""" ==================================================== 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 # 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. # %% import numpy as np # select only 2 classes since the ratio make sense in this case binary_mask = np.bitwise_or(y == 0, y == 2) 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` has 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()