195 lines
8.3 KiB
Python
195 lines
8.3 KiB
Python
import array
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import math
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import random
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import ListedColormap
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import make_moons, make_circles, make_classification
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from sklearn.neural_network import MLPClassifier
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from sklearn.linear_model import LinearRegression, Lasso, Ridge, Perceptron
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from sklearn.metrics import accuracy_score
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from flask import Flask
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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 №1</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_1' Method=get>" \
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"<input type=submit value='Работа с типовыми наборами данных и различными моделями'>" \
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"</form>" \
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"</td>" \
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"</table>" \
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"</html>"
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# Работа с типовыми наборами данных и различными моделями
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# сгенерируйте определенный тип данных и сравните на нем 3 модели
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# 10.Данные: make_moons (noise=0.3, random_state=rs)
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# Модели:
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# · Линейную регрессию
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# · Многослойный персептрон с 10-ю нейронами в скрытом слое (alpha = 0.01)
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# · Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)
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@app.route("/k4_1_task_1", methods=['GET'])
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def k4_1_task_1():
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X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
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random_state=0, n_clusters_per_class=1)
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rng = np.random.RandomState(2)
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X += 2 + rng.uniform(size=X.shape)
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linearly_dataset = (X, y)
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cm_bright = ListedColormap(['#FF0000', '#0000FF'])
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cm_bright2 = ListedColormap(['#FF000066', '#0000FF66'])
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moon_dataset = make_moons(noise=0.3, random_state=0)
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circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
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datasets = [moon_dataset, circles_dataset, linearly_dataset]
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X, y = moon_dataset
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)
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ridge_regression = Ridge(alpha=3, random_state=240)
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ridge_regression.fit(X_train, y_train)
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linear_accuracy = str(ridge_regression.score(X_test, y_test))
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plt.subplot(1, 3, 1)
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
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x_min = moon_dataset[0][:, 0].min()
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g_min = None
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y_min = None
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x_max = moon_dataset[0][:, 0].max()
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g_max = None
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y_max = None
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for k in range(-21, 50):
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elem = np.array([[x_min, k / 20]])
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getted = ridge_regression.predict(elem)
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if (g_min == None or math.fabs(0.5 - getted) < math.fabs(0.5 - g_min)):
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g_min = getted
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y_min = elem[0][1]
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else:
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if(math.fabs(0.5 - getted) > math.fabs(0.5 - g_min)):
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break
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for k in range(-21, 50):
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elem = np.array([[x_max, k / 20]])
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getted = ridge_regression.predict(elem)
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if (g_max == None or math.fabs(0.5 - getted) < math.fabs(0.5 - g_max)):
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g_max = getted
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y_max = elem[0][1]
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else:
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if(math.fabs(0.5 - getted) > math.fabs(0.5 - g_max)):
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break
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x = ridge_regression.predict(X_test)
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plt.plot([x_min, x_max], [y_min, y_max], label="line", color="yellow")
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# plt.show()
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# Перцептрон 10 скрытых слоев
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perceptr = MLPClassifier(random_state=1, max_iter=2000, n_iter_no_change=20, activation="tanh",
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alpha=0.01, hidden_layer_sizes=[10,], tol=0.00000001)
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perceptr.fit(X_train, y_train)
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prediction = perceptr.predict(X_test)
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perceptron_accuracy = str(accuracy_score(y_test, prediction))
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prediction = perceptr.predict(moon_dataset[0])
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perceptron_accuracy_all = str(accuracy_score(moon_dataset[1], prediction))
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params_set = []
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y_elem = None
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g_elem = None
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for data_elem in moon_dataset[0]:
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for k in range(-21, 50):
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elem = np.array([[data_elem[0], k / 20]])
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getted = perceptr.predict(elem)
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if (g_elem == None and getted == 0):
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params_set.append([data_elem[0], -21 / 20])
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g_elem = None
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else:
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if(getted == 1 and (getted == g_elem or g_elem == None)):
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g_elem = getted
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y_elem = elem[0][1]
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else:
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params_set.append([data_elem[0], y_elem])
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g_elem = None
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break
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if (g_elem != None):
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params_set.append([data_elem[0], 50 / 20])
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g_elem = None
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params_set.sort()
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params_set = np.array(params_set)
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plt.subplot(1, 3, 2)
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
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plt.plot(params_set[:, 0], params_set[:, 1], label="line", color="yellow")
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# plt.show()
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# Перцептрон 100 скрытых слоев
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perceptr100 = MLPClassifier(random_state=1, max_iter=2000, n_iter_no_change=20, activation="tanh",
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alpha=0.01, hidden_layer_sizes=[100, ], tol=0.00000001)
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perceptr100.fit(X_train, y_train)
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prediction = perceptr100.predict(X_test)
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perceptron100_accuracy = str(accuracy_score(y_test, prediction))
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prediction = perceptr100.predict(moon_dataset[0])
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perceptron100_accuracy_all = str(accuracy_score(moon_dataset[1], prediction))
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params_set = []
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y_elem = None
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g_elem = None
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for data_elem in moon_dataset[0]:
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for k in range(-21, 30):
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elem = np.array([[data_elem[0], k / 20]])
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getted = perceptr100.predict(elem)
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if (g_elem == None and getted == 0):
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params_set.append([data_elem[0], -21 / 20])
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g_elem = None
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else:
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if(getted == 1 and (getted == g_elem or g_elem == None)):
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g_elem = getted
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y_elem = elem[0][1]
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else:
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params_set.append([data_elem[0], y_elem])
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g_elem = None
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break
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if (g_elem != None):
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params_set.append([data_elem[0], 30 / 20])
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g_elem = None
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params_set.sort()
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params_set = np.array(params_set)
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plt.subplot(1, 3, 3)
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plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright2)
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plt.plot(params_set[:, 0], params_set[:, 1], label="line", color="yellow")
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plt.show()
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return "<html>" \
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"<h1>Работа с типовыми наборами данных и различными моделями</h1>" \
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"<h2>Вариант 10. Данные: make_moons (noise=0.3, random_state=rs)</h2>" \
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"<h2>Модели:\n 1) Линейная регрессия" \
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"\n 2) Многослойный персептрон с 10-ю нейронами в скрытом слое (alpha = 0.01)" \
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"\n 3) Многослойный персептрон со 100-а нейронами в скрытом слое (alpha = 0.01)</h2>" \
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"<h2>Оценка точности линейной регрессии: " + linear_accuracy + "</h2>" \
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"<h2>Оценка точности (тестовые данные) перцептрона 10 нейронов в скрытом слое: " + perceptron_accuracy + "</h2>" \
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"<h2>Оценка точности (тестовые данные) перцептрона 100 нейронов в скрытом слое: " + perceptron100_accuracy + "</h2>" \
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"<h2>Оценка точности (все точки) перцептрона 10 нейронов в скрытом слое: " + perceptron_accuracy_all + "</h2>" \
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"<h2>Оценка точности (все точки) перцептрона 100 нейронов в скрытом слое: " + perceptron100_accuracy_all + "</h2>" \
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"</html>"
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if __name__ == "__main__":
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app.run(debug=True)
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