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train | class_prediction_error | Quick method:
Divides the dataset X and y into train and test splits, fits the model on
the train split, then scores the model on the test split. The visualizer
displays the support for each class in the fitted classification model
displayed as a stacked bar plot Each bar is segmented to show the
di... | yellowbrick/classifier/class_prediction_error.py | def class_prediction_error(
model,
X,
y=None,
ax=None,
classes=None,
test_size=0.2,
random_state=None,
**kwargs):
"""Quick method:
Divides the dataset X and y into train and test splits, fits the model on
the train split, then scores the model on the test split. The visualize... | def class_prediction_error(
model,
X,
y=None,
ax=None,
classes=None,
test_size=0.2,
random_state=None,
**kwargs):
"""Quick method:
Divides the dataset X and y into train and test splits, fits the model on
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train | ClassPredictionError.score | Generates a 2D array where each row is the count of the
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Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
y : ndarray or Series of length n
An array o... | yellowbrick/classifier/class_prediction_error.py | def score(self, X, y, **kwargs):
"""
Generates a 2D array where each row is the count of the
predicted classes and each column is the true class
Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
y :... | def score(self, X, y, **kwargs):
"""
Generates a 2D array where each row is the count of the
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----------
X : ndarray or DataFrame of shape n x m
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train | ClassPredictionError.draw | Renders the class prediction error across the axis. | yellowbrick/classifier/class_prediction_error.py | def draw(self):
"""
Renders the class prediction error across the axis.
"""
indices = np.arange(len(self.classes_))
prev = np.zeros(len(self.classes_))
colors = resolve_colors(
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for idx, ro... | def draw(self):
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Renders the class prediction error across the axis.
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indices = np.arange(len(self.classes_))
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train | ClassPredictionError.finalize | Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize. | yellowbrick/classifier/class_prediction_error.py | def finalize(self, **kwargs):
"""
Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize.
"""
indices = np.arange(len(self.classes_))
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"""
Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize.
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indices = np.arange(len(self.classes_))
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train | missing_bar | The MissingValues Bar visualizer creates a bar graph that lists the total
count of missing values for each selected feature column.
When y targets are supplied to fit, the output is a stacked bar chart where
each color corresponds to the total NaNs for the feature in that column.
Parameters
------... | yellowbrick/contrib/missing/bar.py | def missing_bar(X, y=None, ax=None, classes=None, width=0.5, color='black', **kwargs):
"""The MissingValues Bar visualizer creates a bar graph that lists the total
count of missing values for each selected feature column.
When y targets are supplied to fit, the output is a stacked bar chart where
each ... | def missing_bar(X, y=None, ax=None, classes=None, width=0.5, color='black', **kwargs):
"""The MissingValues Bar visualizer creates a bar graph that lists the total
count of missing values for each selected feature column.
When y targets are supplied to fit, the output is a stacked bar chart where
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train | MissingValuesBar.draw | Called from the fit method, this method generated a horizontal bar plot.
If y is none, then draws a simple horizontal bar chart.
If y is not none, then draws a stacked horizontal bar chart for each nan count per
target values. | yellowbrick/contrib/missing/bar.py | def draw(self, X, y, **kwargs):
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If y is none, then draws a simple horizontal bar chart.
If y is not none, then draws a stacked horizontal bar chart for each nan count per
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nan_c... | def draw(self, X, y, **kwargs):
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train | MissingValuesBar.draw_stacked_bar | Draws a horizontal stacked bar chart with different colors
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"""Draws a horizontal stacked bar chart with different colors
for each count of nan values per label.
"""
for index, nan_values in enumerate(nan_col_counts):
label, nan_col_counts = nan_values
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"""
for index, nan_values in enumerate(nan_col_counts):
label, nan_col_counts = nan_values
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train | MissingValuesBar.finalize | Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize.
Parameters
----------
kwargs: generic keyword arguments. | yellowbrick/contrib/missing/bar.py | def finalize(self, **kwargs):
"""
Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize.
Parameters
----------
kwargs: generic keyword arguments.
