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This commit is contained in:
Raúl Marín 2019-12-19 13:47:15 +01:00
parent b3c5fd756c
commit c321caaefd
33 changed files with 123 additions and 123 deletions
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1
src/py/README.md
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@ -15,7 +15,6 @@ nosetests test/
- Installed through it
- Tested, when they have a test suite.
- Fixed in the `requirements.txt`
* At present we use Python version 2.7.3
---

2
src/py/crankshaft/crankshaft/__init__.py
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@ -4,4 +4,4 @@ import crankshaft.clustering
import crankshaft.space_time_dynamics
import crankshaft.segmentation
import crankshaft.regression
import analysis_data_provider
from . import analysis_data_provider

2
src/py/crankshaft/crankshaft/analysis_data_provider.py
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@ -1,6 +1,6 @@
"""class for fetching data"""
import plpy
import pysal_utils as pu
from . import pysal_utils as pu
NULL_VALUE_ERROR = ('No usable data passed to analysis. Check your input rows '
'for null values and fill in appropriately.')

6
src/py/crankshaft/crankshaft/clustering/__init__.py
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@ -1,4 +1,4 @@
"""Import all functions from for clustering"""
from moran import *
from kmeans import *
from getis import *
from .moran import *
from .kmeans import *
from .getis import *

2
src/py/crankshaft/crankshaft/clustering/getis.py
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@ -47,4 +47,4 @@ class Getis(object):
getis = ps.esda.getisord.G_Local(attr_vals, weight,
star=True, permutations=permutations)
return zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order)
return list(zip(getis.z_sim, getis.p_sim, getis.p_z_sim, weight.id_order))

12
src/py/crankshaft/crankshaft/clustering/kmeans.py
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@ -28,8 +28,8 @@ class Kmeans(object):
ids = result[0]['ids']
km = KMeans(n_clusters=no_clusters, n_init=no_init)
labels = km.fit_predict(zip(xs, ys))
return zip(ids, labels)
labels = km.fit_predict(list(zip(xs, ys)))
return list(zip(ids, labels))
def nonspatial(self, subquery, colnames, no_clusters=5,
standardize=True, id_col='cartodb_id'):
@ -75,18 +75,18 @@ class Kmeans(object):
kmeans = KMeans(n_clusters=no_clusters,
random_state=0).fit(cluster_columns)
centers = [json.dumps(dict(zip(colnames, c)))
centers = [json.dumps(dict(list(zip(colnames, c))))
for c in kmeans.cluster_centers_[kmeans.labels_]]
silhouettes = metrics.silhouette_samples(cluster_columns,
kmeans.labels_,
metric='sqeuclidean')
return zip(kmeans.labels_,
return list(zip(kmeans.labels_,
centers,
silhouettes,
[kmeans.inertia_] * kmeans.labels_.shape[0],
data[0]['rowid'])
data[0]['rowid']))
# -- Preprocessing steps
@ -99,7 +99,7 @@ def _extract_columns(data):
# number of columns minus rowid column
n_cols = len(data[0]) - 1
return np.array([data[0]['arr_col{0}'.format(i+1)]
for i in xrange(n_cols)],
for i in range(n_cols)],
dtype=float).T

