cereja 1.4.7

Cereja is a bundle of useful functions that I don't want to rewrite.

Cereja 🍒

Cereja was written only with the Standard Python Library, and it was a great way to improve knowledge in the Language also to avoid the rewriting of code.

Getting Started DEV

Don't be shy \0/ ... Clone the repository and submit a function or module you made or use some function you liked.

See CONTRIBUTING 💻

Setup

• Python 3.6+
• Pip3

Install

pip install --user cereja

or for all users

pip install cereja

Cereja Example usage

See some of the Cereja tools

To access the Cereja's tools you need to import it import cereja as cj.

📝 FileIO

Create new files

import cereja as cj

file_json = cj.FileIO.create('./json_new_file.json', data={'k': 'v', 'k2': 'v2'})

file_txt = cj.FileIO.create('./txt_new_file.txt', ['line1', 'line2', 'line3'])

file_json.save()
file_txt.save()

print(file_json.exists)
# True
print(file_txt.exists)
# True


# see what you can do .txt file
print(cj.can_do(file_txt))

# see what you can do .json file
print(cj.can_do(file_json))

Load and edit files

import cereja as cj

file_json = cj.FileIO.load('./json_new_file.json')

print(file_json.data)
# {'k': 'v', 'k2': 'v2'}

file_json.add(key='new_key', value='value')
print(file_json.data)
# {'k': 'v', 'k2': 'v2', 'new_key': 'value'}

file_txt = cj.FileIO.load('./txt_new_file.txt')

print(file_txt.data)
# ['line1', 'line2', 'line3']

file_txt.add('line4')
print(file_txt.data)
# ['line1', 'line2', 'line3', 'line4']

file_txt.save(exist_ok=True)  # Override
file_json.save(exist_ok=True)  # Override

📍 Path

import cereja as cj

file_path = cj.Path('/my/path/file.ext')
print(cj.can_do(file_path))
# ['change_current_dir', 'cp', 'created_at', 'exists', 'get_current_dir', 'is_dir', 'is_file', 'is_hidden', 'is_link', 'join', 'last_access', 'list_dir', 'list_files', 'mv', 'name', 'parent', 'parent_name', 'parts', 'path', 'rm', 'root', 'rsplit', 'sep', 'split', 'stem', 'suffix', 'updated_at', 'uri']

🆗 HTTP Requests

import cereja as cj

# Change url, headers and data values.
url = 'localhost:8000/example'
headers = {'Authorization': 'TOKEN'} # optional
data = {'q': 'test'} # optional

response = cj.request.post(url, data=data, headers=headers)

if response.code == 200:
    data = response.data
    # have a fun!

⏳ Progress

import cereja as cj
import time

my_iterable = ['Cereja', 'is', 'very', 'easy']

for i in cj.Progress.prog(my_iterable):
    print(f"current: {i}")
    time.sleep(2)

# Output on terminal ...

# 🍒 Sys[out] » current: Cereja 
# 🍒 Sys[out] » current: is 
# 🍒 Cereja Progress » [▰▰▰▰▰▰▰▰▰▰▰▰▰▰▰▱▱▱▱▱▱▱▱▱▱▱▱▱▱] - 50.00% - 🕢 00:00:02 estimated

🧠 Data Preparation

📊 Freq

import cereja as cj

freq = cj.Freq([1, 2, 3, 3, 10, 10, 4, 4, 4, 4])
# Output -> Freq({1: 1, 2: 1, 3: 2, 10: 2, 4: 4})

freq.most_common(2)
# Output -> {4: 4, 3: 2}

freq.least_freq(2)
# Output -> {2: 1, 1: 1}

freq.probability
# Output -> OrderedDict([(4, 0.4), (3, 0.2), (10, 0.2), (1, 0.1), (2, 0.1)])

freq.sample(min_freq=1, max_freq=2)
# Output -> {3: 2, 10: 2, 1: 1, 2: 1}

# Save json file.
freq.to_json('./freq.json')

🧹 Text Preprocess

import cereja as cj

text = "Oi tudo bem?? meu nome é joab!"

text = cj.preprocess.remove_extra_chars(text)
print(text)
# Output -> 'Oi tudo bem? meu nome é joab!'

text = cj.preprocess.separate(text, sep=['?', '!'])
# Output -> 'Oi tudo bem ? meu nome é joab !'

text = cj.preprocess.accent_remove(text)
# Output -> 'Oi tudo bem ? meu nome e joab !'

