{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "RYmPh1qB_KO2" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2021-02-13T02:42:48.656023Z", "iopub.status.busy": "2021-02-13T02:42:48.655359Z", "iopub.status.idle": "2021-02-13T02:42:48.657357Z", "shell.execute_reply": "2021-02-13T02:42:48.657802Z" }, "id": "oMRm3czy9tLh" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "ooXoR4kx_YL9" }, "source": [ "# TF Lattice 集約関数モデル" ] }, { "cell_type": "markdown", "metadata": { "id": "BR6XNYEXEgSU" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
TensorFlow.orgで表示 Google Colabで実行GitHub でソースを表示{ノートブックをダウンロード/a0}
" ] }, { "cell_type": "markdown", "metadata": { "id": "-ZfQWUmfEsyZ" }, "source": [ "## 概要\n", "\n", "既製の TFL (TensorFlow Lattice) 集約関数モデルは、複雑な集約関数の学習向けの TFL `tf.keras.model`インスタンスを素早く簡単に構築する方法です。このガイドでは、既製の TFL 集約関数モデルを構築し、トレーニングやテストを行うために必要な手順を説明します。 " ] }, { "cell_type": "markdown", "metadata": { "id": "L0lgWoB6Gmk1" }, "source": [ "## セットアップ\n", "\n", "TF Lattice パッケージをインストールします。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:48.667263Z", "iopub.status.busy": "2021-02-13T02:42:48.666523Z", "iopub.status.idle": "2021-02-13T02:42:51.222619Z", "shell.execute_reply": "2021-02-13T02:42:51.221824Z" }, "id": "ivwKrEdLGphZ" }, "outputs": [], "source": [ "#@test {\"skip\": true}\n", "!pip install -q tensorflow-lattice pydot" ] }, { "cell_type": "markdown", "metadata": { "id": "VQsRKS4wGrMu" }, "source": [ "必要なパッケージをインポートします。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:51.228255Z", "iopub.status.busy": "2021-02-13T02:42:51.227556Z", "iopub.status.idle": "2021-02-13T02:42:58.470048Z", "shell.execute_reply": "2021-02-13T02:42:58.469379Z" }, "id": "j41-kd4MGtDS" }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "import collections\n", "import logging\n", "import numpy as np\n", "import pandas as pd\n", "import sys\n", "import tensorflow_lattice as tfl\n", "logging.disable(sys.maxsize)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZHPohKjBIFG5" }, "source": [ "Puzzles データセットをダウンロードします。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:58.475802Z", "iopub.status.busy": "2021-02-13T02:42:58.475072Z", "iopub.status.idle": "2021-02-13T02:42:58.860150Z", "shell.execute_reply": "2021-02-13T02:42:58.860635Z" }, "id": "VjYHpw2dSfHH" }, "outputs": [ { "data": { "text/html": [ "
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puzzle_namestar_ratingword_countis_amazonincludes_photonum_helpfulSales12-18MonthsAgo
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" ], "text/plain": [ " puzzle_name star_rating word_count is_amazon includes_photo \\\n", "0 NightHawks 5 108 1 0 \n", "1 NightHawks 5 31 1 0 \n", "2 NightHawks 5 51 1 0 \n", "3 NightHawks 5 88 1 0 \n", "4 NightHawks 4 37 1 0 \n", "\n", " num_helpful Sales12-18MonthsAgo \n", "0 6 81 \n", "1 5 81 \n", "2 4 81 \n", "3 4 81 \n", "4 2 81 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataframe = pd.read_csv(\n", " 'https://raw.githubusercontent.com/wbakst/puzzles_data/master/train.csv')\n", "train_dataframe.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:58.864991Z", "iopub.status.busy": "2021-02-13T02:42:58.864299Z", "iopub.status.idle": "2021-02-13T02:42:59.204915Z", "shell.execute_reply": "2021-02-13T02:42:59.204274Z" }, "id": "UOsgu3eIEur6" }, "outputs": [ { "data": { "text/html": [ "
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puzzle_namestar_ratingword_countis_amazonincludes_photonum_helpfulSalesLastSixMonths
