code
string | signature
string | docstring
string | loss_without_docstring
float64 | loss_with_docstring
float64 | factor
float64 |
|---|---|---|---|---|---|
try:
func_str = aliasing[func]
except KeyError:
if callable(func):
return func
else:
if func_str in implementations:
return func_str
if func_str.startswith('nan') and \
func_str[3:] in funcs_no_separate_nan:
raise ValueError("%s does not have a nan-version".format(func_str[3:]))
else:
raise NotImplementedError("No such function available")
raise ValueError("func %s is neither a valid function string nor a "
"callable object".format(func)) | def get_func(func, aliasing, implementations) | Return the key of a found implementation or the func itself | 5.513145 | 5.437521 | 1.013908 |
def check_type(x, dtype):
try:
converted = dtype.type(x)
except (ValueError, OverflowError):
return False
# False if some overflow has happened
return converted == x or np.isnan(x)
def type_loop(x, dtype, dtype_dict, default=None):
while True:
try:
dtype = np.dtype(dtype_dict[dtype.name])
if check_type(x, dtype):
return np.dtype(dtype)
except KeyError:
if default is not None:
return np.dtype(default)
raise ValueError("Can not determine dtype of %r" % x)
dtype = np.dtype(dtype)
if check_type(x, dtype):
return dtype
if np.issubdtype(dtype, np.inexact):
return type_loop(x, dtype, _next_float_dtype)
else:
return type_loop(x, dtype, _next_int_dtype, default=np.float32) | def minimum_dtype(x, dtype=np.bool_) | returns the "most basic" dtype which represents `x` properly, which
provides at least the same value range as the specified dtype. | 3.043203 | 2.98662 | 1.018946 |
if n.ndim != 1:
raise ValueError("n is supposed to be 1d array.")
n_mask = n.astype(bool)
n_cumsum = np.cumsum(n)
ret = np.ones(n_cumsum[-1] + 1, dtype=int)
ret[n_cumsum[n_mask]] -= n[n_mask]
ret[0] -= 1
return np.cumsum(ret)[:-1] | def multi_arange(n) | By example:
# 0 1 2 3 4 5 6 7 8
n = [0, 0, 3, 0, 0, 2, 0, 2, 1]
res = [0, 1, 2, 0, 1, 0, 1, 0]
That is it is equivalent to something like this :
hstack((arange(n_i) for n_i in n))
This version seems quite a bit faster, at least for some
possible inputs, and at any rate it encapsulates a task
in a function. | 2.908028 | 2.959193 | 0.98271 |
if X.ndim != 1:
raise ValueError("this is for 1d masks only.")
is_start = np.empty(len(X), dtype=bool)
is_start[0] = X[0] # True if X[0] is True or non-zero
if X.dtype.kind == 'b':
is_start[1:] = ~X[:-1] & X[1:]
M = X
else:
M = X.astype(bool)
is_start[1:] = X[:-1] != X[1:]
is_start[~M] = False
L = np.cumsum(is_start)
L[~M] = 0
return L | def label_contiguous_1d(X) | WARNING: API for this function is not liable to change!!!
By example:
X = [F T T F F T F F F T T T]
result = [0 1 1 0 0 2 0 0 0 3 3 3]
Or:
X = [0 3 3 0 0 5 5 5 1 1 0 2]
result = [0 1 1 0 0 2 2 2 3 3 0 4]
The ``0`` or ``False`` elements of ``X`` are labeled as ``0`` in the output. If ``X``
is a boolean array, each contiguous block of ``True`` is given an integer
label, if ``X`` is not boolean, then each contiguous block of identical values
is given an integer label. Integer labels are 1, 2, 3,..... (i.e. start a 1
and increase by 1 for each block with no skipped numbers.) | 3.621922 | 3.449126 | 1.050098 |
keep_group = np.zeros(np.max(group_idx) + 1, dtype=bool)
keep_group[0] = True
keep_group[group_idx] = True
return relabel_groups_masked(group_idx, keep_group) | def relabel_groups_unique(group_idx) | See also ``relabel_groups_masked``.
keep_group: [0 3 3 3 0 2 5 2 0 1 1 0 3 5 5]
ret: [0 3 3 3 0 2 4 2 0 1 1 0 3 4 4]
Description of above: unique groups in input was ``1,2,3,5``, i.e.
``4`` was missing, so group 5 was relabled to be ``4``.
Relabeling maintains order, just "compressing" the higher numbers
to fill gaps. | 2.864803 | 2.25065 | 1.272878 |
keep_group = keep_group.astype(bool, copy=not keep_group[0])
if not keep_group[0]: # ensuring keep_group[0] is True makes life easier
keep_group[0] = True
relabel = np.zeros(keep_group.size, dtype=group_idx.dtype)
relabel[keep_group] = np.arange(np.count_nonzero(keep_group))
return relabel[group_idx] | def relabel_groups_masked(group_idx, keep_group) | group_idx: [0 3 3 3 0 2 5 2 0 1 1 0 3 5 5]
0 1 2 3 4 5
keep_group: [0 1 0 1 1 1]
ret: [0 2 2 2 0 0 4 0 0 1 1 0 2 4 4]
Description of above in words: remove group 2, and relabel group 3,4, and 5
to be 2, 3 and 4 respecitvely, in order to fill the gap. Note that group 4 was never used
in the input group_idx, but the user supplied mask said to keep group 4, so group
5 is only moved up by one place to fill the gap created by removing group 2.
That is, the mask describes which groups to remove,
the remaining groups are relabled to remove the gaps created by the falsy
elements in ``keep_group``. Note that ``keep_group[0]`` has no particular meaning because it refers
to the zero group which cannot be "removed".
``keep_group`` should be bool and ``group_idx`` int.
Values in ``group_idx`` can be any order, and | 3.456149 | 3.516739 | 0.982771 |
if fill_value is not None and not (np.isscalar(fill_value) or
len(fill_value) == 0):
raise ValueError("fill_value must be None, a scalar or an empty "
"sequence")
order_group_idx = np.argsort(group_idx, kind='mergesort')
counts = np.bincount(group_idx, minlength=size)
ret = np.split(a[order_group_idx], np.cumsum(counts)[:-1])
ret = np.asanyarray(ret)
if fill_value is None or np.isscalar(fill_value):
_fill_untouched(group_idx, ret, fill_value)
return ret | def _array(group_idx, a, size, fill_value, dtype=None) | groups a into separate arrays, keeping the order intact. | 2.817027 | 2.694956 | 1.045296 |
groups = _array(group_idx, a, size, ())
ret = np.full(size, fill_value, dtype=dtype or np.float64)
for i, grp in enumerate(groups):
if np.ndim(grp) == 1 and len(grp) > 0:
ret[i] = func(grp)
return ret | def _generic_callable(group_idx, a, size, fill_value, dtype=None,
func=lambda g: g, **kwargs) | groups a by inds, and then applies foo to each group in turn, placing
the results in an array. | 3.432287 | 3.700979 | 0.9274 |
sortidx = np.argsort(group_idx, kind='mergesort')
invsortidx = np.argsort(sortidx, kind='mergesort')
group_idx_srt = group_idx[sortidx]
a_srt = a[sortidx]
a_srt_cumsum = np.cumsum(a_srt, dtype=dtype)
increasing = np.arange(len(a), dtype=int)
group_starts = _min(group_idx_srt, increasing, size, fill_value=0)[group_idx_srt]
a_srt_cumsum += -a_srt_cumsum[group_starts] + a_srt[group_starts]
return a_srt_cumsum[invsortidx] | def _cumsum(group_idx, a, size, fill_value=None, dtype=None) | N to N aggregate operation of cumsum. Perform cumulative sum for each group.
group_idx = np.array([4, 3, 3, 4, 4, 1, 1, 1, 7, 8, 7, 4, 3, 3, 1, 1])
a = np.array([3, 4, 1, 3, 9, 9, 6, 7, 7, 0, 8, 2, 1, 8, 9, 8])
_cumsum(group_idx, a, np.max(group_idx) + 1)
>>> array([ 3, 4, 5, 6, 15, 9, 15, 22, 7, 0, 15, 17, 6, 14, 31, 39]) | 2.638185 | 2.925824 | 0.90169 |
untouched = np.ones_like(ret, dtype=bool)
untouched[idx] = False
ret[untouched] = fill_value | def _fill_untouched(idx, ret, fill_value) | any elements of ret not indexed by idx are set to fill_value. | 2.419864 | 2.330592 | 1.038305 |
extrafuncs = {'allnan': allnan, 'anynan': anynan,
'first': itemgetter(0), 'last': itemgetter(-1),
'nanfirst': nanfirst, 'nanlast': nanlast}
func = kwargs.pop('func')
func = extrafuncs.get(func, func)
if isinstance(func, str):
raise NotImplementedError("Grouploop needs to be called with a function")
return aggregate_numpy.aggregate(*args, func=lambda x: func(x), **kwargs) | def aggregate_grouploop(*args, **kwargs) | wraps func in lambda which prevents aggregate_numpy from
recognising and optimising it. Instead it groups and loops. | 4.318792 | 4.103914 | 1.052359 |
dtype = minimum_dtype_scalar(fill_value, dtype, a)
ret = np.full(size, fill_value, dtype=dtype)
if fill_value != 1:
ret[group_idx] = 1 # product should start from 1
np.multiply.at(ret, group_idx, a)
return ret | def _prod(group_idx, a, size, fill_value, dtype=None) | Same as aggregate_numpy.py | 3.888837 | 3.82761 | 1.015996 |
varnames = ['group_idx', 'a', 'ret', 'counter']
codebase = c_base_reverse if reverse else c_base
iteration = c_iter_scalar[funcname] if scalar else c_iter[funcname]
if scalar:
varnames.remove('a')
return codebase % dict(init=c_init(varnames), iter=iteration,
finish=c_finish.get(funcname, ''),
ri_redir=(c_ri_redir if nans else c_ri)) | def c_func(funcname, reverse=False, nans=False, scalar=False) | Fill c_funcs with constructed code from the templates | 8.116943 | 7.933195 | 1.023162 |
ilen = step_count(group_idx) + 1
indices = np.empty(ilen, int)
indices[0] = 0
indices[-1] = group_idx.size
inline(c_step_indices, ['group_idx', 'indices'], define_macros=c_macros, extra_compile_args=c_args)
return indices | def step_indices(group_idx) | Get the edges of areas within group_idx, which are filled
with the same value | 5.393188 | 6.065235 | 0.889197 |
# [1]
# return np.random.choice([-np.sqrt(3), 0, np.sqrt(3)], size=size, p=[1 / 6, 2 / 3, 1 / 6])
# [2]
s = 1 / self.density
return np.random.choice([-np.sqrt(s / self.k), 0, np.sqrt(s / self.k)],
size=size,
p=[1 / (2 * s), 1 - 1 / s, 1 / (2 * s)]) | def __create_proj_mat(self, size) | Create a random projection matrix
[1] D. Achlioptas. Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
[2] P. Li, et al. Very sparse random projections.
