diff --git a/automatminer/pipeline.py b/automatminer/pipeline.py index 67275149..3a55aff1 100644 --- a/automatminer/pipeline.py +++ b/automatminer/pipeline.py @@ -4,6 +4,7 @@ import os import copy import pickle +from typing import Dict import pandas as pd @@ -179,7 +180,7 @@ def transform(self, df, **transform_kwargs): return self.predict(df, **transform_kwargs) @check_fitted - def predict(self, df, ignore=None): + def predict(self, df, ignore="all"): """ Predict a target property of a set of materials. @@ -191,17 +192,36 @@ def predict(self, df, ignore=None): Args: df (pandas.DataFrame): Pipe will be fit to this dataframe. - ignore ([str]): String names of columns in all dataframes to ignore. - This will not stop samples from being dropped. + ignore ([str], None): Select which columns to ignore. + These columns will not be used for learning/prediction, but will + simply be appended back to the predicted df at the end of + prediction REGARDLESS of the pipeline configuration. + + This will not stop samples from being dropped. If + columns not present in the fitting are not ignored, they will + be automatically dropped. Similarly, if the AutoFeaturizer + is not configured to preserve inputs and they are not ignored, + they will be automatically dropped. Ignoring columns supercedes + all inner operations. + + Select columns using: + - [str]: String names of columns to ignore. + - None: input columns will be automatically dropped if they are + inputs. User defined features will be preserved if usable + as ML input. Returns: (pandas.DataFrame): The dataframe with target property predictions. """ if ignore: - ignored_df = df[ignore] - df = df.drop(columns=ignored_df) + self.logger.warning( + f"MatPipe will ignore and append (after prediction) the " + f"following columns: \n{ignore}" + ) + ignore_df = df[list(ignore)] + df = df.drop(columns=ignore_df) else: - ignored_df = pd.DataFrame() + ignore_df = pd.DataFrame() self.logger.info("Beginning MatPipe prediction using fitted pipeline.") df = self.autofeaturizer.transform(df, self.target) @@ -209,7 +229,7 @@ def predict(self, df, ignore=None): df = self.reducer.transform(df, self.target) predictions = self.learner.predict(df, self.target) self.logger.info("MatPipe prediction completed.") - merged_df = predictions.join(ignored_df, how="left") + merged_df = predictions.join(ignore_df, how="left") return merged_df @set_fitted @@ -301,7 +321,7 @@ def benchmark(self, df, target, kfold, fold_subset=None, cache=False): return results @check_fitted - def inspect(self, filename=None): + def inspect(self, filename=None) -> Dict[str, str]: """ Get all details of the pipeline in human-readable format. @@ -321,7 +341,7 @@ def inspect(self, filename=None): return attrs @check_fitted - def summarize(self, filename=None): + def summarize(self, filename=None) -> Dict[str, str]: """ Get an executive summary of the most important parts of the pipeline. Useful for understanding the pipeline at a high level. diff --git a/automatminer/tests/test_pipeline.py b/automatminer/tests/test_pipeline.py index d8dbc0c1..263e06aa 100644 --- a/automatminer/tests/test_pipeline.py +++ b/automatminer/tests/test_pipeline.py @@ -65,6 +65,7 @@ def make_matpipe_test(config_preset, skip=None): skippables = [ "transferability", "user_features", + "ignore", "benchmarking", "persistence", "digests", @@ -131,6 +132,39 @@ def test_user_features(self): test = df_test[self.target + " predicted"] self.assertTrue(r2_score(true, test) > 0.75) + + @unittest.skipIf("ignore" in skip, reason) + def test_ignore(self): + df = self.df + # pd.set_option('display.max_rows', 500) + # pd.set_option('display.max_columns', 500) + # pd.set_option('display.width', 1000) + # print(df) + + df_train = df.iloc[:200] + df_test = df.iloc[201:250] + ef = "ExtraFeature" + df_test[ef] = [i + 100 for i in range(df_test.shape[0])] + self.pipe.fit(df_train, self.target) + + self.assertTrue(ef in df_test.columns) + self.assertTrue("composition" in df_test.columns) + + ignore = [ef, "composition"] + predicted_ignored = self.pipe.predict(df_test, ignore=ignore) + self.assertTrue(ef in predicted_ignored.columns) + self.assertTrue("composition" in predicted_ignored.columns) + + predicted_none = self.pipe.predict(df_test, ignore=None) + self.assertFalse(ef in predicted_none.columns) + self.assertFalse("composition" in predicted_none.columns) + + some = ["composition"] + predicted_some = self.pipe.predict(df_test, ignore=some) + self.assertFalse(ef in predicted_some.columns) + self.assertTrue("composition" in predicted_some.columns) + + @unittest.skipIf("benchmarking" in skip, reason) def test_benchmarking_no_cache(self): pipe = self.pipe @@ -212,11 +246,18 @@ def tearDown(self) -> None: @unittest.skipIf(int(os.environ.get("SKIP_INTENSIVE", 0)), "Test too intensive for CircleCI commit builds.") -class MatPipeDebugTest(make_matpipe_test("debug")): - pass +# class MatPipeDebugTest(make_matpipe_test("debug")): +# pass -class MatPipeDebugSingleTest(make_matpipe_test("debug_single")): +class MatPipeDebugSingleTest(make_matpipe_test("debug_single", skip=[ + "transferability", + "user_features", + # "ignore", + "benchmarking", + "persistence", + "digests", + ])): pass diff --git a/docs/source/advanced.rst b/docs/source/advanced.rst index b39e529a..9d642437 100644 --- a/docs/source/advanced.rst +++ b/docs/source/advanced.rst @@ -66,11 +66,14 @@ Time Savers and Practical Tools ------------------------------- ignoring a column +using user defined features + Customizing pipelines --------------------- + Using DFTransformers individually ---------------------------------