def test_pandas_groupby_apply():
""" Test pandas.DataFrame.groupby(...).progress_apply """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 50, (500, 3)))
dfs = pd.DataFrame(randint(0, 50, (500, 3)),
columns=list('abc'))
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
dfs.groupby(['a']).progress_apply(lambda x: None)
our_file.seek(0)
# don't expect final output since no `leave` and
# high dynamic `miniters`
nexres = '100%|##########|'
if nexres in our_file.read():
our_file.seek(0)
raise AssertionError("\nDid not expect:\n{0}\nIn:{1}\n".format(
nexres, our_file.read()))
def run_bootstrap_per_ASV(input_file):
print(f'Reading in ASV file - {input_file} and running with {number_of_experiments} simulations')
df = pd.read_csv(input_file)
tqdm_pandas(tqdm())
results = df.progress_apply(process_row, axis=1)
joined_results = df.merge(results, how='left', on=['ID', 'Site'])
joined_results.to_csv(f'./data/ASV_cultivation_bootstrapped_numbers_max_viability_{number_of_experiments}_simulations.joined.csv', index=False)
def test_pandas_groupby_apply():
""" Test pandas.DataFrame.groupby(...).progress_apply """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 50, (500, 3)))
dfs = pd.DataFrame(randint(0, 50, (500, 3)), columns=list('abc'))
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
dfs.groupby(['a']).progress_apply(lambda x: None)
our_file.seek(0)
# don't expect final output since no `leave` and
# high dynamic `miniters`
nexres = '100%|##########|'
if nexres in our_file.read():
our_file.seek(0)
raise AssertionError("\nDid not expect:\n{0}\nIn:{1}\n".format(
nexres, our_file.read()))
def loadandbuildindex2():
global default
nums = 1000
df_artists, df_en_songs = load_data()
df_en_songs = df_en_songs.iloc[:nums, :]
# -- after load we should create corpus Documents for search system -- #
# -- preprocessed data -- #
tqdm_pandas(tqdm())
clspreproccessed = ClassPreprocessed(text_column='Lyric',
DRAREFREQ_words=True)
df_new_songs = clspreproccessed.preprocessed(df_en_songs)
# -- save -- #
df_new_songs.to_csv('preprocessed_data.csv')
print(df_new_songs['Lyric'][0])
print('Most common words:\n')
print(FREQWORDS)
print('Most rare words:\n')
print(RAREWORDS)
# -- we should preprocessed title of songs -- #
clspreproccessed2 = ClassPreprocessed(text_column='SName',
DRAREFREQ_words=False,
stopwords=False,
lemmatize=False)
df_new_songs = clspreproccessed2.preprocessed(df_new_songs, verbose=1)
df_new_songs.to_csv('preprocessed_data.csv')
try:
index = np.random.choice(df_new_songs.index, size=1)[0]
print('before: \n')
print(df_en_songs.loc[index, ['Lyric', 'SName']].values)
print('after: \n')
print(df_new_songs.loc[index, ['Lyric', 'SName']].values)
except:
print('Choice works bad!')
print('all Done!')
# -- create invert index -- #
# -- We have alseo information about Artist, Popularity, Genre of each music -- #
# -- We can use it for getting more actual results -- #
# -- We should use name artist and Genre and Popularity -- #
# -- merge df_artist and df_songs -- #
merge = pd.merge(df_new_songs,
df_artists,
how='inner',
left_on='ALink',
right_on='Link')
# -- У нас есть ещё жанр Genre и Artist_name -- #
# -- Мы должны этим воспользоваться -- #
# -- Сгруппировать песни по Жанру и Артисту -- #
# -- Тоже сделать что-то вроде базы данных, которая содержит список песен для каждого отдельного взятого певца -- #
createDataInvertIndex(merge)
build_index2(merge, new_columns=True)
# -- Заполняем наш словарь отображений слов -- #
load_embedding_WV()
# -- По умолчанию вектор -- #
default = sum(dictionary_fastWV.values()) / len(dictionary_fastWV)
def flatten_images_directory(self):
tqdm_pandas(tqdm())
logger.INFO("Flattening images data")
self.build_all_images_dir()
self.ref_dataset.progress_apply(
lambda x: copyfile(x['origin_path'], x['destination_path']),
axis=1)
def calculate_featureset3(dataframe):
# Word Mover's Distance
tqdm_pandas(tqdm())
dataframe['wmd'] = dataframe.progress_apply(
lambda x: calc_wordmoversdist(x['question1'], x['question2']), axis=1)
dataframe['norm_wmd'] = dataframe.progress_apply(
lambda x: calc_norm_wordmover(x['question1'], x['question2']), axis=1)
return dataframe
def estimate_viability_range_for_underrepresented_taxa():
print('*** Calculating maximum viability to explain observed number of wells ***')
