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fenago
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import unittest
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import import_ipynb
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import pandas as pd
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import pandas.testing as pd_testing
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import numpy.testing as np_testing
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn import ensemble
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from sklearn import model_selection
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from scipy import stats
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class Test(unittest.TestCase):
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def setUp(self):
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import Activity8_01
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self.exercises = Activity8_01
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self.file_url = '../agaricus-lepiota.data'
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self.mushrooms = pd.read_csv(self.file_url, header=None)
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self.y_raw = self.mushrooms.iloc[:,0]
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self.X_raw = self.mushrooms.iloc[:,1:]
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self.y = (self.y_raw == 'p') * 1
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self.encoder = preprocessing.OneHotEncoder()
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self.encoder.fit(self.X_raw)
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self.X = self.encoder.transform(self.X_raw).toarray()
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self.rfc = ensemble.RandomForestClassifier(n_estimators=100, random_state=100)
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self.grid = {
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'criterion': ['gini', 'entropy'],
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'max_features': [2, 4, 6, 8, 10, 12, 14]
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}
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self.gscv = model_selection.GridSearchCV(estimator=self.rfc, param_grid=self.grid, cv=5, scoring='accuracy')
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self.gscv.fit(self.X,self.y)
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self.results = pd.DataFrame(self.gscv.cv_results_)
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np.random.seed(100)
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self.max_features = X.shape[1]
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self.param_dist = {
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'criterion': ['gini', 'entropy'],
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'max_features': stats.randint(low=1, high=self.max_features)
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}
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self.rscv = model_selection.RandomizedSearchCV(estimator=self.rfc, param_distributions=self.param_dist, n_iter=50, cv=5, scoring='accuracy', random_state=100)
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self.rscv.fit(self.X,self.y)
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self.results = pd.DataFrame(self.rscv.cv_results_)
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def test_result(self):
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self.assertEqual(
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self.exercises.results["mean_test_score"].max()
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, self.results["mean_test_score"].max()
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)
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if __name__ == '__main__':
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unittest.main()
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