From d317a2032924488a30970a536b759f0a3d34e147 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Tue, 4 Jun 2024 09:33:32 +0200 Subject: [PATCH 01/13] ADD: paper draft (#95) --- .github/actions/build-draft.yaml | 23 + .github/actions/build-draft/action.yaml | 23 + .github/workflows/draft_paper.yml | 37 + choice_learn/datasets/expedia.py | 1 + docs/illustrations/expedia_RAM.png | Bin 0 -> 43525 bytes docs/illustrations/fbid_RAM.png | Bin 0 -> 52402 bytes docs/illustrations/features_storage.png | Bin 0 -> 164084 bytes docs/illustrations/features_storage_2.png | Bin 0 -> 119804 bytes docs/illustrations/features_storage_3.png | Bin 0 -> 169363 bytes docs/illustrations/full_ram.png | Bin 0 -> 174330 bytes .../items_vs_shared_features.png | Bin 0 -> 167421 bytes .../illustrations/memory_usage_comparison.png | Bin 0 -> 51490 bytes docs/illustrations/table.png | Bin 0 -> 70134 bytes docs/paper/memory_usage.ipynb | 762 ++++++++++++++++++ docs/paper/non_linear_utility_example.ipynb | 157 ++++ docs/paper/paper.bib | 226 ++++++ docs/paper/paper.md | 181 +++++ docs/paper/table.md | 134 +++ 18 files changed, 1544 insertions(+) create mode 100644 .github/actions/build-draft.yaml create mode 100644 .github/actions/build-draft/action.yaml create mode 100644 .github/workflows/draft_paper.yml create mode 100644 docs/illustrations/expedia_RAM.png create mode 100644 docs/illustrations/fbid_RAM.png create mode 100644 docs/illustrations/features_storage.png create mode 100644 docs/illustrations/features_storage_2.png create mode 100644 docs/illustrations/features_storage_3.png create mode 100644 docs/illustrations/full_ram.png create mode 100644 docs/illustrations/items_vs_shared_features.png create mode 100644 docs/illustrations/memory_usage_comparison.png create mode 100644 docs/illustrations/table.png create mode 100644 docs/paper/memory_usage.ipynb create mode 100644 docs/paper/non_linear_utility_example.ipynb create mode 100644 docs/paper/paper.bib create mode 100644 docs/paper/paper.md create mode 100644 docs/paper/table.md diff --git a/.github/actions/build-draft.yaml b/.github/actions/build-draft.yaml new file mode 100644 index 00000000..dfc4e287 --- /dev/null +++ b/.github/actions/build-draft.yaml @@ -0,0 +1,23 @@ +name: Open Journals PDF Generator +description: Create a draft PDF for an Open Journals paper +inputs: + journal: + description: Target journal, must be either "joss" or "jose" + required: false + default: joss + paper-path: + description: >- + Path to the main paper Markdown file, relative to the + project's root directory. + required: false + default: 'paper.md' +runs: + using: docker + image: 'docker://openjournals/inara:latest' + env: + GIT_SHA: $GITHUB_SHA + JOURNAL: ${{ inputs.journal }} + args: + - -o pdf + - -p + - ${{ inputs.paper-path }} \ No newline at end of file diff --git a/.github/actions/build-draft/action.yaml b/.github/actions/build-draft/action.yaml new file mode 100644 index 00000000..dfc4e287 --- /dev/null +++ b/.github/actions/build-draft/action.yaml @@ -0,0 +1,23 @@ +name: Open Journals PDF Generator +description: Create a draft PDF for an Open Journals paper +inputs: + journal: + description: Target journal, must be either "joss" or "jose" + required: false + default: joss + paper-path: + description: >- + Path to the main paper Markdown file, relative to the + project's root directory. + required: false + default: 'paper.md' +runs: + using: docker + image: 'docker://openjournals/inara:latest' + env: + GIT_SHA: $GITHUB_SHA + JOURNAL: ${{ inputs.journal }} + args: + - -o pdf + - -p + - ${{ inputs.paper-path }} \ No newline at end of file diff --git a/.github/workflows/draft_paper.yml b/.github/workflows/draft_paper.yml new file mode 100644 index 00000000..46385e0d --- /dev/null +++ b/.github/workflows/draft_paper.yml @@ -0,0 +1,37 @@ +name: Paper draft to PDF + +on: + push: + branches: + - paper + +jobs: + paper: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: test + run: | + echo "Hello, World!" + ls + ls choice_learn/ + ls .github/ + ls ./.github/actions/ + + - name: Build draft PDF + uses: ./.github/actions/build-draft + with: + journal: joss + paper-path: docs/paper/paper.md + + - name: Upload + uses: actions/upload-artifact@v4 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: docs/paper/paper.pdf \ No newline at end of file diff --git a/choice_learn/datasets/expedia.py b/choice_learn/datasets/expedia.py index 4844c5be..8db5b2c3 100644 --- a/choice_learn/datasets/expedia.py +++ b/choice_learn/datasets/expedia.py @@ -45,6 +45,7 @@ def load_expedia(as_frame=False, preprocessing="rumnet"): get_path("preprocessed_expedia_rumnet.csv", module=CACHE_MODULE), engine="pyarrow" ) logging.info("Loaded cached preprocessed data.") + except FileNotFoundError: expedia_df.date_time = pd.to_datetime(expedia_df.date_time, format="%Y-%m-%d %H:%M:%S") expedia_df.loc[:, "day_of_week"] = expedia_df.loc[:, "date_time"].dt.dayofweek diff --git a/docs/illustrations/expedia_RAM.png b/docs/illustrations/expedia_RAM.png new file mode 100644 index 0000000000000000000000000000000000000000..681014a721257246dd074e5390ee0d2d9563c273 GIT binary patch literal 43525 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zXEa^GehacgD$}>Y=SKb9d5*sTJoiELUmaUUY56FRS@^qL9a{^lk}i>wN+H^(9rbyk zq&x!fmDg`T@Amx?BAR;C_$_T=Xq1QWh9`I(&8L#v#u%x$*N?M1q&)qJXBxjhkk0O? z|D+jjt!Zl^j1%P{`x_MVM?UytPtS4xX5DY!z9G1Drkc@{lMl~MqfW0fp_3$A z{>_KGyU8yL3f{N5*OIihwwC>QlJEKWadD#4ODJJTXlUtocm^!K3sQD_h;m_JVdBRR z=TB{IO1h8+Pp(Q!;o}13a0Ft^t)L4pT5xx74>dnOKfbcElKk=h#n$^t{`~y>(t&}2 z&7GWI&vJ*Ht^z){wR^`M2M;@lX&sy@Bx)3gk?$W(;RFN(xXR_19UL4)9ABRPz3|)J z+=&tq5!u+-csm4hC!%0gMna%Cc!dAU7hWkiN>?{nl6nXhKKt1Q@8$||{>NVeKY3~l zK4W7`81;Yh7|Ml8;1Gt_&+Gh$hyVZgKd&H9-yhvh5}1Aj0e`gA_0%d= 10]\n", + "expedia_df[\"log_price\"] = expedia_df.price_usd.apply(np.log)\n", + "expedia_df = expedia_df[expedia_df.srch_length_of_stay <= 14]\n", + "expedia_df = expedia_df[expedia_df.srch_booking_window <= 365]\n", + "expedia_df[\"booking_window\"] = np.log(expedia_df[\"srch_booking_window\"] + 1)\n", + "expedia_df = expedia_df.fillna(-1)\n", + "\n", + "logging.info(\"Sorting DF columns\")\n", + "order_cols = [\n", + " \"srch_id\",\n", + " \"prop_id\",\n", + " \"prop_starrating\",\n", + " \"prop_review_score\",\n", + " \"prop_brand_bool\",\n", + " \"prop_location_score1\",\n", + " \"prop_location_score2\",\n", + " \"prop_log_historical_price\",\n", + " \"position\",\n", + " \"promotion_flag\",\n", + " \"srch_length_of_stay\",\n", + " \"srch_adults_count\",\n", + " \"srch_children_count\",\n", + " \"srch_room_count\",\n", + " \"srch_saturday_night_bool\",\n", + " \"orig_destination_distance\",\n", + " \"random_bool\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " \"hour\",\n", + " \"log_price\",\n", + " \"booking_window\",\n", + " \"site_id\",\n", + " \"visitor_location_country_id\",\n", + " \"prop_country_id\",\n", + " \"srch_destination_id\",\n", + " \"click_bool\",\n", + " \"booking_bool\",\n", + "]\n", + "expedia_df = expedia_df[order_cols]\n", + "\n", + "logging.info(\"Creating dummy availabilities\")\n", + "\n", + "# getting rid of search & prop_id and the clickbool and bookingbool\n", + "# adding no_purchase fixed effect\n", + "expedia_df[\"is_no_purchase\"] = 0\n", + "\n", + "logging.info(\"Creating the no purchase option\")\n", + "# adding the no_purchase option to the data\n", + "df1 = (\n", + " expedia_df.groupby(\"srch_id\")\n", + " .filter(lambda x: x.booking_bool.sum() == 1)\n", + " .groupby(\"srch_id\")\n", + " .max()\n", + " .reset_index(drop=False)\n", + ")\n", + "df1.loc[:, \"is_no_purchase\"] = 1\n", + "df1.loc[:, \"log_price\"] = 0\n", + "df1.loc[:, \"booking_bool\"] = 0\n", + "\n", + "df2 = (\n", + " expedia_df.groupby(\"srch_id\")\n", + " .filter(lambda x: x.booking_bool.sum() == 0)\n", + " .groupby(\"srch_id\")\n", + " .max()\n", + " .reset_index(drop=False)\n", + ")\n", + "df2.loc[:, \"is_no_purchase\"] = 1\n", + "df2.loc[:, \"log_price\"] = 0\n", + "df2.loc[:, \"booking_bool\"] = 1\n", + "expedia_df = pd.concat([expedia_df, df1, df2])\n", + "\n", + "site_id_one_hot = pd.get_dummies(expedia_df.site_id, prefix=\"site_id\")\n", + "visitor_location_country_id_one_hot = pd.get_dummies(expedia_df.visitor_location_country_id, prefix=\"visitor_location_country_id\")\n", + "srch_destination_id_one_hot =pd.get_dummies(expedia_df.srch_destination_id, prefix=\"srch_destination_id\")\n", + "prop_country_id_one_hpt = pd.get_dummies(expedia_df.prop_country_id, prefix=\"prop_country_id\")\n", + "expedia_df = pd.concat([expedia_df, site_id_one_hot, visitor_location_country_id_one_hot, srch_destination_id_one_hot, prop_country_id_one_hpt], axis=1)\n", + "\n", + "logging.info(\"Sorting the data frame\")\n", + "expedia_df = expedia_df.sort_values(\"srch_id\")\n", + "choices = [\"booking_bool\"]\n", + "\n", + "logging.info(\"DF to NDarray and creating the ChoiceDataset object\")\n", + "contexts_features_names = [\n", + " \"srch_id\",\n", + " \"srch_length_of_stay\",\n", + " \"srch_adults_count\",\n", + " \"srch_children_count\",\n", + " \"srch_room_count\",\n", + " \"srch_saturday_night_bool\",\n", + " \"booking_window\",\n", + " \"random_bool\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " \"hour\",\n", + " \"site_id\",\n", + " \"visitor_location_country_id\",\n", + " \"srch_destination_id\",\n", + "]\n", + "contexts_features_names += site_id_one_hot.columns.tolist()\n", + "contexts_features_names += visitor_location_country_id_one_hot.columns.tolist()\n", + "contexts_features_names += srch_destination_id_one_hot.columns.tolist()\n", + "contexts_features_names += prop_country_id_one_hpt.columns.tolist()\n", + "\n", + "contexts_items_features_names = [\n", + " \"prop_starrating\",\n", + " \"prop_review_score\",\n", + " \"prop_brand_bool\",\n", + " \"prop_location_score1\",\n", + " \"prop_location_score2\",\n", + " \"prop_log_historical_price\",\n", + " \"position\",\n", + " \"promotion_flag\",\n", + " \"orig_destination_distance\",\n", + " \"log_price\",\n", + " \"prop_country_id\",\n", + "]\n", + "\n", + "long_df = expedia_df[contexts_features_names + contexts_items_features_names + choices]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "long_df_memory_size = []\n", + "for length in data_lengths:\n", + " srch_ids = long_df.srch_id.unique()[:length]\n", + " sub_long_df = long_df[long_df.srch_id.isin(srch_ids)]\n", + " long_df_memory_size.append(get_obj_size(sub_long_df))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pandas.DataFrame wide format (Biogeme)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = load_expedia(as_frame=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transformation of the pandas.DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "site_id_one_hot = pd.get_dummies(df.site_id, prefix=\"site_id\")\n", + "visitor_location_country_id_one_hot = pd.get_dummies(df.visitor_location_country_id, prefix=\"visitor_location_country_id\")\n", + "srch_destination_id_one_hot = pd.get_dummies(df.srch_destination_id, prefix=\"srch_destination_id\")\n", + "prop_country_id_one_hpt = pd.get_dummies(df.prop_country_id, prefix=\"prop_country_id\")\n", + "df = pd.concat([df, site_id_one_hot, visitor_location_country_id_one_hot, srch_destination_id_one_hot, prop_country_id_one_hpt], axis=1)\n", + "\n", + "\n", + "contexts_items_features_names = [\n", + " \"prop_starrating\",\n", + " \"prop_review_score\",\n", + " \"prop_brand_bool\",\n", + " \"prop_location_score1\",\n", + " \"prop_location_score2\",\n", + " \"prop_log_historical_price\",\n", + " \"position\",\n", + " \"promotion_flag\",\n", + " \"orig_destination_distance\",\n", + " \"log_price\",\n", + " \"prop_country_id\",\n", + "]\n", + "contexts_features_names = [\n", + " \"srch_id\",\n", + " \"srch_length_of_stay\",\n", + " \"srch_adults_count\",\n", + " \"srch_children_count\",\n", + " \"srch_room_count\",\n", + " \"srch_saturday_night_bool\",\n", + " \"booking_window\",\n", + " \"random_bool\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " \"hour\",\n", + " \"site_id\",\n", + " \"visitor_location_country_id\",\n", + " \"srch_destination_id\",\n", + "]\n", + "for col in df.columns:\n", + " if col.startswith(\"prop_country_id\"):\n", + " contexts_items_features_names += [col]\n", + " if col.startswith(\"site_id\"):\n", + " contexts_features_names += [col]\n", + " if col.startswith(\"visitor_location_country_id\"):\n", + " contexts_features_names += [col]\n", + " if col.startswith(\"srch_destination_id\"):\n", + " contexts_features_names += [col]\n", + "\n", + "wide_items = []\n", + "for i in range(39):\n", + " sub_df = df.groupby(\"srch_id\").apply(lambda x: x[contexts_items_features_names].iloc[i])\n", + " wide_items.append(sub_df)\n", + "contexts_features = df.groupby(\"srch_id\").apply(lambda x: x[contexts_features_names].iloc[0])\n", + "wide_df = pd.concat(wide_items+[contexts_features], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wide_df_memory_size = []\n", + "for length in data_lengths:\n", + " sub_wide_df = wide_df.iloc[:length].copy()\n", + " wide_df_memory_size.append(get_obj_size(sub_wide_df))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Torch-Choice\n", + "\n", + "For this part you will need the torch-choice package: ```pip install torch-choice```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch_choice.utils.easy_data_wrapper import EasyDatasetWrapper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First create long_df with previous sectio\n", + "\n", + "long_df = long_df.reset_index(drop=True)\n", + "long_df.sort_values(\"srch_id\", inplace=True, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "items_ids = []\n", + "for nit in long_df.srch_id.value_counts().sort_index():\n", + " items_ids.append(np.arange(nit))\n", + "long_df[\"items_id\"] = np.concatenate(items_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mem_sizes = []\n", + "data_lengths = [100, 1000, 10000, 100000, 397618]\n", + "for length in data_lengths:\n", + " ids = long_df.srch_id.unique()[:length]\n", + " sub_long_df = long_df[long_df.srch_id.isin(ids)].copy(deep=True)\n", + "\n", + " data_1 = EasyDatasetWrapper(main_data=sub_long_df,\n", + " purchase_record_column='srch_id',\n", + " choice_column='booking_bool',\n", + " item_name_column='items_id',\n", + " session_index_column='srch_id',\n", + " user_index_column='srch_id',\n", + " # it can be derived from columns of the dataframe or supplied as\n", + " user_observable_columns=['srch_length_of_stay',\n", + " 'srch_adults_count',\n", + " 'srch_children_count',\n", + " 'srch_room_count',\n", + " 'srch_saturday_night_bool'],\n", + " price_observable_columns=['log_price'],\n", + " device=\"cpu\")\n", + " mem_sizes.append(get_obj_size(data_1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Results\n", + "\n", + "| Dataset size | 100 | 1.000 | 10.000 | 100.000 | 397.618 |\n", + "|---|---|---|---|---|---|\n", + "| CD w. FeaturesByIDs | 85.640 | 771.440 | 7.629.440 | 76.209.440 | 302.994.356 |\n", + "| CD wo FeaturesByIDs | 220.400 | 2.198.600 | 21.980.600 | 219.800.600 | 873.964.964 |\n", + "| Torch-Choice | 5.825.168 | 5.5093.676 | 550.480.667 | 5.515.236.600 | 10.448.857.759 |\n", + "| Long format DF | 5.521.360 | 52.463.080 | 524.667.470 | 5.234.198.450 | 20.815.361.140 |\n", + "| Wide format DF | 3.503.109 | 31.784.709 | 314.600.709 | 3.142.760.709 | 12.495.108.741 |\n", + "\n", + "\n", + "data_lengths: [100, 1000, 10000, 100000, 397618]\\\n", + "ChoiceDataset with FeaturesByIDs: [85640, 771440, 7629440, 76209440, 302994356]\\\n", + "ChoiceDataset without FeaturesByIDs: [220400, 2198600, 21980600, 219800600, 873964964]\\\n", + "Torch-Choice: [5.825.168, 55.093.676, 550.480.667, 5.515.236.600, 10.448.857.871]\\\n", + "DF Long Format: [5521360, 52463080, 524667470, 5234198450, 20815361140]\\\n", + "DF Wide Format: [3503109, 31784709, 314600709, 3142760709, 12495108741]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots and Illustrations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "data_lengths = [100, 1000, 10000, 100000, 397618]\n", + "cd_with = [85640, 771440, 7629440, 76209440, 302994356]\n", + "cd_wo = [220400, 2198600, 21980600, 219800600, 873964964]\n", + "df_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", + "df_wide = [6252516976, 6266664976, 6408144976, 7822944976, 12501499936]\n", + "df_wide = [3503109, 31784709, 314600709, 3142760709, 12495108741]\n", + "torch_choice = [1249040, 12481040, 124801040, 1248001040, 4962273680]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data_lengths, cd_with, label=\"ChoiceDataset with FeaturesByIDs\", c=\"teal\")\n", + "plt.plot(data_lengths, cd_wo, label=\"ChoiceDataset withOUT FeaturesByIDs\", c=\"turquoise\")\n", + "plt.plot(data_lengths, df_long, label=\"pd.DataFrame Long format\", c=\"darkblue\")\n", + "plt.plot(data_lengths, df_wide, label=\"pd.DataFrame Wide format\", c=\"cornflowerblue\")\n", + "plt.legend()\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"Dataset Size (x1000)\")\n", + "plt.ylabel(\"Memory Size (bytes)\")\n", + "plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", + " [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data_lengths, cd_with, label=\"Choice-Learn\", c=\"teal\")\n", + "plt.plot(data_lengths, torch_choice, label=\"Torch-Choice\", c=\"turquoise\")\n", + "plt.plot(data_lengths, df_long, label=\"PyLogit (long format)\", c=\"darkblue\")\n", + "plt.plot(data_lengths, df_wide, label=\"Biogeme (wide format)\", c=\"cornflowerblue\")\n", + "plt.scatter(data_lengths, cd_with, c=\"teal\")\n", + "plt.scatter(data_lengths, torch_choice, c=\"turquoise\")\n", + "plt.scatter(data_lengths, df_long, c=\"darkblue\")\n", + "plt.scatter(data_lengths, df_wide, c=\"cornflowerblue\")\n", + "plt.legend()\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size (x1000)\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "# plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", + "# [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data_lengths, cd_with, label=\"ChoiceDataset with FeaturesByIDs\", c=\"teal\")\n", + "plt.plot(data_lengths, cd_wo, label=\"ChoiceDataset withOUT FeaturesByIDs\", c=\"turquoise\")\n", + "plt.plot(data_lengths, df_long, label=\"pd.DataFrame Long format\", c=\"darkblue\")\n", + "plt.plot(data_lengths, df_wide, label=\"pd.DataFrame Wide format\", c=\"cornflowerblue\")\n", + "plt.legend()\n", + "plt.xlabel(\"Dataset Size (x1000)\")\n", + "plt.ylabel(\"Memory Size (bytes)\")\n", + "# plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", + "# [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## FeaturesByIDs Study" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_fixed_features = 10\n", + "n_different_values = 10\n", + "\n", + "normal_sizes = []\n", + "cd_sizes = []\n", + "ds_lengths = [10, 100, 1000, 10000, 100000, 10000000]\n", + "for dataset_len in ds_lengths:\n", + "\n", + " normal_dataset = np.ones((dataset_len, n_fixed_features))\n", + " cd_dataset = (np.ones((dataset_len, 1)), np.ones((n_different_values, n_fixed_features)))\n", + " \n", + " cd_sizes.append(sys.getsizeof(cd_dataset[0]) + sys.getsizeof(cd_dataset[1]))\n", + " normal_sizes.append(sys.getsizeof(normal_dataset))\n", + "\n", + "plt.plot(ds_lengths, normal_sizes, label='w/o FeaturesById - (10, 10)', c=\"darkblue\")\n", + "plt.plot(ds_lengths, cd_sizes, label='w/ FeaturesById - (10, 10)', c=\"turquoise\")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "\n", + "n_fixed_features = 100\n", + "n_different_values = 100\n", + "\n", + "normal_sizes = []\n", + "cd_sizes = []\n", + "ds_lengths = [10, 100, 1000, 10000, 100000, 10000000]\n", + "for dataset_len in ds_lengths:\n", + "\n", + " normal_dataset = np.ones((dataset_len, n_fixed_features))\n", + " cd_dataset = (np.ones((dataset_len, 1)), np.ones((n_different_values, n_fixed_features)))\n", + " \n", + " cd_sizes.append(sys.getsizeof(cd_dataset[0]) + sys.getsizeof(cd_dataset[1]))\n", + " normal_sizes.append(sys.getsizeof(normal_dataset))\n", + "\n", + "plt.plot(ds_lengths, normal_sizes, label='w/o FeaturesById - (100, 100)', c=\"cornflowerblue\")\n", + "plt.plot(ds_lengths, cd_sizes, label='w/ FeaturesById - (100, 100)', c=\"teal\")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/paper/non_linear_utility_example.ipynb b/docs/paper/non_linear_utility_example.ipynb new file mode 100644 index 00000000..d7c22859 --- /dev/null +++ b/docs/paper/non_linear_utility_example.ipynb @@ -0,0 +1,157 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "\n", + "import sys\n", + "\n", + "sys.path.append(\"../../\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "from choice_learn.datasets import load_swissmetro\n", + "from choice_learn.models.base_model import ChoiceModel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ExampleCustomizedModel(ChoiceModel):\n", + " def __init__(self, n_neurons, **kwargs):\n", + " super().__init__(**kwargs)\n", + " self.n_neurons = n_neurons\n", + "\n", + " # Items Features Layer\n", + " self.dense_items = tf.keras.layers.Dense(units=n_neurons, activation=\"sigmoid\")\n", + "\n", + " # Shared Features Layer\n", + " self.dense_shared = tf.keras.layers.Dense(units=n_neurons, activation=\"sigmoid\")\n", + "\n", + " # Third layer: embeddings to utility (dense representation of features > U)\n", + " self.final_layer = tf.keras.layers.Dense(units=1, activation=\"linear\")\n", + "\n", + " @property\n", + " def trainable_weights(self):\n", + " \"\"\"Access model's trainable_weights.\n", + "\n", + " Returns\n", + " -------\n", + " list\n", + " list of trainable_weights\n", + " \"\"\"\n", + " return self.dense_items.trainable_variables\\\n", + " + self.dense_shared.trainable_variables\\\n", + " + self.final_layer.trainable_variables\n", + "\n", + " def compute_batch_utility(self,\n", + " shared_features_by_choice,\n", + " items_features_by_choice,\n", + " available_items_by_choice,\n", + " choices):\n", + " \"\"\"Compute batch utility from features.\"\"\"\n", + " _, _ = available_items_by_choice, choices\n", + " # We apply the neural network to all items_features_by_choice for all the items\n", + " # We then concatenate the utilities of each item of shape (n_choices, 1) into\n", + " # a single one of shape (n_choices, n_items)\n", + " shared_embeddings = self.dense_shared(shared_features_by_choice)\n", + "\n", + " # Iterate over items\n", + " items_features_embeddings = []\n", + " for i in range(items_features_by_choice.shape[1]):\n", + " # Utility is Dense(embeddings sum)\n", + " item_embedding = self.dense_items(items_features_by_choice[:, i])\n", + " item_embedding = tf.keras.activations.sigmoid(shared_embeddings + item_embedding)\n", + " items_features_embeddings.append(self.final_layer(item_embedding))\n", + "\n", + " # Concatenation to get right shape (n_choices, n_items, )\n", + " return tf.concat(items_features_embeddings, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = load_swissmetro()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = ExampleCustomizedModel(n_neurons=10, optimizer=\"Adam\", batch_size=128)\n", + "history = model.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "plt.plot(history[\"train_loss\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(model.evaluate(dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib new file mode 100644 index 00000000..4313d13d --- /dev/null +++ b/docs/paper/paper.bib @@ -0,0 +1,226 @@ +@TechReport{Bierlaire:2023, + author = {Michel Bierlaire}, + title = {A short introduction to {Biogeme}}, + institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne}, + year = {2023}, + type = {Technical Report}, + number = {TRANSP-OR 230620}, + address = {Lausanne, Switzerland} +} + +@article{Brathwaite:2018, + title={Asymmetric, closed-form, finite-parameter models of multinomial choice}, + volume={29}, + ISSN={1755-5345}, + journal={Journal of Choice Modelling}, + publisher={Elsevier BV}, + author={Brathwaite, Timothy and Walker, Joan L.}, + year={2018}, + month=dec, + pages={78–112} +} + +@article{Du:2023, + title={Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python}, + author={Tianyu Du and Ayush Kanodia and Susan Athey}, + year={2023}, + journal={arXiv preprint arXiv:{2304.01906}}, +} + +@article{Aouad:2023, + title={Representing random utility choice models with neural networks}, + author={Aouad, Ali and D{\'e}sir, Antoine}, + journal={arXiv preprint arXiv:2207.12877}, + year={2022} +} + +@article{Han:2022, +title = {A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability}, +journal = {Transportation Research Part B: Methodological}, +volume = {163}, +pages = {166-186}, +year = {2022}, +issn = {0191-2615}, +author = {Yafei Han and Francisco Camara Pereira and Moshe Ben-Akiva and Christopher Zegras}, +keywords = {Discrete choice models, Neural networks, Taste heterogeneity, Interpretability, Utility specification, Machine learning, Deep learning}, +abstract = {Discrete choice models (DCMs) require a priori knowledge of the utility functions, especially how tastes vary across individuals. Utility misspecification may lead to biased estimates, inaccurate interpretations and limited predictability. In this paper, we utilize a neural network to learn taste representation. Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e.g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge. Taste parameters learned by the neural network are fed into the choice model and link the two modules. Our approach extends the L-MNL model (Sifringer et al., 2020) by allowing the neural network to learn the interactions between individual characteristics and alternative attributes. Moreover, we formalize and strengthen the interpretability condition — requiring realistic estimates of behavior indicators (e.g., value-of-time, elasticity) at the disaggregated level, which is crucial for a model to be suitable for scenario analysis and policy decisions. Through a unique network architecture and parameter transformation, we incorporate prior knowledge and guide the neural network to output realistic behavior indicators at the disaggregated level. We show that TasteNet-MNL reaches the ground-truth model’s predictability and recovers the nonlinear taste functions on synthetic data. Its estimated value-of-time and choice elasticities at the individual level are close to the ground truth. In contrast, exemplary logit models with misspecified systematic utility lead to biased parameter estimates and lower prediction accuracy. On a publicly available Swissmetro dataset, TasteNet-MNL outperforms benchmarking MNLs and Mixed Logit model’s predictability. It learns a broader spectrum of taste variations within the population and suggests a higher average value-of-time. Our source code is available for research and application.} +} + +@article{Salvadé:2024, + title={RUMBoost: Gradient Boosted Random Utility Models}, + author={Salvad{\'e}, Nicolas and Hillel, Tim}, + journal={arXiv preprint arXiv:2401.11954}, + year={2024} +} + +@article{Train:1987, + ISSN = {07416261}, + abstract = {We present an empirical model of households' choices among local telephone service options (for example, between flat-rate and measured service) and the interrelation of these choices with the number and average duration of local calls households make at each time of day to each geographical zone. Using a nested logit model with estimation performed on a randomly selected subset of the households' calling patterns, we calculate elasticities of demand for each local service option, number of calls, average duration, and revenues with respect to the fixed monthly charges and the usage charges for calling under each option. We find moderate price elasticities of number of calls with respect to usage charges for households subscribing to measured service. Nevertheless, raising usage charges has a negligible effect on revenues, since a sufficient number of households either originally subscribe to flat-rate service or convert to flat-rate service in response to higher usage charges. We find a high elasticity of demand for each service option with respect to its fixed monthly fee. This indicates high substitutability among service options. The shift among service options induces new calling patterns, which we find to be a small but not negligible indirect effect.}, + author = {Kenneth E. Train and Daniel L. McFadden and Moshe Ben-Akiva}, + journal = {The RAND Journal of Economics}, + number = {1}, + pages = {109--123}, + publisher = {[RAND Corporation, Wiley]}, + title = {The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices}, + volume = {18}, + year = {1987} +} + +@Article{Harris:2020, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, +} + +@software{Abadi:2015, +author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jozefowicz, Rafal and Jia, Yangqing and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mané, Dan and Schuster, Mike and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viégas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang}, +doi = {10.5281/zenodo.4724125}, +license = {Apache-2.0}, +month = nov, +title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}}, +year = {2015} +} + +@Inbook{Nocedal:2006, +title="Large-Scale Unconstrained Optimization", +author ={Nocedal, Jorge and Wright, Stephen J.}, +bookTitle="Numerical Optimization", +year="2006", +publisher="Springer New York", +address="New York, NY", +pages="164--192", +isbn="978-0-387-40065-5", +} + +@article{Kingma:2017, + title={Adam: A Method for Stochastic Optimization}, + author={Diederik P. Kingma and Jimmy Ba}, + year={2017}, + archivePrefix={arXiv}, + primaryClass={cs.LG}, + journal={arXiv preprint arXiv:{1412.6980}}, +} + +@article{Tieleman:2012, + title={Lecture 6.5 {RMSProp}, coursera: Neural networks for machine learning}, + author={Tieleman, Tijmen and Hinton, Geoffrey}, + journal={University of Toronto, Technical Report}, + volume={6}, + year={2012} +} + +@article{Expedia:2013, + title={Personalize Expedia Hotel Searches}, + author={Ben Hamner, Adam and Friedman, Dan}, + journal={ICDM}, + year={2013}, + eprint={https://www.kaggle.com/c/expedia-personalized-sort}, +} + +@article{AouadMarket:2023, + title={Market segmentation trees}, + author={Aouad, Ali and Elmachtoub, Adam N and Ferreira, Kris J and McNellis, Ryan}, + journal={Manufacturing \& Service Operations Management}, + volume={25}, + number={2}, + pages={648--667}, + year={2023}, + publisher={INFORMS} +} + +@article{MendezDiaz:2014, +title = {A branch-and-cut algorithm for the latent-class logit assortment problem}, +journal = {Discrete Applied Mathematics}, +volume = {164}, +pages = {246-263}, +year = {2014}, +note = {Combinatorial Optimization}, +issn = {0166-218X}, +author = {Isabel Méndez-Díaz and Juan José Miranda-Bront and Gustavo Vulcano and Paula Zabala}, +keywords = {Retail operations, Revenue management, Choice behavior, Multinomial logit, Integer programming, Fractional programming}, +abstract = {We study the product assortment problem of a retail operation that faces a stream of customers who are heterogeneous with respect to preferences. Each customer belongs to a market segment characterized by a consideration set that includes the alternatives viewed as options, and by the preference weights that the segment assigns to each of those alternatives. Upon arrival, he checks the offer set displayed by the firm, and either chooses one of those products or quits without purchasing according to a multinomial-logit (MNL) criterion. The firm’s goal is to maximize the expected revenue extracted during a fixed time horizon. This problem also arises in the growing area of choice-based, network revenue management, where computational speed is a critical factor for the practical viability of a solution approach. This so-called latent-class, logit assortment problem is known to be NP-Hard. In this paper, we analyze unconstrained and constrained (i.e., with a limited number of products to display) versions of it, and propose a branch-and-cut algorithm that is computationally fast and leads to (nearly) optimal solutions.} +} + +@software{pandas:2020, + author = {The pandas development team}, + title = {pandas-dev/pandas: Pandas}, + month = feb, + year = 2020, + publisher = {Zenodo}, + version = {latest}, + doi = {10.5281/zenodo.3509134}, +} + +@inproceedings{Bierlaire:2001, + title={The acceptance of modal innovation: The case of Swissmetro}, + author={Bierlaire, Michel and Axhausen, Kay and Abay, Georg}, + booktitle={Swiss transport research conference}, + year={2001} +} + +@article{Pedregosa:2011, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} + +@misc{Helveston:2023, +title={Convert data from wide to long format}, +howpublished={\url{https://xlogit.readthedocs.io/en/latest/notebooks/convert_data_wide_to_long.html}}, +author={Forsythe, C. and Helveston, J}, + year={2023} +} +@misc{Chollet:2015, + title={Keras}, + author={Chollet, Fran\c{c}ois and others}, + year={2015}, + howpublished={\url{https://keras.io}}, +} + +@article{McFadden:2000, + title={Mixed MNL models for discrete response}, + author={McFadden, Daniel and Train, Kenneth}, + journal={Journal of applied Econometrics}, + volume={15}, + number={5}, + pages={447--470}, + year={2000}, + publisher={Wiley Online Library} +} + +@misc{Gurobi:2023, + author = {{Gurobi Optimization, LLC}}, + title = {{Gurobi Optimizer Reference Manual}}, + year = 2023, + url = "https://www.gurobi.com" +} + +@software{ORTools:2024, + title = {OR-Tools}, + version = { v9.9 }, + author = {Laurent Perron and Vincent Furnon}, + organization = {Google}, + url = {https://developers.google.com/optimization/}, + date = { 2024-03-07 } +} diff --git a/docs/paper/paper.md b/docs/paper/paper.md new file mode 100644 index 00000000..9d9f840c --- /dev/null +++ b/docs/paper/paper.md @@ -0,0 +1,181 @@ +--- +title: '`Choice-Learn`: Large-scale choice modeling for operational contexts through the lens of machine learning' + +tags: + - Python + - choice + - operations + - machine learning +authors: + - name: Vincent Auriau + corresponding: true + orcid: 0009-0002-9640-2639 + affiliation: "1, 2" + - name: Ali Aouad + orcid: 0000-0002-9812-6140 + affiliation: 3 + - name: Antoine Désir + orcid: 0000-0001-5654-3301 + affiliation: 4 + - name: Emmanuel Malherbe + orcid: 0009-0006-0898-6873 + affiliation: 2 +affiliations: + - name: CentraleSupélec, Université Paris-Saclay, France + index: 1 + - name: Artefact Research Center, France + index: 2 + - name: London Business School, United Kingdom + index: 3 + - name: INSEAD, France + index: 4 +date: 23 May 2024 +bibliography: paper.bib +output: paper_pdf + +--- + +# Introduction + +Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, which is known as an assortment. Well-known use cases include predicting a commuter's choice of transportation mode or a customer's purchases. A key capability of choice models is their ability to handle assortment variations, such as predicting choices when some alternatives become unavailable or when their features change in different operational contexts. This adaptability to different scenarios allows these models to be used as inputs for optimization problems, including assortment planning or pricing. + +Choice-Learn provides a modular suite of choice modeling tools for practitioners and academic researchers to process choice data, and then formulate, estimate and operationalize choice models. The library is structured into two levels of usage, as illustrated in Figure \ref{fig:gen_org}. The higher-level is designed for fast and easy implementation and the lower-level enables more advanced parameterizations. This structure is inspired by Keras' different endpoints [@Chollet:2015], which enables a user-friendly modeling interface. Choice-Learn is designed with the following objectives: + +- **Streamlined:** The processing of datasets and the estimation of standard choice models are facilitated by a simple code signature. +- **Scalable:** Optimized processes are implemented for data storage and models estimation, allowing the use of large datasets and models with a large number of parameters. +- **Flexible:** The codebase can be customized to fit different use cases. +- **Models Library:** The same package provides implementations of both standard choice models and machine learning-based methods, including neural networks. +- **Downstream operations:** Post-processing tools that leverage choice models for assortment planning and pricing are integrated into the library. + +A summary of the main contributions is provided in Table \ref{tab:comparison1} and \ref{tab:comparison2}. + +![General Organization of the package. \label{fig:gen_org}](../illustrations/choice_learn_levels.png) + +: Comparison of the different packages for data handling and downstream opereations.