From 27c34ddd7dc081e61238c6f40b945bf81c2c5da5 Mon Sep 17 00:00:00 2001
From: Sadra Sabouri <43045767+sadrasabouri@users.noreply.github.com>
Date: Tue, 6 Jun 2023 15:30:07 +0330
Subject: [PATCH] Multi label base architecture (#507)

* add : `MultiLabelCM` base.

* add : `__mlcm_assign_classes__` and `__mlcm_vector_handler__`.

* log : changes logged.

* pep8 : autopep8ed.

* replace : functions moved to `pycm_multilabel_cm.py`.

* update : `__mlcm_vector_filter__` added as a wraper.

* add : `MultiLabelCM` added to `__init__`.

* update : `MultiLabelCM` updated.

* update : base architecture completed.

* fix : removed extra `print`.

* change : `MultiLabelCM.__len__` changed to classes' length.

* fix : `MultiLabelCM.__str__` docstring added.

* add : `actual_vector_multihot` and `predict_vector_multihot`.

* fix : docstring fixed.

* fix : `MultiLabelCM` class docstring fixed.

* remove : extra import removed.

* fix : docstring fixed.

* add : `pycmIndexError` and `pycmNotWorkingError`.

* add : `classwise_cm` and `samplewise_cm` methods.

* test : tests added for `MultiLabelCM`.

* log : changes logged.

* add : new line added.

* rename : `S` -> `input_set` in `__set_to_multihot__`.

* edit : `__str__` updated.

* add : `pycmMultiLabelError` added and extra error classes removed.

* change : support for multi-hop input removed.

* test : tests added.

* fix : verified tests updated

* fix : multilabel tests updated

* fix : multilabel tests updated

* fix : an error message fixed to generlize the error condition.

* rename : `samplewise_cm` -> `get_cm_by_sample`.

* rename : `classwise_cm` -> `get_cm_by_class`.

* doc : multilabel document updated

* doc : README updated

* doc : minor edit in Document

* doc : CHANGELOG updated

* fix : docstring fixed.

* fix : meeting's comment applied.

* test : tests added.

