Merge pull request 'alexandrov_dmitrii_lab_3' (#35) from alexandrov_dmitrii_lab_3 into main

Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/35

alexandrov_dmitrii_lab_3/lab3.py

@@ -0,0 +1,126 @@
+from sklearn.impute import SimpleImputer, MissingIndicator
+from sklearn.pipeline import FeatureUnion, make_pipeline
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+import pandas as pd
+import random as rand
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+def rank_to_dict(ranks, names, n_features):
+    ranks = np.abs(ranks)
+    minmax = MinMaxScaler()
+    ranks = minmax.fit_transform(np.array(ranks).reshape(len(ranks), 1)).ravel()
+    ranks = map(lambda x: round(x, 2), ranks)
+    return dict(zip(names, ranks))
+
+
+def part_one():
+    print('Titanic data analysis\n')
+    data = pd.read_csv('titanic_data.csv', index_col='PassengerId')
+    x = data[['Pclass', 'Name', 'Sex']]
+    y = data[['Survived']]
+
+    names = pd.DataFrame(TfidfVectorizer().fit_transform(x['Name']).toarray())
+    col_names = names[names.columns[1:]].apply(lambda el: sum(el.dropna().astype(float)), axis=1)
+    col_names.index = np.arange(1, len(col_names) + 1)
+    col_sexes = []
+
+    for index, row in x.iterrows():
+        if row['Sex'] == 'male':
+            col_sexes.append(1)
+        else:
+            col_sexes.append(0)
+
+    x = x.drop(columns=['Sex', 'Name'])
+    x['Sex'] = col_sexes
+    x['Name'] = col_names
+
+    dtc = DecisionTreeClassifier(random_state=rand.randint(0, 250))
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.05, random_state=rand.randint(0, 250))
+    dtc.fit(x_train, y_train)
+    print('model score: ' + str(dtc.score(x_test, y_test)))
+    res = dict(zip(['Pclass', 'Sex', 'Name'], dtc.feature_importances_))
+    print('feature importances: ' + str(res))
+
+
+def part_two():
+    print('\n---------------------------------------------------------------------------\nSberbank data analysis\n')
+    data = pd.read_csv('sberbank_data.csv', index_col='id')
+    x = data.drop(columns='price_doc')
+    y = data[['price_doc']]
+
+    x = x.replace(
+        ['NA', 'no', 'yes', 'Investment', 'OwnerOccupier', 'poor', 'satisfactory', 'no data', 'good', 'excellent'],
+        [0, 0, 1, 0, 1, -1, 0, 0, 1, 2])
+    x.fillna(0, inplace=True)
+
+    names = pd.DataFrame(TfidfVectorizer().fit_transform(x['sub_area']).toarray())
+    col_area = names[names.columns[1:]].apply(lambda el: sum(el.dropna().astype(float)), axis=1)
+    col_area.index = np.arange(1, len(col_area) + 1)
+    col_date = []
+
+    for val in x['timestamp']:
+        col_date.append(val.split('-', 1)[0])
+
+    x = x.drop(columns=['sub_area', 'timestamp'])
+    x['sub_area'] = col_area
+    x['timestamp'] = col_date
+
+    col_price = []
+    for val in y['price_doc']:
+        if val < 1500000:
+            col_price.append('low')
+        elif val < 3000000:
+            col_price.append('medium')
+        elif val < 5500000:
+            col_price.append('high')
+        elif val < 10000000:
+            col_price.append('premium')
+        else:
+            col_price.append('oligarch')
+
+    y = pd.DataFrame(col_price)
+
+    transformer = FeatureUnion(
+        transformer_list=[
+            ('features', SimpleImputer(strategy='mean')),
+            ('indicators', MissingIndicator())])
+
+    dtr = make_pipeline(transformer, DecisionTreeClassifier(random_state=rand.randint(0, 250)))
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=rand.randint(0, 250))
+    dtr.fit(x_train, y_train)
+
+    features = list(x.columns)
+    print('model score: ' + str(dtr.score(x_test, y_test)))
+
+    res = sorted(dict(zip(features, dtr.steps[-1][1].feature_importances_)).items(),
+                 key=lambda el: el[1], reverse=True)
+
+    view_y = []
+    view_x = []
+
+    flag = 0
+    print('feature importances:')
+    for val in res:
+        if flag == 8:
+            break
+        print(val[0]+" - "+str(val[1]))
+        view_y.append(val[0])
+        view_x.append(val[1])
+        flag = flag + 1
+
+    plt.figure(1, figsize=(16, 9))
+    plt.bar(view_y, view_x)
+    plt.show()
+
+
+def start():
+    part_one()
+    part_two()
+
+
+start()

