diff --git a/examples/preprocessing/plot_scaling_importance.py b/examples/preprocessing/plot_scaling_importance.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1e599ed753c7f2a5c73ffed79a3c5fc039772d6
--- /dev/null
+++ b/examples/preprocessing/plot_scaling_importance.py
@@ -0,0 +1,131 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+=========================================================
+Importance of Feature Scaling
+=========================================================
+
+Feature scaling though standardization (or Z-score normalization)
+can be an important preprocessing step for many machine learning
+algorithms. Standardization involves rescaling the features such
+that they have the properties of a standard normal distribution
+with a mean of zero and a standard deviation of one.
+
+While many algorithms (such as SVM, K-nearest neighbors, and logistic
+regression) require features to be normalized, intuitively we can
+think of Principle Component Analysis (PCA) as being a prime example
+of when normalization is important. In PCA we are interested in the
+components that maximize the variance. If one component (e.g. human
+height) varies less than another (e.g. weight) because of their
+respective scales (meters vs. kilos), PCA might determine that the
+direction of maximal variance more closely corresponds with the
+'weight' axis, if those features are not scaled. As a change in
+height of one meter can be considered much more important than the
+change in weight of one kilogram, this is clearly incorrect.
+
+To illustrate this, PCA is performed comparing the use of data with
+:class:`StandardScaler <sklearn.preprocessing.StandardScaler>` applied,
+to unscaled data. The results are visualized and a clear difference noted.
+The 1st principal component in the unscaled set can be seen. It can be seen
+that feature #13 dominates the direction, being a whole two orders of
+magnitude above the other features. This is contrasted when observing
+the principal component for the scaled version of the data. In the scaled
+version, the orders of magnitude are roughly the same across all the features.
+
+The dataset used is the Wine Dataset available at UCI. This dataset
+has continuous features that are heterogeneous in scale due to differing
+properties that they measure (i.e alcohol content, and malic acid).
+
+The transformed data is then used to train a naive Bayes classifier, and a
+clear difference in prediction accuracies is observed wherein the dataset
+which is scaled before PCA vastly outperforms the unscaled version.
+
+"""
+from __future__ import print_function
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.naive_bayes import GaussianNB
+from sklearn import metrics
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_wine
+from sklearn.pipeline import make_pipeline
+print(__doc__)
+
+# Code source: Tyler Lanigan <tylerlanigan@gmail.com>
+# Sebastian Raschka <mail@sebastianraschka.com>
+
+# License: BSD 3 clause
+
+RANDOM_STATE = 42
+FIG_SIZE = (10, 7)
+
+
+features, target = load_wine(return_X_y=True)
+
+# Make a train/test split using 30% test size
+X_train, X_test, y_train, y_test = train_test_split(features, target,
+ test_size=0.30,
+ random_state=RANDOM_STATE)
+
+# Fit to data and predict using pipelined GNB and PCA.
+unscaled_clf = make_pipeline(PCA(n_components=2), GaussianNB())
+unscaled_clf.fit(X_train, y_train)
+pred_test = unscaled_clf.predict(X_test)
+
+# Fit to data and predict using pipelined scaling, GNB and PCA.
+std_clf = make_pipeline(StandardScaler(), PCA(n_components=2), GaussianNB())
+std_clf.fit(X_train, y_train)
+pred_test_std = std_clf.predict(X_test)
+
+# Show prediction accuracies in scaled and unscaled data.
+print('\nPrediction accuracy for the normal test dataset with PCA')
+print('{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test)))
+
+print('\nPrediction accuracy for the standardized test dataset with PCA')
+print('{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test_std)))
+
+# Extract PCA from pipeline
+pca = unscaled_clf.named_steps['pca']
+pca_std = std_clf.named_steps['pca']
+
+# Show first principal componenets
+print('\nPC 1 without scaling:\n', pca.components_[0])
+print('\nPC 1 with scaling:\n', pca_std.components_[0])
+
+# Scale and use PCA on X_train data for visualization.
