v3.3 Dataset Diversity Overlap Analysis

notebooks/ToL_catalog_EDA.ipynb

@@ -22,7 +22,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_21438/2566980770.py:1: DtypeWarning: Columns (5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_11858/2566980770.py:1: DtypeWarning: Columns (5,6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df = pd.read_csv(\"../data/catalog.csv\")\n"
      ]
     }
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
        "['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -784,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -793,7 +793,7 @@
        "439910"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -813,7 +813,7 @@
        "9947"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -833,7 +833,7 @@
        "7758"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -862,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1007,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3641,6 +3641,13 @@
     "That's a good number of images, so unlikely to be the cause."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diversity Between Datasets"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3710,11 +3717,197 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "BIOSCAN and iNat21's overlap of genera is completely contained in EOL.\n",
+    "BIOSCAN and iNat21's overlap of genera is almost completely contained in EOL.\n",
     "\n",
     "No changes here."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### More thorough diversity check with 7-tuple taxa\n",
+    "\n",
+    "We'll first filter down to all unique 7-tuple taxa (by data source). Then, we'll reduce down to just EOL and iNat21 to determine how much EOL adds to iNat21's diversity. The remainder from the total is added by BIOSCAN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "source_taxa = ['data_source', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 466741 entries, 956203 to 11000902\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column            Non-Null Count   Dtype  \n",
+      "---  ------            --------------   -----  \n",
+      " 0   split             466741 non-null  object \n",
+      " 1   treeoflife_id     466741 non-null  object \n",
+      " 2   eol_content_id    448910 non-null  float64\n",
+      " 3   eol_page_id       448910 non-null  float64\n",
+      " 4   bioscan_part      7831 non-null    float64\n",
+      " 5   bioscan_filename  7831 non-null    object \n",
+      " 6   inat21_filename   10000 non-null   object \n",
+      " 7   inat21_cls_name   10000 non-null   object \n",
+      " 8   inat21_cls_num    10000 non-null   float64\n",
+      " 9   kingdom           437587 non-null  object \n",
+      " 10  phylum            438050 non-null  object \n",
+      " 11  class             436934 non-null  object \n",
+      " 12  order             437280 non-null  object \n",
+      " 13  family            437137 non-null  object \n",
+      " 14  genus             439711 non-null  object \n",
+      " 15  species           424855 non-null  object \n",
+      " 16  common            466741 non-null  object \n",
+      " 17  data_source       466741 non-null  object \n",
+      " 18  duplicate         466741 non-null  bool   \n",
+      "dtypes: bool(1), float64(4), object(14)\n",
+      "memory usage: 68.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['duplicate'] = df.duplicated(subset = source_taxa, keep = 'first')\n",
+    "df_unique_taxa = df.loc[~df['duplicate']]\n",
+    "df_unique_taxa.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 466,741 unique taxa by source (i.e., the sum of unique 7-tuples within EOL, iNat21, and BIOSCAN, without considering overlaps between them). Our actual unique 7-tuple taxa count for the full dataset is 454,103, so we have about 12,600 taxa shared between our constituent parts.\n",
+    "\n",
+    "Now, let's reduce this down to just EOL and iNat21."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 458910 entries, 956203 to 11000902\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column            Non-Null Count   Dtype  \n",
+      "---  ------            --------------   -----  \n",
+      " 0   split             458910 non-null  object \n",
+      " 1   treeoflife_id     458910 non-null  object \n",
+      " 2   eol_content_id    448910 non-null  float64\n",
+      " 3   eol_page_id       448910 non-null  float64\n",
+      " 4   bioscan_part      0 non-null       float64\n",
+      " 5   bioscan_filename  0 non-null       object \n",
+      " 6   inat21_filename   10000 non-null   object \n",
+      " 7   inat21_cls_name   10000 non-null   object \n",
+      " 8   inat21_cls_num    10000 non-null   float64\n",
+      " 9   kingdom           429756 non-null  object \n",
+      " 10  phylum            430219 non-null  object \n",
+      " 11  class             429103 non-null  object \n",
+      " 12  order             429449 non-null  object \n",
+      " 13  family            429320 non-null  object \n",
+      " 14  genus             432307 non-null  object \n",
+      " 15  species           419345 non-null  object \n",
+      " 16  common            458910 non-null  object \n",
+      " 17  data_source       458910 non-null  object \n",
+      " 18  duplicate         458910 non-null  bool   \n",
+      "dtypes: bool(1), float64(4), object(14)\n",
+      "memory usage: 67.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_taxa_Enat = df_unique_taxa.loc[df_unique_taxa.data_source != \"BIOSCAN\"]\n",
+    "df_taxa_Enat.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 458,910 for EOL and iNat21. Now, we remove taxa duplicates between the two datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 450284 entries, 956203 to 11000902\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column            Non-Null Count   Dtype  \n",
+      "---  ------            --------------   -----  \n",
+      " 0   split             450284 non-null  object \n",
+      " 1   treeoflife_id     450284 non-null  object \n",
+      " 2   eol_content_id    448492 non-null  float64\n",
+      " 3   eol_page_id       448492 non-null  float64\n",
+      " 4   bioscan_part      0 non-null       float64\n",
+      " 5   bioscan_filename  0 non-null       object \n",
+      " 6   inat21_filename   1792 non-null    object \n",
+      " 7   inat21_cls_name   1792 non-null    object \n",
+      " 8   inat21_cls_num    1792 non-null    float64\n",
+      " 9   kingdom           421130 non-null  object \n",
+      " 10  phylum            421593 non-null  object \n",
+      " 11  class             420477 non-null  object \n",
+      " 12  order             420823 non-null  object \n",
+      " 13  family            420694 non-null  object \n",
+      " 14  genus             423681 non-null  object \n",
+      " 15  species           410719 non-null  object \n",
+      " 16  common            450284 non-null  object \n",
+      " 17  data_source       450284 non-null  object \n",
+      " 18  duplicate         450284 non-null  bool   \n",
+      "dtypes: bool(1), float64(4), object(14)\n",
+      "memory usage: 65.7+ MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_11858/532979333.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_taxa_Enat['duplicate'] = df_taxa_Enat.duplicated(subset = taxa, keep = 'first')\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_taxa_Enat['duplicate'] = df_taxa_Enat.duplicated(subset = taxa, keep = 'first')\n",
+    "df_unique_taxa_Enat = df_taxa_Enat.loc[~df_taxa_Enat['duplicate']]\n",
+    "df_unique_taxa_Enat.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Between iNat21 and EOL we have 450,284 unique taxa. That means we have 3,819 unique 7-tuple taxa added by BIOSCAN, and there are 8,626 taxa (7-tuples) shared between EOL and iNat21 (86% of iNat21).\n",
+    "\n",
+    "EOL has 448,910 unique 7-tuple taxa, so it adds 440,284 more unique taxa to iNat21, then the addition of BIOSCAN adds another 3,819 unique taxa."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

