comparison scripts/map_and_average.py @ 9:c4841876a8ff branch-tests

adding notebooks and trying to explain classifier coefficients

+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 16 02:44:07 2017
+
+@author: mariapanteli
+"""
+
+import numpy as np
+import pickle
+
+import util_feature_learning
+    
+WIN_SIZE = 8
+INPUT_FILES = ['/import/c4dm-04/mariap/train_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+               '/import/c4dm-04/mariap/val_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+               '/import/c4dm-04/mariap/test_data_melodia_'+str(WIN_SIZE)+'.pickle']
+OUTPUT_FILES = ['/import/c4dm-04/mariap/lda_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+                '/import/c4dm-04/mariap/pca_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+                '/import/c4dm-04/mariap/nmf_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+                '/import/c4dm-04/mariap/ssnmf_data_melodia_'+str(WIN_SIZE)+'.pickle', 
+                '/import/c4dm-04/mariap/na_data_melodia_'+str(WIN_SIZE)+'.pickle']
+
+
+def remove_inds(features, labels, audiolabels):
+    '''remove instances with unknown country
+    '''
+    remove_inds1 = np.where(labels=='unknown')[0]
+    remove_inds2 = np.where(labels=='Unidentified')[0]
+    keep_inds = np.array(list(set(range(len(labels))) - (set(remove_inds1) | set(remove_inds2))))
+    features = features[keep_inds, :]
+    labels = labels[keep_inds]
+    audiolabels = audiolabels[keep_inds]
+    return features, labels, audiolabels
+
+
+def averageframes(features, audiolabels, classlabels):
+    '''average frame-based features for each recording
+    '''
+    u, ind = np.unique(audiolabels, return_index=True)
+    uniqsorted = u[np.argsort(ind)]
+    newfeatures = []
+    newclasslabels = []
+    newaudiolabels = []
+    for aulabel in uniqsorted:
+        inds = np.where(audiolabels == aulabel)[0]
+        newfeatures.append(np.mean(features[inds, :], axis=0))
+        newclasslabels.append(classlabels[inds[0]])
+        newaudiolabels.append(aulabel)
+    newfeatures = np.array(newfeatures)
+    newaudiolabels = np.array(newaudiolabels)
+    newclasslabels = np.array(newclasslabels)
+    return newfeatures, newaudiolabels, newclasslabels
+
+
+def load_data_from_pickle(pickle_file=None):
+    '''load frame based features and labels from pickle file
+    '''
+    with open(pickle_file,'rb') as f:
+        data, labels, audiolabels = pickle.load(f)
+    # remove 'unknown' and 'unidentified' country
+    data, labels, audiolabels = remove_inds(data, labels, audiolabels)
+    # avoid nan which gives error in feature learning 
+    data[np.isnan(data)] = 0
+    return data, labels, audiolabels
+
+
+def load_train_val_test_sets():
+    '''load train, val, test sets
+    '''
+    print INPUT_FILES[0]
+    trainset = load_data_from_pickle(INPUT_FILES[0])
+    valset = load_data_from_pickle(INPUT_FILES[1])
+    testset = load_data_from_pickle(INPUT_FILES[2])
+    return trainset, valset, testset
+
+
+def get_feat_inds(n_dim=840):
+    '''assume frame with 840 features and return indices for each feature
+    '''
+    if n_dim == 840:
+        rhy_inds = np.arange(400)
+        mel_inds = np.arange(400, 640)
+        mfc_inds = np.arange(640, 720)
+        chr_inds = np.arange(720, 840)
+    elif n_dim == 640:
+        rhy_inds = np.arange(200)
+        mel_inds = np.arange(200, 440)
+        mfc_inds = np.arange(440, 520)
+        chr_inds = np.arange(520, 640)
+    elif n_dim == 460:
+        rhy_inds = np.arange(200)
+        mel_inds = np.arange(200, 260)
+        mfc_inds = np.arange(260, 340)
+        chr_inds = np.arange(340, 460)
+    elif n_dim == 660:
+        rhy_inds = np.arange(400)
+        mel_inds = np.arange(400, 460)
+        mfc_inds = np.arange(460, 540)
+        chr_inds = np.arange(540, 660)
+    feat_inds = [rhy_inds, mel_inds, mfc_inds, chr_inds] 
+    feat_labels = ['rhy', 'mel', 'mfc', 'chr']
+    return feat_labels, feat_inds
+
+
+def map_and_average_frames(dataset=None, n_components=None, min_variance=None):
+    if dataset is None:
+        trainset, valset, testset = load_train_val_test_sets()
+    else:
+        trainset, valset, testset = dataset
+    traindata, trainlabels, trainaudiolabels = trainset
+    valdata, vallabels, valaudiolabels = valset
+    testdata, testlabels, testaudiolabels = testset
+    print traindata.shape, valdata.shape, testdata.shape
+    labels = np.concatenate((trainlabels, vallabels, testlabels)).ravel()
+    audiolabels = np.concatenate((trainaudiolabels, valaudiolabels, testaudiolabels)).ravel()
+    
+    feat_labels, feat_inds = get_feat_inds(n_dim=traindata.shape[1])
+    ldadata_list = []
+    pcadata_list = []
+    nmfdata_list = []
+    ssnmfdata_list = []
+    data_list = []
+    for i in range(len(feat_inds)):
+        print "mapping " + feat_labels[i]
+        inds = feat_inds[i]
