Add catboost support (#542)

Co-authored-by: ajecc <eugenajechiloae@gmail.com>

examples/frameworks/catboost/catboost_example.py

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+# ClearML - Example of CatBoost training, saving model and loading model
+#
+import argparse
+
+from catboost import CatBoostRegressor, Pool
+from catboost.datasets import msrank
+
+from clearml import Task
+
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+
+
+def main(iterations):
+    # Download train and validation datasets
+    train_df, test_df = msrank()
+    # Column 0 contains label values, column 1 contains group ids.
+    X_train, y_train = train_df.drop([0, 1], axis=1).values, train_df[0].values
+    X_test, y_test = test_df.drop([0, 1], axis=1).values, test_df[0].values
+
+    # Split train data into two parts. First part - for baseline model,
+    # second part - for major model
+    splitted_data = train_test_split(X_train, y_train, test_size=0.5)
+    X_train_first, X_train_second, y_train_first, y_train_second = splitted_data
+
+    catboost_model = CatBoostRegressor(iterations=iterations, verbose=False)
+
+    # Prepare simple baselines (just mean target on first part of train pool).
+    baseline_value = y_train_first.mean()
+    train_baseline = np.array([baseline_value] * y_train_second.shape[0])
+    test_baseline = np.array([baseline_value] * y_test.shape[0])
+
+    # Create pools
+    train_pool = Pool(X_train_second, y_train_second, baseline=train_baseline)
+    test_pool = Pool(X_test, y_test, baseline=test_baseline)
+
+    # Train CatBoost model
+    catboost_model.fit(train_pool, eval_set=test_pool, verbose=True, plot=False, save_snapshot=True)
+    catboost_model.save_model("example.cbm")
+
+    catboost_model = CatBoostRegressor()
+    catboost_model.load_model("example.cbm")
+
+    # Apply model on pool with baseline values
+    preds1 = catboost_model.predict(test_pool)
+
+    # Apply model on numpy.array and then add the baseline values
+    preds2 = test_baseline + catboost_model.predict(X_test)
+
+    # Check that preds have small diffs
+    assert (np.abs(preds1 - preds2) < 1e-6).all()
+
+
+if __name__ == "__main__":
+    Task.init(project_name="examples", task_name="CatBoost simple example")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--iterations", default=200)
+    args = parser.parse_args()
+    main(args.iterations)

examples/frameworks/catboost/requirements.txt

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+catboost
+numpy == 1.19.2
+scikit_learn
+clearml