Investments Example

Packages to load

[1]:

from ratingslib.datasets.filenames import FILENAME_INVESTMENTS, dataset_path
from ratingslib.ratings.aggregation import RankingAggregation, RatingAggregation
from ratingslib.utils.enums import ratings
from ratingslib.utils.methods import print_info
import pandas as pd

Set precision to 4 digits

[2]:

pd.set_option('float_format', "{:.4f}".format)

Get filename path and set columns dict (item, ratings)

[3]:

filename = dataset_path(FILENAME_INVESTMENTS)
columns_dict = {'item': 'investment',
                'ratings': ['ROI', 'PP']}

RANKING AGGREGATION: 1. Average Rank 2. Borda count

[4]:

versions = [ratings.RANKINGAVG, ratings.RANKINGBORDA]
for version in versions:
    print_info(version)
    ra = RankingAggregation(version)
    ratings_df = ra.rate_from_file(
        filename, pairwise=False,
        reverse_attributes_cols=['PP'],
        columns_dict=columns_dict)
    print(ratings_df)



=====AvgRank=====
          Item  rating  ranking
0  investment1  2.0000        1
1  investment2  2.0000        1
2  investment3  2.0000        1


=====Borda=====
          Item  rating  ranking
0  investment1  2.0000        1
1  investment2  2.0000        1
2  investment3  2.0000        1

RATING AGGREGATION: 1. Markov 2. OffenseDefense 3. Perron

[5]:

versions = [ratings.AGGREGATIONMARKOV,
            ratings.AGGREGATIONOD,
            ratings.AGGREGATIONPERRON]

for version in versions:
    print_info(version)
    ra = RatingAggregation(version)
    ratings_df = ra.rate_from_file(
        filename, pairwise=False,
        reverse_attributes_cols=['PP'],
        columns_dict=columns_dict)
    print(ratings_df)



=====AggrMarkov=====
          Item  rating  ranking
0  investment1  0.4186        1
1  investment2  0.2847        3
2  investment3  0.2967        2


=====AggrOD=====
          Item  rating  ranking
0  investment1  0.8571        2
1  investment2  1.7097        1
2  investment3  0.8167        3


=====AggrPerron=====
          Item  rating  ranking
0  investment1  0.3660        1
1  investment2  0.3237        2
2  investment3  0.3103        3