def set_backend(self, backend, inplace=False, deep=False):
""" Converts triangle array_backend.
Parameters
----------
backend : str
Currently supported options are 'numpy', 'sparse', and 'cupy'
inplace : bool
Whether to mutate the existing Triangle instance or return a new
one.
Returns
-------
Triangle with updated array_backend
"""
if hasattr(self, "array_backend"):
old_backend = self.array_backend
else:
if hasattr(self, "ldf_"):
old_backend = self.ldf_.array_backend
else:
raise ValueError("Unable to determine array backend.")
if inplace:
if backend in ["numpy", "sparse", "cupy"]:
lookup = {
"numpy": {
"sparse": lambda x: x.todense(),
"cupy": lambda x: cp.asnumpy(x),
},
"cupy": {
"numpy": lambda x: cp.array(x),
"sparse": lambda x: cp.array(x.todense()),
},
"sparse": {
"numpy": lambda x: sp.array(x),
"cupy": lambda x: sp.array(cp.asnumpy(x)),
},
}
if hasattr(self, "values"):
self.values = lookup[backend].get(old_backend, lambda x: x)(
self.values
)
if deep:
for k, v in vars(self).items():
if isinstance(v, Common):
v.set_backend(backend, inplace=True, deep=True)
if hasattr(self, "array_backend"):
self.array_backend = backend
else:
raise AttributeError(backend, "backend is not supported.")
return self
else:
obj = self.copy()
return obj.set_backend(backend=backend, inplace=True, deep=deep)
def set_backend(self, backend, inplace=False):
''' Converts triangle array_backend.
Parameters
----------
backend : str
Currently supported options are 'numpy', 'sparse', and 'cupy'
inplace : bool
Whether to mutate the existing Triangle instance or return a new
one.
Returns
-------
Triangle with updated array_backend
'''
if hasattr(self, 'array_backend'):
old_backend = self.array_backend
else:
if hasattr(self, 'ldf_'):
old_backend = self.ldf_.array_backend
else:
raise ValueError('Unable to determine array backend.')
if inplace:
if backend in ['numpy', 'sparse', 'cupy']:
lookup = {
'numpy': {
'sparse': lambda x: x.todense(),
'cupy': lambda x: cp.asnumpy(x)
},
'cupy': {
'numpy': lambda x: cp.array(x),
'sparse': lambda x: cp.array(x.todense())
},
'sparse': {
'numpy': lambda x: sp.array(x),
'cupy': lambda x: sp.array(cp.asnumpy(x))
}
}
if hasattr(self, 'values'):
self.values = lookup[backend].get(old_backend,
lambda x: x)(self.values)
for k, v in vars(self).items():
if isinstance(v, Common):
v.set_backend(backend, inplace=True)
if hasattr(self, 'array_backend'):
self.array_backend = backend
else:
raise AttributeError(backend, 'backend is not supported.')
