Are the pre-trained layers of the Huggingface BERT models frozen? - nlp

I use the following classification model from Huggingface:
model = AutoModelForSequenceClassification.from_pretrained("dbmdz/bert-base-german-cased", num_labels=2).to(device)
As I understand, this adds a dense layer at the end of the pre-trained model which has 2 output nodes. But are all the pre-trained layers before that frozen? Or are they also updated when fine-tuning? I can't find information about that in the docs...
So do I still have to do something like this?:
for param in model.bert.parameters():
param.requires_grad = False


They are not frozen. All parameters are trainable by default. You can also check that with:
for name, param in model.named_parameters():
print(name, param.requires_grad)
Output:
bert.embeddings.word_embeddings.weight True
bert.embeddings.position_embeddings.weight True
bert.embeddings.token_type_embeddings.weight True
bert.embeddings.LayerNorm.weight True
bert.embeddings.LayerNorm.bias True
bert.encoder.layer.0.attention.self.query.weight True
bert.encoder.layer.0.attention.self.query.bias True
bert.encoder.layer.0.attention.self.key.weight True
bert.encoder.layer.0.attention.self.key.bias True
bert.encoder.layer.0.attention.self.value.weight True
bert.encoder.layer.0.attention.self.value.bias True
bert.encoder.layer.0.attention.output.dense.weight True
bert.encoder.layer.0.attention.output.dense.bias True
bert.encoder.layer.0.attention.output.LayerNorm.weight True
bert.encoder.layer.0.attention.output.LayerNorm.bias True
bert.encoder.layer.0.intermediate.dense.weight True
bert.encoder.layer.0.intermediate.dense.bias True
bert.encoder.layer.0.output.dense.weight True
bert.encoder.layer.0.output.dense.bias True
bert.encoder.layer.0.output.LayerNorm.weight True
bert.encoder.layer.0.output.LayerNorm.bias True
bert.encoder.layer.1.attention.self.query.weight True
bert.encoder.layer.1.attention.self.query.bias True
bert.encoder.layer.1.attention.self.key.weight True
bert.encoder.layer.1.attention.self.key.bias True
bert.encoder.layer.1.attention.self.value.weight True
bert.encoder.layer.1.attention.self.value.bias True
bert.encoder.layer.1.attention.output.dense.weight True
bert.encoder.layer.1.attention.output.dense.bias True
bert.encoder.layer.1.attention.output.LayerNorm.weight True
bert.encoder.layer.1.attention.output.LayerNorm.bias True
bert.encoder.layer.1.intermediate.dense.weight True
bert.encoder.layer.1.intermediate.dense.bias True
bert.encoder.layer.1.output.dense.weight True
bert.encoder.layer.1.output.dense.bias True
bert.encoder.layer.1.output.LayerNorm.weight True
bert.encoder.layer.1.output.LayerNorm.bias True
bert.encoder.layer.2.attention.self.query.weight True
bert.encoder.layer.2.attention.self.query.bias True
bert.encoder.layer.2.attention.self.key.weight True
bert.encoder.layer.2.attention.self.key.bias True
bert.encoder.layer.2.attention.self.value.weight True
bert.encoder.layer.2.attention.self.value.bias True
bert.encoder.layer.2.attention.output.dense.weight True
bert.encoder.layer.2.attention.output.dense.bias True
bert.encoder.layer.2.attention.output.LayerNorm.weight True
bert.encoder.layer.2.attention.output.LayerNorm.bias True
bert.encoder.layer.2.intermediate.dense.weight True
bert.encoder.layer.2.intermediate.dense.bias True
bert.encoder.layer.2.output.dense.weight True
bert.encoder.layer.2.output.dense.bias True
bert.encoder.layer.2.output.LayerNorm.weight True
bert.encoder.layer.2.output.LayerNorm.bias True
bert.encoder.layer.3.attention.self.query.weight True
bert.encoder.layer.3.attention.self.query.bias True
bert.encoder.layer.3.attention.self.key.weight True
bert.encoder.layer.3.attention.self.key.bias True
