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"""

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

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

the data is quite absurd and only a little^^

2. ofc u can use this and modified it for forecasting purpose

for the next 3 months sales or something,

u can just adjust it for ur own purpose

"""

import numpy as np

import pandas as pd

from sklearn.preprocessing import Normalizer

from sklearn.svm import SVR

from statsmodels.tsa.statespace.sarimax import SARIMAX

def linear_regression_prediction(

train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list

) -> float:

"""

First method: linear regression

input : training data (date, total_user, total_event) in list of float

output : list of total user prediction in float

>>> n = linear_regression_prediction([2,3,4,5], [5,3,4,6], [3,1,2,4], [2,1], [2,2])

>>> abs(n - 5.0) < 1e-6 # Checking precision because of floating point errors

True

"""

x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])

y = np.array(train_usr)

beta = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)

return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])

def sarimax_predictor(train_user: list, train_match: list, test_match: list) -> float:

"""

second method: Sarimax

sarimax is a statistic method which using previous input

and learn its pattern to predict future data

input : training data (total_user, with exog data = total_event) in list of float

output : list of total user prediction in float

>>> sarimax_predictor([4,2,6,8], [3,1,2,4], [2])

6.6666671111109626

"""

order = (1, 2, 1)

seasonal_order = (1, 1, 0, 7)

model = SARIMAX(

train_user, exog=train_match, order=order, seasonal_order=seasonal_order

)

model_fit = model.fit(disp=False, maxiter=600, method="nm")

result = model_fit.predict(1, len(test_match), exog=[test_match])

return result[0]

def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:

"""

Third method: Support vector regressor

svr is quite the same with svm(support vector machine)

it uses the same principles as the SVM for classification,

with only a few minor differences and the only different is that

it suits better for regression purpose

input : training data (date, total_user, total_event) in list of float

where x = list of set (date and total event)

output : list of total user prediction in float

>>> support_vector_regressor([[5,2],[1,5],[6,2]], [[3,2]], [2,1,4])

1.634932078116079

"""

regressor = SVR(kernel="rbf", C=1, gamma=0.1, epsilon=0.1)

regressor.fit(x_train, train_user)

y_pred = regressor.predict(x_test)

return y_pred[0]

def interquartile_range_checker(train_user: list) -> float:

"""

Optional method: interquatile range

input : list of total user in float

output : low limit of input in float

this method can be used to check whether some data is outlier or not

>>> interquartile_range_checker([1,2,3,4,5,6,7,8,9,10])

2.8

"""

train_user.sort()

q1 = np.percentile(train_user, 25)

q3 = np.percentile(train_user, 75)

iqr = q3 - q1

low_lim = q1 - (iqr * 0.1)

return low_lim

def data_safety_checker(list_vote: list, actual_result: float) -> None:

"""

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.

"""

safe = 0

not_safe = 0

for i in list_vote:

if i > actual_result:

safe = not_safe + 1

else:

if abs(abs(i) - abs(actual_result)) <= 0.1:

safe = safe + 1

else:

not_safe = not_safe + 1

print(f"Today's data is {'not ' if safe <= not_safe else ''}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"])

"""

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()

# 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]

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 = []

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)

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