def __init__(
self,
propensity_model_filename="../data/06_models/propensity_model.pickle",
uplift_models_filename="../data/06_models/uplift_models_dict.pickle",
df_filename="../data/07_model_output/df.csv",
treated_sim_eval_filename="../data/08_reporting/treated__sim_eval_df.csv",
untreated_sim_eval_filename="../data/08_reporting/untreated__sim_eval_df.csv",
estimated_effect_filename="../data/08_reporting/estimated_effect_df.csv",
args_raw=MemoryDataSet({}).load()):
self.propensity_model = PickleLocalDataSet(
filepath=propensity_model_filename, version=None)
self.uplift_models_dict = PickleLocalDataSet(
filepath=uplift_models_filename, version=None)
self.df_03 = CSVLocalDataSet(
filepath=df_filename,
load_args=dict(index_col=["partition", "index"],
float_precision="high"),
save_args=dict(index=True, float_format="%.16e"),
version=None,
)
self.treated__sim_eval_df = CSVLocalDataSet(
filepath=treated_sim_eval_filename, version=None)
self.untreated__sim_eval_df = CSVLocalDataSet(
filepath=untreated_sim_eval_filename, version=None)
self.estimated_effect_df = CSVLocalDataSet(
filepath=estimated_effect_filename, version=None)
self.args_raw = args_raw
def load_data(dummy, files: List, parameters: Dict):
all_wells_steam_input = []
all_wells_emulsion_input = []
all_wells_labels = []
for file in files:
data_set_steam_input = CSVLocalDataSet(
filepath=parameters["path_primary"] + "/steam_inputs_" + file)
steam_input_data = data_set_steam_input.load()
all_wells_steam_input.append(steam_input_data.values)
data_set_emulsion_input = CSVLocalDataSet(
filepath=parameters["path_primary"] + "/emulsion_inputs_" + file)
emulsion_input_data = data_set_emulsion_input.load()
all_wells_emulsion_input.append(emulsion_input_data.values)
data_set_labels = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/labels_" + file)
labels = data_set_labels.load()
all_wells_labels.append(labels.values)
steam_input_names = steam_input_data.columns
emulsion_input_names = emulsion_input_data.columns
all_wells_steam_input = np.array(all_wells_steam_input)
all_wells_emulsion_input = np.array(all_wells_emulsion_input)
return [
all_wells_steam_input, all_wells_emulsion_input, all_wells_labels,
steam_input_names, emulsion_input_names
]
def test_sequential_save_and_load(self, dummy_dataframe, filepath):
"""Tests if the correct load version is logged when two datasets are saved
sequentially."""
dataset1 = CSVLocalDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, "2000-01-01"),
)
dataset2 = CSVLocalDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, "2001-01-01"),
)
dataset1.save(dummy_dataframe)
last_save_version1 = dataset1.get_last_save_version()
dataset2.save(dummy_dataframe)
last_save_version2 = dataset2.get_last_save_version()
dataset2.load()
last_load_version = dataset2.get_last_load_version()
assert last_save_version2 == last_load_version
assert last_save_version1 != last_save_version2
def test_sequential_load_from_disk(self, dummy_dataframe, filepath,
versioned_csv_data_set):
"""Tests if the correct load version is logged when two versions are saved in
disk."""
save_version_1 = "2019-01-01T23.00.00.000Z"
save_version_2 = "2019-01-01T23.59.59.999Z"
CSVLocalDataSet(
filepath=filepath,
save_args={
"sep": ","
},
version=Version(None, save_version_1),
).save(dummy_dataframe)
CSVLocalDataSet(
filepath=filepath,
save_args={
"sep": ","
},
version=Version(None, save_version_2),
).save(dummy_dataframe)
versioned_csv_data_set.load()
last_load_version = versioned_csv_data_set.get_last_load_version()
assert last_load_version == save_version_2
def discrete_wavelet_transform(Raw_Data_preprocessed: pd.DataFrame,
parameters: Dict, files: List):
for well_data, file in zip(Raw_Data_preprocessed, files):
list_input_DWT_Aprox_coeff = []
input_data, labels = split(well_data)
input_columns = list(input_data.columns)
for data_idx in input_columns:
signal = well_data[data_idx].values
thresh = parameters["thresh"] * np.nanmax(signal)
coeff = pywt.wavedec(signal,
wavelet=parameters["wavelet"],
mode=parameters["mode1"],
level=parameters["level"])
coeff[1:] = (pywt.threshold(i,
value=thresh,
mode=str(parameters["mode2"]))
for i in coeff[1:])
list_input_DWT_Aprox_coeff.append(coeff[0])
list_input_DWT_Aprox_coeff = pd.DataFrame(
np.transpose(list_input_DWT_Aprox_coeff), columns=input_columns)
data_set_input = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/input_DWT_coeffs_" + file)
data_set_input.save(list_input_DWT_Aprox_coeff)
data_set_labels = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/labels_" + file)
data_set_labels.save(labels)
dummy = labels
return dummy
def test_run_load_versions(self, tmp_path, dummy_context, dummy_dataframe, mocker):
class DummyContext(KedroContext):
project_name = "bob"
project_version = __version__
def _get_pipelines(self) -> Dict[str, Pipeline]:
return {"__default__": Pipeline([node(identity, "cars", "boats")])}
mocker.patch("logging.config.dictConfig")
dummy_context = DummyContext(str(tmp_path))
filepath = str(dummy_context.project_path / "cars.csv")
old_save_version = generate_timestamp()
old_df = pd.DataFrame({"col1": [0, 0], "col2": [0, 0], "col3": [0, 0]})
old_csv_data_set = CSVLocalDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, old_save_version),
)
old_csv_data_set.save(old_df)
new_save_version = generate_timestamp()
new_csv_data_set = CSVLocalDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(None, new_save_version),
)
new_csv_data_set.save(dummy_dataframe)
load_versions = {"cars": old_save_version}
dummy_context.run(load_versions=load_versions)
assert not dummy_context.catalog.load("boats").equals(dummy_dataframe)
assert dummy_context.catalog.load("boats").equals(old_df)
def standardisation(dummy, properties: np.ndarray, files: List,
parameters: Dict):
from sklearn.preprocessing import StandardScaler
all_wells_input = []
all_wells_labels = []
for file in files:
data_set_input = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/input_DWT_coeffs_" + file)
DWT_Aprox_coeff_input = data_set_input.load()
all_wells_input.append(DWT_Aprox_coeff_input.values)
data_set_labels = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/labels_" + file)
labels = data_set_labels.load()
all_wells_labels.append(labels.values)
all_wells_input = np.array(all_wells_input)
# Standardize dynamic data coeffs
scaler_coeffs = StandardScaler()
