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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])