def test_df2listOfSeqs():
## NAN ending
Dt = (None, np.max(te_df['t'])) # one big sequence
assert len(aa.df2listOfSeqs(te_df, Dt, time_param='t')[0]) == len(te_df)
Dt = (None, 0) # each element by its own
assert len(aa.df2listOfSeqs(te_df, Dt, time_param='t')) == len(te_df)
## float ending
Dt = (None, np.max(te_df['t'])) # one big sequence
te_df2 = te_df[:-1]
assert len(aa.df2listOfSeqs(te_df2, Dt, time_param='t')[0]) == len(te_df2)
Dt = (None, 0) # each element by its own
assert len(aa.df2listOfSeqs(te_df2, Dt, time_param='t')) == len(te_df2)
def dfDict_to_bigram_matrix(
df_dict,
Dtint,
timeLabel="ici",
callLabel="call",
startTag="_ini",
endTag="_end",
return_values="probs",
minCalls=1,
):
"""Bigrams counts/probs as matrix from DataFrame
Parameters
----------
df_dict: DataFrame
Dtint: tuple
timeLabel: str
callLabel: str
return_values: str
{probs, counts}
Returns
-------
(matrix, sampsLi, condsLi)
"""
cfd = nltk.ConditionalFreqDist() # initialise cond freq dist
calls0 = []
for t in df_dict.keys(): # for reach tape
thisdf = df_dict[t]
# define the sequences
sequences = aa.seqsLi2iniEndSeq(
aa.df2listOfSeqs(thisdf,
Dt=Dtint,
l=callLabel,
time_param=timeLabel),
ini=startTag,
end=endTag,
)
my_bigrams = nltk.bigrams(sequences) # tag bigrams
cfd += bigrams2cfd(my_bigrams) # count bigrams
calls0 += list(thisdf[callLabel].values)
# calls order
calls = [
item[0] for item in sorted(
Counter(calls0).items(), key=lambda x: x[1], reverse=True)
if item[1] >= minCalls
] # order calls
samplesLi = calls[:] + [endTag
] # None #[ 'A', 'B', 'C', 'E', '_ini','_end']
condsLi = calls[:] + [startTag]
if return_values == "counts":
return kykyCountsDict2matrix(cfd, condsLi, samplesLi)
if return_values == "probs":
cpd = condFreqDictC2condProbDict(cfd) # , condsLi, samplesLi)
return kykyCountsDict2matrix(cpd, condsLi, samplesLi)
def shuffled_cfd(df, Dtint, label='call', time_param='ici'):
"""returns the conditional frequencies
of the bigrams in a df after shuffling <label>
Parameters
----------
df : Pandas.DataFrame
Dtint : size two list-like
label : string
name of the label to randomise
time_param : string
name of the time param (Dtint) to define the sequences
Returns
-------
cfd_ns h: nltk.ConditionalFrequencyDist
counts of the randomised sequences
"""
sh_df = shuffleSeries(df, shuffleCol=label) # shuffle the calls
# define the sequences
sequences = aa.seqsLi2iniEndSeq(
aa.df2listOfSeqs(sh_df, Dt=Dtint, l=label, time_param=time_param))
my_bigrams = nltk.bigrams(sequences) # detect bigrams
cfd_nsh = ngr.bigrams2Dict(my_bigrams) # count bigrams
return cfd_nsh
# In[31]:
Dt_chunks = 0.1
Dtint_chunks = (None, Dt_chunks)
## useful numbers
noteFreqs = Counter(
df[call_label].values) # notes = daT.returnSortingKeys(noteFreqs)
notes = list(set(df[call_label].values))
## define the sequences
sequences = []
for t in tape_df.keys(): # for each tape
this_df = tape_df[t]
sequences += aa.df2listOfSeqs(this_df,
Dt=Dtint_chunks,
l=call_label,
time_param=time_param) # define the sequeces
## sequence statistics
ngram_seqs = defaultdict(list)
for s in sequences:
ngram_seqs[len(s)].append(tuple(s))
## count notes in each sequence size
note_chunks_arr = np.zeros((len(noteFreqs), np.max(ngram_seqs.keys()) + 1))
for j in ngram_seqs.keys():
s = ngram_seqs[j]
for i, n in enumerate(notes):
note_chunks_arr[i, j] = sum(x.count(n) for x in s)
seqSizes = np.arange(1, np.shape(note_chunks_arr)[1] + 1)
## sequence stats
Ngrams_dist = np.array([len(ngram_seqs[k]) for k in ngram_seqs.keys()])
