def shuffled_split(X, y, shuffle=True, train_size=0.75, random_seed=None):
"""Splits feature and target arrays into training and test subsets.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Initial dataset, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
shuffle : bool (default: True)
Doesn't shuffle the arrays if False
train_size : float (default: 0.75)
Proportion of data in the training arrays. For example, 0.75 will
put 75% of the data into the training array, and 25% of the data
into the test array.
random_seed : int (default: None)
Sets the random state.
Returns
----------
X_train : array-like, shape = [n_samples * train_size, n_features]
Training dataset, where n_samples is the number of samples and
n_features is the number of features.
y_train : array-like, shape = [n_samples * train_size]
Training target values.
X_test : array-like, shape = [n_samples * (1-train_size), n_features]
Dataset for testing, where n_samples is the number of samples and
n_features is the number of features.
y_test : array-like, shape = [n_samples * (1-train_size)]
Target values for testing.
For usage examples, please see
http://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffled_split/
"""
check_Xy(X, y, y_int=False)
if train_size <= 0.0 or train_size >= 1.0:
raise ValueError('train_size must be a float in the range (0.0, 1.0)')
if shuffle:
X_ary, y_ary = shuffle_arrays_unison(arrays=[X.copy(),
y.copy()],
random_seed=random_seed)
else:
X_ary, y_ary = X.copy(), y.copy()
train_absize = round(train_size * y.shape[0])
X_train, y_train = X_ary[:train_absize], y_ary[:train_absize]
X_test, y_test = X_ary[train_absize:], y_ary[train_absize:]
return X_train, y_train, X_test, y_test
def shuffled_split(X, y, shuffle=True, train_size=0.75, random_seed=None):
"""Splits feature and target arrays into training and test subsets.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Initial dataset, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
shuffle : bool (default: True)
Doesn't shuffle the arrays if False
train_size : float (default: 0.75)
Proportion of data in the training arrays. For example, 0.75 will
put 75% of the data into the training array, and 25% of the data
into the test array.
random_seed : int (default: None)
Sets the random state.
Returns
----------
X_train : array-like, shape = [n_samples * train_size, n_features]
Training dataset, where n_samples is the number of samples and
n_features is the number of features.
y_train : array-like, shape = [n_samples * train_size]
Training target values.
X_test : array-like, shape = [n_samples * (1-train_size), n_features]
Dataset for testing, where n_samples is the number of samples and
n_features is the number of features.
y_test : array-like, shape = [n_samples * (1-train_size)]
Target values for testing.
For usage examples, please see
http://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffled_split/
"""
check_Xy(X, y, y_int=False)
if train_size <= 0.0 or train_size >= 1.0:
raise ValueError('train_size must be a float in the range (0.0, 1.0)')
if shuffle:
X_ary, y_ary = shuffle_arrays_unison(arrays=[X.copy(), y.copy()],
random_seed=random_seed)
else:
X_ary, y_ary = X.copy(), y.copy()
train_absize = round(train_size * y.shape[0])
X_train, y_train = X_ary[:train_absize], y_ary[:train_absize]
X_test, y_test = X_ary[train_absize:], y_ary[train_absize:]
return X_train, y_train, X_test, y_test
def test_float16_y():
check_Xy(X, y.astype(np.int16))
def test_float16_X():
check_Xy(X.astype(np.float16), y)
def test_ok():
check_Xy(X, y)
def test_ok():
check_Xy(X, y)
def plot_decision_regions(X,
y,
clf,
feature_index=None,
filler_feature_values=None,
filler_feature_ranges=None,
ax=None,
X_highlight=None,
res=None,
legend=1,
hide_spines=True,
markers='s^oxv<>',
colors=('#1f77b4,#ff7f0e,#3ca02c,#d62728,'
'#9467bd,#8c564b,#e377c2,'
'#7f7f7f,#bcbd22,#17becf'),
scatter_kwargs=None,
contourf_kwargs=None,
scatter_highlight_kwargs=None):
"""Plot decision regions of a classifier.
