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gridSearch copy.py
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import torch.optim as optim
from ray import tune
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from customLSTM import baseLSTM
from trainer import train
from tester import test
import os
import pandas as po
from sklearn.metrics import f1_score
input_dim = 23
num_output_classes = 5
hidden_dim = 2000
num_epochs = 1
window_size = 5
batch_size = 1 #number of tickers to be passed at the same time
ticker = 'CLc1'
train_from = '2009-01-01' # >= applied
train_until = '2009-02-01' # <= applied
test_from = '2009-02-02' # >= applied
test_until = '2009-02-10' # <= applied
df = po.read_csv('merged_data_without_embeddings/' + ticker + 'merged.csv')
df = df[df['No. Trades'] != 0].reset_index(drop = True)
val_cts = df['class'].value_counts().to_numpy()
del df
val_cts = val_cts/sum(val_cts)
weights = torch.tensor(val_cts, dtype = torch.float)
weights
model = baseLSTM(input_dim, hidden_dim, num_output_classes, window_size)
loss_function = nn.CrossEntropyLoss(ignore_index = 5, weight = weights)
optimizer = optim.Adam(model.parameters(), lr=0.1)
import os
os.getcwd()
os.listdir('merged_data_without_embeddings/')
df = po.read_csv('/Users/VarunMadhavan/Desktop/Notes/NLP/ISB/LSTM/LSTM_attention/merged_data_without_embeddings/' + ticker + 'merged.csv')
os.chdir('/Users/VarunMadhavan/Desktop')
def gridd(config):
input_dim = 23
num_output_classes = 5
hidden_dim = 2000
num_epochs = 1
window_size = 5
batch_size = 1
ticker = 'CLc1'
os.chdir('/Users/VarunMadhavan/Desktop/Notes/NLP/ISB/LSTM/LSTM_attention')
df = po.read_csv('merged_data_without_embeddings/' + ticker + 'merged.csv')
df = df[df['No. Trades'] != 0].reset_index(drop = True)
val_cts = df['class'].value_counts().to_numpy()
del df
val_cts = val_cts/sum(val_cts)
weights = torch.tensor(val_cts, dtype = torch.float)
#weights
train_from = '2009-01-01' # >= applied
train_until = '2009-02-01' # <= applied
test_from = '2009-02-02' # >= applied
test_until = '2009-02-10' # <= applied
model = baseLSTM(input_dim, hidden_dim, num_output_classes, window_size)
loss_function = nn.CrossEntropyLoss(ignore_index = 5, weight = weights)
optimizer = optim.Adam(model.parameters(), lr=config["lr"])
for i in range(3):
model, (hidden_state, cell_state) = train(ticker, window_size, train_from, train_until, model, loss_function, optimizer, num_epochs, input_dim, num_output_classes, hidden_dim)
predictions_df = test(ticker, window_size, test_from, test_until, input_dim, model, hidden_state, cell_state)
acc=f1_score(predictions_df['Actual_Value'], predictions_df['Predictions'],average='weighted')
#acc=test()
tune.track.log(mean_accuracy=acc)
analysis = tune.run(gridd,config={"lr": tune.grid_search([0.1,0.3])})
print("Best config: ",analysis.get_best_config(metric="mean_accuracy"))
analysis = tune.run(train,config={"num_epochs": tune.grid_search([5,10,15,20,25])})
print("Best config: ",analysis.get_best_config(metric="mean_accuracy"))
analysis = tune.run(train,config={"dropout": tune.grid_search([0.1,0.2,0.3])})
print("Best config: ",analysis.get_best_config(metric="mean_accuracy"))
analysis.dataframe()
# !ray
import ray
ray.tune.track.init()
ray.tune.track.init()
track.log
from ray import tune
import ray
from ray.tune import track
track.init()