Merge remote-tracking branch 'origin/master'

classifier/nn_ff.py

@@ -1,32 +1,25 @@
 """
 Simple feed-forward neural network in PyTorch for baseline results on Scicite data.
-Date: July 5th, 2020
+Created: July 5th, 2020
 """
 
 import torch
-#import os
-#os.chdir('/Users/iriley/code/citation-analysis')
-import sys
-sys.path.append('/Users/iriley/code/citation-analysis')
 from utils.nn_reader import read_csv_nn
-from utils.nn_reader2 import read_csv_nn_dev
-from sklearn.metrics import confusion_matrix
-import pandas as pd
 import numpy as np
 
-class Feedforward(torch.nn.Module):
+
+class FeedForward(torch.nn.Module):
     """
     Creates and trains a basic feedforward neural network.
     """
-        #
+
     def __init__(self, input_size, hidden_size, output_size):
         """ Sets up all basic elements of NN. """
-            super(Feedforward, self).__init__()
+        super(FeedForward, self).__init__()
         self.input_size = input_size
         self.hidden_size = hidden_size
         self.output_size = output_size
         self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
-            #self.relu = torch.nn.ReLU()
         self.tanh = torch.nn.Tanh()
         self.fc2 = torch.nn.Linear(self.hidden_size, self.output_size)
         self.sigmoid = torch.nn.Sigmoid()
@@ -35,131 +28,100 @@ class Feedforward(torch.nn.Module):
     def forward(self, x):
         """ Computes output from a given input x. """
         hidden = self.fc1(x)
-            #relu = self.relu(hidden)
         tanh = self.tanh(hidden)
-            #output = self.fc2(relu)
         output = self.fc2(tanh)
         output = self.softmax(output)
         return output
 
-
+    def read_data(self):
-
+        """" Reads in training and test data and converts it to proper format. """
-if __name__=='__main__':
+        self.X_train_, self.y_train_, self.X_test_ = read_csv_nn()
-    """ Reads in the data, then trains and evaluates the neural network. """
+        yclass = np.array([(x[1] == 1) + 2 * (x[2] == 1) for x in self.y_train_])
-
-    X_train, y_train, X_test = read_csv_nn()
-
-    # balance classes
-    yclass = np.array([(x[1]==1)+2*(x[2]==1) for x in y_train])
         is0 = yclass == 0
         is1 = yclass == 1
         is2 = yclass == 2
-    X0 = torch.FloatTensor(X_train[is0])
+        self.X0 = torch.FloatTensor(self.X_train_[is0])
-    X1 = torch.FloatTensor(X_train[is1])
+        self.X1 = torch.FloatTensor(self.X_train_[is1])
-    X2 = torch.FloatTensor(X_train[is2])
+        self.X2 = torch.FloatTensor(self.X_train_[is2])
-    y0 = torch.LongTensor(np.zeros((sum(is0),)))
+        self.y0 = torch.LongTensor(np.zeros((sum(is0),)))
-    y1 = torch.LongTensor(np.ones((sum(is1),)))
+        self.y1 = torch.LongTensor(np.ones((sum(is1),)))
-    y2 = torch.LongTensor(2*np.ones((sum(is2),)))
+        self.y2 = torch.LongTensor(2 * np.ones((sum(is2),)))
-    l0 = sum(is0)
+        self.l0 = sum(is0)
-    l1 = sum(is1)
+        self.l1 = sum(is1)
-    l2 = sum(is2)
+        self.l2 = sum(is2)
-    p0 = torch.randperm(l0)
+
-    p1 = torch.randperm(l1)
+    def fit(self, epochs=100, batch_size=16, lr=0.01, samples0=1000, samples1=1000, samples2=1000):
-    p2 = torch.randperm(l2)
+        """ Trains model, using cross entropy loss and SGD optimizer. """
-    p = torch.randperm(3000)
+        self.criterion = torch.nn.CrossEntropyLoss()
-    X_train = torch.cat((X0[p0][:1000], X1[p1][:1000], X2[p2][:1000]))[p]
+        self.optimizer = torch.optim.SGD(self.parameters(), lr)
-    y_train = torch.cat((y0[:1000], y1[:1000], y2[:1000]))[p]
+        self.samples0 = samples0
-
+        self.samples1 = samples1
-    #X_train = torch.FloatTensor(X_train)
+        self.samples2 = samples2
-    X_test = torch.FloatTensor(X_test)
+
-    #y_train_ = torch.FloatTensor(y_train)
+        model.eval()  # put into eval mode
-    #y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
+
-
+        # initialize training data
-    model = Feedforward(28, 9, 3)
+        self.shuffle()
-    criterion = torch.nn.CrossEntropyLoss()
+        y_pred = self.forward(self.X_train)
-    optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
+        before_train = self.criterion(y_pred, self.y_train)
-
-    model.eval()
-    y_pred = model(X_train)
-    before_train = criterion(y_pred, y_train)
         print('Test loss before training', before_train.item())
 