14
src/py/crankshaft/crankshaft/clustering/moran.py
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@ -75,7 +75,7 @@ class Moran(object):
moran_global = ps.esda.moran.Moran(attr_vals, weight,
permutations=permutations)
return zip([moran_global.I], [moran_global.EI])
return list(zip([moran_global.I], [moran_global.EI]))
def local_stat(self, subquery, attr,
w_type, num_ngbrs, permutations, geom_col, id_col):
@ -139,7 +139,7 @@ class Moran(object):
lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
return zip(
return list(zip(
quads,
lisa.p_sim,
lag,
@ -148,7 +148,7 @@ class Moran(object):
lisa.z,
lisa.Is,
weight.id_order
)
))
def global_rate_stat(self, subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
@ -194,7 +194,7 @@ class Moran(object):
lisa_rate = ps.esda.moran.Moran_Rate(numer, denom, weight,
permutations=permutations)
return zip([lisa_rate.I], [lisa_rate.EI])
return list(zip([lisa_rate.I], [lisa_rate.EI]))
def local_rate_stat(self, subquery, numerator, denominator,
w_type, num_ngbrs, permutations, geom_col, id_col):
@ -262,7 +262,7 @@ class Moran(object):
lag = ps.weights.spatial_lag.lag_spatial(weight, lisa.y)
lag_std = ps.weights.spatial_lag.lag_spatial(weight, lisa.z)
return zip(
return list(zip(
quads,
lisa.p_sim,
lag,
@ -271,7 +271,7 @@ class Moran(object):
lisa.z,
lisa.Is,
weight.id_order
)
))
def local_bivariate_stat(self, subquery, attr1, attr2,
permutations, geom_col, id_col,
@ -303,7 +303,7 @@ class Moran(object):
# find clustering of significance
lisa_sig = quad_position(lisa.q)
return zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order)
return list(zip(lisa.Is, lisa_sig, lisa.p_sim, weight.id_order))
# Low level functions ----------------------------------------

2
src/py/crankshaft/crankshaft/pysal_utils/pysal_utils.py
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@ -27,7 +27,7 @@ def get_weight(query_res, w_type='knn', num_ngbrs=5):
"""
neighbors = {x['id']: x['neighbors'] for x in query_res}
print 'len of neighbors: %d' % len(neighbors)
print('len of neighbors: %d' % len(neighbors))
built_weight = ps.W(neighbors)
built_weight.transform = 'r'

8
src/py/crankshaft/crankshaft/regression/glm/__init__.py
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@ -1,4 +1,4 @@
import glm
import family
import utils
import iwls
from . import glm
from . import family
from . import utils
from . import iwls

5
src/py/crankshaft/crankshaft/regression/glm/base.py
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@ -1,8 +1,9 @@
from __future__ import print_function
import numpy as np
from scipy import stats
from utils import cache_readonly
from .utils import cache_readonly
from functools import reduce
class Results(object):
"""

4
src/py/crankshaft/crankshaft/regression/glm/family.py
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@ -7,8 +7,8 @@ The one parameter exponential family distributions used by GLM.
import numpy as np
from scipy import special
import links as L
import varfuncs as V
from . import links as L
from . import varfuncs as V
FLOAT_EPS = np.finfo(float).eps

8
src/py/crankshaft/crankshaft/regression/glm/glm.py
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@ -3,10 +3,10 @@ import numpy as np
import numpy.linalg as la
from pysal.spreg.utils import RegressionPropsY, spdot
import pysal.spreg.user_output as USER
from utils import cache_readonly
from base import LikelihoodModelResults
import family
from iwls import iwls
from .utils import cache_readonly
from .base import LikelihoodModelResults
from . import family
from .iwls import iwls
__all__ = ['GLM']

2
src/py/crankshaft/crankshaft/regression/glm/iwls.py
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@ -3,7 +3,7 @@ import numpy.linalg as la
from scipy import sparse as sp
from scipy.sparse import linalg as spla
from pysal.spreg.utils import spdot, spmultiply
from family import Binomial, Poisson
from .family import Binomial, Poisson
def _compute_betas(y, x):
"""

4
src/py/crankshaft/crankshaft/regression/glm/utils.py
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@ -1,5 +1,5 @@
from __future__ import absolute_import, print_function
import numpy as np
import warnings
@ -17,7 +17,7 @@ try:
from scipy.lib._version import NumpyVersion
except ImportError:
import re
string_types = basestring
string_types = str
class NumpyVersion():
"""Parse and compare numpy version strings.

