from future import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import seaborn as sb
import tensorflow as tf
from tensorflow import keras as ks
from tensorflow.estimator import LinearRegressor
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
print(tf.version)
"""## Load and configure the Boston Housing Dataset"""
boston_load = datasets.load_boston()
feature_columns = boston_load.feature_names
target_column = boston_load.target
boston_data = pd.DataFrame(boston_load.data, columns=feature_columns).astype(np.float32)
boston_data'MEDV' = target_column.astype(np.float32)
boston_data.head()
"""## Checking the relation between the variables using Pairplot and Correlation Graph"""
sb.pairplot(boston_data, diag_kind="kde", height=3, aspect=0.6)
correlation_data = boston_data.corr()
correlation_data.style.background_gradient(cmap='coolwarm', axis=None)
"""## Descriptive Statistics - Central Tendency and Dispersion"""
stats = boston_data.describe()
boston_stats = stats.transpose()
boston_stats
"""## Select the required columns"""
X_data = boston_data\[i for i in boston_data.columns if i not in \['MEDV']]
Y_data = boston_data\['MEDV']
"""## Train Test Split"""
training_features , test_features ,training_labels, test_labels = train_test_split(X_data , Y_data , test_size=0.2)
print('No. of rows in Training Features: ', training_features.shape0)
print('No. of rows in Test Features: ', test_features.shape0)
print('No. of columns in Training Features: ', training_features.shape1)
print('No. of columns in Test Features: ', test_features.shape1)
print('No. of rows in Training Label: ', training_labels.shape0)
print('No. of rows in Test Label: ', test_labels.shape0)
print('No. of columns in Training Label: ', training_labels.shape1)
print('No. of columns in Test Label: ', test_labels.shape1)
stats = training_features.describe()
stats = stats.transpose()
stats
stats = test_features.describe()
stats = stats.transpose()
stats
"""## Normalize Data"""
def norm(x):
stats = x.describe()
stats = stats.transpose()
return (x - stats'mean') / stats'std'
normed_train_features = norm(training_features)
normed_test_features = norm(test_features)
"""## Build the Input Pipeline for TensorFlow model"""
def feed_input(features_dataframe, target_dataframe, num_of_epochs=10, shuffle=True, batch_size=32):
def input_feed_function():
dataset = tf.data.Dataset.from_tensor_slices((dict(features_dataframe), target_dataframe))
if shuffle:
dataset = dataset.shuffle(2000)
dataset = dataset.batch(batch_size).repeat(num_of_epochs)
return dataset
return input_feed_function
train_feed_input = feed_input(normed_train_features, training_labels)
train_feed_input_testing = feed_input(normed_train_features, training_labels, num_of_epochs=1, shuffle=False)
test_feed_input = feed_input(normed_test_features, test_labels, num_of_epochs=1, shuffle=False)
"""## Model Training"""
feature_columns_numeric = tf.feature_column.numeric_column(m) for m in training_features.columns
linear_model = LinearRegressor(feature_columns=feature_columns_numeric, optimizer='RMSProp')
linear_model.train(train_feed_input)
"""## Predictions"""
train_predictions = linear_model.predict(train_feed_input_testing)
test_predictions = linear_model.predict(test_feed_input)
train_predictions_series = pd.Series(p\['predictions'0 for p in train_predictions])
test_predictions_series = pd.Series(p\['predictions'0 for p in test_predictions])
train_predictions_df = pd.DataFrame(train_predictions_series, columns='predictions')
test_predictions_df = pd.DataFrame(test_predictions_series, columns='predictions')
training_labels.reset_index(drop=True, inplace=True)
train_predictions_df.reset_index(drop=True, inplace=True)
test_labels.reset_index(drop=True, inplace=True)
test_predictions_df.reset_index(drop=True, inplace=True)
train_labels_with_predictions_df = pd.concat(training_labels, train_predictions_df, axis=1)
test_labels_with_predictions_df = pd.concat(test_labels, test_predictions_df, axis=1)
"""## Validation"""
def calculate_errors_and_r2(y_true, y_pred):
mean_squared_err = (mean_squared_error(y_true, y_pred))
root_mean_squared_err = np.sqrt(mean_squared_err)
r2 = round(r2_score(y_true, y_pred)*100,0)
return mean_squared_err, root_mean_squared_err, r2
train_mean_squared_error, train_root_mean_squared_error, train_r2_score_percentage = calculate_errors_and_r2(training_labels, train_predictions_series)
test_mean_squared_error, test_root_mean_squared_error, test_r2_score_percentage = calculate_errors_and_r2(test_labels, test_predictions_series)
print('Training Data Mean Squared Error = ', train_mean_squared_error)
print('Training Data Root Mean Squared Error = ', train_root_mean_squared_error)
print('Training Data R2 = ', train_r2_score_percentage)
print('Test Data Mean Squared Error = ', test_mean_squared_error)
print('Test Data Root Mean Squared Error = ', test_root_mean_squared_error)
print('Test Data R2 = ', test_r2_score_percentage)