import pandas as pd
from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit
[docs]def load_data(path, index, label, n_rows=None, drop=None, verbose=True, **kwargs):
"""Load features and labels from file.
Arguments:
path (str): Path to file or a http/ftp/s3 URL
index (str): Column for index
label (str): Column for labels
n_rows (int): Number of rows to return
drop (list): List of columns to drop
verbose (bool): If True, prints information about features and labels
Returns:
pd.DataFrame, pd.Series: features and labels
"""
feature_matrix = pd.read_csv(path, index_col=index, nrows=n_rows, **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 total training examples
info = 'Number of training examples: {}'
print(info.format(len(X)), end='\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.
Arguments:
X (pd.DataFrame or np.array): data of shape [n_samples, n_features]
y (pd.Series): labels of length [n_samples]
regression (bool): if true, do not use stratified split
test_size (float): percent of train set to holdout for testing
random_state (int, np.random.RandomState): seed for the random number generator
Returns:
pd.DataFrame, pd.DataFrame, pd.Series, pd.Series: features and labels each split into train and test sets
"""
if not isinstance(X, pd.DataFrame):
X = pd.DataFrame(X)
if not isinstance(y, pd.Series):
y = pd.Series(y)
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
[docs]def number_of_features(dtypes):
"""Get the number of features for specific dtypes.
Arguments:
dtypes (pd.Series): dtypes to get the number of features for
Returns:
pd.Series: dtypes and the number of features for each input type
"""
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')
[docs]def label_distribution(labels):
"""Get the label distributions.
Arguments:
labels (pd.Series): Label values
Returns:
pd.Series: Label values and their frequency distribution as percentages.
"""
distribution = labels.value_counts() / len(labels)
return distribution.mul(100).apply('{:.2f}%'.format).rename_axis('Labels')
[docs]def drop_nan_target_rows(X, y):
"""Drops rows in X and y when row in the target y has a value of NaN.
Arguments:
X (pd.DataFrame): Data to transform
y (pd.Series): Target values
Returns:
pd.DataFrame: Transformed X (and y, if passed in) with rows that had a NaN value removed.
"""
X_t = X
y_t = y
if not isinstance(X_t, pd.DataFrame):
X_t = pd.DataFrame(X_t)
if not isinstance(y_t, pd.Series):
y_t = pd.Series(y_t)
# drop rows where corresponding y is NaN
y_null_indices = y_t.index[y_t.isna()]
X_t = X_t.drop(index=y_null_indices)
y_t.dropna(inplace=True)
return X_t, y_t