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
- Preprocessing
- Text Utilities
- Pandas Utilities
- File IO Utilities
- Scikit-learn Utilities
- Math Utilities
- Matplotlib Utilities
- Installation
- Changelog
A collection of different functions for various data preprocessing procedures.
The preprocessing utilities can be imported via
from mxtend.preprocessing import ...
### MeanCenterer
A transformer class that performs column-based mean centering on a NumPy array.
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.
"""
Note that using generalize_names with few firstname_output_letters can result in duplicate entries. E.g., if your dataset contains the names "Adam Johnson" and "Andrew Johnson", the default setting (i.e., 1 first name letter) will produce the generalized name "johnson a" in both cases.
One solution is to increase the number of first name letters in the output by setting the parameter firstname_output_letters to a value larger than 1.
An alternative solution is to use the generalize_names_duplcheck function if you are working with pandas DataFrames.
The generalize_names_duplcheck function can be imported via
from mlxtend.text import generalize_names_duplcheck
By default, generalize_names_duplcheck will apply generalize_names to a pandas DataFrame column with the minimum number of first name letters and append as many first name letters as necessary until no duplicates are present in the given DataFrame column. An example dataset column that contains the names
Reading in a CSV file that has column Name for which we want to generalize the names:
- Samuel Eto'o
- Adam Johnson
- Andrew Johnson
df = pd.read_csv(path)
Applying generalize_names_duplcheck to generate a new DataFrame with the generalized names without duplicates:
df_new = generalize_names_duplcheck(df=df, col_name='Name')
- etoo s
- johnson ad
- johnson an
The pandas utilities can be imported via
from mxtend.pandas import ...
### Minmax Scaling
A function that applies minmax scaling to pandas DataFrame columns.
from mlxtend.pandas import minmax_scaling
def minmax_scaling(df, columns, min_val=0, max_val=1):
"""
Min max scaling for pandas DataFrames
Parameters
----------
df : pandas DataFrame object.
columns : array-like, shape = [n_columns]
Array-like with pandas DataFrame column names, e.g., ['col1', 'col2', ...]
min_val : `int` or `float`, optional (default=`0`)
minimum value after rescaling.
min_val : `int` or `float`, optional (default=`1`)
maximum value after rescaling.
Returns
----------
df_new: pandas DataFrame object.
Copy of the DataFrame with rescaled columns.
"""
The file_io utilities can be imported via
from mxtend.file_io import ...
### Find Files
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']
def find_files(substring, path, recursive=False, check_ext=None, ignore_invisible=True):
"""
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.
recursive: `bool`, optional, (default=`False`)
If true, searches subdirectories recursively.
check_ext: `str`, optional, (default=`None`)
If string (e.g., '.txt'), only returns files that
match the specified file extension.
ignore_invisible : `bool`, optional, (default=`True`)
If `True`, ignores invisible files (i.e., files starting with a period).
Returns
----------
results : `list`
List of the matched files.
"""
A function that finds files that belong together (i.e., differ only by file extension) in different directories and collects them in a Python dictionary for further processing tasks.
d1 = os.path.join(master_path, 'dir_1')
d2 = os.path.join(master_path, 'dir_2')
d3 = os.path.join(master_path, 'dir_3')
find_filegroups(paths=[d1,d2,d3], substring='file_1')
# Returns:
# {'file_1': ['/Users/sebastian/github/mlxtend/tests/data/find_filegroups/dir_1/file_1.log',
# '/Users/sebastian/github/mlxtend/tests/data/find_filegroups/dir_2/file_1.csv',
# '/Users/sebastian/github/mlxtend/tests/data/find_filegroups/dir_3/file_1.txt']}
#
# Note: Setting `substring=''` would return a
# dictionary of all file paths for
# file_1.*, file_2.*, file_3.*
def find_filegroups(paths, substring='', extensions=None, validity_check=True, ignore_invisible=True):
"""
Function that finds and groups files from different directories in a python dictionary.
