import numpy as np
import pandas as pd
[docs]def sklearn_to_df(sklearn_dataset):
"""Converts (as well as it can) an sklearn dataset to a Pandas dataframe.
Args:
sklearn_dataset (sklearn.utils.Bunch): this parameter is usually the result
of using sklearn to quickly get a dataset, e.g. the object resulting
from calling sklearn.load_datasets.load_boston().
Returns:
Pandas dataframe df where df['target'] is the target variable in the original
dataset.
"""
df = pd.DataFrame(sklearn_dataset.data, columns=sklearn_dataset.feature_names)
df['target'] = pd.Series(sklearn_dataset.target)
return df
[docs]def add_intercept(X):
"""Prepends a column of 1s (an intercept column) to a a 2D numpy array.
Args:
X (numpy array): 2D numpy array.
Returns:
X with an appended column of 1s.
"""
# X has to be a 2D numpy array
# prepends intercept
intercept = np.ones(X.shape[0])
return np.c_[intercept, X]
[docs]def get_np_columns(df, columns, intercept=False):
"""Helper used to retreive columns as numpy array.
Args:
df (pandas dataframe): dataframe containing desired columns
columns (list of strings): list of names of desired columns.
Must be a list even if only 1
column is desired.
intercept (bool): set to True if You'd like resulting numpy array
to have a column of 1s appended to it.
Returns:
2D numpy array with columns of array consisting of feature vectors,
i.e. the first column of the result is a numpy vector of the first
column named in columns argument.
"""
# dataframe is a pandas datafram
# columns is a list of column names
# if intercept is true a column of 1s will be appended to the result matrix
# returns columns as float64 matrix
if columns == []:
return None
else:
res = np.expand_dims(a=df[columns[0]].to_numpy().astype('float64'), axis=1)
if len(columns) > 1:
for name in columns[1:]:
res = np.c_[res, np.expand_dims(a=df[name].to_numpy().astype('float64'), axis=1)]
if intercept:
res = add_intercept(res)
return res