bert.encoder.layer.3.attention.self.value.weight True
bert.encoder.layer.3.attention.self.value.bias True
bert.encoder.layer.3.attention.output.dense.weight True
bert.encoder.layer.3.attention.output.dense.bias True
bert.encoder.layer.3.attention.output.LayerNorm.weight True
bert.encoder.layer.3.attention.output.LayerNorm.bias True
bert.encoder.layer.3.intermediate.dense.weight True
bert.encoder.layer.3.intermediate.dense.bias True
bert.encoder.layer.3.output.dense.weight True
bert.encoder.layer.3.output.dense.bias True
bert.encoder.layer.3.output.LayerNorm.weight True
bert.encoder.layer.3.output.LayerNorm.bias True
bert.encoder.layer.4.attention.self.query.weight True
bert.encoder.layer.4.attention.self.query.bias True
bert.encoder.layer.4.attention.self.key.weight True
bert.encoder.layer.4.attention.self.key.bias True
bert.encoder.layer.4.attention.self.value.weight True
bert.encoder.layer.4.attention.self.value.bias True
bert.encoder.layer.4.attention.output.dense.weight True
bert.encoder.layer.4.attention.output.dense.bias True
bert.encoder.layer.4.attention.output.LayerNorm.weight True
bert.encoder.layer.4.attention.output.LayerNorm.bias True
bert.encoder.layer.4.intermediate.dense.weight True
bert.encoder.layer.4.intermediate.dense.bias True
bert.encoder.layer.4.output.dense.weight True
bert.encoder.layer.4.output.dense.bias True
bert.encoder.layer.4.output.LayerNorm.weight True
bert.encoder.layer.4.output.LayerNorm.bias True
bert.encoder.layer.5.attention.self.query.weight True
bert.encoder.layer.5.attention.self.query.bias True
bert.encoder.layer.5.attention.self.key.weight True
bert.encoder.layer.5.attention.self.key.bias True
bert.encoder.layer.5.attention.self.value.weight True
bert.encoder.layer.5.attention.self.value.bias True
bert.encoder.layer.5.attention.output.dense.weight True
bert.encoder.layer.5.attention.output.dense.bias True
bert.encoder.layer.5.attention.output.LayerNorm.weight True
bert.encoder.layer.5.attention.output.LayerNorm.bias True
bert.encoder.layer.5.intermediate.dense.weight True
bert.encoder.layer.5.intermediate.dense.bias True
bert.encoder.layer.5.output.dense.weight True
bert.encoder.layer.5.output.dense.bias True
bert.encoder.layer.5.output.LayerNorm.weight True
bert.encoder.layer.5.output.LayerNorm.bias True
bert.encoder.layer.6.attention.self.query.weight True
bert.encoder.layer.6.attention.self.query.bias True
bert.encoder.layer.6.attention.self.key.weight True
bert.encoder.layer.6.attention.self.key.bias True
bert.encoder.layer.6.attention.self.value.weight True
bert.encoder.layer.6.attention.self.value.bias True
bert.encoder.layer.6.attention.output.dense.weight True
bert.encoder.layer.6.attention.output.dense.bias True
bert.encoder.layer.6.attention.output.LayerNorm.weight True
bert.encoder.layer.6.attention.output.LayerNorm.bias True
bert.encoder.layer.6.intermediate.dense.weight True
bert.encoder.layer.6.intermediate.dense.bias True
bert.encoder.layer.6.output.dense.weight True
bert.encoder.layer.6.output.dense.bias True
bert.encoder.layer.6.output.LayerNorm.weight True
bert.encoder.layer.6.output.LayerNorm.bias True
bert.encoder.layer.7.attention.self.query.weight True
bert.encoder.layer.7.attention.self.query.bias True
bert.encoder.layer.7.attention.self.key.weight True
bert.encoder.layer.7.attention.self.key.bias True
bert.encoder.layer.7.attention.self.value.weight True
bert.encoder.layer.7.attention.self.value.bias True
bert.encoder.layer.7.attention.output.dense.weight True
bert.encoder.layer.7.attention.output.dense.bias True
bert.encoder.layer.7.attention.output.LayerNorm.weight True
bert.encoder.layer.7.attention.output.LayerNorm.bias True
bert.encoder.layer.7.intermediate.dense.weight True
