Change file structure

Library/example_timeseries.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Dec  9 09:31:31 2022
+
+@author: Daniel Häfliger
+
+
+This file shows an example of how to use the Influxclient and Influxdataframe.
+The example try to fit an prediction of a temperature of a specific measure
+with time and a given prediction of the temperature as features with time series
+forecasting depending ond them. 
+
+There is no data cleaning ect. in this example, its just to show how the lib
+works...
+
+Be aware that you need to add a influx- and weatherapi-token and aviable datas 
+to the config file. Otherwise the example, as the lib to, wont work.
+
+
+"""
+import pandas as pd
+import Influxdataframe as Idf
+import Influxclient as I
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import r2_score
+from sklearn.neural_network import MLPRegressor
+
+
+target_variable = "W_AT_5M_0"
+interval = 60
+valuesince = 10
+prediction_hours = 24
+
+shift_number=4
+
+df = Idf.Get_Df()
+
+df = df[::12]
+
+pred =I.GetPrediction(prediction_hours, interval, "temp_c")
+    
+
+data = {
+
+    "y_r": pred[0],
+    "y_i": pred[1],
+    "d_r": pred[2],
+    "d_i": pred[3],
+
+    "P_AT_5M_0": pred[5],
+}
+
+
+preddf = pd.DataFrame(data)
+
+df = pd.concat([df, preddf], axis=0).reset_index(drop=True)
+
+
+df = Idf.ShiftTarget(df, shift_number, target_variable)
+
+
+
+df_predict = df.dropna()
+
+train_df, test_df = train_test_split(df_predict, test_size=0.3, random_state=42)
+
+scaler = MinMaxScaler()
+
+train_df = pd.DataFrame(scaler.fit_transform(
+    train_df), columns=train_df.columns, index=train_df.index)
+test_df = pd.DataFrame(scaler.transform(
+    test_df), columns=test_df.columns, index=test_df.index)
+
+y = train_df[[target_variable+str(0)]]
+
+X = train_df
+X.pop(target_variable+str(0))
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42)
+
+dt = MLPRegressor(activation='relu')
+dt.fit(X_train, y_train)
+
+print(r2_score(y_test, dt.predict(X_test)))
+
+
+
+for i in range(int(prediction_hours*60/interval)):
+    preddf = pd.DataFrame(df.iloc[(int(df_predict.shape[0]+shift_number)-1+i)]).transpose()
+    preddf = pd.DataFrame(scaler.transform(
+        preddf), columns=preddf.columns, index=preddf.index)
+    preddf.pop(target_variable+str(0))
+    at = dt.predict(preddf)
+    preddf[target_variable+str(0)]=at
+    newval = pd.DataFrame(scaler.inverse_transform(preddf))
+    df.at[(int(df_predict.shape[0]+shift_number)-1+i), target_variable+str(0)] = newval.at[0, 5]
+    
+    for j in range(shift_number):    
+        df.at[int(df_predict.shape[0]+shift_number)+i, target_variable+str(j+1)] =    df.at[int(df_predict.shape[0]+shift_number)-1+i, target_variable+str(j)] 
+        
+ 
+
+
+sns.lineplot(df.index, df[target_variable+str(0)])
+