def main(): filter_options = Options.crystal_progression output_foler = settings.OUTPUT_DIR df_getter = cu.getCrystalDecJanLogDF w = Workflow(filter_options=filter_options, nested_folder_output=False) w.pca_dimension_count = 2 w.clustering_counts = [4] w.RunWorkflow(get_df_func=df_getter)
def main(): filter_options = Options.crystal_feedback df_getter = cu.getCrystalDecJanLogDF w = Workflow(filter_options=filter_options, nested_folder_output=False) w.pca_dimension_count = 2 # w.clustering_counts = range(3, 8) w.clustering_counts = [6] w.verbose = True w.RunWorkflow(get_df_func=df_getter)
def main(): filter_options = Options.crystal_actions df_getter = cu.getCrystalDecJanLogDF w = Workflow(filter_options=filter_options, nested_folder_output=False) w.pca_dimension_count = 2 w.further_filter_query_list = [ f'sum_lvl_0_to_4_avgMoleculeDragDurationInSecs < {5*60}' ] # 5 mins - one person averaged 8+ minutes w.clustering_counts = [4] w.RunWorkflow(get_df_func=df_getter)
from src import settings from src.cluster_workflow import Workflow from src import cluster_utils as cu from src.options import Options if __name__ == '__main__': # import setup # setup.init_path() filter_options = Options.waves_actions_lv016 output_foler = settings.OUTPUT_DIR df_getter = cu.getWavesDecJanLogDF w = Workflow(filter_options=filter_options) w.pca_dimension_count = 2 for k in range(4, 8): w.clustering_count = k w.RunWorkflow(get_df_func=df_getter)
from src import settings from src.cluster_workflow import Workflow from src import cluster_utils as cu from src.options import Options if __name__ == '__main__': # import setup # setup.init_path() filter_options = Options.waves_progression output_foler = settings.OUTPUT_DIR df_getter = cu.getWavesDecJanLogDF w = Workflow(filter_options=filter_options) w.further_filter_query_list = [ f'sum_lvl_0_to_34_totalLevelTime < {50*60}' ] # 50 mins w.clustering_counts = range(3, 8) w.verbose = True w.RunWorkflow(get_df_func=df_getter)
from src import settings from src.cluster_workflow import Workflow from src import cluster_utils as cu from src.options import Options if __name__ == '__main__': # import setup # setup.init_path() filter_options = Options.lakeland_actions_lvl01 output_foler = settings.OUTPUT_DIR df_getter = cu.getLakelandDecJanLogDF w = Workflow(filter_options=filter_options, nested_folder_output=False) w.pca_dimension_count = 2 w.clustering_counts = [6] w.RunWorkflow(get_df_func=df_getter)
from src import settings from src.cluster_workflow import Workflow from src import cluster_utils as cu from src.options import Options if __name__ == '__main__': # import setup # setup.init_path() filter_options = Options.waves_feedback_lv016 output_foler = settings.OUTPUT_DIR df_getter = cu.getWavesDecJanLogDF w = Workflow(filter_options=filter_options) w.do_logtransform = False w.pca_dimension_count = 2 w.clustering_counts = range(3, 8) w.verbose = True w.RunWorkflow(get_df_func=df_getter)
from src import settings from src.cluster_workflow import Workflow from src import cluster_utils as cu from src.options import Options if __name__ == '__main__': # import setup # setup.init_path() filter_options = Options.lakeland_feedback_lv01_with_bloom output_foler = settings.OUTPUT_DIR df_getter = cu.getLakelandDecJanLogDF w = Workflow(filter_options=filter_options, nested_folder_output=False) w.clustering_method = "KMeans" w.pca_dimension_count = 2 w.eps_min_list = [(eps, min_samples) for eps in [.01, .02, .05, .07, .1, .2, .3] for min_samples in [5]] w.min_cluster_size_list = [15, 30, 60, 100] w.plot_silhouettes = True w.plot_radars = True w.clustering_counts = [7] w.RunWorkflow(get_df_func=df_getter)