title: how to print estimated coefficients after a (GridSearchCV) fit a model? (SGDRegressor)
body: <p>I am new to <code>scikit-learn</code>, but it did what I was hoping for. Now, maddeningly, the only remaining issue is that I don't find how I could print (or even better, write to a small text file) all the coefficients it estimated, all the features it selected. What is the way to do this?</p>
  
  <p>Same with SGDClassifier, but I think it is the same for all base objects that can be fit, with cross validation or without. Full script below.</p>
  
  <pre><code>import scipy as sp
  import numpy as np
  import pandas as pd
  import multiprocessing as mp
  from sklearn import grid_search
  from sklearn import cross_validation
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import SGDClassifier
  
  
  def main():
      print("Started.")
      # n = 10**6
      # notreatadapter = iopro.text_adapter('S:/data/controls/notreat.csv', parser='csv')
      # X = notreatadapter[1:][0:n]
      # y = notreatadapter[0][0:n]
      notreatdata = pd.read_stata('S:/data/controls/notreat.dta')
      notreatdata = notreatdata.iloc[:10000,:]
      X = notreatdata.iloc[:,1:]
      y = notreatdata.iloc[:,0]
      n = y.shape[0]
  
      print("Data lodaded.")
      X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
  
      print("Data split.")
      scaler = StandardScaler()
      scaler.fit(X_train)  # Don't cheat - fit only on training data
      X_train = scaler.transform(X_train)
      X_test = scaler.transform(X_test)  # apply same transformation to test data
  
      print("Data scaled.")
      # build a model
      model = SGDClassifier(penalty='elasticnet',n_iter = np.ceil(10**6 / n),shuffle=True)
      #model.fit(X,y)
  
      print("CV starts.")
      # run grid search
      param_grid = [{'alpha' : 10.0**-np.arange(1,7),'l1_ratio':[.05, .15, .5, .7, .9, .95, .99, 1]}]
      gs = grid_search.GridSearchCV(model,param_grid,n_jobs=8,verbose=1)
      gs.fit(X_train, y_train)
  
      print("Scores for alphas:")
      print(gs.grid_scores_)
      print("Best estimator:")
      print(gs.best_estimator_)
      print("Best score:")
      print(gs.best_score_)
      print("Best parameters:")
      print(gs.best_params_)
  
  
  if __name__=='__main__':
      mp.freeze_support()
      main()
  </code></pre>