"""
# Set the title
self.set_title(
... | def finalize(self, **kwargs):
"""
Finalize executes any subclass-specific axes finalization steps.
The user calls poof and poof calls finalize.
Parameters
----------
kwargs: generic keyword arguments.
"""
# Set the title
self.set_title(
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train | feature_correlation | Displays the correlation between features and dependent variables.
This visualizer can be used side-by-side with
yellowbrick.features.JointPlotVisualizer that plots a feature
against the target and shows the distribution of each via a
histogram on each axis.
Parameters
----------
X : ndarr... | yellowbrick/target/feature_correlation.py | def feature_correlation(X, y, ax=None, method='pearson',
labels=None, sort=False, feature_index=None,
feature_names=None, **kwargs):
"""
Displays the correlation between features and dependent variables.
This visualizer can be used side-by-side with
yello... | def feature_correlation(X, y, ax=None, method='pearson',
labels=None, sort=False, feature_index=None,
feature_names=None, **kwargs):
"""
Displays the correlation between features and dependent variables.
This visualizer can be used side-by-side with
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train | FeatureCorrelation.fit | Fits the estimator to calculate feature correlation to
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Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
y : ndarray or Series of length n
An array or series of target or class value... | yellowbrick/target/feature_correlation.py | def fit(self, X, y, **kwargs):
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Fits the estimator to calculate feature correlation to
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Parameters
----------
X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
y : ndarray or Series of length n
... | def fit(self, X, y, **kwargs):
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Fits the estimator to calculate feature correlation to
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X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
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train | FeatureCorrelation.draw | Draws the feature correlation to dependent variable, called from fit. | yellowbrick/target/feature_correlation.py | def draw(self):
"""
Draws the feature correlation to dependent variable, called from fit.
"""
pos = np.arange(self.scores_.shape[0]) + 0.5
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self.ax.set_yticks(pos)
self.ax.set_yticklabels(self.... | def draw(self):
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Draws the feature correlation to dependent variable, called from fit.
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train | FeatureCorrelation.finalize | Finalize the drawing setting labels and title. | yellowbrick/target/feature_correlation.py | def finalize(self):
"""
Finalize the drawing setting labels and title.
"""
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self.ax.set_xlabel(self.correlation_labels[self.method])
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train | FeatureCorrelation._create_labels_for_features | Create labels for the features
NOTE: this code is duplicated from MultiFeatureVisualizer | yellowbrick/target/feature_correlation.py | def _create_labels_for_features(self, X):
"""
Create labels for the features
NOTE: this code is duplicated from MultiFeatureVisualizer
"""
if self.labels is None:
# Use column names if a dataframe
if is_dataframe(X):
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train | FeatureCorrelation._select_features_to_plot | Select features to plot.
feature_index is always used as the filter and
if filter_names is supplied, a new feature_index
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"""
Select features to plot.
feature_index is always used as the filter and
if filter_names is supplied, a new feature_index
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"""
if self.feature_index:
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"""
Select features to plot.
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is computed from those names.
"""
if self.feature_index:
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train | feature_analysis | Create figures for feature analysis | paper/figures/figures.py | def feature_analysis(fname="feature_analysis.png"):
"""
Create figures for feature analysis
"""
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_, axes = plt.subplots(ncols=2, figsize=(18,6))
# Draw RadViz on the left
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oz = RadViz(ax=axes[0], classes=["unoccupied", "o... | def feature_analysis(fname="feature_analysis.png"):
"""
Create figures for feature analysis
"""
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train | regression | Create figures for regression models | paper/figures/figures.py | def regression(fname="regression.png"):
"""
Create figures for regression models
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title: string, default: None
Add title to figure or if None leave untitled.
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train | MultiModelMixin.generate_subplots | Generates the subplots for the number of given models. | yellowbrick/base.py | def generate_subplots(self):
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Generates the subplots for the number of given models.
