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Update Decision_tree_NCI-60.py #2

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11 changes: 1 addition & 10 deletions Compared_ML_algorithms/Decision_tree_NCI-60.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@
import matplotlib.pyplot as plt
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report, matthews_corrcoef
from sklearn.metrics import f1_score, accuracy_score, precision_score, recall_score, roc_auc_score, roc_curve
Expand Down Expand Up @@ -127,7 +126,7 @@ def smiles_to_onehot(smiles, character_maximum, dist_char):
smiles_to_onehot, character_maximum=largest_smiles_len, dist_char=dist_char
)

#SMOTE oversampling, train_test_split and normalization
#SMOTE oversampling, train-test split
X = df[['dist_char_ohe','Activity']]
X.columns = ['feature_N' + str(i + 1) for i in range(X.shape[1])]
x = X['feature_N1'].explode().to_frame()
Expand All @@ -146,14 +145,6 @@ def smiles_to_onehot(smiles, character_maximum, dist_char):
X_over, y_over = smote.fit_resample(X1, y1)
X_train2, X_test2, Y_train2, Y_test2 = train_test_split(X_over, y_over, random_state=42)

#Normalization
scaler = StandardScaler()
scaler.fit(X_train2)
X_train2 = scaler.transform(X_train2)
scaler.fit(X_test2)
X_test2 = scaler.transform(X_test2)


#Implementing Decision Tree algorithm

dt = DecisionTreeClassifier(criterion='entropy', max_depth=20, min_samples_leaf=5,random_state=42)
Expand Down