Please note that this functions assumes that class labels are
labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class
labels with integer labels > 4, you may want to provide additional colors
and/or markers as `colors` and `markers` arguments.
See http://matplotlib.org/examples/color/named_colors.html for more
information.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Feature Matrix.
y : array-like, shape = [n_samples]
True class labels.
clf : Classifier object.
Must have a .predict method.
feature_index : array-like (default: (0,) for 1D, (0, 1) otherwise)
Feature indices to use for plotting. The first index in
`feature_index` will be on the x-axis, the second index will be
on the y-axis.
filler_feature_values : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted.
filler_feature_ranges : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted. Will use the
ranges provided to select training samples for plotting.
ax : matplotlib.axes.Axes (default: None)
An existing matplotlib Axes. Creates
one if ax=None.
X_highlight : array-like, shape = [n_samples, n_features] (default: None)
An array with data points that are used to highlight samples in `X`.
res : float or array-like, shape = (2,) (default: None)
This parameter was used to define the grid width,
but it has been deprecated in favor of
determining the number of points given the figure DPI and size
automatically for optimal results and computational efficiency.
To increase the resolution, it's is recommended to use to provide
a `dpi argument via matplotlib, e.g., `plt.figure(dpi=600)`.
hide_spines : bool (default: True)
Hide axis spines if True.
legend : int (default: 1)
Integer to specify the legend location.
No legend if legend is 0.
markers : str (default: 's^oxv<>')
Scatterplot markers.
colors : str (default: 'red,blue,limegreen,gray,cyan')
Comma separated list of colors.
scatter_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib scatter function.
contourf_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib contourf function.
scatter_highlight_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib scatter function.
Returns
---------
ax : matplotlib.axes.Axes object
Examples
-----------
For usage examples, please see
http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/
"""
check_Xy(X, y, y_int=True) # Validate X and y arrays
dim = X.shape[1]
if ax is None:
ax = plt.gca()
if res is not None:
warnings.warn(
"The 'res' parameter has been deprecated."
"To increase the resolution, it's is recommended"
"to use to provide a `dpi argument via matplotlib,"
"e.g., `plt.figure(dpi=600)`.", DeprecationWarning)
plot_testdata = True
if not isinstance(X_highlight, np.ndarray):
if X_highlight is not None:
raise ValueError('X_highlight must be a NumPy array or None')
else:
plot_testdata = False
elif len(X_highlight.shape) < 2:
raise ValueError('X_highlight must be a 2D array')
if feature_index is not None:
# Unpack and validate the feature_index values
if dim == 1:
raise ValueError(
'feature_index requires more than one training feature')
try:
x_index, y_index = feature_index
except ValueError:
raise ValueError(
'Unable to unpack feature_index. Make sure feature_index '
'only has two dimensions.')
try:
X[:, x_index], X[:, y_index]
except IndexError:
raise IndexError(
'feature_index values out of range. X.shape is {}, but '
'feature_index is {}'.format(X.shape, feature_index))
else:
feature_index = (0, 1)
x_index, y_index = feature_index
# Extra input validation for higher number of training features
if dim > 2:
if filler_feature_values is None:
raise ValueError('Filler values must be provided when '
'X has more than 2 training features.')
if filler_feature_ranges is not None:
if not set(filler_feature_values) == set(filler_feature_ranges):
raise ValueError(
'filler_feature_values and filler_feature_ranges must '
'have the same keys')
# Check that all columns in X are accounted for
column_check = np.zeros(dim, dtype=bool)
for idx in filler_feature_values:
column_check[idx] = True
for idx in feature_index:
column_check[idx] = True
if not all(column_check):
missing_cols = np.argwhere(~column_check).flatten()
raise ValueError(
'Column(s) {} need to be accounted for in either '
'feature_index or filler_feature_values'.format(missing_cols))
marker_gen = cycle(list(markers))
n_classes = np.unique(y).shape[0]
colors = colors.split(',')
colors_gen = cycle(colors)
colors = [next(colors_gen) for c in range(n_classes)]
# Get minimum and maximum
x_min, x_max = X[:, x_index].min() - 1, X[:, x_index].max() + 1
if dim == 1:
y_min, y_max = -1, 1
else:
y_min, y_max = X[:, y_index].min() - 1, X[:, y_index].max() + 1
xnum, ynum = plt.gcf().dpi * plt.gcf().get_size_inches()
xnum, ynum = floor(xnum), ceil(ynum)
xx, yy = np.meshgrid(np.linspace(x_min, x_max, num=xnum),
np.linspace(y_min, y_max, num=ynum))
if dim == 1:
X_predict = np.array([xx.ravel()]).T
else:
X_grid = np.array([xx.ravel(), yy.ravel()]).T
X_predict = np.zeros((X_grid.shape[0], dim))
X_predict[:, x_index] = X_grid[:, 0]
X_predict[:, y_index] = X_grid[:, 1]
if dim > 2:
for feature_idx in filler_feature_values:
X_predict[:, feature_idx] = filler_feature_values[feature_idx]
Z = clf.predict(X_predict.astype(X.dtype))
Z = np.array(Z)
Z = Z.reshape(xx.shape)
# Plot decisoin region
# Make sure contourf_kwargs has backwards compatible defaults
contourf_kwargs_default = {'alpha': 0.45, 'antialiased': True}
contourf_kwargs = format_kwarg_dictionaries(
default_kwargs=contourf_kwargs_default,
user_kwargs=contourf_kwargs,
protected_keys=['colors', 'levels'])
cset = ax.contourf(xx,
yy,
Z,
colors=colors,
levels=np.arange(Z.max() + 2) - 0.5,
**contourf_kwargs)
ax.contour(xx,
yy,
Z,
cset.levels,
colors='k',
linewidths=0.5,
antialiased=True)
ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())
# Scatter training data samples
# Make sure scatter_kwargs has backwards compatible defaults
scatter_kwargs_default = {'alpha': 0.8, 'edgecolor': 'black'}
scatter_kwargs = format_kwarg_dictionaries(
default_kwargs=scatter_kwargs_default,
user_kwargs=scatter_kwargs,
protected_keys=['c', 'marker', 'label'])
for idx, c in enumerate(np.unique(y)):
if dim == 1:
y_data = [0 for i in X[y == c]]
x_data = X[y == c]
elif dim == 2:
y_data = X[y == c, y_index]
x_data = X[y == c, x_index]
elif dim > 2 and filler_feature_ranges is not None:
class_mask = y == c
feature_range_mask = get_feature_range_mask(
X,
filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X[class_mask & feature_range_mask, y_index]
x_data = X[class_mask & feature_range_mask, x_index]
else:
continue
ax.scatter(x=x_data,
y=y_data,
c=colors[idx],
marker=next(marker_gen),
label=c,
**scatter_kwargs)
if hide_spines:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
if dim == 1:
ax.axes.get_yaxis().set_ticks([])
if plot_testdata:
if dim == 1:
x_data = X_highlight
y_data = [0 for i in X_highlight]
elif dim == 2:
x_data = X_highlight[:, x_index]
y_data = X_highlight[:, y_index]
else:
feature_range_mask = get_feature_range_mask(
X_highlight,
filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X_highlight[feature_range_mask, y_index]
x_data = X_highlight[feature_range_mask, x_index]
# Make sure scatter_highlight_kwargs backwards compatible defaults
scatter_highlight_defaults = {
'c': '',
'edgecolor': 'black',
'alpha': 1.0,
'linewidths': 1,
'marker': 'o',
's': 80
}
scatter_highlight_kwargs = format_kwarg_dictionaries(
default_kwargs=scatter_highlight_defaults,
user_kwargs=scatter_highlight_kwargs)
ax.scatter(x_data, y_data, **scatter_highlight_kwargs)
if legend:
if dim > 2 and filler_feature_ranges is None:
pass
else:
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles,
labels,
framealpha=0.3,
scatterpoints=1,
loc=legend)
return ax
def plot_decision_regions(X, y, clf,
feature_index=None,
filler_feature_values=None,
filler_feature_ranges=None,
ax=None,
X_highlight=None,
res=None,
legend=1,
hide_spines=True,
markers='s^oxv<>',
colors=('#1f77b4,#ff7f0e,#3ca02c,#d62728,'
'#9467bd,#8c564b,#e377c2,'
'#7f7f7f,#bcbd22,#17becf'),
scatter_kwargs=None,
contourf_kwargs=None,
scatter_highlight_kwargs=None):
"""Plot decision regions of a classifier.