+scaler = std_clf.named_steps['standardscaler']
+X_train_std = pca_std.transform(scaler.transform(X_train))
+
+# visualize standardized vs. untouched dataset with PCA performed
+fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=FIG_SIZE)
+
+
+for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
+ ax1.scatter(X_train[y_train == l, 0], X_train[y_train == l, 1],
+ color=c,
+ label='class %s' % l,
+ alpha=0.5,
+ marker=m
+ )
+
+for l, c, m in zip(range(0, 3), ('blue', 'red', 'green'), ('^', 's', 'o')):
+ ax2.scatter(X_train_std[y_train == l, 0], X_train_std[y_train == l, 1],
+ color=c,
+ label='class %s' % l,
+ alpha=0.5,
+ marker=m
+ )
+
+ax1.set_title('Training dataset after PCA')
+ax2.set_title('Standardized training dataset after PCA')
+
+for ax in (ax1, ax2):
+ ax.set_xlabel('1st principal component')
+ ax.set_ylabel('2nd principal component')
+ ax.legend(loc='upper right')
+ ax.grid()
+
+plt.tight_layout()
+
+plt.show()
diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py
index c38e99acd3d5b17122c41f88cd064c74400217ca..c43c0c4758b10dd5720e42b9e290d676140fbb3c 100644
--- a/sklearn/datasets/__init__.py
+++ b/sklearn/datasets/__init__.py
@@ -3,18 +3,18 @@ The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
-
+from .base import load_breast_cancer
+from .base import load_boston
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
-from .base import load_breast_cancer
from .base import load_linnerud
-from .base import load_boston
-from .base import get_data_home
-from .base import clear_data_home
from .base import load_sample_images
from .base import load_sample_image
+from .base import load_wine
+from .base import get_data_home
+from .base import clear_data_home
from .covtype import fetch_covtype
from .kddcup99 import fetch_kddcup99
from .mlcomp import load_mlcomp
@@ -78,6 +78,7 @@ __all__ = ['clear_data_home',
'load_sample_images',
'load_svmlight_file',
'load_svmlight_files',
+ 'load_wine',
'make_biclusters',
'make_blobs',
'make_circles',
diff --git a/sklearn/datasets/base.py b/sklearn/datasets/base.py
index b83f9d4985e4630ad213e6dd95702891e588bdd6..2325d971428d2f74f5a2708c3b24142f81ab2b19 100644
--- a/sklearn/datasets/base.py
+++ b/sklearn/datasets/base.py
@@ -242,6 +242,122 @@ def load_files(container_path, description=None, categories=None,
DESCR=description)
+def load_data(module_path, data_file_name):
+ """Loads data from module_path/data/data_file_name.
+
+ Parameters
+ ----------
+ data_file_name : String. Name of csv file to be loaded from
+ module_path/data/data_file_name. For example 'wine_data.csv'.
+
+ Returns
+ -------
+ data : Numpy Array
+ A 2D array with each row representing one sample and each column
+ representing the features of a given sample.
+
+ target : Numpy Array
+ A 1D array holding target variables for all the samples in `data.
+ For example target[0] is the target varible for data[0].
+
+ target_names : Numpy Array
+ A 1D array containing the names of the classifications. For example
+ target_names[0] is the name of the target[0] class.
+ """
+ with open(join(module_path, 'data', data_file_name)) as csv_file:
+ data_file = csv.reader(csv_file)
+ temp = next(data_file)
+ n_samples = int(temp[0])
+ n_features = int(temp[1])
+ target_names = np.array(temp[2:])
+ data = np.empty((n_samples, n_features))
+ target = np.empty((n_samples,), dtype=np.int)
+
+ for i, ir in enumerate(data_file):
+ data[i] = np.asarray(ir[:-1], dtype=np.float64)
+ target[i] = np.asarray(ir[-1], dtype=np.int)
+
+ return data, target, target_names
+
+
+def load_wine(return_X_y=False):
+ """Load and return the wine dataset (classification).
+
+ .. versionadded:: 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 :ref:`User Guide <datasets>`.
+
+ Parameters
+ ----------
+ return_X_y : boolean, default=False.
+ If True, returns ``(data, target)`` instead of a Bunch object.
+ See below for more information about the `data` and `target` object.
