From 8407246d71b70df166c8cc67b03aa13ff83ded2f Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Wed, 22 Nov 2023 10:04:54 -0500 Subject: [PATCH 1/9] predict method --- README.md | 12 +++-- denseclus/DenseClus.py | 104 ++++++++++++++++++++++++++++++----- denseclus/utils.py | 4 +- tests/conftest.py | 56 +++++++++++++++---- tests/denseclus_test.py | 115 +++++++++++++++++++++++++++++++-------- tests/utils_test.py | 116 ++++++++++++++++++++++++++++++++++------ tox.ini | 5 +- 7 files changed, 343 insertions(+), 69 deletions(-) diff --git a/README.md b/README.md index 954c505..10c39ab 100644 --- a/README.md +++ b/README.md @@ -33,12 +33,14 @@ All preprocessing and extraction are done under the hood, just call fit and then from denseclus import DenseClus from denseclus.utils import make_dataframe -df = make_dataframe() - +df = make_dataframe(50000) clf = DenseClus() clf.fit(df) -print(clf.score()) +scores = clf.score() +predictions = clf.predict(df.tail()) + +print(predictions[0:10]) ``` ## Usage @@ -50,11 +52,13 @@ clf = DenseClus( umap_combine_method="intersection_union_mapper", ) ``` +**This method does not support prediction and you'll need to refit to the entire dataset.* + ### Advanced Usage For advanced users, it's possible to select more fine-grained control of the underlying algorithms by passing -dictionaries into `DenseClus` class. +dictionaries into `DenseClus` class for either UMAP or HDBSCAN. For example: ```python diff --git a/denseclus/DenseClus.py b/denseclus/DenseClus.py index 582e61a..ca1f86c 100644 --- a/denseclus/DenseClus.py +++ b/denseclus/DenseClus.py @@ -44,6 +44,11 @@ ) logger.addHandler(sh) +warnings.filterwarnings( + "ignore", + message="gradient function is not yet implemented for dice distance metric; inverse_transform will be unavailable", +) + class DenseClus(BaseEstimator, ClassifierMixin): """DenseClus @@ -59,7 +64,7 @@ class DenseClus(BaseEstimator, ClassifierMixin): Setting a seed may help to offset the stochastic nature of UMAP by setting it with fixed random seed. - umap_combine_method : str, default=contrast + umap_combine_method : str, default=intersection Method by which to combine embeddings spaces. Options include: intersection, union, contrast, methods for combining the embeddings: including @@ -85,14 +90,6 @@ class DenseClus(BaseEstimator, ClassifierMixin): important than the other. See: https://umap-learn.readthedocs.io/en/latest/composing_models.html - prediction_data: bool, default=False - Whether to generate extra cached data for predicting labels or - membership vectors few new unseen points later. If you wish to - persist the clustering object for later reuse you probably want - to set this to True. - See: - https://hdbscan.readthedocs.io/en/latest/soft_clustering.html - verbose : bool, default=False Level of verbosity to print when fitting and predicting. Setting to False will only show Warnings that appear. @@ -122,8 +119,7 @@ class DenseClus(BaseEstimator, ClassifierMixin): def __init__( self, random_state: int = 42, - umap_combine_method: str = "contrast", - prediction_data: bool = False, + umap_combine_method: str = "intersection", verbose: bool = False, umap_params=None, hdbscan_params=None, @@ -139,7 +135,6 @@ def __init__( self.random_state = random_state self.umap_combine_method = umap_combine_method - self.prediction_data = prediction_data # Default parameters default_umap_params = { @@ -278,6 +273,89 @@ def _fit_numerical(self): logger.error("Failed to fit numerical UMAP: %s", str(e)) raise + def _predict_base(self, df_new: pd.DataFrame): + """ + Base method for prediction. Transforms the new data using the existing UMAP embeddings and then uses the HDBSCAN's approximate_predict function to predict the cluster labels and strengths. + + Parameters + ---------- + df_new : pd.DataFrame + The new data for which to predict cluster labels. This should be a DataFrame with the same structure as the one used in the fit method. + + Returns + ------- + labels : np.array + The predicted cluster labels for each row in df_new. + strengths : np.array + The strengths of the predictions for each row in df_new. + """ + + categorical_new = extract_categorical(df_new, **self.kwargs) + numerical_new = extract_numerical(df_new, **self.kwargs) + + categorical_embedding = self.categorical_umap_.transform(categorical_new) + numerical_embedding = self.numerical_umap_.transform(numerical_new) + + # Combine the categorical and numerical embeddings using the method + if self.umap_combine_method == "intersection": + mapper_new = numerical_embedding * categorical_embedding + elif self.umap_combine_method == "union": + mapper_new = numerical_embedding + categorical_embedding + elif self.umap_combine_method == "contrast": + mapper_new = numerical_embedding - categorical_embedding + elif self.umap_combine_method == "intersection_union_mapper": + logger.info("Refitting new UMAP for Intersection Mapper") + intersection_mapper = umap.UMAP( + random_state=self.random_state, + n_jobs=1 if self.random_state is not None else -1, + **self.umap_params["combined"], + ).fit(numerical_embedding) + mapper_new = intersection_mapper * (numerical_embedding + categorical_embedding) + else: + raise ValueError("Select valid UMAP combine method") + + labels, strengths = hdbscan.approximate_predict(self.hdbscan_, mapper_new) + + return labels, strengths + + def predict(self, df_new: pd.DataFrame): + """ + Predict the cluster labels for new data. + + This method uses the base prediction method to get the labels and strengths, but only returns the labels. + + Parameters + ---------- + df_new : pd.DataFrame + The new data for which to predict cluster labels. + + Returns + ------- + labels : np.array + The predicted cluster labels for each row in df_new. + """ + labels, _ = self._predict_base(df_new) + return labels + + def predict_proba(self, df_new: pd.DataFrame): + """ + Predict the probabilities of the cluster labels for new data. + + This method uses the base prediction method to get the labels and strengths, but only returns the strengths as probabilities. + + Parameters + ---------- + df_new : pd.DataFrame + The new data for which to predict cluster probabilities. + + Returns + ------- + probabilities : np.array + The probabilities of the predicted cluster labels for each row in df_new. + """ + _, strengths = self._predict_base(df_new) + return strengths + def _fit_categorical(self): """ Fit a UMAP based on categorical data @@ -347,7 +425,7 @@ def _fit_hdbscan(self): """ logger.info("Fitting HDBSCAN with default parameters") hdb_ = hdbscan.HDBSCAN( - prediction_data=self.prediction_data, + prediction_data=True, **self.hdbscan_params, ).fit(self.mapper_.embedding_) self.hdbscan_ = hdb_ diff --git a/denseclus/utils.py b/denseclus/utils.py index c182609..3da1342 100644 --- a/denseclus/utils.py +++ b/denseclus/utils.py @@ -177,13 +177,13 @@ def transform_numerics(numerical: pd.DataFrame) -> pd.DataFrame: return numerical -def make_dataframe() -> pd.DataFrame: +def make_dataframe(n_samples: int = 1000) -> pd.DataFrame: """This will create dataframe for demonstration purposes. Returns: pd.DataFrame: dataframe of categorical and numerical data """ - X, _ = make_blobs(n_samples=1000, n_features=8, random_state=10) # ruff: noqa: W0632 + X, _ = make_blobs(n_samples=n_samples, n_features=8, random_state=10) # ruff: noqa: W0632 numerical = StandardScaler().fit_transform(X[:, :6]) categorical = KBinsDiscretizer(n_bins=3, encode="ordinal").fit_transform(X[:, 6:]) categorical = np.where( diff --git a/tests/conftest.py b/tests/conftest.py index bc1d4b6..94b502d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,42 +1,76 @@ +#!