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SeqToDF and starting with Test class

parent de1b1540
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Showing with 487 additions and 39 deletions
Examples/ExampleDirectory/Documento sin título copy.txt 0 → 100644
Hola, ¿Como estas?
Hola, ¿Como estas?
\ No newline at end of file
Examples/df_trans_merged_textflow.csv 0 → 100644
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poetry.lock
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. # This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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{file = "statsmodels-0.14.0.tar.gz", hash = "sha256:6875c7d689e966d948f15eb816ab5616f4928706b180cf470fd5907ab6f647a4"},
]
[package.dependencies]
numpy = [
{version = ">=1.22.3", markers = "python_version == \"3.10\" and platform_system == \"Windows\" and platform_python_implementation != \"PyPy\""},
{version = ">=1.18", markers = "python_version != \"3.10\" or platform_system != \"Windows\" or platform_python_implementation == \"PyPy\""},
]
packaging = ">=21.3"
pandas = ">=1.0"
patsy = ">=0.5.2"
scipy = ">=1.4,<1.9.2 || >1.9.2"
[package.extras]
build = ["cython (>=0.29.26)"]
develop = ["colorama", "cython (>=0.29.26)", "cython (>=0.29.28,<3.0.0)", "flake8", "isort", "joblib", "matplotlib (>=3)", "oldest-supported-numpy (>=2022.4.18)", "pytest (>=7.0.1,<7.1.0)", "pytest-randomly", "pytest-xdist", "pywinpty", "setuptools-scm[toml] (>=7.0.0,<7.1.0)"]
docs = ["ipykernel", "jupyter-client", "matplotlib", "nbconvert", "nbformat", "numpydoc", "pandas-datareader", "sphinx"]
[[package]]
name = "thinc" name = "thinc"
version = "8.1.10" version = "8.1.10"
description = "A refreshing functional take on deep learning, compatible with your favorite libraries" description = "A refreshing functional take on deep learning, compatible with your favorite libraries"
...@@ -1553,6 +1784,17 @@ files = [ ...@@ -1553,6 +1784,17 @@ files = [
] ]
[[package]] [[package]]
name = "tzdata"
version = "2023.3"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
files = [
{file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
{file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
]
[[package]]
name = "urllib3" name = "urllib3"
version = "2.0.3" version = "2.0.3"
description = "HTTP library with thread-safe connection pooling, file post, and more." description = "HTTP library with thread-safe connection pooling, file post, and more."
...@@ -1627,4 +1869,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more ...@@ -1627,4 +1869,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.7" python-versions = "^3.7"
content-hash = "f21eda5036a869c794538ff1a680d2e5455ac082c4722f873708e03c1c98f19f" content-hash = "2287d1241d8eafcd7cf77c2716e00fcfa2fa56d8a06b96b59a57d6090c24b02a"
pyproject.toml
...@@ -12,6 +12,10 @@ transformers = "^4.19.0" ...@@ -12,6 +12,10 @@ transformers = "^4.19.0"
torch = {version = "^1.11.0", python = "^3.7", platform = "linux"} torch = {version = "^1.11.0", python = "^3.7", platform = "linux"}
lexical-diversity = "^0.1.1" lexical-diversity = "^0.1.1"
emoji = "^1.7.0" emoji = "^1.7.0"
pandas = {version = "^2.1.1", python = ">=3.9,<4.0"}
numpy = {version = "^1.26.0", python = ">=3.9,<3.13"}
scipy = {version = "^1.11.3", python = ">=3.9,<3.13"}
statsmodels = {version = "^0.14.0", python = ">=3.8,<4.0"}
[tool.poetry.dev-dependencies] [tool.poetry.dev-dependencies]
pytest = "^5.2" pytest = "^5.2"
......
textflow/Sequence.py
from typing import Optional from typing import Optional
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
import pandas as pd
from collections import defaultdict
class SequenceIterator: class SequenceIterator:
""" """
...@@ -253,4 +255,78 @@ class Sequence(ABC): ...@@ -253,4 +255,78 @@ class Sequence(ABC):
for r in gen: for r in gen:
yield gen[cont] yield gen[cont]
cont+=1 cont+=1
\ No newline at end of file @abstractmethod
def toDF(self, level= "metadata"):
'''
Convert a Sequence to a pandas DataFrame
#Args:
# level: the route of the level as string, separating each level with "/"
Returns:
A pandas DataFrame with the sequence information
'''
path = level.split("/")
children = [self.children]
metadata = [self.metadata]
columns = []
values = []
for idx, p in enumerate(path):
print(idx,p)
if p == "metadata":
print("Es metadata",metadata)
for metadataDic in metadata:
for m in metadataDic:
columns.append(m+str(idx))
values.append({str(m)+str(idx):metadataDic[m]})
elif p == "children":
childrenAux=[]
for child in children: #Accedemos a los hijos
for ch in child: #Cada hijo tiene un diccionario de valores
#print(ch,child[ch])
columns.append(ch+str(idx))
auxDic={}
for c in child[ch]: #Dentro de cada diccionario de valores tenemos más sequencias
#print(c.metadata)
#print(c.children)
childrenAux.append(c.children)
for metadataKey in c.metadata: #Cada Sequencia tiene sus metadatos, como todas las sequencias de este nivel pertenecen al mismo, todas deberían tener los mismos metadatos
#print(metadataKey)
if metadataKey not in auxDic:
auxDic[metadataKey]=[c.metadata[metadataKey]]
else:
auxDic[metadataKey].append(c.metadata[metadataKey])
values.append({str(ch)+str(idx):auxDic})
children = childrenAux
finalColumns = []
finalRows = {}
