Pipeline Assessment Machine includes Langchain Agents and XGBOost of transit workers of automated data
In this lesson, we include XGBOST analysis power in Belangchain. We build a final pipe in which you can produce synthetic datasets, train the XGBOost model, evaluate its performance, and evaluate the main understanding of the Modar Langchain. By doing this, we show how AI can work on how to work outside the seams of a machinery, making an agent of good governance all the ML Liffectricle system in an orderly and child. With this process, we find that the consolidation of the automation prepared can enable the program to interact with the interpretation. Look Full codes here.
!pip install langchain langchain-community langchain-core xgboost scikit-learn pandas numpy matplotlib seaborn
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
from langchain.tools import Tool
from langchain.agents import AgentType, initialize_agent
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_community.llms.fake import FakeListLLM
import jsonWe start by installing and importing all the important libraries required in this lesson. We use Langchain of Agentic Ai, Xgboard and Skikit-Learn the study of the machine, and panda, inpy, and the oceans of managing data and monitoring. Look Full codes here.
class DataManager:
"""Manages dataset generation and preprocessing"""
def __init__(self, n_samples=1000, n_features=20, random_state=42):
self.n_samples = n_samples
self.n_features = n_features
self.random_state = random_state
self.X_train, self.X_test, self.y_train, self.y_test = None, None, None, None
self.feature_names = [f'feature_{i}' for i in range(n_features)]
def generate_data(self):
"""Generate synthetic classification dataset"""
X, y = make_classification(
n_samples=self.n_samples,
n_features=self.n_features,
n_informative=15,
n_redundant=5,
random_state=self.random_state
)
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
X, y, test_size=0.2, random_state=self.random_state
)
return f"Dataset generated: {self.X_train.shape[0]} train samples, {self.X_test.shape[0]} test samples"
def get_data_summary(self):
"""Return summary statistics of the dataset"""
if self.X_train is None:
return "No data generated yet. Please generate data first."
summary = {
"train_samples": self.X_train.shape[0],
"test_samples": self.X_test.shape[0],
"features": self.X_train.shape[1],
"class_distribution": {
"train": {0: int(np.sum(self.y_train == 0)), 1: int(np.sum(self.y_train == 1))},
"test": {0: int(np.sum(self.y_test == 0)), 1: int(np.sum(self.y_test == 1))}
}
}
return json.dumps(summary, indent=2)It describes the Datamanager class to manage data generation and jobs to install forward. Here, we build a separation division data using the Make_classification function of the Make_classification function, division for training 'sets, and produces a short summary containing sample statistics, the size of the feature, and distribution of classes. Look Full codes here.
class XGBoostManager:
"""Manages XGBoost model training and evaluation"""
def __init__(self):
self.model = None
self.predictions = None
self.accuracy = None
self.feature_importance = None
def train_model(self, X_train, y_train, params=None):
"""Train XGBoost classifier"""
if params is None:
params = {
'max_depth': 6,
'learning_rate': 0.1,
'n_estimators': 100,
'objective': 'binary:logistic',
'random_state': 42
}
self.model = xgb.XGBClassifier(**params)
self.model.fit(X_train, y_train)
return f"Model trained successfully with {params['n_estimators']} estimators"
def evaluate_model(self, X_test, y_test):
"""Evaluate model performance"""
if self.model is None:
return "No model trained yet. Please train model first."
self.predictions = self.model.predict(X_test)
self.accuracy = accuracy_score(y_test, self.predictions)
report = classification_report(y_test, self.predictions, output_dict=True)
result = {
"accuracy": float(self.accuracy),
"precision": float(report['1']['precision']),
"recall": float(report['1']['recall']),
"f1_score": float(report['1']['f1-score'])
}
return json.dumps(result, indent=2)
def get_feature_importance(self, feature_names, top_n=10):
"""Get top N most important features"""
if self.model is None:
return "No model trained yet."
importance = self.model.feature_importances_
feature_imp_df = pd.DataFrame({
'feature': feature_names,
'importance': importance
}).sort_values('importance', ascending=False)
return feature_imp_df.head(top_n).to_string()
def visualize_results(self, X_test, y_test, feature_names):
"""Create visualizations for model results"""
if self.model is None:
print("No model trained yet.")
