From d2bc77450ba71f44813a8c198cb63ec5c993baa2 Mon Sep 17 00:00:00 2001 From: Jo Kristian Bergum Date: Wed, 11 Dec 2024 09:45:43 +0100 Subject: [PATCH] Make labels integers for xgboost 2.x (#1595) * Make labels integers for xgboost 2.x * update dependencies in notebook * new xgboost model --------- Co-authored-by: thomasht86 --- .../models/product_ranker_simple_xgboost.json | 169920 +++++---------- .../notebooks/Train-xgboost.ipynb | 864 +- 2 files changed, 54905 insertions(+), 115879 deletions(-) diff --git a/commerce-product-ranking/application/models/product_ranker_simple_xgboost.json b/commerce-product-ranking/application/models/product_ranker_simple_xgboost.json index 8e07c6112..4d3a5bb5f 100644 --- a/commerce-product-ranking/application/models/product_ranker_simple_xgboost.json +++ b/commerce-product-ranking/application/models/product_ranker_simple_xgboost.json @@ -1,365 +1,362 @@ [ - { "nodeid": 0, "depth": 0, "split": "bi_encoder", "split_condition": 0.503149986, "yes": 1, "no": 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"nativeRank(title)", "split_condition": 0.168165147, "yes": 163, "no": 164, "missing": 163 , "children": [ - { "nodeid": 163, "depth": 7, "split": "bm25(title)", "split_condition": 42.3180504, "yes": 243, "no": 244, "missing": 243 , "children": [ - { "nodeid": 243, "leaf": 0.0139708864 }, - { "nodeid": 244, "leaf": -0.0262684561 } - ]}, - { "nodeid": 164, "leaf": -0.0397165976 } ]}, - { "nodeid": 102, "depth": 6, "split": "bi_encoder", "split_condition": 0.614453256, "yes": 165, "no": 166, "missing": 165 , "children": [ - { "nodeid": 165, "depth": 7, "split": "bm25(description)", "split_condition": 60.8653717, "yes": 245, "no": 246, "missing": 245 , "children": [ - { "nodeid": 245, "leaf": 0.0173159968 }, - { "nodeid": 246, "leaf": -0.0128993159 } + { "nodeid": 126, "depth": 6, "split": "bm25(description)", "split_condition": 31.6232204, "yes": 251, "no": 252, "missing": 252 , "children": [ + { "nodeid": 251, "depth": 7, "split": "nativeRank(title)", "split_condition": 0.248927489, "yes": 485, "no": 486, "missing": 486 , "children": [ + { "nodeid": 485, "leaf": 0.0115763089 }, + { "nodeid": 486, "leaf": 0.0225994121 } ]}, - { "nodeid": 166, "depth": 7, "split": "bm25(description)", "split_condition": 0.96317333, "yes": 247, "no": 248, "missing": 247 , "children": [ - { "nodeid": 247, "leaf": -0.0311170015 }, - { "nodeid": 248, "leaf": 0.0167287942 } + { "nodeid": 252, "depth": 7, "split": "bm25(description)", "split_condition": 33.7122955, "yes": 487, "no": 488, "missing": 488 , "children": [ + { "nodeid": 487, "leaf": -0.013486946 }, + { "nodeid": 488, "leaf": 0.0104234843 } ]} ]} ]} diff --git a/commerce-product-ranking/notebooks/Train-xgboost.ipynb b/commerce-product-ranking/notebooks/Train-xgboost.ipynb index 83444850e..5b97949dd 100644 --- a/commerce-product-ranking/notebooks/Train-xgboost.ipynb +++ b/commerce-product-ranking/notebooks/Train-xgboost.ipynb @@ -3,13 +3,15 @@ { "cell_type": "markdown", "id": "28cb53b1", - "metadata": {}, + "metadata": { + "id": "28cb53b1" + }, "source": [ - "# Learning to Rank using XGBoost \n", + "# Learning to Rank using XGBoost\n", "\n", "This notebook demonstrates how to train a ranking model using [XGBoost](https://xgboost.readthedocs.io/). \n", "\n", - "This notebook is part of the [commerce product ranking sample app](https://github.com/vespa-engine/sample-apps/tree/master/commerce-product-ranking). \n", + "This notebook is part of the [commerce product ranking sample app](https://github.com/vespa-engine/sample-apps/tree/master/commerce-product-ranking).\n", "\n", "Blog post series:\n", "\n", @@ -21,43 +23,35 @@ "\n", ">We introduce the “Shopping Queries Data Set”, a large dataset of difficult search queries, released with the aim of fostering research in the area of semantic matching of queries and products. For each query, the dataset provides a list of up to 40 potentially relevant results, together with ESCI relevance judgements (Exact, Substitute, Complement, Irrelevant) indicating the relevance of the product to the query. Each query-product pair is accompanied by additional information. The dataset is multilingual, as it contains queries in English, Japanese, and Spanish.\n", "\n", - "The dataset is found at [amazon-science/esci-data](https://github.com/amazon-science/esci-data). \n", + "The dataset is found at [amazon-science/esci-data](https://github.com/amazon-science/esci-data).\n", "The dataset and is released under the [Apache 2.0 license](https://github.com/amazon-science/esci-data/blob/main/LICENSE).\n", - "\n" + "\n", + "Note that xgboost requires libomp to be installed. On MacOS, run `brew install libomp`. See also https://xgboost.readthedocs.io/en/latest/install.html" ] }, { "cell_type": "code", "execution_count": 1, "id": "d18a6151", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /usr/local/lib/python3.10/site-packages (1.5.1)\n", - "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/site-packages (1.7.1)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/site-packages (1.23.4)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas) (2022.5)\n", - "Requirement already satisfied: scipy in /usr/local/lib/python3.10/site-packages (from xgboost) (1.9.3)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.10 -m pip install --upgrade pip\u001b[0m\n" - ] - } - ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d18a6151", + "outputId": "94f82cbf-8c91-47e0-95c8-c41742e5f786" + }, + "outputs": [], "source": [ - "!pip3 install pandas xgboost numpy" + "!pip3 install pandas xgboost numpy matplotlib pyarrow fastparquet" ] }, { "cell_type": "code", "execution_count": 2, "id": "12d09526", - "metadata": {}, + "metadata": { + "id": "12d09526" + }, "outputs": [], "source": [ "import pandas\n", @@ -67,7 +61,9 @@ { "cell_type": "markdown", "id": "e9dd7c77", - "metadata": {}, + "metadata": { + "id": "e9dd7c77" + }, "source": [ "Read the published training data. This dataframe contains scraped features from a running Vespa instance, using a scrape rank-profile, returning calculated features using [match-features](https://docs.vespa.ai/en/reference/schema-reference.html#match-features)." ] @@ -76,7 +72,9 @@ "cell_type": "code", "execution_count": 