A library of Python tools and extensions for data science.
Link to the mlxtend repository on GitHub: https://github.com/rasbt/mlxtend.
Sebastian Raschka 2014
A collection of different functions for various data preprocessing procedures.
The preprocessing utilities can be imported via
from mxtend.preprocessing import ...
class MeanCenterer(TransformerObj):
"""
Class for column centering of vectors and matrices.
Keyword arguments:
X: NumPy array object where each attribute/variable is
stored in an individual column.
Also accepts 1-dimensional Python list objects.
Class methods:
fit: Fits column means to MeanCenterer object.
transform: Uses column means from `fit` for mean centering.
fit_transform: Fits column means and performs mean centering.
The class methods `transform` and `fit_transform` return a new numpy array
object where the attributes are centered at the column means.
"""
Examples:
Use the fit method to fit the column means of a dataset (e.g., the training dataset) to a new MeanCenterer object. Then, call the transform method on the same dataset to center it at the sample mean.
>>> X_train
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> mc = MeanCenterer().fit(X_train)
>>> mc.transform(X_train)
array([[-3, -3, -3],
[ 0, 0, 0],
[ 3, 3, 3]])
To use the same parameters that were used to center the training dataset, simply call the transform method of the MeanCenterer instance on a new dataset (e.g., test dataset).
>>> X_test
array([[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
>>> mc.transform(X_test)
array([[-3, -4, -5],
[-3, -4, -5],
[-3, -4, -5]])
The MeanCenterer also supports Python list objects, and the fit_transform method allows you to directly fit and center the dataset.
>>> Z
[1, 2, 3]
>>> MeanCenterer().fit_transform(Z)
array([-1, 0, 1])
import matplotlib.pyplot as plt
import numpy as np
X = 2 * np.random.randn(100,2) + 5
plt.scatter(X[:,0], X[:,1])
plt.grid()
plt.title('Random Gaussian data w. mean=5, sigma=2')
plt.show()
Y = MeanCenterer.fit_transform(X)
plt.scatter(Y[:,0], Y[:,1])
plt.grid()
plt.title('Data after mean centering')
plt.show()
The text utilities can be imported via
from mxtend.text import ...
A function that converts a name into a general format <last_name><separator><firstname letter(s)> (all lowercase), which is useful if data is collected from different sources and is supposed to be compared or merged based on name identifiers. E.g., if names are stored in a pandas DataFrame column, the apply function can be used to generalize names: df['name'] = df['name'].apply(generalize_names)
from mlxtend.text import generalize_names
# defaults
>>> generalize_names('Pozo, José Ángel')
'pozo j'
>>> generalize_names('Pozo, José Ángel')
'pozo j'
>>> assert(generalize_names('José Ángel Pozo')
'pozo j'
>>> generalize_names('José Pozo')
'pozo j'
# optional parameters
>>> generalize_names("Eto'o, Samuel", firstname_output_letters=2)
'etoo sa'
>>> generalize_names("Eto'o, Samuel", firstname_output_letters=0)
'etoo'
>>> generalize_names("Eto'o, Samuel", output_sep=', ')
'etoo, s'
def generalize_names(name, output_sep=' ', firstname_output_letters=1):
"""
Function that outputs a person's name in the format
<last_name><separator><firstname letter(s)> (all lowercase)
Parameters
----------
name : `str`
Name of the player
output_sep : `str` (default: ' ')
String for separating last name and first name in the output.
firstname_output_letters : `int`
Number of letters in the abbreviated first name.
Returns
----------
gen_name : `str`
The generalized name.
"""
The file_io utilities can be imported via
from mxtend.file_io import ...
A function that finds files in a given directory based on substring matches and returns a list of the file names found.
from mlxtend.file_io import find_files
>>> find_files('mlxtend', '/Users/sebastian/Desktop')
['/Users/sebastian/Desktop/mlxtend-0.1.6.tar.gz',
'/Users/sebastian/Desktop/mlxtend-0.1.7.tar.gz']
"""
Function that finds files in a directory based on substring matching.
Parameters
----------
substring : `str`
Substring of the file to be matched.
path : `str`
Path where to look.
