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..7aa3d09d --- /dev/null +++ b/.github/workflows/draft_paper.yml @@ -0,0 +1,29 @@ +name: Paper draft to PDF + +on: + push: + branches: + - paper + +jobs: + paper: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v4 + + - 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 d5e01af6..e081d16e 100644 --- a/choice_learn/datasets/expedia.py +++ b/choice_learn/datasets/expedia.py @@ -46,6 +46,7 @@ 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/features_storage.png b/docs/illustrations/features_storage.png new file mode 100644 index 00000000..8183e016 Binary files /dev/null and b/docs/illustrations/features_storage.png differ diff --git a/docs/illustrations/features_storage_2.png b/docs/illustrations/features_storage_2.png new file mode 100644 index 00000000..38ba91ad Binary files /dev/null and b/docs/illustrations/features_storage_2.png differ diff --git a/docs/illustrations/features_storage_3.png b/docs/illustrations/features_storage_3.png new file mode 100644 index 00000000..7409f4fa Binary files /dev/null and b/docs/illustrations/features_storage_3.png differ diff --git a/docs/illustrations/full_ram.png b/docs/illustrations/full_ram.png new file mode 100644 index 00000000..b7b3cc43 Binary files /dev/null and b/docs/illustrations/full_ram.png differ diff --git a/docs/illustrations/items_vs_shared_features.png b/docs/illustrations/items_vs_shared_features.png new file mode 100644 index 00000000..659c0c6d Binary files /dev/null and b/docs/illustrations/items_vs_shared_features.png differ diff --git a/docs/illustrations/ram_usage_comparison.png b/docs/illustrations/ram_usage_comparison.png new file mode 100644 index 00000000..6a85f05f Binary files /dev/null and b/docs/illustrations/ram_usage_comparison.png differ diff --git a/docs/illustrations/table.png b/docs/illustrations/table.png new file mode 100644 index 00000000..8a882d20 Binary files /dev/null and b/docs/illustrations/table.png differ diff --git a/docs/paper/memory_usage.ipynb b/docs/paper/memory_usage.ipynb new file mode 100644 index 00000000..eed5a041 --- /dev/null +++ b/docs/paper/memory_usage.ipynb @@ -0,0 +1,978 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RAM usage with Choice-Learn\n", + "\n", + "## On the ICDM 2013 Expedia Dataset\n", + "\n", + "- [Choice-Learn's FeaturesStorage](#features-storage-efficiency-with-dummy-data)\n", + "- [pandas.DataFrame on Long format](#pandasdataframe-long-format-(pylogit))\n", + "- [pandas.DataFrame on Wide format](#pandasdataframe-wide-format-(biogeme))\n", + "- [Torch-Choice](#torch-choice)\n", + "- [Plots and Illustrations](#plots-and-illustrations)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"../../\")\n", + "import logging\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from choice_learn.datasets import load_expedia\n", + "from choice_learn.data import ChoiceDataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accessing the RAM usag is actually not that obvious. Here is a code snippet finding all references link to an object in order to addition their memory consumption.\n", + "This is explained [here](https://stackoverflow.com/questions/13530762/how-to-know-bytes-size-of-python-object-like-arrays-and-dictionaries-the-simp)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gc\n", + "import sys\n", + "\n", + "def get_obj_size(obj):\n", + " marked = {id(obj)}\n", + " obj_q = [obj]\n", + " sz = 0\n", + "\n", + " while obj_q:\n", + " sz += sum(map(sys.getsizeof, obj_q))\n", + "\n", + " # Lookup all the object referred to by the object in obj_q.\n", + " all_refr = ((id(o), o) for o in gc.get_referents(*obj_q))\n", + "\n", + " # Filter object that are already marked.\n", + " new_refr = {o_id: o for o_id, o in all_refr if o_id not in marked and not isinstance(o, type)}\n", + "\n", + " obj_q = new_refr.values()\n", + " marked.update(new_refr.keys())\n", + "\n", + " return sz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Features Storage efficiency with dummy data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from choice_learn.data import OneHotStorage\n", + "\n", + "### Small Example\n", + "\n", + "n_locations = 10\n", + "n_data = 100\n", + "\n", + "indexes = np.random.randint(n_locations, size=(n_data, ))\n", + "\n", + "dense_features = np.zeros((n_data, n_locations))\n", + "dense_features[np.arange(n_data), indexes] = 1\n", + "\n", + "storage = OneHotStorage(ids=list(range(n_locations)))\n", + "\n", + "assert (storage.batch[indexes] == dense_features).all()\n", + "\n", + "### Dense features memory usage:\n", + "print(\"Dense memory usage:\", get_obj_size(dense_features))\n", + "\n", + "### FeaturesByIDs memory usage:\n", + "# Storage memory usage + ids memory stirage\n", + "print(\"FeaturesByIDs memory usage:\", get_obj_size(storage)+get_obj_size(indexes))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from choice_learn.data import OneHotStorage\n", + "\n", + "\n", + "dense_sizes = []\n", + "fbid_sizes = []\n", + "ds_lengths = [10, 100, 1000, 10000, 100000, 10000000]\n", + "\n", + "\n", + "n_locations = 10\n", + "for n_data in ds_lengths:\n", + "\n", + " # random