From: https://www.kaggle.com/nubatama/eda-imet
Author: nubatama
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
# basically libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# image libaries
import cv2
import matplotlib.pyplot as plt
# for split train and test
from sklearn.model_selection import train_test_split
# for model
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization, Flatten
from tensorflow.keras.layers import Add, Concatenate, GlobalAvgPool2D
from tensorflow.keras.layers import MaxPooling2D, SeparableConv2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
# Any results you write to the current directory are saved as output.
['test', 'train', 'train.csv', 'labels.csv', 'sample_submission.csv']
I am not good at English. So, I think my description is difficult to read and understand.
Everyone, Please pardon.
Read 'labels.csv' and confirm contents.
There are 1,103 attibutes.
# Label.csv
labels_ds = pd.read_csv(filepath_or_buffer='../input/labels.csv', dtype={'attribute_id':np.object, 'attribute_name':np.object})
print(labels_ds.head())
print(labels_ds.tail())
print("")
print(labels_ds.info())
attribute_id attribute_name 0 0 culture::abruzzi 1 1 culture::achaemenid 2 2 culture::aegean 3 3 culture::afghan 4 4 culture::after british attribute_id attribute_name 1098 1098 tag::writing implements 1099 1099 tag::writing systems 1100 1100 tag::zeus 1101 1101 tag::zigzag pattern 1102 1102 tag::zodiac <class 'pandas.core.frame.DataFrame'> RangeIndex: 1103 entries, 0 to 1102 Data columns (total 2 columns): attribute_id 1103 non-null object attribute_name 1103 non-null object dtypes: object(2) memory usage: 17.3+ KB None
Read train.csv to pandas data frame.
train.csv contains no n/a data.
attribute_ids contains multi values, so need to split.
# train.csv
train_ds = pd.read_csv(filepath_or_buffer='../input/train.csv')
print(train_ds.head())
print("")
print(train_ds.info())
print("")
print(train_ds.head())
id attribute_ids 0 1000483014d91860 147 616 813 1 1000fe2e667721fe 51 616 734 813 2 1001614cb89646ee 776 3 10041eb49b297c08 51 671 698 813 1092 4 100501c227f8beea 13 404 492 903 1093 <class 'pandas.core.frame.DataFrame'> RangeIndex: 109237 entries, 0 to 109236 Data columns (total 2 columns): id 109237 non-null object attribute_ids 109237 non-null object dtypes: object(2) memory usage: 1.7+ MB None id attribute_ids 0 1000483014d91860 147 616 813 1 1000fe2e667721fe 51 616 734 813 2 1001614cb89646ee 776 3 10041eb49b297c08 51 671 698 813 1092 4 100501c227f8beea 13 404 492 903 1093
Image files are exist in '../input/train/' folder.
Image file name is represented by '
print(os.listdir("../input/train/")[0:12])
['e232597c213332cd.png', '4c3e9596dafb4d13.png', '4712bc2351789604.png', 'be77f39c438a3448.png', '2ffa9ece3a622644.png', '9caa967cba20461c.png', 'a4a099220bcafb7.png', '5c6675ae34aa5307.png', '371705db8277fa72.png', '6c934d937be3d500.png', '2f305db4e14e9246.png', '635e5a6ef19476c.png']
Show first 12 images, image height, width, and relative attributes.
# image data
# Check image data size and image by first 12 files
image_file_list = os.listdir("../input/train/")[0:12]
image_data_list = []
fig = plt.figure(figsize=(10, 15))
for image_index in range(12):
image_file_name = train_ds.iloc[image_index, 0]
image_np = cv2.imread("../input/train/" + image_file_name + ".png")
image_label = "{}\n height:{} width:{}\nattr:{}".format(
image_file_name, image_np.shape[0], image_np.shape[1], train_ds.iloc[image_index, 1]
)
image_area = fig.add_subplot(4,3,image_index + 1, title=image_label)
image_area.imshow(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
fig.tight_layout()
fig.show()
Encoding the image attibutes, number to binary. In here, use original simple function, because MultiLabelBinarizer don't work expectly...