df = pd.read_csv(f'./data/ASV_cultivation_bootstrapped_numbers_max_viability_{number_of_experiments}_simulations.joined.csv')
lower_than_expected_df = df[df.deviance <0]
tqdm_pandas(tqdm())
results = lower_than_expected_df.progress_apply(process_viability, axis=1)
results.to_csv(f'./data/estimate_viability_range_for_underrepresented_taxa_{number_of_experiments}_simulations.csv', index=False)
def test_pandas_apply_args_deprecation():
"""Test warning info in
`pandas.Dataframe(Series).progress_apply(func, *args)`"""
try:
from tqdm import tqdm_pandas
except ImportError as err:
skip(str(err))
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.progress_apply(lambda x: None, 1) # 1 shall cause a warning
# Check deprecation message
res = our_file.getvalue()
assert all([i in res for i in (
"TqdmDeprecationWarning", "not supported",
"keyword arguments instead")])
def test_pandas_apply_args_deprecation():
"""Test warning info in
`pandas.Dataframe(Series).progress_apply(func, *args)`"""
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except ImportError:
raise SkipTest
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.progress_apply(lambda x: None, 1) # 1 shall cause a warning
# Check deprecation message
res = our_file.getvalue()
assert all([i in res for i in (
"TqdmDeprecationWarning", "not supported",
"keyword arguments instead")])
def test_pandas_apply():
""" Test pandas.DataFrame.progress_apply """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.progress_apply(lambda x: None)
our_file.seek(0)
if '/6' not in our_file.read():
our_file.seek(0)
raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
'/6', our_file.read()))
def test_pandas():
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
try:
# don't expect final output since no `leave` and
# high dynamic `miniters`
assert '100%|##########| 101/101' not in our_file.read()
except:
raise AssertionError('Did not expect:\n\t100%|##########| 101/101')
def test_pandas_leave():
""" Test pandas with `leave=True` """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
exres = '100%|##########| 101/101'
if exres not in our_file.read():
our_file.seek(0)
raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
exres, our_file.read()))
def test_pandas_leave():
""" Test pandas with `leave=True` """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
exres = '100%|##########| 101/101'
if exres not in our_file.read():
our_file.seek(0)
raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
exres, our_file.read()))
def test_pandas():
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
try:
# don't expect final output since no `leave` and
# high dynamic `miniters`
assert '100%|##########| 101/101' not in our_file.read()
except:
raise AssertionError('Did not expect:\n\t100%|##########| 101/101')
def test_pandas_deprecation():
"""Test bar object instance as argument deprecation"""
try:
from tqdm import tqdm_pandas
except ImportError as err:
skip(str(err))
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.groupby(0).progress_apply(lambda x: None)
# Check deprecation message
assert "TqdmDeprecationWarning" in our_file.getvalue()
assert "instead of `tqdm_pandas(tqdm(...))`" in our_file.getvalue()
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm, file=our_file, leave=False, ascii=True, ncols=20)
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.groupby(0).progress_apply(lambda x: None)
# Check deprecation message
assert "TqdmDeprecationWarning" in our_file.getvalue()
assert "instead of `tqdm_pandas(tqdm, ...)`" in our_file.getvalue()
def test_pandas_leave():
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
try:
assert '100%|##########| 101/101' in our_file.read()
except:
our_file.seek(0)
raise AssertionError('\n'.join(('Expected:',
'100%|##########| 101/101', 'Got:',
our_file.read())))
def test_pandas_leave():
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 100, (1000, 6)))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.groupby(0).progress_apply(lambda x: None)
our_file.seek(0)
try:
assert '100%|##########| 101/101' in our_file.read()
except:
our_file.seek(0)
raise AssertionError('\n'.join(
('Expected:', '100%|##########| 101/101', 'Got:',
our_file.read())))
def test_pandas_apply():
