|
@ -0,0 +1,124 @@ |
|
|
|
|
|
import torch |
|
|
|
|
|
import torch.nn as nn |
|
|
|
|
|
import torch.optim as optim |
|
|
|
|
|
import numpy as np |
|
|
|
|
|
import pandas as pd |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Net(nn.Module): |
|
|
|
|
|
def __init__(self, input_shape): |
|
|
|
|
|
super().__init__() |
|
|
|
|
|
self.fc1 = nn.Linear(input_shape, 8) |
|
|
|
|
|
self.fc2 = nn.Linear(8, 4) |
|
|
|
|
|
|
|
|
|
|
|
def forward(self, x): |
|
|
|
|
|
x = torch.sigmoid(self.fc1(x)) |
|
|
|
|
|
return self.fc2(x) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_argmax(array): |
|
|
|
|
|
max = 0 |
|
|
|
|
|
index = 0 |
|
|
|
|
|
for i in range(len(array)): |
|
|
|
|
|
if array[i] > max: |
|
|
|
|
|
max = array[i] |
|
|
|
|
|
index = i |
|
|
|
|
|
|
|
|
|
|
|
one_hot = [0,0,0,0] |
|
|
|
|
|
one_hot[index] = 1 |
|
|
|
|
|
return one_hot |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def get_trainset(dataset, k, n0, x_columns, y_columns): |
|
|
|
|
|
inp = dataset[x_columns] |
|
|
|
|
|
out = dataset[y_columns] |
|
|
|
|
|
col = "day" |
|
|
|
|
|
x = [] |
|
|
|
|
|
y = [] |
|
|
|
|
|
input_shape = 0 |
|
|
|
|
|
output_shape = 0 |
|
|
|
|
|
for player in out["playerID"].unique(): |
|
|
|
|
|
XPlayer = inp[inp["playerID"] == player] |
|
|
|
|
|
YPlayer = out[out["playerID"] == player] |
|
|
|
|
|
for day in YPlayer[col][n0 - 1:]: |
|
|
|
|
|
prev = day - k |
|
|
|
|
|
xprev = XPlayer[XPlayer[col] == prev].drop(columns=[col, "playerID"]).to_numpy() |
|
|
|
|
|
if xprev.shape[0] != 1: |
|
|
|
|
|
continue |
|
|
|
|
|
else: |
|
|
|
|
|
xprev = xprev[0, :] |
|
|
|
|
|
yt = YPlayer[YPlayer[col] == day].drop(columns=[col, "playerID"]).to_numpy()[0, :] |
|
|
|
|
|
if input_shape == 0: |
|
|
|
|
|
input_shape = xprev.shape[0] |
|
|
|
|
|
else: |
|
|
|
|
|
if input_shape != xprev.shape[0]: |
|
|
|
|
|
print("INCONSISTENT INPUT DIMENSION") |
|
|
|
|
|
exit(2) |
|
|
|
|
|
if output_shape == 0: |
|
|
|
|
|
output_shape = yt.shape[0] |
|
|
|
|
|
else: |
|
|
|
|
|
if output_shape != yt.shape[0]: |
|
|
|
|
|
print("INCONSISTENT OUTPUT DIMENSION") |
|
|
|
|
|
exit(2) |
|
|
|
|
|
x.append(xprev) |
|
|
|
|
|
y.append(yt) |
|
|
|
|
|
|
|
|
|
|
|
x = torch.FloatTensor(x) |
|
|
|
|
|
y = torch.LongTensor(y) |
|
|
|
|
|
return x, y |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def time_series_sigmoid_classification(steps, dataset, k, n0, x_columns, y_columns): |
|
|
|
|
|
net = Net(1) |
|
|
|
|
|
optimizer = optim.Adam(net.parameters(), lr=.001) |
|
|
|
|
|
loss = nn.CrossEntropyLoss() |
|
|
|
|
|
|
|
|
|
|
|
x, y = get_trainset(dataset, k, n0, x_columns, y_columns) |
|
|
|
|
|
accuracy(net, x, y) |
|
|
|
|
|
|
|
|
|
|
|
for step in range(steps): |
|
|
|
|
|
optimizer.zero_grad() |
|
|
|
|
|
|
|
|
|
|
|
x, y = get_trainset(dataset, k, n0, x_columns, y_columns) |
|
|
|
|
|
pred = net(x) |
|
|
|
|
|
net_loss = loss(pred, torch.max(y, 1)[1]) |
|
|
|
|
|
net_loss.backward() |
|
|
|
|
|
optimizer.step() |
|
|
|
|
|
|
|
|
|
|
|
print("Loss at Step {}: {}".format(step, net_loss)) |
|
|
|
|
|
|
|
|
|
|
|
x, y = get_trainset(dataset, k, n0, x_columns, y_columns) |
|
|
|
|
|
accuracy(net, x, y) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def accuracy(net, x, y): |
|
|
|
|
|
pred = net(x) |
|
|
|
|
|
pred = pred.detach().numpy() |
|
|
|
|
|
for row in range(len(pred)): |
|
|
|
|
|
pred[row] = get_argmax(pred[row]) |
|
|
|
|
|
|
|
|
|
|
|
total = len(pred) |
|
|
|
|
|
correct = 0 |
|
|
|
|
|
for i in range(len(pred)): |
|
|
|
|
|
equal = True |
|
|
|
|
|
for j in range(len(pred[i])): |
|
|
|
|
|
if pred[i][j] != y[i][j]: |
|
|
|
|
|
equal = False |
|
|
|
|
|
if equal: |
|
|
|
|
|
correct += 1 |
|
|
|
|
|
|
|
|
|
|
|
accuracy = (correct / total) * 100 |
|
|
|
|
|
print("Accuracy for set: {}%".format(accuracy)) |
|
|
|
|
|
torch.save(net, "model.ckpt") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main(): |
|
|
|
|
|
filename = "personal.csv" |
|
|
|
|
|
df = pd.read_csv(filename) |
|
|
|
|
|
x = ["day", "playerID", "fatigueSliding"] |
|
|
|
|
|
y = ["day", "playerID", "BestOutOfMyselfAbsolutely", "BestOutOfMyselfSomewhat", "BestOutOfMyselfNotAtAll", "BestOutOfMyselfUnknown"] |
|
|
|
|
|
time_series_sigmoid_classification(2, df, 0, 30, x, y) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
|
|
|
main() |