Quillot Mathias / volia

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Commit 765b51bc7741c15001b3983ef6df4d68eedbcd62

Authored by Quillot Mathias
1 parent d27fe6fcc5
Exists in master

Little modification to synchronize

Showing 2 changed files with 51 additions and 2 deletions Side-by-side Diff

... ... @@ -7,7 +7,9 @@
7 7 import sys
8 8  
9 9 # Defining some types
10   -from typing import List, Dict
  10 +from typing import List, Dict, Tuple
  11 +
  12 +from numpy.lib.shape_base import expand_dims
11 13 KeyToList = Dict[str, List[str]]
12 14 KeyToLabels = Dict[str, List[str]]
13 15 KeyToIntLabels = Dict[str, List[int]]
... ... @@ -66,6 +68,27 @@
66 68 '''
67 69 return read_id_values(file_path, np.float64)
68 70  
  71 +
  72 +def read_features_with_matrix(file_path: str) -> Tuple[List[str], np.ndarray]:
  73 + """Read a features file and returns the keys (utterances ids)
  74 + with the corresponding matrix of values.
  75 +
  76 + Args:
  77 + file_path (str): path of the features file
  78 +
  79 + Returns:
  80 + [Tuple(List[str], np.ndarray)]: a tuple with a list of keys and the matrix
  81 + """
  82 + data = read_id_values(file_path, np.float64)
  83 + keys = []
  84 + matrix = None
  85 + for key, values in data.items():
  86 + keys.append(key)
  87 + if matrix is None:
  88 + matrix = np.expand_dims(values, axis=0)
  89 + matrix = np.append(matrix, np.expand_dims(values, axis=0), axis=0)
  90 +
  91 + return (keys, matrix)
69 92  
70 93 def read_labels(file_path: str) -> KeyToLabels:
71 94 '''
... ... @@ -93,6 +93,24 @@
93 93 print("Decisions")
94 94  
95 95  
  96 +def pred_distribution_wt_sel(predictions: str, labels: str, labelencoder: str, outdir: str):
  97 +
  98 + keys_preds, matrix_preds = core.data.read_features_with_matrix(predictions)
  99 + n = 3
  100 + print(matrix_preds.shape)
  101 + for j in range(matrix_preds.shape[1]):
  102 + indices = (-matrix_preds[:, j]).argsort()[:n]
  103 + print(f"INDICE: {j}")
  104 + print("indices")
  105 + print(indices)
  106 + print("Best values")
  107 + print(matrix_preds[indices, j])
  108 + print("All dimensions of best values")
  109 + print(matrix_preds[indices])
  110 + # Select the n best for each column
  111 + pass
  112 +
  113 +
96 114 def utt2dur(utt2dur: str, labels: str):
97 115 if labels == None:
98 116 pass
... ... @@ -128,6 +146,13 @@
128 146 parser_pred_dist.add_argument("--outdir", type=str, help="output file", required=True)
129 147 parser_pred_dist.set_defaults(which="pred_distribution")
130 148  
  149 + # pred-distribution-with-selection
  150 + parser_pred_dist_wt_sel = subparsers.add_parser("pred-distribution-with-selection", help="plot distributions of prediction through labels with a selection of the n best records by column/class prediction.")
  151 + parser_pred_dist_wt_sel.add_argument("--predictions", type=str, help="prediction file", required=True)
  152 + parser_pred_dist_wt_sel.add_argument("--labels", type=str, help="label file", required=True)
  153 + parser_pred_dist_wt_sel.add_argument("--labelencoder", type=str, help="label encode pickle file", required=True)
  154 + parser_pred_dist_wt_sel.add_argument("--outdir", type=str, help="output file", required=True)
  155 + parser_pred_dist_wt_sel.set_defaults(which="pred_distribution_with_selection")
131 156 # duration-stats
132 157 parser_utt2dur = subparsers.add_parser("utt2dur", help="distribution of utt2dur")
133 158 parser_utt2dur.add_argument("--utt2dur", type=str, help="utt2dur file", required=True)
... ... @@ -139,7 +164,8 @@
139 164  
140 165 # Run commands
141 166 runner = SubCommandRunner({
142   - "pred-distribution": pred_distribution,
  167 + "pred_distribution": pred_distribution,
  168 + "pred_distribution_with_selection": pred_distribution_wt_sel,
143 169 "utt2dur": utt2dur
144 170 })
145 171