SYMBOL INDEX (85 symbols across 7 files)

FILE: compile_table.py
  function nice_grouping (line 41) | def nice_grouping(df):
  function fprint (line 47) | def fprint(x):
  function gather_metrics (line 56) | def gather_metrics(results):
  function output_latex (line 107) | def output_latex(nice_subset):
  function output_html (line 111) | def output_html(nice_subset):
  function output_console (line 117) | def output_console(subset):
  function __flatten_dict (line 121) | def __flatten_dict(d, joiner="_"):
  function read_json_log (line 132) | def read_json_log(filename):
  function main (line 161) | def main():

FILE: hypernymysuite/base.py
  class HypernymySuiteModel (line 21) | class HypernymySuiteModel(object):
    method __init__ (line 45) | def __init__(self):
    method predict (line 48) | def predict(self, hypo, hyper):
    method predict_many (line 64) | def predict_many(self, hypos, hypers):
  class Precomputed (line 80) | class Precomputed(HypernymySuiteModel):
    method __init__ (line 85) | def __init__(self, precomputed):
    method predict (line 104) | def predict(self, hypo, hyper):

FILE: hypernymysuite/evaluation.py
  class Dataset (line 41) | class Dataset(object):
    method __init__ (line 53) | def __init__(self, filename, vocabdict, ycolumn="label"):
    method __len__ (line 89) | def __len__(self):
    method hypos (line 93) | def hypos(self):
    method hypers (line 97) | def hypers(self):
    method invocab_mask (line 101) | def invocab_mask(self):
    method oov_mask (line 105) | def oov_mask(self):
    method val_mask (line 109) | def val_mask(self):
    method test_mask (line 113) | def test_mask(self):
    method train_mask (line 117) | def train_mask(self):
    method train_inv_mask (line 121) | def train_inv_mask(self):
    method val_inv_mask (line 125) | def val_inv_mask(self):
    method test_inv_mask (line 129) | def test_inv_mask(self):
    method y (line 133) | def y(self):
  function correlation_setup (line 137) | def correlation_setup(filename, model):
  function bless_directionality_setup (line 172) | def bless_directionality_setup(model):
  function wbless_setup (line 213) | def wbless_setup(model):
  function bibless_setup (line 252) | def bibless_setup(model):
  function ap_at_k (line 302) | def ap_at_k(y_true, y_score, k):
  function siege_setup (line 314) | def siege_setup(filename, model):
  function all_evaluations (line 353) | def all_evaluations(model, extra_args=None):

FILE: hypernymysuite/pattern.py
  class PatternBasedModel (line 25) | class PatternBasedModel(HypernymySuiteModel):
    method __init__ (line 30) | def __init__(self, patterns_filename):
    method predict (line 39) | def predict(self, hypo, hyper):
    method __str__ (line 42) | def __str__(self):
  class RawCountModel (line 46) | class RawCountModel(PatternBasedModel):
    method predict (line 51) | def predict(self, hypo, hyper):
    method __str__ (line 56) | def __str__(self):
  class PPMIModel (line 60) | class PPMIModel(RawCountModel):
    method __init__ (line 66) | def __init__(self, patterns_filename):
    method __str__ (line 84) | def __str__(self):
  class _SvdMixIn (line 88) | class _SvdMixIn(object):
    method __init__ (line 94) | def __init__(self, pattern_filename, k):
    method predict (line 102) | def predict(self, hypo, hyper):
    method predict_many (line 107) | def predict_many(self, hypos, hypers):
    method __str__ (line 114) | def __str__(self):
  class SvdRawModel (line 118) | class SvdRawModel(_SvdMixIn, RawCountModel):
  class SvdPpmiModel (line 126) | class SvdPpmiModel(_SvdMixIn, PPMIModel):
  class RandomBaseline (line 134) | class RandomBaseline(PatternBasedModel):
    method __init__ (line 140) | def __init__(self, filename, k=10, seed=42):
    method predict (line 146) | def predict(self, hypo, hyper):
    method __str__ (line 151) | def __str__(self):

FILE: hypernymysuite/reader.py
  function __try_three_columns (line 30) | def __try_three_columns(string):
  function __load_sparse_matrix (line 42) | def __load_sparse_matrix(filename, same_vocab):
  function read_sparse_matrix (line 87) | def read_sparse_matrix(filename, allow_binary_cache=False, same_vocab=Fa...

FILE: hypernymysuite/unsup.py
  function invCL (line 24) | def invCL(x_row, y_row):
  function clarkeDE (line 37) | def clarkeDE(x_row, y_row):
  function weeds_prec (line 55) | def weeds_prec(x_row, y_row):
  function mdot (line 75) | def mdot(x, y):
  function cosine (line 88) | def cosine(x, y):
  class POSSearchDict (line 101) | class POSSearchDict(object):
    method __init__ (line 108) | def __init__(self, lookup):
    method __contains__ (line 112) | def __contains__(self, item):
    method get (line 119) | def get(self, item, default):
    method __getitem__ (line 125) | def __getitem__(self, item):
  class SparseMatrixBaseline (line 137) | class SparseMatrixBaseline(HypernymySuiteModel):
    method __init__ (line 142) | def __init__(self, space_filename, pos_tagged_space=None):
    method forward (line 162) | def forward(self, inputs):
  class UnsupervisedBaseline (line 166) | class UnsupervisedBaseline(SparseMatrixBaseline):
    method __init__ (line 177) | def __init__(self, space_filename, measurefn):
    method predict (line 181) | def predict(self, hypo, hyper):
  class SLQS (line 189) | class SLQS(SparseMatrixBaseline):
    method __init__ (line 200) | def __init__(self, space_filename, topk):
    method compute_row_entropy (line 220) | def compute_row_entropy(self, i):
    method predict (line 235) | def predict(self, hypo, hyper):
  class SLQS_Cos (line 246) | class SLQS_Cos(SLQS):
    method __init__ (line 251) | def __init__(self, space_filename, topk):
    method predict (line 255) | def predict(self, hypo, hyper):

FILE: main.py
  function main (line 25) | def main():