Upload tfdecisiontrees_final.py

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:[email protected]/tdubon/TF_Decision_Trees
+
+!cd TFClassificationForest
+!git config --global user.email "[email protected]"
+# 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"