Some info on multiple targets?

Tournament
4

This post made me curious and so here is the correlation matrix between targets and the dendrogram to better highlight relationships.

Figure_1
Figure_11123×876 223 KB

Figure_2
Figure_21000×978 66.8 KB

Here is the code.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt


targets = [
    'target_nomi_v4_20',
    'target_nomi_v4_60',
    'target_jerome_v4_20',
    'target_jerome_v4_60',
    'target_janet_v4_20',
    'target_janet_v4_60',
    'target_ben_v4_20',
    'target_ben_v4_60',
    'target_alan_v4_20',
    'target_alan_v4_60',
    'target_paul_v4_20',
    'target_paul_v4_60',
    'target_george_v4_20',
    'target_george_v4_60',
    'target_william_v4_20',
    'target_william_v4_60',
    'target_arthur_v4_20',
    'target_arthur_v4_60',
    'target_thomas_v4_20',
    'target_thomas_v4_60']

# analyse the validation data, but we could do the same on the training data
df = pd.read_parquet('v4/validation.parquet', columns=targets + ['era'])

# compute the mean of the era correlation of every target with any other target
corr = df.groupby('era').corr(method='spearman').mean(axis=0, level=1)

# arrange the order of the columns and rows (for visualization) so that they
# are sorted by correlation with the target 'target_nomi_v4_20' 
corr = corr.sort_values(
    'target_nomi_v4_20',
    axis=0,
    ascending=False).sort_values(
        'target_nomi_v4_20',
        axis=1,
    ascending=False)

sns.heatmap(corr, annot=True)
plt.show()

sns.clustermap(corr)
plt.show()
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