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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" Compare\n",
" ROC Curve\n",
" Precision-Recall Curve\n",
+ " Multilabel Confusion Matrix\n",
" Online Help\n",
" Acceptable Data Types\n",
" \n",
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+ "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": [
+ "\n",
+ " - Notice : new in version 4.0
\n",
+ "
"
+ ]
+ },
+ {
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+ "source": [
+ "MultiLabelCM"
+ ]
+ },
+ {
+ "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 @@
]
},
{
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@@ -3841,7 +3765,6 @@
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@@ -3870,7 +3793,6 @@
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@@ -3878,7 +3800,6 @@
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@@ -3907,7 +3828,6 @@
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@@ -3915,7 +3835,6 @@
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@@ -3944,7 +3863,6 @@
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@@ -3952,7 +3870,6 @@
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@@ -3981,7 +3898,6 @@
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@@ -3989,7 +3905,6 @@
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@@ -3998,7 +3913,6 @@
]
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@@ -4026,7 +3940,6 @@
]
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@@ -4034,7 +3947,6 @@
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@@ -4042,7 +3954,6 @@
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@@ -4070,7 +3981,6 @@
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@@ -4078,7 +3988,6 @@
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@@ -4086,7 +3995,6 @@
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@@ -4114,7 +4022,6 @@
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@@ -4122,7 +4029,6 @@
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@@ -4130,7 +4036,6 @@
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@@ -4158,7 +4063,6 @@
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@@ -4166,7 +4070,6 @@
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@@ -4174,7 +4077,6 @@
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@@ -4202,7 +4104,6 @@
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@@ -4210,7 +4111,6 @@
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@@ -4218,7 +4118,6 @@
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@@ -4226,7 +4125,6 @@
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@@ -4236,7 +4134,6 @@
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@@ -4272,7 +4168,6 @@
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@@ -4282,7 +4177,6 @@
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@@ -4310,7 +4204,6 @@
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@@ -4318,7 +4211,6 @@
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@@ -4357,7 +4248,6 @@
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@@ -4376,7 +4265,6 @@
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@@ -4412,7 +4299,6 @@
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@@ -4422,7 +4308,6 @@
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@@ -4516,7 +4396,6 @@
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@@ -4544,7 +4423,6 @@
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@@ -4600,7 +4475,6 @@
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@@ -4610,7 +4484,6 @@
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@@ -4646,7 +4518,6 @@
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@@ -4654,7 +4525,6 @@
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@@ -4682,7 +4552,6 @@
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@@ -4692,7 +4561,6 @@
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@@ -4700,7 +4568,6 @@
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@@ -4841,7 +4702,6 @@
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@@ -4849,7 +4709,6 @@
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@@ -4861,7 +4720,6 @@
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@@ -4889,7 +4747,6 @@
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@@ -4897,7 +4754,6 @@
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@@ -14739,7 +13946,6 @@
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@@ -14776,7 +13981,6 @@
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@@ -14843,7 +14042,6 @@
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@@ -14863,7 +14060,6 @@
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@@ -14873,7 +14069,6 @@
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@@ -15036,7 +14227,6 @@
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@@ -15070,7 +14260,6 @@
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@@ -15099,7 +14288,6 @@
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@@ -15107,7 +14295,6 @@
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@@ -15122,7 +14309,6 @@
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@@ -15182,7 +14363,6 @@
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@@ -15211,7 +14391,6 @@
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@@ -15242,7 +14421,6 @@
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@@ -15273,7 +14451,6 @@
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{
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@@ -15302,7 +14479,6 @@
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{
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@@ -15310,7 +14486,6 @@
]
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{
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@@ -15321,7 +14496,6 @@
]
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{
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@@ -15331,7 +14505,6 @@
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@@ -15341,7 +14514,6 @@
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@@ -15351,7 +14523,6 @@
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@@ -15380,7 +14551,6 @@
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@@ -15409,7 +14579,6 @@
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{
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@@ -15417,7 +14586,6 @@
]
},
{
- "attachments": {},
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@@ -15426,7 +14594,6 @@
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},
{
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@@ -15436,7 +14603,6 @@
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},
{
- "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": [
"\n",
- " - Notice : updated in version 3.7
\n",
+ " - Notice : updated in version 4.0
\n",
"
"
]
},
{
- "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
-
