81 lines
3.9 KiB
Python
81 lines
3.9 KiB
Python
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from flask import Flask, render_template, request
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import pandas as pd
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.inspection import permutation_importance
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app = Flask(__name__)
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# Загрузите данные из файла
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bgg_data = pd.read_csv("bgg_dataset.csv", delimiter=";")
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# Преобразуйте столбцы Rating Average и Complexity Average в числа с плавающей точкой
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bgg_data["Rating Average"] = bgg_data["Rating Average"].apply(lambda x: float(x.replace(',', '.')))
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bgg_data["Complexity Average"] = bgg_data["Complexity Average"].apply(lambda x: float(x.replace(',', '.')))
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# Создайте целевую переменную (успешность игры)
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bgg_data["Success"] = bgg_data["Rating Average"].apply(lambda x: 1 if x > 7.5 else 0)
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# Определите признаки и целевую переменную
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features_bgg = ["Year Published", "Users Rated", "BGG Rank", "Owned Users", "Complexity Average"]
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X_bgg = bgg_data[features_bgg]
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y_bgg = bgg_data["Success"]
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# Разделите данные на обучающий и тестовый наборы
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X_train_bgg, X_test_bgg, y_train_bgg, y_test_bgg = train_test_split(X_bgg, y_bgg, test_size=0.01, random_state=42)
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# Создайте и обучите модель дерева решений
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model_bgg = DecisionTreeClassifier()
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model_bgg.fit(X_train_bgg, y_train_bgg)
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# Оцените модель на тестовом наборе данных
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y_pred_bgg = model_bgg.predict(X_test_bgg)
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accuracy_bgg = accuracy_score(y_test_bgg, y_pred_bgg)
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# Оценка важности признаков с использованием permutation_importance
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result = permutation_importance(model_bgg, X_test_bgg, y_test_bgg, n_repeats=30, random_state=42)
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# Важности признаков
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feature_importances_bgg = result.importances_mean
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@app.route("/", methods=["GET", "POST"])
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def index():
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if request.method == "POST":
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# Получите данные из запроса
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year_published = int(request.form["year_published"])
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users_rated = int(request.form["users_rated"])
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bgg_rank = int(request.form["bgg_rank"])
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owned_users = int(request.form["owned_users"])
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complexity_average = float(request.form["complexity_average"].replace(',', '.'))
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# Создайте DataFrame с введенными данными
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input_data_bgg = pd.DataFrame({
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"Year Published": [year_published],
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"Users Rated": [users_rated],
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"BGG Rank": [bgg_rank],
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"Owned Users": [owned_users],
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"Complexity Average": [complexity_average]
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})
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# Прогнозируйте успешность игры
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prediction_bgg = model_bgg.predict(input_data_bgg)[0]
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# Определите результат
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result_bgg = "Высокая оценка" if prediction_bgg == 1 else "Низкая оценка"
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# Передайте переменные в шаблон
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return render_template("index.html", accuracy_bgg=accuracy_bgg, success_count_bgg=sum(y_test_bgg),
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failure_count_bgg=len(y_test_bgg) - sum(y_test_bgg),
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feature_importances_bgg=feature_importances_bgg, prediction_result_bgg=result_bgg,
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X_train_bgg_columns=X_train_bgg.columns)
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# Передайте переменные в шаблон
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return render_template("index.html", accuracy_bgg=accuracy_bgg, success_count_bgg=sum(y_test_bgg),
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failure_count_bgg=len(y_test_bgg) - sum(y_test_bgg),
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feature_importances_bgg=feature_importances_bgg,
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X_train_bgg_columns=X_train_bgg.columns)
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if __name__ == "__main__":
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app.run(host="localhost", port=5000)
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