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* Update perceptron.py * Update binary_tree_traversals.py * fix machine_learning * Update build.yml * Update perceptron.py * Update machine_learning/forecasting/run.py Co-authored-by: Christian Clauss <cclauss@me.com>
157 lines
5.5 KiB
Python
157 lines
5.5 KiB
Python
"""
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this is code for forecasting
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but i modified it and used it for safety checker of data
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for ex: you have a online shop and for some reason some data are
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missing (the amount of data that u expected are not supposed to be)
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then we can use it
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*ps : 1. ofc we can use normal statistic method but in this case
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the data is quite absurd and only a little^^
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2. ofc u can use this and modified it for forecasting purpose
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for the next 3 months sales or something,
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u can just adjust it for ur own purpose
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"""
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import numpy as np
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import pandas as pd
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from sklearn.preprocessing import Normalizer
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from sklearn.svm import SVR
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from statsmodels.tsa.statespace.sarimax import SARIMAX
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def linear_regression_prediction(
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train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list
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) -> float:
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"""
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First method: linear regression
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input : training data (date, total_user, total_event) in list of float
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output : list of total user prediction in float
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>>> n = linear_regression_prediction([2,3,4,5], [5,3,4,6], [3,1,2,4], [2,1], [2,2])
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>>> abs(n - 5.0) < 1e-6 # Checking precision because of floating point errors
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True
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"""
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x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])
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y = np.array(train_usr)
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beta = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)
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return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])
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def sarimax_predictor(train_user: list, train_match: list, test_match: list) -> float:
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"""
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second method: Sarimax
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sarimax is a statistic method which using previous input
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and learn its pattern to predict future data
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input : training data (total_user, with exog data = total_event) in list of float
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output : list of total user prediction in float
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>>> sarimax_predictor([4,2,6,8], [3,1,2,4], [2])
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6.6666671111109626
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"""
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order = (1, 2, 1)
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seasonal_order = (1, 1, 0, 7)
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model = SARIMAX(
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train_user, exog=train_match, order=order, seasonal_order=seasonal_order
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)
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model_fit = model.fit(disp=False, maxiter=600, method="nm")
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result = model_fit.predict(1, len(test_match), exog=[test_match])
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return result[0]
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def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:
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"""
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Third method: Support vector regressor
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svr is quite the same with svm(support vector machine)
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it uses the same principles as the SVM for classification,
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with only a few minor differences and the only different is that
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it suits better for regression purpose
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input : training data (date, total_user, total_event) in list of float
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where x = list of set (date and total event)
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output : list of total user prediction in float
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>>> support_vector_regressor([[5,2],[1,5],[6,2]], [[3,2]], [2,1,4])
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1.634932078116079
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"""
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regressor = SVR(kernel="rbf", C=1, gamma=0.1, epsilon=0.1)
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regressor.fit(x_train, train_user)
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y_pred = regressor.predict(x_test)
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return y_pred[0]
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def interquartile_range_checker(train_user: list) -> float:
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"""
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Optional method: interquatile range
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input : list of total user in float
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output : low limit of input in float
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this method can be used to check whether some data is outlier or not
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>>> interquartile_range_checker([1,2,3,4,5,6,7,8,9,10])
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2.8
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"""
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train_user.sort()
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q1 = np.percentile(train_user, 25)
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q3 = np.percentile(train_user, 75)
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iqr = q3 - q1
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low_lim = q1 - (iqr * 0.1)
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return low_lim
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def data_safety_checker(list_vote: list, actual_result: float) -> None:
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"""
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Used to review all the votes (list result prediction)
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and compare it to the actual result.
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input : list of predictions
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output : print whether it's safe or not
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>>> data_safety_checker([2,3,4],5.0)
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Today's data is not safe.
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"""
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safe = 0
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not_safe = 0
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for i in list_vote:
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if i > actual_result:
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safe = not_safe + 1
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else:
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if abs(abs(i) - abs(actual_result)) <= 0.1:
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safe = safe + 1
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else:
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not_safe = not_safe + 1
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print(f"Today's data is {'not ' if safe <= not_safe else ''}safe.")
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# data_input_df = pd.read_csv("ex_data.csv", header=None)
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data_input = [[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
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data_input_df = pd.DataFrame(data_input, columns=["total_user", "total_even", "days"])
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"""
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data column = total user in a day, how much online event held in one day,
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what day is that(sunday-saturday)
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"""
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# start normalization
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normalize_df = Normalizer().fit_transform(data_input_df.values)
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# split data
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total_date = normalize_df[:, 2].tolist()
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total_user = normalize_df[:, 0].tolist()
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total_match = normalize_df[:, 1].tolist()
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# for svr (input variable = total date and total match)
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x = normalize_df[:, [1, 2]].tolist()
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x_train = x[: len(x) - 1]
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x_test = x[len(x) - 1 :]
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# for linear reression & sarimax
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trn_date = total_date[: len(total_date) - 1]
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trn_user = total_user[: len(total_user) - 1]
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trn_match = total_match[: len(total_match) - 1]
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tst_date = total_date[len(total_date) - 1 :]
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tst_user = total_user[len(total_user) - 1 :]
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tst_match = total_match[len(total_match) - 1 :]
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# voting system with forecasting
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res_vote = []
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res_vote.append(
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linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match)
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)
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res_vote.append(sarimax_predictor(trn_user, trn_match, tst_match))
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res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
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# check the safety of todays'data^^
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data_safety_checker(res_vote, tst_user)
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