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Commit f3e06440 by Estrella Vallecillo
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Fixing Analyzer.py and ComplexityAnalyzer.py

parent 90b019e9
Inline Side-by-side
Showing with 1418 additions and 39 deletions
poetry.lock
......@@ -21,6 +21,44 @@ tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)"
tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
[[package]]
name = "blis"
version = "0.7.7"
description = "The Blis BLAS-like linear algebra library, as a self-contained C-extension."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
numpy = ">=1.15.0"
[[package]]
name = "catalogue"
version = "2.0.7"
description = "Super lightweight function registries for your library"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "certifi"
version = "2021.10.8"
description = "Python package for providing Mozilla's CA Bundle."
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "charset-normalizer"
version = "2.0.12"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
category = "main"
optional = false
python-versions = ">=3.5.0"
[package.extras]
unicode_backport = ["unicodedata2"]
[[package]]
name = "click"
version = "8.1.2"
description = "Composable command line interface toolkit"
......@@ -40,6 +78,80 @@ optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "cymem"
version = "2.0.6"
description = "Manage calls to calloc/free through Cython"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "filelock"
version = "3.6.0"
description = "A platform independent file lock."
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
docs = ["furo (>=2021.8.17b43)", "sphinx (>=4.1)", "sphinx-autodoc-typehints (>=1.12)"]
testing = ["covdefaults (>=1.2.0)", "coverage (>=4)", "pytest (>=4)", "pytest-cov", "pytest-timeout (>=1.4.2)"]
[[package]]
name = "functools"
version = "0.5"
description = "Fast tools for functional programming"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "huggingface-hub"
version = "0.5.1"
description = "Client library to download and publish models on the huggingface.co hub"
category = "main"
optional = false
python-versions = ">=3.7.0"
[package.dependencies]
filelock = "*"
packaging = ">=20.9"
pyyaml = "*"
requests = "*"
tqdm = "*"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["pytest", "datasets", "soundfile", "black (>=22.0,<23.0)", "isort (>=5.5.4)", "flake8 (>=3.8.3)"]
dev = ["pytest", "datasets", "soundfile", "black (>=22.0,<23.0)", "isort (>=5.5.4)", "flake8 (>=3.8.3)"]
quality = ["black (>=22.0,<23.0)", "isort (>=5.5.4)", "flake8 (>=3.8.3)"]
tensorflow = ["tensorflow", "pydot", "graphviz"]
testing = ["pytest", "datasets", "soundfile"]
torch = ["torch"]
[[package]]
name = "idna"
version = "3.3"
description = "Internationalized Domain Names in Applications (IDNA)"
category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "jinja2"
version = "3.1.2"
description = "A very fast and expressive template engine."
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
[[package]]
name = "joblib"
version = "1.1.0"
description = "Lightweight pipelining with Python functions"
......@@ -48,6 +160,25 @@ optional = false
python-versions = ">=3.6"
[[package]]
name = "langcodes"
version = "3.3.0"
description = "Tools for labeling human languages with IETF language tags"
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
data = ["language-data (>=1.1,<2.0)"]
[[package]]
name = "markupsafe"
version = "2.1.1"
description = "Safely add untrusted strings to HTML/XML markup."
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "more-itertools"
version = "8.12.0"
description = "More routines for operating on iterables, beyond itertools"
......@@ -56,6 +187,14 @@ optional = false
python-versions = ">=3.5"
[[package]]
name = "murmurhash"
version = "1.0.7"
description = "Cython bindings for MurmurHash"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "nltk"
version = "3.7"
description = "Natural Language Toolkit"
......@@ -78,10 +217,18 @@ tgrep = ["pyparsing"]
twitter = ["twython"]
[[package]]
name = "numpy"
version = "1.22.3"
description = "NumPy is the fundamental package for array computing with Python."
