view experiment-reverb/code/supervised_training_pca-bak.py @ 2:c87a9505f294 tip

Added LICENSE for code, removed .wav files

#!/usr/bin/python2
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 23 11:53:17 2015

@author: mmxgn
"""

# This file does the cluster estimation and the removal of outliers

from sys import argv, exit
from essentia.standard import YamlInput, YamlOutput
from essentia import Pool
from pca import *

from numpy import *
from sklearn import cluster
from sklearn.metrics import pairwise_distances
from sklearn.cluster import KMeans, MiniBatchKMeans
import matplotlib.pyplot as plt
from sklearn.mixture import GMM
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from scipy.signal import decimate
#from hmmlearn import hmm
from sklearn import hmm
#from adpcm import adm, adm_reconstruct


mse = lambda A,B: ((array(A)-array(B)) ** 2).mean()



    
def adm_reconstruct(codeword, h, dmin=.01, dmax=.28):
    x = zeros((1, codeword.shape[1]))

    delta1 = dmin
    delta2 = dmin
    Sum = h
    
    x[0] = h
    for i in range(0, codeword.shape[1]):
        if codeword[0,i] == 0:
            delta1 = dmin
            delta2 = dmin
            
        elif codeword[0,i] == 1:
            delta2 = dmin
            Sum += delta1
            delta1 *= 2
            if delta1 > dmax:
                delta1 = dmax
                
        elif codeword[0,i] == -1:
            delta1 = dmin
            Sum -= delta2
            delta2 *= 2
            if delta2 > dmax:
                delta2 = dmax
        x[0,i] = Sum
    return x
        
def adm(x, dmin=.01, dmax=.28, tol=0.0001):
    
    # Adaptive delta modulation adapted by code:
    # (adeltamod.m)
    #  
    #   Adaptive Delta Modulator
    #   by Gandhar Desai (gdesai)
    #   BITS Pilani Goa Campus
    #   Date: 28 Sept, 2013     

    xsig = x

    Lx = len(x)

    ADMout = zeros((1, Lx))        
    codevec = zeros((1, Lx))
    

    Sum = x[0]
    delta1 = dmin
    delta2 = dmin
    mult1 = 2
    mult2 = 2
    for i in range(0, Lx):
        #print abs(xsig[i] - Sum)
        if (abs(xsig[i] - Sum) < tol):
            bit = 0
            delta2 = dmin
            delta1 = dmin
            
            
        elif (xsig[i] >= Sum):
            bit = 1
            delta2 = dmin
            Sum += delta1
            delta1 *=  mult1
            if delta1 > dmax:
                delta1 = dmax


        else:
            bit = -1
            delta1 = dmin            
            Sum -= delta2
            delta2 *= mult2
            if delta2 > dmax:
                delta2 = dmax

        
   
        ADMout[0, i] = Sum
        codevec[0, i]= bit
        
    return ADMout,codevec, x[0]

if __name__=="__main__":
    if len(argv) != 2:
        print "[EE] Wrong number of arguments"
        print "[II] Correct syntax is:"
        print "[II] \t%s <training_file>"
        print "[II] where <training_file> is a .yaml file containing the"
        print "[II] features of the dataset (try output2_stage/fulltraining-last.yaml)"
        exit(-1)
        
    
    infile = argv[1]
    
    features_pool = YamlInput(filename = infile)()
    
    
    
    feature_captions = features_pool.descriptorNames()   
    parameter_captions = []
    
    
    for c in features_pool.descriptorNames():
        if c.split('.')[0] == 'parameter':
            parameter_captions.append(c)
        if c.split('.')[0] == 'metadata' or c.split('.')[0] == 'parameter':
            feature_captions.remove(c)
            
            

    close('all')
                
    print "[II] Loaded training data from %s (%s) " % (infile, features_pool['metadata.date'][0])
    print "[II] %d Features Available: " % len(feature_captions)


    
    print str(feature_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
    
    nfeatures_in = len(feature_captions)
    nparameters_in = len(parameter_captions)
    features_vector = zeros((nfeatures_in, len(features_pool[feature_captions[0]])))
    
    parameters_vector = zeros((nparameters_in, len(features_pool[parameter_captions[0]])))
    
    
    for i in range(0, nfeatures_in):
        features_vector[i, :] = features_pool[feature_captions[i]].T
    for i in range(0, nparameters_in):
        parameters_vector[i, :] = features_pool[parameter_captions[0]].T
        
    print "[II] %d parameters used:" % len(parameter_captions)
    print str(parameter_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7].replace('parameter.','')
    
    print "[II] Marking silent parts"

    silent_parts = zeros((1, len(features_pool[feature_captions[i]].T)))     

    rms = features_vector[feature_captions.index('rms'), :]    
    
