"""Model understanding graphing utilities."""
import copy
import os
import warnings
from collections import OrderedDict
import numpy as np
import pandas as pd
from sklearn.exceptions import NotFittedError
from sklearn.manifold import TSNE
from sklearn.metrics import auc as sklearn_auc
from sklearn.metrics import confusion_matrix as sklearn_confusion_matrix
from sklearn.metrics import (
precision_recall_curve as sklearn_precision_recall_curve,
)
from sklearn.metrics import roc_curve as sklearn_roc_curve
from sklearn.preprocessing import LabelBinarizer
from sklearn.tree import export_graphviz
from sklearn.utils.multiclass import unique_labels
from evalml.exceptions import NoPositiveLabelException
from evalml.model_family import ModelFamily
from evalml.model_understanding.permutation_importance import (
calculate_permutation_importance,
)
from evalml.objectives.utils import get_objective
from evalml.problem_types import ProblemTypes
from evalml.utils import import_or_raise, infer_feature_types, jupyter_check
[docs]def confusion_matrix(y_true, y_predicted, normalize_method="true"):
"""Confusion matrix for binary and multiclass classification.
Args:
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:
pd.DataFrame: Confusion matrix. The column header represents the predicted labels while row header represents the actual labels.
"""
y_true = infer_feature_types(y_true)
y_predicted = infer_feature_types(y_predicted)
y_true = y_true.to_numpy()
y_predicted = y_predicted.to_numpy()
labels = unique_labels(y_true, y_predicted)
conf_mat = sklearn_confusion_matrix(y_true, y_predicted)
conf_mat = pd.DataFrame(conf_mat, index=labels, columns=labels)
if normalize_method is not None:
return normalize_confusion_matrix(conf_mat, normalize_method=normalize_method)
return conf_mat
[docs]def normalize_confusion_matrix(conf_mat, normalize_method="true"):
"""Normalizes a confusion matrix.
Args:
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:
pd.DataFrame: normalized version of the input confusion matrix. The column header represents the predicted labels while row header represents the actual labels.
Raises:
ValueError: If configuration is invalid, or if the sum of a given axis is zero and normalization by axis is specified.
"""
conf_mat = infer_feature_types(conf_mat)
col_names = conf_mat.columns
conf_mat = conf_mat.to_numpy()
with warnings.catch_warnings(record=True) as w:
if normalize_method == "true":
conf_mat = conf_mat.astype("float") / conf_mat.sum(axis=1)[:, np.newaxis]
elif normalize_method == "pred":
conf_mat = conf_mat.astype("float") / conf_mat.sum(axis=0)
elif normalize_method == "all":
conf_mat = conf_mat.astype("float") / conf_mat.sum().sum()
else:
raise ValueError(
'Invalid value provided for "normalize_method": {}'.format(
normalize_method
)
)
if w and "invalid value encountered in" in str(w[0].message):
raise ValueError(
"Sum of given axis is 0 and normalization is not possible. Please select another option."
)
conf_mat = pd.DataFrame(conf_mat, index=col_names, columns=col_names)
return conf_mat
[docs]def graph_confusion_matrix(
y_true, y_pred, normalize_method="true", title_addition=None
):
"""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'.
Args:
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.