# One hot encoding for multi labels.
def OneHotEncoding(rec):
attribute_id_list = rec["attribute_ids"].split()
for attribute_id in attribute_id_list:
rec[attribute_id] = 1
return rec
# Append new columns from list
def AppendColumns(df, columnList):
for newColumn in columnList:
df[newColumn] = 0
return df
# Create MultiLabelBinarizer instance and fit to attibute id in labels.csv
train_ds_encoded = AppendColumns(train_ds, labels_ds['attribute_id'])
train_ds_encoded = train_ds_encoded.apply(OneHotEncoding, axis=1)
train_ds_encoded.head()
id | attribute_ids | 0 | 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 | ... | 1063 | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1000483014d91860 | 147 616 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1 | 1000fe2e667721fe | 51 616 734 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 1001614cb89646ee | 776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 10041eb49b297c08 | 51 671 698 813 1092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 100501c227f8beea | 13 404 492 903 1093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
# Append filename column
train_ds_encoded["filename"] = train_ds_encoded["id"] + ".png"
train_ds_encoded.head()
id | attribute_ids | 0 | 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 | ... | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | filename | |
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0 | 1000483014d91860 | 147 616 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000483014d91860.png |
1 | 1000fe2e667721fe | 51 616 734 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000fe2e667721fe.png |
2 | 1001614cb89646ee | 776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1001614cb89646ee.png |
3 | 10041eb49b297c08 | 51 671 698 813 1092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10041eb49b297c08.png |
4 | 100501c227f8beea | 13 404 492 903 1093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100501c227f8beea.png |
Check frequency of attribute.
81 attributes (about 7% attributes) are less than 5. So, we need to increase data that is infrequent.
summary_df = pd.DataFrame(data={'id':labels_ds['attribute_id'], 'attibute':labels_ds['attribute_name'], 'count':np.array(train_ds_encoded.iloc[:, 2:].sum(numeric_only=True))})
summary_df = summary_df.sort_values(by='count')
print(summary_df.head())
print(summary_df.tail(20))
id attibute count 199 199 culture::kholmogory 1 81 81 culture::chinese with european decoration 1 221 221 culture::macedonian 1 230 230 culture::mennecy or sceaux 1 366 366 culture::tsimshian 1 id attibute count 1034 1034 tag::textile fragments 3570 738 738 tag::human figures 3665 477 477 tag::birds 3692 744 744 tag::inscriptions 3890 369 369 culture::turkish or venice 4416 156 156 culture::german 5163 780 780 tag::leaves 5259 79 79 culture::china 5382 1046 1046 tag::trees 5591 896 896 tag::portraits 5955 121 121 culture::egyptian 6542 1059 1059 tag::utilitarian objects 6564 194 194 culture::japan 7394 51 51 culture::british 7615 671 671 tag::flowers 8419 13 13 culture::american 9151 189 189 culture::italian 10375 147 147 culture::french 13522 1092 1092 tag::women 14281 813 813 tag::men 19970
rare_attr_df = summary_df.sort_values(by='count').loc[summary_df['count'] <= 5]
rare_data_df = train_ds_encoded.loc[train_ds_encoded.apply(lambda x: set(x['attribute_ids'].split(' ')).isdisjoint(rare_attr_df['id']) == False, axis=1)]
rare_data_df
id | attribute_ids | 0 | 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 | ... | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | filename | |
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25 | 1014ac8807369589 | 103 180 573 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1014ac8807369589.png |
199 | 107ea49bc5e84c1a | 147 203 554 612 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 107ea49bc5e84c1a.png |
451 | 110f113afcbd53e1 | 160 257 1061 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 110f113afcbd53e1.png |
661 | 1178c36f22819170 | 160 257 1061 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1178c36f22819170.png |
805 | 11c7143510270a1f | 43 51 6 584 813 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11c7143510270a1f.png |
1220 | 12afadc4c66ffa6a | 100 744 922 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12afadc4c66ffa6a.png |
1515 | 1344b255698c5067 | 71 616 1059 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1344b255698c5067.png |
1673 | 13a0960b5a90e861 | 30 147 758 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13a0960b5a90e861.png |
2062 | 1491e4ded34a118d | 268 437 462 767 369 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1491e4ded34a118d.png |
2621 | 15ca08f805bebbff | 146 156 584 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15ca08f805bebbff.png |
