print(f'num of test sentiment files: {len(test_sentiment_files)}')
# ### Train
# In[ ]:
# Images:
train_df_ids = train[['PetID']]
print(train_df_ids.shape)
# Metadata:
train_df_ids = train[['PetID']]
train_df_metadata = pd.DataFrame(train_metadata_files)
train_df_metadata.columns = ['metadata_filename']
train_metadata_pets = train_df_metadata['metadata_filename'].apply(
lambda x: x.split(split_char)[-1].split('-')[0])
train_df_metadata = train_df_metadata.assign(PetID=train_metadata_pets)
print(len(train_metadata_pets.unique()))
pets_with_metadatas = len(
np.intersect1d(train_metadata_pets.unique(),
train_df_ids['PetID'].unique()))
print(
f'fraction of pets with metadata: {pets_with_metadatas / train_df_ids.shape[0]:.3f}'
)
# Sentiment:
train_df_ids = train[['PetID']]
train_df_sentiment = pd.DataFrame(train_sentiment_files)
train_df_sentiment.columns = ['sentiment_filename']
train_sentiment_pets = train_df_sentiment['sentiment_filename'].apply(
test_image_files = sorted(glob.glob('../input/petfinder-adoption-prediction/test_images/*.jpg'))
test_metadata_files = sorted(glob.glob('../input/petfinder-adoption-prediction/test_metadata/*.json'))
test_sentiment_files = sorted(glob.glob('../input/petfinder-adoption-prediction/test_sentiment/*.json'))
print(f'num of test images files: {len(test_image_files)}')
print(f'num of test metadata files: {len(test_metadata_files)}')
print(f'num of test sentiment files: {len(test_sentiment_files)}')
# Images:
train_df_ids = train[['PetID']]
print(train_df_ids.shape)
# Metadata:
train_df_ids = train[['PetID']]
train_df_metadata = pd.DataFrame(train_metadata_files)
train_df_metadata.columns = ['metadata_filename']
train_metadata_pets = train_df_metadata['metadata_filename'].apply(lambda x: x.split(split_char)[-1].split('-')[0])
train_df_metadata = train_df_metadata.assign(PetID=train_metadata_pets)
print(len(train_metadata_pets.unique()))
pets_with_metadatas = len(np.intersect1d(train_metadata_pets.unique(), train_df_ids['PetID'].unique()))
print(f'fraction of pets with metadata: {pets_with_metadatas / train_df_ids.shape[0]:.3f}')
# Sentiment:
train_df_ids = train[['PetID']]
train_df_sentiment = pd.DataFrame(train_sentiment_files)
train_df_sentiment.columns = ['sentiment_filename']
train_sentiment_pets = train_df_sentiment['sentiment_filename'].apply(lambda x: x.split(split_char)[-1].split('.')[0])
train_df_sentiment = train_df_sentiment.assign(PetID=train_sentiment_pets)
print(len(train_sentiment_pets.unique()))
pets_with_sentiments = len(np.intersect1d(train_sentiment_pets.unique(), train_df_ids['PetID'].unique()))
