def search_cluster(cluster, uhistdict):
'''
Given a cluster, find the true entry.
'''
cluster_centre = collections.OrderedDict()
field_name = []
field_clarity = []
field_altdist = []
for col in cluster.columns:
if col=='Id':
# Skip the unique ID
continue
# Take the histogram of entries in the cluster
x,y = utils.uniquehist(cluster[col].values, uhistdict[col][0])
# Normalise y so it is a distribution
y = y/sum(y)
# Find the index which occurs more than you'd expect by chance
idx = np.argmax(y - uhistdict[col][1])
# This value is the value of the true datapoint for this column
cluster_centre[col] = uhistdict[col][0][idx]
# Remember how obvious the field was
sort_idx = np.argsort(y - uhistdict[col][1])
field_name.append(col)
field_clarity.append((y[sort_idx[-1]] - uhistdict[col][1][sort_idx[-1]]) /
(y[sort_idx[-2]] - uhistdict[col][1][sort_idx[-2]]))
field_altdist.append(y[sort_idx[-2]] - uhistdict[col][1][sort_idx[-2]])
return cluster_centre, field_name, field_clarity, field_altdist
# Main
#########################
print('Loading the raw test data')
# Load the 100,000 strong test data
test = pd.read_csv('data/test.csv')
# Add an Age column
end_dt = datetime.datetime.strptime('2015-1-1', "%Y-%m-%d")
test['Age'] = [(end_dt - datetime.datetime.strptime(open_dt, "%m/%d/%Y")).days for open_dt in test['Open Date']]
# Build a dictionary of occurances of each element for each column
uhistdict = {}
for col in test.columns:
x,y = utils.uniquehist(test[col].values)
uhistdict[col] = (x,y/sum(y))
# Will use the highest entropy column: Opening Date (equivalent to Age)
# This is almost a unique identifier for the restaurant
trigger_column = 'Age'
unique_triggers = uhistdict[trigger_column][0]
# Note how many entries we expect to see
# If there is more than 50% more than this, there are probably two the same
expected_num_entries = len(test) * np.median(uhistdict[trigger_column][1])
genuinetestmap = collections.OrderedDict()
genuinetestdict = collections.OrderedDict()
for i,trigger in enumerate(unique_triggers):