+        ssm_feat = util_feature_learning.Transformer()
+        if min_variance is not None:
+            ssm_feat.fit_data(traindata[:, inds], trainlabels, n_components=len(inds), pca_only=True)
+            n_components = np.where(ssm_feat.pca_transformer.explained_variance_ratio_.cumsum()>min_variance)[0][0]+1
+            print n_components, len(inds)
+            ssm_feat.fit_data(traindata[:, inds], trainlabels, n_components=n_components)
+        elif n_components is not None:
+            ssm_feat.fit_data(traindata[:, inds], trainlabels, n_components=n_components)
+        else:
+            ssm_feat.fit_data(traindata[:, inds], trainlabels, n_components=len(inds))
+        all_data = np.concatenate((traindata[:, inds], valdata[:, inds], testdata[:, inds]), axis=0)
+        transformed_data_dict = ssm_feat.transform_data(all_data)
+        for key in transformed_data_dict.keys():
+            average_data, audiolabs, classlabs = averageframes(transformed_data_dict[key], audiolabels, labels)
+            transformed_data_dict[key] = average_data
+        data_list.append(transformed_data_dict['none'])
+        pcadata_list.append(transformed_data_dict['pca'])
+        ldadata_list.append(transformed_data_dict['lda'])
+        nmfdata_list.append(transformed_data_dict['nmf'])
+        ssnmfdata_list.append(transformed_data_dict['ssnmf'])
+    return data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs
+
+
+def lda_map_and_average_frames(dataset=None, n_components=None, min_variance=None):
+    if dataset is None:
+        trainset, valset, testset = load_train_val_test_sets()
+    else:
+        trainset, valset, testset = dataset
+    traindata, trainlabels, trainaudiolabels = trainset
+    valdata, vallabels, valaudiolabels = valset
+    testdata, testlabels, testaudiolabels = testset
+    print traindata.shape, valdata.shape, testdata.shape
+    labels = np.concatenate((trainlabels, vallabels, testlabels)).ravel()
+    audiolabels = np.concatenate((trainaudiolabels, valaudiolabels, testaudiolabels)).ravel()
+    
+    feat_labels, feat_inds = get_feat_inds(n_dim=traindata.shape[1])
+    ldadata_list = []
+    pcadata_list = []
+    nmfdata_list = []
+    ssnmfdata_list = []
+    data_list = []
+    for i in range(len(feat_inds)):
+        print "mapping " + feat_labels[i]
+        inds = feat_inds[i]
+        ssm_feat = util_feature_learning.Transformer()
+        if min_variance is not None:
+            ssm_feat.fit_lda_data(traindata[:, inds], trainlabels, n_components=len(inds), pca_only=True)
+            n_components = np.where(ssm_feat.pca_transformer.explained_variance_ratio_.cumsum()>min_variance)[0][0]+1
+            print n_components, len(inds)
+            ssm_feat.fit_lda_data(traindata[:, inds], trainlabels, n_components=n_components)
+        elif n_components is not None:
+            ssm_feat.fit_lda_data(traindata[:, inds], trainlabels, n_components=n_components)
+        else:
+            ssm_feat.fit_lda_data(traindata[:, inds], trainlabels, n_components=len(inds))
+        all_data = np.concatenate((traindata[:, inds], valdata[:, inds], testdata[:, inds]), axis=0)
+        transformed_data_dict = ssm_feat.transform_lda_data(all_data)
+        for key in transformed_data_dict.keys():
+            if len(transformed_data_dict[key])==0:
+                continue
+            average_data, audiolabs, classlabs = averageframes(transformed_data_dict[key], audiolabels, labels)
+            transformed_data_dict[key] = average_data
+        data_list.append(transformed_data_dict['none'])
+        pcadata_list.append(transformed_data_dict['pca'])
+        ldadata_list.append(transformed_data_dict['lda'])
+        nmfdata_list.append(transformed_data_dict['nmf'])
+        ssnmfdata_list.append(transformed_data_dict['ssnmf'])
+    return data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs
+
+
+def write_output(data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs):
+    pickle.dump([ldadata_list, classlabs, audiolabs], open(OUTPUT_FILES[0], 'wb'))
+    pickle.dump([pcadata_list, classlabs, audiolabs], open(OUTPUT_FILES[1], 'wb'))
+    pickle.dump([nmfdata_list, classlabs, audiolabs], open(OUTPUT_FILES[2], 'wb'))
+    pickle.dump([ssnmfdata_list, classlabs, audiolabs], open(OUTPUT_FILES[3], 'wb'))
+    pickle.dump([data_list, classlabs, audiolabs], open(OUTPUT_FILES[4], 'wb'))
+    
+
+if __name__ == '__main__':
+    # first only lda - because it goes fast
+    data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs = lda_map_and_average_frames(min_variance=0.99)    
+    write_output(data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs)
+    # then add nmf,ssnmf    
+    #data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs = map_and_average_frames(min_variance=0.99)
+    #write_output(data_list, pcadata_list, ldadata_list, nmfdata_list, ssnmfdata_list, classlabs, audiolabs)
+