Further edits - batching

broadinstitute · Dec 2, 2024 · d75af10 · d75af10
1 parent d822ecb
commit d75af10
Showing 1 changed file with 45 additions and 6 deletions.
diff --git a/scripts/notebooks/Batching.ipynb b/scripts/notebooks/Batching.ipynb
@@ -354,7 +354,7 @@
     "        None.\n",
     "    \"\"\"\n",
     "    for i in input_vals:\n",
-    "        if not isinstance(i, str) or not i:\n",
+    "        if not isinstance(i, str):\n",
     "            raise Exception('Value input must be a string.')\n",
     "    \n",
     "    if log:\n",
@@ -427,6 +427,41 @@
     "    print(\"Inputs are valid - please proceed to the next cell.\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hidden": true
+   },
+   "outputs": [],
+   "source": [
+    "def validate_batch_sizes(df, target_batch_size, min_batch_size, max_batch_size):\n",
+    "    \"\"\"\n",
+    "    Validates user input to check whether they are valid bins to use when batching. \n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Dataframe with sample data.\n",
+    "        target_batch_size (int): Target size of each batch.\n",
+    "        min_batch_size (int): Minimum size of each batch.\n",
+    "        max_batch_size (int): Maximum size of each batch.\n",
+    "    \n",
+    "    Returns:\n",
+    "        None.\n",
+    "    \"\"\"\n",
+    "    validate_numeric_inputs([target_batch_size, min_batch_size, max_batch_size], log=False)\n",
+    "    \n",
+    "    if (target_batch_size < min_batch_size):\n",
+    "        raise Exception(\"TARGET_BATCH_SIZE must exceed MIN_BATCH_SIZE.\")\n",
+    "        \n",
+    "    if (max_batch_size < target_batch_size):\n",
+    "        raise Exception(\"MAX_BATCH_SIZE must exceed TARGET_BATCH_SIZE.\")\n",
+    "    \n",
+    "    if (len(df) < min_batch_size):\n",
+    "        raise Exception(\"MIN_BATCH_SIZE must exceed the number of samples.\")\n",
+    "    \n",
+    "    print(\"Inputs are valid - please proceed to the next cell.\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -883,9 +918,13 @@
     "        Tuple of batches dictionary and batches metadata dictionary.\n",
     "    \"\"\"\n",
     "    # Validation\n",
-    "    if not min_batch_size <= len(df):\n",
-    "        raise Exception('MIN_BATCH_SIZE must not exceed the number of samples.')\n",
-    "        \n",
+    "    estimated_samples_per_split = len(df)\n",
+    "    for bins in include_bins:\n",
+    "        estimated_samples_per_split /= bins\n",
+    "        if estimated_samples_per_split < min_batch_size:\n",
+    "            raise Exception(f\"Based on INCLUDE_BINS, expected samples per split ({estimated_samples_per_split}) \" +\n",
+    "                            \"is less than MIN_BATCH_SIZE ({min_batch_size}). Please adjust parameters accordingly.\")\n",
+    "            \n",
     "    # Group related samples and select family representatives\n",
     "    related_groups = group_related_samples(df, reference_ped)\n",
     "    family_representatives = select_family_representatives(reference_ped, related_groups) if reference_ped is not None else set(df['sample_id'])\n",
@@ -1021,7 +1060,7 @@
     "duplicates_dict = id_counts[id_counts > 1].to_dict()\n",
     "\n",
     "if (len(duplicates_dict) > 0):\n",
-    "    print(f\"{len(duplicates_dict)} duplicate 'sample_id' exist in the dataset.\")\n",
+    "    print(f\"{len(duplicates_dict)} duplicate 'sample_id' e xist in the dataset.\")\n",
     "    for sample_id, count in duplicates_dict.items():\n",
     "        print(f\"Sample ID: {sample_id}, Count: {count}\")\n",
     "    raise Exception(\"Batching requires unique 'sample_id' - please resolve this before proceeding.\")\n",
@@ -1118,7 +1157,7 @@
     "MINIMUM_BATCH_SIZE = None\n",
     "MAXIMUM_BATCH_SIZE = None\n",
     "\n",
-    "validate_numeric_inputs([TARGET_BATCH_SIZE, MINIMUM_BATCH_SIZE, MAXIMUM_BATCH_SIZE])"
+    "validate_batch_sizes(pass_df, TARGET_BATCH_SIZE, MINIMUM_BATCH_SIZE, MAXIMUM_BATCH_SIZE)"
    ]
   },
   {