def test_run_cross_validation(self):
print "test cross validation..."
train_test_model_class = SklearnRandomForestTrainTestModel
model_param = {'norm_type': 'normalize', 'random_state': 0}
indices_train = range(9)
indices_test = range(9)
output = ModelCrossValidation.run_cross_validation(
train_test_model_class, model_param, self.features, indices_train,
indices_test)
self.assertAlmostEquals(output['stats']['SRCC'],
0.93333333333333324,
places=4)
self.assertAlmostEquals(output['stats']['PCC'],
0.97754442316039469,
places=4)
self.assertAlmostEquals(output['stats']['KENDALL'],
0.83333333333333337,
places=4)
self.assertAlmostEquals(output['stats']['RMSE'],
0.17634739353518517,
places=4)
self.assertEquals(output['model'].TYPE, "RANDOMFOREST")
def test_run_cross_validation(self):
print "test cross validation..."
train_test_model_class = RandomForestTrainTestModel
model_param = {'norm_type': 'normalize', 'random_state': 0}
feature_df_file = config.ROOT + \
"/python/test/resource/sample_feature_extraction_results.json"
feature_df = pd.DataFrame.from_dict(
eval(open(feature_df_file, "r").read()))
indices_train = range(250)
indices_test = range(250, 300)
output = ModelCrossValidation.run_cross_validation(
train_test_model_class, model_param, feature_df, indices_train,
indices_test)
self.assertAlmostEquals(output['stats']['SRCC'],
0.93493301443051136,
places=4)
self.assertAlmostEquals(output['stats']['PCC'],
0.9413390374529329,
places=4)
self.assertAlmostEquals(output['stats']['KENDALL'],
0.78029280419726044,
places=4)
self.assertAlmostEquals(output['stats']['RMSE'],
0.32357946626958406,
places=4)
self.assertEquals(output['model'].TYPE, "RANDOMFOREST")
def test_run_cross_validation(self):
print "test cross validation..."
train_test_model_class = SklearnRandomForestTrainTestModel
model_param = {'norm_type':'normalize', 'random_state': 0}
indices_train = range(9)
indices_test = range(9)
output = ModelCrossValidation.run_cross_validation(
train_test_model_class, model_param, self.features,
indices_train, indices_test)
self.assertAlmostEquals(output['stats']['SRCC'], 0.93333333333333324, places=4)
self.assertAlmostEquals(output['stats']['PCC'], 0.97754442316039469, places=4)
self.assertAlmostEquals(output['stats']['KENDALL'], 0.83333333333333337, places=4)
self.assertAlmostEquals(output['stats']['RMSE'], 0.17634739353518517, places=4)
self.assertEquals(output['model'].TYPE, "RANDOMFOREST")
def test_run_cross_validation(self):
print "test cross validation..."
train_test_model_class = RandomForestTrainTestModel
model_param = {'norm_type':'normalize', 'random_state': 0}
feature_df_file = config.ROOT + \
"/python/test/resource/sample_feature_extraction_results.json"
feature_df = pd.DataFrame.from_dict(eval(open(feature_df_file, "r").read()))
indices_train = range(250)
indices_test = range(250, 300)
output = ModelCrossValidation.run_cross_validation(
train_test_model_class, model_param, feature_df,
indices_train, indices_test)
self.assertAlmostEquals(output['stats']['SRCC'], 0.93493301443051136)
self.assertAlmostEquals(output['stats']['PCC'], 0.9413390374529329)
self.assertAlmostEquals(output['stats']['KENDALL'], 0.78029280419726044)
self.assertAlmostEquals(output['stats']['RMSE'], 0.32357946626958406)
self.assertEquals(output['model'].TYPE, "RANDOMFOREST")
