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Bayangkan Anda ingin memperkirakan tingkat bunga kartu kredit Anda satu tahun dari sekarang. Misalkan tarif utama saat ini adalah 2% dan perusahaan kartu kredit Anda menagih Anda 10% ditambah tarif utama. Mengingat kekuatan perekonomian saat ini, Anda yakin bahwa Federal Reserve kemungkinan besar akan menaikkan suku bunga dibandingkan tidak. The Fed akan bertemu delapan kali dalam dua belas bulan ke depan dan akan menaikkan suku bunga dana federal sebesar 0,25% atau mempertahankannya pada level sebelumnya.
Kami menggunakan distribusi binomial untuk memodelkan tingkat bunga kartu kredit Anda pada akhir periode dua belas bulan. Secara khusus, kita akan menggunakan kelas distribusi TensorFlow Probability Binomial dengan parameter berikut: total_count = 8 (jumlah uji coba atau pertemuan), probs = {.6, .7, .8, .9}, untuk rentang perkiraan kita tentang kemungkinan The Fed menaikkan suku bunga dana federal sebesar 0,25% pada setiap pertemuan.
Ketergantungan & Prasyarat
Pengaturan Instalasi Probabilitas TensorFlow
TFP_Installation = "Stable TFP"
if TFP_Installation == "Most Recent TFP":
!pip install -q tfp-nightly
print("Most recent TFP version installed")
elif TFP_Installation == "Stable TFP":
!pip install -q --upgrade tensorflow-probability
print("Up-to-date, stable TFP version installed")
elif TFP_Installation == "Stable TFP-GPU":
!pip install -q --upgrade tensorflow-probability-gpu
print("Up-to-date, stable TFP-GPU version installed")
print("(make sure GPU is properly configured)")
elif TFP_Installation == "Most Recent TFP-GPU":
!pip install -q tfp-nightly-gpu
print("Most recent TFP-GPU version installed")
print("(make sure GPU is properly configured)")
elif TFP_Installation == "TFP Already Installed":
print("TFP already installed in this environment")
pass
else:
print("Installation Error: Please select a viable TFP installation option.")
Impor dan Variabel Global (pastikan untuk menjalankan sel ini)
from __future__ import absolute_import, division, print_function
warning_status = "ignore"
import warnings
warnings.filterwarnings(warning_status)
with warnings.catch_warnings():
warnings.filterwarnings(warning_status, category=DeprecationWarning)
warnings.filterwarnings(warning_status, category=UserWarning)
import numpy as np
import os
matplotlib_style = 'fivethirtyeight'
import matplotlib.pyplot as plt; plt.style.use(matplotlib_style)
import matplotlib.axes as axes;
from matplotlib.patches import Ellipse
%matplotlib inline
import seaborn as sns; sns.set_context('notebook')
notebook_screen_res = 'png'
%config InlineBackend.figure_format = notebook_screen_res
import tensorflow as tf
# Eager Execution
use_tf_eager = True
# Use try/except so we can easily re-execute the whole notebook.
if use_tf_eager:
try:
tf.compat.v1.enable_eager_execution()
except:
reset_session()
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
def default_session_options(enable_gpu_ram_resizing=True,
enable_xla=False):
"""Creates default options for Graph-mode session."""
config = tf.ConfigProto()
config.log_device_placement = True
if enable_gpu_ram_resizing:
# `allow_growth=True` makes it possible to connect multiple
# colabs to your GPU. Otherwise the colab malloc's all GPU ram.
config.gpu_options.allow_growth = True
if enable_xla:
# Enable on XLA. https://www.tensorflow.org/performance/xla/.
config.graph_options.optimizer_options.global_jit_level = (
tf.OptimizerOptions.ON_1)
return config
def reset_session(options=None):
"""Creates a new global, interactive session in Graph-mode."""
if tf.executing_eagerly():
return
global sess
try:
tf.reset_default_graph()
sess.close()
except:
pass
if options is None:
options = default_session_options()
sess = tf.InteractiveSession(config=options)
def evaluate(tensors):
"""Evaluates Tensor or EagerTensor to Numpy `ndarray`s.
Args:
tensors: Object of `Tensor` or EagerTensor`s; can be `list`, `tuple`,
`namedtuple` or combinations thereof.
Returns:
ndarrays: Object with same structure as `tensors` except with `Tensor` or
`EagerTensor`s replaced by Numpy `ndarray`s.
"""
if tf.executing_eagerly():
return tf.contrib.framework.nest.pack_sequence_as(
tensors,
[t.numpy() if tf.contrib.framework.is_tensor(t) else t
for t in tf.contrib.framework.nest.flatten(tensors)])
return sess.run(tensors)
class _TFColor(object):
"""Enum of colors used in TF docs."""
red = '#F15854'
blue = '#5DA5DA'
orange = '#FAA43A'
green = '#60BD68'
pink = '#F17CB0'
brown = '#B2912F'
purple = '#B276B2'
yellow = '#DECF3F'
gray = '#4D4D4D'
def __getitem__(self, i):
return [
self.red,
self.orange,
self.green,
self.blue,
self.pink,
self.brown,
self.purple,
self.yellow,
self.gray,
][i % 9]
TFColor = _TFColor()
Hitung Probabilitas
Hitung probabilitas kemungkinan suku bunga kartu kredit dalam 12 bulan.
# First we encode our assumptions.
num_times_fed_meets_per_year = 8.
possible_fed_increases = tf.range(
start=0.,
limit=num_times_fed_meets_per_year + 1)
possible_cc_interest_rates = 2. + 10. + 0.25 * possible_fed_increases
prob_fed_raises_rates = tf.constant([0.6, 0.7, 0.8, 0.9]) # Wild guesses.
# Now we use TFP to compute probabilities in a vectorized manner.
# Pad a dim so we broadcast fed probs against CC interest rates.
prob_fed_raises_rates = prob_fed_raises_rates[..., tf.newaxis]
prob_cc_interest_rate = tfd.Binomial(
total_count=num_times_fed_meets_per_year,
probs=prob_fed_raises_rates).prob(possible_fed_increases)
Jalankan Kode TF
# Convert from TF to numpy.
[
possible_cc_interest_rates_,
prob_cc_interest_rate_,
prob_fed_raises_rates_,
] = evaluate([
possible_cc_interest_rates,
prob_cc_interest_rate,
prob_fed_raises_rates,
])
Visualisasikan Hasil
plt.figure(figsize=(14, 9))
for i, pf in enumerate(prob_fed_raises_rates_):
plt.subplot(2, 2, i+1)
plt.bar(possible_cc_interest_rates_,
prob_cc_interest_rate_[i],
color=TFColor[i],
width=0.23,
label="$p = {:.1f}$".format(pf[0]),
alpha=0.6,
edgecolor=TFColor[i],
lw="3")
plt.xticks(possible_cc_interest_rates_ + 0.125, possible_cc_interest_rates_)
plt.xlim(12, 14.25)
plt.ylim(0, 0.5)
plt.ylabel("Probability of cc interest rate")
plt.xlabel("Credit card interest rate (%)")
plt.title("Credit card interest rates: "
"prob_fed_raises_rates = {:.1f}".format(pf[0]));
plt.suptitle("Estimates of credit card interest rates in 12 months.",
fontsize="x-large",
y=1.02)
plt.tight_layout()