3, "id": "d6ff3429", - "metadata": {}, + "metadata": { + "id": "d6ff3429" + }, "outputs": [], "source": [ "train = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/product_ranking_train.parquet')" @@ -85,42 +83,198 @@ { "cell_type": "markdown", "id": "190747b5", - "metadata": {}, + "metadata": { + "id": "190747b5" + }, "source": [ "## Dataset and split processing\n", "Convert the dataset's textual labels Exact, Substitute, Complement, Irrelevant to numeric labels. XGBoost does not\n", - "support custom label gains, so we convert directly to the label gain. " + "support custom label gains, so we convert directly to the label gain." ] }, { "cell_type": "code", "execution_count": 4, "id": "8a08fc7b", - "metadata": {}, + "metadata": { + "id": "8a08fc7b" + }, "outputs": [], "source": [ "esci_2_numeric = {\n", - " 'E' : 1,\n", - " 'S' : 0.1,\n", - " 'C' : 0.01,\n", + " 'E' : 20,\n", + " 'S' : 5,\n", + " 'C' : 1,\n", " 'I' : 0,\n", "}\n", "train['label'] = train['esci_label'].apply(lambda esci_label: esci_2_numeric[esci_label])" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "Ao103WsR6jiP", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 266 + }, + "id": "Ao103WsR6jiP", + "outputId": "0b69d196-670b-4bed-8254-f0f57cb910ab" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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example_idqueryquery_idproduct_idproduct_localeesci_labelsmall_versionlarge_versionsplitbm25(brand)...max_title_significancemean_title_significanceterm(0).significanceterm(1).significanceterm(2).significanceterm(3).significanceterm(4).significancetextSimilarity(title).ordertextSimilarity(title).proximitylabel
016!awnmower tires without rims1B075SCHMPYusI11train0.0...1.00.7469491.00.6941410.5887690.7048870.50.00.00000
117!awnmower tires without rims1B08L3B9B9PusE11train0.0...1.00.7469491.00.6941410.5887690.7048870.51.00.937520
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" + ], + "text/plain": [ + " example_id query query_id product_id \\\n", + "0 16 !awnmower tires without rims 1 B075SCHMPY \n", + "1 17 !awnmower tires without rims 1 B08L3B9B9P \n", + "\n", + " product_locale esci_label small_version large_version split bm25(brand) \\\n", + "0 us I 1 1 train 0.0 \n", + "1 us E 1 1 train 0.0 \n", + "\n", + " ... max_title_significance mean_title_significance term(0).significance \\\n", + "0 ... 1.0 0.746949 1.0 \n", + "1 ... 1.0 0.746949 1.0 \n", + "\n", + " term(1).significance term(2).significance term(3).significance \\\n", + "0 0.694141 0.588769 0.704887 \n", + "1 0.694141 0.588769 0.704887 \n", + "\n", + " term(4).significance textSimilarity(title).order \\\n", + "0 0.5 0.0 \n", + "1 0.5 1.0 \n", + "\n", + " textSimilarity(title).proximity label \n", + "0 0.0000 0 \n", + "1 0.9375 20 \n", + "\n", + "[2 rows x 53 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.head(2)" + ] + }, { "cell_type": "markdown", "id": "a664c4a3", - "metadata": {}, + "metadata": { + "id": "a664c4a3" + }, "source": [ "We create a dev split from the original train split (80:20 split)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1294b310", - "metadata": {}, + "metadata": { + "id": "1294b310" + }, "outputs": [], "source": [ "train_queries = pandas.read_parquet('https://data.vespa-cloud.com/sample-apps-data/train_query_ids.parquet')['query_id'].unique()\n", @@ -130,16 +284,20 @@ { "cell_type": "markdown", "id": "84872077", - "metadata": {}, + "metadata": { + "id": "84872077" + }, "source": [ - "We filter so that we end up with two splits or the original train dataset. This way we can evaluate performance on the dev set and we avoid tuning hyperparameters based on observed test set performance. " + "We filter so that we end up with two splits or the original train dataset. This way we can evaluate performance on the dev set and we avoid tuning hyperparameters based on observed test set performance." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "de18b91c", - "metadata": {}, + "metadata": { + "id": "de18b91c" + }, "outputs": [], "source": [ "df_train = train[train['query_id'].isin(train_queries)]\n", @@ -149,16 +307,20 @@ { "cell_type": "markdown", "id": "f6548de0", - "metadata": {}, + "metadata": { + "id": "f6548de0" + }, "source": [ "We need to group by query_id so that XGBoost knows which products belongs to which query." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "acf75548", - "metadata": {}, + "metadata": { + "id": "acf75548" + }, "outputs": [], "source": [ "train_query_groups=df_train['query_id'].value_counts().sort_index().values\n", @@ -168,16 +330,20 @@ { "cell_type": "markdown", "id": "73177d51", - "metadata": {}, + "metadata": { + "id": "73177d51" + }, "source": [ "The label we want to predict " ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "5fdadc19", - "metadata": {}, + "metadata": { + "id": "5fdadc19" + }, "outputs": [], "source": [ "train_target = df_train['label'].values\n", @@ -187,16 +353,20 @@ { "cell_type": "markdown", "id": "398c14a9", - "metadata": {}, + "metadata": { + "id": "398c14a9" + }, "source": [ "Drop columns that we don't need during training, after this we are left with just the pure features" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "42e3d3b9", - "metadata": {}, + "metadata": { + "id": "42e3d3b9" + }, "outputs": [], "source": [ "drop_fields = ['example_id', 'query', 'query_id', 'product_id', 'product_locale', 'esci_label', 'small_version', 'large_version', 'split', 'label']\n" @@ -204,9 +374,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "ea3cba73", - "metadata": {}, + "metadata": { + "id": "ea3cba73" + }, "outputs": [], "source": [ "df_dev = df_dev.drop(drop_fields, axis=1)\n", @@ -216,37 +388,127 @@ { "cell_type": "markdown", "id": "9dde7561", - "metadata": {}, + "metadata": { + "id": "9dde7561" + }, "source": [ - "In this case, we choose a subset of features to train with. Deleting this cell, trains on all available features. " + "In this case, we choose a subset of features to train with. Deleting this cell, trains on all available features." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "3584d6b1", - "metadata": {}, + "metadata": { + "id": "3584d6b1" + }, "outputs": [], "source": [ - "df_dev = df_dev[['bi_encoder', 'bm25(title)', 'bm25(description)', 'bm25(bullets)', 'nativeRank(title)']]\n", - "df_train = df_train[['bi_encoder', 'bm25(title)', 'bm25(description)', 'bm25(bullets)', 'nativeRank(title)']]" + "keep_features = ['bi_encoder', 'bm25(title)', 'bm25(description)', 'bm25(bullets)', 'nativeRank(title)']\n", + "df_dev = df_dev[keep_features]\n", + "df_train = df_train[keep_features]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "IeAc2bkJ676U", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "id": "IeAc2bkJ676U", + "outputId": "749948b7-ed31-4b64-e817-61f0b0883ad8" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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bi_encoderbm25(title)bm25(description)bm25(bullets)nativeRank(title)
