1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
"""
====================================================
How to use ``sampling_strategy`` in imbalanced-learn
====================================================
This example shows the different usage of the parameter ``sampling_strategy``
for the different family of samplers (i.e. over-sampling, under-sampling. or
cleaning methods).
"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
# %%
print(__doc__)
import seaborn as sns
sns.set_context("poster")
# %% [markdown]
# Create an imbalanced dataset
# ----------------------------
#
# First, we will create an imbalanced data set from a the iris data set.
# %%
from sklearn.datasets import load_iris
from imblearn.datasets import make_imbalance
iris = load_iris(as_frame=True)
sampling_strategy = {0: 10, 1: 20, 2: 47}
X, y = make_imbalance(iris.data, iris.target, sampling_strategy=sampling_strategy)
# %%
import matplotlib.pyplot as plt
fig, axs = plt.subplots(ncols=2, figsize=(10, 5))
autopct = "%.2f"
iris.target.value_counts().plot.pie(autopct=autopct, ax=axs[0])
axs[0].set_title("Original")
y.value_counts().plot.pie(autopct=autopct, ax=axs[1])
axs[1].set_title("Imbalanced")
fig.tight_layout()
# %% [markdown]
# Using ``sampling_strategy`` in resampling algorithms
# ====================================================
#
# `sampling_strategy` as a `float`
# --------------------------------
#
# `sampling_strategy` can be given a `float`. For **under-sampling
# methods**, it corresponds to the ratio :math:`\alpha_{us}` defined by
# :math:`N_{rM} = \alpha_{us} \times N_{m}` where :math:`N_{rM}` and
# :math:`N_{m}` are the number of samples in the majority class after
# resampling and the number of samples in the minority class, respectively.
# %%
# select only 2 classes since the ratio make sense in this case
binary_mask = y.isin([0, 1])
binary_y = y[binary_mask]
binary_X = X[binary_mask]
# %%
from imblearn.under_sampling import RandomUnderSampler
sampling_strategy = 0.8
rus = RandomUnderSampler(sampling_strategy=sampling_strategy)
X_res, y_res = rus.fit_resample(binary_X, binary_y)
ax = y_res.value_counts().plot.pie(autopct=autopct)
_ = ax.set_title("Under-sampling")
# %% [markdown]
# For **over-sampling methods**, it correspond to the ratio
# :math:`\alpha_{os}` defined by :math:`N_{rm} = \alpha_{os} \times N_{M}`
# where :math:`N_{rm}` and :math:`N_{M}` are the number of samples in the
# minority class after resampling and the number of samples in the majority
# class, respectively.
# %%
from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(sampling_strategy=sampling_strategy)
X_res, y_res = ros.fit_resample(binary_X, binary_y)
ax = y_res.value_counts().plot.pie(autopct=autopct)
_ = ax.set_title("Over-sampling")
# %% [markdown]
# `sampling_strategy` as a `str`
# -------------------------------
#
# `sampling_strategy` can be given as a string which specify the class
# targeted by the resampling. With under- and over-sampling, the number of
# samples will be equalized.
#
# Note that we are using multiple classes from now on.
# %%
sampling_strategy = "not minority"
fig, axs = plt.subplots(ncols=2, figsize=(10, 5))
rus = RandomUnderSampler(sampling_strategy=sampling_strategy)
X_res, y_res = rus.fit_resample(X, y)
y_res.value_counts().plot.pie(autopct=autopct, ax=axs[0])
axs[0].set_title("Under-sampling")
sampling_strategy = "not majority"
ros = RandomOverSampler(sampling_strategy=sampling_strategy)
X_res, y_res = ros.fit_resample(X, y)
y_res.value_counts().plot.pie(autopct=autopct, ax=axs[1])
_ = axs[1].set_title("Over-sampling")
# %% [markdown]
# With **cleaning method**, the number of samples in each class will not be
# equalized even if targeted.
# %%
from imblearn.under_sampling import TomekLinks
sampling_strategy = "not minority"
tl = TomekLinks(sampling_strategy=sampling_strategy)
X_res, y_res = tl.fit_resample(X, y)
ax = y_res.value_counts().plot.pie(autopct=autopct)
_ = ax.set_title("Cleaning")
# %% [markdown]
# `sampling_strategy` as a `dict`
# -------------------------------
#
# When `sampling_strategy` is a `dict`, the keys correspond to the targeted
# classes. The values correspond to the desired number of samples for each
# targeted class. This is working for both **under- and over-sampling**
# algorithms but not for the **cleaning algorithms**. Use a `list` instead.
# %%
fig, axs = plt.subplots(ncols=2, figsize=(10, 5))
sampling_strategy = {0: 10, 1: 15, 2: 20}
rus = RandomUnderSampler(sampling_strategy=sampling_strategy)
X_res, y_res = rus.fit_resample(X, y)
y_res.value_counts().plot.pie(autopct=autopct, ax=axs[0])
axs[0].set_title("Under-sampling")
sampling_strategy = {0: 25, 1: 35, 2: 47}
ros = RandomOverSampler(sampling_strategy=sampling_strategy)
X_res, y_res = ros.fit_resample(X, y)
y_res.value_counts().plot.pie(autopct=autopct, ax=axs[1])
_ = axs[1].set_title("Under-sampling")
# %% [markdown]
# `sampling_strategy` as a `list`
# -------------------------------
#
# When `sampling_strategy` is a `list`, the list contains the targeted
# classes. It is used only for **cleaning methods** and raise an error
# otherwise.
# %%
sampling_strategy = [0, 1, 2]
tl = TomekLinks(sampling_strategy=sampling_strategy)
X_res, y_res = tl.fit_resample(X, y)
ax = y_res.value_counts().plot.pie(autopct=autopct)
_ = ax.set_title("Cleaning")
# %% [markdown]
# `sampling_strategy` as a callable
# ---------------------------------
#
# When callable, function taking `y` and returns a `dict`. The keys
# correspond to the targeted classes. The values correspond to the desired
# number of samples for each class.
# %%
def ratio_multiplier(y):
from collections import Counter
multiplier = {1: 0.7, 2: 0.95}
target_stats = Counter(y)
for key, value in target_stats.items():
if key in multiplier:
target_stats[key] = int(value * multiplier[key])
return target_stats
X_res, y_res = RandomUnderSampler(sampling_strategy=ratio_multiplier).fit_resample(X, y)
ax = y_res.value_counts().plot.pie(autopct=autopct)
ax.set_title("Under-sampling")
plt.show()
|
