Merge pull request #41 from sunlabuiuc/develop

(Redo) Improved doc & logging

pyhealth/__init__.py

@@ -10,7 +10,10 @@
  os.makedirs(BASE_CACHE_PATH)
 
 # logging
-logger = logging.getLogger()
+logger = logging.getLogger(__name__)
+logger.propagate = False
 logger.setLevel(logging.INFO)
 handler = logging.StreamHandler()
+formatter = logging.Formatter("%(message)s")
+handler.setFormatter(formatter)
 logger.addHandler(handler)

pyhealth/datasets/base_dataset.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from abc import ABC, abstractmethod
+from abc import ABC
 from collections import Counter
 from copy import deepcopy
 from typing import Dict, Callable, Tuple, Union, List, Optional
@@ -15,6 +15,8 @@
 from pyhealth.medcode import CrossMap
 from pyhealth.utils import load_pickle, save_pickle
 
+logger = logging.getLogger(__name__)
+
 INFO_MSG = """
 dataset.patients: patient_id -> <Patient>
 
@@ -117,18 +119,19 @@ def __init__(
  # check if cache exists or refresh_cache is True
  if os.path.exists(self.filepath) and (not refresh_cache):
  # load from cache
- logging.debug(f"Loaded {self.dataset_name} base dataset from {self.filepath}")
+ logger.debug(
+ f"Loaded {self.dataset_name} base dataset from {self.filepath}")
  self.patients = load_pickle(self.filepath)
  else:
  # load from raw data
- logging.debug(f"Processing {self.dataset_name} base dataset...")
+ logger.debug(f"Processing {self.dataset_name} base dataset...")
  # parse tables
  patients = self.parse_tables()
  # convert codes
  patients = self._convert_code_in_patient_dict(patients)
  self.patients = patients
  # save to cache
- logging.debug(f"Saved {self.dataset_name} base dataset to {self.filepath}")
+ logger.debug(f"Saved {self.dataset_name} base dataset to {self.filepath}")
  save_pickle(self.patients, self.filepath)
 
  def _load_code_mapping_tools(self) -> Dict[str, CrossMap]:
@@ -535,6 +538,7 @@ def stat(self) -> str:
  def base_stat(self) -> str:
  """Returns some statistics of the base dataset."""
  lines = list()
+ lines.append("")
  lines.append(f"Statistics of {self.dataset_name} dataset (dev={self.dev}):")
  lines.append(f"\t- Number of patients: {len(self.patients)}")
  num_visits = [len(p) for p in self.patients.values()]
@@ -550,6 +554,7 @@ def base_stat(self) -> str:
  f"\t- Number of events per visit in {table}: "
  f"{sum(num_events) / len(num_events):.4f}"
  )
+ lines.append("")
  print("\n".join(lines))
  return "\n".join(lines)
 
@@ -558,6 +563,7 @@ def task_stat(self) -> str:
  if self.task is None:
  raise ValueError("Please set task first.")
  lines = list()
+ lines.append("")
  lines.append(f"Statistics of {self.task} task:")
  lines.append(f"\t- Dataset: {self.dataset_name} (dev={self.dev})")
  lines.append(f"\t- Number of samples: {len(self)}")
@@ -596,6 +602,7 @@ def task_stat(self) -> str:
  top10 = sorted(distribution.items(), key=lambda x: x[1], reverse=True)[:10]
  lines.append(
  f"\t\t- Distribution of {key} (Top-10): {top10}")
+ lines.append("")
  print("\n".join(lines))
  return "\n".join(lines)
 
@@ -604,6 +611,7 @@ def info():
  """Prints the output format."""
  print(INFO_MSG)
 
+
 class SampleDataset(ABC, Dataset):
  """Abstract sample dataset class.
 
@@ -668,7 +676,7 @@ def _index_visit(self) -> Dict[str, List[int]]:
  for idx, sample in enumerate(self.samples):
  visit_to_index.setdefault(sample["visit_id"], []).append(idx)
  return visit_to_index
- 
+
  def get_all_tokens(
  self,
  key: str,
@@ -782,7 +790,7 @@ def get_distribution_tokens(self, key: str) -> Dict[str, int]:
  Returns:
  distribution: a dict mapping token to count.
  """
- 
+
  tokens = self.get_all_tokens(key, remove_duplicates=False, sort=False)
  counter = Counter(tokens)
  return counter
@@ -856,36 +864,36 @@ def stat(self) -> None:
  ]
  samples2 = [
  {'patient_id': 'patient-0',
-  'visit_id': 'visit-0',
-  'conditions': ['cond-33',
- 'cond-86',
- 'cond-80'],
-  'procedures': ['prod-11',
- 'prod-8',
- 'prod-15',
- 'prod-66',
- 'prod-91',
- 'prod-94'],
-  'label': 1},
+ 'visit_id': 'visit-0',
+ 'conditions': ['cond-33',
+  'cond-86',
+  'cond-80'],
+ 'procedures': ['prod-11',
+  'prod-8',
+  'prod-15',
+  'prod-66',
+  'prod-91',
+  'prod-94'],
+ 'label': 1},
  {'patient_id': 'patient-0',
-  'visit_id': 'visit-0',
-  'conditions': ['cond-33',
- 'cond-86',
- 'cond-80'],
-  'procedures': ['prod-11',
- 'prod-8',
- 'prod-15',
- 'prod-66',
- 'prod-91',
- 'prod-94'],
-  'label': 1}
+ 'visit_id': 'visit-0',
+ 'conditions': ['cond-33',
+  'cond-86',
+  'cond-80'],
+ 'procedures': ['prod-11',
+  'prod-8',
+  'prod-15',
+  'prod-66',
+  'prod-91',
+  'prod-94'],
+ 'label': 1}
  ]
- 
+
  dataset = SampleDataset(
  samples=samples2,
  dataset_name="test")
- 
- print (dataset.stat())
+
+ print(dataset.stat())
  data = iter(dataset)
- print (next(data))
- print (next(data))
+ print(next(data))
+ print(next(data))

