This example predicts the species of iris flower based on different factors.
Run AutoML to acquire the most effective model with the following specifications:
- Use early stopping timer to 100 sec.
- Include only 'xgboost' model for training.
- Opt for verbose level 2 to get detailed log.
- Add customization for some specific processes of AutoClassifier.
- Load data and split it to train and test datasets.
- Load the example data and create teradataml DataFrame.
>>> load_example_data("teradataml", "iris_input") - Perform sampling to get 80% for training and 20% for testing.
>>> iris_sample = iris.sample(frac = [0.8, 0.2])
- Fetch train and test data.
>>> iris_train= iris_sample[iris_sample['sampleid'] == 1].drop('sampleid', axis=1)>>> iris_test = iris_sample[iris_sample['sampleid'] == 2].drop('sampleid', axis=1)
- Load the example data and create teradataml DataFrame.
- Add customization.
>>> AutoClassifier.generate_custom_config("custom_iris")Generating custom config JSON for AutoML ... Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 2 Customizing Data Preparation Phase ... Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 1,3 Customizing Data Imbalance Handling ... Available data sampling methods with corresponding indices: Index 1: SMOTE Index 2: NearMiss Enter the corresponding index data imbalance handling method: 1 Customization of data imbalance handling has been completed successfully. Available feature scaling methods with corresponding indices: Index 1: maxabs Index 2: mean Index 3: midrange Index 4: range Index 5: rescale Index 6: std Index 7: sum Index 8: ustd Enter the corresponding index feature scaling method: 6 Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 1 Customization of feature scaling has been completed successfully. Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 4 Customization of data preparation phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 3 Customizing Model Training Phase ... Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 1 Customizing Model Hyperparameter ... Available models for hyperparameter tuning with corresponding indices: Index 1: decision_forest Index 2: xgboost Index 3: knn Index 4: glm Index 5: svm Available hyperparamters update methods with corresponding indices: Index 1: ADD Index 2: REPLACE Enter the list of model indices for performing hyperparameter tuning: 2 Available hyperparameters for model 'xgboost' with corresponding indices: Index 1: min_impurity Index 2: max_depth Index 3: min_node_size Index 4: shrinkage_factor Index 5: iter_num Enter the list of hyperparameter indices for model 'xgboost': 2 Enter the index of corresponding update method for hyperparameters 'max_depth' for model 'xgboost': 2 Enter the list of value for hyperparameter 'max_depth' for model 'xgboost': 3, 4 Customization of model hyperparameter has been completed successfully. Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 3 Customization of model training phase has been completed successfully. Process of generating custom config file for AutoML has been completed successfully. 'custom_iris.json' file is generated successfully under the current working directory.
- Create an AutoML instance.
>>> aml = AutoClassifier(include=['xgboost'], >>> verbose=2, >>> max_runtime_secs=100, >>> custom_config_file='custom_iris.json')
- Fit training data.
