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{
"cells": [
{
"cell_type": "markdown",
"id": "db8736a7-ed94-441c-9556-831fa57b5a10",
"metadata": {},
"source": [
"# The Product Pricer Continued\n",
"\n",
"A model that can estimate how much something costs, from its description.\n",
"\n",
"## Baseline Models\n",
"\n",
"Today we work on the simplest models to act as a starting point that we will beat."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import math\n",
"import json\n",
"import random\n",
"from dotenv import load_dotenv\n",
"from huggingface_hub import login\n",
"from items import Item\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pickle\n",
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "933b6e75-3661-4f30-b0b5-c28d04e3748e",
"metadata": {},
"outputs": [],
"source": [
"# More imports for our traditional machine learning\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42cf33b7-7abd-44ba-9780-c156b70473b5",
"metadata": {},
"outputs": [],
"source": [
"# And more imports for our NLP related machine learning\n",
"\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from gensim.models import Word2Vec\n",
"from gensim.utils import simple_preprocess"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1ac3ec0-183c-4a12-920b-b06397f86815",
"metadata": {},
"outputs": [],
"source": [
"# Finally, more imports for more advanced machine learning\n",
"\n",
"from sklearn.svm import LinearSVR\n",
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c01ee5f-c4fc-44fe-9d3a-907e8a0426d2",
"metadata": {},
"outputs": [],
"source": [
"# Constants - used for printing to stdout in color\n",
"\n",
"GREEN = \"\\033[92m\"\n",
"YELLOW = \"\\033[93m\"\n",
"RED = \"\\033[91m\"\n",
"RESET = \"\\033[0m\"\n",
"COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv()\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4dd3aad2-6f99-433c-8792-e461d2f06622",
"metadata": {},
"outputs": [],
"source": [
"# Log in to HuggingFace\n",
"\n",
"hf_token = os.environ['HF_TOKEN']\n",
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985",
"metadata": {},
"outputs": [],
"source": [
"# Let's avoid curating all our data again! Load in the pickle files:\n",
"\n",
"with open('train.pkl', 'rb') as file:\n",
" train = pickle.load(file)\n",
"\n",
"with open('test.pkl', 'rb') as file:\n",
" test = pickle.load(file)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a84638f7-5ff7-4f54-8751-3ef156264aee",
"metadata": {},
"outputs": [],
"source": [
"# Remind ourselves the training prompt\n",
"\n",
"print(train[0].prompt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7619c85-6e9e-48a1-8efe-c6a60471b87c",
"metadata": {},
"outputs": [],
"source": [
"# Remind a test prompt\n",
"\n",
"print(train[0].price)"
]
},
{
"cell_type": "markdown",
"id": "bcccf130-125a-4958-bac3-f46dfcb29b3f",
"metadata": {},
"source": [
"## Unveiling a mighty script that we will use a lot!\n",
"\n",
"A rather pleasing Test Harness that will evaluate any model against 250 items from the Test set\n",
"\n",
"And show us the results in a visually satisfying way.\n",
"\n",
"You write a function of this form:\n",
"\n",
"```\n",
"def my_prediction_function(item):\n",
" # my code here\n",
" return my_estimate\n",
"```\n",
"\n",
"And then you call:\n",
"\n",
"`Tester.test(my_prediction_function)`\n",
"\n",
"To evaluate your model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5793f5c-e23e-4a74-9496-1e30dd1e8935",
"metadata": {},
"outputs": [],
"source": [
"class Tester:\n",
"\n",
" def __init__(self, predictor, title=None, data=test, size=250):\n",
" self.predictor = predictor\n",
" self.data = data\n",
" self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
" self.size = size\n",
