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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)
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