category = "main"
optional = false
python-versions = ">=3.8"
[[package]]
name = "packaging"
version = "21.3"
description = "Core utilities for Python packages"
category = "dev"
category = "main"
optional = false
python-versions = ">=3.6"
......@@ -89,6 +236,24 @@ python-versions = ">=3.6"
pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
[[package]]
name = "pathy"
version = "0.6.1"
description = "pathlib.Path subclasses for local and cloud bucket storage"
category = "main"
optional = false
python-versions = ">= 3.6"
[package.dependencies]
smart-open = ">=5.0.0,<6.0.0"
typer = ">=0.3.0,<1.0.0"
[package.extras]
all = ["google-cloud-storage (>=1.26.0,<2.0.0)", "boto3", "pytest", "pytest-coverage", "mock", "typer-cli"]
gcs = ["google-cloud-storage (>=1.26.0,<2.0.0)"]
s3 = ["boto3"]
test = ["pytest", "pytest-coverage", "mock", "typer-cli"]
[[package]]
name = "pluggy"
version = "0.13.1"
description = "plugin and hook calling mechanisms for python"
......@@ -100,6 +265,18 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
dev = ["pre-commit", "tox"]
[[package]]
name = "preshed"
version = "3.0.6"
description = "Cython hash table that trusts the keys are pre-hashed"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=0.28.0,<1.1.0"
[[package]]
name = "py"
version = "1.11.0"
description = "library with cross-python path, ini-parsing, io, code, log facilities"
......@@ -108,10 +285,25 @@ optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "pydantic"
version = "1.8.2"
description = "Data validation and settings management using python 3.6 type hinting"
category = "main"
optional = false
python-versions = ">=3.6.1"
[package.dependencies]
typing-extensions = ">=3.7.4.3"
[package.extras]
dotenv = ["python-dotenv (>=0.10.4)"]
email = ["email-validator (>=1.0.3)"]
[[package]]
name = "pyparsing"
version = "3.0.7"
description = "Python parsing module"
category = "dev"
category = "main"
optional = false
python-versions = ">=3.6"
......@@ -141,6 +333,14 @@ checkqa-mypy = ["mypy (==v0.761)"]
testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
[[package]]
name = "pyyaml"
version = "6.0"
description = "YAML parser and emitter for Python"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "regex"
version = "2022.4.24"
description = "Alternative regular expression module, to replace re."
......@@ -149,6 +349,197 @@ optional = false
python-versions = ">=3.6"
[[package]]
name = "requests"
version = "2.27.1"
description = "Python HTTP for Humans."
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
[package.dependencies]
certifi = ">=2017.4.17"
charset-normalizer = {version = ">=2.0.0,<2.1.0", markers = "python_version >= \"3\""}
idna = {version = ">=2.5,<4", markers = "python_version >= \"3\""}
urllib3 = ">=1.21.1,<1.27"
[package.extras]
socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"]
[[package]]
name = "sacremoses"
version = "0.0.53"
description = "SacreMoses"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
click = "*"
joblib = "*"
regex = "*"
six = "*"
tqdm = "*"
[[package]]
name = "six"
version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
[[package]]
name = "smart-open"
version = "5.2.1"
description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)"
category = "main"
optional = false
python-versions = ">=3.6,<4.0"
[package.extras]
all = ["boto3", "google-cloud-storage", "azure-storage-blob", "azure-common", "azure-core", "requests"]
azure = ["azure-storage-blob", "azure-common", "azure-core"]
gcs = ["google-cloud-storage"]
http = ["requests"]
s3 = ["boto3"]
test = ["boto3", "google-cloud-storage", "azure-storage-blob", "azure-common", "azure-core", "requests", "moto[server] (==1.3.14)", "pathlib2", "responses", "paramiko", "parameterizedtestcase", "pytest", "pytest-rerunfailures"]
webhdfs = ["requests"]
[[package]]
name = "spacy"
version = "3.3.0"
description = "Industrial-strength Natural Language Processing (NLP) in Python"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
blis = ">=0.4.0,<0.8.0"
catalogue = ">=2.0.6,<2.1.0"
cymem = ">=2.0.2,<2.1.0"
jinja2 = "*"
langcodes = ">=3.2.0,<4.0.0"
murmurhash = ">=0.28.0,<1.1.0"
numpy = ">=1.15.0"
packaging = ">=20.0"
