def __init__(self,
y,
X,
family=family.Gaussian(),
offset=None,
y_fix=None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
if offset is None:
self.offset = np.ones(shape=(self.n, 1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n, 1))
else:
self.y_fix = y_fix
self.fit_params = {}
def __init__(self, y, X, family=family.Gaussian(), offset=None, y_fix = None,
constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
self.df_model = self.X.shape[1] - 1
self.df_resid = self.n - self.df_model - 1
if offset is None:
self.offset = np.ones(shape=(self.n,1))
else:
self.offset = offset * 1.0
if y_fix is None:
self.y_fix = np.zeros(shape=(self.n,1))
else:
self.y_fix = y_fix
self.fit_params = {}
def __init__(self, y, X, family=family.Gaussian(), constant=True):
"""
Initialize class
"""
self.n = USER.check_arrays(y, X)
USER.check_y(y, self.n)
self.y = y
if constant:
self.X = USER.check_constant(X)
else:
self.X = X
self.family = family
self.k = self.X.shape[1]
self.fit_params = {}
def __init__(self, flows, cost, cost_func='pow', o_vars=None, d_vars=None,
origins=None, destinations=None, constant=True, framework='GLM',
SF=None, CD=None, Lag=None, Quasi=False):
n = User.check_arrays(flows, cost)
#User.check_y(flows, n)
self.n = n
self.f = flows
self.c = cost
self.ov = o_vars
self.dv = d_vars
if type(cost_func) == str:
if cost_func.lower() == 'pow':
self.cf = np.log
if (self.c==0).any():
raise ValueError("Zero values detected: cost function 'pow'"
"requires the logarithm of the cost variable which"
"is undefined at 0")
elif cost_func.lower() == 'exp':
self.cf = lambda x: x*1.0
elif (type(cost_func) == FunctionType) | (type(cost_func) == np.ufunc):
self.cf = cost_func
else:
raise ValueError("cost_func must be 'exp', 'pow' or a valid "
" function that has a scalar as a input and output")
y = np.reshape(self.f, (-1,1))
if isinstance(self, Gravity):
X = np.empty((self.n, 0))
else:
X = sp.csr_matrix((self.n, 1))
if isinstance(self, Production) | isinstance(self, Doubly):
o_dummies = spcategorical(origins.flatten())
if constant:
o_dummies = o_dummies[:,1:]
X = sphstack(X, o_dummies, array_out=False)
if isinstance(self, Attraction) | isinstance(self, Doubly):
d_dummies = spcategorical(destinations.flatten())
if constant | isinstance(self, Doubly):
d_dummies = d_dummies[:,1:]
X = sphstack(X, d_dummies, array_out=False)
if self.ov is not None:
if isinstance(self, Gravity):
for each in range(self.ov.shape[1]):
if (self.ov[:,each] == 0).any():
raise ValueError("Zero values detected in column %s "
"of origin variables, which are undefined for "
"Poisson log-linear spatial interaction models" % each)
X = np.hstack((X, np.log(np.reshape(self.ov[:,each], (-1,1)))))
else:
for each in range(self.ov.shape[1]):
if (self.ov[:,each] == 0).any():
raise ValueError("Zero values detected in column %s "
"of origin variables, which are undefined for "
"Poisson log-linear spatial interaction models" % each)
ov = sp.csr_matrix(np.log(np.reshape(self.ov[:,each], ((-1,1)))))
X = sphstack(X, ov, array_out=False)
if self.dv is not None:
if isinstance(self, Gravity):
for each in range(self.dv.shape[1]):
if (self.dv[:,each] == 0).any():
raise ValueError("Zero values detected in column %s "
"of destination variables, which are undefined for "
"Poisson log-linear spatial interaction models" % each)
X = np.hstack((X, np.log(np.reshape(self.dv[:,each], (-1,1)))))
else:
for each in range(self.dv.shape[1]):
if (self.dv[:,each] == 0).any():
raise ValueError("Zero values detected in column %s "
"of destination variables, which are undefined for "
"Poisson log-linear spatial interaction models" % each)
dv = sp.csr_matrix(np.log(np.reshape(self.dv[:,each], ((-1,1)))))
X = sphstack(X, dv, array_out=False)
if isinstance(self, Gravity):
X = np.hstack((X, self.cf(np.reshape(self.c, (-1,1)))))
else:
c = sp.csr_matrix(self.cf(np.reshape(self.c, (-1,1))))
X = sphstack(X, c, array_out=False)
X = X[:,1:]#because empty array instantiated with extra column
if not isinstance(self, (Gravity, Production, Attraction, Doubly)):
X = self.cf(np.reshape(self.c, (-1,1)))
if SF:
raise NotImplementedError("Spatial filter model not yet implemented")
if CD:
raise NotImplementedError("Competing destination model not yet implemented")
if Lag:
raise NotImplementedError("Spatial Lag autoregressive model not yet implemented")
CountModel.__init__(self, y, X, constant=constant)
if (framework.lower() == 'glm'):
if not Quasi:
results = self.fit(framework='glm')
else:
results = self.fit(framework='glm', Quasi=True)
else:
raise NotImplementedError('Only GLM is currently implemented')
self.params = results.params