/usr/bin/env python3 """ Fixture configs for tests """ import pandas as pd +import numpy as np import pytest from denseclus.DenseClus import DenseClus from denseclus.utils import make_dataframe -@pytest.fixture -def n_components(): - return 3 +N_COMPONENTS = 3 -@pytest.fixture +@pytest.fixture(scope="module") def df(): df = make_dataframe() return df -@pytest.fixture -def clf(df, n_components): +@pytest.fixture(scope="module") +def fitted_clf(): + clf = DenseClus() + df_small = make_dataframe(n_samples=100) + clf.fit(df_small) + return clf + + +@pytest.fixture(scope="module") +def union_mapper_clf(): + df = make_dataframe() umap_params = { - "categorical": {"n_components": n_components}, - "numerical": {"n_components": n_components}, - "combined": {"n_components": n_components}, + "categorical": {"n_components": N_COMPONENTS}, + "numerical": {"n_components": N_COMPONENTS}, + "combined": {"n_components": N_COMPONENTS}, } clf = DenseClus(umap_combine_method="intersection_union_mapper", umap_params=umap_params) clf.fit(df) return clf -@pytest.fixture +@pytest.fixture(scope="module") +def default_clf(): + df = make_dataframe() + clf = DenseClus() + clf.fit(df) + return clf + + +@pytest.fixture(scope="module") def categorical_df(): return pd.DataFrame({"col1": ["A", "B", "A", "B"], "col2": ["C", "D", "E", "C"]}) -@pytest.fixture +@pytest.fixture(scope="module") def numerical_df(): return pd.DataFrame({"col3": [23.0, 43.0, 50.0], "col4": [33.0, 34.0, 55.0]}) + + +@pytest.fixture(scope="module") +def missing_numerical_df(): + df = pd.DataFrame({"A": [1, np.nan, 3], "B": [4, 5, np.nan]}) + return df + + +@pytest.fixture(scope="module") +def missing_categorical_df(): + df = pd.DataFrame({"A": ["alpha", np.nan, "beta"], "B": ["gamma", "delta", np.nan]}) + return df + + +@pytest.fixture(scope="module") +def df_new(): + return make_dataframe() diff --git a/tests/denseclus_test.py b/tests/denseclus_test.py index 125590c..cc3feae 100644 --- a/tests/denseclus_test.py +++ b/tests/denseclus_test.py @@ -6,52 +6,125 @@ from denseclus.DenseClus import DenseClus -def test_fit_categorical(clf, df): - assert clf.categorical_umap_.embedding_.shape == (len(df), clf.categorical_umap_.n_components) +def check_embedding_shape(embedding, expected_shape): + assert embedding.shape == expected_shape -def test_fit_numerical(clf, df): - assert clf.numerical_umap_.embedding_.shape == (len(df), clf.numerical_umap_.n_components) +@pytest.mark.slow +def test_fit_categorical(union_mapper_clf, df): + check_embedding_shape( + union_mapper_clf.categorical_umap_.embedding_, + (len(df), union_mapper_clf.categorical_umap_.n_components), + ) -def test_umap_embeddings(clf, df): - assert clf.mapper_.embedding_.shape == (len(df), clf.mapper_.n_components[-1]) +@pytest.mark.slow +def test_fit_numerical(union_mapper_clf, df): + check_embedding_shape( + union_mapper_clf.numerical_umap_.embedding_, + (len(df), union_mapper_clf.numerical_umap_.n_components), + ) -def test_hdbscan_labels(clf, df): - assert clf.hdbscan_.labels_.shape[0] == df.shape[0] +@pytest.mark.slow +def test_umap_embeddings(union_mapper_clf, df): + assert union_mapper_clf.mapper_.embedding_.shape == ( + len(df), + union_mapper_clf.mapper_.n_components[-1], + ) -def test_denseclus_fit_is_df(clf): +@pytest.mark.slow +def test_hdbscan_labels(union_mapper_clf, df): + assert union_mapper_clf.hdbscan_.labels_.shape[0] == df.shape[0] + + +@pytest.mark.slow +def test_denseclus_fit_is_df(union_mapper_clf): with pytest.raises(TypeError): - clf.fit([1, 2, 3]) + union_mapper_clf.fit([1, 2, 3]) -def test_denseclus_score(clf, df): - assert len(clf.score()) == len(df) +@pytest.mark.slow +def test_denseclus_score(union_mapper_clf, df): + assert len(union_mapper_clf.score()) == len(df) +@pytest.mark.fast def test_denseclus_method(df): with pytest.raises(ValueError): _ = DenseClus(umap_combine_method="notamethod").fit(df) -def test_repr(clf): +@pytest.mark.slow +def test_repr(union_mapper_clf): warnings.filterwarnings("ignore", category=UserWarning, module="umap.umap_") - assert str(type(clf.__repr__)) == "" + assert str(type(union_mapper_clf.__repr__)) == "" -def test_fit_known_output(df): - warnings.filterwarnings("ignore", category=UserWarning, module="umap.umap_") - df_small = df.head(100) - clf = DenseClus() - clf.fit(df_small) - scores = clf.score() +@pytest.mark.fast +def test_denseclus_score_length(fitted_clf): + scores = fitted_clf.score() + assert len(scores) == 100 + + +@pytest.mark.fast +def test_denseclus_score_output(fitted_clf): + scores = fitted_clf.score() expected_output = np.array([-1] * 100) - assert len(clf.numerical_umap_.embedding_) == len(expected_output) assert np.all(expected_output == scores) +@pytest.mark.fast def test_fit_empty_df(): with pytest.raises(ValueError): DenseClus().fit(pd.DataFrame()) + + +@pytest.mark.slow +def test_predict(default_clf, df_new): + labels = default_clf.predict(df_new) + assert isinstance(labels, np.ndarray) + assert len(labels) == len(df_new) + + +@pytest.mark.fast +def test_predict_output_shape(default_clf, df_new): + labels = default_clf.predict(df_new) + assert labels.shape == ( + len(df_new), + ), "Predicted labels should have the same number of rows as input data" + + +@pytest.mark.fast +def test_predict_output_type(default_clf, df_new): + labels = default_clf.predict(df_new) + assert issubclass(labels.dtype.type, np.integer), "Predicted labels should be integers" + + +@pytest.mark.fast +def test_predict_proba_output_range(default_clf, df_new): + probabilities = default_clf.predict_proba(df_new) + assert np.all( + (probabilities >= 0) & (probabilities <= 1), + ), "Probabilities should be between 0 and 1" + + +@pytest.mark.fast +def test_predict_proba_output_shape(default_clf, df_new): + probabilities = default_clf.predict_proba(df_new) + assert probabilities.shape == ( + len(df_new), + ), "Probabilities should have the same number of rows as input data" + + +@pytest.mark.fast +def test_predict_input_type(union_mapper_clf): + with pytest.raises(TypeError): + union_mapper_clf.predict("not a dataframe") + + +@pytest.mark.fast +def test_predict_proba_input_type(union_mapper_clf): + with pytest.raises(TypeError): + union_mapper_clf.predict_proba("not a dataframe") diff --git a/tests/utils_test.py b/tests/utils_test.py index 63c54fb..c87398a 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 import numpy as np import pandas as pd import pytest @@ -12,6 +13,7 @@ ) +@pytest.mark.fast def test_extract_categorical(categorical_df): categories = extract_categorical(categorical_df) assert categories.shape == (4, 3) @@ -19,58 +21,69 @@ def test_extract_categorical(categorical_df): assert categories.max().min() == 1 +@pytest.mark.fast def test_extract_numerical(numerical_df): numerics = extract_numerical(numerical_df) assert str(numerics["col3"].dtypes) == "float64" assert str(numerics["col4"].dtypes) == "float64" +@pytest.mark.fast def test_extract_categorical_is_df(): with pytest.raises(TypeError): extract_categorical(["A", "B", "C"]) +@pytest.mark.fast def test_extract_categorical_is_object(numerical_df): with pytest.raises(ValueError): extract_categorical(numerical_df) +@pytest.mark.fast def test_extract_numerical_is_df(): with pytest.raises(TypeError): extract_numerical([1, 2, 3]) +@pytest.mark.fast def test_extract_numerical_is_numeric(categorical_df): with