Code stringlengths 103 85.9k | Summary listlengths 0 94 |
|---|---|
Please provide a description of the function:def from_pretrained(
cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs
):
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MA... | [
"\n Instantiate a GPT2PreTrainedModel from a pre-trained model file or a pytorch state dict.\n Download and cache the pre-trained model file if needed.\n\n Params:\n pretrained_model_name_or_path: either:\n - a str with the name of a pre-trained model to load selected ... |
Please provide a description of the function:def convert_examples_to_features(examples, seq_length, tokenizer):
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tok... | [
"Loads a data file into a list of `InputFeature`s."
] |
Please provide a description of the function:def read_examples(input_file):
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.strip()
... | [
"Read a list of `InputExample`s from an input file."
] |
Please provide a description of the function:def read_squad_examples(input_file, is_training, version_2_with_negative):
with open(input_file, "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or... | [
"Read a SQuAD json file into a list of SquadExample."
] |
Please provide a description of the function:def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training):
unique_id = 1000000000
features = []
for (example_index, example) in enumerate(examples):
query_token... | [
"Loads a data file into a list of `InputBatch`s."
] |
Please provide a description of the function:def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can... | [
"Returns tokenized answer spans that better match the annotated answer."
] |
Please provide a description of the function:def _check_is_max_context(doc_spans, cur_span_index, position):
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# ... | [
"Check if this is the 'max context' doc span for the token."
] |
Please provide a description of the function:def write_predictions(all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, verbose_logging,
version_2_wi... | [
"Write final predictions to the json file and log-odds of null if needed."
] |
Please provide a description of the function:def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the... | [
"Project the tokenized prediction back to the original text."
] |
Please provide a description of the function:def _get_best_indexes(logits, n_best_size):
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(inde... | [
"Get the n-best logits from a list."
] |
Please provide a description of the function:def _compute_softmax(scores):
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x... | [
"Compute softmax probability over raw logits."
] |
Please provide a description of the function:def convert_examples_to_features(examples, tokenizer, max_seq_length,
is_training):
# Swag is a multiple choice task. To perform this task using Bert,
# we will use the formatting proposed in "Improving Language
# Understand... | [
"Loads a data file into a list of `InputBatch`s."
] |
Please provide a description of the function:def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_mode):
label_map = {label : i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
... | [
"Loads a data file into a list of `InputBatch`s."
] |
Please provide a description of the function:def _read_tsv(cls, input_file, quotechar=None):
with open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_i... | [
"Reads a tab separated value file."
] |
Please provide a description of the function:def get_train_examples(self, data_dir):
logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | [
"See base class."
] |
Please provide a description of the function:def _create_examples(self, lines, set_type):
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[3]
text_b = line[4]
... | [
"Creates examples for the training and dev sets."
] |
Please provide a description of the function:def get_train_examples(self, data_dir):
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") | [
"See base class."
] |
Please provide a description of the function:def get_dev_examples(self, data_dir):
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
"dev_matched") | [
"See base class."
] |
Please provide a description of the function:def top_k_logits(logits, k):
if k == 0:
return logits
else:
values = torch.topk(logits, k)[0]
batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
return torch.where(logits < batch_mins, torch.ones_like(logits) * -1e10, logits... | [
"\n Masks everything but the k top entries as -infinity (1e10).\n Used to mask logits such that e^-infinity -> 0 won't contribute to the\n sum of the denominator.\n "
] |
Please provide a description of the function:def load_tf_weights_in_bert(model, tf_checkpoint_path):
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "... | [
" Load tf checkpoints in a pytorch model\n "
] |
Please provide a description of the function:def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
state_dict = kwargs.get('state_dict', None)
kwargs.pop('state_dict', None)
cache_dir = kwargs.get('cache_dir', None)
kwargs.pop('cache_dir', None)
fro... | [
"\n Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.\n Download and cache the pre-trained model file if needed.\n\n Params:\n pretrained_model_name_or_path: either:\n - a str with the name of a pre-trained model to load selected ... |
Please provide a description of the function:def load_tf_weights_in_openai_gpt(model, openai_checkpoint_folder_path):
import re
import numpy as np
print("Loading weights...")
names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8'))
shapes = json.l... | [
" Load tf pre-trained weights in a pytorch model (from NumPy arrays here)\n "
] |
Please provide a description of the function:def from_dict(cls, json_object):
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config | [
"Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters."
