Introduction to sensAI: Supervised Learning with VectorModels#
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%load_ext autoreload
%autoreload 2
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import sys
sys.path.append("../src")
import sensai
import numpy as np
Logging#
sensAI will log relevant activies and inform about ongoing processes as well as results via the log. It is therefore highly recommended that logging be enabled when using sensAI.
sensAI provides a logging module which includes Python’s standard logging module and adds some additional functionality. To enable logging, simply use its configureLogging function.
from sensai.util import logging
logging.configure(level=logging.INFO)
To additionally write log output to a file, use the function logging.add_file_logger.
VectorModels#
The central base class for supervised learning problems in sensAI is VectorModel. A VectorModel is any model which operates on data points that can be reprsented as vectors of data. Here, vector is to be understood not in the mathematical sense but in the computer science sense, where a vector is simply an array of (potentially arbitaririly complex) data. (The mathematical equivalent is a tuple.) Models are typically expected to be able to process more than one data point at a time and thus should be able to process a sequence of vectors.
We use pandas DataFrames to represent such sequences of data points. Note that pandas DataFrames are not limited to primitive data types but can hold arbitrary objects in every cell. When dealing with a large number of inputs, DataFrames also provide at least limited meta-information in the form of column names, so we do not lose track of what is contained in which element of a data point (vector).
VectorModel itself is an abstract base class, which provides a lot of useful functionality that all its specialisations inherit (as we will see later, particularly in the more advanced tutorials). The class is specialised in VectorClassificationModel and VectorRegressionModel, which in turn are specialised for various machine learning frameworks (such as sklearn and PyTorch) or can be directly subclassed to create your own model.
In this tutorial, we will be dealing with a classification problem. Therefore, we will apply subclasses of VectorClassificationModel such as SkLearnRandomForestVectorClassificationModel. As an sklearn classification model which uses a well-defined training and inference interface, the implementation of the class is essentially justa few lines of code (given the intermediate abstraction AbstractSkLearnVectorClassificationModel for all classification models that use the sklearn protocol).
Training and Evaluating Models#
First, let us load a dataset which we can experiment. sklearn provides, for example, the Iris classification dataset, where the task is to differentiate three different types of flowers based on measurements of their petals and sepals.
import sklearn.datasets
import pandas as pd
iris_data = sklearn.datasets.load_iris()
iris_input_df = pd.DataFrame(iris_data["data"], columns=iris_data["feature_names"]).reset_index(drop=True)
iris_output_df = pd.DataFrame({"class": [iris_data["target_names"][idx] for idx in iris_data["target"]]}).reset_index(drop=True)
Here’s a sample of the data, combining both the inputs and outputs:
iris_combined_df = pd.concat((iris_input_df, iris_output_df), axis=1)
iris_combined_df.sample(10)
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | class | |
|---|---|---|---|---|---|
| 30 | 4.8 | 3.1 | 1.6 | 0.2 | setosa |
| 48 | 5.3 | 3.7 | 1.5 | 0.2 | setosa |
| 96 | 5.7 | 2.9 | 4.2 | 1.3 | versicolor |
| 107 | 7.3 | 2.9 | 6.3 | 1.8 | virginica |
| 108 | 6.7 | 2.5 | 5.8 | 1.8 | virginica |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 | virginica |
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 111 | 6.4 | 2.7 | 5.3 | 1.9 | virginica |
| 80 | 5.5 | 2.4 | 3.8 | 1.1 | versicolor |
| 114 | 5.8 | 2.8 | 5.1 | 2.4 | virginica |
When working with sensAI, we typically use DataFrames such as this as the starting point.
We create an instance of InputOutputData from the two data frames.
iris_io_data = sensai.InputOutputData(iris_input_df, iris_output_df)
Low-Level Training and Inference#
We use a DataSplitter (see subclasses) to split the data into a training and test set, specifically a DataSplitterFractional.
data_splitter = sensai.data.DataSplitterFractional(0.8, shuffle=True)
training_io_data, test_io_data = data_splitter.split(iris_io_data)
Now we are ready to train a model. Let us train a random forest classifier, which should work well for this sort of problem. sensAI provides models from various libraries, including scikit-learn, PyTorch, lightgbm, xgboost, catboost, and TensorFlow.
