def fit_from_geodataframe(self,
gdf,
attr,
spatially_extensive_attr,
threshold,
max_it=10,
objective_func=ObjectiveFunctionPairwise(),
contiguity="rook"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
attr : str or list
The clustering criteria (columns of the GeoDataFrame `gdf`) are
specified as string (for one column) or list of strings (for
multiple columns).
spatially_extensive_attr : str or list
The name (`str`) or names (`list` of strings) of column(s) in `gdf`
containing the spatially extensive attributes.
threshold : numbers.Real or :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
max_it : int, default: 10
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
spat_ext_attr = array_from_df_col(gdf, spatially_extensive_attr)
self.fit_from_w(w,
attr,
spat_ext_attr,
threshold=threshold,
max_it=max_it,
objective_func=objective_func)
def fit_from_geodataframe(self,
gdf,
attr,
spatially_extensive_attr,
threshold,
solver="cbc",
metric="euclidean",
contiguity="rook"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
gdf : GeoDataFrame
attr : str or list
The clustering criteria (columns of the GeoDataFrame `gdf`) are
specified as string (for one column) or list of strings (for
multiple columns).
spatially_extensive_attr : str or list
The name (`str`) or names (`list` of strings) of column(s) in `gdf`
containing the spatially extensive attributes.
threshold : numbers.Real or :class:`numpy.ndarray`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
solver : {"cbc", "cplex", "glpk", "gurobi"}, default: "cbc"
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
metric : str or function, default: "euclidean"
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
spat_ext_attr = array_from_df_col(gdf, spatially_extensive_attr)
self.fit_from_w(w,
attr,
spat_ext_attr,
threshold=threshold,
solver=solver,
metric=metric)
def fit_from_geodataframe(self,
gdf,
attr,
n_regions,
method="flow",
solver="cbc",
metric="euclidean",
contiguity="rook"):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix:.
Parameters
----------
gdf : GeoDataFrame
attr : str or list
The clustering criteria (columns of the GeoDataFrame `gdf`) are
specified as string (for one column) or list of strings (for
multiple columns).
n_regions : int
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
method : str
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
solver : str
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
metric : str or function, default: "euclidean"
See the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
self.fit_from_w(w,
attr,
n_regions,
method=method,
solver=solver,
metric=metric)
def fit_from_geodataframe(self,
gdf,
attr,
n_regions,
contiguity="rook",
initial_labels=None,
cooling_factor=0.85,
objective_func=ObjectiveFunctionPairwise()):
"""
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
attr : `str` or `list`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
n_regions : `int`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
contiguity : `str`
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
initial_labels : :class:`numpy.ndarray` or None, default: None
Refer to the corresponding argument in
:meth:`AZP.fit_from_geodataframe`.
cooling_factor : float, default: 0.85
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
self.fit_from_w(
w,
attr,
n_regions,
initial_labels,
cooling_factor=cooling_factor,
objective_func=objective_func)
def fit_from_geodataframe(self,
gdf,
attr,
n_regions,
contiguity="rook",
initial_labels=None,
objective_func=ObjectiveFunctionPairwise()):
"""
Alternative API for :meth:`fit_from_scipy_sparse_matrix`.
Parameters
----------
gdf : :class:`geopandas.GeoDataFrame`
attr : `str` or `list`
The clustering-relevant attributes (columns of the GeoDataFrame
`gdf`) are specified as string (for one column) or list of strings
(for multiple columns).
n_regions : `int`
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
contiguity : {"rook", "queen"}, default: "rook"
Defines the contiguity relationship between areas. Possible
contiguity definitions are:
* "rook" - Rook contiguity.
* "queen" - Queen contiguity.
initial_labels : :class:`numpy.ndarray` or None, default: None
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
objective_func : :class:`region.ObjectiveFunction`, default: ObjectiveFunctionPairwise()
Refer to the corresponding argument in
:meth:`fit_from_scipy_sparse_matrix`.
"""
w = w_from_gdf(gdf, contiguity)
attr = array_from_df_col(gdf, attr)
self.fit_from_w(
w, attr, n_regions, initial_labels, objective_func=objective_func)
