import gym
from gym import spaces
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LogisticRegression, SGDClassifier
from onnxmltools import convert_sklearn
import numpy as np
import onnx
import tf2onnx
import onnxruntime as rt
from onnxmltools import convert_sklearn
from onnxruntime import InferenceSession
from onnx import optimizer

Convert your model to ONNX format

onnx_model = onnx.convert(your_model, target_opset=11) # Adjust target_opset based on your needs

Optimize the ONNX model (optional but recommended)

optimized_model = optimizer.optimize(onnx_model)

Define a scikit-learn model (e.g., Linear Regression)

model = LinearRegression()

class SelfLearningEnv(gym.Env):
def init(self):
super(SelfLearningEnv, self).init()

    # Define action and observation spaces
    self.action_space = spaces.Discrete(2)  # Example: Two possible actions (0 and 1)
    self.observation_space = spaces.Box(low=0, high=1, shape=(4,), dtype=np.float32)  # Example: Four-dimensional state space

    # Define other parameters of the environment
    self.max_steps = 100  # Maximum number of steps per episode
    self.current_step = 0  # Current step

    # Define any additional variables or parameters needed

def reset(self):
    # Reset the environment to the initial state
    self.current_step = 0

    # Return initial state as a numpy array
    return np.array([0, 0, 0, 0])  # Example: Initial state with all zeros

def step(self, action):
    # Take an action and return the new state, reward, termination condition, and additional info

    # Example: Updating the state based on the action
    new_state = np.random.rand(4)  # Example: Generating a random new state

    # Example: Calculating the reward based on the action and state
    reward = np.sum(new_state) if action == 1 else -np.sum(new_state)

    # Update current step
    self.current_step += 1

    # Check if the episode is done (termination condition)
    done = self.current_step >= self.max_steps

    # Return state, reward, done flag, and additional info (if any)
    return new_state, reward, done, {}

def render(self):
    # Define how to visualize or display the environment (optional)
    pass

def close(self):
    # Clean up resources, if any (optional)
    pass

Register the environment with Gym

gym.envs.register(
id='SelfLearning-v0',
entry_point='custom_envs.self_learning_env:SelfLearningEnv'
)

Assuming you have a Gym environment named SelfLearningEnv

env = gym.make('SelfLearning-v0')

Define a scikit-learn model (e.g., Linear Regression)

model = LinearRegression()

for episode in range(100): # Train for 100 episodes
state = env.reset()

# Collect data for training
states = []
actions = []

while True:
    # Generate a fake action for demonstration purposes
    action = env.action_space.sample()

    states.append(state)
    actions.append(action)

    next_state, _, done, _ = env.step(action)
    state = next_state

    if done:
        break

# Train scikit-learn model on collected data
X = PolynomialFeatures(degree=2).fit_transform(states)
y = actions
model.fit(X, y)

Export the model to ONNX format

initial_type = [('float_input', FloatTensorType([None, X.shape[1]]))]
onnx_model = convert_sklearn(model, initial_types=initial_type)

with open("self_learning_model.onnx", "wb") as f:
f.write(onnx_model.SerializeToString())

Generate some example data for a linear regression model

np.random.seed(0)
X_test = np.random.rand(10, 1) * 10

Assuming a linear relationship y = 3*X + noise

y_test = 3 * X_test.squeeze() + np.random.randn(10)

Save the test data to a file

np.savetxt('test_data.csv', np.concatenate((X_test, y_test.reshape(-1, 1)), axis=1), delimiter=',', header='Feature,Target', comments='')

print("Test data generated and saved as 'test_data.csv'.")

Step 1: Train a scikit-learn model

np.random.seed(0)
X_train = np.random.rand(100, 1) * 10
y_train = 3 * X_train.squeeze() + np.random.randn(100)

model = LinearRegression()
model.fit(X_train, y_train)

Step 2: Convert the model to ONNX format

import skl2onnx
from skl2onnx import convert_sklearn

initial_type = [('float_input', skl2onnx.common.data_types.FloatTensorType([None, 1]))]
onnx_model = convert_sklearn(model, initial_types=initial_type)

Save the ONNX model

onnx.save_model(onnx_model, 'linear_regression.onnx')