2
src/py/crankshaft/crankshaft/regression/gwr/__init__.py
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@ -1 +1 @@
from base import *
from .base import *

8
src/py/crankshaft/crankshaft/regression/gwr/base/__init__.py
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@ -1,4 +1,4 @@
import gwr
import sel_bw
import diagnostics
import kernels
from . import gwr
from . import sel_bw
from . import diagnostics
from . import kernels

4
src/py/crankshaft/crankshaft/regression/gwr/base/gwr.py
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@ -7,8 +7,8 @@ __author__ = "Taylor Oshan Tayoshan@gmail.com"
import numpy as np
import numpy.linalg as la
from scipy.stats import t
from kernels import *
from diagnostics import get_AIC, get_AICc, get_BIC
from .kernels import *
from .diagnostics import get_AIC, get_AICc, get_BIC
import pysal.spreg.user_output as USER
from crankshaft.regression.glm.family import Gaussian, Binomial, Poisson
from crankshaft.regression.glm.glm import GLM, GLMResults

2
src/py/crankshaft/crankshaft/regression/gwr/base/kernels.py
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@ -117,4 +117,4 @@ class _Kernel(object):
elif self.function =='exponential':
return np.exp(-zs)
else:
print('Unsupported kernel function', self.function)
print(('Unsupported kernel function', self.function))

4
src/py/crankshaft/crankshaft/regression/gwr/base/search.py
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@ -146,7 +146,7 @@ def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
gwr_func, bw_func, sel_func):
if init:
bw = sel_func(bw_func(y, X))
print bw
print(bw)
optim_model = gwr_func(y, X, bw)
err = optim_model.resid_response.reshape((-1,1))
est = optim_model.params
@ -198,7 +198,7 @@ def flexible_bw(init, y, X, n, k, family, tol, max_iter, rss_score,
new_rss = np.sum((y - predy)**2)
score = np.abs((new_rss - rss)/new_rss)
rss = new_rss
print score
print(score)
scores.append(score)
delta = score
BWs.append(bws)

10
src/py/crankshaft/crankshaft/regression/gwr/base/sel_bw.py
View File

@ -8,12 +8,12 @@
__author__ = "Taylor Oshan Tayoshan@gmail.com"
from kernels import *
from search import golden_section, equal_interval, flexible_bw
from gwr import GWR
from .kernels import *
from .search import golden_section, equal_interval, flexible_bw
from .gwr import GWR
from crankshaft.regression.glm.family import Gaussian, Poisson, Binomial
import pysal.spreg.user_output as USER
from diagnostics import get_AICc, get_AIC, get_BIC, get_CV
from .diagnostics import get_AICc, get_AIC, get_BIC, get_CV
from scipy.spatial.distance import pdist, squareform
from pysal.common import KDTree
import numpy as np
@ -197,7 +197,7 @@ class Sel_BW(object):
if self.fb:
self._fbw()
print self.bw[1]
print(self.bw[1])
self.XB = self.bw[4]
self.err = self.bw[5]
else:

92
src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_gwr.py
View File

@ -14,7 +14,7 @@ import pysal
class TestGWRGaussian(unittest.TestCase):
def setUp(self):
data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
self.coords = zip(data.by_col('X'), data.by_col('Y'))
self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
rural = np.array(data.by_col('PctRural')).reshape((-1,1))
pov = np.array(data.by_col('PctPov')).reshape((-1,1))
@ -56,10 +56,10 @@ class TestGWRGaussian(unittest.TestCase):
BIC = get_BIC(rslt)
CV = get_CV(rslt)
self.assertAlmostEquals(np.floor(AICc), 894.0)
self.assertAlmostEquals(np.floor(AIC), 890.0)
self.assertAlmostEquals(np.floor(BIC), 944.0)
self.assertAlmostEquals(np.round(CV,2), 18.25)
self.assertAlmostEqual(np.floor(AICc), 894.0)
self.assertAlmostEqual(np.floor(AIC), 890.0)
self.assertAlmostEqual(np.floor(BIC), 944.0)
self.assertAlmostEqual(np.round(CV,2), 18.25)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@ -107,10 +107,10 @@ class TestGWRGaussian(unittest.TestCase):
BIC = get_BIC(rslt)
CV = get_CV(rslt)
self.assertAlmostEquals(np.floor(AICc), 896.0)
self.assertAlmostEquals(np.floor(AIC), 892.0)
self.assertAlmostEquals(np.floor(BIC), 941.0)
self.assertAlmostEquals(np.around(CV, 2), 19.19)
self.assertAlmostEqual(np.floor(AICc), 896.0)
self.assertAlmostEqual(np.floor(AIC), 892.0)
self.assertAlmostEqual(np.floor(BIC), 941.0)
self.assertAlmostEqual(np.around(CV, 2), 19.19)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@ -159,10 +159,10 @@ class TestGWRGaussian(unittest.TestCase):
BIC = get_BIC(rslt)
CV = get_CV(rslt)
self.assertAlmostEquals(np.floor(AICc), 895.0)
self.assertAlmostEquals(np.floor(AIC), 890.0)
self.assertAlmostEquals(np.floor(BIC), 943.0)
self.assertAlmostEquals(np.around(CV, 2), 18.21)
self.assertAlmostEqual(np.floor(AICc), 895.0)
self.assertAlmostEqual(np.floor(AIC), 890.0)
self.assertAlmostEqual(np.floor(BIC), 943.0)
self.assertAlmostEqual(np.around(CV, 2), 18.21)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@ -211,10 +211,10 @@ class TestGWRGaussian(unittest.TestCase):
BIC = get_BIC(rslt)
CV = get_CV(rslt)
self.assertAlmostEquals(np.floor(AICc), 896)
self.assertAlmostEquals(np.floor(AIC), 894.0)
self.assertAlmostEquals(np.floor(BIC), 922.0)
self.assertAlmostEquals(np.around(CV, 2), 17.91)
self.assertAlmostEqual(np.floor(AICc), 896)
self.assertAlmostEqual(np.floor(AIC), 894.0)
self.assertAlmostEqual(np.floor(BIC), 922.0)
self.assertAlmostEqual(np.around(CV, 2), 17.91)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-04)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-04)
@ -314,7 +314,7 @@ class TestGWRGaussian(unittest.TestCase):
class TestGWRPoisson(unittest.TestCase):
def setUp(self):
data = pysal.open(pysal.examples.get_path('Tokyomortality.csv'), mode='Ur')
self.coords = zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID'))
self.coords = list(zip(data.by_col('X_CENTROID'), data.by_col('Y_CENTROID')))
self.y = np.array(data.by_col('db2564')).reshape((-1,1))
self.off = np.array(data.by_col('eb2564')).reshape((-1,1))
OCC = np.array(data.by_col('OCC_TEC')).reshape((-1,1))
@ -355,9 +355,9 @@ class TestGWRPoisson(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 13294.0)
self.assertAlmostEquals(np.floor(AIC), 13247.0)
self.assertAlmostEquals(np.floor(BIC), 13485.0)
self.assertAlmostEqual(np.floor(AICc), 13294.0)
self.assertAlmostEqual(np.floor(AIC), 13247.0)
self.assertAlmostEqual(np.floor(BIC), 13485.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-05)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-03)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-03)
@ -404,9 +404,9 @@ class TestGWRPoisson(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 13285)
self.assertAlmostEquals(np.floor(AIC), 13259.0)
self.assertAlmostEquals(np.floor(BIC), 13442.0)