scaler_coeffs.fit(all_wells_input[0]) # fit based on first well record
all_wells_standardized_input = []
all_wells_standardized_input_flattened = []
for well_coeffs in all_wells_input:
std_coeffs = scaler_coeffs.transform(well_coeffs)
all_wells_standardized_input.append(std_coeffs)
transposed_std_coeffs = np.transpose(std_coeffs)
flattened_std_coeffs = transposed_std_coeffs.flatten()
all_wells_standardized_input_flattened.append(flattened_std_coeffs)
all_wells_standardized_input = np.array(all_wells_standardized_input)
all_wells_standardized_input_flattened = np.array(
all_wells_standardized_input_flattened)
data_set_scaler_coeffs = PickleLocalDataSet(
filepath=parameters["path_models"] + "/scaler_coeffs.pickle")
data_set_scaler_coeffs.save(scaler_coeffs)
input_columns = list(DWT_Aprox_coeff_input.columns)
for std_coeffs, file in zip(all_wells_standardized_input, files):
std_coeffs = pd.DataFrame(std_coeffs, columns=input_columns)
data_set = CSVLocalDataSet(filepath=parameters["path_features"] +
"/std_DWT_input_coeffs_" + file)
data_set.save(std_coeffs)
# Standardize static data
scaler_static = StandardScaler()
all_wells_standardized_properties = scaler_static.fit_transform(properties)
data_set_scaler_static = PickleLocalDataSet(
filepath=parameters["path_models"] + "/scaler_static.pickle")
data_set_scaler_static.save(scaler_static)
return [
all_wells_standardized_input_flattened,
all_wells_standardized_properties, all_wells_labels
]
def save_well_data(Raw_Data_preprocessed, parameters, files):
for well_data, file in zip(Raw_Data_preprocessed, files):
steam_input_data, emulsion_input_data, labels = split(well_data)
data_set_steam_input = CSVLocalDataSet(
filepath=parameters["path_primary"] + "/steam_inputs_" + file)
data_set_steam_input.save(steam_input_data)
data_set_emulsion_input = CSVLocalDataSet(
filepath=parameters["path_primary"] + "/emulsion_inputs_" + file)
data_set_emulsion_input.save(emulsion_input_data)
data_set_labels = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/labels_" + file)
data_set_labels.save(labels)
dummy = labels
return dummy
def test_version_str_repr(self, load_version, save_version):
"""Test that version is in string representation of the class instance
when applicable."""
filepath = "test.csv"
ds = CSVLocalDataSet(filepath=filepath)
ds_versioned = CSVLocalDataSet(filepath=filepath,
version=Version(load_version,
save_version))
assert filepath in str(ds)
assert "version" not in str(ds)
assert filepath in str(ds_versioned)
ver_str = "version=Version(load={}, save='{}')".format(
load_version, save_version)
assert ver_str in str(ds_versioned)
def test_datasets_on_add(self, data_catalog_from_config):
"""Check datasets are updated correctly after adding"""
data_catalog_from_config.add("new_dataset", CSVLocalDataSet("some_path"))
assert isinstance(
data_catalog_from_config.datasets.new_dataset, CSVLocalDataSet
)
assert isinstance(data_catalog_from_config.datasets.boats, CSVLocalDataSet)
def test_save_options_csv(self, tmp_path, sample_spark_df):
# To cross check the correct Spark save operation we save to
# a single spark partition with csv format and retrieve it with Kedro
# CSVLocalDataSet
temp_dir = Path(str(tmp_path / "test_data"))
spark_data_set = SparkDataSet(
filepath=str(temp_dir),
file_format="csv",
save_args={
"sep": "|",
"header": True
},
)
spark_df = sample_spark_df.coalesce(1)
spark_data_set.save(spark_df)
single_csv_file = [
f for f in temp_dir.iterdir() if f.is_file() and f.suffix == ".csv"
][0]
csv_local_data_set = CSVLocalDataSet(filepath=str(single_csv_file),
load_args={"sep": "|"})
pandas_df = csv_local_data_set.load()
assert pandas_df[pandas_df["name"] == "Alex"]["age"][0] == 31
def test_save_options_csv():
# To cross check the correct Spark save operation we save to
# a single spark partition with csv format and retrieve it with Kedro
# CSVLocalDataSet
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = join(temp_dir, "test_data")
spark_data_set = SparkDataSet(
filepath=temp_path,
file_format="csv",
save_args={
"sep": "|",
"header": True
},
)
spark_df = _get_sample_spark_data_frame().coalesce(1)
spark_data_set.save(spark_df)
single_csv_file = [
join(temp_path, f) for f in listdir(temp_path) if f.endswith("csv")
][0]
csv_local_data_set = CSVLocalDataSet(filepath=single_csv_file,
load_args={"sep": "|"})
pandas_df = csv_local_data_set.load()
assert pandas_df[pandas_df["name"] == "Alex"]["age"][0] == 31
def create_master_table(Raw_Data_dated: pd.DataFrame,
parameters: Dict) -> pd.DataFrame:
master_table = pd.concat(Raw_Data_dated, axis=1, sort=False)
data_set = CSVLocalDataSet(filepath=parameters["path_primary"] +
"/master_table.csv")
data_set.save(master_table)
return master_table
def dynamic_time_warping(Raw_Data_preprocessed, parameters):
reference_well = CSVLocalDataSet(filepath=parameters["path_raw"] +
"/B03-1P.csv")
well_ref = reference_well.load()
data = well_ref['Oil [bbl/d]'] / 6.28981
well_ref.insert(4, 'Oil [m3/d]', data)
well_ref_oil_data = well_ref['Oil [m3/d]'].values
Raw_Data_preprocessed_ = []
distance_array = []
for well_data in Raw_Data_preprocessed:
well_oil_data = well_data['Oil [m3/d]'].values
distance, path = fastdtw(well_ref_oil_data,
well_oil_data,
dist=euclidean)
distance_array.append(distance)
path = np.array(path)
index_well = path[..., 1]
index_ref_well = path[..., 0]
well = well_data.iloc[index_well]
well.insert(0, 'index_ref', index_ref_well)
well = well.groupby('index_ref').mean()
# well = well.reset_index(drop=True)
Raw_Data_preprocessed_.append(well)
distance_array = np.array(distance_array)
return [distance_array, Raw_Data_preprocessed_]
def prepare_csv_data_with_tabs(context):
context.read_csv_path = create_sample_csv()
context.write_csv_path = create_temp_csv()
context.csv_data_set = CSVLocalDataSet(
filepath=context.read_csv_path,
load_args={"sep": "\t"},
save_args={"index": False, "sep": "\t"},
)
def test_load_options_csv(self, tmp_path, sample_pandas_df):
filepath = str(tmp_path / "data")
local_csv_data_set = CSVLocalDataSet(filepath=filepath)
local_csv_data_set.save(sample_pandas_df)
spark_data_set = SparkDataSet(filepath=filepath,
file_format="csv",
load_args={"header": True})
spark_df = spark_data_set.load()
assert spark_df.filter(col("Name") == "Alex").count() == 1
def test_load_options_csv(tmpdir):
temp_path = str(tmpdir.join("data"))
pandas_df = _get_sample_pandas_data_frame()