self.yhat = results.yhat
self.cov_params = results.cov_params
self.std_err = results.std_err
self.pvalues = results.pvalues
self.tvalues = results.tvalues
self.deviance = results.deviance
self.resid_dev = results.resid_dev
self.llf = results.llf
self.llnull = results.llnull
self.AIC = results.AIC
self.k = results.k
self.D2 = results.D2
self.adj_D2 = results.adj_D2
self.pseudoR2 = results.pseudoR2
self.adj_pseudoR2 = results.adj_pseudoR2
self.results = results
self._cache = {}
def __init__(self,
flows,
cost,
cost_func='pow',
o_vars=None,
d_vars=None,
origins=None,
destinations=None,
constant=True,
framework='GLM',
SF=None,
CD=None,
Lag=None,
Quasi=False):
n = User.check_arrays(flows, cost)
#User.check_y(flows, n)
self.n = n
self.f = flows
self.c = cost
self.ov = o_vars
self.dv = d_vars
if type(cost_func) == str:
if cost_func.lower() == 'pow':
self.cf = np.log
if (self.c == 0).any():
raise ValueError(
"Zero values detected: cost function 'pow'"
"requires the logarithm of the cost variable which"
"is undefined at 0")
elif cost_func.lower() == 'exp':
self.cf = lambda x: x * 1.0
elif (type(cost_func) == FunctionType) | (type(cost_func) == np.ufunc):
self.cf = cost_func
else:
raise ValueError(
"cost_func must be 'exp', 'pow' or a valid "
" function that has a scalar as a input and output")
y = np.reshape(self.f, (-1, 1))
if isinstance(self, Gravity):
X = np.empty((self.n, 0))
else:
X = sp.csr_matrix((self.n, 1))
if isinstance(self, Production) | isinstance(self, Doubly):
o_dummies = spcategorical(origins.flatten())
if constant:
o_dummies = o_dummies[:, 1:]
X = sphstack(X, o_dummies, array_out=False)
if isinstance(self, Attraction) | isinstance(self, Doubly):
d_dummies = spcategorical(destinations.flatten())
if constant | isinstance(self, Doubly):
d_dummies = d_dummies[:, 1:]
X = sphstack(X, d_dummies, array_out=False)
if self.ov is not None:
if isinstance(self, Gravity):
for each in range(self.ov.shape[1]):
if (self.ov[:, each] == 0).any():
raise ValueError(
"Zero values detected in column %s "
"of origin variables, which are undefined for "
"Poisson log-linear spatial interaction models" %
each)
X = np.hstack(
(X, np.log(np.reshape(self.ov[:, each], (-1, 1)))))
else:
for each in range(self.ov.shape[1]):
if (self.ov[:, each] == 0).any():
raise ValueError(
"Zero values detected in column %s "
"of origin variables, which are undefined for "
"Poisson log-linear spatial interaction models" %
each)
ov = sp.csr_matrix(
np.log(np.reshape(self.ov[:, each], ((-1, 1)))))
X = sphstack(X, ov, array_out=False)
if self.dv is not None:
if isinstance(self, Gravity):
for each in range(self.dv.shape[1]):
if (self.dv[:, each] == 0).any():
raise ValueError(
"Zero values detected in column %s "
"of destination variables, which are undefined for "
"Poisson log-linear spatial interaction models" %
each)
X = np.hstack(
(X, np.log(np.reshape(self.dv[:, each], (-1, 1)))))
else:
for each in range(self.dv.shape[1]):
if (self.dv[:, each] == 0).any():
raise ValueError(
"Zero values detected in column %s "
"of destination variables, which are undefined for "
"Poisson log-linear spatial interaction models" %
each)
dv = sp.csr_matrix(
np.log(np.reshape(self.dv[:, each], ((-1, 1)))))
X = sphstack(X, dv, array_out=False)
if isinstance(self, Gravity):
X = np.hstack((X, self.cf(np.reshape(self.c, (-1, 1)))))
else:
c = sp.csr_matrix(self.cf(np.reshape(self.c, (-1, 1))))
X = sphstack(X, c, array_out=False)
X = X[:, 1:] #because empty array instantiated with extra column
if not isinstance(self, (Gravity, Production, Attraction, Doubly)):
X = self.cf(np.reshape(self.c, (-1, 1)))
if SF:
raise NotImplementedError(
"Spatial filter model not yet implemented")
if CD:
raise NotImplementedError(
"Competing destination model not yet implemented")
if Lag:
raise NotImplementedError(
"Spatial Lag autoregressive model not yet implemented")
CountModel.__init__(self, y, X, constant=constant)
if (framework.lower() == 'glm'):
if not Quasi:
results = self.fit(framework='glm')
else:
results = self.fit(framework='glm', Quasi=True)
else:
raise NotImplementedError('Only GLM is currently implemented')
self.params = results.params
self.yhat = results.yhat
self.cov_params = results.cov_params
self.std_err = results.std_err
self.pvalues = results.pvalues
self.tvalues = results.tvalues
self.deviance = results.deviance
self.resid_dev = results.resid_dev
self.llf = results.llf
self.llnull = results.llnull
self.AIC = results.AIC
self.k = results.k
self.D2 = results.D2
self.adj_D2 = results.adj_D2
self.pseudoR2 = results.pseudoR2
self.adj_pseudoR2 = results.adj_pseudoR2
self.results = results
self._cache = {}