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" ], "text/plain": [ " puzzle_name star_rating word_count is_amazon includes_photo \\\n", "0 NightHawks 4 138 0 0 \n", "1 NightHawks 5 115 0 0 \n", "2 NightHawks 5 127 0 0 \n", "3 NightHawks 5 108 1 0 \n", "4 NightHawks 5 31 1 0 \n", "\n", " num_helpful SalesLastSixMonths \n", "0 0 40 \n", "1 0 40 \n", "2 0 40 \n", "3 6 40 \n", "4 5 40 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_dataframe = pd.read_csv(\n", " 'https://raw.githubusercontent.com/wbakst/puzzles_data/master/test.csv')\n", "test_dataframe.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "XG7MPCyzVr22" }, "source": [ "特徴量とラベルを抽出して変換します。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:59.209678Z", "iopub.status.busy": "2021-02-13T02:42:59.208978Z", "iopub.status.idle": "2021-02-13T02:42:59.211348Z", "shell.execute_reply": "2021-02-13T02:42:59.210790Z" }, "id": "bYdJicq5bBuz" }, "outputs": [], "source": [ "# Features:\n", "# - star_rating rating out of 5 stars (1-5)\n", "# - word_count number of words in the review\n", "# - is_amazon 1 = reviewed on amazon; 0 = reviewed on artifact website\n", "# - includes_photo if the review includes a photo of the puzzle\n", "# - num_helpful number of people that found this review helpful\n", "# - num_reviews total number of reviews for this puzzle (we construct)\n", "#\n", "# This ordering of feature names will be the exact same order that we construct\n", "# our model to expect.\n", "feature_names = [\n", " 'star_rating', 'word_count', 'is_amazon', 'includes_photo', 'num_helpful',\n", " 'num_reviews'\n", "]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:59.223521Z", "iopub.status.busy": "2021-02-13T02:42:59.222726Z", "iopub.status.idle": "2021-02-13T02:42:59.225032Z", "shell.execute_reply": "2021-02-13T02:42:59.224503Z" }, "id": "kx0ZX2HR-4qb" }, "outputs": [], "source": [ "def extract_features(dataframe, label_name):\n", " # First we extract flattened features.\n", " flattened_features = {\n", " feature_name: dataframe[feature_name].values.astype(float)\n", " for feature_name in feature_names[:-1]\n", " }\n", "\n", " # Construct mapping from puzzle name to feature.\n", " star_rating = collections.defaultdict(list)\n", " word_count = collections.defaultdict(list)\n", " is_amazon = collections.defaultdict(list)\n", " includes_photo = collections.defaultdict(list)\n", " num_helpful = collections.defaultdict(list)\n", " labels = {}\n", "\n", " # Extract each review.\n", " for i in range(len(dataframe)):\n", " row = dataframe.iloc[i]\n", " puzzle_name = row['puzzle_name']\n", " star_rating[puzzle_name].append(float(row['star_rating']))\n", " word_count[puzzle_name].append(float(row['word_count']))\n", " is_amazon[puzzle_name].append(float(row['is_amazon']))\n", " includes_photo[puzzle_name].append(float(row['includes_photo']))\n", " num_helpful[puzzle_name].append(float(row['num_helpful']))\n", " labels[puzzle_name] = float(row[label_name])\n", "\n", " # Organize data into list of list of features.\n", " names = list(star_rating.keys())\n", " star_rating = [star_rating[name] for name in names]\n", " word_count = [word_count[name] for name in names]\n", " is_amazon = [is_amazon[name] for name in names]\n", " includes_photo = [includes_photo[name] for name in names]\n", " num_helpful = [num_helpful[name] for name in names]\n", " num_reviews = [[len(ratings)] * len(ratings) for ratings in star_rating]\n", " labels = [labels[name] for name in names]\n", "\n", " # Flatten num_reviews\n", " flattened_features['num_reviews'] = [len(reviews) for reviews in num_reviews]\n", "\n", " # Convert data into ragged tensors.