# -*- coding: utf-8 -*-
import pickle
import pandas as pd
def pickle_save(object_to_save, fname):
"""
Save data to file using pickle,
"""
pickle.dump(object_to_save, open(fname, 'wb'))
def pickle_load(fname):
"""
Load data from file using pickle.
"""
# load the model
loaded_object = pickle.load(open(fname, 'rb'))
return loaded_object
def get_data(file_path, drop_columns=True, drop_nans=True):
# load data
data = pd.read_csv(file_path)
# define column names
column_names = list(data.columns)
if drop_columns:
# drop columns with nans
for c in column_names:
if not(any(x in c for x in ["file_name", "mfcc", "duration", "emotion"])):
del data[c]
# drop erroneous rows
if drop_nans:
data = data.dropna()
# round data
data = data.round(3)
data = data.iloc[:, :]
# re-define column names
column_names = list(data.columns)
return data, list(data.columns)
def balance_dataset(data):
# define column names
column_names = list(data.columns)
# assert equal number o samples per class
samples_pro_emotion = {e: len(data[data.emotion == e]) for e in data.emotion.unique()}
balanced_data = pd.concat([data[data.emotion == e].sample(min(samples_pro_emotion.values()))
for e in data.emotion.unique()],
axis=0,
keys=list(data.columns))
# split data
X = balanced_data.iloc[:, :-1]
y = balanced_data.iloc[:, -1:].astype('category')
# print("%25s : %s" % ("Data with balanced sets", str(balanced_data.shape)))
return balanced_data, X, y, column_names
