Sourcery Starbot ⭐ refactored jobindj/data-science-from-scratch #1

SourceryAI · 2021-03-10T16:08:04Z

Thanks for starring sourcery-ai/sourcery ✨ 🌟 ✨

Here's your pull request refactoring your most popular Python repo.

If you want Sourcery to refactor all your Python repos and incoming pull requests install our bot.

Review changes via command line

To manually merge these changes, make sure you're on the master branch, then run:

git fetch https://github.com/sourcery-ai-bot/data-science-from-scratch master
git merge --ff-only FETCH_HEAD
git reset HEAD^

SourceryAI · 2021-03-10T16:08:06Z

first-edition/code/introduction.py

-  if years_experience < 3.0: return "paid"
-  elif years_experience < 8.5: return "unpaid"
-  else: return "paid"
+    if years_experience < 3.0 or years_experience >= 8.5: return "paid"
+    else: return "unpaid"


Function predict_paid_or_unpaid refactored with the following changes:

Merge duplicate blocks in conditional (merge-duplicate-blocks)

Remove redundant conditional (remove-redundant-if)

SourceryAI · 2021-03-10T16:08:06Z

first-edition/code/natural_language_processing.py

 document_lengths = map(len, documents)

-distinct_words = set(word for document in documents for word in document)
+distinct_words = {word for document in documents for word in document}


Lines 181-181 refactored with the following changes:

Replace list(), dict() or set() with comprehension (collection-builtin-to-comprehension)

SourceryAI · 2021-03-10T16:08:06Z

first-edition/code/statistics.py

-    
+
    if n % 2 == 1:
        # if odd, return the middle value
        return sorted_v[midpoint]
-    else:
-        # if even, return the average of the middle values
-        lo = midpoint - 1
-        hi = midpoint
-        return (sorted_v[lo] + sorted_v[hi]) / 2
+    # if even, return the average of the middle values
+    lo = midpoint - 1
+    hi = midpoint
+    return (sorted_v[lo] + sorted_v[hi]) / 2


Function median refactored with the following changes:

Remove unnecessary else after guard condition (remove-unnecessary-else)

SourceryAI · 2021-03-10T16:08:06Z

first-edition/code-python3/clustering.py

-        c1, c2 = min([(cluster1, cluster2)
-                     for i, cluster1 in enumerate(clusters)
-                     for cluster2 in clusters[:i]],
-                     key=lambda p: cluster_distance(p[0], p[1], distance_agg))
+        c1, c2 = min(
+            (
+                (cluster1, cluster2)
+                for i, cluster1 in enumerate(clusters)
+                for cluster2 in clusters[:i]
+            ),
+            key=lambda p: cluster_distance(p[0], p[1], distance_agg),
+        )
+

        # remove them from the list of clusters
-        clusters = [c for c in clusters if c != c1 and c != c2]
+        clusters = [c for c in clusters if c not in [c1, c2]]


Function bottom_up_cluster refactored with the following changes:

Replace multiple comparisons of same variable with in operator (merge-comparisons)

Replace unneeded comprehension with generator (comprehension-to-generator)

SourceryAI · 2021-03-10T16:08:06Z

first-edition/code-python3/introduction.py

-  if years_experience < 3.0: return "paid"
-  elif years_experience < 8.5: return "unpaid"
-  else: return "paid"
+    if years_experience < 3.0 or years_experience >= 8.5: return "paid"
+    else: return "unpaid"


Function predict_paid_or_unpaid refactored with the following changes:

Merge duplicate blocks in conditional (merge-duplicate-blocks)

Remove redundant conditional (remove-redundant-if)

SourceryAI · 2021-03-10T16:08:08Z

scratch/neural_networks.py

-    for i in range(10):
+    for _ in range(10):
        binary.append(x % 2)
-        x = x // 2
+        x //= 2


Function binary_encode refactored with the following changes:

Replace assignment with augmented assignment (aug-assign)

Replace unused for index with underscore (for-index-underscore)

SourceryAI · 2021-03-10T16:08:08Z

scratch/neural_networks.py

-    
+
    # training data
    xs = [[0., 0], [0., 1], [1., 0], [1., 1]]
    ys = [[0.], [1.], [1.], [0.]]
-    
+


Function main refactored with the following changes:

Replace unused for index with underscore (for-index-underscore)

SourceryAI · 2021-03-10T16:08:08Z

scratch/nlp.py

 document_lengths = [len(document) for document in documents]

-distinct_words = set(word for document in documents for word in document)
+distinct_words = {word for document in documents for word in document}


Lines 208-208 refactored with the following changes:

Replace list(), dict() or set() with comprehension (collection-builtin-to-comprehension)

SourceryAI · 2021-03-10T16:08:08Z

scratch/nlp.py

 import tqdm

-for iter in tqdm.trange(1000):
+for _ in tqdm.trange(1000):


Lines 252-252 refactored with the following changes:

Replace unused for index with underscore (for-index-underscore)

SourceryAI · 2021-03-10T16:08:09Z

scratch/simple_linear_regression.py

-    
+
    num_epochs = 10000
    random.seed(0)
-    
+
    guess = [random.random(), random.random()]  # choose random value to start
-
-    learning_rate = 0.00001
-
+
    with tqdm.trange(num_epochs) as t:
+        learning_rate = 0.00001
+
        for _ in t:
            alpha, beta = guess
-    
+
            # Partial derivative of loss with respect to alpha
            grad_a = sum(2 * error(alpha, beta, x_i, y_i)
                         for x_i, y_i in zip(num_friends_good,
                                             daily_minutes_good))
-    
+
            # Partial derivative of loss with respect to beta
            grad_b = sum(2 * error(alpha, beta, x_i, y_i) * x_i
                         for x_i, y_i in zip(num_friends_good,
                                             daily_minutes_good))
-    
+
            # Compute loss to stick in the tqdm description
            loss = sum_of_sqerrors(alpha, beta,
                                   num_friends_good, daily_minutes_good)
            t.set_description(f"loss: {loss:.3f}")
-    
+
            # Finally, update the guess
            guess = gradient_step(guess, [grad_a, grad_b], -learning_rate)
-    
+


Function main refactored with the following changes:

Move assignments closer to their usage (move-assign)

SourceryAI · 2021-03-10T16:08:11Z

scratch/working_with_data.py

            for i in t:
                # i is prime if no smaller prime divides it.
-                i_is_prime = not any(i % p == 0 for p in primes)
+                i_is_prime = all(i % p != 0 for p in primes)


Function remove_projection_from_vector.main.primes_up_to refactored with the following changes:

Invert any/all to simplify comparisons (invert-any-all)

'Refactored by Sourcery'

f632b59

SourceryAI commented Mar 10, 2021

View reviewed changes

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Sourcery Starbot ⭐ refactored jobindj/data-science-from-scratch #1

Sourcery Starbot ⭐ refactored jobindj/data-science-from-scratch #1

Uh oh!

SourceryAI commented Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

SourceryAI Mar 10, 2021

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

1 participant

Sourcery Starbot ⭐ refactored jobindj/data-science-from-scratch #1

Are you sure you want to change the base?

Sourcery Starbot ⭐ refactored jobindj/data-science-from-scratch #1

Uh oh!

Conversation

SourceryAI commented Mar 10, 2021

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Reviewers

Assignees

Labels

Projects

Milestone

Development

Uh oh!

1 participant