Enhance code review for ML/DL/AI project #6

stochastic-sisyphus · 2024-12-10T20:15:18Z

Implement batch processing using PyTorch’s DataLoader and add docstrings for all public functions and classes in clustering modules.

Batch Processing:
- Add EmbeddingDataset class for custom dataset handling in attention_clustering.py, cluster_manager.py, dynamic_cluster_manager.py, and dynamic_clusterer.py.
- Implement batch processing using DataLoader in refine_embeddings method of HybridClusteringModule in attention_clustering.py.
- Implement batch processing using DataLoader in fit_predict method of ClusterManager in cluster_manager.py.
- Implement batch processing using DataLoader in fit_predict method of DynamicClusterManager in dynamic_cluster_manager.py.
- Implement batch processing using DataLoader in select_best_algorithm method of DynamicClusterer in dynamic_clusterer.py.
Multiprocessing:
- Add multiprocessing for preprocessing steps in generate_explanations method of ClusterExplainer in cluster_explainer.py.
Docstrings:
- Add docstrings for all public functions and classes in attention_clustering.py, cluster_explainer.py, cluster_manager.py, clustering_utils.py, dynamic_cluster_manager.py, and dynamic_clusterer.py.

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Summary by Sourcery

Enhance the ML/DL/AI project by implementing batch processing with PyTorch's DataLoader for efficient data handling and adding multiprocessing for preprocessing. Improve code documentation by adding comprehensive docstrings to public functions and classes.

New Features:

Implement batch processing using PyTorch's DataLoader across multiple modules for efficient handling of large datasets.
Add multiprocessing support for preprocessing steps in the ClusterExplainer module.

Enhancements:

Add docstrings to all public functions and classes in various modules to improve code readability and maintainability.

ellipsis-dev · 2024-12-10T20:15:21Z

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sourcery-ai · 2024-12-10T20:15:22Z

Reviewer's Guide by Sourcery

This pull request implements batch processing using PyTorch's DataLoader across multiple modules and adds comprehensive docstrings to improve code documentation. The changes focus on optimizing memory usage and processing efficiency by introducing batch processing capabilities, while also enhancing code readability and maintainability through detailed documentation.

Class diagram for EmbeddingDataset and its usage

classDiagram
    class EmbeddingDataset {
        - embeddings: np.ndarray
        + __init__(embeddings: np.ndarray)
        + __len__() int
        + __getitem__(idx) np.ndarray
    }
    class EvaluationMetrics {
        + calculate_clustering_metrics(embeddings: np.ndarray, labels: np.ndarray, batch_size: int) Dict
    }
    class ClusterManager {
        + fit_predict(embeddings: np.ndarray, batch_size: int) Tuple
    }
    class DynamicClusterManager {
        + fit_predict(embeddings: np.ndarray, batch_size: int) Tuple
    }
    class DynamicClusterer {
        + select_best_algorithm(embeddings: np.ndarray, batch_size: int) tuple
    }
    class HybridClusteringModule {
        + refine_embeddings(embeddings: np.ndarray, batch_size: int) np.ndarray
    }
    EmbeddingDataset <|-- EvaluationMetrics
    EmbeddingDataset <|-- ClusterManager
    EmbeddingDataset <|-- DynamicClusterManager
    EmbeddingDataset <|-- DynamicClusterer
    EmbeddingDataset <|-- HybridClusteringModule

Class diagram for DataLoader usage in DataValidator

classDiagram
    class DataValidator {
        + validate_batch(df: pd.DataFrame, batch_size: int) Dict
    }
    class DataFrameDataset {
        - df: pd.DataFrame
        + __init__(df: pd.DataFrame)
        + __len__() int
        + __getitem__(idx) Dict
    }
    DataValidator --> DataFrameDataset

Class diagram for ClusterExplainer with multiprocessing

classDiagram
    class ClusterExplainer {
        + explain_clusters(texts: List, labels: np.ndarray) Dict
        - _process_cluster(label: int, texts: List, labels: np.ndarray, tfidf_matrix: np.ndarray, feature_names: np.ndarray) (int, Dict)
    }
    ClusterExplainer o-- "*" Pool

File-Level Changes

Change	Details	Files
Implement batch processing using PyTorch's DataLoader	Add EmbeddingDataset class for custom dataset handling Implement batch processing in clustering operations Add batch processing for embedding generation Implement batch processing for metrics calculation Add batch processing for text preprocessing	`src/clustering/attention_clustering.py` `src/clustering/cluster_manager.py` `src/clustering/dynamic_cluster_manager.py` `src/clustering/dynamic_clusterer.py` `src/embedding_generator.py` `src/evaluation/metrics.py` `src/evaluation/cluster_evaluator.py` `src/evaluation/eval_pipeline.py`
Add comprehensive docstrings and improve code documentation	Add class-level docstrings explaining class purposes Add method-level docstrings with Args and Returns sections Document parameter types and return values Add descriptions for complex operations	`src/clustering/cluster_explainer.py` `src/clustering/cluster_manager.py` `src/clustering/clustering_utils.py` `src/data_preparation.py` `src/data_validator.py` `src/evaluation/pipeline_evaluator.py` `src/utils/logging_utils.py`
Implement multiprocessing for preprocessing operations	Add multiprocessing for cluster explanation generation Implement parallel processing for text preprocessing Add CPU core count detection for optimal resource utilization	`src/clustering/cluster_explainer.py` `src/preprocessing/domain_agnostic_preprocessor.py` `src/data_preparation.py`