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10.48093610.47056617.00916010.3678300.065069
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" + ], + "text/plain": [ + " bi_encoder bm25(title) bm25(description) bm25(bullets) \\\n", + "0 0.468920 4.984932 14.295459 13.659327 \n", + "1 0.480936 10.470566 17.009160 10.367830 \n", + "\n", + " nativeRank(title) \n", + "0 0.030665 \n", + "1 0.065069 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_train.head(2)" ] }, { "cell_type": "markdown", "id": "582c12c6", - "metadata": {}, + "metadata": { + "id": "582c12c6" + }, "source": [ "## Prepare for training the model\n", - "Create xgboost DMatrix of the train and dev splits. Please notice the set_group calls, \n", - "so that the xgboost knows which data points belongs to which query. " + "Create xgboost DMatrix of the train and dev splits. Please notice the set_group calls,\n", + "so that the xgboost knows which data points belongs to which query." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "805cfe29", - "metadata": {}, + "metadata": { + "id": "805cfe29" + }, "outputs": [], "source": [ "train_ds = xgboost.DMatrix(df_train, train_target)\n", @@ -258,11 +520,13 @@ { "cell_type": "markdown", "id": "82fbf5db", - "metadata": {}, + "metadata": { + "id": "82fbf5db" + }, "source": [ "## Model Training\n", "\n", - "Configure hyperparameters. We set `base_score` to 0 as the XGBoost export routine does not include the `base_score` bias. Vespa assumes that `base_score` is 0. \n", + "Configure hyperparameters. We set `base_score` to 0 as the XGBoost export routine does not include the `base_score` bias. Vespa assumes that `base_score` is 0.\n", "\n", "We use `rank:ndcg` objective, see [XGBoost objectives](https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters)\n", "\n", @@ -270,19 +534,21 @@ "- `rank:ndcg` listwise loss\n", "- `rank:pairwise` pairwise loss\n", "\n", - "The ranking objective does not change how we do inference with the model, only how the model is trained. " + "The ranking objective does not change how we do inference with the model, only how the model is trained." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "a39a794f", - "metadata": {}, + "metadata": { + "id": "a39a794f" + }, "outputs": [], "source": [ "params = {\n", - " 'objective': 'rank:pairwise',\n", - " 'eval_metric': ['ndcg'],\n", + " 'objective': 'rank:ndcg',\n", + " 'eval_metric': ['ndcg@10'],\n", " 'verbosity': 1,\n", " 'booster': 'gbtree',\n", " 'base_score': 0,\n", @@ -294,259 +560,134 @@ { "cell_type": "markdown", "id": "430b272f", - "metadata": {}, + "metadata": { + "id": "430b272f" + }, "source": [ - "Train the model, output's NDCG for both the train and our dev split. We use `early_stopping_rounds=50`, which will stop training if the `dev` dataset NDCG metric does not improve. " + "Train the model, output's NDCG for both the train and our dev split. We use `early_stopping_rounds=50`, which will stop training if the `dev` dataset NDCG metric does not improve." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "574a6fe8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0]\ttrain-ndcg:0.82792\tdev-ndcg:0.80515\n", - "[1]\ttrain-ndcg:0.83283\tdev-ndcg:0.80881\n", - "[2]\ttrain-ndcg:0.83441\tdev-ndcg:0.81017\n", - "[3]\ttrain-ndcg:0.83610\tdev-ndcg:0.81104\n", - "[4]\ttrain-ndcg:0.83696\tdev-ndcg:0.81169\n", - "[5]\ttrain-ndcg:0.83784\tdev-ndcg:0.81200\n", - "[6]\ttrain-ndcg:0.83853\tdev-ndcg:0.81200\n", - "[7]\ttrain-ndcg:0.83915\tdev-ndcg:0.81220\n", - "[8]\ttrain-ndcg:0.83969\tdev-ndcg:0.81238\n", - "[9]\ttrain-ndcg:0.83992\tdev-ndcg:0.81290\n", - "[10]\ttrain-ndcg:0.84013\tdev-ndcg:0.81295\n", - "[11]\ttrain-ndcg:0.84039\tdev-ndcg:0.81340\n", - "[12]\ttrain-ndcg:0.84025\tdev-ndcg:0.81327\n", - "[13]\ttrain-ndcg:0.84053\tdev-ndcg:0.81340\n", - 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"[94]\ttrain-ndcg@10:0.77072\tdev-ndcg@10:0.71093\n", + "[95]\ttrain-ndcg@10:0.77102\tdev-ndcg@10:0.71078\n", + "[96]\ttrain-ndcg@10:0.77112\tdev-ndcg@10:0.71056\n", + "[97]\ttrain-ndcg@10:0.77128\tdev-ndcg@10:0.71073\n", + "[98]\ttrain-ndcg@10:0.77143\tdev-ndcg@10:0.71091\n", + "[99]\ttrain-ndcg@10:0.77148\tdev-ndcg@10:0.71088\n", + "[100]\ttrain-ndcg@10:0.77164\tdev-ndcg@10:0.71092\n", + "[101]\ttrain-ndcg@10:0.77173\tdev-ndcg@10:0.71106\n", + "[102]\ttrain-ndcg@10:0.77181\tdev-ndcg@10:0.71082\n", + "[103]\ttrain-ndcg@10:0.77186\tdev-ndcg@10:0.71083\n", + "[104]\ttrain-ndcg@10:0.77203\tdev-ndcg@10:0.71108\n" ] } ], @@ -554,37 +695,46 @@ "model = xgboost.train(\n", " params, train_ds, evals=[(train_ds, 'train'), (dev_ds, 'dev')],\n", " num_boost_round=1000,\n", - " early_stopping_rounds=50\n", + " early_stopping_rounds=20\n", ")" ] }, { "cell_type": "markdown", "id": "b90a40cf", - "metadata": {}, + "metadata": { + "id": "b90a40cf" + }, "source": [ - "Plot feature importance " + "Plot feature