PranjalDureja0002

	providerID	y_predicted
0	PRV57070	  0
1	PRV57070	  1
2	PRV57070	  0
3	PRV57070	  0
4	PRV57070	  0
5	PRV57070	  1
6	PRV57070	  0
7	PRV57070	  1
8	PRV57070	  0

PranjalDureja0002

def final_fun_1(X):
    """ function takes raw data as input,preprocessing is done,
    feature engineering is performed and predictions made on the
    best model already trained"""
            
    d_beneficiary = pd.read_csv('health_cs_data/' + X[0])
    d_inpatient = pd.read_csv('health_cs_data/' + X[1])
    d_outpatient = pd.read_csv('health_cs_data/' + X[2])
    d_labels = pd.read_csv('health_cs_data/' + X[3])
    

PranjalDureja0002

<annotation verified="yes">
	<folder>MARMOT_ANNOTATION</folder>
	<filename>10.1.1.1.2006_3.bmp</filename>
	<path>/home/monika/Desktop/MARMOT_ANNOTATION/10.1.1.1.2006_3.bmp</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>793</width>
		<height>1123</height>

PranjalDureja0002

res_dim = 1024

if __name__ == "__main__":
    """loading the data, 
    reading the file annotations,
    appending the tabular coordinates to formulate a dataframe
    """
    df_org = pd.DataFrame()  
    directory = '/content/drive/MyDrive/data_cs2'
    final_col_directory = '/content/drive/MyDrive/cs2_col'

PranjalDureja0002

res_dim = 1024

if __name__ == "__main__":
    """loading the data, 
    reading the file annotations,
    appending the tabular coordinates to formulate a dataframe
    """
    df_org = pd.DataFrame()  
    directory = '/content/drive/MyDrive/data_cs2'
    final_col_directory = '/content/drive/MyDrive/cs2_col'

PranjalDureja0002

destination_t = '\content\drive\MyDrive\cs2_table'
destination_c = '\content\drive\MyDrive\cs2_col'


for i in df_org['filename'].unique():

    # for each unique file, we take the height,width,depth from dataframe
    file_width = int(df_org[df_org['filename']==i]['width'].unique())
    file_height = int(df_org[df_org['filename']==i]['height'].unique())
    # Creating an image array of dtype int32

PranjalDureja0002

def normalize_image(input_image):
  input_image = tf.cast(input_image, tf.float32) / 255.0
  return input_image

def decode_image(img):  
  img = tf.image.decode_jpeg(img)  
  return tf.image.resize(img, [img_height, img_width])

def decode_mask_image(img):  
  img = tf.image.decode_jpeg(img, channels=1)  

PranjalDureja0002

Model: "tablenet"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_7 (InputLayer)            [(None, 228, 228, 3) 0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 234, 234, 3)  0           input_7[0][0]                    
__________________________________________________________________________________________________
conv1_conv (Conv2D)             (None, 114, 114, 64) 9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________

PranjalDureja0002

def fun_table(x,output):
    x = Conv2D(128, (1, 1), activation = 'relu', name='conv7_table')(x)
    concatenated = Concatenate()([x, output])
    concatenated = Concatenate()([concatenated, output])
    x = UpSampling2D(size=(2, 2))(concatenated)
    x = UpSampling2D(size=(2, 2))(x)
    x = UpSampling2D(size=(2, 2))(x)
    x = UpSampling2D(size=(2, 2))(x)

    last = tf.keras.layers.Conv2DTranspose(3, 3, strides=2,padding='same', name='table_output')     

PranjalDureja0002

def fun_column(x,output):
    x = Conv2D(128, (1, 1), activation = 'relu', name='conv7_column')(x)
    x = Dropout(0.8, name='block7_dropout_1')(x)
    concatenated = Concatenate()([x,output])
    concatenated = Concatenate()([concatenated,output])
    x = UpSampling2D(size=(2, 2))(concatenated)
    x = UpSampling2D(size=(2, 2))(x)
    x = UpSampling2D(size=(2, 2))(x)
    x = UpSampling2D(size=(2, 2))(x)