Please note that this functions assumes that class labels are
labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class
labels with integer labels > 4, you may want to provide additional colors
and/or markers as `colors` and `markers` arguments.
See http://matplotlib.org/examples/color/named_colors.html for more
information.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Feature Matrix.
y : array-like, shape = [n_samples]
True class labels.
clf : Classifier object.
Must have a .predict method.
feature_index : array-like (default: (0,) for 1D, (0, 1) otherwise)
Feature indices to use for plotting. The first index in
`feature_index` will be on the x-axis, the second index will be
on the y-axis.
filler_feature_values : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted.
filler_feature_ranges : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted. Will use the
ranges provided to select training samples for plotting.
ax : matplotlib.axes.Axes (default: None)
An existing matplotlib Axes. Creates
one if ax=None.
X_highlight : array-like, shape = [n_samples, n_features] (default: None)
An array with data points that are used to highlight samples in `X`.
res : float or array-like, shape = (2,) (default: None)
This parameter was used to define the grid width,
but it has been deprecated in favor of
determining the number of points given the figure DPI and size
automatically for optimal results and computational efficiency.
To increase the resolution, it's is recommended to use to provide
a `dpi argument via matplotlib, e.g., `plt.figure(dpi=600)`.
hide_spines : bool (default: True)
Hide axis spines if True.
legend : int (default: 1)
Integer to specify the legend location.
No legend if legend is 0.
markers : str (default: 's^oxv<>')
Scatterplot markers.
colors : str (default: 'red,blue,limegreen,gray,cyan')
Comma separated list of colors.
scatter_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib scatter function.
contourf_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib contourf function.
scatter_highlight_kwargs : dict (default: None)
Keyword arguments for underlying matplotlib scatter function.
Returns
---------
ax : matplotlib.axes.Axes object
Examples
-----------
For usage examples, please see
http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/
"""
check_Xy(X, y, y_int=True) # Validate X and y arrays
dim = X.shape[1]
if ax is None:
ax = plt.gca()
if res is not None:
warnings.warn("The 'res' parameter has been deprecated."
"To increase the resolution, it's is recommended"
"to use to provide a `dpi argument via matplotlib,"
"e.g., `plt.figure(dpi=600)`.",
DeprecationWarning)
plot_testdata = True
if not isinstance(X_highlight, np.ndarray):
if X_highlight is not None:
raise ValueError('X_highlight must be a NumPy array or None')
else:
plot_testdata = False
elif len(X_highlight.shape) < 2:
raise ValueError('X_highlight must be a 2D array')
if feature_index is not None:
# Unpack and validate the feature_index values
if dim == 1:
raise ValueError(
'feature_index requires more than one training feature')
try:
x_index, y_index = feature_index
except ValueError:
raise ValueError(
'Unable to unpack feature_index. Make sure feature_index '
'only has two dimensions.')
try:
X[:, x_index], X[:, y_index]
except IndexError:
raise IndexError(
'feature_index values out of range. X.shape is {}, but '
'feature_index is {}'.format(X.shape, feature_index))
else:
feature_index = (0, 1)
x_index, y_index = feature_index
# Extra input validation for higher number of training features
if dim > 2:
if filler_feature_values is None:
raise ValueError('Filler values must be provided when '
'X has more than 2 training features.')
