diff --git a/docs/key.html b/docs/key.html
index 8f4e8281..dfea2913 100644
--- a/docs/key.html
+++ b/docs/key.html
@@ -66,9 +66,9 @@
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h2 id="Key" class="doc_header"><code>class</code> <code>Key</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/key.py#L11" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>Key</code>(<strong><code>pub_id</code></strong>:<code>str</code>, <strong><code>secret_key</code></strong>:<code>str</code>)</p>
+<h2 id="Key" class="doc_header"><code>class</code> <code>Key</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/key.py#L9" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>Key</code>(<strong><code>pub_id</code></strong>:<code>str</code>, <strong><code>secret_key</code></strong>:<code>str</code>)</p>
 </blockquote>
-<p>Immutable Numerai credentials.</p>
+<p>Numerai credentials.</p>
 
 </div>
 
@@ -91,7 +91,7 @@ <h2 id="Key" class="doc_header"><code>class</code> <code>Key</code><a href="http
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h4 id="load_key_from_json" class="doc_header"><code>load_key_from_json</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/key.py#L30" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>load_key_from_json</code>(<strong><code>file_path</code></strong>:<code>str</code>, <strong>*<code>args</code></strong>, <strong>**<code>kwargs</code></strong>)</p>
+<h4 id="load_key_from_json" class="doc_header"><code>load_key_from_json</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/key.py#L22" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>load_key_from_json</code>(<strong><code>file_path</code></strong>:<code>str</code>, <strong>*<code>args</code></strong>, <strong>**<code>kwargs</code></strong>)</p>
 </blockquote>
 <p>Initialize Key object from JSON file.</p>
 <p>Credentials file must have the following format:</p>
@@ -119,14 +119,14 @@ <h4 id="load_key_from_json" class="doc_header"><code>load_key_from_json</code><a
 
 
 
-<div id="dabf124b-00b6-4cfe-9a42-38caaadd09ef" class="output_subarea output_javascript ">
+<div id="0df8e837-bcb8-4e8e-88d2-c17ca0ef587e" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#dabf124b-00b6-4cfe-9a42-38caaadd09ef');
+var element = $('#0df8e837-bcb8-4e8e-88d2-c17ca0ef587e');
 
             setTimeout(function() {
                 var nbb_cell_id = 5;
-                var nbb_unformatted_code = "# export\nclass Key:\n    \"\"\"Immutable Numerai credentials.\"\"\"\n    def __init__(self, pub_id: str, secret_key: str):\n        __slots__ = []\n        self.pub_id = pub_id\n        self.secret_key = secret_key\n\n    def __post_init__(self):\n        rich_print(\n            f\":key: Numerai Auth key initialized with pub_id = '{self.pub_id}' :key:\"\n        )\n\n    def __repr__(self):\n        return f\"Numerai Auth Key. pub_id = '{self.pub_id}'\"\n\n    def __str__(self):\n        return self.__repr__()\n\n\ndef load_key_from_json(file_path: str, *args, **kwargs):\n    \"\"\"\n    Initialize Key object from JSON file. \\n\n    Credentials file must have the following format: \\n\n    `{\"pub_id\": \"PUBLIC_ID\", \"secret_key\": \"SECRET_KEY\"}`\n    \"\"\"\n    with open(file_path) as json_file:\n        json_data = json.load(json_file, *args, **kwargs)\n    pub_id = json_data[\"pub_id\"]\n    secret_key = json_data[\"secret_key\"]\n    return Key(pub_id=pub_id, secret_key=secret_key)";
-                var nbb_formatted_code = "# export\nclass Key:\n    \"\"\"Immutable Numerai credentials.\"\"\"\n\n    def __init__(self, pub_id: str, secret_key: str):\n        __slots__ = []\n        self.pub_id = pub_id\n        self.secret_key = secret_key\n\n    def __post_init__(self):\n        rich_print(\n            f\":key: Numerai Auth key initialized with pub_id = '{self.pub_id}' :key:\"\n        )\n\n    def __repr__(self):\n        return f\"Numerai Auth Key. pub_id = '{self.pub_id}'\"\n\n    def __str__(self):\n        return self.__repr__()\n\n\ndef load_key_from_json(file_path: str, *args, **kwargs):\n    \"\"\"\n    Initialize Key object from JSON file. \\n\n    Credentials file must have the following format: \\n\n    `{\"pub_id\": \"PUBLIC_ID\", \"secret_key\": \"SECRET_KEY\"}`\n    \"\"\"\n    with open(file_path) as json_file:\n        json_data = json.load(json_file, *args, **kwargs)\n    pub_id = json_data[\"pub_id\"]\n    secret_key = json_data[\"secret_key\"]\n    return Key(pub_id=pub_id, secret_key=secret_key)";
+                var nbb_unformatted_code = "# export\nclass Key:\n    \"\"\"Numerai credentials.\"\"\"\n    def __init__(self, pub_id: str, secret_key: str):\n        self.pub_id = pub_id\n        self.secret_key = secret_key\n\n    def __repr__(self):\n        return f\"Numerai Auth Key. pub_id = '{self.pub_id}'\"\n\n    def __str__(self):\n        return self.__repr__()\n\n\ndef load_key_from_json(file_path: str, *args, **kwargs):\n    \"\"\"\n    Initialize Key object from JSON file. \\n\n    Credentials file must have the following format: \\n\n    `{\"pub_id\": \"PUBLIC_ID\", \"secret_key\": \"SECRET_KEY\"}`\n    \"\"\"\n    with open(file_path) as json_file:\n        json_data = json.load(json_file, *args, **kwargs)\n    pub_id = json_data[\"pub_id\"]\n    secret_key = json_data[\"secret_key\"]\n    return Key(pub_id=pub_id, secret_key=secret_key)";
+                var nbb_formatted_code = "# export\nclass Key:\n    \"\"\"Numerai credentials.\"\"\"\n\n    def __init__(self, pub_id: str, secret_key: str):\n        self.pub_id = pub_id\n        self.secret_key = secret_key\n\n    def __repr__(self):\n        return f\"Numerai Auth Key. pub_id = '{self.pub_id}'\"\n\n    def __str__(self):\n        return self.__repr__()\n\n\ndef load_key_from_json(file_path: str, *args, **kwargs):\n    \"\"\"\n    Initialize Key object from JSON file. \\n\n    Credentials file must have the following format: \\n\n    `{\"pub_id\": \"PUBLIC_ID\", \"secret_key\": \"SECRET_KEY\"}`\n    \"\"\"\n    with open(file_path) as json_file:\n        json_data = json.load(json_file, *args, **kwargs)\n    pub_id = json_data[\"pub_id\"]\n    secret_key = json_data[\"secret_key\"]\n    return Key(pub_id=pub_id, secret_key=secret_key)";
                 var nbb_cells = Jupyter.notebook.get_cells();
                 for (var i = 0; i < nbb_cells.length; ++i) {
                     if (nbb_cells[i].input_prompt_number == nbb_cell_id) {
@@ -178,9 +178,9 @@ <h3 id="Example-usage-1:-direct-initialization">Example usage 1: direct initiali
 
 
 
-<div id="f626ac14-f081-47b3-baed-7aeab8a70484" class="output_subarea output_javascript ">
+<div id="ef54303d-8926-4d44-bb35-c50953b85df8" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#f626ac14-f081-47b3-baed-7aeab8a70484');
+var element = $('#ef54303d-8926-4d44-bb35-c50953b85df8');
 
             setTimeout(function() {
                 var nbb_cell_id = 6;
@@ -230,9 +230,9 @@ <h3 id="Example-usage-1:-direct-initialization">Example usage 1: direct initiali
 
 
 
-<div id="b2846aa8-f920-4cda-9ba2-1061f5d7415b" class="output_subarea output_javascript ">
+<div id="576ee839-e765-4b72-84df-2272d09cbb0c" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#b2846aa8-f920-4cda-9ba2-1061f5d7415b');
+var element = $('#576ee839-e765-4b72-84df-2272d09cbb0c');
 
             setTimeout(function() {
                 var nbb_cell_id = 7;
@@ -288,9 +288,9 @@ <h3 id="Example-usage-2:--loading-from-JSON">Example usage 2:  loading from JSON
 
 
 
-<div id="6456dac9-ee35-49e4-a195-bbeb00456d70" class="output_subarea output_javascript ">
+<div id="7af6a109-9b6b-4200-9643-08449407d72c" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#6456dac9-ee35-49e4-a195-bbeb00456d70');
+var element = $('#7af6a109-9b6b-4200-9643-08449407d72c');
 
             setTimeout(function() {
                 var nbb_cell_id = 8;
@@ -350,9 +350,9 @@ <h3 id="Example-usage-2:--loading-from-JSON">Example usage 2:  loading from JSON
 
 
 
-<div id="71beac83-df34-44d0-9496-9ecaa2307580" class="output_subarea output_javascript ">
+<div id="cff49d04-13ac-4a3f-826e-43e52b00b63d" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#71beac83-df34-44d0-9496-9ecaa2307580');
+var element = $('#cff49d04-13ac-4a3f-826e-43e52b00b63d');
 
             setTimeout(function() {
                 var nbb_cell_id = 9;
diff --git a/docs/preprocessing.html b/docs/preprocessing.html
index dec86fc8..37e4c972 100644
--- a/docs/preprocessing.html
+++ b/docs/preprocessing.html
@@ -100,9 +100,9 @@ <h2 id="BaseProcessor" class="doc_header"><code>class</code> <code>BaseProcessor
 
 
 
-<div id="18f7abc5-667a-4da1-9703-2566488da9de" class="output_subarea output_javascript ">
+<div id="59d2c548-db28-4ec4-aa7a-a8d3244b004a" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#18f7abc5-667a-4da1-9703-2566488da9de');
+var element = $('#59d2c548-db28-4ec4-aa7a-a8d3244b004a');
 
             setTimeout(function() {
                 var nbb_cell_id = 5;
@@ -189,9 +189,9 @@ <h4 id="display_processor_info" class="doc_header"><code>display_processor_info<
 
 
 
-<div id="c69d718d-2415-453a-9d75-c0c0cf934e6a" class="output_subarea output_javascript ">
+<div id="6310bf94-b01f-4278-aae9-5a9cd03d2e15" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#c69d718d-2415-453a-9d75-c0c0cf934e6a');
+var element = $('#6310bf94-b01f-4278-aae9-5a9cd03d2e15');
 
             setTimeout(function() {
                 var nbb_cell_id = 6;
@@ -229,7 +229,7 @@ <h4 id="display_processor_info" class="doc_header"><code>display_processor_info<
 <div class="output_area">
 
 
-<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">TestDisplay</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">314</span><span style="font-weight: bold">)</span>. Time taken for step: <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:02</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">002627</span>. ✅
+<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">TestDisplay</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">314</span><span style="font-weight: bold">)</span>. Time taken for step: <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:02</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">000485</span>. ✅
 </pre>
 </div>
 
@@ -532,9 +532,9 @@ <h4 id="display_processor_info" class="doc_header"><code>display_processor_info<
 
 
 
-<div id="eca10f33-f332-4753-9da8-c13d879b8551" class="output_subarea output_javascript ">
+<div id="1fc9f5fa-78ff-4a83-8b67-af2c62cdc056" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#eca10f33-f332-4753-9da8-c13d879b8551');
+var element = $('#1fc9f5fa-78ff-4a83-8b67-af2c62cdc056');
 
             setTimeout(function() {
                 var nbb_cell_id = 7;
@@ -632,9 +632,9 @@ <h2 id="CopyPreProcessor" class="doc_header"><code>class</code> <code>CopyPrePro
 
 
 
-<div id="f5395ed7-bea5-4a8c-a08a-e9ddac551822" class="output_subarea output_javascript ">
+<div id="fb29f159-3fad-4fce-b495-d5decf45715e" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#f5395ed7-bea5-4a8c-a08a-e9ddac551822');
+var element = $('#fb29f159-3fad-4fce-b495-d5decf45715e');
 
             setTimeout(function() {
                 var nbb_cell_id = 8;
@@ -688,7 +688,7 @@ <h2 id="CopyPreProcessor" class="doc_header"><code>class</code> <code>CopyPrePro
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">CopyPreProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">314</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">002955</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">003298</span>. ✅
 </pre>
 </div>
 
@@ -699,9 +699,9 @@ <h2 id="CopyPreProcessor" class="doc_header"><code>class</code> <code>CopyPrePro
 
 
 
-<div id="7ebbd7d6-df81-4c05-a83e-9f9971c82d75" class="output_subarea output_javascript ">
+<div id="cee40d7e-b395-42a0-a51c-97b589a8480e" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#7ebbd7d6-df81-4c05-a83e-9f9971c82d75');
+var element = $('#cee40d7e-b395-42a0-a51c-97b589a8480e');
 
             setTimeout(function() {
                 var nbb_cell_id = 9;
@@ -773,9 +773,9 @@ <h2 id="FeatureSelectionPreProcessor" class="doc_header"><code>class</code> <cod
 
 
 
-<div id="9e8fa96f-b480-406a-a980-c9b21a8e47f2" class="output_subarea output_javascript ">
+<div id="37bf97ee-63f8-412f-b3cf-c2e853e5011f" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#9e8fa96f-b480-406a-a980-c9b21a8e47f2');
+var element = $('#37bf97ee-63f8-412f-b3cf-c2e853e5011f');
 
             setTimeout(function() {
                 var nbb_cell_id = 10;
@@ -829,7 +829,7 @@ <h2 id="FeatureSelectionPreProcessor" class="doc_header"><code>class</code> <cod
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">FeatureSelectionPreProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">5</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">002415</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">001208</span>. ✅
 </pre>
 </div>
 
@@ -840,9 +840,9 @@ <h2 id="FeatureSelectionPreProcessor" class="doc_header"><code>class</code> <cod
 
 
 
-<div id="364a2edb-1443-4013-9a2b-ced437962cc0" class="output_subarea output_javascript ">
+<div id="ad997b54-9578-492d-9795-4e82661d0888" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#364a2edb-1443-4013-9a2b-ced437962cc0');
+var element = $('#ad997b54-9578-492d-9795-4e82661d0888');
 
             setTimeout(function() {
                 var nbb_cell_id = 11;
@@ -943,9 +943,9 @@ <h2 id="FeatureSelectionPreProcessor" class="doc_header"><code>class</code> <cod
 
 
 
-<div id="83b25d44-dd0d-49ba-abb8-43aae6b0349f" class="output_subarea output_javascript ">
+<div id="c0278606-3894-424c-a757-94b326f59cee" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#83b25d44-dd0d-49ba-abb8-43aae6b0349f');
+var element = $('#c0278606-3894-424c-a757-94b326f59cee');
 
             setTimeout(function() {
                 var nbb_cell_id = 12;
@@ -1018,9 +1018,9 @@ <h2 id="TargetSelectionPreProcessor" class="doc_header"><code>class</code> <code
 
 
 
-<div id="cecfd0ea-0b29-4522-a3e5-b444a35181bb" class="output_subarea output_javascript ">
+<div id="49373994-e91d-4a0c-a431-17a5c847e5eb" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#cecfd0ea-0b29-4522-a3e5-b444a35181bb');
+var element = $('#49373994-e91d-4a0c-a431-17a5c847e5eb');
 
             setTimeout(function() {
                 var nbb_cell_id = 13;
@@ -1077,7 +1077,7 @@ <h2 id="TargetSelectionPreProcessor" class="doc_header"><code>class</code> <code
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">TargetSelectionPreProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1055</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">025411</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">028618</span>. ✅
 </pre>
 </div>
 
@@ -1212,9 +1212,9 @@ <h2 id="TargetSelectionPreProcessor" class="doc_header"><code>class</code> <code
 
 
 
-<div id="d0c8e440-a15d-4c3b-b1e2-e872ec75c0d1" class="output_subarea output_javascript ">
+<div id="78a919ae-b60b-4459-92e1-aadf99935c48" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#d0c8e440-a15d-4c3b-b1e2-e872ec75c0d1');
+var element = $('#78a919ae-b60b-4459-92e1-aadf99935c48');
 
             setTimeout(function() {
                 var nbb_cell_id = 14;
@@ -1292,9 +1292,9 @@ <h2 id="ReduceMemoryProcessor" class="doc_header"><code>class</code> <code>Reduc
 
 
 
-<div id="c25a2572-a069-449d-836c-6fa40782f62a" class="output_subarea output_javascript ">
+<div id="9c212f9a-e398-4eb0-a112-ca6e1eac531e" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#c25a2572-a069-449d-836c-6fa40782f62a');
+var element = $('#9c212f9a-e398-4eb0-a112-ca6e1eac531e');
 
             setTimeout(function() {
                 var nbb_cell_id = 15;
@@ -1372,7 +1372,7 @@ <h2 id="ReduceMemoryProcessor" class="doc_header"><code>class</code> <code>Reduc
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">ReduceMemoryProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1073</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">888276</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">788016</span>. ✅
 </pre>
 </div>
 
@@ -1383,9 +1383,9 @@ <h2 id="ReduceMemoryProcessor" class="doc_header"><code>class</code> <code>Reduc
 
 
 