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_, axes = plt.subplots(len(self.models), sharex=True, sharey=True)
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train | MultiModelMixin.predict | Returns a generator containing the predictions for each of the
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X : ndarray or DataFrame of shape n x m
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X : ndarray or DataFrame of shape n x m
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Returns a generator containing the predictions for each of the
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train | load_corpus | Loads and wrangles the passed in text corpus by path. | docs/api/text/corpus.py | def load_corpus(path):
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train | get_data_home | Return the path of the Yellowbrick data directory. This folder is used by
dataset loaders to avoid downloading data several times.
By default, this folder is colocated with the code in the install directory
so that data shipped with the package can be easily located. Alternatively
it can be set by the ... | yellowbrick/datasets/path.py | def get_data_home(path=None):
"""
Return the path of the Yellowbrick data directory. This folder is used by
dataset loaders to avoid downloading data several times.
By default, this folder is colocated with the code in the install directory
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Return the path of the Yellowbrick data directory. This folder is used by
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train | find_dataset_path | Looks up the path to the dataset specified in the data home directory,
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"""
Looks up the path to the dataset specified in the data home directory,
which is found using the ``get_data_home`` function. By default data home
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Looks up the path to the dataset specified in the data home directory,
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train | dataset_exists | Checks to see if a directory with the name of the specified dataset exists
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Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
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"""
Checks to see if a directory with the name of the specified dataset exists
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Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
... | def dataset_exists(dataset, data_home=None):
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Checks to see if a directory with the name of the specified dataset exists
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train | dataset_archive | Checks to see if the dataset archive file exists in the data home directory,
found with ``get_data_home``. By specifying the signature, this function
also checks to see if the archive is the latest version by comparing the
sha256sum of the local archive with the specified signature.
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----... | yellowbrick/datasets/path.py | def dataset_archive(dataset, signature, data_home=None, ext=".zip"):
"""
Checks to see if the dataset archive file exists in the data home directory,
found with ``get_data_home``. By specifying the signature, this function
also checks to see if the archive is the latest version by comparing the
sha2... | def dataset_archive(dataset, signature, data_home=None, ext=".zip"):
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Checks to see if the dataset archive file exists in the data home directory,
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train | cleanup_dataset | Removes the dataset directory and archive file from the data home directory.
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dataset : str
The name of the dataset; should either be a folder in data home or
specified in the yellowbrick.datasets.DATASETS variable.
data_home : str, optional
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"""
Removes the dataset directory and archive file from the data home directory.
Parameters
----------
dataset : str
The name of the dataset; should either be a folder in data home or
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Removes the dataset directory and archive file from the data home directory.
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order to set up various non-instance related figure elements.
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train | color_palette | Return a color palette object with color definition and handling.
Calling this function with ``palette=None`` will return the current
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This function can also be used in a ``with`` statement to temporarily
set the color cycle for a plot or set of plots.
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"""
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train | set_color_codes | Change how matplotlib color shorthands are interpreted.
Calling this will change how shorthand codes like "b" or "g"
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Parameters
----------
palette : str
Named yellowbrick palette to use as the source of colors.
See Also
--------
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Change how matplotlib color shorthands are interpreted.
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Parameters
----------
palette : str
Named yellowbrick palette to use as... | def set_color_codes(palette="accent"):
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Change how matplotlib color shorthands are interpreted.
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train | color_sequence | Return a `ListedColormap` object from a named sequence palette. Useful
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Calling this function with ``palette=None`` will return the default
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Parameters
----------
palette : None or str or sequence
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Return a `ListedColormap` object from a named sequence palette. Useful
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train | ColorPalette.as_hex | Return a color palette with hex codes instead of RGB values. | yellowbrick/style/palettes.py | def as_hex(self):
"""
Return a color palette with hex codes instead of RGB values.
"""
hex = [mpl.colors.rgb2hex(rgb) for rgb in self]
return ColorPalette(hex) | def as_hex(self):
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train | ColorPalette.as_rgb | Return a color palette with RGB values instead of hex codes. | yellowbrick/style/palettes.py | def as_rgb(self):
"""
Return a color palette with RGB values instead of hex codes.
