pavan
/
citation-analysis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

added balanced classes to ff model

Browse Source
isaac
yelircaasi 5 years ago
parent 0faf344a00
commit 2870975861
2 changed files with 50 additions and 83 deletions
Show Stats Download Patch File Download Diff File

71
classifier/nn_ff.py
Unescape View File

@ -4,8 +4,10 @@ Date: July 5th, 2020
"""
import torch
import os
os.chdir('/Users/iriley/code/citation-analysis')
#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
@ -47,10 +49,31 @@ if __name__=='__main__':
X_train, y_train, X_test = read_csv_nn()
X_train = torch.FloatTensor(X_train)
# 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])
X1 = torch.FloatTensor(X_train[is1])
X2 = torch.FloatTensor(X_train[is2])
y0 = torch.LongTensor(np.zeros((sum(is0),)))
y1 = torch.LongTensor(np.ones((sum(is1),)))
y2 = torch.LongTensor(2*np.ones((sum(is2),)))
l0 = sum(is0)
l1 = sum(is1)
l2 = sum(is2)
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]
#X_train = torch.FloatTensor(X_train)
X_test = torch.FloatTensor(X_test)
y_train_ = torch.FloatTensor(y_train)
y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
#y_train_ = torch.FloatTensor(y_train)
#y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
model = Feedforward(28, 9, 3)
criterion = torch.nn.CrossEntropyLoss()
@ -61,13 +84,13 @@ if __name__=='__main__':
before_train = criterion(y_pred, y_train)
print('Test loss before training' , before_train.item())
l = X_train.shape[0]
l = 3000 # X_train.shape[0]
batch_indices = list(zip(list(range(0,l,16))[:-1], list(range(16,l,16))))# + [(l-l%16,l)]
batch_indices[-1] = (batch_indices[-1][0], l)
# train model
model.train()
epochs = 50
epochs = 100
for epoch in range(epochs):
batch = 0
for a,b in batch_indices:
@ -85,9 +108,15 @@ if __name__=='__main__':
loss = criterion(y_pred, y_train)
print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
# shuffle dataset
p = torch.randperm(l)
X_train = X_train[p]
y_train = y_train[p]
#p = torch.randperm(l)
#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)
@ -102,32 +131,32 @@ if __name__=='__main__':
#y_train = torch.max(torch.FloatTensor(y_train_),1)[1]
# get dev set to make predictions
X_dev, y_dev = read_csv_nn_dev()
X_dev = torch.FloatTensor(X_dev)
y_dev_pre = torch.FloatTensor(y_dev)
y_dev = torch.max(torch.FloatTensor(y_dev_pre),1)[1]
#X_dev, y_dev = read_csv_nn_dev()
#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_dev)
y_pred = model.forward(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)
y_dev_ = y_dev.detach().numpy()
#y_dev_ = y_dev.detach().numpy()
# create confusion matrix
cm = confusion_matrix(y_dev_, preds)
print(cm)
#cm = confusion_matrix(y_dev_, preds)
#print(cm)
# save predictions
df = pd.DataFrame()
df['preds'] = preds
df['true'] = y_dev_
#df['true'] = y_dev_
probs = y_pred.detach().numpy()
df['pr0'] = probs[:,0]
df['pr1'] = probs[:,1]
df['pr2'] = probs[:,2]
df['correct'] = df.preds==df.true
df.to_csv('/Users/iriley/code/machine_learning/lab2020/preds_ffnn.csv', index=False)
#df['correct'] = df.preds==df.true
df.to_csv('/Users/iriley/code/machine_learning/lab2020/y_pred_model2.csv', index=False)

62
testing/save_dev_results.py
Unescape View File

@ -1,62 +0,0 @@
import os
from classifier.linear_model import MultiClassPerceptron
from sklearn.metrics import confusion_matrix as cm
from utils.csv import read_csv_file
from eval.metrics import f1_score
import utils.constants as const
import pandas as pd
import numpy as np
train_file_path = '/Users/iriley/code/citation-analysis/data/tsv/train.tsv'
dev_file_path = '/Users/iriley/code/citation-analysis/data/tsv/dev.tsv'
# Read the training dataset
X_train_inst = read_csv_file(train_file_path, '\t')
# set of labels from Training data
labels = set([inst.true_label for inst in X_train_inst])
# Read test data set
X_dev_inst = read_csv_file(dev_file_path, '\t')
# number of training iterations
epochs = 50
# create MultiClassPerceptron classifier object
clf = MultiClassPerceptron(epochs=epochs, learning_rate=0.5, random_state=101)
# train the model
clf.fit(X_train=X_train_inst, labels=list(labels))
# predict
y_pred = clf.predict(X_dev_inst)
y_scores = np.array(clf.get_class_scores(X_dev_inst))
y_true = [inst.true_label for inst in X_dev_inst]
labeldict = {'background': 0, 'method': 1, 'result': 2}
y_pred = np.array([labeldict[x] for x in y_pred])
y_true = np.array([labeldict[x] for x in y_true])
conmat = cm(y_true, y_pred)
df = pd.DataFrame()
df['pred'] = y_pred
df['true'] = y_true
df['correct'] = y_pred==y_true
df['score0'] = np.round(y_scores[:,0],3)
df['score1'] = np.round(y_scores[:,1],3)
df['score2'] = np.round(y_scores[:,2],3)
df.to_csv('/Users/iriley/code/machine_learning/lab2020/preds_perceptron.csv', index=False)
## Model Evaluation
#f1_score_micro = f1_score(y_true, y_pred, labels, const.AVG_MICRO)
#f1_score_macro = f1_score(y_true, y_pred, labels, const.AVG_MACRO)
#f1_score_none = f1_score(y_true, y_pred, labels, None)
## Print F1 Score
#for result in f1_score_micro + f1_score_macro + f1_score_none:
# result.print_result()
Write Preview
Loading…
Cancel
Save