] |
Please provide a description of the function:def set_num_special_tokens(self, num_special_tokens):
" Update input embeddings with new embedding matrice if needed "
if self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_token... | [] |
Please provide a description of the function:def set_num_special_tokens(self, num_special_tokens):
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight) | [
" Update input and output embeddings with new embedding matrice\n Make sure we are sharing the embeddings\n "
] |
Please provide a description of the function:def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
... | [
"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n "
] |
Please provide a description of the function:def get_lr(self, step, nowarn=False):
if self.t_total < 0:
return 1.
progress = float(step) / self.t_total
ret = self.get_lr_(progress)
# warning for exceeding t_total (only active with warmup_linear
if not nowarn ... | [
"\n :param step: which of t_total steps we're on\n :param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps\n :return: learning rate multiplier for current update\n "
] |
Please provide a description of the function:def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
... | [
"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n "
] |
Please provide a description of the function:def whitespace_tokenize(text):
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens | [
"Runs basic whitespace cleaning and splitting on a piece of text."
] |
Please provide a description of the function:def _is_punctuation(char):
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((c... | [
"Checks whether `chars` is a punctuation character."
] |
Please provide a description of the function:def convert_tokens_to_ids(self, tokens):
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specifi... | [
"Converts a sequence of tokens into ids using the vocab."
] |
Please provide a description of the function:def convert_ids_to_tokens(self, ids):
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens | [
"Converts a sequence of ids in wordpiece tokens using the vocab."
] |
Please provide a description of the function:def save_vocabulary(self, vocab_path):
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(... | [
"Save the tokenizer vocabulary to a directory or file."
] |
Please provide a description of the function:def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
if ... | [
"\n Instantiate a PreTrainedBertModel from a pre-trained model file.\n Download and cache the pre-trained model file if needed.\n "
] |
Please provide a description of the function:def tokenize(self, text):
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models... | [
"Tokenizes a piece of text."
] |
Please provide a description of the function:def _run_strip_accents(self, text):
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
... | [
"Strips accents from a piece of text."
] |
Please provide a description of the function:def _tokenize_chinese_chars(self, text):
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | [
"Adds whitespace around any CJK character."
] |
Please provide a description of the function:def _is_chinese_char(self, cp):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanes... | [
"Checks whether CP is the codepoint of a CJK character."