" self.guesses = []\n",
" self.truths = []\n",
" self.errors = []\n",
" self.sles = []\n",
" self.colors = []\n",
"\n",
" def color_for(self, error, truth):\n",
" if error<40 or error/truth < 0.2:\n",
" return \"green\"\n",
" elif error<80 or error/truth < 0.4:\n",
" return \"orange\"\n",
" else:\n",
" return \"red\"\n",
" \n",
" def run_datapoint(self, i):\n",
" datapoint = self.data[i]\n",
" guess = self.predictor(datapoint)\n",
" truth = datapoint.price\n",
" error = abs(guess - truth)\n",
" log_error = math.log(truth+1) - math.log(guess+1)\n",
" sle = log_error ** 2\n",
" color = self.color_for(error, truth)\n",
" title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+\"...\"\n",
" self.guesses.append(guess)\n",
" self.truths.append(truth)\n",
" self.errors.append(error)\n",
" self.sles.append(sle)\n",
" self.colors.append(color)\n",
" print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
"\n",
" def chart(self, title):\n",
" max_error = max(self.errors)\n",
" plt.figure(figsize=(12, 8))\n",
" max_val = max(max(self.truths), max(self.guesses))\n",
" plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
" plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
" plt.xlabel('Ground Truth')\n",
" plt.ylabel('Model Estimate')\n",
" plt.xlim(0, max_val)\n",
" plt.ylim(0, max_val)\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
" def report(self):\n",
" average_error = sum(self.errors) / self.size\n",
" rmsle = math.sqrt(sum(self.sles) / self.size)\n",
" hits = sum(1 for color in self.colors if color==\"green\")\n",
" title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
" self.chart(title)\n",
"\n",
" def run(self):\n",
" self.error = 0\n",
" for i in range(self.size):\n",
" self.run_datapoint(i)\n",
" self.report()\n",
"\n",
" @classmethod\n",
" def test(cls, function):\n",
" cls(function).run()"
]
},
{
"cell_type": "markdown",
"id": "066fef03-8338-4526-9df3-89b649ad4f0a",
"metadata": {},
"source": [
"# Now for something basic\n",
"\n",
"What's the very simplest model you could imagine?\n",
"\n",
"Let's start with a random number generator!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66ea68e8-ab1b-4f0d-aba4-a59574d8f85e",
"metadata": {},
"outputs": [],
"source": [
"def random_pricer(item):\n",
" return random.randrange(1,1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53d941cb-5b73-44ea-b893-3a0ce9997066",
"metadata": {},
"outputs": [],
"source": [
"# Set the random seed\n",
"\n",
"random.seed(42)\n",
"\n",
"# Run our TestRunner\n",
"Tester.test(random_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97451c73-9c1b-43a8-b3b9-9c41942e48a2",
"metadata": {},
"outputs": [],
"source": [
"# That was fun!\n",
"# We can do better - here's another rather trivial model\n",
"\n",
"training_prices = [item.price for item in train]\n",
"training_average = sum(training_prices) / len(training_prices)\n",
"\n",
"def constant_pricer(item):\n",
" return training_average"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cf384eb-30c2-40d8-b7e5-48942ac6a969",
"metadata": {},
"outputs": [],
"source": [
"# Run our constant predictor\n",
"Tester.test(constant_pricer)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6173a4b9-f0b9-407b-a1de-dc8f045adb8f",
"metadata": {},
"outputs": [],
"source": [
"train[0].details"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce16eee8-bb34-4914-9aa5-57e30a567842",
"metadata": {},
"outputs": [],
"source": [
"# Create a new \"features\" field on items, and populate it with json parsed from the details dict\n",
"\n",
"for item in train:\n",
" item.features = json.loads(item.details)\n",
"for item in test:\n",
" item.features = json.loads(item.details)\n",
"\n",
