Created
November 29, 2019 08:03
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unbalance_dask_dataframe.py
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%pylab inline | |
import pandas as pd | |
import dask.dataframe as dd | |
def get_unbal_df(size = 100, balance=None): | |
"""Get a randomly unbalanced df""" | |
if balance is None: | |
balance = np.random.randint(-100, 100) | |
if balance<0: | |
data = [0]*abs(balance * size) + [1] * size | |
else: | |
data = [1]*abs(balance * size) + [0] * size | |
random.shuffle(data) | |
df = pd.DataFrame(data, columns=['label']) | |
return df | |
def balance_ddf(df, label_col, balance): | |
""" | |
Balance a dask dataframe however you want | |
- df has two classes in label col e.g. 0 and 1 (alphebetical or numberic order) | |
- balance: how many more of the first class | |
- label_col: name of label col | |
""" | |
groups = df.groupby([label_col]) | |
a = groups.get_group(0) | |
b = groups.get_group(1) | |
la = len(a) | |
lb = len(b) | |
sizes = [la//balance, lb] | |
min_len = min(sizes) | |
a = a.head(min_len*balance, compute=False, npartitions=-1) | |
b = b.head(min_len, compute=False, npartitions=-1) | |
return dd.concat([a, b]).sample(frac=1).repartition(npartitions=5) | |
df = get_unbal_df() | |
ddf = dd.from_pandas(df, npartitions=5) | |
print('label_mean', df['label'].mean()) | |
target_bal = 2 | |
target_mean = 1/(target_bal+1) | |
df2 = balance_ddf(ddf, 'label', target_bal).compute() | |
assert df2['label'].mean()==target_mean |
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