""" Test pandas.DataFrame[.series].progress_apply """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 50, (500, 3)))
dfs = pd.DataFrame(randint(0, 50, (500, 3)), columns=list('abc'))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.progress_apply(lambda x: None)
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
dfs.a.progress_apply(lambda x: None)
our_file.seek(0)
if our_file.read().count('100%') < 2:
our_file.seek(0)
raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
'100% at least twice', our_file.read()))
def test_pandas_apply():
""" Test pandas.DataFrame[.series].progress_apply """
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except:
raise SkipTest
with closing(StringIO()) as our_file:
df = pd.DataFrame(randint(0, 50, (500, 3)))
dfs = pd.DataFrame(randint(0, 50, (500, 3)),
columns=list('abc'))
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
df.progress_apply(lambda x: None)
tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
dfs.a.progress_apply(lambda x: None)
our_file.seek(0)
if our_file.read().count('100%') < 2:
our_file.seek(0)
raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
'100% at least twice', our_file.read()))
def test_pandas_deprecation():
"""Test bar object instance as argument deprecation"""
try:
from numpy.random import randint
from tqdm import tqdm_pandas
import pandas as pd
except ImportError:
raise SkipTest
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm(file=our_file, leave=False, ascii=True, ncols=20))
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.groupby(0).progress_apply(lambda x: None)
# Check deprecation message
assert "TqdmDeprecationWarning" in our_file.getvalue()
assert "instead of `tqdm_pandas(tqdm(...))`" in our_file.getvalue()
with closing(StringIO()) as our_file:
tqdm_pandas(tqdm, file=our_file, leave=False, ascii=True, ncols=20)
df = pd.DataFrame(randint(0, 50, (500, 3)))
df.groupby(0).progress_apply(lambda x: None)
# Check deprecation message
assert "TqdmDeprecationWarning" in our_file.getvalue()
assert "instead of `tqdm_pandas(tqdm, ...)`" in our_file.getvalue()
start_close_time['start_time'] = map(int,
start_close_time['start_time'] / 1000)
start_close_time['close_time'] = map(int,
start_close_time['close_time'] / 1000)
unique_app_name = np.unique(start_close_time['app_name'])
dict_label = dict(
zip(list(unique_app_name), list(np.arange(0, len(unique_app_name), 1))))
import time
start_close_time['app_name'] = start_close_time['app_name'].apply(
lambda row: str(dict_label[row]))
del start_close_time['start_time'], start_close_time['close_time']
from tqdm import tqdm, tqdm_pandas
tqdm_pandas(tqdm())
def dealed_row(row):
app_name_list = list(row['app_name'])
return ' '.join(app_name_list)
data_feature = start_close_time.groupby('id').progress_apply(
lambda row: dealed_row(row)).reset_index()
data_feature = pd.merge(data_all, data_feature, on='id', how='left')
del data_feature['id']
count_vec = CountVectorizer()
count_csr_basic = count_vec.fit_transform(data_feature[0])
tfidf_vec = TfidfVectorizer()
test_path = 'D:/data_science/kaggle_sound_classification/audio_test/'
#TQDM build
def tqdm_pandas(t):
from pandas.core.frame import Series
def inner(series, func, *args, **kwargs):
t.total = series.size
def wrapper(*args, **kwargs):
t.update(1)
return func(*args, **kwargs)
result = series.apply(wrapper, *args, **kwargs)
t.close()
return result
Series.progress_apply = inner
tqdm_pandas(tqdm_notebook())
tqdm.pandas(desc="my bar!")
#Feature engineering
SAMPLE_RATE = 22050
from scipy.stats import skew
print(os.listdir(os.getcwd()))
tqdm.pandas
train_files = os.listdir(train_path)
test_files = os.listdir(test_path)
train_files = glob(train_path + '*.wav')
test_files = glob(test_path + '*.wav')
SAMPLE_RATE = 22050
def get_feature(fname):
#b,_ = librosa.load(fname, res_type = 'kaiser_fast')
b,_ = librosa.load(fname, res_type = 'kaiser_fast')
import pandas as pd
from src.tools.tokenizer import tokenize, is_chinese, combine
import os
from os.path import join
from start_experiment import MANIFEST_FOLDER
from tqdm import tqdm, tqdm_pandas
from collections import Counter
import sentencepiece as spm
from start_experiment import MANIFEST_FOLDER
from tqdm import tqdm
tqdm_pandas(tqdm)
CORPUS = 'data/corpus.txt'
ENG_CORPUS = 'data/eng_corpus.txt'
TRAIN_MANIFEST_LIST_FORCORPUS_CH = [