pytest.raises(ValueError): extract_numerical(categorical_df) +@pytest.mark.fast def test_transform_numerics(numerical_df): numerics = transform_numerics(numerical_df) assert len(numerics) == 3 +@pytest.mark.fast def test_transform_numerics_is_df(): with pytest.raises(AttributeError): transform_numerics([1, 2, 3]) +@pytest.mark.fast def test_extract_categorical_empty_df(): with pytest.raises(ValueError): extract_categorical(pd.DataFrame()) +@pytest.mark.fast def test_extract_numerical_empty_df(): with pytest.raises(ValueError): extract_numerical(pd.DataFrame()) +@pytest.mark.fast def test_extract_categorical_no_categorical(categorical_df): df = categorical_df.select_dtypes(include=["float", "int"]) with pytest.raises(ValueError): extract_categorical(df) +@pytest.mark.fast def test_extract_categorical_with_categorical(categorical_df): result = extract_categorical(categorical_df) expected = pd.DataFrame( @@ -83,6 +96,7 @@ def test_extract_categorical_with_categorical(categorical_df): assert_frame_equal(result, expected) +@pytest.mark.fast def test_extract_categorical_with_high_cardinality(categorical_df): df = pd.DataFrame( {"col1": ["A", "B", "A", "B"], "col2": ["C", "D", "E", "C"], "col3": ["A", "A", "B", "B"]}, @@ -93,50 +107,47 @@ def test_extract_categorical_with_high_cardinality(categorical_df): assert result.shape == (4, 3) -def test_impute_categorical_no_missing_values(categorical_df): - result = impute_categorical(categorical_df, strategy="constant", fill_value="Missing") - assert_frame_equal(result, categorical_df) - - -def test_impute_categorical_with_missing_values(): - df = pd.DataFrame({"col1": ["A", "B", np.nan, "B"], "col2": ["C", np.nan, "E", "C"]}) - expected = pd.DataFrame( - {"col1": ["A", "B", "Missing", "B"], "col2": ["C", "Missing", "E", "C"]}, - ) - result = impute_categorical(df, strategy="constant", fill_value="Missing") +@pytest.mark.fast +def test_impute_categorical_with_missing_values(missing_categorical_df): + expected = pd.DataFrame({"A": ["alpha", "Missing", "beta"], "B": ["gamma", "delta", "Missing"]}) + result = impute_categorical(missing_categorical_df, strategy="constant", fill_value="Missing") assert_frame_equal(result, expected) -def test_impute_categorical_with_custom_strategy(): - df = pd.DataFrame({"col1": ["A", "B", np.nan, "B"], "col2": ["C", np.nan, "E", "C"]}) - expected = pd.DataFrame({"col1": ["A", "B", "Z", "B"], "col2": ["C", "Z", "E", "C"]}) +@pytest.mark.skip(reason="expiremental feature") +def test_impute_categorical_with_custom_strategy(missing_categorical_df): + expected = pd.DataFrame({"A": ["alpha", "Z", "beta"], "B": ["gamma", "delta", "Z"]}) result = impute_categorical( - df, + missing_categorical_df, strategy="constant", fill_value="Missing", custom_strategy=lambda s: "Z", ) - assert_frame_equal(result, expected) + pd.testing.assert_frame_equal(result, expected) +@pytest.mark.fast def test_extract_categorical_empty_dataframe(): df = pd.DataFrame() with pytest.raises(ValueError, match="Input DataFrame should not be empty."): extract_categorical(df) +@pytest.mark.fast def test_extract_categorical_no_columns(): df = pd.DataFrame(index=[0, 1, 2]) with pytest.raises(ValueError, match="Input DataFrame should not be empty."): extract_categorical(df) +@pytest.mark.fast def test_extract_categorical_no_categorical_data(): df = pd.DataFrame({"col1": [1, 2, 3, 4]}) with pytest.raises(ValueError, match="No categorical data found in the input DataFrame."): extract_categorical(df) +@pytest.mark.fast def test_impute_numerical(numerical_df): df = numerical_df.copy() median = numerical_df["col3"].median() @@ -148,6 +159,7 @@ def test_impute_numerical(numerical_df): assert imputed_df.loc[1, "col3"] == median, "Imputation failed, expected median value" +@pytest.mark.fast def test_extract_numerical_columns(df, numerical_df): df = pd.concat([df, numerical_df], axis=1) @@ -174,11 +186,83 @@ def test_extract_numerical_columns(df, numerical_df): extract_numerical(pd.DataFrame({"col1": ["a", "b", "c"]})) +@pytest.mark.fast def test_impute_categorical_strategy_error(categorical_df): with pytest.raises(ValueError): impute_categorical(categorical_df, strategy="median", fill_value="Error") +@pytest.mark.fast def test_impute_numerical_strategy_error(numerical_df): with pytest.raises(ValueError): impute_numerical(numerical_df, strategy="constant") + + +@pytest.mark.fast +def test_impute_categorical_constant(missing_categorical_df): + result = impute_categorical(missing_categorical_df, "constant", "Missing") + expected = pd.DataFrame({"A": ["alpha", "Missing", "beta"], "B": ["gamma", "delta", "Missing"]}) + pd.testing.assert_frame_equal(result, expected) + + +@pytest.mark.fast +def test_impute_categorical_most_frequent(): + df = pd.DataFrame({"A": ["alpha", np.nan, "alpha"], "B": ["gamma", "gamma", np.nan]}) + result = impute_categorical(df, "most_frequent", "Missing") + expected = pd.DataFrame({"A": ["alpha", "alpha", "alpha"], "B": ["gamma", "gamma", "gamma"]}) + pd.testing.assert_frame_equal(result, expected) + + +@pytest.mark.skip(reason="expiremental feature") +def test_impute_categorical_custom_strategy(missing_categorical_df): + def second_most_frequent(s): + return s.value_counts().index[1] if len(s.value_counts()) > 1 else s.value_counts().index[0] + + result = impute_categorical( + missing_categorical_df, + "constant", + "Missing", + custom_strategy=second_most_frequent, + ) + expected = pd.DataFrame({"A": ["alpha", "alpha", "beta"], "B": ["gamma", "delta", "delta"]}) + pd.testing.assert_frame_equal(result, expected) + + +@pytest.mark.fast +def test_impute_categorical_no_missing_values(): + df = pd.DataFrame({"A": ["alpha", "beta", "alpha"], "B": ["gamma", "delta", "gamma"]}) + result = impute_categorical(df, "constant", "Missing") + pd.testing.assert_frame_equal(result, df) + + +@pytest.mark.fast +def test_impute_categorical_invalid_strategy(missing_categorical_df): + with pytest.raises(ValueError): + impute_categorical(missing_categorical_df, "invalid", "Missing") + + +@pytest.mark.fast +def test_impute_numerical_median(missing_numerical_df): + result = impute_numerical(missing_numerical_df, "median") + expected = pd.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, 5, 4.5]}) + pd.testing.assert_frame_equal(result, expected) + + +@pytest.mark.fast +def test_impute_numerical_mean(missing_numerical_df): + result = impute_numerical(missing_numerical_df, "mean") + expected = pd.DataFrame({"A": [1.0, 2.0, 3.0], "B": [4.0, 5, 4.5]}) + pd.testing.assert_frame_equal(result, expected) + + +@pytest.mark.fast +def test_impute_numerical_no_missing_values(): + df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + result = impute_numerical(df, "median") + pd.testing.assert_frame_equal(result, df) + + +@pytest.mark.fast +def test_impute_numerical_invalid_strategy(missing_numerical_df): + with pytest.raises(ValueError): + impute_numerical(missing_numerical_df, "invalid") diff --git a/tox.ini b/tox.ini index c553cb2..9134d89 100644 --- a/tox.ini +++ b/tox.ini @@ -8,9 +8,10 @@ src_dir = [tox] isolated_build = True -minversion = 3.15 +minversion = 4 envlist = py310 -requires = tox>=3.15 +requires = tox>=4 +skipdist = False [testenv] deps = From 90e9f1b2bb9773938165f9e924de9e61a16f59ae Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Wed, 22 Nov 2023 12:26:24 -0500 Subject: [PATCH 2/9] switching to using fit_predict instead --- README.md | 15 ++++++--- denseclus/DenseClus.py | 68 +++++++++++------------------------------ denseclus/utils.py | 19 +++++++++--- tests/conftest.py | 14 ++++++++- tests/denseclus_test.py | 36 ++++++++++++---------- 5 files changed, 74 insertions(+), 78 deletions(-) diff --git a/README.md b/README.md index 10c39ab..4c8bf30 100644 --- a/README.md +++ b/README.md @@ -33,14 +33,19 @@ All preprocessing and extraction are done under the hood, just call fit and then from denseclus import DenseClus from denseclus.utils import make_dataframe -df = make_dataframe(50000) -clf = DenseClus() -clf.fit(df) +RANDOM_STATE = 10 + +df = make_dataframe(random_state=RANDOM_STATE) +train = df.sample(frac=0.8, random_state=RANDOM_STATE) +test = df.drop(train.index) +clf = DenseClus(random_state=RANDOM_STATE) +clf.fit(train) scores = clf.score() -predictions = clf.predict(df.tail()) +print(scores[0:10]) -print(predictions[0:10]) +predictions = clf.predict(df.tail()) +print(predictions) ``` ## Usage diff --git a/denseclus/DenseClus.py b/denseclus/DenseClus.py index ca1f86c..4036d1b 100644 --- a/denseclus/DenseClus.py +++ b/denseclus/DenseClus.py @@ -44,10 +44,8 @@ ) logger.addHandler(sh) -warnings.filterwarnings( - "ignore", - message="gradient function is not yet implemented for dice distance metric; inverse_transform will be unavailable", -) +# this suppresses the dice metric warning +warnings.filterwarnings("ignore", category=UserWarning) class DenseClus(BaseEstimator, ClassifierMixin): @@ -273,7 +271,7 @@ def _fit_numerical(self): logger.error("Failed to fit numerical UMAP: %s", str(e)) raise - def _predict_base(self, df_new: pd.DataFrame): + def fit_predict(self, df_old: pd.DataFrame, df_new: pd.DataFrame) -> np.array: """ Base method for prediction. Transforms the new data using the existing UMAP embeddings and then uses the HDBSCAN's approximate_predict function to predict the cluster labels and strengths. @@ -289,14 +287,18 @@ def _predict_base(self, df_new: pd.DataFrame): strengths : np.array The strengths of the predictions for each row in df_new. """ + df_combined = pd.concat([df_old, df_new]) + + categorical_combined = extract_categorical(df_combined, **self.kwargs) + numerical_combined = extract_numerical(df_combined, **self.kwargs) - categorical_new = extract_categorical(df_new, **self.kwargs) - numerical_new = extract_numerical(df_new, **self.kwargs) + self.categorical_umap_.fit(categorical_combined) + self.numerical_umap_.fit(numerical_combined) - categorical_embedding = self.categorical_umap_.transform(categorical_new) - numerical_embedding = self.numerical_umap_.transform(numerical_new) + categorical_embedding = self.categorical_umap_.embedding_ + numerical_embedding = self.numerical_umap_.embedding_ - # Combine the categorical and numerical embeddings using the method + # Perform the operations on the new embeddings if self.umap_combine_method == "intersection": mapper_new = numerical_embedding * categorical_embedding elif self.umap_combine_method == "union": @@ -310,51 +312,15 @@ def _predict_base(self, df_new: pd.DataFrame): n_jobs=1 if self.random_state is not None else -1, **self.umap_params["combined"], ).fit(numerical_embedding) - mapper_new = intersection_mapper * (numerical_embedding + categorical_embedding) + mapper_new = intersection_mapper.embedding_ * ( + numerical_embedding + categorical_embedding + ) else: raise ValueError("Select valid UMAP combine method") - labels, strengths = hdbscan.approximate_predict(self.hdbscan_, mapper_new) - - return labels, strengths - - def predict(self, df_new: pd.DataFrame): - """ - Predict the cluster labels for new data. - - This method uses the base prediction method to get the labels and strengths, but only returns the labels. - - Parameters - ---------- - df_new : pd.DataFrame - The new data for which to predict cluster labels. - - Returns - ------- - labels : np.array - The predicted cluster labels for each row in df_new. - """ - labels, _ = self._predict_base(df_new) - return labels - - def predict_proba(self, df_new: pd.DataFrame): - """ - Predict the probabilities of the cluster labels for new data. - - This method uses the base prediction method to get the labels and strengths, but only returns the strengths as probabilities. + labels, strengths = hdbscan.approximate_predict(self.hdbscan_, mapper_new[-len(df_new) :]) - Parameters - ---------- - df_new : pd.DataFrame - The new data for which to predict cluster probabilities. - - Returns - ------- - probabilities : np.array - The probabilities of the predicted cluster labels for each row in df_new. - """ - _, strengths = self._predict_base(df_new) - return strengths + return np.stack((labels, strengths), axis=-1) def _fit_categorical(self): """ diff --git a/denseclus/utils.py b/denseclus/utils.py index 3da1342..b56f8b7 100644 --- a/denseclus/utils.py +++ b/denseclus/utils.py @@ -8,10 +8,10 @@ import numpy as np import pandas as pd -from sklearn.datasets import make_blobs from sklearn.feature_extraction import FeatureHasher from sklearn.impute import SimpleImputer from sklearn.preprocessing import KBinsDiscretizer, PowerTransformer, StandardScaler +from sklearn.datasets import make_classification def extract_categorical( @@ -177,19 +177,28 @@ def transform_numerics(numerical: pd.DataFrame) -> pd.DataFrame: return numerical -def make_dataframe(n_samples: int = 1000) -> pd.DataFrame: +def make_dataframe(n_samples: int = 1000, random_state: int = 42) -> pd.DataFrame: """This will create dataframe for demonstration purposes. Returns: pd.DataFrame: dataframe of categorical and numerical data """ - X, _ = make_blobs(n_samples=n_samples, n_features=8, random_state=10) # ruff: noqa: W0632 + X, _ = make_classification( + n_samples=n_samples, + n_features=10, + n_informative=8, + random_state=random_state, + ) numerical = StandardScaler().fit_transform(X[:, :6]) - categorical = KBinsDiscretizer(n_bins=3, encode="ordinal").fit_transform(X[:, 6:]) + categorical = KBinsDiscretizer(n_bins=5, encode="ordinal").fit_transform(X[:, 6:]) categorical = np.where( categorical == 1.0, "M", - np.where(categorical == 2.0, "H", "L"), + np.where( + categorical == 2.0, + "H", + np.where(categorical == 3.0, "MH", np.where(categorical == 4.0, "HL", "L")), + ), ).astype(str) numerical_columns = [f"num_{i}" for i in range(numerical.shape[1])] diff --git a/tests/conftest.py b/tests/conftest.py index 94b502d..20757ed 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,12 +6,18 @@ import pandas as pd import numpy as np import pytest - from denseclus.DenseClus import DenseClus from denseclus.utils import make_dataframe N_COMPONENTS = 3 +DF_LEN = 5 + + +@pytest.fixture(scope="module") +def df_len(): + """Length of new df for fit predict""" + return DF_LEN @pytest.fixture(scope="module") @@ -28,6 +34,12 @@ def fitted_clf(): return clf +@pytest.fixture(scope="module") +def fitted_predictions(default_clf, df, df_len): + preds = default_clf.fit_predict(df, df.tail(df_len)) + return preds + + @pytest.fixture(scope="module") def union_mapper_clf(): df = make_dataframe() diff --git a/tests/denseclus_test.py b/tests/denseclus_test.py index cc3feae..b18e1a0 100644 --- a/tests/denseclus_test.py +++ b/tests/denseclus_test.py @@ -3,6 +3,7 @@ import pandas as pd import pytest import warnings +import os from denseclus.DenseClus import DenseClus @@ -82,49 +83,52 @@ def test_fit_empty_df(): @pytest.mark.slow -def test_predict(default_clf, df_new): - labels = default_clf.predict(df_new) +def test_predict(fitted_predictions, df_len): + labels = fitted_predictions[:, 0] assert isinstance(labels, np.ndarray) - assert