bert.encoder.layer.7.intermediate.dense.bias True
bert.encoder.layer.7.output.dense.weight True
bert.encoder.layer.7.output.dense.bias True
bert.encoder.layer.7.output.LayerNorm.weight True
bert.encoder.layer.7.output.LayerNorm.bias True
bert.encoder.layer.8.attention.self.query.weight True
bert.encoder.layer.8.attention.self.query.bias True
bert.encoder.layer.8.attention.self.key.weight True
bert.encoder.layer.8.attention.self.key.bias True
bert.encoder.layer.8.attention.self.value.weight True
bert.encoder.layer.8.attention.self.value.bias True
bert.encoder.layer.8.attention.output.dense.weight True
bert.encoder.layer.8.attention.output.dense.bias True
bert.encoder.layer.8.attention.output.LayerNorm.weight True
bert.encoder.layer.8.attention.output.LayerNorm.bias True
bert.encoder.layer.8.intermediate.dense.weight True
bert.encoder.layer.8.intermediate.dense.bias True
bert.encoder.layer.8.output.dense.weight True
bert.encoder.layer.8.output.dense.bias True
bert.encoder.layer.8.output.LayerNorm.weight True
bert.encoder.layer.8.output.LayerNorm.bias True
bert.encoder.layer.9.attention.self.query.weight True
bert.encoder.layer.9.attention.self.query.bias True
bert.encoder.layer.9.attention.self.key.weight True
bert.encoder.layer.9.attention.self.key.bias True
bert.encoder.layer.9.attention.self.value.weight True
bert.encoder.layer.9.attention.self.value.bias True
bert.encoder.layer.9.attention.output.dense.weight True
bert.encoder.layer.9.attention.output.dense.bias True
bert.encoder.layer.9.attention.output.LayerNorm.weight True
bert.encoder.layer.9.attention.output.LayerNorm.bias True
bert.encoder.layer.9.intermediate.dense.weight True
bert.encoder.layer.9.intermediate.dense.bias True
bert.encoder.layer.9.output.dense.weight True
bert.encoder.layer.9.output.dense.bias True
bert.encoder.layer.9.output.LayerNorm.weight True
bert.encoder.layer.9.output.LayerNorm.bias True
bert.encoder.layer.10.attention.self.query.weight True
bert.encoder.layer.10.attention.self.query.bias True
bert.encoder.layer.10.attention.self.key.weight True
bert.encoder.layer.10.attention.self.key.bias True
bert.encoder.layer.10.attention.self.value.weight True
bert.encoder.layer.10.attention.self.value.bias True
bert.encoder.layer.10.attention.output.dense.weight True
bert.encoder.layer.10.attention.output.dense.bias True
bert.encoder.layer.10.attention.output.LayerNorm.weight True
bert.encoder.layer.10.attention.output.LayerNorm.bias True
bert.encoder.layer.10.intermediate.dense.weight True
bert.encoder.layer.10.intermediate.dense.bias True
bert.encoder.layer.10.output.dense.weight True
bert.encoder.layer.10.output.dense.bias True
bert.encoder.layer.10.output.LayerNorm.weight True
bert.encoder.layer.10.output.LayerNorm.bias True
bert.encoder.layer.11.attention.self.query.weight True
bert.encoder.layer.11.attention.self.query.bias True
bert.encoder.layer.11.attention.self.key.weight True
bert.encoder.layer.11.attention.self.key.bias True
bert.encoder.layer.11.attention.self.value.weight True
bert.encoder.layer.11.attention.self.value.bias True
bert.encoder.layer.11.attention.output.dense.weight True
bert.encoder.layer.11.attention.output.dense.bias True
bert.encoder.layer.11.attention.output.LayerNorm.weight True
bert.encoder.layer.11.attention.output.LayerNorm.bias True
bert.encoder.layer.11.intermediate.dense.weight True
bert.encoder.layer.11.intermediate.dense.bias True
bert.encoder.layer.11.output.dense.weight True
bert.encoder.layer.11.output.dense.bias True
bert.encoder.layer.11.output.LayerNorm.weight True
bert.encoder.layer.11.output.LayerNorm.bias True
bert.pooler.dense.weight True
bert.pooler.dense.bias True
classifier.weight True
classifier.bias True


