MLA_Learn_Periodic_Functions/code/wilshire_5000/nn.py

import numpy as np
import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
import wilshire
import tensorflow_addons as tfa
from statsmodels.tsa.arima.model import ARIMA

def snake(x):
    return(x+(tf.math.sin(50*x)**2)/50)
def sinus(x):
    return(tf.math.sin(x))
def sinus_cosinus(x):
    return(tf.math.sin(x)+tf.math.cos(x))
def swish(x):
    return(x*tf.math.sigmoid(x))

def arima_pred(y_train,y_test,orders=[[2,1,1],[2,2,1],[3,1,1],[2,1,2]],n=5):
    mse=[]
    for order in orders :
        mean_err=np.array()
        for k in range(n):
            train = y_train
            preds = []
            for test in range(len(y_test)):
                model = ARIMA(train, order=(order[0],order[1],order[2]))
                model = model.fit()
                output = model.forecast() 
                preds.append(output[0])
                #train.append(y_test[te
            mean_err.append((np.square(np.array(preds) - np.array(y_test))).mean())
        mse.append(mean_err.mean())
    return(mse)


#activations = [tf.keras.activations.relu,swish,sinus_cosinus,sinus,snake]
activations = [snake]


def prepare_data(filename="WILL5000INDFC2.csv"):
    df_train,df_test,index = wilshire.preprocess(filename)
    x_train = np.arange(df_train.shape[0])
    maximum = np.max(x_train)
    x_train = x_train / maximum
    y_train=df_train["WILL5000INDFC"]
    y_train.to_numpy()

    x_test = np.arange(df_train.shape[0]+1,df_train.shape[0]+df_test.shape[0]+1)
    y_test = df_test["WILL5000INDFC"]
    y_test.to_numpy()
    x_test=x_test / maximum
    return x_train,x_test,y_train,y_test




def create_model(activation):
    model =  tf.keras.Sequential()

    model.add(tf.keras.layers.Dense(1,input_shape=[1,],activation=activation))
    model.add(tf.keras.layers.Dense(64,activation=activation))
    model.add(tf.keras.layers.Dense(64,activation=activation))
    model.add(tf.keras.layers.Dense(1))

    opt = tf.keras.optimizers.SGD(learning_rate=0.01,momentum=0.9)
    model.compile(optimizer=opt, loss='mse')
    model.build()
    model.summary()
    return model

def training_testing(n,activations):
    models = []
    errors_train,errors_test = [],[]
    mean_y_train,mean_y_test,std_y_test=[],[],[]
    for activation in activations :
        y_train_5=[]
        y_test_5=[]
        errors_train_5=[]
        errors_test_5=[]
        for k in range(n):

            model = create_model(activations)
            model.fit(x_train,y_train, batch_size=1, epochs=1)

            y_pred_test = model.predict(x_test)
            y_pred_train = model.predict(x_train)
            y_train_5.append(y_pred_train)
            y_test_5.append(y_pred_test)
            errors_test_5.append(model.evaluate(x_test,y_test))
            errors_train_5.append(model.evaluate(x_train,y_train))

        models.append(model)
        mean_y_train.append(np.mean(y_train_5,axis=0))
        mean_y_test.append(np.mean(y_test_5,axis=0))
        std_y_test.append(np.std(y_test_5,axis=0))
        errors_train.append([np.mean(errors_train_5),np.std(errors_train_5)])
        errors_test.append([np.mean(errors_test_5),np.std(errors_test_5)])
        # y_preds_train.append(y_pred_train)
        # y_preds_test.append(y_pred_test)
        return models,errors_train,errors_test


def final_plot():
    x = np.arange(9000)
    x_n = x / maximum
    future_preds = model.predict(x_n)  ## Calculated with a website the number of working days between 01-06-2020 and 01-01-2024

    #x=np.arange(df_train.shape[0]+df_test.shape[0]+908)
    y_true = np.concatenate((y_train,y_test))
    x_cut = np.arange(df_train.shape[0]+df_test.shape[0])
    plt.figure()
    plt.plot(x_cut,y_true,label="True data")
    plt.plot(x,future_preds,label="Predictions")
    plt.xticks(range(0, 9000, 250), range(1995, 2031, 1))
    plt.xlabel("Années")
    plt.ylabel("Index Willshire5000 normalisé")
    plt.vlines([index,index+85],ymin=0,ymax=1,colors="r",label="Test Samples")
    plt.legend()
    plt.show()