01-introduction.ipynb

Kernel: Python [conda env:py37]

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

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

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.]]

# 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

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

%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")

[<matplotlib.lines.Line2D at 0x7f1d73e83ba8>]

pandas

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

# 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

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

Meet the Data

from sklearn.datasets import load_iris
iris_dataset = load_iris()

print("Keys of iris_dataset:\n", iris_dataset.keys())

Keys of iris_dataset:
 dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])

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
...

print("Target names:", iris_dataset['target_names'])

Target names: ['setosa' 'versicolor' 'virginica']

print("Feature names:\n", iris_dataset['feature_names'])

Feature names:
 ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

print("Type of data:", type(iris_dataset['data']))

Type of data: <class 'numpy.ndarray'>

print("Shape of data:", iris_dataset['data'].shape)

Shape of data: (150, 4)

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]]

print("Type of target:", type(iris_dataset['target']))

Type of target: <class 'numpy.ndarray'>

print("Shape of target:", iris_dataset['target'].shape)

Shape of target: (150,)

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

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)

print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)

X_train shape: (112, 4)
y_train shape: (112,)

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

# 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)

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

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)

knn.fit(X_train, y_train)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=None, n_neighbors=1, p=2,
           weights='uniform')

Making Predictions

X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape:", X_new.shape)

X_new.shape: (1, 4)

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

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]

print("Test set score: {:.2f}".format(np.mean(y_pred == y_test)))

Test set score: 0.97

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

Test set score: 0.97

Summary and Outlook

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