Merge dev to dev_tool_selection

metagpt/actions/write_analysis_code.py

@@ -62,14 +62,14 @@ class BaseWriteAnalysisCode(Action):
         return messages
 
     async def run(
-        self, context: List[Message], plan: Plan = None, task_guide: str = ""
+        self, context: List[Message], plan: Plan = None, code_steps: str = ""
     ) -> str:
         """Run of a code writing action, used in data analysis or modeling
 
         Args:
             context (List[Message]): Action output history, source action denoted by Message.cause_by
             plan (Plan, optional): Overall plan. Defaults to None.
-            task_guide (str, optional): suggested step breakdown for the current task. Defaults to "".
+            code_steps (str, optional): suggested step breakdown for the current task. Defaults to "".
 
         Returns:
             str: The code string.
@@ -86,7 +86,7 @@ class WriteCodeByGenerate(BaseWriteAnalysisCode):
         self,
         context: [List[Message]],
         plan: Plan = None,
-        task_guide: str = "",
+        code_steps: str = "",
         system_msg: str = None,
         **kwargs,
     ) -> str:
@@ -152,7 +152,8 @@ class WriteCodeWithTools(BaseWriteAnalysisCode):
         self,
         context: List[Message],
         plan: Plan = None,
-        task_guide: str = "",
+        code_steps: str = "",
+        data_desc: str = "",
     ) -> str:
         task_type = plan.current_task.task_type
         available_tools = registry.get_all_schema_by_module(task_type)
@@ -164,7 +165,7 @@ class WriteCodeWithTools(BaseWriteAnalysisCode):
                 for tool in available_tools
             ]
 
-            recommend_tools = await self._tool_recommendation(context, task_guide, available_tools)
+            recommend_tools = await self._tool_recommendation(context, code_steps, available_tools)
             tool_catalog = self._parse_recommend_tools(task_type, recommend_tools)
             logger.info(f"Recommended tools: \n{recommend_tools}")
 
@@ -172,7 +173,7 @@ class WriteCodeWithTools(BaseWriteAnalysisCode):
             output_desc = TOOL_OUTPUT_DESC.get(task_type, "")
             prompt = TOO_ORGANIZATION_PROMPT.format(
                 special_prompt=special_prompt,
-                code_steps=task_guide,
+                code_steps=code_steps,
                 module_name=module_name,
                 output_desc=output_desc,
                 function_catalog=tool_catalog,

metagpt/actions/write_code_steps.py

@@ -0,0 +1,77 @@
+
+import json
+from typing import Dict, List, Union
+
+from metagpt.actions import Action
+from metagpt.schema import Message, Task, Plan
+
+
+CODE_STEPS_PROMPT_TEMPLATE = """
+# Context
+{context}
+
+##  Format example
+1.
+2.
+3.
+...
+
+-----
+Tasks are all code development tasks.
+You are a professional engineer, the main goal is to plan out concise solution steps for Current Task before coding.
+A planning process can reduce the difficulty and improve the quality of coding.
+You may be given some code plans for the tasks ahead, but you don't have to follow the existing plan when planning the current task.
+The output plan should following the subsequent principles:
+1.The plan is a rough checklist of steps outlining the entire program's structure.Try to keep the number of steps fewer than 5.
+2.The steps should be written concisely and at a high level, avoiding overly detailed implementation specifics.
+3.The execution of the plan happens sequentially, but the plan can incorporate conditional (if) and looping(loop) keywords for more complex structures.
+4.Output carefully referenced "Format example" in format.
+"""
+
+STRUCTURAL_CONTEXT = """
+## User Requirement
+{user_requirement}
+## Current Plan
+{tasks}
+## Current Task
+{current_task}
+"""
+
+
+class WriteCodeSteps(Action):
+
+    async def run(self, plan: Plan) -> str:
+        """Run of a task guide writing action, used in ml engineer
+
+        Args:
+            plan (plan): task plan
+            useful_memories (list): useful_memories
+        Returns:
+            str: The dataset_descriptions string.
+        """
+
+        context = self.get_context(plan)
+        code_steps_prompt = CODE_STEPS_PROMPT_TEMPLATE.format(
+            context=context,
+        )
+        code_steps = await self._aask(code_steps_prompt)
+        return code_steps
+
+    def get_context(self, plan: Plan):
+        user_requirement = plan.goal
+        select_task_keys = ['task_id', 'instruction', 'is_finished', 'code_steps']
+
+        def process_task(task):
+            task_dict = task.dict()
+            ptask = {k: task_dict[k] for k in task_dict if k in select_task_keys}
+            return ptask
+        tasks = json.dumps(
+            [process_task(task) for task in plan.tasks], indent=4, ensure_ascii=False
+        )
+        current_task = json.dumps(process_task(plan.current_task)) if plan.current_task else {}
+        context = STRUCTURAL_CONTEXT.format(
+            user_requirement=user_requirement, tasks=tasks, current_task=current_task
+        )
+        # print(context)
+        return context
+

