# (conda or mamba) Create New Env and install ``scikit-plots``
# Create a new environment and install Python 3.11 with IPython kernel support
mamba create -n py311 python=3.11 ipykernel -y

# Activate the environment
conda activate py311

# Now Install scikit-plots (via pip, conda, or local source)
pip install scikit-plots

## (Optionally) Install the lost packages "Runtime dependencies"
## https://github.com/celik-muhammed/scikit-plots/tree/main/requirements
## wget https://raw.githubusercontent.com/scikit-plots/scikit-plots/main/requirements/default.txt
curl -O https://raw.githubusercontent.com/scikit-plots/scikit-plots/main/requirements/default.txt
pip install -r default.txt

## Try After Ensure all "Runtime dependencies" installed
pip install -U -i https://pypi.anaconda.org/scikit-plots-wheels-staging-nightly/simple scikit-plots

## pip install git+https://github.com/scikit-plots/scikit-plots.git@<branches>
## Latest in Development
pip install git+https://github.com/scikit-plots/scikit-plots.git@main
##
## (Added C, Cpp, Fortran Support) Works with standard Python (CPython)
pip install git+https://github.com/scikit-plots/scikit-plots.git@maintenance/0.4.x
##
## (Works with PyPy interpreter) Works with standard Python (CPython)
pip install git+https://github.com/scikit-plots/scikit-plots.git@maintenance/0.3.x
pip install git+https://github.com/scikit-plots/scikit-plots.git@maintenance/0.3.7

## pip install git+https://github.com/scikit-plots/scikit-plots.git@<tags>
pip install git+https://github.com/scikit-plots/[email protected]
pip install git+https://github.com/scikit-plots/[email protected]
pip install git+https://github.com/scikit-plots/[email protected]

## pip install package  Installs wheel (.whl) if available, else source
## pip install --no-binary=package package  # Forces source installation only the specified package
pip install --no-binary=scikit-plots scikit-plots

## pip install --no-binary=:all: package  # Forces source installation for Package + all dependencies
## This forces scikit-plots and all its dependencies to be installed from source (from .tar.gz).
pip install --no-binary=:all: scikit-plots

## Forked repo: https://github.com/scikit-plots/scikit-plots.git
git clone https://github.com/YOUR-USER-NAME/scikit-plots.git
cd scikit-plots

## (Optionally) Add safe directories for git
# bash docker/script/safe_dirs.sh
git config --global --add safe.directory '*'

## download submodules
git submodule update --init

# pip install -r ./requirements/all.txt
pip install -r ./requirements/build.txt

## Install development version
pip install --no-cache-dir -e . -v

## https://github.com/celik-muhammed/scikit-plots/tree/main/requirements
## (Optionally) Try Development [build,dev,test,doc]
## For More in Doc: https://scikit-plots.github.io/
python -m pip install --no-cache-dir --no-build-isolation -e .[build,dev,test,doc] -v

## https://github.com/celik-muhammed/scikit-plots/tree/main/requirements
## [cpu] refer tensorflow-cpu, transformers, tf-keras
## [gpu] refer Cupy tensorflow lib require NVIDIA CUDA support
pip install "scikit-plots[cpu]"

# Before tf {'0':'All', '1':'Warnings+', '2':'Errors+', '3':'Fatal Only'} if any
import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Disable GPU and force TensorFlow to use CPU
import os; os.environ['CUDA_VISIBLE_DEVICES'] = ''
import tensorflow as tf
# Set TensorFlow's logging level to Fatal
import logging; tf.get_logger().setLevel(logging.CRITICAL)
import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

# Loading the dataset
X, y = load_digits(
  return_X_y=True,
)
# Split the dataset into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
  X, y, test_size=0.33, random_state=0
)
# Convert labels to one-hot encoding
Y_train = tf.keras.utils.to_categorical(y_train)
Y_val = tf.keras.utils.to_categorical(y_val)
# Define a simple TensorFlow model
tf.keras.backend.clear_session()
model = tf.keras.Sequential([
    # tf.keras.layers.Input(shape=(X_train.shape[1],)),  # Input (Functional API)
    tf.keras.layers.InputLayer(shape=(X_train.shape[1],)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(
  optimizer='adam',
  loss='categorical_crossentropy',
  metrics=['accuracy'],
)
# Train the model
model.fit(
    X_train, Y_train,
    batch_size=32,
    epochs=2,
    validation_data=(X_val, Y_val),
    verbose=0
)
# Predict probabilities on the validation set
y_probas = model.predict(X_val)
# Plot the data
import matplotlib.pyplot as plt
import scikitplot as sp
# sp.get_logger().setLevel(sp.sp_logging.WARNING)
sp.logger.setLevel(sp.logger.INFO)  # default WARNING
# Plot precision-recall curves
sp.metrics.plot_precision_recall(
  y_val, y_probas,
)