*****
Quickstart
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To get started, simply import and call one of the implemented algorithms. According to Astrakhantsev 2016, combo_basic is the most precise of the five algorithms, though basic and cvalues is not too far behind (see Precision). The same study shows that PU-ATR and KeyConceptRel have higher precision than combo_basic but are not implemented and PU-ATR take significantly more time since it uses machine learning.

::

    from pyate import combo_basic

    # source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994795/
    string = """Central to the development of cancer are genetic changes that endow these “cancer cells” with many of the
    hallmarks of cancer, such as self-sufficient growth and resistance to anti-growth and pro-death signals. However, while the
    genetic changes that occur within cancer cells themselves, such as activated oncogenes or dysfunctional tumor suppressors,
    are responsible for many aspects of cancer development, they are not sufficient. Tumor promotion and progression are
    dependent on ancillary processes provided by cells of the tumor environment but that are not necessarily cancerous 
    themselves. Inflammation has long been associated with the development of cancer. This review will discuss the reflexive 
    relationship between cancer and inflammation with particular focus on how considering the role of inflammation in physiologic 
    processes such as the maintenance of tissue homeostasis and repair may provide a logical framework for understanding the U
    connection between the inflammatory response and cancer."""

    print(combo_basic(string).sort_values(ascending=False))
    """ (Output)
    dysfunctional tumor                1.443147
    tumor suppressors                  1.443147
    genetic changes                    1.386294
    cancer cells                       1.386294
    dysfunctional tumor suppressors    1.298612
    logical framework                  0.693147
    sufficient growth                  0.693147
    death signals                      0.693147
    many aspects                       0.693147
    inflammatory response              0.693147
    tumor promotion                    0.693147
    ancillary processes                0.693147
    tumor environment                  0.693147
    reflexive relationship             0.693147
    particular focus                   0.693147
    physiologic processes              0.693147
    tissue homeostasis                 0.693147
    cancer development                 0.693147
    dtype: float64
    """

If you would like to add this to a spacy pipeline, simply use add spaCy's add_pipe method.

::

    import spacy
    from pyate.term_extraction_pipeline import TermExtractionPipeline

    nlp = spacy.load("en_core_web_sm")
    nlp.add_pipe(TermExtractionPipeline())
    doc = nlp(string)
    print(doc._.combo_basic.sort_values(ascending=False).head(5))
    """ (Output)
    dysfunctional tumor                1.443147
    tumor suppressors                  1.443147
    genetic changes                    1.386294
    cancer cells                       1.386294
    dysfunctional tumor suppressors    1.298612
    dtype: float64
    """

``TermExtractionPipeline.__init__`` is defined as follows

::
    
    __init__(
      self,
      func: Callable[..., pd.Series] = combo_basic,
      *args,
      **kwargs
    )   

where ``func`` is essentially your term extracting algorithm that takes in a corpus (either a string or iterator of strings) and outputs a Pandas Series of term-value pairs of terms and their respective termhoods. ``func`` is by default ``combo_basic``. ``args`` and ``kwargs`` are for you to overide default values for the function, which you can find by running help.