> [!NOTE] > **Note:** You can run this example right now in a Jupyter-style notebook, no setup required! Just click "Run in Google Colab" |---|---|---|---| | [![](https://www.tensorflow.org/images/tf_logo_32px.png)View on TensorFlow.org](https://www.tensorflow.org/tfx/tutorials/model_analysis/tfma_basic) | [![](https://www.tensorflow.org/images/colab_logo_32px.png)Run in Google Colab](https://colab.sandbox.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/model_analysis/tfma_basic.ipynb) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png)View source on GitHub](https://github.com/tensorflow/tfx/blob/master/docs/tutorials/model_analysis/tfma_basic.ipynb) | [![](https://www.tensorflow.org/images/download_logo_32px.png)Download notebook](https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/model_analysis/tfma_basic.ipynb) | ***An Example of a Key Component of TensorFlow Extended (TFX)*** [TensorFlow Model Analysis (TFMA)](https://www.tensorflow.org/tfx/guide/tfma) is a library for performing model evaluation across different slices of data. TFMA performs its computations in a distributed manner over large amounts of data using [Apache Beam](https://beam.apache.org/documentation/programming-guide/). This example colab notebook illustrates how TFMA can be used to investigate and visualize the performance of a model with respect to characteristics of the dataset. We'll use a model that we trained previously, and now you get to play with the results! The model we trained was for the [Chicago Taxi Example](https://github.com/tensorflow/tfx/tree/master/tfx/examples/chicago_taxi_pipeline), which uses the [Taxi Trips dataset](https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew) released by the City of Chicago. Explore the full dataset in the [BigQuery UI](https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_taxi_trips). As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is a feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/). > [!NOTE] > **Note:** In order to understand TFMA and how it works with Apache Beam, you'll need to know a little bit about Apache Beam itself. The [Beam Programming Guide](https://beam.apache.org/documentation/programming-guide/) is a great place to start. The columns in the dataset are:

|---|---|---| | pickup_community_area | fare | trip_start_month | | trip_start_hour | trip_start_day | trip_start_timestamp | | pickup_latitude | pickup_longitude | dropoff_latitude | | dropoff_longitude | trip_miles | pickup_census_tract | | dropoff_census_tract | payment_type | company | | trip_seconds | dropoff_community_area | tips |
## Install Jupyter Extensions > [!NOTE] > **Note:** If running in a local Jupyter notebook, then these Jupyter extensions must be installed in the environment before running Jupyter. jupyter nbextension enable --py widgetsnbextension --sys-prefix jupyter nbextension install --py --symlink tensorflow_model_analysis --sys-prefix jupyter nbextension enable --py tensorflow_model_analysis --sys-prefix ## Install TensorFlow Model Analysis (TFMA) This will pull in all the dependencies, and will take a minute. # Upgrade pip to the latest, and install TFMA. pip install -U pip pip install tensorflow-model-analysis **Now you must restart the runtime before running the cells below.** # This setup was tested with TF 2.10 and TFMA 0.41 (using colab), but it should # also work with the latest release. import sys # Confirm that we're using Python 3 assert sys.version_info.major==3, 'This notebook must be run using Python 3.' import tensorflow as tf print('TF version: {}'.format(tf.__version__)) import apache_beam as beam print('Beam version: {}'.format(beam.__version__)) import tensorflow_model_analysis as tfma print('TFMA version: {}'.format(tfma.__version__)) ``` 2024-04-30 10:58:28.448131: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-04-30 10:58:28.448179: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-04-30 10:58:28.449816: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered TF version: 2.15.1 Beam version: 2.55.1 TFMA version: 0.46.0 ``` > [!NOTE] > **Note:** The output above should be clear of errors before proceeding. Re-run the install if you are still seeing errors. Also, make sure to restart the runtime/kernel before moving to the next step.