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VNet_test.py
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VNet_test.py
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import torch
import torch.nn as nn
import pandas as pd
from sklearn.model_selection import LeaveOneOut
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from sklearn.preprocessing import LabelEncoder
import torch.multiprocessing as mp
from sklearn.model_selection import KFold
import sys
import os
import copy
import numpy as np
from sklearn.model_selection import StratifiedKFold
def set_seed(seed):
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
# random.seed(seed)
#%%
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0, path='checkpoint.pt', trace_func=print):
"""
Args:
patience (int): How long to wait after last time validation loss improved.
Default: 7
verbose (bool): If True, prints a message for each validation loss improvement.
Default: False
delta (float): Minimum change in the monitored quantity to qualify as an improvement.
Default: 0
path (str): Path for the checkpoint to be saved to.
Default: 'checkpoint.pt'
trace_func (function): trace print function.
Default: print
"""
self.patience = patience
self.verbose = verbose
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
self.delta = delta
self.path = path
self.trace_func = trace_func
def __call__(self, val_loss, model):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.save_checkpoint(val_loss, model)
elif score < self.best_score + self.delta:
self.counter += 1
# self.trace_func(f'EarlyStopping counter: {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.save_checkpoint(val_loss, model)
self.counter = 0
def save_checkpoint(self, val_loss, model):
'''Saves model when validation loss decrease.'''
if self.verbose:
self.trace_func(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), self.path)
self.val_loss_min = val_loss
#%%
class LogisticRegressionModel(nn.Module):
def __init__(self, input_size, output_dim):
super(LogisticRegressionModel, self).__init__()
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, x):
out = self.linear(x)
return out
class FeedforwardNeuralNetModel(nn.Module):
def __init__(self, input_size, hidden_dim, output_dim):
super(FeedforwardNeuralNetModel, self).__init__()
# Input -> Hidden Layer
self.fc1 = nn.Linear(input_dim, hidden_dim)
# ReLU
self.relu = nn.ReLU()
# Hidden Layer -> Output
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
class PopulationDataset(Dataset):
"""Dataset class for column dataset.
Args:
"""
def __init__(self, features, labels):
self.features = features
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
return [self.features[idx], self.labels[idx] ]
class FoldTorch():
"""
"""
def __init__(self, Train, Test, Model, Optimizer, Criterion, Epoch):
self.train_dataset = Train
self.test_dataset = Test
self.model = Model
self.optimizer = Optimizer
self.criterion = Criterion
self.epoch = Epoch
self.result = []
self.train_losses = []
self.valid_losses = []
def train_epoch(self, TLoader):
self.model.train()
for batch_idx, (feats, labs) in enumerate(TLoader):
# feats = feats.view(-1, 28*28).requires_grad_()
feats = feats.requires_grad_()
self.optimizer.zero_grad()
outputs = self.model(feats)
loss = self.criterion(outputs, labs.max(1)[1])
loss.backward()
self.optimizer.step()
self.train_losses.append(loss.item())
def test_epoch(self,TLoader):
self.model.eval()
with torch.no_grad():
for (fs, l) in TLoader:
outputs = self.model(fs)
loss = self.criterion(outputs, l.max(1)[1])
