def pipeline(
df: pd.DataFrame,
config: Config,
train_csv: str = None,
test_csv: str = None,
prediction_csv: str = None) -> (pd.DataFrame, Optional[np.float64]):
if config.is_train():
config['stages'] = {}
for ids, stage in enumerate(config['graph']):
if len(stage) == 0 or stage[0] is None or stage[0] == '':
config["stage"] = '{0}/n{1}'.format(
config["stage"],
'Error value stage "{0}" in pipeline'.format(stage))
raise ValueError(config["stage"])
config["stage"] = stage[0]
config["stage_nb"] = ids
if config.is_train():
config['stages'][config["stage"]] = {}
config['stages'][config["stage"]]['time'] = 0
start_time = time.time()
if stage[0] == 'Start':
continue
# elif stage[0] == 'End':
# break
elif not stage[0] in config['params']['pipeline']:
config["stage"] = '{0}/n{1}'.format(
config["stage"],
'Unknow node "{0}" in pipeline'.format(stage[0]))
raise ValueError(config["stage"])
elif not config['params']['pipeline'][stage[0]]['node'] in _node_map:
config["stage"] = '{0}/n{1}'.format(
config["stage"], 'Unknow node "{0}" in _node_map'.format(
config['params']['pipeline'][stage[0]]['node']))
raise ValueError(config["stage"])
node = _node_map[config['params']['pipeline'][stage[0]]['node']]
if node.name == 'read_df':
if config.is_train():
df = node(train_csv, config)
elif 'args' in config['params']['pipeline'][stage[0]] \
and len(config['params']['pipeline'][stage[0]]['args'])!=0:
node.function(df, config,
**config['params']['pipeline'][stage[0]]['args'])
else:
node(df, config)
stage_time_inc(config, start_time, stage[0])
def feature_selection(df: pd.DataFrame, config: Config):
if config.is_train():
df_size_mb = df.memory_usage(deep=True).sum() / 1024 / 1024
if df_size_mb < 2 * 1024:
return
selected_columns = []
config_sample = copy.deepcopy(config)
for i in range(10):
df_sample = df.sample(min(1000, len(df)),
random_state=i).copy(deep=True)
preprocess_pipeline(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
if len(selected_columns) > 0:
X = X.drop(selected_columns, axis=1)
if len(X.columns) > 0:
selected_columns += select_features(X, y, config["mode"])
else:
break
log("Selected columns: {}".format(selected_columns))
drop_number_columns = [
c for c in df if (c.startswith("number_") or c.startswith("id_"))
and c not in selected_columns
]
if len(drop_number_columns) > 0:
config["drop_number_columns"] = drop_number_columns
config["date_columns"] = {}
for c in [c for c in selected_columns if c.startswith("datetime_")]:
d = c.split("_")
date_col = d[0] + "_" + d[1]
date_part = d[2]
if date_col not in config["date_columns"]:
config["date_columns"][date_col] = []
config["date_columns"][date_col].append(date_part)
drop_datetime_columns = [
c for c in df
if c.startswith("datetime_") and c not in config["date_columns"]
]
if len(drop_datetime_columns) > 0:
config["drop_datetime_columns"] = drop_datetime_columns
if "drop_number_columns" in config:
log("Drop number columns: {}".format(config["drop_number_columns"]))
df.drop(config["drop_number_columns"], axis=1, inplace=True)
if "drop_datetime_columns" in config:
log("Drop datetime columns: {}".format(
config["drop_datetime_columns"]))
df.drop(config["drop_datetime_columns"], axis=1, inplace=True)
def read_df(csv_path: str, config: Config) -> pd.DataFrame:
if "dtype" not in config:
preview_df(csv_path, config)
df = optimize_dataframe(pandas_read_csv(csv_path, config))
if config.is_train():
config["nrows"] = len(df)
return df
def read_df(csv_path: str, config: Config) -> pd.DataFrame:
if "dtype" not in config:
preview_df(csv_path, config)
df = pandas_read_csv(csv_path, config)
if config.is_train():
config["nrows"] = len(df)
config["target_data"] = df['target'].copy()
return df
def read_df(csv_path: str, config: Config) -> pd.DataFrame:
if "dtype" not in config:
preview_df(csv_path, config)
df = pandas_read_csv(csv_path, config)
if config.is_train():
config["nrows_stage_nb"] = 0
config["nrows"] = len(df)
return df
def feature_selection(df: pd.DataFrame, config: Config):
