Python concat Exemples

Exemple #1

                                               cl.CapeCod(trend=0.034))])

# Define X
X = cl.load_sample('xyz')['Incurred']

# Separately apply on-level factors for premium
sample_weight = cl.ParallelogramOLF(rate_history,
                                    change_col='rate_change',
                                    date_col='date',
                                    vertical_line=True).fit_transform(
                                        xyz['Premium'].latest_diagonal)

#  Fit Cod Estimator
pipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_

# Create a Cape Cod pipeline without onleveling
pipe2 = cl.Pipeline(steps=[('dev', cl.Development(
    n_periods=2)), ('model', cl.CapeCod(trend=0.034))])

# Finally fit Cod Estimator without on-leveling
pipe2.fit(
    X,
    sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_

# Plot results
cl.concat(
    (pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),
     pipe2.named_steps.model.ultimate_.rename('columns',
                                              ['Without On-level'])),
    1).T.plot(title='Cape Cod sensitivity to on-leveling', grid=True)

Exemple #2

Fichier : test_triangle.py Projet : attiguyas/chainladder-python

def test_array_protocol(raa, clrd):
    assert np.sqrt(raa) == raa.sqrt()
    assert np.concatenate((clrd.iloc[:200], clrd.iloc[200:]),0) == cl.concat((clrd.iloc[:200], clrd.iloc[200:]),0)

Exemple #3

# Separately apply on-level factors for premium
sample_weight = cl.ParallelogramOLF(rate_history,
                                    change_col='rate_change',
                                    date_col='date',
                                    vertical_line=True).fit_transform(
                                        xyz['Premium'].latest_diagonal)

#  Fit Cod Estimator
pipe.fit(X, sample_weight=sample_weight).named_steps.model.ultimate_

# Create a Cape Cod pipeline without onleveling
pipe2 = cl.Pipeline(steps=[('dev', cl.Development(
    n_periods=2)), ('model', cl.CapeCod(trend=0.034))])

# Finally fit Cod Estimator without on-leveling
pipe2.fit(
    X,
    sample_weight=xyz['Premium'].latest_diagonal).named_steps.model.ultimate_

# Plot results
cl.concat(
    (pipe.named_steps.model.ultimate_.rename('columns', ['With On-level']),
     pipe2.named_steps.model.ultimate_.rename('columns',
                                              ['Without On-level'])),
    1).T.plot(kind='bar',
              title='Cape Cod sensitivity to on-leveling',
              grid=True,
              subplots=True,
              legend=False)

Exemple #4

def test_concat():
    tri = cl.load_sample('clrd').groupby('LOB').sum()
    assert cl.concat([tri.loc['wkcomp'], tri.loc['comauto']], axis=0) == \
           tri.loc[['wkcomp', 'comauto']]

Exemple #5

Fichier : test_utilities.py Projet : synapticarbors/chainladder-python

def test_concat():
    tri = cl.load_sample("clrd").groupby("LOB").sum()
    assert (cl.concat([tri.loc["wkcomp"], tri.loc["comauto"]],
                      axis=0) == tri.loc[["wkcomp", "comauto"]])