neulab/docprompting-conala huggingface dataset:
sklearn.datasets.load_boston(*, return_X_y=False) [source]
 
Load and return the boston house-prices dataset (regression).  
Samples total 506  
Dimensionality 13  
Features real, positive  
Targets real 5. - 50.   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
datandarray of shape (506, 13) 

The data matrix.  
targetndarray of shape (506, ) 

The regression target.  
filenamestr 

The physical location of boston csv dataset.  New in version 0.20.   
DESCRstr 

The full description of the dataset.  
feature_namesndarray 

The names of features    
(data, target)tuple if return_X_y is True 

 New in version 0.18.      Notes  Changed in version 0.20: Fixed a wrong data point at [445, 0].  Examples >>> from sklearn.datasets import load_boston
>>> X, y = load_boston(return_X_y=True)
>>> print(X.shape)
(506, 13)
sklearn.datasets.load_breast_cancer(*, return_X_y=False, as_frame=False) [source]
 
Load and return the breast cancer wisconsin dataset (classification). The breast cancer dataset is a classic and very easy binary classification dataset.  
Classes 2  
Samples per class 212(M),357(B)  
Samples total 569  
Dimensionality 30  
Features real, positive   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.   
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (569, 30) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (569,)

The classification target. If as_frame=True, target will be a pandas Series.  feature_names: list

The names of the dataset columns.  target_names: list

The names of target classes.  frame: DataFrame of shape (569, 31)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.  filename: str

The path to the location of the data.  New in version 0.20.     
(data, target)tuple if return_X_y is True 

 New in version 0.18.   The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
downloaded from:
https://goo.gl/U2Uwz2
   Examples Let’s say you are interested in the samples 10, 50, and 85, and want to know their class name. >>> from sklearn.datasets import load_breast_cancer
>>> data = load_breast_cancer()
>>> data.target[[10, 50, 85]]
array([0, 1, 0])
>>> list(data.target_names)
['malignant', 'benign']
sklearn.datasets.load_diabetes(*, return_X_y=False, as_frame=False) [source]
 
Load and return the diabetes dataset (regression).  
Samples total 442  
Dimensionality 10  
Features real, -.2 < x < .2  
Targets integer 25 - 346   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False. 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.   
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (442, 10) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (442,)

The regression target. If as_frame=True, target will be a pandas Series.  feature_names: list

The names of the dataset columns.  frame: DataFrame of shape (442, 11)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.  data_filename: str

The path to the location of the data.  target_filename: str

The path to the location of the target.    
(data, target)tuple if return_X_y is True 

 New in version 0.18.
sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=False) [source]
 
Load and return the digits dataset (classification). Each datapoint is a 8x8 image of a digit.  
Classes 10  
Samples per class ~180  
Samples total 1797  
Dimensionality 64  
Features integers 0-16   Read more in the User Guide.  Parameters 
 
n_classint, default=10 

The number of classes to return. Between 0 and 10.  
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.   
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (1797, 64) 

The flattened data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (1797,)

The classification target. If as_frame=True, target will be a pandas Series.  feature_names: list

The names of the dataset columns.  target_names: list

The names of target classes.  New in version 0.20.   frame: DataFrame of shape (1797, 65)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   images: {ndarray} of shape (1797, 8, 8)

The raw image data.  DESCR: str

The full description of the dataset.    
(data, target)tuple if return_X_y is True 

 New in version 0.18.   This is a copy of the test set of the UCI ML hand-written digits datasets
https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
   Examples To load the data and visualize the images: >>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import matplotlib.pyplot as plt 
>>> plt.gray() 
>>> plt.matshow(digits.images[0]) 
>>> plt.show()
sklearn.datasets.load_iris(*, return_X_y=False, as_frame=False) [source]
 
Load and return the iris dataset (classification). The iris dataset is a classic and very easy multi-class classification dataset.  
Classes 3  
Samples per class 50  
Samples total 150  
Dimensionality 4  
Features real, positive   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.   
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (150, 4) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (150,)

The classification target. If as_frame=True, target will be a pandas Series.  feature_names: list

The names of the dataset columns.  target_names: list

The names of target classes.  frame: DataFrame of shape (150, 5)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.  filename: str

The path to the location of the data.  New in version 0.20.     
(data, target)tuple if return_X_y is True 

 New in version 0.18.      Notes  Changed in version 0.20: Fixed two wrong data points according to Fisher’s paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.  Examples Let’s say you are interested in the samples 10, 25, and 50, and want to know their class name. >>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']
sklearn.datasets.load_linnerud(*, return_X_y=False, as_frame=False) [source]
 
Load and return the physical excercise linnerud dataset. This dataset is suitable for multi-ouput regression tasks.  
Samples total 20  
Dimensionality 3 (for both data and target)  
Features integer  
Targets integer   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  New in version 0.18.   
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (20, 3) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, dataframe} of shape (20, 3)

The regression targets. If as_frame=True, target will be a pandas DataFrame.  feature_names: list

