Update README.md

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README.md

@@ -24,24 +24,22 @@ Examples are available in [/examples](examples/). For basic usage, you can evalu
 import pandas as pd
 
 from tods import schemas as schemas_utils
-from tods.utils import generate_dataset_problem, evaluate_pipeline
+from tods import generate_dataset, evaluate_pipeline
 
 table_path = 'datasets/yahoo_sub_5.csv'
 target_index = 6 # what column is the target
-#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
-time_limit = 30 # How many seconds you wanna search
-#metric = 'F1' # F1 on label 1
 metric = 'F1_MACRO' # F1 on both label 0 and 1
 
-# Read data and generate dataset and problem
+# Read data and generate dataset
 df = pd.read_csv(table_path)
-dataset, problem_description = generate_dataset_problem(df, target_index=target_index, metric=metric)
+dataset = generate_dataset(df, target_index)
 
 # Load the default pipeline
 pipeline = schemas_utils.load_default_pipeline()
 
 # Run the pipeline
-pipeline_result = evaluate_pipeline(problem_description, dataset, pipeline)
+pipeline_result = evaluate_pipeline(dataset, pipeline, metric)
+print(pipeline_result)
 ```
 We also provide AutoML support to help you automatically find a good pipeline for a your data.
 ```python
@@ -49,29 +47,26 @@ import pandas as pd
 
 from axolotl.backend.simple import SimpleRunner
 
-from tods.utils import generate_dataset_problem
-from tods.search import BruteForceSearch
+from tods import generate_dataset, generate_problem
+from tods.searcher import BruteForceSearch
 
 # Some information
-#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_GOOG.csv' # The path of the dataset
-#target_index = 2 # what column is the target
-
 table_path = 'datasets/yahoo_sub_5.csv'
 target_index = 6 # what column is the target
-#table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
 time_limit = 30 # How many seconds you wanna search
-#metric = 'F1' # F1 on label 1
 metric = 'F1_MACRO' # F1 on both label 0 and 1
 
 # Read data and generate dataset and problem
 df = pd.read_csv(table_path)
-dataset, problem_description = generate_dataset_problem(df, target_index=target_index, metric=metric)
+dataset = generate_dataset(df, target_index=target_index)
+problem_description = generate_problem(dataset, metric)
 
 # Start backend
 backend = SimpleRunner(random_seed=0)
 
 # Start search algorithm
-search = BruteForceSearch(problem_description=problem_description, backend=backend)
+search = BruteForceSearch(problem_description=problem_description,
+                          backend=backend)
 
 # Find the best pipeline
 best_runtime, best_pipeline_result = search.search_fit(input_data=[dataset], time_limit=time_limit)