algorithm/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-07-05 09:21:13 +08:00
Code Issues Packages Projects Releases Wiki Activity

Remove some print statements within algorithmic functions (#7499)

Browse Source 
* Remove commented-out print statements in algorithmic functions

* Encapsulate non-algorithmic code in __main__

* Remove unused print_matrix function

* Remove print statement in __init__

* Remove print statement from doctest

* Encapsulate non-algorithmic code in __main__

* Modify algorithm to return instead of print

* Encapsulate non-algorithmic code in __main__

* Refactor data_safety_checker to return instead of print

* updating DIRECTORY.md

* updating DIRECTORY.md

* Apply suggestions from code review

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Tianyi Zheng
2022-10-22 07:33:51 -04:00
committed by GitHub
parent 717f0e46d9
commit cc10b20beb
10 changed files with 69 additions and 64 deletions
Download Patch File Download Diff File Expand all files Collapse all files

86
machine_learning/forecasting/run.py
View File

@ -1,7 +1,7 @@
"""
this is code for forecasting
but i modified it and used it for safety checker of data
for ex: you have a online shop and for some reason some data are
for ex: you have an online shop and for some reason some data are
missing (the amount of data that u expected are not supposed to be)
then we can use it
*ps : 1. ofc we can use normal statistic method but in this case
@ -91,14 +91,14 @@ def interquartile_range_checker(train_user: list) -> float:
return low_lim
def data_safety_checker(list_vote: list, actual_result: float) -> None:
def data_safety_checker(list_vote: list, actual_result: float) -> bool:
"""
Used to review all the votes (list result prediction)
and compare it to the actual result.
input : list of predictions
output : print whether it's safe or not
>>> data_safety_checker([2,3,4],5.0)
Today's data is not safe.
>>> data_safety_checker([2, 3, 4], 5.0)
False
"""
safe = 0
not_safe = 0
@ -107,50 +107,54 @@ def data_safety_checker(list_vote: list, actual_result: float) -> None:
safe = not_safe + 1
else:
if abs(abs(i) - abs(actual_result)) <= 0.1:
safe = safe + 1
safe += 1
else:
not_safe = not_safe + 1
print(f"Today's data is {'not ' if safe <= not_safe else ''}safe.")
not_safe += 1
return safe > not_safe
# data_input_df = pd.read_csv("ex_data.csv", header=None)
data_input = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
data_input_df = pd.DataFrame(data_input, columns=["total_user", "total_even", "days"])
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
data_input = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
data_input_df = pd.DataFrame(
data_input, columns=["total_user", "total_even", "days"]
)
"""
data column = total user in a day, how much online event held in one day,
what day is that(sunday-saturday)
"""
"""
data column = total user in a day, how much online event held in one day,
what day is that(sunday-saturday)
"""
# start normalization
normalize_df = Normalizer().fit_transform(data_input_df.values)
# split data
total_date = normalize_df[:, 2].tolist()
total_user = normalize_df[:, 0].tolist()
total_match = normalize_df[:, 1].tolist()
# start normalization
normalize_df = Normalizer().fit_transform(data_input_df.values)
# split data
total_date = normalize_df[:, 2].tolist()
total_user = normalize_df[:, 0].tolist()
total_match = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
x = normalize_df[:, [1, 2]].tolist()
x_train = x[: len(x) - 1]
x_test = x[len(x) - 1 :]
# for svr (input variable = total date and total match)
x = normalize_df[:, [1, 2]].tolist()
x_train = x[: len(x) - 1]
x_test = x[len(x) - 1 :]
# for linear reression & sarimax
trn_date = total_date[: len(total_date) - 1]
trn_user = total_user[: len(total_user) - 1]
trn_match = total_match[: len(total_match) - 1]
# for linear regression & sarimax
trn_date = total_date[: len(total_date) - 1]
trn_user = total_user[: len(total_user) - 1]
trn_match = total_match[: len(total_match) - 1]
tst_date = total_date[len(total_date) - 1 :]
tst_user = total_user[len(total_user) - 1 :]
tst_match = total_match[len(total_match) - 1 :]
tst_date = total_date[len(total_date) - 1 :]
tst_user = total_user[len(total_user) - 1 :]
tst_match = total_match[len(total_match) - 1 :]
# voting system with forecasting
res_vote = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# voting system with forecasting
res_vote = []
res_vote.append(
linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match)
)
res_vote.append(sarimax_predictor(trn_user, trn_match, tst_match))
res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
# check the safety of todays'data^^
data_safety_checker(res_vote, tst_user)
# check the safety of today's data
not_str = "" if data_safety_checker(res_vote, tst_user) else "not "
print("Today's data is {not_str}safe.")