# and more ..

# You can use class Preprocessor ...
preprocessor = cj.Preprocessor(stop_words=(),
                               punctuation='!?,.', to_lower=True, is_remove_punctuation=False,
                               is_remove_stop_words=False,
                               is_remove_accent=True)

print(preprocessor.preprocess(text))
# Output -> 'oi tudo bem ? meu nome e joab !'

print(preprocessor.preprocess(text, is_destructive=True))
# Output -> 'oi tudo bem meu nome e joab'

🔣 Tokenizer

import cereja as cj

text = ['oi tudo bem meu nome é joab']

tokenizer = cj.Tokenizer(text, use_unk=True)

# tokens 0 to 9 is UNK
# hash_ used to replace UNK
token_sequence, hash_ = tokenizer.encode('meu nome é Neymar Júnior')
# Output -> [([10, 12, 11, 0, 1], 'eeb755960ce70c')]

decoded_sequence = tokenizer.decode(token_sequence)
# Output -> ['meu', 'nome', 'é', '{0}', '{1}']

decoded_sequence = ' '.join(decoded_sequence)

tokenizer.replace_unks(decoded_sequence, hash_)
# Output -> 'meu nome é Neymar Júnior'

⏸ Corpus

Great training and test separator.

import cereja as cj

X = ['how are you?', 'my name is Joab', 'I like coffee', 'how are you joab?', 'how', 'we are the world']
Y = ['como você está?', 'meu nome é Joab', 'Eu gosto de café', 'Como você está joab?', 'como', 'Nós somos o mundo']

corpus = cj.Corpus(source_data=X, target_data=Y, source_name='en', target_name='pt')
print(corpus)  # Corpus(examples: 6 - source_vocab_size: 13 - target_vocab_size:15)
print(corpus.source)  # LanguageData(examples: 6 - vocab_size: 13)
print(corpus.target)  # LanguageData(examples: 6 - vocab_size: 15)

corpus.source.phrases_freq
# Counter({'how are you': 1, 'my name is joab': 1, 'i like coffee': 1, 'how are you joab': 1, 'how': 1, 'we are the world': 1})

corpus.source.word_freq
# Counter({'how': 3, 'are': 3, 'you': 2, 'joab': 2, 'my': 1, 'name': 1, 'is': 1, 'i': 1, 'like': 1, 'coffee': 1, 'we': 1, 'the': 1, 'world': 1})

corpus.target.phrases_freq
# Counter({'como você está': 1, 'meu nome é joab': 1, 'eu gosto de café': 1, 'como você está joab': 1, 'como': 1, 'nós somos o mundo': 1})

corpus.target.words_freq
# Counter({'como': 3, 'você': 2, 'está': 2, 'joab': 2, 'meu': 1, 'nome': 1, 'é': 1, 'eu': 1, 'gosto': 1, 'de': 1, 'café': 1, 'nós': 1, 'somos': 1, 'o': 1, 'mundo': 1})

# split_data function guarantees test data without data identical to training
# and only with vocabulary that exists in training
train, test = corpus.split_data()  # default percent of training is 80%

