import pandas as pd
from dask import dataframe as dd
from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit
[docs]def load_data(path, index, label, drop=None, verbose=True, **kwargs):
"""Load features and labels from file(s).
Args:
path (str) : path to file(s)
index (str) : column for index
label (str) : column for labels
drop (list) : columns to drop
verbose (bool) : whether to print information about features and labels
Returns:
DataFrame, Series : features and labels
"""
if '*' in path:
feature_matrix = dd.read_csv(path, **kwargs).set_index(index, sorted=True)
labels = [label] + (drop or [])
y = feature_matrix[label].compute()
X = feature_matrix.drop(labels=labels, axis=1).compute()
else:
feature_matrix = pd.read_csv(path, index_col=index, **kwargs)
labels = [label] + (drop or [])
y = feature_matrix[label]
X = feature_matrix.drop(columns=labels)
if verbose:
# number of features
print(number_of_features(X.dtypes), end='\n\n')
# number of training examples
info = 'Number of training examples: {}'
print(info.format(len(X)), end='\n\n')
# label distribution
print(label_distribution(y))
return X, y
[docs]def split_data(X, y, regression=False, test_size=.2, random_state=None):
"""Splits data into train and test sets.
Args:
X (DataFrame) : features
y (Series) : labels
regression (bool): if true, do not use stratified split
test_size (float) : percent of train set to holdout for testing
random_state (int) : seed for the random number generator
Returns:
DataFrame, DataFrame, Series, Series : features and labels each split into train and test sets
"""
if regression:
CV_method = ShuffleSplit(n_splits=1,
test_size=test_size,
random_state=0)
else:
CV_method = StratifiedShuffleSplit(
n_splits=1,
test_size=test_size,
random_state=random_state)
train, test = next(CV_method.split(X, y))
X_train = X.iloc[train]
X_test = X.iloc[test]
y_train = y.iloc[train]
y_test = y.iloc[test]
return X_train, X_test, y_train, y_test
def number_of_features(dtypes):
dtype_to_vtype = {
'bool': 'Boolean',
'int32': 'Numeric',
'int64': 'Numeric',
'float64': 'Numeric',
'object': 'Categorical',
'datetime64[ns]': 'Datetime',
}
vtypes = dtypes.astype(str).map(dtype_to_vtype).value_counts()
return vtypes.sort_index().to_frame('Number of Features')
def label_distribution(labels):
distribution = labels.value_counts() / len(labels)
return distribution.mul(100).apply('{:.2f}%'.format).rename_axis('Labels')