>>> aml.fit(iris_train, iris_train.species)
Received below input for customization : { "DataImbalanceIndicator": true, "DataImbalanceMethod": "SMOTE", "FeatureScalingIndicator": true, "FeatureScalingParam": { "FeatureScalingMethod": "std", "volatile": true }, "HyperparameterTuningIndicator": true, "HyperparameterTuningParam": { "xgboost": { "max_depth": { "Method": "REPLACE", "Value": [ 3, 4 ] } } } } 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Feature Exploration started ... Data Overview: Total Rows in the data: 120 Total Columns in the data: 6 Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage sepal_width FLOAT 120 0 None 0 120 0 0.0 100.0 petal_length FLOAT 120 0 None 0 120 0 0.0 100.0 id INTEGER 120 0 None 0 120 0 0.0 100.0 species INTEGER 120 0 None 0 120 0 0.0 100.0 sepal_length FLOAT 120 0 None 0 120 0 0.0 100.0 petal_width FLOAT 120 0 None 0 120 0 0.0 100.0 id sepal_length sepal_width petal_length petal_width species func min 1.000 4.300 2.200 1.000 0.100 1.000 std 43.378 0.799 0.445 1.729 0.745 0.809 25% 37.750 5.200 2.800 1.500 0.300 1.000 50% 73.500 5.750 3.000 4.300 1.300 2.000 75% 111.250 6.300 3.300 5.025 1.800 3.000 max 150.000 7.900 4.400 6.900 2.500 3.000 mean 74.150 5.810 3.055 3.718 1.177 1.983 count 120.000 120.000 120.000 120.000 120.000 120.000 Statistics of Data: func id sepal_length sepal_width petal_length petal_width species 50% 73.5 5.75 3 4.3 1.3 2 count 120 120 120 120 120 120 mean 74.15 5.81 3.055 3.718 1.177 1.983 min 1 4.3 2.2 1 0.1 1 max 150 7.9 4.4 6.9 2.5 3 75% 111.25 6.3 3.3 5.025 1.8 3 25% 37.75 5.2 2.8 1.5 0.3 1 std 43.378 0.799 0.445 1.729 0.745 0.809 Target Column Distribution: Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 2.5 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Feature Engineering started ... Handling duplicate records present in dataset ... Analysis completed. No action taken. Total time to handle duplicate records: 1.52 sec Starting customized anti-select columns ... Skipping customized anti-select columns. Handling less significant features from data ... Total time to handle less significant features: 6.13 sec Handling Date Features ... Analysis Completed. Dataset does not contain any feature related to dates. No action needed. Total time to handle date features: 0.00 sec Proceeding with default option for missing value imputation. Proceeding with default option for handling remaining missing values. Checking Missing values in dataset ... Analysis Completed. No Missing Values Detected. Total time to find missing values in data: 7.43 sec Imputing Missing Values ... Analysis completed. No imputation required. Time taken to perform imputation: 0.00 sec No information provided for Variable-Width Transformation. Skipping customized string manipulation. Starting Customized Categorical Feature Encoding ... AutoML will proceed with default encoding technique. Performing encoding for categorical columns ... Analysis completed. No categorical columns were found. Time taken to encode the columns: 1.44 sec Starting customized mathematical transformation ... Skipping customized mathematical transformation. Starting customized non-linear transformation ... Skipping customized non-linear transformation. 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Data preparation started ... Starting customized outlier processing ... No information provided for customized outlier processing. AutoML will proceed with default settings. Outlier preprocessing ... Columns with outlier percentage :- ColumnName OutlierPercentage 0 sepal_width 2.5 Deleting rows of these columns: ['sepal_width'] Sample of dataset after removing outlier rows: sepal_length sepal_width petal_length petal_width species id 5.4 3.0 4.5 1.5 2 63 5.4 3.9 1.3 0.4 1 9 5.4 3.4 1.7 0.2 1 115 4.4 3.0 1.3 0.2 1 95 4.4 3.2 1.3 0.2 1 40 6.6 2.9 4.6 1.3 2 27 4.4 2.9 1.4 0.2 1 41 5.4 3.9 1.7 0.4 1 77 5.4 3.4 1.5 0.4 1 39 5.4 3.7 1.5 0.2 1 33 117 rows X 6 columns Time Taken by Outlier processing: 39.01 sec Checking imbalance data ... Imbalance Not Found. Feature selection using lasso ... feature selected by lasso: ['sepal_width', 'petal_length', 'petal_width', 'sepal_length'] Total time taken by feature selection: 2.63 sec scaling Features of lasso data ... columns that will be scaled: ['sepal_width', 'petal_length', 'petal_width', 'sepal_length'] Dataset sample after scaling: id species sepal_width petal_length petal_width sepal_length 10 3 -2.0350680494119757 0.7177312821556723 0.40679582737559905 0.22565555366314133 12 2 -1.0481127674173594 -0.1618905056817609 -0.2731343412379006 -0.14795340546781913 