-<div id="512fcbf7-4c63-4c01-9b68-8671f8ab9d36" class="output_subarea output_javascript ">
+<div id="59dce8c6-1907-4fb1-aada-24e03c98b119" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#512fcbf7-4c63-4c01-9b68-8671f8ab9d36');
+var element = $('#59dce8c6-1907-4fb1-aada-24e03c98b119');
 
             setTimeout(function() {
                 var nbb_cell_id = 16;
@@ -1474,9 +1474,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="8857ac5b-e72c-48ba-8610-f678a01ae08c" class="output_subarea output_javascript ">
+<div id="891b9f9c-3fd3-40f1-afaa-3c236b11d8d7" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#8857ac5b-e72c-48ba-8610-f678a01ae08c');
+var element = $('#891b9f9c-3fd3-40f1-afaa-3c236b11d8d7');
 
             setTimeout(function() {
                 var nbb_cell_id = 18;
@@ -1557,7 +1557,7 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 <div class="output_area">
 
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>2022-04-29 18:47:51.245693: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+<pre>2022-05-02 13:15:13.405602: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
 </pre>
 </div>
@@ -1568,9 +1568,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="d7e4b4e6-8c39-4f24-a3b7-e2fe50b0bae7" class="output_subarea output_javascript ">
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             setTimeout(function() {
                 var nbb_cell_id = 21;
@@ -1620,9 +1620,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="7f65a349-674a-4097-a659-69b15dca2242" class="output_subarea output_javascript ">
+<div id="e0225701-9884-44aa-8c56-240812329f4f" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#7f65a349-674a-4097-a659-69b15dca2242');
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                 var nbb_cell_id = 22;
@@ -1671,7 +1671,7 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">DeepDreamGenerator</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">199</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1073</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">346651</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">271320</span>. ✅
 </pre>
 </div>
 
@@ -1682,9 +1682,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="ee859cc7-722a-42a2-b2b3-b98ee43e209c" class="output_subarea output_javascript ">
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@@ -1795,121 +1795,121 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
       <th>94</th>
       <td>NaN</td>
       <td>NaN</td>
-      <td>0.466837</td>
-      <td>0.207750</td>
-      <td>0.000000</td>
-      <td>0.275315</td>
       <td>1.000000</td>
-      <td>0.000000</td>
-      <td>0.025781</td>
-      <td>0.312666</td>
+      <td>0.823009</td>
+      <td>1.000000</td>
+      <td>0.992251</td>
+      <td>0.495938</td>
+      <td>0.262902</td>
+      <td>0.782719</td>
+      <td>0.753482</td>
       <td>...</td>
       <td>0.50</td>
       <td>0.50</td>
-      <td>0.75</td>
-      <td>0.50</td>
+      <td>0.5</td>
+      <td>0.25</td>
       <td>0.500000</td>
       <td>0.500000</td>
-      <td>0.333333</td>
       <td>0.500000</td>
+      <td>0.333333</td>
       <td>0.500000</td>
-      <td>0.666667</td>
+      <td>0.333333</td>
     </tr>
     <tr>
       <th>95</th>
       <td>NaN</td>
       <td>NaN</td>
-      <td>1.000000</td>
-      <td>0.497070</td>
-      <td>0.723670</td>
-      <td>0.170731</td>
-      <td>0.993114</td>
-      <td>0.741450</td>
-      <td>0.516449</td>
-      <td>0.455713</td>
+      <td>0.008697</td>
+      <td>0.081895</td>
+      <td>0.727875</td>
+      <td>0.046860</td>
+      <td>0.508427</td>
+      <td>0.046550</td>
+      <td>0.003431</td>
+      <td>0.695417</td>
       <td>...</td>
+      <td>0.25</td>
       <td>0.50</td>
-      <td>0.75</td>
-      <td>0.50</td>
-      <td>0.75</td>
-      <td>0.666667</td>
-      <td>1.000000</td>
-      <td>0.666667</td>
-      <td>0.833333</td>
-      <td>0.666667</td>
-      <td>0.833333</td>
+      <td>0.5</td>
+      <td>0.25</td>
+      <td>0.333333</td>
+      <td>0.333333</td>
+      <td>0.333333</td>
+      <td>0.333333</td>
+      <td>0.333333</td>
+      <td>0.166667</td>
     </tr>
     <tr>
       <th>96</th>
       <td>NaN</td>
       <td>NaN</td>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>0.180825</td>
-      <td>0.269748</td>
-      <td>0.788405</td>
-      <td>0.236521</td>
-      <td>0.290303</td>
+      <td>0.232997</td>
       <td>1.000000</td>
+      <td>0.000000</td>
+      <td>0.278891</td>
+      <td>0.292622</td>
+      <td>0.010535</td>
+      <td>0.490047</td>
+      <td>0.213381</td>
       <td>...</td>
-      <td>0.25</td>
       <td>0.50</td>
       <td>0.50</td>
+      <td>0.5</td>
       <td>0.50</td>
-      <td>0.333333</td>
       <td>0.500000</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
+      <td>0.666667</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.666667</td>
+      <td>0.666667</td>
     </tr>
     <tr>
       <th>97</th>
       <td>NaN</td>
       <td>NaN</td>
-      <td>1.000000</td>
-      <td>0.017941</td>
-      <td>0.990947</td>
-      <td>0.959095</td>
-      <td>0.009090</td>
-      <td>0.746303</td>
-      <td>0.771566</td>
-      <td>0.944655</td>
+      <td>0.984097</td>
+      <td>0.985342</td>
+      <td>0.752239</td>
+      <td>0.955584</td>
+      <td>0.479505</td>
+      <td>0.461875</td>
+      <td>0.247469</td>
+      <td>0.867348</td>
       <td>...</td>
-      <td>0.25</td>
-      <td>0.25</td>
-      <td>0.25</td>
-      <td>0.25</td>
-      <td>0.166667</td>
-      <td>0.166667</td>
-      <td>0.166667</td>
-      <td>0.166667</td>
-      <td>0.166667</td>
-      <td>0.166667</td>
+      <td>0.50</td>
+      <td>0.50</td>
+      <td>0.0</td>
+      <td>0.50</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.333333</td>
+      <td>0.500000</td>
     </tr>
     <tr>
       <th>98</th>
       <td>NaN</td>
       <td>NaN</td>
-      <td>0.483026</td>
-      <td>0.200206</td>
-      <td>0.956238</td>
-      <td>0.950899</td>
-      <td>0.989366</td>
+      <td>0.253801</td>
+      <td>0.313556</td>
+      <td>0.581586</td>
+      <td>0.551521</td>
+      <td>0.786020</td>
+      <td>0.514845</td>
       <td>0.000000</td>
-      <td>0.985511</td>
-      <td>0.193696</td>
+      <td>0.000479</td>
       <td>...</td>
-      <td>0.25</td>
-      <td>0.25</td>
+      <td>1.00</td>
+      <td>0.75</td>
+      <td>1.0</td>
       <td>0.50</td>
-      <td>0.25</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
-      <td>0.333333</td>
-      <td>0.500000</td>
-      <td>0.500000</td>
+      <td>1.000000</td>
+      <td>0.833333</td>
+      <td>0.833333</td>
+      <td>0.833333</td>
+      <td>0.833333</td>
+      <td>0.833333</td>
     </tr>
   </tbody>
 </table>
@@ -1923,9 +1923,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="7e0a2cc6-c5e3-42c5-8bc0-f4c3a09083ca" class="output_subarea output_javascript ">
+<div id="acf36072-8ce0-4e06-8e2c-5ba62641adb2" class="output_subarea output_javascript ">
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-var element = $('#7e0a2cc6-c5e3-42c5-8bc0-f4c3a09083ca');
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             setTimeout(function() {
                 var nbb_cell_id = 24;
@@ -1982,9 +1982,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
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+<div id="39c03392-d17e-497c-9083-112ad6b25a3e" class="output_subarea output_javascript ">
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-var element = $('#a14e3de5-c0ab-4c3c-9ee3-6644348fdff3');
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@@ -2085,123 +2085,123 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
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     <tr>
       <th>0</th>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>0.000000</td>
-      <td>0.042107</td>
-      <td>0.063387</td>
-      <td>0.979339</td>
+      <td>0.996130</td>
+      <td>0.012258</td>
       <td>1.000000</td>
-      <td>0.754362</td>
-      <td>0.000000</td>
-      <td>0.731277</td>
+      <td>0.955254</td>
+      <td>0.815248</td>
+      <td>0.997856</td>
+      <td>0.956926</td>
+      <td>0.791413</td>
+      <td>0.670447</td>
+      <td>0.510484</td>
       <td>...</td>
       <td>0.50</td>
+      <td>0.5</td>
+      <td>0.75</td>
+      <td>0.75</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+      <td>0.500000</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>0.253277</td>
+      <td>0.315667</td>
+      <td>0.533756</td>
+      <td>0.213132</td>
+      <td>0.490001</td>
+      <td>0.725381</td>
+      <td>0.685841</td>
+      <td>0.464807</td>
+      <td>0.105545</td>
+      <td>0.989926</td>
+      <td>...</td>
       <td>0.50</td>
+      <td>1.0</td>
       <td>0.50</td>
       <td>0.75</td>
+      <td>0.500000</td>
       <td>0.666667</td>
-      <td>0.666667</td>
-      <td>0.666667</td>
+      <td>0.500000</td>
       <td>0.666667</td>
       <td>0.500000</td>
       <td>0.666667</td>
     </tr>
     <tr>
-      <th>1</th>
-      <td>0.717585</td>
-      <td>1.000000</td>
-      <td>0.119904</td>
-      <td>0.719409</td>
-      <td>0.766565</td>
-      <td>0.030814</td>
-      <td>0.222251</td>
-      <td>0.517912</td>
-      <td>0.000000</td>
-      <td>0.024772</td>
+      <th>2</th>
+      <td>0.522252</td>
+      <td>0.707459</td>
+      <td>0.654992</td>
+      <td>0.475255</td>
+      <td>0.264495</td>
+      <td>0.498145</td>
+      <td>0.479104</td>
+      <td>0.277687</td>
+      <td>0.768365</td>
+      <td>0.169280</td>
       <td>...</td>
+      <td>0.00</td>
+      <td>0.5</td>
       <td>0.25</td>
-      <td>0.25</td>
-      <td>0.75</td>
       <td>0.50</td>
       <td>0.166667</td>
-      <td>0.333333</td>
+      <td>0.666667</td>
       <td>0.166667</td>
+      <td>0.666667</td>
       <td>0.333333</td>
-      <td>0.500000</td>
-      <td>0.333333</td>
+      <td>0.666667</td>
     </tr>
     <tr>
-      <th>2</th>
-      <td>0.165843</td>
-      <td>0.706939</td>
-      <td>0.563268</td>
-      <td>0.184079</td>
-      <td>0.021587</td>
-      <td>0.803588</td>
-      <td>1.000000</td>
-      <td>0.997696</td>
-      <td>0.163117</td>
-      <td>1.000000</td>
+      <th>3</th>
+      <td>0.060471</td>
+      <td>0.249417</td>
+      <td>0.521879</td>
+      <td>0.249297</td>
+      <td>0.000000</td>
+      <td>0.055882</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.000000</td>
+      <td>0.045899</td>
       <td>...</td>
       <td>0.50</td>
+      <td>0.5</td>
       <td>0.50</td>
       <td>0.50</td>
-      <td>0.25</td>
       <td>0.500000</td>
       <td>0.500000</td>
       <td>0.500000</td>
-      <td>0.333333</td>
-      <td>0.500000</td>
-      <td>0.166667</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.463913</td>
-      <td>0.043398</td>
-      <td>0.540559</td>
-      <td>0.780988</td>
-      <td>1.000000</td>
-      <td>0.472640</td>
-      <td>0.000000</td>
-      <td>0.991466</td>
-      <td>0.375541</td>
-      <td>0.986749</td>
-      <td>...</td>
-      <td>0.50</td>
-      <td>0.50</td>
-      <td>0.50</td>
-      <td>0.25</td>
-      <td>0.666667</td>
       <td>0.500000</td>
       <td>0.500000</td>
-      <td>0.333333</td>
       <td>0.500000</td>
-      <td>0.333333</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>0.178387</td>
-      <td>0.420503</td>
-      <td>0.930323</td>
-      <td>0.720097</td>
-      <td>0.051137</td>
-      <td>0.524874</td>
-      <td>0.000000</td>
+      <td>0.996391</td>
+      <td>0.470405</td>
+      <td>0.823283</td>
       <td>1.000000</td>
-      <td>0.702171</td>
-      <td>0.516301</td>
+      <td>1.000000</td>
+      <td>0.768204</td>
+      <td>0.706590</td>
+      <td>0.269626</td>
+      <td>0.000000</td>
+      <td>0.264364</td>
       <td>...</td>
       <td>0.25</td>
-      <td>0.50</td>
-      <td>0.25</td>
+      <td>0.5</td>
       <td>0.25</td>
+      <td>0.75</td>
       <td>0.333333</td>
       <td>0.500000</td>
-      <td>0.166667</td>
-      <td>0.333333</td>
       <td>0.333333</td>
+      <td>0.500000</td>
       <td>0.333333</td>
+      <td>0.500000</td>
     </tr>
   </tbody>
 </table>
@@ -2215,9 +2215,9 @@ <h2 id="DeepDreamGenerator" class="doc_header"><code>class</code> <code>DeepDrea
 
 
 
-<div id="f72c265a-da78-4a49-b716-1bd2f252e3d1" class="output_subarea output_javascript ">
+<div id="d68ab8c9-241e-4aa1-af32-3cd84bde1b82" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#f72c265a-da78-4a49-b716-1bd2f252e3d1');
+var element = $('#d68ab8c9-241e-4aa1-af32-3cd84bde1b82');
 
             setTimeout(function() {
                 var nbb_cell_id = 26;
@@ -2302,9 +2302,9 @@ <h2 id="UMAPFeatureGenerator" class="doc_header"><code>class</code> <code>UMAPFe
 
 
 
-<div id="2f271dfa-1af3-4877-9dab-c9c3f4244fa5" class="output_subarea output_javascript ">
+<div id="72b16342-8bfb-4d8b-b7c8-b21155e88da0" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#2f271dfa-1af3-4877-9dab-c9c3f4244fa5');
+var element = $('#72b16342-8bfb-4d8b-b7c8-b21155e88da0');
 
             setTimeout(function() {
                 var nbb_cell_id = 27;
@@ -2365,9 +2365,9 @@ <h2 id="UMAPFeatureGenerator" class="doc_header"><code>class</code> <code>UMAPFe
 
 
 
-<div id="30bc6537-7511-4e7c-82f3-70ae535c435d" class="output_subarea output_javascript ">
+<div id="1fe3177b-c4e2-4ed7-850c-07b11e92d526" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#30bc6537-7511-4e7c-82f3-70ae535c435d');
+var element = $('#1fe3177b-c4e2-4ed7-850c-07b11e92d526');
 
             setTimeout(function() {
                 var nbb_cell_id = 28;
@@ -2455,21 +2455,21 @@ <h2 id="UMAPFeatureGenerator" class="doc_header"><code>class</code> <code>UMAPFe
   <tbody>
     <tr>
       <th>n559bd06a8861222</th>
-      <td>0.416404</td>
-      <td>0.057189</td>
-      <td>0.793482</td>
+      <td>0.767670</td>
+      <td>0.000000</td>
+      <td>0.516831</td>
     </tr>
     <tr>
       <th>n9d39dea58c9e3cf</th>
-      <td>0.858917</td>
-      <td>0.329835</td>
-      <td>0.826034</td>
+      <td>0.537398</td>
+      <td>0.088487</td>
+      <td>1.000000</td>
     </tr>
     <tr>
       <th>nb64f06d3a9fc9f1</th>
-      <td>0.700607</td>
-      <td>0.096857</td>
-      <td>0.176513</td>
+      <td>0.014322</td>
+      <td>0.400093</td>
+      <td>0.000000</td>
     </tr>
   </tbody>
 </table>
@@ -2482,9 +2482,9 @@ <h2 id="UMAPFeatureGenerator" class="doc_header"><code>class</code> <code>UMAPFe
 
 
 
-<div id="ac10b6e1-2aad-4743-a64b-274dd033d144" class="output_subarea output_javascript ">
+<div id="1ec3daff-fe9f-47b0-a457-37ad8985b212" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#ac10b6e1-2aad-4743-a64b-274dd033d144');
+var element = $('#1ec3daff-fe9f-47b0-a457-37ad8985b212');
 
             setTimeout(function() {
                 var nbb_cell_id = 29;
@@ -2580,9 +2580,9 @@ <h2 id="BayesianGMMTargetProcessor" class="doc_header"><code>class</code> <code>
 
 
 
-<div id="465a63a3-02b3-4f79-86db-6dc501b38017" class="output_subarea output_javascript ">
+<div id="25e2632d-91ac-4fd1-9212-ee8f2ad6cfa4" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#465a63a3-02b3-4f79-86db-6dc501b38017');
+var element = $('#25e2632d-91ac-4fd1-9212-ee8f2ad6cfa4');
 
             setTimeout(function() {
                 var nbb_cell_id = 30;
@@ -2642,7 +2642,7 @@ <h2 id="BayesianGMMTargetProcessor" class="doc_header"><code>class</code> <code>
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">BayesianGMMTargetProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">100</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1074</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:03:28</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">855996</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:02:31</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">198486</span>. ✅
 </pre>
 </div>
 
@@ -2680,19 +2680,19 @@ <h2 id="BayesianGMMTargetProcessor" class="doc_header"><code>class</code> <code>
   </thead>
   <tbody>
     <tr>
-      <th>n44914183f920b7c</th>
-      <td>0.50</td>
+      <th>n16478df93c0ea62</th>
+      <td>0.5</td>
       <td>0.50</td>
     </tr>
     <tr>
-      <th>nbe23e3334634ee5</th>
-      <td>0.25</td>
-      <td>0.25</td>
+      <th>n86afa201ca85b27</th>
+      <td>0.5</td>
+      <td>0.50</td>
     </tr>
     <tr>
-      <th>nbb8bd1f4937f251</th>
-      <td>0.50</td>
-      <td>0.75</td>
+      <th>n78313c76c46b833</th>
+      <td>0.0</td>
+      <td>0.25</td>
     </tr>
   </tbody>
 </table>
@@ -2705,9 +2705,9 @@ <h2 id="BayesianGMMTargetProcessor" class="doc_header"><code>class</code> <code>
 
 
 
-<div id="03f2ae1d-863a-4900-8334-91b21ea05ba8" class="output_subarea output_javascript ">
+<div id="e9be4dc0-c125-4d42-b377-6461eddf73f9" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#03f2ae1d-863a-4900-8334-91b21ea05ba8');
+var element = $('#e9be4dc0-c125-4d42-b377-6461eddf73f9');
 
             setTimeout(function() {
                 var nbb_cell_id = 31;
@@ -2798,9 +2798,9 @@ <h2 id="GroupStatsPreProcessor" class="doc_header"><code>class</code> <code>Grou
 
 
 
-<div id="45666021-dcb8-446c-80d7-04d574c54851" class="output_subarea output_javascript ">
+<div id="6754a740-321d-4961-8f18-cb3eaa0dbdc2" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#45666021-dcb8-446c-80d7-04d574c54851');
+var element = $('#6754a740-321d-4961-8f18-cb3eaa0dbdc2');
 
             setTimeout(function() {
                 var nbb_cell_id = 33;
@@ -2854,7 +2854,7 @@ <h2 id="GroupStatsPreProcessor" class="doc_header"><code>class</code> <code>Grou
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">GroupStatsPreProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">332</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">054868</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">043740</span>. ✅
 </pre>
 </div>
 
@@ -2865,9 +2865,9 @@ <h2 id="GroupStatsPreProcessor" class="doc_header"><code>class</code> <code>Grou
 
 
 
-<div id="85cbe8de-e320-4580-b08e-128aaa3d8454" class="output_subarea output_javascript ">
+<div id="6b05246e-2a97-4913-85a0-fa635c7c69b0" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#85cbe8de-e320-4580-b08e-128aaa3d8454');
+var element = $('#6b05246e-2a97-4913-85a0-fa635c7c69b0');
 
             setTimeout(function() {
                 var nbb_cell_id = 34;
@@ -2931,9 +2931,9 @@ <h3 id="1.1.2.-Numerai-Classic:-Version-2-specific">1.1.2. Numerai Classic: Vers
 
 
 
-<div id="0b10ead6-9e9a-41d7-9227-b4d8f39cad64" class="output_subarea output_javascript ">
+<div id="35b4a2b1-24b0-4104-b7c7-1c003dc77dbf" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#0b10ead6-9e9a-41d7-9227-b4d8f39cad64');
+var element = $('#35b4a2b1-24b0-4104-b7c7-1c003dc77dbf');
 
             setTimeout(function() {
                 var nbb_cell_id = 37;
@@ -3020,9 +3020,9 @@ <h2 id="TalibFeatureGenerator" class="doc_header"><code>class</code> <code>Talib
 
 
 
-<div id="96c023ec-e85a-4932-8b13-3dba6c8d48cb" class="output_subarea output_javascript ">
+<div id="722b18cd-2730-4db5-aded-35bf63ea1354" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#96c023ec-e85a-4932-8b13-3dba6c8d48cb');
+var element = $('#722b18cd-2730-4db5-aded-35bf63ea1354');
 
             setTimeout(function() {
                 var nbb_cell_id = 38;
@@ -3114,9 +3114,9 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 
 
 
-<div id="55b4b438-4d61-436f-b3fa-4e7af9354369" class="output_subarea output_javascript ">
+<div id="d153f40c-73e8-4587-b0a5-977f91354eb9" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#55b4b438-4d61-436f-b3fa-4e7af9354369');
+var element = $('#d153f40c-73e8-4587-b0a5-977f91354eb9');
 
             setTimeout(function() {
                 var nbb_cell_id = 40;
@@ -3179,9 +3179,9 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 
 
 
-<div id="85e75de2-965f-44a1-a45c-28d7efa544c2" class="output_subarea output_javascript ">
+<div id="2340f104-274d-4fa3-8b8e-82fd83cfbbb3" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#85e75de2-965f-44a1-a45c-28d7efa544c2');
+var element = $('#2340f104-274d-4fa3-8b8e-82fd83cfbbb3');
 
             setTimeout(function() {
                 var nbb_cell_id = 41;
@@ -3234,9 +3234,9 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 
 
 
-<div id="1e934c67-17c1-42db-8fec-c00d7582e80b" class="output_subarea output_javascript ">
+<div id="dfbafad6-6694-41e2-aae5-3ba80479e487" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#1e934c67-17c1-42db-8fec-c00d7582e80b');
+var element = $('#dfbafad6-6694-41e2-aae5-3ba80479e487');
 
             setTimeout(function() {
                 var nbb_cell_id = 42;
@@ -3294,22 +3294,22 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 <div class="output_area">
 
 <div class="output_subarea output_stream output_stderr output_text">
-<pre>2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,115 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,120 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,118 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,120 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,123 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,125 INFO numexpr.utils: NumExpr defaulting to 8 threads.
-2022-04-29 18:52:41,130 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
-2022-04-29 18:52:41,135 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+<pre>2022-05-02 13:19:03,685 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,689 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,691 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,696 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,695 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,700 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,698 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,703 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,710 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,716 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,721 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,729 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,728 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,733 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+2022-05-02 13:19:03,738 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:03,744 INFO numexpr.utils: NumExpr defaulting to 8 threads.
 </pre>
 </div>
 </div>
@@ -3317,8 +3317,8 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 <div class="output_area">
 
 
-<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">KatsuFeatureGenerator</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2532555</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">21</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:06</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">882092</span>. ✅
+<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">KatsuFeatureGenerator</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2532770</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">21</span><span style="font-weight: bold">)</span>. Time taken for step: 
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:06</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">930909</span>. ✅
 </pre>
 </div>
 
@@ -3329,9 +3329,9 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 
 
 
-<div id="83ba127f-a5ec-4cc7-816f-42c20a77698c" class="output_subarea output_javascript ">
+<div id="24419b5d-f616-440b-8b93-ea46cf86f3e9" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#83ba127f-a5ec-4cc7-816f-42c20a77698c');
+var element = $('#24419b5d-f616-440b-8b93-ea46cf86f3e9');
 
             setTimeout(function() {
                 var nbb_cell_id = 43;
@@ -3420,34 +3420,34 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
   </thead>
   <tbody>
     <tr>
-      <th>19786946</th>
-      <td>0.086860</td>
-      <td>0.011198</td>
-      <td>0.986855</td>
-      <td>-0.045010</td>
-      <td>0.012140</td>
-      <td>0.995106</td>
-      <td>-0.148342</td>
-      <td>0.011382</td>
-      <td>0.961008</td>
-      <td>45.064623</td>
-      <td>-0.162609</td>
-      <td>-0.189706</td>
+      <th>19772309</th>
+      <td>0.172932</td>
+      <td>0.029689</td>
+      <td>1.072902</td>
+      <td>0.300000</td>
+      <td>0.023119</td>
+      <td>1.158129</td>
+      <td>0.248</td>
+      <td>0.022138</td>
+      <td>1.186312</td>
+      <td>67.052407</td>
+      <td>3.865221</td>
+      <td>3.950395</td>
     </tr>
     <tr>
-      <th>19792087</th>
-      <td>0.076923</td>
-      <td>0.011128</td>
-      <td>0.987406</td>
-      <td>-0.016064</td>
-      <td>0.011934</td>
-      <td>0.999592</td>
-      <td>-0.109091</td>
-      <td>0.011055</td>
-      <td>0.966851</td>
-      <td>46.363268</td>
-      <td>-0.233052</td>
-      <td>-0.198375</td>
+      <th>19777095</th>
+      <td>0.174242</td>
+      <td>0.029667</td>
+      <td>1.057659</td>
+      <td>0.291667</td>
+      <td>0.023161</td>
+      <td>1.143279</td>
+      <td>0.250</td>
+      <td>0.022128</td>
+      <td>1.174094</td>
+      <td>65.378476</td>
+      <td>3.914410</td>
+      <td>3.943198</td>
     </tr>
   </tbody>
 </table>
@@ -3460,9 +3460,9 @@ <h2 id="KatsuFeatureGenerator" class="doc_header"><code>class</code> <code>Katsu
 
 
 
-<div id="b4d4b298-556c-48fb-8715-cf08c2324a39" class="output_subarea output_javascript ">
+<div id="8cce6085-e422-4ef8-8367-b5e4ba15c2f4" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#b4d4b298-556c-48fb-8715-cf08c2324a39');
+var element = $('#8cce6085-e422-4ef8-8367-b5e4ba15c2f4');
 
             setTimeout(function() {
                 var nbb_cell_id = 44;
@@ -3535,9 +3535,9 @@ <h2 id="EraQuantileProcessor" class="doc_header"><code>class</code> <code>EraQua
 
 
 
-<div id="0527ad68-a76c-4e98-8389-1b5518f7a3d8" class="output_subarea output_javascript ">
+<div id="b3b6077a-af00-45f2-a0f9-d3f985013066" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#0527ad68-a76c-4e98-8389-1b5518f7a3d8');
+var element = $('#b3b6077a-af00-45f2-a0f9-d3f985013066');
 
             setTimeout(function() {
                 var nbb_cell_id = 45;
@@ -3597,7 +3597,7 @@ <h2 id="EraQuantileProcessor" class="doc_header"><code>class</code> <code>EraQua
 
 
 <div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">EraQuantileProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10000</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">33</span><span style="font-weight: bold">)</span>. Time taken for step: 
-<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:08</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">582187</span>. ✅
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:08</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">814294</span>. ✅
 </pre>
 </div>
 
@@ -3608,9 +3608,9 @@ <h2 id="EraQuantileProcessor" class="doc_header"><code>class</code> <code>EraQua
 
 
 
-<div id="9a044fbe-0f92-45c9-892e-e220b2393458" class="output_subarea output_javascript ">
+<div id="737e3644-464b-466b-b60c-20732e973bac" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#9a044fbe-0f92-45c9-892e-e220b2393458');
+var element = $('#737e3644-464b-466b-b60c-20732e973bac');
 
             setTimeout(function() {
                 var nbb_cell_id = 46;
@@ -3701,52 +3701,52 @@ <h2 id="EraQuantileProcessor" class="doc_header"><code>class</code> <code>EraQua
   </thead>
   <tbody>
     <tr>
-      <th>2927639</th>
-      <td>-0.014571</td>
-      <td>0.001810</td>
-      <td>0.990366</td>
-      <td>-0.006188</td>
-      <td>0.002102</td>
-      <td>0.993832</td>
-      <td>-0.000723</td>
-      <td>0.001986</td>
-      <td>1.001429</td>
-      <td>47.416878</td>
+      <th>14327556</th>
+      <td>0.348092</td>
+      <td>0.006448</td>
+      <td>1.226758</td>
+      <td>0.237165</td>
+      <td>0.005366</td>
+      <td>1.261297</td>
+      <td>0.559683</td>
+      <td>0.005031</td>
+      <td>1.269734</td>
+      <td>74.890404</td>
       <td>...</td>
+      <td>1.0</td>
+      <td>1.0</td>
       <td>0.5</td>
+      <td>1.0</td>
+      <td>1.0</td>
       <td>0.5</td>
-      <td>0.0</td>
-      <td>0.5</td>
-      <td>0.5</td>
-      <td>0.0</td>
-      <td>0.5</td>
-      <td>0.5</td>
-      <td>0.5</td>
-      <td>0.5</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
     </tr>
     <tr>
-      <th>3368644</th>
-      <td>0.042708</td>
-      <td>0.003863</td>
-      <td>1.018052</td>
-      <td>-0.206815</td>
-      <td>0.004804</td>
-      <td>0.960008</td>
-      <td>-0.211190</td>
-      <td>0.005068</td>
-      <td>0.901031</td>
-      <td>45.971828</td>
+      <th>2622418</th>
+      <td>0.099745</td>
+      <td>0.001118</td>
+      <td>1.051795</td>
+      <td>0.068323</td>
+      <td>0.001183</td>
+      <td>1.064950</td>
+      <td>0.068323</td>
+      <td>0.001486</td>
+      <td>1.055776</td>
+      <td>71.793104</td>
       <td>...</td>
+      <td>1.0</td>
       <td>0.0</td>
       <td>0.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
       <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
-      <td>0.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
+      <td>1.0</td>
     </tr>
   </tbody>
 </table>
@@ -3760,9 +3760,9 @@ <h2 id="EraQuantileProcessor" class="doc_header"><code>class</code> <code>EraQua
 
 
 
-<div id="f5e2b30e-e2e8-4478-89f1-22fa4afd05aa" class="output_subarea output_javascript ">
+<div id="39462cdd-9a8b-4ffb-8d1e-06d3ec0c2e68" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#f5e2b30e-e2e8-4478-89f1-22fa4afd05aa');
+var element = $('#39462cdd-9a8b-4ffb-8d1e-06d3ec0c2e68');
 
             setTimeout(function() {
                 var nbb_cell_id = 47;
@@ -3837,14 +3837,14 @@ <h2 id="TickerMapper" class="doc_header"><code>class</code> <code>TickerMapper</
 
 
 
-<div id="5377fd37-7715-4cc2-83fc-6041b166f091" class="output_subarea output_javascript ">
+<div id="b4585090-1bbb-4951-8115-ac92ce4e7db0" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#5377fd37-7715-4cc2-83fc-6041b166f091');
+var element = $('#b4585090-1bbb-4951-8115-ac92ce4e7db0');
 
             setTimeout(function() {
                 var nbb_cell_id = 49;
-                var nbb_unformatted_code = "#export\nclass TickerMapper(BaseProcessor):\n    def __init__(self, ticker_col: str = \"ticker\", target_ticker_format: str = \"bloomberg_ticker\"):\n        super().__init__()\n        self.ticker_col = ticker_col\n        self.target_ticker_format = target_ticker_format\n        \n        self.signals_map_path = \"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\"\n        self.ticker_map = pd.read_csv(self.signals_map_path)\n        \n        assert self.ticker_col in self.ticker_map.columns, f\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\"\n        assert self.target_ticker_format in self.ticker_map.columns, f\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\"\n        \n        self.mapping = dict(self.ticker_map[[self.ticker_col, self.target_ticker_format]].values)\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\n        dataf[\"bloomberg_ticker\"] = dataf[self.ticker_col].map(self.mapping)\n        return NumerFrame(dataf)";
-                var nbb_formatted_code = "# export\nclass TickerMapper(BaseProcessor):\n    def __init__(\n        self, ticker_col: str = \"ticker\", target_ticker_format: str = \"bloomberg_ticker\"\n    ):\n        super().__init__()\n        self.ticker_col = ticker_col\n        self.target_ticker_format = target_ticker_format\n\n        self.signals_map_path = \"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\"\n        self.ticker_map = pd.read_csv(self.signals_map_path)\n\n        assert (\n            self.ticker_col in self.ticker_map.columns\n        ), f\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\"\n        assert (\n            self.target_ticker_format in self.ticker_map.columns\n        ), f\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\"\n\n        self.mapping = dict(\n            self.ticker_map[[self.ticker_col, self.target_ticker_format]].values\n        )\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\n        dataf[\"bloomberg_ticker\"] = dataf[self.ticker_col].map(self.mapping)\n        return NumerFrame(dataf)";
+                var nbb_unformatted_code = "#export\nclass TickerMapper(BaseProcessor):\n    \"\"\"\n    Map ticker from one format to another. \\n\n    :param ticker_col: Column used for mapping. Must already be present in the input data. \\n\n    :param target_ticker_format: Format to map tickers to. Must be present in the ticker map. \\n\n    Supported ticker formats are: ['ticker', 'bloomberg_ticker', 'yahoo']\n    \"\"\"\n    def __init__(self, ticker_col: str = \"ticker\", target_ticker_format: str = \"bloomberg_ticker\"):\n        super().__init__()\n        self.ticker_col = ticker_col\n        self.target_ticker_format = target_ticker_format\n\n        self.signals_map_path = \"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\"\n        self.ticker_map = pd.read_csv(self.signals_map_path)\n\n        assert self.ticker_col in self.ticker_map.columns, f\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\"\n        assert self.target_ticker_format in self.ticker_map.columns, f\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\"\n\n        self.mapping = dict(self.ticker_map[[self.ticker_col, self.target_ticker_format]].values)\n\n    @display_processor_info\n    def transform(self, dataf: Union[pd.DataFrame, NumerFrame], *args, **kwargs) -> NumerFrame:\n        dataf[self.target_ticker_format] = dataf[self.ticker_col].map(self.mapping)\n        return NumerFrame(dataf)";
+                var nbb_formatted_code = "# export\nclass TickerMapper(BaseProcessor):\n    \"\"\"\n    Map ticker from one format to another. \\n\n    :param ticker_col: Column used for mapping. Must already be present in the input data. \\n\n    :param target_ticker_format: Format to map tickers to. Must be present in the ticker map. \\n\n    Supported ticker formats are: ['ticker', 'bloomberg_ticker', 'yahoo']\n    \"\"\"\n\n    def __init__(\n        self, ticker_col: str = \"ticker\", target_ticker_format: str = \"bloomberg_ticker\"\n    ):\n        super().__init__()\n        self.ticker_col = ticker_col\n        self.target_ticker_format = target_ticker_format\n\n        self.signals_map_path = \"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\"\n        self.ticker_map = pd.read_csv(self.signals_map_path)\n\n        assert (\n            self.ticker_col in self.ticker_map.columns\n        ), f\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\"\n        assert (\n            self.target_ticker_format in self.ticker_map.columns\n        ), f\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\"\n\n        self.mapping = dict(\n            self.ticker_map[[self.ticker_col, self.target_ticker_format]].values\n        )\n\n    @display_processor_info\n    def transform(\n        self, dataf: Union[pd.DataFrame, NumerFrame], *args, **kwargs\n    ) -> NumerFrame:\n        dataf[self.target_ticker_format] = dataf[self.ticker_col].map(self.mapping)\n        return NumerFrame(dataf)";
                 var nbb_cells = Jupyter.notebook.get_cells();
                 for (var i = 0; i < nbb_cells.length; ++i) {
                     if (nbb_cells[i].input_prompt_number == nbb_cell_id) {
@@ -3889,7 +3889,7 @@ <h2 id="TickerMapper" class="doc_header"><code>class</code> <code>TickerMapper</
 <div class="output_area">
 
