Create app.py

app.py

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+import gradio as gr
+from time import time
+from scipy import sparse
+from scipy import linalg
+
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Lasso
+
+
+def load_dataset():
+  X, y = make_regression(n_samples=200, n_features=5000, random_state=0)
+  # create a copy of X in sparse format
+  X_sp = sparse.coo_matrix(X)
+  return X,X_sp,y
+
+def compare_lasso_dense():
+  alpha = 1
+  sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
+  dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=1000)
+
+  t0 = time()
+  sparse_lasso.fit(X_sp, y)
+  # print(f"Sparse Lasso done in {(time() - t0):.3f}s")
+  elapse1 = time() - t0
+
+  t0 = time()
+  dense_lasso.fit(X, y)
+  # print(f"Dense Lasso done in {(time() - t0):.3f}s")
+  elapse2 = time() - t0
+
+  # compare the regression coefficients
+  coeff_diff = linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
+  # print(f"Distance between coefficients : {coeff_diff:.2e}")
+  return f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Dense Lasso done in {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n"
+
+def compare_lasso_sparse():
+  # make a copy of the previous data
+  Xs = X.copy()
+  # make Xs sparse by replacing the values lower than 2.5 with 0s
+  Xs[Xs < 2.5] = 0.0
+  # create a copy of Xs in sparse format
+  Xs_sp = sparse.coo_matrix(Xs)
+  Xs_sp = Xs_sp.tocsc()
+
+  # compute the proportion of non-zero coefficient in the data matrix
+  print(f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%")
+  matrix_density = Xs_sp.nnz / float(X.size) * 100 
+
+  alpha = 0.1
+  sparse_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
+  dense_lasso = Lasso(alpha=alpha, fit_intercept=False, max_iter=10000)
+
+  t0 = time()
+  sparse_lasso.fit(Xs_sp, y)
+  print(f"Sparse Lasso done in {(time() - t0):.3f}s")
+  elapses1 = time() - t0
+
+  t0 = time()
+  dense_lasso.fit(Xs, y)
+  print(f"Dense Lasso done in  {(time() - t0):.3f}s")
+  elapses2 = time() - t0
+
+  # compare the regression coefficients
+  coeff_diff = linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
+  print(f"Distance between coefficients : {coeff_diff:.2e}")
+  return f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%\t\n"+ f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n" 
+
+
+X,X_sp,y = load_dataset()
+# compare_lasso_dense(X,X_sp,y)
+# compare_lasso_sparse(X,X_sp,y)
+
+
+
+title = " Lasso on Dense and Sparse data "
+info = '''**Comparing the two Lasso implementations on Dense data**
+We create a linear regression problem that is suitable for the Lasso, that is to say, with more features than samples. 
+We then store the data matrix in both dense (the usual) and sparse format, and train a Lasso on each. We compute the 
+runtime of both and check that they learned the same model by 
+computing the Euclidean norm of the difference between the coefficients they learned. 
+Because the data is dense, we expect better runtime with a dense data format.
+'''
+
+info2='''***Comparing the two Lasso implementations on Sparse data***
+We make the previous problem sparse by replacing all small values with 0 
+and run the same comparisons as above. Because the data is now sparse, 
+we expect the implementation that uses the sparse data format to be faster.
+'''
+
+conclusion = '''**We show that linear_model.Lasso provides 
+the same results for dense and sparse data 
+and that in the case of sparse data the speed is improved**.
+'''
+with gr.Blocks() as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(info)
+    
+    txt_3 = gr.Textbox(value="", label="Dense Lasso comparison")
+    btn = gr.Button(value="Dense Lasso comparison")
+    btn.click(compare_lasso_dense, outputs=[txt_3])
+
+    gr.Markdown(info2)
+    
+    txt_4 = gr.Textbox(value="", label="Sparse Lasso comparison")
+    btn = gr.Button(value="Sparse Lasso comparison")
+    btn.click(compare_lasso_sparse, outputs=[txt_4])
+
+    gr.Markdown(conclusion)
+ 
+
+if __name__ == "__main__":
+    demo.launch()