2692 | 15f5523afc30f7d7 | 189 367 650 671 889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15f5523afc30f7d7.png |
4039 | 18d1e3f331733ffb | 115 671 835 974 1092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18d1e3f331733ffb.png |
5189 | 1b5fd7de9a5bc566 | 189 376 489 663 707 1046 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1b5fd7de9a5bc566.png |
5437 | 1be7d768f8f51d8e | 189 291 671 780 965 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1be7d768f8f51d8e.png |
6563 | 1e5aa2fc8e2a3354 | 189 813 873 1072 1092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1e5aa2fc8e2a3354.png |
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107586 | fc6968c3cee2a2aa | 104 138 616 369 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | fc6968c3cee2a2aa.png |
108314 | fdfbd92127981390 | 359 447 482 803 1060 1099 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | fdfbd92127981390.png |
108557 | fe79cb978d582542 | 701 855 1059 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | fe79cb978d582542.png |
251 rows × 1106 columns
train_df_2 = train_ds_encoded
for count in range(10):
train_df_2 = train_df_2.append(rare_data_df)
train_df_2.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 111747 entries, 0 to 108557 Columns: 1106 entries, id to filename dtypes: int64(1103), object(3) memory usage: 943.8+ MB
# Separate data and label
train_df_X = train_df_2.iloc[:, 0]
train_df_y = train_df_2.iloc[:, 2:]
# Split train and test
X_train, X_test, y_train, y_test = train_test_split(train_df_X, train_df_y, test_size=0.10, random_state=42)
train_df_train = train_df_2.sample(frac=0.9, random_state=42)
train_df_test = train_df_2.drop(train_df_train.index)
print("{} {} {}".format(len(train_df_2), len(train_df_train), len(train_df_test)))
111747 100572 10899
# Split lable
splitted_attr = labels_ds['attribute_name'].str.split('::', expand = True)
splitted_attr.columns = ['main', 'sub']
splitted_attr
main | sub | |
---|---|---|
0 | culture | abruzzi |
1 | culture | achaemenid |
2 | culture | aegean |
3 | culture | afghan |
4 | culture | after british |
5 | culture | after german |
6 | culture | after german original |
7 | culture | after italian |
8 | culture | after russian original |
9 | culture | akkadian |
10 | culture | alexandria-hadra |
11 | culture | algerian |
12 | culture | alsace |
13 | culture | american |
14 | culture | american or european |
15 | culture | amsterdam |
16 | culture | ansbach |
17 | culture | antwerp |
18 | culture | apulian |
19 | culture | arabian |
20 | culture | aragon |
21 | culture | arica |
22 | culture | asia minor |
23 | culture | assyrian |
24 | culture | atlantic watershed |
25 | culture | attic |
26 | culture | augsburg |
27 | culture | augsburg decoration |
28 | culture | augsburg original |
29 | culture | austrian |
... | ... | ... |
1073 | tag | vishnu |
1074 | tag | volcanoes |
1075 | tag | vulcan |
1076 | tag | wagons |
1077 | tag | walking |
1078 | tag | wars |
1079 | tag | washing |
1080 | tag | watches |
1081 | tag | waterfalls |
1082 | tag | watermills |
1083 | tag | waves |
1084 | tag | weapons |
1085 | tag | weights and measures |
1086 | tag | wells |
1087 | tag | wind |
1088 | tag | windmills |
1089 | tag | windows |
1090 | tag | wine |
1091 | tag | winter |
1092 | tag | women |
1093 | tag | working |
1094 | tag | world war i |
1095 | tag | worshiping |
1096 | tag | wreaths |
1097 | tag | writing |
1098 | tag | writing implements |
1099 | tag | writing systems |
1100 | tag | zeus |
1101 | tag | zigzag pattern |
1102 | tag | zodiac |
1103 rows × 2 columns
print(splitted_attr['main'].drop_duplicates())
print('culture : {}; tag : {}'.format(len(splitted_attr.loc[splitted_attr.main == 'culture']), len(splitted_attr.loc[splitted_attr.main == 'tag'])))
0 culture 398 tag Name: main, dtype: object culture : 398; tag : 705
print(splitted_attr['sub'].drop_duplicates())
0 abruzzi 1 achaemenid 2 aegean 3 afghan 4 after british 5 after german 6 after german original 7 after italian 8 after russian original 9 akkadian 10 alexandria-hadra 11 algerian 12 alsace 13 american 14 american or european 15 amsterdam 16 ansbach 17 antwerp 18 apulian 19 arabian 20 aragon 21 arica 22 asia minor 23 assyrian 24 atlantic watershed 25 attic 26 augsburg 27 augsburg decoration 28 augsburg original 29 austrian ... 1073 vishnu 1074 volcanoes 1075 vulcan 1076 wagons 1077 walking 1078 wars 1079 washing 1080 watches 1081 waterfalls 1082 watermills 1083 waves 1084 weapons 1085 weights and measures 1086 wells 1087 wind 1088 windmills 1089 windows 1090 wine 1091 winter 1092 women 1093 working 1094 world war i 1095 worshiping 1096 wreaths 1097 writing 1098 writing implements 1099 writing systems 1100 zeus 1101 zigzag pattern 1102 zodiac Name: sub, Length: 1103, dtype: object
main category is 2, 'culture' and 'tag'. sub category is 1103, not duplicated.