 
-<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">TickerMapper</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span><span style="font-weight: bold">)</span>. Time taken for step: <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">002790</span>. ✅
+<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">TickerMapper</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">2</span><span style="font-weight: bold">)</span>. Time taken for step: <span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:00</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">003475</span>. ✅
 </pre>
 </div>
 
@@ -3942,9 +3942,9 @@ <h2 id="TickerMapper" class="doc_header"><code>class</code> <code>TickerMapper</
 
 
 
-<div id="b807075a-6e58-4947-a718-0770b4b8190e" class="output_subarea output_javascript ">
+<div id="8c5bb5b7-6a65-418e-b346-1a9f290ccdbc" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#b807075a-6e58-4947-a718-0770b4b8190e');
+var element = $('#8c5bb5b7-6a65-418e-b346-1a9f290ccdbc');
 
             setTimeout(function() {
                 var nbb_cell_id = 50;
@@ -3969,6 +3969,177 @@ <h2 id="TickerMapper" class="doc_header"><code>class</code> <code>TickerMapper</
 </div>
 </div>
 
+</div>
+    {% endraw %}
+
+<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="1.2.5.-SignalsTargetProcessor">1.2.5. SignalsTargetProcessor<a class="anchor-link" href="#1.2.5.-SignalsTargetProcessor"> </a></h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Numerai provides <a href="https://docs.numer.ai/numerai-signals/signals-overview#universe">targets for 5000 stocks</a> that are neutralized against all sorts of factors. However, it can be helpful to experiment with creating your own targets. You might want to explore different windows, different target binning and/or neutralization. <a href="/numerbloxpreprocessing.html#SignalsTargetProcessor"><code>SignalsTargetProcessor</code></a> engineers 3 different targets for every given windows:</p>
+<ul>
+<li><code>_raw</code>: Raw return based on price movements.</li>
+<li><code>_rank</code>: Ranks of raw return.</li>
+<li><code>_group</code>: Binned returns based on rank.</li>
+</ul>
+<p>Note that Numerai provides targets based on 4-day returns and 20-day returns. While you can explore any window you like, it makes sense to start with <code>windows</code> close to these timeframes.</p>
+<p>For the <code>bins</code> argument there are also many options possible. The followed are commonly used binning:</p>
+<ul>
+<li>Nomi bins: <code>[0, 0.05, 0.25, 0.75, 0.95, 1]</code></li>
+<li>Uniform bins: <code>[0, 0.20, 0.40, 0.60, 0.80, 1]</code></li>
+</ul>
+
+</div>
+</div>
+</div>
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+
+<div class="output_wrapper">
+<div class="output">
+
+<div class="output_area">
+
+
+<div class="output_markdown rendered_html output_subarea ">
+<h2 id="SignalsTargetProcessor" class="doc_header"><code>class</code> <code>SignalsTargetProcessor</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/preprocessing.py#L728" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>SignalsTargetProcessor</code>(<strong><code>price_col</code></strong>:<code>str</code>=<em><code>'close'</code></em>, <strong><code>windows</code></strong>:<code>list</code>=<em><code>None</code></em>, <strong><code>bins</code></strong>:<code>list</code>=<em><code>None</code></em>, <strong><code>labels</code></strong>:<code>list</code>=<em><code>None</code></em>) :: <a href="/numerbloxpreprocessing.html#BaseProcessor"><code>BaseProcessor</code></a></p>
+</blockquote>
+<p>Engineer targets for Numerai Signals.</p>
+<p>More information on implements Numerai Signals targets:</p>
+<p><a href="https://forum.numer.ai/t/decoding-the-signals-target/2501">https://forum.numer.ai/t/decoding-the-signals-target/2501</a></p>
+<p>:param price_col: Column from which target will be derived.</p>
+<p>:param windows: Timeframes to use for engineering targets. 10 and 20-day by default.</p>
+<p>:param bins: Binning used to create group targets. Nomi binning by default.</p>
+<p>:param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.</p>
+
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+    {% endraw %}
+
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+
+<div class="output_wrapper">
+<div class="output">
+
+<div class="output_area">
+
+
+
+
+<div id="10ee4ac1-7393-4d4f-82dc-cb1dc06d8e65" class="output_subarea output_javascript ">
+<script type="text/javascript">
+var element = $('#10ee4ac1-7393-4d4f-82dc-cb1dc06d8e65');
+
+            setTimeout(function() {
+                var nbb_cell_id = 51;
+                var nbb_unformatted_code = "# export\nclass SignalsTargetProcessor(BaseProcessor):\n    \"\"\"\n    Engineer targets for Numerai Signals. \\n\n    More information on implements Numerai Signals targets: \\n\n    https://forum.numer.ai/t/decoding-the-signals-target/2501\n\n    :param price_col: Column from which target will be derived. \\n\n    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \\n\n    :param bins: Binning used to create group targets. Nomi binning by default. \\n\n    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.\n    \"\"\"\n    def __init__(self, price_col: str = 'close', windows: list = None, bins: list = None, labels: list = None):\n        super().__init__()\n        self.price_col = price_col\n        self.windows = windows if windows else [10, 20]\n        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]\n        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame) -> NumerFrame:\n        for window in tqdm(self.windows, desc=\"Signals target engineering windows\"):\n            dataf.loc[:, f\"target_{window}d_raw\"] = (\n                dataf[self.price_col].pct_change(periods=window).shift(-window)\n            )\n            era_groups = dataf.groupby(dataf.meta.era_col)\n\n            dataf.loc[:, f\"target_{window}d_rank\"] = era_groups[f\"target_{window}d_raw\"].rank(pct=True,\n                                                                                              method=\"first\"\n                                                                                              )\n            dataf.loc[:, f\"target_{window}d_group\"] = era_groups[f\"target_{window}d_rank\"].transform(\n                lambda group: pd.cut(\n                    group,\n                    bins=self.bins,\n                    labels=self.labels,\n                    include_lowest=True\n                )\n            )\n        return NumerFrame(dataf)";
+                var nbb_formatted_code = "# export\nclass SignalsTargetProcessor(BaseProcessor):\n    \"\"\"\n    Engineer targets for Numerai Signals. \\n\n    More information on implements Numerai Signals targets: \\n\n    https://forum.numer.ai/t/decoding-the-signals-target/2501\n\n    :param price_col: Column from which target will be derived. \\n\n    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \\n\n    :param bins: Binning used to create group targets. Nomi binning by default. \\n\n    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.\n    \"\"\"\n\n    def __init__(\n        self,\n        price_col: str = \"close\",\n        windows: list = None,\n        bins: list = None,\n        labels: list = None,\n    ):\n        super().__init__()\n        self.price_col = price_col\n        self.windows = windows if windows else [10, 20]\n        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]\n        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame) -> NumerFrame:\n        for window in tqdm(self.windows, desc=\"Signals target engineering windows\"):\n            dataf.loc[:, f\"target_{window}d_raw\"] = (\n                dataf[self.price_col].pct_change(periods=window).shift(-window)\n            )\n            era_groups = dataf.groupby(dataf.meta.era_col)\n\n            dataf.loc[:, f\"target_{window}d_rank\"] = era_groups[\n                f\"target_{window}d_raw\"\n            ].rank(pct=True, method=\"first\")\n            dataf.loc[:, f\"target_{window}d_group\"] = era_groups[\n                f\"target_{window}d_rank\"\n            ].transform(\n                lambda group: pd.cut(\n                    group, bins=self.bins, labels=self.labels, include_lowest=True\n                )\n            )\n        return NumerFrame(dataf)";
+                var nbb_cells = Jupyter.notebook.get_cells();
+                for (var i = 0; i < nbb_cells.length; ++i) {
+                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {
+                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {
+                             nbb_cells[i].set_text(nbb_formatted_code);
+                        }
+                        break;
+                    }
+                }
+            }, 500);
+            
+</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+    {% endraw %}
+
+    {% raw %}
+    
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stp</span> <span class="o">=</span> <span class="n">SignalsTargetProcessor</span><span class="p">()</span>
+<span class="n">era_dataf</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">era_col</span> <span class="o">=</span> <span class="s2">&quot;date&quot;</span>
+<span class="n">new_target_dataf</span> <span class="o">=</span> <span class="n">stp</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">era_dataf</span><span class="p">)</span>
+<span class="n">new_target_dataf</span><span class="o">.</span><span class="n">get_target_data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+
+    </div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+<div class="output_area">
+
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>2022-05-02 13:19:22,723 INFO numexpr.utils: Note: NumExpr detected 16 cores but &#34;NUMEXPR_MAX_THREADS&#34; not set, so enforcing safe limit of 8.
+2022-05-02 13:19:22,725 INFO numexpr.utils: NumExpr defaulting to 8 threads.
+</pre>
+</div>
+</div>
+
+<div class="output_area">
+
+
+<div class="output_html rendered_html output_subarea "><pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">✅ Finished step <span style="font-weight: bold">SignalsTargetProcessor</span>. Output <span style="color: #808000; text-decoration-color: #808000">shape</span>=<span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">10000</span>, <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">39</span><span style="font-weight: bold">)</span>. Time taken for step: 
+<span style="color: #000080; text-decoration-color: #000080; font-weight: bold">0:00:25</span><span style="color: #000080; text-decoration-color: #000080">.</span><span style="color: #000080; text-decoration-color: #000080; font-weight: bold">177302</span>. ✅
+</pre>
+</div>
+
+</div>
+
+<div class="output_area">
+
+
+
+
+<div id="00fd844f-89b6-4c25-ad4c-e05135326697" class="output_subarea output_javascript ">
+<script type="text/javascript">
+var element = $('#00fd844f-89b6-4c25-ad4c-e05135326697');
+
+            setTimeout(function() {
+                var nbb_cell_id = 52;
+                var nbb_unformatted_code = "stp = SignalsTargetProcessor()\nera_dataf.meta.era_col = \"date\"\nnew_target_dataf = stp.transform(era_dataf)";
+                var nbb_formatted_code = "stp = SignalsTargetProcessor()\nera_dataf.meta.era_col = \"date\"\nnew_target_dataf = stp.transform(era_dataf)";
+                var nbb_cells = Jupyter.notebook.get_cells();
+                for (var i = 0; i < nbb_cells.length; ++i) {
+                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {
+                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {
+                             nbb_cells[i].set_text(nbb_formatted_code);
+                        }
+                        break;
+                    }
+                }
+            }, 500);
+            
+</script>
+</div>
+
+</div>
+
+</div>
+</div>
+
 </div>
     {% endraw %}
 
@@ -3998,7 +4169,7 @@ <h2 id="2.-Custom-preprocessors">2. Custom preprocessors<a class="anchor-link" h
 
 
 <div class="output_markdown rendered_html output_subarea ">
-<h2 id="AwesomePreProcessor" class="doc_header"><code>class</code> <code>AwesomePreProcessor</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/preprocessing.py#L728" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AwesomePreProcessor</code>() :: <a href="/numerbloxpreprocessing.html#BaseProcessor"><code>BaseProcessor</code></a></p>
+<h2 id="AwesomePreProcessor" class="doc_header"><code>class</code> <code>AwesomePreProcessor</code><a href="https://github.com/crowdcent/numerblox/tree/master/numerblox/preprocessing.py#L768" class="source_link" style="float:right">[source]</a></h2><blockquote><p><code>AwesomePreProcessor</code>() :: <a href="/numerbloxpreprocessing.html#BaseProcessor"><code>BaseProcessor</code></a></p>
 </blockquote>
 <p>TEMPLATE - Do some awesome preprocessing.</p>
 
@@ -4024,12 +4195,12 @@ <h2 id="AwesomePreProcessor" class="doc_header"><code>class</code> <code>Awesome
 
 
 
-<div id="e52869b5-e7fb-492b-a124-2ada36306809" class="output_subarea output_javascript ">
+<div id="0f444362-dec1-4ea4-b5b9-dd9a4ff04545" class="output_subarea output_javascript ">
 <script type="text/javascript">
-var element = $('#e52869b5-e7fb-492b-a124-2ada36306809');
+var element = $('#0f444362-dec1-4ea4-b5b9-dd9a4ff04545');
 
