graphs#

Model understanding graphing utilities.

Module Contents#

Functions#

`binary_objective_vs_threshold`	Computes objective score as a function of potential binary classification decision thresholds for a fitted binary classification pipeline.
`confusion_matrix`	Confusion matrix for binary and multiclass classification.
`decision_tree_data_from_estimator`	Return data for a fitted tree in a restructured format.
`decision_tree_data_from_pipeline`	Return data for a fitted pipeline in a restructured format.
`get_linear_coefficients`	Returns a dataframe showing the features with the greatest predictive power for a linear model.
`get_prediction_vs_actual_data`	Combines y_true and y_pred into a single dataframe and adds a column for outliers. Used in graph_prediction_vs_actual().
`get_prediction_vs_actual_over_time_data`	Get the data needed for the prediction_vs_actual_over_time plot.
`graph_binary_objective_vs_threshold`	Generates a plot graphing objective score vs. decision thresholds for a fitted binary classification pipeline.
`graph_confusion_matrix`	Generate and display a confusion matrix plot.
`graph_permutation_importance`	Generate a bar graph of the pipeline's permutation importance.
`graph_precision_recall_curve`	Generate and display a precision-recall plot.
`graph_prediction_vs_actual`	Generate a scatter plot comparing the true and predicted values. Used for regression plotting.
`graph_prediction_vs_actual_over_time`	Plot the target values and predictions against time on the x-axis.
`graph_roc_curve`	Generate and display a Receiver Operating Characteristic (ROC) plot for binary and multiclass classification problems.
`graph_t_sne`	Plot high dimensional data into lower dimensional space using t-SNE.
`normalize_confusion_matrix`	Normalizes a confusion matrix.
`precision_recall_curve`	Given labels and binary classifier predicted probabilities, compute and return the data representing a precision-recall curve.
`roc_curve`	Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve. Works with binary or multiclass problems.
`t_sne`	Get the transformed output after fitting X to the embedded space using t-SNE.
`visualize_decision_tree`	Generate an image visualizing the decision tree.

Contents#

evalml.model_understanding.graphs.binary_objective_vs_threshold(pipeline, X, y, objective, steps=100)[source]#

Computes objective score as a function of potential binary classification decision thresholds for a fitted binary classification pipeline.

Parameters

pipeline (BinaryClassificationPipeline obj) – Fitted binary classification pipeline.
X (pd.DataFrame) – The input data used to compute objective score.
y (pd.Series) – The target labels.
objective (ObjectiveBase obj, str) – Objective used to score.
steps (int) – Number of intervals to divide and calculate objective score at.

Returns

DataFrame with thresholds and the corresponding objective score calculated at each threshold.

Return type

pd.DataFrame

Raises

ValueError – If objective is not a binary classification objective.
ValueError – If objective’s score_needs_proba is not False.

evalml.model_understanding.graphs.confusion_matrix(y_true, y_predicted, normalize_method='true')[source]#

Confusion matrix for binary and multiclass classification.

Parameters

y_true (pd.Series or np.ndarray) – True binary labels.
y_predicted (pd.Series or np.ndarray) – Predictions from a binary classifier.
normalize_method ({'true', 'pred', 'all', None}) – Normalization method to use, if not None. Supported options are: ‘true’ to normalize by row, ‘pred’ to normalize by column, or ‘all’ to normalize by all values. Defaults to ‘true’.

Returns

Confusion matrix. The column header represents the predicted labels while row header represents the actual labels.

Return type

pd.DataFrame

evalml.model_understanding.graphs.decision_tree_data_from_estimator(estimator)[source]#

Return data for a fitted tree in a restructured format.

Parameters

estimator (ComponentBase) – A fitted DecisionTree-based estimator.

Returns

An OrderedDict of OrderedDicts describing a tree structure.

Return type

OrderedDict

Raises

ValueError – If estimator is not a decision tree-based estimator.
NotFittedError – If estimator is not yet fitted.

evalml.model_understanding.graphs.decision_tree_data_from_pipeline(pipeline_)[source]#

Return data for a fitted pipeline in a restructured format.

