def transform_fn(models, data, input_content_type, output_content_type):
"""
Transform a request using the Gluon model. Called once per request.
:param models: The Gluon model and the column info.
:param data: The request payload.
:param input_content_type: The request content type. ('text/csv')
:param output_content_type: The (desired) response content type. ('text/csv')
:return: response payload and content type.
"""
start = timer()
net = models[0]
column_dict = models[1]
label_map = net.class_labels_internal_map ###
# text/csv
if "text/csv" in input_content_type:
# Load dataset
columns = column_dict["columns"]
if type(data) == str:
# Load dataset
df = pd.read_csv(StringIO(data), header=None)
else:
df = pd.read_csv(StringIO(data.decode()), header=None)
df_preprosessed = preprocess(df, columns, net.label)
ds = TabularDataset(data=df_preprosessed)
try:
predictions = net.predict_proba(ds)
predictions_ = net.predict(ds)
except:
try:
predictions = net.predict_proba(ds.fillna(0.0))
predictions_ = net.predict(ds.fillna(0.0))
warnings.warn("Filled NaN's with 0.0 in order to predict.")
except Exception as e:
response_body = e
return response_body, output_content_type
# threshold = 0.5
# predictions_label = [[k for k, v in label_map.items() if v == 1][0] if i > threshold else [k for k, v in label_map.items() if v == 0][0] for i in predictions]
predictions_label = predictions_.tolist()
# Print prediction counts, limit in case of regression problem
pred_counts = Counter(predictions_label)
n_display_items = 30
if len(pred_counts) > n_display_items:
print(f"Top {n_display_items} prediction counts: "
f"{dict(take(n_display_items, pred_counts.items()))}")
else:
print(f"Prediction counts: {pred_counts}")
# Form response
output = StringIO()
pd.DataFrame(predictions).to_csv(output, header=False, index=False)
response_body = output.getvalue()
# If target column passed, evaluate predictions performance
target = net.label
if target in ds:
print(f"Label column ({target}) found in input data. "
"Therefore, evaluating prediction performance...")
try:
performance = net.evaluate_predictions(
y_true=ds[target],
y_pred=np.array(predictions_label),
auxiliary_metrics=True)
print(
json.dumps(performance,
indent=4,
default=pd.DataFrame.to_json))
time.sleep(0.1)
except Exception as e:
# Print exceptions on evaluate, continue to return predictions
print(f"Exception: {e}")
else:
raise NotImplementedError("content_type must be 'text/csv'")
elapsed_time = round(timer() - start, 3)
print(f"Elapsed time: {round(timer()-start,3)} seconds")
return response_body, output_content_type
def transform_fn(models, data, input_content_type, output_content_type):
"""
Transform a request using the Gluon model. Called once per request.
:param models: The Gluon model and the column info.
:param data: The request payload.
:param input_content_type: The request content type. ('text/csv')
:param output_content_type: The (desired) response content type. ('text/csv')
:return: response payload and content type.
"""
start = timer()
net = models[0]
column_dict = models[1]
# text/csv
if input_content_type == 'text/csv':
# Load dataset
columns = column_dict['columns']
df = pd.read_csv(StringIO(data), header=None)
df_preprosessed = preprocess(df, columns, net.label)
ds = TabularDataset(data=df_preprosessed)
try:
predictions = net.predict(ds)
except:
try:
predictions = net.predict(ds.fillna(0.0))
warnings.warn('Filled NaN\'s with 0.0 in order to predict.')
except Exception as e:
response_body = e
return response_body, output_content_type
# Print prediction counts, limit in case of regression problem
pred_counts = Counter(predictions.tolist())
n_display_items = 30
if len(pred_counts) > n_display_items:
print(f'Top {n_display_items} prediction counts: '
f'{dict(take(n_display_items, pred_counts.items()))}')
else:
print(f'Prediction counts: {pred_counts}')
# Form response
output = StringIO()
pd.DataFrame(predictions).to_csv(output, header=False, index=False)
response_body = output.getvalue()
# If target column passed, evaluate predictions performance
target = net.label
if target in ds:
print(f'Label column ({target}) found in input data. '
'Therefore, evaluating prediction performance...')
try:
performance = net.evaluate_predictions(y_true=ds[target],
y_pred=predictions,
auxiliary_metrics=True)
print(json.dumps(performance, indent=4, default=pd.DataFrame.to_json))
time.sleep(0.1)
except Exception as e:
# Print exceptions on evaluate, continue to return predictions
print(f'Exception: {e}')
else:
raise NotImplementedError("content_type must be 'text/csv'")
elapsed_time = round(timer()-start,3)
print(f'Elapsed time: {round(timer()-start,3)} seconds')
return response_body, output_content_type
