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Md Wasi Ul Kabir

updated scripts and configuration for MVPpred project

1dc9670 6 months ago

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# scripts/train_eval.py
from __future__ import annotations

import pandas as pd
import sys
from pathlib import Path
import numpy as np
import os
os.environ["OMP_NUM_THREADS"] = "64"
os.environ["OPENBLAS_NUM_THREADS"] = "64"
os.environ["MKL_NUM_THREADS"] = "64"
os.environ["NUMEXPR_NUM_THREADS"] = "64"


# Add project root to PYTHONPATH so `import src...` works when running as a script
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from src.pipeline import run_10fold_cv_paper_like

def load_data() -> tuple[pd.DataFrame, list[str], list[str]]:
    morph_path = "Data/morph_dataset.csv"
    perf_path  = "Data/performance_dataset.csv"

    morph_df = pd.read_csv(morph_path)
    perf_df  = pd.read_csv(perf_path)

    # Convert -1 to NaN
    morph_df = morph_df.replace(-1, np.nan)
    perf_df  = perf_df.replace(-1, np.nan)

    # If row counts differ, stop (can't safely align)
    if len(morph_df) != len(perf_df):
        raise ValueError(
            f"Row counts differ: morph={len(morph_df)} perf={len(perf_df)}. "
            "Need a shared ID column to merge."
        )

    # Row-aligned join
    data = pd.concat([morph_df.reset_index(drop=True),
                      perf_df.reset_index(drop=True)], axis=1)

    # Define targets exactly like your performance CSV columns (excluding ecomorph label)
    targets = [
        "sprint", "endurance", "bite", "distance_capacity",
        "jump_distance", "jump_vel", "jump_accel", "jump_power", "angle"
    ]

    cat_cols = ["sex"]

    # Morphology numeric columns (everything in morph_df except categorical)
    # Your morph headers: taxon, genus, species, sex, mass, svl, hl, hw, ...
    morph_numeric = [c for c in morph_df.columns if c not in cat_cols]

    # One-hot encode categorical columns (keep NaNs as its own category if any)
    data[cat_cols] = data[cat_cols].astype("Int64")  # keep missing-safe ints
    data = pd.get_dummies(data, columns=cat_cols, dummy_na=True)

    # After get_dummies, morph_cols should be:
    # - numeric morphology columns (mass, svl, hl, ...)
    # - plus the created one-hot columns for taxon/genus/species/sex/ecomorph
    dummy_cols = [c for c in data.columns if any(c.startswith(f"{cc}_") for cc in cat_cols)]
    morph_cols = morph_numeric + dummy_cols

    # Sanity check: ensure targets exist
    missing = [t for t in targets if t not in data.columns]
    if missing:
        raise ValueError(f"Targets missing from merged data: {missing}. Check perf CSV header.")

    return data, morph_cols, targets


def main():
    data, morph_cols, targets = load_data()

    results = run_10fold_cv_paper_like(
        data=data,
        morph_cols=morph_cols,
        targets=targets,
        experiment_tag="NewData",
        max_folds=None,  
    )
    print("\nCV Results:")
    print(results.sort_values("target"))

if __name__ == "__main__":
    main()

	# scripts/train_eval.py
	from __future__ import annotations

	import pandas as pd
	import sys
	from pathlib import Path
	import numpy as np
	import os
	os.environ["OMP_NUM_THREADS"] = "64"
	os.environ["OPENBLAS_NUM_THREADS"] = "64"
	os.environ["MKL_NUM_THREADS"] = "64"
	os.environ["NUMEXPR_NUM_THREADS"] = "64"


	# Add project root to PYTHONPATH so `import src...` works when running as a script
	ROOT = Path(__file__).resolve().parents[1]
	if str(ROOT) not in sys.path:
	sys.path.insert(0, str(ROOT))

	from src.pipeline import run_10fold_cv_paper_like

	def load_data() -> tuple[pd.DataFrame, list[str], list[str]]:
	morph_path = "Data/morph_dataset.csv"
	perf_path = "Data/performance_dataset.csv"

	morph_df = pd.read_csv(morph_path)
	perf_df = pd.read_csv(perf_path)

	# Convert -1 to NaN
	morph_df = morph_df.replace(-1, np.nan)
	perf_df = perf_df.replace(-1, np.nan)

	# If row counts differ, stop (can't safely align)
	if len(morph_df) != len(perf_df):
	raise ValueError(
	f"Row counts differ: morph={len(morph_df)} perf={len(perf_df)}. "
	"Need a shared ID column to merge."
	)

	# Row-aligned join
	data = pd.concat([morph_df.reset_index(drop=True),
	perf_df.reset_index(drop=True)], axis=1)

	# Define targets exactly like your performance CSV columns (excluding ecomorph label)
	targets = [
	"sprint", "endurance", "bite", "distance_capacity",
	"jump_distance", "jump_vel", "jump_accel", "jump_power", "angle"
	]

	cat_cols = ["sex"]

	# Morphology numeric columns (everything in morph_df except categorical)
	# Your morph headers: taxon, genus, species, sex, mass, svl, hl, hw, ...
	morph_numeric = [c for c in morph_df.columns if c not in cat_cols]

	# One-hot encode categorical columns (keep NaNs as its own category if any)
	data[cat_cols] = data[cat_cols].astype("Int64") # keep missing-safe ints
	data = pd.get_dummies(data, columns=cat_cols, dummy_na=True)

	# After get_dummies, morph_cols should be:
	# - numeric morphology columns (mass, svl, hl, ...)
	# - plus the created one-hot columns for taxon/genus/species/sex/ecomorph
	dummy_cols = [c for c in data.columns if any(c.startswith(f"{cc}_") for cc in cat_cols)]
	morph_cols = morph_numeric + dummy_cols

	# Sanity check: ensure targets exist
	missing = [t for t in targets if t not in data.columns]
	if missing:
	raise ValueError(f"Targets missing from merged data: {missing}. Check perf CSV header.")

	return data, morph_cols, targets


	def main():
	data, morph_cols, targets = load_data()

	results = run_10fold_cv_paper_like(
	data=data,
	morph_cols=morph_cols,
	targets=targets,
	experiment_tag="NewData",
	max_folds=None,
	)
	print("\nCV Results:")
	print(results.sort_values("target"))

	if __name__ == "__main__":
	main()