MeriDK commited on
Commit
d9e8f74
·
1 Parent(s): ca06ac5

Updated loading logic

Browse files
Files changed (1) hide show
  1. AstroM3Dataset.py +14 -20
AstroM3Dataset.py CHANGED
@@ -112,21 +112,18 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
112
  "val": f"splits/{sub}/{seed}/val.csv",
113
  "test": f"splits/{sub}/{seed}/test.csv",
114
  "info": f"splits/{sub}/{seed}/info.json",
115
- "spectra": "spectra.zip"
116
  }
 
117
 
118
- extracted_path = dl_manager.download_and_extract(urls)
 
119
 
120
- # df1 = pd.read_csv(extracted_path["train"])
121
- # df2 = pd.read_csv(extracted_path["val"])
122
- # df3 = pd.read_csv(extracted_path["test"])
123
- # df_combined = pd.concat([df1, df2, df3], ignore_index=True)
124
- #
125
- # # Load all spectra files
126
- # spectra_urls = {}
127
- # for _, row in df_combined.iterrows():
128
- # spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
129
- # spectra_files = dl_manager.download(spectra_urls)
130
 
131
  # Load photometry and init reader
132
  photometry_path = dl_manager.download(f"photometry.zip")
@@ -136,27 +133,24 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
136
  datasets.SplitGenerator(
137
  name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
138
  "info_path": extracted_path["info"],
139
- # "spectra_files": spectra_files,
140
- "spectra_path": extracted_path["spectra"],
141
  "split": "train"}
142
  ),
143
  datasets.SplitGenerator(
144
  name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
145
  "info_path": extracted_path["info"],
146
- # "spectra_files": spectra_files,
147
- "spectra_path": extracted_path["spectra"],
148
  "split": "val"}
149
  ),
150
  datasets.SplitGenerator(
151
  name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
152
  "info_path": extracted_path["info"],
153
- # "spectra_files": spectra_files,
154
- "spectra_path": extracted_path["spectra"],
155
  "split": "test"}
156
  ),
157
  ]
158
 
159
- def _generate_examples(self, csv_path, info_path, spectra_path, split):
160
  """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
161
 
162
  df = pd.read_csv(csv_path)
@@ -166,7 +160,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
166
 
167
  for idx, row in df.iterrows():
168
  photometry = self._get_photometry(row["name"])
169
- spectra = self._get_spectra(os.path.join(spectra_path, row["target"], row["spec_filename"]))
170
 
171
  yield idx, {
172
  "photometry": photometry,
 
112
  "val": f"splits/{sub}/{seed}/val.csv",
113
  "test": f"splits/{sub}/{seed}/test.csv",
114
  "info": f"splits/{sub}/{seed}/info.json",
 
115
  }
116
+ extracted_path = dl_manager.download(urls)
117
 
118
+ # Load all spectra files
119
+ spectra_urls = {}
120
 
121
+ for split in ("train", "val", "test"):
122
+ df = pd.read_csv(extracted_path[split])
123
+ for _, row in df.iterrows():
124
+ spectra_urls[row["spec_filename"]] = f"spectra/{row['target']}/{row['spec_filename']}"
125
+
126
+ spectra_files = dl_manager.download(spectra_urls)
 
 
 
 
127
 
128
  # Load photometry and init reader
129
  photometry_path = dl_manager.download(f"photometry.zip")
 
133
  datasets.SplitGenerator(
134
  name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
135
  "info_path": extracted_path["info"],
136
+ "spectra_files": spectra_files,
 
137
  "split": "train"}
138
  ),
139
  datasets.SplitGenerator(
140
  name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
141
  "info_path": extracted_path["info"],
142
+ "spectra_files": spectra_files,
 
143
  "split": "val"}
144
  ),
145
  datasets.SplitGenerator(
146
  name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
147
  "info_path": extracted_path["info"],
148
+ "spectra_files": spectra_files,
 
149
  "split": "test"}
150
  ),
151
  ]
152
 
153
+ def _generate_examples(self, csv_path, info_path, spectra_files, split):
154
  """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
155
 
156
  df = pd.read_csv(csv_path)
 
160
 
161
  for idx, row in df.iterrows():
162
  photometry = self._get_photometry(row["name"])
163
+ spectra = self._get_spectra(spectra_files[row["spec_filename"]])
164
 
165
  yield idx, {
166
  "photometry": photometry,