http://scikit-learn.org/stable/modules/random_projection.html#sparse-random-projection | 2.954024 | 2.586419 | 1.142129 |
all_genres = ['Action',
'Adventure',
'Animation',
"Children's",
'Comedy',
'Crime',
'Documentary',
'Drama',
'Fantasy',
'Film-Noir',
'Horror',
'Musical',
'Mystery',
'Romance',
'Sci-Fi',
'Thriller',
'War',
'Western']
n_genre = len(all_genres)
movies = {}
if size == '100k':
with open(os.path.join(data_home, 'u.item'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: l.rstrip().split('|'), f.readlines()))
for line in lines:
movie_vec = np.zeros(n_genre)
for i, flg_chr in enumerate(line[-n_genre:]):
if flg_chr == '1':
movie_vec[i] = 1.
movie_id = int(line[0])
movies[movie_id] = movie_vec
elif size == '1m':
with open(os.path.join(data_home, 'movies.dat'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: l.rstrip().split('::'), f.readlines()))
for item_id_str, title, genres in lines:
movie_vec = np.zeros(n_genre)
for genre in genres.split('|'):
i = all_genres.index(genre)
movie_vec[i] = 1.
item_id = int(item_id_str)
movies[item_id] = movie_vec
return movies | def load_movies(data_home, size) | Load movie genres as a context.
Returns:
dict of movie vectors: item_id -> numpy array (n_genre,) | 1.711679 | 1.659708 | 1.031313 |
ages = [1, 18, 25, 35, 45, 50, 56, 999]
users = {}
if size == '100k':
all_occupations = ['administrator',
'artist',
'doctor',
'educator',
'engineer',
'entertainment',
'executive',
'healthcare',
'homemaker',
'lawyer',
'librarian',
'marketing',
'none',
'other',
'programmer',
'retired',
'salesman',
'scientist',
'student',
'technician',
'writer']
with open(os.path.join(data_home, 'u.user'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: l.rstrip().split('|'), f.readlines()))
for user_id_str, age_str, sex_str, occupation_str, zip_code in lines:
user_vec = np.zeros(1 + 1 + 21) # 1 categorical, 1 value, 21 categorical
user_vec[0] = 0 if sex_str == 'M' else 1 # sex
# age (ML1M is "age group", but 100k has actual "age")
age = int(age_str)
for i in range(7):
if age >= ages[i] and age < ages[i + 1]:
user_vec[1] = i
break
user_vec[2 + all_occupations.index(occupation_str)] = 1 # occupation (1-of-21)
users[int(user_id_str)] = user_vec
elif size == '1m':
with open(os.path.join(data_home, 'users.dat'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: l.rstrip().split('::'), f.readlines()))
for user_id_str, sex_str, age_str, occupation_str, zip_code in lines:
user_vec = np.zeros(1 + 1 + 21) # 1 categorical, 1 value, 21 categorical
user_vec[0] = 0 if sex_str == 'M' else 1 # sex
user_vec[1] = ages.index(int(age_str)) # age group (1, 18, ...)
user_vec[2 + int(occupation_str)] = 1 # occupation (1-of-21)
users[int(user_id_str)] = user_vec
return users | def load_users(data_home, size) | Load user demographics as contexts.User ID -> {sex (M/F), age (7 groupd), occupation(0-20; 21)}
Returns:
dict of user vectors: user_id -> numpy array (1+1+21,); (sex_flg + age_group + n_occupation, ) | 2.301309 | 2.170929 | 1.060057 |
if size == '100k':
with open(os.path.join(data_home, 'u.data'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: list(map(int, l.rstrip().split('\t'))), f.readlines()))
elif size == '1m':
with open(os.path.join(data_home, 'ratings.dat'), encoding='ISO-8859-1') as f:
lines = list(map(lambda l: list(map(int, l.rstrip().split('::'))), f.readlines()))
ratings = []
for l in lines:
# Since we consider positive-only feedback setting, ratings < 5 will be excluded.
if l[2] == 5:
ratings.append(l)
ratings = np.asarray(ratings)
# sorted by timestamp
return ratings[np.argsort(ratings[:, 3])] | def load_ratings(data_home, size) | Load all samples in the dataset. | 2.459126 | 2.463173 | 0.998357 |
delta = 0
if opt == 'm':
while True:
mdays = monthrange(d1.year, d1.month)[1]
d1 += timedelta(days=mdays)
if d1 <= d2:
delta += 1
else:
break
else:
delta = (d2 - d1).days
return delta | def delta(d1, d2, opt='d') | Compute difference between given 2 dates in month/day. | 2.579663 | 2.288958 | 1.127003 |
vec = np.zeros(sum(dims))
offset = 0
for seed, dim in zip(seeds, dims):
vec[offset:(offset + dim)] = feature_hash(feature, dim, seed)
offset += dim
return vec | def n_feature_hash(feature, dims, seeds) | N-hot-encoded feature hashing.
Args:
feature (str): Target feature represented as string.
dims (list of int): Number of dimensions for each hash value.
seeds (list of float): Seed of each hash function (mmh3).
Returns:
numpy 1d array: n-hot-encoded feature vector for `s`. | 2.945976 | 3.367965 | 0.874705 |
vec = np.zeros(dim)
i = mmh3.hash(feature, seed) % dim
vec[i] = 1
return vec | def feature_hash(feature, dim, seed=123) | Feature hashing.
Args:
feature (str): Target feature represented as string.
dim (int): Number of dimensions for a hash value.
seed (float): Seed of a MurmurHash3 hash function.
Returns:
numpy 1d array: one-hot-encoded feature vector for `s`. | 4.142386 | 4.412919 | 0.938695 |
tp = 0
for r in recommend:
if r in truth:
tp += 1
return tp | def count_true_positive(truth, recommend) | Count number of true positives from given sets of samples.
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
Returns:
int: Number of true positives. | 2.369937 | 4.30554 | 0.550439 |
if len(truth) == 0:
if len(recommend) == 0:
return 1.
return 0.
if k is None:
k = len(recommend)
return count_true_positive(truth, recommend[:k]) / float(truth.size) | def recall(truth, recommend, k=None) | Recall@k.
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
k (int): Top-k items in `recommend` will be recommended.
Returns:
float: Recall@k. | 2.966065 | 3.305001 | 0.897447 |
if len(recommend) == 0:
if len(truth) == 0:
return 1.
return 0.
if k is None:
k = len(recommend)
return count_true_positive(truth, recommend[:k]) / float(k) | def precision(truth, recommend, k=None) | Precision@k.
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
k (int): Top-k items in `recommend` will be recommended.
Returns:
float: Precision@k. | 2.607529 | 2.978189 | 0.875542 |
if len(truth) == 0:
if len(recommend) == 0:
return 1.
return 0.
tp = accum = 0.
for n in range(recommend.size):
if recommend[n] in truth:
tp += 1.
accum += (tp / (n + 1.))
return accum / truth.size | def average_precision(truth, recommend) | Average Precision (AP).
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
Returns:
float: AP. | 2.922138 | 3.164897 | 0.923297 |
tp = correct = 0.
for r in recommend:
if r in truth:
# keep track number of true positives placed before
tp += 1.
else:
correct += tp
# number of all possible tp-fp pairs
pairs = tp * (recommend.size - tp)
# if there is no TP (or no FP), it's meaningless for this metric (i.e., AUC=0.5)
if pairs == 0:
return 0.5
return correct / pairs | def auc(truth, recommend) | Area under the ROC curve (AUC).
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
Returns:
float: AUC. | 7.308402 | 8.729234 | 0.837233 |
for n in range(recommend.size):
if recommend[n] in truth:
return 1. / (n + 1)
return 0. | def reciprocal_rank(truth, recommend) | Reciprocal Rank (RR).
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
Returns:
float: RR. | 3.423032 | 4.429127 | 0.772846 |
if len(recommend) == 0 and len(truth) == 0:
return 0. # best
elif len(truth) == 0 or len(truth) == 0:
return 100. # worst
accum = 0.
n_recommend = recommend.size
for t in truth:
r = np.where(recommend == t)[0][0] / float(n_recommend)
accum += r
return accum * 100. / truth.size | def mpr(truth, recommend) | Mean Percentile Rank (MPR).
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
Returns:
float: MPR. | 3.295807 | 3.475816 | 0.948211 |
if k is None:
k = len(recommend)
def idcg(n_possible_truth):
res = 0.
for n in range(n_possible_truth):
res += 1. / np.log2(n + 2)
return res
dcg = 0.
for n, r in enumerate(recommend[:k]):
if r not in truth:
continue
dcg += 1. / np.log2(n + 2)
res_idcg = idcg(np.min([truth.size, k]))
if res_idcg == 0.:
return 0.
return dcg / res_idcg | def ndcg(truth, recommend, k=None) | Normalized Discounted Cumulative Grain (NDCG).
Args:
truth (numpy 1d array): Set of truth samples.
recommend (numpy 1d array): Ordered set of recommended samples.
k (int): Top-k items in `recommend` will be recommended.
Returns:
float: NDCG. | 2.432622 | 2.702034 | 0.900293 |
# number of observed users
self.n_user = 0
# store user data
self.users = {}
# number of observed items
self.n_item = 0
# store item data
self.items = {} | def initialize(self, *args) | Initialize a recommender by resetting stored users and items. | 3.97389 | 3.176641 | 1.250972 |
self.users[user.index] = {'known_items': set()}
self.n_user += 1 | def register_user(self, user) | For new users, append their information into the dictionaries.
Args:
user (User): User. | 12.633882 | 14.049193 | 0.89926 |
sorted_indices = np.argsort(scores)
if rev:
sorted_indices = sorted_indices[::-1]
return candidates[sorted_indices], scores[sorted_indices] | def scores2recos(self, scores, candidates, rev=False) | Get recommendation list for a user u_index based on scores.
Args:
scores (numpy array; (n_target_items,)):
Scores for the target items. Smaller score indicates a promising item.
candidates (numpy array; (# target items, )): Target items' indices. Only these items are considered as the recommendation candidates.
rev (bool): If true, return items in an descending order. A ascending order (i.e., smaller scores are more promising) is default.
