<jupyter_start><jupyter_text>15 classes<jupyter_code>label = raw_label.iloc[:,1:]
label.head()
label.shape
total_No_label_count = 0

for i in range(len(label)):
    if sum(label.iloc[i].values)==0:
        total_No_label_count += 1
total_No_label_count
count_of_disease = {}

for name in list(label.columns.values):
    count_of_disease[name] = sum(label[name].values)
   
count_of_disease["No label"] = total_No_label_count
count_of_disease
plt.figure(figsize=(20,10))
plt.bar(*zip(*count_of_disease.items()))
plt.show()<jupyter_output><empty_output><jupyter_text>8 classes<jupyter_code>label = raw_label.iloc[:,1:9]
label.head()
label.shape
total_No_label_count = 0

for i in range(len(label)):
    if sum(label.iloc[i].values)==0:
        total_No_label_count += 1
total_No_label_count
count_of_disease = {}
for name in list(label.columns.values):
    count_of_disease[name] = sum(label[name].values)
   
count_of_disease["No label"] = total_No_label_count
count_of_disease
plt.figure(figsize=(20,10))
plt.bar(*zip(*count_of_disease.items()))
plt.show()
bbox_list_path="./dataset/BBox_List_2017.csv"
bbox = pd.read_csv(bbox_list_path)
bbox = bbox.iloc[:,:6]
image_index = bbox.iloc[:,0].values
disease = bbox.iloc[:,1].values<jupyter_output><empty_output><jupyter_text>Binary Dataset<jupyter_code>train_label_binary="./dataset/binary_train.txt" 
train_binary_meta_data = pd.read_csv(train_label_binary,sep=" ", header=None)


data_entry="./dataset/label_index.csv"
train_meta_data = pd.read_csv(data_entry)
train_meta_data
'00019733_003.png' in train_binary_meta_data.iloc[:,0].values
disease_vector = []
for x in range(len(train_meta_data)):
    if train_meta_data.iloc[x,0] in train_binary_meta_data.iloc[:,0].values:
        disease_vector.append(train_meta_data.iloc[x,1:])     
disease_vector = np.array(disease_vector)
disease_vector.sum(axis=0)
total_No_label_count = 0

for i in range(len(disease_vector)):
    if sum(disease_vector[i,:])==0:
        total_No_label_count += 1
total_No_label_count        
disease_vector = []
count = 0
#len(train_meta_data)
for x in range(10):
#     if x % 2000 ==0 :
#         print(x)
    print(train_meta_data[train_meta_data.iloc[:,0] == train_binary_meta_data.iloc[x,0]].iloc[:,:].values)
    print(train_binary_meta_data.iloc[x,1])
count                
sum(train_meta_data.iloc[x,1:])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14285714285714285, 'fraction_numerical': 0.024513947590870666, 'mean_word_length': 6.065671641791044, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '402910', 'license': 'MIT', 'n_tokens_mistral': 989, 'n_tokens_neox': 945, 'n_words': 121, 'path': 'dataset/explore_dataset.ipynb', 'repo_name': 'alinstein/X_RAY', 'size': '2366'}