.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gyexamples/plot_usparse_xgboost.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_gyexamples_plot_usparse_xgboost.py: .. _example-sparse-tfidf: TfIdf and sparse matrices ========================= .. index:: xgboost, lightgbm, sparse, ensemble `TfidfVectorizer `_ usually creates sparse data. If the data is sparse enough, matrices usually stays as sparse all along the pipeline until the predictor is trained. Sparse matrices do not consider null and missing values as they are not present in the datasets. Because some predictors do the difference, this ambiguity may introduces discrepencies when converter into ONNX. This example looks into several configurations. .. contents:: :local: Imports, setups +++++++++++++++ All imports. It also registered onnx converters for :epgk:`xgboost` and :epkg:`lightgbm`. .. GENERATED FROM PYTHON SOURCE LINES 27-62 .. code-block:: default import warnings import numpy import pandas from tqdm import tqdm from sklearn.compose import ColumnTransformer from sklearn.datasets import load_iris from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer from sklearn.ensemble import ( RandomForestClassifier, HistGradientBoostingClassifier) from xgboost import XGBClassifier from lightgbm import LGBMClassifier from skl2onnx.common.data_types import FloatTensorType, StringTensorType from skl2onnx import to_onnx, update_registered_converter from skl2onnx.sklapi import CastTransformer, ReplaceTransformer from skl2onnx.common.shape_calculator import ( calculate_linear_classifier_output_shapes) from onnxmltools.convert.xgboost.operator_converters.XGBoost import ( convert_xgboost) from onnxmltools.convert.lightgbm.operator_converters.LightGbm import ( convert_lightgbm) from mlprodict.onnxrt import OnnxInference update_registered_converter( XGBClassifier, 'XGBoostXGBClassifier', calculate_linear_classifier_output_shapes, convert_xgboost, options={'nocl': [True, False], 'zipmap': [True, False, 'columns']}) update_registered_converter( LGBMClassifier, 'LightGbmLGBMClassifier', calculate_linear_classifier_output_shapes, convert_lightgbm, options={'nocl': [True, False], 'zipmap': [True, False]}) .. GENERATED FROM PYTHON SOURCE LINES 63-67 Artificial datasets +++++++++++++++++++++++++++ Iris + a text column. .. GENERATED FROM PYTHON SOURCE LINES 67-85 .. code-block:: default cst = ['class zero', 'class one', 'class two'] data = load_iris() X = data.data[:, :2] y = data.target df = pandas.DataFrame(X) df.columns = [f"c{i}" for i in range(X.shape[1])] df["text"] = [cst[i] for i in y] ind = numpy.arange(X.shape[0]) numpy.random.shuffle(ind) X = X[ind, :].copy() y = y[ind].copy() .. GENERATED FROM PYTHON SOURCE LINES 86-92 Train ensemble after sparse +++++++++++++++++++++++++++ The example use the Iris datasets with artifical text datasets preprocessed with a tf-idf. `sparse_threshold=1.` avoids sparse matrices to be converted into dense matrices. .. GENERATED FROM PYTHON SOURCE LINES 92-238 .. code-block:: default def make_pipeline(model, insert_replace, sparse_threshold): if model == HistGradientBoostingClassifier: kwargs = dict(max_iter=5) elif model == XGBClassifier: kwargs = dict(n_estimators=5, use_label_encoder=False) else: kwargs = dict(n_estimators=5) if insert_replace: pipe = Pipeline([ ('union', ColumnTransformer([ ('scale1', StandardScaler(), [0, 1]), ('subject', Pipeline([ ('count', CountVectorizer()), ('tfidf', TfidfTransformer()), ('repl', ReplaceTransformer()), # added transformer ]), "text"), ], sparse_threshold=sparse_threshold)), ('cast', CastTransformer()), ('cls', model(max_depth=3, **kwargs)), ]) else: pipe = Pipeline([ ('union', ColumnTransformer([ ('scale1', StandardScaler(), [0, 1]), ('subject', Pipeline([ ('count', CountVectorizer()), ('tfidf', TfidfTransformer()) ]), "text"), ], sparse_threshold=sparse_threshold)), ('cast', CastTransformer()), ('cls', model(max_depth=3, **kwargs)), ]) return pipe def model_to_onnx(pipe, options): with warnings.catch_warnings(record=False): warnings.simplefilter("ignore", (FutureWarning, UserWarning)) model_onnx = to_onnx( pipe, initial_types=[('input', FloatTensorType([None, 2])), ('text', StringTensorType([None, 1]))], target_opset={'': 14, 'ai.onnx.ml': 2}, options=options) with open('model.onnx', 'wb') as f: f.write(model_onnx.SerializeToString()) return model_onnx def print_status(obs, inputs, pipe, model_onnx, pred_onx, diff, verbose): if verbose: def td(a): if hasattr(a, 'todense'): b = a.todense() ind = set(a.indices) for i in range(b.shape[1]): if i not in ind: b[0, i] = numpy.nan return b return a oinf = OnnxInference(model_onnx) pred_onx2 = oinf.run(inputs) diff2 = numpy.abs( pred_onx2['probabilities'].ravel() - pipe.predict_proba(df).ravel()).sum() obs['discrepency2'] = diff2 if diff > 0.1: for i, (l1, l2) in enumerate( zip(pipe.predict_proba(df), pred_onx['probabilities'])): d = numpy.abs(l1 - l2).sum() if verbose and d > 0.1: print("\nDISCREPENCY DETAILS") print(d, i, l1, l2) pre = pipe.steps[0][-1].transform(df) print("idf", pre[i].dtype, td(pre[i])) pre2 = pipe.steps[1][-1].transform(pre) print("cas", pre2[i].dtype, td(pre2[i])) inter = oinf.run(inputs, intermediate=True) onx = inter['tfidftr_norm'] print("onx", onx.dtype, onx[i]) onx = inter['variable3'] def make_pipelines(df_train, y_train, models=None, sparse_threshold=1., replace_nan=False, insert_replace=False, verbose=False): if models is None: models = [ RandomForestClassifier, HistGradientBoostingClassifier, XGBClassifier, LGBMClassifier] models = [_ for _ in models if _ is not None] pipes = [] for model in tqdm(models): pipe = make_pipeline(model, insert_replace, sparse_threshold) try: pipe.fit(df_train, y_train) except TypeError as e: obs = dict(model=model.__name__, pipe=pipe, error=e, model_onnx=None) pipes.append(obs) continue options = {model: {'zipmap': False}} if replace_nan: options[TfidfTransformer] = {'nan': True} model_onnx = model_to_onnx(pipe, options) # convert oinf = OnnxInference(model_onnx) inputs = {"input": df[["c0", "c1"]].values.astype(numpy.float32), "text": df[["text"]].values} pred_onx = oinf.run(inputs) # check diff = numpy.abs( pred_onx['probabilities'].ravel() - pipe.predict_proba(df).ravel()).sum() obs = dict(model=model.__name__, discrepencies=diff, model_onnx=model_onnx, pipe=pipe) print_status(obs, inputs, pipe, model_onnx, pred_onx, diff, verbose) pipes.append(obs) return pipes data_sparse = make_pipelines(df, y) stat = pandas.DataFrame(data_sparse).drop(['model_onnx', 'pipe'], axis=1) if 'error' in stat.columns: print(stat.drop('error', axis=1)) stat .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/4 [00:00
