text: # Literally just Gino's task1 code, with 2 strings changed...
  
  from traceback import clear_frames
  import numpy as np
  import matplotlib.pyplot as plt
  import sklearn as sk
  import pandas as pd
  
  from sklearn.pipeline import Pipeline
  from sklearn.experimental import enable_iterative_imputer
  from sklearn.impute import SimpleImputer
  from sklearn.feature_selection import f_regression
  from sklearn.feature_selection import SelectKBest
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.kernel_ridge import KernelRidge
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.metrics import mean_absolute_error
  from sklearn.svm import SVR
  from sklearn.linear_model import RidgeCV
  
  feature_headers = headers = [*pd.read_csv('../data/train_descriptors.csv', 
                               nrows=1)]
  features = pd.read_csv(
      '../data/train_descriptors.csv',
      usecols=[c for c in headers if not c in [
          'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
  )
  data = pd.read_csv('../data/train_centroid_distances.csv')
  #%% Train / test splitting
  target = data['mean']
  X_train, X_test, y_train, y_test = train_test_split(
      features, target, test_size=0.3, random_state=42)
  y_train = y_train.to_numpy()
  # # %% Full model defn as pipeline
  pclf = Pipeline([
      ('imputer', SimpleImputer(strategy='mean', verbose=1)),
      ('scaler', MinMaxScaler()),
      ('feature_sel', SelectKBest(f_regression, k = 1551)), # using it all seems better..!?   
      ('fitting', KernelRidge())
  ])
  #%% Fitting
  pclf.fit(X_train, y_train)
  #%% Prediction
  y_pred = pclf.predict(X_test)
  print("MAE::::::", mean_absolute_error(y_test, y_pred))
  #%% plotting
  plt.plot(y_test, y_pred, 'r.')
  plt.show()
  #%% saving
  test_data = pd.read_csv(
      '../data/test_descriptors.csv',
      usecols=[c for c in headers if not c in [
          'identifiers', 'Unnamed: 0', 'name', 'InchiKey', 'SMILES']]
  ).to_numpy()
  test_pred = pclf.predict(test_data)
  np.savetxt('task_3_predictions.csv', test_pred)
  