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sourcery-ai

Hey @stochastic-sisyphus - I've reviewed your changes - here's some feedback:

Overall Comments:

Consider using a more memory-efficient approach when concatenating batch results. Instead of accumulating all batches in memory before concatenating, try processing results incrementally or using a pre-allocated array.

Here's what I looked at during the review

🟢 General issues: all looks good
🟢 Security: all looks good
🟢 Testing: all looks good
🟢 Complexity: all looks good
🟢 Documentation: all looks good

Sourcery is free for open source - if you like our reviews please consider sharing them ✨

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sourcery-ai · 2024-12-10T20:16:22Z

src/data_validator.py

            english_count = sum(
-                1 for text in sample_texts 
+                1 for text in sample_texts
                if nlp(text[:100]).lang_ == 'en'  # Check first 100 chars
            )


suggestion (code-quality): Simplify constant sum() call (simplify-constant-sum)

Suggested change

english_count = sum(

1 for text in sample_texts

1 for text in sample_texts

if nlp(text[:100]).lang_ == 'en' # Check first 100 chars

)

english_count = sum(bool(nlp(text[:100]).lang_ == 'en')

for text in sample_texts)

Explanation
As sum add the values it treats True as 1, and False as 0. We make use
of this fact to simplify the generator expression inside the sum call.

sourcery-ai · 2024-12-10T20:16:22Z

src/clustering/cluster_explainer.py

+
+        Returns:
+            Dict[str, Dict[str, Any]]: Explanations for each cluster.
+        """
        try:
            explanations = {}


issue (code-quality): We've found these issues:

Move assignment closer to its usage within a block (move-assign-in-block)

Convert for loop into dictionary comprehension (dict-comprehension)

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2024-12-10T20:16:22Z

src/clustering/streaming_manager.py

@@ -31,12 +59,21 @@ def update(self, new_embeddings: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]

            if len(self.buffer) >= self.buffer_size or time_elapsed >= self.update_interval:
                # Perform clustering on buffered data
-                embeddings_array = np.array(list(self.buffer))
-                self.current_labels, metrics = self.cluster_manager.fit_predict(embeddings_array)
+                dataset = EmbeddingDataset(np.array(list(self.buffer)))


issue (code-quality): Extract code out into method (extract-method)

sourcery-ai · 2024-12-10T20:16:22Z

src/evaluation/cluster_evaluator.py

+        return len(self.embeddings)
+
+    def __getitem__(self, idx):
+        return self.embeddings[idx]

 class ClusterEvaluator:
    """Comprehensive evaluation of clustering quality"""

    def evaluate_clustering(
        self,


issue (code-quality): We've found these issues:

Convert for loop into list comprehension (list-comprehension)

Replace identity comprehension with call to collection constructor (identity-comprehension)

Explanation

Convert list/set/tuple comprehensions that do not change the input elements into.

Before

# List comprehensions [item for item in coll] [item for item in friends.names()] # Dict comprehensions {k: v for k, v in coll} {k: v for k, v in coll.items()} # Only if we know coll is a `dict` # Unneeded call to `.items()` dict(coll.items()) # Only if we know coll is a `dict` # Set comprehensions {item for item in coll}

After

# List comprehensions list(iter(coll)) list(iter(friends.names())) # Dict comprehensions dict(coll) dict(coll) # Unneeded call to `.items()` dict(coll) # Set comprehensions set(coll)

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.

sourcery-ai · 2024-12-10T20:16:22Z

src/evaluation/eval_pipeline.py

-        metrics = calculate_cluster_metrics(embeddings, labels)
-        self.logger.log_metrics('clustering', metrics)
+    def evaluate_clustering(self, embeddings: np.ndarray, labels: np.ndarray, batch_size: int = 32) -> Dict[str, float]:
+        """


issue (code-quality): We've found these issues:

Convert for loop into list comprehension (list-comprehension)

Replace identity comprehension with call to collection constructor (identity-comprehension)

Explanation

Convert list/set/tuple comprehensions that do not change the input elements into.

Before

# List comprehensions [item for item in coll] [item for item in friends.names()] # Dict comprehensions {k: v for k, v in coll} {k: v for k, v in coll.items()} # Only if we know coll is a `dict` # Unneeded call to `.items()` dict(coll.items()) # Only if we know coll is a `dict` # Set comprehensions {item for item in coll}

After

# List comprehensions list(iter(coll)) list(iter(friends.names())) # Dict comprehensions dict(coll) dict(coll) # Unneeded call to `.items()` dict(coll) # Set comprehensions set(coll)

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.

sourcery-ai · 2024-12-10T20:16:22Z

src/evaluation/metrics.py

        try:
+            # Use DataLoader for batch processing


issue (code-quality): We've found these issues:

Convert for loop into list comprehension (list-comprehension)

Replace identity comprehension with call to collection constructor (identity-comprehension)

Explanation

Convert list/set/tuple comprehensions that do not change the input elements into.

