Delete RF.py

RF.py

@@ -1,190 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Sep  4 10:38:59 2023
-
-@author: BM109X32G-10GPU-02
-"""
-
-
-from sklearn.metrics import confusion_matrix
-import matplotlib.pyplot as plt
-import numpy as np
-
-from sklearn.datasets import make_blobs
-import json
-import numpy as np
-import math
-from tqdm import tqdm
-from scipy import sparse
-from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
-import pickle
-
-
-import pandas as pd
-import matplotlib.pyplot as plt
-from rdkit import Chem
-
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import MinMaxScaler
-from sklearn.neural_network import MLPClassifier
-from sklearn.svm import SVC
-from tensorflow.keras.models import Model, load_model
-from tensorflow.keras.layers import Dense, Input, Flatten, Conv1D, MaxPooling1D, concatenate
-from tensorflow.keras import metrics, optimizers
-from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
-
-def split_smiles(smiles, kekuleSmiles=True):
-    try:
-        mol = Chem.MolFromSmiles(smiles)
-        smiles = Chem.MolToSmiles(mol, kekuleSmiles=kekuleSmiles)
-    except:
-        pass
-    splitted_smiles = []
-    for j, k in enumerate(smiles):
-        if len(smiles) == 1:
-            return [smiles]
-        if j == 0:
-            if k.isupper() and smiles[j + 1].islower() and smiles[j + 1] != "c":
-                splitted_smiles.append(k + smiles[j + 1])
-            else:
-                splitted_smiles.append(k)
-        elif j != 0 and j < len(smiles) - 1:
-            if k.isupper() and smiles[j + 1].islower() and smiles[j + 1] != "c":
-                splitted_smiles.append(k + smiles[j + 1])
-            elif k.islower() and smiles[j - 1].isupper() and k != "c":
-                pass
-            else:
-                splitted_smiles.append(k)
-
-        elif j == len(smiles) - 1:
-            if k.islower() and smiles[j - 1].isupper() and k != "c":
-                pass
-            else:
-                splitted_smiles.append(k)
-    return splitted_smiles
-
-def get_maxlen(all_smiles, kekuleSmiles=True):
-    maxlen = 0
-    for smi in tqdm(all_smiles):
-        spt = split_smiles(smi, kekuleSmiles=kekuleSmiles)
-        if spt is None:
-            continue
-        maxlen = max(maxlen, len(spt))
-    return maxlen
-def get_dict(all_smiles, save_path, kekuleSmiles=True):
-    words = [' ']
-    for smi in tqdm(all_smiles):
-        spt = split_smiles(smi, kekuleSmiles=kekuleSmiles)
-        if spt is None:
-            continue
-        for w in spt:
-            if w in words:
-                continue
-            else:
-                words.append(w)
-    with open(save_path, 'w') as js:
-        json.dump(words, js)
-    return words
-
-def one_hot_coding(smi, words, kekuleSmiles=True, max_len=1000):
-    coord_j = []
-    coord_k = []
-    spt = split_smiles(smi, kekuleSmiles=kekuleSmiles)
-    if spt is None:
-        return None
-    for j,w in enumerate(spt):
-        if j >= max_len:
-            break
-        try:
-            k = words.index(w)
-        except:
-            continue
-        coord_j.append(j)
-        coord_k.append(k)
-    data = np.repeat(1, len(coord_j))
-    output = sparse.csr_matrix((data, (coord_j, coord_k)), shape=(max_len, len(words)))
-    return output
-def split_dataset(dataset, ratio):
-    """Shuffle and split a dataset."""
-   # np.random.seed(111)  # fix the seed for shuffle.
-    #np.random.shuffle(dataset)
-    n = int(ratio * len(dataset))
-    return dataset[:n], dataset[n:]
-def plot_confusion_matrix(cm, savename, title='Confusion Matrix'):
-
-    plt.figure(figsize=(12, 8), dpi=100)
-    np.set_printoptions(precision=2)
- 
-    ind_array = [np.arange(3)]
-    x, y = np.meshgrid(ind_array, ind_array)
-    for x_val, y_val in zip(x.flatten(), y.flatten()):
-        c = cm[y_val][x_val]
-        if c > 0.001:
-            plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=15, va='center', ha='center')
-    
-    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.binary)
-    plt.title(title)
-    plt.colorbar()
-    xlocations = np.array(range(len(classes)))
-    plt.xticks(xlocations, classes, rotation=90)
-    plt.yticks(xlocations, classes)
-    plt.ylabel('Actual label')
-    plt.xlabel('Predict label')
-    
-    # offset the tick
-    tick_marks = np.array(range(len(classes))) + 0.5
-    plt.gca().set_xticks(tick_marks, minor=True)
-    plt.gca().set_yticks(tick_marks, minor=True)
-    plt.gca().xaxis.set_ticks_position('none')
-    plt.gca().yaxis.set_ticks_position('none')
-    plt.grid(True, which='minor', linestyle='-')
-    plt.gcf().subplots_adjust(bottom=0.15)
-    
-    # show confusion matrix
-    plt.savefig(savename, format='png')
-    plt.show()
-def main(sm):
-        with open("dict.json", "r", encoding="utf-8") as f:
-            words = json.load(f)
- 
-        inchis = list([sm])
-        rts = list([0])
-        
-        smiles, targets = [], []
-        for i, inc in enumerate(tqdm(inchis)):
-            mol = Chem.MolFromSmiles(inc)
-            if mol is None:
-                continue
-            else:
-                smi = Chem.MolToSmiles(mol)
-                smiles.append(smi)
-                targets.append(rts[i])
-                
-       
-        
-        features = []
-        for i, smi in enumerate(tqdm(smiles)):
-            xi = one_hot_coding(smi, words, max_len=600)
-            if xi is not None:
-                features.append(xi.todense())
-        features = np.asarray(features)
-        targets = np.asarray(targets)
-        X_test=features
-        Y_test=targets
-        n_features=10
-        
-        model = RandomForestRegressor(n_estimators=100,  criterion='friedman_mse', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=1.0, max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None)
-       
-        from tensorflow.keras import backend as K
-        
-        load_model = pickle.load(open(r"predict.dat","rb"))
-        Y_predict = load_model.predict(K.cast_to_floatx(X_test).reshape((np.size(X_test,0),np.size(X_test,1)*np.size(X_test,2))))
-         #Y_predict = model.predict(X_test) 
-        x = list(Y_test)
-        y = list(Y_predict)
-       
-        return Y_predict
-        
-if __name__ == "__main__":
-    x = main("CCCCCCC1=CC=C(C2(C3=CC=C(CCCCCC)C=C3)C3=CC4=C(C=C3C3=C2C=C(/C=C2\SC(=S)N(CC)C2=O)S3)C(C2=CC=C(CCCCCC)C=C2)(C2=CC=C(CCCCCC)C=C2)C2=C4SC(/C=C3\SC(=S)N(CC)C3=O)=C2)C=C1")