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import numpy as np
from typing import List, Tuple, Any
from sklearn.preprocessing import StandardScaler, RobustScaler
def min_max_normalize_dataset(train_dataset, val_dataset, test_dataset):
"""Min-max normalization (0-1 scaling)."""
labels = [e["label"] for e in train_dataset]
min_label, max_label = min(labels), max(labels)
normalized_train_dataset = []
normalized_val_dataset = []
normalized_test_dataset = []
for e in train_dataset:
e["label"] = (e["label"] - min_label) / (max_label - min_label)
normalized_train_dataset.append(e)
for e in val_dataset:
e["label"] = (e["label"] - min_label) / (max_label - min_label)
normalized_val_dataset.append(e)
for e in test_dataset:
e["label"] = (e["label"] - min_label) / (max_label - min_label)
normalized_test_dataset.append(e)
print(normalized_train_dataset[0])
return normalized_train_dataset, normalized_val_dataset, normalized_test_dataset
def standard_normalize_dataset(train_dataset, val_dataset, test_dataset):
"""Z-score normalization (standardization)."""
train_labels = np.array([e["label"] for e in train_dataset])
mean_label = np.mean(train_labels)
std_label = np.std(train_labels)
normalized_train_dataset = []
normalized_val_dataset = []
normalized_test_dataset = []
for e in train_dataset:
e["label"] = (e["label"] - mean_label) / std_label
normalized_train_dataset.append(e)
for e in val_dataset:
e["label"] = (e["label"] - mean_label) / std_label
normalized_val_dataset.append(e)
for e in test_dataset:
e["label"] = (e["label"] - mean_label) / std_label
normalized_test_dataset.append(e)
return normalized_train_dataset, normalized_val_dataset, normalized_test_dataset
def robust_normalize_dataset(train_dataset, val_dataset, test_dataset):
"""Robust scaling using statistics that are robust to outliers."""
scaler = RobustScaler()
train_labels = np.array([e["label"] for e in train_dataset]).reshape(-1, 1)
scaler.fit(train_labels)
normalized_train_dataset = []
normalized_val_dataset = []
normalized_test_dataset = []
for e in train_dataset:
e["label"] = scaler.transform([[e["label"]]])[0][0]
normalized_train_dataset.append(e)
for e in val_dataset:
e["label"] = scaler.transform([[e["label"]]])[0][0]
normalized_val_dataset.append(e)
for e in test_dataset:
e["label"] = scaler.transform([[e["label"]]])[0][0]
normalized_test_dataset.append(e)
return normalized_train_dataset, normalized_val_dataset, normalized_test_dataset
def log_normalize_dataset(train_dataset, val_dataset, test_dataset, offset=1.0):
"""Log normalization, useful for skewed data."""
normalized_train_dataset = []
normalized_val_dataset = []
normalized_test_dataset = []
for e in train_dataset:
e["label"] = np.log(e["label"] + offset)
normalized_train_dataset.append(e)
for e in val_dataset:
e["label"] = np.log(e["label"] + offset)
normalized_val_dataset.append(e)
for e in test_dataset:
e["label"] = np.log(e["label"] + offset)
normalized_test_dataset.append(e)
return normalized_train_dataset, normalized_val_dataset, normalized_test_dataset
def quantile_normalize_dataset(train_dataset, val_dataset, test_dataset, n_quantiles=1000):
"""Quantile normalization to achieve a uniform distribution."""
from sklearn.preprocessing import QuantileTransformer
transformer = QuantileTransformer(n_quantiles=n_quantiles, output_distribution='uniform')
train_labels = np.array([e["label"] for e in train_dataset]).reshape(-1, 1)
transformer.fit(train_labels)
normalized_train_dataset = []
normalized_val_dataset = []
normalized_test_dataset = []
for e in train_dataset:
e["label"] = transformer.transform([[e["label"]]])[0][0]
normalized_train_dataset.append(e)
for e in val_dataset:
e["label"] = transformer.transform([[e["label"]]])[0][0]
normalized_val_dataset.append(e)
for e in test_dataset:
e["label"] = transformer.transform([[e["label"]]])[0][0]
normalized_test_dataset.append(e)
return normalized_train_dataset, normalized_val_dataset, normalized_test_dataset
def normalize_dataset(train_dataset, val_dataset, test_dataset, method='min_max', **kwargs):
"""
Unified interface for different normalization methods.
Args:
train_dataset: Training dataset
val_dataset: Validation dataset
test_dataset: Test dataset
method: Normalization method ('min_max', 'standard', 'robust', 'log', 'quantile')
**kwargs: Additional arguments for specific normalization methods
Returns:
Normalized datasets (train, val, test)
"""
normalization_methods = {
'min_max': min_max_normalize_dataset,
'standard': standard_normalize_dataset,
'robust': robust_normalize_dataset,
'log': log_normalize_dataset,
'quantile': quantile_normalize_dataset
}
if method not in normalization_methods:
raise ValueError(f"Unsupported normalization method: {method}. "
f"Available methods: {list(normalization_methods.keys())}")
return normalization_methods[method](train_dataset, val_dataset, test_dataset, **kwargs)
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