] |
Please provide a description of the function:def tokenize(self, text):
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
cont... | [
"Tokenizes a piece of text into its word pieces.\n\n This uses a greedy longest-match-first algorithm to perform tokenization\n using the given vocabulary.\n\n For example:\n input = \"unaffable\"\n output = [\"un\", \"##aff\", \"##able\"]\n\n Args:\n text: A s... |
Please provide a description of the function:def load_rocstories_dataset(dataset_path):
with open(dataset_path, encoding='utf_8') as f:
f = csv.reader(f)
output = []
next(f) # skip the first line
for line in tqdm(f):
output.append((' '.join(line[1:5]), line[5], line[... | [
" Output a list of tuples(story, 1st continuation, 2nd continuation, label) "
] |
Please provide a description of the function:def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
tensor_datasets = []
for dataset in encoded_datasets:
n_batch = len(dataset)
input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)... | [
" Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)\n\n To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:\n input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] +... |
Please provide a description of the function:def random_word(tokens, tokenizer):
output_label = []
for i, token in enumerate(tokens):
prob = random.random()
# mask token with 15% probability
if prob < 0.15:
prob /= 0.15
# 80% randomly change token to mask t... | [
"\n Masking some random tokens for Language Model task with probabilities as in the original BERT paper.\n :param tokens: list of str, tokenized sentence.\n :param tokenizer: Tokenizer, object used for tokenization (we need it's vocab here)\n :return: (list of str, list of int), masked tokens and relate... |
Please provide a description of the function:def convert_example_to_features(example, max_seq_length, tokenizer):
tokens_a = example.tokens_a
tokens_b = example.tokens_b
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CL... | [
"\n Convert a raw sample (pair of sentences as tokenized strings) into a proper training sample with\n IDs, LM labels, input_mask, CLS and SEP tokens etc.\n :param example: InputExample, containing sentence input as strings and is_next label\n :param max_seq_length: int, maximum length of sequence.\n ... |
Please provide a description of the function:def random_sent(self, index):
t1, t2 = self.get_corpus_line(index)
if random.random() > 0.5:
label = 0
else:
t2 = self.get_random_line()
label = 1
assert len(t1) > 0
assert len(t2) > 0
... | [
"\n Get one sample from corpus consisting of two sentences. With prob. 50% these are two subsequent sentences\n from one doc. With 50% the second sentence will be a random one from another doc.\n :param index: int, index of sample.\n :return: (str, str, int), sentence 1, sentence 2, isNe... |
Please provide a description of the function:def get_corpus_line(self, item):
t1 = ""
t2 = ""
assert item < self.corpus_lines
if self.on_memory:
sample = self.sample_to_doc[item]
t1 = self.all_docs[sample["doc_id"]][sample["line"]]
t2 = self.a... | [
"\n Get one sample from corpus consisting of a pair of two subsequent lines from the same doc.\n :param item: int, index of sample.\n :return: (str, str), two subsequent sentences from corpus\n "
] |
Please provide a description of the function:def get_random_line(self):
# Similar to original tf repo: This outer loop should rarely go for more than one iteration for large
# corpora. However, just to be careful, we try to make sure that
# the random document is not the same as the doc... | [
"\n Get random line from another document for nextSentence task.\n :return: str, content of one line\n "
] |
Please provide a description of the function:def get_next_line(self):
try:
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
self.current_random_doc ... | [
" Gets next line of random_file and starts over when reaching end of file"
] |
Please provide a description of the function:def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
cand_indices = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
cand_indices.append(i)
num_to... | [
"Creates the predictions for the masked LM objective. This is mostly copied from the Google BERT repo, but\n with several refactors to clean it up and remove a lot of unnecessary variables."
] |
Please provide a description of the function:def create_instances_from_document(
