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comparison galaxy_micropita/src/breadcrumbs/src/under_development/PCA.py @ 3:8fb4630ab314 draft default tip

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author sagun98
date Thu, 03 Jun 2021 17:07:36 +0000
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2:1c5736dc85ab 3:8fb4630ab314
1 """
2 Author: Timothy Tickle
3 Description: Performs and plots Principle Components Analysis.
4 """
5
6 #####################################################################################
7 #Copyright (C) <2012>
8 #
9 #Permission is hereby granted, free of charge, to any person obtaining a copy of
10 #this software and associated documentation files (the "Software"), to deal in the
11 #Software without restriction, including without limitation the rights to use, copy,
12 #modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
13 #and to permit persons to whom the Software is furnished to do so, subject to
14 #the following conditions:
15 #
16 #The above copyright notice and this permission notice shall be included in all copies
17 #or substantial portions of the Software.
18 #
19 #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
20 #INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
21 #PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
22 #HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
23 #OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
24 #SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
25 #####################################################################################
26
27 __author__ = "Timothy Tickle"
28 __copyright__ = "Copyright 2013"
29 __credits__ = ["Timothy Tickle"]
30 __license__ = "MIT"
31 __maintainer__ = "Timothy Tickle"
32 __email__ = "ttickle@sph.harvard.edu"
33 __status__ = "Development"
34
35 #External libraries
36 from AbundanceTable import AbundanceTable
37 from ConstantsFiguresBreadCrumbs import ConstantsFiguresBreadCrumbs
38 from Ordination import Ordination
39 import matplotlib.cm as cm
40 from math import sqrt,asin
41 from matplotlib.mlab import PCA as mplPCA
42 from matplotlib import pyplot as plt
43 from numpy import *
44 from UtilityMath import UtilityMath
45 from ValidateData import ValidateData
46
47 class PCA(Ordination):
48 """
49 Class to Run Principle Components Analysis on an abundance table object
50 """
51
52 def __init__(self):
53 Ordination.__init__(self)
54 self.c_strComponents = "components"
55 self.c_strVariance = "percent_variance"
56
57 def run(self,fScale=True,fCenter=True,fASTransform=False):
58 if not self.dataMatrix is None:
59 mtrxPrepped = self.dataMatrix.T
60 if fASTransform:
61 mtrxPrepped = array([self.doAsinOnList(row) for row in sqrt(mtrxPrepped)])
62 if fCenter:
63 mtrxPrepped = mtrxPrepped-mean(mtrxPrepped,0)
64 if fScale:
65 # This is consistent to R's prcomp method.
66 vStd = std(a=mtrxPrepped,axis=0) if fCenter else [sqrt(sum(square(ldRow))/len(ldRow)) for ldRow in mtrxPrepped.T]
67 mtrxPrepped /= vStd
68 iRows, iCols = mtrxPrepped.shape
69 U,S,V = linalg.svd(a=mtrxPrepped,full_matrices=False)
70 ldVariance = square(S*(iCols-1))
71 ldVariance = ldVariance/sum(ldVariance)
72 # Here components are row-wise so each component is a row.
73 # Here percent variance is given and it is in the order of the components.
74 self.dataProcessed = {self.c_strComponents:V, self.c_strVariance:ldVariance}
75 return True
76 else:
77 print("PCA:run::Error Tried to run analysis on no data load data first.")
78 return False
79
80 def getVariance(self,iIndex=None):
81 if not self.dataProcessed is None:
82 if not iIndex is None:
83 return self.dataProcessed[self.c_strVariance][iIndex]
84 return self.dataProcessed[self.c_strVariance]
85 else:
86 print("PCA:getVariance::Error Tried to run analysis on no data load data first.")
87 return False
88
89 def getComponents(self,iIndex=None):
90 if not self.dataProcessed is None:
91 if not iIndex is None:
92 return self.dataProcessed[self.c_strComponents].T[iIndex]
93 return self.dataProcessed[self.c_strComponents].T
94 else:
95 print("PCA:getComponents::Error Tried to run analysis on no data load data first.")
96 return False
97
98 def doAsinOnList(self, lsValues):
99 return([asin(element) for element in lsValues])
100
101 def convertMetadataForPCA(self,abndTable):
102 """ This takes a metadata dictionary from an abundance table and formats the metadata for use in the PCA.
103 This formatting includes reducing discontinuous data to leveles and replacing NA values to the means of the value (continuous data only)
104 This returns a numpy array of the format needed for this PCA object.
105 """
106
107 # Replace missing values with the mean
108 # dummy the discrete data
109 dictMetadata = abndTable.funcGetMetadataCopy()
110 if(len(dictMetadata) < 2):
111 return None
112
113 ## Remove the metadata id
114 dictMetadata.pop(abndTable.funcGetIDMetadataName(),None)
115 lMetadata = []
116 for lxItem in dictMetadata.values():
117 ## If this is not numeric data then dummy
118 ## Treat NA as a seperate category
119 if not (sum([ ValidateData.funcIsValidStringFloat(xItem) for xItem in lxItem]) == len(lxItem)):
120 # Get levels
121 setsLevels = set(lxItem)
122 # Go through each level and dummy the metadata
123 for sLevel in setsLevels:
124 lMetadata.append([1.0 if xItem==sLevel else 0.0 for xItem in lxItem])
125 else:
126 # Change NA to Mean and store numeric data as float
127 # Also add to the metadata so that there are no negative numbers
128 ldNONA = [float(xItem) for xItem in lxItem if not xItem.strip().lower() in ["na",""]]
129 dMean = sum(ldNONA)/float(len(ldNONA))
130 lsMetadataValues = [dMean if xItem.strip().lower() in ["na",""] else float(xItem) for xItem in lxItem]
131 dMinValueAdj = abs(min(lsMetadataValues))
132 lMetadata.append([sValue + dMinValueAdj for sValue in lsMetadataValues])
133 return(array(lMetadata).T)