-    l = 3000 # X_train.shape[0]
+        # setup for batches
-    batch_indices = list(zip(list(range(0,l,16))[:-1], list(range(16,l,16))))# + [(l-l%16,l)]
+        l = self.samples0 + self.samples1 + self.samples2  # total length
+        batch_indices = list(zip(list(range(0, l, batch_size))[:-1], list(range(16, l, batch_size))))
         batch_indices[-1] = (batch_indices[-1][0], l)
 
         # train model
-    model.train()
+        self.train()  # put into training mode
-    epochs = 100
         for epoch in range(epochs):
             batch = 0
             for a, b in batch_indices:
-            optimizer.zero_grad()
+                self.optimizer.zero_grad()
+
                 # forward pass
                 y_pred = model(X_train[a:b])
-            loss = criterion(y_pred, y_train[a:b])
+                loss = self.criterion(y_pred, self.y_train[a:b])
-            print('Epoch {}, batch {}: train loss: {}'.format(epoch, batch, loss.item()))
+
                 # backward pass
                 loss.backward()
-            optimizer.step()
+                self.optimizer.step()
                 batch += 1
+
             # get loss following epoch
-        y_pred = model.forward(X_train)
+            y_pred = self.forward(self.X_train)
-        loss = criterion(y_pred, y_train)
+            loss = self.criterion(y_pred, self.y_train)
             print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
+
             # shuffle dataset
-        #p = torch.randperm(l)
+            self.shuffle()
-        #X_train = X_train[p]
-        #y_train = y_train[p]
-        p0 = torch.randperm(l0)
-        p1 = torch.randperm(l1)
-        p2 = torch.randperm(l2)
-        p = torch.randperm(3000)
-        X_train = torch.cat((X0[p0][:1000], X1[p1][:1000], X2[p2][:1000]))[p]
-        y_train = torch.cat((y0[:1000], y1[:1000], y2[:1000]))[p]
-
-    model.eval()
-    y_pred = model.forward(X_train)
-    after_train = criterion(y_pred, y_train) 
-    print('Training loss after training' , after_train.item())
 
-    ## reload the data to get original order
+        # display final loss
-    #X_train, y_train, X_test = read_csv_nn()
+        self.eval()  # back to eval mode
-    #X_train = torch.FloatTensor(X_train)
+        y_pred = self.forward(self.X_train)
-    #X_test = torch.FloatTensor(X_test)
+        after_train = self.criterion(y_pred, self.y_train)
-    #y_train_ = torch.FloatTensor(y_train)
+        print('Training loss after training', after_train.item())
-    #y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
 
-    # get dev set to make predictions
+    def predict(self):
-    #X_dev, y_dev = read_csv_nn_dev()
+        """ Generates predictions from model, using test data. """
-    #X_dev = torch.FloatTensor(X_dev)
-    #y_dev_pre = torch.FloatTensor(y_dev)
-    #y_dev = torch.max(torch.FloatTensor(y_dev_pre),1)[1]
 
         # post-process to get predictions & get back to np format
-    y_pred = model.forward(X_test)
+        y_pred = self.forward(self.X_test)
         y_pred_np = y_pred.detach().numpy()
         predmax = np.amax(y_pred_np, axis=1)
-    preds = 1*(y_pred_np[:,1]==predmax) + 2*(y_pred_np[:,2]==predmax)
+        self.preds = 1 * (y_pred_np[:, 1] == predmax) + 2 * (y_pred_np[:, 2] == predmax)
-    #y_dev_ = y_dev.detach().numpy()
+        self.probs = y_pred.detach().numpy()
-    
+
-    # create confusion matrix
+    def shuffle(self):
-    #cm = confusion_matrix(y_dev_, preds)
+        """ Samples and shuffles training data. """
-    #print(cm)
+
-
+        # create permutations for shuffling
-    # save predictions
+        p0 = torch.randperm(self.l0)
-    df = pd.DataFrame()
+        p1 = torch.randperm(self.l1)
-    df['preds'] = preds
+        p2 = torch.randperm(self.l2)
-    #df['true']  = y_dev_
+        n = self.l0 + self.l1 + self.l2
-    probs = y_pred.detach().numpy()
+        p = torch.randperm(n)
-    df['pr0']  = probs[:,0]
+
-    df['pr1']  = probs[:,1]
+        # sample and shuffle data
-    df['pr2']  = probs[:,2]
+        self.X_train = \
-    #df['correct'] = df.preds==df.true
+        torch.cat((self.X0[p0][:self.samples0], self.X1[p1][:self.samples1], self.X2[p2][:self.samples2]))[p]
-    df.to_csv('/Users/iriley/code/machine_learning/lab2020/y_pred_model2.csv', index=False)
+        self.y_train = torch.cat((self.y0[:self.samples0], self.y1[:self.samples1], self.y2[:self.samples2]))[p]
-
-
-
-
-
-

testing/ff_model_testing.py

@@ -0,0 +1,9 @@
+from classifier.nn_ff import FeedForward
+
+
+model = FeedForward(28, 9, 3)
+model.fit()
+model.predict()
+
+
+