\*\* ## Load The Files We'll download a tar file that has everything we need. That includes: - Training and evaluation datasets - Data schema - Training and serving saved models (keras and estimator) and eval saved models (estimator). # Download the tar file from GCP and extract it import io, os, tempfile TAR_NAME = 'saved_models-2.2' BASE_DIR = tempfile.mkdtemp() DATA_DIR = os.path.join(BASE_DIR, TAR_NAME, 'data') MODELS_DIR = os.path.join(BASE_DIR, TAR_NAME, 'models') SCHEMA = os.path.join(BASE_DIR, TAR_NAME, 'schema.pbtxt') OUTPUT_DIR = os.path.join(BASE_DIR, 'output') !curl -O https://storage.googleapis.com/artifacts.tfx-oss-public.appspot.com/datasets/{TAR_NAME}.tar !tar xf {TAR_NAME}.tar !mv {TAR_NAME} {BASE_DIR} !rm {TAR_NAME}.tar print("Here's what we downloaded:") !ls -R {BASE_DIR} ``` % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 100 6800k 100 6800k 0 0 26.9M 0 --:--:-- --:--:-- --:--:-- 26.9M Here's what we downloaded: /tmpfs/tmp/tmpo4hj24ht: saved_models-2.2 /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2: data models schema.pbtxt /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/data: eval train /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/data/eval: data.csv /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/data/train: data.csv /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models: estimator keras /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator: eval_model_dir serving_model_dir /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/eval_model_dir: 1591221811 /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/eval_model_dir/1591221811: saved_model.pb tmp.pbtxt variables /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/eval_model_dir/1591221811/variables: variables.data-00000-of-00001 variables.index /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir: checkpoint eval_chicago-taxi-eval events.out.tfevents.1591221780.my-pipeline-b57vp-237544850 export graph.pbtxt model.ckpt-100.data-00000-of-00001 model.ckpt-100.index model.ckpt-100.meta /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir/eval_chicago-taxi-eval: events.out.tfevents.1591221799.my-pipeline-b57vp-237544850 /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir/export: chicago-taxi /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir/export/chicago-taxi: 1591221801 /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir/export/chicago-taxi/1591221801: saved_model.pb variables /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/serving_model_dir/export/chicago-taxi/1591221801/variables: variables.data-00000-of-00001 variables.index /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras: 0 1 2 /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/0: saved_model.pb variables /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/0/variables: variables.data-00000-of-00001 variables.index /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/1: saved_model.pb variables /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/1/variables: variables.data-00000-of-00001 variables.index /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/2: saved_model.pb variables /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/keras/2/variables: variables.data-00000-of-00001 variables.index ``` ## Parse the Schema Among the things we downloaded was a schema for our data that was created by [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started/). Let's parse that now so that we can use it with TFMA. import tensorflow as tf from google.protobuf import text_format from tensorflow.python.lib.io import file_io from tensorflow_metadata.proto.v0 import schema_pb2 from tensorflow.core.example import example_pb2 schema = schema_pb2.Schema() contents = file_io.read_file_to_string(SCHEMA) schema = text_format.Parse(contents, schema) ## Use the Schema to Create TFRecords We need to give TFMA access to our dataset, so let's create a TFRecords file. We can use our schema to create it, since it gives us the correct type for each feature. import csv datafile = os.path.join(DATA_DIR, 'eval', 'data.csv') reader = csv.DictReader(open(datafile, 'r')) examples = [] for line in reader: example = example_pb2.Example() for feature in schema.feature: key = feature.name if feature.type == schema_pb2.FLOAT: example.features.feature[key].float_list.value[:] = ( [float(line[key])] if len(line[key]) > 0 else []) elif feature.type == schema_pb2.INT: example.features.feature[key].int64_list.value[:] = ( [int(line[key])] if len(line[key]) > 0 else []) elif feature.type == schema_pb2.BYTES: example.features.feature[key].bytes_list.value[:] = ( [line[key].encode('utf8')] if len(line[key]) > 0 else []) # Add a new column 'big_tipper' that indicates if tips was > 20% of the fare. # TODO(b/157064428): Remove after label transformation is supported for Keras. big_tipper = float(line['tips']) > float(line['fare']) * 0.2 example.features.feature['big_tipper'].float_list.value[:] = [big_tipper] examples.append(example) tfrecord_file = os.path.join(BASE_DIR, 'train_data.rio') with tf.io.TFRecordWriter(tfrecord_file) as writer: for example in examples: writer.write(example.SerializeToString()) !ls {tfrecord_file} ``` /tmpfs/tmp/tmpo4hj24ht/train_data.rio ``` ## Setup and Run TFMA TFMA supports a number of different model types including TF keras models, models based on generic TF2 signature APIs, as well TF estimator based models. The [get_started](https://www.tensorflow.org/tfx/model_analysis/get_started) guide has the full list of model types supported and any restrictions. For this example we are going to show how to configure a keras based model as well as an estimator based model that was saved