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
_ff = import_or_raise(
"plotly.figure_factory",
error_msg="Cannot find dependency plotly.figure_factory",
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
conf_mat = confusion_matrix(y_true, y_pred, normalize_method=None)
conf_mat_normalized = confusion_matrix(
y_true, y_pred, normalize_method=normalize_method or "true"
)
labels = conf_mat.columns.tolist()
title = "Confusion matrix{}{}".format(
"" if title_addition is None else (" " + title_addition),
""
if normalize_method is None
else (', normalized using method "' + normalize_method + '"'),
)
z_data, custom_data = (
(conf_mat, conf_mat_normalized)
if normalize_method is None
else (conf_mat_normalized, conf_mat)
)
z_data = z_data.to_numpy()
z_text = [["{:.3f}".format(y) for y in x] for x in z_data]
primary_heading, secondary_heading = (
("Raw", "Normalized") if normalize_method is None else ("Normalized", "Raw")
)
hover_text = (
"<br><b>"
+ primary_heading
+ " Count</b>: %{z}<br><b>"
+ secondary_heading
+ " Count</b>: %{customdata} <br>"
)
# the "<extra> tags at the end are necessary to remove unwanted trace info
hover_template = (
"<b>True</b>: %{y}<br><b>Predicted</b>: %{x}" + hover_text + "<extra></extra>"
)
layout = _go.Layout(
title={"text": title},
xaxis={"title": "Predicted Label", "type": "category", "tickvals": labels},
yaxis={"title": "True Label", "type": "category", "tickvals": labels},
)
fig = _ff.create_annotated_heatmap(
z_data,
x=labels,
y=labels,
annotation_text=z_text,
customdata=custom_data,
hovertemplate=hover_template,
colorscale="Blues",
showscale=True,
)
fig.update_layout(layout)
# put xaxis text on bottom to not overlap with title
fig["layout"]["xaxis"].update(side="bottom")
# plotly Heatmap y axis defaults to the reverse of what we want: https://community.plotly.com/t/heatmap-y-axis-is-reversed-by-default-going-against-standard-convention-for-matrices/32180
fig.update_yaxes(autorange="reversed")
return fig
[docs]def precision_recall_curve(y_true, y_pred_proba, pos_label_idx=-1):
"""Given labels and binary classifier predicted probabilities, compute and return the data representing a precision-recall curve.
Args:
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:
list: 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.
Raises:
NoPositiveLabelException: If predicted probabilities do not contain a column at the specified label.
"""
y_true = infer_feature_types(y_true)
y_pred_proba = infer_feature_types(y_pred_proba)
if isinstance(y_pred_proba, pd.DataFrame):
y_pred_proba_shape = y_pred_proba.shape
try:
y_pred_proba = y_pred_proba.iloc[:, pos_label_idx]
except IndexError:
raise NoPositiveLabelException(
f"Predicted probabilities of shape {y_pred_proba_shape} don't contain a column at index {pos_label_idx}"
)
precision, recall, thresholds = sklearn_precision_recall_curve(y_true, y_pred_proba)
auc_score = sklearn_auc(recall, precision)
return {
"precision": precision,
"recall": recall,
"thresholds": thresholds,
"auc_score": auc_score,
}
[docs]def graph_precision_recall_curve(y_true, y_pred_proba, title_addition=None):
"""Generate and display a precision-recall plot.
Args:
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
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
precision_recall_curve_data = precision_recall_curve(y_true, y_pred_proba)
title = "Precision-Recall{}".format(
"" if title_addition is None else (" " + title_addition)
)
layout = _go.Layout(
title={"text": title},
xaxis={"title": "Recall", "range": [-0.05, 1.05]},
yaxis={"title": "Precision", "range": [-0.05, 1.05]},
)
data = []
data.append(
_go.Scatter(
x=precision_recall_curve_data["recall"],
y=precision_recall_curve_data["precision"],
name="Precision-Recall (AUC {:06f})".format(
precision_recall_curve_data["auc_score"]
),
line=dict(width=3),
)
)
return _go.Figure(layout=layout, data=data)
[docs]def roc_curve(y_true, y_pred_proba):
"""Given labels and classifier predicted probabilities, compute and return the data representing a Receiver Operating Characteristic (ROC) curve. Works with binary or multiclass problems.
Args:
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:
list(dict): 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.
"""
y_true = infer_feature_types(y_true).to_numpy()
y_pred_proba = infer_feature_types(y_pred_proba).to_numpy()
if len(y_pred_proba.shape) == 1:
y_pred_proba = y_pred_proba.reshape(-1, 1)
if y_pred_proba.shape[1] == 2:
y_pred_proba = y_pred_proba[:, 1].reshape(-1, 1)
nan_indices = np.logical_or(pd.isna(y_true), np.isnan(y_pred_proba).any(axis=1))
y_true = y_true[~nan_indices]
y_pred_proba = y_pred_proba[~nan_indices]
lb = LabelBinarizer()
lb.fit(np.unique(y_true))
y_one_hot_true = lb.transform(y_true)
n_classes = y_one_hot_true.shape[1]
curve_data = []
for i in range(n_classes):
fpr_rates, tpr_rates, thresholds = sklearn_roc_curve(
y_one_hot_true[:, i], y_pred_proba[:, i]
)
auc_score = sklearn_auc(fpr_rates, tpr_rates)
curve_data.append(
{
"fpr_rates": fpr_rates,
"tpr_rates": tpr_rates,
"thresholds": thresholds,
"auc_score": auc_score,
}
)
return curve_data
[docs]def graph_roc_curve(y_true, y_pred_proba, custom_class_names=None, title_addition=None):
"""Generate and display a Receiver Operating Characteristic (ROC) plot for binary and multiclass classification problems.