doc_database, doc_idx, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_list):
document = doc_database[doc_idx]
# Account for [CLS], [SEP], [SEP]
max_num_tokens = max_seq_lengt... | [
"This code is mostly a duplicate of the equivalent function from Google BERT's repo.\n However, we make some changes and improvements. Sampling is improved and no longer requires a loop in this function.\n Also, documents are sampled proportionally to the number of sentences they contain, which means each sen... |
Please provide a description of the function:def sample_logits(embedding, bias, labels, inputs, sampler):
true_log_probs, samp_log_probs, neg_samples = sampler.sample(labels)
n_sample = neg_samples.size(0)
b1, b2 = labels.size(0), labels.size(1)
all_ids = torch.cat([labels.view(-1), neg_samples])
... | [
"\n embedding: an nn.Embedding layer\n bias: [n_vocab]\n labels: [b1, b2]\n inputs: [b1, b2, n_emb]\n sampler: you may use a LogUniformSampler\n Return\n logits: [b1, b2, 1 + n_sample]\n "
] |
Please provide a description of the function:def forward(self, hidden, target=None, keep_order=False):
'''
Params:
hidden :: [len*bsz x d_proj]
target :: [len*bsz]
Return:
if target is None:
out :: [len*bsz] Negative log... | [] |
Please provide a description of the function:def log_prob(self, hidden):
r
if self.n_clusters == 0:
logit = self._compute_logit(hidden, self.out_layers[0].weight,
self.out_layers[0].bias, self.out_projs[0])
return F.log_softmax(logit, dim=-... | [
" Computes log probabilities for all :math:`n\\_classes`\n From: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/adaptive.py\n Args:\n hidden (Tensor): a minibatch of examples\n Returns:\n log-probabilities of for each class :math:`c`\n in range ... |
Please provide a description of the function:def sample(self, labels):
# neg_samples = torch.empty(0).long()
n_sample = self.n_sample
n_tries = 2 * n_sample
with torch.no_grad():
neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()
... | [
"\n labels: [b1, b2]\n Return\n true_log_probs: [b1, b2]\n samp_log_probs: [n_sample]\n neg_samples: [n_sample]\n "
] |
Please provide a description of the function:def build_tf_to_pytorch_map(model, config):
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading in a TransfoXLLMHeadModel => we will load also the Adaptive Softmax
tf_to_pt_map.update({
"transformer/adaptive_softmax/... | [
" A map of modules from TF to PyTorch.\n This time I use a map to keep the PyTorch model as identical to the original PyTorch model as possible.\n "
] |
Please provide a description of the function:def load_tf_weights_in_transfo_xl(model, config, tf_path):
try:
import numpy as np
import tensorflow as tf
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"h... | [
" Load tf checkpoints in a pytorch model\n "
] |
Please provide a description of the function:def init_weights(self, m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
if hasattr(m, 'weight') and m.weight is not None:
self.init_weight(m.weight)
if hasattr(m, 'bias') and m.bias is no... | [
" Initialize the weights.\n "
] |
Please provide a description of the function:def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
from_tf=False, *inputs, **kwargs):
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_... | [
"\n Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.\n Download and cache the pre-trained model file if needed.\n\n Params:\n pretrained_model_name_or_path: either:\n - a str with the name of a pre-trained model to load sele... |
Please provide a description of the function:def forward(self, input_ids, mems=None):
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
input_ids = input_ids.trans... | [
" Params:\n input_ids :: [bsz, len]\n mems :: optional mems from previous forwar passes (or init_mems)\n list (num layers) of mem states at the entry of each layer\n shape :: [self.config.mem_len, bsz, self.config.d_model]\n ... |
Please provide a description of the function:def tie_weights(self):
# sampled softmax
if self.sample_softmax > 0:
if self.config.tie_weight:
self.out_layer.weight = self.transformer.word_emb.weight
# adaptive softmax (including standard softmax)
else:... | [
" Run this to be sure output and input (adaptive) softmax weights are tied "
] |
Please provide a description of the function:def forward(self, input_ids, target=None, mems=None):
bsz = input_ids.size(0)
tgt_len = input_ids.size(1)