             setTimeout(function() {
-                var nbb_cell_id = 51;
+                var nbb_cell_id = 54;
                 var nbb_unformatted_code = "# export\nclass AwesomePreProcessor(BaseProcessor):\n    \"\"\" TEMPLATE - Do some awesome preprocessing. \"\"\"\n    def __init__(self):\n        super().__init__()\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\n        # Do processing\n        ...\n        # Parse all contents of NumerFrame to the next pipeline step\n        return NumerFrame(dataf)";
                 var nbb_formatted_code = "# export\nclass AwesomePreProcessor(BaseProcessor):\n    \"\"\"TEMPLATE - Do some awesome preprocessing.\"\"\"\n\n    def __init__(self):\n        super().__init__()\n\n    @display_processor_info\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\n        # Do processing\n        ...\n        # Parse all contents of NumerFrame to the next pipeline step\n        return NumerFrame(dataf)";
                 var nbb_cells = Jupyter.notebook.get_cells();
diff --git a/nbs/03_preprocessing.ipynb b/nbs/03_preprocessing.ipynb
index 1784617b..b0cb5798 100644
--- a/nbs/03_preprocessing.ipynb
+++ b/nbs/03_preprocessing.ipynb
@@ -242,8 +242,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mTestDisplay\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m314\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \u001B[1;34m0:00:02\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m002627\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TestDisplay</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">314</span><span style=\"font-weight: bold\">)</span>. Time taken for step: <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:02</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">002627</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mTestDisplay\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m314\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \u001B[1;34m0:00:02\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m000485\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TestDisplay</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">314</span><span style=\"font-weight: bold\">)</span>. Time taken for step: <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:02</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">000485</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -360,8 +360,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mCopyPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m314\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m002955\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">CopyPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">314</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">002955</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mCopyPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m314\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m003298\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">CopyPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">314</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">003298</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -438,8 +438,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mFeatureSelectionPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m5\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m002415\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">FeatureSelectionPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">002415</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mFeatureSelectionPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m5\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m001208\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">FeatureSelectionPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">001208</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -545,8 +545,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mTargetSelectionPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m1055\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m025411\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TargetSelectionPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1055</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">025411</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mTargetSelectionPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m1055\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m028618\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TargetSelectionPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1055</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">028618</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -709,8 +709,8 @@
     },
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mReduceMemoryProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m1073\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m888276\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">ReduceMemoryProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1073</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">888276</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mReduceMemoryProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m1073\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m788016\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">ReduceMemoryProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1073</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">788016</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -926,8 +926,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-29 18:46:32,054 INFO numerapi.utils: starting download\n",
-      "deepdream_test/numerai_validation_data.parquet: 228MB [01:09, 3.25MB/s]                            \n"
+      "2022-05-02 13:13:59,142 INFO numerapi.utils: starting download\n",
+      "deepdream_test/numerai_validation_data.parquet: 228MB [01:09, 3.28MB/s]                              \n"
      ]
     },
     {
@@ -979,8 +979,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-29 18:47:47,152 INFO numerapi.utils: starting download\n",
-      "deepdream_test/features.json: 441kB [00:03, 112kB/s]                             \n"
+      "2022-05-02 13:15:12,503 INFO numerapi.utils: starting download\n",
+      "deepdream_test/features.json: 441kB [00:00, 603kB/s]                             \n"
      ]
     },
     {
@@ -1021,7 +1021,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-29 18:47:51.245693: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2022-05-02 13:15:13.405602: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     },
@@ -1078,7 +1078,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "971f0e9423064a6d929a5b29b2c49b07"
+       "model_id": "0479b64b099042c19ac98ae5cff37624"
       }
      },
      "metadata": {},
@@ -1086,8 +1086,8 @@
     },
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mDeepDreamGenerator\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m199\u001B[0m, \u001B[1;36m1073\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m346651\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">DeepDreamGenerator</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">199</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1073</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">346651</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mDeepDreamGenerator\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m199\u001B[0m, \u001B[1;36m1073\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m271320\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">DeepDreamGenerator</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">199</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1073</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">271320</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1135,8 +1135,8 @@
     },
     {
      "data": {
-      "text/plain": "    era data_type  feature_dichasial_hammier_spawner  \\\n94  NaN       NaN                           0.466837   \n95  NaN       NaN                           1.000000   \n96  NaN       NaN                           0.000000   \n97  NaN       NaN                           1.000000   \n98  NaN       NaN                           0.483026   \n\n    feature_rheumy_epistemic_prancer  feature_pert_performative_hormuz  \\\n94                          0.207750                          0.000000   \n95                          0.497070                          0.723670   \n96                          0.000000                          0.180825   \n97                          0.017941                          0.990947   \n98                          0.200206                          0.956238   \n\n    feature_hillier_unpitied_theobromine  \\\n94                              0.275315   \n95                              0.170731   \n96                              0.269748   \n97                              0.959095   \n98                              0.950899   \n\n    feature_perigean_bewitching_thruster  feature_renegade_undomestic_milord  \\\n94                              1.000000                            0.000000   \n95                              0.993114                            0.741450   \n96                              0.788405                            0.236521   \n97                              0.009090                            0.746303   \n98                              0.989366                            0.000000   \n\n    feature_koranic_rude_corf  feature_demisable_expiring_millepede  ...  \\\n94                   0.025781                              0.312666  ...   \n95                   0.516449                              0.455713  ...   \n96                   0.290303                              1.000000  ...   \n97                   0.771566                              0.944655  ...   \n98                   0.985511                              0.193696  ...   \n\n    target_paul_20  target_paul_60  target_george_20  target_george_60  \\\n94            0.50            0.50              0.75              0.50   \n95            0.50            0.75              0.50              0.75   \n96            0.25            0.50              0.50              0.50   \n97            0.25            0.25              0.25              0.25   \n98            0.25            0.25              0.50              0.25   \n\n    target_william_20  target_william_60  target_arthur_20  target_arthur_60  \\\n94           0.500000           0.500000          0.333333          0.500000   \n95           0.666667           1.000000          0.666667          0.833333   \n96           0.333333           0.500000          0.333333          0.333333   \n97           0.166667           0.166667          0.166667          0.166667   \n98           0.333333           0.333333          0.333333          0.333333   \n\n    target_thomas_20  target_thomas_60  \n94          0.500000          0.666667  \n95          0.666667          0.833333  \n96          0.333333          0.333333  \n97          0.166667          0.166667  \n98          0.500000          0.500000  \n\n[5 rows x 1073 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>era</th>\n      <th>data_type</th>\n      <th>feature_dichasial_hammier_spawner</th>\n      <th>feature_rheumy_epistemic_prancer</th>\n      <th>feature_pert_performative_hormuz</th>\n      <th>feature_hillier_unpitied_theobromine</th>\n      <th>feature_perigean_bewitching_thruster</th>\n      <th>feature_renegade_undomestic_milord</th>\n      <th>feature_koranic_rude_corf</th>\n      <th>feature_demisable_expiring_millepede</th>\n      <th>...</th>\n      <th>target_paul_20</th>\n      <th>target_paul_60</th>\n      <th>target_george_20</th>\n      <th>target_george_60</th>\n      <th>target_william_20</th>\n      <th>target_william_60</th>\n      <th>target_arthur_20</th>\n      <th>target_arthur_60</th>\n      <th>target_thomas_20</th>\n      <th>target_thomas_60</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>94</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.466837</td>\n      <td>0.207750</td>\n      <td>0.000000</td>\n      <td>0.275315</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.025781</td>\n      <td>0.312666</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.75</td>\n      <td>0.50</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.497070</td>\n      <td>0.723670</td>\n      <td>0.170731</td>\n      <td>0.993114</td>\n      <td>0.741450</td>\n      <td>0.516449</td>\n      <td>0.455713</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.75</td>\n      <td>0.50</td>\n      <td>0.75</td>\n      <td>0.666667</td>\n      <td>1.000000</td>\n      <td>0.666667</td>\n      <td>0.833333</td>\n      <td>0.666667</td>\n      <td>0.833333</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.180825</td>\n      <td>0.269748</td>\n      <td>0.788405</td>\n      <td>0.236521</td>\n      <td>0.290303</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.017941</td>\n      <td>0.990947</td>\n      <td>0.959095</td>\n      <td>0.009090</td>\n      <td>0.746303</td>\n      <td>0.771566</td>\n      <td>0.944655</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n      <td>0.166667</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.483026</td>\n      <td>0.200206</td>\n      <td>0.956238</td>\n      <td>0.950899</td>\n      <td>0.989366</td>\n      <td>0.000000</td>\n      <td>0.985511</td>\n      <td>0.193696</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.50</td>\n      <td>0.25</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1073 columns</p>\n</div>"
+      "text/plain": "    era data_type  feature_dichasial_hammier_spawner  \\\n94  NaN       NaN                           1.000000   \n95  NaN       NaN                           0.008697   \n96  NaN       NaN                           0.232997   \n97  NaN       NaN                           0.984097   \n98  NaN       NaN                           0.253801   \n\n    feature_rheumy_epistemic_prancer  feature_pert_performative_hormuz  \\\n94                          0.823009                          1.000000   \n95                          0.081895                          0.727875   \n96                          1.000000                          0.000000   \n97                          0.985342                          0.752239   \n98                          0.313556                          0.581586   \n\n    feature_hillier_unpitied_theobromine  \\\n94                              0.992251   \n95                              0.046860   \n96                              0.278891   \n97                              0.955584   \n98                              0.551521   \n\n    feature_perigean_bewitching_thruster  feature_renegade_undomestic_milord  \\\n94                              0.495938                            0.262902   \n95                              0.508427                            0.046550   \n96                              0.292622                            0.010535   \n97                              0.479505                            0.461875   \n98                              0.786020                            0.514845   \n\n    feature_koranic_rude_corf  feature_demisable_expiring_millepede  ...  \\\n94                   0.782719                              0.753482  ...   \n95                   0.003431                              0.695417  ...   \n96                   0.490047                              0.213381  ...   \n97                   0.247469                              0.867348  ...   \n98                   0.000000                              0.000479  ...   \n\n    target_paul_20  target_paul_60  target_george_20  target_george_60  \\\n94            0.50            0.50               0.5              0.25   \n95            0.25            0.50               0.5              0.25   \n96            0.50            0.50               0.5              0.50   \n97            0.50            0.50               0.0              0.50   \n98            1.00            0.75               1.0              0.50   \n\n    target_william_20  target_william_60  target_arthur_20  target_arthur_60  \\\n94           0.500000           0.500000          0.500000          0.333333   \n95           0.333333           0.333333          0.333333          0.333333   \n96           0.500000           0.666667          0.500000          0.500000   \n97           0.500000           0.500000          0.500000          0.500000   \n98           1.000000           0.833333          0.833333          0.833333   \n\n    target_thomas_20  target_thomas_60  \n94          0.500000          0.333333  \n95          0.333333          0.166667  \n96          0.666667          0.666667  \n97          0.333333          0.500000  \n98          0.833333          0.833333  \n\n[5 rows x 1073 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>era</th>\n      <th>data_type</th>\n      <th>feature_dichasial_hammier_spawner</th>\n      <th>feature_rheumy_epistemic_prancer</th>\n      <th>feature_pert_performative_hormuz</th>\n      <th>feature_hillier_unpitied_theobromine</th>\n      <th>feature_perigean_bewitching_thruster</th>\n      <th>feature_renegade_undomestic_milord</th>\n      <th>feature_koranic_rude_corf</th>\n      <th>feature_demisable_expiring_millepede</th>\n      <th>...</th>\n      <th>target_paul_20</th>\n      <th>target_paul_60</th>\n      <th>target_george_20</th>\n      <th>target_george_60</th>\n      <th>target_william_20</th>\n      <th>target_william_60</th>\n      <th>target_arthur_20</th>\n      <th>target_arthur_60</th>\n      <th>target_thomas_20</th>\n      <th>target_thomas_60</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>94</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.823009</td>\n      <td>1.000000</td>\n      <td>0.992251</td>\n      <td>0.495938</td>\n      <td>0.262902</td>\n      <td>0.782719</td>\n      <td>0.753482</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.5</td>\n      <td>0.25</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.008697</td>\n      <td>0.081895</td>\n      <td>0.727875</td>\n      <td>0.046860</td>\n      <td>0.508427</td>\n      <td>0.046550</td>\n      <td>0.003431</td>\n      <td>0.695417</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.50</td>\n      <td>0.5</td>\n      <td>0.25</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.166667</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.232997</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.278891</td>\n      <td>0.292622</td>\n      <td>0.010535</td>\n      <td>0.490047</td>\n      <td>0.213381</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.5</td>\n      <td>0.50</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.984097</td>\n      <td>0.985342</td>\n      <td>0.752239</td>\n      <td>0.955584</td>\n      <td>0.479505</td>\n      <td>0.461875</td>\n      <td>0.247469</td>\n      <td>0.867348</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.0</td>\n      <td>0.50</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.253801</td>\n      <td>0.313556</td>\n      <td>0.581586</td>\n      <td>0.551521</td>\n      <td>0.786020</td>\n      <td>0.514845</td>\n      <td>0.000000</td>\n      <td>0.000479</td>\n      <td>...</td>\n      <td>1.00</td>\n      <td>0.75</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>1.000000</td>\n      <td>0.833333</td>\n      <td>0.833333</td>\n      <td>0.833333</td>\n      <td>0.833333</td>\n      <td>0.833333</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1073 columns</p>\n</div>"
      },
      "execution_count": 24,
      "metadata": {},
@@ -1181,7 +1181,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "b9c661eb1ad04f18b121ec5ad987f470"
+       "model_id": "d807e800e7224efbad0cc2339623d4c7"
       }
      },
      "metadata": {},
@@ -1219,8 +1219,8 @@
     },
     {
      "data": {
-      "text/plain": "   feature_abstersive_emotional_misinterpreter  \\\n0                                     0.000000   \n1                                     0.717585   \n2                                     0.165843   \n3                                     0.463913   \n4                                     0.178387   \n\n   feature_accessorial_aroused_crochet  feature_acerb_venusian_piety  \\\n0                             0.000000                      0.000000   \n1                             1.000000                      0.119904   \n2                             0.706939                      0.563268   \n3                             0.043398                      0.540559   \n4                             0.420503                      0.930323   \n\n   feature_affricative_bromic_raftsman  feature_agile_unrespited_gaucho  \\\n0                             0.042107                         0.063387   \n1                             0.719409                         0.766565   \n2                             0.184079                         0.021587   \n3                             0.780988                         1.000000   \n4                             0.720097                         0.051137   \n\n   feature_agronomic_cryptal_advisor  feature_alkaline_pistachio_sunstone  \\\n0                           0.979339                             1.000000   \n1                           0.030814                             0.222251   \n2                           0.803588                             1.000000   \n3                           0.472640                             0.000000   \n4                           0.524874                             0.000000   \n\n   feature_altern_unnoticed_impregnation  feature_ambisexual_boiled_blunderer  \\\n0                               0.754362                             0.000000   \n1                               0.517912                             0.000000   \n2                               0.997696                             0.163117   \n3                               0.991466                             0.375541   \n4                               1.000000                             0.702171   \n\n   feature_amoebaean_wolfish_heeler  ...  target_paul_20  target_paul_60  \\\n0                          0.731277  ...            0.50            0.50   \n1                          0.024772  ...            0.25            0.25   \n2                          1.000000  ...            0.50            0.50   \n3                          0.986749  ...            0.50            0.50   \n4                          0.516301  ...            0.25            0.50   \n\n   target_george_20  target_george_60  target_william_20  target_william_60  \\\n0              0.50              0.75           0.666667           0.666667   \n1              0.75              0.50           0.166667           0.333333   \n2              0.50              0.25           0.500000           0.500000   \n3              0.50              0.25           0.666667           0.500000   \n4              0.25              0.25           0.333333           0.500000   \n\n   target_arthur_20  target_arthur_60  target_thomas_20  target_thomas_60  \n0          0.666667          0.666667          0.500000          0.666667  \n1          0.166667          0.333333          0.500000          0.333333  \n2          0.500000          0.333333          0.500000          0.166667  \n3          0.500000          0.333333          0.500000          0.333333  \n4          0.166667          0.333333          0.333333          0.333333  \n\n[5 rows x 441 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_abstersive_emotional_misinterpreter</th>\n      <th>feature_accessorial_aroused_crochet</th>\n      <th>feature_acerb_venusian_piety</th>\n      <th>feature_affricative_bromic_raftsman</th>\n      <th>feature_agile_unrespited_gaucho</th>\n      <th>feature_agronomic_cryptal_advisor</th>\n      <th>feature_alkaline_pistachio_sunstone</th>\n      <th>feature_altern_unnoticed_impregnation</th>\n      <th>feature_ambisexual_boiled_blunderer</th>\n      <th>feature_amoebaean_wolfish_heeler</th>\n      <th>...</th>\n      <th>target_paul_20</th>\n      <th>target_paul_60</th>\n      <th>target_george_20</th>\n      <th>target_george_60</th>\n      <th>target_william_20</th>\n      <th>target_william_60</th>\n      <th>target_arthur_20</th>\n      <th>target_arthur_60</th>\n      <th>target_thomas_20</th>\n      <th>target_thomas_60</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.042107</td>\n      <td>0.063387</td>\n      <td>0.979339</td>\n      <td>1.000000</td>\n      <td>0.754362</td>\n      <td>0.000000</td>\n      <td>0.731277</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.75</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.717585</td>\n      <td>1.000000</td>\n      <td>0.119904</td>\n      <td>0.719409</td>\n      <td>0.766565</td>\n      <td>0.030814</td>\n      <td>0.222251</td>\n      <td>0.517912</td>\n      <td>0.000000</td>\n      <td>0.024772</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.75</td>\n      <td>0.50</td>\n      <td>0.166667</td>\n      <td>0.333333</td>\n      <td>0.166667</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.165843</td>\n      <td>0.706939</td>\n      <td>0.563268</td>\n      <td>0.184079</td>\n      <td>0.021587</td>\n      <td>0.803588</td>\n      <td>1.000000</td>\n      <td>0.997696</td>\n      <td>0.163117</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.25</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.166667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.463913</td>\n      <td>0.043398</td>\n      <td>0.540559</td>\n      <td>0.780988</td>\n      <td>1.000000</td>\n      <td>0.472640</td>\n      <td>0.000000</td>\n      <td>0.991466</td>\n      <td>0.375541</td>\n      <td>0.986749</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.25</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.178387</td>\n      <td>0.420503</td>\n      <td>0.930323</td>\n      <td>0.720097</td>\n      <td>0.051137</td>\n      <td>0.524874</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.702171</td>\n      <td>0.516301</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.50</td>\n      <td>0.25</td>\n      <td>0.25</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.166667</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n      <td>0.333333</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 441 columns</p>\n</div>"
