Teradata MCP Server

import argparse import os from urllib.parse import urlparse from teradataml import * def main(): parser = argparse.ArgumentParser(description="Teradata MCP Server") parser.add_argument('--database_uri', type=str, required=False, help='Database URI to connect to: teradata://username:password@host:1025/schemaname') parser.add_argument('--action', type=str, choices=['setup', 'cleanup'], required=True, help='Action to perform: setup, test or cleanup') # Extract known arguments and load them into the environment if provided args, unknown = parser.parse_known_args() connection_url = args.database_uri or os.getenv("DATABASE_URI") eng = None if args.action in ['setup', 'cleanup']: if not connection_url: raise ValueError("DATABASE_URI must be provided either as an argument or as an environment variable.") parsed_url = urlparse(connection_url) user = parsed_url.username password = parsed_url.password host = parsed_url.hostname database = user eng = create_context(host=host, username=user, password=password) if args.action=='setup': # Set up the analytic functions test data. # Setup for ANOVA. load_example_data("teradataml", ["insect_sprays"]) # Setup for Attibution. load_example_data("attribution", ["attribution_sample_table1", "attribution_sample_table2", "conversion_event_table", "optional_event_table", "model1_table", "model2_table"]) # Setup for Antiselect. load_example_data("dataframe", ["sales"]) # Setup for Apriori. load_example_data("apriori", ["trans_dense", "trans_sparse"]) # Setup for BincodeFit & Transform. load_example_data("teradataml", ["titanic", "bin_fit_ip"]) titanic = DataFrame.from_table("titanic") bin_code_2 = BincodeFit(data=titanic, target_columns='age', method_type='Equal-Width', nbins=2, label_prefix='label_prefix' ) bin_code_2.output.to_sql(table_name="bin_fit_op", if_exists="replace") # Setup for CFilter load_example_data("dataframe", ["grocery_transaction"]) # Setup for CategoricalSummary load_example_data("teradataml", ["titanic"]) # Setup for ChiSq load_example_data("teradataml", "chi_sq") # Setup for ClassificationEvaluator load_example_data("teradataml", ["titanic"]) df = DataFrame("titanic") ndf = df.assign(pcol=df.survived) ndf.to_sql(table_name="CVTable", if_exists="replace") # Setup for ColumnSummary load_example_data("teradataml", ["titanic"]) # Setup for ColumnTransformer titanic = DataFrame.from_table("titanic") bin_code = BincodeFit(data=titanic, target_columns='age', method_type='Equal-Width', nbins=2, label_prefix='label_prefix') bin_code.output.to_sql(table_name="bin_fit_op", if_exists="replace") # Setup for DecisionForest & Predict. load_example_data("decisionforest", ["boston"]) boston = DataFrame.from_table("boston") DecisionForest_out = DecisionForest(data=boston, input_columns=['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'black', 'lstat'], response_column='medv', max_depth=12, num_trees=4, min_node_size=1, mtry=3, mtry_seed=1, seed=1, tree_type='REGRESSION') DecisionForest_out.result.to_sql(table_name="decision_forest_op", if_exists="replace") # Setup for GLM. load_example_data('glm', ['housing_train_segment']) # Setup for GLMPerSegment & GLMPerSegmentPredict. load_example_data("decisionforestpredict", ["housing_train"]) # Filter the rows from train dataset with homestyle as Classic and Eclectic. binomial_housing_train = DataFrame('housing_train', index_label="homestyle") binomial_housing_train = binomial_housing_train.filter(like = 'ic', axis = 'rows') # GLMPerSegment() function requires features in numeric format for processing, # so dropping the non-numeric columns. binomial_housing_train = binomial_housing_train.drop(columns=["driveway", "recroom", "gashw", "airco", "prefarea", "fullbase"]) gaussian_housing_train = binomial_housing_train.drop(columns="homestyle") gaussian_housing_train.to_sql(table_name="gaussian_housing_train", if_exists="replace") GLMPerSegment_out_1 = GLMPerSegment(data=gaussian_housing_train, data_partition_column="stories", input_columns=['garagepl', 'lotsize', 'bedrooms', 