"# Look at one"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ac702a10-dccb-43d4-887b-6f92a0fb298f",
"metadata": {},
"outputs": [],
"source": [
"train[0].features.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd7a41c5-0c51-41be-a61d-8e80c3e90930",
"metadata": {},
"outputs": [],
"source": [
"# Look at 20 most common features in training set\n",
"\n",
"feature_count = Counter()\n",
"for item in train:\n",
" for f in item.features.keys():\n",
" feature_count[f]+=1\n",
"\n",
"feature_count.most_common(40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cef84a9-4932-48fd-9f7a-51cfc06e3216",
"metadata": {},
"outputs": [],
"source": [
"# Now some janky code to pluck out the Item Weight\n",
"# Don't worry too much about this: spoiler alert, it's not going to be much use in training!\n",
"\n",
"def get_weight(item):\n",
" weight_str = item.features.get('Item Weight')\n",
" if weight_str:\n",
" parts = weight_str.split(' ')\n",
" amount = float(parts[0])\n",
" unit = parts[1].lower()\n",
" if unit==\"pounds\":\n",
" return amount\n",
" elif unit==\"ounces\":\n",
" return amount / 16\n",
" elif unit==\"grams\":\n",
" return amount / 453.592\n",
" elif unit==\"milligrams\":\n",
" return amount / 453592\n",
" elif unit==\"kilograms\":\n",
" return amount / 0.453592\n",
" elif unit==\"hundredths\" and parts[2].lower()==\"pounds\":\n",
" return amount / 100\n",
" else:\n",
" print(weight_str)\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4848b4a-3c5a-4168-83a5-57a1f3ff270d",
"metadata": {},
"outputs": [],
"source": [
"weights = [get_weight(t) for t in train]\n",
"weights = [w for w in weights if w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cd11cc8-f16e-4991-b531-482189ddc4b6",
"metadata": {},
"outputs": [],
"source": [
"average_weight = sum(weights)/len(weights)\n",
"average_weight"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "efe8ec7f-9777-464f-a809-b06b7033bdb2",
"metadata": {},
"outputs": [],
"source": [
"def get_weight_with_default(item):\n",
" weight = get_weight(item)\n",
" return weight or average_weight"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2659fef-a455-431a-9a0e-59342b80084b",
"metadata": {},
"outputs": [],
"source": [
"def get_rank(item):\n",
" rank_dict = item.features.get(\"Best Sellers Rank\")\n",
" if rank_dict:\n",
" ranks = rank_dict.values()\n",
" return sum(ranks)/len(ranks)\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20b9b5be-30bc-4d3a-8492-fbae119421a0",
"metadata": {},
"outputs": [],
"source": [
"ranks = [get_rank(t) for t in train]\n",
"ranks = [r for r in ranks if r]\n",
"average_rank = sum(ranks)/len(ranks)\n",
"average_rank"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "081e646a-ea50-4ec3-9512-6d5f96f8aef6",
"metadata": {},
"outputs": [],
"source": [
"def get_rank_with_default(item):\n",
" rank = get_rank(item)\n",
" return rank or average_rank"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afd5daf7-cb2b-47af-bf17-dd71a9db65d0",
"metadata": {},
"outputs": [],
"source": [
"def get_text_length(item):\n",
" return len(item.test_prompt())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85c89012-a922-401b-8a3b-94af641bf27a",
"metadata": {},
"outputs": [],
"source": [
"# investigate the brands\n",
"\n",
"brands = Counter()\n",
"for t in train:\n",
" brand = t.features.get(\"Brand\")\n",
" if brand:\n",
" brands[brand]+=1\n",
"\n",
"# Look at most common 40 brands\n",
"\n",
"brands.most_common(40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "386dde54-e028-4a6d-b291-cce889ac1fa3",
"metadata": {},
"outputs": [],
"source": [
"TOP_ELECTRONICS_BRANDS = [\"hp\", \"dell\", \"lenovo\", \"samsung\", \"asus\", \"sony\", \"canon\", \"apple\", \"intel\"]\n",
