Note

Go to the end to download the full example code

Visualize a scikit-learn pipeline#

Pipeline can be big with scikit-learn, let’s dig into a visual way to look a them.

Simple model#

Let’s vizualize a simple pipeline, a single model not even trained.

from numpy.random import randn
import pandas
from PIL import Image
from sphinx_runpython.runpython import run_cmd
from sklearn import datasets
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import (
    OneHotEncoder,
    StandardScaler,
    MinMaxScaler,
    PolynomialFeatures,
)
from mlinsights.helpers.pipeline import (
    alter_pipeline_for_debugging,
    enumerate_pipeline_models,
)
from mlinsights.plotting import pipeline2dot, pipeline2str


iris = datasets.load_iris()
X = iris.data[:, :4]
df = pandas.DataFrame(X)
df.columns = ["X1", "X2", "X3", "X4"]
clf = LogisticRegression()
clf

LogisticRegression()
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The trick consists in converting the pipeline in a graph through the DOT language.

dot = pipeline2dot(clf, df)
print(dot)

digraph{
  orientation=portrait;
  nodesep=0.05;
  ranksep=0.25;
  sch0[label="<f0> X1|<f1> X2|<f2> X3|<f3> X4",shape=record,fontsize=8];

  node1[label="union",shape=box,style="filled,rounded",color=cyan,fontsize=12];
  sch0:f0 -> node1;
  sch0:f1 -> node1;
  sch0:f2 -> node1;
  sch0:f3 -> node1;
  sch1[label="<f0> -v-0",shape=record,fontsize=8];
  node1 -> sch1:f0;

  node2[label="LogisticRegression",shape=box,style="filled,rounded",color=yellow,fontsize=12];
  sch1:f0 -> node2;
  sch2[label="<f0> PredictedLabel|<f1> Probabilities",shape=record,fontsize=8];
  node2 -> sch2:f0;
  node2 -> sch2:f1;
}

It is lot better with an image.

dot_file = "graph.dot"
with open(dot_file, "w", encoding="utf-8") as f:
    f.write(dot)

cmd = "dot -G=300 -Tpng {0} -o{0}.png".format(dot_file)
run_cmd(cmd, wait=True)


img = Image.open("graph.dot.png")
img

<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=200x351 at 0x7FED7ACEB550>

Complex pipeline#

scikit-learn instroduced a couple of transform to play with features in a single pipeline. The following example is taken from Column Transformer with Mixed Types.

columns = [
    "pclass",
    "name",
    "sex",
    "age",
    "sibsp",
    "parch",
    "ticket",
    "fare",
    "cabin",
    "embarked",
    "boat",
    "body",
    "home.dest",
]

numeric_features = ["age", "fare"]
numeric_transformer = Pipeline(
    steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
)

categorical_features = ["embarked", "sex", "pclass"]
categorical_transformer = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="constant", fill_value="missing")),
        ("onehot", OneHotEncoder(handle_unknown="ignore")),
    ]
)

preprocessor = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features),
    ]
)

clf = Pipeline(
    steps=[
        ("preprocessor", preprocessor),
        ("classifier", LogisticRegression(solver="lbfgs")),
    ]
)
clf

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(strategy='median')),
                                                                  ('scaler',
                                                                   StandardScaler())]),
                                                  ['age', 'fare']),
                                                 ('cat',
                                                  Pipeline(steps=[('imputer',
                                                                   SimpleImputer(fill_value='missing',
                                                                                 strategy='constant')),
                                                                  ('onehot',
                                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                                  ['embarked', 'sex',
                                                   'pclass'])])),
                ('classifier', LogisticRegression())])
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Let’s see it first as a simplified text.

print(pipeline2str(clf))

Pipeline
   ColumnTransformer
      Pipeline(age,fare)
         SimpleImputer
         StandardScaler
      Pipeline(embarked,sex,pclass)
         SimpleImputer
         OneHotEncoder
   LogisticRegression

dot = pipeline2dot(clf, columns)

dot_file = "graph2.dot"
with open(dot_file, "w", encoding="utf-8") as f:
    f.write(dot)

cmd = "dot -G=300 -Tpng {0} -o{0}.png".format(dot_file)
run_cmd(cmd, wait=True)

img = Image.open("graph2.dot.png")
img

<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=591x787 at 0x7FED7ACE8A60>

Example with FeatureUnion#

model = Pipeline(
    [
        ("poly", PolynomialFeatures()),
        (
            "union",
            FeatureUnion([("scaler2", MinMaxScaler()), ("scaler3", StandardScaler())]),
        ),
    ]
)
dot = pipeline2dot(model, columns)

dot_file = "graph3.dot"
with open(dot_file, "w", encoding="utf-8") as f:
    f.write(dot)

cmd = "dot -G=300 -Tpng {0} -o{0}.png".format(dot_file)
run_cmd(cmd, wait=True)

img = Image.open("graph3.dot.png")
img

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=306x569 at 0x7FED7ACEAD10>

Compute intermediate outputs#

# It is difficult to access intermediate outputs with *scikit-learn* but
# it may be interesting to do so. The method
# `alter_pipeline_for_debugging <find://alter_pipeline_for_debugging>`_
# modifies the pipeline to intercept intermediate outputs.


