139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
import pandas
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import numpy as np
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from flask import Flask
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from sklearn.feature_selection import RFE
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from sklearn.linear_model import Lasso, LinearRegression
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from sklearn.preprocessing import MinMaxScaler
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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 №2</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_2' Method=get>" \
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"<input type=submit value='Ранжирование признаков'>" \
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"</form>" \
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"</td>" \
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"</table>" \
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"</html>"
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# Ранжирование признаков
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# Линейная регрессия, Лассо, Рекурсивное сокращение признаков
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# Отобразить оценки/значения каждого признака каждым методом, и среднюю оценку
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# 4 самых важных признака (индексы/названия)
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@app.route("/k4_1_task_2", methods=['GET'])
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def k4_1_task_2():
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# Генерация даных
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np.random.seed(0)
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size = 750
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X = np.random.uniform(0, 1, (size, 14))
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# Функция выход: регрессионная проблема Фридмана
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Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5)**2 +
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10*X[:, 3] + 5*X[:, 4]**5 + np.random.normal(0, 1))
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# Зависимость признаков
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X[:, 10:] = X[:, : 4] + np.random.normal(0, .025, (size, 4))
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linear_Regr = LinearRegression()
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linear_Regr.fit(X, Y)
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lasso = Lasso(alpha=.05)
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lasso.fit(X, Y)
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recurs_sokr_RFE = RFE(linear_Regr, n_features_to_select=1)
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recurs_sokr_RFE.fit(X, Y)
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new_RFE = []
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elem_ind = 0
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for value in recurs_sokr_RFE.ranking_:
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new_RFE.append(recurs_sokr_RFE.ranking_.max() - value + 1)
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elem_ind += 1
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names = ["x%s" % i for i in range(1, 15)]
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ranks = {}
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ranks["Linear"] = rank_to_dict(linear_Regr.coef_, names)
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ranks["Lasso"] = rank_to_dict(lasso.coef_, names)
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ranks["RFE"] = rank_to_dict(np.array(new_RFE), names)
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mean = {}
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# Пройти по словарю
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for key, value in ranks.items():
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# Пройти по списку значений которые являются парой имя:оценка
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for item in value.items():
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# Имя = ключ для mean
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if(item[0] not in mean):
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mean[item[0]] = 0
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mean[item[0]] += item[1]
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for key, value in mean.items():
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res = value/len(ranks)
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mean[key] = round(res, 2)
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mean_1 = [None, None]
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mean_2 = [None, None]
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mean_3 = [None, None]
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mean_4 = [None, None]
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for key, value in mean.items():
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if(mean_1[0] == None or value >= mean_1[0]):
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mean_4 = mean_3
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mean_3 = mean_2
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mean_2 = mean_1
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mean_1 = [value, key]
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else :
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if (mean_2[0] == None or value >= mean_2[0]):
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mean_4 = mean_3
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mean_3 = mean_2
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mean_2 = [value, key]
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else:
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if (mean_3[0] == None or value >= mean_3[0]):
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mean_4 = mean_3
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mean_3 = [value, key]
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else:
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if (mean_4[0] == None or value >= mean_4[0]):
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mean_4 = [value, key]
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mean_mass = []
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mass_linear = []
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mass_lasso = []
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mass_RFE = []
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for key, value in mean.items():
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mean_mass.append(value)
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for key, value in ranks['Linear'].items():
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mass_linear.append(value)
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for key, value in ranks['Lasso'].items():
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mass_lasso.append(value)
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for key, value in ranks['RFE'].items():
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mass_RFE.append(value)
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data = {"Linear": mass_linear,
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"Lasso": mass_lasso,
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"RFE": mass_RFE,
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"Mean": mean_mass}
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df = pandas.DataFrame(data, index=names)
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return "<html>" \
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"<h1>Ранжирование признаков</h1>" \
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"<h2>Вариант 10. Линейная регрессия, Лассо, Рекурсивное сокращение признаков</h2>" \
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"<h2>Отобразить оценки/значения каждого признака каждым методом, и среднюю оценку</h2>" \
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"<h2>4 самых важных признака: 1) " + mean_1[1] + "; 2) " + mean_2[1] + "; 3) " + mean_3[1] + "; 4) " + mean_4[1] + "</h2>" \
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"<div>" + df.to_html() + "</div>" \
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"</html>"
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def rank_to_dict(ranks, names):
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ranks = np.abs(ranks)
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minmax = MinMaxScaler()
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ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel()
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ranks = map(lambda x: round(x, 2), ranks)
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return dict(zip(names, ranks))
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if __name__ == "__main__":
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app.run(debug=True) |