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# -*- coding: utf-8 -*-
import tensorflow_decision_forests as tfdf
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import gradio as gr
import urllib
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)
#subset data
train_data = train_data.loc[:, ["education", "sex", "capital_gains", "capital_losses", "income_level"]]
test_data = test_data.loc[:, ["education", "sex", "capital_gains", "capital_losses", "income_level"]]
def encode_df(df):
sex_mapping = {" Male": 0, " Female": 1}
df = df.replace({"sex": sex_mapping})
education_mapping = {" High school graduate": 1, " Some college but no degree": 2,
" 10th grade": 3, " Children": 4, " Bachelors degree(BA AB BS)": 5,
" Masters degree(MA MS MEng MEd MSW MBA)": 6, " Less than 1st grade": 7,
" Associates degree-academic program": 8, " 7th and 8th grade": 9,
" 12th grade no diploma": 10, " Associates degree-occup /vocational": 11,
" Prof school degree (MD DDS DVM LLB JD)": 12, " 5th or 6th grade": 13,
" 11th grade": 14, " Doctorate degree(PhD EdD)": 15, " 9th grade": 16,
" 1st 2nd 3rd or 4th grade": 17}
df = df.replace({"education": education_mapping})
income_mapping = {' - 50000.': 0, ' 50000+.': 1}
df = df.replace({"income_level": income_mapping})
return df
train_data = encode_df(train_data)
test_data = encode_df(test_data)
feature_a = tfdf.keras.FeatureUsage(name="education", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_b = tfdf.keras.FeatureUsage(name="sex", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_c = tfdf.keras.FeatureUsage(name="capital_gains", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_d = tfdf.keras.FeatureUsage(name="capital_losses", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
# Convert the dataset into a TensorFlow dataset.
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_data, label="income_level")
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_data, label="income_level")
# Train a GB Trees model
model = tfdf.keras.GradientBoostedTreesModel(
features = [feature_a, feature_b, feature_c, feature_d],
exclude_non_specified_features = True,
growing_strategy = "BEST_FIRST_GLOBAL",
num_trees = 350,
max_depth = 7,
min_examples = 6,
subsample = 0.65,
sampling_method = "GOSS",
validation_ratio = 0.1,
task = tfdf.keras.Task.CLASSIFICATION,
loss = "DEFAULT",
verbose=0)
model.compile(metrics=[keras.metrics.BinaryAccuracy(name="accuracy")])
model.fit(train_ds)
model.evaluate(test_ds, verbose=0)
#prepare user input for the model
def process_inputs(education, sex, capital_gains, capital_losses):
df = pd.DataFrame.from_dict(
{
"education": [edu_in],
"sex": [sex_in],
"capital_gains": [cap_gains_in],
"capital_losses": [cap_losses_in]
}
)
df = encode_df(df)
feature_a = tfdf.keras.FeatureUsage(name="education", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_b = tfdf.keras.FeatureUsage(name="sex", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_c = tfdf.keras.FeatureUsage(name="capital_gains", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
feature_d = tfdf.keras.FeatureUsage(name="capital_losses", semantic=tfdf.keras.FeatureSemantic.CATEGORICAL)
df = tfdf.keras.pd_dataframe_to_tf_dataset(df)
pred = model.predict(df)
if pred > .5:
pred_bi = 1
return {"> $50,000": pred_bi}
elif pred <=.5:
pred_bi = 0
return {"<= $50,000": pred_bi}
iface = gr.Interface(
process_inputs,
[
gr.inputs.Dropdown([" 1st 2nd 3rd or 4th grade", " High school graduate",
" Bachelors degree(BA AB BS)", " Masters degree(MA MS MEng MEd MSW MBA)",
" Prof school degree (MD DDS DVM LLB JD)",
" Doctorate degree(PhD EdD)"], type="index", label="education"),
gr.inputs.Radio([" Male", " Female"], label="sex", type="index"),
gr.inputs.Slider(minimum = 0, maximum = 99999, label="capital_gains"),
gr.inputs.Slider(minimum = 0, maximum = 4608, label="capital_losses")
],
gr.outputs.Label(num_top_classes=2),
live=True,
analytics_enabled=False
)
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