Adding perceptron code and results

python/perceptron.py

@@ -0,0 +1,124 @@
+import pandas as pd
+import numpy as np
+import sys
+from sklearn.model_selection import cross_val_score
+from scipy.stats import mode
+from linearDiscriminantAnalysis import mergePredictions
+
+sys.path.insert(0,'../lib/seqlearn/')
+
+from seqlearn.perceptron import StructuredPerceptron
+
+
+
+def perceptronTest(input_data, test, actual, actual8):
+	model = StructuredPerceptron(verbose=False, random_state=37, max_iter=1000)
+
+	l = 9*len(input_data)/10 - 1
+	scores = cross_val_score(model, input_data.iloc[:,:-1], input_data.iloc[:,-1], cv = 10, fit_params={'lengths':[l]})
+
+	print 'Cross Validation Accuracy = ' + str(scores.mean())
+
+	model.fit(input_data.iloc[:,:-1], input_data.iloc[:,-1], [len(input_data)])
+	pred = model.predict(test)
+	accuracy = sum(pred == actual)/float(len(actual))
+	pred8 = mergePredictions(pred, 8)
+	accuracy8 = sum(pred8 == actual8)/float(len(actual8))
+	print 'Test Accuracy for the subject is = ' + str(accuracy)
+	print 'Test Accuracy for the subject at step 8 is = ' + str(accuracy8)
+
+
+def mainRawData():
+	#Loading Input data (training)
+	d1 = pd.read_csv('../data/training/train_subject1_psd01.csv',header=None)
+	d2 = pd.read_csv('../data/training/train_subject1_psd02.csv',header=None)
+	d3 = pd.read_csv('../data/training/train_subject1_psd03.csv',header=None)
+	input_data_s1 = pd.concat([d1, d2, d3], axis=0)
+
+	d1 = pd.read_csv('../data/training/train_subject2_psd01.csv',header=None)
+	d2 = pd.read_csv('../data/training/train_subject2_psd02.csv',header=None)
+	d3 = pd.read_csv('../data/training/train_subject2_psd03.csv',header=None)
+	input_data_s2 = pd.concat([d1, d2, d3], axis=0)
+
+	d1 = pd.read_csv('../data/training/train_subject3_psd01.csv',header=None)
+	d2 = pd.read_csv('../data/training/train_subject3_psd02.csv',header=None)
+	d3 = pd.read_csv('../data/training/train_subject3_psd03.csv',header=None)
+	input_data_s3 = pd.concat([d1, d2, d3], axis=0)
+
+	#Loading Test data (all subjects)
+	test_s1 = pd.read_csv('../data/testing/test_subject1_psd04.csv', header=None)
+	actual_s1 = pd.read_csv('../data/testing/ActualLables/labels_subject1_psd.csv', header=None)
+	actual8_s1 = pd.read_csv('../data/testing/ActualLables/labels8_subject1_psd.csv', header=None)
+	actual_s1 = actual_s1[0]
+	actual8_s1 = actual8_s1[0]
+
+	test_s2 = pd.read_csv('../data/testing/test_subject2_psd04.csv', header=None)
+	actual_s2 = pd.read_csv('../data/testing/ActualLables/labels_subject2_psd.csv', header=None)
+	actual8_s2 = pd.read_csv('../data/testing/ActualLables/labels8_subject2_psd.csv', header=None)
+	actual_s2 = actual_s2[0]
+	actual8_s2 = actual8_s2[0]
+
+	test_s3 = pd.read_csv('../data/testing/test_subject3_psd04.csv', header=None)
+	actual_s3 = pd.read_csv('../data/testing/ActualLables/labels_subject3_psd.csv', header=None)
+	actual8_s3 = pd.read_csv('../data/testing/ActualLables/labels8_subject3_psd.csv', header=None)
+	actual_s3 = actual_s3[0]
+	actual8_s3 = actual8_s3[0]
+
+	perceptronTest(input_data_s1, test_s1, actual_s1, actual8_s1)
+	perceptronTest(input_data_s2, test_s2, actual_s2, actual8_s2)
+	perceptronTest(input_data_s3, test_s3, actual_s3, actual8_s3)
+
+	# Results:
+	# Cross Validation Accuracy = 0.71953708592
+	# Test Accuracy for the subject is = 0.768835616438
+	# Test Accuracy for the subject at step 8 is = 0.762557077626
+	# Cross Validation Accuracy = 0.571821113391
+	# Test Accuracy for the subject is = 0.616359447005
+	# Test Accuracy for the subject at step 8 is = 0.612903225806
+	# Cross Validation Accuracy = 0.383837959864
+	# Test Accuracy for the subject is = 0.406823394495
+	# Test Accuracy for the subject at step 8 is = 0.389908256881
+
+def mainPCAData():
+	input_data_s1 = pd.read_csv('../data/training/pca_data_v2/pca_subject1.csv')
+
+	input_data_s2 = pd.read_csv('../data/training/pca_data_v2/pca_subject2.csv')
+
+	input_data_s3 = pd.read_csv('../data/training/pca_data_v2/pca_subject3.csv')
+
+	#Loading Test data (all subjects)
+	test_s1 = pd.read_csv('../data/testing/pca_data_v2/pca_subject1.csv')
+	actual_s1 = pd.read_csv('../data/testing/ActualLables/labels_subject1_psd.csv', header=None)
+	actual8_s1 = pd.read_csv('../data/testing/ActualLables/labels8_subject1_psd.csv', header=None)
+	actual_s1 = actual_s1[0]
+	actual8_s1 = actual8_s1[0]
+
+	test_s2 = pd.read_csv('../data/testing/pca_data_v2/pca_subject2.csv')
+	actual_s2 = pd.read_csv('../data/testing/ActualLables/labels_subject2_psd.csv', header=None)
+	actual8_s2 = pd.read_csv('../data/testing/ActualLables/labels8_subject2_psd.csv', header=None)
+	actual_s2 = actual_s2[0]
+	actual8_s2 = actual8_s2[0]
+
+	test_s3 = pd.read_csv('../data/testing/pca_data_v2/pca_subject3.csv')
+	actual_s3 = pd.read_csv('../data/testing/ActualLables/labels_subject3_psd.csv', header=None)
+	actual8_s3 = pd.read_csv('../data/testing/ActualLables/labels8_subject3_psd.csv', header=None)
+	actual_s3 = actual_s3[0]
+	actual8_s3 = actual8_s3[0]
+
+	perceptronTest(input_data_s1, test_s1, actual_s1, actual8_s1)
+	perceptronTest(input_data_s2, test_s2, actual_s2, actual8_s2)
+	perceptronTest(input_data_s3, test_s3, actual_s3, actual8_s3)
+
+	# Results:
+	# Cross Validation Accuracy = 0.870042431112
+	# Test Accuracy for the subject is = 0.882705479452
+	# Test Accuracy for the subject at step 8 is = 0.876712328767
+	# Cross Validation Accuracy = 0.754951838489
+	# Test Accuracy for the subject is = 0.802131336406
+	# Test Accuracy for the subject at step 8 is = 0.797235023041
+	# Cross Validation Accuracy = 0.523014108367
+	# Test Accuracy for the subject is = 0.579128440367
+	# Test Accuracy for the subject at step 8 is = 0.584862385321
+
+if __name__ == '__main__':
+	mainRawData()