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Basic Ranking - Predictions Histogram Distribution #593

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04c937c
predictions histogram distribution added
steven-ja Dec 15, 2022
ef6c8cb
removed cells output
steven-ja Dec 15, 2022
99cfb9c
Update docs/examples/basic_ranking.ipynb
steven-ja Dec 15, 2022
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90 changes: 69 additions & 21 deletions docs/examples/basic_ranking.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -39,20 +39,20 @@
"source": [
"# Recommending movies: ranking\n",
"\n",
"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/basic_ranking\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/basic_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/table\u003e\n",
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/basic_ranking\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/basic_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>\n",
"\n"
]
},
Expand Down Expand Up @@ -360,11 +360,11 @@
" metrics=[tf.keras.metrics.RootMeanSquaredError()]\n",
" )\n",
"\n",
" def call(self, features: Dict[str, tf.Tensor]) -\u003e tf.Tensor:\n",
" def call(self, features: Dict[str, tf.Tensor]) -> tf.Tensor:\n",
" return self.ranking_model(\n",
" (features[\"user_id\"], features[\"movie_title\"]))\n",
"\n",
" def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -\u003e tf.Tensor:\n",
" def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:\n",
" labels = features.pop(\"user_rating\")\n",
" \n",
" rating_predictions = self(features)\n",
Expand Down Expand Up @@ -395,7 +395,7 @@
"outputs": [],
"source": [
"model = MovielensModel()\n",
"model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))"
"callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)"
]
},
{
Expand Down Expand Up @@ -436,7 +436,8 @@
},
"outputs": [],
"source": [
"model.fit(cached_train, epochs=3)"
"model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001))\n",
"model.fit(cached_train, epochs=100, validation_data=cached_test, callbacks=[callback])"
]
},
{
Expand Down Expand Up @@ -477,6 +478,49 @@
"The lower the RMSE metric, the more accurate our model is at predicting ratings."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x0p5KVl-3w1w"
},
"source": [
"### Test predictions distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e_GB-NMw3syS"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set_style('darkgrid')\n",
"\n",
"res_data = pd.DataFrame()\n",
"\n",
"# get predictions from cached test \n",
"res_data['predictions'] = model.predict(cached_test)[:, 0]\n",
"\n",
"# get user rating from test dataset\n",
"test_labels = []\n",
"for r in cached_test:\n",
" test_labels.append((r['user_rating']).numpy())\n",
"\n",
"res_data['test_labels'] = np.concatenate(test_labels)\n",
"\n",
"# plot everythin as kde\n",
"plt.figure(figsize=(10,6), dpi=100)\n",
"#sns.kdeplot(data=res_data, fill=True, bw_adjust=0.9, alpha=0.6, linewidth=0, legend=False)\n",
"sns.histplot(data=res_data, legend=False, alpha=0.8, bins=20)\n",
"plt.legend([\"Predictions\", \"Test Labels\"][::-1], title=\"Legend\", fontsize=12, title_fontsize=16)\n",
"plt.title('Predictions vs. test labels', fontsize=20);"
]
},
{
"cell_type": "markdown",
"metadata": {
Expand Down Expand Up @@ -635,14 +679,13 @@
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "basic_ranking.ipynb",
"private_outputs": true,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3.9.12 ('base')",
"language": "python",
"name": "python3"
},
Expand All @@ -656,7 +699,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.9.12"
},
"vscode": {
"interpreter": {
"hash": "4f523f7c76dd18e7ed336217f32f6f704c23c323644912475b9d3570cf04b060"
}
}
},
"nbformat": 4,
Expand Down