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rgb = [mpl.colors.colorConverter.to_rgb(hex) for hex in self]
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train | ColorPalette.plot | Plot the values in the color palette as a horizontal array.
See Seaborn's palplot function for inspiration.
Parameters
----------
size : int
scaling factor for size of the plot | yellowbrick/style/palettes.py | def plot(self, size=1):
"""
Plot the values in the color palette as a horizontal array.
See Seaborn's palplot function for inspiration.
Parameters
----------
size : int
scaling factor for size of the plot
"""
n = len(self)
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"""
Plot the values in the color palette as a horizontal array.
See Seaborn's palplot function for inspiration.
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----------
size : int
scaling factor for size of the plot
"""
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train | SCurveExample._make_path | Makes directories as needed | docs/api/features/manifold.py | def _make_path(self, path, name):
"""
Makes directories as needed
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if not os.path.exists(path):
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train | SCurveExample.plot_manifold_embedding | Draw the manifold embedding for the specified algorithm | docs/api/features/manifold.py | def plot_manifold_embedding(self, algorithm="lle", path="images"):
"""
Draw the manifold embedding for the specified algorithm
"""
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Draw the manifold embedding for the specified algorithm
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train | download_data | Downloads the zipped data set specified at the given URL, saving it to
the data directory specified by ``get_data_home``. This function verifies
the download with the given signature and extracts the archive.
Parameters
----------
url : str
The URL of the dataset on the Internet to GET
... | yellowbrick/datasets/download.py | def download_data(url, signature, data_home=None, replace=False, extract=True):
"""
Downloads the zipped data set specified at the given URL, saving it to
the data directory specified by ``get_data_home``. This function verifies
the download with the given signature and extracts the archive.
Parame... | def download_data(url, signature, data_home=None, replace=False, extract=True):
"""
Downloads the zipped data set specified at the given URL, saving it to
the data directory specified by ``get_data_home``. This function verifies
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train | set_aesthetic | Set aesthetic parameters in one step.
Each set of parameters can be set directly or temporarily, see the
referenced functions below for more information.
Parameters
----------
palette : string or sequence
Color palette, see :func:`color_palette`
font : string
Font family, see m... | yellowbrick/style/rcmod.py | def set_aesthetic(palette="yellowbrick", font="sans-serif", font_scale=1,
color_codes=True, rc=None):
"""
Set aesthetic parameters in one step.
Each set of parameters can be set directly or temporarily, see the
referenced functions below for more information.
Parameters
-----... | def set_aesthetic(palette="yellowbrick", font="sans-serif", font_scale=1,
color_codes=True, rc=None):
"""
Set aesthetic parameters in one step.
Each set of parameters can be set directly or temporarily, see the
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train | _axes_style | Return a parameter dict for the aesthetic style of the plots.
NOTE: This is an internal method from Seaborn that is simply used to
create a default aesthetic in yellowbrick. If you'd like to use these
styles then import Seaborn!
This affects things like the color of the axes, whether a grid is
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"""
Return a parameter dict for the aesthetic style of the plots.
NOTE: This is an internal method from Seaborn that is simply used to
create a default aesthetic in yellowbrick. If you'd like to use these
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This affects things lik... | def _axes_style(style=None, rc=None):
"""
Return a parameter dict for the aesthetic style of the plots.
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train | set_style | Set the aesthetic style of the plots.
This affects things like the color of the axes, whether a grid is
enabled by default, and other aesthetic elements.
Parameters
----------
style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks}
A dictionary of parameters or the name of ... | yellowbrick/style/rcmod.py | def set_style(style=None, rc=None):
"""
Set the aesthetic style of the plots.
This affects things like the color of the axes, whether a grid is
enabled by default, and other aesthetic elements.
Parameters
----------
style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks}
... | def set_style(style=None, rc=None):
"""
Set the aesthetic style of the plots.
This affects things like the color of the axes, whether a grid is
enabled by default, and other aesthetic elements.
Parameters
----------
style : dict, None, or one of {darkgrid, whitegrid, dark, white, ticks}
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train | _set_context | Set the plotting context parameters.
