Update readme

Co-authored-by: Gustavo Viera López <[email protected]>

README.md

@@ -29,82 +29,55 @@ pip install pactus
 
 ## Getting started
 
-This is quick example of how to test a Random Forest classifier on a preprocessed
-version of the GeoLife dataset:
+This is quick example of how to test a Random Forest classifier on the Animals dataset:
 
 ```python
 from pactus import Dataset, featurizers
 from pactus.models import RandomForestModel
 
-# Load Dataset
-dataset = Dataset.geolife()
-
-# Select the desired features to be extracted from the trajectories
-featurizer = featurizers.UniversalFeaturizer()
-
-# Defining the model
-model = RandomForestModel(
-    featurizer=featurizer,
-    max_features=16,
-    n_estimators=200,
-    bootstrap=False,
-    random_state=SEED,
-    warm_start=True,
-    n_jobs=6,
-)
-
-# Preprocess the dataset and split it into train and test sets
-use_classes = {"car", "taxi-bus", "walk", "bike", "subway", "train"}
-train, test = (
-    # Remove short and pourly time sampled trajectories
-    dataset.filter(lambda traj, _: len(traj) > 10 and traj.dt < 8)
-    # Join "taxi" and "bus" into "taxi-bus"
-    .map(lambda _, label: (_, "taxi-bus" if label in ("bus", "taxi") else label))
-    # Only use the classes defined in use_classes
-    .filter(lambda _, label: label in use_classes)
-    # Split the dataset into train and test
-    .split(train_size=0.7, random_state=SEED)
-)
-
-# Train the model
-model.train(data=train, cross_validation=5)
-
-# Evaluate the model on a test dataset
-evaluation = model.evaluate(test)
+# Load dataset
+dataset = Dataset.animals()
+
+# Split data into train and test subsets
+train, test = dataset.split(0.9)
+
+# Convert trajectories into feature vectors
+ft = featurizers.UniversalFeaturizer()
+
+# Build and train the model
+model = RandomForestModel(featurizer=ft)
+model.train(train, cross_validation=5)
 
-# Show the evaluation results
+# Evaluate the results on the test subset
+evaluation = model.evaluate(test)
 evaluation.show()
 ```
 
-It should output the evaluation results as:
+It should output evaluation results similar to:
 
-```
+```text
 General statistics:
 
-Accuracy: 0.913
-F1-score: 0.892
-Mean precision: 0.910
-Mean recall: 0.877
+Accuracy: 0.962
+F1-score: 0.951
+Mean precision: 0.976
+Mean recall: 0.933
 
 Confusion matrix:
 
-bike      car       subway    taxi-bus  train     walk      precision 
-======================================================================
-89.83     0.56      0.74      1.29      0.0       1.41      94.03     
-0.25      79.1      0.74      1.94      0.0       0.11      90.32     
-0.0       0.56      82.35     1.46      0.0       0.22      90.32     
-2.48      18.08     10.29     91.42     12.12     2.5       87.19     
-0.0       0.56      0.0       0.32      87.88     0.0       90.62     
-7.44      1.13      5.88      3.56      0.0       95.76     93.43     
-----------------------------------------------------------------------
-89.83     79.1      82.35     91.42     87.88     95.76     
+Cattle  Deer    Elk     precision
+================================
+100.0   0.0     0.0     100.0   
+0.0     80.0    0.0     100.0   
+0.0     20.0    100.0   92.86   
+--------------------------------
+100.0   80.0    100.0   
 ```
 
 ## Available datasets
 
 See the whole [list of datasets](https://github.com/yupidevs/trajectory-datasets) compatible with pactus
 
-
 ## Contributing
 
-Follow the guidlines from [pactus documentation](https://pactus.readthedocs.io/en/latest/)
+Follow the guidlines from [pactus documentation](https://pactus.readthedocs.io/en/latest/)