Quick fix to precision recall

README.md

@@ -63,6 +63,7 @@ Currently, five metric operations are supported:
 3. mp-value
 4. Grit
 5. Enrichment
+6. Hit@k
 
 ## Demos
 

cytominer_eval/evaluate.py

@@ -144,6 +144,7 @@ def evaluate(
         metric_result = precision_recall(
             similarity_melted_df=similarity_melted_df,
             replicate_groups=replicate_groups,
+            groupby_columns=groupby_columns,
             k=precision_recall_k,
         )
     elif operation == "grit":

cytominer_eval/operations/precision_recall.py

@@ -14,9 +14,10 @@
 def precision_recall(
     similarity_melted_df: pd.DataFrame,
     replicate_groups: List[str],
+    groupby_columns: List[str],
     k: Union[int, List[int]],
 ) -> pd.DataFrame:
-    """Determine the precision and recall at k for all unique replicate groups
+    """Determine the precision and recall at k for all unique groupby_columns samples
     based on a predefined similarity metric (see cytominer_eval.transform.metric_melt)
 
     Parameters
@@ -26,15 +27,20 @@ def precision_recall(
         samples. Importantly, it must follow the exact structure as output from
         :py:func:`cytominer_eval.transform.transform.metric_melt`.
     replicate_groups : List
-        a list of metadata column names in the original profile dataframe to use as
-        replicate columns.
+        a list of metadata column names in the original profile dataframe to use as replicate columns.
+    groupby_columns : List of str
+        Column by which the similarity matrix is grouped and by which the precision/recall is calculated.
+        For example, if groupby_column = Metadata_sample then the precision is calculated for each sample.
+        Calculating the precision by sample is the default
+        but it is mathematically not incorrect to calculate the precision at the MOA level.
+        This is just less intuitive to understand.
     k : List of ints or int
         an integer indicating how many pairwise comparisons to threshold.
 
     Returns
     -------
     pandas.DataFrame
-        precision and recall metrics for all replicate groups given k
+        precision and recall metrics for all groupby_column groups given k
     """
     # Determine pairwise replicates and make sure to sort based on the metric!
     similarity_melted_df = assign_replicates(
@@ -46,9 +52,9 @@ def precision_recall(
 
     # Extract out specific columns
     pair_ids = set_pair_ids()
-    replicate_group_cols = [
+    groupby_cols_suffix = [
         "{x}{suf}".format(x=x, suf=pair_ids[list(pair_ids)[0]]["suffix"])
-        for x in replicate_groups
+        for x in groupby_columns
     ]
     # iterate over all k
     precision_recall_df = pd.DataFrame()
@@ -57,11 +63,11 @@ def precision_recall(
     for k_ in k:
         # Calculate precision and recall for all groups
         precision_recall_df_at_k = similarity_melted_df.groupby(
-            replicate_group_cols
+            groupby_cols_suffix
         ).apply(lambda x: calculate_precision_recall(x, k=k_))
         precision_recall_df = precision_recall_df.append(precision_recall_df_at_k)
 
     # Rename the columns back to the replicate groups provided
-    rename_cols = dict(zip(replicate_group_cols, replicate_groups))
+    rename_cols = dict(zip(groupby_cols_suffix, groupby_columns))
 
     return precision_recall_df.reset_index().rename(rename_cols, axis="columns")

cytominer_eval/tests/test_evaluate.py

@@ -132,6 +132,9 @@ def test_evaluate_precision_recall():
         },
     }
 
+    gene_groupby_columns = ["Metadata_pert_name"]
+    compound_groupby_columns = ["Metadata_broad_sample"]
+
     for k in ks:
 
         # first test the function with k = float, later we test with k = list of floats
@@ -140,6 +143,7 @@ def test_evaluate_precision_recall():
             features=gene_features,
             meta_features=gene_meta_features,
             replicate_groups=gene_groups,
+            groupby_columns=gene_groupby_columns,
             operation="precision_recall",
             similarity_metric="pearson",
             precision_recall_k=k,
@@ -159,6 +163,7 @@ def test_evaluate_precision_recall():
             features=compound_features,
             meta_features=compound_meta_features,
             replicate_groups=["Metadata_broad_sample"],
+            groupby_columns=compound_groupby_columns,
             operation="precision_recall",
             similarity_metric="pearson",
             precision_recall_k=[k],

cytominer_eval/tests/test_operations/test_precision_recall.py

@@ -1,16 +1,13 @@
 import os
 import random
-import pytest
 import pathlib
-import tempfile
-import numpy as np
 import pandas as pd
 
+
 from cytominer_eval.transform import metric_melt
 from cytominer_eval.operations import precision_recall
 
-random.seed(123)
-tmpdir = tempfile.gettempdir()
+random.seed(42)
 
 # Load CRISPR dataset
 example_file = "SQ00014610_normalized_feature_select.csv.gz"
@@ -37,32 +34,36 @@
 
 replicate_groups = ["Metadata_gene_name", "Metadata_cell_line"]
 
+groupby_columns = ["Metadata_pert_name"]
+
 
 def test_precision_recall():
     result_list = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
+        groupby_columns=groupby_columns,
         k=[5, 10],
     )
 
     result_int = precision_recall(
         similarity_melted_df=similarity_melted_df,
         replicate_groups=replicate_groups,
+        groupby_columns=groupby_columns,
         k=5,
     )
 
     assert len(result_list.k.unique()) == 2
     assert result_list.k.unique()[0] == 5
 
-    # ITGAV has a really strong profile
+    # ITGAV-1 has a really strong profile
     assert (
         result_list.sort_values(by="recall", ascending=False)
         .reset_index(drop=True)
         .iloc[0, :]
-        .Metadata_gene_name
-        == "ITGAV"
+        .Metadata_pert_name
+        == "ITGAV-1"
     )
 
-    assert all(x in result_list.columns for x in replicate_groups)
+    assert all(x in result_list.columns for x in groupby_columns)
 
     assert result_int.equals(result_list.query("k == 5"))