JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Draws the feature importances as a bar chart; called from fit. def draw(self, **kwargs): """ Draws the feature importances as a bar chart; called from fit. """ # Quick validation for param in ('feature_importances_', 'features_'): if not hasattr(self, param): ...
Finalize the drawing setting labels and title. def finalize(self, **kwargs): """ Finalize the drawing setting labels and title. """ # Set the title self.set_title('Feature Importances of {} Features using {}'.format( len(self.features_), self.name)) # Se...
Searches the wrapped model for the classes_ parameter. def _find_classes_param(self): """ Searches the wrapped model for the classes_ parameter. """ for attr in ["classes_"]: try: return getattr(self.estimator, attr) except AttributeError: ...
Determines the xlabel based on the underlying data structure def _get_xlabel(self): """ Determines the xlabel based on the underlying data structure """ # Return user-specified label if self.xlabel: return self.xlabel # Label for coefficients if hasa...
Determines the type of color space that will best represent the target variable y, e.g. either a discrete (categorical) color space or a continuous color space that requires a colormap. This function can handle both 1D or column vectors as well as multi-output targets. Parameters ---------- y :...
Creates the grid layout for the joint plot, adding new axes for the histograms if necessary and modifying the aspect ratio. Does not modify the axes or the layout if self.hist is False or None. def _layout(self): """ Creates the grid layout for the joint plot, adding new axes for the hi...
Fits the JointPlot, creating a correlative visualization between the columns specified during initialization and the data and target passed into fit: - If self.columns is None then X and y must both be specified as 1D arrays or X must be a 2D array with only 2 columns. - I...
Draw the joint plot for the data in x and y. Parameters ---------- x, y : 1D array-like The data to plot for the x axis and the y axis xlabel, ylabel : str The labels for the x and y axes. def draw(self, x, y, xlabel=None, ylabel=None): """ Draw...
Finalize executes any remaining image modifications making it ready to show. def finalize(self, **kwargs): """ Finalize executes any remaining image modifications making it ready to show. """ # Set the aspect ratio to make the visualization square # TODO: still unable to make pl...
Attempts to get the column from the data using the specified index, raises an exception if this is not possible from this point in the stack. def _index_into(self, idx, data): """ Attempts to get the column from the data using the specified index, raises an exception if this is not poss...
The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features ...
Test various estimators. def visualize_model(X, y, estimator, path, **kwargs): """ Test various estimators. """ y = LabelEncoder().fit_transform(y) model = Pipeline([ ('one_hot_encoder', OneHotEncoder()), ('estimator', estimator) ]) _, ax = plt.subplots() # Instant...
Displays cross validation scores as a bar chart and the average of the scores as a horizontal line This helper function is a quick wrapper to utilize the CVScores visualizer for one-off analysis. Parameters ---------- model : a scikit-learn estimator An object that implements ``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...
Creates the bar chart of the cross-validated scores generated from the fit method and places a dashed horizontal line that represents the average value of the scores. def draw(self, **kwargs): """ Creates the bar chart of the cross-validated scores generated from the fit method ...
Add the title, legend, and other visual final touches to the plot. def finalize(self, **kwargs): """ Add the title, legend, and other visual final touches to the plot. """ # Set the title of the figure self.set_title('Cross Validation Scores for {}'.format(self.name)) ...
Accepts a matrix X and returns a correlation matrix so that each column is the variable and each row is the observations. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features def kendalltau(X): """ Accepts a matrix X and returns a correl...
Scores each feature with the algorithm and ranks them in a bar plot. This helper function is a quick wrapper to utilize the Rank1D Visualizer (Transformer) for one-off analysis. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : n...
Displays pairwise comparisons of features with the algorithm and ranks them in a lower-left triangle heatmap plot. This helper function is a quick wrapper to utilize the Rank2D Visualizer (Transformer) for one-off analysis. Parameters ---------- X : ndarray or DataFrame of shape n x m ...
The transform method is the primary drawing hook for ranking classes. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features kwargs : dict Pass generic arguments to the drawing method Returns ------...
Returns the feature ranking. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features algorithm : str or None The ranking mechanism to use, or None for the default Returns ------- ranks : ndar...
Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: dict generic keyword arguments def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finaliza...
