71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
from flask import Flask, render_template
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import numpy as np
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from matplotlib import pyplot as plt
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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.preprocessing import StandardScaler
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from sklearn.datasets import make_moons
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.pipeline import make_pipeline
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from sklearn.metrics import accuracy_score
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import io
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from flask import Response
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import matplotlib
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import base64
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app = Flask(__name__)
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matplotlib.use('Agg')
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matplotlib.rcParams['figure.max_open_warning'] = 0
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# Создаем данные
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moon_dataset = make_moons(noise=0.3, random_state=None)
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X, y = moon_dataset
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X = StandardScaler().fit_transform(X)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
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# Создаем модели
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models = {
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"Линейная регрессия": LogisticRegression(),
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"Полиномиальная регрессия": make_pipeline(PolynomialFeatures(degree=4), LogisticRegression()),
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"Гребневая полиномиальная регрессия": make_pipeline(PolynomialFeatures(degree=4), LogisticRegression(penalty='l2', C=1.0))
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}
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background_color1 = '#CE5A57'
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background_color2 = '#78A5A3'
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data_color1 = 'red'
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data_color2 = 'green'
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# Обучаем и оцениваем модели
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model_results = {}
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for name, model in models.items():
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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model_results[name] = {
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'accuracy': accuracy,
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'X_test': X_test,
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'y_test': y_test,
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'model': model
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}
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@app.route('/')
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def index():
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plot_images = {}
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for model_name, results in model_results.items():
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fig, ax = plt.subplots(figsize=(8, 6))
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cm_data = ListedColormap([data_color1, data_color2])
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scatter = ax.scatter(results['X_test'][:, 0], results['X_test'][:, 1], c=results['model'].predict(results['X_test']), cmap=cm_data, alpha=0.6)
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_title(model_name)
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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buf.seek(0)
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plot_images[model_name] = base64.b64encode(buf.read()).decode('utf-8')
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return render_template('index.html', model_results=model_results, plot_images=plot_images)
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if __name__ == '__main__':
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app.run(threaded=True)
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