text_plain_output_2.png text_plain_output_1.png image_output_1.png
features = 'esm2_t33_650M'
if features == 'T5':
    fn = '/kaggle/input/t5embeds/train_embeds.npy'
    fn4submit = '/kaggle/input/t5embeds/test_embeds.npy'
    str_data_inf = 'CAFA5 T5 embeddings '
elif features == 'esm2_t33_650M':
    fn = '/kaggle/input/23468234/train_embeds_esm2_t33_650M_UR50D.npy'
    fn4submit = '/kaggle/input/23468234/test_embeds_esm2_t33_650M_UR50D.npy'
    str_data_inf = 'CAFA5 ' + features
elif features == 'esm2_t30_150M':
    fn = '/kaggle/input/8947923/train_embeds_esm2_t30_150M_UR50D.npy'
    fn4submit = '/kaggle/input/8947923/test_embeds_esm2_t30_150M_UR50D.npy'
    str_data_inf = 'CAFA5 ' + features
elif features == 'esm2_t12_35M':
    fn = '/kaggle/input/3023750/train_embeds_esm2_t12_35M_UR50D.npy'
    fn4submit = '/kaggle/input/3023750/test_embeds_esm2_t12_35M_UR50D.npy'
    str_data_inf = 'CAFA5 ' + features
elif features == 'esm2_t6_8M':
    fn = '/kaggle/input/315701375/train_embeds_esm2_t6_8M_UR50D.npy'
    fn4submit = '/kaggle/input/315701375/test_embeds_esm2_t6_8M_UR50D.npy'
    str_data_inf = 'CAFA5 ' + features
elif features == 'SGTv12':
    fn = '/kaggle/input/cafa5-sgt-protein-embeddings/run12_fit_on_15000x2/sgt_embendings_train_15000_15000.csv'
    fn4submit = '/kaggle/input/cafa5-sgt-protein-embeddings/run12_fit_on_15000x2/sgt_embendings_test_15000_15000.csv'
    str_data_inf = 'CAFA5 SGT embeddings v12 '
elif features == 'Leven5000':
    fn = '/kaggle/input/cafa5-levenshtein-distance-features-big/run9_achnor_set5000/df_Levenshtein_distance_5000_features_train.csv'
    fn4submit = '/kaggle/input/cafa5-levenshtein-distance-features-big/run9_achnor_set5000/df_Levenshtein_distance_5000_features_test.csv'
    str_data_inf = 'CAFA5 Levenshtein distance features anchor 5000 '
elif features == 'Leven3000':
    fn = '/kaggle/input/cafa5-levenshtein-distance-features-big/run8_anchor_set_3000/df_Levenshtein_distance_3000_features_train.csv'
    fn4submit = '/kaggle/input/cafa5-levenshtein-distance-features-big/run8_anchor_set_3000/df_Levenshtein_distance_3000_features_test.csv'
    str_data_inf = 'CAFA5 Levenshtein distance features anchor 3000 '
elif features == 'Leven1000':
    fn = '/kaggle/input/cafa5-features-etc/df_Levenshtein_distance_1000_features_train.csv'
    fn4submit = '/kaggle/input/cafa5-features-etc/df_Levenshtein_distance_1000_features_test.csv'
    str_data_inf = 'CAFA5 Levenshtein distance features anchor 1000 '
elif features == 'Leven100':
    fn = '/kaggle/input/cafa5-features-etc/df_Levenshtein_distance_100_features_train.csv'
    fn4submit = '/kaggle/input/cafa5-features-etc/df_Levenshtein_distance_100_features_test.csv'
    str_data_inf = 'CAFA5 Levenshtein distance features anchor 100 '
def load_features(fn):
    print(fn)
    if '.csv' in fn:
        df = pd.read_csv(fn, index_col=0)
        X = df.values
    elif '.npy' in fn:
        X = np.load(fn)
    print(X.shape)
    return X
X = load_features(fn)
str_data_inf += ' train'
print(X.shape)
X[:10, :10]