train_ds_encoded.head()
id | attribute_ids | 0 | 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 | ... | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | filename | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1000483014d91860 | 147 616 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000483014d91860.png |
1 | 1000fe2e667721fe | 51 616 734 813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1000fe2e667721fe.png |
2 | 1001614cb89646ee | 776 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1001614cb89646ee.png |
3 | 10041eb49b297c08 | 51 671 698 813 1092 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10041eb49b297c08.png |
4 | 100501c227f8beea | 13 404 492 903 1093 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 100501c227f8beea.png |
corr_df = train_ds_encoded.iloc[:, 2:-1].corr()
corr_df.head()
0 | 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 | ... | 1063 | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1.000000 | -0.000389 | -0.000145 | -0.000067 | -0.000140 | -0.000160 | -0.000078 | -0.000067 | -0.000145 | -0.000206 | -0.000123 | -0.000039 | -0.000145 | -0.003882 | -0.000786 | -0.000227 | -0.000135 | -0.000174 | -0.000427 | -0.000103 | -0.000078 | -0.000087 | -0.000182 | -0.000607 | -0.000249 | -0.001013 | -0.000517 | -0.000140 | -0.000135 | -0.000896 | -0.000087 | -0.000087 | -0.000330 | -0.000665 | -0.000078 | -0.000424 | -0.000067 | -0.000103 | -0.000123 | -0.000067 | ... | -0.000269 | -0.000597 | -0.000408 | -0.000389 | -0.000182 | -0.000426 | -0.000465 | -0.000227 | -0.000413 | -0.001114 | -0.000283 | -0.000246 | -0.000227 | -0.000145 | -0.000343 | -0.000280 | -0.000194 | -0.000373 | -0.000465 | -0.000223 | -0.000415 | -0.001209 | -0.000587 | -0.000269 | -0.000155 | -0.000306 | -0.000616 | -0.000160 | -0.000330 | -0.004979 | -0.000971 | -0.000135 | -0.000306 | -0.000455 | -0.000489 | -0.000859 | -0.001894 | -0.000213 | -0.000358 | -0.000216 |
1 | -0.000389 | 1.000000 | -0.000343 | -0.000159 | -0.000330 | -0.000378 | -0.000183 | -0.000159 | -0.000343 | -0.000485 | -0.000290 | -0.000092 | -0.000343 | -0.009153 | -0.001853 | -0.000534 | -0.000317 | -0.000410 | -0.001008 | -0.000242 | -0.000183 | -0.000205 | -0.000430 | -0.001432 | -0.000587 | -0.002389 | -0.001219 | -0.000330 | -0.000317 | -0.002114 | -0.000205 | -0.000205 | -0.000777 | -0.001567 | -0.000183 | -0.001000 | -0.000159 | -0.000242 | -0.000290 | -0.000159 | ... | -0.000635 | -0.001408 | -0.000961 | -0.000916 | -0.000430 | -0.001004 | -0.001096 | -0.000534 | -0.000974 | -0.002626 | -0.000667 | -0.000579 | -0.000534 | -0.000343 | -0.000809 | -0.000661 | -0.000458 | -0.000879 | -0.001096 | -0.000526 | -0.000978 | 0.000390 | -0.001384 | -0.000635 | -0.000366 | -0.000721 | -0.001453 | -0.000378 | -0.000777 | -0.011739 | -0.002291 | -0.000317 | -0.000721 | -0.001073 | -0.001152 | -0.002026 | -0.002369 | -0.000502 | -0.000845 | -0.000510 |