pathy = ">=0.3.5"
preshed = ">=3.0.2,<3.1.0"
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.9.0"
requests = ">=2.13.0,<3.0.0"
spacy-legacy = ">=3.0.9,<3.1.0"
spacy-loggers = ">=1.0.0,<2.0.0"
srsly = ">=2.4.3,<3.0.0"
thinc = ">=8.0.14,<8.1.0"
tqdm = ">=4.38.0,<5.0.0"
typer = ">=0.3.0,<0.5.0"
wasabi = ">=0.9.1,<1.1.0"
[package.extras]
apple = ["thinc-apple-ops (>=0.0.4,<1.0.0)"]
cuda = ["cupy (>=5.0.0b4,<11.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4,<11.0.0)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4,<11.0.0)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4,<11.0.0)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4,<11.0.0)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4,<11.0.0)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4,<11.0.0)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4,<11.0.0)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4,<11.0.0)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4,<11.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4,<11.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<11.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<11.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<11.0.0)"]
ja = ["sudachipy (>=0.5.2,!=0.6.1)", "sudachidict-core (>=20211220)"]
ko = ["natto-py (==0.9.0)"]
lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
ray = ["spacy-ray (>=0.1.0,<1.0.0)"]
th = ["pythainlp (>=2.0)"]
transformers = ["spacy-transformers (>=1.1.2,<1.2.0)"]
[[package]]
name = "spacy-legacy"
version = "3.0.9"
description = "Legacy registered functions for spaCy backwards compatibility"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "spacy-loggers"
version = "1.0.2"
description = "Logging utilities for SpaCy"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
wasabi = ">=0.8.1,<1.1.0"
[[package]]
name = "srsly"
version = "2.4.3"
description = "Modern high-performance serialization utilities for Python"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
catalogue = ">=2.0.3,<2.1.0"
[[package]]
name = "thinc"
version = "8.0.15"
description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
blis = ">=0.4.0,<0.8.0"
catalogue = ">=2.0.4,<2.1.0"
cymem = ">=2.0.2,<2.1.0"
murmurhash = ">=1.0.2,<1.1.0"
numpy = ">=1.15.0"
preshed = ">=3.0.2,<3.1.0"
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<1.9.0"
srsly = ">=2.4.0,<3.0.0"
wasabi = ">=0.8.1,<1.1.0"
[package.extras]
cuda = ["cupy (>=5.0.0b4)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4)"]
datasets = ["ml-datasets (>=0.2.0,<0.3.0)"]
mxnet = ["mxnet (>=1.5.1,<1.6.0)"]
tensorflow = ["tensorflow (>=2.0.0,<2.6.0)"]
torch = ["torch (>=1.6.0)"]
[[package]]
name = "tokenizers"
version = "0.12.1"
description = "Fast and Customizable Tokenizers"
category = "main"
optional = false
python-versions = "*"
[package.extras]
docs = ["sphinx", "sphinx-rtd-theme", "setuptools-rust"]
testing = ["pytest", "requests", "numpy", "datasets"]
[[package]]
name = "tqdm"
version = "4.64.0"
description = "Fast, Extensible Progress Meter"
......@@ -166,6 +557,112 @@ slack = ["slack-sdk"]
telegram = ["requests"]
[[package]]
name = "transformers"
version = "4.18.0"
description = "State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch"
category = "main"
optional = false
python-versions = ">=3.6.0"
[package.dependencies]
filelock = "*"
huggingface-hub = ">=0.1.0,<1.0"
numpy = ">=1.17"
packaging = ">=20.0"
pyyaml = ">=5.1"
regex = "!=2019.12.17"
requests = "*"
sacremoses = "*"
tokenizers = ">=0.11.1,<0.11.3 || >0.11.3,<0.13"
tqdm = ">=4.27"
[package.extras]
all = ["tensorflow (>=2.3)", "onnxconverter-common", "tf2onnx", "torch (>=1.0)", "jax (>=0.2.8,!=0.3.2)", "jaxlib (>=0.1.65)", "flax (>=0.3.5)", "optax (>=0.0.8)", "sentencepiece (>=0.1.91,!=0.1.92)", "protobuf", "tokenizers (>=0.11.1,!=0.11.3,<0.13)", "torchaudio", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer", "pillow", "optuna", "ray", "sigopt", "timm", "codecarbon (==1.2.0)"]
audio = ["librosa", "pyctcdecode (>=0.3.0)", "phonemizer"]
codecarbon = ["codecarbon (==1.2.0)"]
deepspeed = ["deepspeed (>=0.6.0)"]