alexandrov_dmitrii_lab_3/readme.md

@@ -0,0 +1,60 @@
+### Задание
+1. По данным о пассажирах Титаника решить задачу классификации с помощью дерева решений, в которой по различным характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака из трех рассматриваемых.
+
+Вариант 1: Pclass,Name,Sex.
+
+2. По данным курсовой работы с помощью дерева решений решить выбранную задачу: классификация - зависимость категории цены от всех остальных факторов, оценка результата и отбор наиболее значимых признаков.
+
+### Запуск программы
+Файл lab3.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
+
+### Описание программы
+Программа состоит из двух частей:
+1. Она считывает файл с данными по пассажирам "Титаника", признаки "класс", "имя", "пол" и запись о том, выжил ли пассажир. Данные предобрабатываются: запись о поле кодируется (ж - 0, м - 1), запись об имени кодируется (Tfidf). После этого дерево решений тренируется на данных и результаты выводятся в консоль.
+2. Она считывает файл с данными сбербанка по рынку недвижимости. Далее данные предобрабатываются: названия районов кодируется (Tfidf), нечисловые записи цифровизируются, запоняются нулевые записи, записи подразделяются на классы. После этого на данных обучается дерево решений и результат выводится в консоль и на форму. Поскольку признаков слишком много, выводимые результаты ограничены восемью наиболее значимыми.
+
+### Результаты тестирования
+По результатам тестирования, можно сказать следующее:
+
+По первой задаче:
+* Дерево решений показывает неплохие результаты, около 70-75%. 
+* Однако оценка важности признаков даёт абсолютно неверный результат: наиболее значимым признаком назначается имя пассажира. Это значит, что кодировка не подходит для правильной обработки данных. Возможные решения: обнуление или исключение признака как аналитически очевидно незначимого.
+* Помимо неправильной оценки роли имени, пол определяется более чем в два раза более значимым, нежели класс. Действительная статистика (среди спасшихся пассажиров 74% женщин и детей (из которых многие также были мужского пола) и 26% мужчин, 60% первого класса, 44% - второго, 25% - третьего) скорее подтверждает правильность этого вывода.
+
+По второй задаче:
+* Дерево решений показывает неплохие результаты, около 70-75%.
+* Оценка важности признаков показывает наиболее важным признаком площадь недвижимости, что скорее всего верно. 
+* После площади с небольшим отрывом идёт количество спортивных объектов в округе. Это неверно хотя бы потому, что в данных присустствуют коррелирующие признаки - площадь жилого пространства и другие. К тому же доступна информация по действительному ранжированию.
+* Дальнейшие оценки содержат как правильные, так и неправильные признаки: этаж, количество этажей в доме, район - действительно значимые признаки, но они перемешаны с незначимыми.
+
+Итого. Дерево решений даёт неплохие результаты при классификации. Однако для задач регрессии не подходят, т.к. неверно определяют значимые признаки. При работе также следует тщательнее предобрабатывать данные, в особенности малозначащие текстовые - предложенные методы кодирования показали себя неэффективно на лабораторных данных.
+
+Пример консольных результатов:
+
+>Titanic data analysis
+
+>model score: 0.7777777777777778
+
+>feature importances: {'Pclass': 0.1287795817634186, 'Sex': 0.3381642167551354, 'Name': 0.533056201481446}
+
+>Sberbank data analysis
+
+>model score: 0.7162629757785467
+
+>feature importances:
+
+>full_sq - 0.1801327274709341
+
+>sport_count_3000 - 0.14881362533480907
+
+>floor - 0.03169232872469085 
+
+>power_transmission_line_km - 0.027978416524911377
+
+>timestamp - 0.020092007662845194
+
+>max_floor - 0.019985442431576052
+
+>cafe_count_5000_price_2500 - 0.019397048405749438
+
+>sub_area - 0.017477163456413432

alexandrov_dmitrii_lab_3/titanic_data.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
+100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
+101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
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