初始化 MLflow 客户端

下载此笔记本

根据您运行此 notebook 的位置，您初始化 MLflow 客户端的配置可能会有所不同。

在此示例中，我们使用的是本地运行的跟踪服务器，但还有其他选项可用（最简单的方法是使用Databricks 免费试用版中的免费托管服务）。

有关设置跟踪服务器 URI 和配置对托管或自托管 MLflow 跟踪服务器的访问权限的更多信息，请参阅此处的运行 notebook 指南。

# NOTE: review the links mentioned above for guidance on connecting to a managed tracking server, such as the Databricks Managed MLflow
from mlflow import MlflowClient

client = MlflowClient(tracking_uri="http://127.0.0.1:8080")

使用 MLflow 客户端 API 搜索实验

让我们来看看为我们创建的默认实验。

如果我们不指定新的实验，这个安全的“回退”实验将存储我们创建的 Runs。

# Search experiments without providing query terms behaves effectively as a 'list' action

all_experiments = client.search_experiments()

print(all_experiments)

[<Experiment: artifact_location='./mlruns/0', creation_time=None, experiment_id='0', last_update_time=None, lifecycle_stage='active', name='Default', tags={}>]

from pprint import pprint

# Extract the experiment name and lifecycle_stage
default_experiment = [
  {"name": experiment.name, "lifecycle_stage": experiment.lifecycle_stage}
  for experiment in all_experiments
  if experiment.name == "Default"
][0]

pprint(default_experiment)

{'lifecycle_stage': 'active', 'name': 'Default'}

创建一个新实验

在本节中，我们将

• 创建一个新的 MLflow 实验
• 应用元数据，以实验标签的形式

experiment_description = (
  "This is the grocery forecasting project. "
  "This experiment contains the produce models for apples."
)

experiment_tags = {
  "project_name": "grocery-forecasting",
  "store_dept": "produce",
  "team": "stores-ml",
  "project_quarter": "Q3-2023",
  "mlflow.note.content": experiment_description,
}

produce_apples_experiment = client.create_experiment(name="Apple_Models", tags=experiment_tags)

# Use search_experiments() to search on the project_name tag key

apples_experiment = client.search_experiments(
  filter_string="tags.`project_name` = 'grocery-forecasting'"
)

pprint(apples_experiment[0])

<Experiment: artifact_location='mlflow-artifacts:/977454266300166282', creation_time=1696346036899, experiment_id='977454266300166282', last_update_time=1696346036899, lifecycle_stage='active', name='Apple_Models', tags={'mlflow.note.content': 'This is the grocery forecasting project. This '
                      'experiment contains the produce models for apples.',
'project_name': 'grocery-forecasting',
'project_quarter': 'Q3-2023',
'store_dept': 'produce',
'team': 'stores-ml'}>

# Access individual tag data

print(apples_experiment[0].tags["team"])

stores-ml

运行我们的第一个模型训练

在本节中，我们将

• 创建一个与简单需求预测任务相关的综合数据集
• 启动 MLflow 运行
• 将指标、参数和标签记录到运行中
• 将模型保存到运行中
• 在模型记录期间注册模型

苹果需求的合成数据生成器

请记住，这纯粹是为了演示目的。

需求值纯粹是人为的，并且故意与特征协变。 这不是一个特别现实的现实世界场景（如果是的话，我们就不需要数据科学家了！）。

from datetime import datetime, timedelta

import numpy as np
import pandas as pd


def generate_apple_sales_data_with_promo_adjustment(base_demand: int = 1000, n_rows: int = 5000):
  """
  Generates a synthetic dataset for predicting apple sales demand with seasonality and inflation.

  This function creates a pandas DataFrame with features relevant to apple sales.
  The features include date, average_temperature, rainfall, weekend flag, holiday flag,
  promotional flag, price_per_kg, and the previous day's demand. The target variable,
  'demand', is generated based on a combination of these features with some added noise.