Draws the bar plot of the ranking array of features. def draw(self, **kwargs): """ Draws the bar plot of the ranking array of features. """ if self.orientation_ == 'h': # Make the plot self.ax.barh(np.arange(len(self.ranks_)), self.ranks_, color='b') ...
Draws the heatmap of the ranking matrix of variables. def draw(self, **kwargs): """ Draws the heatmap of the ranking matrix of variables. """ # Set the axes aspect to be equal self.ax.set_aspect("equal") # Generate a mask for the upper triangle mask = np.zeros_l...
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...
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 : ndarray or Series of length n An array o...
Renders the class prediction error across the axis. 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( colors=self.colors, ...
Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. """ indices = np...
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 ------...
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. def draw(self, X, y, **kwargs): """Called from the fit meth...
Draws a horizontal stacked bar chart with different colors for each count of nan values per label. def draw_stacked_bar(self, nan_col_counts): """Draws a horizontal stacked bar chart with different colors for each count of nan values per label. """ for index, nan_values in enume...
Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. ...
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...
Fits the estimator to calculate feature correlation to dependent variable. 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...
Draws the feature correlation to dependent variable, called from fit. def draw(self): """ Draws the feature correlation to dependent variable, called from fit. """ pos = np.arange(self.scores_.shape[0]) + 0.5 self.ax.barh(pos, self.scores_) # Set the labels for the bar...
Finalize the drawing setting labels and title. def finalize(self): """ Finalize the drawing setting labels and title. """ self.set_title('Features correlation with dependent variable') self.ax.set_xlabel(self.correlation_labels[self.method]) self.ax.grid(False, axis='y...
Create labels for the features NOTE: this code is duplicated from MultiFeatureVisualizer 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...
Select features to plot. feature_index is always used as the filter and if filter_names is supplied, a new feature_index is computed from those names. def _select_features_to_plot(self, X): """ Select features to plot. feature_index is always used as the filter and ...
Create figures for feature analysis def feature_analysis(fname="feature_analysis.png"): """ Create figures for feature analysis """ # Create side-by-side axes grid _, axes = plt.subplots(ncols=2, figsize=(18,6)) # Draw RadViz on the left data = load_occupancy(split=False) oz = RadViz(...
Create figures for regression models def regression(fname="regression.png"): """ Create figures for regression models """ _, axes = plt.subplots(ncols=2, figsize=(18, 6)) alphas = np.logspace(-10, 1, 300) data = load_concrete(split=True) # Plot prediction error in the middle oz = Predi...
The matplotlib axes that the visualizer draws upon (can also be a grid of multiple axes objects). The visualizer automatically creates an axes for the user if one has not been specified. def ax(self): """ The matplotlib axes that the visualizer draws upon (can also be a grid of ...
Returns the actual size in pixels as set by matplotlib, or the user provided size if available. def size(self): """ Returns the actual size in pixels as set by matplotlib, or the user provided size if available. """ if not hasattr(self, "_size") or self._size is None: ...
Poof makes the magic happen and a visualizer appear! You can pass in a path to save the figure to disk with various backends, or you can call it with no arguments to show the figure either in a notebook or in a GUI window that pops up on screen. Parameters ---------- out...
Sets the title on the current axes. Parameters ---------- title: string, default: None Add title to figure or if None leave untitled. def set_title(self, title=None): """ Sets the title on the current axes. Parameters ---------- title: strin...
Generates the subplots for the number of given models. def generate_subplots(self): """ Generates the subplots for the number of given models. """ _, axes = plt.subplots(len(self.models), sharex=True, sharey=True) return axes
Returns a generator containing the predictions for each of the internal models (using cross_val_predict and a CV=12). 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 ...
Loads and wrangles the passed in text corpus by path. def load_corpus(path): """ Loads and wrangles the passed in text corpus by path. """ # Check if the data exists, otherwise download or raise if not os.path.exists(path): raise ValueError(( "'{}' dataset has not been download...
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 ...
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 is colocated with the code, but can be modified with the YELLOWBRICK_DATA environment variable, or passing in a different directory. The file returned will be...
