util.py

import os

import numpy as np
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle

FTRAIN = 'training.csv'
FTEST = 'test.csv'

def load(test=False, cols=None):
    """Loads data from FTEST if *test* is True, otherwise from FTRAIN.
    Pass a list of *cols* if you're only interested in a subset of the
    target columns.
    """
    fname = FTEST if test else FTRAIN
    df = read_csv(os.path.expanduser(fname))  # load pandas dataframe

    # The Image column has pixel values separated by space; convert
    # the values to numpy arrays:
    df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))

    if cols:  # get a subset of columns
        df = df[list(cols) + ['Image']]

    print(df.count())  # prints the number of values for each column
    df = df.dropna()  # drop all rows that have missing values in them

    X = np.vstack(df['Image'].values) / 255.  # scale pixel values to [0, 1]
    X = X.astype(np.float32)
    X = X.reshape((-1,96,96,1))


    if not test:  # only FTRAIN has any target columns
        y = df[df.columns[:-1]].values
        y = (y - 48) / 48  # scale target coordinates to [-1, 1]
        X, y = shuffle(X, y, random_state=42)  # shuffle train data
        y = y.astype(np.float32)
        cols_names = df.columns[:-1].values.tolist()
    else:
        y = None
        cols_names = None
    return X, y,cols_names

def load_lookup(lookupfile):
    df = read_csv(lookupfile)
    grouped = df.groupby(by=['ImageId'])
    lookup = {}
    for group_name, group in grouped:
        lookup[group_name] = {'RowId':group['RowId'].values,'FeatureName' : group['FeatureName'].values}
    return lookup