model = Pipeline(
    [
        ("scaler1", StandardScaler()),
        (
            "union",
            FeatureUnion([("scaler2", StandardScaler()), ("scaler3", MinMaxScaler())]),
        ),
        ("lr", LinearRegression()),
    ]
)

X = randn(4, 5)
y = randn(4)
model.fit(X, y)

Pipeline(steps=[('scaler1', StandardScaler()),
                ('union',
                 FeatureUnion(transformer_list=[('scaler2', StandardScaler()),
                                                ('scaler3', MinMaxScaler())])),
                ('lr', LinearRegression())])
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print(pipeline2str(model))

Pipeline
   StandardScaler
   FeatureUnion
      StandardScaler
      MinMaxScaler
   LinearRegression

Let’s now modify the pipeline to get the intermediate outputs.

alter_pipeline_for_debugging(model)

The function adds a member _debug which stores inputs and outputs in every piece of the pipeline.

model.steps[0][1]._debug

BaseEstimatorDebugInformation(StandardScaler)

model.predict(X)

array([-0.94327916,  1.83009631, -0.28141013,  0.12719898])

The member was populated with inputs and outputs.

model.steps[0][1]._debug

BaseEstimatorDebugInformation(StandardScaler)
  transform(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 1.2762825   1.40392169 -0.44070616 -1.73411973 -0.20499451]
    [-1.21217604 -0.33528123 -0.66585633 -0.24197224  0.11465946]
    [-0.17971264 -0.80838722  1.22547855  1.9416219   1.58399645]
    [ 2.22639797 -0.97041703  1.1955906  -0.10650412 -1.54463857]]
  ) -> (
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  )

Every piece behaves the same way.

for coor, model, vars in enumerate_pipeline_models(model):
    print(coor)
    print(model._debug)

(0,)
BaseEstimatorDebugInformation(Pipeline)
  predict(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 1.2762825   1.40392169 -0.44070616 -1.73411973 -0.20499451]
    [-1.21217604 -0.33528123 -0.66585633 -0.24197224  0.11465946]
    [-0.17971264 -0.80838722  1.22547855  1.9416219   1.58399645]
    [ 2.22639797 -0.97041703  1.1955906  -0.10650412 -1.54463857]]
  ) -> (
   shape=(4,) type=<class 'numpy.ndarray'>
   [-0.94327916  1.83009631 -0.28141013  0.12719898]
  )
(0, 0)
BaseEstimatorDebugInformation(StandardScaler)
  transform(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 1.2762825   1.40392169 -0.44070616 -1.73411973 -0.20499451]
    [-1.21217604 -0.33528123 -0.66585633 -0.24197224  0.11465946]
    [-0.17971264 -0.80838722  1.22547855  1.9416219   1.58399645]
    [ 2.22639797 -0.97041703  1.1955906  -0.10650412 -1.54463857]]
  ) -> (
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  )
(0, 1)
BaseEstimatorDebugInformation(FeatureUnion)
  transform(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  ) -> (
   shape=(4, 10) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293   0.7236891
      1.          0.119043    0.          0.42818803]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355  0.
      0.26750008  0.          0.40594461  0.53035845]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961  0.30025918
   ...
  )
(0, 1, 0)
BaseEstimatorDebugInformation(StandardScaler)
  transform(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  ) -> (
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  )
(0, 1, 1)
BaseEstimatorDebugInformation(MinMaxScaler)
  transform(
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293 ]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961]
    [ 1.28652927 -0.84133064  0.97900498 -0.05448639 -1.37714387]]
  ) -> (
   shape=(4, 5) type=<class 'numpy.ndarray'>
   [[0.7236891  1.         0.119043   0.         0.42818803]
    [0.         0.26750008 0.         0.40594461 0.53035845]
    [0.30025918 0.06824208 1.         1.         1.        ]
    [1.         0.         0.98419743 0.44279924 0.        ]]
  )
(0, 2)
BaseEstimatorDebugInformation(LinearRegression)
  predict(
   shape=(4, 10) type=<class 'numpy.ndarray'>
   [[ 0.5669488   1.67810961 -0.86875663 -1.29897394 -0.1728293   0.7236891
      1.          0.119043    0.          0.42818803]
    [-1.31771283 -0.16738017 -1.12300381 -0.15806637  0.11453355  0.
      0.26750008  0.          0.40594461  0.53035845]
    [-0.53576524 -0.6693988   1.01275546  1.5115267   1.43543961  0.30025918
   ...
  ) -> (
   shape=(4,) type=<class 'numpy.ndarray'>
   [-0.94327916  1.83009631 -0.28141013  0.12719898]
  )

Total running time of the script: (0 minutes 0.203 seconds)

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