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mppt-level-training
Commit 91322ba4 authored by aakash.bedi's avatar aakash.bedi
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updated destructors

parent b16a85d1
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    scripts/core/engine/ml_modelling.py
    View file @ 91322ba4
    ...@@ -19,74 +19,99 @@ class AiModelling: ...@@ -19,74 +19,99 @@ class AiModelling:
    for inv_id in list(self.df_raw_tags['inv_id'].unique()): for inv_id in list(self.df_raw_tags['inv_id'].unique()):
    df = self.df_raw_tags[self.df_raw_tags['inv_id'] == inv_id] df = self.df_raw_tags[self.df_raw_tags['inv_id'] == inv_id]
    for mppt_id in list(self.df_raw_tags['mppt_id'].unique()): for mppt_id in list(self.df_raw_tags['mppt_id'].unique()):
    print(f'1st memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') print(f'memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect() data_modelling = DataModelling(start_timestamp=self.start_timestamp,
    tracemalloc.reset_peak() end_timestamp=self.end_timestamp,
    print(f'2nd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') df_coefficients=self.df_coefficients)
    tracemalloc.clear_traces() data_modelling.get_data(inv_id=inv_id, mppt_id=mppt_id, df=df)
    print(f'3rd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') del data_modelling
    df_mppt_level = df[df['mppt_id'] == mppt_id] except Exception as e:
    df_kairos_data = get_tags_data(df_input_tags=df_mppt_level, start_timestamp=self.start_timestamp, logger.exception(f'Exception - {e}')
    end_timestamp=self.end_timestamp, inv_id=inv_id, mppt_id=mppt_id)
    def __del__(self):
    print(f'4th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') try:
    gc.collect() print('destructor called, AiModelling die!')
    tracemalloc.reset_peak() except Exception as e:
    print(f'5th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') logger.exception(f'Exception - {e}')
    tracemalloc.clear_traces()
    print(f'6th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    class DataModelling:
    logger.info(f'Shape of final df - {df_kairos_data.shape}') def __init__(self, start_timestamp, end_timestamp, df_coefficients):
    self.start_timestamp = start_timestamp
    mppt_data = GetData() self.end_timestamp = end_timestamp
    df_mppt = mppt_data.associate_inv_mppt_id(df=df_kairos_data) self.df_coefficients = df_coefficients
    print(f'7th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') def get_data(self, inv_id, mppt_id, df):
    gc.collect() try:
    tracemalloc.reset_peak() print(f'1st memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    print(f'8th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') gc.collect()
    tracemalloc.clear_traces() tracemalloc.reset_peak()
    print(f'9th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') print(f'2nd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    df_coefficient_multiply = mppt_data.multiply_mppt_coefficients(df_mppt=df_mppt, print(f'3rd memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    df_coefficients=self.df_coefficients)
    df_mppt_level = df[df['mppt_id'] == mppt_id]
    print(f'10th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') df_kairos_data = get_tags_data(df_input_tags=df_mppt_level, start_timestamp=self.start_timestamp,
    gc.collect() end_timestamp=self.end_timestamp, inv_id=inv_id, mppt_id=mppt_id)
    tracemalloc.reset_peak()
    print(f'11th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') print(f'4th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces() gc.collect()
    print(f'12th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') tracemalloc.reset_peak()
    print(f'5th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    data_preprocessing = DataPreprocessing() tracemalloc.clear_traces()
    df_clean = data_preprocessing.remove_outliers(df=df_coefficient_multiply, print(f'6th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    param_list=['tilt_irradiance', 'voltage_mppt',
    'current_mppt']) logger.info(f'Shape of final df - {df_kairos_data.shape}')
    print(f'13th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect() mppt_data = GetData()
    tracemalloc.reset_peak() df_mppt = mppt_data.associate_inv_mppt_id(df=df_kairos_data)
    print(f'14th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces() print(f'7th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    print(f'15th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') gc.collect()
    tracemalloc.reset_peak()
    Training(df=df_clean).data_training(inv_id=inv_id, mppt_id=mppt_id) print(f'8th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    del df_kairos_data print(f'9th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    del df_mppt
    del df_coefficient_multiply df_coefficient_multiply = mppt_data.multiply_mppt_coefficients(df_mppt=df_mppt,
    df_coefficients=self.df_coefficients)
    print(f'16th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect() print(f'10th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.reset_peak() gc.collect()
    print(f'17th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') tracemalloc.reset_peak()
    tracemalloc.clear_traces() print(f'11th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    print(f'18th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}') tracemalloc.clear_traces()
    print(f'12th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    data_preprocessing = DataPreprocessing()
    df_clean = data_preprocessing.remove_outliers(df=df_coefficient_multiply,
    param_list=['tilt_irradiance', 'voltage_mppt',
    'current_mppt'])
    print(f'13th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    tracemalloc.reset_peak()
    print(f'14th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'15th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    Training(df=df_clean).data_training(inv_id=inv_id, mppt_id=mppt_id)
    del df_kairos_data
    del df_mppt
    del df_coefficient_multiply
    print(f'16th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    gc.collect()
    tracemalloc.reset_peak()
    print(f'17th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    tracemalloc.clear_traces()
    print(f'18th memory allocation for {inv_id} & {mppt_id} - {tracemalloc.get_traced_memory()}')
    except Exception as e: except Exception as e:
    logger.exception(f'Exception - {e}') logger.exception(f'Exception - {e}')
    def __del__(self): def __del__(self):
    try: try:
    print('destructor called, die!') print('destructor called, DataModelling die!')
    except Exception as e: except Exception as e:
    logger.exception(f'Exception - {e}') logger.exception(f'Exception - {e}')
    \ No newline at end of file
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