'bathrms'], response_column="price", family="Gaussian", iter_max=1000, batch_size=9) # Print the result DataFrame. GLMPerSegment_out_1.result.to_sql(table_name="glm_per_segment_op", if_exists="replace") # Setup for OneHotEncodingFit & Transform one_hot_encoding = OneHotEncodingFit(data=titanic, is_input_dense=True, target_column="sex", categorical_values=["male", "female"], other_column="other") one_hot_encoding.result.to_sql(table_name="one_hot_op", if_exists="replace") # Setup for GetFutileColumns. load_example_data("teradataml", ["titanic"]) CategoricalSummary_out = CategoricalSummary(data=titanic, target_columns=["cabin", "sex", "ticket"]) CategoricalSummary_out.result.to_sql(table_name="cat_summary_op", if_exists="replace") # Setup for Fit. load_example_data("teradataml", ["iris_input", "transformation_table"]) # Setup for KMeans. load_example_data("kmeans", "computers_train1") load_example_data("kmeans",'kmeans_table') computers_train1 = DataFrame.from_table("computers_train1") KMeans_out = KMeans(id_column="id", target_columns=['price', 'speed'], data=computers_train1, num_clusters=2) KMeans_out.result.to_sql(table_name="kmeans_op", if_exists="replace") # Setup for KNN. load_example_data("knn", ["computers_train1_clustered", "computers_test1"]) # Create teradataml DataFrame objects. computers_test1 = DataFrame.from_table("computers_test1") computers_train1_clustered = DataFrame.from_table("computers_train1_clustered") # Generate fit object for column "computer_category". fit_obj = OneHotEncodingFit(data=computers_train1_clustered, is_input_dense=True, target_column="computer_category", categorical_values=["ultra", "special"], other_column="other") # Encode "ultra" and "special" values of column "computer_category". computers_train1_encoded = OneHotEncodingTransform(data=computers_train1_clustered, object=fit_obj.result, is_input_dense=True) computers_train1_encoded.result.to_sql(table_name="knn_OHE_op", if_exists="replace") # Setup for MovingAverage. load_example_data("movavg", ["ibm_stock"]) # Setup for NerExtractor. load_example_data("tdnerextractor", ["ner_input_eng", "ner_dict", "ner_rule"]) # Setup for NGramSplitter. load_example_data("ngrams", ["paragraphs_input"]) # Setup for NPath. load_example_data("NPath", ["impressions", "clicks2", "tv_spots", "clickstream"]) # Setup for NaiveBayesTextClassifierPredict & Trainer. load_example_data("NaiveBayesTextClassifierPredict", ["complaints_tokens_test", "token_table"]) token_table = DataFrame("token_table") # Create a model which is output of NaiveBayesTextClassifierTrainer. nbt_out = NaiveBayesTextClassifierTrainer(data=token_table, token_column='token', doc_id_column='doc_id', doc_category_column='category', model_type="Bernoulli") nbt_out.result.to_sql(table_name="nbt_op", if_exists="replace") # Setup for NonLinearCombineFit & Transform Fit_out = NonLinearCombineFit(data=titanic, target_columns=["sibsp", "parch", "fare"], formula="Y=(X0+X1+1)*X2", result_column="total_cost") Fit_out.result.to_sql(table_name="non_linear_fit_op", if_exists="replace") # Setup for NumApply load_example_data("teradataml", ["numerics"]) # setup for OneClassSVM & Predict load_example_data("larpredict", ["diabetes"]) load_example_data("teradataml", ["cal_housing_ex_raw"]) # Create teradataml DataFrame objects. data_input = DataFrame.from_table("cal_housing_ex_raw") # Scale "target_columns" with respect to 'STD' value of the column. fit_obj = ScaleFit(data=data_input, target_columns=['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], scale_method="STD") # Transform the data. transform_obj = ScaleTransform(data=data_input, object=fit_obj.output, accumulate=["id", "MedHouseVal"]) transform_obj.result.to_sql(table_name="scale_transform_op", if_exists="replace") # Train the input data by OneClassSVM which helps model # to find anomalies in transformed data. one_class_svm = OneClassSVM(data=transform_obj.result, input_columns=['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], local_sgd_iterations=537, batch_size=1, learning_rate='constant', initial_eta=0.01, lambda1=0.1, alpha=0.0, momentum=0.0, iter_max=1 ) one_class_svm.result.to_sql(table_name="one_class_svm_op", if_exists="replace") # Setup for OneHotEncodingFit & Transform load_example_data("teradataml", ["titanic"]) fit_obj = OneHotEncodingFit(data=titanic, is_input_dense=True, target_column="sex", categorical_values=["male", "female"], other_column="other") fit_obj.result.to_sql(table_name="one_hot_fit_op", if_exists="replace") # Setup for OrdinalEncodingFit & Transform load_example_data("teradataml", ["titanic"]) ordinal_encodingfit_res_2 = OrdinalEncodingFit(target_column='sex', approach='LIST', categories=['category0', 'category1'], ordinal_values=[1, 2], start_value=0, default_value=-1, data=titanic) ordinal_encodingfit_res_2.result.to_sql(table_name="ordinal_fit_op", if_exists="replace") # Setup for OutlierFilterFit & Transform fit_obj = OutlierFilterFit(data=titanic, target_columns="fare", lower_percentile=0.1, upper_percentile=0.9, outlier_method="PERCENTILE", replacement_value="MEDIAN", percentile_method="PERCENTILECONT") fit_obj.result.to_sql(table_name="outlier_fit_op", if_exists="replace") # Setup for Pack. load_example_data("pack", ["ville_temperature"]) # Setup for Pivoting & Unpivoting. load_example_data('unpivot', 'titanic_dataset_unpivoted') # Setup for PolynomialFeaturesFit & Transform load_example_data("teradataml", ["numerics"]) numerics = DataFrame.from_table("numerics") fit_obj = PolynomialFeaturesFit(data=numerics, target_columns=["integer_col", "smallint_col"], degree=2) fit_obj.output.to_sql(table_name="poly_fit_op", if_exists="replace") # Setup for QQNorm load_example_data("teradataml", ["rank_table"]) # Setup for ROC load_example_data("roc", ["roc_input"]) # Setup for RandomProjectionFit, RandomProjectionMinComponents & Transform load_example_data("teradataml", "stock_movement") stock_movement = DataFrame.from_table("stock_movement") fit_obj = RandomProjectionFit(data=stock_movement, target_columns="1:", epsilon=0.9, num_components=343) fit_obj.result.to_sql(table_name="random_proj_fit_op", if_exists="replace") # Setup for RowNormalizeFit & Transform load_example_data("teradataml", ["numerics"]) numerics = DataFrame.from_table("numerics") fit_obj = RowNormalizeFit(data=numerics, target_columns=["integer_col", "smallint_col"], approach="INDEX", base_column="integer_col", base_value=100.0) fit_obj.output.to_sql(table_name="row_normalize_fit_op", if_exists="replace") # Setup for SMOTE load_example_data("dataframe", "iris_test") # Setup for SVM, SVMPredict & SVMSparsePredict # Load the example data. load_example_data("teradataml", ["cal_housing_ex_raw"]) # Create teradataml DataFrame objects. data_input = DataFrame.from_table("cal_housing_ex_raw") # Scale "target_columns" with respect to 'STD' value of the column. fit_obj = ScaleFit(data=data_input, target_columns=['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], scale_method="STD") # Transform the data. transform_obj = ScaleTransform(data=data_input, object=fit_obj.output, accumulate=["id", "MedHouseVal"]) transform_obj.result.to_sql(table_name="scale_transform_op2", if_exists="replace") # Train the transformed data using SVM() where "model_type" is 'Regression'. svm_obj1 = SVM(data=transform_obj.result, input_columns=['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], response_column="MedHouseVal", model_type="Regression" ) svm_obj1.result.to_sql(table_name="svm_op", if_exists="replace") # Setup for ScaleFit & Transform load_example_data("teradataml", ["scale_housing"]) scaling_house = DataFrame.from_table("scale_housing") fit_obj = ScaleFit(data=scaling_house, target_columns="lotsize", scale_method="MEAN", miss_value="KEEP", global_scale=False, multiplier="1", intercept="0") fit_obj.output.to_sql(table_name="scale_fit_op", if_exists="replace") # Setup for SentimentExtractor load_example_data("sentimentextractor", ["sentiment_extract_input"]) # Setup for sessionize load_example_data("sessionize", ["sessionize_table"]) # Setup for Shap