from flask import Flask, request, render_template
from sklearn.linear_model import Lasso, LinearRegression
from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
import numpy as np

app = Flask(__name__)

# Generate random data
np.random.seed(0)
size = 750
X = np.random.uniform(0, 1, (size, 14))
Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 +
     10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1, size))
X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))

# Define and train models
lasso = Lasso(alpha=0.1)  # Example alpha value, you can adjust
rfe = RFE(estimator=LinearRegression(), n_features_to_select=1)
rf_regressor = RandomForestRegressor(n_estimators=100, random_state=0)

# Number of bootstrap samples
n_bootstrap = 100

# Dictionary to store rankings
feature_rankings = {}


# Function to rank features using Lasso
def rank_lasso():
    lasso.fit(X, Y)
    coef = lasso.coef_
    ranking = np.abs(coef)
    ranking = min_max_scale(ranking)
    return ranking


# Function to rank features using RFE
def rank_rfe():
    rfe.fit(X, Y)
    ranking = rfe.ranking_
    ranking = min_max_scale(ranking)
    return ranking


# Function to rank features using Random Forest Regressor
def rank_random_forest_regressor():
    rf_regressor.fit(X, Y)
    ranking = rf_regressor.feature_importances_
    ranking = min_max_scale(ranking)
    return ranking


# Function to scale scores to the range [0, 1]
def min_max_scale(arr):
    scaler = MinMaxScaler()
    scaled = scaler.fit_transform(np.array(arr).reshape(-1, 1))
    return scaled


# Function to perform ranking and compute mean ranking
def rank_features():
    feature_rankings['Lasso'] = rank_lasso()
    feature_rankings['RFE'] = rank_rfe()
    feature_rankings['Random Forest Regressor'] = rank_random_forest_regressor()

    # Mean ranking
    mean_ranking = np.mean(list(feature_rankings.values()), axis=0)
    feature_rankings['Mean Ranking'] = mean_ranking

    # Get indices of the top 4 most important features
    top_4_indices = np.argsort(mean_ranking)[-4:][::-1]

    # Get feature names based on indices
    top_4_feature_names = [f'Feature {i + 1}' for i in top_4_indices]

    # Add X and Y values to the context
    return {
        'feature_rankings': feature_rankings,
        'X_values': X[:, top_4_indices].tolist(),
        'Y_values': Y.tolist(),
        'top_4_feature_names': top_4_feature_names  # Add the most important features
    }


@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        context = rank_features()
        return render_template('index.html', **context)
    return render_template('index.html', feature_rankings=feature_rankings)


if __name__ == '__main__':
    app.run(debug=True)