Fixes #60, visualization of a pipeline with passthrough

README.rst

@@ -53,3 +53,12 @@ which partitions the data before fitting a model on each bucket.
 * `GitHub/mlinsights <https://github.com/sdpython/mlinsights/>`_
 * `documentation <http://www.xavierdupre.fr/app/mlinsights/helpsphinx/index.html>`_
 * `Blog <http://www.xavierdupre.fr/app/mlinsights/helpsphinx/blog/main_0000.html#ap-main-0>`_
+
+Function ``pipeline2dot`` converts a pipeline into a graph:
+
+::
+
+    from mlinsights.plotting import pipeline2dot
+    dot = pipeline2dot(clf, df)
+
+.. image:: https://github.com/sdpython/mlinsights/raw/master/_doc/pipeline.png

_doc/sphinxdoc/source/HISTORY.rst

@@ -55,4 +55,4 @@ current - 2019-05-23 - 0.00Mb
 * :issue:`7`: add quantile regression (2018-05-07)
 * :issue:`5`: replace flake8 by code style (2018-04-14)
 * :issue:`1`: change background for cells in notebooks converted into rst then in html, highlight-ipython3 (2018-01-05)
-* :issue:`2`: save features and metadatas for the search engine and retrieves them (2017-12-03)
+* :issue:`2`: save features and metadatas for the search engine and retrieves them (2017-12-03)

_unittests/ut_plotting/test_dot.py

@@ -3,12 +3,15 @@
 @brief      test log(time=2s)
 """
 import unittest
+from io import StringIO
+from textwrap import dedent
 import pandas
 from sklearn import datasets
 from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.decomposition import PCA
 from sklearn.preprocessing import StandardScaler, MinMaxScaler
 from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline, FeatureUnion
+from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
 from sklearn.impute import SimpleImputer
 from sklearn.preprocessing import OneHotEncoder
 from pyquickhelper.pycode import ExtTestCase
@@ -91,6 +94,51 @@ def test_union_features(self):
         self.assertIn("StandardScaler", dot)
         self.assertIn("MinMaxScaler", dot)
 
+    def test_onehotencoder_dot(self):
+        data = dedent("""
+            date,value,notrend,trend,weekday,lag1,lag2,lag3,lag4,lag5,lag6,lag7,lag8
+            2017-07-10 13:27:04.669830,0.003463591425601385,0.0004596547917981044,0.0030039366338032807,
+            ###0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+            2017-07-11 13:27:04.669830,0.004411953385609647,0.001342107238927262,0.003069846146682385,1,
+            ###0.003463591425601385,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+            2017-07-12 13:27:04.669830,0.004277700876279705,0.0011426168863444912,0.0031350839899352135,2,
+            ###0.004411953385609647,0.003463591425601385,0.0,0.0,0.0,0.0,0.0,0.0
+            2017-07-13 13:27:04.669830,0.006078151848127084,0.0028784976490072987,0.003199654199119785,3,
+            ###0.004277700876279705,0.004411953385609647,0.003463591425601385,0.0,0.0,0.0,0.0,0.0
+            2017-07-14 13:27:04.669830,0.006336617719481035,0.003073056920386795,0.0032635607990942395,
+            ###4,0.006078151848127084,0.004277700876279705,0.004411953385609647,0.003463591425601385,0.0,0.0,0.0,0.0
+            2017-07-15 13:27:04.669830,0.008716378909294038,0.0053895711052771985,0.0033268078040168394,5,
+            ###0.006336617719481035,0.006078151848127084,0.004277700876279705,0.004411953385609647,0.003463591425601385,0.0,0.0,0.0
+            2017-07-17 13:27:04.669830,0.0035533180858140765,0.00010197905397394454,0.003451339031840132,0,
+            ###0.008716378909294038,0.006336617719481035,0.006078151848127084,0.004277700876279705,0.004411953385609647,0.003463591425601385,0.0,0.0
+            2017-07-18 13:27:04.669830,0.0038464710972236286,0.0003338398676656705,0.003512631229557958,
+            ###1,0.0035533180858140765,0.008716378909294038,0.006336617719481035,0.006078151848127084,0.004277700876279705,
+            ###0.004411953385609647,0.003463591425601385,0.0
+            2017-07-19 13:27:04.669830,0.004200435956007872,0.0006271561741496745,0.003573279781858197,2,0.0038464710972236286,
+            ###0.0035533180858140765,0.008716378909294038,0.006336617719481035,0.006078151848127084,0.004277700876279705,
+            ###0.004411953385609647,0.003463591425601385
+            2017-07-20 13:27:04.669830,0.004773874566436903,0.0011405859170371827,0.00363328864939972,3,0.004200435956007872,
+            ###0.0038464710972236286,0.0035533180858140765,0.008716378909294038,0.006336617719481035,0.006078151848127084,
+            ###0.004277700876279705,0.004411953385609647
+            2017-07-21 13:27:04.669830,0.005866058541412791,0.00217339675927127,0.0036926617821415207,4,0.004773874566436903,
+            ###0.004200435956007872,0.0038464710972236286,0.0035533180858140765,0.008716378909294038,0.006336617719481035,
+            ###0.006078151848127084,0.004277700876279705
+            """).replace("\n###", "")
+        df = pandas.read_csv(StringIO(data))
+        cols = ['lag1', 'lag2', 'lag3',
+                'lag4', 'lag5', 'lag6', 'lag7', 'lag8']
+        model = make_pipeline(
+            make_pipeline(
+                ColumnTransformer(
+                    [('pass', "passthrough", cols),
+                     ("dummies", OneHotEncoder(), ["weekday"])]),
+                PCA(n_components=2)),
+            LinearRegression())
+        train_cols = cols + ['weekday']
+        model.fit(df, df[train_cols])
+        dot = pipeline2dot(model, df)
+        self.assertIn('label="Identity"', dot)
+
 
 if __name__ == "__main__":
     unittest.main()

mlinsights/plotting/visualize.py

@@ -110,6 +110,16 @@ def _get_name(context, prefix='-v-'):
                      'type': 'transform'}, info]
         return info
 
+    elif isinstance(pipe, str):
+        if pipe == "passthrough":
+            info = {'name': 'Identity', 'type': 'transform'}
+            info['outputs'] = data
+            info['inputs'] = data
+            info = [info]
+        else:
+            raise NotImplementedError(
+                "Not yet implemented for keyword '{}'.".format(type(pipe)))
+        return info
     else:
         raise NotImplementedError(
             "Not yet implemented for {}.".format(type(pipe)))