models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducción a MLFlow y Databricks: acelerando el Machine Learning LifeCycle - Python Sevilla 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLFlow Models\n",
    "In this section, we can see an example of using a trained model from MLFlow experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After decide which model will be used, paste here both experiment_id and run_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_id = \"...\"\n",
    "run_id = \"...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()\n",
    "X = np.array(cancer.data)\n",
    "y = np.array(cancer.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, train_size=426, test_size=143, random_state=0)\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel = np.random.randint(len(X))\n",
    "x_sel = x_test[sel]\n",
    "y_sel = y_test[sel]\n",
    "print(y_sel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = mlflow.sklearn.load_model(f'./mlruns/{experiment_id}/{run_id}/artifacts/model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict([x_sel])"
   ]
  }
 ],
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