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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 re\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\n",
"from openai import OpenAI"
]
},
{
"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": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b",
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI()"
]
},
{
"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": "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",
"`TestRunner(my_prediction_function).run()`\n",
"\n",
"To evaluate your model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5793f5c-e23e-4a74-9496-1e30dd1e8935",
"metadata": {},
"outputs": [],
"source": [
"class TestRunner:\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__\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()"
]
},
{
"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 messages_for(item):\n",
" system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n",
" user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\")\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1af1888-f94a-4106-b0d8-8a70939eec4e",
"metadata": {},
"outputs": [],
"source": [
"def get_price(s):\n",
" s = s.replace('$','').replace(',','')\n",
" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n",
" return float(match.group()) if match else 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f49e90d6-6749-4eb8-9347-5922b189d379",
"metadata": {},
"outputs": [],
"source": [
"def gpt_4o_frontier(item):\n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-2024-08-06\", \n",
" messages=messages_for(item),\n",
" seed=42,\n",
" max_tokens=5\n",
" )\n",
" reply = response.choices[0].message.content\n",
" return get_price(reply)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "766e697e-55bf-4521-b301-3b07d20045e0",
"metadata": {},
"outputs": [],
"source": [
"TestRunner(gpt_4o_frontier).run()"
]
},
{
"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",
"\n",
"TestRunner(random_predictor).run()"
]
},
{
"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_predictor(prompt):\n",
" return training_average"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cf384eb-30c2-40d8-b7e5-48942ac6a969",
"metadata": {},
"outputs": [],
"source": [
"# Run our constant predictor\n",
"\n",
"TestRunner(constant_predictor).run()"
]
},
{
"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": "fd7a41c5-0c51-41be-a61d-8e80c3e90930",
"metadata": {},
"outputs": [],
"source": [
"# Look at 20 most common features in training set\n",
"\n",
"import json\n",
"feature_count = Counter()\n",
"for item in train:\n",
" for f in item.features.keys():\n",
" feature_count[f]+=1\n"
]
},
{
"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"
]
},
{
"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 training ite,"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee9b5298-68b7-497d-8b2e-875287bb25b2",
"metadata": {},
"outputs": [],
"source": [
"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\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": [
"np.random.seed(42)\n",
"\n",
"# Separate features and target\n",
"feature_columns = [col for col in train_df.columns if col != 'price']\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",
"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",
"# Create and train the model\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Print coefficients\n",
"for feature, coef in zip(feature_columns, model.coef_):\n",
" print(f\"{feature}: {coef}\")\n",
"print(f\"Intercept: {model.intercept_}\")\n",
"\n",
"# Make predictions\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Evaluate the model\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}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6561c3c7-ac7f-458b-983c-4a164b9d02c3",
"metadata": {},
"outputs": [],
"source": [
"# Function to predict price for a new item\n",
"def linear_regression_predictor(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",
"TestRunner(linear_regression_predictor).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e126c22e-53e7-4967-9ebb-6b7dd7fe4ade",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"np.random.seed(42)\n",
"\n",
"prices = np.array([float(item.price) for item in train])\n",
"documents = [item.test_prompt() for item in train]\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 linear_regression_nlp_predictor(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",
"TestRunner(linear_regression_nlp_predictor).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b623079e-54fa-418f-b209-7d54ebbcc23a",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression\n",
"from gensim.models import Word2Vec\n",
"from gensim.utils import simple_preprocess\n",
"from sklearn.metrics import mean_squared_error, r2_score\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=300, window=5, min_count=1, workers=8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3de4efc7-68a6-4443-b9fd-70ee9d722362",
"metadata": {},
"outputs": [],
"source": [
"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 = np.array([document_vector(doc) for doc in documents])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f05eeec-dab8-4007-8e8c-dcf4175b8861",
"metadata": {},
"outputs": [],
"source": [
"word2vec_lr_regressor = LinearRegression()\n",
"word2vec_lr_regressor.fit(X, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e43d3fb9-e013-4573-90bf-9a522132b555",
"metadata": {},
"outputs": [],
"source": [
"def word2vec_lr_predictor(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": [
"TestRunner(word2vec_lr_predictor).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fefaa7ff-23e8-4834-9c13-a8c074a2b2ed",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"rf_model = RandomForestRegressor(n_estimators=100, random_state=42, n_jobs=8)\n",
"rf_model.fit(X, prices)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aec04c82-0d5d-4d2b-9c3c-f619a5dc766b",
"metadata": {},
"outputs": [],
"source": [
"def random_forest_predictor(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": "aa836ee2-f4ff-4e03-a0e4-c7faa4d99ed5",
"metadata": {},
"outputs": [],
"source": [
"TestRunner(random_forest_predictor).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f4ff4e3c-5abc-4647-a637-43e1ff05148f",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVR\n",
"np.random.seed(42)\n",
"\n",
"svr_regressor = SVR(kernel='linear')\n",
"svr_regressor.fit(X[:100_000], prices[:100_000])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d026284-a6ba-44e8-85aa-dad86a094e85",
"metadata": {},
"outputs": [],
"source": [
"def svr_predictor(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": "d47bc121-9717-4d42-b1f1-e9f54dcf7fa6",
"metadata": {},
"outputs": [],
"source": [
"TestRunner(svr_predictor).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6a41596-9065-414f-9c45-4a4b18494e13",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}