1 files changed, 64 insertions, 0 deletions
diff --git a/sklearn_pandas/pipeline.py b/sklearn_pandas/pipeline.py
new file mode 100644
index 0000000..04cb053
--- /dev/null
+++ b/sklearn_pandas/pipeline.py
@@ -0,0 +1,64 @@
+import six
+from sklearn.pipeline import _name_estimators, Pipeline
+from sklearn.utils import tosequence
+
+
+class TransformerPipeline(Pipeline):
+    """
+    Pipeline that expects all steps to be transformers taking a single argument
+    and having fit and transform methods.
+
+    Code is copied from sklearn's Pipeline, leaving out the `y=None` argument.
+    """
+    def __init__(self, steps):
+        names, estimators = zip(*steps)
+        if len(dict(steps)) != len(steps):
+            raise ValueError("Provided step names are not unique: %s" % (names,))
+
+        # shallow copy of steps
+        self.steps = tosequence(steps)
+        estimator = estimators[-1]
+
+        for e in estimators:
+            if (not (hasattr(e, "fit") or hasattr(e, "fit_transform")) or not
+                    hasattr(e, "transform")):
+                raise TypeError("All steps of the chain should "
+                                "be transforms and implement fit and transform"
+                                " '%s' (type %s) doesn't)" % (e, type(e)))
+
+        if not hasattr(estimator, "fit"):
+            raise TypeError("Last step of chain should implement fit "
+                            "'%s' (type %s) doesn't)"
+                            % (estimator, type(estimator)))
+
+    def _pre_transform(self, X, **fit_params):
+        fit_params_steps = dict((step, {}) for step, _ in self.steps)
+        for pname, pval in six.iteritems(fit_params):
+            step, param = pname.split('__', 1)
+            fit_params_steps[step][param] = pval
+        Xt = X
+        for name, transform in self.steps[:-1]:
+            if hasattr(transform, "fit_transform"):
+                Xt = transform.fit_transform(Xt, **fit_params_steps[name])
+            else:
+                Xt = transform.fit(Xt, **fit_params_steps[name]) \
+                              .transform(Xt)
+        return Xt, fit_params_steps[self.steps[-1][0]]
+
+    def fit(self, X, **fit_params):
+        Xt, fit_params = self._pre_transform(X, **fit_params)
+        self.steps[-1][-1].fit(Xt, **fit_params)
+        return self
+
+    def fit_transform(self, X, **fit_params):
+        Xt, fit_params = self._pre_transform(X, **fit_params)
+        if hasattr(self.steps[-1][-1], 'fit_transform'):
+            return self.steps[-1][-1].fit_transform(Xt, **fit_params)
+        else:
+            return self.steps[-1][-1].fit(Xt, **fit_params).transform(Xt)
+
+
+def make_transformer_pipeline(*steps):
+    """Construct a TransformerPipeline from the given estimators.
+    """
+    return TransformerPipeline(_name_estimators(steps))