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adding passing tests

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This commit is contained in:
Andy Eschbacher 2016-03-23 14:50:52 -04:00
parent 9943d4de58
commit d4621a6e9c
2 changed files with 63 additions and 23 deletions
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31
src/py/crankshaft/crankshaft/space_time_dynamics/markov.py
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@ -54,7 +54,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
## rebin time data
if num_time_per_bin > 1:
## rebin
t_data = rebin_data(t_data, num_time_per_bin)
t_data = rebin_data(t_data, int(num_time_per_bin))
sp_markov_result = ps.Spatial_Markov(t_data, weights, k=7, fixed=False)
@ -68,7 +68,7 @@ def spatial_markov_trend(subquery, time_cols, num_time_per_bin, permutations, ge
prob_dist = get_prob_dist(lag_classes, sp_markov_result.classes)
## find the ups and down and overall distribution of each cell
trend, trend_up, trend_down, volatility = get_prob_stats(prob_dist)
trend_up, trend_down, trend, volatility = get_prob_stats(prob_dist)
## output the results
@ -127,12 +127,29 @@ def get_prob_dist(transition_matrix, lag_indices, unit_indices):
return np.array([transition_matrix[(lag_indices[i], unit_indices[i])] for i in range(len(lag_indices))])
def get_prob_stats(prob_dist, unit_indices):
# trend, trend_up, trend_down, volatility = get_prob_stats(prob_dist)
"""
get the statistics of the probability distributions
trend_up = np.array([prob_dist[:, i:].sum() for i in unit_indices])
trend_down = np.array([prob_dist[:, :i].sum() for i in unit_indices])
trend = trend_up - trend_down
Outputs:
@param trend_up ndarray(float): sum of probabilities for upward
movement (relative to the unit index of that prob)
@param trend_down ndarray(float): sum of probabilities for downard
movement (relative to the unit index of that prob)
@param trend ndarray(float): difference of upward and downward
movements
"""
num_elements = len(prob_dist)
trend_up = np.empty(num_elements)
trend_down = np.empty(num_elements)
trend = np.empty(num_elements)
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]]
## calculate volatility of distribution
volatility = prob_dist.std(axis=1)
return trend_up, trend_down, trend, volatility

23
src/py/crankshaft/test/test_space_time_dynamics.py
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@ -122,5 +122,28 @@ class SpaceTimeTests(unittest.TestCase):
self.assertTrue(np.array_equal(result, answer))
def test_get_prob_stats(self):
"""Test get_prob_stats"""
probs = np.array([
[ 0.0754717 , 0.88207547, 0.04245283, 0. , 0. ],
[ 0. , 0. , 0.09411765, 0.87058824, 0.03529412],
[ 0.0049505 , 0.09405941, 0.77722772, 0.11881188, 0.0049505 ],
[ 0. , 0. , 0. , 0.02352941, 0.97647059]
])
unit_indices = np.array([1, 3, 2, 4])
answer_up = np.array([0.04245283, 0.03529412, 0.12376238, 0.])
answer_down = np.array([0.0754717, 0.09411765, 0.0990099, 0.02352941])
answer_trend = np.array([-0.03301887 / 0.88207547, -0.05882353 / 0.87058824, 0.02475248 / 0.77722772, -0.02352941 / 0.97647059])
answer_volatility = np.array([ 0.34221495, 0.33705421, 0.29226542, 0.38834223])
result = std.get_prob_stats(probs, unit_indices)
result_up = result[0]
result_down = result[1]
result_trend = result[2]
result_volatility = result[3]
self.assertTrue(np.allclose(result_up, answer_up))
self.assertTrue(np.allclose(result_down, answer_down))
self.assertTrue(np.allclose(result_trend, answer_trend))
self.assertTrue(np.allclose(result_volatility, answer_volatility))