load_example_data("byom", "iris_input") load_example_data("teradataml", ["cal_housing_ex_raw"]) iris_input = DataFrame("iris_input") XGBoost_out = XGBoost(data=iris_input, input_columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], response_column='species', model_type='Classification', iter_num=25) XGBoost_out.result.to_sql(table_name="xgboost_op", if_exists="replace") # Setup for Silhouette load_example_data("teradataml", ["mobile_data"]) # Setup for SimpleImputeFit & Transform fit_obj = SimpleImputeFit(data=titanic, stats_columns="age", literals_columns="cabin", stats="median", literals="General") fit_obj.output.to_sql(table_name="simple_impute_fit_op", if_exists="replace") # Setup for StrApply # Setup for StringSimilarity load_example_data("stringsimilarity", ["strsimilarity_input"]) # Setup for TDNaiveBayesPredict # Load the example data. load_example_data("decisionforestpredict", ["housing_train", "housing_test"]) # Create teradataml DataFrame objects. housing_train = DataFrame.from_table("housing_train") # Import function TDNaiveBayesPredict. from teradataml import NaiveBayes # Example 1: TDNaiveBayesPredict function to predict the classification label using Dense input. NaiveBayes_out = NaiveBayes(data=housing_train, response_column='homestyle', numeric_inputs=['price', 'lotsize', 'bedrooms', 'bathrms', 'stories', 'garagepl'], categorical_inputs=['driveway', 'recroom', 'fullbase', 'gashw', 'airco', 'prefarea']) NaiveBayes_out.result.to_sql(table_name="naive_bayes_op", if_exists="replace") # Setup for TFIDF load_example_data('naivebayestextclassifier', "token_table") # Setup for TargetEncodingFit & Transform # Create teradataml DataFrame objects. data_input = DataFrame.from_table("titanic") # Find the distinct values and counts for column 'sex' and 'embarked'. categorical_summ = CategoricalSummary(data=data_input, target_columns = ["sex", "embarked"] ) # Find the distinct count of 'sex' and 'embarked' in which only 2 column should be present # name 'ColumnName' and 'CategoryCount'. category_data=categorical_summ.result.groupby('ColumnName').count() category_data = category_data.assign(drop_columns=True, ColumnName=category_data.ColumnName, CategoryCount=category_data.count_DistinctValue) category_data.to_sql(table_name="category_data_op", if_exists="replace") # Generates the required hyperparameters when "encoder_method" is 'CBM_BETA'. TargetEncodingFit_out = TargetEncodingFit(data=data_input, category_data=category_data, encoder_method='CBM_BETA', target_columns=['sex', 'embarked'], response_column='survived', default_values=[-1, -2] ) TargetEncodingFit_out.result.to_sql(table_name="target_encoding_fit_op", if_exists="replace") # Setup for TextMorph load_example_data("textmorph", ["words_input", "pos_input"]) # Setup for TextParser load_example_data("textparser", ["complaints", "stop_words"]) # Setup for TrainTestSplit # Setup for Transform load_example_data("teradataml", ["iris_input", "transformation_table"]) # Create teradataml DataFrame objects. iris_input = DataFrame.from_table("iris_input") transformation_df = DataFrame.from_table("transformation_table") transformation_df = transformation_df.drop(['id'], axis=0) # Example 1: Run Fit() with all arguments and pass the output to Transform(). fit_obj = Fit(data=iris_input, object=transformation_df, object_order_column='TargetColumn' ) fit_obj.result.to_sql(table_name="fit_op", if_exists="replace") # Run Transform() with persist as True in order to save the result. transform_result = Transform(data=iris_input, data_partition_column='sepal_length', data_order_column='sepal_length', object=fit_obj.result, object_order_column='TargetColumn', id_columns=['species', 'id'], persist=True ) # Setup for UnivariateStatistics # Setup for Unpack load_example_data("Unpack", ["ville_tempdata", "ville_tempdata1"]) # Setup for Unpivoting load_example_data('unpivot', 'unpivot_input') # Setup for VectorDistance load_example_data("vectordistance", ["target_mobile_data_dense", "ref_mobile_data_dense"]) # Setup for WordEmbeddings load_example_data("teradataml", ["word_embed_model", "word_embed_input_table1"]) # Setup