last_hidden, new_mems = self.transformer(input_ids, mems)
pred_hid = last_hidden[:, -tgt_len:]
if self.sample_softmax ... | [
" Params:\n input_ids :: [bsz, len]\n target :: [bsz, len]\n Returns:\n tuple(softmax_output, new_mems) where:\n new_mems: list (num layers) of hidden states at the entry of each layer\n shape :: [mem_len, bsz, self.co... |
Please provide a description of the function:def to_offset(freq):
if freq is None:
return None
if isinstance(freq, DateOffset):
return freq
if isinstance(freq, tuple):
name = freq[0]
stride = freq[1]
if isinstance(stride, str):
name, stride = stride... | [
"\n Return DateOffset object from string or tuple representation\n or datetime.timedelta object\n\n Parameters\n ----------\n freq : str, tuple, datetime.timedelta, DateOffset or None\n\n Returns\n -------\n DateOffset\n None if freq is None.\n\n Raises\n ------\n ValueError\... |
Please provide a description of the function:def get_offset(name):
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_rule_alias.get(name.lower(), name)
else:
name = libfreqs._lite_rule_alias.ge... | [
"\n Return DateOffset object associated with rule name\n\n Examples\n --------\n get_offset('EOM') --> BMonthEnd(1)\n "
] |
Please provide a description of the function:def infer_freq(index, warn=True):
import pandas as pd
if isinstance(index, ABCSeries):
values = index._values
if not (is_datetime64_dtype(values) or
is_timedelta64_dtype(values) or
values.dtype == object):
... | [
"\n Infer the most likely frequency given the input index. If the frequency is\n uncertain, a warning will be printed.\n\n Parameters\n ----------\n index : DatetimeIndex or TimedeltaIndex\n if passed a Series will use the values of the series (NOT THE INDEX)\n warn : boolean, default True\n\... |
Please provide a description of the function:def get_freq(self):
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
if _is_multiple(delta, _ONE_DAY):
return self._infer_daily_rule()
# Business hourly, maybe. 17: on... | [
"\n Find the appropriate frequency string to describe the inferred\n frequency of self.values\n\n Returns\n -------\n str or None\n "
] |
Please provide a description of the function:def load(fh, encoding=None, is_verbose=False):
try:
fh.seek(0)
if encoding is not None:
up = Unpickler(fh, encoding=encoding)
else:
up = Unpickler(fh)
up.is_verbose = is_verbose
return up.load()
e... | [
"load a pickle, with a provided encoding\n\n if compat is True:\n fake the old class hierarchy\n if it works, then return the new type objects\n\n Parameters\n ----------\n fh : a filelike object\n encoding : an optional encoding\n is_verbose : show exception output\n "
] |
Please provide a description of the function:def _new_Index(cls, d):
# required for backward compat, because PI can't be instantiated with
# ordinals through __new__ GH #13277
if issubclass(cls, ABCPeriodIndex):
from pandas.core.indexes.period import _new_PeriodIndex
return _new_PeriodI... | [
"\n This is called upon unpickling, rather than the default which doesn't\n have arguments and breaks __new__.\n "
] |
Please provide a description of the function:def ensure_index_from_sequences(sequences, names=None):
from .multi import MultiIndex
if len(sequences) == 1:
if names is not None:
names = names[0]
return Index(sequences[0], name=names)
else:
return MultiIndex.from_arra... | [
"\n Construct an index from sequences of data.\n\n A single sequence returns an Index. Many sequences returns a\n MultiIndex.\n\n Parameters\n ----------\n sequences : sequence of sequences\n names : sequence of str\n\n Returns\n -------\n index : Index or MultiIndex\n\n Examples\n ... |
Please provide a description of the function:def ensure_index(index_like, copy=False):
if isinstance(index_like, Index):
if copy:
index_like = index_like.copy()
return index_like
if hasattr(index_like, 'name'):
return Index(index_like, name=index_like.name, copy=copy)
... | [
"\n Ensure that we have an index from some index-like object.\n\n Parameters\n ----------\n index : sequence\n An Index or other sequence\n copy : bool\n\n Returns\n -------\n index : Index or MultiIndex\n\n Examples\n --------\n >>> ensure_index(['a', 'b'])\n Index(['a', ... |
Please provide a description of the function:def _trim_front(strings):
trimmed = strings
while len(strings) > 0 and all(x[0] == ' ' for x in trimmed):
trimmed = [x[1:] for x in trimmed]
return trimmed | [
"\n Trims zeros and decimal points.\n "
] |