as an [`EvalSavedModel`](https://www.tensorflow.org/tfx/model_analysis/eval_saved_model). See the [FAQ](https://www.tensorflow.org/tfx/model_analysis/faq) for examples of other configurations. TFMA provides support for calculating metrics that were used at training time (i.e. built-in metrics) as well metrics defined after the model was saved as part of the TFMA configuration settings. For our keras [setup](https://www.tensorflow.org/tfx/model_analysis/setup) we will demonstrate adding our metrics and plots manually as part of our configuration (see the [metrics](https://www.tensorflow.org/tfx/model_analysis/metrics) guide for information on the metrics and plots that are supported). For the estimator setup we will use the built-in metrics that were saved with the model. Our setups also include a number of slicing specs which are discussed in more detail in the following sections. After creating a [`tfma.EvalConfig`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/EvalConfig) and [`tfma.EvalSharedModel`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/EvalSharedModel) we can then run TFMA using [`tfma.run_model_analysis`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/run_model_analysis). This will create a [`tfma.EvalResult`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/EvalResult) which we can use later for rendering our metrics and plots. ### Keras import tensorflow_model_analysis as tfma # Setup tfma.EvalConfig settings keras_eval_config = text_format.Parse(""" ## Model information model_specs { # For keras (and serving models) we need to add a `label_key`. label_key: "big_tipper" } ## Post training metric information. These will be merged with any built-in ## metrics from training. metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "AUC" } metrics { class_name: "Precision" } metrics { class_name: "Recall" } metrics { class_name: "MeanPrediction" } metrics { class_name: "Calibration" } metrics { class_name: "CalibrationPlot" } metrics { class_name: "ConfusionMatrixPlot" } # ... add additional metrics and plots ... } ## Slicing information slicing_specs {} # overall slice slicing_specs { feature_keys: ["trip_start_hour"] } slicing_specs { feature_keys: ["trip_start_day"] } slicing_specs { feature_values: { key: "trip_start_month" value: "1" } } """, tfma.EvalConfig()) # Create a tfma.EvalSharedModel that points at our keras model. keras_model_path = os.path.join(MODELS_DIR, 'keras', '2') keras_eval_shared_model = tfma.default_eval_shared_model( eval_saved_model_path=keras_model_path, eval_config=keras_eval_config) keras_output_path = os.path.join(OUTPUT_DIR, 'keras') # Run TFMA keras_eval_result = tfma.run_model_analysis( eval_shared_model=keras_eval_shared_model, eval_config=keras_eval_config, data_location=tfrecord_file, output_path=keras_output_path) ``` WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:apache_beam.runners.interactive.interactive_environment:Dependencies required for Interactive Beam PCollection visualization are not available, please use: `pip install apache-beam[interactive]` to install necessary dependencies to enable all data visualization features. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:112: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)` WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/writers/metrics_plots_and_validations_writer.py:112: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version. Instructions for updating: Use eager execution and: `tf.data.TFRecordDataset(path)` ``` ### Estimator import tensorflow_model_analysis as tfma # Setup tfma.EvalConfig settings estimator_eval_config = text_format.Parse(""" ## Model information model_specs { # To use EvalSavedModel set `signature_name` to "eval". signature_name: "eval" } ## Post training metric information. These will be merged with any built-in ## metrics from training. metrics_specs { metrics { class_name: "ConfusionMatrixPlot" } # ... add additional metrics and plots ... } ## Slicing information slicing_specs {} # overall slice slicing_specs { feature_keys: ["trip_start_hour"] } slicing_specs { feature_keys: ["trip_start_day"] } slicing_specs { feature_values: { key: "trip_start_month" value: "1" } } """, tfma.EvalConfig()) # Create a tfma.EvalSharedModel that points at our eval saved model. estimator_base_model_path = os.path.join( MODELS_DIR, 'estimator', 'eval_model_dir') estimator_model_path = os.path.join( estimator_base_model_path, os.listdir(estimator_base_model_path)[0]) estimator_eval_shared_model = tfma.default_eval_shared_model( eval_saved_model_path=estimator_model_path, eval_config=estimator_eval_config) estimator_output_path = os.path.join(OUTPUT_DIR, 'estimator') # Run TFMA estimator_eval_result = tfma.run_model_analysis( eval_shared_model=estimator_eval_shared_model, eval_config=estimator_eval_config, data_location=tfrecord_file, output_path=estimator_output_path) ``` WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:163: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.saved_model.load` instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/eval_saved_model/load.py:163: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.saved_model.load` instead. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/eval_model_dir/1591221811/variables/variables INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpo4hj24ht/saved_models-2.2/models/estimator/eval_model_dir/1591221811/variables/variables WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. 2024-04-30 10:58:52.926791: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1: type_id: TFT_OPTIONAL args { type_id: TFT_PRODUCT args { type_id: TFT_TENSOR args { type_id: TFT_INT64 } } } is neither a subtype nor a supertype of the combined inputs preceding it: type_id: TFT_OPTIONAL args { type_id: TFT_PRODUCT args { type_id: TFT_TENSOR args { type_id: TFT_INT32 } } } for Tuple type infernce function 0 while inferring type of node 'dnn/zero_fraction/cond/output/_9' WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/eval_saved_model/graph_ref.py:184: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. 2024-04-30 10:58:53.077553: W tensorflow/c/c_api.cc:305] Operation '{name:'head/metrics/true_positives_1/Assign' id:674 op device:{requested: '', assigned: ''} def:{ { {node head/metrics/true_positives_1/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](head/metrics/true_positives_1, head/metrics/true_positives_1/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session. 2024-04-30 10:58:53.204776: W tensorflow/c/c_api.cc:305] Operation '{name:'head/metrics/true_positives_1/Assign' id:674 op device:{requested: '', assigned: ''} def:{ { {node head/metrics/true_positives_1/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](head/metrics/true_positives_1, head/metrics/true_positives_1/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) ``` ## Visualizing Metrics and Plots Now that we've run the evaluation, let's take a look at our visualizations using TFMA. For the following examples, we will visualize the results from running the evaluation on the keras model. To view the estimator based model update the `eval_result_path` to point at our `estimator_output_path` variable. eval_result_path = keras_output_path # eval_result_path = estimator_output_path eval_result = keras_eval_result # eval_result = estimator_eval_result ### Rendering Metrics TFMA provides dataframe APIs in [`tfma.experimental.dataframe`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/experimental) to load the materalized output as [`Pandas DataFrames`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html). To view metrics you can use `metrics_as_dataframes(tfma.load_metrics(eval_path))`, which returns an object which potentially contains several DataFrames, one for each metric value type (`double_value`, `confusion_matrix_at_thresholds`, `bytes_value`, and `array_value`). The specific DataFrames populated depends on the eval result. Here, we show the `double_value` DataFrame as an example. import tensorflow_model_analysis.experimental.dataframe as tfma_dataframe dfs = tfma_dataframe.metrics_as_dataframes( tfma.load_metrics(eval_result_path)) display(dfs.double_value.head()) Each of the DataFrames has a column multi-index with the top-level columns: `slices`, `metric_keys`, and `metric_values`. The exact columns of each group can change according to the payload. we can use `DataFrame.columns` API to inspect all the multi-index columns. For example, the slices columns are 'Overall', 'trip_start_day', 'trip_start_hour', and 'trip_start_month', which is configured by the `slicing_specs` in the `eval_config`. print(dfs.double_value.columns) ``` MultiIndex([( 'slices', 'trip_start_hour'), ( 'slices', 'Overall'), ( 'slices', 'trip_start_day'), ( 'slices', 'trip_start_month'), ( 'metric_keys', 'name'), ( 'metric_keys', 'model_name'), ( 'metric_keys', 'output_name'), ( 'metric_keys', 'example_weighted'), ( 'metric_keys', 'is_diff'), ('metric_values', 'double_value')], ) ``` ### Auto pivoting The DataFrame is verbose by design so that there is no loss of information from the payload. However, sometimes, for direct consumption, we might want to organize the information in a more concise but lossy form: slices as rows and metrics as columns. TFMA provides an `auto_pivot` API for this purpose. The util pivots on all of the non-unique columns inside `metric_keys`, and condenses all the slices into one `stringified_slices` column by default. tfma_dataframe.auto_pivot(dfs.double_value).head() ### Filtering slices Since the outputs are DataFrames, any native DataFrame APIs can be used to slice and dice the DataFrame. For example, if we are only interested in `trip_start_hour` of 1, 3, 5, 7 and not in `trip_start_day`, we can use DataFrame's `.loc` filtering logic. Again, we use the `auto_pivot` function to re-organize the DataFrame in the slice vs. metrics view after the filtering is performed. df_double = dfs.double_value df_filtered = (df_double .loc[df_double.slices.trip_start_hour.isin([1,3,5,7])] ) display(tfma_dataframe.auto_pivot(df_filtered)) ### Sorting by metric values We can also sort slices by metrics value. As an example, we show how to sort slices in the above DataFrame by ascending AUC, so that we can find poorly performing slices. This involves two steps: auto-pivoting so that slices are represented as rows and columns are metrics, and then sorting the pivoted DataFrame by the AUC column. # Pivoted table sorted by AUC in ascending order. df_sorted = ( tfma_dataframe.auto_pivot(df_double) .sort_values(by='auc', ascending=True) ) display(df_sorted.head()) ### Rendering Plots Any plots that were added to the [`tfma.EvalConfig`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/EvalConfig) as post training `metric_specs` can be displayed using [`tfma.view.render_plot`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/view/render_plot). As with metrics, plots can be viewed by slice. Unlike metrics, only plots for a particular slice value can be displayed so the [`tfma.SlicingSpec`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/SlicingSpec) must be used and it must specify both a slice feature name and value. If no slice is provided then the plots for the `Overall` slice is used. In the example below we are displaying the `CalibrationPlot` and `ConfusionMatrixPlot` plots that were computed for the `trip_start_hour:1` slice. tfma.view.render_plot( eval_result, tfma.SlicingSpec(feature_values={'trip_start_hour': '1'})) ``` PlotViewer(config={'sliceName': 'trip_start_hour:1', 'metricKeys': {'calibrationPlot': {'metricName': 'calibra… ``` ## Tracking Model Performance Over Time Your training dataset will be used for training your model, and will hopefully be representative of your test dataset and the data that will be sent to your model in production. However, while the data in inference requests may remain the same as your training data, in many cases it will start to change enough so that the performance of your model will change. That means that you need to monitor and measure your model's performance on an ongoing basis, so that you can be aware of and react to changes. Let's take a look at how TFMA can help. Let's load 3 different model runs and use TFMA to see how they compare using [`render_time_series`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/view/render_time_series). # Note this re-uses the EvalConfig from the keras setup. # Run eval on each saved model output_paths = [] for i in range(3): # Create a tfma.EvalSharedModel that points at our saved model. eval_shared_model = tfma.default_eval_shared_model( eval_saved_model_path=os.path.join(MODELS_DIR, 'keras', str(i)), eval_config=keras_eval_config) output_path = os.path.join(OUTPUT_DIR, 'time_series', str(i)) output_paths.append(output_path) # Run TFMA tfma.run_model_analysis(eval_shared_model=eval_shared_model, eval_config=keras_eval_config, data_location=tfrecord_file, output_path=output_path) ``` WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives ``` First, we'll imagine that we've trained and deployed our model yesterday, and now we want to see how it's doing on the new data coming in today. The visualization will start by displaying AUC. From the UI you can: - Add other metrics using the "Add metric series" menu. - Close unwanted graphs by clicking on x - Hover over data points (the ends of line segments in the graph) to get more details > [!NOTE] > **Note:** In the metric series charts the X axis is the model directory name of the model run that you're examining. These names themselves are not meaningful. eval_results_from_disk = tfma.load_eval_results(output_paths[:2]) tfma.view.render_time_series(eval_results_from_disk) ``` TimeSeriesViewer(config={'isModelCentric': True}, data=[{'metrics': {'': {'': {'example_count': {'doubleValue'… ``` Now we'll imagine that another day has passed and we want to see how it's doing on the new data coming in today, compared to the previous two days: eval_results_from_disk = tfma.load_eval_results(output_paths) tfma.view.render_time_series(eval_results_from_disk) ``` TimeSeriesViewer(config={'isModelCentric': True}, data=[{'metrics': {'': {'': {'example_count': {'doubleValue'… ``` ## Model Validation TFMA can be configured to evaluate multiple models at the same time. Typically this is done to compare a new model against a baseline (such as the currently serving model) to determine what the performance differences in metrics (e.g. AUC, etc) are relative to the baseline. When [thresholds](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/MetricThreshold) are configured, TFMA will produce a [`tfma.ValidationResult`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/ValidationResult) record indicating whether the performance matches expecations. Let's re-configure our keras evaluation to compare two models: a candidate and a baseline. We will also validate the candidate's performance against the baseline by setting