+      "text/plain": "   feature_abstersive_emotional_misinterpreter  \\\n0                                     0.996130   \n1                                     0.253277   \n2                                     0.522252   \n3                                     0.060471   \n4                                     0.996391   \n\n   feature_accessorial_aroused_crochet  feature_acerb_venusian_piety  \\\n0                             0.012258                      1.000000   \n1                             0.315667                      0.533756   \n2                             0.707459                      0.654992   \n3                             0.249417                      0.521879   \n4                             0.470405                      0.823283   \n\n   feature_affricative_bromic_raftsman  feature_agile_unrespited_gaucho  \\\n0                             0.955254                         0.815248   \n1                             0.213132                         0.490001   \n2                             0.475255                         0.264495   \n3                             0.249297                         0.000000   \n4                             1.000000                         1.000000   \n\n   feature_agronomic_cryptal_advisor  feature_alkaline_pistachio_sunstone  \\\n0                           0.997856                             0.956926   \n1                           0.725381                             0.685841   \n2                           0.498145                             0.479104   \n3                           0.055882                             0.000000   \n4                           0.768204                             0.706590   \n\n   feature_altern_unnoticed_impregnation  feature_ambisexual_boiled_blunderer  \\\n0                               0.791413                             0.670447   \n1                               0.464807                             0.105545   \n2                               0.277687                             0.768365   \n3                               0.000000                             0.000000   \n4                               0.269626                             0.000000   \n\n   feature_amoebaean_wolfish_heeler  ...  target_paul_20  target_paul_60  \\\n0                          0.510484  ...            0.50             0.5   \n1                          0.989926  ...            0.50             1.0   \n2                          0.169280  ...            0.00             0.5   \n3                          0.045899  ...            0.50             0.5   \n4                          0.264364  ...            0.25             0.5   \n\n   target_george_20  target_george_60  target_william_20  target_william_60  \\\n0              0.75              0.75           0.500000           0.500000   \n1              0.50              0.75           0.500000           0.666667   \n2              0.25              0.50           0.166667           0.666667   \n3              0.50              0.50           0.500000           0.500000   \n4              0.25              0.75           0.333333           0.500000   \n\n   target_arthur_20  target_arthur_60  target_thomas_20  target_thomas_60  \n0          0.500000          0.500000          0.500000          0.500000  \n1          0.500000          0.666667          0.500000          0.666667  \n2          0.166667          0.666667          0.333333          0.666667  \n3          0.500000          0.500000          0.500000          0.500000  \n4          0.333333          0.500000          0.333333          0.500000  \n\n[5 rows x 441 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_abstersive_emotional_misinterpreter</th>\n      <th>feature_accessorial_aroused_crochet</th>\n      <th>feature_acerb_venusian_piety</th>\n      <th>feature_affricative_bromic_raftsman</th>\n      <th>feature_agile_unrespited_gaucho</th>\n      <th>feature_agronomic_cryptal_advisor</th>\n      <th>feature_alkaline_pistachio_sunstone</th>\n      <th>feature_altern_unnoticed_impregnation</th>\n      <th>feature_ambisexual_boiled_blunderer</th>\n      <th>feature_amoebaean_wolfish_heeler</th>\n      <th>...</th>\n      <th>target_paul_20</th>\n      <th>target_paul_60</th>\n      <th>target_george_20</th>\n      <th>target_george_60</th>\n      <th>target_william_20</th>\n      <th>target_william_60</th>\n      <th>target_arthur_20</th>\n      <th>target_arthur_60</th>\n      <th>target_thomas_20</th>\n      <th>target_thomas_60</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.996130</td>\n      <td>0.012258</td>\n      <td>1.000000</td>\n      <td>0.955254</td>\n      <td>0.815248</td>\n      <td>0.997856</td>\n      <td>0.956926</td>\n      <td>0.791413</td>\n      <td>0.670447</td>\n      <td>0.510484</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.5</td>\n      <td>0.75</td>\n      <td>0.75</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.253277</td>\n      <td>0.315667</td>\n      <td>0.533756</td>\n      <td>0.213132</td>\n      <td>0.490001</td>\n      <td>0.725381</td>\n      <td>0.685841</td>\n      <td>0.464807</td>\n      <td>0.105545</td>\n      <td>0.989926</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>1.0</td>\n      <td>0.50</td>\n      <td>0.75</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n      <td>0.500000</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.522252</td>\n      <td>0.707459</td>\n      <td>0.654992</td>\n      <td>0.475255</td>\n      <td>0.264495</td>\n      <td>0.498145</td>\n      <td>0.479104</td>\n      <td>0.277687</td>\n      <td>0.768365</td>\n      <td>0.169280</td>\n      <td>...</td>\n      <td>0.00</td>\n      <td>0.5</td>\n      <td>0.25</td>\n      <td>0.50</td>\n      <td>0.166667</td>\n      <td>0.666667</td>\n      <td>0.166667</td>\n      <td>0.666667</td>\n      <td>0.333333</td>\n      <td>0.666667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.060471</td>\n      <td>0.249417</td>\n      <td>0.521879</td>\n      <td>0.249297</td>\n      <td>0.000000</td>\n      <td>0.055882</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.045899</td>\n      <td>...</td>\n      <td>0.50</td>\n      <td>0.5</td>\n      <td>0.50</td>\n      <td>0.50</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.996391</td>\n      <td>0.470405</td>\n      <td>0.823283</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.768204</td>\n      <td>0.706590</td>\n      <td>0.269626</td>\n      <td>0.000000</td>\n      <td>0.264364</td>\n      <td>...</td>\n      <td>0.25</td>\n      <td>0.5</td>\n      <td>0.25</td>\n      <td>0.75</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n      <td>0.333333</td>\n      <td>0.500000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 441 columns</p>\n</div>"
      },
      "execution_count": 26,
      "metadata": {},
@@ -1364,8 +1364,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "                  feature_umap_0  feature_umap_1  feature_umap_2\nid                                                              \nn559bd06a8861222        0.416404        0.057189        0.793482\nn9d39dea58c9e3cf        0.858917        0.329835        0.826034\nnb64f06d3a9fc9f1        0.700607        0.096857        0.176513",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_umap_0</th>\n      <th>feature_umap_1</th>\n      <th>feature_umap_2</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>n559bd06a8861222</th>\n      <td>0.416404</td>\n      <td>0.057189</td>\n      <td>0.793482</td>\n    </tr>\n    <tr>\n      <th>n9d39dea58c9e3cf</th>\n      <td>0.858917</td>\n      <td>0.329835</td>\n      <td>0.826034</td>\n    </tr>\n    <tr>\n      <th>nb64f06d3a9fc9f1</th>\n      <td>0.700607</td>\n      <td>0.096857</td>\n      <td>0.176513</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                  feature_umap_0  feature_umap_1  feature_umap_2\nid                                                              \nn559bd06a8861222        0.767670        0.000000        0.516831\nn9d39dea58c9e3cf        0.537398        0.088487        1.000000\nnb64f06d3a9fc9f1        0.014322        0.400093        0.000000",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_umap_0</th>\n      <th>feature_umap_1</th>\n      <th>feature_umap_2</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>n559bd06a8861222</th>\n      <td>0.767670</td>\n      <td>0.000000</td>\n      <td>0.516831</td>\n    </tr>\n    <tr>\n      <th>n9d39dea58c9e3cf</th>\n      <td>0.537398</td>\n      <td>0.088487</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>nb64f06d3a9fc9f1</th>\n      <td>0.014322</td>\n      <td>0.400093</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 29,
      "metadata": {},
@@ -1532,11 +1532,11 @@
     },
     {
      "data": {
-      "text/plain": "Generating fake target:   0%|          | 0/69 [00:00<?, ?it/s]",
+      "text/plain": "Generating fake target:   0%|          | 0/60 [00:00<?, ?it/s]",
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "3943d9cef55d46b78adc142550d30bda"
+       "model_id": "85d21ec12d304042b147adf4f2bcf1e8"
       }
      },
      "metadata": {},
@@ -1544,16 +1544,16 @@
     },
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mBayesianGMMTargetProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m100\u001B[0m, \u001B[1;36m1074\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:03:28\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m855996\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">BayesianGMMTargetProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1074</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:03:28</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">855996</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mBayesianGMMTargetProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m100\u001B[0m, \u001B[1;36m1074\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:02:31\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m198486\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">BayesianGMMTargetProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1074</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:02:31</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">198486</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": "                  target  target_fake\nid                                   \nn44914183f920b7c    0.50         0.50\nnbe23e3334634ee5    0.25         0.25\nnbb8bd1f4937f251    0.50         0.75",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target</th>\n      <th>target_fake</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>n44914183f920b7c</th>\n      <td>0.50</td>\n      <td>0.50</td>\n    </tr>\n    <tr>\n      <th>nbe23e3334634ee5</th>\n      <td>0.25</td>\n      <td>0.25</td>\n    </tr>\n    <tr>\n      <th>nbb8bd1f4937f251</th>\n      <td>0.50</td>\n      <td>0.75</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "                  target  target_fake\nid                                   \nn16478df93c0ea62     0.5         0.50\nn86afa201ca85b27     0.5         0.50\nn78313c76c46b833     0.0         0.25",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>target</th>\n      <th>target_fake</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>n16478df93c0ea62</th>\n      <td>0.5</td>\n      <td>0.50</td>\n    </tr>\n    <tr>\n      <th>n86afa201ca85b27</th>\n      <td>0.5</td>\n      <td>0.50</td>\n    </tr>\n    <tr>\n      <th>n78313c76c46b833</th>\n      <td>0.0</td>\n      <td>0.25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 31,
      "metadata": {},
@@ -1702,8 +1702,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mGroupStatsPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m332\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m054868\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">GroupStatsPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">332</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">054868</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mGroupStatsPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m332\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m043740\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">GroupStatsPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">332</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">043740</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1789,8 +1789,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mGroupStatsPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m332\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m032068\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">GroupStatsPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">332</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">032068</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mGroupStatsPreProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10\u001B[0m, \u001B[1;36m332\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m041156\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">GroupStatsPreProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">332</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">041156</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2240,7 +2240,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "2dcc78e024634237972b4ddf11808b03"
+       "model_id": "9ffa7e2bd8494c838e1d5b2d6650c9fe"
       }
      },
      "metadata": {},
@@ -2250,7 +2250,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n"
+      "2022-05-02 13:19:03,685 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,689 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n"
      ]
     },
     {
@@ -2259,7 +2260,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "c8064bb30b764c9390222e291b0584d0"
+       "model_id": "caaae62650bf40158f16fae02ef1e0a6"
       }
      },
      "metadata": {},
@@ -2269,27 +2270,26 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,103 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,112 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,115 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,120 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,118 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,120 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,123 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,125 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
-      "2022-04-29 18:52:41,130 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
-      "2022-04-29 18:52:41,135 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n"
+      "2022-05-02 13:19:03,691 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,696 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,695 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,700 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,698 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,703 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,710 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,716 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,721 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,729 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,728 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,733 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n",
+      "2022-05-02 13:19:03,738 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:03,744 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n"
      ]
     },
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mKatsuFeatureGenerator\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m2532555\u001B[0m, \u001B[1;36m21\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:06\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m882092\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">KatsuFeatureGenerator</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2532555</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:06</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">882092</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mKatsuFeatureGenerator\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m2532770\u001B[0m, \u001B[1;36m21\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:06\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m930909\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">KatsuFeatureGenerator</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2532770</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">21</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:06</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">930909</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2323,8 +2323,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "          feature_close_ROCP_20  feature_close_VOL_20  \\\n19786946               0.086860              0.011198   \n19792087               0.076923              0.011128   \n\n          feature_close_MA_gap_20  feature_close_ROCP_40  \\\n19786946                 0.986855              -0.045010   \n19792087                 0.987406              -0.016064   \n\n          feature_close_VOL_40  feature_close_MA_gap_40  \\\n19786946              0.012140                 0.995106   \n19792087              0.011934                 0.999592   \n\n          feature_close_ROCP_60  feature_close_VOL_60  \\\n19786946              -0.148342              0.011382   \n19792087              -0.109091              0.011055   \n\n          feature_close_MA_gap_60  feature_RSI  feature_MACD  \\\n19786946                 0.961008    45.064623     -0.162609   \n19792087                 0.966851    46.363268     -0.233052   \n\n          feature_MACD_signal  \n19786946            -0.189706  \n19792087            -0.198375  ",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_close_ROCP_20</th>\n      <th>feature_close_VOL_20</th>\n      <th>feature_close_MA_gap_20</th>\n      <th>feature_close_ROCP_40</th>\n      <th>feature_close_VOL_40</th>\n      <th>feature_close_MA_gap_40</th>\n      <th>feature_close_ROCP_60</th>\n      <th>feature_close_VOL_60</th>\n      <th>feature_close_MA_gap_60</th>\n      <th>feature_RSI</th>\n      <th>feature_MACD</th>\n      <th>feature_MACD_signal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19786946</th>\n      <td>0.086860</td>\n      <td>0.011198</td>\n      <td>0.986855</td>\n      <td>-0.045010</td>\n      <td>0.012140</td>\n      <td>0.995106</td>\n      <td>-0.148342</td>\n      <td>0.011382</td>\n      <td>0.961008</td>\n      <td>45.064623</td>\n      <td>-0.162609</td>\n      <td>-0.189706</td>\n    </tr>\n    <tr>\n      <th>19792087</th>\n      <td>0.076923</td>\n      <td>0.011128</td>\n      <td>0.987406</td>\n      <td>-0.016064</td>\n      <td>0.011934</td>\n      <td>0.999592</td>\n      <td>-0.109091</td>\n      <td>0.011055</td>\n      <td>0.966851</td>\n      <td>46.363268</td>\n      <td>-0.233052</td>\n      <td>-0.198375</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": "          feature_close_ROCP_20  feature_close_VOL_20  \\\n19772309               0.172932              0.029689   \n19777095               0.174242              0.029667   \n\n          feature_close_MA_gap_20  feature_close_ROCP_40  \\\n19772309                 1.072902               0.300000   \n19777095                 1.057659               0.291667   \n\n          feature_close_VOL_40  feature_close_MA_gap_40  \\\n19772309              0.023119                 1.158129   \n19777095              0.023161                 1.143279   \n\n          feature_close_ROCP_60  feature_close_VOL_60  \\\n19772309                  0.248              0.022138   \n19777095                  0.250              0.022128   \n\n          feature_close_MA_gap_60  feature_RSI  feature_MACD  \\\n19772309                 1.186312    67.052407      3.865221   \n19777095                 1.174094    65.378476      3.914410   \n\n          feature_MACD_signal  \n19772309             3.950395  \n19777095             3.943198  ",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_close_ROCP_20</th>\n      <th>feature_close_VOL_20</th>\n      <th>feature_close_MA_gap_20</th>\n      <th>feature_close_ROCP_40</th>\n      <th>feature_close_VOL_40</th>\n      <th>feature_close_MA_gap_40</th>\n      <th>feature_close_ROCP_60</th>\n      <th>feature_close_VOL_60</th>\n      <th>feature_close_MA_gap_60</th>\n      <th>feature_RSI</th>\n      <th>feature_MACD</th>\n      <th>feature_MACD_signal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>19772309</th>\n      <td>0.172932</td>\n      <td>0.029689</td>\n      <td>1.072902</td>\n      <td>0.300000</td>\n      <td>0.023119</td>\n      <td>1.158129</td>\n      <td>0.248</td>\n      <td>0.022138</td>\n      <td>1.186312</td>\n      <td>67.052407</td>\n      <td>3.865221</td>\n      <td>3.950395</td>\n    </tr>\n    <tr>\n      <th>19777095</th>\n      <td>0.174242</td>\n      <td>0.029667</td>\n      <td>1.057659</td>\n      <td>0.291667</td>\n      <td>0.023161</td>\n      <td>1.143279</td>\n      <td>0.250</td>\n      <td>0.022128</td>\n      <td>1.174094</td>\n      <td>65.378476</td>\n      <td>3.914410</td>\n      <td>3.943198</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "execution_count": 44,
      "metadata": {},
@@ -2441,7 +2441,7 @@
       "application/vnd.jupyter.widget-view+json": {
        "version_major": 2,
        "version_minor": 0,
-       "model_id": "4c08054acf2046fa8c666b9d673a92a4"
+       "model_id": "40b1ecfce12e490cb307daefed090b27"
       }
      },
      "metadata": {},
@@ -2449,8 +2449,8 @@
     },
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mEraQuantileProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10000\u001B[0m, \u001B[1;36m33\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:08\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m582187\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">EraQuantileProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:08</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">582187</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mEraQuantileProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10000\u001B[0m, \u001B[1;36m33\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:08\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m814294\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">EraQuantileProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">33</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:08</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">814294</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2478,8 +2478,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "         feature_close_ROCP_20  feature_close_VOL_20  feature_close_MA_gap_20  \\\n2927639              -0.014571              0.001810                 0.990366   \n3368644               0.042708              0.003863                 1.018052   \n\n         feature_close_ROCP_40  feature_close_VOL_40  feature_close_MA_gap_40  \\\n2927639              -0.006188              0.002102                 0.993832   \n3368644              -0.206815              0.004804                 0.960008   \n\n         feature_close_ROCP_60  feature_close_VOL_60  feature_close_MA_gap_60  \\\n2927639              -0.000723              0.001986                 1.001429   \n3368644              -0.211190              0.005068                 0.901031   \n\n         feature_RSI  ...  feature_close_MA_gap_20_quantile  \\\n2927639    47.416878  ...                               0.5   \n3368644    45.971828  ...                               0.0   \n\n         feature_close_ROCP_40_quantile  feature_close_VOL_40_quantile  \\\n2927639                             0.5                            0.0   \n3368644                             0.0                            0.0   \n\n         feature_close_MA_gap_40_quantile  feature_close_ROCP_60_quantile  \\\n2927639                               0.5                             0.5   \n3368644                               0.0                             0.0   \n\n         feature_close_VOL_60_quantile  feature_close_MA_gap_60_quantile  \\\n2927639                            0.0                               0.5   \n3368644                            0.0                               0.0   \n\n         feature_RSI_quantile  feature_MACD_quantile  \\\n2927639                   0.5                    0.5   \n3368644                   0.0                    0.0   \n\n         feature_MACD_signal_quantile  \n2927639                           0.5  \n3368644                           0.0  \n\n[2 rows x 24 columns]",
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_close_ROCP_20</th>\n      <th>feature_close_VOL_20</th>\n      <th>feature_close_MA_gap_20</th>\n      <th>feature_close_ROCP_40</th>\n      <th>feature_close_VOL_40</th>\n      <th>feature_close_MA_gap_40</th>\n      <th>feature_close_ROCP_60</th>\n      <th>feature_close_VOL_60</th>\n      <th>feature_close_MA_gap_60</th>\n      <th>feature_RSI</th>\n      <th>...</th>\n      <th>feature_close_MA_gap_20_quantile</th>\n      <th>feature_close_ROCP_40_quantile</th>\n      <th>feature_close_VOL_40_quantile</th>\n      <th>feature_close_MA_gap_40_quantile</th>\n      <th>feature_close_ROCP_60_quantile</th>\n      <th>feature_close_VOL_60_quantile</th>\n      <th>feature_close_MA_gap_60_quantile</th>\n      <th>feature_RSI_quantile</th>\n      <th>feature_MACD_quantile</th>\n      <th>feature_MACD_signal_quantile</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2927639</th>\n      <td>-0.014571</td>\n      <td>0.001810</td>\n      <td>0.990366</td>\n      <td>-0.006188</td>\n      <td>0.002102</td>\n      <td>0.993832</td>\n      <td>-0.000723</td>\n      <td>0.001986</td>\n      <td>1.001429</td>\n      <td>47.416878</td>\n      <td>...</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.0</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.0</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>3368644</th>\n      <td>0.042708</td>\n      <td>0.003863</td>\n      <td>1.018052</td>\n      <td>-0.206815</td>\n      <td>0.004804</td>\n      <td>0.960008</td>\n      <td>-0.211190</td>\n      <td>0.005068</td>\n      <td>0.901031</td>\n      <td>45.971828</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 24 columns</p>\n</div>"
+      "text/plain": "          feature_close_ROCP_20  feature_close_VOL_20  \\\n14327556               0.348092              0.006448   \n2622418                0.099745              0.001118   \n\n          feature_close_MA_gap_20  feature_close_ROCP_40  \\\n14327556                 1.226758               0.237165   \n2622418                  1.051795               0.068323   \n\n          feature_close_VOL_40  feature_close_MA_gap_40  \\\n14327556              0.005366                 1.261297   \n2622418               0.001183                 1.064950   \n\n          feature_close_ROCP_60  feature_close_VOL_60  \\\n14327556               0.559683              0.005031   \n2622418                0.068323              0.001486   \n\n          feature_close_MA_gap_60  feature_RSI  ...  \\\n14327556                 1.269734    74.890404  ...   \n2622418                  1.055776    71.793104  ...   \n\n          feature_close_MA_gap_20_quantile  feature_close_ROCP_40_quantile  \\\n14327556                               1.0                             1.0   \n2622418                                1.0                             0.0   \n\n          feature_close_VOL_40_quantile  feature_close_MA_gap_40_quantile  \\\n14327556                            0.5                               1.0   \n2622418                             0.0                               1.0   \n\n          feature_close_ROCP_60_quantile  feature_close_VOL_60_quantile  \\\n14327556                             1.0                            0.5   \n2622418                              1.0                            0.0   \n\n          feature_close_MA_gap_60_quantile  feature_RSI_quantile  \\\n14327556                               1.0                   1.0   \n2622418                                1.0                   1.0   \n\n          feature_MACD_quantile  feature_MACD_signal_quantile  \n14327556                    1.0                           1.0  \n2622418                     1.0                           1.0  \n\n[2 rows x 24 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>feature_close_ROCP_20</th>\n      <th>feature_close_VOL_20</th>\n      <th>feature_close_MA_gap_20</th>\n      <th>feature_close_ROCP_40</th>\n      <th>feature_close_VOL_40</th>\n      <th>feature_close_MA_gap_40</th>\n      <th>feature_close_ROCP_60</th>\n      <th>feature_close_VOL_60</th>\n      <th>feature_close_MA_gap_60</th>\n      <th>feature_RSI</th>\n      <th>...</th>\n      <th>feature_close_MA_gap_20_quantile</th>\n      <th>feature_close_ROCP_40_quantile</th>\n      <th>feature_close_VOL_40_quantile</th>\n      <th>feature_close_MA_gap_40_quantile</th>\n      <th>feature_close_ROCP_60_quantile</th>\n      <th>feature_close_VOL_60_quantile</th>\n      <th>feature_close_MA_gap_60_quantile</th>\n      <th>feature_RSI_quantile</th>\n      <th>feature_MACD_quantile</th>\n      <th>feature_MACD_signal_quantile</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14327556</th>\n      <td>0.348092</td>\n      <td>0.006448</td>\n      <td>1.226758</td>\n      <td>0.237165</td>\n      <td>0.005366</td>\n      <td>1.261297</td>\n      <td>0.559683</td>\n      <td>0.005031</td>\n      <td>1.269734</td>\n      <td>74.890404</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2622418</th>\n      <td>0.099745</td>\n      <td>0.001118</td>\n      <td>1.051795</td>\n      <td>0.068323</td>\n      <td>0.001183</td>\n      <td>1.064950</td>\n      <td>0.068323</td>\n      <td>0.001486</td>\n      <td>1.055776</td>\n      <td>71.793104</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 24 columns</p>\n</div>"
      },
      "execution_count": 47,
      "metadata": {},
