basic.ipynb

From preamble import *
%matplotlib inline
Introduction
Why Machine Learning?
Problems Machine Learning Can Solve
Knowing Your Task and Knowing Your Data
Why Python?
scikit-learn
Installing scikit-learn
Essential Libraries and Tools
Jupyter Notebook
NumPy
In [2]:
import numpy as np

x = np.array([[1, 2, 3], [4, 5, 6]])
print("x:\n{}".format(x))
x:
[[1 2 3]
 [4 5 6]]
SciPy
In [3]:
from scipy import sparse

# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else
eye = np.eye(4)
print("NumPy array:\n", eye)
NumPy array:
 [[1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [0. 0. 1. 0.]
 [0. 0. 0. 1.]]
In [4]:
# Convert the NumPy array to a SciPy sparse matrix in CSR format
# Only the nonzero entries are stored
sparse_matrix = sparse.csr_matrix(eye)
print("\nSciPy sparse CSR matrix:\n", sparse_matrix)
SciPy sparse CSR matrix:
   (0, 0)	1.0
  (1, 1)	1.0
  (2, 2)	1.0
  (3, 3)	1.0
In [5]:
data = np.ones(4)
row_indices = np.arange(4)
col_indices = np.arange(4)
eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))
print("COO representation:\n", eye_coo)
COO representation:   (0, 0)	1.0
  (1, 1)	1.0
  (2, 2)	1.0
  (3, 3)	1.0
matplotlib
In [6]:
%matplotlib inline
import matplotlib.pyplot as plt

# Generate a sequence of numbers from -10 to 10 with 100 steps in between
x = np.linspace(-10, 10, 100)
# Create a second array using sine
y = np.sin(x)
# The plot function makes a line chart of one array against another
plt.plot(x, y, marker="x")
Out[6]:
[<matplotlib.lines.Line2D at 0x7f1d73e83ba8>]

pandas
In [7]:
import pandas as pd

# create a simple dataset of people
data = {'Name': ["John", "Anna", "Peter", "Linda"],
        'Location' : ["New York", "Paris", "Berlin", "London"],
        'Age' : [24, 13, 53, 33]
       }

data_pandas = pd.DataFrame(data)
# IPython.display allows "pretty printing" of dataframes
# in the Jupyter notebook
display(data_pandas)
Name	Location	Age
0	John	New York	24
1	Anna	Paris	13
2	Peter	Berlin	53
3	Linda	London	33
In [8]:
# Select all rows that have an age column greater than 30
display(data_pandas[data_pandas.Age > 30])
Name	Location	Age
2	Peter	Berlin	53
3	Linda	London	33
mglearn
Python 2 versus Python 3
Versions Used in this Book
In [9]:
import sys
print("Python version:", sys.version)

import pandas as pd
print("pandas version:", pd.__version__)

import matplotlib
print("matplotlib version:", matplotlib.__version__)

import numpy as np
print("NumPy version:", np.__version__)

import scipy as sp
print("SciPy version:", sp.__version__)

import IPython
print("IPython version:", IPython.__version__)

import sklearn
print("scikit-learn version:", sklearn.__version__)
Python version: 3.7.0 (default, Jun 28 2018, 13:15:42) 
[GCC 7.2.0]
pandas version: 0.23.4
matplotlib version: 3.0.0
NumPy version: 1.15.2
SciPy version: 1.1.0
IPython version: 6.4.0
scikit-learn version: 0.21.dev0
A First Application: Classifying Iris Species
sepal_petal
Meet the Data
In [10]:
from sklearn.datasets import load_iris
iris_dataset = load_iris()
In [11]:
print("Keys of iris_dataset:\n", iris_dataset.keys())
Keys of iris_dataset:
 dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])
In [12]:
print(iris_dataset['DESCR'][:193] + "\n...")
.. _iris_dataset:

Iris plants dataset
--------------------

**Data Set Characteristics:**

    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, pre
...
In [13]:
print("Target names:", iris_dataset['target_names'])
Target names: ['setosa' 'versicolor' 'virginica']
In [14]:
print("Feature names:\n", iris_dataset['feature_names'])
Feature names:
 ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
In [15]:
print("Type of data:", type(iris_dataset['data']))
Type of data: <class 'numpy.ndarray'>
In [16]:
print("Shape of data:", iris_dataset['data'].shape)
Shape of data: (150, 4)
In [17]:
print("First five rows of data:\n", iris_dataset['data'][:5])
First five rows of data:
 [[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]]
In [18]:
print("Type of target:", type(iris_dataset['target']))
Type of target: <class 'numpy.ndarray'>
In [19]:
print("Shape of target:", iris_dataset['target'].shape)
Shape of target: (150,)
In [20]:
print("Target:\n", iris_dataset['target'])
Target:
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]
Measuring Success: Training and Testing Data
In [21]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    iris_dataset['data'], iris_dataset['target'], random_state=0)
In [22]:
print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)
X_train shape: (112, 4)
y_train shape: (112,)
In [23]:
print("X_test shape:", X_test.shape)
print("y_test shape:", y_test.shape)
X_test shape: (38, 4)
y_test shape: (38,)
First Things First: Look at Your Data
In [24]:
# create dataframe from data in X_train
# label the columns using the strings in iris_dataset.feature_names
iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)
# create a scatter matrix from the dataframe, color by y_train
pd.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15),
                           marker='o', hist_kwds={'bins': 20}, s=60,
                           alpha=.8, cmap=mglearn.cm3)
Out[24]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9a3ef0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9520f0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9794e0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c91f978>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8c7e48>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8f8320>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8a27f0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c84acf8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c84ad30>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c822710>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7cec18>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7fe160>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7a5668>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c74db70>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c77f0b8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7265c0>]],
      dtype=object)

Building Your First Model: k-Nearest Neighbors
In [25]:
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
In [26]:
knn.fit(X_train, y_train)
Out[26]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=None, n_neighbors=1, p=2,
           weights='uniform')
Making Predictions
In [27]:
X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape:", X_new.shape)
X_new.shape: (1, 4)
In [28]:
prediction = knn.predict(X_new)
print("Prediction:", prediction)
print("Predicted target name:",
       iris_dataset['target_names'][prediction])
Prediction: [0]
Predicted target name: ['setosa']
Evaluating the Model
In [29]:
y_pred = knn.predict(X_test)
print("Test set predictions:\n", y_pred)
Test set predictions:
 [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
 2]
In [30]:
print("Test set score: {:.2f}".format(np.mean(y_pred == y_test)))
Test set score: 0.97
In [31]:
print("Test set score: {:.2f}".format(knn.score(X_test, y_test)))
Test set score: 0.97
Summary and Outlook
In [32]:
X_train, X_test, y_train, y_test = train_test_split(
    iris_dataset['data'], iris_dataset['target'], random_state=0)

knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

print("Test set score: {:.2f}".format(knn.score(X_test, y_test)))
Test set score: 0.97