Please provide a description of the function:def _simple_new(cls, values, name=None, dtype=None, **kwargs):
if not hasattr(values, 'dtype'):
if (values is None or not len(values)) and dtype is not None:
values = np.empty(0, dtype=dtype)
else:
valu... | [
"\n We require that we have a dtype compat for the values. If we are passed\n a non-dtype compat, then coerce using the constructor.\n\n Must be careful not to recurse.\n "
] |
Please provide a description of the function:def _shallow_copy_with_infer(self, values, **kwargs):
attributes = self._get_attributes_dict()
attributes.update(kwargs)
attributes['copy'] = False
if not len(values) and 'dtype' not in kwargs:
attributes['dtype'] = self.d... | [
"\n Create a new Index inferring the class with passed value, don't copy\n the data, use the same object attributes with passed in attributes\n taking precedence.\n\n *this is an internal non-public method*\n\n Parameters\n ----------\n values : the values to create ... |
Please provide a description of the function:def is_(self, other):
# use something other than None to be clearer
return self._id is getattr(
other, '_id', Ellipsis) and self._id is not None | [
"\n More flexible, faster check like ``is`` but that works through views.\n\n Note: this is *not* the same as ``Index.identical()``, which checks\n that metadata is also the same.\n\n Parameters\n ----------\n other : object\n other object to compare against.\n\n... |
Please provide a description of the function:def _assert_take_fillable(self, values, indices, allow_fill=True,
fill_value=None, na_value=np.nan):
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
if allow_fill and f... | [
"\n Internal method to handle NA filling of take.\n "
] |
Please provide a description of the function:def _format_data(self, name=None):
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
(self.inferred_type == 'categorical' and
is... | [
"\n Return the formatted data as a unicode string.\n "
] |
Please provide a description of the function:def format(self, name=False, formatter=None, **kwargs):
header = []
if name:
header.append(pprint_thing(self.name,
escape_chars=('\t', '\r', '\n')) if
self.name is not None ... | [
"\n Render a string representation of the Index.\n "
] |
Please provide a description of the function:def to_native_types(self, slicer=None, **kwargs):
values = self
if slicer is not None:
values = values[slicer]
return values._format_native_types(**kwargs) | [
"\n Format specified values of `self` and return them.\n\n Parameters\n ----------\n slicer : int, array-like\n An indexer into `self` that specifies which values\n are used in the formatting process.\n kwargs : dict\n Options for specifying how th... |
Please provide a description of the function:def _format_native_types(self, na_rep='', quoting=None, **kwargs):
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, dtype=object, copy=True)... | [
"\n Actually format specific types of the index.\n "
] |
Please provide a description of the function:def _summary(self, name=None):
if len(self) > 0:
head = self[0]
if hasattr(head, 'format') and not isinstance(head, str):
head = head.format()
tail = self[-1]
if hasattr(tail, 'format') and not ... | [
"\n Return a summarized representation.\n\n Parameters\n ----------\n name : str\n name to use in the summary representation\n\n Returns\n -------\n String with a summarized representation of the index\n "
] |
Please provide a description of the function:def summary(self, name=None):
warnings.warn("'summary' is deprecated and will be removed in a "
"future version.", FutureWarning, stacklevel=2)
return self._summary(name) | [
"\n Return a summarized representation.\n\n .. deprecated:: 0.23.0\n "
] |
Please provide a description of the function:def to_series(self, index=None, name=None):
from pandas import Series
if index is None:
index = self._shallow_copy()
if name is None:
name = self.name
return Series(self.values.copy(), index=index, name=name... | [
"\n Create a Series with both index and values equal to the index keys\n useful with map for returning an indexer based on an index.\n\n Parameters\n ----------\n index : Index, optional\n index of resulting Series. If None, defaults to original index\n name : st... |
Please provide a description of the function:def to_frame(self, index=True, name=None):
from pandas import DataFrame
if name is None:
name = self.name or 0
result = DataFrame({name: self._values.copy()})
