flake8 fixes in test

tests/test_pyglmnet.py

@@ -179,7 +179,7 @@ def test_glmnet():
 
  X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
  y_train = simulate_glm(distr, beta0, beta, X_train,
- sample=False)
+  sample=False)
  alpha = 0.5
  loss_trace = list()
 
@@ -190,54 +190,65 @@ def callback(beta):
 
  loss_trace.append(
  _loss(distr, alpha, Tau, reg_lambda,
- X_train, y_train, eta, group, beta))
+  X_train, y_train, eta, group, beta))
 
  glm = GLM(distr, learning_rate=learning_rate,
- reg_lambda=reg_lambda, tol=1e-3, max_iter=5000,
- alpha=alpha, solver=solver, score_metric=score_metric,
- random_state=random_state, callback=callback)
+ reg_lambda=reg_lambda, tol=1e-3, max_iter=5000,
+ alpha=alpha, solver=solver,
+ score_metric=score_metric,
+ random_state=random_state, callback=callback)
  assert(repr(glm))
 
  glm.fit(X_train, y_train)
 
  # verify loss decreases or increases only slightly
- loss_trace_decreased = np.diff(loss_trace) <= 1e-3 
+ loss_trace_decreased = np.diff(loss_trace) <= 1e-3
  if np.any(~loss_trace_decreased):
- idx_of_first_increase = 2+np.min(np.nonzero(~loss_trace_decreased))
- assert np.all(loss_trace_decreased), (
- 'Loss increased between iterations'
- ' on distr={d} solver={s} with reg_lambda={l}'
- '\n Loss trace:\n {tr}'
- ).format(d=distr, s=solver, l=reg_lambda,
- tr=loss_trace[:idx_of_first_increase])
+ idx_of_first_increase = 2 + \
+ np.min(np.nonzero(~loss_trace_decreased))
+ assert np.all(loss_trace_decreased), \
+ ('Loss increased between iterations'
+ ' on distr={d} solver={s} with'
+ ' reg_lambda={rl}'
+ '\n Loss trace:\n {lt}'
+ ).format(d=distr, s=solver,
+ rl=reg_lambda,
+ lt=loss_trace[:idx_of_first_increase])
 
  if reg_lambda == 0.0:
- # check that the true model can be recreated almost perfectly
- # when no regularization is applied
+ # check that the true model can be recreated
+ # almost perfectly when no regularization is applied
  # verify loss at convergence = loss when beta=beta_
- l_true = _loss(distr, alpha, np.eye(beta.shape[0]), reg_lambda,
- X_train, y_train, 2.0, None,
- np.concatenate(([beta0], beta)))
- assert_allclose(loss_trace[-1], l_true, rtol=1e-4, atol=1e-5,
- err_msg=('Final loss trace value different from true loss '
- ' on distr={d} solver={s} with reg_lambda={l}'
- ).format(d=distr, s=solver, l=reg_lambda))
+ l_true = _loss(distr, alpha, np.eye(beta.shape[0]),
+ reg_lambda, X_train, y_train, 2.0, None,
+ np.concatenate(([beta0], beta)))
+ assert_allclose(loss_trace[-1], l_true,
+ rtol=1e-4, atol=1e-5,
+ err_msg=('Final loss trace value different'
+ ' from true loss '
+ ' on distr={d} solver={s}'
+ ' with reg_lambda={rl}'
+ ).format(d=distr, s=solver,
+ rl=reg_lambda))
  # beta=beta_ when reg_lambda = 0.
  assert_allclose(beta, glm.beta_, rtol=0.05, atol=1e-2,
- err_msg=('Fitted beta too different from true beta '
- 'in distr={} solver={}'.format(distr, solver)))
+ err_msg=('Fitted beta too different'
+ ' from true beta'
+ ' in distr={} solver={}'
+ ).format(distr, solver))
  betas_.append(glm.beta_)
 
  y_pred = glm.predict(X_train)
- assert y_pred.shape[0] == X_train.shape[0], (
- 'Fitted values have wrong number of rows in '
- ' on distr={d} solver={s} with reg_lambda={l}'
- ).format(d=distr, s=solver, l=reg_lambda)
+ assert y_pred.shape[0] == X_train.shape[0], \
+ ('Fitted values have wrong number of rows in '
+  ' on distr={d} solver={s} with reg_lambda={rl}'
+  ).format(d=distr, s=solver, rl=reg_lambda)
 
  # compare all solvers pairwise to make sure they're close
  for i, first_beta in enumerate(betas_[:-1]):
  for second_beta in betas_[i + 1:]:
- assert_allclose(first_beta, second_beta, rtol=0.05, atol=1e-2)
+ assert_allclose(first_beta, second_beta,
+ rtol=0.05, atol=1e-2)
 
  # test fit_predict
  glm_poisson = GLM(distr='softplus')