if filler_feature_ranges is not None:
if not set(filler_feature_values) == set(filler_feature_ranges):
raise ValueError(
'filler_feature_values and filler_feature_ranges must '
'have the same keys')
# Check that all columns in X are accounted for
column_check = np.zeros(dim, dtype=bool)
for idx in filler_feature_values:
column_check[idx] = True
for idx in feature_index:
column_check[idx] = True
if not all(column_check):
missing_cols = np.argwhere(~column_check).flatten()
raise ValueError(
'Column(s) {} need to be accounted for in either '
'feature_index or filler_feature_values'.format(missing_cols))
marker_gen = cycle(list(markers))
n_classes = np.unique(y).shape[0]
colors = colors.split(',')
colors_gen = cycle(colors)
colors = [next(colors_gen) for c in range(n_classes)]
# Get minimum and maximum
x_min, x_max = X[:, x_index].min() - 1, X[:, x_index].max() + 1
if dim == 1:
y_min, y_max = -1, 1
else:
y_min, y_max = X[:, y_index].min() - 1, X[:, y_index].max() + 1
xnum, ynum = plt.gcf().dpi * plt.gcf().get_size_inches()
xnum, ynum = floor(xnum), ceil(ynum)
xx, yy = np.meshgrid(np.linspace(x_min, x_max, num=xnum),
np.linspace(y_min, y_max, num=ynum))
if dim == 1:
X_predict = np.array([xx.ravel()]).T
else:
X_grid = np.array([xx.ravel(), yy.ravel()]).T
X_predict = np.zeros((X_grid.shape[0], dim))
X_predict[:, x_index] = X_grid[:, 0]
X_predict[:, y_index] = X_grid[:, 1]
if dim > 2:
for feature_idx in filler_feature_values:
X_predict[:, feature_idx] = filler_feature_values[feature_idx]
Z = clf.predict(X_predict.astype(X.dtype))
Z = Z.reshape(xx.shape)
# Plot decisoin region
# Make sure contourf_kwargs has backwards compatible defaults
contourf_kwargs_default = {'alpha': 0.45, 'antialiased': True}
contourf_kwargs = format_kwarg_dictionaries(
default_kwargs=contourf_kwargs_default,
user_kwargs=contourf_kwargs,
protected_keys=['colors', 'levels'])
cset = ax.contourf(xx, yy, Z,
colors=colors,
levels=np.arange(Z.max() + 2) - 0.5,
**contourf_kwargs)
ax.contour(xx, yy, Z, cset.levels,
colors='k',
linewidths=0.5,
antialiased=True)
ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())
# Scatter training data samples
# Make sure scatter_kwargs has backwards compatible defaults
scatter_kwargs_default = {'alpha': 0.8, 'edgecolor': 'black'}
scatter_kwargs = format_kwarg_dictionaries(
default_kwargs=scatter_kwargs_default,
user_kwargs=scatter_kwargs,
protected_keys=['c', 'marker', 'label'])
for idx, c in enumerate(np.unique(y)):
if dim == 1:
y_data = [0 for i in X[y == c]]
x_data = X[y == c]
elif dim == 2:
y_data = X[y == c, y_index]
x_data = X[y == c, x_index]
elif dim > 2 and filler_feature_ranges is not None:
class_mask = y == c
feature_range_mask = get_feature_range_mask(
X, filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X[class_mask & feature_range_mask, y_index]
x_data = X[class_mask & feature_range_mask, x_index]
else:
continue
ax.scatter(x=x_data,
y=y_data,
c=colors[idx],
marker=next(marker_gen),
label=c,
**scatter_kwargs)
if hide_spines:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
if dim == 1:
ax.axes.get_yaxis().set_ticks([])
if plot_testdata:
if dim == 1:
x_data = X_highlight
y_data = [0 for i in X_highlight]
elif dim == 2:
x_data = X_highlight[:, x_index]
y_data = X_highlight[:, y_index]
else:
feature_range_mask = get_feature_range_mask(
X_highlight, filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X_highlight[feature_range_mask, y_index]
x_data = X_highlight[feature_range_mask, x_index]
# Make sure scatter_highlight_kwargs backwards compatible defaults
scatter_highlight_defaults = {'c': '',
'edgecolor': 'black',
