import numpy as np
import plotly.graph_objects as go
import sklearn.metrics
from IPython.display import display
from scipy import interp
from evalml.problem_types import ProblemTypes
class SearchIterationPlot():
def __init__(self, data, show_plot=True):
self.data = data
self.best_score_by_iter_fig = None
self.curr_iteration_scores = list()
self.best_iteration_scores = list()
title = 'Pipeline Search: Iteration vs. {}<br><sub>Gray marker indicates the score at current iteration</sub>'.format(self.data.objective.name)
data = [
go.Scatter(x=[], y=[], mode='lines+markers', name='Best Score'),
go.Scatter(x=[], y=[], mode='markers', name='Iter score', marker={'color': 'gray'})
]
layout = {
'title': title,
'xaxis': {
'title': 'Iteration',
'rangemode': 'tozero'
},
'yaxis': {
'title': 'Score'
}
}
self.best_score_by_iter_fig = go.FigureWidget(data, layout)
self.best_score_by_iter_fig.update_layout(showlegend=False)
self.update()
def update(self):
if len(self.data.results['search_order']) > 0 and len(self.data.results['pipeline_results']) > 0:
iter_idx = self.data.results['search_order']
pipeline_res = self.data.results['pipeline_results']
iter_scores = [pipeline_res[i]['score'] for i in iter_idx]
iter_score_pairs = zip(iter_idx, iter_scores)
iter_score_pairs = sorted(iter_score_pairs, key=lambda value: value[0])
sorted_iter_idx, sorted_iter_scores = zip(*iter_score_pairs)
# Create best score data
best_iteration_scores = list()
curr_best = None
for score in sorted_iter_scores:
if curr_best is None:
best_iteration_scores.append(score)
curr_best = score
else:
if self.data.objective.greater_is_better and score > curr_best \
or not self.data.objective.greater_is_better and score < curr_best:
best_iteration_scores.append(score)
curr_best = score
else:
best_iteration_scores.append(curr_best)
# Update entire line plot
best_score_trace = self.best_score_by_iter_fig.data[0]
best_score_trace.x = sorted_iter_idx
best_score_trace.y = best_iteration_scores
curr_score_trace = self.best_score_by_iter_fig.data[1]
curr_score_trace.x = sorted_iter_idx
curr_score_trace.y = sorted_iter_scores
class PipelineSearchPlots:
"""Plots for the AutoClassificationSearch/AutoRegressionSearch class.
"""
def __init__(self, data):
"""Make plots for the AutoClassificationSearch/AutoRegressionSearch class.
Args:
data (AutoClassificationSearch or AutoRegressionSearch): Automated pipeline search object
"""
self.data = data
def get_roc_data(self, pipeline_id):
"""Gets data that can be used to create a ROC plot.
Returns:
Dictionary containing metrics used to generate an ROC plot.
"""
if self.data.problem_type != ProblemTypes.BINARY:
raise RuntimeError("ROC plots can only be generated for binary classification problems.")
results = self.data.results['pipeline_results']
if len(results) == 0:
raise RuntimeError("You must first call search() to generate ROC data.")
if pipeline_id not in results:
raise RuntimeError("Pipeline {} not found".format(pipeline_id))
pipeline_results = results[pipeline_id]
cv_data = pipeline_results["cv_data"]
mean_fpr = np.linspace(0, 1, 100)
tprs = []
roc_aucs = []
fpr_tpr_data = []
for fold in cv_data:
fpr = fold["all_objective_scores"]["ROC"][0]
tpr = fold["all_objective_scores"]["ROC"][1]
tprs.append(interp(mean_fpr, fpr, tpr))
tprs[-1][0] = 0.0
roc_auc = sklearn.metrics.auc(fpr, tpr)
roc_aucs.append(roc_auc)
fpr_tpr_data.append((fpr, tpr))
mean_tpr = np.mean(tprs, axis=0)
mean_auc = sklearn.metrics.auc(mean_fpr, mean_tpr)
std_auc = np.std(roc_aucs)
roc_data = {"fpr_tpr_data": fpr_tpr_data,
"mean_fpr": mean_fpr,
"mean_tpr": mean_tpr,
"roc_aucs": roc_aucs,
"mean_auc": mean_auc,
"std_auc": std_auc}
return roc_data
def generate_roc_plot(self, pipeline_id):
"""Generate Receiver Operating Characteristic (ROC) plot for a given pipeline using cross-validation
using the data returned from get_roc_data().