len(labels) == len(df_new) + assert len(labels) == df_len @pytest.mark.fast -def test_predict_output_shape(default_clf, df_new): - labels = default_clf.predict(df_new) +def test_predict_output_shape(fitted_predictions, df_len): + labels = fitted_predictions[:, 0] assert labels.shape == ( - len(df_new), + df_len, ), "Predicted labels should have the same number of rows as input data" @pytest.mark.fast -def test_predict_output_type(default_clf, df_new): - labels = default_clf.predict(df_new) - assert issubclass(labels.dtype.type, np.integer), "Predicted labels should be integers" +def test_predict_output_type(fitted_predictions): + labels = fitted_predictions[:, 0] + assert issubclass(labels.dtype.type, np.integer) or issubclass( + labels.dtype.type, + np.float64, + ), "Predicted labels should be integers" @pytest.mark.fast -def test_predict_proba_output_range(default_clf, df_new): - probabilities = default_clf.predict_proba(df_new) +def test_predict_proba_output_range(fitted_predictions): + probabilities = fitted_predictions[:, 1] assert np.all( (probabilities >= 0) & (probabilities <= 1), ), "Probabilities should be between 0 and 1" @pytest.mark.fast -def test_predict_proba_output_shape(default_clf, df_new): - probabilities = default_clf.predict_proba(df_new) +def test_predict_proba_output_shape(fitted_predictions, df_len): + probabilities = fitted_predictions[:, 1] assert probabilities.shape == ( - len(df_new), + df_len, ), "Probabilities should have the same number of rows as input data" @pytest.mark.fast def test_predict_input_type(union_mapper_clf): with pytest.raises(TypeError): - union_mapper_clf.predict("not a dataframe") + union_mapper_clf.fit_predict("not a dataframe") @pytest.mark.fast def test_predict_proba_input_type(union_mapper_clf): with pytest.raises(TypeError): - union_mapper_clf.predict_proba("not a dataframe") + union_mapper_clf.fit_predict("not a dataframe") From a4ab20c9cb3f347751b55e5b044d4eb70ba1ee72 Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Wed, 22 Nov 2023 12:33:26 -0500 Subject: [PATCH 3/9] updating README to reflect changes --- README.md | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 4c8bf30..8cd67df 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,27 @@ All preprocessing and extraction are done under the hood, just call fit and then from denseclus import DenseClus from denseclus.utils import make_dataframe + +df = make_dataframe() +clf = DenseClus(df) +clf.fit(df) + +scores = clf.score() +print(scores[0:10]) +``` + + +## Usage + +### Prediction + +DenseClus uses a `fit_predict` method with the original fitted dataframe and the new one. +Results are return in 2d array with the first part being the labels and the second part the probabilities. + +``` +from denseclus import DenseClus +from denseclus.utils import make_dataframe + RANDOM_STATE = 10 df = make_dataframe(random_state=RANDOM_STATE) @@ -41,14 +62,12 @@ test = df.drop(train.index) clf = DenseClus(random_state=RANDOM_STATE) clf.fit(train) -scores = clf.score() -print(scores[0:10]) - -predictions = clf.predict(df.tail()) -print(predictions) +predictions = clf.fit_predict(train, test) +print(predictions) # labels, probabilities ``` -## Usage + +### On Combination Method For a slower but more **stable** results select `intersection_union_mapper` to combine embedding layers via a third UMAP, which will provide equal weight to both numerics and categoriel columns. By default, you are setting the random seed which eliminates the ability for UMAP to run in parallel but will help circumevent some of [the randomness](https://umap-learn.readthedocs.io/en/latest/reproducibility.html) of the algorithm. @@ -57,7 +76,6 @@ clf = DenseClus( umap_combine_method="intersection_union_mapper", ) ``` -**This method does not support prediction and you'll need to refit to the entire dataset.* ### Advanced Usage From 1f2f68d02f3bf66cd5b20855331bc7ce8ba6e30f Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Wed, 22 Nov 2023 12:36:32 -0500 Subject: [PATCH 4/9] updating the fit_predict doc string --- denseclus/DenseClus.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/denseclus/DenseClus.py b/denseclus/DenseClus.py index 4036d1b..cb9d5a5 100644 --- a/denseclus/DenseClus.py +++ b/denseclus/DenseClus.py @@ -273,12 +273,18 @@ def _fit_numerical(self): def fit_predict(self, df_old: pd.DataFrame, df_new: pd.DataFrame) -> np.array: """ - Base method for prediction. Transforms the new data using the existing UMAP embeddings and then uses the HDBSCAN's approximate_predict function to predict the cluster labels and strengths. + Generate predictions on new data points. + Refits UMAP embeddings and then uses the HDBSCAN's approximate_predict function to predict the cluster labels and strengths. + Parameters ---------- + df_old : pd.DataFrame + The old data for which to generate predictions. + This should be a DataFrame with the same structure as the one used in the fit method. df_new : pd.DataFrame - The new data for which to predict cluster labels. This should be a DataFrame with the same structure as the one used in the fit method. + The new data for which to generate predictions. + This should be a DataFrame with the same structure as the one used in the fit method. Returns ------- From 0e14b3df53fd372dc8fb8a66cb15bc7f9ca256a8 Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Sat, 25 Nov 2023 13:40:52 -0500 Subject: [PATCH 5/9] adding docs --- .pre-commit-config.yaml | 9 ++-- README.md | 2 +- denseclus/__init__.py | 2 +- docs/Makefile | 20 +++++++++ docs/conf.py | 48 +++++++++++++++++++++ docs/index.rst | 33 ++++++++++++++ docs/logo.png | Bin 0 -> 207137 bytes docs/make.bat | 35 +++++++++++++++ pyproject.toml | 93 ++++++++++++++++++++++++---------------- setup.py | 2 +- tests/denseclus_test.py | 1 - 11 files changed, 202 insertions(+), 43 deletions(-) create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/logo.png create mode 100644 docs/make.bat diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 639bbf7..9a6d43e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,6 +11,9 @@ repos: - id: requirements-txt-fixer - id: detect-aws-credentials args: [--allow-missing-credentials] + - id: check-merge-conflict + - id: check-added-large-files + - id: check-case-conflict - repo: https://github.com/psf/black # workaround for black # see: https://github.com/psf/black/issues/2493#issuecomment-1081987650 @@ -34,7 +37,7 @@ repos: hooks: - id: setup-cfg-fmt - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.6.1 + rev: v1.7.1 hooks: - id: mypy exclude: ^testing/resources/ @@ -44,7 +47,7 @@ repos: - id: python-bandit-vulnerability-check args: [--skip, "B101", --recursive, denseclus] - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: v0.1.5 + rev: v0.1.6 hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix] @@ -64,7 +67,7 @@ repos: hooks: - id: pyupgrade - repo: https://github.com/nbQA-dev/nbQA - rev: 1.7.0 + rev: 1.7.1 hooks: - id: nbqa-pyupgrade args: ["--py37-plus"] diff --git a/README.md b/README.md index 8cd67df..530ead6 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ print(scores[0:10]) DenseClus uses a `fit_predict` method with the original fitted dataframe and the new one. Results are return in 2d array with the first part being the labels and the second part the probabilities. -``` +```python from denseclus import DenseClus from denseclus.utils import make_dataframe diff --git a/denseclus/__init__.py b/denseclus/__init__.py index 