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fix train_gpu
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Default user committed Feb 6, 2024
1 parent 7597589 commit c5832dd
Showing 1 changed file with 69 additions and 54 deletions.
123 changes: 69 additions & 54 deletions src/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,7 @@ def crossvalid(train_df, test_df, clfs, run_index, fold_index):
print("FIT X Y")
print(X)
print(y)
print(len(X), len(y))

results = pd.DataFrame()
for name, clf in clfs:
Expand Down Expand Up @@ -198,8 +199,8 @@ def train(
}

today = date.today()
results_file = f"./data/results/drugbank_drug_targets_scores_{today}.csv"
agg_results_file = f"./data/results/drugbank_drug_targets_agg_{today}.csv"
results_file = f"./data/results/openpredict_drug_targets_scores_{today}.csv"
agg_results_file = f"./data/results/openpredict_drug_targets_agg_{today}.csv"

# Get pairs
pairs, labels = generate_dt_pairs(df_known_interactions)
Expand All @@ -212,41 +213,44 @@ def train(
# nb_model = GaussianNB()
# lr_model = linear_model.LogisticRegression()
# rf_model = ensemble.RandomForestClassifier(n_estimators=200, n_jobs=-1)
# rf_model = ensemble.RandomForestClassifier(
# n_estimators=200,
# criterion="log_loss",
# max_depth=config.max_depth,
# min_samples_split=2,
# min_samples_leaf=1,
# max_features="sqrt",
# n_jobs=-1,
# )
xgb_model = XGBClassifier(
rf_model = ensemble.RandomForestClassifier(
n_estimators=200,
criterion="log_loss",
max_depth=config.max_depth,
learning_rate=0.1,
subsample=0.8,
colsample_bytree=0.8,
gamma=0,
reg_alpha=0,
reg_lambda=1,
objective='binary:logistic', # For binary classification
min_samples_split=2,
min_samples_leaf=1,
max_features="sqrt",
n_jobs=-1,
random_state=42,
tree_method='hist', # Use GPU optimized histogram algorithm
# device='gpu',
class_weight="balanced",
)
# xgb_model = XGBClassifier(
# n_estimators=200,
# max_depth=config.max_depth,
# learning_rate=0.1,
# subsample=0.8,
# colsample_bytree=0.8,
# gamma=0,
# reg_alpha=0,
# reg_lambda=1,
# objective='binary:logistic', # For binary classification
# n_jobs=-1,
# random_state=42,
# tree_method='hist', # Use GPU optimized histogram algorithm
# # device='gpu',
# )

# clfs = [('Naive Bayes',nb_model),('Logistic Regression',lr_model),('Random Forest',rf_model)]
clfs = [("XGBoost", xgb_model)] # "Random Forest", rf_model
clfs = [("Random Forest", rf_model)] # "XGBoost", xgb_model

n_seed = 100
n_fold = config.cv_nfold
n_fold = 10
n_run = 2
n_proportion = 1
sc = None

# Run training
all_scores_df = kfold_cv(pairs, labels, embeddings, clfs, n_run, n_fold, n_proportion, n_seed)

all_scores_df.to_csv(results_file, sep=",", index=False)

agg_df = all_scores_df.groupby(["method", "run"]).mean().groupby("method").mean()
Expand All @@ -256,9 +260,9 @@ def train(

os.makedirs("models", exist_ok=True)
with open(save_model, "wb") as f:
pickle.dump(xgb_model, f) #rf_model
pickle.dump(rf_model, f) # xgb_model

return agg_df
return agg_df.mean()
# return agg_df.to_dict(orient="records")


Expand Down Expand Up @@ -295,6 +299,8 @@ def train_gpu(
# we add pairs for missing drug/targets combinations as 0 (not known as interacting)
pairs, labels = generate_dt_pairs(df_known_interactions)

pairs, labels = balance_data(pairs, labels, 1)

log.info("Merging drug/target labels to the DF")
# Merge drug/target pairs and their labels in a DF
train_df = pd.DataFrame(
Expand All @@ -312,29 +318,26 @@ def train_gpu(
embedding_cols = train_df.columns.difference(["drug", "target", "Class"])
X = train_df[embedding_cols].values
y = train_df["Class"].values.ravel()
log.info(f"Features count: {len(embedding_cols)}")
# print(X)
# print(y)
log.info(f"Features count: {len(embedding_cols)} | Length training df: {len(X)}, {len(y)}")

ndrugs = len(embeddings["drug"])
ntargets = len(embeddings["target"])
_unique, counts = np.unique(labels, return_counts=True)
ndrugtargets = counts[1]
log.info(f"Training based on {ndrugtargets} Drug-Targets known interactions: {ndrugs} drugs | {ntargets} targets")
random_state=123 # Or 42?
n_jobs = 2 # Or -1
random_state=42 # Or 123?
n_jobs = -1 # Or 2?
n_splits = 5