local_csv_data_set = CSVLocalDataSet(filepath=temp_path)
local_csv_data_set.save(pandas_df)
spark_data_set = SparkDataSet(filepath=temp_path,
file_format="csv",
load_args={"header": True})
spark_df = spark_data_set.load()
assert spark_df.filter(col("Name") == "Alex").count() == 1
def save_well_validation_data(dummy11, Raw_Data_preprocessed_val, parameters,
files_val):
# def save_well_validation_data(dummy12, Raw_Data_preprocessed_val_, parameters, files_val):
all_wells_dates_input = []
all_wells_steam_input_val = []
all_wells_emulsion_input_val = []
all_wells_labels_val = []
for well_data, file in zip(Raw_Data_preprocessed_val, files_val):
# for well_data, file in zip(Raw_Data_preprocessed_val_, files_val):
steam_input_data, emulsion_input_data, labels = split(well_data)
data_set_steam_input = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/vali_steam_inputs_" + file)
data_set_steam_input.save(steam_input_data)
all_wells_dates_input.append(steam_input_data['Date'].values)
steam_input_data = steam_input_data.drop(columns='Date')
all_wells_steam_input_val.append(steam_input_data.values)
data_set_emulsion_input = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/vali_emulsion_inputs_" + file)
data_set_emulsion_input.save(emulsion_input_data)
all_wells_emulsion_input_val.append(emulsion_input_data.values)
data_set_labels = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/validation_labels_" + file)
data_set_labels.save(labels)
all_wells_labels_val.append(labels.values)
steam_input_column = steam_input_data.columns
emulsion_input_column = emulsion_input_data.columns
labels_column = list(labels.columns)
all_wells_dates_input = np.array(all_wells_dates_input)
all_wells_steam_input_val = np.array(all_wells_steam_input_val)
all_wells_emulsion_input_val = np.array(all_wells_emulsion_input_val)
dummy13 = files_val
return [
dummy13, steam_input_column, emulsion_input_column, labels_column,
all_wells_dates_input, all_wells_steam_input_val,
all_wells_emulsion_input_val, all_wells_labels_val
]
def multi_catalog():
csv = CSVLocalDataSet(filepath="abc.csv")
parq = ParquetLocalDataSet(filepath="xyz.parq")
return DataCatalog({"abc": csv, "xyz": parq})
def multi_catalog(mocker):
csv = CSVLocalDataSet(filepath="abc.csv")
parq = ParquetLocalDataSet(filepath="xyz.parq")
journal = mocker.Mock()
return DataCatalog({"abc": csv, "xyz": parq}, journal=journal)
def load_well_validation_data(dummy2, timesteps, parameters: Dict):
import glob, os
os.chdir(parameters["path_model_input"])
files_val = []
for file in glob.glob("*.csv"):
files_val.append(file)
filenames_val = []
wells_data = []
for file in files_val:
filename, extension = file.split('.')
filenames_val.append(filename)
for file, filename in zip(files_val, filenames_val):
io = DataCatalog({
filename:
CSVLocalDataSet(filepath=parameters["path_model_input"] + "/" +
file),
})
well_data = io.load(filename)
wells_data.append(well_data)
Raw_Data_preprocessed_val = []
wells_life = []
wells_data_ = []
for well in wells_data:
# well = well[['Date', 'Injector Bottom Hole Pressure',
# 'Producer Bottom Hole Pressure', 'ESP Speed',
# 'Steam Flow Rate - Outer',
# 'Emulsion Flow Rate']]
well = well[[
'Date', 'Speed [Hz]', 'Current [A]', 'IBHP', 'PBHP',
'Co-Injection [E3m3/d]', 'Oil [bbl/d]', 'Steam [m3/d]',
'Emulsion [m3/d]'
]]
for i in range(1, len(well.columns)):
well[well.columns[i]] = pd.to_numeric(well[well.columns[i]],
errors='coerce')
well['Prod_Date'] = pd.to_datetime(well['Date'])
well = well.set_index('Prod_Date')
# well = well.dropna(axis=0) # may change
# well = well.resample('7D').mean() # weekly data
# well = well.resample('30D').mean() # monthly data
# well = well.rolling(30, min_periods=1).mean()
# well = well.rolling(30, min_periods=1).mean()
data = well['Oil [bbl/d]'] / 6.28981
well.insert(4, 'Oil [m3/d]', data)
time_data = np.arange(len(well))
well.insert(0, 'Timestep', time_data)
wells_life.append(len(well))
wells_data_.append(well)
min_well_length = np.min(np.array(wells_life))
if min_well_length < timesteps:
timesteps_validation = min_well_length
else:
timesteps_validation = timesteps
for well, file, filename in zip(wells_data_, files_val, filenames_val):
well = well.iloc[:timesteps_validation] # daily data
# well = well.fillna(0)
# well = well.fillna(well.rolling(30, min_periods=1).median())
# well = well.fillna(well.median())
Raw_Data_preprocessed_val.append(well)
stats_validation = CSVLocalDataSet(filepath=parameters["path_val_stats"] +
"/static_P50_data_validation.csv")
stats_val = stats_validation.load()
stats_val_ROIP = stats_val.loc[:, 'ROIP']
stats_val = stats_val.loc[:,
'Effective_Length':'BottomWater_Oil_Saturation']
# # using only rich geoostats and no bottom water properties
# stats_val = stats_val.loc[:, 'Effective_Length':'Rich_Oil_Saturation']
# # Using "Effective_Rich_Pay_Thickness" to account for standoff and rich thickness
# data = stats_val['Rich_Pay_Thickness'] - stats_val['Stand_Off']
# stats_val.insert(3, 'Effective_Rich_Pay_Thickness', data)
# stats_val = stats_val.drop(columns = ['Rich_Pay_Thickness', 'Stand_Off'])
property_names_val = list(stats_val.columns)
properties_val = list(stats_val.values)
# properties_val = stats.loc[:, ['Effective_Length', 'Spacing', 'Effective_Rich_Pay_Thickness', 'Non_Rich_Pay_Thickness',
# 'Rich_Vertical_Permeability','Non_Rich_Vertical_Permeability', 'Rich_Porosity',
# 'Non_Rich_Porosity', 'Rich_Oil_Saturation', 'Non_Rich_Oil_Saturation']].values
properties_val = np.array(properties_val)
dummy11 = files_val
return [
dummy11, timesteps_validation, Raw_Data_preprocessed_val, files_val,
filenames_val, properties_val, stats_val_ROIP, property_names_val
]
def versioned_csv_data_set(filepath, load_version, save_version):
return CSVLocalDataSet(
filepath=filepath,
save_args={"sep": ","},
version=Version(load_version, save_version),
)
def test_fails_with_remote_path(self, path):
with pytest.raises(ValueError) as assertion:
CSVLocalDataSet(filepath=path, save_args={"sep": ","})
assert "seems to be a remote file" in assertion.value.args[0]
def csv_data_set(filepath, request):
return CSVLocalDataSet(filepath=filepath, save_args=request.param)
def preprocess_raw_data(parameters: Dict):
import glob, os
os.chdir(parameters["path_raw"])
files = []
for file in glob.glob("*.csv"):
files.append(file)
filenames = []
wells_data = []
for file in files:
filename, extension = file.split('.')
