65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import unittest
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import numpy as np
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from helper import plpy, fixture_file
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import crankshaft.segmentation as segmentation
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import json
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class SegmentationTest(unittest.TestCase):
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"""Testing class for Moran's I functions"""
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def setUp(self):
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plpy._reset()
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def generate_random_data(self,n_samples,random_state, row_type=False):
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x1 = random_state.uniform(size=n_samples)
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x2 = random_state.uniform(size=n_samples)
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x3 = random_state.randint(0, 4, size=n_samples)
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y = x1+x2*x2+x3
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cartodb_id = range(len(x1))
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if row_type:
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return [ {'features': vals} for vals in zip(x1,x2,x3)], y
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else:
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return [dict( zip(['x1','x2','x3','target', 'cartodb_id'],[x1,x2,x3,y,cartodb_id]))]
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def test_replace_nan_with_mean(self):
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test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
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def test_create_and_predict_segment(self):
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n_samples = 1000
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random_state_train = np.random.RandomState(13)
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random_state_test = np.random.RandomState(134)
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training_data = self.generate_random_data(n_samples, random_state_train)
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test_data, test_y = self.generate_random_data(n_samples, random_state_test, row_type=True)
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ids = [{'cartodb_ids': range(len(test_data))}]
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rows = [{'x1': 0,'x2':0,'x3':0,'y':0,'cartodb_id':0}]
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plpy._define_result('select \* from \(select \* from training\) a limit 1',rows)
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plpy._define_result('.*from \(select \* from training\) as a' ,training_data)
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plpy._define_result('select array_agg\(cartodb\_id order by cartodb\_id\) as cartodb_ids from \(.*\) a',ids)
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plpy._define_result('.*select \* from test.*' ,test_data)
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model_parameters = {'n_estimators': 1200,
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'max_depth': 3,
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'subsample' : 0.5,
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'learning_rate': 0.01,
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'min_samples_leaf': 1}
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result = segmentation.create_and_predict_segment(
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'select * from training',
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'target',
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'select * from test',
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model_parameters)
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prediction = [r[1] for r in result]
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accuracy =np.sqrt(np.mean( np.square( np.array(prediction) - np.array(test_y))))
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self.assertEqual(len(result),len(test_data))
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self.assertTrue( result[0][2] < 0.01)
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self.assertTrue( accuracy < 0.5*np.mean(test_y) )
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