model = AutoModelForSequenceClassification.from_pretrained("dbmdz/bert-base-german-cased", num_labels=2).to(device)
all(p.requires_grad for p in model.parameters()
# True
So you'll need to freeze them yourself if you want them frozen.

Related

pandas dataframe makes a boolean column based on the date in the list

Assuming there is a data frame with 2 columns ds, y (the input of fbprophet https://facebook.github.io/prophet/docs/quick_start.html)
ds y
2021-03-17 3135.73
2021-03-18 3027.99
2021-03-19 3074.96
2021-03-22 3110.87
2021-03-23 3110.87
2021-03-24 3110.87
2021-03-25 3110.87
Here is the code to generate this dataframe
data = \
{
'ds':['2021-03-17','2021-03-18','2021-03-19','2021-03-22','2021-03-23','2021-03-24','2021-03-25'],
'y':['3135.73','3027.99','3074.96','3110.87','3110.87','3110.87','3110.87']
}
df = pd.DataFrame(data)
How could I generate 2 new boolean columns on_season, off_season
based on the "date" in a loop?
So for example, if the ds column is in between date and date-2, then on_season=TRUE, off_season = OFF
desired output since the date in the loop here are '2021-03-19','2021-03-25'. So on_season is between '2021-03-19' and '2021-03-17' and '2021-03-25' and '2021-03-23'.
ds y on_season off_season
2021-03-17 3135.73 TRUE FALSE
2021-03-18 3027.99 TRUE FALSE
2021-03-19 3074.96 TRUE FALSE
2021-03-22 3110.87 FALSE TRUE
2021-03-23 3110.87 TRUE FALSE
2021-03-24 3110.87 TRUE FALSE
2021-03-25 3110.87 TRUE FALSE
Here is the working progress code
# the date in a loop
for date in ['2021-03-19','2021-03-25']:
date_str=''
date_str=date_str.join(date) # convert date from list into string
on_season=pd.to_datetime(date_str)
on_season = on_season.strftime("%Y-%m-%d")
#try to make the day range
off_season=pd.to_datetime(date_str) - timedelta(2)
off_season=off_season.strftime("%Y-%m-%d") # convert datetime object to string
# I am stuck in the dataframe part

It seems you just need to calculate the ds diff between adjacent rows:
df.ds = pd.to_datetime(df.ds)
# check if difference between two rows are smaller than 2
df['on_season'] = df.ds.diff().dt.days.fillna(0) < 2
df['off_season'] = ~df['on_season']
df
ds y on_season off_season
0 2021-03-17 3135.73 True False
1 2021-03-18 3027.99 True False
2 2021-03-19 3074.96 True False
3 2021-03-22 3110.87 False True
4 2021-03-23 3110.87 True False
5 2021-03-24 3110.87 True False
6 2021-03-25 3110.87 True False

How to add 1 more breakdown color in plotly funnel?

I have 1 state on "og"- false, and 2 states on "no og" true and false
is_retargeting
media_source
step
Values
False
og
step1
31992
True
no og
step1
33644
False
no og
step1
32199
False
no og
step2
20699
False
og
step2
47796
True
no og
step2
23668
True
no og
step3
10744
False
no og
step3
27622
False
og
step3
20979
False
no og
step4
16459
False
og
step4
28444
True
no og
step4
49749
i come up with this code, but it shows only 2 colors but breaks "no og" with no explanation.
here is the screenshot what i get
How can add one more breakdown column is_retargeting for this funnel so it will show true or false for "no og"?
import pandas as pd
pd.set_option('max_columns', None)
df = pd.read_csv("name.csv")
df.loc[df["media_source"] != "organic", "media_source"] = 'non organic'
df = df.groupby(['is_retargeting', 'media_source']).sum().reset_index()
df1 = df.melt(id_vars=["is_retargeting", "media_source"],
var_name="step",
value_name="Values")
fig = px.funnel(df1, x='Values', y='step', color='media_source')
fig.show()

Number of occurrences between datetime

I have the following code:
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import datetime
import numpy as np
stock = '^GSPC'
start = datetime.date(2000,1,1)
end = datetime.date.today()
data = web.DataReader(stock, 'yahoo',start, end)
data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data['pct_day']= data['Adj Close'].pct_change()
How can I find the number of occurrences of value over 0.05 in column pct_day and the date when it occurred?
--Edit 1:
In the comment i was suggested :
df[data['pct_day'].gt(0.005)]
which produces the following series:
Date
2001-01-03 True
2002-07-24 True
2002-07-29 True
2008-09-30 True
2008-10-13 True
2008-10-28 True
2008-11-13 True
2008-11-21 True
2008-11-24 True
2008-12-16 True
2009-03-10 True
2009-03-23 True
Name: pct_day, dtype: bool
But when i use:
df[data['pct_day'].gt(0.03)]
it produces the following series:
Date
2000-03-16 False
2000-04-17 False
2000-04-25 False
2000-05-30 False
2000-10-13 False
...
2011-10-27 False
2011-11-30 False
2015-08-26 False
2018-12-26 False
2019-01-04 False
Name: pct_day, Length: 66, dtype: bool
Why is it including the ones with False in the last example but not the first one? Does the length of the second which is 66, also include False? I ask this because data has over 5000 entries but the length in the last example is 66.