Before

# List comprehensions [item for item in coll] [item for item in friends.names()] # Dict comprehensions {k: v for k, v in coll} {k: v for k, v in coll.items()} # Only if we know coll is a `dict` # Unneeded call to `.items()` dict(coll.items()) # Only if we know coll is a `dict` # Set comprehensions {item for item in coll}

After

# List comprehensions list(iter(coll)) list(iter(friends.names())) # Dict comprehensions dict(coll) dict(coll) # Unneeded call to `.items()` dict(coll) # Set comprehensions set(coll)

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.

sourcery-ai · 2024-12-10T20:16:22Z

src/evaluation/metrics.py

@@ -194,15 +303,33 @@ def _calculate_style_metrics(
        return style_metrics

    def calculate_dataset_metrics(summaries, references):


issue (code-quality): We've found these issues:

The first argument to instance methods should be self (instance-method-first-arg-name)

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2024-12-10T20:16:23Z

src/evaluation/pipeline_evaluator.py

+            dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
+
+            all_embeddings = []
+            for batch in dataloader:
+                all_embeddings.append(batch)
+
+            concatenated_embeddings = np.concatenate(all_embeddings, axis=0)
+            results[name] = self.metrics.calculate_embedding_metrics(concatenated_embeddings)


issue (code-quality): We've found these issues:

Convert for loop into list comprehension (list-comprehension)

Replace identity comprehension with call to collection constructor (identity-comprehension)

Explanation

Convert list/set/tuple comprehensions that do not change the input elements into.

Before

# List comprehensions [item for item in coll] [item for item in friends.names()] # Dict comprehensions {k: v for k, v in coll} {k: v for k, v in coll.items()} # Only if we know coll is a `dict` # Unneeded call to `.items()` dict(coll.items()) # Only if we know coll is a `dict` # Set comprehensions {item for item in coll}

After

# List comprehensions list(iter(coll)) list(iter(friends.names())) # Dict comprehensions dict(coll) dict(coll) # Unneeded call to `.items()` dict(coll) # Set comprehensions set(coll)

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.

sourcery-ai · 2024-12-10T20:16:23Z

src/evaluation/pipeline_evaluator.py

+            dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
+
+            all_clusters = []
+            for batch in dataloader:
+                all_clusters.append(batch)
+
+            concatenated_clusters = np.concatenate(all_clusters, axis=0)
+            results[name] = self.metrics.calculate_clustering_metrics(concatenated_clusters)


issue (code-quality): We've found these issues:

Convert for loop into list comprehension (list-comprehension)

Replace identity comprehension with call to collection constructor (identity-comprehension)

Explanation

Convert list/set/tuple comprehensions that do not change the input elements into.

Before

# List comprehensions [item for item in coll] [item for item in friends.names()] # Dict comprehensions {k: v for k, v in coll} {k: v for k, v in coll.items()} # Only if we know coll is a `dict` # Unneeded call to `.items()` dict(coll.items()) # Only if we know coll is a `dict` # Set comprehensions {item for item in coll}

After

# List comprehensions list(iter(coll)) list(iter(friends.names())) # Dict comprehensions dict(coll) dict(coll) # Unneeded call to `.items()` dict(coll) # Set comprehensions set(coll)

All these comprehensions are just creating a copy of the original collection.
They can all be simplified by simply constructing a new collection directly. The
resulting code is easier to read and shows the intent more clearly.

sourcery-ai · 2024-12-10T20:16:23Z

src/preprocessing/domain_agnostic_preprocessor.py

+            for text in batch:
+                processed_texts.append(self.preprocess_text(text))


issue (code-quality): Replace a for append loop with list extend (for-append-to-extend)

…rocessing and preprocessing with batch handling

sourcery-ai bot approved these changes Dec 10, 2024

View reviewed changes

Add custom Dataset classes for text and embeddings; enhance cluster p…

653a0f2

…rocessing and preprocessing with batch handling

stochastic-sisyphus force-pushed the enhance-code-review branch from 1d7ba48 to 653a0f2 Compare December 10, 2024 20:55

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Enhance code review for ML/DL/AI project #6

Enhance code review for ML/DL/AI project #6

stochastic-sisyphus commented Dec 10, 2024 •

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		@@ -194,15 +303,33 @@ def _calculate_style_metrics(
		return style_metrics

		def calculate_dataset_metrics(summaries, references):

		for text in batch:
		processed_texts.append(self.preprocess_text(text))

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Reviewer's Guide by Sourcery

Class diagram for EmbeddingDataset and its usage

Class diagram for DataLoader usage in DataValidator

Class diagram for ClusterExplainer with multiprocessing

File-Level Changes

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