\label{tab:comparison1} + ++--------------+------------------+--------------------+--------------+----------------+ +| | Data | Data | | | +| Package | Format | Batching | Assortment | Pricing | ++:============:+:================:+:==================:+:============:+:==============:+ +| Biogeme | wide | $\times$ | $\times$ | $\times$ | ++--------------+------------------+--------------------+--------------+----------------+ +| PyLogit | long | $\times$ | $\times$ | $\times$ | ++--------------+------------------+--------------------+--------------+----------------+ +| Torch-Choice | Multi Index | $\checkmark$ | $\times$ | $\times$ | ++--------------+------------------+--------------------+--------------+----------------+ +| Choice-Learn | Features Storage | $\checkmark$ | $\checkmark$ | $\checkmark$ | ++==============+==================+====================+==============+================+ + + +: Comparison of the different packages for modelization. CondL, NestL, MixL, and LatC respectively indicate the Conditional Logit, Nested Logit, Mixed Logit and Latent Class models.\label{tab:comparison2} + ++--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ +| Package | Traditional\ | NeuralNet\ | Custom | Non-Stochastic | Stochastic | +| | Models | | Models | Optimizer | Optimizer | +| | | Models | | | | ++:============:+:===============================:+:==================:+:==============:+:==============:+:====================:+ +| Biogeme | CondL, NestL,\ | $\times$ | $\checkmark$ | Newton BFGS | $\quad \times \quad$ | +| | MixL, LatC\ | | | | | +| | & more | | | | | ++--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ +| PyLogit | CondL, NestL,\ | $\times$ | $\times$ | BFGS | $\times$ | +| | MixL,\ | | | | | +| | Asymmetric | | | | | ++--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ +| Torch-Choice | CondL, NestL\ | $\times$ | $\times$ | L-BFGS | $\checkmark$ | +| | | | | | | ++--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ +| Choice-Learn | CondL, NestL,\ | $\checkmark$ | $\checkmark$ | L-BFGS | $\checkmark$ | +| | LatC | | | | | ++==============+=================================+====================+================+================+======================+ + +# Statement of need + +## Streamlined signatures +`Choice-Learn` proposes short code signatures. The *ChoiceDataset* object, which handles the data, takes only 4 inputs: 'items_features' describing each available alternative, 'shared_features' describing the context of the choice, 'available_items' indicating the subset of alternatives offered in the assortment, and finally 'choices', the chosen alternative. Methods to seamlessly integrate popular data formats, such as long and wide format dataframes [@Helveston:2023] are also provided. + +```python +dataset = ChoiceDataset(choices, shared_features, items_features, available_items) +``` + +Finally, data usage, model estimation and evaluation are designed to be consistent with mainstream machine learning packages such as scikit-learn [@Pedregosa:2011]. + +## Data and model scalability + +Choice modeling is widely used in retail and e-commerce sectors to better understand customer behavior and optimize product offerings. With the continuous development of firms' data architectures, larger-scale choice datasets are often available and valuable for managing customer-facing operations. + +`Choice-Learn`'s data management relies on NumPy [@Harris:2020] with the objective of limiting the memory footprint. It minimizes the repetition of items or customers features and defers the jointure of the full data structure until processing batches of data. Moreover, the *FeaturesStorage* object, illustrated in Figure \ref{fig:fbi}, allows feature values to be referenced only by their ID. These feature values are substituted to the ID placeholder on the fly in the batching process. For instance, supermarkets features such as surface, position, or number of employees are often stationary. Thus, they can be stored in an auxiliary data structure and in the main dataset, the store where the choice is recorded is only referenced with its ID. + +The package stands on Tensorflow [@Abadi:2015] for model estimation, offering the possibility to use fast second-order optimization algorithm such as L-BFGS [@Nocedal:2006] as well as various gradient-descent optimizers [@Tieleman:2012; @Kingma:2017] specialized in handling batches of data. GPU usage is also possible, which can prove to be time-saving. +Finally, the TensorFlow backbone ensures an efficient usage in a production environment, for instance within an assortment recommendation software, through deployment and serving tools, such as TFLite and TFServing. + +![Functioning of the *FeaturesStorage*. \label{fig:fbi}](../illustrations/features_storage_3.png) + +## Flexible usage: From linear utility to customized specification + +Choice models following the *Random Utility Maximization* principle [@McFadden:2000] define the utility of an alternative $i \in \mathcal{A}$ as the sum of a deterministic part $U(i)$ and a random error $\epsilon_i$. If the terms $(\epsilon_i)_{i \in \mathcal{A}}$ are assumed to be independent and Gumbel-distributed, the probability to choose alternative $i$ can be written as the softmax normalization over the available alternatives $j\in \mathcal{A}$: + +$$\mathbb{P}(i|\mathcal{A}) = \frac{e^{U(i)}}{\sum_{j \in \mathcal{A}} e^{U(j)}}$$ + +The choice-modeler's job is to formulate an adequate utility function $U(.)$ depending on the context. In `Choice-Learn`, the user can parametrize predefined models or freely specify a custom utility function by overriding the *compute_batch_utility* method from the *ChoiceModel* class. + +## Library of traditional random utility models and machine learning-based models + +Traditional parametric choice models, including the Conditional Logit [@Train:1987], often specify the utility function in a linear form. This provides interpretable coefficients, such as price elasticities, but also limits the predictive power of the model. +The availability of detailed customer choice data, paired with advances in machine learning, enables the estimation of more complex models, with neural networks approaches [@Han:2022; @Aouad:2023] and tree-based models [@Salvadé:2024; @AouadMarket:2023]. While existing choice libraries [@Bierlaire:2023; @Brathwaite:2018; @Du:2023] are often not designed to integrate such machine learning-based approaches, `Choice-Learn` proposes a collection including both types of models. + +## Downstream operations: Assortment and pricing optimization +`Choice-Learn` also offers additional tools for downstream operations, that are not usually integrated in choice modeling libraries. In particular, assortment optimization is a common use case that leverages a choice model in order to determine the optimal subset of alternatives to offer customers in order to maximize a certain objective, such as the expected revenue, conversion rate, or social welfare. This framework captures a variety of applications such as assortment planning, display location optimization, and pricing. We provide a generic implementation based on the mixed-integer programming formulation described in [@MendezDiaz:2014], with the option to choose the solver between Gurobi [@Gurobi:2023] and OR-Tools [@ORTools:2024]. + + +# Experiments and examples + +## Memory usage: a case study + +We provide numerical examples of memory usage to showcase the efficiency of the *FeaturesStorage*. Consider a feature repeated in a dataset, such as a one-hot encoding for locations, represented by a matrix of shape (*#locations, #locations*) where each row refers to one location. In Figure \ref{fig:xps} (a), we compare the memory usage for different dataset sizes. + +We conduct a similar comparison on the ICDM 2013 Expedia dataset [@Expedia:2013] with four data handling methods: pandas.DataFrames [@pandas:2020] in long and wide format, both used in choice modeling packages, as well as Torch-Choice and `Choice-Learn`. Figure \ref{fig:xps} (b) shows the results for various sample sizes. + +Finally, in Figure \ref{fig:xps} (c) and (d), we observe performance gains in terms of memory management on a proprietary dataset in brick-and-mortar retailing consisting of the aggregation of more than 4 million purchases over 5 years in Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which products were available, their prices, as well as a one-hot representation of the supermarket. + +![Memory usage experiments. \label{fig:xps}](../illustrations/full_ram.png) + +## Customized choice models +We provide an example of the custom model definition with the following formulation of utility $U(i)$ with alternative features $x_i$ and customer features $z$: +$$U(x_i, z) = \beta_l \cdot \sigma(\sigma(\Gamma_x \cdot x_i) + \sigma(\Gamma_z \cdot z)) + \epsilon_i,$$ +where $\Gamma_x$, $\Gamma_z$ are matrices and $\beta_l$ is a vector, all three to be estimated and $\sigma$ being the sigmoid activation function. +To declare a custom model, one needs to inherit the *ChoiceModel* class and overwrite the `compute_batch_utility` method. + + +```python + def __init__(self, n_neurons, **kwargs): + super().__init__(**kwargs) + + self.gamma_x = Dense(units=n_neurons, activation="sigmoid") + self.gamma_z = Dense(units=n_neurons, activation="sigmoid") + self.beta_l = Dense(units=1, activation="linear") + + def compute_batch_utility(self, + shared_features_by_choice, + items_features_by_choice, + **kwargs): + + z_embedding = self.gamma_z(shared_features_by_choice) + + item_utility_by_choice = [] + for i in range(n_items): + embedding = sigmoid(shared_embeddings +\ + self.gamma_w(items_features_by_choice[:, i])) + item_utility_by_choice.append(self.beta_l(item_embedding)) + + item_utility_by_choice = tf.concat(item_utility_by_choice, axis=1) + + return item_utility_by_choice +``` + +# Acknowledgments +The authors thank Fortenova[^1] and Martin Možina for their helpful collaboration and providing of the proprietary dataset. + +# References + +[^1]: https://fortenova.hr/en/home/ diff --git a/docs/paper/table.md b/docs/paper/table.md new file mode 100644 index 00000000..f0bb7775 --- /dev/null +++ b/docs/paper/table.md @@ -0,0 +1,134 @@ + + +: Comparison of implemented sequence aggregation methods (average word error rate is used).\label{tab:sequence} ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Package | Data Processing | Estimation | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| | Format | Batching | Non-stochastic | Stochastic| Regularization | ++:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ +| Biogeme | wide | X | Newton BFGS | | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| PyLogit | format | X | BFGS | | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Torch-Choice | | :white_check_mark: | L-BFGS | - body | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Choice-Learn | | :white_check_mark: | L-BFGS | - here | | ++==============+=============+====================+================+===========+================+ +| Package | Models | Downstream Operations | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| | Traditional | NeuralNet | Custom | Assortment| Pricing | ++:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ +| Biogeme | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| PyLogit | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Torch-Choice | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Choice-Learn | cells span columns | cells span columns | columns | ++==============+=============+====================+================+===========+================+ + ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Package | Data Processing | Estimation | +| |-------------+--------------------+----------------+-----------+----------------+ +| | Format | Batching | Non-stochastic | Stochastic| Regularization | ++:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ +| Biogeme | wide | X | Newton BFGS | | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| PyLogit | format | X | BFGS | | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Torch-Choice | | :white_check_mark: | L-BFGS | - body | | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Choice-Learn | | :white_check_mark: | L-BFGS | - here | | ++==============+=============+====================+================+===========+================+ + ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Package | Models | Downstream Operations | +| +:===========:+:==================:+:==============:+:=========:+:==============:+ +| | Traditional | NeuralNet | Custom | Assortment| Pricing | ++:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ +| Biogeme | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| PyLogit | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Torch-Choice | cells span columns | cells span columns | columns | ++--------------+-------------+--------------------+----------------+-----------+----------------+ +| Choice-Learn | cells span columns | cells span columns | columns | ++==============+=============+====================+================+===========+================+ + + ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Package | Data Processing | Estimation | +|--------------+------------------+--------------------+----------------+--------------+----------------+ +| | Format | Batching | Non-stochastic | Stochastic | Regularization | ++:============:+:================:+:==================:+:==============:+:============:+:==============:+ +| Biogeme | wide | X | Newton BFGS | X | X | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| PyLogit | format | X | BFGS | X | X | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Torch-Choice | Multi Index | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Choice-Learn | Features Storage | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++==============+==================+====================+================+==============+================+ + + ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Package | Models | Downstream Operations | +| +---------------------------------+--------------------+----------------+--------------+----------------+ +| | Traditional | NeuralNet | Custom | Assortment | Pricing | ++:============:+:===============================:+:==================:+:==============:+:============:+:==============:+ +| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Torch-Choice | CondL, NestL | $\times$ | $\times$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | ++==============+=================================+====================+================+==============+================+ + + +: Comparison of \label{tab:tab2} ++---------------------------------+ +|![](../illustrations/table.png) | ++---------------------------------+ + + + +: Comparison of implemented sequence aggregation methods (average word error rate is used).