From fa240df01991c14e4a16de9550699ae2b4e6f7a8 Mon Sep 17 00:00:00 2001
From: Yaksher <yaksher.git@gmail.com>
Date: Sat, 11 Nov 2023 22:46:25 -0800
Subject: [PATCH] Revert "Rename outcomes to categories; unpack model_dict
global"
This reverts commit eb1b7980030e898d910ce48b14ec88698ffa313e.
---
bayes.py | 49 +++++++++++++++++++++++++------------------------
main.py | 22 +++++++++-------------
2 files changed, 34 insertions(+), 37 deletions(-)
diff --git a/bayes.py b/bayes.py
index 54bb9878..6752762a 100644
--- a/bayes.py
+++ b/bayes.py
@@ -4,56 +4,57 @@ from math import log, inf
from functools import cache
from typing import List
+"""After training, model_dict is a global variable which is accessible inside this module"""
+
@cache
def tokenize(text):
return [y for y in [re.sub('[^a-z0-9]', '', x) for x in text.lower().split(" ")] if len(y)]
def train(dataset):
- count_of_word_by_category = {}
+ model_dict = {}
+ count_of_word_by_outcome = {}
num_data_points = len(dataset)
- count_of_data_points_in_category = Counter()
+ count_of_data_points_with_outcome = Counter()
for point in dataset:
name = point['name']
classification = point['classification']
- count_of_data_points_in_category[classification] += 1
- if classification not in count_of_word_by_category:
- count_of_word_by_category[classification] = Counter()
+ count_of_data_points_with_outcome[classification] += 1
+ if classification not in count_of_word_by_outcome:
+ count_of_word_by_outcome[classification] = Counter()
words = set(tokenize(point['contents']))
for word in words:
- count_of_word_by_category[classification][word] += 1
+ count_of_word_by_outcome[classification][word] += 1
+
+ model_dict['count_of_word_by_outcome'] = count_of_word_by_outcome
+ model_dict['num_data_points'] = num_data_points
+ model_dict['count_of_data_points_with_outcome'] = count_of_data_points_with_outcome
- return (count_of_word_by_category, num_data_points, count_of_data_points_in_category)
+ return model_dict
"""
TODO - Implement the following functions.
-
-After training (which will be run before your code), the following global
-variables are available:
- count_of_word_by_category[category][word] = Total number of documents in the
- category 'category' in which this word appears
-
- num_data_points = Total number of documents in the data set
-
- count_of_data_points_in_category[category] = Total number of documents in
- the category 'category'
+In each of these functions, you can use the MODEL variable which is a dictionary which has three members:
+ MODEL['count_of_word_by_outcome'][outcome][word] = Total number of documents in the category 'outcome' in which this word appears
+ MODEL['num_data_points'] = Total number of documents in the data set
+ MODEL['count_of_data_points_with_outcome'][outcome] = Total number of documents in the category 'outcome'
"""
@cache
-def pr_category(category : str) : # Pr(category)
+def pr_outcome(outcome : str) : # Pr(outcome)
return 0
@cache
-def pr_word_given_category(word : str, category : str, num_words_in_document : int): # Pr(word | category)
+def pr_word_given_outcome(word : str, outcome : str, num_words_in_document : int): # Pr(word | outcome)
return 0
-def pr_category_given_words(words : List[str], category : str): # Pr(category | words)
+def pr_outcome_given_words(words : List[str], outcome : str): # Pr(outcome | words)
return 0
-def predict(categories, words):
+def predict(outcomes, words):
best = None
best_likelihood = -inf
- for category in categories:
- pr = pr_category_given_words(words, category)
+ for outcome in outcomes:
+ pr = pr_outcome_given_words(words, outcome)
if pr > best_likelihood:
- best = category
+ best = outcome
best_likelihood = pr
return best
diff --git a/main.py b/main.py
index b15ff249..191ba508 100644
--- a/main.py
+++ b/main.py
@@ -8,29 +8,25 @@ VALIDATE = 'data/validate.json'
train = json.loads(open(TRAIN).read())
validate = json.loads(open(VALIDATE).read())
-def test(dataset, categories):
+def test(dataset, outcomes):
answers = dict([x.split(" ") for x in open(dataset + "_validate.txt").read().split("\n")[:-1]])
- (
- bayes.count_of_word_by_category,
- bayes.num_data_points,
- bayes.count_of_data_points_in_category
- ) = bayes.train(train[dataset])
+ bayes.model_dict = bayes.train(train[dataset])
- correct_by_category = Counter()
- incorrect_by_category = Counter()
+ correct_by_outcome = Counter()
+ incorrect_by_outcome = Counter()
for point in validate[dataset]:
words = set(bayes.tokenize(point['contents']))
- prediction = bayes.predict(categories, words)
+ prediction = bayes.predict(outcomes, words)
answer = answers[point['name']]
if prediction == answer:
- correct_by_category[answer] += 1
+ correct_by_outcome[answer] += 1
else:
- incorrect_by_category[answer] += 1
+ incorrect_by_outcome[answer] += 1
- print(correct_by_category)
- print(incorrect_by_category)
+ print(correct_by_outcome)
+ print(incorrect_by_outcome)
test('tweets', set(['positive', 'negative']))
test('emails', set(['spam', 'ham']))
--
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