Parameters

pipeline (PipelineBase) – A pipeline with a DecisionTree-based estimator.

Returns

An OrderedDict of OrderedDicts describing a tree structure.

Return type

OrderedDict

Raises

ValueError – If estimator is not a decision tree-based estimator.
NotFittedError – If estimator is not yet fitted.

evalml.model_understanding.graphs.get_linear_coefficients(estimator, features=None)[source]#

Returns a dataframe showing the features with the greatest predictive power for a linear model.

Parameters

estimator (Estimator) – Fitted linear model family estimator.
features (list[str]) – List of feature names associated with the underlying data.

Returns

Displaying the features by importance.

Return type

pd.DataFrame

Raises

ValueError – If the model is not a linear model.
NotFittedError – If the model is not yet fitted.

evalml.model_understanding.graphs.get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold=None)[source]#

Combines y_true and y_pred into a single dataframe and adds a column for outliers. Used in graph_prediction_vs_actual().

Parameters

y_true (pd.Series, or np.ndarray) – The real target values of the data
y_pred (pd.Series, or np.ndarray) – The predicted values outputted by the regression model.
outlier_threshold (int, float) – A positive threshold for what is considered an outlier value. This value is compared to the absolute difference between each value of y_true and y_pred. Values within this threshold will be blue, otherwise they will be yellow. Defaults to None.

Returns

prediction: Predicted values from regression model.
actual: Real target values.
outlier: Colors indicating which values are in the threshold for what is considered an outlier value.

Return type

pd.DataFrame with the following columns

Raises

ValueError – If threshold is not positive.

evalml.model_understanding.graphs.get_prediction_vs_actual_over_time_data(pipeline, X, y, X_train, y_train, dates)[source]#

Get the data needed for the prediction_vs_actual_over_time plot.

Parameters

pipeline (TimeSeriesRegressionPipeline) – Fitted time series regression pipeline.
X (pd.DataFrame) – Features used to generate new predictions.
y (pd.Series) – Target values to compare predictions against.
X_train (pd.DataFrame) – Data the pipeline was trained on.
y_train (pd.Series) – Target values for training data.
dates (pd.Series) – Dates corresponding to target values and predictions.

Returns

Predictions vs. time.

Return type

pd.DataFrame

evalml.model_understanding.graphs.graph_binary_objective_vs_threshold(pipeline, X, y, objective, steps=100)[source]#

Generates a plot graphing objective score vs. decision thresholds for a fitted binary classification pipeline.

Parameters

pipeline (PipelineBase or subclass) – Fitted pipeline
X (pd.DataFrame) – The input data used to score and compute scores
y (pd.Series) – The target labels
objective (ObjectiveBase obj, str) – Objective used to score, shown on the y-axis of the graph
steps (int) – Number of intervals to divide and calculate objective score at

Returns

plotly.Figure representing the objective score vs. threshold graph generated

evalml.model_understanding.graphs.graph_confusion_matrix(y_true, y_pred, normalize_method='true', title_addition=None)[source]#

Generate and display a confusion matrix plot.

If normalize_method is set, hover text will show raw count, otherwise hover text will show count normalized with method ‘true’.

Parameters

y_true (pd.Series or np.ndarray) – True binary labels.
y_pred (pd.Series or np.ndarray) – Predictions from a binary classifier.
normalize_method ({'true', 'pred', 'all', None}) – Normalization method to use, if not None. Supported options are: ‘true’ to normalize by row, ‘pred’ to normalize by column, or ‘all’ to normalize by all values. Defaults to ‘true’.
title_addition (str) – If not None, append to plot title. Defaults to None.

Returns

plotly.Figure representing the confusion matrix plot generated.

evalml.model_understanding.graphs.graph_permutation_importance(pipeline, X, y, objective, importance_threshold=0)[source]#

Generate a bar graph of the pipeline’s permutation importance.

Parameters

pipeline (PipelineBase or subclass) – Fitted pipeline.
X (pd.DataFrame) – The input data used to score and compute permutation importance.
y (pd.Series) – The target data.
objective (str, ObjectiveBase) – Objective to score on.
importance_threshold (float, optional) – If provided, graph features with a permutation importance whose absolute value is larger than importance_threshold. Defaults to 0.