importance" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "0449c0a2", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "0449c0a2", + "outputId": "5f019913-6773-4525-fa78-aafd41da99da" + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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d3Lt3T3wcPnxYXBYYGIh//vkHU6dOxZkzZ/DZZ5+he/fuOHv2rGQdM2fOlKzjyy+/FJfdvHkTnTp1wscff4xz585h9+7duH//Pj777LOXjisgIADx8fGIjY3Fjh07cOTIkVLtHwNpORUXF4dOnTqhWrVqMDIyQsOGDREVFSXpExkZCYVCIXno6+sXWteUKVPw5ZdfwsTERHydubl5oX4nT57E4MGDxecKhQLbtm17rf2wtLREYGAggoODX2s9RERE7zMdHR1YW1uLD0tLS3HZkSNHMHz4cHz44YdwdHTE119/DXNzc5w+fVqyDhMTE8k6jIz+/wjk6dOnoVKpMGvWLNSsWRPu7u6YMGECzp07h7y8vCLHdOXKFezatQurV69G06ZN0aJFC8yfPx8AcO/ePY32j4G0nDpy5AhcXV2xefNmXLhwAf3790dgYCB27Ngh6Wdqair5tPPidHxSUhJ27NiBfv36vXKbVlZWMDQ0LMvdAAD0798fUVFRePjwYZmvm4iI6H1w9epV2NjYwNHREQEBAUhKShKXeXl54ddff0VmZibUajWio6Px5MkTtGnTRrKOuXPnonLlynBzc8P8+fORn58vLmvUqBG0tLSwdu1aqFQqpKen48cff4S3tzd0dXWLHNPRo0dhbm4ODw8Psa1gm6dOndJo/xhIy0h+fj5GjhwJMzMzWFpaYvr06RAEAcCzQ+GzZs1CYGAgjI2NYWdnh+3bt+O///5Dp06dYGxsDFdXV8kvb+rUqQgLC4OXlxdq1qyJ0aNHw9fXF1u2bJFsV6FQSD7tVK1aVbJ806ZNaNCgAapXrw7g2cxr//79kZ6eLs6qhoSEiOMsOGRfcL5Ily5doFAoXnr+yOrVq+Hi4gJ9fX3Url0b33//vWR53bp1YWNjg61bt2paViIiovde06ZNERkZiV27dmH58uW4efMmWrZsKZ6Kt2nTJuTl5aFPnz4wNjbGkCFDsHXrVjg5OYnrGDVqFKKjo7F//34MGTIEc+bMwVdffSUud3BwwJ49ezB16lQolUqYm5vjzp072LRpU7HjSklJQZUqVSRtOjrPLk/6999/NdpHXtRURtatW4eBAwfixIkTOHXqFAYPHowaNWpg0KBBAICIiAjMmTMH06dPR0REBPr06QMvLy8MGDAA8+fPx6RJkxAYGIj4+HgoFEWfwJ2eng4XFxdJW1ZWFuzs7KBWq+Hu7o45c+agbt264vJDhw5JPrl4eXlh0aJFmDFjBhISEgAAxsbGhbZ18uRJVKlSBWvXroWvry+0tbWLHFNUVBRmzJiBpUuXws3NDWfPnsWgQYNgZGSEvn37iv2aNGmCQ4cOYeDAgUWuJzc3F7m5ueLzjIwMAIBSS4C2tlDka0hKqSVI/kuvxpppjjXTHGumOdbs/+Xl5cHb21t87uLiAnd3dzg5OWHjxo3o378/pk2bhkePHiE0NBSffPIJYmJi0L17d/z555+oX78+AEjOF3VxcYG2tjaGDx+OmTNnQqlUIiUlBUFBQejduzd69OiBrKwshIaGomvXrti5c2eR2USlUkEQBMkh/eIO778KA2kZsbW1RUREBBQKBZydnXHx4kVERESIgdTPzw9DhgwBAMyYMQPLly9H48aN0a1bNwDApEmT4OnpiX///RfW1taF1r9p0yacPHkSK1euFNucnZ2xZs0auLq6Ij09HQsWLICXlxfi4+PxwQcfAABu3bolCaR6enowMzMTZ1aLY2VlBQAwNzd/ab/g4GAsXLhQPOnZwcEBly9fxsqVKyWB1MbGptDJ1c8LDw8vdAUhAHztpoahoarY11FhYR5quYfwzmHNNMeaaY410xxrBsTExBTZXqVKFezZswdqtRrff/89vvvuO9SoUQP//vsvGjVqBDs7O0ydOhXDhg0r8vVPnjxBfn4+1q9fj+rVq4vXqbRq1Uo8/zMwMBBBQUFYtGgRnJ2dC60jNTUVycnJkjEWzNq+eMT2VRhIy0izZs0knx48PT2xcOFCqFTPwpSrq6u4rOCXVPCp5fm21NTUQgFw//796N+/P1atWiWZ/fT09ISnp6f43MvLCy4uLli5ciXCwsIAADk5OUVe6FQWHj9+jOvXr2PgwIFi8Aaenb5gZmYm6WtgYIDs7Oxi1zVlyhSMGzdOfJ6RkQFbW1vMOquFfN2iZ2dJSqklIMxDjemntJCrfr9vk1JSrJnmWDPNsWaaY83+36UQn0JtWVlZePDgAZo3b44mTZoAAJo3b47bt2+jXbt20NXVxbJly/DBBx/Az8+vyPVu2LABWlpa+Pzzz1GpUiXExcUhMTFR0r8gmDZr1kySNwo4ODhg6dKlsLa2hru7OwDgt99+AwDJZFhJMJC+Jc+fEFwQXItqU6ulnwYPHDgAf39/REREIDAw8JXbcHNzw7Vr18Q2S0tLPHr06LXHX5SsrCwAwKpVq9C0aVPJshcP8T98+FCcdS2KUqmEUqks1J6rViD/Pb8HnaZy1Yr3/r59mmLNNMeaaY410xxr9uy9fcKECfD394ednR2Sk5MRHBwMbW1t9O7dG+bm5nBycsLo0aPx6aefwtnZGX/88Qf27t2LHTt2QFdXF0ePHsXx48fx0UcfwcTEBEePHsXEiRPRu3dv8RxQf39/LF68GOHh4ejVqxcyMzMxdepU2NnZoXHjxtDV1cWJEycQGBiIffv2oXr16nB1dYWvry+GDRuGFStWIC8vT7yvabVq1TTaT17UVEaOHz8ueX7s2DHUqlWr2HMvSyIuLg4dOnTAN998I7kdU3FUKhUuXrwo+SNwc3PD5cuXJf309PTEmduX0dXVfWm/qlWrwsbGBjdu3ICTk5Pk4eDgIOl76dIluLm5vXKbREREJHXnzh306tULzs7O6N69OypXroxjx47BysoKurq6iImJgaWlJWbPno1GjRph/fr1WLdunTjbqVQqER0djdatW6Nu3bqYPXs2xo4dix9++EHcxscff4wNGzZg27ZtcHNzg6+vL5RKJXbt2gUDAwMAQHZ2NhISEiTniUZFRaF27dpo27Yt/Pz8ipxJLQnOkJaRpKQkjBs3DkOGDMGZM2ewZMkSLFy4sNTr279/Pzp27IjRo0eja9euSElJAfAsTFpYWAB4doPbZs2awcnJCWlpaZg/fz5u3bqFoKAgcT0+Pj4ICgqCSqUSw7G9vT2ysrKwb98+NGjQAIaGhkXe7sne3h779u1D8+bNoVQqUalSpUJ9QkNDMWrUKJiZmcHX1xe5ubk4deoUHj16JB6Cz87OxunTpzFnzpxS14OIiOh9FR0d/dLltWrVwqZNmxATEwM/P79Ct2lyd3fHsWPHXrmdnj17omfPnsUub9OmjXgHoQIWFhbYsGGD+DwjIwM//fTTK7f1Is6QlpHAwEDk5OSgSZMmGDFiBEaPHl2iWc3irFu3DtnZ2QgPD0e1atXEx/PfmPDo0SMMGjQILi4u8PPzQ0ZGBo4cOYI6deqIfdq3bw8dHR3s3btXbPPy8sLQoUPRo0cPWFlZYd68eUWOYeHChYiNjYWtrW2xs5tBQUFYvXo11q5di/r166N169aIjIyUzJD+9ttvqFGjBlq2bFnqehAREVHFpRBejLpU4Sxbtgzbt2/H7t27Zdl+s2bNMGrUKHzxxRclfk1GRgbMzMxw//59VK5c+Q2OruLIy8sr9tMxFY010xxrpjnWTHOsmebKS80K3r/T09Nhampa4tfxkP17YMiQIUhLS0NmZqb49aFvS8H34Pbq1eutbpeIiIjeHQyk7wEdHR1MmzZNlm1bWlpKvgmCiIiI6EU8h5SIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsduQdA9DJNw/chX8dI7mG8E5TaAuY1AeqF7EauSiH3cN4JrJnmWDPNsWaaK+81S5zbQe4hVDicISUiIiIqhZCQECgUCsmjdu3a4vI2bdoUWj506FDJOkaNGoVGjRpBqVSiYcOGhbbx5MkT9OvXD/Xr14eOjg46d+5corE9fPgQAQEBMDU1hbm5OQYOHIisrKzX2d036r0NpG3atMGYMWPkHkaxEhISYG1tjczMTABAZGQkzM3NX3u9L+63vb09Fi1a9NrrfZlmzZph8+bNb3QbREREcqhbty7u3bsnPg4fPixZPmjQIMnyefPmFVrHgAED0KNHjyLXr1KpYGBggFGjRsHb27vE4woICEB8fDxiY2OxY8cOHDx4EIMHD9Zs596i9zaQvglxcXHo1KkTqlWrBiMjIzRs2BBRUVGSPpGRkYU+Lenr6xda15QpU/Dll1/CxMTkbQ2/REJCQor8BPcyX3/9NSZPngy1Wv1mBkVERCQTHR0dWFtbiw9LS0vJckNDQ8lyU1NTyfLvvvsOI0aMgKOjY5HrNzIywvLlyzFo0CBYW1uXaExXrlzBrl27sHr1ajRt2hQtWrTAkiVLEB0djeTk5NLt6BvGQFqGjhw5AldXV2zevBkXLlxA//79ERgYiB07dkj6mZqaSj4t3bp1S7I8KSkJO3bsQL9+/d7i6N+c9u3bIzMzEzt37pR7KERERGXq6tWrsLGxgaOjIwICApCUlCRZHhUVBUtLS9SrVw9TpkxBdnb2Gx/T0aNHYW5uDg8PD7HN29sbWlpaOH78+Bvffmm81xc15efnY+TIkfjxxx+hq6uLYcOGYebMmVAoFLC3t0dQUBD++ecfbNmyBZUrV8aSJUvg6emJoKAg7Nu3D46OjlizZo34C586dapk/aNHj8aePXuwZcsWdOzYUWxXKBQv/ZSzadMmNGjQANWrVy+0bNu2bZg4cSJu376N1q1bY/Xq1bC1tQUA9OvXD2lpadi2bZvYf8yYMTh37hzi4uJKVJO0tDRMmDABv/32G3Jzc+Hh4YGIiAg0aNAAkZGRCA0NFfcBANauXYu+ffsiNDQUa9aswb///ovKlSvj888/x3fffQcA0NbWhp+fH6Kjo9GhQ9Engufm5iI3N1d8npGRAQBQagnQ1hZKNPb3nVJLkPyXXo010xxrpjnWTHPlvWZ5eXkAgEaNGmH16tX48MMPkZKSglmzZqFly5Y4e/YsTExM0KNHD9SoUQPVqlXDxYsXMW3aNFy5cgW//PJLoXWqVCoIgiCuuyhqtRpqtbrIPgVteXl5uHv3LqysrAr1s7CwwN27d1+6jddV2nW/14F03bp1GDhwIE6cOIFTp05h8ODBqFGjBgYNGgQAiIiIwJw5czB9+nRERESgT58+8PLywoABAzB//nxMmjQJgYGBiI+PFwPai9LT0+Hi4iJpy8rKgp2dHdRqNdzd3TFnzhzUrVtXXH7o0CHJp5oC2dnZmD17NtavXw89PT0MHz4cPXv2xF9//VVmNenWrRsMDAywc+dOmJmZYeXKlWjbti3++ecf9OjRA5cuXcKuXbuwd+9eAICZmRk2b96MiIgIREdHo27dukhJScH58+cl623SpAnmzp1b7HbDw8PFsPu8r93UMDRUldn+vQ/CPHhqhKZYM82xZppjzTRXXmsWExMj/mxoaIg7d+4AAEaOHInBgwdjxowZaNeuHWxsbJCfn4/bt2/D3NwcQ4YMwYwZM/C///0P1apVk6zz6tWryMjIkKz7RXfu3MHjx49f2ic2NhYJCQlF9nv69CkuXbr00te/rtLOAL/XgdTW1hYRERFQKBRwdnbGxYsXERERIQZSPz8/DBkyBAAwY8YMLF++HI0bN0a3bt0AAJMmTYKnpyf+/fffImc8N23ahJMnT2LlypVim7OzM9asWQNXV1ekp6djwYIF8PLyQnx8PD744AMAwK1bt4oMpHl5eVi6dCmaNm0K4FmgdnFxwYkTJ9CkSZPXrsfhw4dx4sQJpKamQqlUAgAWLFiAbdu24ddff8XgwYNhbGwsni9TICkpCdbW1vD29oauri5q1KhRaDw2Nja4ffs21Go1tLQKnykyZcoUjBs3TnyekZEBW1tbzDqrhXxd7dfet/eBUktAmIca009pIVdd/m6TUh6xZppjzTTHmmmuvNfsUohPscsiIiJgaGgIPz+/Qstat26NGTNmwNbWFp988olk2alTp3DlypUiX1dg8+bNSEtLK7JPXl4eYmNj0a5dO6SmpuKPP/6Q9MvPz0dWVhbatm370m28roIjnJp6rwNps2bNJDObnp6eWLhwIVSqZzNyrq6u4rKqVasCAOrXr1+oLTU1tVAg3b9/P/r3749Vq1ZJZj89PT3h6ekpPvfy8oKLiwtWrlyJsLAwAEBOTk6RFzrp6OigcePG4vPatWvD3NwcV65cKZNAev78eWRlZaFy5cqS9pycHFy/fr3Y13Xr1g2LFi2Co6MjfH194efnB39/f+jo/P+fl4GBAdRqNXJzc2FgYFBoHUqlUgzBz8tVK5BfDu9BV57lqhXl8r595RlrpjnWTHOsmebKa810dXWLbM/KysKNGzcQGBhYZJ/4+HgAzybEXlyura0NhUJR7LoBQEtLC1paWi/to6urixYtWiAtLQ0XLlxAo0aNADzLJWq1Gs2bN3/p619Xadf9XgfSV3m+qAXBtai2F68eP3DgAPz9/REREYHAwMBXbsPNzQ3Xrl0T2ywtLfHo0SONx6ulpQVBkJ5vo8m5HFlZWahWrVqR55u+7JZTtra2SEhIwN69exEbG4vhw4dj/vz5OHDggFivhw8fwsjIqMgwSkRE9C6aMGEC/P39YWdnh+TkZAQHB0NbWxu9evXC9evXsWHDBvj5+aFy5cq4cOECxo4di1atWkkmvK5du4asrCykpKQgJycH586dAwDUqVMHenp