old_contents: import chainladder as cl
  raa = cl.load_sample('RAA')
  raa_1989 = raa[raa.valuation < raa.valuation_date]
  cl_ult = cl.Chainladder().fit(raa).ultimate_  # Chainladder Ultimate
  apriori = cl_ult*0+(float(cl_ult.sum())/10)  # Mean Chainladder Ultimate
  apriori_1989 = apriori[apriori.origin < '1990']
  
  
  def test_cc_predict():
      cc = cl.CapeCod().fit(raa_1989, sample_weight=apriori_1989)
      cc.predict(raa, sample_weight=apriori)
  
  
  def test_bf_predict():
      cc = cl.BornhuetterFerguson().fit(raa_1989, sample_weight=apriori_1989)
      cc.predict(raa, sample_weight=apriori)
  
  def test_mack_predict():
      mack = cl.MackChainladder().fit(raa_1989)
      mack.predict(raa)
  
  def test_bs_random_state_predict():
      tri = cl.load_sample('clrd').groupby('LOB').sum().loc['wkcomp', ['CumPaidLoss', 'EarnedPremNet']]
      X = cl.BootstrapODPSample(random_state=100).fit_transform(tri['CumPaidLoss'])
      bf = cl.BornhuetterFerguson(apriori=0.6, apriori_sigma=0.1, random_state=42).fit(X, sample_weight=tri['EarnedPremNet'].latest_diagonal)
      assert abs(bf.predict(X, sample_weight=tri['EarnedPremNet'].latest_diagonal).ibnr_.sum().sum()/bf.ibnr_.sum().sum()-1)<1e-3
  
  def test_basic_transform():
      tri = cl.load_sample('raa')
      cl.Development().fit_transform(tri)
      cl.ClarkLDF().fit_transform(tri)
      cl.TailClark().fit_transform(tri)
      cl.TailBondy().fit_transform(tri)
      cl.TailConstant().fit_transform(tri)
      cl.TailCurve().fit_transform(tri)
      cl.BootstrapODPSample().fit_transform(tri)
      cl.IncrementalAdditive().fit_transform(tri, sample_weight=tri.latest_diagonal)
  
new_contents: import chainladder as cl
  raa = cl.load_sample('RAA')
  raa_1989 = raa[raa.valuation < raa.valuation_date]
  cl_ult = cl.Chainladder().fit(raa).ultimate_  # Chainladder Ultimate
  apriori = cl_ult*0+(float(cl_ult.sum())/10)  # Mean Chainladder Ultimate
  apriori_1989 = apriori[apriori.origin < '1990']
  
  
  def test_cc_predict():
      cc = cl.CapeCod().fit(raa_1989, sample_weight=apriori_1989)
      cc.predict(raa, sample_weight=apriori)
  
  
  def test_bf_predict():
      cc = cl.BornhuetterFerguson().fit(raa_1989, sample_weight=apriori_1989)
      cc.predict(raa, sample_weight=apriori)
  
  def test_mack_predict():
      mack = cl.MackChainladder().fit(raa_1989)
      mack.predict(raa)
  
  def test_bs_random_state_predict():
      tri = cl.load_sample('clrd').groupby('LOB').sum().loc['wkcomp', ['CumPaidLoss', 'EarnedPremNet']]
      X = cl.BootstrapODPSample(random_state=100).fit_transform(tri['CumPaidLoss'])
      bf = cl.BornhuetterFerguson(apriori=0.6, apriori_sigma=0.1, random_state=42).fit(X, sample_weight=tri['EarnedPremNet'].latest_diagonal)
      assert abs(bf.predict(X, sample_weight=tri['EarnedPremNet'].latest_diagonal).ibnr_.sum().sum()/bf.ibnr_.sum().sum()-1)<5e-3
  
  def test_basic_transform():
      tri = cl.load_sample('raa')
      cl.Development().fit_transform(tri)
      cl.ClarkLDF().fit_transform(tri)
      cl.TailClark().fit_transform(tri)
      cl.TailBondy().fit_transform(tri)
      cl.TailConstant().fit_transform(tri)
      cl.TailCurve().fit_transform(tri)
      cl.BootstrapODPSample().fit_transform(tri)
      cl.IncrementalAdditive().fit_transform(tri, sample_weight=tri.latest_diagonal)
  
subject: Update test_predict.py