@@ -2545,7 +2545,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 49;\n                var nbb_unformatted_code = \"#export\\nclass TickerMapper(BaseProcessor):\\n    def __init__(self, ticker_col: str = \\\"ticker\\\", target_ticker_format: str = \\\"bloomberg_ticker\\\"):\\n        super().__init__()\\n        self.ticker_col = ticker_col\\n        self.target_ticker_format = target_ticker_format\\n        \\n        self.signals_map_path = \\\"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\\\"\\n        self.ticker_map = pd.read_csv(self.signals_map_path)\\n        \\n        assert self.ticker_col in self.ticker_map.columns, f\\\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\\\"\\n        assert self.target_ticker_format in self.ticker_map.columns, f\\\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\\\"\\n        \\n        self.mapping = dict(self.ticker_map[[self.ticker_col, self.target_ticker_format]].values)\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        dataf[\\\"bloomberg_ticker\\\"] = dataf[self.ticker_col].map(self.mapping)\\n        return NumerFrame(dataf)\";\n                var nbb_formatted_code = \"# export\\nclass TickerMapper(BaseProcessor):\\n    def __init__(\\n        self, ticker_col: str = \\\"ticker\\\", target_ticker_format: str = \\\"bloomberg_ticker\\\"\\n    ):\\n        super().__init__()\\n        self.ticker_col = ticker_col\\n        self.target_ticker_format = target_ticker_format\\n\\n        self.signals_map_path = \\\"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\\\"\\n        self.ticker_map = pd.read_csv(self.signals_map_path)\\n\\n        assert (\\n            self.ticker_col in self.ticker_map.columns\\n        ), f\\\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\\\"\\n        assert (\\n            self.target_ticker_format in self.ticker_map.columns\\n        ), f\\\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\\\"\\n\\n        self.mapping = dict(\\n            self.ticker_map[[self.ticker_col, self.target_ticker_format]].values\\n        )\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        dataf[\\\"bloomberg_ticker\\\"] = dataf[self.ticker_col].map(self.mapping)\\n        return NumerFrame(dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 49;\n                var nbb_unformatted_code = \"#export\\nclass TickerMapper(BaseProcessor):\\n    \\\"\\\"\\\"\\n    Map ticker from one format to another. \\\\n\\n    :param ticker_col: Column used for mapping. Must already be present in the input data. \\\\n\\n    :param target_ticker_format: Format to map tickers to. Must be present in the ticker map. \\\\n\\n    Supported ticker formats are: ['ticker', 'bloomberg_ticker', 'yahoo']\\n    \\\"\\\"\\\"\\n    def __init__(self, ticker_col: str = \\\"ticker\\\", target_ticker_format: str = \\\"bloomberg_ticker\\\"):\\n        super().__init__()\\n        self.ticker_col = ticker_col\\n        self.target_ticker_format = target_ticker_format\\n\\n        self.signals_map_path = \\\"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\\\"\\n        self.ticker_map = pd.read_csv(self.signals_map_path)\\n\\n        assert self.ticker_col in self.ticker_map.columns, f\\\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\\\"\\n        assert self.target_ticker_format in self.ticker_map.columns, f\\\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\\\"\\n\\n        self.mapping = dict(self.ticker_map[[self.ticker_col, self.target_ticker_format]].values)\\n\\n    @display_processor_info\\n    def transform(self, dataf: Union[pd.DataFrame, NumerFrame], *args, **kwargs) -> NumerFrame:\\n        dataf[self.target_ticker_format] = dataf[self.ticker_col].map(self.mapping)\\n        return NumerFrame(dataf)\";\n                var nbb_formatted_code = \"# export\\nclass TickerMapper(BaseProcessor):\\n    \\\"\\\"\\\"\\n    Map ticker from one format to another. \\\\n\\n    :param ticker_col: Column used for mapping. Must already be present in the input data. \\\\n\\n    :param target_ticker_format: Format to map tickers to. Must be present in the ticker map. \\\\n\\n    Supported ticker formats are: ['ticker', 'bloomberg_ticker', 'yahoo']\\n    \\\"\\\"\\\"\\n\\n    def __init__(\\n        self, ticker_col: str = \\\"ticker\\\", target_ticker_format: str = \\\"bloomberg_ticker\\\"\\n    ):\\n        super().__init__()\\n        self.ticker_col = ticker_col\\n        self.target_ticker_format = target_ticker_format\\n\\n        self.signals_map_path = \\\"https://numerai-signals-public-data.s3-us-west-2.amazonaws.com/signals_ticker_map_w_bbg.csv\\\"\\n        self.ticker_map = pd.read_csv(self.signals_map_path)\\n\\n        assert (\\n            self.ticker_col in self.ticker_map.columns\\n        ), f\\\"Ticker column '{self.ticker_col}' is not available in ticker mapping.\\\"\\n        assert (\\n            self.target_ticker_format in self.ticker_map.columns\\n        ), f\\\"Target ticker column '{self.target_ticker_format}' is not available in ticker mapping.\\\"\\n\\n        self.mapping = dict(\\n            self.ticker_map[[self.ticker_col, self.target_ticker_format]].values\\n        )\\n\\n    @display_processor_info\\n    def transform(\\n        self, dataf: Union[pd.DataFrame, NumerFrame], *args, **kwargs\\n    ) -> NumerFrame:\\n        dataf[self.target_ticker_format] = dataf[self.ticker_col].map(self.mapping)\\n        return NumerFrame(dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2591,8 +2591,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "✅ Finished step \u001B[1mTickerMapper\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m, \u001B[1;36m2\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m002790\u001B[0m. ✅\n",
-      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TickerMapper</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>. Time taken for step: <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">002790</span>. ✅\n</pre>\n"
+      "text/plain": "✅ Finished step \u001B[1mTickerMapper\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m2\u001B[0m, \u001B[1;36m2\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \u001B[1;34m0:00:00\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m003475\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">TickerMapper</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>. Time taken for step: <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:00</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">003475</span>. ✅\n</pre>\n"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2627,6 +2627,149 @@
     }
    }
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### 1.2.5. SignalsTargetProcessor\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Numerai provides [targets for 5000 stocks](https://docs.numer.ai/numerai-signals/signals-overview#universe) that are neutralized against all sorts of factors. However, it can be helpful to experiment with creating your own targets. You might want to explore different windows, different target binning and/or neutralization. `SignalsTargetProcessor` engineers 3 different targets for every given windows:\n",
+    "- `_raw`: Raw return based on price movements.\n",
+    "- `_rank`: Ranks of raw return.\n",
+    "- `_group`: Binned returns based on rank.\n",
+    "\n",
+    "Note that Numerai provides targets based on 4-day returns and 20-day returns. While you can explore any window you like, it makes sense to start with `windows` close to these timeframes.\n",
+    "\n",
+    "For the `bins` argument there are also many options possible. The followed are commonly used binning:\n",
+    "- Nomi bins: `[0, 0.05, 0.25, 0.75, 0.95, 1]`\n",
+    "- Uniform bins: `[0, 0.20, 0.40, 0.60, 0.80, 1]`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<IPython.core.display.Javascript object>",
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 51;\n                var nbb_unformatted_code = \"# export\\nclass SignalsTargetProcessor(BaseProcessor):\\n    \\\"\\\"\\\"\\n    Engineer targets for Numerai Signals. \\\\n\\n    More information on implements Numerai Signals targets: \\\\n\\n    https://forum.numer.ai/t/decoding-the-signals-target/2501\\n\\n    :param price_col: Column from which target will be derived. \\\\n\\n    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \\\\n\\n    :param bins: Binning used to create group targets. Nomi binning by default. \\\\n\\n    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.\\n    \\\"\\\"\\\"\\n    def __init__(self, price_col: str = 'close', windows: list = None, bins: list = None, labels: list = None):\\n        super().__init__()\\n        self.price_col = price_col\\n        self.windows = windows if windows else [10, 20]\\n        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]\\n        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame) -> NumerFrame:\\n        for window in tqdm(self.windows, desc=\\\"Signals target engineering windows\\\"):\\n            dataf.loc[:, f\\\"target_{window}d_raw\\\"] = (\\n                dataf[self.price_col].pct_change(periods=window).shift(-window)\\n            )\\n            era_groups = dataf.groupby(dataf.meta.era_col)\\n\\n            dataf.loc[:, f\\\"target_{window}d_rank\\\"] = era_groups[f\\\"target_{window}d_raw\\\"].rank(pct=True,\\n                                                                                              method=\\\"first\\\"\\n                                                                                              )\\n            dataf.loc[:, f\\\"target_{window}d_group\\\"] = era_groups[f\\\"target_{window}d_rank\\\"].transform(\\n                lambda group: pd.cut(\\n                    group,\\n                    bins=self.bins,\\n                    labels=self.labels,\\n                    include_lowest=True\\n                )\\n            )\\n        return NumerFrame(dataf)\";\n                var nbb_formatted_code = \"# export\\nclass SignalsTargetProcessor(BaseProcessor):\\n    \\\"\\\"\\\"\\n    Engineer targets for Numerai Signals. \\\\n\\n    More information on implements Numerai Signals targets: \\\\n\\n    https://forum.numer.ai/t/decoding-the-signals-target/2501\\n\\n    :param price_col: Column from which target will be derived. \\\\n\\n    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \\\\n\\n    :param bins: Binning used to create group targets. Nomi binning by default. \\\\n\\n    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.\\n    \\\"\\\"\\\"\\n\\n    def __init__(\\n        self,\\n        price_col: str = \\\"close\\\",\\n        windows: list = None,\\n        bins: list = None,\\n        labels: list = None,\\n    ):\\n        super().__init__()\\n        self.price_col = price_col\\n        self.windows = windows if windows else [10, 20]\\n        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]\\n        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame) -> NumerFrame:\\n        for window in tqdm(self.windows, desc=\\\"Signals target engineering windows\\\"):\\n            dataf.loc[:, f\\\"target_{window}d_raw\\\"] = (\\n                dataf[self.price_col].pct_change(periods=window).shift(-window)\\n            )\\n            era_groups = dataf.groupby(dataf.meta.era_col)\\n\\n            dataf.loc[:, f\\\"target_{window}d_rank\\\"] = era_groups[\\n                f\\\"target_{window}d_raw\\\"\\n            ].rank(pct=True, method=\\\"first\\\")\\n            dataf.loc[:, f\\\"target_{window}d_group\\\"] = era_groups[\\n                f\\\"target_{window}d_rank\\\"\\n            ].transform(\\n                lambda group: pd.cut(\\n                    group, bins=self.bins, labels=self.labels, include_lowest=True\\n                )\\n            )\\n        return NumerFrame(dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# export\n",
+    "class SignalsTargetProcessor(BaseProcessor):\n",
+    "    \"\"\"\n",
+    "    Engineer targets for Numerai Signals. \\n\n",
+    "    More information on implements Numerai Signals targets: \\n\n",
+    "    https://forum.numer.ai/t/decoding-the-signals-target/2501\n",
+    "\n",
+    "    :param price_col: Column from which target will be derived. \\n\n",
+    "    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \\n\n",
+    "    :param bins: Binning used to create group targets. Nomi binning by default. \\n\n",
+    "    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, price_col: str = 'close', windows: list = None, bins: list = None, labels: list = None):\n",
+    "        super().__init__()\n",
+    "        self.price_col = price_col\n",
+    "        self.windows = windows if windows else [10, 20]\n",
+    "        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]\n",
+    "        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]\n",
+    "\n",
+    "    @display_processor_info\n",
+    "    def transform(self, dataf: NumerFrame) -> NumerFrame:\n",
+    "        for window in tqdm(self.windows, desc=\"Signals target engineering windows\"):\n",
+    "            dataf.loc[:, f\"target_{window}d_raw\"] = (\n",
+    "                dataf[self.price_col].pct_change(periods=window).shift(-window)\n",
+    "            )\n",
+    "            era_groups = dataf.groupby(dataf.meta.era_col)\n",
+    "\n",
+    "            dataf.loc[:, f\"target_{window}d_rank\"] = era_groups[f\"target_{window}d_raw\"].rank(pct=True,\n",
+    "                                                                                              method=\"first\"\n",
+    "                                                                                              )\n",
+    "            dataf.loc[:, f\"target_{window}d_group\"] = era_groups[f\"target_{window}d_rank\"].transform(\n",
+    "                lambda group: pd.cut(\n",
+    "                    group,\n",
+    "                    bins=self.bins,\n",
+    "                    labels=self.labels,\n",
+    "                    include_lowest=True\n",
+    "                )\n",
+    "            )\n",
+    "        return NumerFrame(dataf)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Signals target engineering windows:   0%|          | 0/2 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "637c17503b2c4d5eb2ed124119b163a9"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-05-02 13:19:22,723 INFO numexpr.utils: Note: NumExpr detected 16 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+      "2022-05-02 13:19:22,725 INFO numexpr.utils: NumExpr defaulting to 8 threads.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "✅ Finished step \u001B[1mSignalsTargetProcessor\u001B[0m. Output \u001B[33mshape\u001B[0m=\u001B[1m(\u001B[0m\u001B[1;36m10000\u001B[0m, \u001B[1;36m39\u001B[0m\u001B[1m)\u001B[0m. Time taken for step: \n\u001B[1;34m0:00:25\u001B[0m\u001B[34m.\u001B[0m\u001B[1;34m177302\u001B[0m. ✅\n",
+      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">✅ Finished step <span style=\"font-weight: bold\">SignalsTargetProcessor</span>. Output <span style=\"color: #808000; text-decoration-color: #808000\">shape</span>=<span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39</span><span style=\"font-weight: bold\">)</span>. Time taken for step: \n<span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">0:00:25</span><span style=\"color: #000080; text-decoration-color: #000080\">.</span><span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">177302</span>. ✅\n</pre>\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "<IPython.core.display.Javascript object>",
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 52;\n                var nbb_unformatted_code = \"stp = SignalsTargetProcessor()\\nera_dataf.meta.era_col = \\\"date\\\"\\nnew_target_dataf = stp.transform(era_dataf)\";\n                var nbb_formatted_code = \"stp = SignalsTargetProcessor()\\nera_dataf.meta.era_col = \\\"date\\\"\\nnew_target_dataf = stp.transform(era_dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# other\n",
+    "stp = SignalsTargetProcessor()\n",
+    "era_dataf.meta.era_col = \"date\"\n",
+    "new_target_dataf = stp.transform(era_dataf)\n",
+    "new_target_dataf.get_target_data.head(2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2647,13 +2790,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 51;\n                var nbb_unformatted_code = \"# export\\nclass AwesomePreProcessor(BaseProcessor):\\n    \\\"\\\"\\\" TEMPLATE - Do some awesome preprocessing. \\\"\\\"\\\"\\n    def __init__(self):\\n        super().__init__()\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        # Do processing\\n        ...\\n        # Parse all contents of NumerFrame to the next pipeline step\\n        return NumerFrame(dataf)\";\n                var nbb_formatted_code = \"# export\\nclass AwesomePreProcessor(BaseProcessor):\\n    \\\"\\\"\\\"TEMPLATE - Do some awesome preprocessing.\\\"\\\"\\\"\\n\\n    def __init__(self):\\n        super().__init__()\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        # Do processing\\n        ...\\n        # Parse all contents of NumerFrame to the next pipeline step\\n        return NumerFrame(dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 54;\n                var nbb_unformatted_code = \"# export\\nclass AwesomePreProcessor(BaseProcessor):\\n    \\\"\\\"\\\" TEMPLATE - Do some awesome preprocessing. \\\"\\\"\\\"\\n    def __init__(self):\\n        super().__init__()\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        # Do processing\\n        ...\\n        # Parse all contents of NumerFrame to the next pipeline step\\n        return NumerFrame(dataf)\";\n                var nbb_formatted_code = \"# export\\nclass AwesomePreProcessor(BaseProcessor):\\n    \\\"\\\"\\\"TEMPLATE - Do some awesome preprocessing.\\\"\\\"\\\"\\n\\n    def __init__(self):\\n        super().__init__()\\n\\n    @display_processor_info\\n    def transform(self, dataf: NumerFrame, *args, **kwargs) -> NumerFrame:\\n        # Do processing\\n        ...\\n        # Parse all contents of NumerFrame to the next pipeline step\\n        return NumerFrame(dataf)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2683,7 +2826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -2707,7 +2850,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 52;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 55;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2723,13 +2866,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 52;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 55;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
diff --git a/nbs/08_key.ipynb b/nbs/08_key.ipynb
index 97367b65..fff447be 100644
--- a/nbs/08_key.ipynb
+++ b/nbs/08_key.ipynb
@@ -86,7 +86,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 4;\n                var nbb_unformatted_code = \"# export\\nimport json\\nfrom dataclasses import dataclass, FrozenInstanceError\";\n                var nbb_formatted_code = \"# export\\nimport json\\nfrom dataclasses import dataclass, FrozenInstanceError\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 4;\n                var nbb_unformatted_code = \"# export\\nimport json\";\n                var nbb_formatted_code = \"# export\\nimport json\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -94,8 +94,7 @@
    ],
    "source": [
     "# export\n",
-    "import json\n",
-    "from dataclasses import dataclass, FrozenInstanceError"
+    "import json"
    ]
   },
   {
@@ -118,7 +117,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 5;\n                var nbb_unformatted_code = \"# export\\n@dataclass(frozen=True)\\nclass Key:\\n    \\\"\\\"\\\"Immutable Numerai credentials.\\\"\\\"\\\"\\n    pub_id: str\\n    secret_key: str\\n\\n    def __repr__(self):\\n        return f\\\"Numerai Auth Key. pub_id = '{self.pub_id}'\\\"\\n\\n    def __str__(self):\\n        return self.__repr__()\\n\\n\\ndef load_key_from_json(file_path: str, *args, **kwargs):\\n    \\\"\\\"\\\"\\n    Initialize Key object from JSON file. \\\\n\\n    Credentials file must have the following format: \\\\n\\n    `{\\\"pub_id\\\": \\\"PUBLIC_ID\\\", \\\"secret_key\\\": \\\"SECRET_KEY\\\"}`\\n    \\\"\\\"\\\"\\n    with open(file_path) as json_file:\\n        json_data = json.load(json_file, *args, **kwargs)\\n    pub_id = json_data[\\\"pub_id\\\"]\\n    secret_key = json_data[\\\"secret_key\\\"]\\n    return Key(pub_id=pub_id, secret_key=secret_key)\";\n                var nbb_formatted_code = \"# export\\n@dataclass(frozen=True)\\nclass Key:\\n    \\\"\\\"\\\"Immutable Numerai credentials.\\\"\\\"\\\"\\n\\n    pub_id: str\\n    secret_key: str\\n\\n    def __repr__(self):\\n        return f\\\"Numerai Auth Key. pub_id = '{self.pub_id}'\\\"\\n\\n    def __str__(self):\\n        return self.__repr__()\\n\\n\\ndef load_key_from_json(file_path: str, *args, **kwargs):\\n    \\\"\\\"\\\"\\n    Initialize Key object from JSON file. \\\\n\\n    Credentials file must have the following format: \\\\n\\n    `{\\\"pub_id\\\": \\\"PUBLIC_ID\\\", \\\"secret_key\\\": \\\"SECRET_KEY\\\"}`\\n    \\\"\\\"\\\"\\n    with open(file_path) as json_file:\\n        json_data = json.load(json_file, *args, **kwargs)\\n    pub_id = json_data[\\\"pub_id\\\"]\\n    secret_key = json_data[\\\"secret_key\\\"]\\n    return Key(pub_id=pub_id, secret_key=secret_key)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 5;\n                var nbb_unformatted_code = \"# export\\nclass Key:\\n    \\\"\\\"\\\"Numerai credentials.\\\"\\\"\\\"\\n    def __init__(self, pub_id: str, secret_key: str):\\n        self.pub_id = pub_id\\n        self.secret_key = secret_key\\n\\n    def __repr__(self):\\n        return f\\\"Numerai Auth Key. pub_id = '{self.pub_id}'\\\"\\n\\n    def __str__(self):\\n        return self.__repr__()\\n\\n\\ndef load_key_from_json(file_path: str, *args, **kwargs):\\n    \\\"\\\"\\\"\\n    Initialize Key object from JSON file. \\\\n\\n    Credentials file must have the following format: \\\\n\\n    `{\\\"pub_id\\\": \\\"PUBLIC_ID\\\", \\\"secret_key\\\": \\\"SECRET_KEY\\\"}`\\n    \\\"\\\"\\\"\\n    with open(file_path) as json_file:\\n        json_data = json.load(json_file, *args, **kwargs)\\n    pub_id = json_data[\\\"pub_id\\\"]\\n    secret_key = json_data[\\\"secret_key\\\"]\\n    return Key(pub_id=pub_id, secret_key=secret_key)\";\n                var nbb_formatted_code = \"# export\\nclass Key:\\n    \\\"\\\"\\\"Numerai credentials.\\\"\\\"\\\"\\n\\n    def __init__(self, pub_id: str, secret_key: str):\\n        self.pub_id = pub_id\\n        self.secret_key = secret_key\\n\\n    def __repr__(self):\\n        return f\\\"Numerai Auth Key. pub_id = '{self.pub_id}'\\\"\\n\\n    def __str__(self):\\n        return self.__repr__()\\n\\n\\ndef load_key_from_json(file_path: str, *args, **kwargs):\\n    \\\"\\\"\\\"\\n    Initialize Key object from JSON file. \\\\n\\n    Credentials file must have the following format: \\\\n\\n    `{\\\"pub_id\\\": \\\"PUBLIC_ID\\\", \\\"secret_key\\\": \\\"SECRET_KEY\\\"}`\\n    \\\"\\\"\\\"\\n    with open(file_path) as json_file:\\n        json_data = json.load(json_file, *args, **kwargs)\\n    pub_id = json_data[\\\"pub_id\\\"]\\n    secret_key = json_data[\\\"secret_key\\\"]\\n    return Key(pub_id=pub_id, secret_key=secret_key)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -126,11 +125,11 @@
    ],
    "source": [
     "# export\n",
-    "@dataclass(frozen=True)\n",
     "class Key:\n",
-    "    \"\"\"Immutable Numerai credentials.\"\"\"\n",
-    "    pub_id: str\n",
-    "    secret_key: str\n",
+    "    \"\"\"Numerai credentials.\"\"\"\n",
+    "    def __init__(self, pub_id: str, secret_key: str):\n",
+    "        self.pub_id = pub_id\n",
+    "        self.secret_key = secret_key\n",
     "\n",
     "    def __repr__(self):\n",
     "        return f\"Numerai Auth Key. pub_id = '{self.pub_id}'\"\n",
@@ -197,47 +196,6 @@
     "assert (example_key.pub_id, example_key.secret_key) == (pub_id, secret_key)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "Variables in the `Key` class cannot be changed after initialization."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 8;\n                var nbb_unformatted_code = \"# hide\\ndef test_immutability(key):\\n    try:\\n        key.secret_key = \\\"HELLO\\\"\\n    except FrozenInstanceError:\\n        return True\\n    return False\\n\\n# Manipulating Key should raise FrozenInstanceError\\nassert test_immutability(example_key)\";\n                var nbb_formatted_code = \"# hide\\ndef test_immutability(key):\\n    try:\\n        key.secret_key = \\\"HELLO\\\"\\n    except FrozenInstanceError:\\n        return True\\n    return False\\n\\n\\n# Manipulating Key should raise FrozenInstanceError\\nassert test_immutability(example_key)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# hide\n",
-    "def test_immutability(key):\n",
-    "    try:\n",
-    "        key.secret_key = \"HELLO\"\n",
-    "    except FrozenInstanceError:\n",
-    "        return True\n",
-    "    return False\n",
-    "\n",
-    "# Manipulating Key should raise FrozenInstanceError\n",
-    "assert test_immutability(example_key)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -247,13 +205,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 9;\n                var nbb_unformatted_code = \"example_key2 = load_key_from_json(\\\"test_assets/test_credentials.json\\\")\";\n                var nbb_formatted_code = \"example_key2 = load_key_from_json(\\\"test_assets/test_credentials.json\\\")\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 8;\n                var nbb_unformatted_code = \"example_key2 = load_key_from_json(\\\"test_assets/test_credentials.json\\\")\";\n                var nbb_formatted_code = \"example_key2 = load_key_from_json(\\\"test_assets/test_credentials.json\\\")\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -272,13 +230,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 10;\n                var nbb_unformatted_code = \"assert (example_key2.pub_id, example_key2.secret_key) == (\\n    \\\"UFVCTElDX0lE\\\",\\n    \\\"U1VQRVJfU0VDUkVUX0tFWQ==\\\",\\n)\";\n                var nbb_formatted_code = \"assert (example_key2.pub_id, example_key2.secret_key) == (\\n    \\\"UFVCTElDX0lE\\\",\\n    \\\"U1VQRVJfU0VDUkVUX0tFWQ==\\\",\\n)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 9;\n                var nbb_unformatted_code = \"assert (example_key2.pub_id, example_key2.secret_key) == (\\n    \\\"UFVCTElDX0lE\\\",\\n    \\\"U1VQRVJfU0VDUkVUX0tFWQ==\\\",\\n)\";\n                var nbb_formatted_code = \"assert (example_key2.pub_id, example_key2.secret_key) == (\\n    \\\"UFVCTElDX0lE\\\",\\n    \\\"U1VQRVJfU0VDUkVUX0tFWQ==\\\",\\n)\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -300,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +282,7 @@
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 11;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 10;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
@@ -340,12 +298,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "outputs": [
     {
      "data": {
       "text/plain": "<IPython.core.display.Javascript object>",
-      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 11;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
+      "application/javascript": "\n            setTimeout(function() {\n                var nbb_cell_id = 10;\n                var nbb_unformatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_formatted_code = \"# hide\\n# Run this cell to sync all changes with library\\nfrom nbdev.export import notebook2script\\n\\nnotebook2script()\";\n                var nbb_cells = Jupyter.notebook.get_cells();\n                for (var i = 0; i < nbb_cells.length; ++i) {\n                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n                             nbb_cells[i].set_text(nbb_formatted_code);\n                        }\n                        break;\n                    }\n                }\n            }, 500);\n            "
      },
      "metadata": {},
      "output_type": "display_data"
diff --git a/numerblox/_nbdev.py b/numerblox/_nbdev.py
index cad73849..26ed6714 100644
--- a/numerblox/_nbdev.py
+++ b/numerblox/_nbdev.py
@@ -25,6 +25,7 @@
          "KatsuFeatureGenerator": "03_preprocessing.ipynb",
          "EraQuantileProcessor": "03_preprocessing.ipynb",
          "TickerMapper": "03_preprocessing.ipynb",
+         "SignalsTargetProcessor": "03_preprocessing.ipynb",
          "AwesomePreProcessor": "03_preprocessing.ipynb",
          "BaseModel": "04_model.ipynb",
          "DirectoryModel": "04_model.ipynb",
diff --git a/numerblox/key.py b/numerblox/key.py
index 98695de0..fc777d3e 100644
--- a/numerblox/key.py
+++ b/numerblox/key.py
@@ -4,14 +4,13 @@
 