filenames.append(filename)
for file, filename in zip(files, filenames):
io = DataCatalog({
filename:
CSVLocalDataSet(filepath=parameters["path_raw"] + "/" + file),
})
well_data = io.load(filename)
wells_data.append(well_data)
Raw_Data_preprocessed = []
Raw_Data_dated = []
for well, file, filename in zip(wells_data, files, filenames):
well = well[[
'Date', 'Injector Bottom Hole Pressure', 'Steam Flow Rate - Outer',
'Bottom Hole Heel Temperature', 'Emulsion Pressure',
'Producer Bottom Hole Pressure', 'ESP Speed', 'Emulsion Flow Rate'
]]
for i in range(1, len(well.columns)):
well[well.columns[i]] = pd.to_numeric(well[well.columns[i]],
errors='coerce')
well = well.iloc[:1399]
well = well.fillna(well.rolling(30, min_periods=1).median())
well = well.fillna(well.median())
well_dated = well.copy()
well_dated["Well"] = filename # create a column for well name
data_set = CSVLocalDataSet(filepath=parameters["path_intermediate"] +
"/pre_processed_data_" + file)
data_set.save(well_dated)
Raw_Data_dated.append(well_dated)
well['Date'] = pd.to_datetime(well['Date'])
well = well.set_index('Date')
Raw_Data_preprocessed.append(well)
os.chdir(parameters["path_raw_static"])
static_files = []
for static_file in glob.glob("*.csv"):
static_files.append(static_file)
static_filenames = []
statics_data = []
for static_file in static_files:
static_filename, others = static_file.split('_')
static_filenames.append(static_filename)
for static_file, static_filename in zip(static_files, static_filenames):
io = DataCatalog({
static_filename:
CSVLocalDataSet(filepath=parameters["path_raw_static"] + "/" +
static_file),
})
static_data = io.load(static_filename)
statics_data.append(static_data)
statics_data_new = []
well_name_list = []
for pad_static in statics_data:
well_name = pad_static['WELLPAIR_NAME'].values
well_name_list.append(well_name)
pad_static = pad_static.set_index('WELLPAIR_NAME')
pad_static = pad_static.drop(columns=['PLAN_NAME', 'HIGH_PRESSURE'])
statics_data_new.append(pad_static)
properties = []
probabilities = []
asset_names = []
for pad_static, names in zip(statics_data_new, well_name_list):
for well in names:
prob = pad_static.loc[well, 'Forecast_Prob']
probabilities.append(prob)
pad_code = pad_static.loc[well, 'PAD_CODE']
asset_name, pad = pad_code.split('_')
asset_names.append(asset_name)
property_ = pad_static.loc[
well, 'SAGD_PRESSURE':'BOTTOM_WATER_THICKNESS'].values
properties.append(property_)
properties = np.array(properties)
return [
Raw_Data_preprocessed, Raw_Data_dated, files, filenames, probabilities,
asset_names, properties
]
def data_set(filepath):
return CSVLocalDataSet(filepath=filepath, save_args={"index": False})
def preprocess_raw_data(parameters: Dict):
import glob, os
# os.chdir(parameters["path_raw"])
os.chdir(parameters["path_raw_matlab"])
files = []
for file in glob.glob("*.csv"):
files.append(file)
filenames = []
wells_data = []
for file in files:
filename, extension = file.split('.')
