0.25,0,0,0,0,Female 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+ "id": "f5229180", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2d3b5bee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "zsh:1: command not found: kaggle\r\n" + ] + } + ], + "source": [ + "!kaggle datasets download -d gender_classification_v7.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "fbbeb52d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [long_hair, forehead_width_cm, forehead_height_cm, nose_wide, nose_long, lips_thin, distance_nose_to_lip_long, gender]\n", + "Index: []" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = clean_data(raw_data)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "id": "bd27b530", + "metadata": {}, + "source": [ + "Dokona normalizacji danych w zbiorze (np. normalizacja wartości float do zakresu 0.0 - 1.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b81c3005", + "metadata": {}, + "outputs": [], + "source": [ + "def normalize_data(data):\n", + " # znormalizuj wartości float do zakresu 0.0 - 1.0\n", + " for col in data.columns:\n", + " if data[col].dtype == float:\n", + " data[col] = (data[col] - data[col].min()) / (data[col].max() - data[col].min())\n", + "\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "611929ca", + "metadata": {}, + "outputs": [], + "source": [ + "normalized_data = normalize_data(raw_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "64724998", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [long_hair, forehead_width_cm, forehead_height_cm, nose_wide, nose_long, lips_thin, distance_nose_to_lip_long, gender]\n", + "Index: []" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "normalized_data" + ] + }, + { + "cell_type": "markdown", + "id": "7013b00e", + "metadata": {}, + "source": [ + "2. Jeśli brak w zbiorze gotowego podziału na podzbiory train/dev/test, to dokona takiego podziału" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "9eb24b71", + "metadata": {}, + "outputs": [], + "source": [ + "train, dev, test = np.split(normalized_data.sample(frac=1, random_state=42), [int(.7*len(normalized_data)), int(.85*len(normalized_data))])\n", + "\n", + "# zapisz dane w osobnych plikach csv\n", + "train.to_csv('train.csv', index=False)\n", + "dev.to_csv('dev.csv', index=False)\n", + "test.to_csv('test.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "81d1cd62", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [long_hair, forehead_width_cm, forehead_height_cm, nose_wide, nose_long, lips_thin, distance_nose_to_lip_long, gender]\n", + "Index: []" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dev" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "851d9aa0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " long_hair forehead_width_cm forehead_height_cm nose_wide \\\n", + "count 5001.000000 5001.000000 5001.000000 5001.000000 \n", + "mean 0.869626 13.181484 5.946311 0.493901 \n", + "std 0.336748 1.107128 0.541268 0.500013 \n", + "min 0.000000 11.400000 5.100000 0.000000 \n", + "25% 1.000000 12.200000 5.500000 0.000000 \n", + "50% 1.000000 13.100000 5.900000 0.000000 \n", + "75% 1.000000 14.000000 6.400000 1.000000 \n", + "max 1.000000 15.500000 7.100000 1.000000 \n", + "\n", + " nose_long lips_thin distance_nose_to_lip_long \n", + "count 5001.000000 5001.000000 5001.000000 \n", + "mean 0.507898 0.493101 0.498900 \n", + "std 0.499988 0.500002 0.500049 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 1.000000 0.000000 0.000000 \n", + "75% 1.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 \n", + " long_hair forehead_width_cm forehead_height_cm nose_wide \\\n", + "count 3500.000000 3500.000000 3500.000000 3500.000000 \n", + "mean 0.870000 13.187686 5.951800 0.505714 \n", + "std 0.336351 1.109019 0.542695 0.500039 \n", + "min 0.000000 11.400000 5.100000 0.000000 \n", + "25% 1.000000 12.200000 5.500000 0.000000 \n", + "50% 1.000000 13.100000 5.900000 1.000000 \n", + "75% 1.000000 14.000000 6.400000 1.000000 \n", + "max 1.000000 15.500000 7.100000 1.000000 \n", + "\n", + " nose_long lips_thin distance_nose_to_lip_long \n", + "count 3500.000000 3500.000000 3500.000000 \n", + "mean 0.522000 0.499429 0.507714 \n", + "std 0.499587 0.500071 0.500012 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 1.000000 0.000000 1.000000 \n", + "75% 1.