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styles then import Seaborn!
This affects things like the size of the labels, lines, and other
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"""
Set the plotting context parameters.
NOTE: This is an internal method from Seaborn that is simply used to
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Set the plotting context parameters.
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train | set_palette | Set the matplotlib color cycle using a seaborn palette.
Parameters
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palette : yellowbrick color palette | seaborn color palette (with ``sns_`` prepended)
Palette definition. Should be something that :func:`color_palette`
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n_colors : int
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Set the matplotlib color cycle using a seaborn palette.
Parameters
----------
palette : yellowbrick color palette | seaborn color palette (with ``sns_`` prepended)
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Set the matplotlib color cycle using a seaborn palette.
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palette : yellowbrick color palette | seaborn color palette (with ``sns_`` prepended)
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train | dispersion | Displays lexical dispersion plot for words in a corpus
This helper function is a quick wrapper to utilize the DisperstionPlot
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Parameters
----------
words : list
A list of words whose dispersion will be examined within a corpus
y : ndarray or Series of ... | yellowbrick/text/dispersion.py | def dispersion(words, corpus, y=None, ax=None, colors=None, colormap=None,
labels=None, annotate_docs=False, ignore_case=False, **kwargs):
""" Displays lexical dispersion plot for words in a corpus
This helper function is a quick wrapper to utilize the DisperstionPlot
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train | DispersionPlot.fit | The fit method is the primary drawing input for the dispersion
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Parameters
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X : list or generator
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The fit method is the primary drawing input for the dispersion
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Parameters
----------
X : list or generator
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train | DispersionPlot.draw | Called from the fit method, this method creates the canvas and
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Parameters
----------
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"""
Called from the fit method, this method creates the canvas and
draws the plot on it.
Parameters
----------
kwargs: generic keyword arguments.
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# Resolve the labels with the classes
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"""
Called from the fit method, this method creates the canvas and
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Parameters
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kwargs: generic keyword arguments.
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Parameters
----------
kwargs: generic keyword arguments. | yellowbrick/text/dispersion.py | def finalize(self, **kwargs):
"""
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Parameters
----------
kwargs: generic keyword arguments.
"""
self.ax.set_ylim(-1, len(self.indexed_wor... | def finalize(self, **kwargs):
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The finalize method executes any subclass-specific axes
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kwargs: generic keyword arguments.
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train | manifold_embedding | Quick method for Manifold visualizer.
The Manifold visualizer provides high dimensional visualization for feature
analysis by embedding data into 2 dimensions using the sklearn.manifold
package for manifold learning. In brief, manifold learning algorithms are
unsuperivsed approaches to non-linear dimen... | yellowbrick/features/manifold.py | def manifold_embedding(
X,
y=None,
ax=None,
manifold="lle",
n_neighbors=10,
colors=None,
target=AUTO,
alpha=0.7,
random_state=None,
**kwargs):
"""Quick method for Manifold visualizer.
The Manifold visualizer provides high dimensional visualization for feature
analysi... | def manifold_embedding(
X,
y=None,
ax=None,
manifold="lle",
n_neighbors=10,
colors=None,
target=AUTO,
alpha=0.7,
random_state=None,
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train | Manifold.manifold | Creates the manifold estimator if a string value is passed in,
validates other objects passed in. | yellowbrick/features/manifold.py | def manifold(self, transformer):
"""
Creates the manifold estimator if a string value is passed in,
validates other objects passed in.
"""
if not is_estimator(transformer):
if transformer not in self.ALGORITHMS:
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"""
Creates the manifold estimator if a string value is passed in,
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"""
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if transformer not in self.ALGORITHMS:
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train | Manifold.fit_transform | Fits the manifold on X and transforms the data to plot it on the axes.
The optional y specified can be used to declare discrete colors. If
the target is set to 'auto', this method also determines the target
type, and therefore what colors will be used.
Note also that fit records the amo... | yellowbrick/features/manifold.py | def fit_transform(self, X, y=None):
"""
Fits the manifold on X and transforms the data to plot it on the axes.