self.assertAlmostEqual(np.floor(AICc), 13285)
self.assertAlmostEqual(np.floor(AIC), 13259.0)
self.assertAlmostEqual(np.floor(BIC), 13442.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@ -452,9 +452,9 @@ class TestGWRPoisson(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 367.0)
self.assertAlmostEquals(np.floor(AIC), 361.0)
self.assertAlmostEquals(np.floor(BIC), 451.0)
self.assertAlmostEqual(np.floor(AICc), 367.0)
self.assertAlmostEqual(np.floor(AIC), 361.0)
self.assertAlmostEqual(np.floor(BIC), 451.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-02,
atol=1e-02)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02, atol=1e-02)
@ -511,9 +511,9 @@ class TestGWRPoisson(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 11283.0)
self.assertAlmostEquals(np.floor(AIC), 11211.0)
self.assertAlmostEquals(np.floor(BIC), 11497.0)
self.assertAlmostEqual(np.floor(AICc), 11283.0)
self.assertAlmostEqual(np.floor(AIC), 11211.0)
self.assertAlmostEqual(np.floor(BIC), 11497.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-03)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@ -559,9 +559,9 @@ class TestGWRPoisson(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 21070.0)
self.assertAlmostEquals(np.floor(AIC), 21069.0)
self.assertAlmostEquals(np.floor(BIC), 21111.0)
self.assertAlmostEqual(np.floor(AICc), 21070.0)
self.assertAlmostEqual(np.floor(AIC), 21069.0)
self.assertAlmostEqual(np.floor(BIC), 21111.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-04)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-02)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-02)
@ -583,7 +583,7 @@ class TestGWRPoisson(unittest.TestCase):
class TestGWRBinomial(unittest.TestCase):
def setUp(self):
data = pysal.open(pysal.examples.get_path('landslides.csv'))
self.coords = zip(data.by_col('X'), data.by_col('Y'))
self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
self.y = np.array(data.by_col('Landslid')).reshape((-1,1))
ELEV = np.array(data.by_col('Elev')).reshape((-1,1))
SLOPE = np.array(data.by_col('Slope')).reshape((-1,1))
@ -630,9 +630,9 @@ class TestGWRBinomial(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 275.0)
self.assertAlmostEquals(np.floor(AIC), 271.0)
self.assertAlmostEquals(np.floor(BIC), 349.0)
self.assertAlmostEqual(np.floor(AICc), 275.0)
self.assertAlmostEqual(np.floor(AIC), 271.0)
self.assertAlmostEqual(np.floor(BIC), 349.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@ -693,9 +693,9 @@ class TestGWRBinomial(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 277.0)
self.assertAlmostEquals(np.floor(AIC), 271.0)
self.assertAlmostEquals(np.floor(BIC), 358.0)
self.assertAlmostEqual(np.floor(AICc), 277.0)
self.assertAlmostEqual(np.floor(AIC), 271.0)
self.assertAlmostEqual(np.floor(BIC), 358.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@ -756,9 +756,9 @@ class TestGWRBinomial(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 276.0)
self.assertAlmostEquals(np.floor(AIC), 272.0)
self.assertAlmostEquals(np.floor(BIC), 341.0)
self.assertAlmostEqual(np.floor(AICc), 276.0)
self.assertAlmostEqual(np.floor(AIC), 272.0)
self.assertAlmostEqual(np.floor(BIC), 341.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)
@ -819,9 +819,9 @@ class TestGWRBinomial(unittest.TestCase):
AIC = get_AIC(rslt)
BIC = get_BIC(rslt)
self.assertAlmostEquals(np.floor(AICc), 276.0)
self.assertAlmostEquals(np.floor(AIC), 273.0)
self.assertAlmostEquals(np.floor(BIC), 331.0)
self.assertAlmostEqual(np.floor(AICc), 276.0)
self.assertAlmostEqual(np.floor(AIC), 273.0)
self.assertAlmostEqual(np.floor(BIC), 331.0)
np.testing.assert_allclose(est_Int, rslt.params[:,0], rtol=1e-00)
np.testing.assert_allclose(se_Int, rslt.bse[:,0], rtol=1e-00)
np.testing.assert_allclose(t_Int, rslt.tvalues[:,0], rtol=1e-00)