Checks to see if a directory with the name of the specified dataset exists in the data home directory, found with ``get_data_home``. Parameters ---------- dataset : str The name of the dataset; should either be a folder in data home or specified in the yellowbrick.datasets.DATASETS vari...
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. Parameters ----...
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 specified in the yellowbrick.datasets.DATASETS variable. data_home : str, optional The path on dis...
Fit to data, transform it, then visualize it. Fits the visualizer to X and y with opetional parameters by passing in all of kwargs, then calls poof with the same kwargs. This method must return the result of the transform method. def fit_transform_poof(self, X, y=None, **kwargs): """ ...
This method performs preliminary computations in order to set up the figure or perform other analyses. It can also call drawing methods in order to set up various non-instance related figure elements. This method must return self. def fit(self, X, y=None, **fit_params): """ Thi...
The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features ...
Return a color palette object with color definition and handling. Calling this function with ``palette=None`` will return the current matplotlib color cycle. This function can also be used in a ``with`` statement to temporarily set the color cycle for a plot or set of plots. Parameters ------...
Change how matplotlib color shorthands are interpreted. Calling this will change how shorthand codes like "b" or "g" are interpreted by matplotlib in subsequent plots. Parameters ---------- palette : str Named yellowbrick palette to use as the source of colors. See Also -------- ...
Return a `ListedColormap` object from a named sequence palette. Useful for continuous color scheme values and color maps. Calling this function with ``palette=None`` will return the default color sequence: Color Brewer RdBu. Parameters ---------- palette : None or str or sequence Name...
Return a color palette with hex codes instead of RGB values. 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)
Return a color palette with RGB values instead of hex codes. def as_rgb(self): """ Return a color palette with RGB values instead of hex codes. """ rgb = [mpl.colors.colorConverter.to_rgb(hex) for hex in self] return ColorPalette(rgb)
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 def plot(self, size=1): """ Plot the values in the color palette as a horizontal ...
Makes directories as needed def _make_path(self, path, name): """ Makes directories as needed """ if not os.path.exists(path): os.mkdirs(path) if os.path.isdir(path) : return os.path.join(path, name) return path
Draw the manifold embedding for the specified algorithm def plot_manifold_embedding(self, algorithm="lle", path="images"): """ Draw the manifold embedding for the specified algorithm """ _, ax = plt.subplots(figsize=(9,6)) path = self._make_path(path, "s_curve_{}_manifold.png".f...
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 ...
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...
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 ena...
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 ...
Set the plotting context parameters. 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 size of the labels, lines, and other elements of the plot, but no...
Set the matplotlib color cycle using a seaborn palette. Parameters ---------- palette : yellowbrick color palette \| seaborn color palette (with ``sns_`` prepended) Palette definition. Should be something that :func:`color_palette` can process. n_colors : int Number of colors in ...
Displays lexical dispersion plot for words in a corpus This helper function is a quick wrapper to utilize the DisperstionPlot Visualizer for one-off analysis Parameters ---------- words : list A list of words whose dispersion will be examined within a corpus y : ndarray or Series of ...
The fit method is the primary drawing input for the dispersion visualization. Parameters ---------- X : list or generator Should be provided as a list of documents or a generator that yields a list of documents that contain a list of words in the ord...
Called from the fit method, this method creates the canvas and draws the plot on it. Parameters ---------- kwargs: generic keyword arguments. def draw(self, points, target=None, **kwargs): """ Called from the fit method, this method creates the canvas and draws t...
The finalize method executes any subclass-specific axes finalization steps. The user calls poof & poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ The finalize method executes any subclass-specific axes ...
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...
Creates the manifold estimator if a string value is passed in, validates other objects passed in. 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): ...
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...
Returns the transformed data points from the manifold embedding. Parameters ---------- X : array-like of shape (n, m) A matrix or data frame with n instances and m features Returns ------- Xprime : array-like of shape (n, 2) Returns the 2-dimensi...
Draws the points described by X and colored by the points in y. Can be called multiple times before finalize to add more scatter plots to the axes, however ``fit()`` must be called before use. Parameters ---------- X : array-like of shape (n, 2) The matrix produced b...