\label{tab:comparison} + ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Package | Format | Batching | Non-Stochastic | Stochastic | Regularization | ++:============:+:================:+:==================:+:==============:+:============:+:==============:+ +| Biogeme | wide | $\times$ | Newton BFGS | $\times$ | $\times$ | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| PyLogit | long | $\times$ | BFGS | $\times$ | $\times$ | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Torch-Choice | Multi Index | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++--------------+------------------+--------------------+----------------+--------------+----------------+ +| Choice-Learn | Features Storage | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++==============+==================+====================+================+==============+================+ + + ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Package | Traditional | NeuralNet | Custom | Assortment | Pricing | ++:============:+:===============================:+:==================:+:==============:+:============:+:==============:+ +| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Torch-Choice | CondL, NestL | $\times$ | $\times$ | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+--------------+----------------+ +| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | ++==============+=================================+====================+================+==============+================+ + ++--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ +| Package | Traditional | NeuralNet | Custom | Non-Stochastic | Stochastic | Regularization | +| | Models | Models | Models | Estimation | Estimation | | ++:============:+:===============================:+:==================:+:==============:+:==============:+:============:+:==============:+ +| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | Newton BFGS | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ +| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | BFGS | $\times$ | $\times$ | ++--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ +| Torch-Choice | CondL, NestL | $\times$ | $\times$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ +| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | ++==============+=================================+====================+================+================+==============+================+ From ade6adcfcc55706fc3e222f2c4fe5031f5f22ccd Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Thu, 13 Jun 2024 09:38:22 +0200 Subject: [PATCH 02/13] Paper Editions (#99) --- docs/paper/paper.bib | 25 +++++++++------------ docs/paper/paper.md | 9 +++++--- notebooks/introduction/3_model_clogit.ipynb | 6 ++--- 3 files changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib index 4313d13d..155c8d7b 100644 --- a/docs/paper/paper.bib +++ b/docs/paper/paper.bib @@ -83,13 +83,11 @@ @Article{Harris:2020 volume = {585}, number = {7825}, pages = {357--362}, - doi = {10.1038/s41586-020-2649-2}, publisher = {Springer Science and Business Media {LLC}}, } @software{Abadi:2015, author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jozefowicz, Rafal and Jia, Yangqing and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mané, Dan and Schuster, Mike and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viégas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang}, -doi = {10.5281/zenodo.4724125}, license = {Apache-2.0}, month = nov, title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}}, @@ -129,7 +127,7 @@ @article{Expedia:2013 author={Ben Hamner, Adam and Friedman, Dan}, journal={ICDM}, year={2013}, - eprint={https://www.kaggle.com/c/expedia-personalized-sort}, + howpublished={\url{https://www.kaggle.com/c/expedia-personalized-sort} (last checked on 2024-06-07)}, } @article{AouadMarket:2023, @@ -157,13 +155,11 @@ @article{MendezDiaz:2014 } @software{pandas:2020, - author = {The pandas development team}, - title = {pandas-dev/pandas: Pandas}, + author = {{The pandas development team}}, + title = {pandas-dev/pandas: {P}andas}, month = feb, year = 2020, publisher = {Zenodo}, - version = {latest}, - doi = {10.5281/zenodo.3509134}, } @inproceedings{Bierlaire:2001, @@ -187,15 +183,16 @@ @article{Pedregosa:2011 @misc{Helveston:2023, title={Convert data from wide to long format}, -howpublished={\url{https://xlogit.readthedocs.io/en/latest/notebooks/convert_data_wide_to_long.html}}, +howpublished={\url{https://xlogit.readthedocs.io/en/latest/notebooks/convert_data_wide_to_long.html} (last checked on 2024-06-07)}, author={Forsythe, C. and Helveston, J}, - year={2023} + year={2023}, + urldate = {2024-06-07}, } @misc{Chollet:2015, title={Keras}, - author={Chollet, Fran\c{c}ois and others}, + author={{Chollet et al.}}, year={2015}, - howpublished={\url{https://keras.io}}, + howpublished={\url{https://keras.io} (last checked on 2024-06-07)}, } @article{McFadden:2000, @@ -213,7 +210,7 @@ @misc{Gurobi:2023 author = {{Gurobi Optimization, LLC}}, title = {{Gurobi Optimizer Reference Manual}}, year = 2023, - url = "https://www.gurobi.com" + howpublished = {\url{https://www.gurobi.com} (last checked on 2024-06-07)} } @software{ORTools:2024, @@ -221,6 +218,6 @@ @software{ORTools:2024 version = { v9.9 }, author = {Laurent Perron and Vincent Furnon}, organization = {Google}, - url = {https://developers.google.com/optimization/}, - date = { 2024-03-07 } + date = { 2024-03-07 }, + howpublished = {\url{https://developers.google.com/optimization/} (last checked on 2024-06-07)} } diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 9d9f840c..9d82d051 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -76,13 +76,16 @@ A summary of the main contributions is provided in Table \ref{tab:comparison1} a +:============:+:===============================:+:==================:+:==============:+:==============:+:====================:+ | Biogeme | CondL, NestL,\ | $\times$ | $\checkmark$ | Newton BFGS | $\quad \times \quad$ | | | MixL, LatC\ | | | | | -| | & more | | | | | +| | & more\ | | | | | +| | | | | | | +--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ | PyLogit | CondL, NestL,\ | $\times$ | $\times$ | BFGS | $\times$ | | | MixL,\ | | | | | -| | Asymmetric | | | | | +| | Asymmetric\ | | | | | +| | | | | | | +--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ -| Torch-Choice | CondL, NestL\ | $\times$ | $\times$ | L-BFGS | $\checkmark$ | +| Torch- | CondL, NestL\ | $\times$ | $\times$ | L-BFGS | $\checkmark$ | +| Choice\ | | | | | | | | | | | | | +--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ | Choice-Learn | CondL, NestL,\ | $\checkmark$ | $\checkmark$ | L-BFGS | $\checkmark$ | diff --git a/notebooks/introduction/3_model_clogit.ipynb b/notebooks/introduction/3_model_clogit.ipynb index a3ea2062..03a65867 100644 --- a/notebooks/introduction/3_model_clogit.ipynb +++ b/notebooks/introduction/3_model_clogit.ipynb @@ -52,13 +52,13 @@ "source": [ "## Example 1: SwissMetro\n", "\n", - "The choice-learn package offers a high level API to conceive and estimate discrete choice models. Several models are ready to be used, you can check the list [here](../README.md). If you want to create your own model or another one that is not in the list, the lower level API can help you. Check the notebook [here](./4_model_customization.ipynb).\n", + "The choice-learn package offers a high level API to conceive and estimate discrete choice models. Several models are ready to be used, you can check the list [here](../../README.md). If you want to create your own model or another one that is not in the list, the lower level API can help you. Check the notebook [here](./4_model_customization.ipynb).\n", "\n", "Let's begin this tutorial with the estimation of a Conditional Logit Model on the SwissMetro dataset[3].\n", "It follows the specifications described in [PyLogit](https://github.com/timothyb0912/pylogit/blob/master/examples/notebooks/Main%20PyLogit%20Example.ipynb) and [Biogeme](https://github.com/mncosta/biogeme_tutorial/blob/master/6.1-MultinomialLogitAndProbitModels/01-biogeme-basics.ipynb).\n", "\n", "\n", - "First, we download our data as a ChoiceDataset. See the [data management tutorial](./choice_learn_introduction_data.ipynb) first if needed." + "First, we download our data as a ChoiceDataset. See the [data management tutorial](./2_data_handling.ipynb) first if needed." ] }, { @@ -1173,7 +1173,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.11.4" } }, "nbformat": 4, From 433a70ff9a9f6b2da8f4c900dc89295b4c327115 Mon Sep 17 00:00:00 2001 From: VincentAuriau Date: Thu, 13 Jun 2024 09:39:56 +0200 Subject: [PATCH 03/13] RM: useless file --- docs/paper/table.md | 134 -------------------------------------------- 1 file changed, 134 deletions(-) delete mode 100644 docs/paper/table.md diff --git a/docs/paper/table.md b/docs/paper/table.md deleted file mode 100644 index f0bb7775..00000000 --- a/docs/paper/table.md +++ /dev/null @@ -1,134 +0,0 @@ - - -: Comparison of implemented sequence aggregation methods (average word error rate is used).\label{tab:sequence} -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Package | Data Processing | Estimation | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| | Format | Batching | Non-stochastic | Stochastic| Regularization | -+:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ -| Biogeme | wide | X | Newton BFGS | | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| PyLogit | format | X | BFGS | | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Torch-Choice | | :white_check_mark: | L-BFGS | - body | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Choice-Learn | | :white_check_mark: | L-BFGS | - here | | -+==============+=============+====================+================+===========+================+ -| Package | Models | Downstream Operations | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| | Traditional | NeuralNet | Custom | Assortment| Pricing | -+:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ -| Biogeme | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| PyLogit | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Torch-Choice | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Choice-Learn | cells span columns | cells span columns | columns | -+==============+=============+====================+================+===========+================+ - -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Package | Data Processing | Estimation | -| |-------------+--------------------+----------------+-----------+----------------+ -| | Format | Batching | Non-stochastic | Stochastic| Regularization | -+:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ -| Biogeme | wide | X | Newton BFGS | | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| PyLogit | format | X | BFGS | | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Torch-Choice | | :white_check_mark: | L-BFGS | - body | | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Choice-Learn | | :white_check_mark: | L-BFGS | - here | | -+==============+=============+====================+================+===========+================+ - -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Package | Models | Downstream Operations | -| +:===========:+:==================:+:==============:+:=========:+:==============:+ -| | Traditional | NeuralNet | Custom | Assortment| Pricing | -+:============:+:===========:+:==================:+:==============:+:=========:+:==============:+ -| Biogeme | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| PyLogit | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Torch-Choice | cells span columns | cells span columns | columns | -+--------------+-------------+--------------------+----------------+-----------+----------------+ -| Choice-Learn | cells span columns | cells span columns | columns | -+==============+=============+====================+================+===========+================+ - - -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Package | Data Processing | Estimation | -|--------------+------------------+--------------------+----------------+--------------+----------------+ -| | Format | Batching | Non-stochastic | Stochastic | Regularization | -+:============:+:================:+:==================:+:==============:+:============:+:==============:+ -| Biogeme | wide | X | Newton BFGS | X | X | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| PyLogit | format | X | BFGS | X | X | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Torch-Choice | Multi Index | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Choice-Learn | Features Storage | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+==============+==================+====================+================+==============+================+ - - -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Package | Models | Downstream Operations | -| +---------------------------------+--------------------+----------------+--------------+----------------+ -| | Traditional | NeuralNet | Custom | Assortment | Pricing | -+:============:+:===============================:+:==================:+:==============:+:============:+:==============:+ -| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Torch-Choice | CondL, NestL | $\times$ | $\times$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | -+==============+=================================+====================+================+==============+================+ - - -: Comparison of \label{tab:tab2} -+---------------------------------+ -|![](../illustrations/table.png) | -+---------------------------------+ - - - -: Comparison of implemented sequence aggregation methods (average word error rate is used).\label{tab:comparison} - -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Package | Format | Batching | Non-Stochastic | Stochastic | Regularization | -+:============:+:================:+:==================:+:==============:+:============:+:==============:+ -| Biogeme | wide | $\times$ | Newton BFGS | $\times$ | $\times$ | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| PyLogit | long | $\times$ | BFGS | $\times$ | $\times$ | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Torch-Choice | Multi Index | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+--------------+------------------+--------------------+----------------+--------------+----------------+ -| Choice-Learn | Features Storage | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+==============+==================+====================+================+==============+================+ - - -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Package | Traditional | NeuralNet | Custom | Assortment | Pricing | -+:============:+:===============================:+:==================:+:==============:+:============:+:==============:+ -| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Torch-Choice | CondL, NestL | $\times$ | $\times$ | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+--------------+----------------+ -| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | -+==============+=================================+====================+================+==============+================+ - -+--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ -| Package | Traditional | NeuralNet | Custom | Non-Stochastic | Stochastic | Regularization | -| | Models | Models | Models | Estimation | Estimation | | -+:============:+:===============================:+:==================:+:==============:+:==============:+:============:+:==============:+ -| Biogeme | CondL, NestL, MixL, LatC & more | $\times$ | $\checkmark$ | Newton BFGS | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ -| PyLogit | CondL, NestL, MixL, Asymmetric | $\times$ | $\times$ | BFGS | $\times$ | $\times$ | -+--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ -| Torch-Choice | CondL, NestL | $\times$ | $\times$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+--------------+---------------------------------+--------------------+----------------+----------------+--------------+----------------+ -| Choice-Learn | CondL, NestL, LatC | $\checkmark$ | $\checkmark$ | L-BFGS | $\checkmark$ | $\checkmark$ | -+==============+=================================+====================+================+================+==============+================+ From 3a469ec296e5e12739c625935f76521e90b10e33 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Thu, 13 Jun 2024 17:10:52 +0200 Subject: [PATCH 04/13] ADD: DOIs (#105) --- docs/paper/paper.bib | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib index 155c8d7b..0a0495ea 100644 --- a/docs/paper/paper.bib +++ b/docs/paper/paper.bib @@ -15,6 +15,7 @@ @article{Brathwaite:2018 journal={Journal of Choice Modelling}, publisher={Elsevier BV}, author={Brathwaite, Timothy and Walker, Joan L.}, + doi = {https://doi.org/10.1016/j.jocm.2018.01.002}, year={2018}, month=dec, pages={78–112} @@ -25,12 +26,14 @@ @article{Du:2023 author={Tianyu Du and Ayush Kanodia and Susan Athey}, year={2023}, journal={arXiv preprint arXiv:{2304.01906}}, + doi = {https://doi.org/10.48550/arXiv.2304.01906}, } @article{Aouad:2023, title={Representing random utility choice models with neural networks}, author={Aouad, Ali and D{\'e}sir, Antoine}, journal={arXiv preprint arXiv:2207.12877}, + doi = {https://doi.org/10.48550/arXiv.2207.12877}, year={2022} } @@ -43,6 +46,7 @@ @article{Han:2022 issn = {0191-2615}, author = {Yafei Han and Francisco Camara Pereira and Moshe Ben-Akiva and Christopher Zegras}, keywords = {Discrete choice models, Neural networks, Taste heterogeneity, Interpretability, Utility specification, Machine learning, Deep learning}, +doi = {https://doi.org/10.1016/j.trb.2022.07.001}, abstract = {Discrete choice models (DCMs) require a priori knowledge of the utility functions, especially how tastes vary across individuals. Utility misspecification may lead to biased estimates, inaccurate interpretations and limited predictability. In this paper, we utilize a neural network to learn taste representation. Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e.g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge. Taste parameters learned by the neural network are fed into the choice model and link the two modules. Our approach extends the L-MNL model (Sifringer et al., 2020) by allowing the neural network to learn the interactions between individual characteristics and alternative attributes. Moreover, we formalize and strengthen the interpretability condition — requiring realistic estimates of behavior indicators (e.g., value-of-time, elasticity) at the disaggregated level, which is crucial for a model to be suitable for scenario analysis and policy decisions. Through a unique network architecture and parameter transformation, we incorporate prior knowledge and guide the neural network to output realistic behavior indicators at the disaggregated level. We show that TasteNet-MNL reaches the ground-truth model’s predictability and recovers the nonlinear taste functions on synthetic data. Its estimated value-of-time and choice elasticities at the individual level are close to the ground truth. In contrast, exemplary logit models with misspecified systematic utility lead to biased parameter estimates and lower prediction accuracy. On a publicly available Swissmetro dataset, TasteNet-MNL outperforms benchmarking MNLs and Mixed Logit model’s predictability. It learns a broader spectrum of taste variations within the population and suggests a higher average value-of-time. Our source code is available for research and application.