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


### Fonctions d'activations ###
def snake(x):
    return(x+(tf.math.sin(30*x)**2)/30)
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 prepare_data(filename="WILL5000INDFC.csv"):
    """
    Prepare data by preprocessing, normalizing and cutting it in train and test sets
    Return x and y train and test sets, as well as the maximum for later plots and the index separating both sets

    """
    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,maximum,index

def arima_pred(y_train,y_test,orders=[[2,1,1],[2,2,1],[3,1,1],[2,1,2]],n=5):
    """
    Computes the ARIMA errors (mse) for several orders to compare with the article

    """
    mse=[]
    for order in orders :
        
        mean_err= []
        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() 
                #print(output)
                preds.append(output)
                #train.append(y_test[te
            mean_err.append((np.square(np.array(preds) - np.array(y_test))).mean())
        mse.append([np.array(mean_err).mean(),np.array(mean_err).std()])
    return(mse)


def create_model(activation):
    """
    Create the neural network with the requested activation function

    """
    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=0.8)
    model.compile(optimizer=opt, loss='mse')
    model.build()
    model.summary()
    return model

def training_testing(n=5,activations = [tf.keras.activations.relu,swish,sinus_cosinus,sinus,snake],epochs = 50):
    """
    Trains models and computes means and std of test errors on n tries for each activation function requested.

    """
    x_train,x_test,y_train,y_test,maximum,index = prepare_data(filename="WILL5000INDFC.csv")
    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(activation)
            model.fit(x_train,y_train, batch_size=1, epochs=epochs)

            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(models,errors_test,arima_err,activations=["ReLU","Swish","Sinus Cosinus","Sinus","Snake"],orders_ARIMA = ["[2,1,1]","[2,2,1]","[3,1,1]","[2,1,2]"]):
    """
    Prints the results to compare with the table of the article and plot the same plot as the article
    """
    x_train,x_test,y_train,y_test,maximum,index = prepare_data(filename="WILL5000INDFC.csv")
    x = np.arange(9000) ## 9000 data points bring us to ~2031 to try and predict future data
    x_n = x / maximum
    future_preds = models[-1].predict(x_n) 

    y_true = np.concatenate((y_train,y_test))
    x_cut = np.arange(x_train.shape[0]+x_test.shape[0])

    print("----- ARIMA Test MSE -----")
    for k in range(len(orders_ARIMA)):
        print("ARIMA"+orders_ARIMA[k]+" : "+str(arima_err[k]))
    
    print("----- DNN Test MSE -----")
    for k in range(len(activations)):
        print("DNN "+activations[k]+" : "+str(errors_test[k]))


    ### PLOT ###
    plt.figure()
    plt.plot(x_cut,y_true,label="True data")
    plt.plot(x,future_preds,label="Predictions")
    plt.xticks(range(0, 9000, 500), range(1995, 2031, 2))
    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()




def plot_all_a(a=["1","10","20","30","100"]):
    """
    Plots the varying a values plot by loading pre-existing models (they are uploaded on GitHub)
    """
    models=[]
    for param in a :
        models.append(tf.keras.models.load_model("Snake"+param+"a"))
    x_train,x_test,y_train,y_test,maximum,index = prepare_data(filename="WILL5000INDFC.csv")
    x = np.arange(9000)
    x_n = x / maximum
    y_true = np.concatenate((y_train,y_test))
    x_cut = np.arange(x_train.shape[0]+x_test.shape[0])
    future_preds=[]
    for k in range(len(models)):
        future_preds.append(models[k].predict(x_n) )

    
    
    plt.figure()
    plt.plot(x_cut,y_true,label="True data")
    for k in range(len(models)):
        plt.plot(x,future_preds[k],label="a = "+a[k])
    plt.xticks(range(0, 9000, 500), range(1995, 2031, 2))
    plt.xlabel("Années")
    plt.ylabel("Index Willshire5000 normalisé")
    plt.legend()
    plt.show()

# plot_all_a()