def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle
Set of LDFs to which the munich adjustment will be applied.
y : Ignored
sample_weight : Ignored
Returns
-------
self : object
Returns the instance itself.
"""
backend = "cupy" if X.array_backend == "cupy" else "numpy"
self.X_ = X.copy()
paid_tri = self.X_[self.paid_to_incurred[0]]
incurred_tri = self.X_[self.paid_to_incurred[1]]
self.paid_w_ = Development(n_periods=self.paid_n_periods).fit(self.X_.iloc[0,0]).w_
self.case_w_ = Development(n_periods=self.case_n_periods).fit(self.X_.iloc[0,0]).w_
self.case_ldf_ = self.case_to_prior_case_.mean(2)
self.paid_ldf_ = self.paid_to_prior_case_.mean(2)
case = incurred_tri-paid_tri
patterns = ((1 - np.nan_to_num(case.nan_triangle[..., 1:])) *
self.case_ldf_.values)
for i in range(np.isnan(case.nan_triangle[-1]).sum()):
increment = (
(case - case[case.valuation < case.valuation_date]).iloc[..., :-1] *
patterns)
increment.ddims = case.ddims[1:]
increment.valuation_date = case.valuation[case.valuation>=case.valuation_date].drop_duplicates()[1]
case = case + increment
def fit(self, X, y=None, sample_weight=None):
if (type(X.ddims) != np.ndarray):
raise ValueError(
'Triangle must be expressed with development lags')
obj = copy.copy(X)
self.w_ = X.nan_triangle() if not self.drop else self._drop(X)
lag = {'M': 1, 'Q': 3, 'Y': 12}[X.development_grain]
if type(self.drop) is not list and self.drop is not None:
self.drop = [self.drop]
drop = [(item[0], item[1] - lag) for item in self.drop]
else:
drop = self.drop
obj = Development(n_periods=self.n_periods,
drop=drop).fit_transform(obj)
obj = Chainladder().fit(obj)
# Works for only a single triangle - can we generalize this
exp_incr_triangle = obj.full_expectation_.cum_to_incr() \
.values[0, 0, :, :X.shape[-1]]
exp_incr_triangle = np.nan_to_num(exp_incr_triangle) * \
obj.X_.nan_triangle()
self.design_matrix_ = self._get_design_matrix(X)
self.hat_ = self._get_hat(X, exp_incr_triangle)
self.resampled_triangles_, self.scale_ = \
self._get_simulation(X, exp_incr_triangle)
n_obs = np.nansum(self.w_)
n_origin_params = X.shape[2]
n_dev_params = X.shape[3] - 1
deg_free = n_obs - n_origin_params - n_dev_params
deg_free_adj_fctr = np.sqrt(n_obs / deg_free)
return self
def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Triangle to which the incremental method is applied. Triangle must
be cumulative.
y : Ignored
sample_weight : Exposure used in the method.
Returns
-------
self : object
Returns the instance itself.