2 | -0.000145 | -0.000343 | 1.000000 | -0.000059 | -0.000124 | -0.000141 | -0.000069 | -0.000059 | -0.000128 | -0.000181 | -0.000108 | -0.000034 | -0.000128 | -0.003423 | -0.000693 | -0.000200 | -0.000119 | -0.000153 | -0.000377 | -0.000091 | -0.000069 | -0.000077 | -0.000161 | -0.000536 | -0.000219 | -0.000893 | -0.000456 | -0.000124 | -0.000119 | -0.000791 | -0.000077 | -0.000077 | -0.000291 | -0.000586 | -0.000069 | -0.000374 | -0.000059 | -0.000091 | -0.000108 | -0.000059 | ... | -0.000237 | -0.000527 | -0.000359 | -0.000343 | -0.000161 | -0.000375 | -0.000410 | -0.000200 | -0.000364 | -0.000982 | -0.000249 | -0.000217 | -0.000200 | -0.000128 | -0.000303 | -0.000247 | -0.000171 | -0.000329 | -0.000410 | -0.000197 | -0.000366 | -0.001067 | -0.000518 | -0.000237 | -0.000137 | -0.000270 | -0.000543 | -0.000141 | -0.000291 | -0.004391 | -0.000857 | -0.000119 | -0.000270 | -0.000401 | -0.000431 | -0.000758 | -0.001670 | -0.000188 | -0.000316 | -0.000191 |
3 | -0.000067 | -0.000159 | -0.000059 | 1.000000 | -0.000057 | -0.000065 | -0.000032 | -0.000027 | -0.000059 | -0.000084 | -0.000050 | -0.000016 | -0.000059 | -0.001585 | -0.000321 | -0.000092 | -0.000055 | -0.000071 | -0.000175 | -0.000042 | -0.000032 | -0.000035 | -0.000074 | -0.000248 | -0.000102 | -0.000414 | -0.000211 | -0.000057 | -0.000055 | -0.000366 | -0.000035 | -0.000035 | -0.000135 | -0.000271 | -0.000032 | -0.000173 | -0.000027 | -0.000042 | -0.000050 | -0.000027 | ... | -0.000110 | -0.000244 | -0.000166 | -0.000159 | -0.000074 | -0.000174 | -0.000190 | -0.000092 | -0.000169 | -0.000455 | -0.000115 | -0.000100 | -0.000092 | -0.000059 | -0.000140 | -0.000114 | -0.000079 | -0.000152 | -0.000190 | -0.000091 | -0.000169 | -0.000494 | -0.000240 | -0.000110 | -0.000063 | -0.000125 | -0.000251 | -0.000065 | -0.000135 | -0.002032 | -0.000397 | -0.000055 | -0.000125 | -0.000186 | -0.000199 | -0.000351 | -0.000773 | -0.000087 | -0.000146 | -0.000088 |
4 | -0.000140 | -0.000330 | -0.000124 | -0.000057 | 1.000000 | -0.000136 | -0.000066 | -0.000057 | -0.000124 | -0.000175 | -0.000104 | -0.000033 | -0.000124 | -0.003299 | -0.000668 | -0.000193 | -0.000114 | -0.000148 | -0.000363 | -0.000087 | -0.000066 | -0.000074 | -0.000155 | -0.000516 | -0.000211 | -0.000861 | -0.000440 | -0.000119 | -0.000114 | -0.000762 | -0.000074 | -0.000074 | -0.000280 | -0.000565 | -0.000066 | -0.000360 | -0.000057 | -0.000087 | -0.000104 | -0.000057 | ... | -0.000229 | -0.000508 | -0.000346 | -0.000330 | -0.000155 | -0.000362 | -0.000395 | -0.000193 | -0.000351 | -0.000946 | -0.000240 | -0.000209 | -0.000193 | -0.000124 | -0.000292 | -0.000238 | -0.000165 | -0.000317 | -0.000395 | -0.000190 | -0.000353 | 0.016946 | -0.000499 | -0.000229 | -0.000132 | -0.000260 | -0.000524 | -0.000136 | -0.000280 | -0.004231 | -0.000826 | -0.000114 | -0.000260 | -0.000387 | -0.000415 | -0.000730 | -0.001610 | -0.000181 | -0.000304 | -0.000184 |
corr_df2 = corr_df.replace(1, 0).abs()
corr_df2['id'] = corr_df2.index
corr_df2.head()