Returns:
(numpy array, numpy array) : (Sorted list of items, Sorted scores). | 2.564451 | 3.125538 | 0.820483 |
# make initial status for batch training
for e in train_events:
self.__validate(e)
self.rec.users[e.user.index]['known_items'].add(e.item.index)
self.item_buffer.append(e.item.index)
# for batch evaluation, temporarily save new users info
for e in test_events:
self.__validate(e)
self.item_buffer.append(e.item.index)
self.__batch_update(train_events, test_events, n_epoch)
# batch test events are considered as a new observations;
# the model is incrementally updated based on them before the incremental evaluation step
for e in test_events:
self.rec.users[e.user.index]['known_items'].add(e.item.index)
self.rec.update(e) | def fit(self, train_events, test_events, n_epoch=1) | Train a model using the first 30% positive events to avoid cold-start.
Evaluation of this batch training is done by using the next 20% positive events.
After the batch SGD training, the models are incrementally updated by using the 20% test events.
Args:
train_events (list of Event): Positive training events (0-30%).
test_events (list of Event): Test events (30-50%).
n_epoch (int): Number of epochs for the batch training. | 5.539002 | 5.112032 | 1.083523 |
for i, e in enumerate(test_events):
self.__validate(e)
# target items (all or unobserved depending on a detaset)
unobserved = set(self.item_buffer)
if not self.repeat:
unobserved -= self.rec.users[e.user.index]['known_items']
# item i interacted by user u must be in the recommendation candidate
# even if it is a new item
unobserved.add(e.item.index)
candidates = np.asarray(list(unobserved))
# make top-{at} recommendation for the 1001 items
start = time.clock()
recos, scores = self.__recommend(e, candidates)
recommend_time = (time.clock() - start)
rank = np.where(recos == e.item.index)[0][0]
# Step 2: update the model with the observed event
self.rec.users[e.user.index]['known_items'].add(e.item.index)
start = time.clock()
self.rec.update(e)
update_time = (time.clock() - start)
self.item_buffer.append(e.item.index)
# (top-1 score, where the correct item is ranked, rec time, update time)
yield scores[0], rank, recommend_time, update_time | def evaluate(self, test_events) | Iterate recommend/update procedure and compute incremental recall.
Args:
test_events (list of Event): Positive test events.
Returns:
list of tuples: (rank, recommend time, update time) | 6.064999 | 5.606741 | 1.081733 |
for epoch in range(n_epoch):
# SGD requires us to shuffle events in each iteration
# * if n_epoch == 1
# => shuffle is not required because it is a deterministic training (i.e. matrix sketching)
if n_epoch != 1:
np.random.shuffle(train_events)
# train
for e in train_events:
self.rec.update(e, batch_train=True)
# test
MPR = self.__batch_evaluate(test_events)
if self.debug:
logger.debug('epoch %2d: MPR = %f' % (epoch + 1, MPR)) | def __batch_update(self, train_events, test_events, n_epoch) | Batch update called by the fitting method.
Args:
train_events (list of Event): Positive training events.
test_events (list of Event): Test events.
n_epoch (int): Number of epochs for the batch training. | 5.472105 | 5.961437 | 0.917917 |
percentiles = np.zeros(len(test_events))
all_items = set(self.item_buffer)
for i, e in enumerate(test_events):
# check if the data allows users to interact the same items repeatedly
unobserved = all_items
if not self.repeat:
# make recommendation for all unobserved items
unobserved -= self.rec.users[e.user.index]['known_items']
# true item itself must be in the recommendation candidates
unobserved.add(e.item.index)
candidates = np.asarray(list(unobserved))
recos, scores = self.__recommend(e, candidates)
pos = np.where(recos == e.item.index)[0][0]
percentiles[i] = pos / (len(recos) - 1) * 100
return np.mean(percentiles) | def __batch_evaluate(self, test_events) | Evaluate the current model by using the given test events.
Args:
test_events (list of Event): Current model is evaluated by these events.
Returns:
float: Mean Percentile Rank for the test set. | 5.91242 | 5.910385 | 1.000344 |
'''Scale X values to new width'''
if type(values) == dict:
values = self._scale_x_values_timestamps(values=values, max_width=max_width)
adjusted_values = list(values)
if len(adjusted_values) > max_width:
def get_position(current_pos):
return len(adjusted_values) * current_pos // max_width
adjusted_values = [statistics.mean(adjusted_values[get_position(i):get_position(i + 1)]) for i in range(max_width)]
return adjusted_values | def _scale_x_values(self, values, max_width) | Scale X values to new width | 3.516097 | 3.262423 | 1.077756 |
'''Scale X values to new width based on timestamps'''
first_timestamp = float(values[0][0])
last_timestamp = float(values[-1][0])
step_size = (last_timestamp - first_timestamp) / max_width
values_by_column = [[] for i in range(max_width)]
for timestamp, value in values:
if value is None:
continue
timestamp = float(timestamp)
column = (timestamp - first_timestamp) // step_size
column = int(min(column, max_width - 1)) # Don't go beyond the last column
values_by_column[column].append(value)
adjusted_values = [statistics.mean(values) if values else 0 for values in values_by_column] # Average each column, 0 if no values
return adjusted_values | def _scale_x_values_timestamps(self, values, max_width) | Scale X values to new width based on timestamps | 2.944258 | 2.745307 | 1.072469 |
'''
Take values and transmute them into a new range
'''
# Scale Y values - Create a scaled list of values to use for the visual graph
scaled_values = []
y_min_value = min(values)
if scale_old_from_zero:
y_min_value = 0
y_max_value = max(values)
new_min = 0
OldRange = (y_max_value - y_min_value) or 1 # Prevents division by zero if all values are the same
NewRange = (new_max - new_min) # max_height is new_max
for old_value in values:
new_value = (((old_value - y_min_value) * NewRange) / OldRange) + new_min
scaled_values.append(new_value)
return scaled_values | def _scale_y_values(self, values, new_min, new_max, scale_old_from_zero=True) | Take values and transmute them into a new range | 3.573125 | 3.119069 | 1.145574 |
'''Create a representation of an ascii graph using two lists in this format: field[x][y] = "char"'''
empty_space = ' '
# This formats as field[x][y]
field = [[empty_space for y in range(max(values) + 1)] for x in range(len(values))]
# Draw graph into field
for x in range(len(values)):
y = values[x]
y_prev = values[x - 1] if x - 1 in range(len(values)) else y
y_next = values[x + 1] if x + 1 in range(len(values)) else y
# Fill empty space
if abs(y_prev - y) > 1:
# Fill space between y and y_prev
step = 1 if y_prev - y > 0 else -1
# We don't want the first item to be inclusive, so we use step instead of y+1 since step can be negative
for h in range(y + step, y_prev, step):
if field[x][h] is empty_space:
field[x][h] = '|'
# Assign the character to be placed into the graph
char = self._assign_ascii_character(y_prev, y, y_next)
field[x][y] = char
return field | def _get_ascii_field(self, values) | Create a representation of an ascii graph using two lists in this format: field[x][y] = "char" | 4.396677 | 3.433986 | 1.280342 |
char = '-'
elif y_next < y and y_prev < y:
char = '-'
elif y_prev < y and y == y_next:
char = '-'
elif y_prev == y and y_next < y:
char = '-'
elif y_next > y:
char = '/'
elif y_next < y:
char = '\\'
elif y_prev < y:
char = '/'
elif y_prev > y:
char = '\\'
elif y_next == y:
char = '-'
elif y == y_prev:
char = '-'
return char | def _assign_ascii_character(self, y_prev, y, y_next): # noqa for complexity
'''Assign the character to be placed into the graph'''
char = '?'
if y_next > y and y_prev > y | Assign the character to be placed into the graph | 2.23483 | 1.925216 | 1.160821 |
'''Draw graph from field double nested list, format field[x][y] = char'''
row_strings = []
for y in range(len(field[0])):
row = ''
for x in range(len(field)):
row += field[x][y]
row_strings.insert(0, row)
graph_string = '\n'.join(row_strings)
return graph_string | def _draw_ascii_graph(self, field) | Draw graph from field double nested list, format field[x][y] = char | 4.007404 | 2.408173 | 1.664085 |
'''
Accepts a list of y values and returns an ascii graph
Optionally values can also be a dictionary with a key of timestamp, and a value of value. InGraphs returns data in this format for example.
'''
result = ''
border_fill_char = '*'
start_ctime = None
end_ctime = None
if not max_width:
max_width = 180
# If this is a dict of timestamp -> value, sort the data, store the start/end time, and convert values to a list of values
if isinstance(values, dict):
time_series_sorted = sorted(list(values.items()), key=lambda x: x[0]) # Sort timestamp/value dict by the timestamps
start_timestamp = time_series_sorted[0][0]
end_timestamp = time_series_sorted[-1][0]
start_ctime = datetime.fromtimestamp(float(start_timestamp)).ctime()
end_ctime = datetime.fromtimestamp(float(end_timestamp)).ctime()
values = self._scale_x_values_timestamps(values=time_series_sorted, max_width=max_width)
values = [value for value in values if value is not None]
if not max_height:
max_height = min(20, max(values))
stdev = statistics.stdev(values)
mean = statistics.mean(values)
# Do value adjustments
adjusted_values = list(values)
adjusted_values = self._scale_x_values(values=values, max_width=max_width)
upper_value = max(adjusted_values) # Getting upper/lower after scaling x values so we don't label a spike we can't see
lower_value = min(adjusted_values)
adjusted_values = self._scale_y_values(values=adjusted_values, new_min=0, new_max=max_height, scale_old_from_zero=False)
adjusted_values = self._round_floats_to_ints(values=adjusted_values)
# Obtain Ascii Graph String
field = self._get_ascii_field(adjusted_values)
graph_string = self._draw_ascii_graph(field=field)
# Label the graph
if label:
top_label = 'Upper value: {upper_value:.2f} '.format(upper_value=upper_value).ljust(max_width, border_fill_char)
result += top_label + '\n'
result += '{graph_string}\n'.format(graph_string=graph_string)
if label:
lower = f'Lower value: {lower_value:.2f} '
stats = f' Mean: {mean:.2f} *** Std Dev: {stdev:.2f} ******'
fill_length = max_width - len(lower) - len(stats)
stat_label = f'{lower}{"*" * fill_length}{stats}\n'
result += stat_label
if start_ctime and end_ctime:
fill_length = max_width - len(start_ctime) - len(end_ctime)
result += f'{start_ctime}{" " * fill_length}{end_ctime}\n'
return result | def asciigraph(self, values=None, max_height=None, max_width=None, label=False) | Accepts a list of y values and returns an ascii graph
Optionally values can also be a dictionary with a key of timestamp, and a value of value. InGraphs returns data in this format for example. | 3.570115 | 3.008437 | 1.186701 |
if isinstance(expression, Pattern):
expression = expression.expression
return _substitute(expression, substitution)[0] | def substitute(expression: Union[Expression, Pattern], substitution: Substitution) -> Replacement | Replaces variables in the given *expression* using the given *substitution*.