metagpt/roles/ml_engineer.py

@@ -10,12 +10,12 @@ from metagpt.actions import Action
 from metagpt.actions.execute_code import ExecutePyCode
 from metagpt.actions.write_analysis_code import WriteCodeByGenerate, WriteCodeWithTools
 from metagpt.actions.write_plan import WritePlan
-from metagpt.actions.write_task_guide import WriteTaskGuide
 from metagpt.logs import logger
 from metagpt.prompts.ml_engineer import GEN_DATA_DESC_PROMPT
 from metagpt.roles import Role
 from metagpt.schema import Message, Plan
 from metagpt.utils.common import CodeParser
+from metagpt.actions.write_code_steps import WriteCodeSteps
 
 STRUCTURAL_CONTEXT = """
 ## User Requirement
@@ -123,11 +123,6 @@ class AskReview(Action):
         return rsp, confirmed
 
 
-# class WriteTaskGuide(Action):
-#     async def run(self, task_instruction: str, data_desc: dict = None) -> str:
-#         return ""
-
-
 class GenerateDataDesc(Action):
     async def run(self, files: list) -> dict:
         data_desc = {}
@@ -154,7 +149,7 @@ class MLEngineer(Role):
         self._set_react_mode(react_mode="plan_and_act")
         self.plan = Plan(goal=goal)
         self.use_tools = True
-        self.use_task_guide = True
+        self.use_code_steps = True
         self.execute_code = ExecutePyCode()
         self.auto_run = auto_run
         self.data_path = data_path
@@ -172,7 +167,7 @@ class MLEngineer(Role):
             logger.info(f"ready to take on task {task}")
 
             # take on current task
-            code, result, success = await self._write_and_exec_code()
+            code, result, success, code_steps = await self._write_and_exec_code()
 
             # ask for acceptance, users can other refuse and change tasks in the plan
             task_result_confirmed = await self._ask_review()
@@ -181,6 +176,7 @@ class MLEngineer(Role):
                 # tick off this task and record progress
                 task.code = code
                 task.result = result
+                task.code_steps = code_steps
                 self.plan.finish_current_task()
                 self.working_memory.clear()
 
@@ -194,9 +190,9 @@ class MLEngineer(Role):
         return data_desc
 
     async def _write_and_exec_code(self, max_retry: int = 3):
-        task_guide = (
-            await WriteTaskGuide().run(self.plan)
-            if self.use_task_guide
+        code_steps = (
+            await WriteCodeSteps().run(self.plan)
+            if self.use_code_steps
             else ""
         )
 
@@ -214,13 +210,13 @@ class MLEngineer(Role):
                 logger.info("Write code with pure generation")
                 # code = "print('abc')"
                 code = await WriteCodeByGenerate().run(
-                    context=context, plan=self.plan, task_guide=task_guide, temperature=0.0
+                    context=context, plan=self.plan, code_steps=code_steps, temperature=0.0
                 )
                 cause_by = WriteCodeByGenerate
             else:
                 logger.info("Write code with tools")
                 code = await WriteCodeWithTools().run(
-                    context=context, plan=self.plan, task_guide=task_guide
+                    context=context, plan=self.plan, code_steps=code_steps,
                 )
                 cause_by = WriteCodeWithTools
 
@@ -243,7 +239,7 @@ class MLEngineer(Role):
 
             counter += 1
 
-        return code, result, success
+        return code, result, success, code_steps
 
     async def _ask_review(self):
         if not self.auto_run:
@@ -272,7 +268,7 @@ class MLEngineer(Role):
 
     def get_useful_memories(self) -> List[Message]:
         """find useful memories only to reduce context length and improve performance"""
-
+        # TODO dataset description , code steps
         user_requirement = self.plan.goal
         tasks = json.dumps(
             [task.dict() for task in self.plan.tasks], indent=4, ensure_ascii=False
@@ -294,11 +290,11 @@ class MLEngineer(Role):
 