The optional y specified can be used to declare discrete colors. If
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A matrix or data frame with n instances and m features
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Xprime : array-like of shape (n, 2)
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"""
Returns the transformed data points from the manifold embedding.
Parameters
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X : array-like of shape (n, m)
A matrix or data frame with n instances and m features
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Xprime : array-like of sh... | def transform(self, X):
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X : array-like of shape (n, 2)
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train | Manifold.finalize | Add title and modify axes to make the image ready for display. | yellowbrick/features/manifold.py | def finalize(self):
"""
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"""
self.set_title(
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self.ax.set_xticklabels([])
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"""
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train | Manifold._determine_target_color_type | Determines the target color type from the vector y as follows:
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- if target is auto: determine if y is continuous or discrete
- otherwise specify supplied target type
This property will be used to compute the colors for each point. | yellowbrick/features/manifold.py | def _determine_target_color_type(self, y):
"""
Determines the target color type from the vector y as follows:
- if y is None: only a single color is used
- if target is auto: determine if y is continuous or discrete
- otherwise specify supplied target type
T... | def _determine_target_color_type(self, y):
"""
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train | rfecv | Performs recursive feature elimination with cross-validation to determine
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This helper function is a quick wrapper to utilize the RFECV visualizer
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Performs recursive feature elimination with cross-validation to determine
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Renders the rfecv curve.
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train | scatterviz | Displays a bivariate scatter plot.
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X : ndarray or DataFrame of shape n x m
A matrix of n instances with m features
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alpha=1.0,
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train | ScatterVisualizer.draw | Called from the fit method, this method creates a scatter plot that
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train | ScatterVisualizer.finalize | Finalize executes any subclass-specific axes finalization steps.
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kwargs: generic keyword arguments.
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train | gridsearch_color_plot | Quick method:
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This helper function is a quick wrapper to utilize GridSearchColorPlot
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If no `X` data is passed, the model is assumed to be fit already. This
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"""Quick method:
Create a color plot showing the best grid search scores across two
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This helper function is a quick wrapper to utilize GridSearchColorPlot
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train | filter_missing | Removes rows that contain np.nan values in data. If y is given,
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train | filter_missing_X_and_y | Remove rows from X and y where either contains nans. | yellowbrick/utils/nan_warnings.py | def filter_missing_X_and_y(X, y):
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train | count_rows_with_nans | Count the number of rows in 2D arrays that contain any nan values. | yellowbrick/utils/nan_warnings.py | def count_rows_with_nans(X):
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train | StatsModelsWrapper.fit | Pretend to be a sklearn estimator, fit is called on creation | yellowbrick/contrib/statsmodels/base.py | def fit(self, X, y):
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Pretend to be a sklearn estimator, fit is called on creation
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# note that GLM takes endog (y) and then exog (X):
# this is the reverse of sklearn's methods
self.glm_model = self.glm_partial(y, X)
self.glm_results = self.glm_model.fit()... | def fit(self, X, y):
"""
Pretend to be a sklearn estimator, fit is called on creation
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# note that GLM takes endog (y) and then exog (X):
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train | missing_dispersion | The Missing Values Dispersion visualizer shows the locations of missing (nan)
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When y targets are supplied to fit, the output dispersion plot is color
coded according to the target y that the element refers to.
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"""
The Missing Values Dispersion visualizer shows the locations of missing (nan)
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train | MissingValuesDispersion.draw | Called from the fit method, this method creates a scatter plot that
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If y is not None, then it draws a scatter plot where each class is in a
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train | MissingValuesDispersion.draw_multi_dispersion_chart | Draws a multi dimensional dispersion chart, each color corresponds
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for index, nan_values in enumerate(nan_locs):
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train | param_projection | Projects the grid search results onto 2 dimensions.
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cv_results : dict
A dictionary of results from the `GridSearchCV` object's `cv_results_`
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x_param : string
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Projects the grid search results onto 2 dimensions.
