Wikipedia embeddings to enrich the data#

When the data comprises common entities (cities, companies or famous people), bringing new information assembled from external sources may be the key to improving the analysis.

Embeddings, or vectorial representations of entities, are a conveniant way to capture and summarize the information on an entity. Relational data embeddings capture all common entities from Wikipedia. [1] These will be called KEN embeddings in the following example.

We will see that these embeddings of common entities significantly improve our results.


This example requires pyarrow to be installed.

The data#

We will take a look at the video game sales dataset. Let’s retrieve the dataset:

import pandas as pd

X = pd.read_csv(
# Shuffle the data
X = X.sample(frac=1, random_state=11, ignore_index=True)
Rank Name Platform Year Genre Publisher NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales
0 6500 Star Wars: Bounty Hunter GC 2002 Shooter LucasArts 0.20 0.05 0.0 0.01 0.26
1 13442 Thrillville: Off the Rails DS 2007 Strategy LucasArts 0.03 0.01 0.0 0.00 0.05
2 15074 Thomas and Friends: Steaming around Sodor 3DS 2015 Action Avanquest Software 0.00 0.02 0.0 0.00 0.02

Our goal will be to predict the sales amount (y, our target column):

y = X["Global_Sales"]
0        0.26
1        0.05
2        0.02
3        1.16
4        0.03
16567    0.25
16568    0.49
16569    0.22
16570    0.53
16571    0.11
Name: Global_Sales, Length: 16572, dtype: float64

Let’s take a look at the distribution of our target variable:

import matplotlib.pyplot as plt
import seaborn as sns


06 ken embeddings

It seems better to take the log of sales rather than the absolute values:

import numpy as np

y = np.log(y)
06 ken embeddings

Before moving further, let’s carry out some basic preprocessing:

# Get a mask of the rows with missing values in "Publisher" and "Global_Sales"
mask = X.isna()["Publisher"] | X.isna()["Global_Sales"]
# And remove them
X.dropna(subset=["Publisher", "Global_Sales"], inplace=True)
y = y[~mask]

Extracting entity embeddings#

We will use KEN embeddings to enrich our data.

We will start by checking out the available tables with fetch_ken_table_aliases:

from skrub.datasets import fetch_ken_table_aliases

{'companies', 'games', 'all_entities', 'schools', 'movies', 'albums'}

The games table is the most relevant to our case. Let’s see what kind of types we can find in it with the function fetch_ken_types:

from skrub.datasets import fetch_ken_types

0 wikicat_1994_video_games
1 wikicat_irem_games
2 wikicat_ea_guingamp_players
3 wikicat_video_game_companies_of_the_united_kin...
4 wikicat_asian_games_medalists_in_swimming
... ...
636 wikicat_college_football_games
637 wikicat_sonic_team_games
638 wikicat_space_opera_video_games
639 wikicat_boxers_at_the_2002_asian_games
640 wikicat_motorcycle_video_games

641 rows × 1 columns

Interesting, we have a broad range of topics!

Next, we’ll use fetch_ken_embeddings to extract the embeddings of entities we need:

from skrub.datasets import fetch_ken_embeddings

KEN Embeddings are classified by types. See the example on fetch_ken_embeddings to understand how you can filter types you are interested in.

The fetch_ken_embeddings function allows us to specify the types to be included and/or excluded so as not to load all Wikipedia entity embeddings in a table.

In a first table, we include all embeddings with the type name “game” and exclude those with type name “companies” or “developer”.

embedding_games = fetch_ken_embeddings(

In a second table, we include all embeddings containing the type name “game_development_companies”, “game_companies” or “game_publish”:

embedding_publisher = fetch_ken_embeddings(

# We keep the 200 embeddings column names in a list (for the |Pipeline|):
n_dim = 200

emb_columns = [f"X{j}" for j in range(n_dim)]

emb_columns2 = [f"X{j}_aux" for j in range(n_dim)]

Merging the entities#

We will now merge the entities from Wikipedia with their equivalent match in our video game sales table:

The entities from the ‘embedding_games’ table will be merged along the column “Name” and the ones from ‘embedding_publisher’ table with the column “Publisher”

from skrub import Joiner

fa1 = Joiner(embedding_games, aux_key="Entity", main_key="Name")
fa2 = Joiner(embedding_publisher, aux_key="Entity", main_key="Publisher", suffix="_aux")

X_full = fa1.fit_transform(X)
X_full = fa2.fit_transform(X_full)

Prediction with base features#

We will forget for now the KEN Embeddings and build a typical learning pipeline, where will we try to predict the amount of sales only using the base features contained in the initial table.

