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An expirement to show how stratify option works
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# Experiment to confirm the effect of stratify option in Scikit Learn, tran_test_split() method. | |
# by Shayan Amani | |
from sklearn.model_selection import train_test_split | |
import pandas as pd | |
raw_data = pd.read_csv("codebase/adrel/dataset/train.csv") | |
cnt = raw_data.groupby('label').count() | |
''' experiment begins ''' | |
''' Part One: stratify is ON ''' | |
train, validate = train_test_split(raw_data, test_size=0.1, random_state=seed, stratify=raw_data['label']) | |
tr = train.groupby('label').count() | |
for i in range(9): | |
ratio = tr.iloc[i][0] / cnt.iloc[i][0] | |
print(ratio) | |
# assert that all train label classes has 90% of raw data | |
assert 0.89 < ratio < 0.91, 'Ratio is not following the rules {}'.format(i) | |
''' Output: | |
0.9000484027105518 | |
0.9000853970964987 | |
0.8999281781182668 | |
0.9000229832222477 | |
0.900049115913556 | |
0.8998682476943346 | |
0.8999274836838289 | |
0.9000227221086117 | |
0.9000738370662072 | |
''' | |
''' Part Two: stratify is OFF''' | |
train, validate = train_test_split(raw_data, test_size=0.1, random_state=seed) | |
tr = train.groupby('label').count() | |
for i in range(9): | |
ratio = tr.iloc[i][0] / cnt.iloc[i][0] | |
print(ratio) | |
assert 0.89 < ratio < 0.91, 'Ratio is not following the rules {}'.format(i) | |
''' Output: | |
0.9010164569215876 | |
0.8936806148590948 | |
0.8889154895858271 | |
Traceback (most recent call last): | |
File "/home/shi-on/anaconda3/envs/PyON36/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3267, in run_code | |
exec(code_obj, self.user_global_ns, self.user_ns) | |
File "<ipython-input-115-5835d3554147>", line 4, in <module> | |
assert 0.89 < ratio < 0.91, 'Ratio is not following the rules {}'.format(i) | |
AssertionError: Ratio is not following the rules 2 | |
''' |
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