linearReg.py

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

import tensorflow.compat.v2.feature_column as fc


import tensorflow as tf

#print(tf.version)

# Load dataset.
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data
dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data
y_train = dftrain.pop('survived')
y_eval = dfeval.pop('survived')

# Prepping dataset
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
                       'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']

feature_columns = []

for feature_name in CATEGORICAL_COLUMNS:
    vocabulary = dftrain[feature_name].unique()
    feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
    feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))

#print(feature_columns)

def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
    def inputFunction(): # inner function, this will be returned
        ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) # create tf.data.Dataset object with data and its label
        if shuffle:
            ds = ds.shuffle(1000) #randomize order of data
        ds = ds.batch(batch_size).repeat(num_epochs)
         # split dataset into batches of 32 and repeat process for number of epochs
        return ds  # return a batch of the dataset
    return inputFunction

train_input_fn = make_input_fn(dftrain, y_train) #here we will call the input_function that was returned to us to get a dataset object we can feed to the model
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
# We create a linear estimtor by passing the feature columns we created earlier

linear_est.train(train_input_fn) # train
result = linear_est.evaluate(eval_input_fn) # get model metrics/stats by testing on tetsing data

clear_output()
print(result['accuracy'])

pred_dicts = list(linear_est.predict(eval_input_fn))
probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])

probs.plot(kind='hist', bins=20, title='predicted probabilities')
plt.show()