Note
Go to the end to download the full example code.
Dataset upload tutorial¶
A tutorial on how to create and upload a dataset to OpenML.
# License: BSD 3-Clause
import numpy as np
import pandas as pd
import sklearn.datasets
from scipy.sparse import coo_matrix
import openml
from openml.datasets.functions import create_dataset
Warning
This example uploads data. For that reason, this example connects to the test server at test.openml.org. This prevents the main server from crowding with example datasets, tasks, runs, and so on. The use of this test server can affect behaviour and performance of the OpenML-Python API.
openml.config.start_using_configuration_for_example()
/home/runner/work/openml-python/openml-python/examples/30_extended/create_upload_tutorial.py:22: UserWarning: Switching to the test server https://test.openml.org/api/v1/xml to not upload results to the live server. Using the test server may result in reduced performance of the API!
openml.config.start_using_configuration_for_example()
Below we will cover the following cases of the dataset object:
A numpy array
A list
A pandas dataframe
A sparse matrix
A pandas sparse dataframe
Dataset is a numpy array¶
A numpy array can contain lists in the case of dense data or it can contain OrderedDicts in the case of sparse data.
Prepare dataset¶
Load an example dataset from scikit-learn which we will upload to OpenML.org via the API.
diabetes = sklearn.datasets.load_diabetes()
name = "Diabetes(scikit-learn)"
X = diabetes.data
y = diabetes.target
attribute_names = diabetes.feature_names
description = diabetes.DESCR
OpenML does not distinguish between the attributes and targets on the data level and stores all data in a single matrix.
The target feature is indicated as meta-data of the dataset (and tasks on that data).
data = np.concatenate((X, y.reshape((-1, 1))), axis=1)
attribute_names = list(attribute_names)
attributes = [(attribute_name, "REAL") for attribute_name in attribute_names] + [
("class", "INTEGER")
]
citation = (
"Bradley Efron, Trevor Hastie, Iain Johnstone and "
"Robert Tibshirani (2004) (Least Angle Regression) "
"Annals of Statistics (with discussion), 407-499"
)
paper_url = "https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf"
Create the dataset object¶
The definition of all fields can be found in the XSD files describing the expected format:
diabetes_dataset = create_dataset(
# The name of the dataset (needs to be unique).
# Must not be longer than 128 characters and only contain
# a-z, A-Z, 0-9 and the following special characters: _\-\.(),
name=name,
# Textual description of the dataset.
description=description,
# The person who created the dataset.
creator="Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani",
# People who contributed to the current version of the dataset.
contributor=None,
# The date the data was originally collected, given by the uploader.
collection_date="09-01-2012",
# Language in which the data is represented.
# Starts with 1 upper case letter, rest lower case, e.g. 'English'.
language="English",
# License under which the data is/will be distributed.
licence="BSD (from scikit-learn)",
# Name of the target. Can also have multiple values (comma-separated).
default_target_attribute="class",
# The attribute that represents the row-id column, if present in the
# dataset.
row_id_attribute=None,
# Attribute or list of attributes that should be excluded in modelling, such as
# identifiers and indexes. E.g. "feat1" or ["feat1","feat2"]
ignore_attribute=None,
# How to cite the paper.
citation=citation,
# Attributes of the data
attributes=attributes,
data=data,
# A version label which is provided by the user.
version_label="test",
original_data_url="https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html",
paper_url=paper_url,
)
diabetes_dataset.publish()
print(f"URL for dataset: {diabetes_dataset.openml_url}")
URL for dataset: https://test.openml.org/d/887
Dataset is a list¶
A list can contain lists in the case of dense data or it can contain OrderedDicts in the case of sparse data.
Weather dataset: https://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html
data = [
["sunny", 85, 85, "FALSE", "no"],
["sunny", 80, 90, "TRUE", "no"],
["overcast", 83, 86, "FALSE", "yes"],
["rainy", 70, 96, "FALSE", "yes"],
["rainy", 68, 80, "FALSE", "yes"],
["rainy", 65, 70, "TRUE", "no"],
["overcast", 64, 65, "TRUE", "yes"],
["sunny", 72, 95, "FALSE", "no"],
["sunny", 69, 70, "FALSE", "yes"],
["rainy", 75, 80, "FALSE", "yes"],
["sunny", 75, 70, "TRUE", "yes"],
["overcast", 72, 90, "TRUE", "yes"],
["overcast", 81, 75, "FALSE", "yes"],
["rainy", 71, 91, "TRUE", "no"],
]
attribute_names = [
("outlook", ["sunny", "overcast", "rainy"]),
("temperature", "REAL"),
("humidity", "REAL"),
("windy", ["TRUE", "FALSE"]),
("play", ["yes", "no"]),
]
description = (
"The weather problem is a tiny dataset that we will use repeatedly"
" to illustrate machine learning methods. Entirely fictitious, it "
"supposedly concerns the conditions that are suitable for playing "
"some unspecified game. In general, instances in a dataset are "
"characterized by the values of features, or attributes, that measure "
"different aspects of the instance. In this case there are four "
"attributes: outlook, temperature, humidity, and windy. "
"The outcome is whether to play or not."
