I called this notebook ubiquitous numpy as the main goal of this section is to show examples of how much is the impact of NumPy over the Scientific Python Ecosystem.
Later on, see also this extra notebook: Extra Torch Tensor - Requires PyTorch
Machine Learning is about building programs with tunable parameters (typically an array of floating point values) that are adjusted automatically so as to improve their behavior by adapting to previously seen data.
Machine Learning can be considered a subfield of Artificial Intelligence since those algorithms can be seen as building blocks to make computers learn to behave more intelligently by somehow generalizing rather that just storing and retrieving data items like a database system would do.
We'll take a look at a very simple machine learning tasks here: the clustering task
Data in machine learning algorithms, with very few exceptions, is assumed to be stored as a
two-dimensional array, of size [n_samples, n_features].
The arrays can be
either numpy arrays, or in some cases scipy.sparse matrices.
The size of the array is expected to be [n_samples, n_features]
n_samples: The number of samples: each sample is an item to process (e.g. classify). A sample can be a document, a picture, a sound, a video, an astronomical object, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits.
n_features: The number of features or distinct traits that can be used to describe each item in a quantitative manner. Features are generally real-valued, but may be boolean or discrete-valued in some cases.
The number of features must be fixed in advance. However it can be very high dimensional (e.g. millions of features) with most of them being zeros for a given sample.
This is a case where scipy.sparse matrices can be useful, in that they are much more memory-efficient than numpy arrays.
There is a dedicated notebook in the training material, explicitly dedicated to scipy.sparse: 07_1_Sparse_Matrices
In [1]:
from IPython.core.display import Image, display
display(Image(filename='images/iris_setosa.jpg'))
print("Iris Setosa\n")
display(Image(filename='images/iris_versicolor.jpg'))
print("Iris Versicolor\n")
display(Image(filename='images/iris_virginica.jpg'))
print("Iris Virginica")
Features in the Iris dataset:
Target classes to predict:
In [2]:
from sklearn.datasets import load_iris
iris = load_iris()
Try by yourself one of the following commands where 'd' is the variable containing the dataset:
print(iris.keys()) # Structure of the contained data
print(iris.DESCR) # A complete description of the dataset
print(iris.data.shape) # [n_samples, n_features]
print(iris.target.shape) # [n_samples,]
print(iris.feature_names)
datasets.get_data_home() # This is where the datasets are stored
In [3]:
print(iris.keys())
In [4]:
print(iris.DESCR)
In [5]:
print(type(iris.data))
In [6]:
X = iris.data
print(X.size, X.shape)
In [7]:
y = iris.target
type(y)
Out[7]:
In [8]:
from sklearn.cluster import KMeans
In [9]:
kmean = KMeans(n_clusters=3)
kmean.fit(iris.data)
kmean.cluster_centers_
Out[9]:
In [10]:
kmean.cluster_centers_.shape
Out[10]:
In [11]:
from itertools import combinations
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
In [12]:
rgb = np.empty(shape=y.shape, dtype='<U1')
rgb[y==0] = 'r'
rgb[y==1] = 'g'
rgb[y==2] = 'b'
In [13]:
for cols in combinations(range(4), 2):
f, ax = plt.subplots(figsize=(7.5, 7.5))
ax.scatter(X[:, cols[0]], X[:, cols[1]], c=rgb)
ax.scatter(kmean.cluster_centers_[:, cols[0]],
kmean.cluster_centers_[:, cols[1]], marker='*', s=250,
color='black', label='Centers')
feature_x = iris.feature_names[cols[0]]
feature_y = iris.feature_names[cols[1]]
ax.set_title("Features: {} vs {}".format(feature_x.title(),
feature_y.title()))
ax.set_xlabel(feature_x)
ax.set_ylabel(feature_y)
ax.legend(loc='best')
plt.show()