dev = ["tensorflow (>=2.3)", "onnxconverter-common", "tf2onnx", "torch (>=1.0)", "jax (>=0.2.8,!=0.3.2)", "jaxlib (>=0.1.65)", "flax (>=0.3.5)", "optax (>=0.0.8)", "sentencepiece (>=0.1.91,!=0.1.92)", "protobuf", "tokenizers (>=0.11.1,!=0.11.3,<0.13)", "torchaudio", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer", "pillow", "optuna", "ray", "sigopt", "timm", "codecarbon (==1.2.0)", "pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "pytest-timeout", "black (>=22.0,<23.0)", "sacrebleu (>=1.4.12,<2.0.0)", "rouge-score", "nltk", "GitPython (<3.1.19)", "hf-doc-builder (>=0.2.0)", "faiss-cpu", "cookiecutter (==1.7.3)", "isort (>=5.5.4)", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "unidic-lite (>=1.0.7)", "unidic (>=1.0.2)", "hf-doc-builder", "scikit-learn"]
dev-tensorflow = ["pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "pytest-timeout", "black (>=22.0,<23.0)", "sacrebleu (>=1.4.12,<2.0.0)", "rouge-score", "nltk", "GitPython (<3.1.19)", "hf-doc-builder (>=0.2.0)", "faiss-cpu", "cookiecutter (==1.7.3)", "tensorflow (>=2.3)", "onnxconverter-common", "tf2onnx", "sentencepiece (>=0.1.91,!=0.1.92)", "protobuf", "tokenizers (>=0.11.1,!=0.11.3,<0.13)", "pillow", "isort (>=5.5.4)", "flake8 (>=3.8.3)", "hf-doc-builder", "scikit-learn", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer"]
dev-torch = ["pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "pytest-timeout", "black (>=22.0,<23.0)", "sacrebleu (>=1.4.12,<2.0.0)", "rouge-score", "nltk", "GitPython (<3.1.19)", "hf-doc-builder (>=0.2.0)", "faiss-cpu", "cookiecutter (==1.7.3)", "torch (>=1.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "protobuf", "tokenizers (>=0.11.1,!=0.11.3,<0.13)", "torchaudio", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer", "pillow", "optuna", "ray", "sigopt", "timm", "codecarbon (==1.2.0)", "isort (>=5.5.4)", "flake8 (>=3.8.3)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "unidic-lite (>=1.0.7)", "unidic (>=1.0.2)", "hf-doc-builder", "scikit-learn", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
docs = ["tensorflow (>=2.3)", "onnxconverter-common", "tf2onnx", "torch (>=1.0)", "jax (>=0.2.8,!=0.3.2)", "jaxlib (>=0.1.65)", "flax (>=0.3.5)", "optax (>=0.0.8)", "sentencepiece (>=0.1.91,!=0.1.92)", "protobuf", "tokenizers (>=0.11.1,!=0.11.3,<0.13)", "torchaudio", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer", "pillow", "optuna", "ray", "sigopt", "timm", "codecarbon (==1.2.0)", "hf-doc-builder"]
docs_specific = ["hf-doc-builder"]
fairscale = ["fairscale (>0.3)"]
flax = ["jax (>=0.2.8,!=0.3.2)", "jaxlib (>=0.1.65)", "flax (>=0.3.5)", "optax (>=0.0.8)"]
flax-speech = ["librosa", "pyctcdecode (>=0.3.0)", "phonemizer"]
ftfy = ["ftfy"]
integrations = ["optuna", "ray", "sigopt"]
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "unidic-lite (>=1.0.7)", "unidic (>=1.0.2)"]
modelcreation = ["cookiecutter (==1.7.3)"]
onnx = ["onnxconverter-common", "tf2onnx", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
quality = ["black (>=22.0,<23.0)", "isort (>=5.5.4)", "flake8 (>=3.8.3)", "GitPython (<3.1.19)", "hf-doc-builder (>=0.2.0)"]
ray = ["ray"]
retrieval = ["faiss-cpu", "datasets"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["sentencepiece (>=0.1.91,!=0.1.92)", "protobuf"]
serving = ["pydantic", "uvicorn", "fastapi", "starlette"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["torchaudio", "librosa", "pyctcdecode (>=0.3.0)", "phonemizer"]
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......
pyproject.toml
......@@ -7,6 +7,8 @@ authors = ["Jaime Collado <jcollado@ujaen.es>", "Estrella Vallecillo <mevr0003@r
[tool.poetry.dependencies]
python = "^3.8"
nltk = "^3.7"
spacy = "^3.3.0"
transformers = "^4.18.0"
[tool.poetry.dev-dependencies]
pytest = "^5.2"
......
textflow/Analyzer.py
import os
import spacy
import spacy.cli
from typing import Optional
from textflow.Sequence import Sequence
#from transformers import pipeline
class Analyzer:
def __init__(self, function, isMetadata: Optional[bool] = False):
def __init__(self, function, isMetadata: Optional[bool] = False,lang : Optional[str] = "es"):
"""Creates an analyzer from an input object.
Args:
function: the function of the analyzer like count word, files...