In this case, let us use the random forest implementation from sklearn, which is provided via the wrapper class SkLearnRandomForestVectorClassificationModel.
sensAI’s VectorModel classes (specialised for classification and regression) provide a common interface with a lot of useful functionality, which we will see later.
random_forest_model = sensai.sklearn.classification.SkLearnRandomForestVectorClassificationModel(
min_samples_leaf=2).with_name("RandomForest")
The class suppports all the parameters supported by the original sklearn model. In this case, we only set the minimum number of samples that must end up in each leaf.
We train the model using the fitInputOutputData method; we could also use the fit method, which is analogous to the sklearn interface and takes two arguments (input, output).
random_forest_model.fit_input_output_data(training_io_data)
random_forest_model
INFO 2025-11-21 11:02:06,789 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:06,790 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:06,894 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
SkLearnRandomForestVectorClassificationModel[id=140111267871424, featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
We can now apply the trained model and predict the outputs for the test set we reserved.
predicted_outputs_df = random_forest_model.predict(test_io_data.inputs)
predicted_outputs_df.head(5)
| class | |
|---|---|
| 91 | versicolor |
| 41 | setosa |
| 58 | versicolor |
| 90 | versicolor |
| 48 | setosa |
Let’s compare some of the predictions to the ground truth.
pd.concat((predicted_outputs_df.rename(columns={"class": "predictedClass"}), test_io_data.outputs), axis=1).sample(10)
| predictedClass | class | |
|---|---|---|
| 99 | versicolor | versicolor |
| 20 | setosa | setosa |
| 87 | versicolor | versicolor |
| 124 | virginica | virginica |
| 116 | virginica | virginica |
| 102 | virginica | virginica |
| 90 | versicolor | versicolor |
| 37 | setosa | setosa |
| 121 | virginica | virginica |
| 144 | virginica | virginica |
Using the ground truth and predicted values, we could now compute the metrics we’re interested in. We could, for example, use the metrics implemented in sklearn to analyse the result. Yet sensAI already provides abstractions that facilitate the generation of metrics and the collection of results. Read on!
Using Evaluators#
sensAI provides evaluator abstractions which facilitate the training and evaluation of models.
For a classification problem, we instantiate a VectorClassificationModelEvaluator. An evaluator serves to evaluate one or more models based on the same data, so we construct it with the data and instructions on how to handle/split the data for evaluation.
evaluator_params = sensai.evaluation.ClassificationEvaluatorParams(data_splitter=data_splitter, compute_probabilities=True)
evaluator = sensai.evaluation.VectorClassificationModelEvaluator(iris_io_data, params=evaluator_params)
We can use this evaluator to evaluate one or more models. Let us evaluate the random forest model from above.
evaluator.fit_model(random_forest_model)
eval_data = evaluator.eval_model(random_forest_model)
INFO 2025-11-21 11:02:06,991 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:06,992 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:07,093 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
The evaluation data holds, in particular, an EvalStats object, which can provide data on the quality of the results. Depending on the type of problem, many metrics will already be computed by default.
eval_stats = eval_data.get_eval_stats()
eval_stats
ClassificationEvalStats[id=140112445775760, accuracy=0.9666666666666667, balancedAccuracy=0.9722222222222222, geoMeanTrueClassProb=0.8890980234758366, N=30]
We can get the metrics in a dictionary as follows:
eval_stats.metrics_dict()
{'accuracy': 0.9666666666666667,
'balancedAccuracy': 0.9722222222222222,
'geoMeanTrueClassProb': np.float64(0.8890980234758366)}
We can compute additional metrics by passing a metric to the compute_metric_value method, but we could also have added additional metrics to the evaluator_params above and have the metric included in all results.
Let’s see how frequently the true class is among the top two most probable classes.
eval_stats.compute_metric_value(sensai.eval_stats_classification.ClassificationMetricTopNAccuracy(2))
1.0
The EvalStats object can also be used to generate plots, such as a confusion matrix or a precision-recall plot for binary classification.
eval_stats.plot_confusion_matrix(normalize=True);
Using the Fully-Integrated Evaluation Utilities#
sensAI’s evaluation utilities take things one step further and assist you in out all the evaluation steps and results computations in a single call.