Args:
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.
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
title = "Receiver Operating Characteristic{}".format(
"" if title_addition is None else (" " + title_addition)
)
layout = _go.Layout(
title={"text": title},
xaxis={"title": "False Positive Rate", "range": [-0.05, 1.05]},
yaxis={"title": "True Positive Rate", "range": [-0.05, 1.05]},
)
all_curve_data = roc_curve(y_true, y_pred_proba)
graph_data = []
n_classes = len(all_curve_data)
if custom_class_names and len(custom_class_names) != n_classes:
raise ValueError(
"Number of custom class names does not match number of classes"
)
for i in range(n_classes):
roc_curve_data = all_curve_data[i]
name = i + 1 if custom_class_names is None else custom_class_names[i]
graph_data.append(
_go.Scatter(
x=roc_curve_data["fpr_rates"],
y=roc_curve_data["tpr_rates"],
hovertemplate="(False Postive Rate: %{x}, True Positive Rate: %{y})<br>"
+ "Threshold: %{text}",
name=f"Class {name} (AUC {roc_curve_data['auc_score']:.06f})",
text=roc_curve_data["thresholds"],
line=dict(width=3),
)
)
graph_data.append(
_go.Scatter(
x=[0, 1], y=[0, 1], name="Trivial Model (AUC 0.5)", line=dict(dash="dash")
)
)
return _go.Figure(layout=layout, data=graph_data)
[docs]def graph_permutation_importance(pipeline, X, y, objective, importance_threshold=0):
"""Generate a bar graph of the pipeline's permutation importance.
Args:
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.
"""
go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
perm_importance = calculate_permutation_importance(pipeline, X, y, objective)
perm_importance["importance"] = perm_importance["importance"]
if importance_threshold < 0:
raise ValueError(
f"Provided importance threshold of {importance_threshold} must be greater than or equal to 0"
)
# Remove features with close to zero importance
perm_importance = perm_importance[
abs(perm_importance["importance"]) >= importance_threshold
]
# List is reversed to go from ascending order to descending order
perm_importance = perm_importance.iloc[::-1]
title = "Permutation Importance"
subtitle = (
"The relative importance of each input feature's "
"overall influence on the pipelines' predictions, computed using "
"the permutation importance algorithm."
)
data = [
go.Bar(
x=perm_importance["importance"],
y=perm_importance["feature"],
orientation="h",
)
]
layout = {
"title": "{0}<br><sub>{1}</sub>".format(title, subtitle),
"height": 800,
"xaxis_title": "Permutation Importance",
"yaxis_title": "Feature",
"yaxis": {"type": "category"},
}
fig = go.Figure(data=data, layout=layout)
return fig
[docs]def binary_objective_vs_threshold(pipeline, X, y, objective, steps=100):
"""Computes objective score as a function of potential binary classification decision thresholds for a fitted binary classification pipeline.
Args:
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:
pd.DataFrame: DataFrame with thresholds and the corresponding objective score calculated at each threshold.
Raises:
ValueError: If objective is not a binary classification objective.
ValueError: If objective's `score_needs_proba` is not False.
"""
objective = get_objective(objective, return_instance=True)
if not objective.is_defined_for_problem_type(ProblemTypes.BINARY):
raise ValueError(
"`binary_objective_vs_threshold` can only be calculated for binary classification objectives"
)
if objective.score_needs_proba:
raise ValueError("Objective `score_needs_proba` must be False")
pipeline_tmp = copy.copy(pipeline)
thresholds = np.linspace(0, 1, steps + 1)
costs = []
for threshold in thresholds:
pipeline_tmp.threshold = threshold
scores = pipeline_tmp.score(X, y, [objective])
costs.append(scores[objective.name])
df = pd.DataFrame({"threshold": thresholds, "score": costs})
return df
[docs]def graph_binary_objective_vs_threshold(pipeline, X, y, objective, steps=100):
"""Generates a plot graphing objective score vs. decision thresholds for a fitted binary classification pipeline.
