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

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machine_learning/forecasting/run.py

@@ -11,20 +11,21 @@
          u can just adjust it for ur own purpose
 """
 
 import logging
 from warnings import simplefilter
 
 import numpy as np
 import pandas as pd
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import Normalizer
 from sklearn.svm import SVR
 from statsmodels.tsa.statespace.sarimax import SARIMAX
 import matplotlib.pyplot as plt
 
 logging.basicConfig(level=logging.Info)
 logger = logging.getLogger(__name__)
 
+
 def linear_regression_prediction(
     train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list
 ) -> float:
@@ -147,13 +148,14 @@
             not_safe += 1
     return safe > not_safe
 
+
 def plot_forecast(actual, predictions):
     plt.figure(figsize=(10, 5))
     plt.plot(range(len(actual)), actual, label="Actual")
-    plt.plot(len(actual), predictions[0], 'ro', label="Linear Reg")
-    plt.plot(len(actual), predictions[1], 'go', label="SARIMAX")
-    plt.plot(len(actual), predictions[2], 'bo', label="SVR")
-    plt.plot(len(actual), predictions[3], 'yo', label="RF")
+    plt.plot(len(actual), predictions[0], "ro", label="Linear Reg")
+    plt.plot(len(actual), predictions[1], "go", label="SARIMAX")
+    plt.plot(len(actual), predictions[2], "bo", label="SVR")
+    plt.plot(len(actual), predictions[3], "yo", label="RF")
     plt.legend()
     plt.title("Data Safety Forecast")
     plt.xlabel("Days")
@@ -204,5 +206,5 @@
     # check the safety of today's data
     not_str = "" if data_safety_checker(res_vote, test_user[0]) else "not "
     logger.info(f"Today's data is {not_str}safe.")
-        
+
     plot_forecast(train_user, res_vote)