* doc : multilabel document updated

* doc : README updated

* doc : minor bug in Document fixed

* fix : minor edit in multilabel tests

* doc : multilabel output added to README

* doc : Document input-error section updated

---------

Co-authored-by: sepandhaghighi <sepand.haghighi@yahoo.com>
---
 CHANGELOG.md               |    9 +
 Document/Document.ipynb    | 1060 +++++-------------------------------
 README.md                  |   74 +--
 Test/error_test.py         |   42 ++
 Test/multilabel_test.py    |   35 ++
 Test/verified_test.py      |   46 +-
 pycm/__init__.py           |    1 +
 pycm/pycm_error.py         |    6 +
 pycm/pycm_multilabel_cm.py |  216 ++++++++
 pycm/pycm_param.py         |    4 +
 10 files changed, 535 insertions(+), 958 deletions(-)
 create mode 100644 Test/multilabel_test.py
 create mode 100644 pycm/pycm_multilabel_cm.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9c3adce2..21748df0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,9 +6,18 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 ## [Unreleased]
 ### Added
+- `pycmMultiLabelError` class
+- `MultiLabelCM` class
+- `get_cm_by_class` method
+- `get_cm_by_sample` method
+- `__mlcm_vector_handler__` function
+- `__mlcm_assign_classes__` function
+- `__mlcm_vectors_filter__` function
+- `__set_to_multihot__` function
 - `deprecated` function
 ### Changed
 - Document modified
+- `README.md` modified
 - Example-4 modified
 - Test system modified
 - Python 3.5 support dropped
diff --git a/Document/Document.ipynb b/Document/Document.ipynb
index 7bbd0499..1843c8cb 100644
--- a/Document/Document.ipynb
+++ b/Document/Document.ipynb
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     "            <li><a href=\"#Compare\">Compare</a></li>\n",
     "            <li><a href=\"#ROC-curve\">ROC Curve</a></li>\n",
     "            <li><a href=\"#Precision-Recall-curve\">Precision-Recall Curve</a></li>\n",
+    "            <li><a href=\"#Multilabel-confusion-matrix\">Multilabel Confusion Matrix</a></li>\n",
     "            <li><a href=\"#Online-help\">Online Help</a></li>\n",
     "            <li><a href=\"#Acceptable-data-types\">Acceptable Data Types</a></li>\n",
     "        </ol>\n",
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    "source": [
@@ -3441,7 +3329,44 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multilabel confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From `version 4.0`, `MultiLabelCM` has been added to calculate class-wise or sample-wise multilabel confusion matrices. In class-wise mode, confusion matrices are calculated for each class, and in sample-wise mode, they are generated per sample. All generated confusion matrices are binarized with a one-vs-rest transformation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mlcm = MultiLabelCM(actual_vector=[{\"cat\", \"bird\"}, {\"dog\"}], \n",
+    "                    predict_vector=[{\"cat\"}, {\"dog\", \"bird\"}], \n",
+    "                    classes=[\"cat\", \"dog\", \"bird\"])\n",
+    "print(mlcm.actual_vector_multihot)\n",
+    "print(mlcm.predict_vector_multihot)\n",
+    "mlcm.get_cm_by_class(\"cat\").print_matrix()\n",
+    "mlcm.get_cm_by_sample(0).print_matrix()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<ul>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  new in <span style=\"color:red;\">version 4.0</span> </li>\n",
+    "</ul>"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3449,7 +3374,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3457,7 +3381,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3473,7 +3396,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3481,7 +3403,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3641,7 +3562,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3649,7 +3569,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3658,7 +3577,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3668,7 +3586,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3676,7 +3593,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3684,7 +3600,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3702,7 +3617,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3710,7 +3624,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3720,7 +3633,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3728,7 +3640,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3741,7 +3652,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3749,7 +3659,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3759,7 +3668,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3769,7 +3677,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3777,7 +3684,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3789,7 +3695,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3797,7 +3702,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3805,7 +3709,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3817,7 +3720,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3825,7 +3727,30 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<strong>MultiLabelCM</strong>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. `actual_vector` : python `list` or numpy array of `sets`\n",
+    "2. `predict_vector` : python `list` or numpy array of `sets`\n",
+    "3. `sample_weight`: python `list` or numpy `array`\n",
+    "4. `classes` : python `list`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* run `help(MultiLabelCM)` for more information"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3833,7 +3758,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3841,7 +3765,6 @@
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3870,7 +3793,6 @@
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    "source": [
@@ -3878,7 +3800,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3907,7 +3828,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3915,7 +3835,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3944,7 +3863,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3952,7 +3870,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3981,7 +3898,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3989,7 +3905,6 @@