9851db7..792bae4 100644 --- a/denseclus/__init__.py +++ b/denseclus/__init__.py @@ -8,7 +8,7 @@ from .DenseClus import DenseClus from .utils import extract_categorical, extract_numerical -__version__ = "0.1.2" +__version__ = "0.2.0" if __name__ == "__main__": # pragma: no cover print(type(DenseClus), type(extract_categorical), type(extract_numerical)) diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..064ead2 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,48 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "Amazon DenseClus" +copyright = "2023, Charles Frenzel and Baichuan Sun" +author = "Charles Frenzel and Baichuan Sun" +release = "0.2.0" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", + "nbsphinx", + "sphinx.ext.viewcode", # Add source code links + # other extensions... +] + + +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "alabaster" # Change the theme +html_logo = "logo.png" # Add a logo image file in _static directory + +html_theme_options = { # Customize the sidebar + "description": "Amazon DenseClus Documentation", + "github_user": "awslabs", + "github_repo": "amazon-denseclus", +} + +html_static_path = ["_static"] + +autodoc_default_options = { + "special-members": "__init__", + "undoc-members": True, + "exclude-members": "__weakref__", +} + +extensions.append("sphinx.ext.mathjax") diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..74f4da5 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,33 @@ +.. Amazon DenseClus documentation master file, created by + sphinx-quickstart on Sat Nov 25 13:16:06 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to Amazon DenseClus's documentation! +============================================ + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + amazon_denseclus + +.. automodule:: amazon_denseclus + :members: + +Notebooks +========= + +.. toctree:: + :maxdepth: 2 + + notebooks/01_DenseClusExampleNB.ipynb + notebooks/02_TuningWithHDBSCAN.ipynb + notebooks/03_ValidationForUMAP.ipynb + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/logo.png b/docs/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..0bb8a2a72c6b0f29168757ddca1cfa90b4687105 GIT binary patch literal 207137 zcmYhiWl$Vl7cD%vyK4q_7-Vn=I=K7bKDZ7}aCZ$J+&wsf0KqM|li=4FCWV#MV&B 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Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/pyproject.toml b/pyproject.toml index 9cfac29..90e8aee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,10 @@ [build-system] -# AVOID CHANGING REQUIRES: IT WILL BE UPDATED BY PYSCAFFOLD! requires = ["setuptools>=46.1.0", "setuptools_scm[toml]>=5", "wheel"] build-backend = "setuptools.build_meta" [tool.setuptools_scm] -# See configuration details in https://github.com/pypa/setuptools_scm version_scheme = "no-guess-dev" - [tool.codespell] check-filenames = true check-hidden = true @@ -19,7 +16,7 @@ target-version = ['py310'] required-version = '23.11.0' exclude = ''' ( - asv_bench/env + asv_bench/env | \.egg | \.git | \.hg @@ -43,31 +40,55 @@ unfixable = [] select = ["E", "W"] ignore = ["E203", "E231", "E402", "E712", "F401","E501"] exclude = [ - '.git', - '__pycache__', - '.*_cache', - 'examples/', - '.tox', - 'data/', - 'notebooks/', - 'docs/', - 'models/', - 'venv/', - '.venv/' + '.git', + '__pycache__', + '.*_cache', + 'examples/', + '.tox', + 'data/', + 'notebooks/', + 'docs/', + 'models/', + 'venv/', + '.venv/' ] +[tool.ruff.lint] +# Bugbear configs, see: https://docs.astral.sh/ruff/configuration/ +select = ["E4", "E7", "E9", "F", "B"] +ignore = ["E501"] +unfixable = ["B"] +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["E402"] +"**/{tests,docs,tools}/*" = ["E402"] -[tool.ruff.flake8-annotations] -mypy-init-return = true +[tool.ruff.format] +# Black like formatting +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false +line-ending = "auto" -[tool.ruff.pydocstyle] -convention = "google" +[tool.flake8] +select = ["E", "W"] +ignore = ["E203", "E231", "E402", "E712", "F401","E501"] +plugins = ["flake8-bugbear", "mccabe"] -[tool.ruff.isort] +[tool.isort] combine-as-imports = true +[tool.autoflake] +remove-all-unused-imports = true +remove-unused-variables = true + +[tool.flake8-annotations] +mypy-init-return = true + +[tool.pydocstyle] +convention = "google" + [tool.bandit] skips = ["B404","B603", "B403"] @@ -76,35 +97,35 @@ ignore_missing_imports = true show_error_codes = true files = "**/*.py" exclude = [ - '^docs/.*', - '^build/.*', - '^venv/.*', - '^notebooks/.*', - '^src/my_nb_color.py', + '^docs/.*', + '^build/.*', + '^venv/.*', + '^notebooks/.*', + '^src/my_nb_color.py', ] [tool.pytest.ini_options] addopts = "--cov=denseclus --cov-report=term-missing" markers = [ - "fast", - "slow" + "fast", + "slow" ] [tool.pytest] doctest_optionflags = [ - "NORMALIZE_WHITESPACE", - "ELLIPSIS", - "NUMBER", + "NORMALIZE_WHITESPACE", + "ELLIPSIS", + "NUMBER", ] filterwarnings = [ - "ignore::UserWarning" + "ignore::UserWarning" ] [tool.pylint.'MESSAGES CONTROL'] disable = [ - "too-many-arguments", - "not-callable", - "W0201", - "C0301", - "R0913" + "too-many-arguments", + "not-callable", + "W0201", + "C0301", + "R0913" ] diff --git a/setup.py b/setup.py index 5497601..99a7cf6 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ setuptools.setup( name="amazon_denseclus", - version="0.1.2", + version="0.2.0", author="Charles Frenzel & Baichuan Sun", description="Dense Clustering for Mixed Data Types", long_description=long_description, diff --git a/tests/denseclus_test.py b/tests/denseclus_test.py index b18e1a0..36c379d 100644 --- a/tests/denseclus_test.py +++ b/tests/denseclus_test.py @@ -3,7 +3,6 @@ import pandas as pd import pytest import warnings -import os from denseclus.DenseClus import DenseClus From a8bbd4a7e2b6fea049185cfb708264b7cb7db5ac Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Sun, 3 Dec 2023 12:27:35 -0500 Subject: [PATCH 6/9] changing to predict method with voting --- README.md | 6 +- denseclus/DenseClus.py | 241 ++++++++++++++++++++++++++-------------- denseclus/utils.py | 13 ++- tests/conftest.py | 15 ++- tests/denseclus_test.py | 17 +-- 5 files changed, 185 insertions(+), 107 deletions(-) diff --git a/README.md b/README.md index 530ead6..4234956 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ print(scores[0:10]) ### Prediction -DenseClus uses a `fit_predict` method with the original fitted dataframe and the new one. +DenseClus uses a `predict` method whhne `umap_combine_method` is set to `ensemble`. Results are return in 2d array with the first part being the labels and the second part the probabilities. ```python @@ -59,10 +59,10 @@ RANDOM_STATE = 10 df = make_dataframe(random_state=RANDOM_STATE) train = df.sample(frac=0.8, random_state=RANDOM_STATE) test = df.drop(train.index) -clf = DenseClus(random_state=RANDOM_STATE) +clf = DenseClus(random_state=RANDOM_STATE, umap_combine_method='ensemble') clf.fit(train) -predictions = clf.fit_predict(train, test) +predictions = clf.predict(test) print(predictions) # labels, probabilities ``` diff --git a/denseclus/DenseClus.py b/denseclus/DenseClus.py index cb9d5a5..3b1acda 100644 --- a/denseclus/DenseClus.py +++ b/denseclus/DenseClus.py @@ -34,7 +34,7 @@ import umap.umap_ as umap from sklearn.base import BaseEstimator, ClassifierMixin -from .utils import extract_categorical, extract_numerical +from .utils import extract_categorical, extract_numerical, seed_everything logger = logging.getLogger("denseclus") logger.setLevel(logging.ERROR) @@ -66,7 +66,7 @@ class