for XGBoost & Predict XGBoost_out_1 = XGBoost(data=titanic, input_columns=["age", "survived", "pclass"], response_column='fare', max_depth=3, lambda1=1000.0, model_type='Regression', seed=-1, shrinkage_factor=0.1, iter_num=2) XGBoost_out_1.result.to_sql(table_name="xgboost_op2", if_exists="replace") # Setup for ZTest elif args.action in ('cleanup'): # Cleanup for ANOVA. db_drop_table(table_name="insect_sprays", suppress_error=True) # Cleanup for Attibution. for tbl in ["attribution_sample_table1", "attribution_sample_table2", "conversion_event_table", "optional_event_table", "model1_table", "model2_table"]: db_drop_table(table_name=tbl, suppress_error=True) # Cleanup for Antiselect. db_drop_table(table_name="antiselect_input", suppress_error=True) # Cleanup for Apriori. db_drop_table(table_name="trans_dense", suppress_error=True) db_drop_table(table_name="trans_sparse", suppress_error=True) # Cleanup for BincodeFit & Transform. db_drop_table(table_name="titanic", suppress_error=True) db_drop_table(table_name="bin_fit_ip", suppress_error=True) db_drop_table(table_name="bin_fit_op", suppress_error=True) # Cleanup for CFilter db_drop_table(table_name="grocery_transaction", suppress_error=True) # Cleanup for CategoricalSummary db_drop_table(table_name="cat_summary_op", suppress_error=True) # Cleanup for ChiSq db_drop_table(table_name="chi_sq_input", suppress_error=True) # Cleanup for ClassificationEvaluator db_drop_table(table_name='CVTable', suppress_error=True) # Cleanup for ColumnSummary - uses titanic table (cleaned elsewhere) # Cleanup for ColumnTransformer - uses bin_fit_op (cleaned elsewhere) # Cleanup for ConvertTo db_drop_table(table_name="convert_to_output_tbl", suppress_error=True) # Cleanup for DecisionForest & Predict db_drop_table(table_name="boston", suppress_error=True) db_drop_table(table_name="decision_forest_op", suppress_error=True) # Clean up for GLM. db_drop_table(table_name="housing_train_segment", suppress_error=True) # Cleanup for GLMPerSegment. db_drop_table(table_name="housing_train", suppress_error=True) db_drop_table(table_name="gaussian_housing_train", suppress_error=True) db_drop_table(table_name="glm_per_segment_op", suppress_error=True) # Cleanup for OneHotEncodingFit & Transform db_drop_table(table_name="one_hot_op", suppress_error=True) # Cleanup for GetFutileColumns - uses cat_summary_op (cleaned elsewhere) # Cleanup for Fit db_drop_table(table_name="iris_input", suppress_error=True) db_drop_table(table_name="transformation_table", suppress_error=True) db_drop_table(table_name="fit_op", suppress_error=True) # Cleanup for KMeans db_drop_table(table_name="computers_train1", suppress_error=True) db_drop_table(table_name="kmeans_table", suppress_error=True) db_drop_table(table_name="kmeans_op", suppress_error=True) # Cleanup for KNN db_drop_table(table_name="computers_train1_clustered", suppress_error=True) db_drop_table(table_name="computers_test1", suppress_error=True) db_drop_table(table_name="knn_OHE_op", suppress_error=True) # Cleanup for MovingAverage db_drop_table(table_name="ibm_stock", suppress_error=True) # Cleanup for NerExtractor db_drop_table(table_name="ner_input_eng", suppress_error=True) db_drop_table(table_name="ner_dict", suppress_error=True) db_drop_table(table_name="ner_rule", suppress_error=True) # Cleanup for NGramSplitter db_drop_table(table_name="paragraphs_input", suppress_error=True) # Cleanup for NPath db_drop_table(table_name="impressions", suppress_error=True) db_drop_table(table_name="clicks2", suppress_error=True) db_drop_table(table_name="tv_spots", suppress_error=True) # Cleanup for NaiveBayesTextClassifierPredict & Trainer db_drop_table(table_name="complaints_tokens_test", suppress_error=True) db_drop_table(table_name="token_table", suppress_error=True) db_drop_table(table_name="nbt_op", suppress_error=True) # Cleanup for NonLinearCombineFit & Transform db_drop_table(table_name="non_linear_fit_op", suppress_error=True) # Cleanup for NumApply db_drop_table(table_name="numerics", suppress_error=True) # Cleanup