\n", " star_rating = tf.ragged.constant(star_rating)\n", " word_count = tf.ragged.constant(word_count)\n", " is_amazon = tf.ragged.constant(is_amazon)\n", " includes_photo = tf.ragged.constant(includes_photo)\n", " num_helpful = tf.ragged.constant(num_helpful)\n", " num_reviews = tf.ragged.constant(num_reviews)\n", " labels = tf.constant(labels)\n", "\n", " # Now we can return our extracted data.\n", " return (star_rating, word_count, is_amazon, includes_photo, num_helpful,\n", " num_reviews), labels, flattened_features" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:42:59.275958Z", "iopub.status.busy": "2021-02-13T02:42:59.239327Z", "iopub.status.idle": "2021-02-13T02:43:01.253095Z", "shell.execute_reply": "2021-02-13T02:43:01.252473Z" }, "id": "Nd6j_J5CbNiz" }, "outputs": [], "source": [ "train_xs, train_ys, flattened_features = extract_features(train_dataframe, 'Sales12-18MonthsAgo')\n", "test_xs, test_ys, _ = extract_features(test_dataframe, 'SalesLastSixMonths')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:01.258921Z", "iopub.status.busy": "2021-02-13T02:43:01.257824Z", "iopub.status.idle": "2021-02-13T02:43:01.260493Z", "shell.execute_reply": "2021-02-13T02:43:01.259895Z" }, "id": "KfHHhCRsHejl" }, "outputs": [], "source": [ "# Let's define our label minimum and maximum.\n", "min_label, max_label = float(np.min(train_ys)), float(np.max(train_ys))\n", "min_label, max_label = float(np.min(train_ys)), float(np.max(train_ys))" ] }, { "cell_type": "markdown", "metadata": { "id": "9TwqlRirIhAq" }, "source": [ "このガイドのトレーニングに使用するデフォルト値を設定します。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:01.265494Z", "iopub.status.busy": "2021-02-13T02:43:01.264466Z", "iopub.status.idle": "2021-02-13T02:43:01.267237Z", "shell.execute_reply": "2021-02-13T02:43:01.266693Z" }, "id": "GckmXFzRIhdD" }, "outputs": [], "source": [ "LEARNING_RATE = 0.1\n", "BATCH_SIZE = 128\n", "NUM_EPOCHS = 500\n", "MIDDLE_DIM = 3\n", "MIDDLE_LATTICE_SIZE = 2\n", "MIDDLE_KEYPOINTS = 16\n", "OUTPUT_KEYPOINTS = 8" ] }, { "cell_type": "markdown", "metadata": { "id": "TpDKon4oIh2W" }, "source": [ "## 特徴量の構成\n", "\n", "特徴量の較正と特徴量あたりの構成は tfl.configs.FeatureConfig によって設定します。特徴量の構成には、単調性制約、特徴量あたりの正規化（tfl.configs.RegularizerConfig を参照）、および格子モデルの格子のサイズが含まれます。\n", "\n", "モデルが認識する必要のあるすべての特徴量に対し、完全な特徴量の構成を指定する必要があります。指定されていない場合、モデルは特徴量の存在を認識できません。集約モデルの場合は、これらの特徴量は自動的に考慮され、不規則な特徴として適切に処理されます。" ] }, { "cell_type": "markdown", "metadata": { "id": "_IMwcDh7Xs5n" }, "source": [ "### 分位数を計算する\n", "\n", "`tfl.configs.FeatureConfig` の `pwl_calibration_input_keypoints` のデフォルト設定は 'quantiles' ですが、既製のモデルについては、入力キーポイントを手動で定義する必要があります。これを行うには、まず、分位数を計算するためのヘルパー関数を独自に定義します。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:01.274485Z", "iopub.status.busy": "2021-02-13T02:43:01.273459Z", "iopub.status.idle": "2021-02-13T02:43:01.276194Z", "shell.execute_reply": "2021-02-13T02:43:01.275599Z" }, "id": "l0uYl9ZpXtW1" }, "outputs": [], "source": [ "def compute_quantiles(features,\n", " num_keypoints=10,\n", " clip_min=None,\n", " clip_max=None,\n", " missing_value=None):\n", " # Clip min and max if desired.