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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# from IPython.core.interactiveshell import InteractiveShell
from pathlib import Path

# matplotlib inline
# import os
# import sys
import pandas as pd

from machine_learning.helper import (
    impute_encode_categorical_features,
    prepare_cross_validator,
    prepare_sklearn_data_format,
    run_all_classification_models,
)

# InteractiveShell.ast_node_interactivity = "all"
#
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
#     sys.path.append(nb_dir)


# %%
CV_METHOD = "logo"  # logo, half_logo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
print("CV_METHOD: " + CV_METHOD)
N_SL = 3  # Number of largest/smallest accuracies (of particular CV) outputs
UNDERSAMPLING = False
# (bool) If True this will train and test data on balanced dataset
# (using undersampling method)

# %% jupyter={"outputs_hidden": false, "source_hidden": false}
PATH_BASE = Path("E:/STRAWresults/20230415")

SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
TARGET_VARIABLE = "JCQ_job_control"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)

if ("appraisal" in TARGET_VARIABLE) and ("stressfulness" in TARGET_VARIABLE):
    TARGET_VARIABLE += "_"
    TARGET_VARIABLE += SEGMENT_TYPE

PATH_FULL = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")

model_input = pd.read_csv(PATH_FULL)

if SEGMENT_LENGTH == "daily":
    DAY_LENGTH = "daily"  # or "working"
    print(DAY_LENGTH)
    model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]

# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input["target"].value_counts()

# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets
BINS = [-1, 0, 4]  # bins for stressfulness (0-4) target
print("BINS: ", BINS)
model_input["target"], edges = pd.cut(
    model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True
)  # ['low', 'medium', 'high']
print(model_input["target"].value_counts())
REMOVE_MEDIUM = True
if ("medium" in model_input["target"]) and REMOVE_MEDIUM:
    model_input = model_input[model_input["target"] != "medium"]
    model_input["target"] = (
        model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
    )
else:
    model_input["target"] = model_input["target"].map(
        {"low": 0, "medium": 1, "high": 2}
    )
    print(model_input["target"].value_counts())


# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# UnderSampling
if UNDERSAMPLING:
    no_stress = model_input[model_input["target"] == 0]
    stress = model_input[model_input["target"] == 1]

    no_stress = no_stress.sample(n=len(stress))
    model_input = pd.concat([stress, no_stress], axis=0)


# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
    model_input_encoded, CV_METHOD
)
cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)

# %%
data_y.head()

# %%
data_y.tail()
# %%
data_y.shape
# %%
scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
    TARGET_VARIABLE
    + "_"
    + SEGMENT_LENGTH
    + "_classification"
    + str(BINS)
    + "_"
    + CV_METHOD
    + ".csv"
)
scores.to_csv(path_output_full, index=False)