  Args:
      base_demand (int, optional): Base demand for apples. Defaults to 1000.
      n_rows (int, optional): Number of rows (days) of data to generate. Defaults to 5000.

  Returns:
      pd.DataFrame: DataFrame with features and target variable for apple sales prediction.

  Example:
      >>> df = generate_apple_sales_data_with_seasonality(base_demand=1200, n_rows=6000)
      >>> df.head()
  """

  # Set seed for reproducibility
  np.random.seed(9999)

  # Create date range
  dates = [datetime.now() - timedelta(days=i) for i in range(n_rows)]
  dates.reverse()

  # Generate features
  df = pd.DataFrame(
      {
          "date": dates,
          "average_temperature": np.random.uniform(10, 35, n_rows),
          "rainfall": np.random.exponential(5, n_rows),
          "weekend": [(date.weekday() >= 5) * 1 for date in dates],
          "holiday": np.random.choice([0, 1], n_rows, p=[0.97, 0.03]),
          "price_per_kg": np.random.uniform(0.5, 3, n_rows),
          "month": [date.month for date in dates],
      }
  )

  # Introduce inflation over time (years)
  df["inflation_multiplier"] = 1 + (df["date"].dt.year - df["date"].dt.year.min()) * 0.03

  # Incorporate seasonality due to apple harvests
  df["harvest_effect"] = np.sin(2 * np.pi * (df["month"] - 3) / 12) + np.sin(
      2 * np.pi * (df["month"] - 9) / 12
  )

  # Modify the price_per_kg based on harvest effect
  df["price_per_kg"] = df["price_per_kg"] - df["harvest_effect"] * 0.5

  # Adjust promo periods to coincide with periods lagging peak harvest by 1 month
  peak_months = [4, 10]  # months following the peak availability
  df["promo"] = np.where(
      df["month"].isin(peak_months),
      1,
      np.random.choice([0, 1], n_rows, p=[0.85, 0.15]),
  )

  # Generate target variable based on features
  base_price_effect = -df["price_per_kg"] * 50
  seasonality_effect = df["harvest_effect"] * 50
  promo_effect = df["promo"] * 200

  df["demand"] = (
      base_demand
      + base_price_effect
      + seasonality_effect
      + promo_effect
      + df["weekend"] * 300
      + np.random.normal(0, 50, n_rows)
  ) * df["inflation_multiplier"]  # adding random noise

  # Add previous day's demand
  df["previous_days_demand"] = df["demand"].shift(1)
  df["previous_days_demand"].fillna(method="bfill", inplace=True)  # fill the first row

  # Drop temporary columns
  df.drop(columns=["inflation_multiplier", "harvest_effect", "month"], inplace=True)

  return df

# Generate the dataset!

data = generate_apple_sales_data_with_promo_adjustment(base_demand=1_000, n_rows=1_000)

data[-20:]