 
 if __name__ == "__main__":
-    # requirement = "Run data analysis on sklearn Iris dataset, include a plot.."
+    # requirement = "Run data analysis on sklearn Iris dataset, include a plot"
     # requirement = "Run data analysis on sklearn Diabetes dataset, include a plot"
     # requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy"
     # requirement = "Run data analysis on sklearn Wisconsin Breast Cancer dataset, include a plot, train a model to predict targets (20% as validation), and show validation accuracy"
-    # requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
+    requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
 
     requirement = "Perform data analysis on the provided data. Train a model to predict the target variable Survived. Include data preprocessing, feature engineering, and modeling in your pipeline. The metric is accuracy."
     data_path = "/data/lidanyang/tabular_data/titanic"

metagpt/schema.py

@@ -81,6 +81,7 @@ class Task(BaseModel):
     code: str = ""
     result: str = ""
     is_finished: bool = False
+    code_steps: str = ""
 
 
 class Plan(BaseModel):

metagpt/tools/functions/libs/data_preprocess.py

@@ -0,0 +1,123 @@
+
+import pandas as pd
+import numpy as np
+
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import KBinsDiscretizer
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MaxAbsScaler
+from sklearn.preprocessing import RobustScaler
+from sklearn.preprocessing import OrdinalEncoder
+
+from metagpt.tools.functions import registry
+from metagpt.tools.functions.schemas.data_preprocess import *
+
+
+@registry.register("data_preprocess", FillMissingValue)
+def fill_missing_value(df: pd.DataFrame, features: list, strategy: str = 'mean', fill_value=None,):
+    df[features] = SimpleImputer(strategy=strategy, fill_value=fill_value).fit_transform(df[features])
+    return df
+
+
+# @registry.register("data_preprocess", FillMissingValue)
+# def label_encode(df: pd.DataFrame, features: list,):
+#     for col in features:
+#         df[col] = LabelEncoder().fit_transform(df[col])
+#     return df
+
+
+@registry.register("data_preprocess", SplitBins)
+def split_bins(df: pd.DataFrame, features: list, strategy: str = 'quantile',):
+    df[features] = KBinsDiscretizer(strategy=strategy, encode='ordinal').fit_transform(df[features])
+    return df
+
+
+@registry.register("data_preprocess", MinMaxScale)
+def min_max_scale(df: pd.DataFrame, features: list, ):
+    df[features] = MinMaxScaler().fit_transform(df[features])
+    return df
+
+
+@registry.register("data_preprocess", StandardScale)
+def standard_scale(df: pd.DataFrame, features: list, ):
+    df[features] = StandardScaler().fit_transform(df[features])
+    return df
+
+
+@registry.register("data_preprocess", LogTransform)
+def log_transform(df: pd.DataFrame, features: list, ):
+    for col in features:
+        if df[col].min() <= 0:
+            df[col] = df[col] - df[col].min() + 2
+        df[col] = np.log(df[col])
+    return df
+
+
+@registry.register("data_preprocess", MaxAbsScale)
+def max_abs_scale(df: pd.DataFrame, features: list, ):
+    df[features] = MaxAbsScaler().fit_transform(df[features])
+    return df
+
+
+@registry.register("data_preprocess", RobustScale)
+def robust_scale(df: pd.DataFrame, features: list, ):
+    df[features] = RobustScaler().fit_transform(df[features])
+    return df
+
+
+@registry.register("data_preprocess", OrdinalEncode)
+def ordinal_encode(df: pd.DataFrame, features: list,):
+    df[features] = OrdinalEncoder().fit_transform(df[features])
+    return df
+
+
+if __name__ == '__main__':
+    def run():
+        V = {
+            'a': [-1, 2, 3, 6, 5, 4],
+            'b': [1.1, 2.2, 3.3, 6.6, 5.5, 4.4],
+            'c': ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'],
+            'd': [1, None, 3, None, 5, 4],
+            'e': [1.1, np.NAN, 3.3, None, 5.5, 4.4],
+            'f': ['aa', np.NAN, 'cc', None, '', 'ff'],
+
+        }
+
+        df = pd.DataFrame(V)
+        print(df.dtypes)
+
+        numeric_features = ['a', 'b', 'd', 'e']
+        numeric_features_wo_miss = ['a', 'b', ]
+        categorial_features = ['c', 'f']
+
+        df_ = fill_missing_value(df.copy(), numeric_features)
+        print(df_)
+        df_ = fill_missing_value(df.copy(), categorial_features, strategy='constant', fill_value='hehe')
+        print(df_)
+
+        df_ = fill_missing_value(df.copy(), numeric_features, strategy='constant', fill_value=999)
+        print(df_)
+
+        # df_ = label_encode(df.copy(), numeric_features + categorial_features, )
+        # print(df_)
+
+        df_ = split_bins(df.copy(), numeric_features_wo_miss, strategy='quantile')
+        print(df_)
+
+        df_ = min_max_scale(df.copy(), numeric_features, )
+        print(df_)
+
+        df_ = standard_scale(df.copy(), numeric_features, )
+        print(df_)
+
+        df_ = log_transform(df.copy(), numeric_features, )
+        print(df_)
+
+        df_ = max_abs_scale(df.copy(), numeric_features, )
+        print(df_)
+
+        df_ = robust_scale(df.copy(), numeric_features, )
+        print(df_)
+    run()