Add title and modify axes to make the image ready for display. def finalize(self): """ Add title and modify axes to make the image ready for display. """ self.set_title( '{} Manifold (fit in {:0.2f} seconds)'.format( self._name, self.fit_time_.interval ...
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 This property will be used to compute the colors for each point....
Performs recursive feature elimination with cross-validation to determine an optimal number of features for a model. Visualizes the feature subsets with respect to the cross-validation score. This helper function is a quick wrapper to utilize the RFECV visualizer for one-off analysis. Parameters ...
Fits the RFECV with the wrapped model to the specified data and draws the rfecv curve with the optimal number of features found. Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_fe...
Renders the rfecv curve. def draw(self, **kwargs): """ Renders the rfecv curve. """ # Compute the curves x = self.n_feature_subsets_ means = self.cv_scores_.mean(axis=1) sigmas = self.cv_scores_.std(axis=1) # Plot one standard deviation above and below ...
Displays a bivariate scatter plot. This helper function is a quick wrapper to utilize the ScatterVisualizer (Transformer) for one-off analysis. 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...
The fit method is the primary drawing input for the parallel coords visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with 2 f...
Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set. def draw(self, X, y, **kwargs): """Called from the fit method, this method creates a scatter plot that draws ...
Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. ...
Quick method: Create a color plot showing the best grid search scores across two parameters. This helper function is a quick wrapper to utilize GridSearchColorPlot for one-off analysis. If no `X` data is passed, the model is assumed to be fit already. This allows quick exploration without wait...
Removes rows that contain np.nan values in data. If y is given, X and y will be filtered together so that their shape remains identical. For example, rows in X with nans will also remove rows in y, or rows in y with np.nans will also remove corresponding rows in X. Parameters ------------ X : a...
Remove rows from X and y where either contains nans. def filter_missing_X_and_y(X, y): """Remove rows from X and y where either contains nans.""" y_nans = np.isnan(y) x_nans = np.isnan(X).any(axis=1) unioned_nans = np.logical_or(x_nans, y_nans) return X[~unioned_nans], y[~unioned_nans]
Warn if nans exist in a numpy array. def warn_if_nans_exist(X): """Warn if nans exist in a numpy array.""" null_count = count_rows_with_nans(X) total = len(X) percent = 100 * null_count / total if null_count > 0: warning_message = \ 'Warning! Found {} rows of {} ({:0.2f}%) with...
Count the number of rows in 2D arrays that contain any nan values. def count_rows_with_nans(X): """Count the number of rows in 2D arrays that contain any nan values.""" if X.ndim == 2: return np.where(np.isnan(X).sum(axis=1) != 0, 1, 0).sum()
Pretend to be a sklearn estimator, fit is called on creation def fit(self, X, y): """ Pretend to be a sklearn estimator, fit is called on creation """ # note that GLM takes endog (y) and then exog (X): # this is the reverse of sklearn's methods self.glm_model = self.glm...
The Missing Values Dispersion visualizer shows the locations of missing (nan) values in the feature dataset by the order of the index. When y targets are supplied to fit, the output dispersion plot is color coded according to the target y that the element refers to. Parameters ---------- alpha...
Gets the locations of nans in feature data and returns the coordinates in the matrix def get_nan_locs(self, **kwargs): """Gets the locations of nans in feature data and returns the coordinates in the matrix """ if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype(self.X.d...
Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set. If y is not None, then it draws a scatter plot where each class is in a different color. def draw(self, X, y...
Draws a multi dimensional dispersion chart, each color corresponds to a different target variable. def draw_multi_dispersion_chart(self, nan_locs): """Draws a multi dimensional dispersion chart, each color corresponds to a different target variable. """ for index, nan_values in ...
Projects the grid search results onto 2 dimensions. The display value is taken as the max over the non-displayed dimensions. Parameters ---------- cv_results : dict A dictionary of results from the `GridSearchCV` object's `cv_results_` attribute. x_param : string The name ...
Projects the grid search results onto 2 dimensions. The wrapped GridSearch object is assumed to be fit already. The display value is taken as the max over the non-displayed dimensions. Parameters ---------- x_param : string The name of the parameter to be visualized...
Calculates the difference threshold for a given difference local maximum. Parameters ----------- ymx_i : float The normalized y value of a local maximum. def __threshold(self, ymx_i): """ Calculates the difference threshold for a given difference loc...