"""
from chainladder import ULT_VAL
from chainladder.utils.utility_functions import num_to_nan
if (type(X.ddims) != np.ndarray):
raise ValueError('Triangle must be expressed with development lags')
if X.array_backend == 'sparse':
X = X.set_backend('numpy')
else:
X = copy.deepcopy(X)
if sample_weight.array_backend == 'sparse':
sample_weight = sample_weight.set_backend('numpy')
else:
sample_weight = copy.deepcopy(sample_weight)
xp = X.get_array_module()
sample_weight.is_cumulative = False
obj = X.cum_to_incr()/sample_weight
x = obj.trend(self.trend)
w_ = Development(n_periods=self.n_periods-1).fit(x).w_
w_ = num_to_nan(w_)
w_ = xp.concatenate((w_, (w_[..., -1:]*x.nan_triangle)[..., -1:]),
axis=-1)
if self.average == 'simple':
y_ = xp.nanmean(w_*x.values, axis=-2)
if self.average == 'volume':
y_ = xp.nansum(w_*x.values*sample_weight.values, axis=-2)
y_ = y_ / xp.nansum(w_*sample_weight.values, axis=-2)
y_ = xp.repeat(y_[..., None, :], len(x.odims), -2)
obj = copy.copy(x)
keeps = 1-xp.nan_to_num(x.nan_triangle) + \
xp.nan_to_num(
x[x.valuation==x.valuation_date].values[0, 0, ...]*0+1)
obj.values = (1+self.trend) ** \
xp.flip((xp.abs(xp.arange(obj.shape[-2])[None].T -
xp.arange(obj.shape[-2])[None])), 0)*y_*keeps
obj.values = obj.values*(1-xp.nan_to_num(x.nan_triangle)) + \
xp.nan_to_num((X.cum_to_incr()/sample_weight).values)
obj.values[obj.values == 0] = xp.nan
obj._set_slicers()
obj.valuation_date = pd.to_datetime(ULT_VAL)
self.ldf_ = obj.incr_to_cum().link_ratio
self.incremental_ = obj*sample_weight
self.sigma_ = self.std_err_ = 0*self.ldf_
return self
def fit(self, X, y=None, sample_weight=None):
obj = copy.deepcopy(X)
obj = Development(n_periods=self.n_periods).fit_transform(obj)
obj = Chainladder().fit(obj)
# Works for only a single triangle - can we generalize this
exp_incr_triangle = obj.full_expectation_ \
.cum_to_incr().triangle[0, 0, :, :X.shape[-1]]
exp_incr_triangle = np.nan_to_num(exp_incr_triangle) * \
obj.X_.nan_triangle()
self.design_matrix_ = self._get_design_matrix(X)
self.hat_ = self._get_hat(X, exp_incr_triangle)
self.resampled_triangles_, self.scale_ = \
self._get_simulation(X, exp_incr_triangle)
return self
def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Triangle to which the incremental method is applied. Triangle must
be cumulative.
y : Ignored
sample_weight : Exposure used in the method.
Returns
-------
self : object
Returns the instance itself.
"""
if (type(X.ddims) != np.ndarray):
raise ValueError(
'Triangle must be expressed with development lags')
obj = X.cum_to_incr() / sample_weight
xp = cp.get_array_module(obj.values)
x = obj.trend(self.trend)
w_ = Development(n_periods=self.n_periods - 1).fit(x).w_
w_[w_ == 0] = xp.nan
w_ = xp.concatenate((w_, (w_[..., -1:] * x._nan_triangle())[..., -1:]),
axis=-1)
if self.average == 'simple':
y_ = xp.nanmean(w_ * x.values, axis=-2)
if self.average == 'volume':
y_ = xp.nansum(w_ * x.values * sample_weight.values, axis=-2)
y_ = y_ / xp.nansum(w_ * sample_weight.values, axis=-2)
y_ = xp.repeat(xp.expand_dims(y_, -2), len(x.odims), -2)
obj = copy.copy(x)
keeps = 1-xp.nan_to_num(x._nan_triangle()) + \
xp.nan_to_num(
x._get_latest_diagonal(compress=False).values[0, 0, ...]*0+1)
obj.values = (1+self.trend) ** \
xp.flip((xp.abs(xp.arange(obj.shape[-2])[xp.newaxis].T -
xp.arange(obj.shape[-2])[xp.newaxis])), 0)*y_*keeps
obj.values = obj.values*(X._expand_dims(1-xp.nan_to_num(x._nan_triangle()))) + \
xp.nan_to_num((X.cum_to_incr()/sample_weight).values)
obj.values[obj.values == 0] = xp.nan
obj.nan_override = True
obj._set_slicers()
self.incremental_ = obj * sample_weight
self.ldf_ = obj.incr_to_cum().link_ratio
self.cdf_ = DevelopmentBase._get_cdf(self.ldf_)
self.sigma_ = self.std_err_ = 0 * self.ldf_
return self
def fit(self, X, y=None, sample_weight=None):
backend = X.array_backend
if backend == "sparse":
X = X.set_backend("numpy")
else:
X = X.copy()
xp = X.get_array_module()
if X.shape[:2] != (1, 1):
raise ValueError(