draw of store apparition\n", + " indexes = np.random.randint(n_locations, size=(n_data, ))\n", + "\n", + " # Creation of the \"dense\" dataset\n", + " dense_dataset = np.zeros((n_data, n_locations))\n", + " dense_dataset[np.arange(n_data), indexes] = 1\n", + "\n", + " # Creation of the Features storage\n", + " storage = OneHotStorage(ids=list(range(n_locations)))\n", + " \n", + " # Memory print of the features storage and the indexes\n", + " fbid_sizes.append(get_obj_size(indexes) + get_obj_size(storage))\n", + " # Memory print of the dense dataset\n", + " dense_sizes.append(get_obj_size(dense_dataset))\n", + "\n", + "plt.plot(ds_lengths, dense_sizes, label='w/o FeaturesById - n_locations=10', c=\"darkblue\")\n", + "plt.plot(ds_lengths, fbid_sizes, label='w/ FeaturesById - n_locations=10', c=\"turquoise\")\n", + "plt.scatter(ds_lengths, dense_sizes, c=\"darkblue\")\n", + "plt.scatter(ds_lengths, fbid_sizes, c=\"turquoise\")\n", + "plt.yscale(\"log\")\n", + "plt.xscale(\"log\")\n", + "plt.xlabel(\"Dataset Size\")\n", + "plt.ylabel(\"Memory usage (bytes)\")\n", + "\n", + "n_locations = 100\n", + "\n", + "\n", + "dense_sizes = []\n", + "fbid_sizes = []\n", + "for n_data in ds_lengths:\n", + "\n", + "\n", + " # random draw of store apparition\n", + " indexes = np.random.randint(n_locations, size=(n_data, ))\n", + "\n", + " # Creation of the \"dense\" dataset\n", + " dense_dataset = np.zeros((n_data, n_locations))\n", + " dense_dataset[np.arange(n_data), indexes] = 1\n", + "\n", + " # Creation of the Features storage\n", + " storage = OneHotStorage(ids=list(range(n_locations)))\n", + "\n", + " # Memory print of the features storage and the indexes\n", + " fbid_sizes.append(get_obj_size(indexes) + get_obj_size(storage))\n", + "\n", + " # Memory print of the dense dataset\n", + " dense_sizes.append(get_obj_size(dense_dataset))\n", + " \n", + "plt.plot(ds_lengths, dense_sizes, label='w/o FeaturesById - n_locations=100', c=\"cornflowerblue\")\n", + "plt.plot(ds_lengths, fbid_sizes, label='w/ FeaturesById - n_locations=100', c=\"teal\")\n", + "plt.scatter(ds_lengths, dense_sizes, c=\"cornflowerblue\")\n", + "plt.scatter(ds_lengths, fbid_sizes, 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": "markdown", + "metadata": {}, + "source": [ + "## Expedia Dataset\n", + "\n", + "You might want to run the different methods individually.\\\n", + "The results are stored later in the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### With Choice-Learn and FeaturesByIDs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Takes some time\n", + "dataset = load_expedia(as_frame=False, preprocessing=\"rumnet\")\n", + "\n", + "# Defining tested data lengths\n", + "data_lengths = [100, 1000, 10000, 100000, 397618]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clearn_memory_size = []\n", + "for length in data_lengths:\n", + " sub_dataset = dataset[:length]\n", + " clearn_memory_size.append(get_obj_size(sub_dataset))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### pandas.DataFrame Long format (PyLogit)\n", + "\n", + "The raw dataframe needs formatting to have the right format. It can take some time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Load DF\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", + "# 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", + "# Sorting\n", + "expedia_df = expedia_df.sort_values(\"srch_id\")\n", + "choices = [\"booking_bool\"]\n", + "\n", + "# Final selection of the columns to match the ChoiceDataset\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": [ + "# 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))\n", + "\n", + "print(long_df_memory_size)" + ] + }, + { + "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": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transformation of the pandas.DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\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", + " \"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 expedia_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", + " 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 = 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)" + ] + }, + { + "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))\n", + "\n", + "print(wide_df_memory_size)" + ] + }, + { + "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 section\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": [ + "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", + " 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", + " tc_mem_sizes.append(get_obj_size(data_1))\n", + "print(tc_mem_sizes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Results\n", + "\n", + "| Dataset size | 100 | 1.000 | 10.000 | 100.000 | 397.618 |\n", + "|---|---|---|---|---|---|\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 | 3.834.744 | 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 [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: [3834744, 35262904, 349542904, 3492342904, 13885163464]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 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, + "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", + "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(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(2, 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)\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", + "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", + "### 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", + "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": {}, + "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..ddabdeda --- /dev/null +++ b/docs/paper/paper.bib @@ -0,0 +1,228 @@ +@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.