jglaser commited on
Commit
db83314
·
1 Parent(s): a18b94f

fix dataset loading script

Browse files
Files changed (1) hide show
  1. binding_affinity.py +14 -24
binding_affinity.py CHANGED
@@ -15,7 +15,6 @@
15
  """TODO: A dataset of protein sequences, ligand SMILES and binding affinities."""
16
 
17
  import huggingface_hub
18
- import pandas as pd
19
  import os
20
  import pyarrow.parquet as pq
21
  import datasets
@@ -92,8 +91,10 @@ class BindingAffinity(datasets.ArrowBasedBuilder):
92
  features = datasets.Features(
93
  {
94
  "seq": datasets.Value("string"),
95
- "smiles_can": datasets.Value("string"),
 
96
  "neg_log10_affinity_M": datasets.Value("float"),
 
97
  "affinity": datasets.Value("float"),
98
  # These are the features of your dataset like images, labels ...
99
  }
@@ -124,16 +125,17 @@ class BindingAffinity(datasets.ArrowBasedBuilder):
124
  # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
125
  # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
126
  files = dl_manager.download_and_extract(_URLs)
 
127
  return [
128
  datasets.SplitGenerator(
129
- name=datasets.Split.TRAIN,
130
  # These kwargs will be passed to _generate_examples
 
131
  gen_kwargs={
132
- "filepath": files["default"],
133
  },
134
  ),
135
  datasets.SplitGenerator(
136
- name=datasets.Split.TRAIN+"_no_kras",
137
  # These kwargs will be passed to _generate_examples
138
  gen_kwargs={
139
  "filepath": files["no_kras"],
@@ -142,24 +144,12 @@ class BindingAffinity(datasets.ArrowBasedBuilder):
142
 
143
  ]
144
 
145
- def _generate_examples(
146
- self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
147
- ):
148
- """ Yields examples as (key, example) tuples. """
149
- # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
150
- # The `key` is here for legacy reason (tfds) and is not important in itself.
151
-
152
- df = pd.read_parquet(filepath)
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- for k, row in df.iterrows():
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- yield k, {
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- "seq": row["seq"],
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- "smiles_can": row["smiles_can"],
157
- "neg_log10_affinity_M": row["neg_log10_affinity_M"],
158
- "affinity_uM": row["affinity_uM"],
159
- }
160
-
161
  def _generate_tables(
162
- self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
163
  ):
164
- df = pq.read_table(filepath)
165
- yield split, df
 
 
 
 
 
15
  """TODO: A dataset of protein sequences, ligand SMILES and binding affinities."""
16
 
17
  import huggingface_hub
 
18
  import os
19
  import pyarrow.parquet as pq
20
  import datasets
 
91
  features = datasets.Features(
92
  {
93
  "seq": datasets.Value("string"),
94
+ "smiles": datasets.Value("string"),
95
+ "affinity_uM": datasets.Value("float"),
96
  "neg_log10_affinity_M": datasets.Value("float"),
97
+ "smiles_can": datasets.Value("string"),
98
  "affinity": datasets.Value("float"),
99
  # These are the features of your dataset like images, labels ...
100
  }
 
125
  # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
126
  # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
127
  files = dl_manager.download_and_extract(_URLs)
128
+ print(files)
129
  return [
130
  datasets.SplitGenerator(
 
131
  # These kwargs will be passed to _generate_examples
132
+ name=datasets.Split.TRAIN,
133
  gen_kwargs={
134
+ 'filepath': files["default"],
135
  },
136
  ),
137
  datasets.SplitGenerator(
138
+ name='no_kras',
139
  # These kwargs will be passed to _generate_examples
140
  gen_kwargs={
141
  "filepath": files["no_kras"],
 
144
 
145
  ]
146
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
147
  def _generate_tables(
148
+ self, filepath
149
  ):
150
+ from pyarrow import fs
151
+ local = fs.LocalFileSystem()
152
+
153
+ for i, f in enumerate([filepath]):
154
+ print(f)
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+ yield i, pq.read_table(f,filesystem=local)