# pairs, classes = balance_data(pairs_all, classes_all, n_proportion)
# skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)
skf = StratifiedKFold(n_splits=n_splits, shuffle=True)

# TODO: xgboost don't support gridsearch on GPU by default
# https://github.com/compomics/ms2pip/blob/a8c61b41044f3f756b4551d7866d8030e68b1570/train_scripts/train_xgboost_c.py#L143

# NOTE: To run XGB on GPU:
params["device"] = "cuda:0"
params["tree_method"] = "hist"
# # NOTE: To run XGB on GPU:
# params["device"] = "cuda:0"
# params["tree_method"] = "hist"

# combinations = get_params_combinations(params_grid)
# print("Working on combination {}/{}".format(count, len(combinations)))
Expand All @@ -349,20 +352,20 @@ def train_gpu(
x_train, x_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]

# Send data to GPU for XGBoost
# # Send data to GPU for XGBoost
send_time = time.time()
dtrain = xgb.DMatrix(x_train, label=y_train)
dtest = xgb.DMatrix(x_test, label=y_test)
# print(f"Sending data to GPU took {time.time() - send_time}s")
# dtrain = xgb.DMatrix(x_train, label=y_train)
# dtest = xgb.DMatrix(x_test, label=y_test)
# # print(f"Sending data to GPU took {time.time() - send_time}s")

# Train XGBoost model
model = xgb.train(params, dtrain, num_boost_round=100)
predictions = model.predict(dtest)
# # Train XGBoost model
# model = xgb.train(params, dtrain, num_boost_round=100)
# predictions = model.predict(dtest)

# Train Random Forest model
# model = RandomForestClassifier(**params)
# model.fit(x_train, y_train)
# predictions = model.predict(x_test)
model = RandomForestClassifier(**params)
model.fit(x_train, y_train)
predictions = model.predict(x_test)

# Evaluate model
predictions_binary = np.round(predictions) # Convert probabilities to binary outputs
Expand Down Expand Up @@ -484,16 +487,26 @@ def exclude_similar(input_dir, subject_sim_threshold: float = 1, object_sim_thre
# object_sim_thresholds = [1, 0.99, 0.98, 0.97, 0.96, 0.95, 0.94, 0.93, 0.92, 0.91, 0.90]
subject_sim_thresholds = [1]
object_sim_thresholds = [1]
# params = { #XGB
# 'max_depth': 3,
# 'n_estimators': 100,
# # For XGB:
# 'learning_rate': 0.1,
# 'subsample': 0.7,
# 'colsample_bytree': 0.7,
# 'gamma': 0,
# 'reg_alpha': 0.1,
# 'reg_lambda': 1,
# }
params = {
'max_depth': 3,
'n_estimators': 100,
# For XGB:
'learning_rate': 0.1,
'subsample': 0.7,
'colsample_bytree': 0.7,
'gamma': 0,
'reg_alpha': 0.1,
'reg_lambda': 1,
'n_estimators': 200,
'criterion': "log_loss",
'max_depth': None, #config.max_depth
'min_samples_split': 2,
'min_samples_leaf': 1,
'max_features': "sqrt",
'n_jobs': -1,
'class_weight': 'balanced',
}
scores_df = pd.DataFrame()
for subject_sim_threshold in subject_sim_thresholds:
Expand All @@ -503,6 +516,8 @@ def exclude_similar(input_dir, subject_sim_threshold: float = 1, object_sim_thre
print(f"Similar excluded for {subject_sim_threshold}/{object_sim_threshold}")

scores = train_gpu(df_known_dt, df_drugs_embeddings, df_targets_embeddings, params)
# scores = train(df_known_dt, df_drugs_embeddings, df_targets_embeddings, params)

scores["subject_sim_threshold"] = subject_sim_threshold
scores["object_sim_threshold"] = object_sim_threshold
scores_df = pd.concat([scores_df, scores], ignore_index=True)
Expand Down

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