'aidatatang_200zh.csv', 'AISHELL-2.csv', 'c_500.csv', 'ce_200.csv',
'data_aishell.csv', 'magic_data_train.csv', 'magic_data_dev.csv',
'magic_data_test.csv', 'stcmds.csv', 'prime.csv'
]
TRAIN_MANIFEST_LIST_FORCORPUS_CH = [
join(MANIFEST_FOLDER, i) for i in TRAIN_MANIFEST_LIST_FORCORPUS_CH
]
TRAIN_MANIFEST_LIST_FORCORPUS_EN = [
'libri_100.csv', 'libri_360.csv', 'libri_500.csv'
]
TRAIN_MANIFEST_LIST_FORCORPUS_EN = [
join(MANIFEST_FOLDER, i) for i in TRAIN_MANIFEST_LIST_FORCORPUS_EN
]
DEV_MANIFEST_LIST_FORCORPUS = [join(MANIFEST_FOLDER, 'ce_20_dev.csv')]
test_df['fuzz_token_set_ratio'] = test_dd.apply(
lambda x: fuzz.token_set_ratio(str(x['question1']), str(x['question2'])),
axis=1,
meta=('a', np.dtype('int64'))).compute(get=dask.multiprocessing.get)
test_df['fuzz_token_sort_ratio'] = test_dd.apply(
lambda x: fuzz.token_sort_ratio(str(x['question1']), str(x['question2'])),
axis=1,
meta=('a', np.dtype('int64'))).compute(get=dask.multiprocessing.get)
print((time.time() - start_time))
del test_dd
###############################################################################
model = gensim.models.KeyedVectors.load_word2vec_format(
'../../input/GoogleNews-vectors-negative300.bin.gz', binary=True)
tqdm_pandas(tqdm(desc="Train wmd:", total=len(train_df)))
train_df['wmd'] = train_df.progress_apply(
lambda x: wmd(x['question1'], x['question2']), axis=1)
tqdm_pandas(tqdm(desc="Test wmd:", total=len(test_df)))
test_df['wmd'] = test_df.progress_apply(
lambda x: wmd(x['question1'], x['question2']), axis=1)
del model
norm_model = gensim.models.KeyedVectors.load_word2vec_format(
'../../input/GoogleNews-vectors-negative300.bin.gz', binary=True)
norm_model.init_sims(replace=True)
tqdm_pandas(tqdm(desc="Train norm wmd:", total=len(train_df)))
train_df['norm_wmd'] = train_df.progress_apply(
lambda x: norm_wmd(x['question1'], x['question2']), axis=1)
tqdm_pandas(tqdm(desc="Test norm wmd:", total=len(test_df)))
test_df['norm_wmd'] = test_df.progress_apply(
import pandas as pd import numpy as np from tqdm import tqdm, tqdm_pandas df = pd.DataFrame(np.random.randint(0, 100, (100000, 6))) # Create and register a new `tqdm` instance with `pandas` # (can use tqdm_gui, optional kwargs, etc.) tqdm_pandas(tqdm()) # Now you can use `progress_apply` instead of `apply` df.progress_apply(lambda x: x**2) # can also groupby: # df.groupby(0).progress_apply(lambda x: x**2) """ Source code for `tqdm_pandas` (really simple!) """ # def tqdm_pandas(t): # from pandas.core.frame import DataFrame # def inner(df, func, *args, **kwargs): # t.total = groups.size // len(groups) # def wrapper(*args, **kwargs): # t.update(1) # return func(*args, **kwargs) # result = df.apply(wrapper, *args, **kwargs) # t.close() # return result # DataFrame.progress_apply = inner
import numpy as np
import pandas
import tqdm
from typing import Callable
from diyrex.cache import cacher
tqdm.tqdm_pandas(tqdm.tqdm) # progress bars for groupby
def compute_implicit_ratings_func(signals: pandas.DataFrame,
agg_func: Callable):
ratings = signals.groupby(['user', 'item']).progress_apply(agg_func)
# normalize
ratings /= ratings.max()
return ratings.to_frame(name='rating').reset_index()
def compute_implicit_ratings_1(signals):
"rating is 1 if any interaction happened"
return compute_implicit_ratings_func(signals, lambda g: 1)
def compute_implicit_ratings_2(signals: pandas.DataFrame):
"rating is the number of days with interactions"
return compute_implicit_ratings_func(signals,
lambda g: g['date'].dt.date.nunique())
import numpy as np
import pandas as pd
import tqdm
import pickle
progress_bar = tqdm.tqdm()
tqdm.tqdm_pandas(progress_bar)
#load data
from gym_splendor_code.envs.mechanics.abstract_observation import DeterministicObservation
from nn_models.utils.vectorizer import Vectorizer
raw_data_small = pd.read_pickle(
'/home/tomasz/ML_Research/splendor/gym_open_ai-splendor/training_data/half_merged.pi'
)
#
vectorizer = Vectorizer()
def obs_to_state(obs: DeterministicObservation):
return obs.recreate_state()
series_of_states = raw_data_small['observation'].progress_map(obs_to_state)
X_list = series_of_states.tolist()
n = len(X_list)
m = n - n % 5
X_list = X_list[0:m]
Y = np.array(raw_data_small['value'].tolist()[0:m]).reshape(m, 1)
indices = [i for i in range(len(Y)) if abs(Y[i]) == 1]