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rf_hyperparameter_tuning.py
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from sklearn.inspection import permutation_importance
import time
from Script import *
df_train = pd.read_csv("Data/train.csv")
df_test = pd.read_csv("Data/test.csv")
df_train = df_train.drop("i", axis=1)
y = df_train.pop("y")
df_train.insert(54,"y", y)
df_test = df_test.drop(["y", "i"], axis = 1)
#Changing variable data types
df_test.loc[:, "c1":"c12"] = df_test.loc[:, "c1":"c12"].astype("category")
df_test.loc[:, ["o1", "o2"]] = df_test.loc[:, ["o1", "o2"]].astype("category")
df_train.loc[:, "c1":"c12"] = df_train.loc[:, "c1":"c12"].astype("category")
df_train.loc[:, ["o1", "o2", "y"]] = df_train.loc[:, ["o1", "o2", "y"]].astype("category")
#Call relevant methods until we have results
analyze_dataset(df_train, df_test)
df_train, df_test = preprocess_data(df_train, df_test)
# HERE JUST DO WHATEVER YOU WANT WITH THE MODELS. I DID A STRATIFIED K-FOLD ON A RANDOM FOREST
# COMMENT OR DELETE WHATEVER YOU DON'T WANT TO USE AND MAKE YOUR OWN MODELS TO TEST AND ALL THAT SHIT
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# skf = StratifiedKFold(n_splits = 3)
# score_list = []
# models = []
# #Stratified fold to keep the data imbalance while training
# #Train 3 times to simulate a 3 way cross-validation
# for train, test in skf.split(df_train, y):
# print('train - {} | test - {}'.format(np.bincount(y[train]), np.bincount(y[test])))
# train_x = df_train.iloc[train]
# train_y = y.iloc[train]
# test_x = df_train.iloc[test]
# test_y = y.iloc[test]
# #CREATE THE MODELS HERE IF POSSIBLE
# #Random Forest
# clf = RandomForestClassifier(random_state = 0)
# clf.fit(train_x, train_y)
# y_pred = clf.predict(test_x)
# print(compute_metrics(y_pred, test_y))
# #SAVE BEST SCORES AND BEST MODELS TO TEST ON THE DATA LATER.
# score_list.append(clf.score(test_x,test_y))
# models.append(clf)
# #Check Metrics attained
# best_model_index = score_list.index(max(score_list))
# preds = models[best_model_index].predict(df_test)
X = df_train.iloc[:, 0:-1]
y = df_train.iloc[:, -1]
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
clf1 = RandomForestClassifier( random_state=1)
clf2 = make_pipeline(StandardScaler(), SVC(gamma='auto', probability=True))
clf3 = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
max_depth=1, random_state=0)
print("Fitting Classifiers")
eclf1 = VotingClassifier(estimators=[
('rf', clf1), ('svm', clf2), ('xgb', clf3)], voting='hard')
eclf2 = VotingClassifier(estimators=[
('rf', clf1), ('svm', clf2), ('xgb', clf3)],
voting='soft')
print("Hard Voting ")
hard_scores = cross_val_score(eclf1, X, y, cv=10)
print("Soft Voting")
soft_scores = cross_val_score(eclf2, X, y, cv=10)
print("aux stop")
import joblib
name_of_model = "rf+svm+xgboost"
joblib.dump(hard_scores, "Temp/"+name_of_model+"_hard_scores"+".joblib")
joblib.dump(hard_scores, "Temp/"+ name_of_model+"_soft_scores"+ ".joblib")
print("hard voting", hard_scores.mean())
print("soft voting", soft_scores.mean())
print("aux stop")
eclf1.fit(X, y)
preds_hard = eclf1.predict(df_test)
save_predictions(preds_hard, "Predictions/"+name_of_model+"_hard")
eclf2.fit(X, y)
preds_soft = eclf2.predict(df_test)
save_predictions(preds_soft, "Predictions/"+ name_of_model+"_soft")