TF_Decision_Trees / tfdecisiontrees_final.py

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	# -- coding: utf-8 --
	"""TFDecisionTrees_Final.ipynb

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/drive/1QCdVlNQ8LszC_v3ek10DUeO9V0IvVzpm

	# Classification with TF Decision Trees
	Source code from https://keras.io/examples/structured_data/classification_with_tfdf/
	"""

	!pip install huggingface_hub

	!pip install numpy==1.20

	!pip install folium==0.2.1

	!pip install imgaug==0.2.6

	!pip install tensorflow==2.8.0

	!pip install -U tensorflow_decision_forests

	!pip install ipykernel==4.10

	!apt-get install -y git-lfs

	!pip install wurlitzer

	from huggingface_hub import notebook_login
	from huggingface_hub.keras_mixin import push_to_hub_keras

	notebook_login()

	import math
	import urllib
	import numpy as np
	import pandas as pd
	import tensorflow as tf
	from tensorflow import keras
	from tensorflow.keras import layers
	import tensorflow_decision_forests as tfdf
	import os
	import tempfile

	tmpdir = tempfile.mkdtemp()

	try:
	from wurlitzer import sys_pipes
	except:
	from colabtools.googlelog import CaptureLog as sys_pipes

	input_path = "https://archive.ics.uci.edu/ml/machine-learning-databases/census-income-mld/census-income"
	input_column_header = "income_level"

	#Load data

	BASE_PATH = input_path
	CSV_HEADER = [ l.decode("utf-8").split(":")[0].replace(" ", "_")
	for l in urllib.request.urlopen(f"{BASE_PATH}.names")
	if not l.startswith(b"\|")][2:]

	CSV_HEADER.append(input_column_header)

	train_data = pd.read_csv(f"{BASE_PATH}.data.gz", header=None, names=CSV_HEADER)
	test_data = pd.read_csv(f"{BASE_PATH}.test.gz", header=None, names=CSV_HEADER)

	train_data["migration_code-change_in_msa"] = train_data["migration_code-change_in_msa"].apply(lambda x: "Unansw" if x == " ?" else x)

	test_data["migration_code-change_in_msa"] = test_data["migration_code-change_in_msa"].apply(lambda x: "Unansw" if x == " ?" else x)

	print(train_data["migration_code-change_in_msa"].unique())

	for i, value in enumerate(CSV_HEADER):
	if value == "fill_inc_questionnaire_for_veteran's_admin":
	CSV_HEADER[i] = "fill_inc_veterans_admin"
	elif value == "migration_code-change_in_msa":
	CSV_HEADER[i] = "migration_code_chx_in_msa"
	elif value == "migration_code-change_in_reg":
	CSV_HEADER[i] = "migration_code_chx_in_reg"
	elif value == "migration_code-move_within_reg":
	CSV_HEADER[i] = "migration_code_move_within_reg"

	#inspect the classes of the label, the input_column_header in this case
	classes = train_data["income_level"].unique().tolist()
	print(f"Label classes: {classes}")

	#rename columns containing invalid characters
	train_data = train_data.rename(columns={"fill_inc_questionnaire_for_veteran's_admin": "fill_inc_veterans_admin", "migration_code-change_in_msa": "migration_code_chx_in_msa", "migration_code-change_in_reg" : "migration_code_chx_in_reg", "migration_code-move_within_reg" : "migration_code_move_within_reg"})
	test_data = train_data.rename(columns={"fill_inc_questionnaire_for_veteran's_admin": "fill_inc_veterans_admin", "migration_code-change_in_msa": "migration_code_chx_in_msa", "migration_code-change_in_reg" : "migration_code_chx_in_reg", "migration_code-move_within_reg" : "migration_code_move_within_reg"})

	#convert from string to integers
	# This stage is necessary if your classification label is represented as a
	# string. Note: Keras expected classification labels to be integers.
	target_labels = [" - 50000.", " 50000+."]
	train_data[input_column_header] = train_data[input_column_header].map(target_labels.index)
	test_data[input_column_header] = test_data[input_column_header].map(target_labels.index)

	#Observe shape of training and test data
	print(f"Train data shape: {train_data.shape}")
	print(f"Test data shape: {test_data.shape}")
	print(train_data.head().T)

	#define metadata

	# Target column name.
	TARGET_COLUMN_NAME = "income_level"
	# Weight column name.
	WEIGHT_COLUMN_NAME = "instance_weight"
	# Numeric feature names.
	NUMERIC_FEATURE_NAMES = [
	"age",
	"wage_per_hour",
	"capital_gains",
	"capital_losses",
	"dividends_from_stocks",
	"num_persons_worked_for_employer",
	"weeks_worked_in_year",
	]

	# Categorical features and their vocabulary lists.
	CATEGORICAL_FEATURES_WITH_VOCABULARY = {
	feature_name: sorted(
	[str(value) for value in list(train_data[feature_name].unique())]
	)
	for feature_name in CSV_HEADER
	if feature_name
	not in list(NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME, TARGET_COLUMN_NAME])
	}
	# All features names.
	FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list(
	CATEGORICAL_FEATURES_WITH_VOCABULARY.keys()
	)

	"""Configure hyperparameters for the tree model."""

