Copyright (c) 2015-2017 Sebastian Raschka
Python Machine Learning - Code Examples¶
Chapter 4 - Building Good Training Sets – Data Pre-Processing¶
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -p numpy,pandas,matplotlib,sklearn
The use of watermark is optional. You can install this IPython extension via "pip install watermark". For more information, please see: https://github.com/rasbt/watermark.
Overview¶
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from IPython.display import Image
%matplotlib inline
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# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
Dealing with missing data¶
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import pandas as pd
from io import StringIO
csv_data = '''A,B,C,D
1.0,2.0,3.0,4.0
5.0,6.0,,8.0
10.0,11.0,12.0,'''
# If you are using Python 2.7, you need
# to convert the string to unicode:
# csv_data = unicode(csv_data)
df = pd.read_csv(StringIO(csv_data))
df
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df.isnull().sum()
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Eliminating samples or features with missing values¶
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df.dropna()
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df.dropna(axis=1)
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# only drop rows where all columns are NaN
df.dropna(how='all')
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# drop rows that have not at least 4 non-NaN values
df.dropna(thresh=4)
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# only drop rows where NaN appear in specific columns (here: 'C')
df.dropna(subset=['C'])
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Imputing missing values¶
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from sklearn.preprocessing import Imputer
imr = Imputer(missing_values='NaN', strategy='mean', axis=0)
imr = imr.fit(df)
imputed_data = imr.transform(df.values)
imputed_data
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df.values
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Understanding the scikit-learn estimator API¶
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Image(filename='./images/04_04.png', width=400)
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Image(filename='./images/04_05.png', width=400)
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Handling categorical data¶
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import pandas as pd
df = pd.DataFrame([['green', 'M', 10.1, 'class1'],
['red', 'L', 13.5, 'class2'],
['blue', 'XL', 15.3, 'class1']])
df.columns = ['color', 'size', 'price', 'classlabel']
df
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Mapping ordinal features¶
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size_mapping = {'XL': 3,
'L': 2,
'M': 1}
df['size'] = df['size'].map(size_mapping)
df
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inv_size_mapping = {v: k for k, v in size_mapping.items()}
df['size'].map(inv_size_mapping)
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Encoding class labels¶
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import numpy as np
class_mapping = {label: idx for idx, label in enumerate(np.unique(df['classlabel']))}
class_mapping
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df['classlabel'] = df['classlabel'].map(class_mapping)
df
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inv_class_mapping = {v: k for k, v in class_mapping.items()}
df['classlabel'] = df['classlabel'].map(inv_class_mapping)
df
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from sklearn.preprocessing import LabelEncoder
class_le = LabelEncoder()
y = class_le.fit_transform(df['classlabel'].values)
y
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class_le.inverse_transform(y)
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Performing one-hot encoding on nominal features¶
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X = df[['color', 'size', 'price']].values
color_le = LabelEncoder()
X[:, 0] = color_le.fit_transform(X[:, 0])
X
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from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(categorical_features=[0])
ohe.fit_transform(X).toarray()
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pd.get_dummies(df[['price', 'color', 'size']])
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Partitioning a dataset in training and test sets¶
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df_wine = pd.read_csv('https://archive.ics.uci.edu/'
'ml/machine-learning-databases/wine/wine.data',
header=None)
df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
'Alcalinity of ash', 'Magnesium', 'Total phenols',
'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
'Color intensity', 'Hue', 'OD280/OD315 of diluted wines',
'Proline']
print('Class labels', np.unique(df_wine['Class label']))
df_wine.head()
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Note:¶
If the link to the Wine dataset provided above does not work for you, you can find a local copy in this repository at ./../datasets/wine/wine.data.
Or you could fetch it via
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df_wine = pd.read_csv('https://raw.githubusercontent.com/rasbt/python-machine-learning-book/master/code/datasets/wine/wine.data', header=None)
df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash',
'Alcalinity of ash', 'Magnesium', 'Total phenols',
'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins',
'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']
df_wine.head()
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if Version(sklearn_version) < '0.18':
from sklearn.cross_validation import train_test_split
else:
from sklearn.model_selection import train_test_split
X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.3, random_state=0)
Bringing features onto the same scale¶
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from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)
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from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)
A visual example:
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ex = pd.DataFrame([0, 1, 2, 3, 4, 5])
# standardize
ex[1] = (ex[0] - ex[0].mean()) / ex[0].std(ddof=0)
# Please note that pandas uses ddof=1 (sample standard deviation)
# by default, whereas NumPy's std method and the StandardScaler
# uses ddof=0 (population standard deviation)
# normalize
ex[2] = (ex[0] - ex[0].min()) / (ex[0].max() - ex[0].min())
ex.columns = ['input', 'standardized', 'normalized']
ex
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Selecting meaningful features¶
...
Sparse solutions with L1-regularization¶
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Image(filename='./images/04_12.png', width=500)
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Image(filename='./images/04_13.png', width=500)
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