how to check if a list value exist in a column value which is also a list

I have the following Dataframe:
Name
0 [Scrap]
1 [Machinery, Scrap]
2 [RMG]
3 [Leather]
4 [Machinery]
5 [RMG, Optical Frame, Machinery]
I want to check if "Scrap" or "Machinery" exist in Name column. like below:
Name Type
0 [Scrap] True
1 [Machinery, Scrap] True
2 [RMG] False
3 [Leather] False
4 [Machinery] True
5 [RMG, Optical Frame, Machinery] True
I have tried the following code.
df['Type'] = df.apply(lambda x: ["Scrap", "Iron","Machinery"] in x["Name"], axis=1)
However, i get the following result:
Name Type
0 [Scrap] False
1 [Machinery, Scrap] False
2 [RMG] False
3 [Leather] False
4 [Machinery] False
5 [RMG, Optical Frame, Machinery] False

This is one approach using .apply.
Ex:
to_check = ("Scrap", "Iron","Machinery")
df = pd.DataFrame({"Name": [["Scrap"], ["Machinery", "Scrap"], ["RMG"], ["Leather"], ["Machinery"], ["RMG", "Optical Frame", "Machinery"]]})
df['Type'] = df["Name"].apply(lambda x: any(i in x for i in to_check))
print(df)
Output:
Name Type
0 [Scrap] True
1 [Machinery, Scrap] True
2 [RMG] False
3 [Leather] False
4 [Machinery] True
5 [RMG, Optical Frame, Machinery] True

You are checking if the whole list can be found, which is why you are getting no True. Try
df['Type'] = df.apply(lambda elem: ("Scrap" in elem["Name"]) or ("Machinery" in elem["Name"]), axis=1)

You can also use set intersection to see whether there exists a common element.
df['Type'] = df.apply(lambda x: bool( set(["Scrap", "Iron","Machinery"]) & set(x["Name"]) ), axis=1)

Test for overlapping times in Python

I am following this word for word, but am getting different results compared to the original post and I have no idea why. I am wanting to compare pairs of times to one another to check where they overlap. I am using Python 3.6.6
Find if dates are overlapping in a list of N pairs
This is what I coded:
import datetime
from collections import namedtuple
from itertools import combinations
timesok = [('09:30', '10:00'), ('10:00', '10:30'), ('10:30', '11:00')]
wrongtimes1 = [('9:30', '10:00'), ('9:00', '10:30'), ('10:30', '11:00')]
wrongtimes2=[('9:30', '10:00'), ('10:00', '10:30'), ('9:15', '9:45')]
def test_overlap(dt1_st, dt1_end, dt2_st, dt2_end):
Range = namedtuple('Range', ['start', 'end'])
r1 = Range(start=dt1_st, end=dt1_end)
r2 = Range(start=dt2_st, end=dt2_end)
latest_start = max(r1.start, r2.start)
earliest_end = min(r1.end, r2.end)
overlap = (earliest_end - latest_start)
return overlap.seconds
def find_overlaps(times):
pairs = list(combinations(times, 2))
print(pairs)
for pair in pairs:
start1 = datetime.datetime.strptime(pair[0][0], '%H:%M')
end1 = datetime.datetime.strptime(pair[0][1], '%H:%M')
start2 = datetime.datetime.strptime(pair[1][0], '%H:%M')
end2 = datetime.datetime.strptime(pair[1][1], '%H:%M')
yield test_overlap(start1, end1, start2, end2) > 0
list(find_overlaps(timesok))
list(find_overlaps(wrongtimes1))
list(find_overlaps(wrongtimes2))
# timesok result:
list(find_overlaps(timesok))
[(('09:30', '10:00'), ('10:00', '10:30')),
(('09:30', '10:00'), ('10:30', '11:00')),
(('10:00', '10:30'), ('10:30', '11:00'))]
Out[7]: [False, True, False]
# wrongtimes1 result:
list(find_overlaps(wrongtimes1))
[(('9:30', '10:00'), ('9:00', '10:30')),
(('9:30', '10:00'), ('10:30', '11:00')),
(('9:00', '10:30'), ('10:30', '11:00'))]
Out[8]: [True, True, False]
# wrongtimes2 result:
list(find_overlaps(wrongtimes2))
[(('9:30', '10:00'), ('10:00', '10:30')),
(('9:30', '10:00'), ('9:15', '9:45')),
(('10:00', '10:30'), ('9:15', '9:45'))]
Out[9]: [False, True, True]
I think the results should be as follows (which corresponds with the original example in the link above):
Out[7]: [False, False, False]
Out[8]: [True, False, False]
Out[9]: [False, True, False]
Have I missed something very obvious here? I am a total Python newbie so be gentle with me (!)