We first use scikit-learn’s ColumnTransformer to define the columns that will be included in the learning process and the appropriate encoding of categorical variables using the MinHashEncoder and OneHotEncoder:

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import OneHotEncoder

from skrub import MinHashEncoder

min_hash = MinHashEncoder(n_components=100)
ohe = OneHotEncoder(handle_unknown="ignore", sparse_output=False)

encoder = make_column_transformer(
    ("passthrough", ["Year"]),
    (ohe, ["Genre"]),
    (min_hash, ["Platform"]),

We incorporate our ColumnTransformer into a Pipeline. We define a predictor, HistGradientBoostingRegressor, fast and reliable for big datasets.

from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.pipeline import make_pipeline

hgb = HistGradientBoostingRegressor(random_state=0)
pipeline = make_pipeline(encoder, hgb)

The Pipeline can now be readily applied to the dataframe for prediction:

from sklearn.model_selection import cross_validate

# We will save the results in a dictionnary:
all_r2_scores = dict()
all_rmse_scores = dict()

cv_results = cross_validate(
    pipeline, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]

all_r2_scores["Base features"] = cv_results["test_r2"]
all_rmse_scores["Base features"] = -cv_results["test_neg_root_mean_squared_error"]

print("With base features:")
    f"Mean R2 is {all_r2_scores['Base features'].mean():.2f} +-"
    f" {all_r2_scores['Base features'].std():.2f} and the RMSE is"
    f" {all_rmse_scores['Base features'].mean():.2f} +-"
    f" {all_rmse_scores['Base features'].std():.2f}"
With base features:
Mean R2 is 0.21 +- 0.01 and the RMSE is 1.30 +- 0.01

Prediction with KEN Embeddings#

We will now build a second learning pipeline using only the KEN embeddings from Wikipedia.

We keep only the embeddings columns:

encoder2 = make_column_transformer(
    ("passthrough", emb_columns), ("passthrough", emb_columns2), remainder="drop"

We redefine the Pipeline:

Let’s look at the results:

cv_results = cross_validate(
    pipeline2, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]

all_r2_scores["KEN features"] = cv_results["test_r2"]
all_rmse_scores["KEN features"] = -cv_results["test_neg_root_mean_squared_error"]

print("With KEN Embeddings:")
    f"Mean R2 is {all_r2_scores['KEN features'].mean():.2f} +-"
    f" {all_r2_scores['KEN features'].std():.2f} and the RMSE is"
    f" {all_rmse_scores['KEN features'].mean():.2f} +-"
    f" {all_rmse_scores['KEN features'].std():.2f}"
With KEN Embeddings:
Mean R2 is 0.36 +- 0.01 and the RMSE is 1.17 +- 0.01

It seems including the embeddings is very relevant for the prediction task at hand!

Prediction with KEN Embeddings and base features#

As we have seen the predictions scores in the case when embeddings are only present and when they are missing, we will do a final prediction with all variables included.

We include both the embeddings and the base features:

encoder3 = make_column_transformer(
    ("passthrough", emb_columns),
    ("passthrough", emb_columns2),
    ("passthrough", ["Year"]),
    (ohe, ["Genre"]),
    (min_hash, ["Platform"]),

We redefine the Pipeline:

Let’s look at the results:

cv_results = cross_validate(
    pipeline3, X_full, y, scoring=["r2", "neg_root_mean_squared_error"]

all_r2_scores["Base + KEN features"] = cv_results["test_r2"]
all_rmse_scores["Base + KEN features"] = -cv_results["test_neg_root_mean_squared_error"]

print("With KEN Embeddings and base features:")
    f"Mean R2 is {all_r2_scores['Base + KEN features'].mean():.2f} +-"
    f" {all_r2_scores['Base + KEN features'].std():.2f} and the RMSE is"
    f" {all_rmse_scores['Base + KEN features'].mean():.2f} +-"
    f" {all_rmse_scores['Base + KEN features'].std():.2f}"
With KEN Embeddings and base features:
Mean R2 is 0.49 +- 0.01 and the RMSE is 1.04 +- 0.01

Plotting the results#

Finally, we plot the scores on a boxplot:

plt.figure(figsize=(5, 3))
# sphinx_gallery_thumbnail_number = -1
ax = sns.boxplot(data=pd.DataFrame(all_r2_scores), orient="h")
plt.xlabel("Prediction accuracy     ", size=15)
06 ken embeddings

There is a clear improvement when including the KEN embeddings among the explanatory variables.

In this case, the embeddings from Wikipedia introduced additional background information on the game and the publisher of the game that would otherwise be missed.

It helped significantly improve the prediction score.

Total running time of the script: (13 minutes 4.578 seconds)

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