6AIDLly/j6dOnePjwITIzM8U+BXe9OXHiBPr06YNJkyYBAFxcXODr64tBgwZhxYoVyMvLw8iRI9GzZ0/Y2Ni8tfpo4r0OpC9eaXbs2DHUqlUL2tqlP0QcFxeHjh074ptvvinR/b5UKhUuXrwomT53c3PD5cuXC/XNz8/HqVOnxNnQhIQEpKWlieeoWllZ4dKlS5LXnDt3rsSfVtzd3ZGSkgIdHR3Y29sX2UdPT0+cQX6egYEB/P394e/vjxEjRqB27dq4ePEi3N3dAQCXLl2Cm5tbicZBRET0Lrhz5w569eqFBw8ewMrKCi1atMCxY8dgZWWFJ0+eYO/evVi0aBEeP34MW1tbdO3aFV9//bVkHUFBQThw4ID4vOC98ubNm+J7sZ+fn+SOPAV9CiahsrOz8c8//yA/P1/sExUVhZEjR6Jt27bQ0tJC165dxYuNy6P3OpAmJSVh3LhxGDJkCM6cOYMlS5Zg4cKFpV7f/v370bFjR4wePRpdu3ZFSkoKgGchzsLCAgAwc+ZMNGvWDE5OTkhLS8P8+fNx69YtBAUFievx8fFBUFAQVCqVJBzr6uriyy+/xHfffQcdHR2MHDkSzZo1EwPqxx9/jPnz52P9+vXw9PTETz/9pFEQ9Pb2hqenJzp37ox58+bhww8/RHJyMv744w906dIFHh4esLe3x82bN3Hu3Dl88MEHMDExwcaNG6FSqdC0aVMYGhrip59+goGBAezs7MR1Hzp0qND5MkRERO+y6OjoYpfZ2tpKgmZxSnIXnMTExJcub9OmDZ4+fSq5WMnCwgIbNmx45brLi/c6kAYGBiInJwdNmjSBtrY2Ro8e/VrfYrBu3TpkZ2cjPDwc4eHhYnvr1q3FP7hHjx5h0KBBSElJQaVKldCoUSMcOXIEderUEfu3b98eOjo62Lt3L3x8/v/EaUNDQ0yaNAlffPEF7t69i5YtW+J///ufuNzHxwfTp0/HV199hSdPnmDAgAEIDAzExYsXSzR+hUKBmJgYTJs2Df3798d///0Ha2trtGrVSjxftmvXrtiyZQs++ugjpKWlYe3atTA3N8fcuXMxbtw4qFQq1K9fH7///rt4Lurdu3dx5MgR/PTTTxrX9PiUtoXOaaWi5eXlISYmBpdCfN7o+UEVCWumOdZMc6yZ5liz949CePGkQyoXli1bhu3bt2P37t1yD+W1TZo0CY8ePcIPP/xQ4tdkZGTAzMwM9+/fZyAtoYJ/wP38/PgPeAmxZppjzTTHmmmONdNcealZwft3enp6oW+lepn3eoa0PBsyZAjS0tKQmZlZ7r4+VFNVqlSR3NKJiIiI6HkMpOWUjo4Opk2bJvcwysT48ePlHgIRERGVY/wueyIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSVZkF0rS0tLJaFRERERG9R0oVSL/55hv8/PPP4vPu3bujcuXKqF69Os6fP19mgyMiIiKiiq9UgXTFihWwtbUFAMTGxiI2NhY7d+5E+/btMXHixDIdIBERERFVbDqleVFKSooYSHfs2IHu3bvjk08+gb29PZo2bVqmAyQiIiKiiq1UM6SVKlXC7du3AQC7du2Ct7c3AEAQBKhUqrIbHRERERFVeKWaIf3ss8/wxRdfoFatWnjw4AHat28PADh79iycnJzKdIBEREREVLGVKpBGRETA3t4et2/fxrx582BsbAwAuHfvHoYPH16mAyQiIiKiiq1UgVRXVxcTJkwo1D527NjXHhARERERvV9KfR/SH3/8ES1atICNjQ1u3boFAFi0aBF+++23MhscEREREVV8pQqky5cvx7hx49C+fXukpaWJFzKZm5tj0aJFZTk+IiIiIqrgShVIlyxZglWrVmHatGnQ1tYW2z08PHDx4sUyGxwRERERVXylCqQ3b96Em5tboXalUonHjx+/9qCIiIiI6P1RqkDq4OCAc+fOFWrftWsXXFxcXndMRERERPQeKdVV9uPGjcOIESPw5MkTCIKAEydOYOPGjQgPD8fq1avLeoxEREREVIGVKpAGBQXBwMAAX3/9NbKzs/HFF1/AxsYGixcvRs+ePct6jERERERUgWkcSPPz87Fhwwb4+PggICAA2dnZyMrKQpUqVd7E+IiIiIiogtP4HFIdHR0MHToUT548AQAYGhoyjBIRERFRqZXqoqYmTZrg7NmzZT0WIiIiInoPleoc0uHDh2P8+PG4c+cOGjVqBCMjI8lyV1fXMhkcEREREVV8pQqkBRcujRo1SmxTKBQQBAEKhUL85iYiIiIiolcpVSC9efNmWY+DiIiIiN5TpQqkdnZ2ZT0OoiI1Dd+HfB2jV3ckKLUFzGsC1AvZjVyVQu7hvBNYM82xZppjzTRXljVLnNsBISEhCA0NlbQ7Ozvj77//xsOHDxEcHIw9e/YgKSkJVlZW6Ny5M8LCwmBmZgYAePDgAQICAnDhwgU8ePAAVapUQadOnTBnzhyYmpoCAO7du4fx48fj1KlTuHbtGkaNGoVFixa9cnxJSUkYNmwY9u/fD2NjY/Tt2xfh4eHQ0SlVRHtnlWpv169f/9LlgYGBpRqMHOzt7TFmzBiMGTNG7qFoLCQkBNu2bSvyW7Oe16dPH7i4uGDq1KnF9omMjMSYMWOQlpb20nUpFAps3boVnTt3LtEYJ0+ejMePH2PJkiUl6k9ERPQm1K1bF3v37hWfFwS+5ORkJCcnY8GCBahTpw5u3bqFoUOHIjk5Gb/++isAQEtLC506dcKsWbNgZWWFa9euYcSIEXj48CE2bNgAAMjNzYWVlRW+/vprRERElGhMKpUKHTp0gLW1NY4cOYJ79+4hMDAQurq6mDNnThlXoHwrVSAdPXq05HleXh6ys7Ohp6cHQ0PDchlIiwtcJ0+eLHRR1utITEyEg4OD+LxSpUqoX78+Zs2ahZYtW5bZdkrq/PnziImJwfLly8W2okJ4jx494OfnJz4vadh9lQkTJsDR0RFjx46Fo6Pja62LiIiotHR0dGBtbV2ovV69eti8ebP4vGbNmpg9ezZ69+6N/Px86OjooFKlShg2bJjYx87ODsOHD8f8+fPFNnt7eyxevBgAsGbNmhKNac+ePbh8+TL27t2LqlWromHDhggLC8OkSZMQEhICPT290u7uO6dUt3169OiR5JGVlYWEhAS0aNECGzduLOsxvlFWVlYwNDQs8/Xu3bsX9+7dw8GDB2FjY4OOHTvi33//LfPtvMqSJUvQrVs3GBsbv7SfgYHBG7mfrKWlJXx8fCSBmIiI6G27evUqbGxs4OjoiICAACQlJRXbNz09HaampsUeNk9OTsaWLVvQunXr1xrT0aNHUb9+fVStWlVs8/HxQUZGBuLj419r3e+aUgXSotSqVQtz584tNHtaVtq0aYNRo0bhq6++goWFBaytrRESEiIu//bbb1G/fn0YGRnB1tYWw4cPR1ZWFgAgLi4O/fv3R3p6OhQKBRQKhfhae3t78RyPL774Aj169JBsNy8vD5aWluJpCmq1GuHh4XBwcICBgQEaNGggTuk/r3LlyrC2tka9evUwdepUZGRk4Pjx4+LyH3/8ER4eHjAxMYG1tTW++OILpKamisvj4uKgUCiwb98+eHh4wNDQEF5eXkhISCi2RtevX4ejoyNGjhwJQRCgUqnw66+/wt/fX1LHW7duYezYsWItgGczyObm5uLPoaGhOH/+vNgnMjKyyG3evn0b3bt3h7m5OSwsLNCpUyckJiZK+vj7+yM6OrrYcRMREb1JTZs2RWRkJHbt2oXly5fj5s2baNmyJTIzMwv1vX//PsLCwjB48OBCy3r16gVDQ0NUr14dpqamWL169WuNKyUlRRJGAYjPU1JSXmvd75oyPWNWR0cHycnJZblKiXXr1mHcuHE4fvw4jh49in79+qF58+Zo164dtLS08N1338HBwQE3btzA8OHD8dVXX+H777+Hl5cXFi1ahBkzZoiBrqgZw4CAAHTr1g1ZWVni8t27dyM7OxtdunQBAISHh+Onn37CihUrUKtWLRw8eBC9e/eGlZVVkZ+UcnJyxDD7/NR7Xl4ewsLC4OzsjNTUVIwbNw79+vVDTEyM5PXTpk3DwoULYWVlhaFDh2LAgAH466+/Cm3nwoUL8PHxwcCBAzFr1iyxLT09HR4eHmK/LVu2oEGDBhg8eDAGDRpUZJ179OiBS5cuYdeuXeL5NgUndj8vLy8PPj4+8PT0xKFDh6Cjo4NZs2bB19cXFy5cEPe3SZMmuHPnDhITE2Fvb1/kNnNzc5Gbmys+z8jIAAAotQRoawtFvoaklFqC5L/0aqyZ5lgzzbFmmivLmuXl5cHb21t87uLiAnd3dzg5OWHjxo3o37+/uCwjIwN+fn5wcXHBtGnTkJeXJ1nXvHnzMHXqVFy9ehVff/01xowZU+Q1EoIgQK1WF3r9i9RqNQRBkPQr+Dk/P/+Vr39xP5//r1xKu/1SBdLt27dLnguCgHv37mHp0qVo3rx5qQZSEq6urggODgbwbEZ26dKl2LdvH9q1ayc5H9Le3h6zZs3C0KFD8f3330NPTw9mZmZQKBRFnj9SwMfHB0ZGRti6dSv69OkDANiwYQM+/fRTmJiYIDc3F3PmzMHevXvh6ekJAHB0dMThw4excuVKSSD18vKClpYWsrOzIQgCGjVqhLZt24rLBwwYIP7s6OiI7777Do0bN5aEYQCYPXu2uN7JkyejQ4cOePLkCfT19cU+R44cQceOHTFt2jSMHz9ebL916xa0tbUlh+ItLCygra0tzswWxcDAAMbGxsWeb1Pg559/hlqtxurVq8WZ1rVr18Lc3BxxcXH45JNPAAA2NjbieIoLpOHh4YWugASAr93UMDTkfW01EeahlnsI7xzWTHOsmeZYM82VRc1enOgpUKVKFezZs0eckczJyUFISAiUSiUGDhyI2NjYYtepra2NPn36YOrUqWjatCksLCwkyx88eICbN28Wu+0CmZmZuHr1qqRfwel9165de+Xri/Kycb8N2dnZpXpdqQLpi1dYKxQKWFlZ4eOPP8bChQtLNZCSePEboKpVqyYe5t67dy/Cw8Px999/IyMjA/n5+Xjy5Amys7NLfI6ojo4OunfvjqioKPTp0wePHz/Gb7/9Jh5uvnbtGrKzs9GuXTvJ654+fQo3NzdJ288//4zatWvj0qVL+OqrrxAZGQldXV1x+enTpxESEoLz58/j0aNHUKuf/U+XlJSEOnXqFLnP1apVAwCkpqaiRo0aYv927dph9uzZhe4UkJOTA6VSKYbFsnb+/Hlcu3YNJiYmkvYnT57g+vXr4nMDAwMAL/8jnTJlCsaNGyc+z8jIgK2tLWad1UK+rnYZj7xiUmoJCPNQY/opLeSqeWuZkmDNNMeaaY4101xZ1uxSiE+htqysLDx48ADNmzeHn58fMjIy0KFDB1StWhXbt28vUW4oeO9r0aJFocmWb7/9Fg4ODpKLhYuipaWFX3/9FR4eHuLk0erVq2FqaopBgwZBqVSWcC+fzUzGxsaiXbt2krzxthUc4dRUqQJpQXh6214ssEKhgFqtRmJiIjp27Ihhw4Zh9uzZsLCwwOHDhzFw4EA8ffpUo4uWAgIC0Lp1a6SmpiI2NhYGBgbw9fUFAPGc1D/++APVq1eXvO7FPxpbW1vUqlULtWrVQn5+Prp06YJLly5BqVTi8ePH8PHxgY+PD6KiomBlZYWkpCT4+Pjg6dOnxe5zQbB8vv5WVlawsbHBxo0bMWDAAPF+aMCzC4qys7Px9OnTN3KlXlZWFho1aoSoqKhCy6ysrMSfHz58WKjtRUqlssj/8XLVCuTzvn0ayVUreK9DDbFmmmPNNMeaaa4saqarq4sJEybA398fdnZ2SE5ORnBwMLS1tdG7d2/k5OSgQ4cOyM7ORlRUFHJycpCTkwPg2fuWtrY2YmJi8O+//6Jx48YwNjZGfHw8Jk6ciObNm6NWrVritgruTPP48WM8ePAA8fHx0NPTEyeatm7diilTpuDvv/8GAPj5+aFOnToYMGAA5s2bh5SUFAQHB2PEiBGvvBj5ZfsrZyAt7bZLdVHTzJkzi5ztysnJwcyZM0s1kNdx+vRpqNVqLFy4EM2aNcOHH35Y6FxWPT29En2lqZeXF2xtbfHzzz8jKioK3bp1E4tbp04dKJVKJCUlwcnJSfKwtbUtdp2ff/45dHR08P333wMA/v77bzx48ABz585Fy5YtUbt2bckFTZowMDDAjh07oK+vDx8fH8kJ2g0bNgQAXL58WfKaktSiJH3c3d1x9epVVKlSpVA9nj/n9NKlS9DV1