} } @@ -50,6 +54,7 @@ @article{Salvadé:2024 title={RUMBoost: Gradient Boosted Random Utility Models}, author={Salvad{\'e}, Nicolas and Hillel, Tim}, journal={arXiv preprint arXiv:2401.11954}, + doi={https://doi.org/10.48550/arXiv.2401.11954}, year={2024} } @@ -63,7 +68,8 @@ @article{Train:1987 publisher = {[RAND Corporation, Wiley]}, title = {The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices}, volume = {18}, - year = {1987} + year = {1987}, + doi = {https://doi.org/10.2307/2555538}, } @Article{Harris:2020, @@ -91,7 +97,8 @@ @software{Abadi:2015 license = {Apache-2.0}, month = nov, title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}}, -year = {2015} +year = {2015}, +doi = {10.5281/zenodo.4724125} } @Inbook{Nocedal:2006, @@ -103,6 +110,7 @@ @Inbook{Nocedal:2006 address="New York, NY", pages="164--192", isbn="978-0-387-40065-5", +doi = {https://doi.org/10.1007/0-306-48332-7_250} } @article{Kingma:2017, @@ -112,6 +120,7 @@ @article{Kingma:2017 archivePrefix={arXiv}, primaryClass={cs.LG}, journal={arXiv preprint arXiv:{1412.6980}}, + doi = {https://doi.org/10.48550/arXiv.1412.6980} } @article{Tieleman:2012, @@ -138,7 +147,8 @@ @article{AouadMarket:2023 number={2}, pages={648--667}, year={2023}, - publisher={INFORMS} + publisher={INFORMS}, + doi={https://doi.org/10.1287/msom.2023.1195}, } @article{MendezDiaz:2014, @@ -151,7 +161,7 @@ @article{MendezDiaz:2014 issn = {0166-218X}, author = {Isabel Méndez-Díaz and Juan José Miranda-Bront and Gustavo Vulcano and Paula Zabala}, keywords = {Retail operations, Revenue management, Choice behavior, Multinomial logit, Integer programming, Fractional programming}, -abstract = {We study the product assortment problem of a retail operation that faces a stream of customers who are heterogeneous with respect to preferences. Each customer belongs to a market segment characterized by a consideration set that includes the alternatives viewed as options, and by the preference weights that the segment assigns to each of those alternatives. Upon arrival, he checks the offer set displayed by the firm, and either chooses one of those products or quits without purchasing according to a multinomial-logit (MNL) criterion. The firm’s goal is to maximize the expected revenue extracted during a fixed time horizon. This problem also arises in the growing area of choice-based, network revenue management, where computational speed is a critical factor for the practical viability of a solution approach. This so-called latent-class, logit assortment problem is known to be NP-Hard. In this paper, we analyze unconstrained and constrained (i.e., with a limited number of products to display) versions of it, and propose a branch-and-cut algorithm that is computationally fast and leads to (nearly) optimal solutions.} +doi = {https://doi.org/10.1016/j.dam.2012.03.003}, } @software{pandas:2020, @@ -160,6 +170,7 @@ @software{pandas:2020 month = feb, year = 2020, publisher = {Zenodo}, + doi = {10.5281/zenodo.3509134}, } @inproceedings{Bierlaire:2001, From 90d9d2ab2236a92b8be9365fb4336e89ab556bd4 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Thu, 13 Jun 2024 17:32:58 +0200 Subject: [PATCH 05/13] Paper (#106) --- docs/paper/paper.bib | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib index 0a0495ea..9ad13efa 100644 --- a/docs/paper/paper.bib +++ b/docs/paper/paper.bib @@ -15,7 +15,7 @@ @article{Brathwaite:2018 journal={Journal of Choice Modelling}, publisher={Elsevier BV}, author={Brathwaite, Timothy and Walker, Joan L.}, - doi = {https://doi.org/10.1016/j.jocm.2018.01.002}, + doi = {10.1016/j.jocm.2018.01.002}, year={2018}, month=dec, pages={78–112} @@ -26,14 +26,14 @@ @article{Du:2023 author={Tianyu Du and Ayush Kanodia and Susan Athey}, year={2023}, journal={arXiv preprint arXiv:{2304.01906}}, - doi = {https://doi.org/10.48550/arXiv.2304.01906}, + doi = {10.48550/arXiv.2304.01906}, } @article{Aouad:2023, title={Representing random utility choice models with neural networks}, author={Aouad, Ali and D{\'e}sir, Antoine}, journal={arXiv preprint arXiv:2207.12877}, - doi = {https://doi.org/10.48550/arXiv.2207.12877}, + doi = {10.48550/arXiv.2207.12877}, year={2022} } @@ -46,7 +46,7 @@ @article{Han:2022 issn = {0191-2615}, author = {Yafei Han and Francisco Camara Pereira and Moshe Ben-Akiva and Christopher Zegras}, keywords = {Discrete choice models, Neural networks, Taste heterogeneity, Interpretability, Utility specification, Machine learning, Deep learning}, -doi = {https://doi.org/10.1016/j.trb.2022.07.001}, +doi = {10.1016/j.trb.2022.07.001}, abstract = {Discrete choice models (DCMs) require a priori knowledge of the utility functions, especially how tastes vary across individuals. Utility misspecification may lead to biased estimates, inaccurate interpretations and limited predictability. In this paper, we utilize a neural network to learn taste representation. Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e.g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge. Taste parameters learned by the neural network are fed into the choice model and link the two modules. Our approach extends the L-MNL model (Sifringer et al., 2020) by allowing the neural network to learn the interactions between individual characteristics and alternative attributes. Moreover, we formalize and strengthen the interpretability condition — requiring realistic estimates of behavior indicators (e.g., value-of-time, elasticity) at the disaggregated level, which is crucial for a model to be suitable for scenario analysis and policy decisions. Through a unique network architecture and parameter transformation, we incorporate prior knowledge and guide the neural network to output realistic behavior indicators at the disaggregated level. We show that TasteNet-MNL reaches the ground-truth model’s predictability and recovers the nonlinear taste functions on synthetic data. Its estimated value-of-time and choice elasticities at the individual level are close to the ground truth. In contrast, exemplary logit models with misspecified systematic utility lead to biased parameter estimates and lower prediction accuracy. On a publicly available Swissmetro dataset, TasteNet-MNL outperforms benchmarking MNLs and Mixed Logit model’s predictability. It learns a broader spectrum of taste variations within the population and suggests a higher average value-of-time. Our source code is available for research and application.} } @@ -54,7 +54,7 @@ @article{Salvadé:2024 title={RUMBoost: Gradient Boosted Random Utility Models}, author={Salvad{\'e}, Nicolas and Hillel, Tim}, journal={arXiv preprint arXiv:2401.11954}, - doi={https://doi.org/10.48550/arXiv.2401.11954}, + doi={10.48550/arXiv.2401.11954}, year={2024} } @@ -69,7 +69,7 @@ @article{Train:1987 title = {The Demand for Local Telephone Service: A Fully Discrete Model of Residential Calling Patterns and Service Choices}, volume = {18}, year = {1987}, - doi = {https://doi.org/10.2307/2555538}, + doi = {10.2307/2555538}, } @Article{Harris:2020, @@ -110,7 +110,7 @@ @Inbook{Nocedal:2006 address="New York, NY", pages="164--192", isbn="978-0-387-40065-5", -doi = {https://doi.org/10.1007/0-306-48332-7_250} +doi = {10.1007/0-306-48332-7_250} } @article{Kingma:2017, @@ -120,7 +120,7 @@ @article{Kingma:2017 archivePrefix={arXiv}, primaryClass={cs.LG}, journal={arXiv preprint arXiv:{1412.6980}}, - doi = {https://doi.org/10.48550/arXiv.1412.6980} + doi = {10.48550/arXiv.1412.6980} } @article{Tieleman:2012, @@ -148,7 +148,7 @@ @article{AouadMarket:2023 pages={648--667}, year={2023}, publisher={INFORMS}, - doi={https://doi.org/10.1287/msom.2023.1195}, + doi={10.1287/msom.2023.1195}, } @article{MendezDiaz:2014, @@ -161,7 +161,7 @@ @article{MendezDiaz:2014 issn = {0166-218X}, author = {Isabel Méndez-Díaz and Juan José Miranda-Bront and Gustavo Vulcano and Paula Zabala}, keywords = {Retail operations, Revenue management, Choice behavior, Multinomial logit, Integer programming, Fractional programming}, -doi = {https://doi.org/10.1016/j.dam.2012.03.003}, +doi = {10.1016/j.dam.2012.03.003}, } @software{pandas:2020, @@ -214,7 +214,8 @@ @article{McFadden:2000 number={5}, pages={447--470}, year={2000}, - publisher={Wiley Online Library} + publisher={Wiley Online Library}, + doi = {10.1002/1099-1255(200009/10)15:5<447::aid-jae570>3.3.co;2-t} } @misc{Gurobi:2023, From fc4f2aed4f48d0e1bcebba94258c894e7c2da847 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Mon, 17 Jun 2024 10:28:00 +0200 Subject: [PATCH 06/13] Paper (#108) --- docs/illustrations/ram_usage_comparison.png | Bin 0 -> 178392 bytes docs/paper/memory_usage.ipynb | 118 ++++++++++++++++++++ docs/paper/paper.md | 4 +- 3 files changed, 120 insertions(+), 2 deletions(-) create mode 100644 docs/illustrations/ram_usage_comparison.png diff --git a/docs/illustrations/ram_usage_comparison.png b/docs/illustrations/ram_usage_comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..fbb63857a031a897a62b26d47798c3439fe9b911 GIT binary patch literal 178392 zcmb@u1yELN+dukXfC7qw3Mzu&10oiai;!ygxoRP-WQ{`MX(9h&eC5e7LSdj#BK*8^4soj!_AXufq;*?E zYIM%SkDBg0+ClT)L~A!EJS`TJIg!ikzsHjG^o?>@gltNV@{=4H<~@>|m!9$Y$$j$c ztQw!#-*5d@O8$yGws&W1({SVY)P8lRYt6=n!4tGd$c;<9|MgFjqVyd*)cc25{_7vz zMB%y1|MP94n?6eOxa;A+6&98>#mAmFVGXx8BfGuLzJQUR6<1sdKvUaaFt_G)kz@FnP!9MRU%wW zN{N`Je62<+SGpW|F@F9_K0ZF>+z}72T<+}Xz;Q0B?)xAXFTQslK3rsFW%YXS`Qa5WakAer37Z(@1<2E`?EV5_t+3d^=l}z%bf`Wo=6g%dJcPm4AI@;QNS5a9__9uDk;h2s&dE- z;t0G)i*Pyp{nXUN4?io=UG=ak83w)kP^1)m8ENj&O{nA4wOT z_g0h4r)ub3Npj?U>ovcKhEv1!<|%dInWNIuDALo@$IGp;;G14v0$Lx@SNLv*$HaU_ zi-gfATo)7TY`89TLl-VKySFw5C^sb@4ROaA~Srmo8mO9n&8v z)cg5W<{}Ob4%dqp94-f&IDyBo$T)nkEG#U3pPe2%;un2wC-9sRIa$MgA4HH$sZM3w zWG{QeGXwiHmz9hR!`|LLf>tSH`pEx28>87cd%AdZ!{N@N(PS0J+1Z)r$Bzb%<9<%K zy1TQHAt8LKs;V*W$b#A|{EK-Ji`;CpGiQhRQ`x&oO{&$Pe|A%wP6)4*XcI<=*(o3V zfqqqw`$>Jtr>eG=$9kbFNhpxyZmvd|1=*{Q*Y$?;wSMMlBntT6kx3K1U30u%NgJpF zbH&WExxYTqI628Zu(sG8E98SuG3Wl~o19}go1AAnx9dH3_jBSkST(P@V_0OeetrFA zTw$}k-M`3V@f+oSx;rGcan4v`Ht{(l z$HPc1be6*g8*w@&|HanbY`wOmECfERF?&sY1 z{JrC!+xO?HRf?CMAIt{&`1^;SPgFbadu@q?c#^S@X`Ii`D|%vJVA!k-K5adA-RWk# zbouhlTeq5E!D$?wl#9$UiSZY0_wt-GSj`E;Fp5e`-%!X^)jB&lwCePWl$g_?jHuE# zH}6dr@I5$M$s5Rl1*kLn-H4dO%JV*(*(UsIzVWYb7XmF8x}uHn50#4aw+2f*Ii-ZzTo5mgR-3Z&8d=1 zr`eMBOnQyiOg2l>l8M~iTeGd@E{BX{WMtos2D~3jB~<;~g6VHKIo#3UOn5JMt3w?| z(tu)OL{wap9@l5TpOy_h`Is_aq2}?90B5lK$-y(H>ICfD)Tv79Nqm>j4i`xeZ7c28 zmL@9Mp4+cC)z`m+#gQVLdGIGIZ*zAyjrRklcpQ5$qh5!u6P>lS_3G-XVyW2!^QtXO zH#awWKdF9-ZHo^QHD{qVN>bLP+RfO4enc2AEi4$Yz23vnUtoAU%BqpWnk7tg1ySvQ;0|W*J1}y;( ze9PX#WAR)e`}zREfJgV{>U3FED;tr^Q_U! zj}viNrE=A7#Bo@E)u^o*xBA@^|D2zn|BGy9zsoua2?^CLZ2j(-X6R+G5eQ+weEITl zW_H$bG2Y(7(z3p}*(;jKpyT-P;DAA|;}(>B!=c;_1CZ4goK1O=V#826X7%p$OoI5nU$57 zBg0TPtevja=*?{S!vj9da(hk$B^W?__2odi1k?n6K|w}`P0ezgZr|M9RFP0asA$E; zzd`~p=UdH-#>K@+XZX#xDyD>ik9%$=z+%MTK(kXyb9%f#kSaoOkJ-?}*B2*CAunRzv1V1RNdMZ}XmJX(7`XkJ`T6D? 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zwQ*-BMn_*LM#tZsc$YOFHObWnD5K`G2Ha0(KkEDyydIWV*DjBf@2~;{<8n85xoUfgc@TJMAZ!9CdrI%OmaA6>v~m;r$q72LlQk z9XocXZZxP!_LSoly_WZ>gFIHE?B+tOtlPE~BjcuzX_(YGI2JSPK5W@6ikk9JVB?hR zM@|T!adj4bLqip^3!Tg?!#q~yFzgkhdStI-mF`qpB=}k+@hAO(e7(KBUm{b+je{L0 zndoqHMtR%oEz|A~c8`>AVf;_IA}SSYLlbob|68$G%Om2q{XfNvt9SKp3j4h7zdtwW zuJZGLN`U3u9pv|n{r9Kjr2*sq`%{0Z)&Hq}T#f#{|1SoX`~S;jJ})tT?Az9^)Md#` Q!Cxm8&d9%(GraqM0F@i%;Q#;t literal 0 HcmV?d00001 diff --git a/docs/paper/memory_usage.ipynb b/docs/paper/memory_usage.ipynb index c1658c8a..e6fc0d31 100644 --- a/docs/paper/memory_usage.ipynb +++ b/docs/paper/memory_usage.ipynb @@ -612,6 +612,13 @@ "torch_choice = [1249040, 12481040, 124801040, 1248001040, 4962273680]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -673,6 +680,117 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors=[\"#e69f00\", \"#56b4e9\", \"#009e73\", \"#0072b2\", \"#d55e00\", \"#cc79a7\", \"#f0e442\"]\n", + "linestyle=[\"-\", \"--\", \"-.\", \":\", \"-\", \"--\", \"-.\"]\n", + "\n", + "fig = plt.figure(figsize=(12, 10))\n", + "fig.tight_layout()\n", + "fig.subplots_adjust(hspace=0.35)\n", + "\n", + "ds_lengths = [10, 100, 1000, 10000, 100000, 10000000]\n", + "dense_1 = [928, 8128, 80128, 800128, 8000128, 800000128]\n", + "fbid_1 =[1036, 1756, 8956, 80956, 800956, 80000956]\n", + "dense_2 = [8128, 80128, 800128, 8000128, 80000128, 8000000128]\n", + "fbid_2 = [7892, 8612, 15812, 87812, 807812, 80007812]\n", + "plt.subplot(2, 2, 1)\n", + "plt.plot(ds_lengths, dense_1, label='w/o FeaturesStorage - 10 locations', c=colors[0], ls=linestyle[0])\n", + "plt.plot(ds_lengths, fbid_1, label='w/ FeaturesStorage - 10 locations', c=colors[1], ls=linestyle[1])\n", + "plt.scatter(ds_lengths, dense_1, c=colors[0])\n", + "plt.scatter(ds_lengths, fbid_1, c=colors[1])\n", + "\n", + "plt.plot(ds_lengths, dense_2, label='w/o FeaturesStorage - 100 locations', c=colors[2], ls=linestyle[2])\n", + "plt.plot(ds_lengths, fbid_2, label='w/ FeaturesStorage - 100 locations', c=colors[3], ls=linestyle[3])\n", + "plt.scatter(ds_lengths, dense_2, c=colors[2])\n", + "plt.scatter(ds_lengths, fbid_2, c=colors[3])\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.legend(prop={'size': 8})\n", + "\n", + "plt.title(\"(a) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "data_lengths = [100, 1000, 10000, 100000, 397618]\n", + "cd_with = [85640, 771440, 7629440, 76209440, 302994356]\n", + "cd_wo = [220400, 2198600, 21980600, 219800600, 873964964]\n", + "df_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", + "df_wide = [6252516976, 6266664976, 6408144976, 7822944976, 12501499936]\n", + "df_wide = [3503109, 31784709, 314600709, 3142760709, 12495108741]\n", + "torch_choice = [1249040, 12481040, 124801040, 1248001040, 4962273680]\n", + "\n", + "plt.plot(data_lengths, cd_with, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", + "plt.plot(data_lengths, torch_choice, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", + "plt.plot(data_lengths, df_long, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", + "plt.plot(data_lengths, df_wide, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", + "plt.scatter(data_lengths, cd_with, c=colors[3])\n", + "plt.scatter(data_lengths, torch_choice, c=colors[1])\n", + "plt.scatter(data_lengths, df_long, c=colors[0])\n", + "plt.scatter(data_lengths, df_wide, c=colors[2])\n", + "plt.legend(prop={'size': 8})\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.title(\"(b) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "\n", + "sizes = [100, 1000, 10000.0, 100000.0, 1000000.0, 4789225]\n", + "tc = [3933312, 4854912, 14070912, 106230912, 1027830912, 4907997312]\n", + "# Wide \n", + "wide = [629748, 5954312, 59178320, 591244240, 5914324260, 28317686484]\n", + "# Long\n", + "long = [729260, 7216256, 79819560, 1000640616, 10190708220, 47241911756]\n", + "# CL\n", + "cl = [163734, 526146, 3921306, 34453314, 319713662, 1546499942]\n", + "\n", + "plt.plot(sizes, cl, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", + "plt.plot(sizes, tc, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", + "plt.plot(sizes, long, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", + "plt.plot(sizes, wide, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", + "plt.scatter(sizes, cl, c=colors[3])\n", + "plt.scatter(sizes, tc, c=colors[1])\n", + "plt.scatter(sizes, long, c=colors[0])\n", + "plt.scatter(sizes, wide, c=colors[2])\n", + "plt.legend(prop={'size': 8})\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.title(\"(c) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "n_stores = [0, 10, 100, 250, 692]\n", + "cl_mem_usage = [352083911, 352084711, 352163911, 352583911, 355914823]\n", + "other_mem = [1027830912, 1027831712, 1027910912, 1028330912, 1031661824]\n", + "long_mem_usage = [4654663932, 4734664572, 5454670332, 6654679932, 10190708220]\n", + "wide_mem_usage = [378279972, 458280612, 1178286372, 2378295972, 5914324260]\n", + "\n", + "plt.plot(n_stores, cl_mem_usage, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", + "plt.plot(n_stores, other_mem, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", + "plt.plot(n_stores, long_mem_usage, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", + "plt.plot(n_stores, wide_mem_usage, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", + "plt.scatter(n_stores, cl_mem_usage, c=colors[3])\n", + "plt.scatter(n_stores, other_mem, c=colors[1])\n", + "plt.scatter(n_stores, long_mem_usage, c=colors[0])\n", + "plt.scatter(n_stores, wide_mem_usage, c=colors[2])\n", + "plt.legend(prop={'size': 8})\n", + "plt.yscale(\"log\")\n", + "plt.xlabel(\"Stores Number\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.title(\"(d) Memory usage of our retail dataset \\n for different number of stores\", y=-.3)\n", + "# plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", + "# [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 9d82d051..7f26af83 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -139,9 +139,9 @@ We provide numerical examples of memory usage to showcase the efficiency of the We conduct a similar comparison on the ICDM 2013 Expedia dataset [@Expedia:2013] with four data handling methods: pandas.DataFrames [@pandas:2020] in long and wide format, both used in choice modeling packages, as well as Torch-Choice and `Choice-Learn`. Figure \ref{fig:xps} (b) shows the results for various sample sizes. -Finally, in Figure \ref{fig:xps} (c) and (d), we observe performance gains in terms of memory management on a proprietary dataset in brick-and-mortar retailing consisting of the aggregation of more than 4 million purchases over 5 years in Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which products were available, their prices, as well as a one-hot representation of the supermarket. +Finally, in Figure \ref{fig:xps} (c) and (d), we observe performance gains in terms of memory management on a proprietary dataset in brick-and-mortar retailing consisting of the aggregation of more than 4 billion purchases in Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which