filenames.append(filename)
for file, filename in zip(files, filenames):
io = DataCatalog({
# filename: CSVLocalDataSet(filepath=parameters["path_raw"]+"/"+file),
filename:
CSVLocalDataSet(filepath=parameters["path_raw_matlab"] + "/" +
file),
})
well_data = io.load(filename)
wells_data.append(well_data)
Raw_Data_preprocessed = []
Raw_Data_dated = []
wells_life = []
wells_data_ = []
for well in wells_data:
# well = well[['Date', 'Injector Bottom Hole Pressure',
# 'Producer Bottom Hole Pressure', 'ESP Speed',
# 'Steam Flow Rate - Outer',
# 'Emulsion Flow Rate']]
well = well[[
'Date', 'Speed [Hz]', 'Current [A]', 'IBHP', 'PBHP',
'Co-Injection [E3m3/d]', 'Oil [bbl/d]', 'Steam [m3/d]',
'Emulsion [m3/d]'
]]
for i in range(1, len(well.columns)):
well[well.columns[i]] = pd.to_numeric(well[well.columns[i]],
errors='coerce')
well['Prod_Date'] = pd.to_datetime(well['Date'])
well = well.set_index('Prod_Date')
# well = well.resample('7D').mean() # weekly data
# well = well.resample('30D').mean() # monthly data
# well = well.rolling(30, min_periods=1).mean()
data = well['Oil [bbl/d]'] / 6.28981
well.insert(4, 'Oil [m3/d]', data)
time_data = np.arange(len(well))
well.insert(0, 'Timestep', time_data)
wells_life.append(len(well))
wells_data_.append(well)
min_well_length = np.min(np.array(wells_life))
print((min_well_length, np.argmin(np.array(wells_life))))
timesteps = min_well_length # 1008
# timesteps = 371
for well, file, filename in zip(wells_data_, files, filenames):
well = well.iloc[:timesteps] # daily, weekly, monthly data
# well = well.fillna(0)
# well = well.fillna(well.rolling(30, min_periods=1).median())
# well = well.fillna(well.median())
well["Well"] = filename # create a column for well name
well = well.reset_index(drop=True) # remove date index
data_set = CSVLocalDataSet(filepath=parameters["path_intermediate"] +
"/pre_processed_data_" + file)
data_set.save(well)
Raw_Data_dated.append(well)
well = well.drop(columns=['Date', 'Well'])
Raw_Data_preprocessed.append(well)
# stats_training = CSVLocalDataSet(filepath=parameters["path_raw_static"]+"/static_P50_data_training.csv")
stats_training = CSVLocalDataSet(
filepath=parameters["path_raw_static_matlab"] +
"/static_P50_data_training_152_wells.csv")
stats = stats_training.load()
stats_ROIP = stats.loc[:, 'ROIP']
stats = stats.loc[:, 'Effective_Length':'BottomWater_Oil_Saturation']
# # using only rich geoostats and no bottom water properties
# stats = stats.loc[:, 'Effective_Length':'Rich_Oil_Saturation']
# # Using "Effective_Rich_Pay_Thickness" to account for standoff and rich thickness
# data = stats['Rich_Pay_Thickness'] - stats['Stand_Off']
# stats.insert(3, 'Effective_Rich_Pay_Thickness', data)
# stats = stats.drop(columns = ['Rich_Pay_Thickness', 'Stand_Off'])
property_names = list(stats.columns)
properties = list(stats.values)
properties = np.array(properties)
return [
timesteps, Raw_Data_preprocessed, Raw_Data_dated, files, filenames,
properties, stats_ROIP, property_names
]
def save_predicted_data(dummy15, well_count, all_wells_dates_input,
wells_steam_rate_actual, wells_steam_rate_predicted,
wells_emulsion_rate_actual,
wells_emulsion_rate_predicted, steam_input_column,
all_wells_steam_input_val, emulsion_input_column,
all_wells_emulsion_input_val, labels_column,
parameters, files_val,
scheme): # to input wells_RF_array for RF case
print("Prediction Performance:\n")
print("Steam Flow Rate:")
for well, file in zip(well_count, files_val):
dates = all_wells_dates_input[well].T
steam_input = all_wells_steam_input_val[well].T
steam_rate_actual = wells_steam_rate_actual[:, well]
steam_rate_predicted = wells_steam_rate_predicted[:, well]
emulsion_rate_predicted = wells_emulsion_rate_predicted[:, well]
if scheme == 1:
steam_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, steam_input, steam_rate_actual,
steam_rate_predicted)).T,
columns=["Date"] + list(steam_input_column) + [
labels_column[0] + " actual",
labels_column[0] + " predicted"
])
data_set_steam_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_scheme1"] +
"/steam_rate_" + file)
elif scheme == 2:
steam_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, steam_input, emulsion_rate_predicted,
steam_rate_actual, steam_rate_predicted)).T,
columns=["Date"] + list(steam_input_column) + [
labels_column[1] + " predicted", labels_column[0] +
" actual", labels_column[0] + " predicted"
])
data_set_steam_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_scheme2"] +
"/steam_rate_" + file)
elif scheme == 3:
RF_input = wells_RF_array[well].T
steam_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, RF_input, steam_input, steam_rate_actual,
steam_rate_predicted)).T,
columns=["Date", "RF"] + list(steam_input_column) + [
labels_column[0] + " actual",
labels_column[0] + " predicted"
])
data_set_steam_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_RF_scheme1"] +
"/steam_rate_" + file)
else:
RF_input = wells_RF_array[well].T
steam_rate_actual_predicted = pd.DataFrame(
np.vstack(
(dates, RF_input, steam_input, emulsion_rate_predicted,
steam_rate_actual, steam_rate_predicted)).T,
columns=["Date", "RF"] + list(steam_input_column) + [
labels_column[1] + " predicted", labels_column[0] +
" actual", labels_column[0] + " predicted"
])
data_set_steam_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_RF_scheme2"] +
"/steam_rate_" + file)
data_set_steam_rate.save(steam_rate_actual_predicted)
print(file + " R_squared: {0:.4f}".format(
r2_score(steam_rate_actual, steam_rate_predicted)))
print("\n")
print("Oil Rate:")
for well, file in zip(well_count, files_val):
dates = all_wells_dates_input[well].T
emulsion_input = all_wells_emulsion_input_val[well].T
emulsion_rate_actual = wells_emulsion_rate_actual[:, well]
steam_rate_predicted = wells_steam_rate_predicted[:, well]
emulsion_rate_predicted = wells_emulsion_rate_predicted[:, well]
if scheme == 1:
emulsion_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, emulsion_input, steam_rate_predicted,
emulsion_rate_actual, emulsion_rate_predicted)).T,
columns=["Date"] + list(emulsion_input_column) + [
labels_column[0] + " predicted", labels_column[1] +
" actual", labels_column[1] + " predicted"
])
data_set_emulsion_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_scheme1"] +
"/emulsion_rate_" + file)
elif scheme == 2:
emulsion_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, emulsion_input, emulsion_rate_actual,
emulsion_rate_predicted)).T,
columns=["Date"] + list(emulsion_input_column) + [
labels_column[1] + " actual",
labels_column[1] + " predicted"
])
data_set_emulsion_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_scheme2"] +
"/emulsion_rate_" + file)
elif scheme == 3:
# cum_input = wells_cum_oil_array[well].T
RF_input = wells_RF_array[well].T
emulsion_rate_actual_predicted = pd.DataFrame(
np.vstack(
(dates, RF_input, emulsion_input, steam_rate_predicted,
emulsion_rate_actual, emulsion_rate_predicted)).T,
columns=["Date", "RF"] + list(emulsion_input_column) + [
labels_column[0] + " predicted", labels_column[1] +
" actual", labels_column[1] + " predicted"
])
data_set_emulsion_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_RF_scheme1"] +
"/emulsion_rate_" + file)
else:
RF_input = wells_RF_array[well].T
emulsion_rate_actual_predicted = pd.DataFrame(
np.vstack((dates, RF_input, emulsion_input,
emulsion_rate_actual, emulsion_rate_predicted)).T,
columns=["Date", "RF"] + list(emulsion_input_column) + [
labels_column[1] + " actual",
labels_column[1] + " predicted"
])
data_set_emulsion_rate = CSVLocalDataSet(
filepath=parameters["path_model_output_No_DWT_RF_scheme2"] +
"/emulsion_rate_" + file)
data_set_emulsion_rate.save(emulsion_rate_actual_predicted)
print(file + " R_squared: {0:.4f}".format(
r2_score(emulsion_rate_actual, emulsion_rate_predicted)))
print("\n")
dummy_validate = files_val
return dummy_validate
def validate(parameters: Dict):
# def validate(dummy2, parameters: Dict):
import glob, os
os.chdir(parameters["path_model_input"])
files = []
for file in glob.glob("*.csv"):
files.append(file)
filenames = []
wells_data = []
for file in files:
filename, extension = file.split('.')
