Stats gen fix

data/stats_avg_std_byClass.csv

@@ -1,9 +1,9 @@
 class,average_all_imgs,standard_deviation,avg_labeled,std_dev_labeled
-kingdom,869710.0833333334,932.582480713279,817063.9166666666,903.9158791982065
-phylum,96634.45370370371,310.86082690442635,90799.6574074074,301.32981499912586
-class,30076.42939481268,173.4255730704462,28203.440922190202,167.93880112168898
-order,6980.9505016722405,83.55208256933061,6542.110367892977,80.88331328458904
-family,1309.8043423694778,36.19121913350637,1223.4554467871485,34.977927994481725
-genus,141.30713405635214,11.887267728807664,120.25013201186076,10.96586211895174
-species,63.770697250957795,7.9856557182837395,53.29990773385801,7.300678580368952
-common,23.511079121780227,4.848822446922575,22.675425435573036,4.761872051575203
+kingdom,838798.0,1996.719846424967,817063.9166666666,1996.5788222797064
+phylum,93199.77777777778,1668.4007961420498,90799.6574074074,1668.382272635818
+class,29007.42363112392,1496.495817288206,28203.440922190202,1496.488372028748
+order,6732.826755852843,600.7988885217314,6542.110367892977,600.7943925756412
+family,1263.25,237.33843036779797,1223.4554467871485,237.33578940480186
+genus,136.28465819082822,,120.25013201186076,
+species,61.50409698332488,,53.29990773385801,
+common,22.675425435573036,102.60825555250013,22.675425435573036,102.60825555250013

notebooks/ToL_catalog_EDA.ipynb

@@ -22,7 +22,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/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",
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_56448/2566980770.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"
      ]
     }
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
        "['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -862,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3919,7 +3919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3928,7 +3928,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_images = df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Process DataFrame with just taxa columns and focus on higher ranks for standard deviation (not `genus` and `species`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3936,22 +3952,38 @@
     "std_all_images = []\n",
     "avgs_labeled_images = []\n",
     "std_labeled_images = []\n",
-    "for taxon in taxa_com[1:]: #taxa + common\n",
-    "    num_taxon = df[taxon].nunique()\n",
-    "    num_img_taxon = len(df.loc[df[taxon].notna()])\n",
-    "    avg_all = 10436521/num_taxon\n",
-    "    std_all = np.sqrt(10436521/num_taxon)\n",
-    "    avg_labeled = num_img_taxon/num_taxon\n",
-    "    std_labeled = np.sqrt(num_img_taxon/num_taxon)\n",
+    "for rank in taxa_com[1:]: #taxa + common\n",
+    "    num_taxa = df_taxa[rank].nunique()\n",
+    "    df_labeled = df_taxa.loc[df_taxa[rank].notna()]\n",
+    "    num_img_rank = len(df_labeled)\n",
+    "    # Get averages\n",
+    "    avg_all = num_images/num_taxa\n",
+    "    avg_labeled = num_img_rank/num_taxa\n",
     "    avgs_all_images.append(avg_all)\n",
-    "    std_all_images.append(std_all)\n",
     "    avgs_labeled_images.append(avg_labeled)\n",
-    "    std_labeled_images.append(std_labeled)"
+    "\n",
+    "    # Get standard deviation\n",
+    "    std_rank_all = 0\n",
+    "    std_rank_labeled = 0\n",
+    "    if rank not in [\"genus\", \"species\"]:\n",
+    "        for taxon in df_labeled[rank].unique():\n",
+    "            num_img_at_taxon = len(df_taxa.loc[df_taxa[rank] == taxon])\n",
+    "            dev_all = np.square(avg_all - num_img_at_taxon)\n",
+    "            dev_labeled = np.square(avg_labeled - num_img_at_taxon)\n",
+    "            std_rank_all = std_rank_all + dev_all\n",
+    "            std_rank_labeled = std_rank_labeled + dev_labeled\n",
+    "        std_rank_all = np.sqrt(std_rank_all/num_images)\n",
+    "        std_rank_labeled = np.sqrt(std_rank_labeled/num_images)\n",
+    "    else:\n",
+    "        std_rank_all = np.nan\n",
+    "        std_rank_labeled = np.nan\n",
+    "    std_all_images.append(std_rank_all)\n",
+    "    std_labeled_images.append(std_rank_labeled)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -3986,94 +4018,94 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>kingdom</td>\n",
-       "      <td>869710.083333</td>\n",
-       "      <td>932.582481</td>\n",
+       "      <td>838798.000000</td>\n",
+       "      <td>1996.719846</td>\n",
        "      <td>817063.916667</td>\n",
-       "      <td>903.915879</td>\n",
+       "      <td>1996.578822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>phylum</td>\n",
-       "      <td>96634.453704</td>\n",
-       "      <td>310.860827</td>\n",
+       "      <td>93199.777778</td>\n",
+       "      <td>1668.400796</td>\n",
        "      <td>90799.657407</td>\n",
-       "      <td>301.329815</td>\n",
+       "      <td>1668.382273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>class</td>\n",
-       "      <td>30076.429395</td>\n",
-       "      <td>173.425573</td>\n",
+       "      <td>29007.423631</td>\n",
+       "      <td>1496.495817</td>\n",
        "      <td>28203.440922</td>\n",
-       "      <td>167.938801</td>\n",
+       "      <td>1496.488372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>order</td>\n",
-       "      <td>6980.950502</td>\n",
-       "      <td>83.552083</td>\n",
+       "      <td>6732.826756</td>\n",
+       "      <td>600.798889</td>\n",
        "      <td>6542.110368</td>\n",
-       "      <td>80.883313</td>\n",
+       "      <td>600.794393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>family</td>\n",
-       "      <td>1309.804342</td>\n",
-       "      <td>36.191219</td>\n",
+       "      <td>1263.250000</td>\n",
+       "      <td>237.338430</td>\n",
        "      <td>1223.455447</td>\n",
-       "      <td>34.977928</td>\n",
+       "      <td>237.335789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>genus</td>\n",
-       "      <td>141.307134</td>\n",
-       "      <td>11.887268</td>\n",
+       "      <td>136.284658</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>120.250132</td>\n",
-       "      <td>10.965862</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>species</td>\n",
-       "      <td>63.770697</td>\n",
-       "      <td>7.985656</td>\n",
+       "      <td>61.504097</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>53.299908</td>\n",
-       "      <td>7.300679</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>common</td>\n",
-       "      <td>23.511079</td>\n",
-       "      <td>4.848822</td>\n",
        "      <td>22.675425</td>\n",
-       "      <td>4.761872</td>\n",
+       "      <td>102.608256</td>\n",
+       "      <td>22.675425</td>\n",
+       "      <td>102.608256</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     class  average_all_imgs  standard_deviation    avg_labeled   \n",
-       "0  kingdom     869710.083333          932.582481  817063.916667  \\\n",
-       "1   phylum      96634.453704          310.860827   90799.657407   \n",
-       "2    class      30076.429395          173.425573   28203.440922   \n",
-       "3    order       6980.950502           83.552083    6542.110368   \n",
-       "4   family       1309.804342           36.191219    1223.455447   \n",
-       "5    genus        141.307134           11.887268     120.250132   \n",
-       "6  species         63.770697            7.985656      53.299908   \n",
-       "7   common         23.511079            4.848822      22.675425   \n",
+       "     class  average_all_imgs  standard_deviation    avg_labeled  \\\n",
+       "0  kingdom     838798.000000         1996.719846  817063.916667   \n",
+       "1   phylum      93199.777778         1668.400796   90799.657407   \n",
+       "2    class      29007.423631         1496.495817   28203.440922   \n",
+       "3    order       6732.826756          600.798889    6542.110368   \n",
+       "4   family       1263.250000          237.338430    1223.455447   \n",
+       "5    genus        136.284658                 NaN     120.250132   \n",
+       "6  species         61.504097                 NaN      53.299908   \n",
+       "7   common         22.675425          102.608256      22.675425   \n",
        "\n",
        "   std_dev_labeled  \n",
-       "0       903.915879  \n",
-       "1       301.329815  \n",
-       "2       167.938801  \n",
-       "3        80.883313  \n",
-       "4        34.977928  \n",
-       "5        10.965862  \n",
-       "6         7.300679  \n",
-       "7         4.761872  "
+       "0      1996.578822  \n",
+       "1      1668.382273  \n",
+       "2      1496.488372  \n",
+       "3       600.794393  \n",
+       "4       237.335789  \n",
+       "5              NaN  \n",
+       "6              NaN  \n",
+       "7       102.608256  "
       ]
      },
-     "execution_count": 62,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4086,7 +4118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4102,7 +4134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4111,7 +4143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -4120,13 +4152,13 @@
        "<Axes: xlabel='kingdom', ylabel='Count'>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]

