Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Refactor _missing_values calculation #638

Merged
merged 2 commits into from
Jan 15, 2024
Merged

Refactor _missing_values calculation #638

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
316e832
Refactor _missing_values calculation
Zethson Jan 15, 2024
32cb8fd
Revert to deepcopy
Zethson Jan 15, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 12 additions & 14 deletions ehrapy/preprocessing/_quality_control.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,26 +65,24 @@ def qc_metrics(


def _missing_values(
arr: np.ndarray, shape: tuple[int, int] = None, df_type: Literal["obs", "var"] = "obs"
arr: np.ndarray, mode: Literal["abs", "pct"] = "abs", df_type: Literal["obs", "var"] = "obs"
) -> np.ndarray:
"""Calculates the absolute or relative amount of missing values.

Args:
arr: Numpy array containing a data row which is a subset of X (mtx).
shape: Shape of X (mtx).
mode: Whether to calculate absolute or percentage of missing values. Defaults to `"abs"`.
df_type: Whether to calculate the proportions for obs or var. One of 'obs' or 'var'. Defaults to 'obs' .

Returns:
Absolute or relative amount of missing values.
"""
if shape is None:
return pd.isnull(arr).sum()
else:
n_rows, n_cols = shape
if df_type == "obs":
return (pd.isnull(arr).sum() / n_cols) * 100
else:
return (pd.isnull(arr).sum() / n_rows) * 100
num_missing = pd.isnull(arr).sum()
if mode == "abs":
return num_missing
elif mode == "pct":
total_elements = arr.shape[0] if df_type == "obs" else len(arr)
return (num_missing / total_elements) * 100


def _obs_qc_metrics(
Expand Down Expand Up @@ -118,8 +116,8 @@ def _obs_qc_metrics(
)
)

obs_metrics["missing_values_abs"] = np.apply_along_axis(_missing_values, 1, mtx)
obs_metrics["missing_values_pct"] = np.apply_along_axis(_missing_values, 1, mtx, shape=mtx.shape, df_type="obs")
obs_metrics["missing_values_abs"] = np.apply_along_axis(_missing_values, 1, mtx, mode="abs")
obs_metrics["missing_values_pct"] = np.apply_along_axis(_missing_values, 1, mtx, mode="pct", df_type="obs")

# Specific QC metrics
for qc_var in qc_vars:
Expand Down Expand Up @@ -164,8 +162,8 @@ def _var_qc_metrics(adata: AnnData, layer: str = None) -> pd.DataFrame:
categorical_indices = np.concatenate([categorical_indices, index])
non_categorical_indices = np.ones(mtx.shape[1], dtype=bool)
non_categorical_indices[categorical_indices] = False
var_metrics["missing_values_abs"] = np.apply_along_axis(_missing_values, 0, mtx)
var_metrics["missing_values_pct"] = np.apply_along_axis(_missing_values, 0, mtx, shape=mtx.shape, df_type="var")
var_metrics["missing_values_abs"] = np.apply_along_axis(_missing_values, 0, mtx, mode="abs")
var_metrics["missing_values_pct"] = np.apply_along_axis(_missing_values, 0, mtx, mode="pct", df_type="var")

var_metrics["mean"] = np.nan
var_metrics["median"] = np.nan
Expand Down
Loading