products were available, their prices, as well as a one-hot representation of the supermarket. -![Memory usage experiments. \label{fig:xps}](../illustrations/full_ram.png) +![Memory usage experiments. \label{fig:xps}](../illustrations/ram_usage_comparison.png) ## Customized choice models We provide an example of the custom model definition with the following formulation of utility $U(i)$ with alternative features $x_i$ and customer features $z$: From 567ffebfc2341c481b9735f59a7d001fd29f210a Mon Sep 17 00:00:00 2001 From: VincentAuriau Date: Mon, 17 Jun 2024 10:47:49 +0200 Subject: [PATCH 07/13] ENH: small enhancements --- docs/paper/memory_usage.ipynb | 96 ++--------------------------------- 1 file changed, 5 insertions(+), 91 deletions(-) diff --git a/docs/paper/memory_usage.ipynb b/docs/paper/memory_usage.ipynb index e6fc0d31..aa79268b 100644 --- a/docs/paper/memory_usage.ipynb +++ b/docs/paper/memory_usage.ipynb @@ -105,9 +105,7 @@ "\n", "### FeaturesByIDs memory usage:\n", "# Storage memory usage + ids memory stirage\n", - "print(\"FeaturesByIDs memory usage:\", get_obj_size(storage)+get_obj_size(indexes))\n", - "\n", - "\n" + "print(\"FeaturesByIDs memory usage:\", get_obj_size(storage)+get_obj_size(indexes))" ] }, { @@ -574,16 +572,16 @@ "\n", "| Dataset size | 100 | 1.000 | 10.000 | 100.000 | 397.618 |\n", "|---|---|---|---|---|---|\n", - "| CD w. FeaturesByIDs | 85.640 | 771.440 | 7.629.440 | 76.209.440 | 302.994.356 |\n", - "| CD wo FeaturesByIDs | 220.400 | 2.198.600 | 21.980.600 | 219.800.600 | 873.964.964 |\n", + "| C-L w. FeaturesByIDs | 85.640 | 771.440 | 7.629.440 | 76.209.440 | 302.994.356 |\n", + "| C-L wo FeaturesByIDs | 220.400 | 2.198.600 | 21.980.600 | 219.800.600 | 873.964.964 |\n", "| Torch-Choice | 5.825.168 | 5.5093.676 | 550.480.667 | 5.515.236.600 | 10.448.857.759 |\n", "| Long format DF | 5.521.360 | 52.463.080 | 524.667.470 | 5.234.198.450 | 20.815.361.140 |\n", "| Wide format DF | 3.503.109 | 31.784.709 | 314.600.709 | 3.142.760.709 | 12.495.108.741 |\n", "\n", "\n", "data_lengths: [100, 1000, 10000, 100000, 397618]\\\n", - "ChoiceDataset with FeaturesByIDs: [85640, 771440, 7629440, 76209440, 302994356]\\\n", - "ChoiceDataset without FeaturesByIDs: [220400, 2198600, 21980600, 219800600, 873964964]\\\n", + "Choice-Learn with FeaturesByIDs: [85640, 771440, 7629440, 76209440, 302994356]\\\n", + "Choice-Learn without FeaturesByIDs: [220400, 2198600, 21980600, 219800600, 873964964]\\\n", "Torch-Choice: [5.825.168, 55.093.676, 550.480.667, 5.515.236.600, 10.448.857.871]\\\n", "DF Long Format: [5521360, 52463080, 524667470, 5234198450, 20815361140]\\\n", "DF Wide Format: [3503109, 31784709, 314600709, 3142760709, 12495108741]" @@ -596,90 +594,6 @@ "## Plots and Illustrations" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "data_lengths = [100, 1000, 10000, 100000, 397618]\n", - "cd_with = [85640, 771440, 7629440, 76209440, 302994356]\n", - "cd_wo = [220400, 2198600, 21980600, 219800600, 873964964]\n", - "df_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", - "df_wide = [6252516976, 6266664976, 6408144976, 7822944976, 12501499936]\n", - "df_wide = [3503109, 31784709, 314600709, 3142760709, 12495108741]\n", - "torch_choice = [1249040, 12481040, 124801040, 1248001040, 4962273680]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(data_lengths, cd_with, label=\"ChoiceDataset with FeaturesByIDs\", c=\"teal\")\n", - "plt.plot(data_lengths, cd_wo, label=\"ChoiceDataset withOUT FeaturesByIDs\", c=\"turquoise\")\n", - "plt.plot(data_lengths, df_long, label=\"pd.DataFrame Long format\", c=\"darkblue\")\n", - "plt.plot(data_lengths, df_wide, label=\"pd.DataFrame Wide format\", c=\"cornflowerblue\")\n", - "plt.legend()\n", - "plt.yscale(\"log\")\n", - "plt.xlabel(\"Dataset Size (x1000)\")\n", - "plt.ylabel(\"Memory Size (bytes)\")\n", - "plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", - " [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(data_lengths, cd_with, label=\"Choice-Learn\", c=\"teal\")\n", - "plt.plot(data_lengths, torch_choice, label=\"Torch-Choice\", c=\"turquoise\")\n", - "plt.plot(data_lengths, df_long, label=\"PyLogit (long format)\", c=\"darkblue\")\n", - "plt.plot(data_lengths, df_wide, label=\"Biogeme (wide format)\", c=\"cornflowerblue\")\n", - "plt.scatter(data_lengths, cd_with, c=\"teal\")\n", - "plt.scatter(data_lengths, torch_choice, c=\"turquoise\")\n", - "plt.scatter(data_lengths, df_long, c=\"darkblue\")\n", - "plt.scatter(data_lengths, df_wide, c=\"cornflowerblue\")\n", - "plt.legend()\n", - "plt.yscale(\"log\")\n", - "plt.xscale(\"log\")\n", - "plt.xlabel(\"Dataset Size (x1000)\")\n", - "plt.ylabel(\"Memory usage (bytes)\")\n", - "# plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", - "# [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(data_lengths, cd_with, label=\"ChoiceDataset with FeaturesByIDs\", c=\"teal\")\n", - "plt.plot(data_lengths, cd_wo, label=\"ChoiceDataset withOUT FeaturesByIDs\", c=\"turquoise\")\n", - "plt.plot(data_lengths, df_long, label=\"pd.DataFrame Long format\", c=\"darkblue\")\n", - "plt.plot(data_lengths, df_wide, label=\"pd.DataFrame Wide format\", c=\"cornflowerblue\")\n", - "plt.legend()\n", - "plt.xlabel(\"Dataset Size (x1000)\")\n", - "plt.ylabel(\"Memory Size (bytes)\")\n", - "# plt.xticks([0, 50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000],\n", - "# [0, 50, 100, 150, 200, 250, 300, 350, 400])\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, From 3618b37d0642a0ee9d1700c188910ab4aa11e7aa Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Mon, 24 Jun 2024 10:30:01 +0200 Subject: [PATCH 08/13] Paper length reduction (#109) * ADD: small enhancements * FIX: typo * ENH/FIX: plots --- docs/illustrations/ram_usage_comparison.png | Bin 178392 -> 179722 bytes docs/paper/memory_usage.ipynb | 304 ++++++++++++++++---- docs/paper/paper.md | 26 +- 3 files changed, 255 insertions(+), 75 deletions(-) diff --git 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zwQ*-BMn_*LM#tZsc$YOFHObWnD5K`G2Ha0(KkEDyydIWV*DjBf@2~;{<8n85xoUfgc@TJMAZ!9CdrI%OmaA6>v~m;r$q72LlQk z9XocXZZxP!_LSoly_WZ>gFIHE?B+tOtlPE~BjcuzX_(YGI2JSPK5W@6ikk9JVB?hR zM@|T!adj4bLqip^3!Tg?!#q~yFzgkhdStI-mF`qpB=}k+@hAO(e7(KBUm{b+je{L0 zndoqHMtR%oEz|A~c8`>AVf;_IA}SSYLlbob|68$G%Om2q{XfNvt9SKp3j4h7zdtwW zuJZGLN`U3u9pv|n{r9Kjr2*sq`%{0Z)&Hq}T#f#{|1SoX`~S;jJ})tT?Az9^)Md#` Q!Cxm8&d9%(GraqM0F@i%;Q#;t diff --git a/docs/paper/memory_usage.ipynb b/docs/paper/memory_usage.ipynb index aa79268b..eed5a041 100644 --- a/docs/paper/memory_usage.ipynb +++ b/docs/paper/memory_usage.ipynb @@ -188,7 +188,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Expedia Dataset" + "## Expedia Dataset\n", + "\n", + "You might want to run the different methods individually.\\\n", + "The results are stored later in the notebook." ] }, { @@ -227,7 +230,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### pandas.DataFrame Long format (PyLogit)" + "### pandas.DataFrame Long format (PyLogit)\n", + "\n", + "The raw dataframe needs formatting to have the right format. It can take some time." ] }, { @@ -239,14 +244,16 @@ "import pandas as pd\n", "import numpy as np\n", "\n", + "# Load DF\n", "expedia_df = load_expedia(as_frame=True)\n", - "logging.info(\"rumnet preprocessing selected, starting preprocessing...\")\n", + "\n", + "# Format dates & time features\n", "expedia_df.date_time = pd.to_datetime(expedia_df.date_time, format=\"%Y-%m-%d %H:%M:%S\")\n", "expedia_df.loc[:, \"day_of_week\"] = expedia_df.loc[:, \"date_time\"].dt.dayofweek\n", "expedia_df.loc[:, \"month\"] = expedia_df.loc[:, \"date_time\"].dt.month\n", "expedia_df.loc[:, \"hour\"] = expedia_df.loc[:, \"date_time\"].dt.hour\n", "\n", - "logging.info(\"Filtering ids with less than 1000 occurrences\")\n", + "# Filtering ids with less than 1000 occurrences\n", "for id_col in [\n", " \"site_id\",\n", " \"visitor_location_country_id\",\n", @@ -259,8 +266,7 @@ " if id_ not in kept_ids:\n", " expedia_df.loc[expedia_df[id_col] == id_, id_col] = -1\n", "\n", - "logging.info(\"Filtering DF for price, stay length, booking window, etc.\")\n", - "# Filtering\n", + "# \"Filtering DF for price, stay length, booking window, etc.\n", "expedia_df = expedia_df[expedia_df.price_usd <= 1000]\n", "expedia_df = expedia_df[expedia_df.price_usd >= 10]\n", "expedia_df[\"log_price\"] = expedia_df.price_usd.apply(np.log)\n", @@ -269,7 +275,7 @@ "expedia_df[\"booking_window\"] = np.log(expedia_df[\"srch_booking_window\"] + 1)\n", "expedia_df = expedia_df.fillna(-1)\n", "\n", - "logging.info(\"Sorting DF columns\")\n", + "# Sorting DF columns\n", "order_cols = [\n", " \"srch_id\",\n", " \"prop_id\",\n", @@ -302,13 +308,11 @@ "]\n", "expedia_df = expedia_df[order_cols]\n", "\n", - "logging.info(\"Creating dummy availabilities\")\n", - "\n", + "# Creating dummy availabilities\n", "# getting rid of search & prop_id and the clickbool and bookingbool\n", "# adding no_purchase fixed effect\n", "expedia_df[\"is_no_purchase\"] = 0\n", "\n", - "logging.info(\"Creating the no purchase option\")\n", "# adding the no_purchase option to the data\n", "df1 = (\n", " expedia_df.groupby(\"srch_id\")\n", @@ -331,19 +335,22 @@ "df2.loc[:, \"is_no_purchase\"] = 1\n", "df2.loc[:, \"log_price\"] = 0\n", "df2.loc[:, \"booking_bool\"] = 1\n", + "\n", + "# Concatenating the created DFs\n", "expedia_df = pd.concat([expedia_df, df1, df2])\n", "\n", + "# One Hot encoding \n", "site_id_one_hot = pd.get_dummies(expedia_df.site_id, prefix=\"site_id\")\n", "visitor_location_country_id_one_hot = pd.get_dummies(expedia_df.visitor_location_country_id, prefix=\"visitor_location_country_id\")\n", "srch_destination_id_one_hot =pd.get_dummies(expedia_df.srch_destination_id, prefix=\"srch_destination_id\")\n", "prop_country_id_one_hpt = pd.get_dummies(expedia_df.prop_country_id, prefix=\"prop_country_id\")\n", "expedia_df = pd.concat([expedia_df, site_id_one_hot, visitor_location_country_id_one_hot, srch_destination_id_one_hot, prop_country_id_one_hpt], axis=1)\n", "\n", - "logging.info(\"Sorting the data frame\")\n", + "# Sorting\n", "expedia_df = expedia_df.sort_values(\"srch_id\")\n", "choices = [\"booking_bool\"]\n", "\n", - "logging.info(\"DF to NDarray and creating the ChoiceDataset object\")\n", + "# Final selection of the columns to match the ChoiceDataset\n", "contexts_features_names = [\n", " \"srch_id\",\n", " \"srch_length_of_stay\",\n", @@ -388,11 +395,14 @@ "metadata": {}, "outputs": [], "source": [ + "# Accessinng RAM usage\n", "long_df_memory_size = []\n", "for length in data_lengths:\n", " srch_ids = long_df.srch_id.unique()[:length]\n", " sub_long_df = long_df[long_df.srch_id.isin(srch_ids)]\n", - " long_df_memory_size.append(get_obj_size(sub_long_df))" + " long_df_memory_size.append(get_obj_size(sub_long_df))\n", + "\n", + "print(long_df_memory_size)" ] }, { @@ -413,6 +423,15 @@ "df = load_expedia(as_frame=True)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.columns" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -426,13 +445,114 @@ "metadata": {}, "outputs": [], "source": [ - "site_id_one_hot = pd.get_dummies(df.site_id, prefix=\"site_id\")\n", - "visitor_location_country_id_one_hot = pd.get_dummies(df.visitor_location_country_id, prefix=\"visitor_location_country_id\")\n", - "srch_destination_id_one_hot = pd.get_dummies(df.srch_destination_id, prefix=\"srch_destination_id\")\n", - "prop_country_id_one_hpt = pd.get_dummies(df.prop_country_id, prefix=\"prop_country_id\")\n", - "df = pd.concat([df, site_id_one_hot, visitor_location_country_id_one_hot, srch_destination_id_one_hot, prop_country_id_one_hpt], axis=1)\n", "\n", + "expedia_df = load_expedia(as_frame=True)\n", + "\n", + "# Format dates & time features\n", + "expedia_df.date_time = pd.to_datetime(expedia_df.date_time, format=\"%Y-%m-%d %H:%M:%S\")\n", + "expedia_df.loc[:, \"day_of_week\"] = expedia_df.loc[:, \"date_time\"].dt.dayofweek\n", + "expedia_df.loc[:, \"month\"] = expedia_df.loc[:, \"date_time\"].dt.month\n", + "expedia_df.loc[:, \"hour\"] = expedia_df.loc[:, \"date_time\"].dt.hour\n", + "\n", + "# Filtering ids with less than 1000 occurrences\n", + "for id_col in [\n", + " \"site_id\",\n", + " \"visitor_location_country_id\",\n", + " \"prop_country_id\",\n", + " \"srch_destination_id\",\n", + "]:\n", + " value_counts = expedia_df[[\"srch_id\", id_col]].drop_duplicates()[id_col].value_counts()\n", + " kept_ids = value_counts.index[value_counts.gt(1000)]\n", + " for id_ in expedia_df[id_col].unique():\n", + " if id_ not in kept_ids:\n", + " expedia_df.loc[expedia_df[id_col] == id_, id_col] = -1\n", + "\n", + "# \"Filtering DF for price, stay length, booking window, etc.\n", + "expedia_df = expedia_df[expedia_df.price_usd <= 1000]\n", + "expedia_df = expedia_df[expedia_df.price_usd >= 10]\n", + "expedia_df[\"log_price\"] = expedia_df.price_usd.apply(np.log)\n", + "expedia_df = expedia_df[expedia_df.srch_length_of_stay <= 14]\n", + "expedia_df = expedia_df[expedia_df.srch_booking_window <= 365]\n", + "expedia_df[\"booking_window\"] = np.log(expedia_df[\"srch_booking_window\"] + 1)\n", + "expedia_df = expedia_df.fillna(-1)\n", + "\n", + "# Sorting DF columns\n", + "order_cols = [\n", + " \"srch_id\",\n", + " \"prop_id\",\n", + " \"prop_starrating\",\n", + " \"prop_review_score\",\n", + " \"prop_brand_bool\",\n", + " \"prop_location_score1\",\n", + " \"prop_location_score2\",\n", + " \"prop_log_historical_price\",\n", + " \"position\",\n", + " \"promotion_flag\",\n", + " \"srch_length_of_stay\",\n", + " \"srch_adults_count\",\n", + " \"srch_children_count\",\n", + " \"srch_room_count\",\n", + " \"srch_saturday_night_bool\",\n", + " \"orig_destination_distance\",\n", + " \"random_bool\",\n", + " \"day_of_week\",\n", + " \"month\",\n", + " \"hour\",\n", + " \"log_price\",\n", + " \"booking_window\",\n", + " \"site_id\",\n", + " \"visitor_location_country_id\",\n", + " \"prop_country_id\",\n", + " \"srch_destination_id\",\n", + " \"click_bool\",\n", + " \"booking_bool\",\n", + "]\n", + "expedia_df = expedia_df[order_cols]\n", + "\n", + "# Creating dummy availabilities\n", + "# getting rid of search & prop_id and the clickbool and bookingbool\n", + "# adding no_purchase fixed effect\n", + "expedia_df[\"is_no_purchase\"] = 0\n", + "\n", + "# adding the no_purchase option to the data\n", + "df1 = (\n", + " expedia_df.groupby(\"srch_id\")\n", + " .filter(lambda x: x.booking_bool.sum() == 1)\n", + " .groupby(\"srch_id\")\n", + " .max()\n", + " .reset_index(drop=False)\n", + ")\n", + "df1.loc[:, \"is_no_purchase\"] = 1\n", + "df1.loc[:, \"log_price\"] = 0\n", + "df1.loc[:, \"booking_bool\"] = 0\n", + "\n", + "df2 = (\n", + " expedia_df.groupby(\"srch_id\")\n", + " .filter(lambda x: x.booking_bool.sum() == 0)\n", + " .groupby(\"srch_id\")\n", + " .max()\n", + " .reset_index(drop=False)\n", + ")\n", + "df2.loc[:, \"is_no_purchase\"] = 1\n", + "df2.loc[:, \"log_price\"] = 0\n", + "df2.loc[:, \"booking_bool\"] = 1\n", + "\n", + "# Concatenating the created DFs\n", + "expedia_df = pd.concat([expedia_df, df1, df2])\n", "\n", + "site_id_one_hot = pd.get_dummies(expedia_df.site_id, prefix=\"site_id\")\n", + "visitor_location_country_id_one_hot = pd.get_dummies(expedia_df.visitor_location_country_id, prefix=\"visitor_location_country_id\")\n", + "srch_destination_id_one_hot = pd.get_dummies(expedia_df.srch_destination_id, prefix=\"srch_destination_id\")\n", + "prop_country_id_one_hpt = pd.get_dummies(expedia_df.prop_country_id, prefix=\"prop_country_id\")\n", + "expedia_df = pd.concat([expedia_df, site_id_one_hot, visitor_location_country_id_one_hot, srch_destination_id_one_hot, prop_country_id_one_hpt], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "contexts_items_features_names = [\n", " \"prop_starrating\",\n", " \"prop_review_score\",\n", @@ -462,7 +582,7 @@ " \"visitor_location_country_id\",\n", " \"srch_destination_id\",\n", "]\n", - "for col in df.columns:\n", + "for col in expedia_df.columns:\n", " if col.startswith(\"prop_country_id\"):\n", " contexts_items_features_names += [col]\n", " if col.startswith(\"site_id\"):\n", @@ -474,9 +594,13 @@ "\n", "wide_items = []\n", "for i in range(39):\n", - " sub_df = df.groupby(\"srch_id\").apply(lambda x: x[contexts_items_features_names].iloc[i])\n", + " try:\n", + " sub_df = expedia_df.groupby(\"srch_id\").apply(lambda x: x[contexts_items_features_names].iloc[i])\n", + " except IndexError:\n", + " # Add dummy row\n", + " sub_df = pd.DataFrame({col: -1 for col in contexts_items_features_names}, index=[0])\n", " wide_items.append(sub_df)\n", - "contexts_features = df.groupby(\"srch_id\").apply(lambda x: x[contexts_features_names].iloc[0])\n", + "contexts_features = expedia_df.groupby(\"srch_id\").apply(lambda x: x[contexts_features_names].iloc[0])\n", "wide_df = pd.concat(wide_items+[contexts_features], axis=1)" ] }, @@ -489,7 +613,9 @@ "wide_df_memory_size = []\n", "for length in data_lengths:\n", " sub_wide_df = wide_df.iloc[:length].copy()\n", - " wide_df_memory_size.append(get_obj_size(sub_wide_df))" + " wide_df_memory_size.append(get_obj_size(sub_wide_df))\n", + "\n", + "print(wide_df_memory_size)" ] }, { @@ -517,7 +643,7 @@ "metadata": {}, "outputs": [], "source": [ - "# First create long_df with previous sectio\n", + "# First create long_df with previous section\n", "\n", "long_df = long_df.reset_index(drop=True)\n", "long_df.sort_values(\"srch_id\", inplace=True, ignore_index=True)" @@ -541,7 +667,7 @@ "metadata": {}, "outputs": [], "source": [ - "mem_sizes = []\n", + "tc_mem_sizes = []\n", "data_lengths = [100, 1000, 10000, 100000, 397618]\n", "for length in data_lengths:\n", " ids = long_df.srch_id.unique()[:length]\n", @@ -561,7 +687,8 @@ " 'srch_saturday_night_bool'],\n", " price_observable_columns=['log_price'],\n", " device=\"cpu\")\n", - " mem_sizes.append(get_obj_size(data_1))" + " tc_mem_sizes.append(get_obj_size(data_1))\n", + "print(tc_mem_sizes)" ] }, { @@ -572,19 +699,17 @@ "\n", "| Dataset size | 100 | 1.000 | 10.000 | 100.000 | 397.618 |\n", "|---|---|---|---|---|---|\n", - "| C-L w. FeaturesByIDs | 85.640 | 771.440 | 7.629.440 | 76.209.440 | 302.994.356 |\n", - "| C-L wo FeaturesByIDs | 220.400 | 2.198.600 | 21.980.600 | 219.800.600 | 873.964.964 |\n", + "| Choice-Learn | 369.157 | 3.574.957 | 35.632.957 | 356.212.957 | 1.416.328.273 |\n", "| Torch-Choice | 5.825.168 | 5.5093.676 | 550.480.667 | 5.515.236.600 | 10.448.857.759 |\n", "| Long format DF | 5.521.360 | 52.463.080 | 524.667.470 | 5.234.198.450 | 20.815.361.140 |\n", - "| Wide format DF | 3.503.109 | 31.784.709 | 314.600.709 | 3.142.760.709 | 12.495.108.741 |\n", + "| Wide format DF | 3.834.744 | 35.262.904 | 349.542.904 | 3.492.342.904 | 13.885.163.464 |\n", "\n", "\n", "data_lengths: [100, 1000, 10000, 100000, 397618]\\\n", - "Choice-Learn with FeaturesByIDs: [85640, 771440, 7629440, 76209440, 302994356]\\\n", - "Choice-Learn without FeaturesByIDs: [220400, 2198600, 21980600, 219800600, 873964964]\\\n", - "Torch-Choice: [5.825.168, 55.093.676, 550.480.667, 5.515.236.600, 10.448.857.871]\\\n", + "Choice-Learn [369157, 3574957, 35632957, 356212957, 1416328273]\\\n", + "Torch-Choice: [5825088, 55093564, 550480555, 5515236488, 10448857759]\\\n", "DF Long Format: [5521360, 52463080, 524667470, 5234198450, 20815361140]\\\n", - "DF Wide Format: [3503109, 31784709, 314600709, 3142760709, 12495108741]" + "DF Wide Format: [3834744, 35262904, 349542904, 3492342904, 13885163464]" ] }, { @@ -594,6 +719,65 @@ "## Plots and Illustrations" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "colors=[\"#e69f00\", \"#56b4e9\", \"#009e73\", \"#0072b2\", \"#d55e00\", \"#cc79a7\", \"#f0e442\"]\n", + "linestyle=[\"-\", \"--\", \"-.