In [12]:

    

import os
import operator
import random
import calendar

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D

from vixstructure.data import FuturesByMonth
from vixstructure.utils import parse_whole_directory_monthwise

mpl.rcParams["figure.figsize"] = 16, 9


    

In [2]:

    

def plot3d_loss(dataframe, zlim=None, rotation=225):
    X = dataframe.index.levels[0]
    Y = dataframe.index.levels[1]
    X, Y = np.meshgrid(X, Y)
    Z = np.reshape(
            np.array(list(map(lambda x: dataframe[x[0], x[1]], np.reshape(np.dstack((X,Y)), (X.shape[0]*X.shape[1],2))))),
            X.shape)
    fig = plt.figure(figsize=(8, 8))
    ax = fig.gca(projection='3d')
    ax.plot_surface(X, Y, Z, linewidth=None, antialiased=True, cmap=cm.coolwarm_r)
    ax.view_init(azim=rotation)
    ax.set_xlabel("Depth")
    ax.set_ylabel("Width")
    ax.set_xlim(X[0,0], X[-1,-1])
    ax.set_ylim(Y[0,0], Y[-1,-1])
    ax.set_zlim(zlim)
    ax.set_zlabel("Loss", rotation=90)


    

In [3]:

    

naive_prediction = []
naive_prediction_test = []
naive_prediction_train = []
for month in range(1, 13):
    futures = FuturesByMonth("data/futures_per_year_and_month.h5", month, yearly=True, spreads=True)
    (x_train, y_train), (x_val, y_val), (x_test, y_test) = futures.splitted_dataset()
    mse = np.mean(np.square(x_val[:,month - 1] - y_val[:,0]))
    naive_prediction.append(mse)
    naive_prediction_test.append(np.mean(np.square(x_test[:,month - 1] - y_test[:,0])))
    naive_prediction_train.append(np.mean(np.square(x_train[:,month - 1] - y_train[:,0])))
naive = pd.DataFrame([naive_prediction_train, naive_prediction, naive_prediction_test],
                     columns=calendar.month_name[1:], index=["Training", "Validation", "Test"]).T


    

In [4]:

    

naive


    

    
Out[4]:







  

    

      

      
Training
      
Validation
      
Test
    
  
  

    

      
January
      
0.128959
      
0.034129
      
0.021595
    
    

      
February
      
0.135883
      
0.035440
      
0.021047
    
    

      
March
      
0.124055
      
0.025579
      
0.017777
    
    

      
April
      
0.215221
      
0.015699
      
0.034429
    
    

      
May
      
0.219204
      
0.015678
      
0.033568
    
    

      
June
      
0.161504
      
0.017682
      
0.021206
    
    

      
July
      
0.188856
      
0.054747
      
0.013939
    
    

      
August
      
0.074138
      
0.019385
      
0.018262
    
    

      
September
      
0.067586
      
0.018339
      
0.123145
    
    

      
October
      
0.078243
      
0.053764
      
0.073775
    
    

      
November
      
0.402545
      
0.031190
      
0.038410
    
    

      
December
      
0.188189
      
0.043425
      
0.088538
    
  

In [5]:

    

data = parse_whole_directory_monthwise("models/experiment09")


    

In [6]:

    

monthly_val = data.groupby(("depth", "width", "month")).min().groupby("month").min().iloc[:, [0, 2]]
monthly_val.index = naive.index


    

In [7]:

    

pd.concat((monthly_val, naive.Validation), axis=1).plot()
plt.xticks(np.arange(12), calendar.month_abbr[1:])
plt.grid()
plt.show()


    

In [8]:

    

data.val_denorm_mse.groupby(("depth", "width", "month")).min().groupby(
    ("depth", "width")).mean().groupby("width").min().plot()
plt.show()


    

In [9]:

    

data.val_denorm_mse.groupby(("depth", "width", "month")).min().groupby(
    ("depth", "width")).mean().groupby("depth").min().plot()
plt.show()


    

In [15]:

    

to_3dplot = data.val_denorm_mse.groupby(("depth", "width", "month")).min().groupby(
                ("depth", "width")).mean()
plot3d_loss(to_3dplot)
plt.show()


    

In [16]:

    

to_3dplot.idxmin()


    

    
Out[16]:





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