You can perform evaluations based on a single split or cross-validation. We simply declare the necessary parameters for both types of computations (or the one type we seek to carry out).
evaluatorParams = sensai.evaluation.ClassificationEvaluatorParams(
data_splitter=data_splitter, compute_probabilities=True,
additional_metrics=[sensai.eval_stats_classification.ClassificationMetricTopNAccuracy(2)])
cross_validator_params = sensai.evaluation.crossval.VectorModelCrossValidatorParams(folds=10,
evaluator_params=evaluator_params)
eval_util = sensai.evaluation.ClassificationModelEvaluation(iris_io_data,
evaluator_params=evaluatorParams, cross_validator_params=cross_validator_params)
In practice, we will usually want to save evaluation results. The evaluation methods of eval_util take a parameter result_writer which allows us to define where results shall be written. Within this notebook, we shall simply inspect the resulting metrics in the log that is printed, and we shall configure plots to be shown directly.
Simple Evaluation#
We can perform the same evaluation as above (which uses a single split) like so:
eval_util.perform_simple_evaluation(random_forest_model, show_plots=True)
INFO 2025-11-21 11:02:07,445 sensai.evaluation.eval_util:perform_simple_evaluation:288 - Evaluating SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)'] via <sensai.evaluation.evaluator.VectorClassificationModelEvaluator object at 0x7f6e788f2230>
INFO 2025-11-21 11:02:07,446 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:07,447 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:07,548 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:07,559 sensai.evaluation.eval_util:gather_results:296 - Evaluation results for class: ClassificationEvalStats[accuracy=0.9666666666666667, balancedAccuracy=0.9722222222222222, geoMeanTrueClassProb=0.8890980234758366, top2Accuracy=1.0, N=30]
<sensai.evaluation.evaluator.VectorClassificationModelEvaluationData at 0x7f6e32056d10>
Customising the Set of Plots#
If we decide that we don’t really want to have the normalised confusion matrix, we can disable it for any further experiments.
eval_util.eval_stats_plot_collector.get_enabled_plots()
['confusion-matrix-rel',
'confusion-matrix-abs',
'precision-recall',
'threshold-precision-recall',
'threshold-counts']
Some of these are only active for binary classification. The one we don’t want is “confusion-matrix-rel”.
eval_util.eval_stats_plot_collector.disable_plots("confusion-matrix-rel")
We could also define our own plot class (by creating a new subclass of ClassificationEvalStatsPlot) and add it to the plot collector in order to have the plot auto-generated whenever we apply one of eval_util’s methods.
Cross-Validation#
We can similarly run cross-validation and produce the respective evaluation metrics with a single call.
eval_util.perform_cross_validation(random_forest_model, show_plots=True)
INFO 2025-11-21 11:02:08,022 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 1/10 ...
INFO 2025-11-21 11:02:08,022 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,024 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,126 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,139 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 1/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9553599022560102, N=15]
INFO 2025-11-21 11:02:08,140 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 2/10 ...
INFO 2025-11-21 11:02:08,140 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,141 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,244 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,255 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 2/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9734228107223192, N=15]
INFO 2025-11-21 11:02:08,255 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 3/10 ...
INFO 2025-11-21 11:02:08,256 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,257 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,359 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,371 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 3/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.976458969438705, N=15]
INFO 2025-11-21 11:02:08,371 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 4/10 ...
INFO 2025-11-21 11:02:08,372 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,373 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,475 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,486 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 4/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9333333333333332, geoMeanTrueClassProb=0.9411926691126593, N=15]
INFO 2025-11-21 11:02:08,487 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 5/10 ...
INFO 2025-11-21 11:02:08,487 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,489 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,591 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,602 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 5/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9298964965102043, N=15]
INFO 2025-11-21 11:02:08,603 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 6/10 ...
INFO 2025-11-21 11:02:08,603 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,604 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,706 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,716 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 6/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8888888888888888, geoMeanTrueClassProb=0.6379522029160006, N=15]
INFO 2025-11-21 11:02:08,717 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 7/10 ...
INFO 2025-11-21 11:02:08,717 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,718 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,820 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,831 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 7/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8611111111111112, geoMeanTrueClassProb=0.8099785263057822, N=15]
INFO 2025-11-21 11:02:08,832 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 8/10 ...
INFO 2025-11-21 11:02:08,833 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,834 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:08,936 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:08,950 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 8/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9657414983648998, N=15]
INFO 2025-11-21 11:02:08,950 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 9/10 ...
INFO 2025-11-21 11:02:08,951 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:08,952 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,054 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,067 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 9/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9560552426688266, N=15]
INFO 2025-11-21 11:02:09,067 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 10/10 ...
