scikit-learn

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

1 Installing the latest release
2 Data preparation
3 Intergration

Installing the latest release

Scikit-learn requires:
Python (>= 2.6 or >= 3.3),
NumPy (>= 1.6.1),
SciPy (>= 0.9).

To verify a certain package among your installed packages via Cmder on windows:

conda list | grep scipy
>> scipy    0.17.1   np111py27_1

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U scikit-learn

or conda:

conda install scikit-learn

Data preparation

1. Loading ready to use data from sklearn

from sklearn import datasets

# eg. Boston house price
boston = datasets.load_boston()
y = boston.target
X = bosston.data

2. Splitting your data

Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would have a perfect score but would fail to predict anything useful on yet-unseen data. This situation is called overfitting. To avoid it, it is common practice when performing a (supervised) machine learning experiment to hold out part of the available data as a test set X_test, y_test

sklearn.model_selection.train_test_split(*arrays, **options)

Parameters:
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_size : float, int, or None (default is None)
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25.

train_size : float, int, or None (default is None)
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_state : int or RandomState
Pseudo-random number generator state used for random sampling.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]
>>>
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]

3. features normalization or standardization

Intergration

from __future__ import print_function
import numpy as np
from scipy.integrate import simps
from numpy import trapz
# The y values.  A numpy array is used here,
# but a python list could also be used.
y = np.array([5, 20, 4, 18, 19, 18, 7, 4])
# Compute the area using the composite trapezoidal rule.
area = trapz(y, dx=5)
print("area =", area)
# Compute the area using the composite Simpson's rule.
area = simps(y, dx=5)
print("area =", area)
Output:
area = 452.5
area = 460.0

From http://stackoverflow.com/questions/13320262/calculating-the-area-under-a-curve-given-a-set-of-coordinates-without-knowing-t

Examples from scipy:

>>>
>>> np.trapz([1,2,3])
4.0
>>> np.trapz([1,2,3], x=[4,6,8])
8.0
>>> np.trapz([1,2,3], dx=2)
8.0
>>> a = np.arange(6).reshape(2, 3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.trapz(a, axis=0)
array([ 1.5,  2.5,  3.5])
>>> np.trapz(a, axis=1)
array([ 2.,  8.])

From https://docs.scipy.org/doc/numpy/reference/generated/numpy.trapz.html

Python scikit-learn ML