Parameters
----------
paths : `list`
Paths of the directories to be searched. Dictionary keys are build from
the first directory.
substring : `str`, optional, (default=`''`)
Substring that all files have to contain to be considered.
extensions : `list`, optional, (default=`None`)
`None` or `list` of allowed file extensions for each path. If provided, the number
of extensions must match the number of `paths`.
validity_check : `bool`, optional, (default=`True`)
If `True`, checks if all dictionary values have the same number of file paths. Prints
a warning and returns an empty dictionary if the validity check failed.
ignore_invisible : `bool`, optional, (default=`True`)
If `True`, ignores invisible files (i.e., files starting with a period).
Returns
----------
groups : `dict`
Dictionary of files paths. Keys are the file names found in the first directory listed
in `paths` (without file extension).
"""
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.
- An example usage of the
ColumnSelectorused 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]))
And ensemble classifier that predicts class labels based on a majority voting rule (hard voting) or average predicted probabilities (soft voting).
Decision regions plotted for 4 different classifiers:
Please see the IPython Notebook for a detailed explanation and examples.
Input:
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
import numpy as np
from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target
np.random.seed(123)
################################
# Initialize classifiers
################################
clf1 = LogisticRegression()
clf2 = RandomForestClassifier()
clf3 = GaussianNB()
################################
# Initialize EnsembleClassifier
################################
# hard voting
eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='hard')
# soft voting (uniform weights)
# eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='soft')
# soft voting with different weights
# eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='soft', weights=[1,2,10])
################################
# 5-fold Cross-Validation
################################
for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']):
scores = cross_validation.cross_val_score(clf, X, y, cv=5, scoring='accuracy')
print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))
Output:
Accuracy: 0.90 (+/- 0.05) [Logistic Regression]
Accuracy: 0.92 (+/- 0.05) [Random Forest]
Accuracy: 0.91 (+/- 0.04) [naive Bayes]
Accuracy: 0.95 (+/- 0.05) [Ensemble]
The following example illustrates how the weighting of different classifiers affects the calculated average probability:
## Math Utilities
The math utilities can be imported via
from mxtend.math import ...
### Combinations and Permutations
Functions to calculate the number of combinations and permutations for creating subsequences of r elements out of a sequence with n elements.
In:
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)
Out:
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.
def num_combinations(n, r, with_replacement=False):
"""
Function to calculate the number of possible combinations.
Parameters
----------
n : `int`
Total number of items.
r : `int`
Number of elements of the target itemset.
with_replacement : `bool`, optional, (default=False)
Allows repeated elements if True.
Returns
----------
comb : `int`
Number of possible combinations.
"""
def num_permutations(n, r, with_replacement=False):
"""
Function to calculate the number of possible permutations.
Parameters
----------
n : `int`
Total number of items.
r : `int`
Number of elements of the target itemset.
with_replacement : `bool`, optional, (default=False)
Allows repeated elements if True.
Returns
----------
permut : `int`
Number of possible permutations.
"""
## Matplotlib Utilities
The matplotlib utilities can be imported via
from mxtend.matplotlib import ...
### Plotting Decision Regions
A function plot decision regions of classifiers. Import plot_decision_regions via
from mlxtend.matplotlib import plot_decision_regions
For more examples, please see this IPython Notebook.
from mlxtend.matplotlib import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X,y)
# Plotting decision regions
plot_decision_regions(X, y, clf=svm, res=0.02)
# Adding axes annotations
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()
##### Default Parameters
def plot_decision_regions(X, y, clf, res=0.02, cmap=None, edgecolors=None):
"""
Plots decision regions of a classifier.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Feature Matrix.
y : array-like, shape = [n_samples]
True class labels.
clf : Classifier object. Must have a .predict method.
res : float (default: 0.02)
Grid width. Lower values increase the resolution but
slow down the plotting.
cmap : Custom colormap object.
Uses matplotlib.cm.rainbow if None.
Returns
---------
None
### Removing Borders
A function to remove borders from matplotlib plots. Import remove_borders via
from mlxtend.matplotlib import remove_borders
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.
"""
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