self.valid_losses.append(loss.item())
def train(self, dataloader_kwargs, seed=131, dataloadertest_kwargs={}, patience=7, earlyStop_opt=False):
set_seed(seed)
train_loader = DataLoader(self.train_dataset, **dataloader_kwargs)
if earlyStop_opt:
early_stopping = EarlyStopping(patience=patience, verbose=True)
test_loader = DataLoader(self.test_dataset, **dataloadertest_kwargs)
for epoch in range(self.epoch):
self.train_epoch(TLoader=train_loader)
self.test_epoch(TLoader=test_loader)
train_loss = np.average(self.train_losses)
valid_loss = np.average(self.valid_losses)
self.train_losses = []
self.valid_losses = []
early_stopping(valid_loss, self.model)
if early_stopping.early_stop:
print("Early stopping at epoch number:",epoch)
break
else:
for epoch in range(self.epoch):
self.train_epoch(TLoader=train_loader)
def test(self, dataloader_kwargs):
self.model.eval()
test_loader = DataLoader(self.test_dataset, **dataloader_kwargs)
with torch.no_grad():
for (fs, l) in test_loader:
# fs = fs.view(-1, 28*28)
outputs = self.model(fs)
predicted = outputs.max(1)[1]
self.result.append((l.max(1)[1], predicted))
# self.result.append((l, predicted))
def LoadStateDict(self):
self.model.load_state_dict(torch.load('checkpoint.pt'))
def __len__(self):
return len(self.result)
def __getitem__(self, idx):
return self.result[idx]
@property
def result(self):
return self._result
@result.setter
def result(self, value):
self._result=value
@property
def model(self):
return self._model
@model.setter
def model(self, value):
self._model=value
#%%
#
# class KFoldTorch():
# """
# """
# def __init__(self, AllFeatures, AllLabels, Split=None, seed=101):
# self.N = len(AllFeatures.index)
# self.M = len(AllLabels)
# self.data = torch.Tensor(AllFeatures.T.values)
# le = LabelEncoder()
# le.fit(AllLabels)
# value_idxs = le.transform(AllLabels)
# self.labels = torch.eye(len(AllLabels.cat.categories))[value_idxs]
# self.LeaveOut = self.M if Split is None else Split
# self.kf = KFold(n_splits=self.LeaveOut, random_state=seed, shuffle=True)
# self.Folds = []
# self.mod = None
# self.opt = None
# self.crit = None
# self.nepoch = None
# self.kwargs_train = None
# self.kwargs_test = None
#
# def RunFold(self, trn_split, tst_split, getAcc=False):
# Trn = PopulationDataset(self.data[trn_split], self.labels[trn_split])
# Tst = PopulationDataset(self.data[tst_split], self.labels[tst_split])
# mod_ = copy.deepcopy(self.mod)
# opt_ = copy.deepcopy(self.opt)
# FoldObj = FoldTorch(Trn, Tst, mod_, opt_, self.crit, self.nepoch)
# FoldObj.train(self.kwargs_train, getAcc=getAcc)
# FoldObj.test(self.kwargs_test)
# return FoldObj
#
# def AppendFolds(self, ReturnedFolds):
# for FoldObj in ReturnedFolds:
# self.Folds.append(FoldObj)
#
# def CollectPredictions(self):
# outlist = []
# for F in self.Folds:
# outlist.append(F.result)
# return outlist
#
# def Run(self, mod, opt, crit, nepoch, kwargs_train, kwargs_test, cores=1, getAcc=False):
# self.mod = mod
# self.opt = opt
# self.crit = crit
# self.nepoch = nepoch
# self.kwargs_train = kwargs_train
# self.kwargs_test = kwargs_test
# if cores<2:
# tot = len([x for x in self.kf.split(self.data)])
# norm_term = 1/tot*100
# i=1
# for train_idxs, test_idxs in self.kf.split(self.data):
# ret = self.RunFold(train_idxs,test_idxs, getAcc=getAcc)
# self.Folds.append(ret)
# print(" ", end="\r", flush=True)
# print("".join([str(i*norm_term),"%"]), end="\r", flush=True)
# i+=1
# # else:
# # p = mp.Pool(cores)
# # for train_idxs, test_idxs in self.kf.split(self.data):
# # ret = p.apply_async(self.RunFold,
# # args=(train_idxs,test_idxs),
# # callback=self.AppendFolds)
# # ret.get()
# # p.close()
# # p.join()
#
# def __len__(self):
# return len(self.Folds)
#
# def __getitem__(self, idx):
# return self.Folds[idx]
#%%
