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driver.py
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driver.py
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from load_data import train_data, test_data, train_label, test_label, positive_train, negative_train
from svm_original import train_svm
from genetic import genetic_algorithm
import random
import numpy as np
import matplotlib.pyplot as plt
from skbio.stats.composition import ilr, ilr_inv
from skbio.stats.composition import clr, clr_inv
from sklearn.decomposition import PCA
from sklearn.decomposition import KernelPCA, SparsePCA
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# Params ::
# iterations : Number of times to run the algorithm to take average
# sample size : sample size of training data
# reduce : The target dimension to reduce to
# train_data_svm : training data
# train_label_svm : training label
def split_train_test( positive_train, negative_train, sample_size ):
k = random.randrange( 0 , len(positive_train) - ( sample_size // 2 ) )
train_positive = positive_train[k: (k + (sample_size // 2) ) , :]
train_postive_label = np.ones( ( sample_size // 2 ) )
p = random.randrange( 0 , len(negative_train) - ( sample_size // 2 ) )
train_negative = negative_train[k: (k + (sample_size // 2) ) , :]
train_negative_label = np.zeros( ( sample_size // 2 ) )
train_sample_data = np.append( train_positive, train_negative, axis = 0)
train_sample_label = np.append( train_postive_label, train_negative_label, axis = 0)
data_label = np.append( train_sample_data, train_sample_label.reshape((( len(train_sample_label) ) , 1 )), axis = 1)
np.random.shuffle( data_label )
features = len(data_label[0])
train_sample_data = data_label[:, 0:features - 1]
train_sample_label = data_label[:,features - 1]
return train_sample_data, train_sample_label
def train( iterations, sample_size, reduce, positive_train, negative_train, test_data, test_label):
f1_original_clr = []
f1_original = []
f1_dca = []
f1_clr = []
f1_ilr = []
roc_original_clr = []
roc_original = []
roc_dca = []
roc_clr = []
roc_ilr = []
for _ in range( iterations ):
# Select a smaller size
#Select a random set from the train data
train_sample_data, train_sample_label = split_train_test( positive_train, negative_train, sample_size )
f1_original_data, roc_original_data = train_svm(train_sample_data, train_sample_label, test_data,
test_label)
f1_original.append( f1_original_data )
roc_original.append( roc_original_data )
train_sample_data[train_sample_data == 0] = 0.1e-32
test_data[test_data == 0] = 0.1e-32
clr_original_train = clr(train_sample_data)
clr_original_test = clr(test_data)
scaler = StandardScaler()
clr_original_train = np.nan_to_num(scaler.fit_transform(clr_original_train))
clr_original_test = np.nan_to_num(scaler.fit_transform(clr_original_test))
f1_original_data_clr, roc_original_data_clr = train_svm( clr_original_train, train_sample_label, clr_original_test, test_label )
f1_original_clr.append ( f1_original_data_clr )
roc_original_clr.append( roc_original_data_clr )
matrices = genetic_algorithm( train_sample_data, reduce )
roc_dca_iterations = []
for br_matrix in matrices:
#br_matrix = matrices[0]
reduced_data = np.matmul(br_matrix, train_sample_data.transpose()).transpose()
reduced_test = np.matmul(br_matrix, test_data.transpose()).transpose()
f1_dca_data, roc_dca_data = train_svm( reduced_data, train_sample_label, reduced_test, test_label )
#f1_dca.append( f1_dca_data )
roc_dca_iterations.append( roc_dca_data )
#print ("DCA max", max(roc_dca_iterations) )
roc_dca.append( max(roc_dca_iterations) )
#print ( " PCA CLR train shape ", train_sample_data.shape )
# Do ILR and CLR transformation
# Set zeros to small values
train_sample_data[train_sample_data == 0] = 0.1e-32
test_data[test_data == 0] = 0.1e-32
clr_data_train = clr(train_sample_data)
clr_test = clr(test_data)
ilr_data_train = ilr( train_sample_data )
ilr_test = ilr( test_data )
np.savetxt("ilr_data.csv", ilr_data_train, delimiter=",")
# Do PCA to reduce dimensions
pca_clr = PCA(n_components = reduce)
pca_ilr = PCA(n_components = reduce)
#print ( "reduce ", reduce )
fit_train_clr = np.ascontiguousarray( pca_clr.fit_transform(clr_data_train) )
fit_test_clr = np.ascontiguousarray( pca_clr.transform(clr_test) )
fit_train_ilr = np.ascontiguousarray( pca_ilr.fit_transform(ilr_data_train) )
fit_test_ilr = np.ascontiguousarray( pca_ilr.transform(ilr_test) )
np.savetxt("ilr_data_pca.csv", fit_train_ilr, delimiter=",")
pca_clr_reduced_train = np.nan_to_num( fit_train_clr )
pca_ilr_reduced_train = np.nan_to_num( fit_train_ilr )
fit_test_clr = np.nan_to_num( fit_test_clr )
fit_test_ilr = np.nan_to_num( fit_test_ilr )
f1_pca_clr_data, roc_pca_clr_data = train_svm( pca_clr_reduced_train, train_sample_label, fit_test_clr, test_label )
f1_pca_ilr_data, roc_pca_ilr_data = train_svm( pca_ilr_reduced_train, train_sample_label, fit_test_ilr, test_label )
f1_clr.append( f1_pca_clr_data )
roc_clr.append( roc_pca_clr_data )
f1_ilr.append( f1_pca_ilr_data )
roc_ilr.append( roc_pca_ilr_data )
#print ( roc_original, roc_dca, roc_clr, roc_ilr)
return ( sum ( roc_original ) / iterations ) , ( sum ( roc_original_clr ) / iterations ), ( sum( roc_dca ) / iterations ) , ( sum( roc_clr ) / iterations ) , ( sum( roc_ilr ) / iterations )
original = []
original_clr_loss_array = []
dca = []
pca_clr = []
pca_ilr = []
for sample_size in range(20, 200, 10):
original_loss, original_clr_loss , dca_loss, clr_loss, ilr_loss = train( 10, sample_size, 20, positive_train, negative_train, test_data, test_label)
print ( original_loss, original_clr_loss, dca_loss, clr_loss, ilr_loss )
original.append( original_loss )
original_clr_loss_array.append( original_clr_loss )
dca.append( dca_loss )
pca_clr.append( clr_loss )
pca_ilr.append( ilr_loss )
print ( original_loss )
print ( dca_loss )
print ( clr_loss )
print ( ilr_loss )
print (" ")
x = list(range(20, 200, 10))
plt.title(" Needle Leaf : Landcover Classification: Dimensions 250 - 20 : Threshold : 0.5 ")
plt.plot(x, dca, label = ' DCA')
plt.plot(x, original, label = 'Original')
plt.plot(x, original_clr_loss_array, label = ' CLR ')
plt.plot(x, pca_clr, label = 'PCA_CLR ')
plt.plot(x, pca_ilr, label = 'PCA_ILR ')
plt.xlabel('Training sample size')
plt.ylabel('Area Under ROC ')
plt.legend()
plt.savefig("clr_z_normalised_needle_leaf.png")