    ]
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   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -3998,7 +3913,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4026,7 +3940,6 @@
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   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4034,7 +3947,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4042,7 +3954,6 @@
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   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4070,7 +3981,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4078,7 +3988,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4086,7 +3995,6 @@
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   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4114,7 +4022,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4122,7 +4029,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4130,7 +4036,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4158,7 +4063,6 @@
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    "metadata": {},
    "source": [
@@ -4166,7 +4070,6 @@
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    "metadata": {},
    "source": [
@@ -4174,7 +4077,6 @@
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    "metadata": {},
    "source": [
@@ -4202,7 +4104,6 @@
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -4210,7 +4111,6 @@
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    "metadata": {},
    "source": [
@@ -4218,7 +4118,6 @@
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    "metadata": {},
    "source": [
@@ -4226,7 +4125,6 @@
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    "metadata": {},
    "source": [
@@ -4236,7 +4134,6 @@
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    "metadata": {},
    "source": [
@@ -4264,7 +4161,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4272,7 +4168,6 @@
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    "metadata": {},
    "source": [
@@ -4282,7 +4177,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4310,7 +4204,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4318,7 +4211,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4329,7 +4221,6 @@
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4357,7 +4248,6 @@
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4365,7 +4255,6 @@
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4376,7 +4265,6 @@
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4404,7 +4292,6 @@
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -4412,7 +4299,6 @@
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    "metadata": {},
    "source": [
@@ -4422,7 +4308,6 @@
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    "source": [
@@ -4450,7 +4335,6 @@
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    "source": [
@@ -4458,7 +4342,6 @@
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    "source": [
@@ -4470,7 +4353,6 @@
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    "source": [
@@ -4498,7 +4380,6 @@
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    "metadata": {},
    "source": [
@@ -4506,7 +4387,6 @@
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    "metadata": {},
    "source": [
@@ -4516,7 +4396,6 @@
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    "source": [
@@ -4544,7 +4423,6 @@
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    "source": [
@@ -4552,7 +4430,6 @@
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    "source": [
@@ -4562,7 +4439,6 @@
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    "metadata": {},
    "source": [
@@ -4592,7 +4468,6 @@
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    "metadata": {},
    "source": [
@@ -4600,7 +4475,6 @@
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    "source": [
@@ -4610,7 +4484,6 @@
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    "source": [
@@ -4638,7 +4511,6 @@
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    "source": [
@@ -4646,7 +4518,6 @@
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    "source": [
@@ -4654,7 +4525,6 @@
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    "metadata": {},
    "source": [
@@ -4682,7 +4552,6 @@
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    "metadata": {},
    "source": [
@@ -4692,7 +4561,6 @@
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    "metadata": {},
    "source": [
@@ -4700,7 +4568,6 @@
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    "source": [
@@ -4711,7 +4578,6 @@
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    "source": [
@@ -4799,7 +4665,6 @@
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    "source": [
@@ -4807,7 +4672,6 @@
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    "source": [
@@ -4815,7 +4679,6 @@
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    "metadata": {},
    "source": [
@@ -4823,7 +4686,6 @@
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    "source": [
@@ -4831,7 +4693,6 @@
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    "metadata": {},
    "source": [
@@ -4841,7 +4702,6 @@
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    "source": [
@@ -4849,7 +4709,6 @@
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    "source": [
@@ -4861,7 +4720,6 @@
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@@ -4889,7 +4747,6 @@
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    "source": [
@@ -4897,7 +4754,6 @@
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    "source": [
@@ -4907,7 +4763,6 @@
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    "source": [
@@ -4937,7 +4792,6 @@
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@@ -4945,7 +4799,6 @@
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@@ -4953,7 +4806,6 @@
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@@ -4981,7 +4833,6 @@
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@@ -4989,7 +4840,6 @@
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@@ -5001,7 +4851,6 @@
