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42aa4d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | from pathlib import Path
import pickle
import numpy as np
import pandas as pd
from keras_preprocessing.text import Tokenizer
from keras_preprocessing.sequence import pad_sequences
MAX_WORDS = 30000
MAX_LEN = 100
OOV_TOKEN = "[OOV]"
def build_label_map(values):
classes = sorted(pd.Series(values).astype(str).unique())
to_index = {v: i for i, v in enumerate(classes)}
to_value = {i: v for i, v in enumerate(classes)}
return {
"classes": classes,
"to_index": to_index,
"to_value": to_value,
}
def encode_labels(values, label_map):
return np.array([label_map["to_index"][str(v)] for v in values], dtype=np.int32)
def build_hierarchy_masks(train_df, label_maps):
y2_to_idx = label_maps["y2"]["to_index"]
y3_to_idx = label_maps["y3"]["to_index"]
y4_to_idx = label_maps["y4"]["to_index"]
y5_to_idx = label_maps["y5"]["to_index"]
y6_to_idx = label_maps["y6"]["to_index"]
mask23 = np.zeros((len(y2_to_idx), len(y3_to_idx)), dtype=bool)
mask34 = np.zeros((len(y3_to_idx), len(y4_to_idx)), dtype=bool)
mask45 = np.zeros((len(y4_to_idx), len(y5_to_idx)), dtype=bool)
mask56 = np.zeros((len(y5_to_idx), len(y6_to_idx)), dtype=bool)
for row in train_df[["y2", "y3", "y4", "y5", "y6"]].drop_duplicates().itertuples(index=False):
y2, y3, y4, y5, y6 = map(str, row)
mask23[y2_to_idx[y2], y3_to_idx[y3]] = True
mask34[y3_to_idx[y3], y4_to_idx[y4]] = True
mask45[y4_to_idx[y4], y5_to_idx[y5]] = True
mask56[y5_to_idx[y5], y6_to_idx[y6]] = True
return {
"mask23": mask23,
"mask34": mask34,
"mask45": mask45,
"mask56": mask56,
}
def check_split_labels(split_df, split_name, label_maps):
for level in ["y2", "y3", "y4", "y5", "y6"]:
unseen = sorted(set(split_df[level].astype(str).unique()) - set(label_maps[level]["classes"]))
if unseen:
raise ValueError(f"{split_name} contains unseen {level} labels: {unseen[:10]}")
def main():
project_dir = Path(__file__).resolve().parents[2]
interim_dir = project_dir / "data" / "interim"
processed_dir = project_dir / "data" / "processed"
processed_dir.mkdir(parents=True, exist_ok=True)
artifacts_dir = project_dir / "training" / "artifacts"
tokenizer_dir = artifacts_dir / "tokenizer"
label_maps_dir = artifacts_dir / "label_maps"
hierarchy_dir = artifacts_dir / "hierarchy"
tokenizer_dir.mkdir(parents=True, exist_ok=True)
label_maps_dir.mkdir(parents=True, exist_ok=True)
hierarchy_dir.mkdir(parents=True, exist_ok=True)
train_df = pd.read_csv(interim_dir / "train.csv")
valid_df = pd.read_csv(interim_dir / "valid.csv")
test_df = pd.read_csv(interim_dir / "test.csv")
# text
X_train_text = train_df["company_description"].astype(str).tolist()
X_valid_text = valid_df["company_description"].astype(str).tolist()
X_test_text = test_df["company_description"].astype(str).tolist()
tokenizer = Tokenizer(num_words=MAX_WORDS, oov_token=OOV_TOKEN)
tokenizer.fit_on_texts(X_train_text)
X_train_seq = tokenizer.texts_to_sequences(X_train_text)
X_valid_seq = tokenizer.texts_to_sequences(X_valid_text)
X_test_seq = tokenizer.texts_to_sequences(X_test_text)
X_train = pad_sequences(X_train_seq, maxlen=MAX_LEN, padding="post", truncating="post")
X_valid = pad_sequences(X_valid_seq, maxlen=MAX_LEN, padding="post", truncating="post")
X_test = pad_sequences(X_test_seq, maxlen=MAX_LEN, padding="post", truncating="post")
# label maps built from train only
label_maps = {
"y2": build_label_map(train_df["y2"]),
"y3": build_label_map(train_df["y3"]),
"y4": build_label_map(train_df["y4"]),
"y5": build_label_map(train_df["y5"]),