Returns

plotly.Figure, a bar graph showing features and their respective permutation importance.

Raises

ValueError – If importance_threshold is not greater than or equal to 0.

evalml.model_understanding.graphs.graph_precision_recall_curve(y_true, y_pred_proba, title_addition=None)[source]#

Generate and display a precision-recall plot.

Parameters

y_true (pd.Series or np.ndarray) – True binary labels.
y_pred_proba (pd.Series or np.ndarray) – Predictions from a binary classifier, before thresholding has been applied. Note this should be the predicted probability for the “true” label.
title_addition (str or None) – If not None, append to plot title. Defaults to None.

Returns

plotly.Figure representing the precision-recall plot generated

evalml.model_understanding.graphs.graph_prediction_vs_actual(y_true, y_pred, outlier_threshold=None)[source]#

Generate a scatter plot comparing the true and predicted values. Used for regression plotting.

Parameters

y_true (pd.Series) – The real target values of the data.
y_pred (pd.Series) – The predicted values outputted by the regression model.
outlier_threshold (int, float) – A positive threshold for what is considered an outlier value. This value is compared to the absolute difference between each value of y_true and y_pred. Values within this threshold will be blue, otherwise they will be yellow. Defaults to None.

Returns

plotly.Figure representing the predicted vs. actual values graph

Raises

ValueError – If threshold is not positive.

evalml.model_understanding.graphs.graph_prediction_vs_actual_over_time(pipeline, X, y, X_train, y_train, dates)[source]#

Plot the target values and predictions against time on the x-axis.

Parameters

pipeline (TimeSeriesRegressionPipeline) – Fitted time series regression pipeline.
X (pd.DataFrame) – Features used to generate new predictions.
y (pd.Series) – Target values to compare predictions against.
X_train (pd.DataFrame) – Data the pipeline was trained on.
y_train (pd.Series) – Target values for training data.
dates (pd.Series) – Dates corresponding to target values and predictions.

Returns

Showing the prediction vs actual over time.

Return type

plotly.Figure

Raises

ValueError – If the pipeline is not a time-series regression pipeline.

evalml.model_understanding.graphs.graph_roc_curve(y_true, y_pred_proba, custom_class_names=None, title_addition=None)[source]#

Generate and display a Receiver Operating Characteristic (ROC) plot for binary and multiclass classification problems.

Parameters

y_true (pd.Series or np.ndarray) – True labels.
y_pred_proba (pd.Series or np.ndarray) – Predictions from a classifier, before thresholding has been applied. Note this should a one dimensional array with the predicted probability for the “true” label in the binary case.
custom_class_names (list or None) – If not None, custom labels for classes. Defaults to None.
title_addition (str or None) – if not None, append to plot title. Defaults to None.

Returns

plotly.Figure representing the ROC plot generated

Raises

ValueError – If the number of custom class names does not match number of classes in the input data.

evalml.model_understanding.graphs.graph_t_sne(X, n_components=2, perplexity=30.0, learning_rate=200.0, metric='euclidean', marker_line_width=2, marker_size=7, **kwargs)[source]#

Plot high dimensional data into lower dimensional space using t-SNE.

Parameters

X (np.ndarray, pd.DataFrame) – Data to be transformed. Must be numeric.
n_components (int) – Dimension of the embedded space. Defaults to 2.
perplexity (float) – Related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. Defaults to 30.
learning_rate (float) – Usually in the range [10.0, 1000.0]. If the cost function gets stuck in a bad local minimum, increasing the learning rate may help. Must be positive. Defaults to 200.
metric (str) – The metric to use when calculating distance between instances in a feature array. The default is “euclidean” which is interpreted as the squared euclidean distance.
marker_line_width (int) – Determines the line width of the marker boundary. Defaults to 2.
marker_size (int) – Determines the size of the marker. Defaults to 7.
kwargs – Arbitrary keyword arguments.

Returns

Figure representing the transformed data.