13 2 -0.801373946918705 0.24859966197570801 0.13482375993019924 -0.27248972517814 14 3 0.43232015557456555 0.8936556397231588 1.4946840971571982 0.7238008325044227 16 2 -1.0481127674173594 0.13131675693071682 -0.0011622737925007988 -0.023417085757499345 18 2 0.6790589760732187 0.5418069245881857 0.5427818610982991 0.5992645127941018 15 3 -0.06115748542274311 0.6004483771106811 0.8147539285436989 0.22565555366314133 11 3 -0.5546351264200517 0.6590898296331769 1.0867259959890987 -0.27248972517814 9 1 2.1594918990651437 -1.4520024611766627 -1.0890505435741 -0.5215623645987796 8 2 -0.06115748542274311 0.3658825670206992 0.270809793652899 0.9728734719250622 117 rows X 6 columns Total time taken by feature scaling: 30.28 sec Feature selection using rfe ... feature selected by RFE: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] Total time taken by feature selection: 7.70 sec scaling Features of rfe data ... columns that will be scaled: ['r_sepal_length', 'r_sepal_width', 'r_petal_length', 'r_petal_width'] Dataset sample after scaling: id species r_sepal_length r_sepal_width r_petal_length r_petal_width 10 3 0.22565555366314133 -2.0350680494119757 0.7177312821556723 0.40679582737559905 12 2 -0.14795340546781913 -1.0481127674173594 -0.1618905056817609 -0.2731343412379006 13 2 -0.27248972517814 -0.801373946918705 0.24859966197570801 0.13482375993019924 14 3 0.7238008325044227 0.43232015557456555 0.8936556397231588 1.4946840971571982 16 2 -0.023417085757499345 -1.0481127674173594 0.13131675693071682 -0.0011622737925007988 18 2 0.5992645127941018 0.6790589760732187 0.5418069245881857 0.5427818610982991 15 3 0.22565555366314133 -0.06115748542274311 0.6004483771106811 0.8147539285436989 11 3 -0.27248972517814 -0.5546351264200517 0.6590898296331769 1.0867259959890987 9 1 -0.5215623645987796 2.1594918990651437 -1.4520024611766627 -1.0890505435741 8 2 0.9728734719250622 -0.06115748542274311 0.3658825670206992 0.270809793652899 117 rows X 6 columns Total time taken by feature scaling: 28.48 sec scaling Features of pca data ... columns that will be scaled: ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'] Dataset sample after scaling: id species sepal_length sepal_width petal_length petal_width 87 3 0.5992645127941051 -0.3078963059213974 1.0695799972906452 0.8147539285436984 128 3 1.7200913901869874 0.43232015557456543 1.3041458073806276 0.8147539285436984 33 1 -0.5215623645987763 1.6660142580678357 -1.3347195561316718 -1.361022611019501 39 1 -0.5215623645987763 0.9257977965718729 -1.3347195561316718 -1.089050543574101 100 3 1.0974097916353864 -1.2948515879160134 1.1868629023356365 0.8147539285436984 35 2 0.3501918733734644 -0.5546351264200516 0.5418069245881857 -0.001162273792501403 28 3 0.3501918733734644 -1.0481127674173591 1.0695799972906452 0.2708097936528984 47 2 1.4710187507663468 0.43232015557456543 0.5418069245881857 0.2708097936528984 10 3 0.22565555366314463 -2.035068049411975 0.7177312821556723 0.4067958273755985 106 3 0.4747281930837853 0.9257977965718729 0.9522970922456546 1.4946840971571977 117 rows X 6 columns Total time taken by feature scaling: 23.24 sec Dimension Reduction using pca ... PCA columns: ['col_0', 'col_1'] Total time taken by PCA: 6.30 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Model Training started ... Starting customized hyperparameter update ... Completed customized hyperparameter update. Hyperparameters used for model training: response_column : species name : xgboost model_type : Classification column_sampling : (1, 0.6) min_impurity : (0.0, 0.1) lambda1 : (0.01, 0.1, 1, 10) shrinkage_factor : (0.5, 0.1, 0.2) max_depth : (3, 4) min_node_size : (1, 2) iter_num : (10, 20) seed : 42 Total number of models for xgboost : 384 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Performing hyperparameter tuning ... xgboost ---------------------------------------------------------------------------------------------------- Leaderboard RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION MICRO-RECALL MICRO-F1 MACRO-PRECISION MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 0 1 XGBOOST_2 pca 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000 1.000000 1.000000 1 2 XGBOOST_3 lasso 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041 2 3 XGBOOST_1 rfe 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041 3 4 XGBOOST_0 lasso 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041 4 rows X 13 columns 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Completed: ｜⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿｜ 100% - 18/18 - Display model leaderboard.