0 | 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 | ... | 1064 | 1065 | 1066 | 1067 | 1068 | 1069 | 1070 | 1071 | 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | 1080 | 1081 | 1082 | 1083 | 1084 | 1085 | 1086 | 1087 | 1088 | 1089 | 1090 | 1091 | 1092 | 1093 | 1094 | 1095 | 1096 | 1097 | 1098 | 1099 | 1100 | 1101 | 1102 | id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.000000 | 0.000389 | 0.000145 | 0.000067 | 0.000140 | 0.000160 | 0.000078 | 0.000067 | 0.000145 | 0.000206 | 0.000123 | 0.000039 | 0.000145 | 0.003882 | 0.000786 | 0.000227 | 0.000135 | 0.000174 | 0.000427 | 0.000103 | 0.000078 | 0.000087 | 0.000182 | 0.000607 | 0.000249 | 0.001013 | 0.000517 | 0.000140 | 0.000135 | 0.000896 | 0.000087 | 0.000087 | 0.000330 | 0.000665 | 0.000078 | 0.000424 | 0.000067 | 0.000103 | 0.000123 | 0.000067 | ... | 0.000597 | 0.000408 | 0.000389 | 0.000182 | 0.000426 | 0.000465 | 0.000227 | 0.000413 | 0.001114 | 0.000283 | 0.000246 | 0.000227 | 0.000145 | 0.000343 | 0.000280 | 0.000194 | 0.000373 | 0.000465 | 0.000223 | 0.000415 | 0.001209 | 0.000587 | 0.000269 | 0.000155 | 0.000306 | 0.000616 | 0.000160 | 0.000330 | 0.004979 | 0.000971 | 0.000135 | 0.000306 | 0.000455 | 0.000489 | 0.000859 | 0.001894 | 0.000213 | 0.000358 | 0.000216 | 0 |
1 | 0.000389 | 0.000000 | 0.000343 | 0.000159 | 0.000330 | 0.000378 | 0.000183 | 0.000159 | 0.000343 | 0.000485 | 0.000290 | 0.000092 | 0.000343 | 0.009153 | 0.001853 | 0.000534 | 0.000317 | 0.000410 | 0.001008 | 0.000242 | 0.000183 | 0.000205 | 0.000430 | 0.001432 | 0.000587 | 0.002389 | 0.001219 | 0.000330 | 0.000317 | 0.002114 | 0.000205 | 0.000205 | 0.000777 | 0.001567 | 0.000183 | 0.001000 | 0.000159 | 0.000242 | 0.000290 | 0.000159 | ... | 0.001408 | 0.000961 | 0.000916 | 0.000430 | 0.001004 | 0.001096 | 0.000534 | 0.000974 | 0.002626 | 0.000667 | 0.000579 | 0.000534 | 0.000343 | 0.000809 | 0.000661 | 0.000458 | 0.000879 | 0.001096 | 0.000526 | 0.000978 | 0.000390 | 0.001384 | 0.000635 | 0.000366 | 0.000721 | 0.001453 | 0.000378 | 0.000777 | 0.011739 | 0.002291 | 0.000317 | 0.000721 | 0.001073 | 0.001152 | 0.002026 | 0.002369 | 0.000502 | 0.000845 | 0.000510 | 1 |
2 | 0.000145 | 0.000343 | 0.000000 | 0.000059 | 0.000124 | 0.000141 | 0.000069 | 0.000059 | 0.000128 | 0.000181 | 0.000108 | 0.000034 | 0.000128 | 0.003423 | 0.000693 | 0.000200 | 0.000119 | 0.000153 | 0.000377 | 0.000091 | 0.000069 | 0.000077 | 0.000161 | 0.000536 | 0.000219 | 0.000893 | 0.000456 | 0.000124 | 0.000119 | 0.000791 | 0.000077 | 0.000077 | 0.000291 | 0.000586 | 0.000069 | 0.000374 | 0.000059 | 0.000091 | 0.000108 | 0.000059 | ... | 0.000527 | 0.000359 | 0.000343 | 0.000161 | 0.000375 | 0.000410 | 0.000200 | 0.000364 | 0.000982 | 0.000249 | 0.000217 | 0.000200 | 0.000128 | 0.000303 | 0.000247 | 0.000171 | 0.000329 | 0.000410 | 0.000197 | 0.000366 | 0.001067 | 0.000518 | 0.000237 | 0.000137 | 0.000270 | 0.000543 | 0.000141 | 0.000291 | 0.004391 | 0.000857 | 0.000119 | 0.000270 | 0.000401 | 0.000431 | 0.000758 | 0.001670 | 0.000188 | 0.000316 | 0.000191 | 2 |
