Upload find-math-fine.py

find-math-fine.py

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+import pandas as pd
+import re
+from concurrent.futures import ProcessPoolExecutor
+from tqdm import tqdm
+import os
+import glob
+
+# Science keywords (formatted for regex word boundaries)
+science_keywords_list = [
+
+    #logic_reasoning_problem_solving_keywords
+    "deductive reasoning", "inductive reasoning", "abductive reasoning", "logical fallacy",
+    "syllogism", "proposition", "premise", "conclusion",
+    "argument", "critical thinking", "analytical skills", "hypothesis testing",
+    "problem analysis", "brainstorming", "decision making", "creative thinking",
+    "heuristic", "algorithm", "data analysis", "causal reasoning",
+    "correlation", "evidence-based reasoning", "validity", "soundness",
+    "cognitive bias", "confirmation bias", "cognitive dissonance", "logical consistency",
+    "counterargument", "debate", "dialectic", "socratic questioning",
+    "root cause analysis", "SWOT analysis", "decision tree", "flow chart",
+    "mind mapping", "ideation", "brainwriting", "lateral thinking",
+    "problem decomposition", "synthesis", "pattern recognition", "inference",
+    "troubleshooting", "risk assessment", "scenario planning", "cost-benefit analysis",
+    "optimization", "simulation", "strategic planning", "logical operator", "chain of thought", 
+    "step by step",
+
+    # Basic arithmetic
+    "addition", "subtraction", "multiplication", "division", "fraction", 
+    "decimal", "percentage", "ratio", "proportion", "absolute value", 
+
+    # Algebra
+    "algebra", "equation", "coefficient", "variable", "polynomial", 
+    "quadratic", "exponential", "logarithm", "factorial", "sum", 
+    "quotient",  
+    "linear equation", "nonlinear equation", "system of equations", 
+
+    # Data analysis and statistics
+    "data", "statistics", "analysis", "median", 
+    "standard deviation",  "probability", 
+    "binomial", "normal distribution", "histogram", 
+    "scatter plot", "correlation", "regression", 
+
+    # Functions and calculus
+    "function", "linear function", "nonlinear function",  
+    "intercept", "coordinate", "domain", "range", "limit", 
+    "derivative", "integral", "differentiation",  
+    "infinite series", "sequence", "convergence", "power series", 
+
+    # Geometry and trigonometry
+    "geometry", "angle", "triangle", "rectangle", "square", 
+    "circle", "polygon", "perimeter", "area", "circumference", 
+    "diameter", "radius", "pythagorean theorem", "trigonometry", 
+    "sine", "cosine", "tangent", "secant", "cosecant", 
+    "cotangent", "arc", "parallelogram", 
+    "rhombus", "trapezoid", "congruence", 
+
+    # Advanced mathematics
+    "calculus", "differential equation", "partial derivative", 
+    "vector", "matrix", "determinant", "eigenvalue", "eigenvector", 
+    "linear transformation", "tensor", "multivariable calculus", 
+
+    # Logic and set theory
+    "logic", "subset", "union", "intersection", 
+    "element", "cardinality", "venn diagram", "truth table", 
+    "proposition", "theorem", "proof", "induction", 
+
+    # Discrete math and combinatorics
+    "combinatorics", "permutation", "combination", "graph theory", 
+    "tree", "vertex",
+    "probability", "random variable", "expected value", 
+
+    # Measurements and units
+    "computation", "measurement", "length", "width", "height", 
+    "area", "volume", "density", "mass",
+    "velocity", "acceleration", "force", 
+
+    # Additional concepts
+    "binary", "decimal", "octal", "hexadecimal", "modulus", 
+    "prime number", "composite number", "greatest common divisor", 
+    "least common multiple", "factorization", "irrational number", 
+    "complex number", "imaginary unit", "real number", 
+    "absolute value"
+    ]
+
+# Escape special regex characters and add word boundaries
+science_keywords = [
+    r"\b" + re.escape(keyword).replace(r'\ ', ' ') + r"\b" for keyword in science_keywords_list
+]
+
+# Combine science keywords into a single regex pattern using non-capturing groups
+science_regex = r'(?:' + r'|'.join(science_keywords) + r')'
+
+# Function to process a chunk of the dataset
+def process_chunk(chunk):
+    # Assign column names if they are not already set
+    if list(chunk.columns) != ['score', 'text', 'url']:
+        chunk.columns = ['score', 'text', 'url']
+    
+    # Use vectorized string operations for efficiency