allData = [f for f in os.listdir("/home/PERSONALE/daniele.dallolio3/NMF_samples/") if f.startswith("NMF_diagnosis_")]
allDataR = [f for f in os.listdir("/home/PERSONALE/daniele.dallolio3/NMF_samples/") if f.startswith("NMF_relapse_")]
allData = sorted(allData, key=lambda x: int(x.replace("NMF_diagnosis_", "").replace(".csv", "")))
allDataR = sorted(allDataR, key=lambda x: int(x.replace("NMF_relapse_", "").replace(".csv", "")))
allData = [os.path.join("/home/PERSONALE/daniele.dallolio3/NMF_samples/", f) for f in allData]
allDataR = [os.path.join("/home/PERSONALE/daniele.dallolio3/NMF_samples/", f) for f in allDataR]
label_df = pd.ExcelFile("Evolution_trajectories_80samples_250620.xlsx")
label_df = label_df.parse(label_df.sheet_names[0])
labels_ = label_df["Trajectory_19_HIGH_RISK"].astype('category').cat.reorder_categories(['S', 'B', 'L', 'D'])
pat_idxs = label_df["CLONALV_new1_SNP"]
AllLabels = labels_
M = len(AllLabels)
le = LabelEncoder()
le.fit(AllLabels)
value_idxs = le.transform(AllLabels)
labels = torch.eye(len(AllLabels.cat.categories))[value_idxs]
LeaveOut = 4
seed = 101
# kf = KFold(n_splits=LeaveOut, random_state=seed, shuffle=True)
kf = StratifiedKFold(n_splits=LeaveOut, random_state=seed, shuffle=True)
kf_split = [(x,y) for x,y in kf.split(AllLabels,AllLabels)]
tot = len(kf_split)
norm_term = 1/tot*100
output_dim = len(set(labels_))
epoch = int(1e4)
#%%
for fn, fnR in zip(allData, allDataR):
idx = os.path.basename(fn).replace("NMF_diagnosis_", "").replace(".csv", "")
print(" ", end="\r", flush=True)
print(idx)
data_diagnosis = pd.read_csv(fn).astype('float64')
data_diagnosis.columns = [ c.replace("_E", "") for c in data_diagnosis.columns ]
data_relapse = pd.read_csv(fnR).astype('float64')
data_relapse.columns = [ c.replace("_R", "") for c in data_relapse.columns ]
# 1 diagnosis
# data_ = data_diagnosis.copy()
# 2 difference
# data_ = (data_diagnosis-data_relapse).copy()
# 3 appended
data_ = data_diagnosis.append(data_relapse)
data_ = data_.reset_index(drop=True).copy()
assert label_df["CLONALV_new1_SNP"].tolist() == data_.columns.tolist()
AllFeatures = data_
# Split=len(data_.T)
N = len(AllFeatures.index)
data = torch.Tensor(AllFeatures.T.values)
input_dim = len(data_)
# batch_size = len(data_.T) -1
i=0
output = pd.DataFrame({"CLONALV_new1_SNP":[], "Expected":[], "Predicted":[]})
for train_idxs, test_idxs in kf_split:
Trn = PopulationDataset(data[train_idxs], labels[train_idxs])
Tst = PopulationDataset(data[test_idxs], labels[test_idxs])
mod_ = FeedforwardNeuralNetModel(input_dim, input_dim, output_dim)
opt_ = torch.optim.Adam(mod_.parameters(131), lr=0.1)
FoldObj = FoldTorch(Trn, Tst, mod_, opt_, nn.CrossEntropyLoss(), epoch)
FoldObj.train({'batch_size': len(Trn), 'shuffle': True, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
dataloadertest_kwargs={'batch_size': 1, 'shuffle': False, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
earlyStop_opt=True,
patience=100)
FoldObj.test({'batch_size': 1, 'shuffle': False, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)})
# output.loc[i,"Expected"] = FoldObj.result[0][0].item()
# output.loc[i,"Predicted"] = FoldObj.result[0][1].item()
tmp = pd.DataFrame({"CLONALV_new1_SNP":pat_idxs[test_idxs].tolist(), "Expected":[x[0].item() for x in FoldObj.result], "Predicted":[x[1].item() for x in FoldObj.result]})
