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How to use filtered_models method in dbt-osmosis

Best Python code snippet using dbt-osmosis_python

process_dataset.py

Source:process_dataset.py

1import argparse2import logging3import pandas as pd4import numpy as np5from sklearn.metrics import average_precision_score6import utils7import models8import soluto_log9def parse_args():10    parser = argparse.ArgumentParser(description='predictive model testing for photography segment')11    parser.add_argument('data_path', help='path to directory with the raw data')12    requiredNamed = parser.add_argument_group('required named arguments')13    requiredNamed.add_argument('--folds',  required=True, type=int,14                               help='number of folds in K-fold cross-validation')15    requiredNamed.add_argument('--test_size', type=float,16                               help='portion of test set out of all records')17    return parser.parse_args()18def models_variations(X_train, X_test, y_train, y_test, folds):19    logger = logging.getLogger('log')20    # NOTE: X sets might contain raws with only zeros.21    apps = utils.get_apps(X_train.columns)22    google_apps = [i for i in apps if 'google.android' in i]23    logger.debug('apps with "google.android" in package name:\n{}'.format(google_apps))24    carrier_apps_pattern = set([i for i in X_train['carrier_name'].unique()])25    carrier_apps = [i for i in apps if (carrier_apps_pattern & set(i.split('.')))]26    logger.debug('apps with a carrier name in package name:\n{}'.format(carrier_apps))27    # .sec. is for samsung apps.28    vendor_apps_pattern = set([i for i in X_train['device_vendor'].unique()] + ['sec'])29    vendor_apps = [i for i in apps if (vendor_apps_pattern & set(i.split('.')))]30    logger.debug('apps with a vendor name in package name:\n{}'.format(vendor_apps))31    pre_installed = {}32    for property in ['carrier_name', 'device_model', 'device_vendor']:33        pre_installed_apps = \34            utils.get_top_by_property(X_train[[property] + apps], property, 100, 0.9)35        pre_installed[property] = pre_installed_apps36        logger.debug('{property} pre installed apps:\n{apps}'.format(37            property=property, apps=pre_installed_apps.to_string()))38    # hot-one encode device model39    # NOTE: X_train and X_test might have inconsistent columns due to rare device models40    device_models = X_train['device_model']41    X_train = pd.get_dummies(X_train, columns=['device_model'], prefix='', prefix_sep='')42    X_train = pd.concat([X_train, device_models], axis=1)43    logger.info('train shape: {}'.format(X_train.shape))44    device_models_test = X_test['device_model']45    X_test = pd.get_dummies(X_test, columns=['device_model'], prefix='', prefix_sep='')46    X_test = pd.concat([X_test, device_models_test], axis=1)47    device_models = X_train['device_model'].unique()48    logger.info('all apps ; devices models with variance > 0.0099')49    filtered_models = utils.filter_low_var_columns(X_train[device_models], 0.0099)50    features = np.append(filtered_models, apps)51    model_df = X_train[features].copy()52    model = models.logistic_regression(model_df, y_train, folds)53    logger.info('test average precision: {}'.format(54        average_precision_score(y_test,55                                model.predict_proba(X_test[model_df.columns])[:, 1])))56    logger.info('apps with variance > 0.09 ; devices models with variance > 0.0099')57    filtered_apps = utils.filter_low_var_columns(X_train[apps], 0.09)58    filtered_models = utils.filter_low_var_columns(X_train[device_models], 0.0099)59    features = np.append(filtered_models, filtered_apps)60    model_df = X_train[features].copy()61    model = models.logistic_regression(model_df, y_train, folds)62    logger.info('test average precision: {}'.format(63        average_precision_score(y_test,64                                model.predict_proba(X_test[model_df.columns])[:, 1])))65    logger.info('apps with variance > 0.09 ; devices models with variance > 0.0099 ; clean rows with 0 apps')66    filtered_apps = utils.filter_low_var_columns(X_train[apps], 0.09)67    filtered_models = utils.filter_low_var_columns(X_train[device_models], 0.0099)68    features = np.append(filtered_models, filtered_apps)69    model_df = X_train[features].copy()70    model_df = model_df[model_df.sum(axis=1) > 0]71    model = models.logistic_regression(model_df, y_train, folds)72    logger.info('test average precision: {}'.format(73        average_precision_score(y_test,74                                model.predict_proba(X_test[model_df.columns])[:, 1])))75    logger.info('apps with variance > 0.09 + remove apps by name ; devices models with variance > 0.0099')76    filtered_apps = [i for i in apps if i not in google_apps + carrier_apps + vendor_apps]77    filtered_apps = utils.filter_low_var_columns(X_train[filtered_apps], 0.09)78    filtered_models = utils.filter_low_var_columns(X_train[device_models], 0.0099)79    features = np.append(filtered_models, filtered_apps)80    model_df = X_train[features].copy()81    model = models.logistic_regression(model_df, y_train, folds)82    logger.info('test average precision: {}'.format(83        average_precision_score(y_test,84                                model.predict_proba(X_test[model_df.columns])[:, 1])))85    logger.info('apps with variance > 0.09 + remove apps by name ; devices models with variance > 0.0099 ; clean rows with 0 apps')86    filtered_apps = [i for i in apps if i not in google_apps + carrier_apps + vendor_apps]87    filtered_apps = utils.filter_low_var_columns(X_train[filtered_apps], 0.09)88    filtered_models = utils.filter_low_var_columns(X_train[device_models], 0.0099)89    features = np.append(filtered_models, filtered_apps)90    model_df = X_train[features].copy()91    model_df = model_df[model_df.sum(axis=1) > 0]92    model = models.logistic_regression(model_df, y_train, folds)93    logger.info('test average precision: {}'.format(94        average_precision_score(y_test,95                                model.predict_proba(X_test[model_df.columns])[:, 1])))96    logger.info('remove apps by pre installed ; devices models with variance > 0.0099')97    model_df = X_train.copy()98    model_df_test = X_test.copy()99    for property in ['carrier_name', 'device_model', 'device_vendor']:100        for p, apps_replace in pre_installed[property].groupby(level=0):101            apps_replace_list = apps_replace.index.get_level_values('level_1').tolist()102            model_df.loc[model_df[property] == p, apps_replace_list] = 0103            model_df_test.loc[model_df_test[property] == p, apps_replace_list] = 0104    filtered_models = utils.filter_low_var_columns(model_df[device_models], 0.0099)105    features = np.append(filtered_models, apps)106    model_df = model_df[features]107    model = models.logistic_regression(model_df, y_train, folds)108    logger.info('test average precision: {}'.format(109        average_precision_score(y_test,110                                model.predict_proba(model_df_test[model_df.columns])[:, 1])))111    logger.info('apps with variance > 0.09 + remove apps by pre installed ; devices models with variance > 0.0099')112    model_df = X_train.copy()113    model_df_test = X_test.copy()114    for property in ['carrier_name', 'device_model', 'device_vendor']:115        for p, apps_replace in pre_installed[property].groupby(level=0):116            apps_replace_list = apps_replace.index.get_level_values('level_1').tolist()117            model_df.loc[model_df[property] == p, apps_replace_list] = 0118            model_df_test.loc[model_df_test[property] == p, apps_replace_list] = 0119    filtered_apps = utils.filter_low_var_columns(model_df[apps], 0.09)120    filtered_models = utils.filter_low_var_columns(model_df[device_models], 0.0099)121    features = np.append(filtered_models, filtered_apps)122    model_df = model_df[features]123    model = models.logistic_regression(model_df, y_train, folds)124    logger.info('test average precision: {}'.format(125        average_precision_score(y_test,126                                model.predict_proba(model_df_test[model_df.columns])[:, 1])))127    logger.info('apps with variance > 0.09')128    filtered_apps = utils.filter_low_var_columns(X_train[apps], 0.09)129    features = filtered_apps130    model_df = X_train[features].copy()131    model = models.logistic_regression(model_df, y_train, folds)132    logger.info('test average precision: {}'.format(133        average_precision_score(y_test,134                                model.predict_proba(X_test[model_df.columns])[:, 1])))135    logger.info('apps with variance > 0.09 + remove apps by pre installed')136    model_df = X_train.copy()137    model_df_test = X_test.copy()138    for property in ['carrier_name', 'device_model', 'device_vendor']:139        for p, apps_replace in pre_installed[property].groupby(level=0):140            apps_replace_list = apps_replace.index.get_level_values('level_1').tolist()141            model_df.loc[model_df[property] == p, apps_replace_list] = 0142            model_df_test.loc[model_df_test[property] == p, apps_replace_list] = 0143    filtered_apps = utils.filter_low_var_columns(model_df[apps], 0.09)144    features = filtered_apps145    model_df = model_df[features]146    model = models.logistic_regression(model_df, y_train, folds)147    logger.info('test average precision: {}'.format(148        average_precision_score(y_test,149                                model.predict_proba(model_df_test[model_df.columns])[:, 1])))150def main():151    args = parse_args()152    logger = soluto_log.get_logger(filename='photography_model')153    try:154        logger.info('start')155        X_train, X_test, y_train, y_test = utils.load_ready_train_test(args.data_path)156        models_variations(X_train, X_test, y_train, y_test, args.folds)157    except Exception as e:158        logger.exception('Exception')159    finally:160        logger.info('end')161if __name__ == '__main__':...