3 | 0.000067 | 0.000159 | 0.000059 | 0.000000 | 0.000057 | 0.000065 | 0.000032 | 0.000027 | 0.000059 | 0.000084 | 0.000050 | 0.000016 | 0.000059 | 0.001585 | 0.000321 | 0.000092 | 0.000055 | 0.000071 | 0.000175 | 0.000042 | 0.000032 | 0.000035 | 0.000074 | 0.000248 | 0.000102 | 0.000414 | 0.000211 | 0.000057 | 0.000055 | 0.000366 | 0.000035 | 0.000035 | 0.000135 | 0.000271 | 0.000032 | 0.000173 | 0.000027 | 0.000042 | 0.000050 | 0.000027 | ... | 0.000244 | 0.000166 | 0.000159 | 0.000074 | 0.000174 | 0.000190 | 0.000092 | 0.000169 | 0.000455 | 0.000115 | 0.000100 | 0.000092 | 0.000059 | 0.000140 | 0.000114 | 0.000079 | 0.000152 | 0.000190 | 0.000091 | 0.000169 | 0.000494 | 0.000240 | 0.000110 | 0.000063 | 0.000125 | 0.000251 | 0.000065 | 0.000135 | 0.002032 | 0.000397 | 0.000055 | 0.000125 | 0.000186 | 0.000199 | 0.000351 | 0.000773 | 0.000087 | 0.000146 | 0.000088 | 3 |
4 | 0.000140 | 0.000330 | 0.000124 | 0.000057 | 0.000000 | 0.000136 | 0.000066 | 0.000057 | 0.000124 | 0.000175 | 0.000104 | 0.000033 | 0.000124 | 0.003299 | 0.000668 | 0.000193 | 0.000114 | 0.000148 | 0.000363 | 0.000087 | 0.000066 | 0.000074 | 0.000155 | 0.000516 | 0.000211 | 0.000861 | 0.000440 | 0.000119 | 0.000114 | 0.000762 | 0.000074 | 0.000074 | 0.000280 | 0.000565 | 0.000066 | 0.000360 | 0.000057 | 0.000087 | 0.000104 | 0.000057 | ... | 0.000508 | 0.000346 | 0.000330 | 0.000155 | 0.000362 | 0.000395 | 0.000193 | 0.000351 | 0.000946 | 0.000240 | 0.000209 | 0.000193 | 0.000124 | 0.000292 | 0.000238 | 0.000165 | 0.000317 | 0.000395 | 0.000190 | 0.000353 | 0.016946 | 0.000499 | 0.000229 | 0.000132 | 0.000260 | 0.000524 | 0.000136 | 0.000280 | 0.004231 | 0.000826 | 0.000114 | 0.000260 | 0.000387 | 0.000415 | 0.000730 | 0.001610 | 0.000181 | 0.000304 | 0.000184 | 4 |
corr_df3 = corr_df2.loc[lambda x: x[0:-1].max() > 0.4]
max_values = corr_df3.iloc[:, 0:-1].max(axis=1)
max_index1 = corr_df3.iloc[:, 0:-1].idxmax(axis=1)
max_index2 = corr_df3['id']
corr_df4 = pd.DataFrame(np.stack((max_values, max_index1, max_index2), axis=-1), columns=['value', 'id1', 'id2'])
corr_df4 = corr_df4.merge(labels_ds, left_on = 'id1', right_on = 'attribute_id')
corr_df4 = corr_df4.merge(labels_ds, left_on = 'id2', right_on = 'attribute_id', suffixes=('_1', '_2'))
corr_df4 = corr_df4.drop(columns=['attribute_id_1', 'attribute_id_2'])
corr_df4
value | id1 | id2 | attribute_name_1 | attribute_name_2 | |
---|---|---|---|---|---|
0 | 0.840149 | 28 | 5 | culture::augsburg original | culture::after german |
1 | 0.659341 | 120 | 10 | culture::egypt | culture::alexandria-hadra |
2 | 0.610194 | 331 | 18 | culture::south italian | culture::apulian |
3 | 0.484815 | 331 | 61 | culture::south italian | culture::campanian |
4 | 0.570598 | 161 | 25 | culture::greek | culture::attic |
5 | 0.807182 | 228 | 27 | culture::meissen with german | culture::augsburg decoration |
6 | 0.840149 | 5 | 28 | culture::after german | culture::augsburg original |
7 | 0.516766 | 383 | 29 | culture::vienna | culture::austrian |
8 | 0.426615 | 582 | 33 | tag::cuneiform | culture::babylonian |
9 | 0.85832 | 582 | 1023 | tag::cuneiform | tag::tablets |
10 | 0.645373 | 102 | 90 | culture::danish | culture::copenhagen |
