



"""simple docstring"""
import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
							# Initialising the CNN
							# (Sequential- Building the model layer by layer)
							UpperCAmelCase						:		Any			= models.Sequential()

							# Step 1 - Convolution
							# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
							# (3,3) is the kernel size (filter matrix)
							classifier.add(
							    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
							)

							# Step 2 - Pooling
							classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

							# Adding a second convolutional layer
							classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
							classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

							# Step 3 - Flattening
							classifier.add(layers.Flatten())

							# Step 4 - Full connection
							classifier.add(layers.Dense(units=128, activation="relu"))
							classifier.add(layers.Dense(units=1, activation="sigmoid"))

							# Compiling the CNN
							classifier.compile(
							    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
							)

							# Part 2 - Fitting the CNN to the images

							# Load Trained model weights

							# from keras.models import load_model
							# regressor=load_model('cnn.h5')

							UpperCAmelCase						:		List[Any]			= tf.keras.preprocessing.image.ImageDataGenerator(
							    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
							)

							UpperCAmelCase						:		List[str]			= tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

							UpperCAmelCase						:		List[Any]			= train_datagen.flow_from_directory(
							    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
							)

							UpperCAmelCase						:		Tuple			= test_datagen.flow_from_directory(
							    "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
							)

							classifier.fit_generator(
							    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
							)

							classifier.save("cnn.h5")

							# Part 3 - Making new predictions

							UpperCAmelCase						:		int			= tf.keras.preprocessing.image.load_img(
							    "dataset/single_prediction/image.png", target_size=(64, 64)
							)
							UpperCAmelCase						:		Dict			= tf.keras.preprocessing.image.img_to_array(test_image)
							UpperCAmelCase						:		Any			= np.expand_dims(test_image, axis=0)
							UpperCAmelCase						:		List[Any]			= classifier.predict(test_image)
							# training_set.class_indices
							if result[0][0] == 0:
														UpperCAmelCase						:		str			= 'Normal'
							if result[0][0] == 1:
														UpperCAmelCase						:		int			= 'Abnormality detected'






"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


  return (data["data"], data["target"])


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


  lowercase_								= XGBRegressor(verbosity=0 ,  random_state=42							)
  xgb.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Predict target for test data
  lowercase_								= xgb.predict(__lowerCAmelCase							)
  lowercase_								= predictions.reshape(len(__lowerCAmelCase							) ,  1							)
  return predictions


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= xgboost(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)
  print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