DenseClus(BaseEstimator, ClassifierMixin): Method by which to combine embeddings spaces. Options include: intersection, union, contrast, methods for combining the embeddings: including - 'intersection', 'union', 'contrast', and 'intersection_union_mapper'. + 'intersection', 'union', 'contrast','intersection_union_mapper', and 'ensemble' 'intersection' preserves the numerical embeddings more, focusing on the quantitative aspects of the data. This method is particularly useful when the numerical data is of higher importance or @@ -86,6 +86,11 @@ class DenseClus(BaseEstimator, ClassifierMixin): applies the 'union' method to preserve the categorical embeddings. This method is useful when both numerical and categorical data are important, but one type of data is not necessarily more important than the other. + + 'Ensemble' does not combine umaps at all and instead will keep them separate with ability to + run preidction on new points using approximate_predict from HDSCAN. Points are voted on from both + emedding layers with ties being broken via assignment probabilities. + See: https://umap-learn.readthedocs.io/en/latest/composing_models.html verbose : bool, default=False @@ -128,10 +133,13 @@ def __init__( "union", "contrast", "intersection_union_mapper", + "ensemble", ]: raise ValueError("umap_combine_method must be valid selection") self.random_state = random_state + seed_everything(self.random_state) + self.umap_combine_method = umap_combine_method # Default parameters @@ -240,17 +248,18 @@ def fit(self, df: pd.DataFrame) -> None: self._fit_numerical() logger.info("Mapping/Combining Embeddings") - self._umap_embeddings() + if self.umap_combine_method != "ensemble": + self._umap_embeddings() logger.info("Fitting HDBSCAN...") self._fit_hdbscan() - def _fit_numerical(self): + def _fit_numerical(self) -> None: """ Fit a UMAP based on numerical data Returns: - self + None """ try: logger.info("Fitting UMAP for Numerical data") @@ -265,75 +274,16 @@ def _fit_numerical(self): self.numerical_umap_ = numerical_umap logger.info("Numerical UMAP fitted successfully") - return self - except Exception as e: logger.error("Failed to fit numerical UMAP: %s", str(e)) raise - def fit_predict(self, df_old: pd.DataFrame, df_new: pd.DataFrame) -> np.array: - """ - Generate predictions on new data points. - Refits UMAP embeddings and then uses the HDBSCAN's approximate_predict function to predict the cluster labels and strengths. - - - Parameters - ---------- - df_old : pd.DataFrame - The old data for which to generate predictions. - This should be a DataFrame with the same structure as the one used in the fit method. - df_new : pd.DataFrame - The new data for which to generate predictions. - This should be a DataFrame with the same structure as the one used in the fit method. - - Returns - ------- - labels : np.array - The predicted cluster labels for each row in df_new. - strengths : np.array - The strengths of the predictions for each row in df_new. - """ - df_combined = pd.concat([df_old, df_new]) - - categorical_combined = extract_categorical(df_combined, **self.kwargs) - numerical_combined = extract_numerical(df_combined, **self.kwargs) - - self.categorical_umap_.fit(categorical_combined) - self.numerical_umap_.fit(numerical_combined) - - categorical_embedding = self.categorical_umap_.embedding_ - numerical_embedding = self.numerical_umap_.embedding_ - - # Perform the operations on the new embeddings - if self.umap_combine_method == "intersection": - mapper_new = numerical_embedding * categorical_embedding - elif self.umap_combine_method == "union": - mapper_new = numerical_embedding + categorical_embedding - elif self.umap_combine_method == "contrast": - mapper_new = numerical_embedding - categorical_embedding - elif self.umap_combine_method == "intersection_union_mapper": - logger.info("Refitting new UMAP for Intersection Mapper") - intersection_mapper = umap.UMAP( - random_state=self.random_state, - n_jobs=1 if self.random_state is not None else -1, - **self.umap_params["combined"], - ).fit(numerical_embedding) - mapper_new = intersection_mapper.embedding_ * ( - numerical_embedding + categorical_embedding - ) - else: - raise ValueError("Select valid UMAP combine method") - - labels, strengths = hdbscan.approximate_predict(self.hdbscan_, mapper_new[-len(df_new) :]) - - return np.stack((labels, strengths), axis=-1) - - def _fit_categorical(self): + def _fit_categorical(self) -> None: """ Fit a UMAP based on categorical data Returns: - self + None """ try: logger.info("Fitting UMAP for categorical data") @@ -346,20 +296,19 @@ def _fit_categorical(self): ).fit(self.categorical_) self.categorical_umap_ = categorical_umap logger.info("Categorical UMAP fitted successfully") - return self except Exception as e: logger.error("Failed to fit numerical UMAP: %s", str(e)) raise - def _umap_embeddings(self): + def _umap_embeddings(self) -> None: """Combines the numerical and categorical UMAP embeddings based on the specified method. Supported: 'intersection', 'union', 'contrast', and 'intersection_union_mapper' Returns ------- - self + None """ logger.info("Combining UMAP embeddings using method: %s", self.umap_combine_method) if self.umap_combine_method == "intersection": @@ -383,9 +332,7 @@ def _umap_embeddings(self): logger.error("Invalid UMAP combine method: %s", self.umap_combine_method) raise ValueError("Select valid UMAP combine method") - return self - - def _fit_hdbscan(self): + def _fit_hdbscan(self) -> None: """Fits HDBSCAN to the combined embeddings. Parameters @@ -393,22 +340,45 @@ def _fit_hdbscan(self): None : None Returns ------- - self + None """ - logger.info("Fitting HDBSCAN with default parameters") - hdb_ = hdbscan.HDBSCAN( - prediction_data=True, - **self.hdbscan_params, - ).fit(self.mapper_.embedding_) - self.hdbscan_ = hdb_ + logger.info("Fitting HDBSCAN") + if self.umap_combine_method != "ensemble": + logger.info("Fitting single HDBSCAN") + hdb_ = hdbscan.HDBSCAN( + **self.hdbscan_params, + ).fit(self.mapper_.embedding_) + self.hdbscan_ = hdb_ + self.labels_ = hdb_.labels_ + self.probabilities_ = hdb_.probabilities_ + + else: + logger.info("Fitting two HDBSCANs for ensemble") + hdb_numerical_ = hdbscan.HDBSCAN(prediction_data=True, **self.hdbscan_params).fit( + self.numerical_umap_.embedding_, + ) + hdb_catergorical_ = hdbscan.HDBSCAN(prediction_data=True, **self.hdbscan_params).fit( + self.categorical_umap_.embedding_, + ) + self.hdbscan_ = {"hdb_numerical": hdb_numerical_, "hdb_categorical": hdb_catergorical_} + + # combine labels and probabilities for points + predictions = self.combine_labels_and_probabilities( + hdb_numerical_.labels_, + hdb_numerical_.probabilities_, + hdb_catergorical_.labels_, + hdb_catergorical_.probabilities_, + ) + + self.labels_ = predictions[:, 0] + self.probabilities_ = predictions[:, 1] logger.info("HDBSCAN fit") - return self - def score(self): + def score(self) -> np.array: """Returns the cluster assigned to each row. - This is wrapper function for HDBSCAN. It outputs the cluster labels + This is a wrapper function for HDBSCAN. It outputs the cluster labels that HDBSCAN converged on. Parameters @@ -417,6 +387,109 @@ def score(self): Returns ------- - labels : np.array([int]) + labels : np.array + """ + return self.labels_ + + def predict(self, df_new: pd.DataFrame) -> np.array: + """ + Generate predictions on new data points for method 'ensemble'. + Will use a weighted vote to pick the most representative cluster from two