\n", " if clip_min is not None:\n", " features = np.maximum(features, clip_min)\n", " features = np.append(features, clip_min)\n", " if clip_max is not None:\n", " features = np.minimum(features, clip_max)\n", " features = np.append(features, clip_max)\n", " # Make features unique.\n", " unique_features = np.unique(features)\n", " # Remove missing values if specified.\n", " if missing_value is not None:\n", " unique_features = np.delete(unique_features,\n", " np.where(unique_features == missing_value))\n", " # Compute and return quantiles over unique non-missing feature values.\n", " return np.quantile(\n", " unique_features,\n", " np.linspace(0., 1., num=num_keypoints),\n", " interpolation='nearest').astype(float)" ] }, { "cell_type": "markdown", "metadata": { "id": "9oYZdVeWEhf2" }, "source": [ "### 特徴量の構成を定義する\n", "\n", "分位数の計算ができるようになったら、モデルが入力として使用する各特徴量に対する特徴量の構成を定義します。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:01.285068Z", "iopub.status.busy": "2021-02-13T02:43:01.283991Z", "iopub.status.idle": "2021-02-13T02:43:01.288051Z", "shell.execute_reply": "2021-02-13T02:43:01.287399Z" }, "id": "rEYlSXhTEmoh" }, "outputs": [], "source": [ "# Feature configs are used to specify how each feature is calibrated and used.\n", "feature_configs = [\n", " tfl.configs.FeatureConfig(\n", " name='star_rating',\n", " lattice_size=2,\n", " monotonicity='increasing',\n", " pwl_calibration_num_keypoints=5,\n", " pwl_calibration_input_keypoints=compute_quantiles(\n", " flattened_features['star_rating'], num_keypoints=5),\n", " ),\n", " tfl.configs.FeatureConfig(\n", " name='word_count',\n", " lattice_size=2,\n", " monotonicity='increasing',\n", " pwl_calibration_num_keypoints=5,\n", " pwl_calibration_input_keypoints=compute_quantiles(\n", " flattened_features['word_count'], num_keypoints=5),\n", " ),\n", " tfl.configs.FeatureConfig(\n", " name='is_amazon',\n", " lattice_size=2,\n", " num_buckets=2,\n", " ),\n", " tfl.configs.FeatureConfig(\n", " name='includes_photo',\n", " lattice_size=2,\n", " num_buckets=2,\n", " ),\n", " tfl.configs.FeatureConfig(\n", " name='num_helpful',\n", " lattice_size=2,\n", " monotonicity='increasing',\n", " pwl_calibration_num_keypoints=5,\n", " pwl_calibration_input_keypoints=compute_quantiles(\n", " flattened_features['num_helpful'], num_keypoints=5),\n", " # Larger num_helpful indicating more trust in star_rating.\n", " reflects_trust_in=[\n", " tfl.configs.TrustConfig(\n", " feature_name=\"star_rating\", trust_type=\"trapezoid\"),\n", " ],\n", " ),\n", " tfl.configs.FeatureConfig(\n", " name='num_reviews',\n", " lattice_size=2,\n", " monotonicity='increasing',\n", " pwl_calibration_num_keypoints=5,\n", " pwl_calibration_input_keypoints=compute_quantiles(\n", " flattened_features['num_reviews'], num_keypoints=5),\n", " )\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "9zoPJRBvPdcH" }, "source": [ "## 集約関数モデル\n", "\n", "既製の TFL モデルの構築には、まず [tfl.configs](https://www.tensorflow.org/lattice/api_docs/python/tfl/configs) からモデル構成を構築します。集約関数モデルは [tfl.configs.AggregateFunctionConfig](https://www.tensorflow.org/lattice/api_docs/python/tfl/configs/AggregateFunctionConfig) を使用して構築します。これには区分的線形較正と分類別較正、それに続いて不規則な入力の各次元に格子モデルを適用します。次に、各次元の出力に集約レイヤーを適用します。さらにオプションで出力の区分的線形較正を適用します。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:01.301317Z", "iopub.status.busy": "2021-02-13T02:43:01.294367Z", "iopub.status.idle": "2021-02-13T02:43:06.611336Z", "shell.execute_reply": "2021-02-13T02:43:06.611804Z" }, "id": "l_4J7EjSPiP3" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Model config defines the model structure for the aggregate function model.