if config.is_train():
df_size_mb = df.memory_usage(deep=True).sum() / 1024 / 1024
if df_size_mb < 2 * 1024:
return
selected_columns = []
for i in range(3):
config_sample = copy.deepcopy(config)
df_sample = df.sample(frac=0.05, random_state=i).copy(deep=True)
df_sample = preprocess_pipeline(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
if len(X.columns) > 0:
selected_columns += select_features(X, y, config["mode"])
else:
break
df_size_mb = df.drop(
list(set(df) - set(selected_columns)), 1,
errors='ignore').memory_usage(deep=True).sum() / 1024 / 1024
if df_size_mb < 2 * 1024:
break
selected_columns = list(set(selected_columns))
log("Selected columns: {}".format(selected_columns))
drop_number_columns = [
c for c in df if (c.startswith("number_") or c.startswith("id_"))
and c not in selected_columns
]
if len(drop_number_columns) > 0:
config["drop_number_columns"] = drop_number_columns
drop_datetime_columns = [
c for c in df
if c.startswith("datetime_") and c not in selected_columns
]
if len(drop_datetime_columns) > 0:
config["drop_datetime_columns"] = drop_datetime_columns
if "drop_number_columns" in config:
log("Drop number columns: {}".format(config["drop_number_columns"]))
df.drop(config["drop_number_columns"], axis=1, inplace=True)
if "drop_datetime_columns" in config:
log("Drop datetime columns: {}".format(
config["drop_datetime_columns"]))
df.drop(config["drop_datetime_columns"], axis=1, inplace=True)
def fillna(df: pd.DataFrame, config: Config, args: dict = {}):
if len(args) != 0:
for k, v in args.items():
if config.is_train():
lst_columns = [c for c in df if c.startswith(k)]
config['stages'][config["stage"]][k] = {
'lst_columns': lst_columns
}
if len(lst_columns) != 0:
if 'agg' in v or 'value' in v:
if config.is_train():
s_fillna_values = calc_columns_metric(
df,
lst_columns,
metric=v['agg'] if 'agg' in v else None,
value=v['value'] if 'value' in v else None)
config['stages'][config["stage"]][k][
'fillna_values'] = deepcopy(s_fillna_values)
if len(config['stages'][config["stage"]][k]['lst_columns']) != 0:
fillna_columns(
df, config['stages'][config["stage"]][k]['fillna_values'])
else:
for c in [c for c in df if c.startswith("number_")]:
df[c].fillna(-1, inplace=True)
for c in [c for c in df if c.startswith("string_")]:
df[c].fillna("", inplace=True)
for c in [c for c in df if c.startswith("datetime_")]:
df[c].fillna(datetime.datetime(1970, 1, 1), inplace=True)
def subsample(df: pd.DataFrame, config: Config, max_size_mb: float=2.0):
if config.is_train():
df_size_mb = df.memory_usage(deep=True).sum() / 1024 / 1024
if df_size_mb > max_size_mb:
mem_per_row = df_size_mb / len(df)
sample_rows = int(max_size_mb / mem_per_row)
log("Size limit exceeded: {:0.2f} Mb. Dataset rows: {}. Subsample to {} rows.".format(df_size_mb, len(df), sample_rows))
_, df_drop = train_test_split(df, train_size=sample_rows, random_state=1)
df.drop(df_drop.index, inplace=True)
config["nrows"] = sample_rows
else:
config["nrows"] = len(df)
def check_columns_exists(df: pd.DataFrame,
config: Config,
key_stage: str,
drop_columns_test: bool = True):
field_target_name = config['params']['field_target_name']
if config.is_train():
if not 'columns_exists' in config['params']['pipeline'][
config["stage"]]:
config['params']['pipeline'][
config["stage"]]['columns_exists'] = {}
if not field_target_name in df.columns:
raise ValueError(
'Column y="{0}" not exists in train dataset'.format(
field_target_name))
config['params']['pipeline'][config["stage"]]['columns_exists'][key_stage] = \
set([x for x in df.columns if x!=field_target_name])
elif 'columns_exists' in config['params']['pipeline'][config["stage"]]:
if key_stage in config['params']['pipeline'][
config["stage"]]['columns_exists']:
set_columns = config['params']['pipeline'][
config["stage"]]['columns_exists'][key_stage] - set(df.columns)
if len(set_columns) != 0:
raise ValueError(
'Columns "{0}" not exists in test dataset on stage {1}'.