>>> aml.leaderboard()
RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION MICRO-RECALL MICRO-F1 MACRO-PRECISION MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 0 1 XGBOOST_2 pca 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000 1.000000 1.000000 1 2 XGBOOST_3 lasso 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041 2 3 XGBOOST_1 rfe 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041 3 4 XGBOOST_0 lasso 0.958333 0.958333 0.958333 0.958333 0.966667 0.958333 0.960234 0.9625 0.958333 0.958041
- Display the best performing model.
>>> aml.leader()
RANK MODEL_ID FEATURE_SELECTION ACCURACY MICRO-PRECISION MICRO-RECALL MICRO-F1 MACRO-PRECISION MACRO-RECALL MACRO-F1 WEIGHTED-PRECISION WEIGHTED-RECALL WEIGHTED-F1 1 XGBOOST_2 pca 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
- Display model hyperparameters for trained model.
>>> aml.model_hyperparameters(rank=1)
{'response_column': 'species', 'name': 'xgboost', 'model_type': 'Classification', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 0.01, 'shrinkage_factor': 0.5, 'max_depth': 3, 'min_node_size': 1, 'iter_num': 10, 'seed': 42, 'persist': False, 'output_prob': True, 'output_responses': ['1', '3', '2']}>>> aml.model_hyperparameters(rank=4)
{'response_column': 'species', 'name': 'xgboost', 'model_type': 'Classification', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 0.01, 'shrinkage_factor': 0.5, 'max_depth': 3, 'min_node_size': 1, 'iter_num': 10, 'seed': 42, 'persist': False, 'output_prob': True, 'output_responses': ['1', '3', '2']} - Generate prediction on test dataset using best performing model.
>>> prediction = aml.predict(iris_test)
Data Transformation started ... Performing transformation carried out in feature engineering phase ... Updated dataset after dropping irrelevent columns : sepal_length sepal_width petal_length petal_width species 7.4 2.8 6.1 1.9 3 7.7 3.8 6.7 2.2 3 6.5 2.8 4.6 1.5 2 5.1 2.5 3.0 1.1 2 5.0 2.0 3.5 1.0 2 6.7 3.1 5.6 2.4 3 5.1 3.4 1.5 0.2 1 5.1 3.7 1.5 0.4 1 6.3 3.3 6.0 2.5 3 7.7 2.8 6.7 2.0 3 30 rows X 5 columns Updated dataset after performing target column transformation : petal_length sepal_width sepal_length id petal_width species 1.5 3.4 5.1 10 0.2 1 3.5 2.0 5.0 14 1.0 2 5.6 3.1 6.7 22 2.4 3 5.0 3.0 6.7 15 1.7 2 6.0 3.3 6.3 9 2.5 3 6.7 2.8 7.7 17 2.0 3 6.1 2.8 7.4 8 1.9 3 5.2 3.0 6.5 16 2.0 3 5.6 3.4 6.3 11 2.4 3 1.6 3.2 4.7 19 0.2 1 30 rows X 6 columns Performing transformation carried out in data preparation phase ... Updated dataset after performing Lasso feature selection: id sepal_width petal_length petal_width sepal_length species 17 2.8 6.7 2.0 7.7 3 26 3.4 1.6 0.4 5.0 1 20 2.8 4.6 1.5 6.5 2 19 3.2 1.6 0.2 4.7 1 36 2.9 4.3 1.3 6.4 2 28 3.1 4.4 1.4 6.7 2 15 3.0 5.0 1.7 6.7 2 32 2.9 4.3 1.3 6.2 2 38 3.1 1.6 0.2 4.8 1 12 3.8 6.7 2.2 7.7 3 30 rows X 6 columns Updated dataset after performing scaling on Lasso selected features : id species sepal_width petal_length petal_width sepal_length 38 1 0.18558133507591124 -1.2760781036091762 -1.3610226110195 -1.2687802828607015 17 3 -0.5546351264200517 1.7146359750380966 1.0867259959890987 2.3427729887385853 34 2 -1.541590408414668 -0.0446076006367697 -0.2731343412379006 -0.39702604488845983 15 2 -0.06115748542274311 0.7177312821556723 0.6787678948209989 1.097409791635383 36 2 -0.30789630592139744 0.30724111449820335 0.13482375993019924 0.7238008325044227 28 2 0.18558133507591124 0.3658825670206992 0.270809793652899 1.097409791635383 19 1 0.43232015557456555 -1.2760781036091762 -1.3610226110195 -1.3933166025710215 30 1 0.43232015557456555 -1.5106439136991585 -1.3610226110195 -1.019707643440061 26 1 0.9257977965718731 -1.2760781036091762 -1.0890505435741 -1.019707643440061 20 2 -0.5546351264200517 0.4831654720656899 0.40679582737559905 0.8483371522147425 30 rows X 6 columns Updated dataset after performing RFE feature selection: id sepal_length sepal_width petal_length petal_width species 22 6.7 3.1 5.6 2.4 3 36 6.4 2.9 4.3 1.3 2 28 6.7 3.1 4.4 1.4 2 19 4.7 3.2 1.6 0.2 1 38 4.8 3.1 1.6 0.2 1 12 7.7 3.8 6.7 2.2 3 15 6.7 3.0 5.0 1.7 2 32 6.2 2.9 4.3 1.3 2 17 7.7 2.8 6.7 2.0 3 34 5.5 2.4 3.7 1.0 2 30 rows X 6 columns Updated dataset after performing scaling on RFE selected features : id species r_sepal_length r_sepal_width r_petal_length r_petal_width 22 3 1.097409791635383 0.18558133507591124 1.0695799972906452 1.6306701308798983 15 2 1.097409791635383 -0.06115748542274311 0.7177312821556723 0.6787678948209989 32 2 0.474728193083782 -0.30789630592139744 0.30724111449820335 0.13482375993019924 36 2 0.7238008325044227 -0.30789630592139744 0.30724111449820335 0.13482375993019924 38 1 -1.2687802828607015 0.18558133507591124 -1.2760781036091762 -1.3610226110195 12 3 2.3427729887385853 1.9127530785664895 1.7146359750380966 1.3586980634344987 19 1 -1.3933166025710215 0.43232015557456555 -1.2760781036091762 -1.3610226110195 30 1 -1.019707643440061 0.43232015557456555 -1.5106439136991585 -1.3610226110195 17 3 2.3427729887385853 -0.5546351264200517 1.7146359750380966 1.0867259959890987 34 2 -0.39702604488845983 -1.541590408414668 -0.0446076006367697 -0.2731343412379006 30 rows X 6 columns Updated dataset after performing scaling for PCA feature selection : id species sepal_length sepal_width petal_length petal_width 38 1 -1.2687802828606982 0.18558133507591118 -1.2760781036091762 -1.361022611019501 19 1 -1.393316602571018 0.43232015557456543 -1.2760781036091762 -1.361022611019501 30 1 -1.0197076434400576 0.43232015557456543 -1.5106439136991585 -1.361022611019501 17 3 2.3427729887385884 -0.5546351264200516 1.7146359750380966 1.0867259959890982 22 3 1.0974097916353864 0.18558133507591118 1.0695799972906452 1.6306701308798979 35 3 -0.023417085757496025 -0.8013739469187048 0.7763727346781676 0.9507399622663981 15 2 1.0974097916353864 -0.061157485422743095 0.7177312821556723 0.6787678948209983 32 2 0.4747281930837853 -0.3078963059213974 0.30724111449820335 0.13482375993019866 26 1 -1.0197076434400576 0.9257977965718729 -1.2760781036091762 -1.089050543574101 