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TFReceiptLinearPredictor / inference.py

Janmejay1198

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

save_directory = 'Generated Data/'
if not os.path.exists(save_directory):
    os.makedirs(save_directory)

# Define the Linear Regression Model
class LinearRegression(tf.Module):
    def __init__(self, input_size, output_size):
        self.W = tf.Variable(tf.random.normal([input_size, output_size]), name='weights')
        self.b = tf.Variable(tf.zeros([output_size]), name='bias')

    def __call__(self, X):
        return tf.matmul(X, self.W) + self.b
    
data = pd.read_csv('data_daily.csv', parse_dates=['# Date'], index_col=None)  # Load the dataset
data.rename(columns={"# Date": "Date"}, inplace=True)                         # Rename Date Column

date_range = pd.date_range(start='2022-01-01', end='2022-12-31', freq='D')      # Creating Inference Dataframe
inference_data = pd.DataFrame({'Date': date_range})

initial_date = np.datetime64('2021-01-01')
num_of_days = (inference_data['Date'] - initial_date).dt.days / 364

X = tf.convert_to_tensor(num_of_days, dtype=tf.float32)[:, tf.newaxis]

# Initialize the model
input_size = 1
output_size = 1
model = LinearRegression(input_size, output_size)
loaded_model = tf.saved_model.load('Receipts_model')
model.W.assign(loaded_model.W)
model.b.assign(loaded_model.b)

model(tf.constant([[0.0]], dtype=tf.float32))  # Run the model to initialize variables

y_predict = model(X)                            # Run the model to make predictions
y_predict *= np.max(data['Receipt_Count'].to_numpy())  

df = pd.DataFrame({
    'Date': pd.date_range(start='2022-01-01', periods=365),
    'Receipts': y_predict.numpy().squeeze()
}).set_index('Date')

# Resample by month and sum up the receipts, then save to CSV
df.resample('M').sum().to_csv(os.path.join(save_directory, 'predicted_receipts.csv'), index=True)

# Convert TensorFlow tensor to NumPy array
y_predict_np = y_predict.numpy()

# Plot the results using Seaborn
plt.figure(figsize=(15, 7))
sns.scatterplot(x=data['Date'], y=data['Receipt_Count'], color='#93E9BE', label='Actual Daily Receipt Count')
sns.lineplot(x=inference_data['Date'], y=y_predict_np.squeeze(), color='#FF5349', label='Model Prediction over Year 2022')
plt.title('Comparison of Model Predictions and Actual Daily Receipt Counts (2021-2022)')
plt.xlabel('Date (Year 2021 - Year 2022)')
plt.ylabel('Number of Receipts')
plt.grid(True, which='both', linestyle='-', linewidth=0.5) 
plt.legend()
plt.savefig(os.path.join(save_directory, 'inference_visualization.png'), bbox_inches='tight')
plt.show()

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

	save_directory = 'Generated Data/'
	if not os.path.exists(save_directory):
	os.makedirs(save_directory)

	# Define the Linear Regression Model
	class LinearRegression(tf.Module):
	def __init__(self, input_size, output_size):
	self.W = tf.Variable(tf.random.normal([input_size, output_size]), name='weights')
	self.b = tf.Variable(tf.zeros([output_size]), name='bias')

	def __call__(self, X):
	return tf.matmul(X, self.W) + self.b

	data = pd.read_csv('data_daily.csv', parse_dates=['# Date'], index_col=None) # Load the dataset
	data.rename(columns={"# Date": "Date"}, inplace=True) # Rename Date Column

	date_range = pd.date_range(start='2022-01-01', end='2022-12-31', freq='D') # Creating Inference Dataframe
	inference_data = pd.DataFrame({'Date': date_range})

	initial_date = np.datetime64('2021-01-01')
	num_of_days = (inference_data['Date'] - initial_date).dt.days / 364

	X = tf.convert_to_tensor(num_of_days, dtype=tf.float32)[:, tf.newaxis]

	# Initialize the model
	input_size = 1
	output_size = 1
	model = LinearRegression(input_size, output_size)
	loaded_model = tf.saved_model.load('Receipts_model')
	model.W.assign(loaded_model.W)
	model.b.assign(loaded_model.b)

	model(tf.constant([[0.0]], dtype=tf.float32)) # Run the model to initialize variables

	y_predict = model(X) # Run the model to make predictions
	y_predict *= np.max(data['Receipt_Count'].to_numpy())

	df = pd.DataFrame({
	'Date': pd.date_range(start='2022-01-01', periods=365),
	'Receipts': y_predict.numpy().squeeze()
	}).set_index('Date')

	# Resample by month and sum up the receipts, then save to CSV
	df.resample('M').sum().to_csv(os.path.join(save_directory, 'predicted_receipts.csv'), index=True)

	# Convert TensorFlow tensor to NumPy array
	y_predict_np = y_predict.numpy()

	# Plot the results using Seaborn
	plt.figure(figsize=(15, 7))
	sns.scatterplot(x=data['Date'], y=data['Receipt_Count'], color='#93E9BE', label='Actual Daily Receipt Count')
	sns.lineplot(x=inference_data['Date'], y=y_predict_np.squeeze(), color='#FF5349', label='Model Prediction over Year 2022')
	plt.title('Comparison of Model Predictions and Actual Daily Receipt Counts (2021-2022)')
	plt.xlabel('Date (Year 2021 - Year 2022)')
	plt.ylabel('Number of Receipts')
	plt.grid(True, which='both', linestyle='-', linewidth=0.5)
	plt.legend()
	plt.savefig(os.path.join(save_directory, 'inference_visualization.png'), bbox_inches='tight')
	plt.show()