for value in values: #Recorremos la lista de valores (diccionarios)
for v in value: # Para cada clave en el diccionario
print(v,type(value))
if type(value[v]) == dict:
for keyValue in value[v]:
if v+keyValue not in finalColumns:
finalColumns.append(v+keyValue)
if type(value[v][keyValue]) == list:
finalRows[v+keyValue]=value[v][keyValue]
else:
finalRows[v+keyValue]=[value[v][keyValue]]
else:
if type(value[v][keyValue]) == list and finalRows[v+keyValue][0] != list: #Solo se ejecutara una vez despues de haber creado el dataset
newList=[]
for element in finalRows[v+keyValue]:
newList.append(element)
finalRows[v+keyValue]=[newList,value[v][keyValue]]
else:
finalRows[v+keyValue].append(value[v][keyValue])
else:
if len(value[v]) != 0:
if v not in finalColumns:
finalColumns.append(v)
finalRows[v]=value[v]
print(finalRows)
df = pd.DataFrame(finalRows)
return df
textflow/SequenceDirectory.py
...@@ -163,4 +163,17 @@ class SequenceDirectory(Sequence): ...@@ -163,4 +163,17 @@ class SequenceDirectory(Sequence):
Returns: Returns:
A generator with the result of the filter A generator with the result of the filter
''' '''
return super().filterMetadata(level,criteria) return super().filterMetadata(level,criteria)
\ No newline at end of file
def toDF(self,level):#, level, criteria):
'''
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
'''
return super().toDF(level)
\ No newline at end of file
textflow/SequenceFile.py
...@@ -147,4 +147,17 @@ class SequenceFile (Sequence): ...@@ -147,4 +147,17 @@ class SequenceFile (Sequence):
Returns: Returns:
A generator with the result of the filter A generator with the result of the filter
''' '''
return super().filterMetadata(level,criteria) return super().filterMetadata(level,criteria)
\ No newline at end of file
def toDF(self,level):#, level, criteria):
'''
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
'''
return super().toDF(level)
\ No newline at end of file
textflow/SequenceString.py
...@@ -147,4 +147,17 @@ class SequenceString (Sequence): ...@@ -147,4 +147,17 @@ class SequenceString (Sequence):
''' '''
return super().filterMetadata(level,criteria) return super().filterMetadata(level,criteria)
def toDF(self,level):#, level, criteria):
'''
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
'''
return super().toDF(level)
\ No newline at end of file
textflow/SequenceToken.py
...@@ -137,3 +137,16 @@ class SequenceToken (Sequence): ...@@ -137,3 +137,16 @@ class SequenceToken (Sequence):
A generator with the result of the filter A generator with the result of the filter
''' '''
return super().filterMetadata(level,criteria) return super().filterMetadata(level,criteria)
def toDF(self,level):#, level, criteria):
'''
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
'''
return super().toDF(level)
textflow/Test.py 0 → 100644
from typing import Optional
from abc import ABC, abstractmethod
import pandas as pd
from collections import defaultdict
from scipy.stats import shapiro, normaltest, kstest, anderson, chisquare, jarque_bera
from statsmodels.stats.diagnostic import lilliefors
class Test():
#https://towardsdatascience.com/normality-tests-in-python-31e04aa4f411
def __init__(self,parametricTest=["Shapiro","D'Agostino","Anderson-Darling","Chi-Square","Lilliefors","Jarque–Bera","Kolmogorov-Smirnov"],nonParametricTest=["mannwhitneyu","wilcoxon","kruskal"]):
self.parametricTest = parametricTest
self.nonParametricTest = nonParametricTest
def apply(self,df1,df2):
#Hay que poner gráficas:
# qUARTIL QUARTIL
# Box Plot
# Histograma
pass
def applyParametric(self,df):
testFinal = pd.DataFrame()
#Numeric Cols, hay que filtrar el df
for i in self.parametricTest:
if i == "Shapiro":
test = df.apply(lambda x: shapiro(x), axis=0)
test.index = ['Shapiro stat', 'Shapiro p-value']
test = test.transpose()
elif i == "D'Agostino":
test = df.apply(lambda x: normaltest(x), axis=0)
test.index = ["D'Agostino stat", "D'Agostino p-value"]
test = test.transpose()
elif i == "Anderson-Darling":
test = df.apply(lambda x: anderson(x), axis=0)
test.index = ['Anderson-Darling stat', 'Anderson-Darling crit_val', 'Anderson-Darling sig_level']
test = test.transpose()
pass
elif i == "Chi-Square":
test = df.apply(lambda x: chisquare(x), axis=0)
test.index = ['Chi-Square stat', 'Chi-Square p-value']
test = test.transpose()
pass
elif i == "Lilliefors":
test = df.apply(lambda x: lilliefors(x), axis=0)
test.index = ['Lilliefors stat', 'Lilliefors p-value']
test = test.transpose()
pass
elif i == "Jarque–Bera":
test = df.apply(lambda x: jarque_bera(x), axis=0)
test.index = ['Shapiro stat', 'Shapiro p-value']
test = test.transpose()
pass
elif i == "Kolmogorov-Smirnov":
test = df.apply(lambda x: kstest(x, 'norm'), axis=0)
test.index = ["Kolmogorov-Smirnov stat", "Kolmogorov-Smirnov p-value"]
test = test.transpose()
for t in self.parametricTest:
if t != "Anderson-Darling":
print("Pass the test of"+t)
print(list(test[test[t+' p-value'] > 0.05].index))
else:
for i in range(len(list(test[t+' crit_val'].index))):
sig_level, crit_val = test[t+' sig_level'][i], test[t+' crit_val'][i]
print("Pass the test of"+t)
print(list(test[test[t+' stat'] < crit_val].index),"at {sig_level} level of significance")
\ No newline at end of file
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