The names of the dataset columns.  target_names: list

The names of the target columns.  frame: DataFrame of shape (20, 6)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.  data_filename: str

The path to the location of the data.  target_filename: str

The path to the location of the target.  New in version 0.20.     
(data, target)tuple if return_X_y is True 

 New in version 0.18.
sklearn.datasets.load_sample_image(image_name) [source]
 
Load the numpy array of a single sample image Read more in the User Guide.  Parameters 
 
image_name{china.jpg, flower.jpg} 

The name of the sample image loaded    Returns 
 
img3D array 

The image as a numpy array: height x width x color     Examples >>> from sklearn.datasets import load_sample_image
>>> china = load_sample_image('china.jpg')   
>>> china.dtype                              
dtype('uint8')
>>> china.shape                              
(427, 640, 3)
>>> flower = load_sample_image('flower.jpg') 
>>> flower.dtype                             
dtype('uint8')
>>> flower.shape                             
(427, 640, 3)
sklearn.datasets.load_sample_images() [source]
 
Load sample images for image manipulation. Loads both, china and flower. Read more in the User Guide.  Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
imageslist of ndarray of shape (427, 640, 3) 

The two sample image.  
filenameslist 

The filenames for the images.  
DESCRstr 

The full description of the dataset.       Examples To load the data and visualize the images: >>> from sklearn.datasets import load_sample_images
>>> dataset = load_sample_images()     
>>> len(dataset.images)                
2
>>> first_img_data = dataset.images[0] 
>>> first_img_data.shape               
(427, 640, 3)
>>> first_img_data.dtype               
dtype('uint8')
sklearn.datasets.load_wine(*, return_X_y=False, as_frame=False) [source]
 
Load and return the wine dataset (classification).  New in version 0.18.  The wine dataset is a classic and very easy multi-class classification dataset.  
Classes 3  
Samples per class [59,71,48]  
Samples total 178  
Dimensionality 13  
Features real, positive   Read more in the User Guide.  Parameters 
 
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.  
as_framebool, default=False 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.  New in version 0.23.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
data{ndarray, dataframe} of shape (178, 13) 

The data matrix. If as_frame=True, data will be a pandas DataFrame.  target: {ndarray, Series} of shape (178,)

The classification target. If as_frame=True, target will be a pandas Series.  feature_names: list

The names of the dataset columns.  target_names: list

The names of target classes.  frame: DataFrame of shape (178, 14)

Only present when as_frame=True. DataFrame with data and target.  New in version 0.23.   DESCR: str

The full description of the dataset.    
(data, target)tuple if return_X_y is True 
The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit
standard format from:
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
   Examples Let’s say you are interested in the samples 10, 80, and 140, and want to know their class name. >>> from sklearn.datasets import load_wine
>>> data = load_wine()
>>> data.target[[10, 80, 140]]
array([0, 1, 2])
>>> list(data.target_names)
['class_0', 'class_1', 'class_2']
sklearn.datasets.make_biclusters(shape, n_clusters, *, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None) [source]
 
Generate an array with constant block diagonal structure for biclustering. Read more in the User Guide.  Parameters 
 
shapeiterable of shape (n_rows, n_cols) 

The shape of the result.  
n_clustersint 

The number of biclusters.  
noisefloat, default=0.0 

The standard deviation of the gaussian noise.  
minvalint, default=10 

Minimum value of a bicluster.  
maxvalint, default=100 

Maximum value of a bicluster.  
shufflebool, default=True 

Shuffle the samples.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
Xndarray of shape shape 

The generated array.  
rowsndarray of shape (n_clusters, X.shape[0]) 

The indicators for cluster membership of each row.  
colsndarray of shape (n_clusters, X.shape[1]) 

The indicators for cluster membership of each column.      See also  
make_checkerboard

  References  
1  
Dhillon, I. S. (2001, August). Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 269-274). ACM.
sklearn.datasets.make_blobs(n_samples=100, n_features=2, *, centers=None, cluster_std=1.0, center_box=- 10.0, 10.0, shuffle=True, random_state=None, return_centers=False) [source]
 
Generate isotropic Gaussian blobs for clustering. Read more in the User Guide.  Parameters 
 
n_samplesint or array-like, default=100 

If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.  Changed in version v0.20: one can now pass an array-like to the n_samples parameter   
n_featuresint, default=2 

The number of features for each sample.  
centersint or ndarray of shape (n_centers, n_features), default=None 

The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.  
cluster_stdfloat or array-like of float, default=1.0 

The standard deviation of the clusters.  
center_boxtuple of float (min, max), default=(-10.0, 10.0) 

The bounding box for each cluster center when centers are generated at random.  
shufflebool, default=True 

Shuffle the samples.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.  
return_centersbool, default=False 

If True, then return the centers of each cluster  New in version 0.23.     Returns 
 
Xndarray of shape (n_samples, n_features) 

The generated samples.  
yndarray of shape (n_samples,) 

The integer labels for cluster membership of each sample.  
centersndarray of shape (n_centers, n_features) 