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@@ -5029,7 +4878,6 @@
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@@ -5039,7 +4887,6 @@
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@@ -5047,7 +4894,6 @@
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@@ -5059,7 +4905,6 @@
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@@ -5087,7 +4932,6 @@
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@@ -5097,7 +4941,6 @@
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@@ -5105,7 +4948,6 @@
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@@ -5115,7 +4957,6 @@
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@@ -5143,7 +4984,6 @@
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@@ -5151,7 +4991,6 @@
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@@ -5161,7 +5000,6 @@
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@@ -5189,7 +5027,6 @@
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@@ -5197,7 +5034,6 @@
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@@ -5207,7 +5043,6 @@
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@@ -5235,7 +5070,6 @@
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@@ -5243,7 +5077,6 @@
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@@ -5251,7 +5084,6 @@
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@@ -5281,7 +5113,6 @@
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@@ -5291,7 +5122,6 @@
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@@ -5299,7 +5129,6 @@
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@@ -5307,7 +5136,6 @@
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@@ -5337,7 +5165,6 @@
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@@ -12976,7 +12258,6 @@
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@@ -12984,7 +12265,6 @@
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@@ -12992,7 +12272,6 @@
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@@ -13000,7 +12279,6 @@
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@@ -13008,7 +12286,6 @@
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@@ -13016,7 +12293,6 @@
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@@ -13064,7 +12340,6 @@
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@@ -13072,7 +12347,6 @@
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@@ -13081,7 +12355,6 @@
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@@ -13089,7 +12362,6 @@
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@@ -13097,7 +12369,6 @@
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@@ -13107,7 +12378,6 @@
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@@ -13115,7 +12385,6 @@
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@@ -13125,7 +12394,6 @@
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@@ -13133,7 +12401,6 @@
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@@ -13182,7 +12449,6 @@
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@@ -13190,7 +12456,6 @@
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@@ -13198,7 +12463,6 @@
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@@ -13206,7 +12470,6 @@
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    "source": [
@@ -13214,7 +12477,6 @@
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    "source": [
@@ -13224,7 +12486,6 @@
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    "source": [
@@ -13234,7 +12495,6 @@
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    "source": [
@@ -13244,7 +12504,6 @@
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@@ -13252,7 +12511,6 @@
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    "source": [
@@ -13264,7 +12522,6 @@
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@@ -13312,7 +12569,6 @@
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@@ -13320,7 +12576,6 @@
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@@ -13329,7 +12584,6 @@
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@@ -13337,7 +12591,6 @@
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@@ -13345,7 +12598,6 @@
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    "source": [
@@ -13355,7 +12607,6 @@
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@@ -13365,7 +12616,6 @@
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@@ -13375,7 +12625,6 @@
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@@ -13383,7 +12632,6 @@
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@@ -13558,7 +12806,6 @@
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@@ -13668,7 +12915,6 @@
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@@ -13676,7 +12922,6 @@
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@@ -13686,7 +12931,6 @@
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@@ -13696,7 +12940,6 @@
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@@ -13706,7 +12949,6 @@
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@@ -13716,7 +12958,6 @@
    ]
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    "source": [
@@ -13817,7 +13058,6 @@
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    "cell_type": "markdown",
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    "source": [
@@ -13825,7 +13065,6 @@
    ]
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    "metadata": {},
    "source": [
@@ -13835,7 +13074,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -13845,7 +13083,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -13855,7 +13092,6 @@
    ]
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   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -13865,7 +13101,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14131,7 +13366,6 @@
    ]
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   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14139,7 +13373,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14150,7 +13383,6 @@
    ]
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   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14160,7 +13392,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14170,7 +13401,6 @@
    ]