"Only single index/column triangles are supported")
if type(X.ddims) != np.ndarray:
raise ValueError(
"Triangle must be expressed with development lags")
lag = {"M": 1, "Q": 3, "Y": 12}[X.development_grain]
obj = Development(
n_periods=self.n_periods,
drop=self.drop,
drop_high=self.drop_high,
drop_low=self.drop_low,
drop_valuation=self.drop_valuation,
).fit_transform(X)
self.w_ = obj.w_
obj = Chainladder().fit(obj)
# Works for only a single triangle - can we generalize this
exp_incr_triangle = obj.full_expectation_.cum_to_incr().values[
0, 0, :, :X.shape[-1]]
exp_incr_triangle = xp.nan_to_num(
exp_incr_triangle) * obj.X_.nan_triangle
self.design_matrix_ = self._get_design_matrix(X)
if self.hat_adj:
try:
self.hat_ = self._get_hat(X, exp_incr_triangle)
except:
warn("Could not compute hat matrix. Setting hat_adj to False")
self.had_adj = False
self.hat_ = None
else:
self.hat_ = None
self.resampled_triangles_, self.scale_ = self._get_simulation(
X, exp_incr_triangle)
n_obs = xp.nansum(self.w_)
n_origin_params = X.shape[2]
n_dev_params = X.shape[3] - 1
deg_free = n_obs - n_origin_params - n_dev_params
deg_free_adj_fctr = xp.sqrt(n_obs / deg_free)
return self
def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Triangle to which the incremental method is applied. Triangle must
be cumulative.
y : Ignored
sample_weight : Exposure used in the method.
Returns
-------
self : object
Returns the instance itself.
"""
obj = X.cum_to_incr() / sample_weight
x = obj.trend(self.trend)
w_ = Development(n_periods=self.n_periods - 1).fit(x).w_
w_[w_ == 0] = np.nan
w_ = np.concatenate((w_, (w_[..., -1:] * x.nan_triangle())[..., -1:]),
axis=-1)
if self.average == 'simple':
y_ = np.nanmean(w_ * x.values, axis=-2)
if self.average == 'volume':
y_ = np.nansum(w_ * x.values * sample_weight.values, axis=-2)
y_ = y_ / np.nansum(w_ * sample_weight.values, axis=-2)
y_ = np.repeat(np.expand_dims(y_, -2), len(x.odims), -2)
obj = copy.deepcopy(x)
keeps = 1-np.nan_to_num(x.nan_triangle()) + \
np.nan_to_num(
x.get_latest_diagonal(compress=False).values[0, 0, ...]*0+1)
obj.values = (1+self.trend) ** \
np.flip((np.abs(np.expand_dims(np.arange(obj.shape[-2]), 0).T -
np.expand_dims(np.arange(obj.shape[-2]), 0))), 0)*y_*keeps
obj.values = obj.values*(x.expand_dims(1-np.nan_to_num(x.nan_triangle()))) + \
np.nan_to_num((X.cum_to_incr()/sample_weight).values)
obj.values[obj.values == 0] = np.nan
obj.nan_override = True
self.incremental_ = obj * sample_weight
self.ldf_ = obj.incr_to_cum().link_ratio
self.cdf_ = DevelopmentBase._get_cdf(self.ldf_)
return self
def fit(self, X, y=None, sample_weight=None):
xp = cp.get_array_module(X.values)
if X.shape[:2] != (1, 1):
raise ValueError(
'Only single index/column triangles are supported')
if (type(X.ddims) != np.ndarray):
raise ValueError(
'Triangle must be expressed with development lags')
obj = copy.copy(X)
lag = {'M': 1, 'Q': 3, 'Y': 12}[X.development_grain]
obj = Development(
n_periods=self.n_periods,
drop=self.drop,
drop_high=self.drop_high,
drop_low=self.drop_low,
drop_valuation=self.drop_valuation).fit_transform(obj)
self.w_ = obj.w_
obj = Chainladder().fit(obj)
# Works for only a single triangle - can we generalize this
exp_incr_triangle = obj.full_expectation_.cum_to_incr() \
.values[0, 0, :, :X.shape[-1]]
exp_incr_triangle = xp.nan_to_num(exp_incr_triangle) * \
obj.X_._nan_triangle()
self.design_matrix_ = self._get_design_matrix(X)
if self.hat_adj:
try:
self.hat_ = self._get_hat(X, exp_incr_triangle)
except:
warn('Could not compute hat matrix. Setting hat_adj to False')
self.had_adj = False
self.hat_ = None
else:
self.hat_ = None
self.resampled_triangles_, self.scale_ = \
self._get_simulation(X, exp_incr_triangle)
n_obs = xp.nansum(self.w_)
n_origin_params = X.shape[2]
n_dev_params = X.shape[3] - 1
deg_free = n_obs - n_origin_params - n_dev_params
deg_free_adj_fctr = np.sqrt(n_obs / deg_free)
return self
def fit(self, X, y=None, sample_weight=None):
"""Fit the model with X.