isMetadata: boolean, if the result of the analyzer is stored in metadata (True) or in children(False)
"""
if lang == "es":
spacy.cli.download("es_core_news_sm")
self.nlp = spacy.load("es_core_news_sm")
elif lang == "en":
spacy.cli.download("en_core_web_sm")
self.nlp = spacy.load("en_core_web_sm")
self.lang = lang
self.function = function
self.isMetadata = isMetadata
def analyze(self, sequence, tag, levelOfAnalyzer, levelOfResult:Optional[str] = None, analyzeMetadata: Optional[bool] = False): #TODO
def analyze(self, sequence, tag, levelOfAnalyzer, levelOfResult:Optional[str] = "", analyzeMetadata: Optional[bool] = False): #TODO
"""Analyze a sequence
Args:
sequence: the Sequence we want to analyze
levelOfAnalyzer: the path of the sequence level to analyze
levelOfResult: the path of the sequence level to store the result. (Podemos querer analizar los tokens pero almacenarlo a nivel de oracion)
tag: the label to store the analysis resut
analyzeMetadata: boolean, if the result of the analyzer is stored in metadata (True) or in children(False)
levelOfAnalyzer: the path of the sequence level to analyze inside of the result(la subsequencia a analizar dentro de la sequencia en la que queremos almacenar el resultado)
levelOfResult: the path of the sequence level to store the result. (Podemos querer analizar los tokens pero almacenarlo a nivel de oracion)
analyzeMetadata: boolean, if the result of the analyzer is applied in metadata (True) or in children(False)
Raises:
ValueError if the levelOfResult is incorrect
"""
ruta = levelOfAnalyzer.split("/")
if levelOfResult == "":
if analyzeMetadata:
analyzeResult = sequence.filterMetadata(levelOfAnalyzer, self.function)
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
if isinstance(resultOfAnalisys[0], Sequence):
sequence.children[tag] = resultOfAnalisys
else:
sequence.metadata[tag] = resultOfAnalisys
else:
children = [sequence.children]
ruta = levelOfResult.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
if analyzeMetadata:
analyzeResult = seq.filterMetadata(levelOfAnalyzer, self.function)
'''for i in analyzeResult:
resultOfAnalisys = i'''
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
if isinstance(resultOfAnalisys[0], Sequence):
seq.children[tag] = resultOfAnalisys
else:
seq.metadata[tag] = resultOfAnalisys
else:
analyzeResult = seq.filter(levelOfAnalyzer, self.function)
for i in analyzeResult:
resultOfAnalisys = i
if isinstance(resultOfAnalisys[0], Sequence):
seq.children[tag] = resultOfAnalisys
else:
seq.metadata[tag] = resultOfAnalisys
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
#La secuencia siempre debe tener un atributo texto para que este funcione
#Contar el numero de palabras, numero de palabras unicas, numero de caracteres y numero medio de caracteres
def volumetry(self,sequence,levelOfAnalyze): #TODO: Revisar
children = [sequence.children]
results=[]
for r in ruta:
for child in children:
if r in child:
ruta = levelOfAnalyze.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
results.extend(child[r])
for seq in child[r]:
if "text" not in seq.metadata:
raise ValueError(f"Level text not found in {seq.metadata.keys()}")
else:
text = seq.metadata["text"].split(" ")
volumetry= {
"words" : len(text),
"uniqueWords" : len(set(text)),
"chars" : len(seq.metadata["text"]),
"avgWordsLen" : round(volumetry["chars"] / volumetry["words"])
}
seq.metadata["volumetry"] = volumetry
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
analyze = self.function(results)
print(analyze)
ruta = levelOfResult.split("/")
def lemmas(self, sequence, levelOfAnalyze): #TODO: Revisar
children = [sequence.children]
for r in ruta:
for child in children:
if r in child:
ruta = levelOfAnalyze.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
#child[r] es una lista de sequencias
#child[r].children[tag]=[]
#print(child[r])
for seq in child[r]:
print(seq)
seq.children[tag] = analyze.pop(0)
print(seq.children)
'''child[r][chi].children[tag]=[]
child[r][chi].children[tag].append(analyze.pop(chi))'''
pass
#results.extend(child[r])
#pass
#Aqui ya dbo almacenar los resultados
if "text" not in seq.metadata:
raise ValueError(f"Level text not found in {seq.metadata.keys()}")
else:
sequenceLemmas = []
setLemmas = set()
lemma ={}
sumaLenLemmas=0
text = seq.metadata["text"]
doc= self.nlp(text)
for token in doc:
if token.pos_ not in ["PUNCT", "SPACE", "SYM"]:
sumaLenLemmas += len(token.lemma_)
setLemmas.add(token.lemma_)
s = Sequence("token",token.lemma_)
sequenceLemmas.append(s)
lemma["uniqueLemmas"] = len(setLemmas)
lemma["avgLemmasLen"] = round(sumaLenLemmas/len(sequenceLemmas))
seq.metadata["lemmas"] = lemma
seq.children["lemmas"] = sequenceLemmas
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
#Es necesario tener una etiqueta de token en children, si esta no existe, se creará
def pos (self, sequence, levelOfAnalyze): #TODO: Revisar
children = [sequence.children]
ruta = levelOfAnalyze.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
if "text" not in seq.metadata:
raise ValueError("The sequence of the level {levelOfAnalyze} don't have atribute text")
else:
doc = self.nlp(seq.metadata["text"])
if "tokens" not in seq.children:
#Creamos uno
pos=[]
for token in doc:
s = Sequence("token",token.text)
s.metadata["pos"] = token.pos_
pos.append(s)
seq.children["tokens"] = pos
else:
pos=[]
for token in doc:
pos.append(token.pos_)
for seqToken in seq.children["tokens"]:
seqToken.metadata["pos"] = pos.pop(0)
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
'''
def polaridad(self, sequence, levelOfAnalyze):
#https://huggingface.co/finiteautomata/beto-sentiment-analysis
if self.lang == "es":
polarityClassifier = pipeline("text-classification",model='finiteautomata/beto-sentiment-analysis', return_all_scores=True)
elif self.lang == "en":
polarityClassifier = pipeline("text-classification",model='finiteautomata/bertweet-base-sentiment-analysis', return_all_scores=True)
children = [sequence.children]
ruta = levelOfAnalyze.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
if "text" not in seq.metadata:
raise ValueError(f"Level text not found in {seq.metadata.keys()}")
else:
prediction = polarityClassifier(seq.metadata["text"])
seq.metadata["polarity"] = prediction
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
pass
def emotions(self, sequence, levelOfAnalyze):
if self.lang == "es":
emotionsClassifier = pipeline("text-classification",model='pysentimiento/robertuito-emotion-analysis', return_all_scores=True)
elif self.lang == "en":
emotionsClassifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
\ No newline at end of file
children = [sequence.children]
ruta = levelOfAnalyze.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
if "text" not in seq.metadata:
raise ValueError(f"Level text not found in {seq.metadata.keys()}")
else:
prediction = emotionsClassifier(seq.metadata["text"])
seq.metadata["emotions"] = prediction
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")'''
\ No newline at end of file
textflow/ComplexityAnalyzer.py 0 → 100644
import os
import spacy
import spacy.cli
from typing import Optional
from textflow.Sequence import Sequence
import re
import numpy as np
import math
from functools import reduce
creaPath = os.path.join(os.path.dirname(__file__), 'Crea-5000.txt')
class ComplexityAnalyzer:
def __init__(self, lang = "es"):
"""Creates an analyzer from an input object.
Args:
function: the function of the analyzer like count word, files...
isMetadata: boolean, if the result of the analyzer is stored in metadata (True) or in children(False)
"""
if lang == "es":
spacy.cli.download("es_core_news_sm")
self.nlp = spacy.load("es_core_news_sm")
#Vamos a cargar CREA:
self.dicFreqWords=self.read(creaPath)
self.function = self.complexity
'''elif lang == "en":
spacy.cli.download("en_core_web_sm")
self.nlp = spacy.load("en_core_web_sm")'''
#Este analizador, solo puede analizar cadenas de texto, por lo que solo tiene sentido que use el atributo text de metadata
def analyze(self, sequence, tag, levelOfAnalyzer, levelOfResult:Optional[str] = ""): #TODO
"""Analyze a sequence
Args:
sequence: the Sequence we want to analyze
tag: the label to store the analysis resut
levelOfAnalyzer: the path of the sequence level to analyze inside of the result(la subsequencia a analizar dentro de la sequencia en la que queremos almacenar el resultado)
levelOfResult: the path of the sequence level to store the result. (Podemos querer analizar los tokens pero almacenarlo a nivel de oracion)
analyzeMetadata: boolean, if the result of the analyzer is applied in metadata (True) or in children(False)
Raises:
ValueError if the levelOfResult is incorrect
"""
if levelOfResult == "":
analyzeResult = sequence.filterMetadata(levelOfAnalyzer,self.function)#TODO
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
sequence.metadata[tag] = resultOfAnalisys
else:
children = [sequence.children]
ruta = levelOfResult.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
analyzeResult = seq.filterMetadata(levelOfAnalyzer,self.function)
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
seq.metadata[tag] = resultOfAnalisys
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
def read(self,fichero):
with open(fichero,'r',encoding='latin-1') as file:
next(file)
lines = file.readlines()
freqWordsCrea ={}
for l in lines[:-2]:
words = l.strip().split()
freqWordsCrea[words[1]] = float(words[2].replace(',',''))
return freqWordsCrea
def complexity(self, arrayText):
arrayResults =[]
for text in arrayText:
doc= self.nlp (text)
self.simplesMetrics(doc)
self.countRareAndLowWord()
self.analyzeLegibility(doc)
self.lexicalIndex()
self.sentenceComplexity()
self.readability()
self.ageReadability()
self.embeddingDepth()
dicResults = {
'nSentences' : self.numContentSentences,
'nComplexSentence' : self.numComplexSents,
'avglenSentence' : self.avgLenSentence,
'nPuntuationMarks': self.numPuntuationMark,
'nWords': self.numWords,
'nRareWords' : self.numRareWord,
'nSyllabes' : self.numSyllabes,
'nChar' : self.numChars,
'ILFW': self.indexLowFreqWords,
'LDI': self.lexicalDistributionIndex,
'LC': self.lexicalComplexity,
'SSR': self.spauldingScore,
'SCI' : self.sentenceComplexityIndex,
'ARI' : self.autoReadabilityIndex,
'huerta': self.readabilityFH,
'IFSZ': self.perspicuityIFSZ,
'polini': self.poliniComprensibility,
'mu': self.muLegibility,
'minage': self.minAge,
'SOL': self.solReadability,
'crawford': self.crawford,
'min_depth' : self.min_max_list,
'max_depth' : self.max_max_list,
'mean_depth' : self.mean_max_list
}
arrayResults.append(dicResults)
return arrayResults
def simplesMetrics(self, doc):
#Simple metrics son los signos de puntuación, el numero de frases, el numero de frases con contenido...
