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spline-diff.py
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spline-diff.py
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import numpy as np
from numpy.core.fromnumeric import reshape
import torch
from torch._C import Value
from tqdm import tqdm
from sklearn import preprocessing
from torch import nn
from torch.utils.data import dataloader
import architectures
import random
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
np.set_printoptions(edgeitems=30, linewidth=100000)
class CustomDataset(torch.utils.data.Dataset):
def __init__(self, inputs, labels):
self.inputs = inputs
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
labels = self.labels[idx]
inputs = self.inputs[idx]
sample = {"inputs": inputs, "labels": labels}
return sample
class Scaler():
def fit(self, data):
shape = data.shape
data = np.reshape(data, newshape=(-1, shape[-1]))
self.mean = np.mean(data, axis=0)
self.sd = np.std(data, axis=0)
def scale(self, data):
return np.copy((data - self.mean) / self.sd)
def scale_back(self, data):
return np.copy(data * self.sd + self.mean)
model_name = "spline_diffs"
number_points = [5,6,7,8,9,10]
prediction_position = 0
features_x = [0,1,3]
features_y = [0,1,3]
f_diff = [0,1,3]
time_intervals = [30, 60, 90, 120]
training_fraction = 0.5
remove_all_zero_ys = True
num_predictions = 10
if prediction_position>0 and num_predictions>1:
raise ValueError("Can't make several consecutive non-immediate predictions")
def to_diff(xs:np.ndarray, ys:np.ndarray, return_diffs_only:bool=False):
xs = np.copy(xs)
ys = np.copy(ys)
originals = np.expand_dims(xs[:,0,:], 1)
if ys is not None:
ys[:,1:,f_diff] = ys[:,1:,f_diff] - ys[:,:-1:,f_diff]
ys[:,0,f_diff] = ys[:,0,f_diff] - xs[:,-1,f_diff]
xs[:,1:,f_diff] = xs[:,1:,f_diff] - xs[:,:-1,f_diff]
xs = xs[:,1:,:]
if return_diffs_only:
xs = xs[:,:,f_diff]
ys = ys[:,:,f_diff]
originals = originals[:,:,f_diff]
return (originals, xs, ys)
def from_diff(originals, xs, ys, diff_indices:list=[]):
xs = np.copy(np.concatenate([originals, xs], 1))
if(len(diff_indices)==0):
xs[:,1:,:] = xs.cumsum(1)[:,1:,:]
else:
xs[:,1:,diff_indices] = xs.cumsum(1)[:,1:,diff_indices]
if ys is not None:
ys = np.copy(np.concatenate([np.expand_dims(xs[:,-1,:], 1), ys], 1))
if(len(diff_indices) == 0):
ys[:,1:,:] = ys.cumsum(1)[:,1:,:]
else:
ys[:,1:,diff_indices] = ys.cumsum(1)[:,1:,diff_indices]
ys = ys[:,1:,:]
return (xs, ys)
for nop in number_points:
seq_size = nop
if(len(f_diff)>0):
seq_size -= 1
all_xs = []
all_ys = []
all_ids = []
for time_interval in time_intervals:
folder_path = f"./training-data/training-data-{time_interval}/{nop}"
xs_path = f"{folder_path}/xs.npy"
ys_path = f"{folder_path}/ys.npy"
xs = np.load(xs_path)
ys = np.load(ys_path)
ids = [f"{time_interval}-{nop}-{i}" for i in range(len(xs))]
all_xs.append(xs)
all_ys.append(ys)
all_ids.extend(ids)
xs:np.ndarray = np.concatenate(all_xs)
ys:np.ndarray = np.concatenate(all_ys)
ids = np.array(all_ids)
# Removal of samples with -999999 (no value) for any of the features.
keep_positions = []
for pos, (x, y) in enumerate(zip(xs, ys)):
if -999999 in x[:,features_x] or -999999 in y[prediction_position:prediction_position+num_predictions,features_y]:
keep_positions.append(False)
else:
keep_positions.append(True)
xs = xs[keep_positions]
ys = ys[keep_positions]
ids = ids[keep_positions]
if(len(f_diff)>0):
originals, xs, ys = to_diff(xs, ys)
xs = xs[:,:,features_x]
ys = ys[:,:,features_y]
if(len(f_diff)>0):
originals = originals[:,:,features_x]
# Removal of samples with all features equal to 0
if remove_all_zero_ys:
keep_positions = []
for x, y in zip(xs, ys):
if (~y[prediction_position:(prediction_position+num_predictions)].any(axis=1)).any() or (~x.any(axis=1)).any():
keep_positions.append(False)
else:
keep_positions.append(True)
xs = xs[keep_positions]
ys = ys[keep_positions]
ids = ids[keep_positions]
if(len(f_diff)>0):
originals = originals[keep_positions]
scaler = Scaler()
scaler.fit(xs)
# Removing anomalous values
keep_positions = []
for x, y in zip(xs, ys):
x_ratios = abs((x-scaler.mean)/scaler.sd)
y_ratios = abs((y[prediction_position:(prediction_position+num_predictions)]-scaler.mean)/scaler.sd)
keep_positions.append(not ((x_ratios > 2).any() or (y_ratios > 2).any()))
xs = xs[keep_positions]
ys = ys[keep_positions]
ids = ids[keep_positions]
if(len(f_diff)>0):
originals = originals[keep_positions]
rnd = np.random.default_rng(1337)
positions = rnd.permutation(len(xs))
num_training = int(len(xs)*training_fraction)
pos_testing = positions[num_training:]
xs_test = xs[pos_testing]
ys_test = ys[pos_testing]
ids_testing = ids[pos_testing]
if(len(f_diff)>0):
originals_testing = originals[pos_testing]
if(len(f_diff)>0):
xs_test_dediff, ys_test_dediff = from_diff(originals_testing, xs_test, ys_test)
def lf(data):
out = data[1]
ys = data[2]
loss = np.mean((out - ys)**2)
return loss
model_predictions = np.zeros((len(xs_test), num_predictions, len(features_y)))
for i in range(num_predictions):
out = architectures.predict_extrapolate(xs_test, 0, 1)
if(len(f_diff)>0):
xs_test_dediff, out_dediff = from_diff(originals_testing, xs_test, out)
predictions = list(zip(xs_test_dediff, out_dediff, ys_test_dediff[:,prediction_position+i,:]))
model_predictions[:,i,:] = np.squeeze(out_dediff)
total_loss = 0
for prediction in predictions:
total_loss += lf(prediction)
avg_loss = total_loss / len(predictions)
print(f"Average loss for prediction {i}: {avg_loss}")
xs_test = np.concatenate([xs_test[:,1:,:], out], 1)
originals_testing = np.expand_dims(xs_test_dediff[:,1,:], 1)
results_folder = f"./test-results/{'-'.join([str(ti) for ti in time_intervals])}/{nop}"
Path(results_folder).mkdir(parents=True, exist_ok=True)
results = {id:model_predictions[i] for i, id in enumerate(ids_testing)}
np.save(f"{results_folder}/{model_name}.npy",results)