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14180,7 +13410,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14190,7 +13419,6 @@
    ]
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    "metadata": {},
    "source": [
@@ -14242,7 +13470,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14261,7 +13488,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14290,7 +13516,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14322,7 +13547,6 @@
    ]
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    "metadata": {},
    "source": [
@@ -14355,7 +13579,6 @@
    ]
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    "source": [
@@ -14386,7 +13609,6 @@
    ]
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    "source": [
@@ -14418,7 +13640,6 @@
    ]
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    "metadata": {},
    "source": [
@@ -14447,7 +13668,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14455,7 +13675,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14469,7 +13688,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14479,7 +13697,6 @@
    ]
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    "metadata": {},
    "source": [
@@ -14489,7 +13706,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14499,7 +13715,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14509,7 +13724,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14519,7 +13733,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14548,7 +13761,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14580,7 +13792,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14613,7 +13824,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14644,7 +13854,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14675,7 +13884,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14707,7 +13915,6 @@
    ]
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-   "attachments": {},
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    "metadata": {},
    "source": [
@@ -14739,7 +13946,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14768,7 +13974,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14776,7 +13981,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14793,7 +13997,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14803,7 +14006,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14813,7 +14015,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14823,7 +14024,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14833,7 +14033,6 @@
    ]
   },
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14843,7 +14042,6 @@
    ]
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-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14853,7 +14051,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14863,7 +14060,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14873,7 +14069,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14902,7 +14097,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14935,7 +14129,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -14969,7 +14162,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15003,7 +14195,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15036,7 +14227,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15070,7 +14260,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15099,7 +14288,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15107,7 +14295,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15122,7 +14309,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15132,7 +14318,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15142,7 +14327,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15152,7 +14336,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15162,7 +14345,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15172,7 +14354,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15182,7 +14363,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15211,7 +14391,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15242,7 +14421,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15273,7 +14451,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15302,7 +14479,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15310,7 +14486,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15321,7 +14496,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15331,7 +14505,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15341,7 +14514,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15351,7 +14523,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15380,7 +14551,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15409,7 +14579,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15417,7 +14586,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15426,7 +14594,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -15436,7 +14603,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16211,17 +15377,100 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM([[0, 1], [1, 1]], [[1, 0], [1, 0]])\n",
+    "except pycmVectorError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])\n",
+    "    mlcm.get_cm_by_class(1)\n",
+    "except pycmMultiLabelError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm.get_cm_by_sample(2)\n",
+    "except pycmMultiLabelError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM(2, [{1, 0}, {1, 0}])\n",
+    "except pycmVectorError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM([{1, 0}, {1, 0}, {1,1}], [{1, 0}, {1, 0}])\n",
+    "except pycmVectorError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM([], [])\n",
+    "except pycmVectorError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    mlcm = MultiLabelCM([{1, 0}, {1, 0}], [{1, 0}, {1, 0}], classes=[1,0,1])\n",
+    "except pycmVectorError as e:\n",
+    "    print(str(e))"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<ul>\n",
-    "    <li><span style=\"color:red;\">Notice </span> :  updated in <span style=\"color:red;\">version 3.7</span> </li>\n",
+    "    <li><span style=\"color:red;\">Notice </span> :  updated in <span style=\"color:red;\">version 4.0</span> </li>\n",