+    # Count the number of matches in each column
+    score_counts = chunk['score'].astype(str).str.count(science_regex, flags=re.IGNORECASE)
+    url_counts = chunk['url'].astype(str).str.count(science_regex, flags=re.IGNORECASE)
+    text_counts = chunk['text'].astype(str).str.count(science_regex, flags=re.IGNORECASE)
+    
+    # Handle NaN values by filling them with zero
+    score_counts = score_counts.fillna(0)
+    url_counts = url_counts.fillna(0)
+    text_counts = text_counts.fillna(0)
+    
+    # Sum the counts to get the science score
+    match_counts = score_counts + url_counts + text_counts
+    match_counts = match_counts.astype(int)
+    
+    #
+    #
+    # Set a threshold for the minimum science score
+    threshold = 50  # Adjust this value as needed
+    #
+    #
+    
+    # Filter rows that meet the threshold
+    filtered_chunk = chunk[match_counts >= threshold].copy()
+    filtered_chunk['science_score'] = match_counts[match_counts >= threshold]
+    
+    # Replace the original 'score' with 'science_score'
+    filtered_chunk['score'] = filtered_chunk['science_score']
+    filtered_chunk = filtered_chunk.drop(columns=['science_score'])
+    
+    return filtered_chunk
+
+# Function to process a single CSV file
+def process_file(input_file, output_file):
+    # Read the CSV file in chunks, assuming no header in the CSV file
+    chunk_size = 10000  # Adjust this value based on your memory constraints
+    reader = pd.read_csv(input_file, chunksize=chunk_size, header=None)
+    
+    # Prepare the output file
+    first_chunk = True
+    
+    # Number of worker processes
+    num_workers = 20  # Adjust based on your CPU cores
+    
+    # Batch size for chunks to process in parallel
+    batch_size = num_workers * 4  # Adjust based on memory constraints
+    
+    chunk_list = []
+    with ProcessPoolExecutor(max_workers=num_workers) as executor:
+        for chunk in tqdm(reader, desc=f'Reading chunks from {os.path.basename(input_file)}'):
+            chunk_list.append(chunk)
+            if len(chunk_list) == batch_size:
+                # Process batch of chunks in parallel
+                futures = [executor.submit(process_chunk, c) for c in chunk_list]
+                for future in tqdm(futures, desc='Processing batch', leave=False):
+                    filtered_chunk = future.result()
+                    if not filtered_chunk.empty:
+                        if first_chunk:
+                            filtered_chunk.to_csv(output_file, mode='w', index=False, header=False)
+                            first_chunk = False
+                        else:
+                            filtered_chunk.to_csv(output_file, mode='a', index=False, header=False)
+                chunk_list = []
+        # Process any remaining chunks
+        if chunk_list:
+            futures = [executor.submit(process_chunk, c) for c in chunk_list]
+            for future in tqdm(futures, desc='Processing last batch', leave=False):
+                filtered_chunk = future.result()
+                if not filtered_chunk.empty:
+                    if first_chunk:
+                        filtered_chunk.to_csv(output_file, mode='w', index=False, header=False)
+                        first_chunk = False
+                    else:
+                        filtered_chunk.to_csv(output_file, mode='a', index=False, header=False)
+    print(f'Finished processing {input_file}')
+
+# List of directories to process
+data_dir = '/media/joe/512-3/csv'
+years = [f'CC-MAIN-{year}' for year in range(2013, 2025)]  # Adjust years as needed
+directories = [os.path.join(data_dir, year) for year in years]
+
+# Process each CSV file in each directory
+for dir_path in directories:
+    if not os.path.isdir(dir_path):
+        print(f'Directory not found: {dir_path}')
+        continue
+    csv_files = glob.glob(os.path.join(dir_path, '*.csv'))
+    print(f'Found {len(csv_files)} CSV files in {dir_path}')
+    for input_file in csv_files:
+        # Construct output file name
+        base_name = os.path.basename(input_file)
+        output_file = os.path.join(
+            dir_path, 'math_' + base_name
+        )
+        
+        # Check if output file already exists to avoid reprocessing
+        if os.path.exists(output_file):
+            print(f'Output file already exists. Skipping: {output_file}')
+            continue
+        
+        process_file(input_file, output_file)
+