INFO 2025-11-21 11:02:09,068 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,070 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,245 sensai.vector_model:fit:388 - Fitting completed in 0.18 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,256 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 10/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9523809523809524, geoMeanTrueClassProb=0.841736920530503, N=15]
INFO 2025-11-21 11:02:09,272 sensai.evaluation.eval_util:perform_cross_validation:351 - Cross-validation results:
mean[accuracy] std[accuracy] mean[balancedAccuracy] std[balancedAccuracy] mean[geoMeanTrueClassProb] std[geoMeanTrueClassProb]
class 0.96 0.053333 0.963571 0.050077 0.89878 0.10223
<sensai.evaluation.crossval.VectorClassificationModelCrossValidationData at 0x7f6e322340a0>
As you can see, the plot we disabled earlier is no longer being generated.
Comparing Models#
A most common use case is to compare the performance of several models. The evaluation utility makes it very simple to compare any number of models.
Let’s say we want to compare the random forest we have been using thus far to a simple decision tree.
results = eval_util.compare_models([
random_forest_model,
sensai.sklearn.classification.SkLearnDecisionTreeVectorClassificationModel(min_samples_leaf=2).with_name("DecisionTree")],
use_cross_validation=True)
INFO 2025-11-21 11:02:09,500 sensai.evaluation.eval_util:compare_models:400 - Evaluating model 1/2 named 'RandomForest' ...
INFO 2025-11-21 11:02:09,505 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 1/10 ...
INFO 2025-11-21 11:02:09,505 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,508 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,609 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,620 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 1/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9553599022560102, N=15]
INFO 2025-11-21 11:02:09,621 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 2/10 ...
INFO 2025-11-21 11:02:09,621 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,622 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,723 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,734 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 2/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9734228107223192, N=15]
INFO 2025-11-21 11:02:09,735 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 3/10 ...
INFO 2025-11-21 11:02:09,735 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,736 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,837 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,851 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 3/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.976458969438705, N=15]
INFO 2025-11-21 11:02:09,851 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 4/10 ...
INFO 2025-11-21 11:02:09,852 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,853 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:09,954 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:09,965 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 4/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9333333333333332, geoMeanTrueClassProb=0.9411926691126593, N=15]
INFO 2025-11-21 11:02:09,966 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 5/10 ...
INFO 2025-11-21 11:02:09,967 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:09,968 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,069 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,080 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 5/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9298964965102043, N=15]
INFO 2025-11-21 11:02:10,081 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 6/10 ...
INFO 2025-11-21 11:02:10,082 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:10,083 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,186 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,197 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 6/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8888888888888888, geoMeanTrueClassProb=0.6379522029160006, N=15]
INFO 2025-11-21 11:02:10,198 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 7/10 ...
INFO 2025-11-21 11:02:10,199 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:10,200 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,301 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,313 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 7/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8611111111111112, geoMeanTrueClassProb=0.8099785263057822, N=15]
INFO 2025-11-21 11:02:10,314 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 8/10 ...
INFO 2025-11-21 11:02:10,314 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:10,315 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,417 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,428 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 8/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9657414983648998, N=15]
INFO 2025-11-21 11:02:10,429 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 9/10 ...
INFO 2025-11-21 11:02:10,429 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:10,430 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,531 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,543 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 9/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=0.9560552426688266, N=15]
INFO 2025-11-21 11:02:10,544 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 10/10 ...
INFO 2025-11-21 11:02:10,544 sensai.vector_model:fit:359 - Fitting SkLearnRandomForestVectorClassificationModel instance
INFO 2025-11-21 11:02:10,546 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type RandomForestClassifier
INFO 2025-11-21 11:02:10,647 sensai.vector_model:fit:388 - Fitting completed in 0.10 seconds: SkLearnRandomForestVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='RandomForest', model='RandomForestClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,658 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 10/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9523809523809524, geoMeanTrueClassProb=0.841736920530503, N=15]
INFO 2025-11-21 11:02:10,674 sensai.evaluation.eval_util:perform_cross_validation:351 - Cross-validation results:
mean[accuracy] std[accuracy] mean[balancedAccuracy] std[balancedAccuracy] mean[geoMeanTrueClassProb] std[geoMeanTrueClassProb]
class 0.96 0.053333 0.963571 0.050077 0.89878 0.10223
INFO 2025-11-21 11:02:10,750 sensai.evaluation.eval_util:compare_models:400 - Evaluating model 2/2 named 'DecisionTree' ...