+
+ Returns
+ -------
+ data : Bunch
+ Dictionary-like object, the interesting attributes are:
+ 'data', the data to learn, 'target', the classification labels,
+ 'target_names', the meaning of the labels, 'feature_names', the
+ meaning of the features, and 'DESCR', 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']
+ """
+ module_path = dirname(__file__)
+ data, target, target_names = load_data(module_path, 'wine_data.csv')
+
+ with open(join(module_path, 'descr', 'wine_data.rst')) as rst_file:
+ fdescr = rst_file.read()
+
+ if return_X_y:
+ return data, target
+
+ return Bunch(data=data, target=target,
+ target_names=target_names,
+ DESCR=fdescr,
+ feature_names=['alcohol',
+ 'malic_acid',
+ 'ash',
+ 'alcalinity_of_ash',
+ 'magnesium',
+ 'total_phenols',
+ 'flavanoids',
+ 'nonflavanoid_phenols',
+ 'proanthocyanins',
+ 'color_intensity',
+ 'hue',
+ 'od280/od315_of_diluted_wines',
+ 'proline'])
+
+
def load_iris(return_X_y=False):
"""Load and return the iris dataset (classification).
@@ -292,18 +408,7 @@ def load_iris(return_X_y=False):
['setosa', 'versicolor', 'virginica']
"""
module_path = dirname(__file__)
- with open(join(module_path, 'data', 'iris.csv')) as csv_file:
- data_file = csv.reader(csv_file)
- temp = next(data_file)
- n_samples = int(temp[0])
- n_features = int(temp[1])
- target_names = np.array(temp[2:])
- data = np.empty((n_samples, n_features))
- target = np.empty((n_samples,), dtype=np.int)
-
- for i, ir in enumerate(data_file):
- data[i] = np.asarray(ir[:-1], dtype=np.float64)
- target[i] = np.asarray(ir[-1], dtype=np.int)
+ data, target, target_names = load_data(module_path, 'iris.csv')
with open(join(module_path, 'descr', 'iris.rst')) as rst_file:
fdescr = rst_file.read()
@@ -370,18 +475,7 @@ def load_breast_cancer(return_X_y=False):
['malignant', 'benign']
"""
module_path = dirname(__file__)
- with open(join(module_path, 'data', 'breast_cancer.csv')) as csv_file:
- data_file = csv.reader(csv_file)
- first_line = next(data_file)
- n_samples = int(first_line[0])
- n_features = int(first_line[1])
- target_names = np.array(first_line[2:4])
- data = np.empty((n_samples, n_features))
- target = np.empty((n_samples,), dtype=np.int)
-
- for count, value in enumerate(data_file):
- data[count] = np.asarray(value[:-1], dtype=np.float64)
- target[count] = np.asarray(value[-1], dtype=np.int)
+ data, target, target_names = load_data(module_path, 'breast_cancer.csv')
with open(join(module_path, 'descr', 'breast_cancer.rst')) as rst_file:
fdescr = rst_file.read()
@@ -517,12 +611,12 @@ def load_diabetes(return_X_y=False):
(data, target) : tuple if ``return_X_y`` is True
- .. versionadded:: 0.18
+ .. versionadded:: 0.18
"""
base_dir = join(dirname(__file__), 'data')
data = np.loadtxt(join(base_dir, 'diabetes_data.csv.gz'))
target = np.loadtxt(join(base_dir, 'diabetes_target.csv.gz'))
-
+
if return_X_y:
return data, target
@@ -554,7 +648,7 @@ def load_linnerud(return_X_y=False):
'targets', the two multivariate datasets, with 'data' corresponding to
the exercise and 'targets' corresponding to the physiological
measurements, as well as 'feature_names' and 'target_names'.
