Predicting the yield of Buchwald Hartwig HTE reactions
import logging
import sklearn
import torch
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
import pandas as pd
from rxnfp.models import SmilesClassificationModel
from rxn_yields.data import generate_buchwald_hartwig_rxns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import os
from IPython.display import display
%matplotlib inline
torch.cuda.is_available()
NAME_SPLIT = [
('FullCV_01', 2768),
('FullCV_02', 2768),
('FullCV_03', 2768),
('FullCV_04', 2768),
('FullCV_05', 2768),
('FullCV_06', 2768),
('FullCV_07', 2768),
('FullCV_08', 2768),
('FullCV_09', 2768),
('FullCV_10', 2768),
('Test1', 3058),
('Test2', 3056),
('Test3', 3059),
('Test4', 3056),
]
def load_model_from_results_folder(name, split, epoch=10, results_folder='../trained_models/buchwald_hartwig', model_type='bert'):
import os
models_folder = os.path.join(results_folder, f"{name}_split_{str(split).replace('-','_')}")
model_path = [os.path.join(models_folder, o) for o in os.listdir(models_folder)
if os.path.isdir(os.path.join(models_folder,o)) and o.endswith(f'epoch-{epoch}')][0]
model = SmilesClassificationModel(model_type, model_path,
num_labels=1, args={
"regression": True
}, use_cuda=torch.cuda.is_available())
return model
def make_plot(y_test, y_pred, rsme, r2_score, mae, name):
fontsize = 16
fig, ax = plt.subplots(figsize=(8,8))
r2_patch = mpatches.Patch(label="R2 = {:.3f}".format(r2_score), color="#5402A3")
rmse_patch = mpatches.Patch(label="RMSE = {:.1f}".format(rmse), color="#5402A3")
mae_patch = mpatches.Patch(label="MAE = {:.1f}".format(mae), color="#5402A3")
plt.xlim(-5,105)
plt.ylim(-5,105)
plt.scatter(y_pred, y_test, alpha=0.2, color="#5402A3")
plt.plot(np.arange(100), np.arange(100), ls="--", c=".3")
plt.legend(handles=[r2_patch, rmse_patch, mae_patch], fontsize=fontsize)
ax.set_ylabel('Measured', fontsize=fontsize)
ax.set_xlabel('Predicted', fontsize=fontsize)
ax.set_title(name, fontsize=fontsize)
return fig
y_predictions = []
y_tests = []
r2_scores = []
rmse_scores = []
for (name, split) in NAME_SPLIT:
df_doyle = pd.read_excel('../data/Buchwald-Hartwig/Dreher_and_Doyle_input_data.xlsx', sheet_name=name)
df_doyle['rxn'] = generate_buchwald_hartwig_rxns(df_doyle)
train_df = df_doyle.iloc[:split-1][['rxn', 'Output']] # paper has starting index 1 not 0
test_df = df_doyle.iloc[split-1:][['rxn', 'Output']] # paper has starting index 1 not 0
train_df.columns = ['text', 'labels']
test_df.columns = ['text', 'labels']
y_test = test_df.labels.values
mean = train_df.labels.mean()
std = train_df.labels.std()
model = load_model_from_results_folder(name, split)
y_preds = model.predict(test_df.text.values)[0]
y_preds = y_preds * std + mean
y_preds = np.clip(y_preds, 0, 100)
r_squared = r2_score(y_test, y_preds)
rmse = mean_squared_error(y_test, y_preds) ** 0.5
mae = mean_absolute_error(y_test, y_preds)
r2_scores.append(r_squared)
print(f"{name} | R2 {r_squared:.2f} | RMSE {rmse:.1f} | MAE {mae:.1f}")
fig = make_plot(y_test, y_preds, rmse, r_squared, mae, name)
# fig.savefig(f'images/mlst_doyle_results/{name}_{split}.pdf')