notebooks/ToL_catalog_EDA.py

@@ -491,6 +491,9 @@ eol_long_all_taxa[taxa].info(show_counts = True)
 # %% [markdown]
 # That's a good number of images, so unlikely to be the cause.
 
+# %% [markdown]
+# ## Diversity Between Datasets
+
 # %% [markdown]
 # ### Label Overlap Check
 
@@ -516,10 +519,45 @@ print(f"There are {len(list(set(inat21_genera) & set(bioscan_genera)))} genera s
 print(f"There are {len(list(set(gen_overlap) & set(bioscan_genera)))} genera shared between all three data sources.")
 
 # %% [markdown]
-# BIOSCAN and iNat21's overlap of genera is completely contained in EOL.
+# BIOSCAN and iNat21's overlap of genera is almost completely contained in EOL.
 #
 # No changes here.
 
+# %% [markdown]
+# #### More thorough diversity check with 7-tuple taxa
+#
+# We'll first filter down to all unique 7-tuple taxa (by data source). Then, we'll reduce down to just EOL and iNat21 to determine how much EOL adds to iNat21's diversity. The remainder from the total is added by BIOSCAN.
+
+# %%
+source_taxa = ['data_source', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']
+
+# %%
+df['duplicate'] = df.duplicated(subset = source_taxa, keep = 'first')
+df_unique_taxa = df.loc[~df['duplicate']]
+df_unique_taxa.info(show_counts=True)
+
+# %% [markdown]
+# We have 466,741 unique taxa by source (i.e., the sum of unique 7-tuples within EOL, iNat21, and BIOSCAN, without considering overlaps between them). Our actual unique 7-tuple taxa count for the full dataset is 454,103, so we have about 12,600 taxa shared between our constituent parts.
+#
+# Now, let's reduce this down to just EOL and iNat21.
+
+# %%
+df_taxa_Enat = df_unique_taxa.loc[df_unique_taxa.data_source != "BIOSCAN"]
+df_taxa_Enat.info(show_counts = True)
+
+# %% [markdown]
+# We have 458,910 for EOL and iNat21. Now, we remove taxa duplicates between the two datasets.
+
+# %%
+df_taxa_Enat['duplicate'] = df_taxa_Enat.duplicated(subset = taxa, keep = 'first')
+df_unique_taxa_Enat = df_taxa_Enat.loc[~df_taxa_Enat['duplicate']]
+df_unique_taxa_Enat.info(show_counts = True)
+
+# %% [markdown]
+# Between iNat21 and EOL we have 450,284 unique taxa. That means we have 3,819 unique 7-tuple taxa added by BIOSCAN, and there are 8,626 taxa (7-tuples) shared between EOL and iNat21 (86% of iNat21).
+#
+# EOL has 448,910 unique 7-tuple taxa, so it adds 440,284 more unique taxa to iNat21, then the addition of BIOSCAN adds another 3,819 unique taxa.
+
 # %% [markdown]
 # ## Overall Stats
 #