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train | KneeLocator.__threshold | Calculates the difference threshold for a
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Calculates the difference threshold for a
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ymx_i : float
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train | KneeLocator.plot_knee_normalized | Plots the normalized curve, the distance curve (xd, ysn) and the
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"""
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"""
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train | PosTagVisualizer._handle_treebank | Create a part-of-speech tag mapping using the Penn Treebank tags
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kwargs: dict
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-------
ax : matplotlib axes
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Called from the fit method, this method creates the canvas and
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Parameters
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kwargs: dict
generic keyword arguments.
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train | tsne | Display a projection of a vectorized corpus in two dimensions using TSNE,
a nonlinear dimensionality reduction method that is particularly well
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scatter plot. TSNE is widely used in text analysis to show clusters or
groups of documents... | yellowbrick/text/tsne.py | def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None,
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"""
Display a projection of a vectorized corpus in two dimensions using TSNE,
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Display a projection of a vectorized corpus in two dimensions using TSNE,
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... | yellowbrick/text/tsne.py | def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}):
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Creates an internal transformer pipeline to project the data set into
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train | get_version | Reads the python file defined in the VERSION_PATH to find the get_version
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] | 59b67236a3862c73363e8edad7cd86da5b69e3b2 |
train | get_requires | Yields a generator of requirements as defined by the REQUIRE_PATH which
should point to a requirements.txt output by `pip freeze`. | setup.py | def get_requires(path=REQUIRE_PATH):
"""
Yields a generator of requirements as defined by the REQUIRE_PATH which
should point to a requirements.txt output by `pip freeze`.
"""
for line in read(path).splitlines():
line = line.strip()
if line and not line.startswith('#'):
y... | def get_requires(path=REQUIRE_PATH):
"""
Yields a generator of requirements as defined by the REQUIRE_PATH which
should point to a requirements.txt output by `pip freeze`.
"""
for line in read(path).splitlines():
line = line.strip()
if line and not line.startswith('#'):
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train | get_description_type | Returns the long_description_content_type based on the extension of the
package describe path (e.g. .txt, .rst, or .md). | setup.py | def get_description_type(path=PKG_DESCRIBE):
"""
Returns the long_description_content_type based on the extension of the
package describe path (e.g. .txt, .rst, or .md).
"""
_, ext = os.path.splitext(path)
return {
".rst": "text/x-rst",
".txt": "text/plain",
".md": "text/... | def get_description_type(path=PKG_DESCRIBE):
"""
Returns the long_description_content_type based on the extension of the
package describe path (e.g. .txt, .rst, or .md).
"""
_, ext = os.path.splitext(path)
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train | learning_curve | Displays a learning curve based on number of samples vs training and
cross validation scores. The learning curve aims to show how a model
learns and improves with experience.
This helper function is a quick wrapper to utilize the LearningCurve
for one-off analysis.
Parameters
----------
mo... | yellowbrick/model_selection/learning_curve.py | def learning_curve(model, X, y, ax=None, groups=None,
train_sizes=DEFAULT_TRAIN_SIZES, cv=None, scoring=None,
exploit_incremental_learning=False, n_jobs=1,
pre_dispatch="all", shuffle=False, random_state=None,
**kwargs):
"""
Displays a learning curve b... | def learning_curve(model, X, y, ax=None, groups=None,
train_sizes=DEFAULT_TRAIN_SIZES, cv=None, scoring=None,
exploit_incremental_learning=False, n_jobs=1,
pre_dispatch="all", shuffle=False, random_state=None,
**kwargs):
"""
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train | LearningCurve.fit | Fits the learning curve with the wrapped model to the specified data.
Draws training and test score curves and saves the scores to the
estimator.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of sam... | yellowbrick/model_selection/learning_curve.py | def fit(self, X, y=None):
"""
Fits the learning curve with the wrapped model to the specified data.
Draws training and test score curves and saves the scores to the
estimator.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Trainin... | def fit(self, X, y=None):
"""
Fits the learning curve with the wrapped model to the specified data.
Draws training and test score curves and saves the scores to the
estimator.
Parameters
----------
X : array-like, shape (n_samples, n_features)
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