Draws the feature importances as a bar chart; called from fit. def draw(self, **kwargs): """ Draws the feature importances as a bar chart; called from fit. """ # Quick validation for param in ('feature_importances_', 'features_'): if not hasattr(self, param): ...

Finalize the drawing setting labels and title. def finalize(self, **kwargs): """ Finalize the drawing setting labels and title. """ # Set the title self.set_title('Feature Importances of {} Features using {}'.format( len(self.features_), self.name)) # Se...

Searches the wrapped model for the classes_ parameter. def _find_classes_param(self): """ Searches the wrapped model for the classes_ parameter. """ for attr in ["classes_"]: try: return getattr(self.estimator, attr) except AttributeError: ...

Determines the xlabel based on the underlying data structure def _get_xlabel(self): """ Determines the xlabel based on the underlying data structure """ # Return user-specified label if self.xlabel: return self.xlabel # Label for coefficients if hasa...

Determines the type of color space that will best represent the target variable y, e.g. either a discrete (categorical) color space or a continuous color space that requires a colormap. This function can handle both 1D or column vectors as well as multi-output targets. Parameters ---------- y :...

Creates the grid layout for the joint plot, adding new axes for the histograms if necessary and modifying the aspect ratio. Does not modify the axes or the layout if self.hist is False or None. def _layout(self): """ Creates the grid layout for the joint plot, adding new axes for the hi...

Fits the JointPlot, creating a correlative visualization between the columns specified during initialization and the data and target passed into fit: - If self.columns is None then X and y must both be specified as 1D arrays or X must be a 2D array with only 2 columns. - I...

Draw the joint plot for the data in x and y. Parameters ---------- x, y : 1D array-like The data to plot for the x axis and the y axis xlabel, ylabel : str The labels for the x and y axes. def draw(self, x, y, xlabel=None, ylabel=None): """ Draw...

Finalize executes any remaining image modifications making it ready to show. def finalize(self, **kwargs): """ Finalize executes any remaining image modifications making it ready to show. """ # Set the aspect ratio to make the visualization square # TODO: still unable to make pl...

Attempts to get the column from the data using the specified index, raises an exception if this is not possible from this point in the stack. def _index_into(self, idx, data): """ Attempts to get the column from the data using the specified index, raises an exception if this is not poss...

The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features ...

Test various estimators. def visualize_model(X, y, estimator, path, **kwargs): """ Test various estimators. """ y = LabelEncoder().fit_transform(y) model = Pipeline([ ('one_hot_encoder', OneHotEncoder()), ('estimator', estimator) ]) _, ax = plt.subplots() # Instant...

Displays cross validation scores as a bar chart and the average of the scores as a horizontal line This helper function is a quick wrapper to utilize the CVScores visualizer for one-off analysis. Parameters ---------- model : a scikit-learn estimator An object that implements ``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...

Creates the bar chart of the cross-validated scores generated from the fit method and places a dashed horizontal line that represents the average value of the scores. def draw(self, **kwargs): """ Creates the bar chart of the cross-validated scores generated from the fit method ...

Add the title, legend, and other visual final touches to the plot. def finalize(self, **kwargs): """ Add the title, legend, and other visual final touches to the plot. """ # Set the title of the figure self.set_title('Cross Validation Scores for {}'.format(self.name)) ...

Accepts a matrix X and returns a correlation matrix so that each column is the variable and each row is the observations. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features def kendalltau(X): """ Accepts a matrix X and returns a correl...

Scores each feature with the algorithm and ranks them in a bar plot. This helper function is a quick wrapper to utilize the Rank1D Visualizer (Transformer) for one-off analysis. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features y : n...

Displays pairwise comparisons of features with the algorithm and ranks them in a lower-left triangle heatmap plot. This helper function is a quick wrapper to utilize the Rank2D Visualizer (Transformer) for one-off analysis. Parameters ---------- X : ndarray or DataFrame of shape n x m ...

The transform method is the primary drawing hook for ranking classes. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features kwargs : dict Pass generic arguments to the drawing method Returns ------...

Returns the feature ranking. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features algorithm : str or None The ranking mechanism to use, or None for the default Returns ------- ranks : ndar...

Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: dict generic keyword arguments def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finaliza...

Draws the bar plot of the ranking array of features. def draw(self, **kwargs): """ Draws the bar plot of the ranking array of features. """ if self.orientation_ == 'h': # Make the plot self.ax.barh(np.arange(len(self.ranks_)), self.ranks_, color='b') ...

