view scripts/load_dataset.py @ 4:e50c63cf96be branch-tests

rearranging folders

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 15 22:52:57 2017

@author: mariapanteli
"""

import numpy as np
import pandas as pd
import pickle

import load_features
import util_dataset
import util_filter_dataset


#METADATA_FILE = 'sample_dataset/metadata.csv'
#OUTPUT_FILES = ['sample_dataset/train_data.pickle', 'sample_dataset/val_data.pickle', 'sample_dataset/test_data.pickle']
WIN_SIZE = 2
METADATA_FILE = 'data/metadata_BLSM_language_all.csv'
#OUTPUT_FILES = ['/import/c4dm-04/mariap/train_data_cf.pickle', '/import/c4dm-04/mariap/val_data_cf.pickle', '/import/c4dm-04/mariap/test_data_cf.pickle']
#OUTPUT_FILES = ['/import/c4dm-04/mariap/train_data_cf_4.pickle', '/import/c4dm-04/mariap/val_data_cf_4.pickle', '/import/c4dm-04/mariap/test_data_cf_4.pickle']
OUTPUT_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']

def extract_features(df, win2sec=8.0):
    """Extract features from melspec and chroma.
    
    Parameters
    ----------
    df : pd.DataFrame
        Metadata including class label and path to audio, melspec, chroma
    win2sec : float
        The window size for the second frame decomposition of the features
        
    Returns
    -------
    X : np.array
        The features for every frame x every audio file in the dataset
    Y : np.array
        The class labels for every frame in the dataset
    Y_audio : np.array
        The audio labels
    """
    feat_loader = load_features.FeatureLoader(win2sec=win2sec)
    frames_rhy, frames_mfcc, frames_chroma, frames_mel, Y_df, Y_audio_df = feat_loader.get_features(df)
    print frames_rhy.shape, frames_mel.shape, frames_mfcc.shape, frames_chroma.shape
    X = np.concatenate((frames_rhy, frames_mel, frames_mfcc, frames_chroma), axis=1)
    Y = Y_df.get_values()
    Y_audio = Y_audio_df.get_values()
    return X, Y, Y_audio


if __name__ == '__main__':
    # load dataset
    df = pd.read_csv(METADATA_FILE)
    df = util_filter_dataset.remove_missing_data(df)
    subset_idx = util_dataset.subset_labels(df['Country'].get_values())
    df = df.iloc[subset_idx, :]
    X, Y = np.arange(len(df)), df['Country'].get_values()
    
    # split in train, val, test set
    train_set, val_set, test_set = util_dataset.get_train_val_test_idx(X, Y) 
    
    # extract features and write output
    X_train, Y_train, Y_audio_train = extract_features(df.iloc[train_set[0], :], win2sec=WIN_SIZE)
    with open(OUTPUT_FILES[0], 'wb') as f:
        pickle.dump([X_train, Y_train, Y_audio_train], f)
        
    X_val, Y_val, Y_audio_val = extract_features(df.iloc[val_set[0], :], win2sec=WIN_SIZE)
    with open(OUTPUT_FILES[1], 'wb') as f:
        pickle.dump([X_val, Y_val, Y_audio_val], f)
        
    X_test, Y_test, Y_audio_test = extract_features(df.iloc[test_set[0], :], win2sec=WIN_SIZE)
    with open(OUTPUT_FILES[2], 'wb') as f:
        pickle.dump([X_test, Y_test, Y_audio_test], f)

#out_file = '/import/c4dm-04/mariap/test_data_melodia_1_test.pickle'
#    pickle.dump([X_test, Y_test, Y_audio_test], f)
#with open(out_file, 'wb') as f: