def barplot_rating_dist(item, single=False, group=None, savefig=None):
with msg("plotting rating distribution"):
ratings = Data.get_ratings()[:,item]
nyms = Data.get_nyms()
plt.xlabel('rating')
plt.ylabel('no. ratings')
step = 1
bins = np.arange(step/2, 5 + 1.5*step, step)
hist = lambda d, **kwargs: plt.hist(d, bins=bins, rwidth=step*0.75, **kwargs)
if group is not None:
plt.title(f'Item {item}, group {group} rating distribution')
hist(ratings[nyms[group]].data)
elif single:
plt.title(f'Item {item} rating distribution')
hist(ratings.data)
else:
plt.title(f'Item {item}, all groups rating distributions')
for nym_n, nym in enumerate(nyms):
hist(ratings[nym].data, histtype='step', linewidth=2 ,label=f'group {nym_n}')
plt.legend()
if savefig is None:
plt.show()
else:
with msg(f'Saving figure to "{savefig}"'):
plt.savefig(savefig, dpi=150)
plt.clf()
def heatmap_rating_dist(item):
# def plot_rating_dists_across_groups(ratings, item, groups, savefig=False):
with msg("plotting rating distribution"):
ratings = Data.get_ratings()[:,item]
nyms = Data.get_nyms()
data = np.zeros((10, len(nyms)))
for nym_n, nym in enumerate(nyms):
unique, count = np.unique(ratings[nym].data, return_counts=True)
for rating, count in dict(zip(unique, count)).items():
data[int(2*rating - 1), nym_n] = count
ax = sns.heatmap(data)
ax.set(
title="Distribution of item #{} ratings by group".format(int(item)),
xlabel="group number",
ylabel="rating",
yticklabels=np.linspace(0.5, 5, 10))
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from myutils import msg
from datareader import DataReader
from dist_model import DiscreteNormal as DiscNorm
rating_count = 5
dist_gen = DiscNorm(np.linspace(0.5, 5.5, num=rating_count + 1))
with msg("Getting data"):
Rtilde = DataReader.get_Rtilde()
Rvar = DataReader.get_Rvar()
R = DataReader.get_ratings()
lam = DataReader.get_lam()
P = DataReader.get_nyms()
def get_data_dist(data):
ratings, counts = np.unique(data, return_counts=True)
dist_data = np.zeros(rating_count)
dist_data[ratings.astype(int) - 1] = counts / counts.sum()
return dist_data
def get_err(data, mean, var):
dist_data = get_data_dist(data)
dist_model = dist_gen.pmf(mean, var)
return abs(dist_data / dist_model)