annotate data.py @ 4:a609d6dc8047 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 7ad344d108076116e702e1c1e91cea73d8fcadc4
author rnateam
date Thu, 28 Jul 2016 05:55:25 -0400
parents 8918de535391
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8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
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1 """Dataset handler."""
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3 import numpy as np
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
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5 import pandas as pd
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7 __author__ = "Gianluca Corrado"
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8 __copyright__ = "Copyright 2016, Gianluca Corrado"
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9 __license__ = "MIT"
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10 __maintainer__ = "Gianluca Corrado"
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11 __email__ = "gianluca.corrado@unitn.it"
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12 __status__ = "Production"
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15 class Dataset(object):
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16 """General dataset."""
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18 def __init__(self, fp, fr, standardize_proteins=False,
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19 standardize_rnas=False):
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20 """
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21 Constructor.
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23 Parameters
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24 ----------
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25 fp : str
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26 Protein features
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28 fr : str
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29 The name of the HDF5 file containing features for the RNAs.
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30 """
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31 self.Fp = fp.astype('float32')
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33 store = pd.io.pytables.HDFStore(fr)
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34 self.Fr = store.features.astype('float32')
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35 store.close()
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37 def load(self):
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38 """Load dataset in memory."""
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39 raise NotImplementedError()
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42 class PredictDataset(Dataset):
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43 """Test dataset."""
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45 def __init__(self, fp, fr):
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46 """
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47 Constructor.
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48
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49 Parameters
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50 ----------
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51 fp : str
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52 The name of the HDF5 file containing features for the proteins.
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53
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54 fr : str
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55 The name of the HDF5 file containing features for the RNAs.
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56 """
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57 super(PredictDataset, self).__init__(fp, fr)
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58
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59 def load(self):
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60 """
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61 Load dataset in memory.
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62
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63 Return
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64 ------
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65 Examples to predict. For each example:
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66 - p contains the protein features,
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67 - r contains the RNA features,
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68 - p_names contains the name of the protein,
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69 - r_names contains the name of the RNA.
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70
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71 """
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72 protein_input_dim = self.Fp.shape[0]
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73 rna_input_dim = self.Fr.shape[0]
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74 num_examples = self.Fp.shape[1] * self.Fr.shape[1]
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75 p = np.zeros((num_examples, protein_input_dim)).astype('float32')
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76 p_names = []
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77 r = np.zeros((num_examples, rna_input_dim)).astype('float32')
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78 r_names = []
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79 index = 0
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80 for protein in self.Fp.columns:
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81 for rna in self.Fr.columns:
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
82 p[index] = self.Fp[protein]
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
83 p_names.append(protein)
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
84 r[index] = self.Fr[rna]
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
85 r_names.append(rna)
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
86 index += 1
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
87
8918de535391 planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 2fc7f3c08f30e2d81dc4ad19759dfe7ba9b0a3a1
rnateam
parents:
diff changeset
88 return (p, np.array(p_names), r, np.array(r_names))