Step 3: Create a Gym environment

class LinearRegressionEnv(gym.Env):
def init(self):
super(LinearRegressionEnv, self).init()
self.action_space = gym.spaces.Discrete(1)
self.observation_space = gym.spaces.Box(low=0, high=10, shape=(1,), dtype=np.float32)

def reset(self):
    self.state = np.random.rand(1) * 10
    return self.state

def step(self, action):
    return self.state, self.state[0]*3, True, {}

Step 4: Test the ONNX model in the Gym environment

env = LinearRegressionEnv()

session = rt.InferenceSession('linear_regression.onnx')
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

for _ in range(10):
state = env.reset()
action = model.predict([state])[0]
onnx_input = {input_name: np.array([[state[0]]], dtype=np.float32)}
prediction = session.run([output_name], onnx_input)[0]
print(f"True Value: {action}, Predicted Value: {prediction[0][0]}")

Define a scikit-learn model

class ScikitLearnModel:
def init(self):
self.model = LogisticRegression()

def train(self, X, y):
    self.model.fit(X, y)

def predict(self, X):
    return self.model.predict(X)

Define a simple neural network using TensorFlow

class SimpleNeuralNetwork(tf.keras.Model):
def init(self):
super(SimpleNeuralNetwork, self).init()
self.dense = tf.keras.layers.Dense(1, activation='sigmoid')

def call(self, inputs):
    return self.dense(inputs)

Convert TensorFlow model to ONNX format

def convert_to_onnx(model):
dummy_input = tf.constant(np.zeros((1, 2), dtype=np.float32)) # Adjust input shape as needed
onnx_model = tf2onnx.convert.from_keras(model, dummy_input)
return onnx_model

Define a simple environment using OpenAI Gym

class SimpleEnvironment(gym.Env):
def init(self):
super(SimpleEnvironment, self).init()
self.action_space = gym.spaces.Discrete(2)
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(2,), dtype=np.float32)

def reset(self):
    return np.random.rand(2)

def step(self, action):
    return np.random.rand(2), 0, False, {}

Main command processing function

def process_command(command):
global scikit_model, tf_model, onnx_model
if command.startswith("train_sklearn"):
# Parse and preprocess training data
X = np.random.rand(100, 2)
y = np.random.randint(2, size=100)
scikit_model.train(X, y)
print("Scikit-learn model trained.")
elif command.startswith("train_tf"):
# Define and train a TensorFlow model
X = np.random.rand(100, 2)
y = np.random.randint(2, size=100)
tf_model = SimpleNeuralNetwork()
tf_model.compile(optimizer='adam', loss='binary_crossentropy')
tf_model.fit(X, y, epochs=5, verbose=0)
print("TensorFlow model trained.")
elif command.startswith("convert_to_onnx"):
# Convert TensorFlow model to ONNX
onnx_model = convert_to_onnx(tf_model)
print("TensorFlow model converted to ONNX.")
elif command.startswith("predict_sklearn"):
# Generate a random input and make a prediction using scikit-learn model
input_data = np.random.rand(2).reshape(1, -1)
prediction = scikit_model.predict(input_data)
print(f"Scikit-learn model prediction: {prediction}")
elif command.startswith("predict_onnx"):
# Simulate inference using ONNX model
input_data = np.random.rand(2).astype(np.float32)
session = tf2onnx.backend.prepare(onnx_model)
prediction = session.run(input_data)
print(f"ONNX model prediction: {prediction}")
elif command.startswith("env_step"):
# Simulate a step in the environment
action = np.random.randint(2)
observation, reward, done, _ = env.step(action)
print(f"Environment step - Observation: {observation}, Reward: {reward}, Done: {done}")
else:
print("Invalid command. Please try again.")

Initialize models and environment

scikit_model = ScikitLearnModel()
tf_model = None
onnx_model = None
env = SimpleEnvironment()

Example Usage

if name == "main":
process_command("train_sklearn")
process_command("train_tf")
process_command("convert_to_onnx")
process_command("predict_sklearn")
process_command("predict_onnx")
process_command("env_step"

Define a scikit-learn model

class ScikitLearnModel:
def init(self):
self.model = LogisticRegression()

def train(self, X, y):
    self.model.fit(X, y)

def predict(self, X):
    return self.model.predict(X)

Define a simple neural network using TensorFlow

class SimpleNeuralNetwork(tf.keras.Model):
def init(self):
super(SimpleNeuralNetwork, self).init()
self.dense = tf.keras.layers.Dense(1, activation='sigmoid')

def call(self, inputs):
    return self.dense(inputs)