INFO 2025-11-21 11:02:10,750 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 1/10 ...
INFO 2025-11-21 11:02:10,751 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,752 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,755 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,760 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 1/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=1.0, N=15]
INFO 2025-11-21 11:02:10,761 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 2/10 ...
INFO 2025-11-21 11:02:10,761 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,762 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,765 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,770 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 2/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=1.0, N=15]
INFO 2025-11-21 11:02:10,771 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 3/10 ...
INFO 2025-11-21 11:02:10,771 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,773 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,776 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,782 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 3/10: ClassificationEvalStats[accuracy=1.0, balancedAccuracy=1.0, geoMeanTrueClassProb=1.0, N=15]
INFO 2025-11-21 11:02:10,782 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 4/10 ...
INFO 2025-11-21 11:02:10,783 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,783 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,786 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,792 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 4/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9333333333333332, geoMeanTrueClassProb=0.6141303814089187, N=15]
INFO 2025-11-21 11:02:10,793 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 5/10 ...
INFO 2025-11-21 11:02:10,793 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,794 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,797 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,803 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 5/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9333333333333332, geoMeanTrueClassProb=0.9548416039104165, N=15]
INFO 2025-11-21 11:02:10,803 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 6/10 ...
INFO 2025-11-21 11:02:10,804 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,805 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,808 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,813 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 6/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8888888888888888, geoMeanTrueClassProb=0.39810717055349726, N=15]
INFO 2025-11-21 11:02:10,814 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 7/10 ...
INFO 2025-11-21 11:02:10,814 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,815 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,818 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,824 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 7/10: ClassificationEvalStats[accuracy=0.8666666666666667, balancedAccuracy=0.8611111111111112, geoMeanTrueClassProb=0.39810717055349726, N=15]
INFO 2025-11-21 11:02:10,825 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 8/10 ...
INFO 2025-11-21 11:02:10,825 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,826 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,829 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,835 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 8/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9444444444444445, geoMeanTrueClassProb=0.9548416039104165, N=15]
INFO 2025-11-21 11:02:10,835 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 9/10 ...
INFO 2025-11-21 11:02:10,836 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,836 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,840 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,846 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 9/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9333333333333332, geoMeanTrueClassProb=0.6141303814089187, N=15]
INFO 2025-11-21 11:02:10,847 sensai.evaluation.crossval:eval_model:192 - Training and evaluating model with fold 10/10 ...
INFO 2025-11-21 11:02:10,847 sensai.vector_model:fit:359 - Fitting SkLearnDecisionTreeVectorClassificationModel instance
INFO 2025-11-21 11:02:10,848 sensai.sklearn.sklearn_base:_fit_classifier:314 - Fitting sklearn classifier of type DecisionTreeClassifier
INFO 2025-11-21 11:02:10,851 sensai.vector_model:fit:388 - Fitting completed in 0.00 seconds: SkLearnDecisionTreeVectorClassificationModel[featureGenerator=None, rawInputTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], featureTransformerChain=DataFrameTransformerChain[dataFrameTransformers=[]], fitArgs={}, useBalancedClassWeights=False, useLabelEncoding=False, name='DecisionTree', model='DecisionTreeClassifier(min_samples_leaf=2, random_state=42)']
INFO 2025-11-21 11:02:10,857 sensai.evaluation.crossval:eval_model:201 - Evaluation result for class, fold 10/10: ClassificationEvalStats[accuracy=0.9333333333333333, balancedAccuracy=0.9523809523809524, geoMeanTrueClassProb=0.6309573444801932, N=15]
INFO 2025-11-21 11:02:10,873 sensai.evaluation.eval_util:perform_cross_validation:351 - Cross-validation results:
mean[accuracy] std[accuracy] mean[balancedAccuracy] std[balancedAccuracy] mean[geoMeanTrueClassProb] std[geoMeanTrueClassProb]
class 0.94 0.046667 0.944683 0.04441 0.756512 0.238693
INFO 2025-11-21 11:02:10,951 sensai.evaluation.eval_util:compare_models:469 - Model comparison results, aggregated across folds:
mean[accuracy] std[accuracy] mean[balancedAccuracy] std[balancedAccuracy] mean[geoMeanTrueClassProb] std[geoMeanTrueClassProb]
model_name
RandomForest 0.96 0.053333 0.963571 0.050077 0.898780 0.102230
DecisionTree 0.94 0.046667 0.944683 0.044410 0.756512 0.238693
In addition to the data frame with the aggregated metrics, which was already printed to the log, the results object contains all the data that was generated during the evaluation. We can, for example, use it to plot the distribution of one of the metrics across all the folds for one of our models.