Args:
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
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
objective = get_objective(objective, return_instance=True)
df = binary_objective_vs_threshold(pipeline, X, y, objective, steps)
title = f"{objective.name} Scores vs. Thresholds"
layout = _go.Layout(
title={"text": title},
xaxis={"title": "Threshold", "range": _calculate_axis_range(df["threshold"])},
yaxis={
"title": f"{objective.name} Scores vs. Binary Classification Decision Threshold",
"range": _calculate_axis_range(df["score"]),
},
)
data = []
data.append(_go.Scatter(x=df["threshold"], y=df["score"], line=dict(width=3)))
return _go.Figure(layout=layout, data=data)
[docs]def get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold=None):
"""Combines y_true and y_pred into a single dataframe and adds a column for outliers. Used in `graph_prediction_vs_actual()`.
Args:
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:
pd.DataFrame with the following columns:
* `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.
Raises:
ValueError: If threshold is not positive.
"""
if outlier_threshold and outlier_threshold <= 0:
raise ValueError(
f"Threshold must be positive! Provided threshold is {outlier_threshold}"
)
y_true = infer_feature_types(y_true)
y_pred = infer_feature_types(y_pred)
predictions = y_pred.reset_index(drop=True)
actual = y_true.reset_index(drop=True)
data = pd.concat([pd.Series(predictions), pd.Series(actual)], axis=1)
data.columns = ["prediction", "actual"]
if outlier_threshold:
data["outlier"] = np.where(
(abs(data["prediction"] - data["actual"]) >= outlier_threshold),
"#ffff00",
"#0000ff",
)
else:
data["outlier"] = "#0000ff"
return data
[docs]def graph_prediction_vs_actual(y_true, y_pred, outlier_threshold=None):
"""Generate a scatter plot comparing the true and predicted values. Used for regression plotting.
Args:
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.
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if jupyter_check():
import_or_raise("ipywidgets", warning=True)
if outlier_threshold and outlier_threshold <= 0:
raise ValueError(
f"Threshold must be positive! Provided threshold is {outlier_threshold}"
)
df = get_prediction_vs_actual_data(y_true, y_pred, outlier_threshold)
data = []
x_axis = _calculate_axis_range(df["prediction"])
y_axis = _calculate_axis_range(df["actual"])
x_y_line = [min(x_axis[0], y_axis[0]), max(x_axis[1], y_axis[1])]
data.append(
_go.Scatter(x=x_y_line, y=x_y_line, name="y = x line", line_color="grey")
)
title = "Predicted vs Actual Values Scatter Plot"
layout = _go.Layout(
title={"text": title},
xaxis={"title": "Prediction", "range": x_y_line},
yaxis={"title": "Actual", "range": x_y_line},
)
for color, outlier_group in df.groupby("outlier"):
if outlier_threshold:
name = (
"< outlier_threshold" if color == "#0000ff" else ">= outlier_threshold"
)
else:
name = "Values"
data.append(
_go.Scatter(
x=outlier_group["prediction"],
y=outlier_group["actual"],
mode="markers",
marker=_go.scatter.Marker(color=color),
name=name,
)
)
return _go.Figure(layout=layout, data=data)
def _tree_parse(est, feature_names):
children_left = est.tree_.children_left
children_right = est.tree_.children_right
features = est.tree_.feature
thresholds = est.tree_.threshold
values = est.tree_.value
def recurse(i):
if children_left[i] == children_right[i]:
return {"Value": values[i]}
return OrderedDict(
{
"Feature": feature_names[features[i]],
"Threshold": thresholds[i],
"Value": values[i],
"Left_Child": recurse(children_left[i]),
"Right_Child": recurse(children_right[i]),
}
)
return recurse(0)
[docs]def decision_tree_data_from_estimator(estimator):
"""Return data for a fitted tree in a restructured format.
Args:
estimator (ComponentBase): A fitted DecisionTree-based estimator.
Returns:
OrderedDict: An OrderedDict of OrderedDicts describing a tree structure.
Raises:
ValueError: If estimator is not a decision tree-based estimator.
NotFittedError: If estimator is not yet fitted.