 # Cell
 import json
-from dataclasses import dataclass, FrozenInstanceError
 
 # Cell
-@dataclass(frozen=True)
 class Key:
-    """Immutable Numerai credentials."""
-    pub_id: str
-    secret_key: str
+    """Numerai credentials."""
+    def __init__(self, pub_id: str, secret_key: str):
+        self.pub_id = pub_id
+        self.secret_key = secret_key
 
     def __repr__(self):
         return f"Numerai Auth Key. pub_id = '{self.pub_id}'"
diff --git a/numerblox/preprocessing.py b/numerblox/preprocessing.py
index 7bf30df5..635b7964 100644
--- a/numerblox/preprocessing.py
+++ b/numerblox/preprocessing.py
@@ -3,7 +3,7 @@
 __all__ = ['BaseProcessor', 'display_processor_info', 'CopyPreProcessor', 'FeatureSelectionPreProcessor',
            'TargetSelectionPreProcessor', 'ReduceMemoryProcessor', 'DeepDreamGenerator', 'UMAPFeatureGenerator',
            'BayesianGMMTargetProcessor', 'GroupStatsPreProcessor', 'TalibFeatureGenerator', 'KatsuFeatureGenerator',
-           'EraQuantileProcessor', 'TickerMapper', 'AwesomePreProcessor']
+           'EraQuantileProcessor', 'TickerMapper', 'SignalsTargetProcessor', 'AwesomePreProcessor']
 
 # Cell
 import os
@@ -724,6 +724,46 @@ def transform(self, dataf: Union[pd.DataFrame, NumerFrame], *args, **kwargs) ->
         dataf[self.target_ticker_format] = dataf[self.ticker_col].map(self.mapping)
         return NumerFrame(dataf)
 
+# Cell
+class SignalsTargetProcessor(BaseProcessor):
+    """
+    Engineer targets for Numerai Signals. \n
+    More information on implements Numerai Signals targets: \n
+    https://forum.numer.ai/t/decoding-the-signals-target/2501
+
+    :param price_col: Column from which target will be derived. \n
+    :param windows: Timeframes to use for engineering targets. 10 and 20-day by default. \n
+    :param bins: Binning used to create group targets. Nomi binning by default. \n
+    :param labels: Scaling for binned target. Must be same length as resulting bins (bins-1). Numerai labels by default.
+    """
+    def __init__(self, price_col: str = 'close', windows: list = None, bins: list = None, labels: list = None):
+        super().__init__()
+        self.price_col = price_col
+        self.windows = windows if windows else [10, 20]
+        self.bins = bins if bins else [0, 0.05, 0.25, 0.75, 0.95, 1]
+        self.labels = labels if labels else [0, 0.25, 0.50, 0.75, 1]
+
+    @display_processor_info
+    def transform(self, dataf: NumerFrame) -> NumerFrame:
+        for window in tqdm(self.windows, desc="Signals target engineering windows"):
+            dataf.loc[:, f"target_{window}d_raw"] = (
+                dataf[self.price_col].pct_change(periods=window).shift(-window)
+            )
+            era_groups = dataf.groupby(dataf.meta.era_col)
+
+            dataf.loc[:, f"target_{window}d_rank"] = era_groups[f"target_{window}d_raw"].rank(pct=True,
+                                                                                              method="first"
+                                                                                              )
+            dataf.loc[:, f"target_{window}d_group"] = era_groups[f"target_{window}d_rank"].transform(
+                lambda group: pd.cut(
+                    group,
+                    bins=self.bins,
+                    labels=self.labels,
+                    include_lowest=True
+                )
+            )
+        return NumerFrame(dataf)
+
 # Cell
 class AwesomePreProcessor(BaseProcessor):
     """ TEMPLATE - Do some awesome preprocessing. """