display(results.results_df)
esc_random_forest = results.result_by_model_name["RandomForest"].cross_validation_data.get_eval_stats_collection()
esc_random_forest.plot_distribution("accuracy", bins=np.linspace(0,1,21), stat="count", kde=False);
| mean[accuracy] | std[accuracy] | mean[balancedAccuracy] | std[balancedAccuracy] | mean[geoMeanTrueClassProb] | std[geoMeanTrueClassProb] | |
|---|---|---|---|---|---|---|
| model_name | ||||||
| RandomForest | 0.96 | 0.053333 | 0.963571 | 0.050077 | 0.898780 | 0.102230 |
| DecisionTree | 0.94 | 0.046667 | 0.944683 | 0.044410 | 0.756512 | 0.238693 |
We can also compute additional aggregations or inspect the full list of metrics.
esc_random_forest.agg_metrics_dict(agg_fns=[np.max, np.min])
{'max[accuracy]': 1.0,
'min[accuracy]': 0.8666666666666667,
'max[balancedAccuracy]': 1.0,
'min[balancedAccuracy]': 0.8611111111111112,
'max[geoMeanTrueClassProb]': 0.976458969438705,
'min[geoMeanTrueClassProb]': 0.6379522029160006}
esc_random_forest.get_values("accuracy")
[1.0,
1.0,
1.0,
0.9333333333333333,
1.0,
0.8666666666666667,
0.8666666666666667,
1.0,
1.0,
0.9333333333333333]
Feature Generators and Data Frame Transformers#
When dealing with the preparation of input data for a model, we often need to cater to technical requirements of various types of models. sensAI seeks to make the process of supporting multiple input pipelines for different types of models as simple as possible - by focusing on concise, declarative semantics and integrating the model-specific data extraction and transformation mechanisms into the models themselves. In essence, this means:
Starting with the raw or most general representation of the data
This could mean simply starting with the data that is straightforward for us to obtain - or using directly using particular domain specific objects.
For example, if the problem is to classify situations, we might already have a
Situationclass in our code which represents all the data that is is relevant to a situation (e.g. the point in time, the affected user, the location, etc.). Pandas DataFrames can represent arbitrary data, so there is no reason to not simply use as the raw input data frame that is fed to our models a single column containing instances of classSituation. Or we might instead directly observe a set of sensor readings, all of which are real numbers; this scenario would certainly be closer to what we typically see in machine learning data sets, but it isn’t always the case in the real world.Whatever the case may be, we can represent it in a data frame. We call the original input data frame, which we pass to a sensAI
VectorModel, the raw data frame.Extracing features from the raw data, using their “natural” representation (using
FeatureGenerators)We extract from the raw data frame pieces of information that we regard as relevant features for the task at hand. A sensAI
FeatureGeneratorcan generate one or more data frame columns (containing arbitrary data), and a model can be associated with any number of feature generators. Several key aspects:FeatureGenerators crititcally decouple the original raw data from the features used by the models, enabling different models to use different sets of features or entirely different representations of the same features.
FeatureGenerators become part of the model and are (where necessary) jointly trained with model. This facilitates model deployment, as every sensAI model becomes a single unit that can directly process raw input data, which is (usually) straightforward to supply at inference time.
FeatureGenerators store meta-data on the features they generate, enabling downstream components to handle them appropriately.
The feature representation that we choose to generate can be arbitrary, but oftentimes, we will want to extract “natural” feature representations, which could, in priciple, be used by many types of models, albeit in different concrete forms. Sequential data can be naturally represented as an array/list, categorical data can be represented using descriptive category names, and numeric data can be represented using unmodified integers and floating point numbers.
Transforming feature representations into a form that is suitable for the model at hand (using
DataFrameTransformers)In the transformation stage, we address the model-specific idiosynchrasies, which may require, for example, that all features be represented as numbers (or even numbers within a limited range) or that all features be discrete, that no values be missing, etc. A
DataFrameTransformercan, in principle perform an arbitary transformation from one data frame to another, but the typical use case is to apply transformations of feature representations that are necessary for specific types of models to work (their best).