🔢 Array

import cereja as cj

cj.array.is_empty(data)  # False
cj.array.get_shape(data)  # (2, 3)

data = cj.array.flatten(data)  # [1, 2, 3, 3, 3, 3]
cj.array.prod(data)  # 162
cj.array.sub(data)  # -13
cj.array.div(data)  # 0.006172839506172839

cj.array.rand_n(0.0, 2.0, n=3)  # [0.3001196087729699, 0.639679494102923, 1.060200897124107]
cj.array.rand_n(1, 10)  # 5.086403830031244
cj.array.array_randn((3, 3,
                      3))  # [[[0.015077210355770374, 0.014298110484612511, 0.030410666810216064], [0.029319083335697604, 0.0072365209507707666, 0.010677361074992], [0.010576754075922935, 0.04146379877648334, 0.02188348813336284]], [[0.0451851551098092, 0.037074906805326824, 0.0032484586475421007], [0.025633380630695347, 0.010312669541918484, 0.0373624007621097], [0.047923908102496145, 0.0027939333359724224, 0.05976224377251878]], [[0.046869510719106486, 0.008325638358172866, 0.0038702998343255893], [0.06475268683502387, 0.0035638592537234623, 0.06551037943638163], [0.043317416824708604, 0.06579372884523939, 0.2477564291871006]]]
cj.array.group_items_in_batches(items=[1, 2, 3, 4], items_per_batch=3, fill=0)  # [[1, 2, 3], [4, 0, 0]]
cj.array.remove_duplicate_items(['hi', 'hi', 'ih'])  # ['hi', 'ih'] 
cj.array.get_cols([['line1_col1', 'line1_col2'],
                   ['line2_col1', 'line2_col2']])  # [['line1_col1', 'line2_col1'], ['line1_col2', 'line2_col2']]
cj.array.dotproduct([1, 2], [1, 2])  # 5

a = cj.array.array_gen((3, 3), 1)  # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
b = cj.array.array_gen((3, 3), 1)  # [[1, 1, 1], [1, 1, 1], [1, 1, 1]]
cj.array.dot(a, b)  # [[3, 3, 3], [3, 3, 3], [3, 3, 3]]
cj.mathtools.theta_angle((2, 2), (0, -2))  # 135.0

🧰 Utils

import cereja as cj

data = {"key1": 'value1', "key2": 'value2', "key3": 'value3', "key4": 'value4'}

cj.utils.chunk(list(range(10)), batch_size=3)
# [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
cj.utils.chunk(list(range(10)), batch_size=3, fill_with=0, is_random=True)
# [[9, 7, 8], [0, 3, 2], [4, 1, 5], [6, 0, 0]]

# Invert Dict
cj.utils.invert_dict(data)
# Output -> {'value1': 'key1', 'value2': 'key2', 'value3': 'key3', 'value4': 'key4'}

# Get sample of large data
cj.utils.sample(data, k=2, is_random=True)
# Output -> {'key1': 'value1', 'key4': 'value4'}

cj.utils.fill([1, 2, 3, 4], max_size=20, with_=0)
# Output -> [1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

cj.utils.rescale_values([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], granularity=4)
# Output -> [1, 3, 5, 7]

cj.utils.import_string('cereja.file._io.FileIO')
# Output -> <class 'cereja.file._io.FileIO'>

cj.utils.list_methods(cj.Path)
# Output -> ['change_current_dir', 'cp', 'get_current_dir', 'join', 'list_dir', 'list_files', 'mv', 'rm', 'rsplit', 'split']


cj.utils.string_to_literal('[1,2,3,4]')
# Output -> [1, 2, 3, 4]

cj.utils.time_format(3600)
# Output -> '01:00:00'

cj.utils.truncate("Cereja is fun.", k=3)
# Output -> 'Cer...'

data = [[1, 2, 3], [3, 3, 3]]
cj.utils.is_iterable(data)  # True
cj.utils.is_sequence(data)  # True
cj.utils.is_numeric_sequence(data)  # True

See Usage - Jupyter Notebook

License

This project is licensed under the MIT License - see the LICENSE file for details