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added notes about venvs to install docs. saner comma syntax in gallery.
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aiqc committed Aug 9, 2023
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8 changes: 8 additions & 0 deletions dev/PyPI_workflow.md
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### Dependencies for build process.

Install pip
```bash
pip install --upgrade pip
```
or
```bash
conda install pip
```

```bash
pip install --upgrade wheel
pip install --upgrade aiqc
```
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" ),\n",
" \n",
" Target(\n",
" dataset = shared_dataset\n",
" , column = 'object'\n",
" , encoder = Target.Encoder(LabelBinarizer())\n",
" dataset = shared_dataset,\n",
" column = 'object',\n",
" encoder = Target.Encoder(LabelBinarizer())\n",
" ),\n",
"\n",
" Stratifier(\n",
" size_test = 0.12\n",
" , size_validation = 0.22\n",
" size_test = 0.12,\n",
" size_validation = 0.22\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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"source": [
"pipeline = Pipeline(\n",
" Input(\n",
" dataset = dataset_img\n",
" , encoders = Input.Encoder(FunctionTransformer(div255, inverse_func=mult255))\n",
" , reshape_indices = (0,2,3)#reshape for Conv1D grayscale.\n",
" dataset = dataset_img,\n",
" encoders = Input.Encoder(FunctionTransformer(div255, inverse_func=mult255)),\n",
" reshape_indices = (0,2,3)#reshape for Conv1D grayscale.\n",
" ),\n",
" \n",
" Target(\n",
" dataset = dataset_label\n",
" , column = 'status'\n",
" dataset = dataset_label,\n",
" column = 'status'\n",
" ),\n",
"\n",
" Stratifier(\n",
" size_test = 0.14\n",
" , size_validation = 0.22\n",
" size_test = 0.14,\n",
" size_validation = 0.22\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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" ),\n",
" \n",
" Target(\n",
" dataset = shared_dataset\n",
" , column = 'species'\n",
" , encoder = Target.Encoder(OrdinalEncoder())\n",
" dataset = shared_dataset,\n",
" column = 'species',\n",
" encoder = Target.Encoder(OrdinalEncoder())\n",
" ),\n",
"\n",
" Stratifier(\n",
" size_test = 0.09\n",
" , size_validation = 0.22\n",
" #, fold_count = 5\n",
" size_test = 0.09,\n",
" size_validation = 0.22\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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" ),\n",
" \n",
" Target(\n",
" dataset = shared_dataset\n",
" , column = 'object'\n",
" , encoder = Target.Encoder(LabelBinarizer(sparse_output=False))\n",
" dataset = shared_dataset,\n",
" column = 'object',\n",
" encoder = Target.Encoder(LabelBinarizer(sparse_output=False))\n",
" ),\n",
"\n",
" Stratifier(\n",
" size_test = 0.12\n",
" , size_validation = 0.22\n",
" size_test = 0.12,\n",
" size_validation = 0.22\n",
" )\n",
")"
]
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" **hp\n",
"):\n",
" model.compile(\n",
" loss = loser\n",
" , optimizer = optimizer\n",
" , metrics = ['accuracy']\n",
" loss = loser,\n",
" optimizer = optimizer,\n",
" metrics = ['accuracy']\n",
" )\n",
" model.fit(\n",
" train_features, train_label\n",
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" ),\n",
" \n",
" Trainer(\n",
" pipeline = pipeline\n",
" , repeat_count = 2\n",
" pipeline = pipeline,\n",
" repeat_count = 2\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -322,7 +322,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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" ),\n",
" \n",
" Target(\n",
" dataset = shared_dataset\n",
" , column = 'Outcome'\n",
" , encoder = Target.Encoder(LabelBinarizer())\n",
" dataset = shared_dataset,\n",
" column = 'Outcome',\n",
" encoder = Target.Encoder(LabelBinarizer())\n",
" ),\n",
" \n",
" Stratifier(size_test=0.26)\n",
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" **hp\n",
"):\n",
" model.compile(\n",
" loss = loser\n",
" , optimizer = optimizer\n",
" , metrics = ['accuracy', tf.keras.metrics.Precision()]\n",
" loss = loser,\n",
" optimizer = optimizer,\n",
" metrics = ['accuracy', tf.keras.metrics.Precision()]\n",
" )\n",
" \n",
" # Early stopping.\n",
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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" ),\n",
" \n",
" Trainer(\n",
" pipeline = pipeline\n",
" , repeat_count = 2\n",
" pipeline = pipeline,\n",
" repeat_count = 2\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -773,7 +773,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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Expand Up @@ -251,19 +251,19 @@
"source": [
"pipeline = Pipeline(\n",
" Input(\n",
" dataset = dataset_img\n",
" , encoders = Input.Encoder(FunctionTransformer(div255, inverse_func=mult255))\n",
" , reshape_indices = (0,2,3)#reshape for Conv1D grayscale.\n",
" dataset = dataset_img,\n",
" encoders = Input.Encoder(FunctionTransformer(div255, inverse_func=mult255)),\n",
" reshape_indices = (0,2,3)#reshape for Conv1D grayscale.\n",
" ),\n",
" \n",
" Target(\n",
" dataset = dataset_label\n",
" , column = 'status'\n",
" dataset = dataset_label,\n",
" column = 'status'\n",
" ),\n",
"\n",
" Stratifier(\n",
" size_test = 0.14\n",
" , size_validation = 0.22\n",
" size_test = 0.14,\n",
" size_validation = 0.22\n",
" )\n",
")"
]
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" ),\n",
" \n",
" Trainer(\n",
" pipeline = pipeline\n",
" , repeat_count = 2\n",
" pipeline = pipeline,\n",
" repeat_count = 2\n",
" )\n",
")"
]
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -485,7 +485,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
"version": "3.7.16"
}
},
"nbformat": 4,
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