     "</ul>"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16229,7 +15478,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16276,7 +15524,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16284,7 +15531,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16292,7 +15538,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16300,7 +15545,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16322,7 +15566,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16330,7 +15573,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16338,7 +15580,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16517,7 +15758,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16551,7 +15791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.5.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/README.md b/README.md
index 538b54c4..6db5a1d8 100644
--- a/README.md
+++ b/README.md
@@ -437,6 +437,32 @@ pycm.ConfusionMatrix(classes: [0, 1, 2])
 'cm2'
 ```
 
+### Multilabel confusion matrix
+
+From `version 4.0`, `MultiLabelCM` has been added to calculate class-wise or sample-wise multilabel confusion matrices. In class-wise mode, confusion matrices are calculated for each class, and in sample-wise mode, they are generated per sample. All generated confusion matrices are binarized with a one-vs-rest transformation.
+
+```pycon
+>>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"])
+>>> mlcm.actual_vector_multihot
+[[1, 0, 1], [0, 1, 0]]
+>>> mlcm.predict_vector_multihot
+[[1, 0, 0], [0, 1, 1]]
+>>> mlcm.get_cm_by_class("cat").print_matrix()
+Predict 0       1       
+Actual
+0       1       0       
+
+1       0       1       
+
+>>> mlcm.get_cm_by_sample(0).print_matrix()
+Predict 0       1       
+Actual
+0       1       0       
+
+1       1       1 
+
+```
+
 ### Online help
 
 `online_help` function is added in `version 1.1` in order to open each statistics definition in web browser
@@ -451,55 +477,9 @@ pycm.ConfusionMatrix(classes: [0, 1, 2])
 * List of items are available by calling `online_help()` (without argument)
 * If PyCM website is not available, set `alt_link = True` (new in `version 2.4`)
 
-### Acceptable data types
-
-**ConfusionMatrix**
-
-1. `actual_vector` : python `list` or numpy `array` of any stringable objects
-2. `predict_vector` : python `list` or numpy `array` of any stringable objects
-3. `matrix` : `dict`
-4. `digit`: `int`
-5. `threshold` : `FunctionType (function or lambda)`
-6. `file` : `File object`
-7. `sample_weight` : python `list` or numpy `array` of numbers
-8. `transpose` : `bool`
-9. `classes` : python `list`
-10. `is_imbalanced` : `bool`
-11. `metrics_off` : `bool`
-
-* Run `help(ConfusionMatrix)` for `ConfusionMatrix` object details
-
-**Compare**
-
-1. `cm_dict` : python `dict` of `ConfusionMatrix` object (`str` : `ConfusionMatrix`)
-2. `by_class` : `bool`
-3. `class_weight` : python `dict` of class weights (`class_name` : `float`)
-4. `class_benchmark_weight`: python `dict` of class benchmark weights (`class_benchmark_name` : `float`)
-5. `overall_benchmark_weight`: python `dict` of overall benchmark weights (`overall_benchmark_name` : `float`)
-6. `digit`: `int`
-
-* Run `help(Compare)` for `Compare` object details
-
-**ROCCurve**
-
-1. `actual_vector` : python `list` or numpy `array` of any stringable objects
-2. `probs` : python `list` or numpy `array`
-3. `classes` : python `list`
-4. `thresholds`: python `list` or numpy `array`
-5. `sample_weight`: python `list` or numpy `array`
-
-**PRCurve**
-
-1. `actual_vector` : python `list` or numpy `array` of any stringable objects
-2. `probs` : python `list` or numpy `array`
-3. `classes` : python `list`
-4. `thresholds`: python `list` or numpy `array`
-5. `sample_weight`: python `list` or numpy `array`
-
-For more information visit [here](https://github.com/sepandhaghighi/pycm/tree/master/Document "Document")
+### Screen record
 
 <div align="center">
-
 <a href="https://asciinema.org/a/171863" target="_blank"><img src="https://asciinema.org/a/171863.png"/></a>
 </div>
 
diff --git a/Test/error_test.py b/Test/error_test.py
index 591e1f23..eeb25f74 100644
--- a/Test/error_test.py
+++ b/Test/error_test.py
@@ -280,4 +280,46 @@
 Traceback (most recent call last):
         ...
 pycm.pycm_error.pycmMatrixError: This method cannot be executed while 'metrics_off=True'.
+>>> mlcm = MultiLabelCM([[0, 1], [1, 1]], [[1, 0], [1, 0]])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: Class extraction from input failed. Input vectors should be a list of sets with unified types.
+>>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], ['cat', {'cat'}])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: Class extraction from input failed. Input vectors should be a list of sets with unified types.
+>>> mlcm = MultiLabelCM(['dog', {'cat', 'dog'}], [{'cat'}, {'cat'}])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: Class extraction from input failed. Input vectors should be a list of sets with unified types.
+>>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])
+>>> mlcm.get_cm_by_class(1)
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmMultiLabelError: Given class name is not among problem's classes.
+>>> mlcm.get_cm_by_sample(2)
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmMultiLabelError: Given index is out of vector's range.
+>>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat', 'bird'}], classes=['dog', 'cat'])
+>>> mlcm.get_cm_by_class('bird')
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmMultiLabelError: Given class name is not among problem's classes.
+>>> mlcm = MultiLabelCM(2, [[1, 0], [1, 0]])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: The type of input vectors is assumed to be a list or a NumPy array
+>>> mlcm = MultiLabelCM([{1, 0}, {1, 0}, {1,1}], [{1, 0}, {1, 0}])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: Input vectors must have same length
+>>> mlcm = MultiLabelCM([], [])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: Input vectors are empty
+>>> mlcm = MultiLabelCM([{1, 0}, {1, 0}], [{1, 0}, {1, 0}], classes=[1,0,1])
+Traceback (most recent call last):
+        ...