"""
if not estimator.model_family == ModelFamily.DECISION_TREE:
raise ValueError(
"Tree structure reformatting is only supported for decision tree estimators"
)
if not estimator._is_fitted:
raise NotFittedError(
"This DecisionTree estimator is not fitted yet. Call 'fit' with appropriate arguments "
"before using this estimator."
)
est = estimator._component_obj
feature_names = estimator.input_feature_names
return _tree_parse(est, feature_names)
[docs]def decision_tree_data_from_pipeline(pipeline_):
"""Return data for a fitted pipeline in a restructured format.
Args:
pipeline_ (PipelineBase): A pipeline with a DecisionTree-based estimator.
Returns:
OrderedDict: An OrderedDict of OrderedDicts describing a tree structure.
Raises:
ValueError: If estimator is not a decision tree-based estimator.
NotFittedError: If estimator is not yet fitted.
"""
if not pipeline_.model_family == ModelFamily.DECISION_TREE:
raise ValueError(
"Tree structure reformatting is only supported for decision tree estimators"
)
if not pipeline_._is_fitted:
raise NotFittedError(
"The DecisionTree estimator associated with this pipeline is not fitted yet. Call 'fit' "
"with appropriate arguments before using this estimator."
)
est = pipeline_.estimator._component_obj
feature_names = pipeline_.input_feature_names[pipeline_.estimator.name]
return _tree_parse(est, feature_names)
[docs]def visualize_decision_tree(
estimator, max_depth=None, rotate=False, filled=False, filepath=None
):
"""Generate an image visualizing the decision tree.
Args:
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:
graphviz.Source: DOT object that can be directly displayed in Jupyter notebooks.
Raises:
ValueError: If estimator is not a decision tree-based estimator.
NotFittedError: If estimator is not yet fitted.
"""
if not estimator.model_family == ModelFamily.DECISION_TREE:
raise ValueError(
"Tree visualizations are only supported for decision tree estimators"
)
if max_depth and (not isinstance(max_depth, int) or not max_depth >= 0):
raise ValueError(
"Unknown value: '{}'. The parameter max_depth has to be a non-negative integer".format(
max_depth
)
)
if not estimator._is_fitted:
raise NotFittedError(
"This DecisionTree estimator is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
)
est = estimator._component_obj
graphviz = import_or_raise(
"graphviz", error_msg="Please install graphviz to visualize trees."
)
graph_format = None
if filepath:
# Cast to str in case a Path object was passed in
filepath = str(filepath)
try:
f = open(filepath, "w")
f.close()
except (IOError, FileNotFoundError):
raise ValueError(
("Specified filepath is not writeable: {}".format(filepath))
)
path_and_name, graph_format = os.path.splitext(filepath)
if graph_format:
graph_format = graph_format[1:].lower() # ignore the dot
supported_filetypes = graphviz.FORMATS
if graph_format not in supported_filetypes:
raise ValueError(
(
"Unknown format '{}'. Make sure your format is one of the "
+ "following: {}"
).format(graph_format, supported_filetypes)
)
else:
graph_format = "pdf" # If the filepath has no extension default to pdf
dot_data = export_graphviz(
decision_tree=est,
max_depth=max_depth,
rotate=rotate,
filled=filled,
feature_names=estimator.input_feature_names,
)
source_obj = graphviz.Source(source=dot_data, format=graph_format)
if filepath:
source_obj.render(filename=path_and_name, cleanup=True)
return source_obj
[docs]def get_prediction_vs_actual_over_time_data(pipeline, X, y, X_train, y_train, dates):
"""Get the data needed for the prediction_vs_actual_over_time plot.
Args:
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:
pd.DataFrame: Predictions vs. time.
"""
dates = infer_feature_types(dates)
prediction = pipeline.predict_in_sample(X, y, X_train=X_train, y_train=y_train)
return pd.DataFrame(
{
"dates": dates.reset_index(drop=True),
"target": y.reset_index(drop=True),
"prediction": prediction.reset_index(drop=True),
}
)
[docs]def graph_prediction_vs_actual_over_time(pipeline, X, y, X_train, y_train, dates):
"""Plot the target values and predictions against time on the x-axis.
Args:
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:
plotly.Figure: Showing the prediction vs actual over time.