format(str(set_columns), key_stage))
set_columns = set(df.columns) - config['params']['pipeline'][
config["stage"]]['columns_exists'][key_stage]
if len(set_columns) != 0:
if drop_columns_test:
df.drop(columns=[x for x in set_columns], inplace=True)
else:
raise ValueError(
'Columns "{0}" not exists in train dataset on stage {1}'
.format(str(set_columns), key_stage))
else:
raise ValueError(
'Preprocess stage "{0}" not exists'.format(key_stage))
def subsample(df: pd.DataFrame, config: Config):
if config.is_train():
# df_size_mb = df.memory_usage(deep=True).sum() / 1024 / 1024
df_size_mb, sample_rows = get_sample_rows(df, config)
if df_size_mb > config['params']['memory']['max_size_mb']:
# mem_per_row = df_size_mb / len(df)
# sample_rows = int(config['params']['memory']['max_size_mb'] / mem_per_row)
log("Size limit exceeded: {:0.2f} Mb. Dataset rows: {}. Subsample to {} rows." \
.format(df_size_mb, len(df), sample_rows), config.verbose)
_, df_drop = train_test_split(df,
train_size=sample_rows,
random_state=1)
df.drop(df_drop.index, inplace=True)
config["nrows"] = sample_rows
elif config["nrows_stage_nb"] == 0:
config["nrows"] = max(sample_rows, len(df))
else:
config["nrows"] = min(sample_rows, config["nrows"])
config["nrows_stage_nb"] = config["stage_nb"]
def scale(df: pd.DataFrame, config: Config):
warnings.filterwarnings(action='ignore', category=DataConversionWarning)
scale_columns = [
c for c in df if c.startswith("number_") and df[c].dtype != np.int8
and c not in config["categorical_columns"]
]
if len(scale_columns) > 0:
if config.is_train():
config['stages'][config["stage"]]['scale_columns'] = deepcopy(
scale_columns)
config['stages'][config["stage"]]['model'] = StandardScaler(
copy=False)
config['stages'][config["stage"]]['scale_columns'] = deepcopy(
scale_columns)
config['stages'][config["stage"]]['model'].fit(
df[scale_columns].astype(np.float32))
df[config['stages'][config["stage"]]['scale_columns']] = \
config['stages'][config["stage"]]['model'].transform( \
df[config['stages'][config["stage"]]['scale_columns']].astype(np.float32) ).astype(np.float32)
def non_negative_target_detect(df: pd.DataFrame, config: Config):
if config.is_train():
config["non_negative_target"] = df["target"].lt(0).sum() == 0
def time_series_detect(df: pd.DataFrame, config: Config):
sample_size = 10000
model_params = {
"objective": "regression" if config["mode"] == "regression" else "binary",
"metric": "rmse" if config["mode"] == "regression" else "auc",
"learning_rate": 0.01,
"verbosity": -1,
"seed": 1,
"max_depth": -1,
}
if config.is_train():
datetime_columns = [c for c in df if c.startswith("datetime_")]
id_columns = [c for c in df if c.startswith("id_")]
sort_columns = []
for dc in datetime_columns:
sort_columns.append([dc])
for ic in id_columns:
sort_columns.append([ic, dc])
else:
for ic in id_columns:
sort_columns.append([ic])
scores = []
config.limit_time_fraction(0.1)
for sc in sort_columns:
if config.is_time_fraction_limit():
break
Log.silent(True)
df.sort_values(sc, inplace=True)
config_sample = copy.deepcopy(config)
df_sample = df.iloc[-sample_size:].copy() if len(df) > sample_size else df.copy()
df_sample = df_sample[[c for c in df_sample if c.startswith("number_") or c == "target" or c in sc]]
shift_columns(df_sample, group= sc[0] if len(sc) > 1 else None)
transform(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
X_train, X_test, y_train, y_test = ts_split(X, y, test_size=0.5)
model_sorted = lgb.train(model_params, lgb.Dataset(X_train, label=y_train), 3000, lgb.Dataset(X_test, label=y_test),
early_stopping_rounds=100, verbose_eval=False)
score_sorted = model_sorted.best_score["valid_0"][model_params["metric"]]
sampled_columns = [c for c in X if "_shift" not in c]
model_sampled = lgb.train(model_params, lgb.Dataset(X_train[sampled_columns], label=y_train), 3000, lgb.Dataset(X_test[sampled_columns], label=y_test),
early_stopping_rounds=100, verbose_eval=False)
score_sampled = model_sampled.best_score["valid_0"][model_params["metric"]]
if config.is_classification():
score_sorted = -score_sorted
score_sampled = -score_sampled
Log.silent(False)
Log.print("Sort: {}. Score sorted: {:0.4f}. Score sampled: {:0.4f}".format(sc, score_sorted, score_sampled))
score_ratio = score_sampled / score_sorted if config.is_regression() else abs(score_sorted / score_sampled)
if score_ratio >= 1.03:
Log.print(score_ratio)
scores.append((score_sorted, sc))
if len(scores) > 0:
scores = sorted(scores, key=lambda x: x[0])