Draws the heatmap of the ranking matrix of variables. def draw(self, **kwargs): """ Draws the heatmap of the ranking matrix of variables. """ # Set the axes aspect to be equal self.ax.set_aspect("equal") # Generate a mask for the upper triangle mask = np.zeros_l...

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...

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 : ndarray or Series of length n An array o...

Renders the class prediction error across the axis. 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( colors=self.colors, ...

Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. """ indices = np...

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 ------...

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. def draw(self, X, y, **kwargs): """Called from the fit meth...

Draws a horizontal stacked bar chart with different colors for each count of nan values per label. def draw_stacked_bar(self, nan_col_counts): """Draws a horizontal stacked bar chart with different colors for each count of nan values per label. """ for index, nan_values in enume...

Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. ...

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...

Fits the estimator to calculate feature correlation to dependent variable. 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...

Draws the feature correlation to dependent variable, called from fit. def draw(self): """ Draws the feature correlation to dependent variable, called from fit. """ pos = np.arange(self.scores_.shape[0]) + 0.5 self.ax.barh(pos, self.scores_) # Set the labels for the bar...

Finalize the drawing setting labels and title. def finalize(self): """ Finalize the drawing setting labels and title. """ self.set_title('Features correlation with dependent variable') self.ax.set_xlabel(self.correlation_labels[self.method]) self.ax.grid(False, axis='y...

Create labels for the features NOTE: this code is duplicated from MultiFeatureVisualizer 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...

Select features to plot. feature_index is always used as the filter and if filter_names is supplied, a new feature_index is computed from those names. def _select_features_to_plot(self, X): """ Select features to plot. feature_index is always used as the filter and ...

Create figures for feature analysis def feature_analysis(fname="feature_analysis.png"): """ Create figures for feature analysis """ # Create side-by-side axes grid _, axes = plt.subplots(ncols=2, figsize=(18,6)) # Draw RadViz on the left data = load_occupancy(split=False) oz = RadViz(...

Create figures for regression models def regression(fname="regression.png"): """ Create figures for regression models """ _, axes = plt.subplots(ncols=2, figsize=(18, 6)) alphas = np.logspace(-10, 1, 300) data = load_concrete(split=True) # Plot prediction error in the middle oz = Predi...

The matplotlib axes that the visualizer draws upon (can also be a grid of multiple axes objects). The visualizer automatically creates an axes for the user if one has not been specified. def ax(self): """ The matplotlib axes that the visualizer draws upon (can also be a grid of ...

Returns the actual size in pixels as set by matplotlib, or the user provided size if available. def size(self): """ Returns the actual size in pixels as set by matplotlib, or the user provided size if available. """ if not hasattr(self, "_size") or self._size is None: ...

Poof makes the magic happen and a visualizer appear! You can pass in a path to save the figure to disk with various backends, or you can call it with no arguments to show the figure either in a notebook or in a GUI window that pops up on screen. Parameters ---------- out...

Sets the title on the current axes. Parameters ---------- title: string, default: None Add title to figure or if None leave untitled. def set_title(self, title=None): """ Sets the title on the current axes. Parameters ---------- title: strin...

Generates the subplots for the number of given models. def generate_subplots(self): """ Generates the subplots for the number of given models. """ _, axes = plt.subplots(len(self.models), sharex=True, sharey=True) return axes

Returns a generator containing the predictions for each of the internal models (using cross_val_predict and a CV=12). 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 ...

Loads and wrangles the passed in text corpus by path. def load_corpus(path): """ Loads and wrangles the passed in text corpus by path. """ # Check if the data exists, otherwise download or raise if not os.path.exists(path): raise ValueError(( "'{}' dataset has not been download...

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 ...

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 is colocated with the code, but can be modified with the YELLOWBRICK_DATA environment variable, or passing in a different directory. The file returned will be...

Checks to see if a directory with the name of the specified dataset exists in the data home directory, found with ``get_data_home``. Parameters ---------- dataset : str The name of the dataset; should either be a folder in data home or specified in the yellowbrick.datasets.DATASETS vari...

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. Parameters ----...

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 specified in the yellowbrick.datasets.DATASETS variable. data_home : str, optional The path on dis...

