cleanup

app.py

@@ -5,26 +5,10 @@ from sklearn.datasets import make_regression
 import pandas as pd
 from sklearn.linear_model import ARDRegression, LinearRegression, BayesianRidge
 import matplotlib.pyplot as plt
-import seaborn as sns
 from matplotlib.colors import SymLogNorm
 import gradio as gr
 
 
-# def make_regression_data(n_samples=100,
-#     n_features=100,
-#     n_informative=10,
-#     noise=8,
-#     coef=True,
-#     random_state=42,):
-#     X, y, true_weights = make_regression(
-#         n_samples=n_samples,
-#         n_features=n_features,
-#         n_informative=n_informative,
-#         noise=noise,
-#         coef=coef,
-#         random_state=random_state,
-#     )
-#     return X, y, true_weights
 
 X, y, true_weights = make_regression(
     n_samples=100,
@@ -41,17 +25,6 @@ X, y, true_weights = make_regression(
 # We now fit both Bayesian models and the OLS to later compare the models'
 # coefficients.
 
-# olr = LinearRegression().fit(X, y)
-# brr = BayesianRidge(compute_score=True, n_iter=30).fit(X, y)
-# ard = ARDRegression(compute_score=True, n_iter=30).fit(X, y)
-# df = pd.DataFrame(
-#     {
-#         "Weights of true generative process": true_weights,
-#         "ARDRegression": ard.coef_,
-#         "BayesianRidge": brr.coef_,
-#         "LinearRegression": olr.coef_,
-#     }
-# )
 
 def fit_regression_models(n_iter=30, X=X, y=y, true_weights=true_weights):
     olr = LinearRegression().fit(X, y)
@@ -77,19 +50,6 @@ def fit_regression_models(n_iter=30, X=X, y=y, true_weights=true_weights):
 # Now we compare the coefficients of each model with the weights of
 # the true generative model.
 
-
-# plt.figure(figsize=(10, 6))
-# ax = sns.heatmap(
-#     df.T,
-#     norm=SymLogNorm(linthresh=10e-4, vmin=-80, vmax=80),
-#     cbar_kws={"label": "coefficients' values"},
-#     cmap="seismic_r",
-# )
-# plt.ylabel("linear model")
-# plt.xlabel("coefficients")
-# plt.tight_layout(rect=(0, 0, 1, 0.95))
-# _ = plt.title("Models' coefficients")
-
 def visualize_coefficients(df=None):
     fig = plt.figure(figsize=(10, 6))
     ax = sns.heatmap(
@@ -119,16 +79,6 @@ def visualize_coefficients(df=None):
 # --------------------------------
 
 
-# ard_scores = -np.array(ard.scores_)
-# brr_scores = -np.array(brr.scores_)
-# plt.plot(ard_scores, color="navy", label="ARD")
-# plt.plot(brr_scores, color="red", label="BayesianRidge")
-# plt.ylabel("Log-likelihood")
-# plt.xlabel("Iterations")
-# plt.xlim(1, 30)
-# plt.legend()
-# _ = plt.title("Models log-likelihood")
-
 def plot_marginal_log_likelihood(ard=None, brr=None, n_iter=30):
     
     fig = plt.figure(figsize=(10, 6))
@@ -240,28 +190,6 @@ def generate_polynomial_dataset(degree = 10):
 # Plotting polynomial regressions with std errors of the scores
 # -------------------------------------------------------------
 
-# ax = sns.scatterplot(
-#     data=full_data, x="input_feature", y="target", color="black", alpha=0.75
-# )
-# ax.plot(X_plot, y_plot, color="black", label="Ground Truth")
-# ax.plot(X_plot, y_brr, color="red", label="BayesianRidge with polynomial features")
-# ax.plot(X_plot, y_ard, color="navy", label="ARD with polynomial features")
-# ax.fill_between(
-#     X_plot.ravel(),
-#     y_ard - y_ard_std,
-#     y_ard + y_ard_std,
-#     color="navy",
-#     alpha=0.3,
-# )
-# ax.fill_between(
-#     X_plot.ravel(),
-#     y_brr - y_brr_std,
-#     y_brr + y_brr_std,
-#     color="red",
-#     alpha=0.3,
-# )
-# ax.legend()
-# _ = ax.set_title("Polynomial fit of a non-linear feature")
 
 
 def visualize_bayes_regressions_polynomial_features(degree = 10):