output = output.append(tmp)
print(" ", end="\r", flush=True)
i+=1
print("".join([str(i*norm_term),"%"]), end="\r", flush=True)
output = output.reset_index(drop=True)
output.to_csv(os.path.join("/home/PERSONALE/daniele.dallolio3/NMF_samples/pop_opt", "".join(["predictions_",idx,".csv"]) ), index=True)
#%%
from sklearn.metrics import f1_score
acc = []
fld = "appendWithEarly"
for idx in range(2,40):
fn = os.path.join("/home/PERSONALE/daniele.dallolio3/NMF_samples/pop_opt", fld,"".join(["predictions_",str(idx),".csv"]) )
df = pd.read_csv(fn)
# acc.append((df["Expected"] == df["Predicted"]).sum()/len(df.index)*100
acc.append(f1_score(df["Expected"].tolist(), df["Predicted"].tolist(), average='weighted'))
import matplotlib.pylab as plb
plb.hist(acc)
max(acc)
plb.plot(np.arange(2,40), acc)
np.arange(2,40)[np.argmax(acc)]
labels_.value_counts()
#%%
labels_naive = [ "L" for i in range(len(labels_))]
f1_score(labels_.tolist(), labels_naive, average='weighted')
#%%
# Train/Test on all dataset
Trn = PopulationDataset(data[train_idxs], labels[train_idxs])
Tst = PopulationDataset(data[test_idxs], labels[test_idxs])
mod_ = FeedforwardNeuralNetModel(input_dim, input_dim, output_dim)
opt_ = torch.optim.Adam(mod_.parameters(131), lr=0.1)
FoldObj = FoldTorch(Trn, Tst, mod_, opt_, nn.CrossEntropyLoss(), epoch)
FoldObj.train({'batch_size': len(Trn), 'shuffle': True, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
dataloadertest_kwargs={'batch_size': 1, 'shuffle': False, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
earlyStop_opt=True,
patience=100)
FoldObj.test({'batch_size': 1, 'shuffle': False, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)})
#%%
#
# TEST CLASS WITH KNOWN DATASET
#
# import torchvision.transforms as transforms
# import torchvision.datasets as dsets
#
# train_dataset = dsets.MNIST(root='./data',
# train=True,
# transform=transforms.ToTensor(),
# download=True)
# test_dataset = dsets.MNIST(root='./data',
# train=False,
# transform=transforms.ToTensor())
# batch_size = 100
# n_iters = 3000
# num_epochs = n_iters / (len(train_dataset) / batch_size)
# num_epochs = int(num_epochs)
#
# input_dim = 28*28
# hidden_dim = 100
# output_dim = 10
# model = FeedforwardNeuralNetModel(input_dim, hidden_dim, output_dim)
# learning_rate = 0.1
# optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# FoldObj = FoldTorch(train_dataset, test_dataset, model, optimizer, nn.CrossEntropyLoss(), num_epochs)
# FoldObj.train({'batch_size': batch_size, 'shuffle': True})
# FoldObj.test({'batch_size': batch_size, 'shuffle': False})
#
# correct = [(x == y).sum().item() for x,y in FoldObj.result]
# sum(correct)/sum([len(x) for x,y in FoldObj.result]) #0.9721
#%%
# input_dim = len(data_)
# batch_size = len(data_.T) -1
# output_dim = len(set(labels_))
# # model = LogisticRegressionModel(input_dim, output_dim)
# model = FeedforwardNeuralNetModel(input_dim, input_dim, output_dim)
# # optimizer_prova = torch.optim.SGD(model.parameters(131), lr=0.001)
#
#
# KFoldObj = KFoldTorch(data_, labels_, Split=len(data_.T))
# KFoldObj.Run(mod=model,
# opt=optimizer_prova,
# crit=nn.CrossEntropyLoss(),
# nepoch=int(1e2),
# kwargs_train={'batch_size': batch_size, 'shuffle': True, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
# kwargs_test={'batch_size': 1, 'shuffle': False, 'num_workers': 0, 'worker_init_fn':np.random.seed(0)},
# cores=1,
# getAcc=True)
# len(KFoldObj)
# [ f.result for f in KFoldObj.Folds ]
#%%
###########
# THE END #
###########