california
CaliforniaHousing
¶
Bases: Dataset
A PyTorch Dataset for regression. A toy data set from scikit-learn. Features: * MedInc median income in block group * HouseAge median house age in block group * AveRooms average number of rooms per household * AveBedrms average number of bedrooms per household * Population block group population * AveOccup average number of household members * Latitude block group latitude * Longitude block group longitude The target variable is the median house value for California districts, expressed in hundreds of thousands of Dollars ($100,000). Samples total: 20640, Dimensionality: 8, Features: real, Target: real 0.15 - 5. This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
feature_type |
dtype
|
The data type of the features. Defaults to torch.float. |
float
|
target_type |
dtype
|
The data type of the targets. Defaults to torch.long. |
float
|
train |
bool
|
Whether the dataset is for training or not. Defaults to True. |
True
|
n_samples |
int
|
The number of samples of the dataset. Defaults to None, which means the entire dataset is used. |
None
|
Attributes:
Name | Type | Description |
---|---|---|
data |
Tensor
|
The data features. |
targets |
Tensor
|
The data targets. |
Examples:
>>> from torch.utils.data import DataLoader
from spotpython.data.diabetes import Diabetes
import torch
dataset = Diabetes(feature_type=torch.float32, target_type=torch.float32)
# Set batch size for DataLoader
batch_size = 5
# Create DataLoader
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
# Iterate over the data in the DataLoader
for batch in dataloader:
inputs, targets = batch
print(f"Batch Size: {inputs.size(0)}")
print("---------------")
print(f"Inputs: {inputs}")
print(f"Targets: {targets}")
Source code in spotpython/data/california.py
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 |
|
__getitem__(idx)
¶
Returns the feature and target at the given index.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
idx |
int
|
The index. |
required |
Returns:
Name | Type | Description |
---|---|---|
tuple |
tuple
|
A tuple containing the feature and target at the given index. |
Examples:
>>> from spotpython.light.csvdataset import CSVDataset
dataset = CSVDataset(filename='./data/spotpython/data.csv', target_column='prognosis')
print(dataset.data.shape)
print(dataset.targets.shape)
torch.Size([11, 65])
torch.Size([11])
Source code in spotpython/data/california.py
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
|
__len__()
¶
Returns the length of the dataset.
Returns:
Name | Type | Description |
---|---|---|
int |
int
|
The length of the dataset. |
Examples:
>>> from spotpython.light import CSVDataset
>>> dataset = CSVDataset()
>>> print(len(dataset))
60000
Source code in spotpython/data/california.py
113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
|
extra_repr()
¶
Returns a string representation of the dataset.
Returns:
Name | Type | Description |
---|---|---|
str |
str
|
A string representation of the dataset. |
Examples:
>>> from spotpython.light import CSVDataset
>>> dataset = CSVDataset()
>>> print(dataset)
Split: Train
Source code in spotpython/data/california.py
129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
|