Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+import tensorflow_decision_forests as tfdf
+import pandas as pd
+import gradio as gr
+import urllib
+from tensorflow import keras
+
+
+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)
+
+#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
+)