97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
from flask import Flask, request, render_template
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from sklearn.linear_model import Lasso, LinearRegression
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from sklearn.feature_selection import RFE
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.preprocessing import MinMaxScaler
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import numpy as np
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app = Flask(__name__)
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# Generate random data
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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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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, size))
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X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
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# Define and train models
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lasso = Lasso(alpha=0.1) # Example alpha value, you can adjust
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rfe = RFE(estimator=LinearRegression(), n_features_to_select=1)
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rf_regressor = RandomForestRegressor(n_estimators=100, random_state=0)
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# Number of bootstrap samples
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n_bootstrap = 100
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# Dictionary to store rankings
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feature_rankings = {}
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# Function to rank features using Lasso
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def rank_lasso():
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lasso.fit(X, Y)
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coef = lasso.coef_
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ranking = np.abs(coef)
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ranking = min_max_scale(ranking)
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return ranking
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# Function to rank features using RFE
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def rank_rfe():
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rfe.fit(X, Y)
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ranking = rfe.ranking_
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ranking = min_max_scale(ranking)
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return ranking
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# Function to rank features using Random Forest Regressor
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def rank_random_forest_regressor():
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rf_regressor.fit(X, Y)
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ranking = rf_regressor.feature_importances_
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ranking = min_max_scale(ranking)
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return ranking
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# Function to scale scores to the range [0, 1]
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def min_max_scale(arr):
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scaler = MinMaxScaler()
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scaled = scaler.fit_transform(np.array(arr).reshape(-1, 1))
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return scaled
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# Function to perform ranking and compute mean ranking
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def rank_features():
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feature_rankings['Lasso'] = rank_lasso()
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feature_rankings['RFE'] = rank_rfe()
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feature_rankings['Random Forest Regressor'] = rank_random_forest_regressor()
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# Mean ranking
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mean_ranking = np.mean(list(feature_rankings.values()), axis=0)
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feature_rankings['Mean Ranking'] = mean_ranking
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# Get indices of the top 4 most important features
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top_4_indices = np.argsort(mean_ranking)[-4:][::-1]
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# Get feature names based on indices
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top_4_feature_names = [f'Feature {i + 1}' for i in top_4_indices]
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# Add X and Y values to the context
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return {
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'feature_rankings': feature_rankings,
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'X_values': X[:, top_4_indices].tolist(),
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'Y_values': Y.tolist(),
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'top_4_feature_names': top_4_feature_names # Add the most important features
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}
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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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context = rank_features()
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return render_template('index.html', **context)
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return render_template('index.html', feature_rankings=feature_rankings)
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if __name__ == '__main__':
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app.run(debug=True)
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