11 | 0.784447 | 254 | 97 | culture::naxos | culture::cyclades |
12 | 0.645373 | 90 | 102 | culture::copenhagen | culture::danish |
13 | 0.672785 | 185 | 110 | culture::irish | culture::dublin |
14 | 0.7131 | 217 | 114 | culture::lydian | culture::east greek/sardis |
15 | 0.659341 | 10 | 120 | culture::alexandria-hadra | culture::egypt |
16 | 0.707097 | 219 | 140 | culture::macao | culture::for iberian market |
17 | 0.441192 | 348 | 154 | culture::swiss | culture::geneva |
18 | 0.570598 | 25 | 161 | culture::attic | culture::greek |
19 | 0.448669 | 97 | 162 | culture::cyclades | culture::greek islands |
20 | 0.784447 | 97 | 254 | culture::cyclades | culture::naxos |
21 | 0.816489 | 181 | 166 | culture::indian or nepalese | culture::gurkha |
22 | 0.42974 | 750 | 180 | tag::jainism | culture::india |
23 | 0.816489 | 166 | 181 | culture::gurkha | culture::indian or nepalese |
24 | 0.672785 | 110 | 185 | culture::dublin | culture::irish |
25 | 0.7131 | 114 | 217 | culture::east greek/sardis | culture::lydian |
26 | 0.707097 | 140 | 219 | culture::for iberian market | culture::macao |
27 | 0.807182 | 27 | 228 | culture::augsburg decoration | culture::meissen with german |
28 | 0.707055 | 357 | 257 | culture::thessaly | culture::neolithic |
29 | 0.41318 | 338 | 308 | culture::st. petersburg | culture::russian |
30 | 0.610194 | 18 | 331 | culture::apulian | culture::south italian |
31 | 0.41318 | 308 | 338 | culture::russian | culture::st. petersburg |
32 | 0.441192 | 154 | 348 | culture::geneva | culture::swiss |
33 | 0.427787 | 61 | 352 | culture::campanian | culture::teano |
34 | 0.707055 | 257 | 357 | culture::neolithic | culture::thessaly |
35 | 0.516766 | 29 | 383 | culture::austrian | culture::vienna |
36 | 0.421642 | 896 | 405 | tag::portraits | tag::actresses |
37 | 0.83523 | 645 | 406 | tag::eve | tag::adam |
38 | 0.585224 | 457 | 445 | tag::baseball | tag::athletes |
39 | 0.585224 | 445 | 457 | tag::athletes | tag::baseball |
40 | 0.443011 | 498 | 482 | tag::buddhism | tag::bodhisattva |
41 | 0.524181 | 498 | 497 | tag::buddhism | tag::buddha |
42 | 0.524181 | 497 | 498 | tag::buddha | tag::buddhism |
43 | 0.85832 | 1023 | 582 | tag::tablets | tag::cuneiform |
44 | 0.83523 | 406 | 645 | tag::adam | tag::eve |
45 | 0.443135 | 780 | 671 | tag::leaves | tag::flowers |
46 | 0.770603 | 757 | 730 | tag::judith | tag::holofernes |
47 | 0.476425 | 748 | 735 | tag::isis | tag::horus |
48 | 0.476425 | 735 | 748 | tag::horus | tag::isis |
49 | 0.42974 | 180 | 750 | culture::india | tag::jainism |
50 | 0.770603 | 730 | 757 | tag::holofernes | tag::judith |
51 | 0.443135 | 671 | 780 | tag::flowers | tag::leaves |
52 | 0.421642 | 405 | 896 | tag::actresses | tag::portraits |
53 | 0.404764 | 1043 | 913 | tag::trains | tag::railways |
54 | 0.577258 | 1011 | 957 | tag::students | tag::schools |
55 | 0.714231 | 1011 | 1029 | tag::students | tag::teachers |
56 | 0.714231 | 1029 | 1011 | tag::teachers | tag::students |
57 | 0.404764 | 913 | 1043 | tag::railways | tag::trains |