+pycm.pycm_error.pycmVectorError: The classes list isn't unique. It contains duplicated labels.
 """
diff --git a/Test/multilabel_test.py b/Test/multilabel_test.py
new file mode 100644
index 00000000..177fb987
--- /dev/null
+++ b/Test/multilabel_test.py
@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+>>> from pycm import *
+>>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])
+>>> len(mlcm)
+2
+>>> print(mlcm)
+pycm.MultiLabelCM(classes: ['cat', 'dog'])
+>>> mlcm
+pycm.MultiLabelCM(classes: ['cat', 'dog'])
+>>> mlcm.get_cm_by_sample(0)
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_sample(0)
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_sample(1)
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_class('cat')
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_class('cat')
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_class('dog')
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.samplewise_cms[0] != mlcm.samplewise_cms[1]
+True
+>>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat', 'bird'}], classes=['dog', 'cat'])
+>>> mlcm.get_cm_by_class('cat')
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.get_cm_by_class('dog')
+pycm.ConfusionMatrix(classes: [0, 1])
+>>> mlcm.classwise_cms['dog'] != mlcm.classwise_cms['cat']
+True
+>>> mlcm2 = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}], sample_weight=[2, 5])
+>>> mlcm2.get_cm_by_class("dog")
+pycm.ConfusionMatrix(classes: [0, 1])
+"""
diff --git a/Test/verified_test.py b/Test/verified_test.py
index ca01363c..6a426bd6 100644
--- a/Test/verified_test.py
+++ b/Test/verified_test.py
@@ -662,5 +662,49 @@
 -0.0012804097311239404
 >>> cm2.distance(metric=DistanceType.KentFosterII)[1]
 -0.002196997436837158
-
+>>> mlcm = MultiLabelCM(actual_vector=[{"cat", "bird"}, {"dog"}], predict_vector=[{"cat"}, {"dog", "bird"}], classes=["cat", "dog", "bird"]) # Verified Case -- (http://bitly.ws/GNq2)
+>>> mlcm.actual_vector_multihot
+[[1, 0, 1], [0, 1, 0]]
+>>> mlcm.predict_vector_multihot
+[[1, 0, 0], [0, 1, 1]]
+>>> mlcm.get_cm_by_class("cat").print_matrix()
+Predict 0       1
+Actual
+0       1       0
+<BLANKLINE>
+1       0       1
+<BLANKLINE>
+<BLANKLINE>
+>>> mlcm.get_cm_by_class("dog").print_matrix()
+Predict 0       1
+Actual
+0       1       0
+<BLANKLINE>
+1       0       1
+<BLANKLINE>
+<BLANKLINE>
+>>> mlcm.get_cm_by_class("bird").print_matrix()
+Predict 0       1
+Actual
+0       0       1
+<BLANKLINE>
+1       1       0
+<BLANKLINE>
+<BLANKLINE>
+>>> mlcm.get_cm_by_sample(0).print_matrix()
+Predict 0       1
+Actual
+0       1       0
+<BLANKLINE>
+1       1       1
+<BLANKLINE>
+<BLANKLINE>
+>>> mlcm.get_cm_by_sample(1).print_matrix()
+Predict 0       1
+Actual
+0       1       1
+<BLANKLINE>
+1       0       1
+<BLANKLINE>
+<BLANKLINE>
 """
diff --git a/pycm/__init__.py b/pycm/__init__.py
index 6d185842..71e855d0 100644
--- a/pycm/__init__.py
+++ b/pycm/__init__.py
@@ -6,5 +6,6 @@
 from .pycm_distance import DistanceType
 from .pycm_obj import ConfusionMatrix
 from .pycm_compare import Compare
+from .pycm_multilabel_cm import MultiLabelCM
 from .pycm_curve import Curve, ROCCurve, PRCurve
 __version__ = PYCM_VERSION
diff --git a/pycm/pycm_error.py b/pycm/pycm_error.py
index acc990bf..7b97a36a 100644
--- a/pycm/pycm_error.py
+++ b/pycm/pycm_error.py
@@ -42,3 +42,9 @@ class pycmCurveError(Exception):
     """Curve error class."""
 
     pass
+
+
+class pycmMultiLabelError(Exception):
+    """Multilabel error class."""
+
+    pass
diff --git a/pycm/pycm_multilabel_cm.py b/pycm/pycm_multilabel_cm.py
new file mode 100644
index 00000000..461414ea
--- /dev/null
+++ b/pycm/pycm_multilabel_cm.py
@@ -0,0 +1,216 @@
+# -*- coding: utf-8 -*-
+"""MultiLabelCM module."""
+from __future__ import division
+from .pycm_error import pycmVectorError, pycmMultiLabelError
+from .pycm_param import *
+from .pycm_obj import ConfusionMatrix
+import numpy
+
+
+class MultiLabelCM():
+    """
+    Multilabel confusion matrix class.
+
+    >>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])
+    >>> mlcm.actual_vector_multihot
+    [[0, 1], [1, 1]]
+    >>> mlcm.predict_vector_multihot
+    [[1, 0], [1, 0]]
+    """
+
+    def __init__(
+            self,
+            actual_vector,
+            predict_vector,
+            sample_weight=None,
+            classes=None):
+        """
+        Init method.
+
+        :param actual_vector: actual vector
+        :type actual_vector: python list or numpy array of sets
+        :param predict_vector: vector of predictions
+        :type predict_vector: python list or numpy array of sets
+        :param sample_weight: sample weights list
+        :type sample_weight: python list or numpy array
+        :param classes: ordered labels of classes
+        :type classes: list
+        """
+        self.actual_vector = actual_vector
+        self.actual_vector_multihot = []
+        self.predict_vector = predict_vector
+        self.predict_vector_multihot = []
+        self.weights = None
+        self.classes = None
+        self.classwise_cms = {}
+        self.samplewise_cms = {}
+        __mlcm_vector_handler__(
+            self,
+            actual_vector,
+            predict_vector,
+            sample_weight,
+            classes)
+        __mlcm_assign_classes__(self, classes)
+        __mlcm_vectors_filter__(self)
+
+    def get_cm_by_class(self, class_name):
+        """
+        Return confusion matrices based on classes.
+
+        :param class_name: target class name for confusion matrix
+        :type class_name: any valid type
+        :return: confusion matrix corresponding to the given class name
+        """
+        if class_name not in self.classwise_cms:
+            try:
+                class_index = self.classes.index(class_name)
+            except ValueError:
+                raise pycmMultiLabelError(INVALID_CLASS_NAME_ERROR)
+            actual_vector_temp = []
+            predict_vector_temp = []
+            for item1, item2 in zip(
+                    self.actual_vector_multihot, self.predict_vector_multihot):
