"""Standard metrics used for model understanding."""
import warnings
import numpy as np
import pandas as pd
from scipy.stats import chisquare, kstest, wilcoxon
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.utils.multiclass import unique_labels
from evalml.exceptions import NoPositiveLabelException
from evalml.problem_types import is_classification, is_regression, is_time_series
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_ww = infer_feature_types(y_true)
y_predicted = infer_feature_types(y_predicted)
labels = unique_labels(y_true_ww, y_predicted)
conf_mat = sklearn_confusion_matrix(y_true_ww, 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 pd.DataFrame 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_ww = infer_feature_types(y_true)
y_pred_proba = infer_feature_types(y_pred_proba)
# Standardize data to be a DataFrame even for binary target
if isinstance(y_pred_proba, pd.Series):
y_pred_proba = pd.DataFrame(y_pred_proba)
# Only use one column for binary inputs that are still a DataFrame
elif y_pred_proba.shape[1] == 2:
y_pred_proba = pd.DataFrame(y_pred_proba.iloc[:, 1])
nan_indices = np.logical_or(pd.isna(y_true_ww), pd.isna(y_pred_proba).any(axis=1))
y_true_ww = y_true_ww[~nan_indices]
y_pred_proba = y_pred_proba[~nan_indices]
lb = LabelBinarizer()
lb.fit(y_true_ww)
# label binarizer will output a numpy array
y_one_hot_true_np = lb.transform(y_true_ww)
n_classes = y_one_hot_true_np.shape[1]
curve_data = []
for i in range(n_classes):
fpr_rates, tpr_rates, thresholds = sklearn_roc_curve(
y_one_hot_true_np[:, i],
y_pred_proba.iloc[:, 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 check_distribution(y_true, y_pred, problem_type, threshold=0.1):
"""Determines if the distribution of the predicted data is likely to match that of the ground truth data.
Will use a different statistical test based on the given problem type:
- Classification (Binary or Multiclass) - chi squared test
- Regression - Kolmogorov-Smirnov test
- Time Series Regression - Wilcoxon signed-rank test
Args:
y_true (pd.Series): The ground truth data.
y_pred (pd.Series): Predictions from a pipeline.
problem_type (str or ProblemType): The pipeline's problem type, used to determine the method.
threshold (float): The threshold for the p value where we choose to accept or reject the null hypothesis.
Should be between 0 and 1, non-inclusive. Defaults to 0.1.
Returns:
int: 0 if the distribution of predicted values is not likely to match the true distribution, 1 if it is.
"""
if is_classification(problem_type):
true_value_counts = y_true.value_counts()
pred_value_counts = y_pred.value_counts()
# Prevents an error in the baseline case where only one class is predicted
if len(true_value_counts) != len(pred_value_counts):
return 0
p_value = chisquare(pred_value_counts, f_exp=true_value_counts).pvalue
elif is_time_series(problem_type):
p_value = wilcoxon(y_true, y_pred).pvalue
elif is_regression(problem_type):
p_value = kstest(y_true, y_pred).pvalue
return 0 if p_value < threshold else 1