self.sentences = [s for s in doc.sents]
self.numSentences = len(self.sentences)
pcs = []
for sent in self.sentences:
docSent = self.nlp(sent.text)
pcs.append([w for w in docSent if re.match('NOUN.*|VERB.*|ADJ.*', w.pos_) ])
numPunt = 0
numWords = 0
numWord3Syllabes = 0
numSyllabes = 0
for token in doc:
if token.pos_ == "PUNCT":
numPunt+=1
else:
numWords +=1
syllabes = self.countSyllabes(token.text)
if syllabes > 2:
numWord3Syllabes +=1
if token.text != "\r\n":
numSyllabes += syllabes
numChars = len(token.text)
self.posContentSentences = pcs
self.numContentSentences = len(pcs)
self.numPuntuationMark = numPunt
self.numWords = numWords
self.numWords3Syllabes = numWord3Syllabes
self.numSyllabes = numSyllabes
self.numChars = numChars
def analyzeLegibility(self,doc):
self.readabilityFH = 206.84 - 0.60*(self.numSyllabes/self.numWords) - 1.02*(self.numWords/self.numSentences)
self.perspicuityIFSZ = 206.835 - ((62.3*self.numSyllabes)/self.numWords) - (self.numWords/self.numSentences)
numLetters = 0
listLenLetters =[]
for token in doc:
if token.text.isalpha(): #Si es una palabra
numLetters += len(token.text)
listLenLetters.append(len(token.text))
avgLettersWords = numLetters/self.numWords
listLenLetters = np.array(listLenLetters)
self.poliniComprensibility = 95.2 - (9.7 * avgLettersWords) - ((0.35*self.numWords)/self.numSentences)
self.muLegibility = (self.numWords/(self.numWords-1))*(avgLettersWords/listLenLetters.var())*100
def lexicalIndex(self):
self.numContentWords = reduce((lambda a, b: a + b), [len(s) for s in self.posContentSentences])
self.numDistinctContentWords = len(set([w.text.lower() for s in self.posContentSentences for w in s]))
if self.numContentWords == 0:
self.numContentWords = 1
self.indexLowFreqWords = self.numLowWord / float(self.numContentWords)
if self.numContentSentences == 0:
self.numContentSentences = 1
self.lexicalDistributionIndex = self.numDistinctContentWords / float(self.numContentSentences)
self.lexicalComplexity = (self.indexLowFreqWords+self.lexicalDistributionIndex) /2
def readability(self):
self.autoReadabilityIndex = 4.71 * self.numChars / self.numWords + 0.5 * self.numWords/self.numContentSentences
self.spauldingScore = 1.609*(self.numWords / self.numContentSentences) + 331.8* (self.numRareWord /self.numWords) + 22.0
def countRareAndLowWord(self):
freqWord = sorted(self.dicFreqWords, key = self.dicFreqWords.__getitem__, reverse = True)[:1500]
countRareWord = 0
countLowWord = 0
for sentence in self.posContentSentences:
for word in sentence:
if word.text.lower() not in freqWord:
countRareWord += 1
if word.text.lower() not in self.dicFreqWords:
countLowWord += 1
self.numRareWord = countRareWord
self.numLowWord = countLowWord
def sentenceComplexity(self):
numComplexSentence=0
for sentence in self.sentences:
verb = False
cont = 0
for token in sentence:
if token.pos_ == "VERB":
if verb:
verb= False
cont+=1
else:
verb = True
else:
verb= False
if cont > 0:
numComplexSentence += 1
self.numComplexSents = numComplexSentence
self.avgLenSentence = self.numWords / self.numContentSentences
self.complexSentence = self.numComplexSents / self.numContentSentences
self.sentenceComplexityIndex = (self.avgLenSentence+self.complexSentence)/2
def countSyllabes(self, text):
t = re.sub(r'y([aáeéiíoóuú])', '\\1', text.lower())
t = re.sub(r'[aáeéioóu][iuy]', 'A', t.lower())
t = re.sub(r'[iu][aáeyéioóu]', 'A', t).lower()
t = re.sub(r'[aáeéiíoóuúy]', 'A', t)
return len(t.split('A'))-1
def treeHeight(self,root, cont):
if not list(root.children):
return 1
else:
cont+=1
if cont == 320:
return 320
return 1 + max(self.treeHeight(x, cont) for x in root.children)
def embeddingDepth(self):
roots = [sent.root for sent in self.sentences]