"y6": build_label_map(train_df["y6"]),
}
check_split_labels(valid_df, "valid", label_maps)
check_split_labels(test_df, "test", label_maps)
y_train = {
"y2": encode_labels(train_df["y2"], label_maps["y2"]),
"y3": encode_labels(train_df["y3"], label_maps["y3"]),
"y4": encode_labels(train_df["y4"], label_maps["y4"]),
"y5": encode_labels(train_df["y5"], label_maps["y5"]),
"y6": encode_labels(train_df["y6"], label_maps["y6"]),
}
y_valid = {
"y2": encode_labels(valid_df["y2"], label_maps["y2"]),
"y3": encode_labels(valid_df["y3"], label_maps["y3"]),
"y4": encode_labels(valid_df["y4"], label_maps["y4"]),
"y5": encode_labels(valid_df["y5"], label_maps["y5"]),
"y6": encode_labels(valid_df["y6"], label_maps["y6"]),
}
y_test = {
"y2": encode_labels(test_df["y2"], label_maps["y2"]),
"y3": encode_labels(test_df["y3"], label_maps["y3"]),
"y4": encode_labels(test_df["y4"], label_maps["y4"]),
"y5": encode_labels(test_df["y5"], label_maps["y5"]),
"y6": encode_labels(test_df["y6"], label_maps["y6"]),
}
hierarchy = build_hierarchy_masks(train_df, label_maps)
# save arrays
np.save(processed_dir / "X_train.npy", X_train)
np.save(processed_dir / "X_valid.npy", X_valid)
np.save(processed_dir / "X_test.npy", X_test)
np.savez(processed_dir / "y_train.npz", **y_train)
np.savez(processed_dir / "y_valid.npz", **y_valid)
np.savez(processed_dir / "y_test.npz", **y_test)
# optional: save raw text and codes for later inspection/evaluation
train_df.to_csv(processed_dir / "train_processed_reference.csv", index=False)
valid_df.to_csv(processed_dir / "valid_processed_reference.csv", index=False)
test_df.to_csv(processed_dir / "test_processed_reference.csv", index=False)
# save artifacts
with open(tokenizer_dir / "tokenizer.pkl", "wb") as f:
pickle.dump(tokenizer, f)
with open(label_maps_dir / "label_maps.pkl", "wb") as f:
pickle.dump(label_maps, f)
with open(hierarchy_dir / "hierarchy.pkl", "wb") as f:
pickle.dump(hierarchy, f)
metadata = {
"max_words": MAX_WORDS,
"max_len": MAX_LEN,
"oov_token": OOV_TOKEN,
"train_rows": len(train_df),
"valid_rows": len(valid_df),
"test_rows": len(test_df),
"vocab_size_raw": len(tokenizer.word_index),
"vocab_size_capped": min(MAX_WORDS, len(tokenizer.word_index) + 1),
"n_classes_y2": len(label_maps["y2"]["classes"]),
"n_classes_y3": len(label_maps["y3"]["classes"]),
"n_classes_y4": len(label_maps["y4"]["classes"]),
"n_classes_y5": len(label_maps["y5"]["classes"]),
"n_classes_y6": len(label_maps["y6"]["classes"]),
}
with open(processed_dir / "metadata.pkl", "wb") as f:
pickle.dump(metadata, f)
print("Saved tokenized arrays:")
print(processed_dir / "X_train.npy")
print(processed_dir / "X_valid.npy")
print(processed_dir / "X_test.npy")
print("\nSaved label arrays:")
print(processed_dir / "y_train.npz")
print(processed_dir / "y_valid.npz")
print(processed_dir / "y_test.npz")
print("\nSaved artifacts:")
print(tokenizer_dir / "tokenizer.pkl")
print(label_maps_dir / "label_maps.pkl")
print(hierarchy_dir / "hierarchy.pkl")
print("\nShapes:")
print("X_train:", X_train.shape)
print("X_valid:", X_valid.shape)
print("X_test: ", X_test.shape)
print("\nClass counts:")
print("y2:", len(label_maps["y2"]["classes"]))
print("y3:", len(label_maps["y3"]["classes"]))
print("y4:", len(label_maps["y4"]["classes"]))
print("y5:", len(label_maps["y5"]["classes"]))
print("y6:", len(label_maps["y6"]["classes"]))
print("\nTokenizer:")
print("Raw vocab size:", len(tokenizer.word_index))
print("Capped vocab size:", min(MAX_WORDS, len(tokenizer.word_index) + 1))
if __name__ == "__main__":
main()
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