Convert TensorFlow model to ONNX format

def convert_to_onnx(model):
dummy_input = tf.constant(np.zeros((1, 2), dtype=np.float32)) # Adjust input shape as needed
onnx_model = tf2onnx.convert.from_keras(model, dummy_input)
return onnx_model

Define a simple environment using OpenAI Gym

class SimpleEnvironment(gym.Env):
def init(self):
super(SimpleEnvironment, self).init()
self.action_space = gym.spaces.Discrete(2)
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(2,), dtype=np.float32)

def reset(self):
    return np.random.rand(2)

def step(self, action):
    return np.random.rand(2), 0, False, {}

Self-learning Algorithm

class SelfLearningAlgorithm:
def init(self):
self.model = SGDClassifier()

def learn(self, X, y):
    self.model.partial_fit(X, y, classes=[0, 1])

Main command processing function

def process_command(command):
global scikit_model, tf_model, onnx_model, self_learner
if command.startswith("train_sklearn"):
# Parse and preprocess training data
X = np.random.rand(100, 2)
y = np.random.randint(2, size=100)
scikit_model.train(X, y)
print("Scikit-learn model trained.")
elif command.startswith("train_tf"):
# Define and train a TensorFlow model
X = np.random.rand(100, 2)
y = np.random.randint(2, size=100)
tf_model = SimpleNeuralNetwork()
tf_model.compile(optimizer='adam', loss='binary_crossentropy')
tf_model.fit(X, y, epochs=5, verbose=0)
print("TensorFlow model trained.")
elif command.startswith("convert_to_onnx"):
# Convert TensorFlow model to ONNX
onnx_model = convert_to_onnx(tf_model)
print("TensorFlow model converted to ONNX.")
elif command.startswith("predict_sklearn"):
# Generate a random input and make a prediction using scikit-learn model
input_data = np.random.rand(2).reshape(1, -1)
prediction = scikit_model.predict(input_data)
print(f"Scikit-learn model prediction: {prediction}")
elif command.startswith("predict_onnx"):
# Simulate inference using ONNX model
input_data = np.random.rand(2).astype(np.float32)
session = tf2onnx.backend.prepare(onnx_model)
prediction = session.run(input_data)
print(f"ONNX model prediction: {prediction}")
elif command.startswith("env_step"):
# Simulate a step in the environment
action = np.random.randint(2)
observation, reward, done, _ = env.step(action)
print(f"Environment step - Observation: {observation}, Reward: {reward}, Done: {done}")
elif command.startswith("self_learn"):
# Simulate self-learning
X = np.random.rand(10, 2)
y = np.random.randint(2, size=10)
self_learner.learn(X, y)
print("Self-learning complete.")
else:
print("Invalid command. Please try again.")

Initialize models, environment, and self-learner

scikit_model = ScikitLearnModel()
tf_model = None
onnx_model = None
env = SimpleEnvironment()
self_learner = SelfLearningAlgorithm()

Step 1: Train a scikit-learn model

np.random.seed(0)
X_train = np.random.rand(100, 1) * 10
y_train = 3 * X_train.squeeze() + np.random.randn(100)

model = LinearRegression()
model.fit(X_train, y_train)

Step 2: Convert the model to ONNX format

import skl2onnx
from skl2onnx import convert_sklearn

initial_type = [('float_input', skl2onnx.common.data_types.FloatTensorType([None, 1]))]
onnx_model = convert_sklearn(model, initial_types=initial_type)

Save the ONNX model

onnx.save_model(onnx_model, 'linear_regression.onnx')

Step 3: Create a Gym environment

class LinearRegressionEnv(gym.Env):
def init(self):
super(LinearRegressionEnv, self).init()
self.action_space = gym.spaces.Discrete(1)
self.observation_space = gym.spaces.Box(low=0, high=10, shape=(1,), dtype=np.float32)

def reset(self):
    self.state = np.random.rand(1) * 10
    return self.state

def step(self, action):
    return self.state, self.state[0]*3, True, {}

Step 4: Test the ONNX model in the Gym environment

env = LinearRegressionEnv()

session = rt.InferenceSession('linear_regression.onnx')
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name

for _ in range(10):
state = env.reset()
action = model.predict([state])[0]
onnx_input = {input_name: np.array([[state[0]]], dtype=np.float32)}
prediction = session.run([output_name], onnx_input)[0]
print(f"True Value: {action}, Predicted Value: {prediction[0][0]}")

bot.run('') # Replace with your bot token