183 lines
6.5 KiB
Python
183 lines
6.5 KiB
Python
import pandas
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from flask import Flask, render_template
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from matplotlib import pyplot as plt
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from numpy import vectorize
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_absolute_error, accuracy_score
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from sklearn.model_selection import train_test_split
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app = Flask(__name__)
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@app.route("/")
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def home():
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return "<html>" \
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"<h1>Жукова Алина ПИбд-41</h1>" \
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"<h1>Лабораторная работа №5</h1>" \
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"<table>" \
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"<td>" \
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"<form Action='http://127.0.0.1:5000/k4_1_task_5' Method=get>" \
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"<input type=submit value='Регрессия'>" \
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"</form>" \
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"</td>" \
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"</table>" \
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"</html>"
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# Регрессия
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# 10 вариант - Линейная регрессия
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@app.route("/k4_1_task_5", methods=['GET'])
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def k4_1_task_5():
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data = pandas.read_csv('Data_chess_games.csv')
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data = data.loc[data['created_at'] - data['last_move_at'] != 0]
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data = data.drop_duplicates()
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data = data.sample(n=5000, replace=True, random_state=1)
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# отбор нужных столбцов
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corr = data[['white_rating', 'black_rating', 'moves', 'opening_eco', 'opening_name', 'winner']]
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def SplitPr(stroke):
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return len(stroke.split(" "))
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corr['len'] = corr['moves'].apply(SplitPr)
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corr = corr.loc[corr['len'] >= 10]
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number_element = 0
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def SelectElemToStolb(elem):
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return elem.split(" ")[number_element]
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def ScoreXod(elem):
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if(elem == "O-O" or elem == "O-O-O"):
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return 50
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if(elem.__contains__("x")):
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return 20
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if (elem.__contains__("+")):
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return 50
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return 10
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def EcoToScore(elem):
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score_eco = 0
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if(str(elem).startswith("A")):
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score_eco = 100
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if (str(elem).startswith("B")):
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score_eco = 200
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if (str(elem).startswith("C")):
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score_eco = 300
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if (str(elem).startswith("D")):
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score_eco = 400
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if (str(elem).startswith("E")):
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score_eco = 500
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score_eco += int(str(elem)[1:])
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return score_eco
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# 0 - white || 1 - black
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gamer_ind = 0
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def ScoreAllXods(elem):
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score = 0
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xods = elem.split(" ")
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ind_temp = 0
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for xod in xods:
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if(ind_temp % 2 == gamer_ind % 2):
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score += ScoreXod(xod)
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ind_temp += 1
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return score
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corr['score_eco'] = corr['opening_eco'].apply(EcoToScore)
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# Ходы
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corr['w_score_all'] = corr['moves'].apply(ScoreAllXods)
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gamer_ind = 1
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corr['b_score_all'] = corr['moves'].apply(ScoreAllXods)
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def WinnerSelect(elem):
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if(elem == "white"):
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return 1
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if(elem == "black"):
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return -1
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return 0
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corr['winner'] = corr['winner'].apply(WinnerSelect)
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y = corr[['winner']]
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# Нормирование к 1
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max_zn = corr['white_rating'].max()
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min_zn = corr['white_rating'].min()
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def normirovanie(elem):
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return ((elem - min_zn) / (max_zn - min_zn))
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corr['white_rating'] = corr['white_rating'].apply(normirovanie)
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max_zn = corr['black_rating'].max()
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min_zn = corr['black_rating'].min()
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corr['black_rating'] = corr['black_rating'].apply(normirovanie)
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corr_st = corr
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corr = corr[['white_rating', 'black_rating', 'b_score_all', 'w_score_all', 'score_eco']]
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X_train, X_test, y_train, y_test = train_test_split(corr, y, test_size=.05, random_state=42)
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linear = LinearRegression()
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linear.fit(X_train, y_train)
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def FunkPorog(elem):
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if(elem >= 0.5):
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return 1
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if(elem > -0.5):
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return 0
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if(elem <= -0.5):
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return -1
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prediction = linear.predict(X_test)
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n_predict_vect = vectorize(FunkPorog)
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# Средняя ошибка
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accuracy_mean_abs_error = mean_absolute_error(prediction, y_test)
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# Предсказание исхода партии, в целых числах
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predict_acc_score = n_predict_vect(prediction)
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accuracy_acc_score = accuracy_score(predict_acc_score, y_test)
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white_win = corr_st.loc[corr_st['winner'] == 1]
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white_score = (white_win['w_score_all'])
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black_score = (white_win['b_score_all'])
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white_win_x = white_score - black_score + white_win['score_eco']
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white_win_y = white_win['winner'] + (white_win['black_rating'] - white_win['white_rating'])
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black_win = corr_st.loc[corr_st['winner'] == -1]
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white_score = (black_win['w_score_all'])
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black_score = (black_win['b_score_all'])
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black_win_x = white_score - black_score + black_win['score_eco']
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black_win_y = black_win['winner'] + (black_win['black_rating'] - black_win['white_rating'])
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no_win = corr_st.loc[corr_st['winner'] == 0]
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white_score = (no_win['w_score_all'])
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black_score = (no_win['b_score_all'])
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no_win_x = white_score - black_score + no_win['score_eco']
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no_win_y = no_win['winner'] + (no_win['black_rating'] - no_win['white_rating'])
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white_win = white_win[['white_rating', 'black_rating', 'b_score_all', 'w_score_all', 'score_eco']]
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black_win = black_win[['white_rating', 'black_rating', 'b_score_all', 'w_score_all', 'score_eco']]
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no_win = no_win[['white_rating', 'black_rating', 'b_score_all', 'w_score_all', 'score_eco']]
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plt.subplot(1, 2, 1)
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plt.scatter(white_win_x, (corr_st.loc[corr_st['winner'] == 1])['winner'], alpha=0.6, s=25, c='red')
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plt.scatter(black_win_x, (corr_st.loc[corr_st['winner'] == -1])['winner'], alpha=0.6, s=25, c='blue')
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plt.scatter(no_win_x, (corr_st.loc[corr_st['winner'] == 0])['winner'], alpha=0.6, s=25, c='green')
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plt.scatter(white_win_x, white_win_y, alpha=0.2, s=25, c='pink')
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plt.scatter(black_win_x, black_win_y, alpha=0.2, s=25, c='cyan')
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plt.scatter(no_win_x, no_win_y, alpha=0.2, s=25, c='yellow')
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plt.savefig('static\k4_1_5_regression.png')
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return "<html>" \
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"<h1>Регрессия</h1>" \
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"<h2>Вариант 10. Задание 5 - Линейная регрессия</h2>" \
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"<h2> Точность модели:</h2>" \
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"<h2> Отклонение по абсолютной ошибке: " + str(accuracy_mean_abs_error) + "</h2>" \
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"<h2> Отклонение по исходу партии в целых числах: " + str(accuracy_acc_score) + "</h2>" \
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"<div align='center'>" + render_template(
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'4_1_l6_figure1.html') + "</div>" \
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"</html>"
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if __name__ == "__main__":
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app.run(debug=True) |