\", \":\", \"-\", \"--\", \"-.\"]\n", + "\n", + "fig = plt.figure(figsize=(12, 5))\n", + "fig.tight_layout()\n", + "fig.subplots_adjust(hspace=0.35)\n", + "\n", + "sizes = [10, 100, 1000, 10000, 100000, 10000000]\n", + "dense_1 = [928, 8128, 80128, 800128, 8000128, 800000128]\n", + "fbid_1 =[1036, 1756, 8956, 80956, 800956, 80000956]\n", + "dense_2 = [8128, 80128, 800128, 8000128, 80000128, 8000000128]\n", + "fbid_2 = [7892, 8612, 15812, 87812, 807812, 80007812]\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(sizes, dense_1, label='w/o FeaturesStorage - 10 locations', c=colors[0], ls=linestyle[0])\n", + "plt.plot(sizes, fbid_1, label='w/ FeaturesStorage - 10 locations', c=colors[1], ls=linestyle[1])\n", + "plt.scatter(sizes, dense_1, c=colors[0])\n", + "plt.scatter(sizes, fbid_1, c=colors[1])\n", + "\n", + "plt.plot(sizes, dense_2, label='w/o FeaturesStorage - 100 locations', c=colors[2], ls=linestyle[2])\n", + "plt.plot(sizes, fbid_2, label='w/ FeaturesStorage - 100 locations', c=colors[3], ls=linestyle[3])\n", + "plt.scatter(sizes, dense_2, c=colors[2])\n", + "plt.scatter(sizes, fbid_2, c=colors[3])\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.legend(prop={'size': 8})\n", + "\n", + "plt.title(\"(a) Memory usage efficiency of FeaturesStorage \\n for one hot encoded features\", y=-.3)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "sizes = [100, 1000, 10000, 100000, 397618]\n", + "expedia_choice_learn= [369157, 3574957, 35632957, 356212957, 1416328273]\n", + "expedia_torch_choice = [5825088, 55093564, 550480555, 5515236488, 10448857759]\n", + "expedia_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", + "expedia_wide = [3834744, 35262904, 349542904, 3492342904, 13885163464]\n", + "\n", + "plt.plot(sizes, expedia_choice_learn, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", + "plt.plot(sizes, expedia_torch_choice, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", + "plt.plot(sizes, expedia_long, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", + "plt.plot(sizes, expedia_wide, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", + "plt.scatter(sizes, expedia_choice_learn, c=colors[3])\n", + "plt.scatter(sizes, expedia_torch_choice, c=colors[1])\n", + "plt.scatter(sizes, expedia_long, c=colors[0])\n", + "plt.scatter(sizes, expedia_wide, c=colors[2])\n", + "plt.legend(prop={'size': 8})\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "plt.title(\"(b) Memory usage of the Expedia dataset \\n for different dataset sizes\", y=-.3)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -607,55 +791,54 @@ "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0.35)\n", "\n", - "ds_lengths = [10, 100, 1000, 10000, 100000, 10000000]\n", + "sizes = [10, 100, 1000, 10000, 100000, 10000000]\n", "dense_1 = [928, 8128, 80128, 800128, 8000128, 800000128]\n", "fbid_1 =[1036, 1756, 8956, 80956, 800956, 80000956]\n", "dense_2 = [8128, 80128, 800128, 8000128, 80000128, 8000000128]\n", "fbid_2 = [7892, 8612, 15812, 87812, 807812, 80007812]\n", "plt.subplot(2, 2, 1)\n", - "plt.plot(ds_lengths, dense_1, label='w/o FeaturesStorage - 10 locations', c=colors[0], ls=linestyle[0])\n", - "plt.plot(ds_lengths, fbid_1, label='w/ FeaturesStorage - 10 locations', c=colors[1], ls=linestyle[1])\n", - "plt.scatter(ds_lengths, dense_1, c=colors[0])\n", - "plt.scatter(ds_lengths, fbid_1, c=colors[1])\n", - "\n", - "plt.plot(ds_lengths, dense_2, label='w/o FeaturesStorage - 100 locations', c=colors[2], ls=linestyle[2])\n", - "plt.plot(ds_lengths, fbid_2, label='w/ FeaturesStorage - 100 locations', c=colors[3], ls=linestyle[3])\n", - "plt.scatter(ds_lengths, dense_2, c=colors[2])\n", - "plt.scatter(ds_lengths, fbid_2, c=colors[3])\n", + "plt.plot(sizes, dense_1, label='w/o FeaturesStorage - 10 locations', c=colors[0], ls=linestyle[0])\n", + "plt.plot(sizes, fbid_1, label='w/ FeaturesStorage - 10 locations', c=colors[1], ls=linestyle[1])\n", + "plt.scatter(sizes, dense_1, c=colors[0])\n", + "plt.scatter(sizes, fbid_1, c=colors[1])\n", + "\n", + "plt.plot(sizes, dense_2, label='w/o FeaturesStorage - 100 locations', c=colors[2], ls=linestyle[2])\n", + "plt.plot(sizes, fbid_2, label='w/ FeaturesStorage - 100 locations', c=colors[3], ls=linestyle[3])\n", + "plt.scatter(sizes, dense_2, c=colors[2])\n", + "plt.scatter(sizes, fbid_2, c=colors[3])\n", "plt.yscale(\"log\")\n", "plt.xscale(\"log\")\n", "plt.xlabel(\"Dataset Size\")\n", "plt.ylabel(\"Memory usage (bytes)\")\n", "plt.legend(prop={'size': 8})\n", "\n", - "plt.title(\"(a) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", + "plt.title(\"(a) Memory usage efficiency of FeaturesStorage \\n for one hot encoded features\", y=-.3)\n", "\n", "plt.subplot(2, 2, 2)\n", - "data_lengths = [100, 1000, 10000, 100000, 397618]\n", - "cd_with = [85640, 771440, 7629440, 76209440, 302994356]\n", - "cd_wo = [220400, 2198600, 21980600, 219800600, 873964964]\n", - "df_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", - "df_wide = [6252516976, 6266664976, 6408144976, 7822944976, 12501499936]\n", - "df_wide = [3503109, 31784709, 314600709, 3142760709, 12495108741]\n", - "torch_choice = [1249040, 12481040, 124801040, 1248001040, 4962273680]\n", - "\n", - "plt.plot(data_lengths, cd_with, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", - "plt.plot(data_lengths, torch_choice, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", - "plt.plot(data_lengths, df_long, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", - "plt.plot(data_lengths, df_wide, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", - "plt.scatter(data_lengths, cd_with, c=colors[3])\n", - "plt.scatter(data_lengths, torch_choice, c=colors[1])\n", - "plt.scatter(data_lengths, df_long, c=colors[0])\n", - "plt.scatter(data_lengths, df_wide, c=colors[2])\n", + "sizes = [100, 1000, 10000, 100000, 397618]\n", + "expedia_choice_learn= [369157, 3574957, 35632957, 356212957, 1416328273]\n", + "expedia_torch_choice = [5825088, 55093564, 550480555, 5515236488, 10448857759]\n", + "expedia_long = [5521360, 52463080, 524667470, 5234198450, 20815361140]\n", + "expedia_wide = [3834744, 35262904, 349542904, 3492342904, 13885163464]\n", + "\n", + "plt.plot(sizes, expedia_choice_learn, label=\"Choice-Learn\", c=colors[3], ls=linestyle[3])\n", + "plt.plot(sizes, expedia_torch_choice, label=\"Torch-Choice\", c=colors[1], ls=linestyle[1])\n", + "plt.plot(sizes, expedia_long, label=\"PyLogit (long format)\", c=colors[0], ls=linestyle[0])\n", + "plt.plot(sizes, expedia_wide, label=\"Biogeme (wide format)\", c=colors[2], ls=linestyle[2])\n", + "plt.scatter(sizes, expedia_choice_learn, c=colors[3])\n", + "plt.scatter(sizes, expedia_torch_choice, c=colors[1])\n", + "plt.scatter(sizes, expedia_long, c=colors[0])\n", + "plt.scatter(sizes, expedia_wide, c=colors[2])\n", "plt.legend(prop={'size': 8})\n", "plt.yscale(\"log\")\n", "plt.xscale(\"log\")\n", "plt.xlabel(\"Dataset Size\")\n", "plt.ylabel(\"Memory usage (bytes)\")\n", - "plt.title(\"(b) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", + "plt.title(\"(b) Memory usage of the Expedia dataset \\n for different dataset sizes\", y=-.3)\n", "\n", "plt.subplot(2, 2, 3)\n", "\n", + "### Results with our proprietary dataset\n", "sizes = [100, 1000, 10000.0, 100000.0, 1000000.0, 4789225]\n", "tc = [3933312, 4854912, 14070912, 106230912, 1027830912, 4907997312]\n", "# Wide \n", @@ -681,6 +864,7 @@ "plt.title(\"(c) Memory usage of our retail dataset \\n for different dataset sizes\", y=-.3)\n", "\n", "plt.subplot(2, 2, 4)\n", + "### Results with our proprietary dataset\n", "n_stores = [0, 10, 100, 250, 692]\n", "cl_mem_usage = [352083911, 352084711, 352163911, 352583911, 355914823]\n", "other_mem = [1027830912, 1027831712, 1027910912, 1028330912, 1031661824]\n", diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 7f26af83..4e990cd5 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -37,9 +37,9 @@ output: paper_pdf # Introduction -Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, which is known as an assortment. Well-known use cases include predicting a commuter's choice of transportation mode or a customer's purchases. A key capability of choice models is their ability to handle assortment variations, such as predicting choices when some alternatives become unavailable or when their features change in different operational contexts. This adaptability to different scenarios allows these models to be used as inputs for optimization problems, including assortment planning or pricing. +Discrete choice models aim at predicting choice decisions made by individuals from a menu of alternatives, called an assortment. Well-known use cases include predicting a commuter's choice of transportation mode or a customer's purchases. Choice models are able to handle assortment variations, when some alternatives become unavailable or when their features change in different contexts. This adaptability to different scenarios allows these models to be used as inputs for optimization problems, including assortment planning or pricing. -Choice-Learn provides a modular suite of choice modeling tools for practitioners and academic researchers to process choice data, and then formulate, estimate and operationalize choice models. The library is structured into two levels of usage, as illustrated in Figure \ref{fig:gen_org}. The higher-level is designed for fast and easy implementation and the lower-level enables more advanced parameterizations. This structure is inspired by Keras' different endpoints [@Chollet:2015], which enables a user-friendly modeling interface. Choice-Learn is designed with the following objectives: +Choice-Learn provides a modular suite of choice modeling tools for practitioners and academic researchers to process choice data, and then formulate, estimate and operationalize choice models. The library is structured into two levels of usage, as illustrated in Figure \ref{fig:gen_org}. The higher-level is designed for fast and easy implementation and the lower-level enables more advanced parameterizations. This structure, inspired by Keras' different endpoints [@Chollet:2015], enables a user-friendly interface. Choice-Learn is designed with the following objectives: - **Streamlined:** The processing of datasets and the estimation of standard choice models are facilitated by a simple code signature. - **Scalable:** Optimized processes are implemented for data storage and models estimation, allowing the use of large datasets and models with a large number of parameters. @@ -47,7 +47,7 @@ Choice-Learn provides a modular suite of choice modeling tools for practitioners - **Models Library:** The same package provides implementations of both standard choice models and machine learning-based methods, including neural networks. - **Downstream operations:** Post-processing tools that leverage choice models for assortment planning and pricing are integrated into the library. -A summary of the main contributions is provided in Table \ref{tab:comparison1} and \ref{tab:comparison2}. +The main contributions are summarized in Tables \ref{tab:comparison1} and \ref{tab:comparison2}. ![General Organization of the package. \label{fig:gen_org}](../illustrations/choice_learn_levels.png) @@ -95,19 +95,15 @@ A summary of the main contributions is provided in Table \ref{tab:comparison1} a # Statement of need ## Streamlined signatures -`Choice-Learn` proposes short code signatures. The *ChoiceDataset* object, which handles the data, takes only 4 inputs: 'items_features' describing each available alternative, 'shared_features' describing the context of the choice, 'available_items' indicating the subset of alternatives offered in the assortment, and finally 'choices', the chosen alternative. Methods to seamlessly integrate popular data formats, such as long and wide format dataframes [@Helveston:2023] are also provided. +`Choice-Learn` proposes short code signatures that are consistent with mainstream machine learning packages such as scikit-learn [@Pedregosa:2011]. The *ChoiceDataset* object, which handles the data, takes only 4 inputs: 'items_features' describing each available alternative, 'shared_features' describing the context of the choice, 'available_items' indicating the subset of available alternatives, and finally 'choices', the chosen alternative. Methods to seamlessly integrate popular data formats, such as long and wide format dataframes [@Helveston:2023] are also provided. ```python dataset = ChoiceDataset(choices, shared_features, items_features, available_items) ``` -Finally, data usage, model estimation and evaluation are designed to be consistent with mainstream machine learning packages such as scikit-learn [@Pedregosa:2011]. - ## Data and model scalability -Choice modeling is widely used in retail and e-commerce sectors to better understand customer behavior and optimize product offerings. With the continuous development of firms' data architectures, larger-scale choice datasets are often available and valuable for managing customer-facing operations. - -`Choice-Learn`'s data management relies on NumPy [@Harris:2020] with the objective of limiting the memory footprint. It minimizes the repetition of items or customers features and defers the jointure of the full data structure until processing batches of data. Moreover, the *FeaturesStorage* object, illustrated in Figure \ref{fig:fbi}, allows feature values to be referenced only by their ID. These feature values are substituted to the ID placeholder on the fly in the batching process. For instance, supermarkets features such as surface, position, or number of employees are often stationary. Thus, they can be stored in an auxiliary data structure and in the main dataset, the store where the choice is recorded is only referenced with its ID. +`Choice-Learn`'s data management relies on NumPy [@Harris:2020] with the objective of limiting the memory footprint. It minimizes the repetition of items or customers features and defers the jointure of the full data structure until processing batches of data. Moreover, the *FeaturesStorage* object, illustrated in Figure \ref{fig:fbi}, allows feature values to be referenced only by their ID. These values are substituted to the ID placeholder on the fly in the batching process. For instance, supermarkets features such as surface or position are often stationary. Thus, they can be stored in an auxiliary data structure and in the main dataset, the store where the choice is recorded is only referenced with its ID. The package stands on Tensorflow [@Abadi:2015] for model estimation, offering the possibility to use fast second-order optimization algorithm such as L-BFGS [@Nocedal:2006] as well as various gradient-descent optimizers [@Tieleman:2012; @Kingma:2017] specialized in handling batches of data. GPU usage is also possible, which can prove to be time-saving. Finally, the TensorFlow backbone ensures an efficient usage in a production environment, for instance within an assortment recommendation software, through deployment and serving tools, such as TFLite and TFServing. @@ -124,22 +120,22 @@ The choice-modeler's job is to formulate an adequate utility function $U(.)