Python 对不均衡数据进行Over sample（重抽样）

需要重采样的数据文件（Libsvm format），如heart_scale

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+1 1:0.708333 2:1 3:1 4:-0.320755 5:-0.105023 6:-1 7:1 8:-0.419847 9:-1 10:-0.225806 12:1 13:-1 -1 1:0.583333 2:-1 3:0.333333 4:-0.603774 5:1 6:-1 7:1 8:0.358779 9:-1 10:-0.483871 12:-1 13:1 ....

 重采样后的数据保存文件（Libsvm format），这里heart_scale_balance.txt

Python code：

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from sklearn.datasets import load_svmlight_file from sklearn.datasets import dump_svmlight_file import numpy as np from sklearn.utils import check_random_state from scipy.sparse import hstack,vstack   def fit_sample(X, y):     """Resample the dataset.     """     label = np.unique(y)     stats_c_ = {}     maj_n = 0     for i in label:         nk = sum(y==i)         stats_c_[i] = nk         if nk > maj_n:             maj_n = nk              maj_c_ = i         # Keep the samples from the majority class     X_resampled = X[y == maj_c_]     y_resampled = y[y == maj_c_]     # Loop over the other classes over picking at random     for key in stats_c_.keys():           # If this is the majority class, skip it         if key == maj_c_:             continue           # Define the number of sample to create         num_samples = int(stats_c_[maj_c_] -stats_c_[key])           # Pick some elements at random         random_state = check_random_state(42)         indx = random_state.randint(low=0, high=stats_c_[key],size=num_samples)           # Concatenate to the majority class         X_resampled = vstack([X_resampled,X[y == key],X[y == key][indx]])         print np.shape(y_resampled),np.shape(y[y == key]),np.shape(y[y == key][indx])         y_resampled = list(y_resampled)+list(y[y == key])+list(y[y == key][indx])     return X_resampled, y_resampled     X_train, y_train = load_svmlight_file("heart_scale")   # Apply the random over-sampling X_train, y_train = fit_sample(X_train,y_train) dump_svmlight_file(X_train, y_train,'heart_scale_balance.txt',zero_based=False)