Parameters
----------
X : Triangle-like
Triangle to which the incremental method is applied. Triangle must
be cumulative.
y : None
Ignored
sample_weight :
Exposure used in the method.
Returns
-------
self : object
Returns the instance itself.
"""
from chainladder import ULT_VAL
from chainladder.utils.utility_functions import num_to_nan
if type(X.ddims) != np.ndarray:
raise ValueError("Triangle must be expressed with development lags")
if X.array_backend == "sparse":
X = X.set_backend("numpy")
else:
X = X.copy()
if sample_weight.array_backend == "sparse":
sample_weight = sample_weight.set_backend("numpy")
xp = X.get_array_module()
sample_weight.is_cumulative = False
obj = X.cum_to_incr() / sample_weight.values
if hasattr(X, "trend_"):
if self.trend != 0:
warnings.warn(
"IncrementalAdditive Trend assumption is ignored when X has a trend_ property."
)
x = obj * obj.trend_.values
else:
x = obj.trend(self.trend, axis='valuation')
w_ = Development(
n_periods=self.n_periods - 1, drop=self.drop,
drop_high=self.drop_high, drop_low=self.drop_low,
drop_valuation=self.drop_valuation).fit(x).w_
# This will miss drops on the latest diagonal
w_ = num_to_nan(w_)
w_ = xp.concatenate((w_, (w_[..., -1:] * x.nan_triangle)[..., -1:]), axis=-1)
if self.average == "simple":
y_ = xp.nanmean(w_ * x.values, axis=-2)
if self.average == "volume":
y_ = xp.nansum(w_ * x.values * sample_weight.values, axis=-2)
y_ = y_ / xp.nansum(w_ * sample_weight.values, axis=-2)
self.zeta_ = X.iloc[..., -1:, :]
self.zeta_.values = y_[:, :, None, :]
y_ = xp.repeat(y_[..., None, :], len(x.odims), -2)
obj = x.copy()
keeps = (
1
- xp.nan_to_num(x.nan_triangle)
+ xp.nan_to_num(
x[x.valuation == x.valuation_date].values[0, 0, ...] * 0 + 1
)
)
obj.values = y_ * keeps
obj.valuation_date = obj.valuation.max()
obj.values = obj.values * (1 - xp.nan_to_num(x.nan_triangle)) + xp.nan_to_num(
(X.cum_to_incr().values / sample_weight.values)
)
obj.values[obj.values == 0] = xp.nan
obj._set_slicers()
obj.valuation_date = obj.valuation.max()
future_trend = self.trend if not self.future_trend else self.future_trend
self.incremental_ = obj * sample_weight.values
self.incremental_ = self.incremental_.trend(
1/(1+future_trend)-1, axis='valuation', start=X.valuation_date,
end=self.incremental_.valuation_date)
self.ldf_ = obj.incr_to_cum().link_ratio
self.sigma_ = self.std_err_ = 0 * self.ldf_
return self