if index:
result.index = self
return resu... | [
"\n Create a DataFrame with a column containing the Index.\n\n .. versionadded:: 0.24.0\n\n Parameters\n ----------\n index : boolean, default True\n Set the index of the returned DataFrame as the original Index.\n\n name : object, default None\n The p... |
Please provide a description of the function:def _validate_names(self, name=None, names=None, deep=False):
from copy import deepcopy
if names is not None and name is not None:
raise TypeError("Can only provide one of `names` and `name`")
elif names is None and name is None:
... | [
"\n Handles the quirks of having a singular 'name' parameter for general\n Index and plural 'names' parameter for MultiIndex.\n "
] |
Please provide a description of the function:def _set_names(self, values, level=None):
if not is_list_like(values):
raise ValueError('Names must be a list-like')
if len(values) != 1:
raise ValueError('Length of new names must be 1, got %d' %
... | [
"\n Set new names on index. Each name has to be a hashable type.\n\n Parameters\n ----------\n values : str or sequence\n name(s) to set\n level : int, level name, or sequence of int/level names (default None)\n If the index is a MultiIndex (hierarchical), le... |
Please provide a description of the function:def set_names(self, names, level=None, inplace=False):
if level is not None and not isinstance(self, ABCMultiIndex):
raise ValueError('Level must be None for non-MultiIndex')
if level is not None and not is_list_like(level) and is_list_... | [
"\n Set Index or MultiIndex name.\n\n Able to set new names partially and by level.\n\n Parameters\n ----------\n names : label or list of label\n Name(s) to set.\n level : int, label or list of int or label, optional\n If the index is a MultiIndex, le... |
Please provide a description of the function:def rename(self, name, inplace=False):
return self.set_names([name], inplace=inplace) | [
"\n Alter Index or MultiIndex name.\n\n Able to set new names without level. Defaults to returning new index.\n Length of names must match number of levels in MultiIndex.\n\n Parameters\n ----------\n name : label or list of labels\n Name(s) to set.\n inpl... |
Please provide a description of the function:def _validate_index_level(self, level):
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError("Too many levels: Index has only 1 level,"
" %d is not a valid level number" % (lev... | [
"\n Validate index level.\n\n For single-level Index getting level number is a no-op, but some\n verification must be done like in MultiIndex.\n\n "
] |
Please provide a description of the function:def sortlevel(self, level=None, ascending=True, sort_remaining=None):
return self.sort_values(return_indexer=True, ascending=ascending) | [
"\n For internal compatibility with with the Index API.\n\n Sort the Index. This is for compat with MultiIndex\n\n Parameters\n ----------\n ascending : boolean, default True\n False to sort in descending order\n\n level, sort_remaining are compat parameters\n\n ... |
Please provide a description of the function:def droplevel(self, level=0):
if not isinstance(level, (tuple, list)):
level = [level]
levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
if len(level) == 0:
return self
if len(level) >= sel... | [
"\n Return index with requested level(s) removed.\n\n If resulting index has only 1 level left, the result will be\n of Index type, not MultiIndex.\n\n .. versionadded:: 0.23.1 (support for non-MultiIndex)\n\n Parameters\n ----------\n level : int, str, or list-like,... |
Please provide a description of the function:def _isnan(self):
if self._can_hold_na:
return isna(self)
else:
# shouldn't reach to this condition by checking hasnans beforehand
values = np.empty(len(self), dtype=np.bool_)
values.fill(False)
... | [
"\n Return if each value is NaN.\n "
] |
Please provide a description of the function:def get_duplicates(self):
warnings.warn("'get_duplicates' is deprecated and will be removed in "
"a future release. You can use "
"idx[idx.duplicated()].unique() instead",
FutureWarning, stack... | [
"\n Extract duplicated index elements.\n\n .. deprecated:: 0.23.0\n Use idx[idx.duplicated()].unique() instead\n\n Returns a sorted list of index elements which appear more than once in\n the index.\n\n Returns\n -------\n array-like\n List of d... |
Please provide a description of the function:def _get_unique_index(self, dropna=False):
