def test_pipeline_first_transform_stage(self):
transform_stages = self.pipeline_event.transformStages
transform_event = transform_stages[0].te
transformer = transform_event.transformer
expected_transformer = modeldb_types.Transformer(
-1,
'PCA',
'decomposition PCA')
utils.is_equal_transformer(transformer, expected_transformer, self)
old_df = transform_event.oldDataFrame
expected_old_df = modeldb_types.DataFrame(
-1,
[
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
],
100,
'digits-dataset')
utils.is_equal_dataframe(expected_old_df, old_df, self)
new_df = transform_event.newDataFrame
expected_new_df = modeldb_types.DataFrame(
-1,
[
modeldb_types.DataFrameColumn('0', 'float64'),
modeldb_types.DataFrameColumn('1', 'float64'),
],
100,
'')
utils.is_equal_dataframe(expected_new_df, new_df, self)
def test_pipeline_first_fit_stage(self):
fit_stages = self.pipeline_event.fitStages
fit_event1 = fit_stages[0].fe
# First Stage
transformer = fit_event1.model
expected_transformer = modeldb_types.Transformer(
-1, 'PCA', 'decomposition PCA')
utils.is_equal_transformer(transformer, expected_transformer, self)
df = fit_event1.df
expected_df = modeldb_types.DataFrame(-1, [
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
], 100, 'digits-dataset')
utils.is_equal_dataframe(df, expected_df, self)
spec = fit_event1.spec
expected_spec = modeldb_types.TransformerSpec(-1, 'PCA', [
modeldb_types.HyperParameter('copy', 'True', 'bool', FMIN, FMAX),
modeldb_types.HyperParameter('n_components', 'None', 'NoneType',
FMIN, FMAX),
modeldb_types.HyperParameter('whiten', 'False', 'bool', FMIN,
FMAX),
], 'decomposition PCA')
utils.is_equal_transformer_spec(spec, expected_spec, self)
self.assertEqual(fit_event1.featureColumns, ['A', 'B'])
def test_gridcv_event(self):
utils.validate_grid_search_cv_event(self.grid_search_event, self)
self.assertEqual(self.grid_search_event.numFolds, 3)
best_fit_event = self.grid_search_event.bestFit
df = best_fit_event.df
expected_df = modeldb_types.DataFrame(
-1,
[
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
modeldb_types.DataFrameColumn('C', 'int64'),
modeldb_types.DataFrameColumn('D', 'int64'),
],
2000,
'digits-dataset')
utils.is_equal_dataframe(df, expected_df, self)
transformer = best_fit_event.model
utils.validate_transformer_struct(transformer, self)
expected_transformer = modeldb_types.Transformer(
-1,
'SVC',
'')
utils.is_equal_transformer(transformer, expected_transformer, self)
self.assertEqual(best_fit_event.featureColumns, [
'A', 'B', 'C', 'D'])
def test_old_dataframe(self):
old_df = self.random_split_event.oldDataFrame
expected_df = modeldb_types.DataFrame(-1, [
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
modeldb_types.DataFrameColumn('C', 'int64'),
modeldb_types.DataFrameColumn('D', 'int64'),
], 100, 'digits-dataset')
utils.is_equal_dataframe(old_df, expected_df, self)
def test_dataframe(self):
df = self.fit_event.df
expected_df = modeldb_types.DataFrame(-1, [
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
modeldb_types.DataFrameColumn('C', 'int64'),
modeldb_types.DataFrameColumn('D', 'int64')
], 100, 'digits-dataset')
utils.is_equal_dataframe(df, expected_df, self)
def test_overall_pipeline_fit_event(self):
fit_event = self.pipeline_event.pipelineFit
utils.validate_fit_event_struct(fit_event, self)
transformer = fit_event.model
expected_transformer = modeldb_types.Transformer(
-1,
'Pipeline',
'pipeline with pca + logistic')
utils.is_equal_transformer(transformer, expected_transformer, self)
df = fit_event.df
expected_df = modeldb_types.DataFrame(
-1,
[
modeldb_types.DataFrameColumn('A', 'int64'),
modeldb_types.DataFrameColumn('B', 'int64'),
],
100,
'digits-dataset')
utils.is_equal_dataframe(df, expected_df, self)
spec = fit_event.spec
expected_spec = modeldb_types.TransformerSpec(
-1,
'Pipeline',
[
modeldb_types.HyperParameter(
'logistic__n_jobs', '1', 'int', FMIN, FMAX),
modeldb_types.HyperParameter(
'pca__copy', 'True', 'bool', FMIN, FMAX),
modeldb_types.HyperParameter(
'pca__n_components', 'None', 'NoneType', FMIN, FMAX),
modeldb_types.HyperParameter(
'logistic__fit_intercept', 'True', 'bool', FMIN, FMAX),
modeldb_types.HyperParameter(
'pca__whiten', 'False', 'bool', FMIN, FMAX),
modeldb_types.HyperParameter(
'steps', "[('pca', PCA(copy=True, n_components=None, whiten=False)), ('logistic', LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False))]", 'list', FMIN, FMAX),
modeldb_types.HyperParameter(
'logistic', 'LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)', 'LinearRegression', FMIN, FMAX),
modeldb_types.HyperParameter(
'pca', 'PCA(copy=True, n_components=None, whiten=False)', 'PCA', FMIN, FMAX),
modeldb_types.HyperParameter(
'logistic__normalize', 'False', 'bool', FMIN, FMAX),
modeldb_types.HyperParameter(
'logistic__copy_X', 'True', 'bool', FMIN, FMAX)
],
'pipeline with pca + logistic')
utils.is_equal_transformer_spec(spec, expected_spec, self)
self.assertItemsEqual(fit_event.featureColumns, ['A', 'B'])
def test_new_dataframe(self):
new_df = self.transform_event.newDataFrame
utils.validate_dataframe_struct(new_df, self)
new_df_column = new_df.schema[0]
df_column = modeldb_types.DataFrameColumn('0', 'int64')
expected_new_df = modeldb_types.DataFrame(-1, [df_column], 100,
'') # fix columns
utils.is_equal_dataframe(expected_new_df, new_df, self)