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 \n", + " long_hair forehead_width_cm forehead_height_cm nose_wide \\\n", + "count 750.000000 750.000000 750.000000 750.000000 \n", + "mean 0.870667 13.119067 5.933867 0.472000 \n", + "std 0.335792 1.084345 0.538999 0.499549 \n", + "min 0.000000 11.400000 5.100000 0.000000 \n", + "25% 1.000000 12.200000 5.500000 0.000000 \n", + "50% 1.000000 13.100000 5.900000 0.000000 \n", + "75% 1.000000 14.000000 6.375000 1.000000 \n", + "max 1.000000 15.500000 7.100000 1.000000 \n", + "\n", + " nose_long lips_thin distance_nose_to_lip_long \n", + "count 750.000000 750.000000 750.000000 \n", + "mean 0.466667 0.481333 0.465333 \n", + "std 0.499221 0.499985 0.499130 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 \n", + "75% 1.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 \n", + " long_hair forehead_width_cm forehead_height_cm nose_wide \\\n", + "count 751.000000 751.000000 751.000000 751.000000 \n", + "mean 0.866844 13.214913 5.933156 0.460719 \n", + "std 0.339969 1.119877 0.537134 0.498787 \n", + "min 0.000000 11.400000 5.100000 0.000000 \n", + "25% 1.000000 12.200000 5.500000 0.000000 \n", + "50% 1.000000 13.200000 5.900000 0.000000 \n", + "75% 1.000000 14.100000 6.300000 1.000000 \n", + "max 1.000000 15.500000 7.100000 1.000000 \n", + "\n", + " nose_long lips_thin distance_nose_to_lip_long \n", + "count 751.000000 751.000000 751.000000 \n", + "mean 0.483356 0.475366 0.491345 \n", + "std 0.500056 0.499726 0.500258 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 \n", + "75% 1.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 \n" + ] + } + ], + "source": [ + "for d in [raw_data,train, dev, test]:\n", + " print( d.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f52a79aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
150110.4390240.301111Male
258610.5609760.450000Female
265300.3658540.100001Female
105510.4390240.401111Male
70500.9268290.251111Male
...........................
208710.0487800.450110Female
188910.0487800.150000Female
462310.5365850.200000Female
159111.0000000.951010Male
134610.5365850.350000Female
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3500 rows × 8 columns

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long_hairforehead_width_cmforehead_height_cmnose_widenose_longlips_thindistance_nose_to_lip_longgender
150110.4390240.301111Male
258610.5609760.450000Female
265300.3658540.100001Female
105510.4390240.401111Male
70500.9268290.251111Male
...........................
208710.0487800.450110Female
188910.0487800.150000Female
462310.5365850.200000Female
159111.0000000.951010Male
134610.5365850.350000Female
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3500 rows × 8 columns

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" + ], + "text/plain": [ + " long_hair forehead_width_cm forehead_height_cm nose_wide nose_long \\\n", + "1501 1 0.439024 0.30 1 1 \n", + "2586 1 0.560976 0.45 0 0 \n", + "2653 0 0.365854 0.10 0 0 \n", + "1055 1 0.439024 0.40 1 1 \n", + "705 0 0.926829 0.25 1 1 \n", + "... ... ... ... ... ... \n", + "2087 1 0.048780 0.45 0 1 \n", + "1889 1 0.048780 0.15 0 0 \n", + "4623 1 0.536585 0.20 0 0 \n", + "1591 1 1.000000 0.95 1 0 \n", + "1346 1 0.536585 0.35 0 0 \n", + "\n", + " lips_thin distance_nose_to_lip_long gender \n", + "1501 1 1 Male \n", + "2586 0 0 Female \n", + "2653 0 1 Female \n", + "1055 1 1 Male \n", + "705 1 1 Male \n", + "... ... ... ... \n", + "2087 1 0 Female \n", + "1889 0 0 Female \n", + "4623 0 0 Female \n", + "1591 1 0 Male \n", + "1346 0 0 Female \n", + "\n", + "[3500 rows x 8 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clean_data(train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb1439e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}