refactor: remove deep_transformer module

.pre-commit-config.yaml

@@ -112,13 +112,13 @@ repos:
         args: ["--profile=black"]
 
   - repo: https://github.com/PyCQA/pylint
-    rev: v2.16.2
+    rev: v2.17.0
     hooks:
       - id: pylint
         args: ["--rcfile=pyproject.toml"]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.0.1
+    rev: v1.1.1
     hooks:
       - id: mypy
         args:
@@ -139,7 +139,7 @@ repos:
       - id: nbstripout
 
   - repo: https://github.com/python-poetry/poetry
-    rev: 1.3.0
+    rev: 1.4.0
     hooks:
       - id: poetry-check
       - id: poetry-lock

docs/README.md

@@ -51,7 +51,6 @@ poetry install
 | ------ | ----------- | ----------- |
 |[`Datetime transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/datetime_transformer/)|[`DateColumnsTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/datetime_transformer/#sk_transformers.datetime_transformer.DateColumnsTransformer)|Splits a date column into multiple columns.|
 |[`Datetime transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/datetime_transformer/)|[`DurationCalculatorTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/datetime_transformer/#sk_transformers.datetime_transformer.DurationCalculatorTransformer)|Calculates the duration between to given dates.|
-|[`Deep transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/deep_transformer/)|[`ToVecTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/deep_transformer/#sk_transformers.deep_transformer.ToVecTransformer)|This transformer trains an [FT-Transformer](https://paperswithcode.com/method/ft-transformer) using the [pytorch-widedeep package](https://github.com/jrzaurin/pytorch-widedeep) and extracts the embeddings from its embedding layer.|
 |[`Encoder transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/encoder_transformer/)|[`MeanEncoderTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/encoder_transformer/#sk_transformers.encoder_transformer.MeanEncoderTransformer)|Scikit-learn API for the [feature-engine MeanEncoder](https://feature-engine.readthedocs.io/en/latest/api_doc/encoding/MeanEncoder.html).|
 |[`Generic transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/generic_transformer/)|[`AggregateTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/generic_transformer/#sk_transformers.generic_transformer.AggregateTransformer)|This transformer uses Pandas groupby method and aggregate to apply function on a column grouped by another column.|
 |[`Generic transformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/generic_transformer/)|[`AllowedValuesTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/generic_transformer/#sk_transformers.generic_transformer.AllowedValuesTransformer)|This transformer replaces values that are *not* in a list with another value.|

examples/playground.ipynb

@@ -116,59 +116,6 @@
     "transformer.fit_transform(X).to_numpy()"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Deep transformer](https://chrislemke.github.io/sk-transformers/API-reference/transformer/deep_transformer/)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### [`ToVecTransformer`](https://chrislemke.github.io/sk-transformers/API-reference/transformer/deep_transformer/#sk_transformers.deep_transformer.ToVecTransformer)\n",
-    "\n",
-    "This transformer trains an [FT-Transformer](https://paperswithcode.com/method/ft-transformer)\n",
-    "using the [pytorch-widedeep package](https://github.com/jrzaurin/pytorch-widedeep) and extracts the embeddings\n",
-    "from its embedding layer. The output shape of the transformer is (number of rows,(`input_dim` * number of columns)).\n",
-    "Please refer to [this example](https://pytorch-widedeep.readthedocs.io/en/latest/examples/09_extracting_embeddings.html)\n",
-    "for pytorch_widedeep example on how to extract embeddings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from pytorch_widedeep.datasets import load_adult\n",
-    "from sk_transformers import ToVecTransformer\n",
-    "\n",
-    "df = load_adult(as_frame=True)\n",
-    "df[\"target\"] = (df[\"income\"].apply(lambda x: \">50K\" in x)).astype(int)\n",
-    "df = df.drop([\"income\", \"educational-num\"], axis=1)\n",
-    "\n",
-    "cat_cols, cont_cols = [], []\n",
-    "for col in df.columns:\n",
-    "    if df[col].dtype == \"O\" or df[col].nunique() < 50 and col != \"target\":\n",
-    "        cat_cols.append(col)\n",
-    "    elif col != \"target\":\n",
-    "        cont_cols.append(col)\n",
-    "\n",
-    "target_col = \"target\"\n",
-    "target = df[target_col].to_numpy()\n",
-    "\n",
-    "transformer = ToVecTransformer(\n",
-    "    cat_cols, cont_cols, verbose=0, training_objective=\"binary\"\n",
-    ")\n",
-    "transformer.fit_transform(df, target).shape"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",