Aprenda a criar soluções de pesquisa de varejo aprimoradas por IA com MongoDB e Databricks

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%pip install pymongo tqdm
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import pandas as pd
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import json
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from collections import Counter
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from tqdm import tqdm
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from pymongo import MongoClient
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from pyspark.sql import functions as F
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from pyspark.sql import types as T
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from pyspark.sql import Window
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import pyspark
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from pyspark import SparkContext
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from pyspark.sql import SparkSession
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conf = pyspark.SparkConf()
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import copy
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import numpy as np
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tqdm.pandas()
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MONGO_CONN = 'mongodb+srv://:@retail-demo.2wqno.mongodb.net/?retryWrites=true&w=majority'     

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atp = spark.readStream.format("mongodb").\ option('spark.mongodb.connection.uri', MONGO_CONN).\ option('spark.mongodb.database', "search").\ option('spark.mongodb.collection', "atp_status_myn").\ option('spark.mongodb.change.stream.publish.full.document.only','true').\ option('spark.mongodb.aggregation.pipeline',[]).\ option("forceDeleteTempCheckpointLocation", "true").load()

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atp = atp.drop("_id")

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atp.writeStream.format("mongodb").\ option('spark.mongodb.connection.uri', MONGO_CONN).\ option('spark.mongodb.database', "search").\ option('spark.mongodb.collection', "catalog_myn").\ option('spark.mongodb.operationType', "update").\ option('spark.mongodb.upsertDocument', True).\ option('spark.mongodb.idFieldList', "id").\

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option("forceDeleteTempCheckpointLocation", "true").\ option("checkpointLocation", "/tmp/retail-atp-myn4/_checkpoint/").\ outputMode("append").\ start()

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@F.udf() def get_vec(text): 
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    return model.encode(text)

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catalog_status = spark.readStream.format("mongodb").\ option('spark.mongodb.connection.uri', MONGO_CONN).\ option('spark.mongodb.database', "search").\ option('spark.mongodb.collection', "catalog_myn").\ option('spark.mongodb.change.stream.publish.full.document.only','true').\ option('spark.mongodb.aggregation.pipeline',[]).\ option("forceDeleteTempCheckpointLocation", "true").load()

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catalog_status = catalog_status.withColumn("discountedPrice", F.col("price") * F.col("pred_price")) catalog_status = catalog_status.withColumn("atp", (F.col("atp").cast("boolean") & F.lit(1).cast("boolean")).cast("integer")) 

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catalog_status.withColumn("vec", get_vec("title")) catalog_status = catalog_status.drop("_id")

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catalog_status.writeStream.format("mongodb").\ option('spark.mongodb.connection.uri', MONGO_CONN).\ option('spark.mongodb.database', "search").\ option('spark.mongodb.collection', "catalog_final_myn").\ option('spark.mongodb.operationType', "update").\ option('spark.mongodb.idFieldList', "id").\ option("forceDeleteTempCheckpointLocation", "true").\ option("checkpointLocation", "/tmp/retail-atp-myn5/_checkpoint/").\ outputMode("append").\ start()

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conf = pyspark.SparkConf() conf.set("spark.jars.packages", "org.mongodb.spark:mongo-spark-connector_2.12:10.1.0")

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spark = SparkSession.builder \
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 .master("local") \
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 .appName("test1") \
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 .config(conf = conf) \
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 .getOrCreate()

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@F.udf(T.FloatType()) 
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def beta_fn(pct,a,b):
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    return float(100*beta.cdf(pct, a,b)) w = Window().partitionBy() df =
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df.withColumn("purchase_alpha", F.avg('purchase').over(w)) df = df.withColumn("cart_alpha", F.avg('cart').over(w)) df = df.withColumn("total_views_mean", F.avg('total_views').over(w)) df = df.withColumn("purchase_beta", F.expr('total_views_mean - purchase_alpha')) 
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df = df.withColumn("cart_beta", F.expr('total_views_mean - cart_alpha')) df = df.withColumn("purchase_pct", F.expr('(purchase+purchase_alpha)/(total_views+purchase_alpha+purchase_beta)')) 
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df = df.withColumn("cart_pct", F.expr('(purchase+cart_alpha)/(total_views+cart_alpha+cart_beta)'))

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model_name = "retail_competitive_pricing_model_1"
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with mlflow.start_run(run_name=model_name):
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    # Create and fit a linear regression model
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    model = RandomForestRegressor(n_estimators=50, max_depth=3)
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    model.fit(X_train, y_train)
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    wrappedModel = CompPriceModelWrapper(model)
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    # Log model parameters and metrics
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    mlflow.log_params(model.get_params())
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    mlflow.log_metric("mse", np.mean((model.predict(X_test) - y_test) ** 2))
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    # Log the model with a signature that defines the schema of the model's inputs and outputs. 
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    # When the model is deployed, this signature will be used to validate inputs.
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    signature = infer_signature(X_train, wrappedModel.predict(None,X_train))
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    # MLflow contains utilities to create a conda environment used to serve models.
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    # The necessary dependencies are added to a conda.yaml file which is logged along with the model.
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    conda_env =  _mlflow_conda_env(
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        additional_conda_deps=None,
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        additional_pip_deps=["scikit-learn=={}".format(sklearn.__version__)],
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        additional_conda_channels=None,
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    )
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    mlflow.pyfunc.log_model(model_name, python_model=wrappedModel, conda_env=conda_env, signature=signature)

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model_name = "retail_competitive_pricing_model_1"
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apply_model_udf = mlflow.pyfunc.spark_udf(spark, f"models:/{model_name}/staging")
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# Apply the model to the new data
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columns = ['old_sales','total_sales','min_price','max_price','avg_price','old_avg_price']
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udf_inputs = struct(*columns)
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udf_inputs

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sales = sales.fillna(0.0)
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sales = sales.withColumn("pred_price",apply_model_udf(udf_inputs))
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sales = sales.withColumn("price_elasticity", F.expr("((old_sales - total_sales)/(old_sales + total_sales))/(((old_avg_price - avg_price)+1)/(old_avg_price + avg_price))"))
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sales = sales.withColumn("discount", F.ceil((F.lit(1) - F.col("pred_price"))*F.lit(100)))

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sales.select("id", "pred_price", "price_elasticity").write.format("mongodb").\ option('spark.mongodb.connection.uri', MONGO_CONN).\ option('spark.mongodb.database', "search").\ option('spark.mongodb.collection', "price_myn").\ option('spark.mongodb.idFieldList', 'id').\ mode('overwrite').\ save()

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{
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  "mappings": {
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    "dynamic": false,
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    "fields": {
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      "query": [
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        {
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          "foldDiacritics": false,
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          "maxGrams": 7,
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          "minGrams": 3,
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          "tokenization": "edgeGram",
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          "type": "autocomplete"
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        }
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      ]
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    }
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  }
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}