    # Implementing Hysteresis Gate -- High threshold is halfway between 
    # the mean and the max and Low is halfway between the mean dn the min
    
    rms_threshold_mean = mean(rms)

    rms_threshold_max = max(rms)
    rms_threshold_min = min(rms)
    
    rms_threshold_high = 0.1 * rms_threshold_mean
    rms_threshold_low = 0.01 * rms_threshold_mean
    
    for n in range(1,  len(rms)):
        prev = rms[n-1]
        curr = rms[n]
        
        if prev >= rms_threshold_high:
            if curr < rms_threshold_low:
                silent_parts[0,n] = 1
            else:
                silent_parts[0,n] = 0
        elif prev <= rms_threshold_low:
            if curr > rms_threshold_high:
                silent_parts[0,n] = 0
            else:
                silent_parts[0,n] = 1
        else:
            silent_parts[0,n] = silent_parts[0,n-1]
                
            
    if silent_parts[0,1] == 1:
        silent_parts[0, 0] = 1
        
    
  #  plot(rms)
 #   plot(silent_parts.T)
  #  plot(ones((len(rms), 1))*rms_threshold_high)
 #   plot(ones((len(rms), 1))*rms_threshold_low)
    
    active_index = invert(silent_parts.flatten().astype(bool))
    
    # Keep only active parts
    
    # Uncomment this
    #features_vector = features_vector[:, active_index]
   # parameters_vector = parameters_vector[:, active_index]
    
    moments_vector = zeros((features_vector.shape[0], 2))
    
    print "[II] Storing moments vector"
    for i in range(0, features_vector.shape[0]):
        mean_ = mean(features_vector[i,:])
        std_ = std(features_vector[i,:], ddof=1)
        moments_vector[i,0] = mean_
        moments_vector[i,1] = std_
        
        features_vector[i,:] = (features_vector[i,:] - mean_)/std_

        
    
    print "[II] Extracting PCA configuration "
    
    kernel, q, featurelist = extract_pca_configuration_from_data(features_vector)
    
    print "[II] Optimal number of PCs to keep: %d" % q
    
    feature_captions_array = array(feature_captions)
    
    features_to_keep = features_vector
    
    features_to_keep = list(feature_captions_array[featurelist])
    print "[II] Decided to keep %d features:" % len(features_to_keep)
    print  str(features_to_keep).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
    
    
    
    # Keep the desired features
    #Uncomment this
    #features_kept_data = features_vector[featurelist,:]
    features_lept_data = features_vector
    
    # Generate the parameter clusters using k-means
    
    # Uncomment this
    features_vector = (kernel.T*features_kept_data)[0:q,:]
    
    
   
 #   parameters_k_means = KMeans(n_clusters = parameters_k, init='k-means++', max_iter=300, tol=0.0000001, verbose = 1)
    parameters_k_means = KMeans(init='k-means++', n_init=10, max_iter=300, tol=0.0000001, verbose = 0)
#   #  parameters_k_means = MiniBatchKMeans(init='k-means++', max_iter=300, tol=0.00001, verbose = 1)
#    
#    # Quantize the differences of the parameters instead of the parameters themselves
#    parameters_vector_diff = concatenate((zeros((shape(parameters_vector)[0],1)),diff(parameters_vector, axis=1)),axis=1)
#    features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
#    
#    # Delete this afterwards
#    # features_vector = features_vector_diff
#    parameters_k_means.fit(parameters_vector_diff.T)
    parameters_k_means.fit(parameters_vector.T)
#    
    parameters_k_means_centers = parameters_k_means.cluster_centers_
    parameters_k_means_labels = parameters_k_means.labels_
#    
    parameters_vector_estimated = zeros(shape(parameters_vector))
#    
    for n in range(0, len(parameters_vector_estimated[0])):
        parameters_vector_estimated[:,n] = parameters_k_means_centers[parameters_k_means_labels[n]]
#
##    plot(parameters_vector[0])
# #   plot(parameters_vector_estimated[0])
#    
#    # PROvLIMA EDW
    print "[II] Parameters MSE for %d clusters: %.3f" % (len(parameters_k_means.cluster_centers_), mse(parameters_vector, parameters_vector_estimated))
    
#    
    
    