-
+
(data, target) : tuple if ``return_X_y`` is True
.. versionadded:: 0.18
@@ -608,7 +702,7 @@ def load_boston(return_X_y=False):
(data, target) : tuple if ``return_X_y`` is True
- .. versionadded:: 0.18
+ .. versionadded:: 0.18
Examples
--------
diff --git a/sklearn/datasets/data/breast_cancer.csv b/sklearn/datasets/data/breast_cancer.csv
index 8eafb958159787376747c74d1e46ae6ed486c0c6..979a3dcb6786a29213bec3ea3a427c514c79975b 100644
--- a/sklearn/datasets/data/breast_cancer.csv
+++ b/sklearn/datasets/data/breast_cancer.csv
@@ -1,4 +1,4 @@
-569,30,malignant,benign,,,,,,,,,,,,,,,,,,,,,,,,,,,
+569,30,malignant,benign
17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189,0
20.57,17.77,132.9,1326,0.08474,0.07864,0.0869,0.07017,0.1812,0.05667,0.5435,0.7339,3.398,74.08,0.005225,0.01308,0.0186,0.0134,0.01389,0.003532,24.99,23.41,158.8,1956,0.1238,0.1866,0.2416,0.186,0.275,0.08902,0
19.69,21.25,130,1203,0.1096,0.1599,0.1974,0.1279,0.2069,0.05999,0.7456,0.7869,4.585,94.03,0.00615,0.04006,0.03832,0.02058,0.0225,0.004571,23.57,25.53,152.5,1709,0.1444,0.4245,0.4504,0.243,0.3613,0.08758,0
diff --git a/sklearn/datasets/data/wine_data.csv b/sklearn/datasets/data/wine_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6c7fe81952aa6129023730ced4581b42ecd085af
--- /dev/null
+++ b/sklearn/datasets/data/wine_data.csv
@@ -0,0 +1,179 @@
+178,13,class_0,class_1,class_2
+14.23,1.71,2.43,15.6,127,2.8,3.06,0.28,2.29,5.64,1.04,3.92,1065,0
+13.2,1.78,2.14,11.2,100,2.65,2.76,0.26,1.28,4.38,1.05,3.4,1050,0
+13.16,2.36,2.67,18.6,101,2.8,3.24,0.3,2.81,5.68,1.03,3.17,1185,0
+14.37,1.95,2.5,16.8,113,3.85,3.49,0.24,2.18,7.8,0.86,3.45,1480,0
+13.24,2.59,2.87,21,118,2.8,2.69,0.39,1.82,4.32,1.04,2.93,735,0
+14.2,1.76,2.45,15.2,112,3.27,3.39,0.34,1.97,6.75,1.05,2.85,1450,0
+14.39,1.87,2.45,14.6,96,2.5,2.52,0.3,1.98,5.25,1.02,3.58,1290,0
+14.06,2.15,2.61,17.6,121,2.6,2.51,0.31,1.25,5.05,1.06,3.58,1295,0
+14.83,1.64,2.17,14,97,2.8,2.98,0.29,1.98,5.2,1.08,2.85,1045,0
+13.86,1.35,2.27,16,98,2.98,3.15,0.22,1.85,7.22,1.01,3.55,1045,0
+14.1,2.16,2.3,18,105,2.95,3.32,0.22,2.38,5.75,1.25,3.17,1510,0
+14.12,1.48,2.32,16.8,95,2.2,2.43,0.26,1.57,5,1.17,2.82,1280,0
+13.75,1.73,2.41,16,89,2.6,2.76,0.29,1.81,5.6,1.15,2.9,1320,0
+14.75,1.73,2.39,11.4,91,3.1,3.69,0.43,2.81,5.4,1.25,2.73,1150,0
+14.38,1.87,2.38,12,102,3.3,3.64,0.29,2.96,7.5,1.2,3,1547,0
+13.63,1.81,2.7,17.2,112,2.85,2.91,0.3,1.46,7.3,1.28,2.88,1310,0
+14.3,1.92,2.72,20,120,2.8,3.14,0.33,1.97,6.2,1.07,2.65,1280,0
+13.83,1.57,2.62,20,115,2.95,3.4,0.4,1.72,6.6,1.13,2.57,1130,0
+14.19,1.59,2.48,16.5,108,3.3,3.93,0.32,1.86,8.7,1.23,2.82,1680,0
+13.64,3.1,2.56,15.2,116,2.7,3.03,0.17,1.66,5.1,0.96,3.36,845,0
+14.06,1.63,2.28,16,126,3,3.17,0.24,2.1,5.65,1.09,3.71,780,0
+12.93,3.8,2.65,18.6,102,2.41,2.41,0.25,1.98,4.5,1.03,3.52,770,0
+13.71,1.86,2.36,16.6,101,2.61,2.88,0.27,1.69,3.8,1.11,4,1035,0
+12.85,1.6,2.52,17.8,95,2.48,2.37,0.26,1.46,3.93,1.09,3.63,1015,0
+13.5,1.81,2.61,20,96,2.53,2.61,0.28,1.66,3.52,1.12,3.82,845,0
+13.05,2.05,3.22,25,124,2.63,2.68,0.47,1.92,3.58,1.13,3.2,830,0
+13.39,1.77,2.62,16.1,93,2.85,2.94,0.34,1.45,4.8,0.92,3.22,1195,0
+13.3,1.72,2.14,17,94,2.4,2.19,0.27,1.35,3.95,1.02,2.77,1285,0
+13.87,1.9,2.8,19.4,107,2.95,2.97,0.37,1.76,4.5,1.25,3.4,915,0
+14.02,1.68,2.21,16,96,2.65,2.33,0.26,1.98,4.7,1.04,3.59,1035,0
+13.73,1.5,2.7,22.5,101,3,3.25,0.29,2.38,5.7,1.19,2.71,1285,0