Return type

plotly.Figure

Raises

ValueError – If marker_line_width or marker_size are not valid values.

evalml.model_understanding.graphs.normalize_confusion_matrix(conf_mat, normalize_method='true')[source]#

Normalizes a confusion matrix.

Parameters

conf_mat (pd.DataFrame or np.ndarray) – Confusion matrix to normalize.
normalize_method ({'true', 'pred', 'all'}) – Normalization method. Supported options are: ‘true’ to normalize by row, ‘pred’ to normalize by column, or ‘all’ to normalize by all values. Defaults to ‘true’.

Returns

normalized version of the input confusion matrix. The column header represents the predicted labels while row header represents the actual labels.

Return type

pd.DataFrame

Raises

ValueError – If configuration is invalid, or if the sum of a given axis is zero and normalization by axis is specified.

evalml.model_understanding.graphs.precision_recall_curve(y_true, y_pred_proba, pos_label_idx=- 1)[source]#

Given labels and binary classifier predicted probabilities, compute and return the data representing a precision-recall curve.

Parameters

y_true (pd.Series or np.ndarray) – True binary labels.
y_pred_proba (pd.Series or np.ndarray) – Predictions from a binary classifier, before thresholding has been applied. Note this should be the predicted probability for the “true” label.
pos_label_idx (int) – the column index corresponding to the positive class. If predicted probabilities are two-dimensional, this will be used to access the probabilities for the positive class.

Returns

Dictionary containing metrics used to generate a precision-recall plot, with the following keys:

precision: Precision values.

recall: Recall values.

thresholds: Threshold values used to produce the precision and recall.

auc_score: The area under the ROC curve.

Return type

list

Raises

NoPositiveLabelException – If predicted probabilities do not contain a column at the specified label.

evalml.model_understanding.graphs.roc_curve(y_true, y_pred_proba)[source]#

Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve. Works with binary or multiclass problems.

Parameters

y_true (pd.Series or np.ndarray) – True labels.
y_pred_proba (pd.Series or np.ndarray) – Predictions from a classifier, before thresholding has been applied.

Returns

A list of dictionaries (with one for each class) is returned. Binary classification problems return a list with one dictionary.

Each dictionary contains metrics used to generate an ROC plot with the following keys:

fpr_rate: False positive rate.
tpr_rate: True positive rate.
threshold: Threshold values used to produce each pair of true/false positive rates.
auc_score: The area under the ROC curve.

Return type

list(dict)

evalml.model_understanding.graphs.t_sne(X, n_components=2, perplexity=30.0, learning_rate=200.0, metric='euclidean', **kwargs)[source]#

Get the transformed output after fitting X to the embedded space using t-SNE.

Args:
X (np.ndarray, pd.DataFrame): Data to be transformed. Must be numeric. n_components (int, optional): Dimension of the embedded space. perplexity (float, optional): Related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50. learning_rate (float, optional): Usually in the range [10.0, 1000.0]. If the cost function gets stuck in a bad local minimum, increasing the learning rate may help. metric (str, optional): The metric to use when calculating distance between instances in a feature array. kwargs: Arbitrary keyword arguments.

Returns: TSNE output.
Return type: np.ndarray (n_samples, n_components)
Raises: ValueError – If specified parameters are not valid values.

evalml.model_understanding.graphs.visualize_decision_tree(estimator, max_depth=None, rotate=False, filled=False, filepath=None)[source]#

Generate an image visualizing the decision tree.

Parameters

estimator (ComponentBase) – A fitted DecisionTree-based estimator.
max_depth (int, optional) – The depth to which the tree should be displayed. If set to None (as by default), tree is fully generated.
rotate (bool, optional) – Orient tree left to right rather than top-down.
filled (bool, optional) – Paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.
filepath (str, optional) – Path to where the graph should be saved. If set to None (as by default), the graph will not be saved.

Returns

DOT object that can be directly displayed in Jupyter notebooks.

Return type

graphviz.Source

Raises

ValueError – If estimator is not a decision tree-based estimator.
NotFittedError – If estimator is not yet fitted.

force_plots

partial_dependence_functions