#    print "[II] Clustering features."
#    
#    n_clusters = 100
#    features_clustering = GMM(n_components = n_clusters, covariance_type='diag')
#    
#    features_clustering.fit( features_vector.T, y=parameters_k_means_labels)
#    
#    features_clustering_means = features_clustering.means_
#    features_clustering_labels = features_clustering.predict(features_vector.T)
#    features_clustering_sigmas = features_clustering.covars_
#    
#    features_vector_estimated = zeros(shape(features_vector))
#    
#
#    for n in range(0, len(features_vector_estimated[0])):
#        features_vector_estimated[:,n] = features_clustering_means[features_clustering_labels[n]]
#        
#  #  for n in range(0,features_vector.shape[0]):
#   # hist(features_vector[1]-features_vector_estimated[1], 256)
#    std(features_vector[1]-features_vector_estimated[1], ddof=1)
#    mean(features_vector[1]-features_vector_estimated[1])
#    
#    print "[II] Features MSE for %d clusters: %.3f" % (n_clusters, mse(features_vector, features_vector_estimated))
#        
#        
#    print "[II] Testing Gaussian Naive Bayes Classifier"
#
#    gnb = GaussianNB()    
#    gnb.fit(features_vector.T, parameters_k_means_labels)
#    parameter_labels_estimated = gnb.predict(features_vector.T)
#    
#    print "[II] Raw Parameters - Gaussian Naive Bayes classifier testing ratio: %.4f" % (float(sum((parameter_labels_estimated == parameters_k_means_labels).astype(float)))/float(len(parameters_k_means_labels)))
#    
#    
#    print "[II] Trying ADM-coded parameters"
#    UpsamplingFactor = 100
#    print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
#    
#
#    # Upsampled features and parameters    
#    features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
#    feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
#    parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
#    
#    parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
#    parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
#    parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
#    
#    # Reconstructed parameters
#    
#    parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
#    
#    dmin = 0.001
#    dmax = 0.28
#    tol = 0.001    
#    for i in range(0, parameters_vector_upsampled.shape[0]):
#        out, code, h =  adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
#        parameters_vector_upsampled_adm[i,:] = out
#        parameters_vector_upsampled_code[i,:] = code
#        parameters_vector_upsampled_firstval[i, 0] = h
#        
#        
#        # Reconstruct-ADM        
#        parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
#        
#    
# #   plot(parameters_vector_upsampled_adm.T, 'r--')
#    
#    
#   # plot(parameters_vector_upsampled.T)
#  #  plot(parameters_vector_upsampled_reconstructed.T, 'g.')
#    
#    
#    
#    parameters_vector_reconstructed = zeros(shape(parameters_vector))
#    for n in range(0, parameters_vector.shape[1]):
#        parameters_vector_reconstructed[:,n] = parameters_vector_upsampled_reconstructed[:,n*UpsamplingFactor]
#        
#        
#    mse_adm = mse(parameters_vector_reconstructed, parameters_vector)
#    
#    print "[II] Expected ADM reconstruction MSE: %.4f" % mse_adm
#    
#   # figure()
#    #plot(parameters_vector.T)
#   # plot(parameters_vector_reconstructed.T)
#    
#    print "[II] Building HMM transition, emission matrices and priors"
#    
#    transmat = zeros((3,3))
#    startprob = zeros((3,))
#    emissionmat = zeros((3, n_clusters))
#    
#            
#    state_labels = parameters_vector_upsampled_code + 1
#    stateseq = state_labels.T
#    
#    for n in range(0,shape(parameters_vector_upsampled_code)[1]):
#        if n>0:
#            transmat[state_labels[0,n-1],state_labels[0,n]] += 1
#        startprob[state_labels[0,n]] +=1
#        emissionmat[state_labels[0,n],feature_labels_upsampled[n]] += 1
#        
#        
#    for n in range(0, transmat.shape[0]):
#        transmat[n,:]/=sum(transmat[n,:])
#        emissionmat[n,:]/=sum(emissionmat[n,:])
#         
#
#    transmat = matrix(transmat)
#    emissionmat = matrix(emissionmat)
#    # Prior
#    startprob = startprob/sum(startprob)
#    startprob = ravel(startprob)
#    
#    # Vocabulary
#        
#    model = hmm.GMMHMM(n_mix=n_clusters, n_components=3, covariance_type="diag")
#    model.means_ = features_clustering.means_
#    model.covars_ = features_clustering.covars_   
#    
#    features_vector_array = array(features_vector)
#    features_vector_upsampled_array=array(features_vector_upsampled)
#    
#    model.fit([features_vector_array.T])
#    stateseq_estimated = model.predict(features_vector_upsampled_array.T)
#    
#    parameters_vector_upsampled_reconstructed_decoded = matrix(zeros(shape(parameters_vector_upsampled)))
# 
#
#    plot(stateseq_estimated)
#    plot(stateseq)
#    
#    code_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
#    code_estimated[0,:] = stateseq_estimated - 1
#    
#    
#   
#    parameters_vector_upsampled_reconstructed_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
#
#    for i in range(0, parameters_vector_upsampled.shape[0]):
#        parameters_vector_upsampled_reconstructed_estimated[i,:] = adm_reconstruct(code_estimated,parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
#    figure()
#    plot(parameters_vector_upsampled_reconstructed_estimated.T)
#    plot(parameters_vector_upsampled.T)