+13.58,1.66,2.36,19.1,106,2.86,3.19,0.22,1.95,6.9,1.09,2.88,1515,0
+13.68,1.83,2.36,17.2,104,2.42,2.69,0.42,1.97,3.84,1.23,2.87,990,0
+13.76,1.53,2.7,19.5,132,2.95,2.74,0.5,1.35,5.4,1.25,3,1235,0
+13.51,1.8,2.65,19,110,2.35,2.53,0.29,1.54,4.2,1.1,2.87,1095,0
+13.48,1.81,2.41,20.5,100,2.7,2.98,0.26,1.86,5.1,1.04,3.47,920,0
+13.28,1.64,2.84,15.5,110,2.6,2.68,0.34,1.36,4.6,1.09,2.78,880,0
+13.05,1.65,2.55,18,98,2.45,2.43,0.29,1.44,4.25,1.12,2.51,1105,0
+13.07,1.5,2.1,15.5,98,2.4,2.64,0.28,1.37,3.7,1.18,2.69,1020,0
+14.22,3.99,2.51,13.2,128,3,3.04,0.2,2.08,5.1,0.89,3.53,760,0
+13.56,1.71,2.31,16.2,117,3.15,3.29,0.34,2.34,6.13,0.95,3.38,795,0
+13.41,3.84,2.12,18.8,90,2.45,2.68,0.27,1.48,4.28,0.91,3,1035,0
+13.88,1.89,2.59,15,101,3.25,3.56,0.17,1.7,5.43,0.88,3.56,1095,0
+13.24,3.98,2.29,17.5,103,2.64,2.63,0.32,1.66,4.36,0.82,3,680,0
+13.05,1.77,2.1,17,107,3,3,0.28,2.03,5.04,0.88,3.35,885,0
+14.21,4.04,2.44,18.9,111,2.85,2.65,0.3,1.25,5.24,0.87,3.33,1080,0
+14.38,3.59,2.28,16,102,3.25,3.17,0.27,2.19,4.9,1.04,3.44,1065,0
+13.9,1.68,2.12,16,101,3.1,3.39,0.21,2.14,6.1,0.91,3.33,985,0
+14.1,2.02,2.4,18.8,103,2.75,2.92,0.32,2.38,6.2,1.07,2.75,1060,0
+13.94,1.73,2.27,17.4,108,2.88,3.54,0.32,2.08,8.9,1.12,3.1,1260,0
+13.05,1.73,2.04,12.4,92,2.72,3.27,0.17,2.91,7.2,1.12,2.91,1150,0
+13.83,1.65,2.6,17.2,94,2.45,2.99,0.22,2.29,5.6,1.24,3.37,1265,0
+13.82,1.75,2.42,14,111,3.88,3.74,0.32,1.87,7.05,1.01,3.26,1190,0
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+13.17,2.59,2.37,20,120,1.65,0.68,0.53,1.46,9.3,0.6,1.62,840,2
+14.13,4.1,2.74,24.5,96,2.05,0.76,0.56,1.35,9.2,0.61,1.6,560,2
diff --git a/sklearn/datasets/descr/wine_data.rst b/sklearn/datasets/descr/wine_data.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3d3341874a584d57cdf200bd062df20a7656a81b
--- /dev/null
+++ b/sklearn/datasets/descr/wine_data.rst
@@ -0,0 +1,95 @@
+Wine Data Database
+====================
+
+Notes
+-----
+Data Set Characteristics:
+ :Number of Instances: 178 (50 in each of three classes)
+ :Number of Attributes: 13 numeric, predictive attributes and the class
+ :Attribute Information:
+ - 1) Alcohol
+ - 2) Malic acid
+ - 3) Ash
+ - 4) Alcalinity of ash
+ - 5) Magnesium
+ - 6) Total phenols
+ - 7) Flavanoids
+ - 8) Nonflavanoid phenols
+ - 9) Proanthocyanins
+ - 10)Color intensity
+ - 11)Hue
+ - 12)OD280/OD315 of diluted wines
+ - 13)Proline
+ - class:
+ - class_0
+ - class_1
+ - class_2
+
+ :Summary Statistics:
+
+ ============================= ==== ===== ======= =====
+ Min Max Mean SD
+ ============================= ==== ===== ======= =====
+ Alcohol: 11.0 14.8 13.0 0.8
+ Malic Acid: 0.74 5.80 2.34 1.12
+ Ash: 1.36 3.23 2.36 0.27
+ Alcalinity of Ash: 10.6 30.0 19.5 3.3
+ Magnesium: 70.0 162.0 99.7 14.3
+ Total Phenols: 0.98 3.88 2.29 0.63
+ Flavanoids: 0.34 5.08 2.03 1.00
+ Nonflavanoid Phenols: 0.13 0.66 0.36 0.12
+ Proanthocyanins: 0.41 3.58 1.59 0.57
+ Colour Intensity: 1.3 13.0 5.1 2.3
+ Hue: 0.48 1.71 0.96 0.23
+ OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71
+ Proline: 278 1680 746 315
+ ============================= ==== ===== ======= =====
+
+ :Missing Attribute Values: None
+ :Class Distribution: class_0 (59), class_1 (71), class_2 (48)
+ :Creator: R.A. Fisher
+ :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
+ :Date: July, 1988
+
+This is a copy of UCI ML Wine recognition datasets.