20 2 0.8483371522147457 -0.5546351264200516 0.4831654720656899 0.4067958273755985 30 rows X 6 columns Updated dataset after performing PCA feature selection : id col_0 col_1 species 0 26 -2.134501 0.376449 1 1 17 2.979318 0.472867 3 2 22 2.068188 0.711279 3 3 19 -2.352833 -0.238068 1 4 38 -2.222807 -0.419043 1 5 36 0.713335 0.002790 2 6 15 1.389572 0.414597 2 7 20 1.099709 -0.157185 2 8 34 0.019662 -1.592394 2 9 35 1.189404 -0.672774 3 10 rows X 4 columns Data Transformation completed.⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿｜ 100% - 15/15 Following model is being picked for evaluation: Model ID : XGBOOST_2 Feature Selection Method : pca Prediction : id Prediction species prob_1 prob_2 prob_3 0 22 3 3 0.001111 0.005453 0.993436 1 38 1 1 0.997480 0.001717 0.000803 2 36 2 2 0.003154 0.986508 0.010337 3 15 3 2 0.001111 0.005453 0.993436 4 34 2 2 0.003659 0.987273 0.009068 5 35 3 3 0.001353 0.019395 0.979252 6 20 2 2 0.003516 0.957607 0.038877 7 19 1 1 0.997480 0.001717 0.000803 8 17 3 3 0.001111 0.005453 0.993436 9 26 1 1 0.997480 0.001717 0.000803 Confusion Matrix : array([[10, 0, 0], [ 0, 7, 1], [ 0, 0, 12]], dtype=int64)>>> prediction.head()
id Prediction species prob_1 prob_2 prob_3 10 1 1 0.9974796820350456 0.001716876719969664 0.0008034412449846343 12 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341 13 2 2 0.002354870676477184 0.6354616937763028 0.36218343554722005 14 2 2 0.003658600107511803 0.9872729931172249 0.009068406775263282 16 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341 17 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341 15 3 2 0.0011112337307529446 0.005452541701712984 0.9934362245675341 11 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341 9 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341 8 3 3 0.0011112337307529446 0.005452541701712984 0.9934362245675341
- Generate evaluation metrics on test dataset using best performing model.
>>> performance_metrics = aml.evaluate(iris_test)
Skipping data transformation as data is already transformed. Following model is being picked for evaluation: Model ID : XGBOOST_2 Feature Selection Method : pca Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 10 0 0 1.000000 1.000 1.000000 10 2 3 CLASS_3 0 1 12 0.923077 1.000 0.960000 12 1 2 CLASS_2 0 7 0 1.000000 0.875 0.933333 8 -------------------------------------------------------------------------------- SeqNum Metric MetricValue 0 3 Micro-Recall 0.966667 1 5 Macro-Precision 0.974359 2 6 Macro-Recall 0.958333 3 7 Macro-F1 0.964444 4 9 Weighted-Recall 0.966667 5 10 Weighted-F1 0.966222 6 8 Weighted-Precision 0.969231 7 4 Micro-F1 0.966667 8 2 Micro-Precision 0.966667 9 1 Accuracy 0.966667>>> performance_metrics
SeqNum Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support 0 1 CLASS_1 10 0 0 1.0 1.0 1.0 10 2 3 CLASS_3 0 1 12 0.9230769230769231 1.0 0.9600000000000001 12 1 2 CLASS_2 0 7 0 1.0 0.875 0.9333333333333333 8
- Generate prediction on test dataset using second best performing model.