pyhealth/medcode/cross_map.py

@@ -8,6 +8,8 @@
 from pyhealth.medcode.utils import MODULE_CACHE_PATH, download_and_read_csv
 from pyhealth.utils import load_pickle, save_pickle
 
+logger = logging.getLogger(__name__)
+
 
 class CrossMap:
  """Contains mapping between two medical code systems.
@@ -30,11 +32,11 @@ def __init__(
  pickle_filename = f"{self.s_vocab}_to_{self.t_vocab}.pkl"
  pickle_filepath = os.path.join(MODULE_CACHE_PATH, pickle_filename)
  if os.path.exists(pickle_filepath) and (not refresh_cache):
- logging.debug(f"Loaded {self.s_vocab}->{self.t_vocab} mapping "
+ logger.debug(f"Loaded {self.s_vocab}->{self.t_vocab} mapping "
  f"from {pickle_filepath}")
  self.mapping = load_pickle(pickle_filepath)
  else:
- logging.debug(f"Processing {self.s_vocab}->{self.t_vocab} mapping...")
+ logger.debug(f"Processing {self.s_vocab}->{self.t_vocab} mapping...")
  try:
  local_filename = f"{self.s_vocab}_to_{self.t_vocab}.csv"
  df = download_and_read_csv(local_filename, refresh_cache)
@@ -44,7 +46,7 @@ def __init__(
  self.mapping = defaultdict(list)
  for _, row in df.iterrows():
  self.mapping[row[self.s_vocab]].append(row[self.t_vocab])
- logging.debug(f"Saved {self.s_vocab}->{self.t_vocab} mapping "
+ logger.debug(f"Saved {self.s_vocab}->{self.t_vocab} mapping "
  f"to {pickle_filepath}")
  save_pickle(self.mapping, pickle_filepath)
 

pyhealth/medcode/inner_map.py

@@ -10,6 +10,8 @@
 from pyhealth.medcode.utils import MODULE_CACHE_PATH, download_and_read_csv
 from pyhealth.utils import load_pickle, save_pickle
 
+logger = logging.getLogger(__name__)
+
 
 # TODO: add this callable method: InnerMap(vocab)
 class InnerMap(ABC):
@@ -22,7 +24,7 @@ class InnerMap(ABC):
  Note:
  This class cannot be instantiated using `__init__()` (throws an error).
  """
- 
+
  @abstractmethod
  def __init__(
  self,
@@ -36,10 +38,10 @@ def __init__(
  pickle_filepath = os.path.join(MODULE_CACHE_PATH, self.vocabulary + ".pkl")
  csv_filename = self.vocabulary + ".csv"
  if os.path.exists(pickle_filepath) and (not refresh_cache):
- logging.debug(f"Loaded {vocabulary} code from {pickle_filepath}")
+ logger.debug(f"Loaded {vocabulary} code from {pickle_filepath}")
  self.graph = load_pickle(pickle_filepath)
  else:
- logging.debug(f"Processing {vocabulary} code...")
+ logger.debug(f"Processing {vocabulary} code...")
  df = download_and_read_csv(csv_filename, refresh_cache)
  # create graph
  df = df.set_index("code")
@@ -54,7 +56,7 @@ def __init__(
  if "parent_code" in row:
  if not pd.isna(row["parent_code"]):
  self.graph.add_edge(row["parent_code"], code)
- logging.debug(f"Saved {vocabulary} code to {pickle_filepath}")
+ logger.debug(f"Saved {vocabulary} code to {pickle_filepath}")
  save_pickle(self.graph, pickle_filepath)
  return
 
@@ -170,11 +172,11 @@ def get_descendants(self, code: str) -> List[str]:
  )
  return descendants
 
