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removing time-binning options, reorganizes signature

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This commit is contained in:
Andy Eschbacher 2016-05-25 12:02:47 -04:00
parent fd7aa4140a
commit e80fdca7fc
1 changed files with 35 additions and 30 deletions
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65
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
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@ -8,23 +8,22 @@ import pysal as ps
import plpy
import crankshaft.pysal_utils as pu
def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
permutations, geom_col, id_col, w_type, num_ngbrs):
def spatial_markov_trend(subquery, time_cols, num_classes = 7,
w_type = 'knn', num_ngbrs = 5, permutations = 999,
geom_col = 'the_geom', id_col = 'cartodb_id'):
"""
Predict the trends of a unit based on:
1. history of its transitions to different classes (e.g., 1st quantile -> 2nd quantile)
2. average class of its neighbors
Inputs:
@param subquery string: e.g., SELECT * FROM table_name
@param time_cols list (string): list of strings of column names
@param num_time_per_bin int: number of bins to divide # of time columns into
@param permutations int: number of permutations for test stats
@param geom_col string: name of column which contains the geometries
@param id_col string: name of column which has the ids of the table
@param w_type string: weight type ('knn' or 'queen')
@param num_ngbrs int: number of neighbors (if knn type)
@param subquery string: e.g., SELECT the_geom, cartodb_id, interesting_time_column FROM table_name
@param time_cols list of strings: list of strings of column names
@param w_type string (optional): weight type ('knn' or 'queen')
@param num_ngbrs int (optional): number of neighbors (if knn type)
@param permutations int (optional): number of permutations for test stats
@param geom_col string (optional): name of column which contains the geometries
@param id_col string (optional): name of column which has the ids of the table
Outputs:
@param trend_up float: probablity that a geom will move to a higher class
@ -34,8 +33,8 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
@param
"""
if num_time_per_bin < 1:
plpy.error('Error: number of time bins must be >= 1')
if len(time_cols) < 2:
plpy.error('More than one time column needs to be passed')
qvals = {"id_col": id_col,
"time_cols": time_cols,
@ -43,13 +42,15 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
"subquery": subquery,
"num_ngbrs": num_ngbrs}
query = pu.construct_neighbor_query(w_type, qvals)
try:
query_result = plpy.execute(query)
query_result = plpy.execute(
pu.construct_neighbor_query(w_type, qvals)
)
if len(query_result) == 0:
return zip([None], [None], [None], [None], [None])
except plpy.SPIError, err:
plpy.notice('** Query failed with exception %s: %s' % (err, query))
plpy.error('Spatial Markov failed: check the input parameters')
plpy.debug('Query failed with exception %s: %s' % (err, query))
plpy.error('Query failed, check the input parameters')
return zip([None], [None], [None], [None], [None])
## build weight
@ -57,34 +58,33 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin,
## prep time data
t_data = get_time_data(query_result, time_cols)
## rebin time data
if num_time_per_bin > 1:
## rebin
t_data = rebin_data(t_data, int(num_time_per_bin))
print 'shape of t_data %d, %d' % t_data.shape
print 'number of weight objects: %d, %d' % (weights.sparse).shape
print 'first num elements: %f' % t_data[0, 0]
plpy.debug('shape of t_data %d, %d' % t_data.shape)
plpy.debug('number of weight objects: %d, %d' % (weights.sparse).shape)
plpy.debug('first num elements: %f' % t_data[0, 0])
# ls = ps.lag_spatial(weights, t_data)
sp_markov_result = ps.Spatial_Markov(t_data,
weights,
k=7,
k=num_classes,
fixed=False,
permutations=permutations)
## get lag classes
lag_classes = ps.Quantiles(ps.lag_spatial(weights, t_data[:, -1]), k=7).yb
lag_classes = ps.Quantiles(
ps.lag_spatial(weights, t_data[:, -1]),
k=num_classes).yb
## look up probablity distribution for each unit according to class and lag class
prob_dist = get_prob_dist(sp_markov_result.P, lag_classes, sp_markov_result.classes[:, -1])
prob_dist = get_prob_dist(sp_markov_result.P,
lag_classes,
sp_markov_result.classes[:, -1])
## find the ups and down and overall distribution of each cell
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)
def get_time_data(markov_data, time_cols):
@ -95,6 +95,7 @@ def get_time_data(markov_data, time_cols):
return np.array([[x['attr' + str(i)] for x in markov_data]
for i in range(1, num_attrs+1)], dtype=float).transpose()
## not currently used
def rebin_data(time_data, num_time_per_bin):
"""
Convert an n x l matrix into an (n/m) x l matrix where the values are
@ -130,6 +131,7 @@ def rebin_data(time_data, num_time_per_bin):
return np.array([time_data[:, num_time_per_bin * i:num_time_per_bin * (i+1)].mean(axis=1)
for i in range(n_max)]).T
def get_prob_dist(transition_matrix, lag_indices, unit_indices):
"""
Given an array of transition matrices, look up the probability
@ -168,7 +170,10 @@ def get_prob_stats(prob_dist, unit_indices):
for i in range(num_elements):
trend_up[i] = prob_dist[i, (unit_indices[i]+1):].sum()
trend_down[i] = prob_dist[i, :unit_indices[i]].sum()
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
if prob_dist[i, unit_indices[i]] > 0.0:
trend[i] = (trend_up[i] - trend_down[i]) / prob_dist[i, unit_indices[i]]
else:
trend[i] = None
## calculate volatility of distribution
volatility = prob_dist.std(axis=1)