'alpha': 1.0,
'linewidths': 1,
'marker': 'o',
's': 80}
scatter_highlight_kwargs = format_kwarg_dictionaries(
default_kwargs=scatter_highlight_defaults,
user_kwargs=scatter_highlight_kwargs)
ax.scatter(x_data,
y_data,
**scatter_highlight_kwargs)
if legend:
if dim > 2 and filler_feature_ranges is None:
pass
else:
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels,
framealpha=0.3, scatterpoints=1, loc=legend)
return ax
def test_check_Xy_float16_y():
check_Xy(X, y.astype(np.int16))
def test_check_Xy_float16_X():
check_Xy(X.astype(np.float16), y)
def plot_decision_regions(X,
y,
clf,
feature_index=None,
filler_feature_values=None,
filler_feature_ranges=None,
ax=None,
X_highlight=None,
res=0.02,
legend=1,
hide_spines=True,
markers='s^oxv<>',
colors='red,blue,limegreen,gray,cyan'):
"""Plot decision regions of a classifier.
Please note that this functions assumes that class labels are
labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class
labels with integer labels > 4, you may want to provide additional colors
and/or markers as `colors` and `markers` arguments.
See http://matplotlib.org/examples/color/named_colors.html for more
information.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Feature Matrix.
y : array-like, shape = [n_samples]
True class labels.
clf : Classifier object.
Must have a .predict method.
feature_index : array-like (default: (0,) for 1D, (0, 1) otherwise)
Feature indices to use for plotting. The first index in
`feature_index` will be on the x-axis, the second index will be
on the y-axis.
filler_feature_values : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted.
filler_feature_ranges : dict (default: None)
Only needed for number features > 2. Dictionary of feature
index-value pairs for the features not being plotted. Will use the
ranges provided to select training samples for plotting.
ax : matplotlib.axes.Axes (default: None)
An existing matplotlib Axes. Creates
one if ax=None.
X_highlight : array-like, shape = [n_samples, n_features] (default: None)
An array with data points that are used to highlight samples in `X`.
res : float or array-like, shape = (2,) (default: 0.02)
Grid width. If float, same resolution is used for both the x- and
y-axis. If array-like, the first item is used on the x-axis, the
second is used on the y-axis. Lower values increase the resolution but
slow down the plotting.
hide_spines : bool (default: True)
Hide axis spines if True.
legend : int (default: 1)
Integer to specify the legend location.
No legend if legend is 0.
markers : str (default 's^oxv<>')
Scatterplot markers.
colors : str (default 'red,blue,limegreen,gray,cyan')
Comma separated list of colors.
Returns
---------
ax : matplotlib.axes.Axes object
"""
check_Xy(X, y, y_int=True) # Validate X and y arrays
dim = X.shape[1]
if ax is None:
ax = plt.gca()
if isinstance(res, float):
xres, yres = res, res
else:
try:
xres, yres = res
except ValueError:
raise ValueError('Unable to unpack res. Expecting '
'array-like input of length 2.')
plot_testdata = True
if not isinstance(X_highlight, np.ndarray):
if X_highlight is not None:
raise ValueError('X_highlight must be a NumPy array or None')
else:
plot_testdata = False
elif len(X_highlight.shape) < 2:
raise ValueError('X_highlight must be a 2D array')
if feature_index is not None:
# Unpack and validate the feature_index values
if dim == 1:
raise ValueError(
'feature_index requires more than one training feature')
try:
x_index, y_index = feature_index
except ValueError:
raise ValueError(
'Unable to unpack feature_index. Make sure feature_index '
'only has two dimensions.')