Log.print("Scores: {}".format(scores))
config["sort_values"] = scores[0][1]
df.sort_values(config["sort_values"], inplace=True)
config_sample = copy.deepcopy(config)
df_sample = df.iloc[-sample_size:].copy() if len(df) > sample_size else df.copy()
shift_columns(df_sample, group=config["sort_values"][0] if len(config["sort_values"]) > 1 else None)
transform(df_sample, config_sample)
y = df_sample["target"]
X = df_sample.drop("target", axis=1)
model = lgb.train(model_params, lgb.Dataset(X, label=y), 1000)
fi = pd.Series(model.feature_importance(importance_type="gain"), index=X.columns)
fi = fi[fi > 0].sort_values()
selected_columns = fi[fi >= fi.quantile(0.75)].index.tolist()
selected_shift_columns = [c.replace("_shift", "") for c in selected_columns if "_shift" in c]
if len(selected_shift_columns) > 0:
Log.print("Shift columns: {}".format(selected_shift_columns))
config["shift_columns"] = selected_shift_columns
if "shift_columns" in config:
shift_columns(df, group=config["sort_values"][0] if len(config["sort_values"]) > 1 else None, number_columns=config["shift_columns"])
def model(df: pd.DataFrame, config: Config,
models: list): # -> (Optional[pd.DataFrame], Optional[pd.Series])
if config.is_train():
X, y = split_X_y(df, config)
if config.is_train():
# train(X, y, config)
if 'args' in config['params']['pipeline'][config["stage"]] and \
'models' in config['params']['pipeline'][config["stage"]]['args']:
if config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'h2o':
train_h2o(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lightgbm':
train_lightgbm(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'vw':
train_vw(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lm':
train_lm(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'rf':
train_rand_forest(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lcv':
train_linear_cv(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'bayes':
train_bayesian(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'arima':
train_arima(X, y, config)
else:
raise ValueError('Train: Unknow model name "{0}"'.format(
config['params']['pipeline'][
config["stage"]]['args']['models'][0]))
else:
config['params']['pipeline'][config["stage"]]['models'] = []
if config["nrows"] < 1000:
train_h2o(X, y, config)
config['params']['pipeline'][
config["stage"]]['models'].append('h2o')
else:
train_lightgbm(X, y, config)
config['params']['pipeline'][
config["stage"]]['models'].append('lightgbm')
if config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'h2o':
df[config["stage"]] = predict_h2o(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lightgbm':
df[config["stage"]] = predict_lightgbm(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'vw':
df[config["stage"]] = predict_vw(X, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lm':
df[config["stage"]] = predict_lm(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'rf':
df[config["stage"]] = predict_rand_forest(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lcv':
df[config["stage"]] = predict_linear_cv(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'bayes':
df[config["stage"]] = predict_bayesian(X, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'arima':
df[config["stage"]] = predict_arima(X, config)
else:
if config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'h2o':
df[config["stage"]] = predict_h2o(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lightgbm':
df[config["stage"]] = predict_lightgbm(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'vw':
df[config["stage"]] = predict_vw(df, y, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lm':
df[config["stage"]] = predict_lm(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'rf':
df[config["stage"]] = predict_rand_forest(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'lcv':
df[config["stage"]] = predict_linear_cv(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'bayes':
df[config["stage"]] = predict_bayesian(df, config)
elif config['params']['pipeline'][
config["stage"]]['args']['models'][0] == 'arima':
df[config["stage"]] = predict_arima(df, config)
if config["non_negative_target"]:
df[config["stage"]] = df[config["stage"]].apply(lambda p: max(0, p))
def working_days_zero_detect(df: pd.DataFrame, config: Config) -> bool:
if config.is_train() & ("is_working_datetime_0" in df):
if (df.loc[df["is_working_datetime_0"] == 0, "target"] == 0).all():
config["working_days_zero"] = True
log("Working days zero detect", config.verbose)
