mirror of
https://github.com/fenago/data-science.git
synced 2026-05-04 08:31:59 +00:00
fenago
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import unittest
|
|
import import_ipynb
|
|
import pandas as pd
|
|
import pandas.testing as pd_testing
|
|
import numpy.testing as np_testing
|
|
|
|
from sklearn import datasets
|
|
from sklearn import ensemble
|
|
from scipy import stats
|
|
from sklearn import model_selection
|
|
import pandas as pd
|
|
import numpy as np
|
|
|
|
class Test(unittest.TestCase):
|
|
def setUp(self):
|
|
import Exercise_8_3
|
|
self.exercises = Exercise_8_3
|
|
|
|
self.digits = datasets.load_digits()
|
|
|
|
self.y = self.digits.target
|
|
|
|
self.X = self.digits.data
|
|
|
|
self.rfc = ensemble.RandomForestClassifier(n_estimators=100, random_state=100)
|
|
|
|
self.n_features = X.shape[1]
|
|
|
|
np.random.seed(100)
|
|
|
|
self.param_dist = {
|
|
'criterion': ['gini', 'entropy'],
|
|
'max_features': stats.randint(low=1, high=self.n_features)
|
|
}
|
|
|
|
self.rscv = model_selection.RandomizedSearchCV(estimator=self.rfc, param_distributions=self.param_dist, n_iter=50, cv=5, scoring='accuracy', random_state=100)
|
|
|
|
self.rscv.fit(self.X,self.y)
|
|
|
|
self.results = pd.DataFrame(self.rscv.cv_results_)
|
|
|
|
self.distinct_results = self.results.loc[:,['params','mean_test_score']]
|
|
|
|
self.distinct_results.loc[:,'params'] = self.distinct_results.loc[:,'params'].astype('str')
|
|
|
|
self.distinct_results.drop_duplicates(inplace=True)
|
|
|
|
def test_result(self):
|
|
self.assertEqual(
|
|
self.exercises.distinct_results["mean_test_score"].max()
|
|
, self.distinct_results["mean_test_score"].max()
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|