notebooks/ToL_catalog_EDA.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.15.2
+#       jupytext_version: 1.16.0
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -566,22 +566,45 @@ df_unique_taxa_Enat.info(show_counts = True)
 # %%
 import numpy as np
 
+# %%
+num_images = df.shape[0]
+
+# %% [markdown]
+# Process DataFrame with just taxa columns and focus on higher ranks for standard deviation (not `genus` and `species`).
+
 # %%
 avgs_all_images = []
 std_all_images = []
 avgs_labeled_images = []
 std_labeled_images = []
-for taxon in taxa_com[1:]: #taxa + common
-    num_taxon = df[taxon].nunique()
-    num_img_taxon = len(df.loc[df[taxon].notna()])
-    avg_all = 10436521/num_taxon
-    std_all = np.sqrt(10436521/num_taxon)
-    avg_labeled = num_img_taxon/num_taxon
-    std_labeled = np.sqrt(num_img_taxon/num_taxon)
+for rank in taxa_com[1:]: #taxa + common
+    num_taxa = df_taxa[rank].nunique()
+    df_labeled = df_taxa.loc[df_taxa[rank].notna()]
+    num_img_rank = len(df_labeled)
+    # Get averages
+    avg_all = num_images/num_taxa
+    avg_labeled = num_img_rank/num_taxa
     avgs_all_images.append(avg_all)
-    std_all_images.append(std_all)
     avgs_labeled_images.append(avg_labeled)
-    std_labeled_images.append(std_labeled)
+
+    # Get standard deviation
+    std_rank_all = 0
+    std_rank_labeled = 0
+    if rank not in ["genus", "species"]:
+        for taxon in df_labeled[rank].unique():
+            num_img_at_taxon = len(df_taxa.loc[df_taxa[rank] == taxon])
+            dev_all = np.square(avg_all - num_img_at_taxon)
+            dev_labeled = np.square(avg_labeled - num_img_at_taxon)
+            std_rank_all = std_rank_all + dev_all
+            std_rank_labeled = std_rank_labeled + dev_labeled
+        std_rank_all = np.sqrt(std_rank_all/num_images)
+        std_rank_labeled = np.sqrt(std_rank_labeled/num_images)
+    else:
+        std_rank_all = np.nan
+        std_rank_labeled = np.nan
+    std_all_images.append(std_rank_all)
+    std_labeled_images.append(std_rank_labeled)
+
 
 # %%
 avg_std = pd.DataFrame(data = {'class': taxa_com[1:], 'average_all_imgs': avgs_all_images, 'standard_deviation': std_all_images,