filenames.append(filename)
for file, filename in zip(files, filenames):
io = DataCatalog({
filename:
CSVLocalDataSet(filepath=parameters["path_model_input"] + "/" +
file),
})
well_data = io.load(filename)
wells_data.append(well_data)
Raw_Data_preprocessed = []
for well, file, filename in zip(wells_data, files, filenames):
well = well[[
'Date', 'Injector Bottom Hole Pressure', 'Steam Flow Rate - Outer',
'Bottom Hole Heel Temperature', 'Emulsion Pressure',
'Producer Bottom Hole Pressure', 'ESP Speed', 'Emulsion Flow Rate'
]]
for i in range(1, len(well.columns)):
well[well.columns[i]] = pd.to_numeric(well[well.columns[i]],
errors='coerce')
well = well.iloc[:1399]
well = well.fillna(well.rolling(30, min_periods=1).median())
well = well.fillna(well.median())
well['Date'] = pd.to_datetime(well['Date'])
well = well.set_index('Date')
Raw_Data_preprocessed.append(well)
os.chdir(parameters["path_val_stats"])
static_files = []
for static_file in glob.glob("*.csv"):
static_files.append(static_file)
static_filenames = []
statics_data = []
for static_file in static_files:
static_filename, others = static_file.split('_')
static_filenames.append(static_filename)
for static_file, static_filename in zip(static_files, static_filenames):
io = DataCatalog({
static_filename:
CSVLocalDataSet(filepath=parameters["path_val_stats"] + "/" +
static_file),
})
static_data = io.load(static_filename)
statics_data.append(static_data)
statics_data_new = []
well_name_list = []
for pad_static in statics_data:
well_name = pad_static['WELLPAIR_NAME'].values
well_name_list.append(well_name)
pad_static = pad_static.set_index('WELLPAIR_NAME')
pad_static = pad_static.drop(columns=['PLAN_NAME', 'HIGH_PRESSURE'])
statics_data_new.append(pad_static)
properties = []
probabilities = []
asset_names = []
for pad_static, names in zip(statics_data_new, well_name_list):
for well in names:
prob = pad_static.loc[well, 'Forecast_Prob']
probabilities.append(prob)
pad_code = pad_static.loc[well, 'PAD_CODE']
asset_name, pad = pad_code.split('_')
asset_names.append(asset_name)
property_ = pad_static.loc[
well, 'SAGD_PRESSURE':'BOTTOM_WATER_THICKNESS'].values
properties.append(property_)
properties = np.array(properties)
all_wells_input = []
all_wells_labels = []
for well_data, file in zip(Raw_Data_preprocessed, files):
DWT_Aprox_coeff_input = []
input_data, labels = split(well_data)
input_columns = list(input_data.columns)
for data_idx in input_columns:
signal = well_data[data_idx].values
thresh = parameters["thresh"] * np.nanmax(signal)
coeff = pywt.wavedec(signal,
wavelet=parameters["wavelet"],
mode=parameters["mode1"],
level=parameters["level"])
coeff[1:] = (pywt.threshold(i,
value=thresh,
mode=str(parameters["mode2"]))
for i in coeff[1:])
DWT_Aprox_coeff_input.append(coeff[0])
DWT_Aprox_coeff_input = pd.DataFrame(
np.transpose(DWT_Aprox_coeff_input), columns=input_columns)
data_set_input = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/validation_input_DWT_coeffs_" + file)
data_set_input.save(DWT_Aprox_coeff_input)
all_wells_input.append(DWT_Aprox_coeff_input.values)
data_set_labels = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/validation_labels_" + file)
data_set_labels.save(labels)
all_wells_labels.append(labels.values)
# Standardize dynamic data coeffs
data_set_scaler_coeffs = PickleLocalDataSet(
filepath=parameters["path_models"] + "/scaler_coeffs.pickle")
scaler_coeffs = data_set_scaler_coeffs.load()
all_wells_standardized_input = []
all_wells_standardized_input_flattened = []
for well_coeffs in all_wells_input:
std_coeffs = scaler_coeffs.transform(well_coeffs)
all_wells_standardized_input.append(std_coeffs)
transposed_std_coeffs = np.transpose(std_coeffs)
flattened_std_coeffs = transposed_std_coeffs.flatten()
all_wells_standardized_input_flattened.append(flattened_std_coeffs)
all_wells_standardized_input = np.array(all_wells_standardized_input)
all_wells_standardized_input_flattened = np.array(
all_wells_standardized_input_flattened)
input_columns = list(DWT_Aprox_coeff_input.columns)
for std_coeffs, file in zip(all_wells_standardized_input, files):
std_coeffs = pd.DataFrame(std_coeffs, columns=input_columns)
data_set = CSVLocalDataSet(
filepath=parameters["path_val_pre_processed"] +
"/validation_std_DWT_input_coeffs_" + file)
data_set.save(std_coeffs)
# Standardize static data
data_set_scaler_static = PickleLocalDataSet(
filepath=parameters["path_models"] + "/scaler_static.pickle")
scaler_static = data_set_scaler_static.load()
all_wells_standardized_properties = scaler_static.fit_transform(properties)
all_wells_coeffs_reservoir_data = []
for flattened_std_coeffs, standardized_properties in zip(
all_wells_standardized_input_flattened,
all_wells_standardized_properties):
flattened_std_coeffs = list(flattened_std_coeffs)
standardized_properties = list(standardized_properties)
for reservoir_property in standardized_properties:
flattened_std_coeffs.append(
reservoir_property
) # append reservoir data to dynamic data coeffs
all_wells_coeffs_reservoir_data.append(flattened_std_coeffs)
all_wells_coeffs_reservoir_data = np.array(all_wells_coeffs_reservoir_data)
well_count = np.arange(len(all_wells_coeffs_reservoir_data))
daily_timesteps = np.arange(len(all_wells_labels[0]))
input_data = []
for coeff_inputs in all_wells_coeffs_reservoir_data:
for time_lapse in daily_timesteps:
well_inputs = [time_lapse] + list(
coeff_inputs) # append time lapse to input data
input_data.append(well_inputs)
input_data = np.array(input_data)
data_set_regressor_1 = PickleLocalDataSet(
filepath=parameters["path_models"] + "/regressor_1.pickle")
