-
Notifications
You must be signed in to change notification settings - Fork 1
/
label_encode.py
88 lines (61 loc) · 2.87 KB
/
label_encode.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
import pandas as pd
from sklearn.preprocessing import LabelEncoder
# Import card dataframe
card_df = pd.read_pickle("data\card_df.pkl")
# Verify length of card dataframe
print(len(card_df))
print(card_df.head())
# Prepare values for encoding
card_df['hp'] = card_df['hp'].astype(int)
card_df['convertedRetreatCost'] = card_df['convertedRetreatCost'].fillna(0)
card_df['convertedRetreatCost'] = card_df['convertedRetreatCost'].astype(int)
card_df['name'] = card_df['name'].astype(str)
card_df['subtypes'] = [str(i) for i in card_df['subtypes']]
card_df['subtypes'] = card_df['subtypes'].astype(str)
card_df['rules'] = card_df['rules'].astype(str)
card_df['types'] = card_df['types'].astype(str)
# create new columns for each element of an attack
card_df['attack_name'] = None
card_df['attack_text'] = None
card_df['attack_damage'] = None
card_df['attack_convertedEnergyCost'] = None
for i, row in card_df.iterrows():
attacks = row['attacks']
if attacks:
# record first attack
attack = attacks[0]
card_df.at[i, 'attack_name'] = attack.name
card_df.at[i, 'attack_text'] = attack.text
card_df.at[i, 'attack_damage'] = attack.damage
card_df.at[i, 'attack_convertedEnergyCost'] = attack.convertedEnergyCost
else:
# set attack information to None or empty string
card_df.at[i, 'attack_name'] = None
card_df.at[i, 'attack_text'] = None
card_df.at[i, 'attack_damage']= None
card_df.at[i, 'attack_convertedEnergyCost'] = None
# Drop unnecesary attacks column
card_df = card_df.drop('attacks', axis=1)
card_df['weaknesses'] = [str(i) for i in card_df['weaknesses']]
card_df['weaknesses'] = card_df['weaknesses'].astype(str)
card_df['evolvesFrom'] = card_df['evolvesFrom'].astype(str)
# Store the label encoders used to encode each column
encoders = {}
le_card_df = pd.DataFrame()
for column in card_df.columns:
if card_df[column].dtype == 'object':
label_encoder = LabelEncoder()
le_card_df[column] = label_encoder.fit_transform(card_df[column].astype(str))
encoders[column] = label_encoder
# Create a dataframe to store the decoding dictionaries
decoding_dict = pd.DataFrame(columns=['column_name', 'encoding', 'decoding'])
# Loop through each column's label encoder and store the decoding dictionary
for column, label_encoder in encoders.items():
decoding = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))
temp_df = pd.DataFrame({'column_name': [column] * len(decoding), 'encoding': list(decoding.keys()), 'decoding': list(decoding.values())})
decoding_dict = pd.concat([decoding_dict, temp_df], ignore_index=True)
pd.to_pickle(le_card_df, 'data\label_encoded_df.pkl')
pd.to_pickle(decoding_dict, 'data\label_decoding_dict.pkl')
pd.to_pickle(encoders, 'data\label_encoder_objects.pkl')
print(le_card_df.head())
print(decoding_dict)