>>> print(substitute(f(x_), {'x': a}))
f(a)
If nothing was substituted, the original expression is returned:
>>> expression = f(x_)
>>> result = substitute(expression, {'y': a})
>>> print(result)
f(x_)
>>> expression is result
True
Note that this function returns a list of expressions iff the expression is a variable and its substitution
is a list of expressions. In other cases were a substitution is a list of expressions, the expressions will
be integrated as operands in the surrounding operation:
>>> print(substitute(f(x_, c), {'x': [a, b]}))
f(a, b, c)
If you substitute with a `Multiset` of values, they will be sorted:
>>> replacement = Multiset([b, a, b])
>>> print(substitute(f(x_, c), {'x': replacement}))
f(a, b, b, c)
Parameters:
expression:
An expression in which variables are substituted.
substitution:
A substitution dictionary. The key is the name of the variable,
the value either an expression or a list of expression to use as a replacement for
the variable.
Returns:
The expression resulting from applying the substitution. | 5.443305 | 10.321874 | 0.527356 |
r
if len(position) == 0:
return replacement
if not isinstance(expression, Operation):
raise IndexError("Invalid position {!r} for expression {!s}".format(position, expression))
if position[0] >= op_len(expression):
raise IndexError("Position {!r} out of range for expression {!s}".format(position, expression))
pos = position[0]
operands = list(op_iter(expression))
subexpr = replace(operands[pos], position[1:], replacement)
if isinstance(subexpr, Sequence):
new_operands = tuple(operands[:pos]) + tuple(subexpr) + tuple(operands[pos + 1:])
return create_operation_expression(expression, new_operands)
operands[pos] = subexpr
return create_operation_expression(expression, operands) | def replace(expression: Expression, position: Sequence[int], replacement: Replacement) -> Replacement | r"""Replaces the subexpression of `expression` at the given `position` with the given `replacement`.
The original `expression` itself is not modified, but a modified copy is returned. If the replacement
is a list of expressions, it will be expanded into the list of operands of the respective operation:
>>> print(replace(f(a), (0, ), [b, c]))
f(b, c)
Parameters:
expression:
An :class:`Expression` where a (sub)expression is to be replaced.
position:
A tuple of indices, e.g. the empty tuple refers to the `expression` itself,
`(0, )` refers to the first child (operand) of the `expression`, `(0, 0)` to the first
child of the first child etc.
replacement:
Either an :class:`Expression` or a list of :class:`Expression`\s to be
inserted into the `expression` instead of the original expression at that `position`.
Returns:
The resulting expression from the replacement.
Raises:
IndexError: If the position is invalid or out of range. | 2.950181 | 2.799921 | 1.053666 |
r
if len(replacements) == 0:
return expression
replacements = sorted(replacements)
if len(replacements[0][0]) == 0:
if len(replacements) > 1:
raise IndexError(
"Cannot replace child positions for expression {}, got {!r}".format(expression, replacements[1:])
)
return replacements[0][1]
if len(replacements) == 1:
return replace(expression, replacements[0][0], replacements[0][1])
if not isinstance(expression, Operation):
raise IndexError("Invalid replacements {!r} for expression {!s}".format(replacements, expression))
operands = list(op_iter(expression))
new_operands = []
last_index = 0
for index, group in itertools.groupby(replacements, lambda r: r[0][0]):
new_operands.extend(operands[last_index:index])
replacements = [(pos[1:], r) for pos, r in group]
if len(replacements) == 1:
replacement = replace(operands[index], replacements[0][0], replacements[0][1])
else:
replacement = replace_many(operands[index], replacements)
if isinstance(replacement, (list, tuple, Multiset)):
new_operands.extend(replacement)
else:
new_operands.append(replacement)
last_index = index + 1
new_operands.extend(operands[last_index:len(operands)])
return create_operation_expression(expression, new_operands) | def replace_many(expression: Expression, replacements: Sequence[Tuple[Sequence[int], Replacement]]) -> Replacement | r"""Replaces the subexpressions of *expression* at the given positions with the given replacements.
The original *expression* itself is not modified, but a modified copy is returned. If the replacement
is a sequence of expressions, it will be expanded into the list of operands of the respective operation.
This function works the same as `replace`, but allows multiple positions to be replaced at the same time.
However, compared to just replacing each position individually with `replace`, this does work when positions are
modified due to replacing a position with a sequence:
>>> expr = f(a, b)
>>> expected_result = replace_many(expr, [((0, ), [c, c]), ((1, ), a)])
>>> print(expected_result)
f(c, c, a)
However, using `replace` for one position at a time gives the wrong result:
>>> step1 = replace(expr, (0, ), [c, c])
>>> print(step1)
f(c, c, b)
>>> step2 = replace(step1, (1, ), a)
>>> print(step2)
f(c, a, b)
Parameters:
expression:
An :class:`Expression` where a (sub)expression is to be replaced.
replacements:
A collection of tuples consisting of a position in the expression and a replacement for that position.
With just a single replacement pair, this is equivalent to using `replace`:
>>> replace(a, (), b) == replace_many(a, [((), b)])
True
Returns:
The resulting expression from the replacements.
Raises:
IndexError: If a position is invalid or out of range or if you try to replace a subterm of a term you are
already replacing. | 2.378458 | 2.393391 | 0.993761 |
rules = [ReplacementRule(pattern, replacement) for pattern, replacement in rules]
expression = expression
replaced = True
replace_count = 0
while replaced and replace_count < max_count:
replaced = False
for subexpr, pos in preorder_iter_with_position(expression):
for pattern, replacement in rules:
try:
subst = next(match(subexpr, pattern))
result = replacement(**subst)
expression = replace(expression, pos, result)
replaced = True
break
except StopIteration:
pass
if replaced:
break
replace_count += 1
return expression | def replace_all(expression: Expression, rules: Iterable[ReplacementRule], max_count: int=math.inf) \
-> Union[Expression, Sequence[Expression]] | Replace all occurrences of the patterns according to the replacement rules.
A replacement rule consists of a *pattern*, that is matched against any subexpression
of the expression. If a match is found, the *replacement* callback of the rule is called with
the variables from the match substitution. Whatever the callback returns is used as a replacement for the
matched subexpression. This can either be a single expression or a sequence of expressions, which is then
integrated into the surrounding operation in place of the subexpression.
Note that the pattern can therefore not be a single sequence variable/wildcard, because only single expressions
will be matched.
Args:
expression:
The expression to which the replacement rules are applied.
rules:
A collection of replacement rules that are applied to the expression.
max_count:
If given, at most *max_count* applications of the rules are performed. Otherwise, the rules
are applied until there is no more match. If the set of replacement rules is not confluent,
the replacement might not terminate without a *max_count* set.
Returns:
The resulting expression after the application of the replacement rules. This can also be a sequence of
expressions, if the root expression is replaced with a sequence of expressions by a rule. | 3.019305 | 3.572991 | 0.845036 |
return _replace_all_post_order(expression, rules)[0] | def replace_all_post_order(expression: Expression, rules: Iterable[ReplacementRule]) \
-> Union[Expression, Sequence[Expression]] | Replace all occurrences of the patterns according to the replacement rules.
A replacement rule consists of a *pattern*, that is matched against any subexpression
of the expression. If a match is found, the *replacement* callback of the rule is called with
the variables from the match substitution. Whatever the callback returns is used as a replacement for the
matched subexpression. This can either be a single expression or a sequence of expressions, which is then
integrated into the surrounding operation in place of the subexpression.
Note that the pattern can therefore not be a single sequence variable/wildcard, because only single expressions
will be matched.
Args:
expression:
The expression to which the replacement rules are applied.
rules:
A collection of replacement rules that are applied to the expression.
max_count:
If given, at most *max_count* applications of the rules are performed. Otherwise, the rules
are applied until there is no more match. If the set of replacement rules is not confluent,
the replacement might not terminate without a *max_count* set.
Returns:
The resulting expression after the application of the replacement rules. This can also be a sequence of
expressions, if the root expression is replaced with a sequence of expressions by a rule. | 4.550606 | 13.261083 | 0.343155 |
return any(True for _ in match(subject, pattern)) | def is_match(subject: Expression, pattern: Expression) -> bool | Check whether the given *subject* matches given *pattern*.
Args:
subject:
The subject.
pattern:
The pattern.
Returns:
True iff the subject matches the pattern. | 15.064775 | 21.282749 | 0.70784 |
raise ImportError('The graphviz package is required to draw the graph.')
graph = Graph()
nodes_left = {} # type: Dict[TLeft, str]
nodes_right = {} # type: Dict[TRight, str]
node_id = 0
for (left, right), value in self._edges.items():
if left not in nodes_left:
name = 'node{:d}'.format(node_id)
nodes_left[left] = name
graph.node(name, label=str(left))
node_id += 1
if right not in nodes_right:
name = 'node{:d}'.format(node_id)
nodes_right[right] = name
graph.node(name, label=str(right))
node_id += 1
edge_label = value is not True and str(value) or ''
graph.edge(nodes_left[left], nodes_right[right], edge_label)
return graph | def as_graph(self) -> Graph: # pragma: no cover
if Graph is None | Returns a :class:`graphviz.Graph` representation of this bipartite graph. | 2.129218 | 1.989867 | 1.07003 |
# The directed graph is represented as a dictionary of edges
# The key is the tail of all edges which are represented by the value
# The value is a set of heads for the all edges originating from the tail (key)
# In addition, the graph stores which part of the bipartite graph a node originated from
# to avoid problems when a value exists in both halfs.
# Only one direction of the undirected edge is needed for the HopcroftKarp class
directed_graph = {} # type: Dict[Tuple[int, TLeft], Set[Tuple[int, TRight]]]
for (left, right) in self._edges:
tail = (LEFT, left)
head = (RIGHT, right)
if tail not in directed_graph:
directed_graph[tail] = {head}
else:
directed_graph[tail].add(head)
matching = HopcroftKarp(directed_graph).maximum_matching()
# Filter out the partitions (LEFT and RIGHT) and only return the matching edges
# that go from LEFT to RIGHT
return dict((tail[1], head[1]) for tail, head in matching.items() if tail[0] == LEFT) | def find_matching(self) -> Dict[TLeft, TRight] | Finds a matching in the bipartite graph.
This is done using the Hopcroft-Karp algorithm with an implementation from the
`hopcroftkarp` package.