)
citation = (
"I. H. Witten, E. Frank, M. A. Hall, and ITPro,"
"Data mining practical machine learning tools and techniques, "
"third edition. Burlington, Mass.: Morgan Kaufmann Publishers, 2011"
)
weather_dataset = create_dataset(
name="Weather",
description=description,
creator="I. H. Witten, E. Frank, M. A. Hall, and ITPro",
contributor=None,
collection_date="01-01-2011",
language="English",
licence=None,
default_target_attribute="play",
row_id_attribute=None,
ignore_attribute=None,
citation=citation,
attributes=attribute_names,
data=data,
version_label="example",
)
weather_dataset.publish()
print(f"URL for dataset: {weather_dataset.openml_url}")
URL for dataset: https://test.openml.org/d/888
Dataset is a pandas DataFrame¶
It might happen that your dataset is made of heterogeneous data which can usually
be stored as a Pandas DataFrame. DataFrames offer the advantage of
storing the type of data for each column as well as the attribute names.
Therefore, when providing a Pandas DataFrame, OpenML can infer this
information without needing to explicitly provide it when calling the
function openml.datasets.create_dataset(). In this regard, you only
need to pass 'auto' to the attributes parameter.
df = pd.DataFrame(data, columns=[col_name for col_name, _ in attribute_names])
# enforce the categorical column to have a categorical dtype
df["outlook"] = df["outlook"].astype("category")
df["windy"] = df["windy"].astype("bool")
df["play"] = df["play"].astype("category")
print(df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14 entries, 0 to 13
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 outlook 14 non-null category
1 temperature 14 non-null int64
2 humidity 14 non-null int64
3 windy 14 non-null bool
4 play 14 non-null category
dtypes: bool(1), category(2), int64(2)
memory usage: 650.0 bytes
None
We enforce the column ‘outlook’ and ‘play’ to be a categorical
dtype while the column ‘windy’ is kept as a boolean column. ‘temperature’
and ‘humidity’ are kept as numeric columns. Then, we can
call openml.datasets.create_dataset() by passing the dataframe and
fixing the parameter attributes to 'auto'.
weather_dataset = create_dataset(
name="Weather",
description=description,
creator="I. H. Witten, E. Frank, M. A. Hall, and ITPro",
contributor=None,
collection_date="01-01-2011",
language="English",
licence=None,
default_target_attribute="play",
row_id_attribute=None,
ignore_attribute=None,
citation=citation,
attributes="auto",
data=df,
version_label="example",
)
weather_dataset.publish()
print(f"URL for dataset: {weather_dataset.openml_url}")
URL for dataset: https://test.openml.org/d/889
Dataset is a sparse matrix¶
sparse_data = coo_matrix(
([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]))
)
column_names = [
("input1", "REAL"),
("input2", "REAL"),
("y", "REAL"),
]
xor_dataset = create_dataset(
name="XOR",
description="Dataset representing the XOR operation",
creator=None,
contributor=None,
collection_date=None,
language="English",
licence=None,
default_target_attribute="y",
row_id_attribute=None,
ignore_attribute=None,
citation=None,
attributes=column_names,
data=sparse_data,
version_label="example",
)
xor_dataset.publish()
print(f"URL for dataset: {xor_dataset.openml_url}")
URL for dataset: https://test.openml.org/d/890
Dataset is a pandas dataframe with sparse columns¶
sparse_data = coo_matrix(
([1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0], ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]))
)
column_names = ["input1", "input2", "y"]
df = pd.DataFrame.sparse.from_spmatrix(sparse_data, columns=column_names)
print(df.info())
xor_dataset = create_dataset(
name="XOR",
description="Dataset representing the XOR operation",
creator=None,
contributor=None,
collection_date=None,
language="English",
licence=None,
default_target_attribute="y",
row_id_attribute=None,
ignore_attribute=None,
citation=None,
attributes="auto",
data=df,
version_label="example",
)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 input1 4 non-null Sparse[float64, 0]
1 input2 4 non-null Sparse[float64, 0]
2 y 4 non-null Sparse[float64, 0]
dtypes: Sparse[float64, 0](3)
memory usage: 212.0 bytes
None
xor_dataset.publish()
print(f"URL for dataset: {xor_dataset.openml_url}")
URL for dataset: https://test.openml.org/d/891
openml.config.stop_using_configuration_for_example()
Total running time of the script: (0 minutes 4.161 seconds)