max_list = []
max_list = [self.treeHeight(root,0) for root in roots]
self.max_max_list = max(max_list)
self.min_max_list = min(max_list)
self.mean_max_list = sum(max_list)/(len(max_list))
return self.max_max_list, self.min_max_list, self.mean_max_list
def ageReadability(self):
self.solReadability = -2.51 + 0.74*(3.1291+1.0430*math.sqrt(self.numWords3Syllabes*(30/self.numSentences)))
self.minAge = 0.2495* (self.numWords/self.numSentences) + 6.4763*(self.numSyllabes/self.numWords) - 7.1395
self.crawford = -20.5*(self.numSentences/self.numWords)+4.9*(self.numSyllabes/self.numWords)-3.407
pass
textflow/Crea-5000.txt 0 → 100644
This diff could not be displayed because it is too large.
textflow/Sequence.py
......@@ -270,5 +270,46 @@ class Sequence:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
yield criteria(results)
cont=0
gen = criteria(results)
for r in gen:
yield gen[cont]
cont+=1
def filterMetadata(self, level, criteria): #TODO
'''
Filter the children of a Sequence according to a criteria
Args:
level: the route of the level as string, separating each level with "/"
criteria: the filter function
Returns:
A generator with the result of the filter
'''
ruta = level.split("/")
children = [self.children]
metadata = [self.metadata]
results=[]
if len(ruta) == 1 and ruta[0] in metadata[0]:
results.append(metadata[0][ruta[0]])
else:
for r in ruta:
if r == ruta[-1]:
for m in metadata:
if r in m:
results.append(m[r])
else:
for child in children:
if r in child:
children = [c.children for c in child[r]]
metadata = [c.metadata for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
#yield criteria(results)
cont=0
gen = criteria(results)
for r in gen:
yield gen[cont]
cont+=1
\ No newline at end of file
textflow/StylometryAnalyzer.py 0 → 100644
from typing import Optional
import spacy
import spacy.cli
class StylometryyAnalyzer: #TODO
def __init__(self, lang = "es"):
if lang == "es":
spacy.cli.download("es_core_news_sm")
self.nlp = spacy.load("es_core_news_sm")
self.function = self.stylometry
pass
#Este analizador, solo puede analizar cadenas de texto, por lo que solo tiene sentido que use el atributo text de metadata
def analyze(self, sequence, tag, levelOfAnalyzer, levelOfResult:Optional[str] = ""): #TODO
"""Analyze a sequence
Args:
sequence: the Sequence we want to analyze
tag: the label to store the analysis resut
levelOfAnalyzer: the path of the sequence level to analyze inside of the result(la subsequencia a analizar dentro de la sequencia en la que queremos almacenar el resultado)
levelOfResult: the path of the sequence level to store the result. (Podemos querer analizar los tokens pero almacenarlo a nivel de oracion)
analyzeMetadata: boolean, if the result of the analyzer is applied in metadata (True) or in children(False)
Raises:
ValueError if the levelOfResult is incorrect
"""
if levelOfResult == "":
analyzeResult = sequence.filterMetadata(levelOfAnalyzer,self.function)#TODO
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
sequence.metadata[tag] = resultOfAnalisys
else:
children = [sequence.children]
ruta = levelOfResult.split("/")
for r in ruta: #Para cada nivel de la ruta
for child in children: #Miramos en todas las secuencias disponibles
if r in child: #Si dentro de la secuencia actual está r
if r == ruta[-1]:
for seq in child[r]:
analyzeResult = seq.filterMetadata(levelOfAnalyzer,self.function)
resultOfAnalisys= []
for i in analyzeResult:
resultOfAnalisys.append(i)
seq.metadata[tag] = resultOfAnalisys
else:
children = [c.children for c in child[r]]
else:
raise ValueError(f"Sequence level '{r}' not found in {child}")
def stylometry(self):
pass
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