Returns:
A dictionary where each edge of the matching is represented by a key-value pair
with the key being from the left part of the graph and the value from te right part. | 5.04389 | 4.945477 | 1.0199 |
return BipartiteGraph(((n1, n2), v) for (n1, n2), v in self._edges.items() if n1 != edge[0] and n2 != edge[1]) | def without_nodes(self, edge: Edge) -> 'BipartiteGraph[TLeft, TRight, TEdgeValue]' | Returns a copy of this bipartite graph with the given edge and its adjacent nodes removed. | 2.4347 | 2.419386 | 1.006329 |
return BipartiteGraph((e2, v) for e2, v in self._edges.items() if edge != e2) | def without_edge(self, edge: Edge) -> 'BipartiteGraph[TLeft, TRight, TEdgeValue]' | Returns a copy of this bipartite graph with the given edge removed. | 4.55515 | 4.609358 | 0.98824 |
return BipartiteGraph(((n1, n2), v) for (n1, n2), v in self._edges.items() if n1 in left and n2 in right) | def limited_to(self, left: Set[TLeft], right: Set[TRight]) -> 'BipartiteGraph[TLeft, TRight, TEdgeValue]' | Returns the induced subgraph where only the nodes from the given sets are included. | 2.733119 | 2.744363 | 0.995903 |
raise ImportError('The graphviz package is required to draw the graph.')
graph = Digraph()
subgraphs = [Digraph(graph_attr={'rank': 'same'}), Digraph(graph_attr={'rank': 'same'})]
nodes = [{}, {}] # type: List[Dict[Union[TLeft, TRight], str]]
edges = [] # type: List [Tuple[str, str]]
node_id = 0
for (tail_part, tail), head_set in self.items():
if tail not in nodes[tail_part]:
name = 'node{:d}'.format(node_id)
nodes[tail_part][tail] = name
subgraphs[tail_part].node(name, label=str(tail))
node_id += 1
for head_part, head in head_set:
if head not in nodes[head_part]:
name = 'node{:d}'.format(node_id)
nodes[head_part][head] = name
subgraphs[head_part].node(name, label=str(head))
node_id += 1
edges.append((nodes[tail_part][tail], nodes[head_part][head]))
graph.subgraph(subgraphs[0])
graph.subgraph(subgraphs[1])
for tail_node, head_node in edges:
graph.edge(tail_node, head_node)
return graph | def as_graph(self) -> Digraph: # pragma: no cover
if Digraph is None | Returns a :class:`graphviz.Digraph` representation of this directed match graph. | 2.251864 | 2.206907 | 1.020371 |
if isinstance(expression, Wildcard):
return False
if isinstance(expression, Expression):
return expression.is_constant
if isinstance(expression, Operation):
return all(is_constant(o) for o in op_iter(expression))
return True | def is_constant(expression) | Check if the given expression is constant, i.e. it does not contain Wildcards. | 3.489348 | 3.078031 | 1.13363 |
if isinstance(expression, Wildcard):
return expression.fixed_size
if isinstance(expression, Expression):
return expression.is_syntactic
if isinstance(expression, (AssociativeOperation, CommutativeOperation)):
return False
if isinstance(expression, Operation):
return all(is_syntactic(o) for o in op_iter(expression))
return True | def is_syntactic(expression) | Check if the given expression is syntactic, i.e. it does not contain sequence wildcards or
associative/commutative operations. | 3.65999 | 3.160872 | 1.157905 |
if isinstance(expression, Wildcard):
if isinstance(expression, SymbolWildcard):
return expression.symbol_type
return None
return type(expression) | def get_head(expression) | Returns the given expression's head. | 5.411252 | 4.986569 | 1.085165 |
if isinstance(pattern, Pattern):
pattern = pattern.expression
pattern_head = get_head(pattern)
if pattern_head is None:
return True
if issubclass(pattern_head, OneIdentityOperation):
return True
subject_head = get_head(subject)
assert subject_head is not None
return issubclass(subject_head, pattern_head) | def match_head(subject, pattern) | Checks if the head of subject matches the pattern's head. | 3.389587 | 3.384139 | 1.00161 |
yield expression
if isinstance(expression, Operation):
for operand in op_iter(expression):
yield from preorder_iter(operand) | def preorder_iter(expression) | Iterate over the expression in preorder. | 4.088847 | 3.870757 | 1.056343 |
yield expression, ()
if isinstance(expression, Operation):
for i, operand in enumerate(op_iter(expression)):
for child, pos in preorder_iter_with_position(operand):
yield child, (i, ) + pos | def preorder_iter_with_position(expression) | Iterate over the expression in preorder.
Also yields the position of each subexpression. | 4.052983 | 4.653429 | 0.870967 |
if hasattr(expression, 'variable_name') and expression.variable_name:
return False
if isinstance(expression, Operation):
return all(is_anonymous(o) for o in op_iter(expression))
return True | def is_anonymous(expression) | Returns True iff the expression does not contain any variables. | 3.926522 | 3.66506 | 1.071339 |
if hasattr(expression, 'variable_name') and expression.variable_name in variables:
return True
if isinstance(expression, Operation):
return any(contains_variables_from_set(o, variables) for o in op_iter(expression))
return False | def contains_variables_from_set(expression, variables) | Returns True iff the expression contains any of the variables from the given set. | 3.055142 | 3.018142 | 1.012259 |
if variables is None:
variables = set()
if hasattr(expression, 'variable_name') and expression.variable_name is not None:
variables.add(expression.variable_name)
if isinstance(expression, Operation):
for operand in op_iter(expression):
get_variables(operand, variables)
return variables | def get_variables(expression, variables=None) | Returns the set of variable names in the given expression. | 2.463719 | 2.306363 | 1.068227 |
if isinstance(expression, Operation):
if hasattr(expression, 'variable_name'):
variable_name = renaming.get(expression.variable_name, expression.variable_name)
return create_operation_expression(
expression, [rename_variables(o, renaming) for o in op_iter(expression)], variable_name=variable_name
)
operands = [rename_variables(o, renaming) for o in op_iter(expression)]
return create_operation_expression(expression, operands)
elif isinstance(expression, Expression):
expression = expression.__copy__()
expression.variable_name = renaming.get(expression.variable_name, expression.variable_name)
return expression | def rename_variables(expression: Expression, renaming: Dict[str, str]) -> Expression | Rename the variables in the expression according to the given dictionary.
Args:
expression:
The expression in which the variables are renamed.
renaming:
The renaming dictionary. Maps old variable names to new ones.
Variable names not occuring in the dictionary are left unchanged.
Returns:
The expression with renamed variables. | 2.552687 | 2.661186 | 0.959229 |
if vector_sum < 0:
raise ValueError("Vector sum must not be negative")
if len(max_vector) == 0:
if vector_sum == 0:
yield tuple()
return
total = sum(max_vector)
if vector_sum <= total:
start = max(max_vector[0] + vector_sum - total, 0)
end = min(max_vector[0], vector_sum)
for j in range(start, end + 1):
for vec in fixed_integer_vector_iter(max_vector[1:], vector_sum - j):
yield (j, ) + vec | def fixed_integer_vector_iter(max_vector: Tuple[int, ...], vector_sum: int) -> Iterator[Tuple[int, ...]] | Return an iterator over the integer vectors which
- are componentwise less than or equal to *max_vector*, and
- are non-negative, and where
- the sum of their components is exactly *vector_sum*.
The iterator yields the vectors in lexicographical order.
Examples:
List all vectors that are between ``(0, 0)`` and ``(2, 2)`` componentwise, where the sum of components is 2:
>>> vectors = list(fixed_integer_vector_iter([2, 2], 2))
>>> vectors
[(0, 2), (1, 1), (2, 0)]
>>> list(map(sum, vectors))
[2, 2, 2]
Args:
max_vector:
Maximum vector for the iteration. Every yielded result will be less than or equal to this componentwise.
vector_sum:
Every iterated vector will have a component sum equal to this value.
Yields:
All non-negative vectors that have the given sum and are not larger than the given maximum.
Raises:
ValueError:
If *vector_sum* is negative. | 2.011697 | 2.449595 | 0.821236 |
if n < 0:
raise ValueError("Total must not be negative")
if num_parts < 0:
raise ValueError("Number of num_parts must not be negative")
if num_parts == 0:
if n == 0:
yield tuple()
return
m = n + num_parts - 1
last = (m, )
first = (-1, )
for t in itertools.combinations(range(m), num_parts - 1):
yield tuple(v - u - 1 for u, v in zip(first + t, t + last)) | def weak_composition_iter(n: int, num_parts: int) -> Iterator[Tuple[int, ...]] | Yield all weak compositions of integer *n* into *num_parts* parts.
Each composition is yielded as a tuple. The generated partitions are order-dependant and not unique when
ignoring the order of the components. The partitions are yielded in lexicographical order.
Example:
>>> compositions = list(weak_composition_iter(5, 2))
>>> compositions
[(0, 5), (1, 4), (2, 3), (3, 2), (4, 1), (5, 0)]
We can easily verify that all compositions are indeed valid:
>>> list(map(sum, compositions))
[5, 5, 5, 5, 5, 5]
The algorithm was adapted from an answer to this `Stackoverflow question`_.
Args:
n:
The integer to partition.
num_parts:
The number of parts for the combination.
Yields:
All non-negative tuples that have the given sum and size.
Raises:
ValueError:
If *n* or *num_parts* are negative.
.. _Stackoverflow question: http://stackoverflow.com/questions/40538923/40540014#40540014 | 3.334639 | 3.683947 | 0.905181 |
if len(variables) == 1:
yield from _commutative_single_variable_partiton_iter(values, variables[0])
return
generators = []
for value, count in values.items():
generators.append(_make_variable_generator_factory(value, count, variables))
initial = dict((var.name, Multiset()) for var in variables) # type: Dict[str, 'Multiset[T]']
for subst in generator_chain(initial, *generators):
valid = True
for var in variables:
if var.default is not None and len(subst[var.name]) == 0:
subst[var.name] = var.default
elif len(subst[var.name]) < var.minimum:
valid = False
break
if valid:
if None in subst:
del subst[None]
yield subst | def commutative_sequence_variable_partition_iter(values: Multiset, variables: List[VariableWithCount]
) -> Iterator[Dict[str, Multiset]] | Yield all possible variable substitutions for given values and variables.
.. note::
The results are not yielded in any particular order because the algorithm uses dictionaries. Dictionaries until
Python 3.6 do not keep track of the insertion order.