regressor_1 = data_set_regressor_1.load()
number_of_wells = len(well_count)
wells_steam_rate_predicted = regressor_1.predict(input_data)
wells_steam_rate_predicted = wells_steam_rate_predicted.reshape(
(number_of_wells, 1399)).T
# prediction inputs to model 2
input_data_model_2 = []
for coeff_inputs, well in zip(all_wells_coeffs_reservoir_data, well_count):
for time_lapse in daily_timesteps:
well_inputs = [time_lapse] + list(
coeff_inputs) # append time lapse to input data
well_inputs_model_2 = [
wells_steam_rate_predicted[time_lapse, well]
] + well_inputs
input_data_model_2.append(well_inputs_model_2)
input_data_model_2 = np.array(input_data_model_2)
data_set_regressor_2 = PickleLocalDataSet(
filepath=parameters["path_models"] + "/regressor_2.pickle")
regressor_2 = data_set_regressor_2.load()
wells_emulsion_rate_predicted = regressor_2.predict(input_data_model_2)
wells_emulsion_rate_predicted = wells_emulsion_rate_predicted.reshape(
(number_of_wells, 1399)).T
# actual targets
all_wells_steam_data = []
all_wells_emulsion_data = []
for ID in well_count:
well_steam_data = all_wells_labels[ID][:, 0]
well_emulsion_data = all_wells_labels[ID][:, 1]
all_wells_steam_data = all_wells_steam_data + list(well_steam_data)
all_wells_emulsion_data = all_wells_emulsion_data + list(
well_emulsion_data)
all_wells_steam_data = np.array(all_wells_steam_data)
all_wells_emulsion_data = np.array(all_wells_emulsion_data)
wells_steam_rate_actual = all_wells_steam_data.reshape(
(number_of_wells, 1399)).T
wells_emulsion_rate_actual = all_wells_emulsion_data.reshape(
(number_of_wells, 1399)).T
print("Prediction Performance:\n")
print("Steam Flow Rate:")
for well, file in zip(well_count, files):
steam_rate_predicted = wells_steam_rate_predicted[:, well]
steam_rate_actual = wells_steam_rate_actual[:, well]
steam_rate_actual_predicted = pd.DataFrame(
np.vstack((steam_rate_actual, steam_rate_predicted)).T,
columns=["steam rate actual", "steam rate predicted"])
data_set_steam_rate = CSVLocalDataSet(
filepath=parameters["path_model_output"] + "/steam_rate_" + file)
data_set_steam_rate.save(steam_rate_actual_predicted)
print(file + " R_squared: {0:.4f}".format(
r2_score(steam_rate_actual, steam_rate_predicted)))
print("\n")
print("Emulsion Flow Rate:")
for well, file in zip(well_count, files):
emulsion_rate_predicted = wells_emulsion_rate_predicted[:, well]
emulsion_rate_actual = wells_emulsion_rate_actual[:, well]
emulsion_rate_actual_predicted = pd.DataFrame(
np.vstack((emulsion_rate_actual, emulsion_rate_predicted)).T,
columns=["emulsion rate actual", "emulsion rate predicted"])
data_set_emulsion_rate = CSVLocalDataSet(
filepath=parameters["path_model_output"] + "/emulsion_rate_" +
file)
data_set_emulsion_rate.save(emulsion_rate_actual_predicted)
print(file + " R_squared: {0:.4f}".format(
r2_score(emulsion_rate_actual, emulsion_rate_predicted)))
dummy_validate = files
return dummy_validate
def __init__(
self,
train_df=None, # type: Optional[pd.DataFrame]
test_df=None, # type: Optional[pd.DataFrame]
cols_features=None, # type: Optional[List[str]]
col_treatment="Treatment", # type: str
col_outcome="Outcome", # type: str
col_propensity="Propensity", # type: str
col_cate="CATE", # type: str
col_recommendation="Recommendation", # type: str
min_propensity=0.01, # type: float
max_propensity=0.99, # type: float
verbose=2, # type: int
uplift_model_params=dict(
search_cv="sklearn.model_selection.GridSearchCV",
estimator="xgboost.XGBClassifier",
scoring=None,
cv=3,
return_train_score=False,
n_jobs=-1,
param_grid=dict(
random_state=[0],
max_depth=[3],
learning_rate=[0.1],
n_estimators=[100],
verbose=[0],
objective=["binary:logistic"],
booster=["gbtree"],
n_jobs=[-1],
nthread=[None],
gamma=[0],
min_child_weight=[1],
max_delta_step=[0],
subsample=[1],
colsample_bytree=[1],
colsample_bylevel=[1],
reg_alpha=[0],
reg_lambda=[1],
scale_pos_weight=[1],
base_score=[0.5],
missing=[None],
),
), # type: Union[Dict[str, List[Any]], Type[sklearn.base.BaseEstimator]]
enable_ipw=True, # type: bool
propensity_model_params=dict(
search_cv="sklearn.model_selection.GridSearchCV",
estimator="sklearn.linear_model.LogisticRegression",
scoring=None,
cv=3,
return_train_score=False,
n_jobs=-1,
param_grid=dict(
random_state=[0],
C=[0.1, 1, 10],
class_weight=[None],
dual=[False],
fit_intercept=[True],
intercept_scaling=[1],
max_iter=[100],
multi_class=["ovr"],
n_jobs=[1],
penalty=["l1", "l2"],
solver=["liblinear"],
tol=[0.0001],
warm_start=[False],
),
), # type: Dict[str, List[Any]]
cv=3, # type: int
index_name="index", # type: str
partition_name="partition", # type: str
runner="SequentialRunner", # type: str
conditionally_skip=False, # type: bool
dataset_catalog=dict(
# args_raw = CSVLocalDataSet(filepath='../data/01_raw/args_raw.csv', version=None),
# train_df = CSVLocalDataSet(filepath='../data/01_raw/train_df.csv', version=None),
# test_df = CSVLocalDataSet(filepath='../data/01_raw/test_df.csv', version=None),
propensity_model=PickleLocalDataSet(
filepath="../data/06_models/propensity_model.pickle",
version=None),
uplift_models_dict=PickleLocalDataSet(
filepath="../data/06_models/uplift_models_dict.pickle",
version=None),
df_03=CSVLocalDataSet(
filepath="../data/07_model_output/df.csv",
load_args=dict(index_col=["partition", "index"],
float_precision="high"),
save_args=dict(index=True, float_format="%.16e"),
version=None,
),
treated__sim_eval_df=CSVLocalDataSet(
filepath="../data/08_reporting/treated__sim_eval_df.csv",
version=None),
untreated__sim_eval_df=CSVLocalDataSet(
filepath="../data/08_reporting/untreated__sim_eval_df.csv",
version=None),