Returns:
plotly.Figure representing the ROC plot generated
"""
roc_data = self.get_roc_data(pipeline_id)
fpr_tpr_data = roc_data["fpr_tpr_data"]
roc_aucs = roc_data["roc_aucs"]
mean_fpr = roc_data["mean_fpr"]
mean_tpr = roc_data["mean_tpr"]
mean_auc = roc_data["mean_auc"]
std_auc = roc_data["std_auc"]
results = self.data.results['pipeline_results']
pipeline_name = results[pipeline_id]["pipeline_name"]
layout = go.Layout(title={'text': 'Receiver Operating Characteristic of<br>{} w/ ID={}'.format(pipeline_name, pipeline_id)},
xaxis={'title': 'False Positive Rate', 'range': [-0.05, 1.05]},
yaxis={'title': 'True Positive Rate', 'range': [-0.05, 1.05]})
data = []
for fold_num, fold in enumerate(fpr_tpr_data):
fpr = fold[0]
tpr = fold[1]
roc_auc = roc_aucs[fold_num]
data.append(go.Scatter(x=fpr, y=tpr,
name='ROC fold %d (AUC = %0.2f)' % (fold_num, roc_auc),
mode='lines+markers'))
data.append(go.Scatter(x=mean_fpr, y=mean_tpr,
name='Mean ROC (AUC = %0.2f ± %0.2f)' % (mean_auc, std_auc),
line=dict(width=3)))
data.append(go.Scatter(x=[0, 1], y=[0, 1],
name='Chance',
line=dict(dash='dash')))
figure = go.Figure(layout=layout, data=data)
return figure
def get_confusion_matrix_data(self, pipeline_id):
"""Gets data that can be used to create a confusion matrix plot.
Returns:
List containing information used to generate a confusion matrix plot. Each element in the list contains the confusion matrix data for that fold.
"""
if self.data.problem_type not in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
raise RuntimeError("Confusion matrix plots can only be generated for classification problems.")
results = self.data.results['pipeline_results']
if len(results) == 0:
raise RuntimeError("You must first call search() to generate confusion matrix data.")
if pipeline_id not in results:
raise RuntimeError("Pipeline {} not found".format(pipeline_id))
pipeline_results = results[pipeline_id]
cv_data = pipeline_results["cv_data"]
confusion_matrix_data = []
for fold in cv_data:
confusion_matrix_data.append(fold["all_objective_scores"]["Confusion Matrix"])
return confusion_matrix_data
def generate_confusion_matrix(self, pipeline_id, fold_num=None):
"""Generate confusion matrix plot for a given pipeline using the data returned from get_confusion_matrix_data().
Returns:
plotly.Figure representing the confusion matrix plot generated
"""
data = self.get_confusion_matrix_data(pipeline_id)
results = self.data.results['pipeline_results']
pipeline_name = results[pipeline_id]["pipeline_name"]
# defaults to last fold if none specified. May need to think of better approach.
if fold_num is None:
fold_num = -1
conf_mat = data[fold_num]
labels = conf_mat.columns
layout = go.Layout(title={'text': 'Confusion matrix of<br>{} w/ ID={}'.format(pipeline_name, pipeline_id)},
xaxis={'title': 'Predicted Label', 'tickvals': labels},
yaxis={'title': 'True Label', 'tickvals': labels})
figure = go.Figure(data=go.Heatmap(x=labels, y=labels, z=conf_mat,
hovertemplate='<b>True</b>: %{y}' +
'<br><b>Predicted</b>: %{x}' +
'<br><b>Number of times</b>: %{z}' +
'<extra></extra>'), # necessary to remove unwanted trace info
layout=layout)
return figure
def search_iteration_plot(self, interactive_plot=False):
"""Shows a plot of the best score at each iteration using data gathered during training.
Returns:
plot
"""
if interactive_plot:
plot_obj = SearchIterationPlot(self.data)
display(plot_obj.best_score_by_iter_fig)
return plot_obj
else:
plot_obj = SearchIterationPlot(self.data)
return go.Figure(plot_obj.best_score_by_iter_fig)