Fit to data, transform it, then visualize it. Fits the visualizer to X and y with opetional parameters by passing in all of kwargs, then calls poof with the same kwargs. This method must return the result of the transform method. def fit_transform_poof(self, X, y=None, **kwargs): """ ...

This method performs preliminary computations in order to set up the figure or perform other analyses. It can also call drawing methods in order to set up various non-instance related figure elements. This method must return self. def fit(self, X, y=None, **fit_params): """ Thi...

The fit method is the primary drawing input for the visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features ...

Return a color palette object with color definition and handling. Calling this function with ``palette=None`` will return the current matplotlib color cycle. This function can also be used in a ``with`` statement to temporarily set the color cycle for a plot or set of plots. Parameters ------...

Change how matplotlib color shorthands are interpreted. Calling this will change how shorthand codes like "b" or "g" are interpreted by matplotlib in subsequent plots. Parameters ---------- palette : str Named yellowbrick palette to use as the source of colors. See Also -------- ...

Return a `ListedColormap` object from a named sequence palette. Useful for continuous color scheme values and color maps. Calling this function with ``palette=None`` will return the default color sequence: Color Brewer RdBu. Parameters ---------- palette : None or str or sequence Name...

Return a color palette with hex codes instead of RGB values. 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)

Return a color palette with RGB values instead of hex codes. def as_rgb(self): """ Return a color palette with RGB values instead of hex codes. """ rgb = [mpl.colors.colorConverter.to_rgb(hex) for hex in self] return ColorPalette(rgb)

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 def plot(self, size=1): """ Plot the values in the color palette as a horizontal ...

Makes directories as needed def _make_path(self, path, name): """ Makes directories as needed """ if not os.path.exists(path): os.mkdirs(path) if os.path.isdir(path) : return os.path.join(path, name) return path

Draw the manifold embedding for the specified algorithm def plot_manifold_embedding(self, algorithm="lle", path="images"): """ Draw the manifold embedding for the specified algorithm """ _, ax = plt.subplots(figsize=(9,6)) path = self._make_path(path, "s_curve_{}_manifold.png".f...

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 ...

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...

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 ena...

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 ...

Set the plotting context parameters. 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 size of the labels, lines, and other elements of the plot, but no...

Set the matplotlib color cycle using a seaborn palette. Parameters ---------- palette : yellowbrick color palette | seaborn color palette (with ``sns_`` prepended) Palette definition. Should be something that :func:`color_palette` can process. n_colors : int Number of colors in ...

Displays lexical dispersion plot for words in a corpus This helper function is a quick wrapper to utilize the DisperstionPlot Visualizer for one-off analysis Parameters ---------- words : list A list of words whose dispersion will be examined within a corpus y : ndarray or Series of ...

The fit method is the primary drawing input for the dispersion visualization. Parameters ---------- X : list or generator Should be provided as a list of documents or a generator that yields a list of documents that contain a list of words in the ord...

Called from the fit method, this method creates the canvas and draws the plot on it. Parameters ---------- kwargs: generic keyword arguments. def draw(self, points, target=None, **kwargs): """ Called from the fit method, this method creates the canvas and draws t...

The finalize method executes any subclass-specific axes finalization steps. The user calls poof & poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ The finalize method executes any subclass-specific axes ...

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...

Creates the manifold estimator if a string value is passed in, validates other objects passed in. 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): ...

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...

Returns the transformed data points from the manifold embedding. Parameters ---------- X : array-like of shape (n, m) A matrix or data frame with n instances and m features Returns ------- Xprime : array-like of shape (n, 2) Returns the 2-dimensi...

Draws the points described by X and colored by the points in y. Can be called multiple times before finalize to add more scatter plots to the axes, however ``fit()`` must be called before use. Parameters ---------- X : array-like of shape (n, 2) The matrix produced b...

Add title and modify axes to make the image ready for display. def finalize(self): """ Add title and modify axes to make the image ready for display. """ self.set_title( '{} Manifold (fit in {:0.2f} seconds)'.format( self._name, self.fit_time_.interval ...

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 This property will be used to compute the colors for each point....

Performs recursive feature elimination with cross-validation to determine an optimal number of features for a model. Visualizes the feature subsets with respect to the cross-validation score. This helper function is a quick wrapper to utilize the RFECV visualizer for one-off analysis. Parameters ...