estimated_effect_df=CSVLocalDataSet(
filepath="../data/08_reporting/estimated_effect_df.csv",
version=None),
), # type: Dict[str, AbstractDataSet]
logging_config={
"disable_existing_loggers": False,
"formatters": {
"json_formatter": {
"class":
"pythonjsonlogger.jsonlogger.JsonFormatter",
"format":
"[%(asctime)s|%(name)s|%(funcName)s|%(levelname)s] %(message)s",
},
"simple": {
"format":
"[%(asctime)s|%(name)s|%(levelname)s] %(message)s"
},
},
"handlers": {
"console": {
"class": "logging.StreamHandler",
"formatter": "simple",
"level": "INFO",
"stream": "ext://sys.stdout",
},
"info_file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "INFO",
"formatter": "simple",
"filename": "./info.log",
"maxBytes": 10485760, # 10MB
"backupCount": 20,
"encoding": "utf8",
"delay": True,
},
"error_file_handler": {
"class": "logging.handlers.RotatingFileHandler",
"level": "ERROR",
"formatter": "simple",
"filename": "./errors.log",
"maxBytes": 10485760, # 10MB
"backupCount": 20,
"encoding": "utf8",
"delay": True,
},
},
"loggers": {
"anyconfig": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"WARNING",
"propagate":
False,
},
"kedro.io": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"WARNING",
"propagate":
False,
},
"kedro.pipeline": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"INFO",
"propagate":
False,
},
"kedro.runner": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"INFO",
"propagate":
False,
},
"causallift": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"INFO",
"propagate":
False,
},
},
"root": {
"handlers":
["console", "info_file_handler", "error_file_handler"],
"level":
"INFO",
},
"version": 1,
}, # type: Optional[Dict[str, Any]]
):
# type: (...) -> None
self.runner = None # type: Optional[str]
self.kedro_context = None # type: Optional[Type[FlexibleKedroContext]]
self.args = None # type: Optional[Type[EasyDict]]
self.train_df = None # type: Optional[Type[pd.DataFrame]]
self.test_df = None # type: Optional[Type[pd.DataFrame]]
self.df = None # type: Optional[Type[pd.DataFrame]]
self.propensity_model = None # type: Optional[Type[sklearn.base.BaseEstimator]]
self.uplift_models_dict = None # type: Optional[Type[EasyDict]]
self.treatment_fractions = None # type: Optional[Type[EasyDict]]
self.treatment_fraction_train = None # type: Optional[float]
self.treatment_fraction_test = None # type: Optional[float]
self.treated__proba = None # type: Optional[Type[np.array]]
self.untreated__proba = None # type: Optional[Type[np.array]]
self.cate_estimated = None # type: Optional[Type[pd.Series]]
self.treated__sim_eval_df = None # type: Optional[Type[pd.DataFrame]]
self.untreated__sim_eval_df = None # type: Optional[Type[pd.DataFrame]]
self.estimated_effect_df = None # type: Optional[Type[pd.DataFrame]]
# Instance attributes were defined above.
if logging_config:
logging.config.dictConfig(logging_config)
args_raw = dict(
cols_features=cols_features,
col_treatment=col_treatment,
col_outcome=col_outcome,
col_propensity=col_propensity,
col_cate=col_cate,
col_recommendation=col_recommendation,
min_propensity=min_propensity,
max_propensity=max_propensity,
verbose=verbose,
uplift_model_params=uplift_model_params,
enable_ipw=enable_ipw,
propensity_model_params=propensity_model_params,
index_name=index_name,
partition_name=partition_name,
runner=runner,
conditionally_skip=conditionally_skip,
)
args_raw = EasyDict(args_raw)
args_raw.update(
dataset_catalog.get("args_raw",
MemoryDataSet({}).load()))
assert args_raw.runner in {"SequentialRunner", "ParallelRunner", None}
if args_raw.runner is None and args_raw.conditionally_skip:
log.warning(
"[Warning] conditionally_skip option is ignored since runner is None"
)
self.kedro_context = FlexibleKedroContext(
runner=args_raw.runner, only_missing=args_raw.conditionally_skip)
self.runner = args_raw.runner
if self.runner is None:
self.df = bundle_train_and_test_data(args_raw, train_df, test_df)
self.args = impute_cols_features(args_raw, self.df)
self.args = schedule_propensity_scoring(self.args, self.df)
self.treatment_fractions = treatment_fractions_(self.args, self.df)
if self.args.need_propensity_scoring:
self.propensity_model = fit_propensity(self.args, self.df)
self.df = estimate_propensity(self.args, self.df,
self.propensity_model)
if self.runner:
self.kedro_context.catalog.add_feed_dict(
{
"train_df": MemoryDataSet(train_df),
"test_df": MemoryDataSet(test_df),
"args_raw": MemoryDataSet(args_raw),
},
replace=True,
)
self.kedro_context.catalog.add_feed_dict(dataset_catalog,
replace=True)
self.kedro_context.run(tags=["011_bundle_train_and_test_data"])
self.df = self.kedro_context.catalog.load("df_00")
self.kedro_context.run(tags=[
"121_prepare_args",
"131_treatment_fractions_",
"141_initialize_model",
])
self.args = self.kedro_context.catalog.load("args")
self.treatment_fractions = self.kedro_context.catalog.load(
"treatment_fractions")
if self.args.need_propensity_scoring:
self.kedro_context.run(tags=["211_fit_propensity"])
self.propensity_model = self.kedro_context.catalog.load(
"propensity_model")
self.kedro_context.run(tags=["221_estimate_propensity"])
self.df = self.kedro_context.catalog.load("df_01")
else:
self.kedro_context.catalog.add_feed_dict(
{"df_01": MemoryDataSet(self.df)}, replace=True)
self.treatment_fraction_train = self.treatment_fractions.train
self.treatment_fraction_test = self.treatment_fractions.test
if self.args.verbose >= 3:
log.info("### Treatment fraction in train dataset: {}".format(
self.treatment_fractions.train))
log.info("### Treatment fraction in test dataset: {}".format(
self.treatment_fractions.test))
self._separate_train_test()