Tensorflow Decision Forests

 #Introducing TensorFlow Decision Forests

#https://blog.tensorflow.org/2021/05/introducing-tensorflow-decision-forests.html

#next step : https://www.tensorflow.org/decision_forests/tutorials

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#!pip install tensorflow_decision_forests 

#!wget "https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv"

# Load TensorFlow Decision Forests

import tensorflow_decision_forests as tfdf

# Load the training dataset using pandas

import pandas

df  = pandas.read_csv("penguins.csv")

from sklearn.model_selection import train_test_split 

train_df , test_df = train_test_split(df, test_size = 0.2)

# Convert the pandas dataframe into a TensorFlow dataset

train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="species")

# Train the model

model = tfdf.keras.RandomForestModel()

model.fit(train_ds)

# Load the testing dataset

#test_df = pandas.read_csv("penguins_test.csv")

# Convert it to a TensorFlow dataset

test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="species")

# Evaluate the model

model.compile(metrics=["accuracy"])

print(model.evaluate(test_ds))

# >> 0.979311

# Note: Cross-validation would be more suited on this small dataset.

# See also the "Out-of-bag evaluation" below.

# Export the model to a TensorFlow SavedModel

model.save("project/my_first_model")

tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0)

model.summary()

model.make_inspector().variable_importances()

#["MEAN_DECREASE_IN_ACCURACY"]

#MEAN_DECREASE_IN_ACCURACY

# List all the other available learning algorithms

tfdf.keras.get_all_models()

# Display the hyper-parameters of the Gradient Boosted Trees model 

#the following will open a new help window describing GradientBoostedTreesModel

#? tfdf.keras.GradientBoostedTreesModel

# Create another model with specified hyper-parameters

model = tfdf.keras.GradientBoostedTreesModel(

    num_trees=500,

    growing_strategy="BEST_FIRST_GLOBAL",

    max_depth=8,

    split_axis="SPARSE_OBLIQUE",

    )

# Evaluate the model

model.compile(metrics=["accuracy"])

print(model.evaluate(test_ds))