a [`tmfa.MetricThreshold`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/MetricThreshold) on the AUC metric. # Setup tfma.EvalConfig setting eval_config_with_thresholds = text_format.Parse(""" ## Model information model_specs { name: "candidate" # For keras we need to add a `label_key`. label_key: "big_tipper" } model_specs { name: "baseline" # For keras we need to add a `label_key`. label_key: "big_tipper" is_baseline: true } ## Post training metric information metrics_specs { metrics { class_name: "ExampleCount" } metrics { class_name: "BinaryAccuracy" } metrics { class_name: "BinaryCrossentropy" } metrics { class_name: "AUC" threshold { # Ensure that AUC is always > 0.9 value_threshold { lower_bound { value: 0.9 } } # Ensure that AUC does not drop by more than a small epsilon # e.g. (candidate - baseline) > -1e-10 or candidate > baseline - 1e-10 change_threshold { direction: HIGHER_IS_BETTER absolute { value: -1e-10 } } } } metrics { class_name: "AUCPrecisionRecall" } metrics { class_name: "Precision" } metrics { class_name: "Recall" } metrics { class_name: "MeanLabel" } metrics { class_name: "MeanPrediction" } metrics { class_name: "Calibration" } metrics { class_name: "CalibrationPlot" } metrics { class_name: "ConfusionMatrixPlot" } # ... add additional metrics and plots ... } ## Slicing information slicing_specs {} # overall slice slicing_specs { feature_keys: ["trip_start_hour"] } slicing_specs { feature_keys: ["trip_start_day"] } slicing_specs { feature_keys: ["trip_start_month"] } slicing_specs { feature_keys: ["trip_start_hour", "trip_start_day"] } """, tfma.EvalConfig()) # Create tfma.EvalSharedModels that point at our keras models. candidate_model_path = os.path.join(MODELS_DIR, 'keras', '2') baseline_model_path = os.path.join(MODELS_DIR, 'keras', '1') eval_shared_models = [ tfma.default_eval_shared_model( model_name=tfma.CANDIDATE_KEY, eval_saved_model_path=candidate_model_path, eval_config=eval_config_with_thresholds), tfma.default_eval_shared_model( model_name=tfma.BASELINE_KEY, eval_saved_model_path=baseline_model_path, eval_config=eval_config_with_thresholds), ] validation_output_path = os.path.join(OUTPUT_DIR, 'validation') # Run TFMA eval_result_with_validation = tfma.run_model_analysis( eval_shared_models, eval_config=eval_config_with_thresholds, data_location=tfrecord_file, output_path=validation_output_path) ``` WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:absl:Tensorflow version (2.15.1) found. Note that TFMA support for TF 2.0 is currently in beta WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named "keras_metadata.pb" in the SavedModel directory. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/confusion_matrix_metrics.py:528: RuntimeWarning: invalid value encountered in divide prec_slope = dtp / np.maximum(dp, 0) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/confusion_matrix_metrics.py:532: RuntimeWarning: divide by zero encountered in divide p[:num_thresholds - 1] / np.maximum(p[1:], 0), np.ones_like(p[1:])) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/confusion_matrix_metrics.py:532: RuntimeWarning: invalid value encountered in divide p[:num_thresholds - 1] / np.maximum(p[1:], 0), np.ones_like(p[1:])) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:152: RuntimeWarning: invalid value encountered in divide f1 = 2 * precision * recall / (precision + recall) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/binary_confusion_matrices.py:155: RuntimeWarning: invalid value encountered in divide false_omission_rate = fn / predicated_negatives /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/confusion_matrix_metrics.py:539: RuntimeWarning: invalid value encountered in divide recall = tp / (tp + fn) /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_model_analysis/metrics/confusion_matrix_metrics.py:534: RuntimeWarning: invalid value encountered in divide prec_slope * (dtp + intercept * np.log(safe_p_ratio)) / ``` When running evaluations with one or more models against a baseline, TFMA automatically adds diff metrics for all of the metrics computed during the evaluation. These metrics are named after the corresponding metric but with `_diff` appended to the metric name. Let's take a look at the metrics produced by our run: tfma.view.render_time_series(eval_result_with_validation) ``` TimeSeriesViewer(config={'isModelCentric': True}, data=[{'metrics': {'': {'': {'binary_crossentropy': {'double… ``` Now let's look at the output from our validation checks. To view the validation results we use [`tfma.load_validator_result`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/load_validation_result). For our example, the validation fails because AUC is below the threshold. validation_result = tfma.load_validation_result(validation_output_path) print(validation_result.validation_ok) ``` False ``` > [!NOTE] > **Note:** This site provides applications using data that has been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one's own risk.