The centers of each cluster. Only returned if return_centers=True.      See also  
make_classification


A more intricate variant.    Examples >>> from sklearn.datasets import make_blobs
>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])
>>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])
sklearn.datasets.make_checkerboard(shape, n_clusters, *, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None) [source]
 
Generate an array with block checkerboard structure for biclustering. Read more in the User Guide.  Parameters 
 
shapetuple of shape (n_rows, n_cols) 

The shape of the result.  
n_clustersint or array-like or shape (n_row_clusters, n_column_clusters) 

The number of row and column clusters.  
noisefloat, default=0.0 

The standard deviation of the gaussian noise.  
minvalint, default=10 

Minimum value of a bicluster.  
maxvalint, default=100 

Maximum value of a bicluster.  
shufflebool, default=True 

Shuffle the samples.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
Xndarray of shape shape 

The generated array.  
rowsndarray of shape (n_clusters, X.shape[0]) 

The indicators for cluster membership of each row.  
colsndarray of shape (n_clusters, X.shape[1]) 

The indicators for cluster membership of each column.      See also  
make_biclusters

  References  
1  
Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003). Spectral biclustering of microarray data: coclustering genes and conditions. Genome research, 13(4), 703-716.
sklearn.datasets.make_circles(n_samples=100, *, shuffle=True, noise=None, random_state=None, factor=0.8) [source]
 
Make a large circle containing a smaller circle in 2d. A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide.  Parameters 
 
n_samplesint or tuple of shape (2,), dtype=int, default=100 

If int, it is the total number of points generated. For odd numbers, the inner circle will have one point more than the outer circle. If two-element tuple, number of points in outer circle and inner circle.  Changed in version 0.23: Added two-element tuple.   
shufflebool, default=True 

Whether to shuffle the samples.  
noisefloat, default=None 

Standard deviation of Gaussian noise added to the data.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary.  
factorfloat, default=.8 

Scale factor between inner and outer circle in the range (0, 1).    Returns 
 
Xndarray of shape (n_samples, 2) 

The generated samples.  
yndarray of shape (n_samples,) 

The integer labels (0 or 1) for class membership of each sample.
sklearn.datasets.make_friedman1(n_samples=100, n_features=10, *, noise=0.0, random_state=None) [source]
 
Generate the “Friedman #1” regression problem. This dataset is described in Friedman [1] and Breiman [2]. Inputs X are independent features uniformly distributed on the interval [0, 1]. The output y is created according to the formula: y(X) = 10 * sin(pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - 0.5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] + noise * N(0, 1).
 Out of the n_features features, only 5 are actually used to compute y. The remaining features are independent of y. The number of features has to be >= 5. Read more in the User Guide.  Parameters 
 
n_samplesint, default=100 

The number of samples.  
n_featuresint, default=10 

The number of features. Should be at least 5.  
noisefloat, default=0.0 

The standard deviation of the gaussian noise applied to the output.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
Xndarray of shape (n_samples, n_features) 

The input samples.  
yndarray of shape (n_samples,) 

The output values.     References  
1  
J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.  
2  
L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.
sklearn.datasets.make_friedman2(n_samples=100, *, noise=0.0, random_state=None) [source]
 
Generate the “Friedman #2” regression problem. This dataset is described in Friedman [1] and Breiman [2]. Inputs X are 4 independent features uniformly distributed on the intervals: 0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= 11.
 The output y is created according to the formula: y(X) = (X[:, 0] ** 2 + (X[:, 1] * X[:, 2]  - 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 + noise * N(0, 1).
 Read more in the User Guide.  Parameters 
 
n_samplesint, default=100 

The number of samples.  
noisefloat, default=0.0 

The standard deviation of the gaussian noise applied to the output.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
Xndarray of shape (n_samples, 4) 

The input samples.  
yndarray of shape (n_samples,) 

The output values.     References  
1  
J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.  
2  
L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.
sklearn.datasets.make_friedman3(n_samples=100, *, noise=0.0, random_state=None) [source]
 
Generate the “Friedman #3” regression problem. This dataset is described in Friedman [1] and Breiman [2]. Inputs X are 4 independent features uniformly distributed on the intervals: 0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= 11.
 The output y is created according to the formula: y(X) = arctan((X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) / X[:, 0]) + noise * N(0, 1).
 Read more in the User Guide.  Parameters 
 
n_samplesint, default=100 

The number of samples.  
noisefloat, default=0.0 

The standard deviation of the gaussian noise applied to the output.  
random_stateint, RandomState instance or None, default=None 

Determines random number generation for dataset noise. Pass an int for reproducible output across multiple function calls. See Glossary.    Returns 
 
Xndarray of shape (n_samples, 4) 

The input samples.  
yndarray of shape (n_samples,) 

The output values.     References  
1  
J. Friedman, “Multivariate adaptive regression splines”, The Annals of Statistics 19 (1), pages 1-67, 1991.  
2  
L. Breiman, “Bagging predictors”, Machine Learning 24, pages 123-140, 1996.