2
src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_kernels.py
View File

@ -12,7 +12,7 @@ class TestKernels(unittest.TestCase):
y = np.arange(5,0, -1)
np.random.shuffle(x)
np.random.shuffle(y)
self.coords = np.array(zip(x, y))
self.coords = np.array(list(zip(x, y)))
self.fix_gauss_kern = np.array([
[ 1. , 0.38889556, 0.48567179, 0.48567179, 0.89483932],
[ 0.38889556, 1. , 0.89483932, 0.64118039, 0.48567179],

2
src/py/crankshaft/crankshaft/regression/gwr/base/tests/test_sel_bw.py
View File

@ -13,7 +13,7 @@ import pysal
class TestSelBW(unittest.TestCase):
def setUp(self):
data = pysal.open(pysal.examples.get_path('GData_utm.csv'))
self.coords = zip(data.by_col('X'), data.by_col('Y'))
self.coords = list(zip(data.by_col('X'), data.by_col('Y')))
self.y = np.array(data.by_col('PctBach')).reshape((-1,1))
rural = np.array(data.by_col('PctRural')).reshape((-1,1))
pov = np.array(data.by_col('PctPov')).reshape((-1,1))

20
src/py/crankshaft/crankshaft/regression/gwr_cs.py
View File

@ -2,8 +2,8 @@
Geographically weighted regression
"""
import numpy as np
from gwr.base.gwr import GWR as PySAL_GWR
from gwr.base.sel_bw import Sel_BW
from .gwr.base.gwr import GWR as PySAL_GWR
from .gwr.base.sel_bw import Sel_BW
import json
from crankshaft.analysis_data_provider import AnalysisDataProvider
import plpy
@ -48,7 +48,7 @@ class GWR:
# x, y are centroids of input geometries
x = np.array(query_result[0]['x'], dtype=np.float)
y = np.array(query_result[0]['y'], dtype=np.float)
coords = zip(x, y)
coords = list(zip(x, y))
# extract dependent variable
Y = np.array(query_result[0]['dep_var'], dtype=np.float).reshape((-1, 1))
@ -88,7 +88,7 @@ class GWR:
bw = np.repeat(float(bw), n)
# create lists of json objs for model outputs
for idx in xrange(n):
for idx in range(n):
coeffs.append(json.dumps({var: model.params[idx, k]
for k, var in enumerate(ind_vars)}))
stand_errs.append(json.dumps({var: model.bse[idx, k]
@ -99,8 +99,8 @@ class GWR:
json.dumps({var: filtered_t[idx, k]
for k, var in enumerate(ind_vars)}))
return zip(coeffs, stand_errs, t_vals, filtered_t_vals,
predicted, residuals, r_squared, bw, rowid)
return list(zip(coeffs, stand_errs, t_vals, filtered_t_vals,
predicted, residuals, r_squared, bw, rowid))
def gwr_predict(self, subquery, dep_var, ind_vars,
bw=None, fixed=False, kernel='bisquare',
@ -133,7 +133,7 @@ class GWR:
x = np.array(query_result[0]['x'], dtype=np.float)
y = np.array(query_result[0]['y'], dtype=np.float)
coords = np.array(zip(x, y), dtype=np.float)
coords = np.array(list(zip(x, y)), dtype=np.float)
# extract dependent variable
Y = np.array(query_result[0]['dep_var']).reshape((-1, 1))
@ -190,7 +190,7 @@ class GWR:
predicted = model.predy.flatten()
m = len(model.predy)
for idx in xrange(m):
for idx in range(m):
coeffs.append(json.dumps({var: model.params[idx, k]
for k, var in enumerate(ind_vars)}))
stand_errs.append(json.dumps({var: model.bse[idx, k]
@ -198,5 +198,5 @@ class GWR:
t_vals.append(json.dumps({var: model.tvalues[idx, k]
for k, var in enumerate(ind_vars)}))
return zip(coeffs, stand_errs, t_vals,
r_squared, predicted, rowid[test])
return list(zip(coeffs, stand_errs, t_vals,
r_squared, predicted, rowid[test]))

2
src/py/crankshaft/crankshaft/segmentation/__init__.py
View File

@ -1,2 +1,2 @@
"""Import all functions from for segmentation"""
from segmentation import *
from .segmentation import *

4
src/py/crankshaft/crankshaft/segmentation/segmentation.py
View File

@ -47,7 +47,7 @@ class Segmentation(object):
model_parameters, 0.2)
prediction = model.predict(target_features)
accuracy_array = [accuracy] * prediction.shape[0]
return zip(target_ids, prediction, accuracy_array)
return list(zip(target_ids, prediction, accuracy_array))
def create_and_predict_segment(self, query, variable, feature_columns,
target_query, model_params,
@ -79,7 +79,7 @@ class Segmentation(object):
'''
rowid = [{'ids': [2.9, 4.9, 4, 5, 6]}]
'''
return zip(rowid[0]['ids'], result, accuracy_array)
return list(zip(rowid[0]['ids'], result, accuracy_array))
def predict_segment(self, model, feature_columns, target_query,
feature_means):