"def is_top_electronics_brand(item):\n",
" brand = item.features.get(\"Brand\")\n",
" return brand and brand.lower() in TOP_ELECTRONICS_BRANDS"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c31c9c59-9d0d-47a8-a046-f20ed8d38d4c",
"metadata": {},
"outputs": [],
"source": [
"def get_features(item):\n",
" return {\n",
" \"weight\": get_weight_with_default(item),\n",
" \"rank\": get_rank_with_default(item),\n",
" \"text_length\": get_text_length(item),\n",
" \"is_top_electronics_brand\": 1 if is_top_electronics_brand(item) else 0\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88850855-f5bd-4be2-9d7c-75bf8a21609b",
"metadata": {},
"outputs": [],
"source": [
"# Look at features in a training item\n",
"get_features(train[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee9b5298-68b7-497d-8b2e-875287bb25b2",
"metadata": {},
"outputs": [],
"source": [
"# A utility function to convert our features into a pandas dataframe\n",
"\n",
"def list_to_dataframe(items):\n",
" features = [get_features(item) for item in items]\n",
" df = pd.DataFrame(features)\n",
" df['price'] = [item.price for item in items]\n",
" return df\n",
"\n",
"train_df = list_to_dataframe(train)\n",
"test_df = list_to_dataframe(test[:250])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc1d68e0-ab33-40f4-9334-461d426af25c",
"metadata": {},
"outputs": [],
"source": [
"# Traditional Linear Regression!\n",
"\n",
"np.random.seed(42)\n",
"\n",
"# Separate features and target\n",
"feature_columns = ['weight', 'rank', 'text_length', 'is_top_electronics_brand']\n",
"\n",
"X_train = train_df[feature_columns]\n",
"y_train = train_df['price']\n",
"X_test = test_df[feature_columns]\n",
"y_test = test_df['price']\n",
"\n",
"# Train a Linear Regression\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n",
"\n",
"for feature, coef in zip(feature_columns, model.coef_):\n",
" print(f\"{feature}: {coef}\")\n",
"print(f\"Intercept: {model.intercept_}\")\n",
"\n",
"# Predict the test set and evaluate\n",
"y_pred = model.predict(X_test)\n",
"mse = mean_squared_error(y_test, y_pred)\n",
"r2 = r2_score(y_test, y_pred)\n",
"\n",
"print(f\"Mean Squared Error: {mse}\")\n",
"print(f\"R-squared Score: {r2}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6561c3c7-ac7f-458b-983c-4a164b9d02c3",
"metadata": {},
"outputs": [],
"source": [
"# Function to predict price for a new item\n",
"\n",
"def linear_regression_pricer(item):\n",
" features = get_features(item)\n",
" features_df = pd.DataFrame([features])\n",
" return model.predict(features_df)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9bf2caa4-657a-4fc6-9dcb-bed7eaf8dd65",
"metadata": {},
"outputs": [],
"source": [
"# test it\n",
"\n",
"Tester.test(linear_regression_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79e1574b-52ef-49cc-bfb5-e97252ed5db8",
"metadata": {},
"outputs": [],
"source": [
"# For the next few models, we prepare our documents and prices\n",
"# Note that we use the test prompt for the documents, otherwise we'll reveal the answer!!\n",
"\n",
"prices = np.array([float(item.price) for item in train])\n",
"documents = [item.test_prompt() for item in train]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e126c22e-53e7-4967-9ebb-6b7dd7fe4ade",
"metadata": {},
"outputs": [],
"source": [
"# Use the CountVectorizer for a Bag of Words model\n",
"\n",
"np.random.seed(42)\n",
"vectorizer = CountVectorizer(max_features=1000, stop_words='english')\n",
"X = vectorizer.fit_transform(documents)\n",
"regressor = LinearRegression()\n",
"regressor.fit(X, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b7148d3-3202-4536-a75c-1627495c51d3",
"metadata": {},
"outputs": [],
"source": [
"def bow_lr_pricer(item):\n",