metagpt/tools/functions/libs/ml_model.py

@@ -0,0 +1,196 @@
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+
+
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+
+from metagpt.tools.functions import registry
+from metagpt.tools.functions.schemas.ml_model import *
+
+
+#########
+## 分类 ##
+#########
+
+
+@registry.register("classification_model", LogisticRegressionClassification)
+def logistic_regression_classification(df, label, test_size=0.2, penalty='l2', dual=False):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+
+    model = LogisticRegression(penalty=penalty, dual=dual)
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict_proba(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+@registry.register("classification_model", RandomForestClassification)
+def random_forest_classification(df, label, test_size=0.2, n_estimators=100, criterion='gini'):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+    model = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion)
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict_proba(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+@registry.register("classification_model", GradientBoostingClassification)
+def gradient_boosting_classification(df, label, test_size=0.2, n_estimators=100, learning_rate=0.1):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+    model = GradientBoostingClassifier(n_estimators=n_estimators, learning_rate=learning_rate)
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict_proba(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+
+#########
+## 回归 ##
+#########
+
+
+@registry.register("regression_model", LinearRegressionRegression)
+def linear_regression(df, label, test_size=0.2, ):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+
+    model = LinearRegression()
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+@registry.register("regression_model", RandomForestRegression)
+def random_forest_regression(df, label, test_size=0.2, n_estimators=100, criterion='squared_error'):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+    model = RandomForestRegressor(n_estimators=n_estimators, criterion=criterion)
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+@registry.register("regression_model", GradientBoostingRegression)
+def gradient_boosting_regression(df, label, test_size=0.2, n_estimators=100, learning_rate=0.1):
+    nonnumeric_columns = [col for col in df if df[col].dtype == 'object']
+    for col in nonnumeric_columns:
+        df[col] = LabelEncoder().fit_transform(df[col])
+    df = df.fillna(0)
+
+    features = [col for col in df if col != label]
+    x, y = df[features], df[label]
+    tr_x, te_x, tr_y, te_y = train_test_split(x, y, test_size=test_size, random_state=1)
+    model = GradientBoostingRegressor(n_estimators=n_estimators, learning_rate=learning_rate)
+    model.fit(tr_x, tr_y, )
+    te_pred_prob = model.predict(te_x)
+
+    res = {
+        'te_pred_prob': te_pred_prob
+    }
+    return res
+
+
+if __name__ == '__main__':
+    def run():
+        from sklearn.datasets import load_iris
+        loader = load_iris(as_frame=True)
+        df = loader['data']
+        df['target'] = loader['target']
+
+        df[df.columns[0]] = df[df.columns[0]].astype(str)
+        df[df.columns[1]] = df[df.columns[1]].astype(int)
+        df['target'] = df['target'].astype(str)
+
+        print(df)
+        print('####'*5)
+        res = logistic_regression_classification(df, 'target', test_size=0.25, penalty='l2', dual=False)
+        print(res['te_pred_prob'])
+
+        print('####'*5)
+        res = random_forest_classification(df, 'target', test_size=0.25, n_estimators=100, criterion='gini')
+        print(res['te_pred_prob'])
+
+        print('####'*5)
+        res = gradient_boosting_classification(df, 'target', test_size=0.25, n_estimators=100, learning_rate=0.1)
+        print(res['te_pred_prob'])
+
+        from sklearn.datasets import make_regression
+        import pandas as pd
+        loader = make_regression()
+        df = pd.DataFrame(loader[0])
+        df['target'] = loader[1]
+
+        df[df.columns[0]] = df[df.columns[0]].astype(str)
+        df[df.columns[1]] = df[df.columns[1]].astype(int)
+        # df['target'] = df['target'].astype(str)
+
+        print(df)
+        print('####' * 5)
+        res = linear_regression(df, 'target', test_size=0.25, )
+        print(res['te_pred_prob'])
+
+        print('####' * 5)
+        res = random_forest_regression(df, 'target', test_size=0.25, n_estimators=100, criterion='squared_error')
+        print(res['te_pred_prob'])
+
+        print('####' * 5)
+        res = gradient_boosting_regression(df, 'target', test_size=0.25, n_estimators=100, learning_rate=0.1)
+        print(res['te_pred_prob'])
+    run()

