From 5545f31f1c3198cb9f6cdb30ad1a7b629bca3617 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Wed, 19 Sep 2018 13:40:40 -0400
Subject: [PATCH] add missing file
---
mhcflurry/allele_encoding_transforms.py | 77 +++++++++++++++++++++++++
1 file changed, 77 insertions(+)
create mode 100644 mhcflurry/allele_encoding_transforms.py
diff --git a/mhcflurry/allele_encoding_transforms.py b/mhcflurry/allele_encoding_transforms.py
new file mode 100644
index 00000000..30092586
--- /dev/null
+++ b/mhcflurry/allele_encoding_transforms.py
@@ -0,0 +1,77 @@
+import time
+
+import pandas
+import sklearn.decomposition
+
+
+class AlleleEncodingTransform(object):
+ def transform(self, data):
+ raise NotImplementedError()
+
+ def get_fit(self):
+ """
+ Get the fit for serialization, which must be in the form of one or more
+ dataframes.
+
+ Returns
+ -------
+ dict : string to DataFrame
+ """
+ raise NotImplementedError()
+
+ def restore_fit(self, fit):
+ """
+ Restore a serialized fit.
+
+ Parameters
+ ----------
+ fit : string to DataFrame
+ """
+
+
+class PCATransform(AlleleEncodingTransform):
+ name = 'pca'
+ serialization_keys = ['data']
+
+ def __init__(self):
+ self.model = None
+
+ def is_fit(self):
+ return self.model is not None
+
+ def fit(self, allele_representations):
+ self.model = sklearn.decomposition.PCA()
+ shape = allele_representations.shape
+ flattened = allele_representations.reshape(
+ (shape[0], shape[1] * shape[2]))
+ print("Fitting PCA allele encoding transform on data of shape: %s" % (
+ str(flattened.shape)))
+ start = time.time()
+ self.model.fit(flattened)
+ print("Fit complete in %0.3f sec." % (time.time() - start))
+
+ def get_fit(self):
+ df = pandas.DataFrame(self.model.components_)
+ df.columns = ["pca_%s" % c for c in df.columns]
+ df["mean"] = self.model.mean_
+ return {
+ 'data': df
+ }
+
+ def restore_fit(self, fit):
+ assert list(fit) == ['data']
+ data = fit["data"]
+ self.model = sklearn.decomposition.PCA()
+ self.model.mean_ = data["mean"].values
+ self.model.components_ = data.drop(columns="mean").values
+
+ def transform(self, allele_representations):
+ if not self.is_fit():
+ self.fit(allele_representations)
+ flattened = allele_representations.reshape(
+ (allele_representations.shape[0],
+ allele_representations.shape[1] * allele_representations.shape[2]))
+ return self.model.transform(flattened)
+
+
+TRANSFORMS = dict((klass.name, klass) for klass in [PCATransform])
--
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