v3.3 EDA update, proper length of catalog, updated stats and stats gen.

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_10277/2566980770.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"
      ]
     }
@@ -3935,9 +3935,16 @@
     "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": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3945,22 +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 = num_images/num_taxon\n",
-    "    std_all = np.sqrt(num_images/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": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -3996,65 +4019,65 @@
        "      <th>0</th>\n",
        "      <td>kingdom</td>\n",
        "      <td>838798.000000</td>\n",
-       "      <td>915.859159</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>93199.777778</td>\n",
-       "      <td>305.286386</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>29007.423631</td>\n",
-       "      <td>170.315659</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>6732.826756</td>\n",
-       "      <td>82.053804</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>1263.250000</td>\n",
-       "      <td>35.542228</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>136.284658</td>\n",
-       "      <td>11.674102</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>61.504097</td>\n",
-       "      <td>7.842455</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>22.675425</td>\n",
-       "      <td>4.761872</td>\n",
+       "      <td>102.608256</td>\n",
        "      <td>22.675425</td>\n",
-       "      <td>4.761872</td>\n",
+       "      <td>102.608256</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4062,27 +4085,27 @@
       ],
       "text/plain": [
        "     class  average_all_imgs  standard_deviation    avg_labeled  \\\n",
-       "0  kingdom     838798.000000          915.859159  817063.916667   \n",
-       "1   phylum      93199.777778          305.286386   90799.657407   \n",
-       "2    class      29007.423631          170.315659   28203.440922   \n",
-       "3    order       6732.826756           82.053804    6542.110368   \n",
-       "4   family       1263.250000           35.542228    1223.455447   \n",
-       "5    genus        136.284658           11.674102     120.250132   \n",
-       "6  species         61.504097            7.842455      53.299908   \n",
-       "7   common         22.675425            4.761872      22.675425   \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": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4095,7 +4118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [

notebooks/ToL_catalog_EDA.py

@@ -569,22 +569,42 @@ 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 = num_images/num_taxon
-    std_all = np.sqrt(num_images/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,