if self.is_unique and not dropna:
return self
values = self.values
if not self.is_unique:
values = self.unique()
if dropna:
try:
if self... | [
"\n Returns an index containing unique values.\n\n Parameters\n ----------\n dropna : bool\n If True, NaN values are dropped.\n\n Returns\n -------\n uniques : index\n "
] |
Please provide a description of the function:def _get_reconciled_name_object(self, other):
name = get_op_result_name(self, other)
if self.name != name:
return self._shallow_copy(name=name)
return self | [
"\n If the result of a set operation will be self,\n return self, unless the name changes, in which\n case make a shallow copy of self.\n "
] |
Please provide a description of the function:def union(self, other, sort=None):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other = ensure_index(other)
if len(other) == 0 or self.equals(other):
return self._get_reconciled_name_object(other)
... | [
"\n Form the union of two Index objects.\n\n Parameters\n ----------\n other : Index or array-like\n sort : bool or None, default None\n Whether to sort the resulting Index.\n\n * None : Sort the result, except when\n\n 1. `self` and `other` are ... |
Please provide a description of the function:def intersection(self, other, sort=False):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other = ensure_index(other)
if self.equals(other):
return self._get_reconciled_name_object(other)
if n... | [
"\n Form the intersection of two Index objects.\n\n This returns a new Index with elements common to the index and `other`.\n\n Parameters\n ----------\n other : Index or array-like\n sort : False or None, default False\n Whether to sort the resulting index.\n\n ... |
Please provide a description of the function:def difference(self, other, sort=None):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
if self.equals(other):
# pass an empty np.ndarray with the appropriate dtype
return self._shallow_copy(self._d... | [
"\n Return a new Index with elements from the index that are not in\n `other`.\n\n This is the set difference of two Index objects.\n\n Parameters\n ----------\n other : Index or array-like\n sort : False or None, default None\n Whether to sort the resulti... |
Please provide a description of the function:def symmetric_difference(self, other, result_name=None, sort=None):
self._validate_sort_keyword(sort)
self._assert_can_do_setop(other)
other, result_name_update = self._convert_can_do_setop(other)
if result_name is None:
r... | [
"\n Compute the symmetric difference of two Index objects.\n\n Parameters\n ----------\n other : Index or array-like\n result_name : str\n sort : False or None, default None\n Whether to sort the resulting index. By default, the\n values are attempted ... |
Please provide a description of the function:def _get_fill_indexer_searchsorted(self, target, method, limit=None):
if limit is not None:
raise ValueError('limit argument for %r method only well-defined '
'if index and target are monotonic' % method)
sid... | [
"\n Fallback pad/backfill get_indexer that works for monotonic decreasing\n indexes and non-monotonic targets.\n "
] |
Please provide a description of the function:def _get_nearest_indexer(self, target, limit, tolerance):
left_indexer = self.get_indexer(target, 'pad', limit=limit)
right_indexer = self.get_indexer(target, 'backfill', limit=limit)
target = np.asarray(target)
left_distances = abs(... | [
"\n Get the indexer for the nearest index labels; requires an index with\n values that can be subtracted from each other (e.g., not strings or\n tuples).\n "
] |
Please provide a description of the function:def _convert_listlike_indexer(self, keyarr, kind=None):
if isinstance(keyarr, Index):
keyarr = self._convert_index_indexer(keyarr)
else:
keyarr = self._convert_arr_indexer(keyarr)
indexer = self._convert_list_indexer(... | [
"\n Parameters\n ----------\n keyarr : list-like\n Indexer to convert.\n\n Returns\n -------\n indexer : numpy.ndarray or None\n Return an ndarray or None if cannot convert.\n keyarr : numpy.ndarray\n Return tuple-safe keys.\n ... |
Please provide a description of the function:def _invalid_indexer(self, form, key):
raise TypeError("cannot do {form} indexing on {klass} with these "
"indexers [{key}] of {kind}".format(
form=form, klass=type(self), key=key,
... | [
"\n Consistent invalid indexer message.\n "
] |
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