UXdunU13DsiIqLXd+fOHfTq1QvOzs7o3r07KleujGPHjsHKygpnzpzB8ePHcfHiRTg5OaFatWri4/bt2wCevdeuWrUKLVq0gIuLC8aOHYtPP/0UO3bskGzHzc0Nbm5uOH36NDZs2AA3NzfJLGl6errk4mRtbW3s2LED2tra8PT0RO/evREYGChLlpJbqQJpaGioOFv4vOzs7CLPA3zTnJyckJeXhyVLluDGjRv48ccfsWLFCkkfe3t7ZGVlYd++fbh///5LDx9/8cUXWLFiBWJjYxEQECC2m5iYYMKECRg7dizWrVuH69ev48yZM1iyZAnWrVtX7PoUCgVGjRqFuXPnIjs7GzVq1ICenp443u3btyMsLKzU+29kZIQ//vgDOjo6aN++vfi7sbKygru7Ow4fPlyoFgcPHsTdu3dx//79Itdpb2+Pmzdv4ty5c7h//77kgqMCAQEBsLS0RKdOnXDo0CHcvHkTcXFxGDVqFO7cuSP2O3ToEFq2bCkeuiciInqboqOjkZycjNzcXNy5cwfR0dGoWbMmgGd3nxEEochHwaH4jz76CEeOHEFaWhpycnLwzz//YO7cueLdaQoUtY7n7zzTr18/CIL0Qi07OzvExMQgOzsb//33HxYsWPDefUsTUMpAKghCkeclnj9/vtCJvW9DgwYN8O233+Kbb75BvXr1EBUVhfDwcEkfLy8vDB06FD169ICVlRXmzZtX7PoCAgJw+fJlVK9evdBFWmFhYZg+fTrCw8Ph4uICX19f/PHHH5Kb4Relb9++yMvLw9KlS2FlZYXIyEj88ssvqFOnDubOnYsFCxaUvgB4dteAnTt3QhAEdOjQAY8fPwYABAUFFTqkPnPmTCQmJqJmzZrFHkbv2rUrfH198dFHH8HKyqrI+8saGhri4MGDqFGjBj777DO4uLhg4MCBePLkieTUgejo6GKv6CciIiJSCC9G9ZeoVKkSFAqFeMPY50OpSqVCVlYWhg4dimXLlr2RwZLmcnJy4OzsjJ9//lm8M8DbtHPnTowfPx4XLlzQ6BNfRkYGzMzMcP/+fVSuXPkNjrDiyMvLQ0xMDPz8/GQ9f+hdwpppjjXTHGumOdZMc+WlZgXv3wVZsaQ0mhNetGgRBEHAgAEDEBoaKjlPUE9PD/b29rKEHiqegYEB1q9fX+yh+Tft8ePHWLt27Xt5+IGIiIhKRqOU0LdvXwCAg4MDvLy8+KnlHdGmTRvZtv3555/Ltm0iIiJ6N5Rq2ur5G8A/efKk0K2KNJmiJSIiIqL3W6kuasrOzsbIkSNRpUoVGBkZoVKlSpIHEREREVFJlSqQTpw4EX/++SeWL18OpVKJ1atXIzQ0FDY2NuL3thMRERERlUSpDtn//vvvWL9+Pdq0aYP+/fujZcuWcHJygp2dHaKioiT37iQiIiIieplSzZA+fPgQjo6OAJ6dL1rw1ZAtWrTAwYMHy250RERERFThlSqQOjo64ubNmwCA2rVrY9OmTQCezZy++K0FREREREQvU6pA2r9/f5w/fx4AMHnyZCxbtgz6+voYO3YsJk6cWKYDJCIiIqKKrVTnkI4dO1b82dvbG3///TdOnz4NJycnuLq6ltngiIiIiKjie+2vz3ny5Ans7OxgZ2dXFuMhIiIiovdMqQ7Zq1QqhIWFoXr16jA2NsaNGzcAANOnT8f//ve/Mh0gEREREVVspQqks2fPRmRkJObNmwc9PT2xvV69eli9enWZDY6IiIiIKr5SBdL169fjhx9+QEBAALS1tcX2Bg0a4O+//y6zwRERERFRxVeqQHr37l04OTkValer1cjLy3vtQRERERHR+6NUgbROnTo4dOhQofZff/0Vbm5urz0oIiIiInp/lOoq+xkzZqBv3764e/cu1Go1tmzZgoSEBKxfvx47duwo6zESERERUQWm0QzpjRs3IAgCOnXqhN9//x179+6FkZERZsyYgStXruD3339Hu3bt3tRYiYiIiKgC0miGtFatWrh37x6qVKmCli1bwsLCAhcvXkTVqlXf1PiIiIiIqILTaIZUEATJ8507d+Lx48dlOiAiIiIier+U6qKmAi8GVCIiIiIiTWkUSBUKBRQKRaE2IiIiIqLS0ugcUkEQ0K9fPyiVSgDPvsd+6NChMDIykvTbsmVL2Y2QiIiIiCo0jQJp3759Jc979+5dpoMhIiIiovePRoF07dq1b2ocRERERPSeeq2LmoiIiIiIXhcDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVgykRERERCQrBlIiIiIikhUDKRERERHJioGUiIiIiGTFQEpEREREsmIgJSIiIiJZMZASERERkawYSImIiIhIVjpyD4DoZZqG70O+jpHcw3gnKLUFzGsC1AvZjVyVQu7hvBNYM82xZpqr6DVLnNtB7iFQBcAZ0jLUpk0bjBkzptjl9vb2WLRo0Vsbz5vQr18/dO7cWe5hEBFROTZ37lwoFArxPTExMREKhaLIxy+//CK+btSoUWjUqBGMjY2LfT/dvXs3mjVrBhMTE1hZWaFr165ITEx86XgePnyIgIAAmJqawtzcHAMHDkRWVlYZ7S2VBQbSt+jkyZMYPHiw3MMgIiJ6Y06ePImVK1fC1dVVbLO1tcW9e/ckj9DQUBgbG6N9+/aS1w8YMADdunUrct03b95Ep06d8PHHH+PcuXPYvXs37t+/j88+++ylYwoICEB8fDxiY2OxY8cOHDx4kO/H5QwP2b9FVlZWcg9BdoIgQKVSQUeHf3pERBVNVlYWAgICsGrVKsyaNUts19bWhrW1taTv1q1b0b17dxgbG4tt3333HQAgJSUFf/31V6H1nz59GiqVCrNmzYKW1rM5tQkTJqBTp07Iy8uDrq5uoddcuXIFu3btwsmTJ+Hh4QEAWLJkCfz8/LBgwQLY2Ni8/o7Ta+MMaRnLz8/HyJEjYWZmBktLS0yfPh2CIADQ7JB9WloagoKCYGVlBVNTU3z88cc4f/68uDwkJAQNGzbEjz/+CHt7e5iZmaFnz57IzMwU+6jVasybNw9OTk5QKpWoUaMGZs+eLS6/ePEiPv74YxgYGKBy5coYPHiw5BCGSqXCuHHjYG5ujsqVK+Orr74S9+X5bYSHh8PBwQEGBgZo0KABfv31V3F5XFwcFAoFdu7ciUaNGkGpVOLw4cMa1ZSIiN4NI0aMQIcOHeDt7f3SfqdPn8a5c+cwcOBAjdbfqFEjaGlpYe3atVCpVEhPT8ePP/4Ib2/vIsMoABw9ehTm5uZiGAUAb29vaGlp4fjx4xptn94cBtIytm7dOujo6ODEiRNYvHgxvv32W6xevVrj9XTr1g2pqanYuXMnTp8+DXd3d7Rt2xYPHz4U+1y/fh3btm3Djh07sGPHDhw4cABz584Vl0+ZMgVz587F9OnTcfnyZWzYsAFVq1YFADx+/Bg+Pj6oVKkSTp48iV9++QV79+7FyJEjxdcvXLgQkZGRWLNmDQ4fPoyHDx9i69atknGGh4dj/fr1WLFiBeLj4zF27Fj07t0bBw4ckPSbPHky5s6diytXrkgO4xARUcUQHR2NM2fOIDw8/JV9//e//8HFxQVeXl4abcPBwQF79uzB1KlToVQqYW5ujjt37mDTpk3FviYlJQVVqlSRtOno6MDCwgIpKSkabZ/eHB43LWO2traIiIiAQqGAs7MzLl68iIiICAwaNKjE6zh8+DBOnDiB1NRUKJVKAMCCBQuwbds2/Prrr+J5L2q1GpGRkTAxMQEA9OnTB/v27cPs2bORmZmJxYsXY+nSpejbty8AoGbNmmjRogUAYMOGDXjy5AnWr18PI6NnV7EvXboU/v7++Oabb1C1alUsWrQIU6ZMEc/NWbFiBXbv3i2OMzc3F3PmzMHevXvh6ekJAHB0dMThw4excuVKtG7dWuw7c+ZMtGvXrth9zs3NRW5urvg8IyMDAKDUEqCtLRT3MnqOUkuQ/JdejTXTHGumuYpes7y8PNy+fRujR49GTEwMtLW1kZeXB0EQoFarkZeXJ+mfk5ODDRs2YOrUqYWWFVCr1eK6n5eSkoKgoCD07t0bPXr0QFZWFkJDQ9G1a1fs3LkTCkXhuxioVCoIglDktlQqVbFjeNcU7Ifc+1Pa7TOQlrFmzZpJ/ofw9PTEwoULoVKpSryO8+fPIysrC5UrV5a05+Tk4Pr16+Jze3t7MYwCQLVq1ZCamgrg2Tkzubm5aNu2bZHbuHLlCho0aCCGUQBo3rw51Go1EhISoK+vj3v37qFp06bich0dHXh4eIiH7a9du4bs7OxCQfPp06dwc3OTtD1/qKQo4eHhCA0NLdT+tZsahoYlrx0BYR5quYfwzmHNNMeaaa6i1iwmJgbHjh1DamoqmjRpIrar1WocOnQIy5Ytwy+//AJtbW0AwP79+/H48WNYW1sjJiamyHUWvNfFxsZK2qOiogAArVq1wr179wAAgYGBCAoKwqJFi+Ds7FxoXampqUhOTpZsS6VS4cGDB7h7926xY3hXvVizty07O7tUr2MgLYeysrJQrVo1xMXFFVpmbm4u/vzi+TIKhUL8VGlgYPAmhwgA4vmmf/zxB6pXry5ZVjCzW+D54FuUKVOmYNy4ceLzjIwM2NraYtZZLeTrapfRiCs2pZaAMA81pp/SQq664t3r8E1gzTTHmmmuotfsUogPWrZsie7du0vaBw0aBGdnZ0yYMAH16tUT27/99lv4+/ujV69exa7zxIkTOH78ONq1ayd5r4uLi0NiYiL8/PzEtoJg2qxZM/Fo3fMcHBywdOlSWFtbw93dHcCz0CYIAoYOHVphLmrKy8tDbGxsoZq9bQVHODXFQFrGXjxB+tixY6hVq5b4ybAk3N3dkZKSAh0dHdjb25dqHLVq1YKBgQH27duHoKCgQstdXFwQGRmJx48fi2Hxr7/+gpaWFpydnWFmZoZq1arh+PHjaNWqFYBnF2wVnM8KAHXq1IFSqURSUpLk8HxpKJXKQiEWAHLVCuRXwBtJv0m5akWFvPn2m8SaaY4101xFrZmuri4sLCxgYWEhaTc2NoaVlZXkiNm1a9dw6NAhxMTEFBmarl27hqysLPz33394+vQp4uPjoaurizp16kBPTw/+/v5YvHgxwsPD0atXL2RmZmLq1Kmws7ND48aNoaurixMnTiAwMBD79u1D9erV4erqCl9fXwwbNgwrVqxAXl4exowZg549e8LOzu6N1+dt09XVlTWQlnbbvKipjCUlJWHcuHFISEjAxo0bsWTJEowePVqjdXh7e8PT0xOdO3fGnj17kJiYiCNHjmDatGk4depUidahr6+PSZMm4auvvsL69etx/fp1HDt2DP/73/8APLsnm76+Pvr27YtLly5h//79+PLLL9GnTx/xwqfRo0dj7ty52LZtG/7++28MHz4caWlp4jZMTEwwYcIEjB07FuvWrcP169dx5swZLFmyBOvWrdNon4mIqOJbs2YNPvjgA3zyySdFLg8KCoKbmxtWrVqF5ORkNGnSBG5ubkhOTgYAfPzxx9iwYQO2bdsGNzc3+Pr6QqlUYteuXeKRwezsbCQkJEjOZYyKikLt2rXRtm1b+Pn5oUWLFvjhhx/e/A5TiXGGtIwFBgYiJycHTZo0gba2NkaPHq3xzXcVCgViYmIwbdo09O/fH//99x+sra3RqlUrMSyWxPTp06Gjo4MZM2YgOTkZ1apVw9ChQwEAhoaG2L17N0aPHo3GjRvD0NAQXbt2xbfffiu+fvz48bh37x769u0LLS0tDBgwAF26dEF6errYJywsDFZWVggPD8eNGzdgbm4Od3d3TJ06VaN9JiKiiqWo087mzJmDOXPmvPI1eXl5iImJgZ+fX6EZt549e6Jnz57FrqNNmzaFblFoYWGBDRs2lHzw9NYphBd/a0TlQEZGBszMzFBz/M/8LvsSevZ92Sp8dUK7Qh4WfBNYM82xZpqr6DV7E99l/7JASkUrLzUreP9OT0+HqalpiV/HGVIq145PaVvobgNUtIJ/jC6F+PAf8BJizTTHmmmONSN6NZ5DKoOoqCgYGxsX+ahbt67cwyMiIiJ6qzhDKoNPP/1Ucn/P5/HTMxEREb1vGEhlYGJiIrmhPREREdH7jIfsiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZKUj9wCIXqZp+D7k6xjJPYx3glJbwLwmQL2Q3chVKeQezjuBNdMca6a5N12zxLkdJM/nzp2LKVOmYPTo0Vi0aBEePnyI4OBg7NmzB0lJSbCyskLnzp0RFhYGMzMz8XX79