metagpt/tools/functions/schemas/data_preprocess.py

@@ -0,0 +1,62 @@
+
+import pandas as pd
+
+from metagpt.tools.functions.schemas.base import tool_field, ToolSchema
+
+
+class FillMissingValue(ToolSchema):
+    """Completing missing values with simple strategies"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+    strategy: str = tool_field(description="the imputation strategy", default='mean')
+    fill_value: int = tool_field(description="fill_value is used to replace all occurrences of missing_values", default=None)
+
+
+# class LabelEncode(ToolSchema):
+#     """Completing missing values with simple strategies"""
+#     df: pd.DataFrame = tool_field(description="input dataframe")
+#     features: list = tool_field(description="columns to be processed")
+
+
+class SplitBins(ToolSchema):
+    """Bin continuous data into intervals and return the bin identifier encoded as an integer value"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+    strategy: str = tool_field(description="Strategy used to define the widths of the bins", default='quantile')
+
+
+class MinMaxScale(ToolSchema):
+    """Transform features by scaling each feature to a range, witch is (0, 1)"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+
+
+class StandardScale(ToolSchema):
+    """Standardize features by removing the mean and scaling to unit variance"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+
+
+class LogTransform(ToolSchema):
+    """Performs a logarithmic transformation on the specified columns"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+
+
+class MaxAbsScale(ToolSchema):
+    """Scale each feature by its maximum absolute value"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+
+
+class RobustScale(ToolSchema):
+    """Scale features using statistics that are robust to outliers, the quantile_range is (25.0, 75.0)"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+
+
+class OrdinalEncode(ToolSchema):
+    """Encode categorical features as an integer array"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    features: list = tool_field(description="columns to be processed")
+

metagpt/tools/functions/schemas/ml_model.py

@@ -0,0 +1,55 @@
+import pandas as pd
+
+from metagpt.tools.functions.schemas.base import tool_field, ToolSchema
+
+
+class LogisticRegressionClassification(ToolSchema):
+    """Logistic Regression (aka logit, MaxEnt) classifier"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+    penalty: str = tool_field(description="Specify the norm of the penalty", default="l2")
+    dual: bool = tool_field(description="Dual (constrained) or primal (regularized) formulation", default="l2")
+
+
+class RandomForestClassification(ToolSchema):
+    """random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+    n_estimators: int = tool_field(description="The number of trees in the forest", default=100)
+    criterion: str = tool_field(description="The function to measure the quality of a split", default="gini")
+
+
+class GradientBoostingClassification(ToolSchema):
+    """Gradient Boosting for classification.This algorithm builds an additive model in a forward stage-wise fashion"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+    n_estimators: int = tool_field(description="The number of boosting stages to perform", default=100)
+    learning_rate: float = tool_field(description="Learning rate shrinks the contribution of each tree by learning_rate", default=0.1)
+
+
+class LinearRegressionRegression(ToolSchema):
+    """Ordinary least squares Linear Regression."""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+
+
+class RandomForestRegression(ToolSchema):
+    """random forest is a meta estimator that fits a number of decision tree on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+    n_estimators: int = tool_field(description="The number of trees in the forest", default=100)
+    criterion: str = tool_field(description="The function to measure the quality of a split", default="squared_error")
+
+
+class GradientBoostingRegression(ToolSchema):
+    """Gradient Boosting for regression.This estimator builds an additive model in a forward stage-wise fashion"""
+    df: pd.DataFrame = tool_field(description="input dataframe")
+    label: str = tool_field(description="target name")
+    test_size: float = tool_field(description="The proportion of the test set to all the data", default=0.2)
+    n_estimators: int = tool_field(description="The number of boosting stages to perform", default=100)
+    learning_rate: float = tool_field(description="Learning rate shrinks the contribution of each tree by learning_rate", default=0.1)