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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
from pathlib import Path

import pandas as pd
import seaborn as sns
from sklearn.decomposition import PCA

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

# %%
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)

PATH_FULL = PATH_BASE / SEGMENT_LENGTH / "features" / "all_sensor_features.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)]

# %%
TARGETS = [
    "PANAS_negative_affect_mean",
    "PANAS_positive_affect_mean",
    "JCQ_job_demand_mean",
    "JCQ_job_control_mean",
    "appraisal_stressfulness_period_mean",
]

# %%
all_features_cleaned = pd.DataFrame()
for target in TARGETS:
    PATH_FULL = (
        PATH_BASE
        / SEGMENT_LENGTH
        / "features"
        / ("all_sensor_features_cleaned_straw_py_(" + target + ").csv")
    )
    current_features = pd.read_csv(PATH_FULL, index_col="local_segment")
    if all_features_cleaned.empty:
        all_features_cleaned = current_features
    else:
        all_features_cleaned = all_features_cleaned.join(
            current_features[("phone_esm_straw_" + target)],
            how="inner",
            rsuffix="_" + target,
        )
    print(all_features_cleaned.shape)

# %%
pca = PCA(n_components=1)
TARGETS_PREFIXED = ["phone_esm_straw_" + target for target in TARGETS]
pca.fit(all_features_cleaned[TARGETS_PREFIXED])
print(pca.explained_variance_ratio_)

# %%
model_input = all_features_cleaned.drop(columns=TARGETS_PREFIXED)
model_input["target"] = pca.fit_transform(all_features_cleaned[TARGETS_PREFIXED])

# %%
sns.histplot(data=model_input, x="target")

# %%
model_input.target.quantile(0.6)

# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets
BINS = [-10, 0, 10]  # 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 / (
    "composite_"
    + SEGMENT_LENGTH
    + "_classification"
    + str(BINS)
    + "_"
    + CV_METHOD
    + ".csv"
)
scores.to_csv(path_output_full, index=False)