Returns
----------
results : `list`
List of the matched files.
"""
The scikit-learn utilities can be imported via
from mxtend.scikit-learn import ...
A feature selector for scikit-learn's Pipeline class that returns specified columns from a NumPy array; extremely useful in combination with scikit-learn's Pipeline in cross-validation.
ColumnSelector used in a pipeline for cross-validation on the Iris dataset.Example in Pipeline:
from mlxtend.sklearn import ColumnSelector
from sklearn.pipeline import Pipeline
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
clf_2col = Pipeline(steps=[
('scaler', StandardScaler()),
('reduce_dim', ColumnSelector(cols=(1,3))), # extracts column 2 and 4
('classifier', GaussianNB())
])
ColumnSelector has a transform method that is used to select and return columns (features) from a NumPy array so that it can be used in the Pipeline like other transformation classes.
### original data
print('First 3 rows before:\n', X_train[:3,:])
First 3 rows before:
[[ 4.5 2.3 1.3 0.3]
[ 6.7 3.3 5.7 2.1]
[ 5.7 3. 4.2 1.2]]
### after selection
cols = ColumnExtractor(cols=(1,3)).transform(X_train)
print('First 3 rows:\n', cols[:3,:])
First 3 rows:
[[ 2.3 0.3]
[ 3.3 2.1]
[ 3. 1.2]]
A simple transformer that converts a sparse into a dense numpy array, e.g., required for scikit-learn's Pipeline when e.g,. CountVectorizers are used in combination with RandomForests.
Example in Pipeline:
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from mlxtend.sklearn import DenseTransformer
pipe_1 = Pipeline([
('vect', CountVectorizer(analyzer='word',
decode_error='replace',
preprocessor=lambda text: re.sub('[^a-zA-Z]', ' ', text.lower()),
stop_words=stopwords,) ),
('to_dense', DenseTransformer()),
('clf', RandomForestClassifier())
])
parameters_1 = dict(
clf__n_estimators=[50, 100, 200],
clf__max_features=['sqrt', 'log2', None],)
grid_search_1 = GridSearchCV(pipe_1,
parameters_1,
n_jobs=1,
verbose=1,
scoring=f1_scorer,
cv=10)
print("Performing grid search...")
print("pipeline:", [name for name, _ in pipe_1.steps])
print("parameters:")
grid_search_1.fit(X_train, y_train)
print("Best score: %0.3f" % grid_search_1.best_score_)
print("Best parameters set:")
best_parameters_1 = grid_search_1.best_estimator_.get_params()
for param_name in sorted(parameters_1.keys()):
print("\t%s: %r" % (param_name, best_parameters_1[param_name]))
The math utilities can be imported via
from mxtend.math import ...
Functions to calculate the number of combinations and permutations for creating subsequences of r elements out of a sequence with n elements.
from mlxtend.math import num_combinations
from mlxtend.math import num_permutations
c = num_combinations(n=20, r=8, with_replacement=False)
print('Number of ways to combine 20 elements into 8 subelements: %d' % c)
d = num_permutations(n=20, r=8, with_replacement=False)
print('Number of ways to permute 20 elements into 8 subelements: %d' % d)
Output:
Number of ways to combine 20 elements into 8 subelements: 125970
Number of ways to permute 20 elements into 8 subelements: 5079110400
This is especially useful in combination with itertools, e.g., in order to estimate the progress via pyprind.
The matplotlib utilities can be imported via
from mxtend.matplotlib import ...
A function to remove borders from matplotlib plots.
def remove_borders(axes, left=False, bottom=False, right=True, top=True):
"""
A function to remove chartchunk from matplotlib plots, such as axes
spines, ticks, and labels.
Keyword arguments:
axes: An iterable containing plt.gca() or plt.subplot() objects, e.g. [plt.gca()].
left, bottom, right, top: Boolean to specify which plot axes to hide.
"""
Example
You can use the following command to install mlxtend:
pip install mlxtend
or
easy_install mlxtend
Alternatively, you download the package manually from the Python Package Index https://pypi.python.org/pypi/mlxtend, unzip it, navigate into the package, and use the command:
python setup.py install