Example:
For a subject like ``fc(a, a, a, b, b, c)`` and a pattern like ``f(x__, y___, y___)`` one can define the
following input parameters for the partitioning:
>>> x = VariableWithCount(name='x', count=1, minimum=1, default=None)
>>> y = VariableWithCount(name='y', count=2, minimum=0, default=None)
>>> values = Multiset('aaabbc')
Then the solutions are found (and sorted to get a unique output):
>>> substitutions = commutative_sequence_variable_partition_iter(values, [x, y])
>>> as_strings = list(str(Substitution(substitution)) for substitution in substitutions)
>>> for substitution in sorted(as_strings):
... print(substitution)
{x ↦ {a, a, a, b, b, c}, y ↦ {}}
{x ↦ {a, a, a, c}, y ↦ {b}}
{x ↦ {a, b, b, c}, y ↦ {a}}
{x ↦ {a, c}, y ↦ {a, b}}
Args:
values:
The multiset of values which are partitioned and distributed among the variables.
variables:
A list of the variables to distribute the values among. Each variable has a name, a count of how many times
it occurs and a minimum number of values it needs.
Yields:
Each possible substitutions that is a valid partitioning of the values among the variables. | 3.099422 | 3.145462 | 0.985363 |
try:
all_source_lines, lnum = inspect.findsource(lambda_func)
source_lines, _ = inspect.getsourcelines(lambda_func)
except (IOError, TypeError):
return None
all_source_lines = [l.rstrip('\r\n') for l in all_source_lines]
block_end = lnum + len(source_lines)
source_ast = None
for i in range(lnum, -1, -1):
try:
block = all_source_lines[i:block_end]
if block[0].startswith(' ') or block[0].startswith('\t'):
block.insert(0, 'with 0:')
source_ast = ast.parse(os.linesep.join(block))
except (SyntaxError, tokenize.TokenError):
pass
else:
break
nv = LambdaNodeVisitor(block)
nv.visit(source_ast)
lambda_code = lambda_func.__code__
for candidate_code, lambda_text in nv.lambdas:
candidate_code = candidate_code.co_consts[0]
# We don't check for direct equivalence since the flags can be different
if (candidate_code.co_code == lambda_code.co_code and
candidate_code.co_consts == lambda_code.co_consts and
candidate_code.co_names == lambda_code.co_names and
candidate_code.co_varnames == lambda_code.co_varnames and
candidate_code.co_cellvars == lambda_code.co_cellvars and
candidate_code.co_freevars == lambda_code.co_freevars):
return lambda_text[lambda_text.index(':')+1:].strip()
return None | def get_short_lambda_source(lambda_func: LambdaType) -> Optional[str] | Return the source of a (short) lambda function.
If it's impossible to obtain, return ``None``.
The source is returned without the ``lambda`` and signature parts:
>>> get_short_lambda_source(lambda x, y: x < y)
'x < y'
This should work well for most lambda definitions, however for multi-line or highly nested lambdas,
the source extraction might not succeed.
Args:
lambda_func:
The lambda function.
Returns:
The source of the lambda function without its signature. | 2.602059 | 2.62796 | 0.990144 |
if b == 0:
return (1, 0, a)
x0, y0, d = extended_euclid(b, a % b)
x, y = y0, x0 - (a // b) * y0
return (x, y, d) | def extended_euclid(a: int, b: int) -> Tuple[int, int, int] | Extended Euclidean algorithm that computes the Bézout coefficients as well as :math:`gcd(a, b)`
Returns ``x, y, d`` where *x* and *y* are a solution to :math:`ax + by = d` and :math:`d = gcd(a, b)`.
*x* and *y* are a minimal pair of Bézout's coefficients.
See `Extended Euclidean algorithm <https://en.wikipedia.org/wiki/Extended_Euclidean_algorithm>`_ or
`Bézout's identity <https://en.wikipedia.org/wiki/B%C3%A9zout%27s_identity>`_ for more information.
Example:
Compute the Bézout coefficients and GCD of 42 and 12:
>>> a, b = 42, 12
>>> x, y, d = extended_euclid(a, b)
>>> x, y, d
(1, -3, 6)
Verify the results:
>>> import math
>>> d == math.gcd(a, b)
True
>>> a * x + b * y == d
True
Args:
a:
The first integer.
b:
The second integer.
Returns:
A tuple with the Bézout coefficients and the greatest common divider of the arguments. | 1.87788 | 2.493742 | 0.753037 |
r
if a <= 0 or b <= 0:
raise ValueError('Coefficients a and b must be positive integers.')
if c < 0:
raise ValueError('Constant c must not be negative.')
d = math.gcd(a, math.gcd(b, c))
a = a // d
b = b // d
c = c // d
if c == 0:
yield (0, 0)
else:
x0, y0, d = extended_euclid(a, b)
# If c is not divisible by gcd(a, b), then there is no solution
if c % d != 0:
return
x, y = c * x0, c * y0
if x <= 0:
while y >= 0:
if x >= 0:
yield (x, y)
x += b
y -= a
else:
while x >= 0:
if y >= 0:
yield (x, y)
x -= b
y += a | def base_solution_linear(a: int, b: int, c: int) -> Iterator[Tuple[int, int]] | r"""Yield solutions for a basic linear Diophantine equation of the form :math:`ax + by = c`.
First, the equation is normalized by dividing :math:`a, b, c` by their gcd.
Then, the extended Euclidean algorithm (:func:`extended_euclid`) is used to find a base solution :math:`(x_0, y_0)`.
All non-negative solutions are generated by using that the general solution is :math:`(x_0 + b t, y_0 - a t)`.
Because the base solution is one of the minimal pairs of Bézout's coefficients, for all non-negative solutions
either :math:`t \geq 0` or :math:`t \leq 0` must hold. Also, all the non-negative solutions are consecutive with
respect to :math:`t`.
Hence, by adding or subtracting :math:`a` resp. :math:`b` from the base solution, all non-negative solutions can
be efficiently generated.
Args:
a:
The first coefficient of the equation.
b:
The second coefficient of the equation.
c:
The constant of the equation.
Yields:
Each non-negative integer solution of the equation as a tuple ``(x, y)``.
Raises:
ValueError:
If any of the coefficients is not a positive integer. | 2.460596 | 2.345567 | 1.049041 |
r
if len(coeffs) == 0:
if total == 0:
yield tuple()
return
if len(coeffs) == 1:
if total % coeffs[0] == 0:
yield (total // coeffs[0], )
return
if len(coeffs) == 2:
yield from base_solution_linear(coeffs[0], coeffs[1], total)
return
# calculate gcd(coeffs[1:])
remainder_gcd = math.gcd(coeffs[1], coeffs[2])
for coeff in coeffs[3:]:
remainder_gcd = math.gcd(remainder_gcd, coeff)
# solve coeffs[0] * x + remainder_gcd * y = total
for coeff0_solution, remainder_gcd_solution in base_solution_linear(coeffs[0], remainder_gcd, total):
new_coeffs = [c // remainder_gcd for c in coeffs[1:]]
# use the solutions for y to solve the remaining variables recursively
for remainder_solution in solve_linear_diop(remainder_gcd_solution, *new_coeffs):
yield (coeff0_solution, ) + remainder_solution | def solve_linear_diop(total: int, *coeffs: int) -> Iterator[Tuple[int, ...]] | r"""Yield non-negative integer solutions of a linear Diophantine equation of the format
:math:`c_1 x_1 + \dots + c_n x_n = total`.
If there are at most two coefficients, :func:`base_solution_linear()` is used to find the solutions.
Otherwise, the solutions are found recursively, by reducing the number of variables in each recursion:
1. Compute :math:`d := gcd(c_2, \dots , c_n)`
2. Solve :math:`c_1 x + d y = total`
3. Recursively solve :math:`c_2 x_2 + \dots + c_n x_n = y` for each solution for :math:`y`
4. Combine these solutions to form a solution for the whole equation
Args:
total:
The constant of the equation.
*coeffs:
The coefficients :math:`c_i` of the equation.
Yields:
The non-negative integer solutions of the equation as a tuple :math:`(x_1, \dots, x_n)`. | 2.777324 | 2.580874 | 1.076118 |
generator_count = len(factories)
if generator_count == 0:
yield initial_data
return
generators = [None] * generator_count # type: List[Optional[Iterator[T]]]
next_data = initial_data
generator_index = 0
while True:
try:
while generator_index < generator_count:
if generators[generator_index] is None:
generators[generator_index] = factories[generator_index](next_data)
next_data = next(generators[generator_index])
generator_index += 1
yield next_data
generator_index -= 1
except StopIteration:
generators[generator_index] = None
generator_index -= 1
if generator_index < 0:
break | def generator_chain(initial_data: T, *factories: Callable[[T], Iterator[T]]) -> Iterator[T] | Chain multiple generators together by passing results from one to the next.
This helper function allows to create a chain of generator where each generator is constructed by a factory that
gets the data yielded by the previous generator. So each generator can generate new data dependant on the data
yielded by the previous one. For each data item yielded by a generator, a new generator is constructed by the
next factory.
Example:
Lets say for every number from 0 to 4, we want to count up to that number. Then we can do
something like this using list comprehensions:
>>> [i for n in range(1, 5) for i in range(1, n + 1)]
[1, 1, 2, 1, 2, 3, 1, 2, 3, 4]
You can use this function to achieve the same thing:
>>> list(generator_chain(5, lambda n: iter(range(1, n)), lambda i: iter(range(1, i + 1))))
[1, 1, 2, 1, 2, 3, 1, 2, 3, 4]
The advantage is, that this is independent of the number of dependant generators you have.
Also, this function does not use recursion so it is safe to use even with large generator counts.
Args:
initial_data:
The initial data that is passed to the first generator factory.
*factories:
The generator factories. Each of them gets passed its predecessors data and has to return an iterable.
The data from this iterable is passed to the next factory.
Yields:
Every data item yielded by the generators of the final factory. | 1.841482 | 2.09144 | 0.880485 |
if variable_name not in self:
self[variable_name] = replacement.copy() if isinstance(replacement, Multiset) else replacement
else:
existing_value = self[variable_name]
if isinstance(existing_value, tuple):
if isinstance(replacement, Multiset):
if Multiset(existing_value) != replacement:
raise ValueError
elif replacement != existing_value:
raise ValueError
elif isinstance(existing_value, Multiset):
if not isinstance(replacement, (tuple, list, Multiset)):
raise ValueError
compare_value = Multiset(replacement)
if existing_value == compare_value:
if not isinstance(replacement, Multiset):
self[variable_name] = replacement
else:
raise ValueError
elif replacement != existing_value:
raise ValueError | def try_add_variable(self, variable_name: str, replacement: VariableReplacement) -> None | Try to add the variable with its replacement to the substitution.
This considers an existing replacement and will only succeed if the new replacement
can be merged with the old replacement. Merging can occur if either the two replacements
are equivalent. Replacements can also be merged if the old replacement for the variable_name was
unordered (i.e. a :class:`~.Multiset`) and the new one is an equivalent ordered version of it:
>>> subst = Substitution({'x': Multiset(['a', 'b'])})
>>> subst.try_add_variable('x', ('a', 'b'))
>>> print(subst)
{x ↦ (a, b)}
Args:
variable:
The name of the variable to add.
replacement:
The replacement for the variable.