	GROWING_STRATEGY = "BEST_FIRST_GLOBAL"
	NUM_TREES = 250
	MIN_EXAMPLES = 6
	MAX_DEPTH = 5
	SUBSAMPLE = 0.65
	SAMPLING_METHOD = "RANDOM"
	VALIDATION_RATIO = 0.1

	#Implement training & evaluation procedure
	def prepare_sample(features, target, weight):
	for feature_name in features:
	if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
	if features[feature_name].dtype != tf.dtypes.string:
	# Convert categorical feature values to string.
	features[feature_name] = tf.strings.as_string(features[feature_name])
	return features, target, weight


	def run_experiment(model, train_data, test_data, num_epochs=1, batch_size=None):

	train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(
	train_data, label="income_level", weight="instance_weight"
	).map(prepare_sample, num_parallel_calls=tf.data.AUTOTUNE)
	test_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(
	test_data, label="income_level", weight="instance_weight"
	).map(prepare_sample, num_parallel_calls=tf.data.AUTOTUNE)

	model.fit(train_dataset, epochs=num_epochs, batch_size=batch_size)
	_, accuracy = model.evaluate(test_dataset, verbose=0)
	push_to_hub = True
	print(f"Test accuracy: {round(accuracy * 100, 2)}%")

	#Create model inputs

	def create_model_inputs():
	inputs = {}
	for feature_name in FEATURE_NAMES:
	if feature_name in NUMERIC_FEATURE_NAMES:
	inputs[feature_name] = layers.Input(
	name=feature_name, shape=(), dtype=tf.float32
	)
	else:
	inputs[feature_name] = layers.Input(
	name=feature_name, shape=(), dtype=tf.string
	)
	return inputs

	"""# Experiment 1: Decision Forests with raw features"""

	#Decision Forest with raw features
	def specify_feature_usages(inputs):
	feature_usages = []

	for feature_name in inputs:
	if inputs[feature_name].dtype == tf.dtypes.float32:
	feature_usage = tfdf.keras.FeatureUsage(
	name=feature_name, semantic=tfdf.keras.FeatureSemantic.NUMERICAL
	)
	else:
	feature_usage = tfdf.keras.FeatureUsage(
	name=feature_name, semantic=tfdf.keras.FeatureSemantic.CATEGORICAL
	)

	feature_usages.append(feature_usage)
	return feature_usages

	#Create GB trees model
	def create_gbt_model():
	gbt_model = tfdf.keras.GradientBoostedTreesModel(
	features = specify_feature_usages(create_model_inputs()),
	exclude_non_specified_features = True,
	growing_strategy = GROWING_STRATEGY,
	num_trees = NUM_TREES,
	max_depth = MAX_DEPTH,
	min_examples = MIN_EXAMPLES,
	subsample = SUBSAMPLE,
	validation_ratio = VALIDATION_RATIO,
	task = tfdf.keras.Task.CLASSIFICATION,
	loss = "DEFAULT",
	)

	gbt_model.compile(metrics=[keras.metrics.BinaryAccuracy(name="accuracy")])
	return gbt_model

	#Train and evaluate model
	gbt_model = create_gbt_model()
	run_experiment(gbt_model, train_data, test_data)

	#Inspect the model: Model type, mask, input features, feature importance
	print(gbt_model.summary())

	inspector = gbt_model.make_inspector()
	[field for field in dir(inspector) if not field.startswith("_")]

	#plot the model
	tfdf.model_plotter.plot_model_in_colab(gbt_model, tree_idx=0, max_depth=3)

	#display variable importance
	inspector.variable_importances()

	print("Model type:", inspector.model_type())
	print("Number of trees:", inspector.num_trees())
	print("Objective:", inspector.objective())
	print("Input features:", inspector.features())

	inspector.features()

	#save_path = os.path.join(tmpdir, "raw/1/")
	gbt_model.save("/Users/tdubon/TF_Model")

	"""# Creating HF Space"""

	from huggingface_hub import KerasModelHubMixin
	from huggingface_hub.keras_mixin import push_to_hub_keras
	push_to_hub_keras(gbt_model, repo_url="https://huggingface.co/keras-io/TF_Decision_Trees")

	#Clone and configure
	!git clone https://tdubon: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@huggingface.co/tdubon/TF_Decision_Trees

	!cd TFClassificationForest
	!git config --global user.email "tdubon6@gmail.com"
	# Tip: using the same email than for your huggingface.co account will link your commits to your profile
	!git config --global user.name "tdubon"

	!git add .
	!git commit -m "Initial commit"
	!git push

	tf.keras.models.save_model(
	gbt_model, "/Users/tdubon/TFClassificationForest", overwrite=True, include_optimizer=True, save_format=None,
	signatures=None, options=None, save_traces=True)

	# Commented out IPython magic to ensure Python compatibility.
	gbt_model.make_inspector().export_to_tensorboard("/tmp/tb_logs/model_1")

	# %load_ext tensorboard
	# %tensorboard --logdir "/tmp/tb_logs"