model discrepencies error
0 RandomForestClassifier 0.914291 NaN
1 HistGradientBoostingClassifier NaN A sparse matrix was passed, but dense data is ...
2 XGBClassifier 5.724362 NaN
3 LGBMClassifier 0.000007 NaN


.. GENERATED FROM PYTHON SOURCE LINES 239-245 Sparse data hurts. Dense data ++++++++++ Let's replace sparse data with dense by using `sparse_threshold=0.` .. GENERATED FROM PYTHON SOURCE LINES 245-253 .. code-block:: default data_dense = make_pipelines(df, y, sparse_threshold=0.) stat = pandas.DataFrame(data_dense).drop(['model_onnx', 'pipe'], axis=1) if 'error' in stat.columns: print(stat.drop('error', axis=1)) stat .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/4 [00:00
model discrepencies
0 RandomForestClassifier 0.376477
1 HistGradientBoostingClassifier 0.000005
2 XGBClassifier 0.000005
3 LGBMClassifier 0.000007


.. GENERATED FROM PYTHON SOURCE LINES 254-256 This is much better. Let's compare how the preprocessing applies on the data. .. GENERATED FROM PYTHON SOURCE LINES 256-263 .. code-block:: default print("sparse") print(data_sparse[-1]['pipe'].steps[0][-1].transform(df)[:2]) print() print("dense") print(data_dense[-1]['pipe'].steps[0][-1].transform(df)[:2]) .. rst-class:: sphx-glr-script-out .. code-block:: none sparse (0, 0) -0.9006811702978088 (0, 1) 1.019004351971607 (0, 2) 0.4323732931220851 (0, 5) 0.9016947018779491 (1, 0) -1.1430169111851105 (1, 1) -0.13197947932162468 (1, 2) 0.4323732931220851 (1, 5) 0.9016947018779491 dense [[-0.90068117 1.01900435 0.43237329 0. 0. 0.9016947 ] [-1.14301691 -0.13197948 0.43237329 0. 0. 0.9016947 ]] .. GENERATED FROM PYTHON SOURCE LINES 264-283 This shows `RandomForestClassifier `_, `XGBClassifier `_ do not process the same way sparse and dense matrix as opposed to `LGBMClassifier `_. And `HistGradientBoostingClassifier `_ fails. Dense data with nan +++++++++++++++++++ Let's keep sparse data in the scikit-learn pipeline but replace null values by nan in the onnx graph. .. GENERATED FROM PYTHON SOURCE LINES 283-291 .. code-block:: default data_dense = make_pipelines(df, y, sparse_threshold=1., replace_nan=True) stat = pandas.DataFrame(data_dense).drop(['model_onnx', 'pipe'], axis=1) if 'error' in stat.columns: print(stat.drop('error', axis=1)) stat .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/4 [00:00
model discrepencies error
0 RandomForestClassifier 37.161304 NaN
1 HistGradientBoostingClassifier NaN A sparse matrix was passed, but dense data is ...
2 XGBClassifier 0.000005 NaN
3 LGBMClassifier 0.000007 NaN


.. GENERATED FROM PYTHON SOURCE LINES 292-301 Dense, 0 replaced by nan ++++++++++++++++++++++++ Instead of using a specific options to replace null values into nan values, a custom transformer called ReplaceTransformer is explicitely inserted into the pipeline. A new converter is added to the list of supported models. It is equivalent to the previous options except it is more explicit. .. GENERATED FROM PYTHON SOURCE LINES 301-309 .. code-block:: default data_dense = make_pipelines(df, y, sparse_threshold=1., replace_nan=False, insert_replace=True) stat = pandas.DataFrame(data_dense).drop(['model_onnx', 'pipe'], axis=1) if 'error' in stat.columns: print(stat.drop('error', axis=1)) stat .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0/4 [00:00
model discrepencies error
0 RandomForestClassifier 43.986234 NaN
1 HistGradientBoostingClassifier NaN A sparse matrix was passed, but dense data is ...
2 XGBClassifier 0.000005 NaN
3 LGBMClassifier 0.000007 NaN


.. GENERATED FROM PYTHON SOURCE LINES 310-316 Conclusion ++++++++++ Unless dense arrays are used, because :epkg:`onnxruntime` ONNX does not support sparse yet, the conversion needs to be tuned depending on the model which follows the TfIdf preprocessing. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 2.279 seconds) .. _sphx_glr_download_gyexamples_plot_usparse_xgboost.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_usparse_xgboost.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_usparse_xgboost.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_