$ dep ## Library of traditional random utility models and machine learning-based models -Traditional parametric choice models, including the Conditional Logit [@Train:1987], often specify the utility function in a linear form. This provides interpretable coefficients, such as price elasticities, but also limits the predictive power of the model. -The availability of detailed customer choice data, paired with advances in machine learning, enables the estimation of more complex models, with neural networks approaches [@Han:2022; @Aouad:2023] and tree-based models [@Salvadé:2024; @AouadMarket:2023]. While existing choice libraries [@Bierlaire:2023; @Brathwaite:2018; @Du:2023] are often not designed to integrate such machine learning-based approaches, `Choice-Learn` proposes a collection including both types of models. +Traditional parametric choice models, including the Conditional Logit [@Train:1987], often specify the utility function in a linear form. This provides interpretable coefficients but limits the predictive power of the model. +Recent works propose the estimation of more complex models, with neural networks approaches [@Han:2022; @Aouad:2023] and tree-based models [@Salvadé:2024; @AouadMarket:2023]. While existing choice libraries [@Bierlaire:2023; @Brathwaite:2018; @Du:2023] are often not designed to integrate such machine learning-based approaches, `Choice-Learn` proposes a collection including both types of models. ## Downstream operations: Assortment and pricing optimization -`Choice-Learn` also offers additional tools for downstream operations, that are not usually integrated in choice modeling libraries. In particular, assortment optimization is a common use case that leverages a choice model in order to determine the optimal subset of alternatives to offer customers in order to maximize a certain objective, such as the expected revenue, conversion rate, or social welfare. This framework captures a variety of applications such as assortment planning, display location optimization, and pricing. We provide a generic implementation based on the mixed-integer programming formulation described in [@MendezDiaz:2014], with the option to choose the solver between Gurobi [@Gurobi:2023] and OR-Tools [@ORTools:2024]. +`Choice-Learn` offers additional tools for downstream operations, that are not usually integrated in choice modeling libraries. In particular, assortment optimization is a common use case that leverages a choice model to determine the optimal subset of alternatives to offer customers maximizing a certain objective, such as the expected revenue, conversion rate, or social welfare. This framework captures a variety of applications such as assortment planning, display location optimization, and pricing. We provide implementations based on the mixed-integer programming formulation described in [@MendezDiaz:2014], with the option to choose the solver between Gurobi [@Gurobi:2023] and OR-Tools [@ORTools:2024]. # Experiments and examples ## Memory usage: a case study -We provide numerical examples of memory usage to showcase the efficiency of the *FeaturesStorage*. Consider a feature repeated in a dataset, such as a one-hot encoding for locations, represented by a matrix of shape (*#locations, #locations*) where each row refers to one location. In Figure \ref{fig:xps} (a), we compare the memory usage for different dataset sizes. +We provide in Figure \ref{fig:xps} (a) numerical examples of memory usage to showcase the efficiency of the *FeaturesStorage*. We consider a feature repeated in a dataset, such as a one-hot encoding for locations, represented by a matrix of shape (*#locations, #locations*) where each row refers to one location. -We conduct a similar comparison on the ICDM 2013 Expedia dataset [@Expedia:2013] with four data handling methods: pandas.DataFrames [@pandas:2020] in long and wide format, both used in choice modeling packages, as well as Torch-Choice and `Choice-Learn`. Figure \ref{fig:xps} (b) shows the results for various sample sizes. +We compare four data handling methods on the Expedia dataset [@Expedia:2013]: pandas.DataFrames [@pandas:2020] in long and wide format, both used in choice modeling packages, Torch-Choice and `Choice-Learn`. Figure \ref{fig:xps} (b) shows the results for various sample sizes. -Finally, in Figure \ref{fig:xps} (c) and (d), we observe performance gains in terms of memory management on a proprietary dataset in brick-and-mortar retailing consisting of the aggregation of more than 4 billion purchases in Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which products were available, their prices, as well as a one-hot representation of the supermarket. +Finally, in Figure \ref{fig:xps} (c) and (d), we observe memory usage gains on a proprietary dataset in brick-and-mortar retailing consisting of the aggregation of more than 4 billion purchases in Konzum supermarkets in Croatia. Focusing on the *coffee* subcategory, the dataset specifies, for each purchase, which products were available, their prices, as well as a one-hot representation of the supermarket. ![Memory usage experiments. \label{fig:xps}](../illustrations/ram_usage_comparison.png) From 9894ea13399d2dd37af9ae756e20d4f3bb510b16 Mon Sep 17 00:00:00 2001 From: VincentAuriau Date: Mon, 15 Jul 2024 13:05:51 +0200 Subject: [PATCH 09/13] removed back quotes from title --- docs/paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 4e990cd5..ffdd538d 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -1,5 +1,5 @@ --- -title: '`Choice-Learn`: Large-scale choice modeling for operational contexts through the lens of machine learning' +title: 'Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning' tags: - Python From ca4371030fe45ac25f00b76b791b8ec3412d5875 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Sun, 11 Aug 2024 19:33:45 +0200 Subject: [PATCH 10/13] Enhance Paper (#147) * ENH: modifs from review * FIX: url * FIX: citation * FIX: citation * ENH: customized model * ENH: add . --- docs/paper/paper.bib | 23 +++++++------------ docs/paper/paper.md | 53 +++++--------------------------------------- 2 files changed, 14 insertions(+), 62 deletions(-) diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib index 9ad13efa..ddabdeda 100644 --- a/docs/paper/paper.bib +++ b/docs/paper/paper.bib @@ -132,11 +132,11 @@ @article{Tieleman:2012 } @article{Expedia:2013, - title={Personalize Expedia Hotel Searches}, - author={Ben Hamner, Adam and Friedman, Dan}, - journal={ICDM}, - year={2013}, - howpublished={\url{https://www.kaggle.com/c/expedia-personalized-sort} (last checked on 2024-06-07)}, + title={Personalize Expedia Hotel Searches - ICDM 2013}, + author={Ben Hamner, Adam and Friedman, Dan and SSA_Expedia}, + journal={Kaggle}, + year={2013}, + url={https://www.kaggle.com/c/expedia-personalized-sort}, } @article{AouadMarket:2023, @@ -192,18 +192,11 @@ @article{Pedregosa:2011 year={2011} } -@misc{Helveston:2023, -title={Convert data from wide to long format}, -howpublished={\url{https://xlogit.readthedocs.io/en/latest/notebooks/convert_data_wide_to_long.html} (last checked on 2024-06-07)}, -author={Forsythe, C. and Helveston, J}, - year={2023}, - urldate = {2024-06-07}, -} @misc{Chollet:2015, title={Keras}, author={{Chollet et al.}}, year={2015}, - howpublished={\url{https://keras.io} (last checked on 2024-06-07)}, + howpublished={\url{https://keras.io}}, } @article{McFadden:2000, @@ -222,7 +215,7 @@ @misc{Gurobi:2023 author = {{Gurobi Optimization, LLC}}, title = {{Gurobi Optimizer Reference Manual}}, year = 2023, - howpublished = {\url{https://www.gurobi.com} (last checked on 2024-06-07)} + howpublished = {\url{https://www.gurobi.com}} } @software{ORTools:2024, @@ -231,5 +224,5 @@ @software{ORTools:2024 author = {Laurent Perron and Vincent Furnon}, organization = {Google}, date = { 2024-03-07 }, - howpublished = {\url{https://developers.google.com/optimization/} (last checked on 2024-06-07)} + howpublished = {\url{https://developers.google.com/optimization/}} } diff --git a/docs/paper/paper.md b/docs/paper/paper.md index ffdd538d..08f9884c 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -41,7 +41,7 @@ Discrete choice models aim at predicting choice decisions made by individuals fr Choice-Learn provides a modular suite of choice modeling tools for practitioners and academic researchers to process choice data, and then formulate, estimate and operationalize choice models. The library is structured into two levels of usage, as illustrated in Figure \ref{fig:gen_org}. The higher-level is designed for fast and easy implementation and the lower-level enables more advanced parameterizations. This structure, inspired by Keras' different endpoints [@Chollet:2015], enables a user-friendly interface. Choice-Learn is designed with the following objectives: -- **Streamlined:** The processing of datasets and the estimation of standard choice models are facilitated by a simple code signature. +- **Streamlined:** The processing of datasets and the estimation of standard choice models are facilitated by a simple code signature that are consistent with mainstream machine learning packages such as scikit-learn [@Pedregosa:2011]. - **Scalable:** Optimized processes are implemented for data storage and models estimation, allowing the use of large datasets and models with a large number of parameters. - **Flexible:** The codebase can be customized to fit different use cases. - **Models Library:** The same package provides implementations of both standard choice models and machine learning-based methods, including neural networks. @@ -94,18 +94,11 @@ The main contributions are summarized in Tables \ref{tab:comparison1} and \ref{t # Statement of need -## Streamlined signatures -`Choice-Learn` proposes short code signatures that are consistent with mainstream machine learning packages such as scikit-learn [@Pedregosa:2011]. The *ChoiceDataset* object, which handles the data, takes only 4 inputs: 'items_features' describing each available alternative, 'shared_features' describing the context of the choice, 'available_items' indicating the subset of available alternatives, and finally 'choices', the chosen alternative. Methods to seamlessly integrate popular data formats, such as long and wide format dataframes [@Helveston:2023] are also provided. - -```python -dataset = ChoiceDataset(choices, shared_features, items_features, available_items) -``` - ## Data and model scalability -`Choice-Learn`'s data management relies on NumPy [@Harris:2020] with the objective of limiting the memory footprint. It minimizes the repetition of items or customers features and defers the jointure of the full data structure until processing batches of data. Moreover, the *FeaturesStorage* object, illustrated in Figure \ref{fig:fbi}, allows feature values to be referenced only by their ID. These values are substituted to the ID placeholder on the fly in the batching process. For instance, supermarkets features such as surface or position are often stationary. Thus, they can be stored in an auxiliary data structure and in the main dataset, the store where the choice is recorded is only referenced with its ID. +`Choice-Learn`'s data management relies on NumPy [@Harris:2020] with the objective of limiting the memory footprint. It minimizes the repetition of items or customers features and defers the jointure of the full data structure until processing batches of data. The package introduces the *FeaturesStorage* object, illustrated in Figure \ref{fig:fbi}, that allows feature values to be referenced only by their ID. These values are substituted to the ID placeholder on the fly in the batching process. For instance, supermarkets features such as surface or position are often stationary. Thus, they can be stored in an auxiliary data structure and in the main dataset, the store where the choice is recorded is only referenced with its ID. -The package stands on Tensorflow [@Abadi:2015] for model estimation, offering the possibility to use fast second-order optimization algorithm such as L-BFGS [@Nocedal:2006] as well as various gradient-descent optimizers [@Tieleman:2012; @Kingma:2017] specialized in handling batches of data. GPU usage is also possible, which can prove to be time-saving. +The package stands on Tensorflow [@Abadi:2015] for model estimation, offering the possibility to use fast quasi-Newton optimization algorithm such as L-BFGS [@Nocedal:2006] as well as various gradient-descent optimizers [@Tieleman:2012; @Kingma:2017] specialized in handling batches of data. GPU usage is also possible, which can prove to be time-saving. Finally, the TensorFlow backbone ensures an efficient usage in a production environment, for instance within an assortment recommendation software, through deployment and serving tools, such as TFLite and TFServing. ![Functioning of the *FeaturesStorage*. \label{fig:fbi}](../illustrations/features_storage_3.png) @@ -116,7 +109,8 @@ Choice models following the *Random Utility Maximization* principle [@McFadden:2 $$\mathbb{P}(i|\mathcal{A}) = \frac{e^{U(i)}}{\sum_{j \in \mathcal{A}} e^{U(j)}}$$ -The choice-modeler's job is to formulate an adequate utility function $U(.)$ depending on the context. In `Choice-Learn`, the user can parametrize predefined models or freely specify a custom utility function by overriding the *compute_batch_utility* method from the *ChoiceModel* class. +The choice-modeler's job is to formulate an adequate utility function $U(.)$ depending on the context. In `Choice-Learn`, the user can parametrize predefined models or freely specify a custom utility function. +To declare a custom model, one needs to inherit the *ChoiceModel* class and overwrite the `compute_batch_utility` method as shown in the [documentation](https://artefactory.github.io/choice-learn/). ## Library of traditional random utility models and machine learning-based models @@ -127,9 +121,7 @@ Recent works propose the estimation of more complex models, with neural networks `Choice-Learn` offers additional tools for downstream operations, that are not usually integrated in choice modeling libraries. In particular, assortment optimization is a common use case that leverages a choice model to determine the optimal subset of alternatives to offer customers maximizing a certain objective, such as the expected revenue, conversion rate, or social welfare. This framework captures a variety of applications such as assortment planning, display location optimization, and pricing. We provide implementations based on the mixed-integer programming formulation described in [@MendezDiaz:2014], with the option to choose the solver between Gurobi [@Gurobi:2023] and OR-Tools [@ORTools:2024]. -# Experiments and examples - -## Memory usage: a case study +# Memory usage: a case study We provide in Figure \ref{fig:xps} (a) numerical examples of memory usage to showcase the efficiency of the *FeaturesStorage*. We consider a feature repeated in a dataset, such as a one-hot encoding for locations, represented by a matrix of shape (*#locations, #locations*) where each row refers to one location. @@ -139,39 +131,6 @@ Finally, in Figure \ref{fig:xps} (c) and (d), we observe memory usage gains on a ![Memory usage experiments. \label{fig:xps}](../illustrations/ram_usage_comparison.png) -## Customized choice models -We provide an example of the custom model definition with the following formulation of utility $U(i)$ with alternative features $x_i$ and customer features $z$: -$$U(x_i, z) = \beta_l \cdot \sigma(\sigma(\Gamma_x \cdot x_i) + \sigma(\Gamma_z \cdot z)) + \epsilon_i,$$ -where $\Gamma_x$, $\Gamma_z$ are matrices and $\beta_l$ is a vector, all three to be estimated and $\sigma$ being the sigmoid activation function. -To declare a custom model, one needs to inherit the *ChoiceModel* class and overwrite the `compute_batch_utility` method. - - -```python - def __init__(self, n_neurons, **kwargs): - super().__init__(**kwargs) - - self.gamma_x = Dense(units=n_neurons, activation="sigmoid") - self.gamma_z = Dense(units=n_neurons, activation="sigmoid") - self.beta_l = Dense(units=1, activation="linear") - - def compute_batch_utility(self, - shared_features_by_choice, - items_features_by_choice, - **kwargs): - - z_embedding = self.gamma_z(shared_features_by_choice) - - item_utility_by_choice = [] - for i in range(n_items): - embedding = sigmoid(shared_embeddings +\ - self.gamma_w(items_features_by_choice[:, i])) - item_utility_by_choice.append(self.beta_l(item_embedding)) - - item_utility_by_choice = tf.concat(item_utility_by_choice, axis=1) - - return item_utility_by_choice -``` - # Acknowledgments The authors thank Fortenova[^1] and Martin Možina for their helpful collaboration and providing of the proprietary dataset. From 74f39bc03071920dac05b3ec8197b7e4bae2af16 Mon Sep 17 00:00:00 2001 From: Vincent Auriau Date: Fri, 6 Sep 2024 16:05:38 +0200 Subject: [PATCH 11/13] FIX: wording (#154) --- docs/paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 08f9884c..7295db2d 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -132,7 +132,7 @@ Finally, in Figure \ref{fig:xps} (c) and (d), we observe memory usage gains on a ![Memory usage experiments. \label{fig:xps}](../illustrations/ram_usage_comparison.png) # Acknowledgments -The authors thank Fortenova[^1] and Martin Možina for their helpful collaboration and providing of the proprietary dataset. +The authors thank Fortenova[^1] and Martin Možina for their helpful collaboration and provision of the proprietary dataset. # References From 2706fbf112a8822fef7cbfa7eee0aff1a451258d Mon Sep 17 00:00:00 2001 From: VincentAuriau Date: Fri, 6 Sep 2024 16:19:54 +0200 Subject: [PATCH 12/13] ENH: some cleaning --- .github/workflows/draft_paper.yml | 8 -------- docs/illustrations/expedia_RAM.png | Bin 43525 -> 0 bytes docs/illustrations/fbid_RAM.png | Bin 52402 -> 0 bytes docs/illustrations/memory_usage_comparison.png | Bin 51490 -> 0 bytes 4 files changed, 8 deletions(-) delete mode 100644 docs/illustrations/expedia_RAM.png delete mode 100644 docs/illustrations/fbid_RAM.png delete mode 100644 docs/illustrations/memory_usage_comparison.png diff --git a/.github/workflows/draft_paper.yml b/.github/workflows/draft_paper.yml index 46385e0d..7aa3d09d 100644 --- a/.github/workflows/draft_paper.yml +++ b/.github/workflows/draft_paper.yml @@ -13,14 +13,6 @@ jobs: - name: Checkout uses: actions/checkout@v4 - - name: test - run: | - echo "Hello, World!" - ls - ls choice_learn/ - ls .github/ - ls ./.github/actions/ - - name: Build draft PDF uses: ./.github/actions/build-draft with: diff --git a/docs/illustrations/expedia_RAM.png b/docs/illustrations/expedia_RAM.png 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