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# Analyse the errors by the distance from the nearest school,
# similar to Q1, but we need to create groups ourselves, and we
# do that using percentiles.

import numpy as np
from pythonql.helpers import print_table
from model_eval import dataset,model1

# Compute mean and std of prices as usual
mean = np.mean( [select d.price for d in dataset ])
std = np.std( [select d.price for d in dataset ])

# Compute percentiles and define a function that
# will map distances to percentiles

percs = [0,25,50,75,100]
distances = [ select x.school_dist for x in dataset ]
perc_values = [ select np.percentile(distances,x) for x in percs ]

# Define a function that maps values to ranges to values,
# according to the percentiles

def get_interval(perc_vals, value):
  for i in range(len(perc_vals)):
    if value < perc_vals[i]:
      break
  return (perc_vals[i-1] as f, perc_vals[i] as t)

# Compute the number of errors in each percentile
res = [ select (range, segment_size, n_errors)
        for d in dataset, m in model1
        where d.record_id == m.record_id
        let err = abs(d.price - m.pred) > std
        group by get_interval(perc_values, d.school_dist) as range
        let n_errors = sum(err),
            segment_size = len(err)
        order by n_errors/segment_size desc ]

print_table(res)