	日期	平均温度	降雨量	周末	假期	每公斤价格	促销	需求	前几天的需求
980	2023-09-14 11:13:56.948267	34.130183	1.454065	0	0	1.449177	0	971.802447	1001.085782
981	2023-09-15 11:13:56.948267	32.353643	9.462859	0	0	2.856503	0	818.951553	971.802447
982	2023-09-16 11:13:56.948266	18.816833	0.391470	1	0	1.326429	0	1281.352029	818.951553
983	2023-09-17 11:13:56.948265	34.533012	2.120477	1	0	0.970131	0	1357.385504	1281.352029
984	2023-09-18 11:13:56.948265	23.057202	2.365705	0	0	1.049931	0	991.427049	1357.385504
985	2023-09-19 11:13:56.948264	34.810165	3.089005	0	0	2.035149	0	974.971149	991.427049
986	2023-09-20 11:13:56.948263	29.208905	3.673292	0	0	2.518098	0	1056.249547	974.971149
987	2023-09-21 11:13:56.948263	16.428676	4.077782	0	0	1.268979	0	1063.118915	1056.249547
988	2023-09-22 11:13:56.948262	32.067512	2.734454	0	0	0.762317	0	1040.492007	1063.118915
989	2023-09-23 11:13:56.948261	31.938203	13.883486	1	0	1.153301	0	1285.040470	1040.492007
990	2023-09-24 11:13:56.948261	18.024055	7.544061	1	0	0.610703	0	1366.644564	1285.040470
991	2023-09-25 11:13:56.948260	20.681067	18.820490	0	0	1.533488	0	973.934924	1366.644564
992	2023-09-26 11:13:56.948259	16.010132	7.705941	0	0	1.632498	1	1188.291256	973.934924
993	2023-09-27 11:13:56.948259	18.766455	6.274840	0	0	2.806554	0	930.089438	1188.291256
994	2023-09-28 11:13:56.948258	27.948793	23.705246	0	0	0.829464	0	1060.576311	930.089438
995	2023-09-29 11:13:56.948257	28.661072	10.329865	0	0	2.290591	0	910.690776	1060.576311
996	2023-09-30 11:13:56.948256	10.821693	3.575645	1	0	0.897473	0	1306.363801	910.690776
997	2023-10-01 11:13:56.948256	21.108560	6.221089	1	0	1.093864	1	1564.422372	1306.363801
998	2023-10-02 11:13:56.948254	29.451301	5.021463	0	0	2.493085	1	1164.303256	1564.422372
999	2023-10-03 11:13:56.948248	19.261458	0.438381	0	0	2.610422	1	1067.963448	1164.303256

训练并记录模型

我们现在可以导入我们的模型类并训练一个 RandomForestRegressor

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

import mlflow

# Use the fluent API to set the tracking uri and the active experiment
mlflow.set_tracking_uri("http://127.0.0.1:8080")

# Sets the current active experiment to the "Apple_Models" experiment and returns the Experiment metadata
apple_experiment = mlflow.set_experiment("Apple_Models")

# Define a run name for this iteration of training.
# If this is not set, a unique name will be auto-generated for your run.
run_name = "apples_rf_test"

# Define an artifact path that the model will be saved to.
artifact_path = "rf_apples"

# Split the data into features and target and drop irrelevant date field and target field
X = data.drop(columns=["date", "demand"])
y = data["demand"]

# Split the data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

params = {
  "n_estimators": 100,
  "max_depth": 6,
  "min_samples_split": 10,
  "min_samples_leaf": 4,
  "bootstrap": True,
  "oob_score": False,
  "random_state": 888,
}

# Train the RandomForestRegressor
rf = RandomForestRegressor(**params)

# Fit the model on the training data
rf.fit(X_train, y_train)

# Predict on the validation set
y_pred = rf.predict(X_val)

# Calculate error metrics
mae = mean_absolute_error(y_val, y_pred)
mse = mean_squared_error(y_val, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_val, y_pred)

# Assemble the metrics we're going to write into a collection
metrics = {"mae": mae, "mse": mse, "rmse": rmse, "r2": r2}

# Initiate the MLflow run context
with mlflow.start_run(run_name=run_name) as run:
  # Log the parameters used for the model fit
  mlflow.log_params(params)

  # Log the error metrics that were calculated during validation
  mlflow.log_metrics(metrics)

  # Log an instance of the trained model for later use
  mlflow.sklearn.log_model(sk_model=rf, input_example=X_val, name=artifact_path)

/Users/benjamin.wilson/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/mlflow/models/signature.py:333: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details.
input_schema = _infer_schema(input_ex)
/Users/benjamin.wilson/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")

成功！

您刚刚记录了您的第一个 MLflow 模型！

导航到 MLflow UI 以查看刚刚创建的运行（命名为“apples_rf_test”，记录到实验“Apple_Models”）。