Raises:
ValueError: If the pipeline is not a time-series regression pipeline.
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if pipeline.problem_type != ProblemTypes.TIME_SERIES_REGRESSION:
raise ValueError(
"graph_prediction_vs_actual_over_time only supports time series regression pipelines! "
f"Received {str(pipeline.problem_type)}."
)
data = get_prediction_vs_actual_over_time_data(
pipeline, X, y, X_train, y_train, dates
)
data = [
_go.Scatter(
x=data["dates"],
y=data["target"],
mode="lines+markers",
name="Target",
line=dict(color="#1f77b4"),
),
_go.Scatter(
x=data["dates"],
y=data["prediction"],
mode="lines+markers",
name="Prediction",
line=dict(color="#d62728"),
),
]
# Let plotly pick the best date format.
layout = _go.Layout(
title={"text": "Prediction vs Target over time"},
xaxis={"title": "Time"},
yaxis={"title": "Target Values and Predictions"},
)
return _go.Figure(data=data, layout=layout)
[docs]def get_linear_coefficients(estimator, features=None):
"""Returns a dataframe showing the features with the greatest predictive power for a linear model.
Args:
estimator (Estimator): Fitted linear model family estimator.
features (list[str]): List of feature names associated with the underlying data.
Returns:
pd.DataFrame: Displaying the features by importance.
Raises:
ValueError: If the model is not a linear model.
NotFittedError: If the model is not yet fitted.
"""
if not estimator.model_family == ModelFamily.LINEAR_MODEL:
raise ValueError(
"Linear coefficients are only available for linear family models"
)
if not estimator._is_fitted:
raise NotFittedError(
"This linear estimator is not fitted yet. Call 'fit' with appropriate arguments "
"before using this estimator."
)
coef_ = estimator.feature_importance
coef_.name = "Coefficients"
coef_.index = features
coef_ = coef_.sort_values()
coef_ = pd.Series(estimator._component_obj.intercept_, index=["Intercept"]).append(
coef_
)
return coef_
[docs]def t_sne(
X,
n_components=2,
perplexity=30.0,
learning_rate=200.0,
metric="euclidean",
**kwargs,
):
"""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:
np.ndarray (n_samples, n_components): TSNE output.
Raises:
ValueError: If specified parameters are not valid values.
"""
if not isinstance(n_components, int) or not n_components > 0:
raise ValueError(
"The parameter n_components must be of type integer and greater than 0"
)
if not perplexity >= 0:
raise ValueError("The parameter perplexity must be non-negative")
X = infer_feature_types(X)
t_sne_ = TSNE(
n_components=n_components,
perplexity=perplexity,
learning_rate=learning_rate,
metric=metric,
**kwargs,
)
X_new = t_sne_.fit_transform(X)
return X_new
[docs]def graph_t_sne(
X,
n_components=2,
perplexity=30.0,
learning_rate=200.0,
metric="euclidean",
marker_line_width=2,
marker_size=7,
**kwargs,
):
"""Plot high dimensional data into lower dimensional space using t-SNE.
Args:
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:
plotly.Figure: Figure representing the transformed data.
Raises:
ValueError: If marker_line_width or marker_size are not valid values.
"""
_go = import_or_raise(
"plotly.graph_objects", error_msg="Cannot find dependency plotly.graph_objects"
)
if not marker_line_width >= 0:
raise ValueError("The parameter marker_line_width must be non-negative")
if not marker_size >= 0:
raise ValueError("The parameter marker_size must be non-negative")
X_embedded = t_sne(
X,
n_components=n_components,
perplexity=perplexity,
learning_rate=learning_rate,
metric=metric,
**kwargs,
)
fig = _go.Figure()
fig.add_trace(_go.Scatter(x=X_embedded[:, 0], y=X_embedded[:, 1], mode="markers"))
fig.update_traces(
mode="markers", marker_line_width=marker_line_width, marker_size=marker_size
)
fig.update_layout(title="t-SNE", yaxis_zeroline=False, xaxis_zeroline=False)
return fig
def _calculate_axis_range(arr):
"""Helper method to help calculate the appropriate range for an axis based on the data to graph."""
max_value = arr.max()
min_value = arr.min()
margins = abs(max_value - min_value) * 0.05
return [min_value - margins, max_value + margins]