2
src/py/crankshaft/crankshaft/space_time_dynamics/__init__.py
View File

@ -1,2 +1,2 @@
"""Import all functions from clustering libraries."""
from markov import *
from .markov import *

2
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
View File

@ -91,7 +91,7 @@ class Markov(object):
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist, sp_markov_result.classes[:, -1])
# output the results
return zip(trend, trend_up, trend_down, volatility, weights.id_order)
return list(zip(trend, trend_up, trend_down, volatility, weights.id_order))

2
src/py/crankshaft/setup.py
View File

@ -26,7 +26,7 @@ setup(
'Intended Audience :: Mapping comunity',
'Topic :: Maps :: Mapping Tools',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python',
],
keywords='maps mapping tools spatial analysis geostatistics',

4
src/py/crankshaft/test/test_clustering_moran.py
View File

@ -72,7 +72,7 @@ class MoranTest(unittest.TestCase):
result = moran.local_stat('subquery', 'value',
'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[6]) for row in result]
zipped_values = zip(result, self.moran_data)
zipped_values = list(zip(result, self.moran_data))
for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)
@ -91,7 +91,7 @@ class MoranTest(unittest.TestCase):
'knn', 5, 99, 'the_geom', 'cartodb_id')
result = [(row[0], row[6]) for row in result]
zipped_values = zip(result, self.moran_data)
zipped_values = list(zip(result, self.moran_data))
for ([res_quad, res_val], [exp_val, exp_quad]) in zipped_values:
self.assertAlmostEqual(res_val, exp_val)

8
src/py/crankshaft/test/test_regression_gwr.py
View File

@ -86,7 +86,7 @@ class GWRTest(unittest.TestCase):
# unpack response
coeffs, stand_errs, t_vals, t_vals_filtered, predicteds, \
residuals, r_squareds, bws, rowids = zip(*gwr_resp)
residuals, r_squareds, bws, rowids = list(zip(*gwr_resp))
# prepare for comparision
coeff_known_pctpov = self.knowns['est_pctpov']
@ -98,13 +98,13 @@ class GWRTest(unittest.TestCase):
# test pctpov coefficient estimates
for idx, val in enumerate(coeff_known_pctpov):
resp_idx = rowids.index(ids[idx])
self.assertAlmostEquals(val,
self.assertAlmostEqual(val,
json.loads(coeffs[resp_idx])['pctpov'],
places=4)
# test pctrural tvals
for idx, val in enumerate(tval_known_pctblack):
resp_idx = rowids.index(ids[idx])
self.assertAlmostEquals(val,
self.assertAlmostEqual(val,
json.loads(t_vals[resp_idx])['pctrural'],
places=4)
@ -119,7 +119,7 @@ class GWRTest(unittest.TestCase):
# unpack response
coeffs, stand_errs, t_vals, \
r_squareds, predicteds, rowid = zip(*gwr_resp)
r_squareds, predicteds, rowid = list(zip(*gwr_resp))
threshold = 0.01
for i, idx in enumerate(self.idx_ids_of_unknowns):

2
src/py/crankshaft/test/test_segmentation.py
View File

@ -118,7 +118,7 @@ class SegmentationTest(unittest.TestCase):
model_parameters,
id_col='cartodb_id')
results = [(row[1], row[2]) for row in result]
zipped_values = zip(results, self.result_seg)
zipped_values = list(zip(results, self.result_seg))
pre_res = [r[0] for r in self.true_result]
acc_res = [r[1] for r in self.result_seg]

2
src/py/crankshaft/test/test_space_time_dynamics.py
View File

@ -98,7 +98,7 @@ class SpaceTimeTests(unittest.TestCase):
self.assertTrue(result is not None)
result = [(row[0], row[1], row[2], row[3], row[4]) for row in result]
print result[0]
print(result[0])
expected = self.markov_data
for ([res_trend, res_up, res_down, res_vol, res_id],
[exp_trend, exp_up, exp_down, exp_vol, exp_id]