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pca_sklearn.py
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pca_sklearn.py
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import copy
import numpy as np
from sklearn.decomposition import PCA
import netCDF4
import json
from functions import extract_n_by_n
class PCA_sklearn:
def fit(self, predictors, locations, **kwargs):
self.locations = locations
self.pcas = []
self.n = predictors['n']
for location in locations:
raw = extract_n_by_n(predictors, location, **kwargs)
#pca = PCA(n_components='mle', whiten=True)
#pca = PCA(n_components=0.95, whiten=True)
pca = PCA(n_components=2)
pca = pca.fit(raw)
components = pca.components_
pca.components_ = components
self.pcas.append(pca.fit(raw))
print "pca: ", location, pca.n_components_, pca.explained_variance_ratio_
def save(self, filename='pca.nc'):
"""
Write sklearn PCA parameters to a netcdf file
"""
max_n_components = max([pca.n_components_ for pca in self.pcas])
n_features = self.pcas[0].components_.shape[1]
outfile = netCDF4.Dataset(filename, 'w')
outfile.createDimension("location", len(self.locations))
outfile.createDimension("feature", n_features)
outfile.createDimension("component", max_n_components)
outfile.createVariable("location", str, ("location"))
outfile.createVariable("n_components", "i4", ("location"))
outfile.createVariable("components", "f4", ("location", "component", "feature"))
outfile.createVariable("means", "f4", ("location", "feature"))
outfile.createVariable("explained_variance_ratio", "f4", ("location", "component"))
outfile.createVariable("noise_variance", "f4", ("location"))
outfile.createVariable("whiten", "c", ("location"))
id = 0
for pca in self.pcas:
#print id, self.locations[id], pca.n_components_, pca.components_.shape, pca.explained_variance_ratio_.shape
outfile.variables['location'][id] = json.dumps(self.locations[id])
outfile.variables["n_components"][id] = pca.n_components_
outfile.variables["components"][id,:pca.n_components_] = pca.components_
outfile.variables["means"][id] = pca.mean_
outfile.variables["explained_variance_ratio"][id,:pca.n_components_] = pca.explained_variance_ratio_
outfile.variables["noise_variance"][id] = pca.noise_variance_
id += 1
outfile.close()
return True
def load(self, filename='pca.nc'):
"""
Read sklearn PCA parameters from a netcdf file
"""
infile = netCDF4.Dataset(filename, 'r')
self.locations = [json.loads(string) for string in list(infile.variables['location'])]
self.pcas = []
id = 0
for location in self.locations:
n_components = infile.variables['n_components'][id]
components = infile.variables['components'][id]
mean = infile.variables['means'][id]
explained_variance_ratio = infile.variables['explained_variance_ratio'][id]
noise_variance = infile.variables['noise_variance'][id]
pca = PCA(n_components=n_components)
pca.components_ = components
pca.mean_ = mean
pca.explained_variance_ratio_ = explained_variance_ratio
pca.noise_variance_ = noise_variance
self.pcas.append(pca)
id += 1
def transform(self, predictors, **kwargs):
result = []
id = 0
for location in self.locations:
raw = extract_n_by_n(predictors, location, **kwargs)
result.append(self.pcas[id].transform(raw))
id += 1
return result