}, + doi = {10.1016/j.jocm.2018.01.002}, + 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}}, + 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 = {10.48550/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}, +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.} +} + +@article{Salvadé:2024, + title={RUMBoost: Gradient Boosted Random Utility Models}, + author={Salvad{\'e}, Nicolas and Hillel, Tim}, + journal={arXiv preprint arXiv:2401.11954}, + doi={10.48550/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}, + doi = {10.2307/2555538}, +} + +@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}, + 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}, +license = {Apache-2.0}, +month = nov, +title = {{TensorFlow, Large-scale machine learning on heterogeneous systems}}, +year = {2015}, +doi = {10.5281/zenodo.4724125} +} + +@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", +doi = {10.1007/0-306-48332-7_250} +} + +@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}}, + doi = {10.48550/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 - 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, + 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}, + doi={10.1287/msom.2023.1195}, +} + +@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}, +doi = {10.1016/j.dam.2012.03.003}, +} + +@software{pandas:2020, + author = {{The pandas development team}}, + title = {pandas-dev/pandas: {P}andas}, + month = feb, + year = 2020, + publisher = {Zenodo}, + 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{Chollet:2015, + title={Keras}, + author={{Chollet et al.}}, + 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}, + doi = {10.1002/1099-1255(200009/10)15:5<447::aid-jae570>3.3.co;2-t} +} + +@misc{Gurobi:2023, + author = {{Gurobi Optimization, LLC}}, + title = {{Gurobi Optimizer Reference Manual}}, + year = 2023, + howpublished = {\url{https://www.gurobi.com}} +} + +@software{ORTools:2024, + title = {OR-Tools}, + version = { v9.9 }, + author = {Laurent Perron and Vincent Furnon}, + organization = {Google}, + date = { 2024-03-07 }, + howpublished = {\url{https://developers.google.com/optimization/}} +} diff --git a/docs/paper/paper.md b/docs/paper/paper.md new file mode 100644 index 00000000..7295db2d --- /dev/null +++ b/docs/paper/paper.md @@ -0,0 +1,139 @@ +--- +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, 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, 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 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. +- **Downstream operations:** Post-processing tools that leverage choice models for assortment planning and pricing are integrated into the library. + +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) + +: 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- | CondL, NestL\ | $\times$ | $\times$ | L-BFGS | $\checkmark$ | +| Choice\ | | | | | | +| | | | | | | ++--------------+---------------------------------+--------------------+----------------+----------------+----------------------+ +| Choice-Learn | CondL, NestL,\ | $\checkmark$ | $\checkmark$ | L-BFGS | $\checkmark$ | +| | LatC | | | | | ++==============+=================================+====================+================+================+======================+ + +# Statement of need + +## 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. 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 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) + +## 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. +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 + +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` 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]. + + +# 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. + +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 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) + +# Acknowledgments +The authors thank Fortenova[^1] and Martin Možina for their helpful collaboration and provision of the proprietary dataset. + +# References + +[^1]: https://fortenova.hr/en/home/ diff --git a/poetry.lock b/poetry.lock index 7881fff0..c765c531 100644 --- a/poetry.lock +++ b/poetry.lock @@ -57,48 +57,37 @@ wheel = ">=0.23.0,<1.0" [[package]] name = "attrs" -version = "23.2.0" +version = "24.2.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.7" files = [ - {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, - {file = "attrs-23.2.0.tar.gz", hash = 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