play.py

Source:play.py

1from sources import play_agent, start_carla, get_hparams, ConsoleStats, CarlaEnvSettings2import os3from threading import Thread4# Replacement for shared multiprocessing object when playing5class PauseObject:6    value = 07if __name__ == '__main__':8    9    # Load hparams if they are being saved by trainer10    hparams = get_hparams(playing=True)11    print('Saved models:')12    # Check paths from hparams, filter out models whose files exist on disk13    filtered_models = {}14    for model in hparams['models']:15        if os.path.isfile(model):16            filtered_models[int(model.split('.')[0].split('_')[-1])] = model17    filtered_models = list(dict(sorted(filtered_models.items(), key=lambda kv: kv[0])).values())18    for index, model in enumerate(filtered_models):19        print(f'{index+1}. {model}')20    # Ask user for a model to choose21    model = filtered_models[int(input('Choose the model (empty - most recent one): ') or '0') - 1]22    print('Starting...')23    # Kill Carla processes if there are any and start simulator24    start_carla(playing=True)25    # Run Carla settings (weather, NPC control) in a separate thread26    pause_object = PauseObject()27    carla_settings = CarlaEnvSettings(0, [pause_object], car_npcs=hparams['car_npcs'])28    carla_settings_thread = Thread(target=carla_settings.update_settings_in_loop, daemon=True)29    carla_settings_thread.start()30    # Play31    print(f'Starting agent ({model})...')...

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