commit
3988033311
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"""
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Simple feed-forward neural network in PyTorch for baseline results on Scicite data.
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Date: July 5th, 2020
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Created: July 5th, 2020
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"""
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import torch
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#import os
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#os.chdir('/Users/iriley/code/citation-analysis')
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import sys
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sys.path.append('/Users/iriley/code/citation-analysis')
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from utils.nn_reader import read_csv_nn
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from utils.nn_reader2 import read_csv_nn_dev
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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import numpy as np
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class Feedforward(torch.nn.Module):
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"""
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Creates and trains a basic feedforward neural network.
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"""
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#
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def __init__(self, input_size, hidden_size, output_size):
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""" Sets up all basic elements of NN. """
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super(Feedforward, self).__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.output_size = output_size
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self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
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#self.relu = torch.nn.ReLU()
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self.tanh = torch.nn.Tanh()
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self.fc2 = torch.nn.Linear(self.hidden_size, self.output_size)
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self.sigmoid = torch.nn.Sigmoid()
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self.softmax = torch.nn.Softmax(dim=1)
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def forward(self, x):
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""" Computes output from a given input x. """
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hidden = self.fc1(x)
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#relu = self.relu(hidden)
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tanh = self.tanh(hidden)
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#output = self.fc2(relu)
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output = self.fc2(tanh)
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output = self.softmax(output)
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return output
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if __name__=='__main__':
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""" Reads in the data, then trains and evaluates the neural network. """
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X_train, y_train, X_test = read_csv_nn()
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# balance classes
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yclass = np.array([(x[1]==1)+2*(x[2]==1) for x in y_train])
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is0 = yclass==0
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is1 = yclass==1
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is2 = yclass==2
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X0 = torch.FloatTensor(X_train[is0])
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X1 = torch.FloatTensor(X_train[is1])
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X2 = torch.FloatTensor(X_train[is2])
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y0 = torch.LongTensor(np.zeros((sum(is0),)))
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y1 = torch.LongTensor(np.ones((sum(is1),)))
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y2 = torch.LongTensor(2*np.ones((sum(is2),)))
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l0 = sum(is0)
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l1 = sum(is1)
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l2 = sum(is2)
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p0 = torch.randperm(l0)
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p1 = torch.randperm(l1)
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p2 = torch.randperm(l2)
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p = torch.randperm(3000)
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X_train = torch.cat((X0[p0][:1000], X1[p1][:1000], X2[p2][:1000]))[p]
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y_train = torch.cat((y0[:1000], y1[:1000], y2[:1000]))[p]
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#X_train = torch.FloatTensor(X_train)
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X_test = torch.FloatTensor(X_test)
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#y_train_ = torch.FloatTensor(y_train)
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#y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
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model = Feedforward(28, 9, 3)
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criterion = torch.nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
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model.eval()
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y_pred = model(X_train)
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before_train = criterion(y_pred, y_train)
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print('Test loss before training' , before_train.item())
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l = 3000 # X_train.shape[0]
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batch_indices = list(zip(list(range(0,l,16))[:-1], list(range(16,l,16))))# + [(l-l%16,l)]
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batch_indices[-1] = (batch_indices[-1][0], l)
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# train model
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model.train()
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epochs = 100
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for epoch in range(epochs):
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batch = 0
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for a,b in batch_indices:
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optimizer.zero_grad()
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# forward pass
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y_pred = model(X_train[a:b])
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loss = criterion(y_pred, y_train[a:b])
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print('Epoch {}, batch {}: train loss: {}'.format(epoch, batch, loss.item()))
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# backward pass
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loss.backward()
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optimizer.step()
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batch += 1
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# get loss following epoch
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y_pred = model.forward(X_train)
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loss = criterion(y_pred, y_train)
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print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
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# shuffle dataset
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#p = torch.randperm(l)
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#X_train = X_train[p]
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#y_train = y_train[p]
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p0 = torch.randperm(l0)
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p1 = torch.randperm(l1)
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p2 = torch.randperm(l2)
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p = torch.randperm(3000)
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X_train = torch.cat((X0[p0][:1000], X1[p1][:1000], X2[p2][:1000]))[p]
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y_train = torch.cat((y0[:1000], y1[:1000], y2[:1000]))[p]
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model.eval()
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y_pred = model.forward(X_train)
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after_train = criterion(y_pred, y_train)
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print('Training loss after training' , after_train.item())
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## reload the data to get original order
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#X_train, y_train, X_test = read_csv_nn()
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#X_train = torch.FloatTensor(X_train)
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#X_test = torch.FloatTensor(X_test)
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#y_train_ = torch.FloatTensor(y_train)
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#y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
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# get dev set to make predictions
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#X_dev, y_dev = read_csv_nn_dev()
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#X_dev = torch.FloatTensor(X_dev)
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#y_dev_pre = torch.FloatTensor(y_dev)
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#y_dev = torch.max(torch.FloatTensor(y_dev_pre),1)[1]
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# post-process to get predictions & get back to np format
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y_pred = model.forward(X_test)
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y_pred_np = y_pred.detach().numpy()
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predmax = np.amax(y_pred_np, axis=1)
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preds = 1*(y_pred_np[:,1]==predmax) + 2*(y_pred_np[:,2]==predmax)
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#y_dev_ = y_dev.detach().numpy()
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# create confusion matrix
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#cm = confusion_matrix(y_dev_, preds)
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#print(cm)
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# save predictions
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df = pd.DataFrame()
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df['preds'] = preds
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#df['true'] = y_dev_
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probs = y_pred.detach().numpy()
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df['pr0'] = probs[:,0]
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df['pr1'] = probs[:,1]
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df['pr2'] = probs[:,2]
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#df['correct'] = df.preds==df.true
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df.to_csv('/Users/iriley/code/machine_learning/lab2020/y_pred_model2.csv', index=False)
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class FeedForward(torch.nn.Module):
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"""
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Creates and trains a basic feedforward neural network.