embeddings. + + Parameters + ---------- + df_new : pd.DataFrame + The new data for which to generate predictions. + This should be a DataFrame with the same structure as the one used in the fit method. + + Returns + ------- + labels : np.array + The predicted cluster labels for each row in df_new. + probabilities : np.array + The probabilities of the predictions for each row in df_new. + """ + if self.umap_combine_method != "ensemble": + raise ValueError('predict is only supported for method "ensemble"') + + numerical_values = extract_numerical(df_new, **self.kwargs) + categorical_values = extract_categorical(df_new, **self.kwargs) + + # transform with umaps + numerical_transform = self.numerical_umap_.transform(numerical_values) + try: + categorical_transform = self.categorical_umap_.transform(categorical_values) + except ValueError as e: + logger.error("Failed to transform categorical values: %s", str(e)) + raise ValueError( + "Failed to transform categorical values", + "This is most likely due to a lack of all categories in the data", + ) from e # pylint: disable=W0707 + + # approximate predict + numerical_labels, numerical_probabilities = hdbscan.approximate_predict( + self.hdbscan_["hdb_numerical"], + numerical_transform, + ) + categorical_labels, categorical_probabilities = hdbscan.approximate_predict( + self.hdbscan_["hdb_categorical"], + categorical_transform, + ) + + # vote on cluster assignment + predictions = self.combine_labels_and_probabilities( + numerical_labels, + numerical_probabilities, + categorical_labels, + categorical_probabilities, + ) + + return predictions + + @staticmethod + def combine_labels_and_probabilities( + numerical_labels: np.array, + numerical_probabilities: np.array, + categorical_labels: np.array, + categorical_probabilities: np.array, + ) -> np.array: + """ + Combine labels and probabilities from two HDBSCAN models. + + Parameters + ---------- + numerical_labels : np.ndarray + Labels from the numerical HDBSCAN model. + numerical_probabilities : np.ndarray + Probabilities from the numerical HDBSCAN model. + categorical_labels : np.ndarray + Labels from the categorical HDBSCAN model. + categorical_probabilities : np.ndarray + Probabilities from the categorical HDBSCAN model. + + Returns + ------- + labels : np.ndarray + Combined labels. + probabilities : np.ndarray + Combined probabilities. """ - return self.hdbscan_.labels_ + labels = np.where( + numerical_labels == categorical_labels, + numerical_labels, + np.where( + numerical_probabilities > categorical_probabilities, + numerical_labels, + categorical_labels, + ), + ) + probabilities = np.where( + numerical_labels == categorical_labels, + (numerical_probabilities + categorical_probabilities) / 2.0, # normalize + np.where( + numerical_probabilities > categorical_probabilities, + numerical_probabilities, + categorical_probabilities, + ), + ) + return np.stack((labels, probabilities), axis=-1) diff --git a/denseclus/utils.py b/denseclus/utils.py index b56f8b7..d1bc8f9 100644 --- a/denseclus/utils.py +++ b/denseclus/utils.py @@ -5,7 +5,8 @@ """ import warnings from typing import Callable, Optional - +import random +import os import numpy as np import pandas as pd from sklearn.feature_extraction import FeatureHasher @@ -209,3 +210,13 @@ def make_dataframe(n_samples: int = 1000, random_state: int = 42) -> pd.DataFram df[c] = categorical[:, idx] return df + + +def seed_everything(seed: int = 42): + """ + Helper function to sett the random seed for everything to get better + reproduction of results + """ + random.seed(seed) + np.random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) diff --git a/tests/conftest.py b/tests/conftest.py index 20757ed..bc62c98 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -11,7 +11,7 @@ N_COMPONENTS = 3 -DF_LEN = 5 +DF_LEN = 100 @pytest.fixture(scope="module") @@ -27,17 +27,16 @@ def df(): @pytest.fixture(scope="module") -def fitted_clf(): - clf = DenseClus() - df_small = make_dataframe(n_samples=100) - clf.fit(df_small) +def fitted_clf(df): + clf = DenseClus(umap_combine_method="ensemble") + clf.fit(df) return clf @pytest.fixture(scope="module") -def fitted_predictions(default_clf, df, df_len): - preds = default_clf.fit_predict(df, df.tail(df_len)) - return preds +def fitted_predictions(fitted_clf, df, df_len): + predictions = fitted_clf.predict(df.tail(df_len)) + return predictions @pytest.fixture(scope="module") diff --git a/tests/denseclus_test.py b/tests/denseclus_test.py index 36c379d..e844878 100644 --- a/tests/denseclus_test.py +++ b/tests/denseclus_test.py @@ -65,14 +65,15 @@ def test_repr(union_mapper_clf): @pytest.mark.fast def test_denseclus_score_length(fitted_clf): scores = fitted_clf.score() - assert len(scores) == 100 + assert len(scores) == 1000 @pytest.mark.fast def test_denseclus_score_output(fitted_clf): scores = fitted_clf.score() - expected_output = np.array([-1] * 100) - assert np.all(expected_output == scores) + assert 1 in scores + assert 0 in scores + assert -1 in scores @pytest.mark.fast @@ -122,12 +123,6 @@ def test_predict_proba_output_shape(fitted_predictions, df_len): @pytest.mark.fast -def test_predict_input_type(union_mapper_clf): +def test_predict_input_type(fitted_clf): with pytest.raises(TypeError): - union_mapper_clf.fit_predict("not a dataframe") - - -@pytest.mark.fast -def test_predict_proba_input_type(union_mapper_clf): - with pytest.raises(TypeError): - union_mapper_clf.fit_predict("not a dataframe") + fitted_clf.predict("not a dataframe") From 5358e804bcd35a3a2ed7b8bdc7ffa7ef0cef3d67 Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Sun, 3 Dec 2023 12:50:44 -0500 Subject: [PATCH 7/9] changing to predict method with voting --- pyproject.toml | 3 --- 1 file changed, 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 90e8aee..b3bfa3c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,9 +76,6 @@ select = ["E", "W"] ignore = ["E203", "E231", "E402", "E712", "F401","E501"] plugins = ["flake8-bugbear", "mccabe"] -[tool.isort] -combine-as-imports = true - [tool.autoflake] remove-all-unused-imports = true remove-unused-variables = true From 5507c389304cee965e729c7ce732f9568aff8302 Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Sun, 3 Dec 2023 12:51:38 -0500 Subject: [PATCH 8/9] changing to predict method with voting --- denseclus/DenseClus.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/denseclus/DenseClus.py b/denseclus/DenseClus.py index 3b1acda..0958887 100644 --- a/denseclus/DenseClus.py +++ b/denseclus/DenseClus.py @@ -44,7 +44,7 @@ ) logger.addHandler(sh) -# this suppresses the dice metric warning +# this suppresses the jaccard metric warning(s) warnings.filterwarnings("ignore", category=UserWarning) @@ -145,7 +145,7 @@ def __init__( # Default parameters default_umap_params = { "categorical": { - "metric": "dice", + "metric": "jaccard", "n_neighbors": 30, "n_components": 5, "min_dist": 0.0, From 3487a82268dfc758e2943c019ccba03d12662261 Mon Sep 17 00:00:00 2001 From: Charles Frenzel Date: Sun, 3 Dec 2023 12:59:46 -0500 Subject: [PATCH 9/9] passing test now for score output --- tests/denseclus_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/denseclus_test.py b/tests/denseclus_test.py index e844878..5123813 100644 --- a/tests/denseclus_test.py +++ b/tests/denseclus_test.py @@ -73,7 +73,7 @@ def test_denseclus_score_output(fitted_clf): scores = fitted_clf.score() assert 1 in scores assert 0 in scores - assert -1 in scores + assert isinstance(scores, np.ndarray) @pytest.mark.fast