" x = vectorizer.transform([item.test_prompt()])\n",
" return max(regressor.predict(x)[0], 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38f7f7d0-d22c-4282-92e5-9666a7b8535d",
"metadata": {},
"outputs": [],
"source": [
"# test it\n",
"\n",
"Tester.test(bow_lr_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b623079e-54fa-418f-b209-7d54ebbcc23a",
"metadata": {},
"outputs": [],
"source": [
"# The amazing word2vec model, implemented in gensim NLP library\n",
"\n",
"np.random.seed(42)\n",
"\n",
"# Preprocess the documents\n",
"processed_docs = [simple_preprocess(doc) for doc in documents]\n",
"\n",
"# Train Word2Vec model\n",
"w2v_model = Word2Vec(sentences=processed_docs, vector_size=400, window=5, min_count=1, workers=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3de4efc7-68a6-4443-b9fd-70ee9d722362",
"metadata": {},
"outputs": [],
"source": [
"# This step of averaging vectors across the document is a weakness in our approach\n",
"\n",
"def document_vector(doc):\n",
" doc_words = simple_preprocess(doc)\n",
" word_vectors = [w2v_model.wv[word] for word in doc_words if word in w2v_model.wv]\n",
" return np.mean(word_vectors, axis=0) if word_vectors else np.zeros(w2v_model.vector_size)\n",
"\n",
"# Create feature matrix\n",
"X_w2v = np.array([document_vector(doc) for doc in documents])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f05eeec-dab8-4007-8e8c-dcf4175b8861",
"metadata": {},
"outputs": [],
"source": [
"# Run Linear Regression on word2vec\n",
"\n",
"word2vec_lr_regressor = LinearRegression()\n",
"word2vec_lr_regressor.fit(X_w2v, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e43d3fb9-e013-4573-90bf-9a522132b555",
"metadata": {},
"outputs": [],
"source": [
"def word2vec_lr_pricer(item):\n",
" doc = item.test_prompt()\n",
" doc_vector = document_vector(doc)\n",
" return max(0, word2vec_lr_regressor.predict([doc_vector])[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6740319d-5c8e-4125-9106-97e2e8ab72c7",
"metadata": {},
"outputs": [],
"source": [
"Tester.test(word2vec_lr_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d6d3265-37c1-464c-a489-5be4df0a7276",
"metadata": {},
"outputs": [],
"source": [
"# Support Vector Machines\n",
"\n",
"np.random.seed(42)\n",
"svr_regressor = LinearSVR()\n",
"\n",
"svr_regressor.fit(X_w2v, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fcc289e6-56a1-4119-864f-2fdf8efde643",
"metadata": {},
"outputs": [],
"source": [
"def svr_pricer(item):\n",
" np.random.seed(42)\n",
" doc = item.test_prompt()\n",
" doc_vector = document_vector(doc)\n",
" return max(float(svr_regressor.predict([doc_vector])[0]),0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80286a48-7cca-40e6-af76-a814a23bb9dc",
"metadata": {},
"outputs": [],
"source": [
"Tester.test(svr_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6c44fe4-e4d9-4559-a8ed-d8f97e25b69f",
"metadata": {},
"outputs": [],
"source": [
"# And the powerful Random Forest regression\n",
"\n",
"rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=8)\n",
"rf_model.fit(X_w2v, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a38812d0-913b-400b-804f-51434d895d05",
"metadata": {},
"outputs": [],
"source": [
"def random_forest_pricer(item):\n",
" doc = item.test_prompt()\n",
" doc_vector = document_vector(doc)\n",
" return max(0, rf_model.predict([doc_vector])[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "88b51c01-c791-4fdc-8010-00b2e486b8ce",
"metadata": {},
"outputs": [],
"source": [
"Tester.test(random_forest_pricer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc85b271-4c92-480c-8843-2d7713b0fa57",
"metadata": {},
"outputs": [],
"source": []
}
],
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