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@simonfqy
simonfqy authored May 9, 2018
1 parent dc376a2 commit c3de9b9
Showing 1 changed file with 43 additions and 9 deletions.
52 changes: 43 additions & 9 deletions dcCustom/utils/evaluate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,8 @@ def compute_model_performance(self,
# No longer need to plot. Could be wasting time calculating metrics again, but they
# are super fast so it is no big deal.
multitask_scores[metric.name], computed_metrics = metric.compute_metric(
y, y_pred, w, per_task_metrics=True, plot=False)
y, y_pred, w, per_task_metrics=True, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)
all_task_scores[metric.name] = computed_metrics

# Now deal with validation or test sets.
Expand All @@ -151,7 +152,8 @@ def compute_model_performance(self,
plot_finished = True
else: # Otherwise don't need to plot.
multitask_scores[metric.name], computed_metrics = metric.compute_metric(
y, y_pred, w, per_task_metrics=True, plot=False)
y, y_pred, w, per_task_metrics=True, plot=False, is_training_set=self.is_training_set,
tasks=self.tasks, model_name=self.model_name)
all_task_scores[metric.name] = computed_metrics

else:
Expand All @@ -167,7 +169,8 @@ def compute_model_performance(self,
plot_finished = True
else:
multitask_scores[metric.name] = metric.compute_metric(
y, y_pred, w, per_task_metrics=False, plot=False)
y, y_pred, w, per_task_metrics=False, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)

elif plot and (i == len(metrics)-1 or metric.metric.__name__ =="concordance_index") and (
not plot_finished):
Expand All @@ -177,7 +180,8 @@ def compute_model_performance(self,
plot_finished = True
else:
multitask_scores[metric.name] = metric.compute_metric(
y, y_pred, w, per_task_metrics=False, plot=False)
y, y_pred, w, per_task_metrics=False, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)

if stats_out is not None:
log("Saving stats to %s" % stats_out, self.verbose)
Expand All @@ -201,6 +205,7 @@ def __init__(self,
generator,
transformers,
labels,
dataset=None,
outputs=None,
n_tasks=1,
n_classes=2,
Expand Down Expand Up @@ -230,6 +235,8 @@ def __init__(self,
self.generator = generator
self.n_tasks = n_tasks
self.n_classes = n_classes
# I added the dataset here to implement output_predictions() function easier.
self.dataset = dataset
self.output_transformers = [
transformer for transformer in transformers if transformer.transform_y
]
Expand All @@ -245,7 +252,26 @@ def __init__(self,
if len(self.label_keys) != len(self.output_keys):
raise ValueError("Must have same number of labels and outputs")

def compute_model_performance(self, metrics, per_task_metrics=False,
# TODO: the following function needs revision to work properly.
def output_predictions(self, y_preds, csv_out):
"""
Writes predictions to file.
Args:
y_preds: np.ndarray
csvfile: Open file object.
"""
mol_ids = self.dataset.ids
y_preds = np.reshape(y_preds, (len(y_preds), self.n_tasks))
assert len(y_preds) == len(mol_ids)
with open(csv_out, "w") as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(["Compound"] + self.dataset.get_task_names())
for mol_id, y_pred in zip(mol_ids, y_preds):
csvwriter.writerow([mol_id] + list(y_pred))

def compute_model_performance(self, metrics, csv_out=None, stats_out=None,
per_task_metrics=False,
no_concordance_index=False, plot=False):
"""
Computes statistics of model on test data and saves results to csv.
Expand Down Expand Up @@ -296,6 +322,10 @@ def generator_closure():
w = np.array(w)
w = np.reshape(w, newshape=y.shape)

if csv_out is not None:
log("Saving predictions to %s" % csv_out, self.verbose)
self.output_predictions(y_pred, csv_out)

plot_finished = False
# Compute multitask metrics
for i, metric in enumerate(metrics):
Expand All @@ -315,7 +345,8 @@ def generator_closure():
plot_finished = True
else:
multitask_scores[metric.name], computed_metrics = metric.compute_metric(
y, y_pred, w, per_task_metrics=True, n_classes=self.n_classes, plot=False)
y, y_pred, w, per_task_metrics=True, n_classes=self.n_classes, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)
all_task_scores[metric.name] = computed_metrics

elif plot and (i == len(metrics)-1 or metric.metric.__name__ =="concordance_index") and (
Expand All @@ -329,7 +360,8 @@ def generator_closure():

else: #Otherwise don't need to plot.
multitask_scores[metric.name], computed_metrics = metric.compute_metric(
y, y_pred, w, per_task_metrics=True, n_classes=self.n_classes, plot=False)
y, y_pred, w, per_task_metrics=True, n_classes=self.n_classes, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)
all_task_scores[metric.name] = computed_metrics

else:
Expand All @@ -346,7 +378,8 @@ def generator_closure():
plot_finished = True
else:
multitask_scores[metric.name] = metric.compute_metric(
y, y_pred, w, per_task_metrics=False, n_classes=self.n_classes, plot=False)
y, y_pred, w, per_task_metrics=False, n_classes=self.n_classes, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)

elif plot and (i == len(metrics)-1 or metric.metric.__name__ =="concordance_index") and (
not plot_finished):
Expand All @@ -358,7 +391,8 @@ def generator_closure():

else: #Otherwise don't need to plot.
multitask_scores[metric.name]= metric.compute_metric(
y, y_pred, w, per_task_metrics=False, n_classes=self.n_classes, plot=False)
y, y_pred, w, per_task_metrics=False, n_classes=self.n_classes, plot=False,
is_training_set=self.is_training_set, tasks=self.tasks, model_name=self.model_name)

if not per_task_metrics:
return multitask_scores
Expand Down

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