hyperparticle / graph-nlu Goto Github PK

So, i already tried to run your babi_dialogue_ridge.ipynb notebooks, and so far i do some trial and error, but i just can't seem to solve this part of your code :

import operator
import scipy
import sklearn

eval_data = dev_data
gold=[]
guess=[]

indeces = list(set(eval_data.index))
print('num instances', len(indeces))
mrr = 0.0
for eid in indeces:
sub = rdata[rdata.index == eid] # grab the RE for this scene
target = compute_target(sub) # compose the predictions of each word to a target vector
distances = [(v['rname'], scipy.spatial.distance.cosine(target,v.ix[start_col:])) for i,v in restaurants.iterrows()]
distances.sort(key=operator.itemgetter(1))
guess += [distances[0][0]]
d = list(zip(*distances))[0]
mrr += compute_mrr(d, sub.iloc[-1].ix['target'])
gold += [sub.iloc[-1].ix['target']] # all the rows in sub have the same matrix

when i run it, it will give me this:

AttributeError Traceback (most recent call last)
in ()
13 sub = rdata[rdata.index == eid] # grab the RE for this scene
14 target = compute_target(sub) # compose the predictions of each word to a target vector
---> 15 distances = [(v['rname'], scipy.spatial.distance.cosine(target,v.ix[start_col:])) for i,v in restaurants.iterrows()]
16 distances.sort(key=operator.itemgetter(1))
17 guess += [distances[0][0]]

in (.0)
13 sub = rdata[rdata.index == eid] # grab the RE for this scene
14 target = compute_target(sub) # compose the predictions of each word to a target vector
---> 15 distances = [(v['rname'], scipy.spatial.distance.cosine(target,v.ix[start_col:])) for i,v in restaurants.iterrows()]
16 distances.sort(key=operator.itemgetter(1))
17 guess += [distances[0][0]]

~\Anaconda3\lib\site-packages\scipy\spatial\distance.py in cosine(u, v, w)
742 # cosine distance is also referred to as 'uncentered correlation',
743 # or 'reflective correlation'
--> 744 return correlation(u, v, w=w, centered=False)
745
746

~\Anaconda3\lib\site-packages\scipy\spatial\distance.py in correlation(u, v, w, centered)
693 u = u - umu
694 v = v - vmu
--> 695 uv = np.average(u * v, weights=w)
696 uu = np.average(np.square(u), weights=w)
697 vv = np.average(np.square(v), weights=w)

~\Anaconda3\lib\site-packages\numpy\lib\function_base.py in average(a, axis, weights, returned)
1127 if weights is None:
1128 avg = a.mean(axis)
-> 1129 scl = avg.dtype.type(a.size/avg.size)
1130 else:
1131 wgt = np.asanyarray(weights)

AttributeError: 'float' object has no attribute 'dtype'

Help, anyone?