for OneClassSVM & Predict db_drop_table(table_name="diabetes", suppress_error=True) db_drop_table(table_name="cal_housing_ex_raw", suppress_error=True) db_drop_table(table_name="scale_transform_op", suppress_error=True) db_drop_table(table_name="one_class_svm_op", suppress_error=True) # Cleanup for OneHotEncodingFit & Transform (additional) db_drop_table(table_name="one_hot_fit_op", suppress_error=True) # Cleanup for OrdinalEncodingFit & Transform db_drop_table(table_name="ordinal_fit_op", suppress_error=True) # Cleanup for OutlierFilterFit & Transform db_drop_table(table_name="outlier_fit_op", suppress_error=True) # Cleanup for Pack db_drop_table(table_name="ville_temperature", suppress_error=True) # Cleanup for Pivoting & Unpivoting db_drop_table(table_name="titanic_dataset_unpivoted", suppress_error=True) # Cleanup for PolynomialFeaturesFit & Transform db_drop_table(table_name="poly_fit_op", suppress_error=True) # Cleanup for QQNorm db_drop_table(table_name="rank_table", suppress_error=True) # Cleanup for ROC db_drop_table(table_name="roc_input", suppress_error=True) # Cleanup for RandomProjectionFit, RandomProjectionMinComponents & Transform db_drop_table(table_name="stock_movement", suppress_error=True) db_drop_table(table_name="random_proj_fit_op", suppress_error=True) # Cleanup for RowNormalizeFit & Transform db_drop_table(table_name="row_normalize_fit_op", suppress_error=True) # Cleanup for SMOTE db_drop_table(table_name="iris_test", suppress_error=True) # Cleanup for SVM, SVMPredict & SVMSparsePredict db_drop_table(table_name="scale_transform_op2", suppress_error=True) db_drop_table(table_name="svm_op", suppress_error=True) # Cleanup for ScaleFit & Transform db_drop_table(table_name="scale_housing", suppress_error=True) db_drop_table(table_name="scale_fit_op", suppress_error=True) # Cleanup for SentimentExtractor db_drop_table(table_name="sentiment_extract_input", suppress_error=True) # Cleanup for sessionize db_drop_table(table_name="sessionize_table", suppress_error=True) # Cleanup for Shap db_drop_table(table_name="xgboost_op", suppress_error=True) # Cleanup for Silhouette db_drop_table(table_name="mobile_data", suppress_error=True) # Cleanup for SimpleImputeFit & Transform db_drop_table(table_name="simple_impute_fit_op", suppress_error=True) # Cleanup for StringSimilarity db_drop_table(table_name="strsimilarity_input", suppress_error=True) # Cleanup for TDNaiveBayesPredict db_drop_table(table_name="housing_train", suppress_error=True) db_drop_table(table_name="housing_test", suppress_error=True) db_drop_table(table_name="naive_bayes_op", suppress_error=True) # Cleanup for TargetEncodingFit & Transform db_drop_table(table_name="category_data_op", suppress_error=True) db_drop_table(table_name="target_encoding_fit_op", suppress_error=True) # Cleanup for TextMorph db_drop_table(table_name="words_input", suppress_error=True) db_drop_table(table_name="pos_input", suppress_error=True) # Cleanup for TextParser db_drop_table(table_name="complaints", suppress_error=True) db_drop_table(table_name="stop_words", suppress_error=True) # Cleanup for Unpack db_drop_table(table_name="ville_tempdata", suppress_error=True) db_drop_table(table_name="ville_tempdata1", suppress_error=True) # Cleanup for Unpivoting db_drop_table(table_name="unpivot_input", suppress_error=True) # Cleanup for VectorDistance db_drop_table(table_name="target_mobile_data_dense", suppress_error=True) db_drop_table(table_name="ref_mobile_data_dense", suppress_error=True) # Cleanup for WordEmbeddings db_drop_table(table_name="word_embed_model", suppress_error=True) db_drop_table(table_name="word_embed_input_table1", suppress_error=True) # Cleanup for XGBoost & Predict db_drop_table(table_name="xgboost_op2", suppress_error=True) print("Or you can run the cleanup action of this script with: `analytic_functions_setup.py --action cleanup`") else: raise ValueError(f"Unknown action: {args.action}") # Drop the context if it was created if eng: remove_context() if __name__ == '__main__': main()