diff data.py @ 0:d04fa5201f51 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 7ad344d108076116e702e1c1e91cea73d8fcadc4

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data.py	Thu Jul 28 05:56:54 2016 -0400
@@ -0,0 +1,88 @@
+"""Dataset handler."""
+
+import numpy as np
+
+import pandas as pd
+
+__author__ = "Gianluca Corrado"
+__copyright__ = "Copyright 2016, Gianluca Corrado"
+__license__ = "MIT"
+__maintainer__ = "Gianluca Corrado"
+__email__ = "[e-mail]"
+__status__ = "Production"
+
+
+class Dataset(object):
+    """General dataset."""
+
+    def __init__(self, fp, fr, standardize_proteins=False,
+                 standardize_rnas=False):
+        """
+        Constructor.
+
+        Parameters
+        ----------
+        fp : str
+            Protein features
+
+        fr : str
+            The name of the HDF5 file containing features for the RNAs.
+        """
+        self.Fp = fp.astype('float32')
+
+        store = pd.io.pytables.HDFStore(fr)
+        self.Fr = store.features.astype('float32')
+        store.close()
+
+    def load(self):
+        """Load dataset in memory."""
+        raise NotImplementedError()
+
+
+class PredictDataset(Dataset):
+    """Test dataset."""
+
+    def __init__(self, fp, fr):
+        """
+        Constructor.
+
+        Parameters
+        ----------
+        fp : str
+            The name of the HDF5 file containing features for the proteins.
+
+        fr : str
+            The name of the HDF5 file containing features for the RNAs.
+        """
+        super(PredictDataset, self).__init__(fp, fr)
+
+    def load(self):
+        """
+        Load dataset in memory.
+
+        Return
+        ------
+        Examples to predict. For each example:
+            - p contains the protein features,
+            - r contains the RNA features,
+            - p_names contains the name of the protein,
+            - r_names contains the name of the RNA.
+
+        """
+        protein_input_dim = self.Fp.shape[0]
+        rna_input_dim = self.Fr.shape[0]
+        num_examples = self.Fp.shape[1] * self.Fr.shape[1]
+        p = np.zeros((num_examples, protein_input_dim)).astype('float32')
+        p_names = []
+        r = np.zeros((num_examples, rna_input_dim)).astype('float32')
+        r_names = []
+        index = 0
+        for protein in self.Fp.columns:
+            for rna in self.Fr.columns:
+                p[index] = self.Fp[protein]
+                p_names.append(protein)
+                r[index] = self.Fr[rna]
+                r_names.append(rna)
+                index += 1
+
+        return (p, np.array(p_names), r, np.array(r_names))