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Extend chapter on feature importance results / robustness checks / outlook👨‍💻 (#405)
1 parent dd69da6 commit 9cf5cad

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‎notebooks/4.0c-mb-feature-importances.ipynb

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‎notebooks/6.0a-mb-results-fttransformer.ipynb

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"execution_count": null,
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"metadata": {
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"colab_type": "text",
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"id": "view-in-github"
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"id": "view-in-github",
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"tags": []
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},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# set globally here\n",
@@ -54,7 +57,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# key used for files and artefacts\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# set project name. Required to access files and artefacts\n",
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"height": 208
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},
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"id": "ah1dofx3TdDj",
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"outputId": "0bd418dd-6b5d-4fa8-9142-89b22d255e2f"
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"outputId": "0bd418dd-6b5d-4fa8-9142-89b22d255e2f",
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"tags": []
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "WmXtH-PEqyQE"
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"id": "WmXtH-PEqyQE",
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"tags": []
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},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"X_test.head()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "zMIOV1jA_ImH"
@@ -134,6 +144,37 @@
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"## FT-Transformer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def count_parameters(model):\n",
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" \"\"\"\n",
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" Count number of parameters, that require gradient-update in model.\n",
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" \n",
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" Found here: https://discuss.pytorch.org/t/how-do-i-check-the-number-of-parameters-of-a-model/4325/9\n",
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" \"\"\"\n",
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" return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
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"\n",
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"for feature_str, model in tqdm(models):\n",
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"\n",
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" model_name = model.split(\"/\")[-1].split(\":\")[0]\n",
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"\n",
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" artifact = run.use_artifact(model)\n",
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" model_dir = artifact.download()\n",
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" \n",
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" with open(Path(model_dir, model_name), 'rb') as f:\n",
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" model = pickle.load(f)\n",
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" \n",
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" print(feature_str)\n",
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" print(count_parameters(model.clf))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [

‎notebooks/6.0e-mb-viz-universal.ipynb

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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "kNTG2a_kf5gS"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "vVE2JK9Af5gW"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "h9mAHJU1f5gX"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "rdBVk3fyf5gZ"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "SyA46Ie6f5gc"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "KLKHwCjOf5gg"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "jGL-HbYlf5gi"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "roRmlg_nf5gl"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "r5ZnoZIG26K_"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"id": "hc7pkVqe4qNw"
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"fi = pd.concat([fi_classical, fi_gbm, fi_transformer], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"fi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"ind = np.arange(len(fi))\n",
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"width = 0.25\n",
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"\n",
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"axes[0].barh(ind, fi[\"quote(best)->quote(ex) values\"], width, xerr=fi[\"quote(best)->quote(ex) std\"], label=\"Classical\")\n",
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"axes[0].barh(ind+width, fi[\"gbm(classical) values\"], width, xerr=fi[\"gbm(classical) std\"], label=\"GBRT\")\n",
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"axes[0].barh(ind+width + width, fi[\"fttransformer(classical) values\"], width, xerr=fi[\"fttransformer(classical) std\"], label=\"Transformer\")\n",
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"axes[0].barh(ind, fi[\"quote(best)->quote(ex)->rev_tick(all) values\"].abs(), width, xerr=fi[\"quote(best)->quote(ex)->rev_tick(all) std\"], label=\"Classical\")\n",
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"axes[0].barh(ind+width, fi[\"gbm(classical) values\"].abs(), width, xerr=fi[\"gbm(classical) std\"], label=\"GBRT\")\n",
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"axes[0].barh(ind+width + width, fi[\"fttransformer(classical) values\"].abs(), width, xerr=fi[\"fttransformer(classical) std\"], label=\"Transformer\")\n",
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"axes[0].axvline(0, color='black', linestyle='--', linewidth=0.5)\n",
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"axes[0].set_xlim([-0.15,0.15])\n",
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"axes[0].set_xlim([0,0.15])\n",
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"\n",
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"axes[1].barh(ind, fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) values\"], width, xerr=fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) std\"], label=\"Classical\")\n",
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"axes[1].barh(ind+width, fi[\"gbm(classical-size) values\"], width, xerr=fi[\"gbm(classical-size) std\"], label=\"GBRT\")\n",
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"axes[1].barh(ind+width + width, fi[\"fttransformer(classical-size) values\"], width, xerr=fi[\"fttransformer(classical-size) std\"], label=\"Transformer\")\n",
1636+
"axes[1].barh(ind, fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) values\"].abs(), width, xerr=fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) std\"], label=\"Classical\")\n",
1637+
"axes[1].barh(ind+width, fi[\"gbm(classical-size) values\"].abs(), width, xerr=fi[\"gbm(classical-size) std\"], label=\"GBRT\")\n",
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"axes[1].barh(ind+width + width, fi[\"fttransformer(classical-size) values\"].abs(), width, xerr=fi[\"fttransformer(classical-size) std\"], label=\"Transformer\")\n",
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"axes[1].axvline(0, color='black', linestyle='--', linewidth=0.5)\n",
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"axes[1].set_xlim([-0.15,0.15])\n",
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"axes[1].set_xlim([0,0.15])\n",
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"\n",
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"axes[2].barh(ind, fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) values\"], width, xerr=fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) std\"], label=\"Classical\")\n",
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"axes[2].barh(ind+width, fi[\"gbm(ml) values\"], width, xerr=fi[\"gbm(ml) std\"], label=\"GBRT\")\n",
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"axes[2].barh(ind+width + width, fi[\"fttransformer(ml) values\"], width, xerr=fi[\"fttransformer(ml) std\"], label=\"Transformer\")\n",
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"axes[2].barh(ind, fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) values\"].abs(), width, xerr=fi[\"trade_size(ex)->quote(best)->quote(ex)->depth(best)->depth(ex)->rev_tick(all) std\"], label=\"Classical\")\n",
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"axes[2].barh(ind+width, fi[\"gbm(ml) values\"].abs(), width, xerr=fi[\"gbm(ml) std\"], label=\"GBRT\")\n",
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"axes[2].barh(ind+width + width, fi[\"fttransformer(ml) values\"].abs(), width, xerr=fi[\"fttransformer(ml) std\"], label=\"Transformer\")\n",
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"axes[2].axvline(0, color='black', linestyle='--', linewidth=0.5)\n",
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"axes[2].set_xlim([-0.15,0.15])\n",
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"axes[2].set_xlim([0.0,0.15])\n",
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"\n",
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"\n",
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"# set y-labels\n",
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"labels = ['Price Lead All', 'Price Lag All', 'Price Lead Ex', 'Price Lag Ex', 'Quotes NBBO', 'Quotes Ex', 'Trade Price', \"Quotes Size\", 'Trade Size', 'Strike Price', 'Time To Maturity', 'Option Type', 'Root', 'Moneyness', \"Day Volume\", 'Issue Type']\n",
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"axes[0].set(yticks=ind + width, yticklabels=labels, ylim=[2*width - 1, len(fi)])\n",
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"\n",
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"# set x-labels\n",
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"axes[0].set_xlabel(\"SAGE Value\")\n",
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"axes[1].set_xlabel(\"SAGE Value\")\n",
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"axes[2].set_xlabel(\"SAGE Value\")\n",
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"axes[0].set_xlabel(r\"\\textbar SAGE Value\\textbar\")\n",
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"axes[1].set_xlabel(r\"\\textbar SAGE Value\\textbar\")\n",
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"axes[2].set_xlabel(r\"\\textbar SAGE Value\\textbar\")\n",
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"\n",
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"# set y-labels\n",
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"axes[0].set_title(\"Set Classical\")\n",
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"axes[1].set_title(\"Set Classical-Size\")\n",
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"axes[1].set_title(\"Set Size\")\n",
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"axes[2].set_title(\"Set Options\")\n",
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"\n",
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"handles, labels = axes[0].get_legend_handles_labels()\n",
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"\n",
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"plt.savefig(f\"../reports/Graphs/sage-importances.pdf\", bbox_inches=\"tight\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.4"
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"version": "3.9.7"
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}
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},
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"nbformat": 4,

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