u3D9OnTcfHiRRgZGaFv376YPXs2dHSeRYUnT55g6NChOH36NK5cuYKOHTti27Ztrxzfw4cP8eWXX+L333+HlpYWunbtisWLF8PY2LhM60DvNllnSNu0aYMxY8bIOYSXSkhIgLW1NTIzM4vtExkZCXNz87c3qCKEhISgYcOGr72euLg4KBQKpKWlvfa6Cty/fx9VqlTBnTt3ymydRERUtJMnT2LlypVwdXUV25KTk5GcnIwFCxbg0qVLiIyMxK5duzBw4ECxz/nz5+Hn5wdfX1+cPXsWP//8M7Zv347JkyeLfVQqFQwMDDBq1Ch4e3uXeEwBAQGIj49HbGwsduzYgYMHD2Lw4MFls8NUYVS4Q/ZxcXHo1KkTqlWrBiMjIzRs2BBRUVGSPpGRkVAoFJKHvr5+oXVNmTIFX375JUxMTN7W8EtlwoQJ2Ldvn0avKerDgJeXF+7duyf5xPy6LC0tERgYiODg4DJbJxERFZaVlYWAgACsWrUKlSpVEtvr1auHzZs3w9/fHzVr1sTHH3+M2bNn4/fff0d+fj4A4Oeff4arqytmzJgBJycntG7dGvPmzcOyZcvESRkjIyMsX74cgwYNgrW1dYnGdOXKFezatQurV69G06ZN0aJFCyxZsgTR0dFITk4u+yLQO6vCBdIjR47A1dUVmzdvxoULF9C/f38EBgZix44dkn6mpqa4d++e+Lh165ZkeVJSEnbs2IF+/fq9xdFrRhAE5Ofnw9jYGJUrV37t9enp6cHa2hoKRdkeUurfvz+ioqLw8OHDMl0vERH9vxEjRqBDhw4lmr1MT0+HqampeDg+Nze30MSMgYEBnjx5gtOnT5d6TEePHoW5uTk8PDzENm9vb2hpaeH48eOlXi9VPLIH0vz8fIwcORJmZmawtLTE9OnTIQgCAMDe3h6zZs1CYGAgjI2NYWdnh+3bt+O///5Dp06dYGxsDFdXV5w6dUpc39SpUxEWFgYvLy/UrFkTo0ePhq+vL7Zs2SLZrkKhgLW1tfioWrWqZPmmTZvQoEEDVK9eXdIeGRmJGjVqwNDQEF26dMGDBw8K7dNvv/0Gd3d36Ovrw9HREaGhoeKnUEEQEBISgho1akCpVMLGxgajRo0SX5ubm4tJkybB1tYWSqUSTk5O+N///gfg/w+p79y5E40aNYJSqcThw4cLHbLv168fOnfujNDQUFhZWcHU1BRDhw7F06dPxeUHDhzA4sWLxRnixMTEIg/Zb968GXXr1oVSqYS9vT0WLlwo2Vd7e3vMmTMHAwYMgImJCWrUqIEffvhB0qdu3bqwsbHB1q1bC/8BEBHRa4uOjsaZM2cQHh7+yr73799HWFiY5LC5j48Pjhw5go0bN0KlUuHu3buYOXMmAODevXulHldKSgqqVKkiadPR0YGFhQVSUlJKvV6qeGS/qGndunUYOHAgTpw4gVOnTmHw4MGoUaMGBg0aBACIiIjAnDlzMH36dERERKBPnz7w8vLCgAEDMH/+fEyaNAmBgYGIj48vdmYvPT0dLi4ukrasrCzY2dlBrVbD3d0dc+bMQd26dcXlhw4dknyiA4Djx49j4MCBCA8PR+fOnbFr165Ch6IPHTqEwMBAfPfdd2jZsiWuX78u/k8fHByMzZs3IyIiAtHR0ahbty5SUlJw/vx58fWBgYE4evQovvvuOzRo0AA3b97E/fv3JduYPHkyFixYAEdHR1SqVAlxcXGF9nnfvn3Q19dHXFwcEhMT0b9/f1SuXBmzZ8/G4sWL8c8//6BevXriPzhWVlZITEyUrOP06dPo3r07QkJC0KNHDxw5cgTDhw9H5cqVJTPHCxcuRFhYGKZOnYpff/0Vw4YNQ+vWreHs7Cz2adKkCQ4dOiQ5Z+l5ubm5yM3NFZ9nZGQAAJRaArS1hSJfQ1JKLUHyX3o11kxzrJnm3nTNbty4gdGjRyMmJgba2trIy8uDIAhQq9XIy8uT9M3IyICfnx9cXFwwbdo0cflHH32EuXPnYujQoejTpw+USiWmTp2KQ4cOFbketVpdZPuLVCoVBEEosp9KpSr29QXtr1o//b/yUrPSbl/2QGpra4uIiAgoFAo4Ozvj4sWLiIiIEAOpn58fhgwZAgCYMWMGli9fjsaNG6Nbt24AgEmTJsHT0xP//vtvkee0bNq0STzJu4CzszPWrFkDV1dXpKenY8GCBfDy8kJ8fDw++OADAMCtW7cKBdLFixfD19cXX331FQDgww8/xJEjR7Br1y6xT2hoKCZPnoy+ffsCABwdHREWFoavvvoKwcHBSEpKgrW1Nby9vaGrq4saNWqgSZMmAIB//vkHmzZtQmxsrHjIxdHRsdA+zZw5E+3atXtpXfX09LBmzRoYGhqibt26mDlzJiZOnCheVamnpwdDQ8OXngf07bffom3btpg+fbq4v5cvX8b8+fMlgdTPzw/Dhw8H8Oz3ERERgf3790sCqY2NDc6ePVvstsLDwxEaGlqo/Ws3NQwNVS/dV5IK81DLPYR3DmumOdZMc2+qZqtWrUJqaqr4XgI8C4yHDh3CsmXL8Msvv0BbWxs5OTkICQmBUqnEwIEDERsbK1nPhx9+iHXr1uHRo0cwMjJCamoqgGczpDExMZK+d+7cwePHjwu1vyg1NRXJycmSfiqVCg8ePMDdu3df+foXx0ivJnfNsrOzS/U62QNps2bNJDObnp6eWLhwIVSqZyHk+SsFCw6r169fv1BbampqoXC1f/9+9O/fH6tWrZLMfnp6esLT01N87uXlBRcXF6xcuRJhYWEAgJycnELn01y5cgVdunSRtHl6ekoC6fnz5/HXX39h9uzZYptKpcKTJ0+QnZ2Nbt26YdGiRXB0dISvry/8/Pzg7+8PHR0dnDt3Dtra2mjduvVLa/ZiUC5KgwYNYGhoKBlnVlYWbt++DTs7u1e+vmB/O3XqJGlr3rw5Fi1aBJVKBW1tbQDS31HBqRAF/5AVMDAweOkf6ZQpUzBu3DjxeUZGBmxtbTHrrBbydbVLNN73nVJLQJiHGtNPaSFXzdvxlARrpjnWTHNvumZHJ05E9+7dJW2DBg2Cs7MzJkyYgHr16iEjIwMdOnRA1apVsX37dsn7Q3FCQkJga2uLkSNHiv/eF9i8eTPS0tLg5+f30nU4ODhg6dKlsLa2hru7O4BngUkQBAwdOhQ2NjZFvi4vLw+xsbFo164ddHV1XzlWKj81KzjCqSnZA+mrPF/UguBaVJtaLf3keeDAAfj7+yMiIgKBgYGv3IabmxuuXbsmtllaWuLRo0cajzcrKwuhoaH47LPPCi3T19eHra0tEhISsHfvXsTGxmL48OGYP38+Dhw4AAMDgxJtw8iofN2X88U/fIVCUej38fDhQ1hZWRW7DqVSCaVSWag9V61APu91qJFctYL3h9QQa6Y51kxzb6pmFhYWsLCwkLQZGxvDysoKbm5uYhjNzs5GVFQUcnJykJOTA+DZ6VoFYXP+/Pnw9fWFlpYWtmzZgvnz52PTpk2SyZnLly/j6dOnSEtLQ2ZmJuLj4wFAvI7hxIkTCAwMxL59+1C9enW4urrC19cXw4YNw4oVK5CXl4cxY8agZ8+eJZoc0dXVZSDVkNw1K+22ZQ+kL15ld+zYMdSqVavQpzFNxMXFoWPHjvjmm29KdK8zlUqFixcvSj7pubm54fLly5J+Li4uRY73ee7u7khISICTk1Ox2zMwMIC/vz/8/f0xYsQI1K5dGxcvXkT9+vWhVqtx4MABje7xVpTz588jJydHDLnHjh2DsbExbG1tATw7pF8wC10cFxcX/PXXX5K2v/76Cx9++KHGv59Lly6hTZs2Gr2GiIhe35kzZ8T3rhffm27evAl7e3sAwM6dOzF79mzk5uaiQYMG+O2339C+fXtJfz8/P8ldadzc3ABAvBg5OzsbCQkJkvMIo6KiMHLkSLRt21a8Mf53331X5vtJ7zbZA2lSUhLGjRuHIUOG4MyZM1iyZEmhK7k1sX//fnTs2BGjR49G165dxav49PT0xE+QM2fORLNmzeDk5IS0tDTMnz8ft27dQlBQkLgeHx8fBAUFSQ5Njxo1Cs2bN8eCBQvQqVMn7N69W3K4Hnh2nmvHjh1Ro0YNfP7559DS0sL58+dx6dIlzJo1C5GRkVCpVGjatCkMDQ3x008/wcDAAHZ2dqhcuTL69u2LAQMGiBc13bp1C6mpqYUOx7zK06dPMXDgQHz99ddITExEcHAwRo4cCS2tZzdWsLe3x/Hjx5GYmAhjY+NCn64BYPz48WjcuDHCwsLQo0cPHD16FEuXLsX333+v0Viys7Nx+vRpzJkzR6PXERFR6Tx/sWubNm3EwPgyf/755yv7vHjx64uK2paFhQU2bNjwynXT+0322z4FBgYiJycHTZo0wYgRIzB69OjX+gaHdevWITs7G+Hh4ahWrZr4eP4Q+qNHjzBo0CC4uLjAz88PGRkZOHLkCOrUqSP2ad++PXR0dLB3716xrVmzZli1ahUWL16MBg0aYM+ePfj6668l2/fx8cGOHTuwZ88eNG7cGM2aNUNERIR4aMLc3ByrVq1C8+bN4erqir179+L3338X7yO6fPlyfP755xg+fDhq166NQYMG4fHjxxrXoW3btqhVqxZatWqFHj164NNPP0VISIi4fMKECdDW1kadOnVgZWWFpKSkQutwd3fHpk2bEB0djXr16mHGjBmYOXOmxvdm/e2331CjRg20bNlS4/0gIiKiik8hlORj03tq2bJl2L59O3bv3i33UDTSr18/pKWlleg7ht+GZs2aYdSoUfjiiy9K/JqMjAyYmZnh/v37ZXLT//dBXl4eYmJi4Ofnx3OuSog10xxrpjnWTHOsmebKS80K3r8LvnyhpGQ/ZF+eDRkyRDxxu7x/fWh5df/+fXz22Wfo1auX3EMhIiKicoqB9CV0dHQwbdo0uYfxTrO0tBTv20pERERUFAbSCigyMlLuIRARERGVmOwXNRERERHR+42BlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLLSkXsAREURBAEAkJmZCV1dXZlH827Iy8tDdnY2MjIyWLMSYs00x5ppjjXTHGumufJSs4yMDAD//z5eUgykVC49ePAAAODg4CDzSIiIiEhTmZmZMDMzK3F/BlIqlywsLAAASUlJGv1Bv88yMjJga2uL27dvw9TUVO7hvBNYM82xZppjzTTHmmmuvNRMEARkZmbCxsZGo9cxkFK5pKX17PRmMzMz/mOkIVNTU9ZMQ6yZ5lgzzbFmmmPNNFcealaaiSRe1EREREREsmIgJSIiIiJZMZBSuaRUKhEcHAylUin3UN4ZrJnmWDPNsWaaY800x5pp7l2vmULQ9Lp8IiIiIqIyxBlSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRERERLJiIKVyadmyZbC3t4e+vj6aNm2KEydOyD2kt+LgwYPw9/eHjY0NFAoFtm3bJlkuCAJmzJiBatWqwcDAAN7e3rh69aqkz8OHDxEQEABTU1OYm5tj4MCByMrKkvS5cOECWrZsCX19fdja2mLevHlvetfemPDwcDRu3BgmJiaoUqUKOnfujISEBEmfJ0+eYMSIEahcuTKMjY3RtWtX/Pvvv5I+SUlJ6NChAwwNDVGlShVMnDgR+fn5kj5xcXFwd3eHUqmEk5MTIiMj3/TuvRHLly+Hq6ureANtT09P7Ny5U1zOer3c3LlzoVAoMGbMGLGNNSssJCQECoVC8qhdu7a4nDUr2t27d9G7d29UrlwZBgYGqF+/Pk6dOiUur7DvAwJRORMdHS3o6ekJa9asEeLj44VBgwYJ5ubmwr///iv30N64mJgYYdq0acKWLVsEAMLWrVsly+fOnSuYmZkJ27ZtE86fPy98+umngoODg5CTkyP28fX1FRo0aCAcO3ZMOHTokODk5CT06tVLXJ6eni5UrVpVCAgIEC5duiRs3LhRMDAwEFauXPm2drNM+fj4CGvXrhUuXboknDt3TvDz8xNq1KghZGVliX2GDh0q2NraCvv27RNOnTolNGvWTPDy8hKX5+fnC/Xq1RO8vb2Fs2fPCjExMYKlpaUwZcoUsc+NGzcEQ0NDYdy4ccLly5eFJUuWCNra2sKuXbve6v6Whe3btwt//PGH8M8//wgJCQnC1KlTBV1dXeHSpUuCILBeL3PixAnB3t5ecHV1FUaPHi22s2aFBQcHC3Xr1hXu3bsnPv777z9xOWtW2MOHDwU7OzuhX79+wvHjx4UbN24Iu3fvFq5duyb2qajvAwykVO40adJEGDFihPhcpVIJNjY2Qnh4uIyjevteDKRqtVqwtrYW5s+fL7alpaUJSqVS2LhxoyAIgnD58mUBgHDy5Emxz86dOwWFQiHcvXtXEARB+P7774VKlSoJubm5Yp9JkyYJzs7Ob3iP3o7U1FQBgHDgwAFBEJ7VSFdXV/jll1/EPleuXBEACEePHhUE4dkHAS0tLSElJUXss3z5csHU1FSs01dffSXUrVtXsq0ePXoIPj4+b3qX3opKlSoJq1evZr1eIjMzU6hVq5YQGxsrtG7dWgykrFnRgoODhQYNGhS5jDUr2qRJk4QWLVoUu7wivw/wkD2VK0+fPsXp06fh7e0ttmlpacHb2xtHjx6VcWTyu3nzJlJSUiS1MTMzQ9OmTcXaHD16FObm5vDw8BD7eHt7Q0tLC8ePHxf7tGrVCnp6emIfHx8fJCQk4NGjR29pb96c9PR0AICFhQUA4PTp08jLy5PUrXbt2qhRo4akbvXr10fVqlXFPj4+PsjIyEB8fLzY5/l1FPR51/8uVSoVoqOj8fjxY3h6erJeLzFixAh06NCh0H6xZsW7evUqbGxs4OjoiICAACQlJQFgzYqzfft2eHh4oFu3bqhSpQrc3NywatUqcXlFfh9gIKVy5f79+1CpVJJ/gACgatWqSElJkWlU5UPB/r+sNikpKahSpYpkuY6ODiwsLCR9ilrH89t4V6nVaowZMwbNmzdHvXr1ADzbJz09PZibm0v6vli3V9WkuD4ZGRnIycl5E7vzRl28eBHGxsZQKpUYOnQotm7dijp16rBexYiOjsaZM2cQHh5eaBlrVrSmTZsiMjISu3btwvLly3Hz5k20bNkSmZmZrFkxbty4geXLl6NWrVrYvXs3hg0bhlGjRmHdunUAKvb7gI4sWyUiegNGjBiBS5cu4fDhw3IPpdxzdnbGuXPnkJ6ejl9//RV9+/bFgQMH5B5WuXT79m2MHj0asbGx0NfXl3s474z27duLP7u6uqJp06aws7PDpk2bYGBgIOPIyi+1Wg0PDw/MmTMHAODm5oZLly5hxYoV6Nu3r8yje7M4Q0rliqWlJbS1tQtdafnvv//C2tpaplGVDwX7/7LaWFtbIzU1VbI8Pz8fDx8+lPQpah3Pb+NdNHLkSOzYsQP79+/HBx98ILZbW1vj6dOnSEtLk/R/sW6vqklxfUxNTd/JN1c9PT04OTmhUaNGCA8PR4MGDbB48WLWqwinT59Gamoq3N3doaOjAx0dHRw4cADfffcddHR0ULVqVdasBMzNzfHhhx/i2rVr/DsrRrVq1VCnTh1Jm4uLi3iqQ0V+H2AgpXJFT08PjRo1wr59+8Q2tVqNffv2wdPTU8aRyc/BwQHW1taS2mRkZOD48eNibTw9PZGWlobTp0+Lff7880+o1Wo0bdpU7HPw4EHk5eWJfWJjY+Hs7IxKlSq9pb0pO4IgYOTIkdi6dSv+/PNPODg4SJY3atQIurq6krolJCQgKSlJUreLFy9K/hGPjY2Fqamp+Obg6ekpWUdBn4ryd6lWq5Gbm8t6FaFt27a4ePEizp07Jz48PDwQEBAg/syavVpWVhauX7+OatWq8e+sGM2bNy9027p//vkHdnZ2ACr4+4Bsl1MRFSM6OlpQKpVCZGSkcPnyZWHw4MGCubm55ErLiiozM1M4e/ascPbsWQGA8O233wpnz54Vbt26JQjCs9t9mJubC7/99ptw4cIFoVOnTkXe7sPNzU04fvy4cPjwYaFWrVqS232kpaUJVatWFfr06SNcunRJiI6OFgwNDd/Z2z4NGzZMMDMzE+Li4iS3l8nOzhb7DB06VKhRo4bw559/CqdOnRI8PT0FT09PcXnB7WU++eQT4dy5c8KuXbsEKyurIm8vM3HiROHKlSvCsmXL3tnby0yePFk4cOCAcPPmTeHChQvC5MmTBYVCIezZs0cQBNarJJ6/yl4QWLOijB8/XoiLixNu3rwp/PXXX4K3t7dgaWkppKamCoLAmhXlxIkTgo6OjjB79mzh6tWrQlRUlGBoaCj89NNPYp+K+j7AQErl0pIlS4QaNWoIenp6QpMmTYRjx47JPaS3Yv/+/QKAQo++ffsKgvDslh/Tp08XqlatKiiVSqFt27ZCQkKCZB0PHjwQevXqJRgbGwumpqZC//79hczMTEmf8+fPCy1atBCUSqVQvXp1Ye7cuW9rF8tcUfUCIKxdu1bsk5OTIwwfPlyoVKmSYGhoKHTp0kW4d++eZD2JiYlC+/btBQMDA8HS0lIYP368kJeXJ+mzf/9+oWHDhoKenp7g6Ogo2ca7ZMCAAYKdnZ2gp6cnWFlZCW3bthXDqCCwXiXxYiBlzQrr0aOHUK1aNUFPT0+oXr260KNHD8n9NFmzov3+++9CvXr1BKVSKdSuXVv44YcfJMsr6vuAQhAEQZ65WSIiIiIinkNKRERERDJjICUiIiIiWTGQEhEREZGsGEiJiIiISFYMpEREREQkKwZSIiIiIpIVAykRERERyYqBlIiIiIhkxUBKRESF9OvXDwqFotDj2rVrcg+NiCogHbkHQERE5ZOvry/Wrl0rabOyspJpNFJ5eXnQ1dWVexhEVEY4Q0pEREVSKpWwtraWPLS1tYvse+vWLfj7+6NSpUowMjJC3bp1ERMTIy6Pj49Hx44dYWpqChMTE7Rs2RLXr18HAKjVasycORMffPABlEolGjZsiF27domvTUxMhEKhwM8//4zWrVtDX18fUVFRAIDVq1fDxcUF+vr6qF27Nr7//vs3WBEielM4Q0pERK9txIgRePr0KQ4ePAgjIyNcvnwZxsbGAIC7d++iVatWaNOmDf7880+Ymprir7/+Qn5+PgBg8eLFWLhwIVauXAk3NzesWbMGn376KeLj41GrVi1xG5MnT8bChQvh5uYmhtIZM2Zg6dKlcHNzw9mzZzFo0CAYGRmhb9++stSBiEpHIQiCIPcgiIiofOnXrx9++ukn6Ovri23t27fHL7/8UmR/V1dXdO3aFcHBwYWWTZ06FdHR0UhISCjyMHv16tUxYsQITJ06VWxr0qQJGjdujGXLliExMREODg5YtGgRRo8eLfZxcnJCWFgYevXqJbbNmjULMTExOHLkSKn2m4jkwRlSIiIq0kcffYTly5eLz42MjIrtO2rUKAwbNgx79uyBt7c3unbtCldXVwDAuXPn0LJlyyLDaEZGBpKTk9G8eXNJe/PmzXH+/HlJm4eHh/jz48ePcf36dQwcOBCDBg0S2/Pz82FmZqbZjhKR7BhIiYioSEZGRnBycipR36CgIPj4+OCPP/7Anj17EB4ejoULF+LLL7+EgYFBmY2nQFZWFgBg1apVaNq0qaRfcee5ElH5xYuaiIioTNja2mLo0KHYsmULxo8fj1WrVgF4djj/0KFDyMvLK/QaU1NT2NjY4K+//pK0//XXX6hTp06x26patSpsbGxw48YNODk5SR4ODg5lu2NE9MZxhpSIiF7bmDFj0L59e3z44Yd49OgR9u/fDxcXFwDAyJEjsWTJEvTs2RNTpkyBmZkZjh07hiZNmsDZ2RkTJ05EcHAwatasiYYNG2Lt2rU4d+6ceCV9cUJDQzFq1CiYmZnB19cXubm5OHXqFB49eoRx48a9jd0mojLCQEpERK9NpVJhxIgRuHPnDkxNTeHr64uIiAgAQOXKlfHnn39i4sSJaN26NbS1tdGwYUPxvNFRo0YhPT0d48ePR2pqKurUqYPt27dLrrAvSlBQEAwNDTF//nxMnDgRRkZGqF+/PsaMGfOmd5eIyhivsiciIiIiWfEcUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESyYiAlIiIiIlkxkBIRERGRrBhIiYiIiEhWDKREREREJCsGUiIiIiKSFQMpEREREcmKgZSIiIiIZMVASkRERESy+j/KCygNuM+clgAAAABJRU5ErkJggg==", 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" ] @@ -601,27 +751,84 @@ { "cell_type": "markdown", "id": "071a0a1d", - "metadata": {}, + "metadata": { + "id": "071a0a1d" + }, "source": [ - "## Export model for serving in Vespa \n", + "## Export model for serving in Vespa\n", "\n", - "See [importing-xgboost models](https://docs.vespa.ai/en/xgboost#exporting-models-from-xgboost) for how to import the exported JSON file into Vespa for serving. Since we used a DataFrame with column names as input, the column names are used as feature names. " + "See [importing-xgboost models](https://docs.vespa.ai/en/xgboost#exporting-models-from-xgboost) for how to import the exported JSON file into Vespa for serving. Since we used a DataFrame with column names as input, the column names are used as feature names." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "00bab1a7", - "metadata": {}, + "metadata": { + "id": "00bab1a7" + }, "outputs": [], "source": [ "model.dump_model('product_ranker_simple_xgboost.json', with_stats=False, dump_format='json')" ] }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9WVz2Q_v-e9Y", + "metadata": { + "id": "9WVz2Q_v-e9Y" + }, + "outputs": [], + "source": [ + "import json\n", + "with open('product_ranker_simple_xgboost.json', 'r') as f:\n", + " model_json = json.load(f)\n" + ] + }, + { + "cell_type": "markdown", + "id": "voiOvVTdJ47J", + "metadata": { + "id": "voiOvVTdJ47J" + }, + "source": [ + "Only the first 71 trees are exported (as we used early stopping). This is important as number of trees does impact inference performance." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "m8qKYN1pAqw9", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m8qKYN1pAqw9", + "outputId": "c0e096b1-efe4-47dd-8b82-0c7a50c3f994" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "105" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(model_json)" + ] + }, { "cell_type": "markdown", "id": "30732710", - "metadata": {}, + "metadata": { + "id": "30732710" + }, "source": [ "Place the exported `product_ranker_simple.json` file in the `models` directory in the Vespa application package and configure the rank-profile:\n", "\n", @@ -636,6 +843,9 @@ } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -651,7 +861,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.14" } }, "nbformat": 4,