Fits the RFECV with the wrapped model to the specified data and draws the rfecv curve with the optimal number of features found. Parameters ---------- X : array-like, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_fe...

Renders the rfecv curve. def draw(self, **kwargs): """ Renders the rfecv curve. """ # Compute the curves x = self.n_feature_subsets_ means = self.cv_scores_.mean(axis=1) sigmas = self.cv_scores_.std(axis=1) # Plot one standard deviation above and below ...

Displays a bivariate scatter plot. This helper function is a quick wrapper to utilize the ScatterVisualizer (Transformer) for one-off analysis. 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...

The fit method is the primary drawing input for the parallel coords visualization since it has both the X and y data required for the viz and the transform method does not. Parameters ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with 2 f...

Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set. def draw(self, X, y, **kwargs): """Called from the fit method, this method creates a scatter plot that draws ...

Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. Parameters ---------- kwargs: generic keyword arguments. def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. ...

Quick method: Create a color plot showing the best grid search scores across two parameters. This helper function is a quick wrapper to utilize GridSearchColorPlot for one-off analysis. If no `X` data is passed, the model is assumed to be fit already. This allows quick exploration without wait...

Removes rows that contain np.nan values in data. If y is given, X and y will be filtered together so that their shape remains identical. For example, rows in X with nans will also remove rows in y, or rows in y with np.nans will also remove corresponding rows in X. Parameters ------------ X : a...

Remove rows from X and y where either contains nans. def filter_missing_X_and_y(X, y): """Remove rows from X and y where either contains nans.""" y_nans = np.isnan(y) x_nans = np.isnan(X).any(axis=1) unioned_nans = np.logical_or(x_nans, y_nans) return X[~unioned_nans], y[~unioned_nans]

Warn if nans exist in a numpy array. def warn_if_nans_exist(X): """Warn if nans exist in a numpy array.""" null_count = count_rows_with_nans(X) total = len(X) percent = 100 * null_count / total if null_count > 0: warning_message = \ 'Warning! Found {} rows of {} ({:0.2f}%) with...

Count the number of rows in 2D arrays that contain any nan values. def count_rows_with_nans(X): """Count the number of rows in 2D arrays that contain any nan values.""" if X.ndim == 2: return np.where(np.isnan(X).sum(axis=1) != 0, 1, 0).sum()

Pretend to be a sklearn estimator, fit is called on creation def fit(self, X, y): """ Pretend to be a sklearn estimator, fit is called on creation """ # note that GLM takes endog (y) and then exog (X): # this is the reverse of sklearn's methods self.glm_model = self.glm...

The Missing Values Dispersion visualizer shows the locations of missing (nan) values in the feature dataset by the order of the index. When y targets are supplied to fit, the output dispersion plot is color coded according to the target y that the element refers to. Parameters ---------- alpha...

Gets the locations of nans in feature data and returns the coordinates in the matrix def get_nan_locs(self, **kwargs): """Gets the locations of nans in feature data and returns the coordinates in the matrix """ if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype(self.X.d...

Called from the fit method, this method creates a scatter plot that draws each instance as a class or target colored point, whose location is determined by the feature data set. If y is not None, then it draws a scatter plot where each class is in a different color. def draw(self, X, y...

Draws a multi dimensional dispersion chart, each color corresponds to a different target variable. def draw_multi_dispersion_chart(self, nan_locs): """Draws a multi dimensional dispersion chart, each color corresponds to a different target variable. """ for index, nan_values in ...

Projects the grid search results onto 2 dimensions. The display value is taken as the max over the non-displayed dimensions. Parameters ---------- cv_results : dict A dictionary of results from the `GridSearchCV` object's `cv_results_` attribute. x_param : string The name ...

Projects the grid search results onto 2 dimensions. The wrapped GridSearch object is assumed to be fit already. The display value is taken as the max over the non-displayed dimensions. Parameters ---------- x_param : string The name of the parameter to be visualized...

Calculates the difference threshold for a given difference local maximum. Parameters ----------- ymx_i : float The normalized y value of a local maximum. def __threshold(self, ymx_i): """ Calculates the difference threshold for a given difference loc...