return
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
cm = confusion_matrix(y_test, self.predictions)
sns.heatmap(cm, annot=True, fmt="d", cmap='Blues', ax=axes[0, 0])
axes[0, 0].set_title('Confusion Matrix')
axes[0, 0].set_ylabel('True Label')
axes[0, 0].set_xlabel('Predicted Label')
importance = self.model.feature_importances_
indices = np.argsort(importance)[-10:]
axes[0, 1].barh(range(10), importance[indices])
axes[0, 1].set_yticks(range(10))
axes[0, 1].set_yticklabels([feature_names[i] for i in indices])
axes[0, 1].set_title('Top 10 Feature Importances')
axes[0, 1].set_xlabel('Importance')
axes[1, 0].hist([y_test, self.predictions], label=['True', 'Predicted'], bins=2)
axes[1, 0].set_title('True vs Predicted Distribution')
axes[1, 0].legend()
axes[1, 0].set_xticks([0, 1])
train_sizes = [0.2, 0.4, 0.6, 0.8, 1.0]
train_scores = [0.7, 0.8, 0.85, 0.88, 0.9]
axes[1, 1].plot(train_sizes, train_scores, marker="o")
axes[1, 1].set_title('Learning Curve (Simulated)')
axes[1, 1].set_xlabel('Training Set Size')
axes[1, 1].set_ylabel('Accuracy')
axes[1, 1].grid(True)
plt.tight_layout()
plt.show()We use xgboostManager to train, test, and translate to our final end. We are equal to XGBCLASFIER, computer accuracy and per-class matrics, uninstall high-risk venture, and logically effects using cardion matrix, significance chart, and easy-to-learn matrix. Look Full codes here.
def create_ml_agent(data_manager, xgb_manager):
"""Create LangChain agent with ML tools"""
tools = [
Tool(
name="GenerateData",
func=lambda x: data_manager.generate_data(),
description="Generate synthetic dataset for training. No input needed."
),
Tool(
name="DataSummary",
func=lambda x: data_manager.get_data_summary(),
description="Get summary statistics of the dataset. No input needed."
),
Tool(
name="TrainModel",
func=lambda x: xgb_manager.train_model(
data_manager.X_train, data_manager.y_train
),
description="Train XGBoost model on the dataset. No input needed."
),
Tool(
name="EvaluateModel",
func=lambda x: xgb_manager.evaluate_model(
data_manager.X_test, data_manager.y_test
),
description="Evaluate trained model performance. No input needed."
),
Tool(
name="FeatureImportance",
func=lambda x: xgb_manager.get_feature_importance(
data_manager.feature_names, top_n=10
),
description="Get top 10 most important features. No input needed."
)
]
return toolsWe describe the creative_ml_agent coordination of the Langchain Ecosystem's study activities. Here, threatened by key performance, data production, exemplary training, assessment, and feature analysis on Langchain tools, which enables the analysis of the language transforming messages. Look Full codes here.
def run_tutorial():
"""Execute the complete tutorial"""
print("=" * 80)
print("ADVANCED LANGCHAIN + XGBOOST TUTORIAL")
print("=" * 80)
data_mgr = DataManager(n_samples=1000, n_features=20)
xgb_mgr = XGBoostManager()
tools = create_ml_agent(data_mgr, xgb_mgr)
print("n1. Generating Dataset...")
result = tools[0].func("")
print(result)
print("n2. Dataset Summary:")
summary = tools[1].func("")
print(summary)
print("n3. Training XGBoost Model...")
train_result = tools[2].func("")
print(train_result)
print("n4. Evaluating Model:")
eval_result = tools[3].func("")
print(eval_result)
print("n5. Top Feature Importances:")
importance = tools[4].func("")
print(importance)
print("n6. Generating Visualizations...")
xgb_mgr.visualize_results(
data_mgr.X_test,
data_mgr.y_test,
data_mgr.feature_names
)
print("n" + "=" * 80)
print("TUTORIAL COMPLETE!")
print("=" * 80)
print("nKey Takeaways:")
print("- LangChain tools can wrap ML operations")
print("- XGBoost provides powerful gradient boosting")
print("- Agent-based approach enables conversational ML pipelines")
print("- Easy integration with existing ML workflows")
if __name__ == "__main__":
run_tutorial()We include the orchestrate full flow of run_tutoIrial (), where we produce data, train and inspect the XGBOost model, as well as importing SUCACE factors. We can imagine the consequences and print the key to take, to give us to see them by the end of the end, variables ml Pipeline.
In conclusion, we build a full-time ML pipe connecting the Langchain's Agentic Frameworky power for predicting XGBOST Classifer. We see how Langchain can work as a discussion of the complex functions of ML as a data function, exemplary training, and assessment, all logical and directed. This hands-in-way-in-way to Walkthrough helps us to inform us to combine the linked llm-powered orchinging orchings can facilitate exploring, improving interpretation, and exposed to the leading method of Dialogue data.
Look Full codes here. Feel free to look our GITHUB page for tutorials, codes and letters of writing. Also, feel free to follow it Sane and don't forget to join ours 100K + ml subreddit Then sign up for Our newspaper.
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