+https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
+
+The data is the results of a chemical analysis of wines grown in the same
+region in Italy by three different cultivators. There are thirteen different
+measurements taken for different constituents found in the three types of
+wine.
+
+Original Owners:
+
+Forina, M. et al, PARVUS -
+An Extendible Package for Data Exploration, Classification and Correlation.
+Institute of Pharmaceutical and Food Analysis and Technologies,
+Via Brigata Salerno, 16147 Genoa, Italy.
+
+Citation:
+
+Lichman, M. (2013). UCI Machine Learning Repository
+[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
+School of Information and Computer Science.
+
+References
+----------
+(1)
+S. Aeberhard, D. Coomans and O. de Vel,
+Comparison of Classifiers in High Dimensional Settings,
+Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+Mathematics and Statistics, James Cook University of North Queensland.
+(Also submitted to Technometrics).
+
+The data was used with many others for comparing various
+classifiers. The classes are separable, though only RDA
+has achieved 100% correct classification.
+(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+(All results using the leave-one-out technique)
+
+(2)
+S. Aeberhard, D. Coomans and O. de Vel,
+"THE CLASSIFICATION PERFORMANCE OF RDA"
+Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+Mathematics and Statistics, James Cook University of North Queensland.
+(Also submitted to Journal of Chemometrics).
diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py
index 92fe96fa106568f0c4124d301e004b08bee4d969..c0dd5101904d911f55bb5df2efe704ef48fe8f32 100644
--- a/sklearn/datasets/tests/test_base.py
+++ b/sklearn/datasets/tests/test_base.py
@@ -17,6 +17,7 @@ from sklearn.datasets import load_linnerud
from sklearn.datasets import load_iris
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import load_boston
+from sklearn.datasets import load_wine
from sklearn.datasets.base import Bunch
from sklearn.externals.six import b, u
@@ -195,6 +196,7 @@ def test_load_linnerud():
assert_array_equal(X_y_tuple[0], bunch.data)
assert_array_equal(X_y_tuple[1], bunch.target)
+
def test_load_iris():
res = load_iris()
assert_equal(res.data.shape, (150, 4))
@@ -210,6 +212,21 @@ def test_load_iris():
assert_array_equal(X_y_tuple[1], bunch.target)
+def test_load_wine():
+ res = load_wine()
+ assert_equal(res.data.shape, (178, 13))
+ assert_equal(res.target.size, 178)
+ assert_equal(res.target_names.size, 3)
+ assert_true(res.DESCR)
+
+ # test return_X_y option
+ X_y_tuple = load_wine(return_X_y=True)
+ bunch = load_wine()
+ assert_true(isinstance(X_y_tuple, tuple))
+ assert_array_equal(X_y_tuple[0], bunch.data)
+ assert_array_equal(X_y_tuple[1], bunch.target)
+
+
def test_load_breast_cancer():
res = load_breast_cancer()
assert_equal(res.data.shape, (569, 30))
@@ -239,6 +256,7 @@ def test_load_boston():
assert_array_equal(X_y_tuple[0], bunch.data)
assert_array_equal(X_y_tuple[1], bunch.target)
+
def test_loads_dumps_bunch():
bunch = Bunch(x="x")
bunch_from_pkl = loads(dumps(bunch))