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"""
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def __init__(self, input_size, hidden_size, output_size):
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""" Sets up all basic elements of NN. """
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super(FeedForward, self).__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.output_size = output_size
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self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
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self.tanh = torch.nn.Tanh()
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self.fc2 = torch.nn.Linear(self.hidden_size, self.output_size)
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self.sigmoid = torch.nn.Sigmoid()
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self.softmax = torch.nn.Softmax(dim=1)
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def forward(self, x):
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""" Computes output from a given input x. """
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hidden = self.fc1(x)
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tanh = self.tanh(hidden)
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output = self.fc2(tanh)
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output = self.softmax(output)
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return output
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def read_data(self):
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"""" Reads in training and test data and converts it to proper format. """
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self.X_train_, self.y_train_, self.X_test_ = read_csv_nn()
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yclass = np.array([(x[1] == 1) + 2 * (x[2] == 1) for x in self.y_train_])
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is0 = yclass == 0
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is1 = yclass == 1
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is2 = yclass == 2
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self.X0 = torch.FloatTensor(self.X_train_[is0])
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self.X1 = torch.FloatTensor(self.X_train_[is1])
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self.X2 = torch.FloatTensor(self.X_train_[is2])
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self.y0 = torch.LongTensor(np.zeros((sum(is0),)))
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self.y1 = torch.LongTensor(np.ones((sum(is1),)))
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self.y2 = torch.LongTensor(2 * np.ones((sum(is2),)))
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self.l0 = sum(is0)
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self.l1 = sum(is1)
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self.l2 = sum(is2)
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def fit(self, epochs=100, batch_size=16, lr=0.01, samples0=1000, samples1=1000, samples2=1000):
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""" Trains model, using cross entropy loss and SGD optimizer. """
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self.criterion = torch.nn.CrossEntropyLoss()
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self.optimizer = torch.optim.SGD(self.parameters(), lr)
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self.samples0 = samples0
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self.samples1 = samples1
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self.samples2 = samples2
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model.eval() # put into eval mode
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# initialize training data
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self.shuffle()
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y_pred = self.forward(self.X_train)
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before_train = self.criterion(y_pred, self.y_train)
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print('Test loss before training', before_train.item())
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# setup for batches
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l = self.samples0 + self.samples1 + self.samples2 # total length
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batch_indices = list(zip(list(range(0, l, batch_size))[:-1], list(range(16, l, batch_size))))
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batch_indices[-1] = (batch_indices[-1][0], l)
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# train model
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self.train() # put into training mode
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for epoch in range(epochs):
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batch = 0
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for a, b in batch_indices:
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self.optimizer.zero_grad()
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# forward pass
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y_pred = model(X_train[a:b])
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loss = self.criterion(y_pred, self.y_train[a:b])
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# backward pass
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loss.backward()
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self.optimizer.step()
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batch += 1
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# get loss following epoch
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y_pred = self.forward(self.X_train)
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loss = self.criterion(y_pred, self.y_train)
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print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
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# shuffle dataset
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self.shuffle()
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# display final loss
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self.eval() # back to eval mode
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y_pred = self.forward(self.X_train)
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after_train = self.criterion(y_pred, self.y_train)
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print('Training loss after training', after_train.item())
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def predict(self):
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""" Generates predictions from model, using test data. """
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# post-process to get predictions & get back to np format
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y_pred = self.forward(self.X_test)
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y_pred_np = y_pred.detach().numpy()
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predmax = np.amax(y_pred_np, axis=1)
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self.preds = 1 * (y_pred_np[:, 1] == predmax) + 2 * (y_pred_np[:, 2] == predmax)
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self.probs = y_pred.detach().numpy()
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def shuffle(self):
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""" Samples and shuffles training data. """
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# create permutations for shuffling
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p0 = torch.randperm(self.l0)
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p1 = torch.randperm(self.l1)
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p2 = torch.randperm(self.l2)
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n = self.l0 + self.l1 + self.l2
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p = torch.randperm(n)
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# sample and shuffle data
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self.X_train = \
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torch.cat((self.X0[p0][:self.samples0], self.X1[p1][:self.samples1], self.X2[p2][:self.samples2]))[p]
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self.y_train = torch.cat((self.y0[:self.samples0], self.y1[:self.samples1], self.y2[:self.samples2]))[p]
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@ -0,0 +1,9 @@
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from classifier.nn_ff import FeedForward
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model = FeedForward(28, 9, 3)
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model.fit()
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model.predict()
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