Raises:
ValueError:
if the variable cannot be merged because it conflicts with the existing
substitution for the variable_name. | 2.565126 | 2.3594 | 1.087194 |
new_subst = Substitution(self)
new_subst.try_add_variable(variable, replacement)
return new_subst | def union_with_variable(self, variable: str, replacement: VariableReplacement) -> 'Substitution' | Try to create a new substitution with the given variable added.
See :meth:`try_add_variable` for a version of this method that modifies the substitution
in place.
Args:
variable_name:
The name of the variable to add.
replacement:
The substitution for the variable.
Returns:
The new substitution with the variable_name added or merged.
Raises:
ValueError:
if the variable cannot be merged because it conflicts with the existing
substitution for the variable. | 3.891778 | 4.692647 | 0.829335 |
if getattr(pattern, 'variable_name', False):
try:
self.try_add_variable(pattern.variable_name, subject)
except ValueError:
return False
return True
elif isinstance(pattern, expressions.Operation):
assert isinstance(subject, type(pattern))
assert op_len(subject) == op_len(pattern)
op_expression = cast(expressions.Operation, subject)
for subj, patt in zip(op_iter(op_expression), op_iter(pattern)):
if not self.extract_substitution(subj, patt):
return False
return True | def extract_substitution(self, subject: 'expressions.Expression', pattern: 'expressions.Expression') -> bool | Extract the variable substitution for the given pattern and subject.
This assumes that subject and pattern already match when being considered as linear.
Also, they both must be :term:`syntactic`, as sequence variables cannot be handled here.
All that this method does is checking whether all the substitutions for the variables can be unified.
So, in case it returns ``False``, the substitution is invalid for the match.
..warning::
This method mutates the substitution and will even do so in case the extraction fails.
Create a copy before using this method if you need to preserve the original substitution.
Example:
With an empty initial substitution and a linear pattern, the extraction will always succeed:
>>> subst = Substitution()
>>> subst.extract_substitution(f(a, b), f(x_, y_))
True
>>> print(subst)
{x ↦ a, y ↦ b}
Clashing values for existing variables will fail:
>>> subst.extract_substitution(b, x_)
False
For non-linear patterns, the extraction can also fail with an empty substitution:
>>> subst = Substitution()
>>> subst.extract_substitution(f(a, b), f(x_, x_))
False
>>> print(subst)
{x ↦ a}
Note that the initial substitution got mutated even though the extraction failed!
Args:
subject:
A :term:`syntactic` subject that matches the pattern.
pattern:
A :term:`syntactic` pattern that matches the subject.
Returns:
``True`` iff the substitution could be extracted successfully. | 3.563608 | 3.901681 | 0.913352 |
new_subst = Substitution(self)
for other in others:
for variable_name, replacement in other.items():
new_subst.try_add_variable(variable_name, replacement)
return new_subst | def union(self, *others: 'Substitution') -> 'Substitution' | Try to merge the substitutions.
If a variable occurs in multiple substitutions, try to merge the replacements.
See :meth:`union_with_variable` to see how replacements are merged.
Does not modify any of the original substitutions.
Example:
>>> subst1 = Substitution({'x': Multiset(['a', 'b']), 'z': a})
>>> subst2 = Substitution({'x': ('a', 'b'), 'y': ('c', )})
>>> print(subst1.union(subst2))
{x ↦ (a, b), y ↦ (c), z ↦ a}
Args:
others:
The other substitutions to merge with this one.
Returns:
The new substitution with the other substitutions merged.
Raises:
ValueError:
if a variable occurs in multiple substitutions but cannot be merged because the
substitutions conflict. | 3.277776 | 4.089457 | 0.801519 |
return Substitution((renaming.get(name, name), value) for name, value in self.items()) | def rename(self, renaming: Dict[str, str]) -> 'Substitution' | Return a copy of the substitution with renamed variables.
Example:
Rename the variable *x* to *y*:
>>> subst = Substitution({'x': a})
>>> subst.rename({'x': 'y'})
{'y': Symbol('a')}
Args:
renaming:
A dictionary mapping old variable names to new ones.
Returns:
A copy of the substitution where variable names have been replaced according to the given renaming
dictionary. Names that are not contained in the dictionary are left unchanged. | 4.30852 | 7.639821 | 0.563956 |
return isinstance(term, type) and issubclass(term, Operation) | def is_operation(term: Any) -> bool | Return True iff the given term is a subclass of :class:`.Operation`. | 6.873792 | 3.346395 | 2.054088 |
return isinstance(term, type) and issubclass(term, Symbol) | def is_symbol_wildcard(term: Any) -> bool | Return True iff the given term is a subclass of :class:`.Symbol`. | 8.980778 | 3.654099 | 2.457727 |
return next((t for t in state.keys() if is_symbol_wildcard(t) and isinstance(symbol, t)), None) | def _get_symbol_wildcard_label(state: '_State', symbol: Symbol) -> Type[Symbol] | Return the transition target for the given symbol type from the the given state or None if it does not exist. | 5.329668 | 4.58464 | 1.162505 |
return term.name + '('
elif is_symbol_wildcard(term):
return '*{!s}'.format(term.__name__)
elif isinstance(term, Wildcard):
return '*{!s}{!s}'.format(term.min_count, (not term.fixed_size) and '+' or '')
elif term == Wildcard:
return '*'
else:
return str(term) | def _term_str(term: TermAtom) -> str: # pragma: no cover
if is_operation(term) | Return a string representation of a term atom. | 4.924381 | 4.605997 | 1.069124 |
for term in self._terms:
if isinstance(term, Wildcard) and not term.fixed_size:
return False
if is_operation(term) and issubclass(term, (AssociativeOperation, CommutativeOperation)):
return False
return True | def is_syntactic(self) | True, iff the flatterm is :term:`syntactic`. | 5.982324 | 5.369843 | 1.114059 |
return cls(cls._combined_wildcards_iter(sum(flatterms, cls.empty()))) | def merged(cls, *flatterms: 'FlatTerm') -> 'FlatTerm' | Concatenate the given flatterms to a single flatterm.
Args:
*flatterms:
The flatterms which are concatenated.
Returns:
The concatenated flatterms. | 15.701852 | 22.73834 | 0.690545 |
if isinstance(expression, Operation):
yield type(expression)
for operand in op_iter(expression):
yield from cls._flatterm_iter(operand)
yield OPERATION_END
elif isinstance(expression, SymbolWildcard):
yield expression.symbol_type
elif isinstance(expression, (Symbol, Wildcard)):
yield expression
else:
assert False, "Unreachable unless a new unsupported expression type is added." | def _flatterm_iter(cls, expression: Expression) -> Iterator[TermAtom] | Generator that yields the atoms of the expressions in prefix notation with operation end markers. | 5.073122 | 4.509571 | 1.124968 |
last_wildcard = None # type: Optional[Wildcard]
for term in flatterm:
if isinstance(term, Wildcard) and not isinstance(term, SymbolWildcard):
if last_wildcard is not None:
new_min_count = last_wildcard.min_count + term.min_count
new_fixed_size = last_wildcard.fixed_size and term.fixed_size
last_wildcard = Wildcard(new_min_count, new_fixed_size)
else:
last_wildcard = Wildcard(term.min_count, term.fixed_size)
else:
if last_wildcard is not None:
yield last_wildcard
last_wildcard = None
yield term
if last_wildcard is not None:
yield last_wildcard | def _combined_wildcards_iter(flatterm: Iterator[TermAtom]) -> Iterator[TermAtom] | Combine consecutive wildcards in a flatterm into a single one. | 1.98983 | 1.891322 | 1.052085 |
labels = set() # type: Set[TransitionLabel]
if self.state1 is not None and self.fixed != 1:
labels.update(self.state1.keys())
if self.state2 is not None and self.fixed != 2:
labels.update(self.state2.keys())
if self.fixed != 0:
if self.fixed == 1 and self.state2 is None:
labels.add(OPERATION_END)
elif self.fixed == 2 and self.state1 is None:
labels.add(OPERATION_END)
labels.add(Wildcard)
return labels | def labels(self) -> Set[TransitionLabel] | Return the set of transition labels to examine for this queue state.
This is the union of the transition label sets for both states.
However, if one of the states is fixed, it is excluded from this union and a wildcard transition is included
instead. Also, when already in a failed state (one of the states is ``None``), the :const:`OPERATION_END` is
also included. | 2.631496 | 2.11056 | 1.246824 |
index = len(self._patterns)
self._patterns.append((pattern, final_label))
flatterm = FlatTerm(pattern.expression) if not isinstance(pattern, FlatTerm) else pattern
if flatterm.is_syntactic or len(flatterm) == 1:
net = self._generate_syntactic_net(flatterm, index)
else:
net = self._generate_net(flatterm, index)
if self._root:
self._root = self._product_net(self._root, net)
else:
self._root = net
return index | def add(self, pattern: Union[Pattern, FlatTerm], final_label: T=None) -> int | Add a pattern to the discrimination net.
Args:
pattern:
The pattern which is added to the DiscriminationNet. If an expression is given, it will be converted to
a `FlatTerm` for internal processing. You can also pass a `FlatTerm` directly.
final_label:
A label that is returned if the pattern matches when using :meth:`match`. This will default to the
pattern itself.
