Address PR comments and pin cryptography

InnerEye/ML/configs/classification/CovidModel.py

@@ -248,6 +248,12 @@ def generate_custom_report(self, report_dir: Path, model_proc: ModelProcessing)
 
  label_prefix = LoggingColumns.Label.value
  output_prefix = LoggingColumns.ModelOutput.value
+ label_key_cvx03vs12 = f"{label_prefix}_CVX03vs12"
+ output_key_cvx03vs12 = f"{output_prefix}_CVX03vs12"
+ label_key_cvx0vs3 = f"{label_prefix}_CVX0vs3"
+ output_key_cvx0vs3 = f"{output_prefix}_CVX0vs3"
+ label_key_cvx1vs2 = f"{label_prefix}_CVX1vs2"
+ output_key_cvx1vs2 = f"{output_prefix}_CVX1vs2"
 
  def get_output_csv_path(mode: ModelExecutionMode) -> Path:
  p = get_best_epoch_results_path(mode=mode, model_proc=model_proc)
@@ -266,29 +272,29 @@ def get_labels_and_predictions(df: pd.DataFrame) -> pd.DataFrame:
  labels.append(target_df[label_prefix].item())
 
  pred_cvx03vs12 = predictions[1] + predictions[2]
- label_cvx03vs12 = 1 if labels[1] or labels[2] else 0
+ label_cvx03vs12 = 1 if int(labels[1] or labels[2]) else 0
 
  if (predictions[0] + predictions[3]) != 0:
  pred_cvx0vs3 = predictions[3] / (predictions[0] + predictions[3])
  else:
  pred_cvx0vs3 = np.NaN
- label_cvx0vs3 = 0 if labels[0] else 1 if labels[3] else np.NaN
+ label_cvx0vs3 = 0 if labels[0] else (1 if labels[3] else np.NaN)
 
  if (predictions[1] + predictions[2]) != 0:
  pred_cvx1vs2 = predictions[2] / (predictions[1] + predictions[2])
  else:
  pred_cvx1vs2 = np.NaN
- label_cvx1vs2 = 0 if labels[1] else 1 if labels[2] else np.NaN
+ label_cvx1vs2 = 0 if labels[1] else (1 if labels[2] else np.NaN)
 
  return pd.DataFrame.from_dict({LoggingColumns.Patient.value: [df.iloc[0][LoggingColumns.Patient.value]],
  output_prefix: [np.argmax(predictions)],
  label_prefix: [np.argmax(labels)],
- f"{output_prefix}_CVX03vs12": pred_cvx03vs12,
- f"{label_prefix}_CVX03vs12": label_cvx03vs12,
- f"{output_prefix}_CVX0vs3": pred_cvx0vs3,
- f"{label_prefix}_CVX0vs3": label_cvx0vs3,
- f"{output_prefix}_CVX1vs2": pred_cvx1vs2,
- f"{label_prefix}_CVX1vs2": label_cvx1vs2})
+ output_key_cvx03vs12: pred_cvx03vs12,
+ label_key_cvx03vs12: label_cvx03vs12,
+ output_key_cvx0vs3: pred_cvx0vs3,
+ label_key_cvx0vs3: label_cvx0vs3,
+ output_key_cvx1vs2: pred_cvx1vs2,
+ label_key_cvx1vs2: label_cvx1vs2})
 
  def get_per_task_output_and_labels(df: pd.DataFrame) -> pd.DataFrame:
  df = df.groupby(LoggingColumns.Patient.value, as_index=False).apply(get_labels_and_predictions).reset_index(drop=True)
@@ -300,27 +306,27 @@ def compute_binary_accuracy(model_outputs: pd.Series, labels: pd.Series) -> floa
  return ((model_outputs[non_nan_indices] > .5) == labels[non_nan_indices]).mean()
 
  outputs_and_labels = get_per_task_output_and_labels(df)
- cvx03vs12_indices = (outputs_and_labels[f"{label_prefix}_CVX03vs12"] == 1)
- cvx03vs12_accuracy = compute_binary_accuracy(model_outputs=outputs_and_labels[f"{output_prefix}_CVX03vs12"],
- labels=outputs_and_labels[f"{label_prefix}_CVX03vs12"])
+ cvx03vs12_indices = (outputs_and_labels[label_key_cvx03vs12] == 1)
+ cvx03vs12_accuracy = compute_binary_accuracy(model_outputs=outputs_and_labels[output_key_cvx03vs12],
+ labels=outputs_and_labels[label_key_cvx03vs12])
  cvx0vs3_outputs_and_labels = outputs_and_labels[~cvx03vs12_indices]
- cvx0vs3_accuracy = compute_binary_accuracy(model_outputs=cvx0vs3_outputs_and_labels[f"{output_prefix}_CVX0vs3"],
- labels=cvx0vs3_outputs_and_labels[f"{label_prefix}_CVX0vs3"])
+ cvx0vs3_accuracy = compute_binary_accuracy(model_outputs=cvx0vs3_outputs_and_labels[output_key_cvx0vs3],
+ labels=cvx0vs3_outputs_and_labels[label_key_cvx0vs3])
  cvx1vs2_outputs_and_labels = outputs_and_labels[cvx03vs12_indices]
- cvx1vs2_accuracy = compute_binary_accuracy(model_outputs=cvx1vs2_outputs_and_labels[f"{output_prefix}_CVX1vs2"],
- labels=cvx1vs2_outputs_and_labels[f"{label_prefix}_CVX1vs2"])
+ cvx1vs2_accuracy = compute_binary_accuracy(model_outputs=cvx1vs2_outputs_and_labels[output_key_cvx1vs2],
+ labels=cvx1vs2_outputs_and_labels[label_key_cvx1vs2])
  multiclass_acc = (outputs_and_labels[output_prefix] == outputs_and_labels[label_prefix]).mean() # type: ignore
 
  report_section_text = f"{data_split.value}\n"
  report_section_text += f"CVX03vs12 Accuracy: {cvx03vs12_accuracy:.4f}\n"
 
  report_section_text += f"CVX0vs3 Accuracy: {cvx0vs3_accuracy:.4f}\n"
- nan_in_cvx0vs3 = cvx0vs3_outputs_and_labels[f"{output_prefix}_CVX0vs3"].isna().sum()
+ nan_in_cvx0vs3 = cvx0vs3_outputs_and_labels[output_key_cvx0vs3].isna().sum()
  if nan_in_cvx0vs3 > 0:
  report_section_text += f"Warning: CVX0vs3 accuracy was computed skipping {nan_in_cvx0vs3} NaN model outputs.\n"
 
  report_section_text += f"CVX1vs2 Accuracy: {cvx1vs2_accuracy:.4f}\n"
- nan_in_cvx1vs2 = cvx1vs2_outputs_and_labels[f"{output_prefix}_CVX1vs2"].isna().sum()
+ nan_in_cvx1vs2 = cvx1vs2_outputs_and_labels[output_key_cvx1vs2].isna().sum()
  if nan_in_cvx1vs2 > 0:
  report_section_text += f"Warning: CVX1vs2 accuracy was computed skipping {nan_in_cvx1vs2} NaN model outputs.\n"
 

environment.yml

@@ -16,6 +16,7 @@ dependencies:
  - azureml-sdk==1.23.0
  - azureml-tensorboard==1.23.0
  - conda-merge==0.1.5
+ - cryptography==3.3.2
  - dataclasses-json==0.5.2
  - docker==4.3.1
  - flake8==3.8.3