def sp_eval(y_pred, y_true):
n, m = y_pred.shape
total = 0
for i in range(n):
rho, pval = sp(y_pred[i, :], y_true[i, :])
total += rho
return total / n
def percentile_bucket(vals, bucket_size=10, scale=1.0, shift=0.0):
""" returns percentile scores for each value
Parameters
----------
bucket_size (float)
The size of each bucket, in percentile points 0-100. Actual bucket
cutoffs are calculated with numpy.arange(), so if 100 isn't divisible
by bucket_size, your top bucket will be small.
scale (float)
All values will be multiplied by this number after bucketing.
shift (float)
All values will have this added to them after scaling.
"""
from scipy.stats import scoreatpercentile as sp
import numpy as np
from bisect import bisect_left
percs = np.concatenate([np.arange(bucket_size,100,bucket_size), [100]]) # arange to get the percentiles
cuts = [sp(vals, p) for p in percs] # to get the cutoff score for each percentile
new_list = np.array([bisect_left(cuts, val)+1 for val in vals]) * scale + shift # turn values into bucket numbers... +1 since we want 1-indexed buckets
return new_list
def data_analysis_and_correlation(df_education, df_gdp):
""" Analysis and Correlation education data with gdp. """
print "[Data Analysis and Correlation of Education to GDP data] ==> Begin"
common_countries = list(set(df_education['Country'].tolist()) & set(df_gdp['Country'].tolist()))
gdp = []
total_school_time = []
men_school_time = []
women_school_time = []
for cntry in common_countries:
df1 = df_education[df_education['Country'] == cntry]
df2 = df_gdp[df_gdp['Country'] == cntry]
if df2['GDP_'+ df1['Year'].iloc[0]].iloc[0] != '':
total_school_time.append(int(df1['Total_School_Time'].iloc[0]))
men_school_time.append(int(df1['Men_School_Time'].iloc[0]))
women_school_time.append(int(df1['Women_School_Time'].iloc[0]))
gdp.append(math.log(df2['GDP_'+ df1['Year'].iloc[0]].iloc[0]))
df_edu_to_gdp = pd.DataFrame({'Total': total_school_time, 'Men': men_school_time, \
'Women': women_school_time, 'GDP': gdp})
print df_edu_to_gdp.corr(), "\n"
gdp_np_array = np.array(df_edu_to_gdp.GDP.tolist())
for col in ['Women', 'Men', 'Total']:
r_val, p_val = sp(gdp_np_array, np.array(df_edu_to_gdp[col].tolist()))
print "Correlation of GDP against {}:".format(col)
print "Pearsons correlation coefficient: {}".format(r_val)
print "2-tailed p-values: {}\n".format(p_val)
# Scatter matrix plot with histogram of data plots in the diagonal
pd.scatter_matrix(df_edu_to_gdp, alpha=0.05, figsize=(10, 10), diagonal='hist')
plt.savefig('figures/education_to_gdp/data_education_gdp_analysis.png')
plt.clf()
#
# ==> Conclusion / Summary
# GDP Men Total Women
# GDP 1.000000 0.495794 0.479050 0.497923
# Men 0.495794 1.000000 0.971663 0.942572
# Total 0.479050 0.971663 1.000000 0.977217
# Women 0.497923 0.942572 0.977217 1.000000
#
print """
def percentile_bucket(vals, bucket_size=10, scale=1.0, shift=0.0):
""" returns percentile scores for each value
Parameters
----------
bucket_size (float)
The size of each bucket, in percentile points 0-100. Actual bucket
cutoffs are calculated with numpy.arange(), so if 100 isn't divisible
by bucket_size, your top bucket will be small.
scale (float)
All values will be multiplied by this number after bucketing.
shift (float)
All values will have this added to them after scaling.
"""
from scipy.stats import scoreatpercentile as sp
import numpy as np
from bisect import bisect_left
# arange to get the percentiles
percs = np.concatenate([np.arange(bucket_size, 100, bucket_size), [100]])
# to get the cutoff score for each percentile
cuts = [sp(vals, p) for p in percs]
# turn values into bucket numbers... +1 since we want 1-indexed buckets
new_list = np.array(
[bisect_left(cuts, val) + 1 for val in vals]) * scale + shift
return new_list
import gensim
from scipy.stats import spearmanr as sp
import pandas as pd
model = gensim.models.KeyedVectors.load_word2vec_format(
'/media/jaya/study stuffs/IITM/2nd_sem/NLP/NLPpa1/google/GoogleNews-vectors-negative300.bin',
binary=True)
#word = model.similarity(word1,word2)
df = pd.read_csv(
'/media/jaya/study stuffs/IITM/2nd_sem/NLP/NLPpa1/google/combined.csv')
word1 = df.values[:, 0]
word2 = df.values[:, 1]
score_353 = df.values[:, 2]
scores = []
for k in range(len(df)):
tmp = model.similarity(word1[k], word2[k])
scores.append(tmp)
cor = sp(scores, score_353)
print(cor)