Upload find-science-fine.py

find-science-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 = [
+    # Physics subfields
+    "classical mechanics", "quantum mechanics", "thermodynamics", "statistical mechanics",
+    "electromagnetism", "optics", "acoustics", "relativity",
+    "particle physics", "nuclear physics", "atomic physics", "molecular physics",
+    "condensed matter physics", "solid state physics", "fluid dynamics", "plasma physics",
+    "astrophysics", "cosmology", "gravitational physics", "space physics",
+    "geophysics", "biophysics", "chemical physics", "material science",
+    "energy physics", "environmental physics", "medical physics", "computational physics",
+    "high energy physics", "theoretical physics", "experimental physics", "quantum field theory",
+    "quantum optics", "quantum computing", "nanophysics", "nanotechnology",
+    "electrodynamics", "magnetohydrodynamics", "photovoltaics", "superconductivity",
+    "non-linear dynamics", "chaos theory", "string theory", "loop quantum gravity",
+
+    # Astronomy and space exploration
+    "astronomy", "astrophysics", "cosmology", "space exploration", "exoplanets",
+    "spacecraft", "space shuttle", "rocket science", "satellites", "International Space Station",
+    "Hubble Space Telescope", "Mars rovers", "moon landing", "lunar mission", "orbital mechanics",
+    "zero gravity", "microgravity", "space colonization", "astrobiology", "planetology",
+    "dark matter", "dark energy", "black holes", "neutron stars", "pulsars",
+    "space probe", "deep space", "interstellar travel", "galaxies", "nebulae",
+    "stellar evolution", "solar system", "comets", "asteroids", "meteorites",
+    "space weather", "cosmic radiation", "extraterrestrial life", "SETI", "alien planets",
+    "space suit", "spacewalk", "gravity assist", "launch vehicle", "reusable rockets",
+    "space tourism", "private spaceflight", "space elevator", "falcon rocket", "starship", "NASA",
+
+    # Keywords for chemistry and physics
+    "chemistry lab", "physics lab", "science lab", "laboratory write-up",
+    "experimental write-up", "lab report", "experiment procedure",
+    "materials and methods", "scientific method", "chemical reaction",
+    "titration", "stoichiometry", "molecular structure", "acid-base experiment",
+    "organic chemistry lab", "Newton's laws experiment", "electric circuits lab",
+    "magnetism experiment", "thermodynamics lab", "momentum lab",
+    "experiment", "science project", "visual perception", "color and light", "optics experiment",
+    "color spectrum", "materials list", "step-by-step instructions", "procedure",
+    "instructions", "kids experiment", "learn about colors", "exploration of light",
+    "hands-on science", "STEM activity",
+    "learning through experimentation", "visual effects",
+    "spin speed", "color change",
+
+    # Math keywords
+    "addition", "subtraction", "multiplication",
+    "fraction", "decimal", "percentage", "ratio", "proportion",
+    "equation", "coefficient",
+    "polynomial", "quadratic", "exponential", "logarithm", "factorial",
+    "sum", "quotient",
+    "median",
+    "data", "analysis",
+    "graph", "function", "linear",
+    "nonlinear", "slope", "intercept", "coordinate",
+    "geometry", "angle", "triangle", "rectangle", "circle",
+    "polygon", "perimeter",
+    "circumference", "diameter", "radius", "pythagorean",
+    "theorem", "trigonometry", "sine", "cosine", "tangent",
+    "calculus",
+    "sequence", "convergence",
+    "vector", "algebra", "arithmetic",
+    "computation", "measurement",
+
+    # 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",
+]
+    
+
+# 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)
+