Doriand Petit
il y a 4 ans
1 fichiers modifiés avec 92 ajouts et 97 suppressions
+ 92
- 97
code/wilshire_5000/nn.py
Voir le fichier
| def swish(x): | def swish(x): | ||||
| return(x*tf.math.sigmoid(x)) | return(x*tf.math.sigmoid(x)) | ||||
| def arima_pred(y_train,y_test,order=[2,1,1]): | |||||
| 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[test]) | |||||
| return((np.square(np.array(preds) - np.array(y_test))).mean(),preds) | |||||
| 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 = [tf.keras.activations.relu,swish,sinus_cosinus,sinus,snake] | ||||
| activations = [snake] | activations = [snake] | ||||
| models = [] | |||||
| errors_train,errors_test = [],[] | |||||
| mean_y_train,mean_y_test,std_y_test=[],[],[] | |||||
| df_train,df_test,index = wilshire.preprocess('WILL5000INDFC2.csv') | |||||
| 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() | |||||
| print("----") | |||||
| print(y_test) | |||||
| x_test=x_test / maximum | |||||
| print(arima_pred(list(y_train),list(y_test))) | |||||
| for activation in activations : | |||||
| y_train_5=[] | |||||
| y_test_5=[] | |||||
| errors_train_5=[] | |||||
| errors_test_5=[] | |||||
| for k in range(1): | |||||
| 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() | |||||
| 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)) | |||||
| 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) | |||||
| 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 | |||||
| def plot_total(x_train,y_train,y_pred_train,x_test,y_test,y_pred_test): | |||||
| x = np.concatenate((x_train,x_test)) | |||||
| 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)) | y_true = np.concatenate((y_train,y_test)) | ||||
| y_pred = np.concatenate((y_pred_train,y_pred_test)) | |||||
| x_cut = np.arange(df_train.shape[0]+df_test.shape[0]) | |||||
| plt.figure() | plt.figure() | ||||
| plt.plot(x,y_true,label="True data") | |||||
| plt.plot(x,y_pred,label="Predictions") | |||||
| plt.vlines([index,index+85]) | |||||
| 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.legend() | ||||
| plt.show() | plt.show() | ||||
| #plot_total(x_train,y_train,y_pred_train,x_test,y_test,y_pred_test) | |||||
| print(errors_test) | |||||
| #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() | |||||
Chargement…