Tensorflow Decision Forests

Python

Published

August 28, 2023

Source

library('reticulate')
use_condaenv('tensorflow')

import tensorflow_decision_forests as tfdf

import os
import numpy as np
import pandas as pd
import tensorflow as tf
import math


print("Found TensorFlow Decision Forests v" + tfdf.__version__)

# Download the dataset
# !wget -q https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv -O /tmp/penguins.csv

# Load a dataset into a Pandas Dataframe.
dataset_df = pd.read_csv("/tmp/penguins.csv")

# Display the first 3 examples.
dataset_df.head(3)


# Encode the categorical labels as integers.
#
# Details:
# This stage is necessary if your classification label is represented as a
# string since Keras expects integer classification labels.
# When using `pd_dataframe_to_tf_dataset` (see below), this step can be skipped.

# Name of the label column.
label = "species"

classes = dataset_df[label].unique().tolist()
print(f"Label classes: {classes}")

dataset_df[label] = dataset_df[label].map(classes.index)

# Split the dataset into a training and a testing dataset.

def split_dataset(dataset, test_ratio=0.30):
  """Splits a panda dataframe in two."""
  test_indices = np.random.rand(len(dataset)) < test_ratio
  return dataset[~test_indices], dataset[test_indices]


train_ds_pd, test_ds_pd = split_dataset(dataset_df)
print("{} examples in training, {} examples for testing.".format(
  len(train_ds_pd), len(test_ds_pd)))


train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_ds_pd, label=label)
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_ds_pd, label=label)


# %set_cell_height 300

# Specify the model.
model_1 = tfdf.keras.RandomForestModel(verbose=2)

# Train the model.
model_1.fit(train_ds)


model_1.compile(metrics=["accuracy"])
evaluation = model_1.evaluate(test_ds, return_dict=True)
print()

for name, value in evaluation.items():
  print(f"{name}: {value:.4f}")


tfdf.model_plotter.plot_model_in_colab(model_1, tree_idx=0, max_depth=3)