try:
X[:, x_index], X[:, y_index]
except IndexError:
raise IndexError(
'feature_index values out of range. X.shape is {}, but '
'feature_index is {}'.format(X.shape, feature_index))
else:
feature_index = (0, 1)
x_index, y_index = feature_index
# Extra input validation for higher number of training features
if dim > 2:
if filler_feature_values is None:
raise ValueError('Filler values must be provided when '
'X has more than 2 training features.')
if filler_feature_ranges is not None:
if not set(filler_feature_values) == set(filler_feature_ranges):
raise ValueError(
'filler_feature_values and filler_feature_ranges must '
'have the same keys')
# Check that all columns in X are accounted for
column_check = np.zeros(dim, dtype=bool)
for idx in filler_feature_values:
column_check[idx] = True
for idx in feature_index:
column_check[idx] = True
if not all(column_check):
missing_cols = np.argwhere(~column_check).flatten()
raise ValueError(
'Column(s) {} need to be accounted for in either '
'feature_index or filler_feature_values'.format(missing_cols))
marker_gen = cycle(list(markers))
n_classes = np.unique(y).shape[0]
colors = colors.split(',')
colors_gen = cycle(colors)
colors = [next(colors_gen) for c in range(n_classes)]
# Get minimum and maximum
x_min, x_max = X[:, x_index].min() - 1, X[:, x_index].max() + 1
if dim == 1:
y_min, y_max = -1, 1
else:
y_min, y_max = X[:, y_index].min() - 1, X[:, y_index].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, xres),
np.arange(y_min, y_max, yres))
if dim == 1:
X_predict = np.array([xx.ravel()]).T
else:
X_grid = np.array([xx.ravel(), yy.ravel()]).T
X_predict = np.zeros((X_grid.shape[0], dim))
X_predict[:, x_index] = X_grid[:, 0]
X_predict[:, y_index] = X_grid[:, 1]
if dim > 2:
for feature_idx in filler_feature_values:
X_predict[:, feature_idx] = filler_feature_values[feature_idx]
Z = clf.predict(X_predict)
Z = Z.reshape(xx.shape)
# Plot decisoin region
ax.contourf(xx,
yy,
Z,
alpha=0.3,
colors=colors,
levels=np.arange(Z.max() + 2) - 0.5)
# ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())
ax.axis(xmin=xx.min(), xmax=xx.max())
# Scatter training data samples
for idx, c in enumerate(np.unique(y)):
if dim == 1:
y_data = [0 for i in X[y == c]]
x_data = X[y == c]
elif dim == 2:
y_data = X[y == c, y_index]
x_data = X[y == c, x_index]
elif dim > 2 and filler_feature_ranges is not None:
class_mask = y == c
feature_range_mask = get_feature_range_mask(
X,
filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X[class_mask & feature_range_mask, y_index]
x_data = X[class_mask & feature_range_mask, x_index]
else:
continue
ax.scatter(x=x_data,
y=y_data,
alpha=0.8,
c=colors[idx],
marker=next(marker_gen),
edgecolor='black',
label=c)
if hide_spines:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
if dim == 1:
ax.axes.get_yaxis().set_ticks([])
if legend:
if dim > 2 and filler_feature_ranges is None:
pass
else:
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles,
labels,
framealpha=0.3,
scatterpoints=1,
loc=legend)
if plot_testdata:
if dim == 1:
x_data = X_highlight
y_data = [0 for i in X_highlight]
elif dim == 2:
x_data = X_highlight[:, x_index]
y_data = X_highlight[:, y_index]
else:
feature_range_mask = get_feature_range_mask(
X_highlight,
filler_feature_values=filler_feature_values,
filler_feature_ranges=filler_feature_ranges)
y_data = X_highlight[feature_range_mask, y_index]
x_data = X_highlight[feature_range_mask, x_index]
ax.scatter(x_data,
y_data,
c='',
edgecolor='black',
alpha=1.0,
linewidths=1,
marker='o',
s=80)
return ax