Default.py

from __future__ import absolute_import, division, print_function, unicode_literals

import pathlib

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import regularizers

print(tf.__version__)

import tensorflow_docs as tfdocs
import tensorflow_docs.plots
import tensorflow_docs.modeling

dataset_path = "default.csv"

column_names = ["LIMIT_BAL", "SEX", "EDUCATION", "MARRIAGE", "AGE", "PAY_0", "PAY_2", "PAY_3", "PAY_4", "PAY_5", "PAY_6", "BILL_AMT1", "BILL_AMT2", "BILL_AMT3", "BILL_AMT4", "BILL_AMT5", "BILL_AMT6", "PAY_AMT1", "PAY_AMT2", "PAY_AMT3", "PAY_AMT4", "PAY_AMT5", "PAY_AMT6", "default"]

raw_dataset = pd.read_csv(dataset_path, names=column_names,
                      na_values = "?", comment='\t',
                      sep=",", skipinitialspace=True)

dataset = raw_dataset.copy()
dataset.tail()

print(dataset.isna().sum())

dataset = dataset.dropna()

dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')
dataset.tail()

train_dataset = dataset.sample(frac=0.8,random_state=0)
test_dataset = dataset.drop(train_dataset.index)

train_stats = train_dataset.describe()
train_stats.pop("default")
train_stats = train_stats.transpose()
print(train_stats)

train_labels = train_dataset.pop('default')
test_labels = test_dataset.pop('default')

def norm(x):
  return (x - train_stats['mean']) / train_stats['std']
normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)

METRICS = [
      keras.metrics.TruePositives(name='tp'),
      keras.metrics.FalsePositives(name='fp'),
      keras.metrics.TrueNegatives(name='tn'),
      keras.metrics.FalseNegatives(name='fn'), 
      keras.metrics.BinaryAccuracy(name='accuracy'),
      keras.metrics.Precision(name='precision'),
      keras.metrics.Recall(name='recall'),
      keras.metrics.AUC(name='auc'),
]

def make_model(metrics = METRICS, output_bias=None):
  if output_bias is not None:
    output_bias = tf.keras.initializers.Constant(output_bias)
  model = keras.Sequential([
      keras.layers.Dense(
          16, activation='relu',
          input_shape=[len(train_dataset.keys())]),
      keras.layers.Dropout(0.5),
      keras.layers.Dense(1, activation='sigmoid',
                         bias_initializer=output_bias),
  ])

  model.compile(
      optimizer=keras.optimizers.Adam(lr=1e-3),
      loss=keras.losses.BinaryCrossentropy(),
      metrics=metrics)

  return model
  
model = make_model(output_bias = -1.258687004)

print(model.summary())

EPOCHS = 100
BATCH_SIZE = 2048

history = model.fit(normed_train_data, train_labels, 
                    epochs=EPOCHS, validation_split = 0.2, verbose=2, callbacks=[tfdocs.modeling.EpochDots()])

hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail()

plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)

print(history)

plotter.plot({'Basic': history}, metric = "accuracy")
plt.ylim([0, 10])
plt.ylabel('accuracy [default]')
plt.show()

test_predictions = model.predict(normed_test_data).flatten()

a = plt.axes(aspect='equal')
plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [default]')
plt.ylabel('Predictions [default]')
lims = [0, 50]
plt.xlim(lims)
plt.ylim(lims)
_ = plt.plot(lims, lims)
plt.show()

error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error [default]")
_ = plt.ylabel("Count")
plt.show()