\n", "aggregate_function_model_config = tfl.configs.AggregateFunctionConfig(\n", " feature_configs=feature_configs,\n", " middle_dimension=MIDDLE_DIM,\n", " middle_lattice_size=MIDDLE_LATTICE_SIZE,\n", " middle_calibration=True,\n", " middle_calibration_num_keypoints=MIDDLE_KEYPOINTS,\n", " middle_monotonicity='increasing',\n", " output_min=min_label,\n", " output_max=max_label,\n", " output_calibration=True,\n", " output_calibration_num_keypoints=OUTPUT_KEYPOINTS,\n", " output_initialization=np.linspace(\n", " min_label, max_label, num=OUTPUT_KEYPOINTS))\n", "# An AggregateFunction premade model constructed from the given model config.\n", "aggregate_function_model = tfl.premade.AggregateFunction(\n", " aggregate_function_model_config)\n", "# Let's plot our model.\n", "tf.keras.utils.plot_model(\n", " aggregate_function_model, show_layer_names=False, rankdir='LR')" ] }, { "cell_type": "markdown", "metadata": { "id": "4F7AwiXgWhe2" }, "source": [ "各集約レイヤーの出力は、不規則な入力にわたる較正格子の平均出力です。ここでは、最初の集約レイヤーの内部で使用するモデルを示します。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:06.618694Z", "iopub.status.busy": "2021-02-13T02:43:06.617851Z", "iopub.status.idle": "2021-02-13T02:43:06.760207Z", "shell.execute_reply": "2021-02-13T02:43:06.760719Z" }, "id": "UM7XF6UIWo4T" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aggregation_layers = [\n", " layer for layer in aggregate_function_model.layers\n", " if isinstance(layer, tfl.layers.Aggregation)\n", "]\n", "tf.keras.utils.plot_model(\n", " aggregation_layers[0].model, show_layer_names=False, rankdir='LR')" ] }, { "cell_type": "markdown", "metadata": { "id": "0ohYOftgTZhq" }, "source": [ "ここで、ほかの [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) と同様に、モデルをコンパイルしてデータに適合させます。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:43:06.782473Z", "iopub.status.busy": "2021-02-13T02:43:06.781374Z", "iopub.status.idle": "2021-02-13T02:44:06.570454Z", "shell.execute_reply": "2021-02-13T02:44:06.570868Z" }, "id": "uB9di3-lTfMy" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aggregate_function_model.compile(\n", " loss='mae',\n", " optimizer=tf.keras.optimizers.Adam(LEARNING_RATE))\n", "aggregate_function_model.fit(\n", " train_xs, train_ys, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE, verbose=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "pwZtGDR-Tzur" }, "source": [ "モデルのトレーニングが終了すると、テストセットを使用してモデルを評価することができます。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-02-13T02:44:06.575991Z", "iopub.status.busy": "2021-02-13T02:44:06.575306Z", "iopub.status.idle": "2021-02-13T02:44:08.121654Z", "shell.execute_reply": "2021-02-13T02:44:08.121135Z" }, "id": "RWj1YfubT0NE" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Set Evaluation...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/7 [===>..........................] - ETA: 8s - loss: 122.9233" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "7/7 [==============================] - ETA: 0s - loss: 46.9455 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "7/7 [==============================] - 2s 9ms/step - loss: 46.9455\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "46.945472717285156\n" ] } ], "source": [ "print('Test Set Evaluation...')\n", "print(aggregate_function_model.evaluate(test_xs, test_ys))" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "aggregate_function_models.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 0 }