Returns:
The index of the newly added pattern. This is used internally to later to get the pattern and its final
label once a match is found. | 3.330115 | 3.288321 | 1.01271 |
# Capture the last sequence wildcard for every level of operation nesting on a stack
# Used to add backtracking edges in case the "match" fails later
last_wildcards = [None]
# Generate a fail state for every level of nesting to backtrack to a sequence wildcard in a parent Expression
# in case no match can be found
fail_states = [None]
operand_counts = [0]
root = state = _State()
states = {root.id: root}
for term in flatterm:
if operand_counts[-1] >= 0:
operand_counts[-1] += 1
# For wildcards, generate a chain of #min_count Wildcard edges
# If the wildcard is unbounded (fixed_size = False),
# add a wildcard self loop at the end
if isinstance(term, Wildcard):
# Generate a chain of #min_count Wildcard edges
for _ in range(term.min_count):
state = cls._create_child_state(state, Wildcard)
states[state.id] = state
# If it is a sequence wildcard, add a self loop
if not term.fixed_size:
state[Wildcard] = state
last_wildcards[-1] = state
operand_counts[-1] = -1
else:
state = cls._create_child_state(state, term)
states[state.id] = state
if is_operation(term):
fail_state = None
if last_wildcards[-1] or fail_states[-1]:
last_fail_state = (
fail_states[-1]
if not isinstance(fail_states[-1], list) else fail_states[-1][operand_counts[-1]]
)
if term.arity.fixed_size:
fail_state = _State()
states[fail_state.id] = fail_state
new_fail_states = [fail_state]
for _ in range(term.arity.min_count):
new_fail_state = _State()
states[new_fail_state.id] = new_fail_state
fail_state[Wildcard] = new_fail_state
fail_state = new_fail_state
new_fail_states.append(new_fail_state)
fail_state[OPERATION_END] = last_wildcards[-1] or last_fail_state
fail_state = new_fail_states
else:
fail_state = _State()
states[fail_state.id] = fail_state
fail_state[OPERATION_END] = last_wildcards[-1] or last_fail_state
fail_state[Wildcard] = fail_state
fail_states.append(fail_state)
last_wildcards.append(None)
operand_counts.append(0)
elif term == OPERATION_END:
fail_states.pop()
last_wildcards.pop()
operand_counts.pop()
if last_wildcards[-1] != state:
if last_wildcards[-1]:
state[EPSILON] = last_wildcards[-1]
elif fail_states[-1]:
last_fail_state = (
fail_states[-1]
if not isinstance(fail_states[-1], list) else fail_states[-1][operand_counts[-1]]
)
state[EPSILON] = last_fail_state
state.payload = [final_label]
return cls._convert_nfa_to_dfa(root, states) | def _generate_net(cls, flatterm: FlatTerm, final_label: T) -> _State[T] | Generates a DFA matching the given pattern. | 3.021951 | 2.991003 | 1.010347 |
for index in self._match(subject):
pattern, label = self._patterns[index]
subst = Substitution()
if subst.extract_substitution(subject, pattern.expression):
for constraint in pattern.constraints:
if not constraint(subst):
break
else:
yield label, subst | def match(self, subject: Union[Expression, FlatTerm]) -> Iterator[Tuple[T, Substitution]] | Match the given subject against all patterns in the net.
Args:
subject:
The subject that is matched. Must be constant.
Yields:
A tuple :code:`(final label, substitution)`, where the first component is the final label associated with
the pattern as given when using :meth:`add()` and the second one is the match substitution. | 5.441216 | 5.550204 | 0.980363 |
try:
next(self.match(subject))
except StopIteration:
return False
return True | def is_match(self, subject: Union[Expression, FlatTerm]) -> bool | Check if the given subject matches any pattern in the net.
Args:
subject:
The subject that is matched. Must be constant.
Returns:
True, if any pattern matches the subject. | 5.941684 | 5.193182 | 1.144132 |
raise ImportError('The graphviz package is required to draw the graph.')
dot = Digraph()
nodes = set()
queue = [self._root]
while queue:
state = queue.pop(0)
if not state.payload:
dot.node('n{!s}'.format(state.id), '', {'shape': ('circle' if state else 'doublecircle')})
else:
dot.node('n{!s}'.format(state.id), '\n'.join(map(str, state.payload)), {'shape': 'box'})
for next_state in state.values():
if next_state.id not in nodes:
queue.append(next_state)
nodes.add(state.id)
nodes = set()
queue = [self._root]
while queue:
state = queue.pop(0)
if state.id in nodes:
continue
nodes.add(state.id)
for (label, other) in state.items():
dot.edge('n{!s}'.format(state.id), 'n{!s}'.format(other.id), _term_str(label))
if other.id not in nodes:
queue.append(other)
return dot | def as_graph(self) -> Digraph: # pragma: no cover
if Digraph is None | Renders the discrimination net as graphviz digraph. | 2.632157 | 2.607529 | 1.009445 |
inner = pattern.expression
if self.operation is None:
if not isinstance(inner, Operation) or isinstance(inner, CommutativeOperation):
raise TypeError("Pattern must be a non-commutative operation.")
self.operation = type(inner)
elif not isinstance(inner, self.operation):
raise TypeError(
"All patterns must be the same operation, expected {} but got {}".format(self.operation, type(inner))
)
if op_len(inner) < 3:
raise ValueError("Pattern has not enough operands.")
operands = list(op_iter(inner))
first_name = self._check_wildcard_and_get_name(operands[0])
last_name = self._check_wildcard_and_get_name(operands[-1])
index = len(self._patterns)
self._patterns.append((pattern, first_name, last_name))
flatterm = FlatTerm.merged(*(FlatTerm(o) for o in operands[1:-1]))
self._net.add(flatterm, index)
return index | def add(self, pattern: Pattern) -> int | Add a pattern that will be recognized by the matcher.
Args:
pattern:
The pattern to add.
Returns:
An internal index for the pattern.
Raises:
ValueError:
If the pattern does not have the correct form.
TypeError:
If the pattern is not a non-commutative operation. | 4.110425 | 3.830736 | 1.073012 |
if not isinstance(pattern.expression, Operation) or isinstance(pattern.expression, CommutativeOperation):
return False
if op_len(pattern.expression) < 3:
return False
first, *_, last = op_iter(pattern.expression)
try:
cls._check_wildcard_and_get_name(first)
cls._check_wildcard_and_get_name(last)
except ValueError:
return False
return True | def can_match(cls, pattern: Pattern) -> bool | Check if a pattern can be matched with a sequence matcher.
Args:
pattern:
The pattern to check.
Returns:
True, iff the pattern can be matched with a sequence matcher. | 4.771484 | 4.757958 | 1.002843 |
if not isinstance(subject, self.operation):
return
subjects = list(op_iter(subject))
flatterms = [FlatTerm(o) for o in subjects]
for i in range(len(flatterms)):
flatterm = FlatTerm.merged(*flatterms[i:])
for index in self._net._match(flatterm, collect=True):
match_index = self._net._patterns[index][1]
pattern, first_name, last_name = self._patterns[match_index]
operand_count = op_len(pattern.expression) - 2
expr_operands = subjects[i:i + operand_count]
patt_operands = list(op_iter(pattern.expression))[1:-1]
substitution = Substitution()
if not all(itertools.starmap(substitution.extract_substitution, zip(expr_operands, patt_operands))):
continue
try:
if first_name is not None:
substitution.try_add_variable(first_name, tuple(subjects[:i]))
if last_name is not None:
substitution.try_add_variable(last_name, tuple(subjects[i + operand_count:]))
except ValueError:
continue
for constraint in pattern.constraints:
if not constraint(substitution):
break
else:
yield pattern, substitution | def match(self, subject: Expression) -> Iterator[Tuple[Pattern, Substitution]] | Match the given subject against all patterns in the sequence matcher.
Args:
subject:
The subject that is matched. Must be constant.
Yields:
A tuple :code:`(pattern, substitution)` for every matching pattern. | 4.245328 | 4.402036 | 0.964401 |
r
if not is_constant(subject):
raise ValueError("The subject for matching must be constant.")
global_constraints = [c for c in pattern.constraints if not c.variables]
local_constraints = set(c for c in pattern.constraints if c.variables)
for subst in _match([subject], pattern.expression, Substitution(), local_constraints):
for constraint in global_constraints:
if not constraint(subst):
break
else:
yield subst | def match(subject: Expression, pattern: Pattern) -> Iterator[Substitution] | r"""Tries to match the given *pattern* to the given *subject*.
Yields each match in form of a substitution.
Parameters:
subject:
An subject to match.
pattern:
The pattern to match.
Yields:
All possible match substitutions.
Raises:
ValueError:
If the subject is not constant. | 4.507358 | 4.336548 | 1.039388 |
if not is_constant(subject):
raise ValueError("The subject for matching must be constant.")
for child, pos in preorder_iter_with_position(subject):
if match_head(child, pattern):
for subst in match(child, pattern):
yield subst, pos | def match_anywhere(subject: Expression, pattern: Pattern) -> Iterator[Tuple[Substitution, Tuple[int, ...]]] | Tries to match the given *pattern* to the any subexpression of the given *subject*.
Yields each match in form of a substitution and a position tuple.
The position is a tuple of indices, e.g. the empty tuple refers to the *subject* itself,
:code:`(0, )` refers to the first child (operand) of the subject, :code:`(0, 0)` to the first child of
the first child etc.
Parameters:
subject:
An subject to match.
pattern:
The pattern to match.
Yields:
All possible substitution and position pairs.
Raises:
ValueError:
If the subject is not constant. | 5.505833 | 5.127566 | 1.073771 |
i = 0
var_index = 0
opt_index = 0
result = []
for operand in op_iter(operation):
wrap_associative = False
if isinstance(operand, Wildcard):
count = operand.min_count if operand.optional is None else 0
if not operand.fixed_size or isinstance(operation, AssociativeOperation):
count += sequence_var_partition[var_index]
var_index += 1
wrap_associative = operand.fixed_size and operand.min_count
elif operand.optional is not None:
count = optional_parts[opt_index]
opt_index += 1
else:
count = 1
operand_expressions = list(op_iter(subjects))[i:i + count]
i += count
if wrap_associative and len(operand_expressions) > wrap_associative:
fixed = wrap_associative - 1
operand_expressions = tuple(operand_expressions[:fixed]) + (
create_operation_expression(operation, operand_expressions[fixed:]),
)
result.append(operand_expressions)
return result | def _build_full_partition(
optional_parts, sequence_var_partition: Sequence[int], subjects: Sequence[Expression], operation: Operation
) -> List[Sequence[Expression]] | Distribute subject operands among pattern operands.
Given a partitoning for the variable part of the operands (i.e. a list of how many extra operands each sequence
variable gets assigned). | 3.525845 | 3.71761 | 0.948417 |
for _ in self._match(self.matcher.root):
yield list(self._internal_iter()) | def grouped(self) | Yield the matches grouped by their final state in the automaton, i.e. structurally identical patterns
only differing in constraints will be yielded together. Each group is yielded as a list of tuples consisting of
a pattern and a match substitution.
Yields:
The grouped matches. | 23.196154 | 21.159632 | 1.096246 |
if label is None:
label = pattern
for i, (p, l, _) in enumerate(self.patterns):
if pattern == p and label == l:
return i
# TODO: Avoid renaming in the pattern, use variable indices instead
renaming = self._collect_variable_renaming(pattern.expression) if self.rename else {}
self._internal_add(pattern, label, renaming) | def add(self, pattern: Pattern, label=None) -> None | Add a new pattern to the matcher.
The optional label defaults to the pattern itself and is yielded during matching. The same pattern can be
added with different labels which means that every match for the pattern will result in every associated label
being yielded with that match individually.
Equivalent patterns with the same label are not added again. However, patterns that are structurally equivalent,
but have different constraints or different variable names are distinguished by the matcher.
Args:
pattern:
The pattern to add.
label:
An optional label for the pattern. Defaults to the pattern itself. | 7.323541 | 7.072753 | 1.035458 |
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