return self
else:
obj = copy.deepcopy(self)
return obj.set_backend(backend=backend, inplace=True)
def _repr_format(self):
if type(self.odims[0]) == np.datetime64:
origin = pd.Series(self.odims).dt.to_period(self.origin_grain)
else:
origin = pd.Series(self.odims)
if len(self.ddims) == 1 and self.ddims[0] is None:
ddims = list(self.vdims)
else:
ddims = self.ddims
if cp.get_array_module(self.values).__name__ == 'cupy':
out = cp.asnumpy(self.values[0, 0])
else:
out = self.values[0, 0]
out = pd.DataFrame(out, index=origin, columns=ddims)
if str(out.columns[0]).find('-') > 0 and not \
isinstance(out.columns, pd.PeriodIndex):
out.columns = [
item.replace('-9999', '-Ult') for item in out.columns
]
if len(out.drop_duplicates()) != 1:
return out
else:
return out.drop_duplicates().set_index(pd.Index(['(All)']))
else:
return out
def to_json(self):
''' Serializes triangle object to json format
Returns
-------
string representation of object in json format
'''
def sparse_out(tri):
k, v, o, d = tri.shape
xp = cp.get_array_module(tri)
if xp == cp != np:
out = cp.asnumpy(tri)
else:
out = tri
coo = coo_matrix(np.nan_to_num(out.reshape((k * v * o, d))))
return json.dumps(
dict(
zip([str(item) for item in zip(coo.row, coo.col)],
coo.data)))
json_dict = {}
if self.is_val_tri:
ddims = self.ddims
json_dict['ddims'] = {
'dtype': str(ddims.dtype),
'array': ddims.values.tolist()
}
attributes = ['kdims', 'vdims', 'odims']
else:
attributes = ['kdims', 'vdims', 'odims', 'ddims']
for attribute in attributes:
json_dict[attribute] = {
'dtype': str(getattr(self, attribute).dtype),
'array': getattr(self, attribute).tolist()
}
xp = cp.get_array_module(self.values)
if xp == cp != np:
out = cp.asnumpy(self.cum_to_incr().values)
else:
out = self.cum_to_incr().values
if np.sum(np.nan_to_num(out) == 0) / np.prod(self.shape) > 0.40:
json_dict['values'] = {
'dtype': str(out.dtype),
'array': sparse_out(out),
'sparse': True
}
else:
json_dict['values'] = {
'dtype': str(out.dtype),
'array': out.tolist(),
'sparse': False
}
json_dict['key_labels'] = self.key_labels
json_dict['origin_grain'] = self.origin_grain
json_dict['development_grain'] = self.development_grain
json_dict['nan_override'] = self.nan_override
json_dict['is_cumulative'] = self.is_cumulative
json_dict['is_val_tri'] = self.is_val_tri
json_dict['valuation_date'] = self.valuation_date.strftime('%Y-%m-%d')
return json.dumps(json_dict)
def sparse_out(tri):
k, v, o, d = tri.shape
xp = cp.get_array_module(tri)
if xp == cp != np:
out = cp.asnumpy(tri)
else:
out = tri
coo = coo_matrix(np.nan_to_num(out.reshape((k*v*o, d))))
return json.dumps(dict(zip([str(item) for item in zip(coo.row, coo.col)], coo.data)))
def _repr_format(self):
odims, ddims = self._repr_date_axes()
if cp.get_array_module(self.values).__name__ == 'cupy':
out = cp.asnumpy(self.values[0, 0])
else:
out = self.values[0, 0]
out = pd.DataFrame(out, index=odims, columns=ddims)
if str(out.columns[0]).find('-') > 0 and not \
isinstance(out.columns, pd.PeriodIndex):
out.columns = [
item.replace('-9999', '-Ult') for item in out.columns
]
if len(out.drop_duplicates()) != 1:
return out
else:
return out.drop_duplicates().set_index(pd.Index(['(All)']))
else:
return out
def dropna(self):
""" Method that removes orgin/development vectors from edge of a
triangle that are all missing values. This may come in handy for a
new line of business that doesn't have origins/developments of an
existing line in the same triangle.
"""
xp = cp.get_array_module(self.values)
obj = self.sum(axis=0).sum(axis=1)
odim = list(obj.sum(axis=-1).values[0, 0, :, 0]*0+1)
min_odim = obj.origin[odim.index(1)]
max_odim = obj.origin[::-1][odim[::-1].index(1)]
if obj.shape[-1] != 1:
if xp.__name__ == 'cupy':
ddim = cp.asnumpy(xp.nan_to_num((obj.sum(axis=-2).values*0+1)[0, 0, 0]))
else:
ddim = np.nan_to_num((obj.sum(axis=-2).values*0+1)[0, 0, 0])
ddim = obj.development.iloc[:, 0][pd.Series(ddim).astype(bool)]
obj = self[(self.development >= ddim.min()) &
(self.development <= ddim.max())]
return obj[(self.origin >= min_odim) & (self.origin <= max_odim)]
obj = self[(self.origin >= min_odim) & (self.origin <= max_odim)]
return obj