overlap.seconds does not return what you think it does.
overlap is the result of a datetime subtraction, which is datetime.timedelta.
timedelta.seconds does not return the total number of seconds. It returns the second part of the delta, and if the timedelta is -1 day, 23:45:00, timedelta.seconds is 85500.
What you meant to write was overlap.total_seconds():
def test_overlap(dt1_st, dt1_end, dt2_st, dt2_end):
Range = namedtuple('Range', ['start', 'end'])
r1 = Range(start=dt1_st, end=dt1_end)
r2 = Range(start=dt2_st, end=dt2_end)
latest_start = max(r1.start, r2.start)
earliest_end = min(r1.end, r2.end)
overlap = (earliest_end - latest_start)
return overlap.total_seconds()
You still have the problem that your times all have to be at the same day and cannot span wrap around midnight. I don't think using datetime is the right approach here, I think it is easier to program something custom.
Here you go:
from itertools import combinations
def test_overlap(t1_st, t1_end, t2_st, t2_end):
def convert_to_minutes(t_str):
hours, minutes = t_str.split(':')
return 60*int(hours)+int(minutes)
t1_st = convert_to_minutes(t1_st)
t1_end = convert_to_minutes(t1_end)
t2_st = convert_to_minutes(t2_st)
t2_end = convert_to_minutes(t2_end)
# Check for wrapping time differences
if t1_end < t1_st:
if t2_end < t2_st:
# Both wrap, therefore they overlap at midnight
return True
# t2 doesn't wrap. Therefore t1 has to start after t2 and end before
return t1_st < t2_end or t2_st < t1_end
if t2_end < t2_st:
# only t2 wraps. Same as before, just reversed
return t2_st < t1_end or t1_st < t2_end
# They don't wrap and the start of one comes after the end of the other,
# therefore they don't overlap
if t1_st >= t2_end or t2_st >= t1_end:
return False
# In all other cases, they have to overlap
return True
times = [('09:30', '00:00'), ('07:00', '00:30'), ('10:30', '00:15'), ('12:15', '13:30'), ('10:00', '11:00'), ('00:15', '01:15')]
pairs = list(combinations(times, 2))
for pair in pairs:
start1 = pair[0][0]
end1 = pair[0][1]
start2 = pair[1][0]
end2 = pair[1][1]
print(str(test_overlap(start1, end1, start2, end2)) + "\t" + str(pair))
True (('09:30', '00:00'), ('07:00', '00:30'))
True (('09:30', '00:00'), ('10:30', '00:15'))
True (('09:30', '00:00'), ('12:15', '13:30'))
True (('09:30', '00:00'), ('10:00', '11:00'))
False (('09:30', '00:00'), ('00:15', '01:15'))
True (('07:00', '00:30'), ('10:30', '00:15'))
True (('07:00', '00:30'), ('12:15', '13:30'))
True (('07:00', '00:30'), ('10:00', '11:00'))
True (('07:00', '00:30'), ('00:15', '01:15'))
True (('10:30', '00:15'), ('12:15', '13:30'))
True (('10:30', '00:15'), ('10:00', '11:00'))
False (('10:30', '00:15'), ('00:15', '01:15'))
False (('12:15', '13:30'), ('10:00', '11:00'))
False (('12:15', '13:30'), ('00:15', '01:15'))
False (('10:00', '11:00'), ('00:15', '01:15'))

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