gene_gene/gene_interacts_gene/datafile/gene_gene_datafile_generator.py
def partitioner(df):
    """
    This function creates a parition rank for the current dataset.
    This algorithm assigns a rank [0-1) for each datapoint inside each group (outlined below):
        1,1 -in hetionet and has sentences
        1,0 - in hetionet and doesn't have sentences
        0,1 - not in hetionet and does have sentences
        0,0, - not in hetionet and doesn't have sentences
        
    This ranking will be used in the get split function to assign each datapoint 
    into its corresponding category (train, dev, test)
    """
    partition_rank = pd.np.linspace(0, 1, num=len(df), endpoint=False)
    pd.np.random.shuffle(partition_rank)
    df['partition_rank'] = partition_rank
    return df

    This function creates a parition rank for the current dataset.
    This algorithm assigns a rank [0-1) for each datapoint inside each group (outlined below):
        1,1 -in hetionet and has sentences
        1,0 - in hetionet and doesn't have sentences
        0,1 - not in hetionet and does have sentences
        0,0, - not in hetionet and doesn't have sentences
        
    This ranking will be used in the get split function to assign each datapoint 
    into its corresponding category (train, dev, test)
    
This function creates a parition rank for the current dataset.

This ranking will be used in the get split function to assign each datapoint
into its corresponding category (train, dev, test)
This function creates a parition rank for the current dataset . This ranking will be used in the get split function to assign each datapoint into its corresponding category ( train dev test )
def partitioner(df):
    partition_rank = pd.np.linspace(0, 1, num=len(df), endpoint=False)
    pd.np.random.shuffle(partition_rank)
    df['partition_rank'] = partition_rank
    return df
def partitioner ( df ) : partition_rank = pd . np . linspace ( 0 , 1 , num = len ( df ) , endpoint = False ) pd . np . random . shuffle ( partition_rank ) df [ 'partition_rank' ] = partition_rank return df
This function creates a parition rank for the current dataset.
This function creates a parition rank for the current dataset .
"""
    This function creates a parition rank for the current dataset.
    This algorithm assigns a rank [0-1) for each datapoint inside each group (outlined below):
        1,1 -in hetionet and has sentences
        1,0 - in hetionet and doesn't have sentences
        0,1 - not in hetionet and does have sentences
        0,0, - not in hetionet and doesn't have sentences
        
    This ranking will be used in the get split function to assign each datapoint 
    into its corresponding category (train, dev, test)
    """
df