+                actual_vector_temp.append(item1[class_index])
+                predict_vector_temp.append(item2[class_index])
+            cm = ConfusionMatrix(
+                actual_vector_temp,
+                predict_vector_temp,
+                sample_weight=self.weights)
+            self.classwise_cms[class_name] = cm
+            return cm
+        return self.classwise_cms[class_name]
+
+    def get_cm_by_sample(self, index):
+        """
+        Return confusion matrices based on samples.
+
+        :param index: sample index for confusion matrix
+        :type index: int
+        :return: confusion matrix corresponding to the given sample number
+        """
+        if index < 0 or index >= len(self.actual_vector):
+            raise pycmMultiLabelError(VECTOR_INDEX_ERROR)
+        if index not in self.samplewise_cms:
+            cm = ConfusionMatrix(
+                self.actual_vector_multihot[index],
+                self.predict_vector_multihot[index],
+                sample_weight=self.weights)
+            self.samplewise_cms[index] = cm
+            return cm
+        return self.samplewise_cms[index]
+
+    def __str__(self):
+        """
+        Multilabel confusion matrix object string representation method.
+
+        :return: representation as str
+        """
+        return self.__repr__()
+
+    def __repr__(self):
+        """
+        Multilabel confusion matrix object representation method.
+
+        :return: representation as str
+        """
+        return "pycm.MultiLabelCM(classes: " + str(self.classes) + ")"
+
+    def __len__(self):
+        """
+        Multilabel confusion matrix object length method.
+
+        :return: length as int
+        """
+        return len(self.classes)
+
+
+def __mlcm_vector_handler__(
+        mlcm,
+        actual_vector,
+        predict_vector,
+        sample_weight,
+        classes):
+    """
+    Handle multilabel object conditions for vectors.
+
+    :param mlcm: multilabel confusion matrix
+    :type mlcm: pycm.MultiLabelCM object
+    :param actual_vector: actual vector
+    :type actual_vector: python list or numpy array of sets
+    :param predict_vector: vector of predictions
+    :type predict_vector: python list or numpy array of sets
+    :param sample_weight: sample weights list
+    :type sample_weight: python list or numpy array
+    :param classes: ordered labels of classes
+    :type classes: list
+    :return: None
+    """
+    if not isinstance(actual_vector, (list, numpy.ndarray)) or not \
+            isinstance(predict_vector, (list, numpy.ndarray)):
+        raise pycmVectorError(VECTOR_TYPE_ERROR)
+    if len(actual_vector) != len(predict_vector):
+        raise pycmVectorError(VECTOR_SIZE_ERROR)
+    if len(actual_vector) == 0 or len(predict_vector) == 0:
+        raise pycmVectorError(VECTOR_EMPTY_ERROR)
+    if not all(map(lambda x: isinstance(x, set), actual_vector)):
+        raise pycmVectorError(NOT_ALL_SET_VECTOR_ERROR)
+    if not all(map(lambda x: isinstance(x, set), predict_vector)):
+        raise pycmVectorError(NOT_ALL_SET_VECTOR_ERROR)
+    if classes is not None and len(set(classes)) != len(classes):
+        raise pycmVectorError(VECTOR_UNIQUE_CLASS_ERROR)
+    if isinstance(sample_weight, (list, numpy.ndarray)):
+        mlcm.weights = sample_weight
+
+
+def __mlcm_assign_classes__(
+        mlcm,
+        classes):
+    """
+    Assign multilabel object class.
+
+    :param mlcm: multilabel confusion matrix
+    :type mlcm: pycm.MultiLabelCM object
+    :param classes: ordered labels of classes
+    :type classes: list
+    :return: None
+    """
+    mlcm.classes = classes
+    if classes is None:
+        mlcm.classes = sorted(
+            list(
+                set.union(
+                    *mlcm.actual_vector,
+                    *mlcm.predict_vector)))
+
+
+def __mlcm_vectors_filter__(mlcm):
+    """
+    Normalize multilabel object vectors.
+
+    :param mlcm: multilabel confusion matrix
+    :type mlcm: pycm.MultiLabelCM object
+    :param classes: ordered labels of classes
+    :type classes: list
+    :return: None
+    """
+    mlcm.actual_vector_multihot = [__set_to_multihot__(
+        x, mlcm.classes) for x in mlcm.actual_vector]
+    mlcm.predict_vector_multihot = [__set_to_multihot__(
+        x, mlcm.classes) for x in mlcm.predict_vector]
+
+
+def __set_to_multihot__(input_set, classes):
+    """
+    Convert a set into a multi-hot vector based in classes.
+
+    :param input_set: input set
+    :type input_set: set
+    :param classes: ordered labels of classes
+    :type classes: list
+    :return: multi-hot vector as a list
+    """
+    result = [0] * len(classes)
+    for i, x in enumerate(classes):
+        if x in input_set:
+            result[i] = 1
+    return result
diff --git a/pycm/pycm_param.py b/pycm/pycm_param.py
index 6c3664be..2f3856d0 100644
--- a/pycm/pycm_param.py
+++ b/pycm/pycm_param.py
@@ -67,6 +67,7 @@
 VECTOR_EMPTY_ERROR = "Input vectors are empty"
 VECTOR_ONLY_ERROR = "This option only works in vector mode"
 VECTOR_UNIQUE_CLASS_ERROR = "The classes list isn't unique. It contains duplicated labels."
+NOT_ALL_SET_VECTOR_ERROR = "Class extraction from input failed. Input vectors should be a list of sets with unified types."
 CLASS_NUMBER_ERROR = "Number of the classes is lower than 2"
 METRICS_OFF_ERROR = "This method cannot be executed while 'metrics_off=True'."
 CLASSES_ERROR = "Used classes is not a subset of matrix's classes."
@@ -74,6 +75,9 @@
 CLASSES_LENGHT_ERROR = "Classes length is not equal to the array length."
 AREA_METHOD_ERROR = "The numeric integral method can only be selected between 'trapezoidal' and 'midpoint'!"
 
+VECTOR_INDEX_ERROR = "Given index is out of vector's range."
+INVALID_CLASS_NAME_ERROR = "Given class name is not among problem's classes."
+
 COMPARE_TYPE_ERROR = "The input is supposed to consist of pycm.ConfusionMatrix object but it's not!"
 COMPARE_DOMAIN_ERROR = "The domain of all ConfusionMatrix objects must be same! The sample size or the number " \
                        "of classes are different."