+
 if __name__ == "__main__":
  icd9cm = InnerMap.load("ICD9CM")
- print (icd9cm.stat())
- print ("428.0" in icd9cm)
- print (icd9cm.lookup("4280"))
- print (icd9cm.get_ancestors("428.0"))
- print (icd9cm.get_descendants("428.0"))
-
+ print(icd9cm.stat())
+ print("428.0" in icd9cm)
+ print(icd9cm.lookup("4280"))
+ print(icd9cm.get_ancestors("428.0"))
+ print(icd9cm.get_descendants("428.0"))

pyhealth/metrics/binary.py

@@ -1,4 +1,5 @@
 from typing import List, Optional, Dict
+
 import numpy as np
 import sklearn.metrics as sklearn_metrics
 
@@ -9,6 +10,43 @@ def binary_metrics_fn(
  metrics: Optional[List[str]] = None,
  threshold: float = 0.5,
 ) -> Dict[str, float]:
+ """Computes metrics for binary classification.
+
+ User can specify which metrics to compute by passing a list of metric names.
+ The accepted metric names are:
+ - pr_auc: area under the precision-recall curve
+ - roc_auc: area under the receiver operating characteristic curve
+ - accuracy: accuracy score
+ - balanced_accuracy: balanced accuracy score (usually used for imbalanced
+ datasets)
+ - f1: f1 score
+ - precision: precision score
+ - recall: recall score
+ - cohen_kappa: Cohen's kappa score
+ - jaccard: Jaccard similarity coefficient score
+ If no metrics are specified, pr_auc, roc_auc and f1 are computed by default.
+
+ This function calls sklearn.metrics functions to compute the metrics. For
+ more information on the metrics, please refer to the documentation of the
+ corresponding sklearn.metrics functions.
+
+ Args:
+ y_true: True target values of shape (n_samples,).
+ y_prob: Predicted probabilities of shape (n_samples,).
+ metrics: List of metrics to compute. Default is ["pr_auc", "roc_auc", "f1"].
+ threshold: Threshold for binary classification. Default is 0.5.
+
+ Returns:
+ Dictionary of metrics whose keys are the metric names and values are
+ the metric values.
+
+ Examples:
+ >>> from pyhealth.metrics import binary_metrics_fn
+ >>> y_true = np.array([0, 0, 1, 1])
+ >>> y_prob = np.array([0.1, 0.4, 0.35, 0.8])
+ >>> binary_metrics_fn(y_true, y_prob, metrics=["accuracy"])
+ {'accuracy': 0.75}
+ """
  if metrics is None:
  metrics = ["pr_auc", "roc_auc", "f1"]
 

pyhealth/metrics/multiclass.py

@@ -9,6 +9,55 @@ def multiclass_metrics_fn(
  y_prob: np.ndarray,
  metrics: Optional[List[str]] = None,
 ) -> Dict[str, float]:
+ """Computes metrics for multiclass classification.
+
+ User can specify which metrics to compute by passing a list of metric names.
+ The accepted metric names are:
+ - roc_auc_macro_ovo: area under the receiver operating characteristic curve,
+ macro averaged over one-vs-one multiclass classification
+ - roc_auc_macro_ovr: area under the receiver operating characteristic curve,
+ macro averaged over one-vs-rest multiclass classification
+ - roc_auc_weighted_ovo: area under the receiver operating characteristic curve,
+ weighted averaged over one-vs-one multiclass classification
+ - roc_auc_weighted_ovr: area under the receiver operating characteristic curve,
+ weighted averaged over one-vs-rest multiclass classification
+ - accuracy: accuracy score
+ - balanced_accuracy: balanced accuracy score (usually used for imbalanced
+ datasets)
+ - f1_micro: f1 score, micro averaged
+ - f1_macro: f1 score, macro averaged
+ - f1_weighted: f1 score, weighted averaged
+ - jaccard_micro: Jaccard similarity coefficient score, micro averaged
+ - jaccard_macro: Jaccard similarity coefficient score, macro averaged
+ - jaccard_weighted: Jaccard similarity coefficient score, weighted averaged
+ - cohen_kappa: Cohen's kappa score
+ If no metrics are specified, accuracy, f1_macro, and f1_micro are computed
+ by default.
+
+ This function calls sklearn.metrics functions to compute the metrics. For
+ more information on the metrics, please refer to the documentation of the
+ corresponding sklearn.metrics functions.
+
+ Args:
+ y_true: True target values of shape (n_samples,).
+ y_prob: Predicted probabilities of shape (n_samples, n_classes).
+ metrics: List of metrics to compute. Default is ["accuracy", "f1_macro",
+ "f1_micro"].
+
+ Returns:
+ Dictionary of metrics whose keys are the metric names and values are
+ the metric values.
+
+ Examples:
+ >>> from pyhealth.metrics import multiclass_metrics_fn
+ >>> y_true = np.array([0, 1, 2, 2])
+ >>> y_prob = np.array([[0.9, 0.05, 0.05],
+ ... [0.05, 0.9, 0.05],
+ ... [0.05, 0.05, 0.9],
+ ... [0.6, 0.2, 0.2]])
+ >>> multiclass_metrics_fn(y_true, y_prob, metrics=["accuracy"])
+ {'accuracy': 0.75}
+ """
  if metrics is None:
  metrics = ["accuracy", "f1_macro", "f1_micro"]