>>> prediction = aml.predict(iris_test,2)
Skipping data transformation as data is already transformed. Following model is being picked for evaluation: Model ID : XGBOOST_3 Feature Selection Method : lasso Prediction : id Prediction species prob_1 prob_2 prob_3 0 38 1 1 0.998938 0.000531 0.000531 1 36 2 2 0.000962 0.998226 0.000813 2 28 2 2 0.000935 0.998252 0.000813 3 15 3 2 0.000871 0.002996 0.996133 4 17 3 3 0.000548 0.000505 0.998947 5 34 2 2 0.000818 0.998369 0.000813 6 19 1 1 0.998938 0.000531 0.000531 7 30 1 1 0.998938 0.000531 0.000531 8 26 1 1 0.998938 0.000531 0.000531 9 20 2 2 0.000942 0.998245 0.000813 Confusion Matrix : array([[10, 0, 0], [ 0, 7, 1], [ 0, 0, 12]], dtype=int64)>>> prediction.head()
id Prediction species prob_1 prob_2 prob_3 10 1 1 0.9989377670822677 0.0005312951795478172 0.000530937738184492 12 3 3 0.0006801883644343667 0.0009692414607328684 0.9983505701748328 13 2 2 0.0008180359310818579 0.9983690346735865 0.0008129293953315769 14 2 2 0.0008180359310818579 0.9983690346735865 0.0008129293953315769 16 3 3 0.000543436036706532 0.0005265372144978478 0.9989300267487955 17 3 3 0.0005478504971630089 0.0005047187958750853 0.9989474307069619 15 3 2 0.0008712061087393717 0.0029962703677184187 0.9961325235235423 11 3 3 0.0006801883644343667 0.0009692414607328684 0.9983505701748328 9 3 3 0.0006801883644343667 0.0009692414607328684 0.9983505701748328 8 3 3 0.0005478504971630089 0.0005047187958750853 0.9989474307069619
- Generate evaluation metrics on test dataset using second best performing model.
>>> performance_metrics = aml.evaluate(iris_test,2)
Skipping data transformation as data is already transformed. Following model is being picked for evaluation: Model ID : XGBOOST_3 Feature Selection Method : lasso Performance Metrics : Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support SeqNum 0 1 CLASS_1 10 0 0 1.000000 1.000 1.000000 10 2 3 CLASS_3 0 1 12 0.923077 1.000 0.960000 12 1 2 CLASS_2 0 7 0 1.000000 0.875 0.933333 8 -------------------------------------------------------------------------------- SeqNum Metric MetricValue 0 3 Micro-Recall 0.966667 1 5 Macro-Precision 0.974359 2 6 Macro-Recall 0.958333 3 7 Macro-F1 0.964444 4 9 Weighted-Recall 0.966667 5 10 Weighted-F1 0.966222 6 8 Weighted-Precision 0.969231 7 4 Micro-F1 0.966667 8 2 Micro-Precision 0.966667 9 1 Accuracy 0.966667>>> performance_metrics
SeqNum Prediction Mapping CLASS_1 CLASS_2 CLASS_3 Precision Recall F1 Support 2 3 CLASS_3 0 1 12 0.92307 1.0 0.9600000000000001 12 1 2 CLASS_2 0 7 0 1.0 0.875 0.9333333333333333 8 0 1 CLASS_1 10 0 0 1.0 1.0 1.0 10