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‎README.md‎

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# SAIL
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ![main branch](https://github.com/IBM/sail/actions/workflows/build.yml/badge.svg?branch=main) [![](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/) <img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ![main branch](https://github.com/IBM/autosail/actions/workflows/build.yml/badge.svg?branch=main) [![](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/) <img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
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The library is for experimenting with streaming processing engines (SPEs) and incremental machine learning (IML) models. The main features of Sail are:
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@@ -12,7 +12,7 @@ The library is for experimenting with streaming processing engines (SPEs) and in
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## Documentation
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See the [**SAIL Wiki**](https://github.com/IBM/sail/wiki) for full documentation, installation guide, operational details and other information.
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See the [**SAIL Wiki**](https://github.com/IBM/autosail/wiki) for full documentation, installation guide, operational details and other information.
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## Architecture
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@@ -41,7 +41,7 @@ Sail could have been parallelized using Spark as well. However, to keep the stre
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Sail is intended to work with **Python 3.8 and above**. You can install the latest version from GitHub as so:
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```sh
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git clone https://github.com/IBM/sail.git
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git clone https://github.com/IBM/autosail.git
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cd sail
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pip install -e ".[OPTION]"
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```

‎examples/ensemble/simple_regression.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"import pandas as pd\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from river import stream\n",
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"from sail.transformers.river.preprocessing import StandardScaler\n",
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"from sail.models.river.linear_model import LinearRegression\n",
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"from autosail.transformers.river.preprocessing import StandardScaler\n",
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"from autosail.models.river.linear_model import LinearRegression\n",
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"import time\n",
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"from sail.models.ensemble.distAggregateRegressor import DistAggregateRegressor\n",
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"from autosail.models.ensemble.distAggregateRegressor import DistAggregateRegressor\n",
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"import numpy as np\n",
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"from river import optim\n",
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"import matplotlib.pyplot as plt "
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"df_X = pd.DataFrame(boston_data, columns=range(0, 13))\n",
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"df_y = pd.Series(boston_target)\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size=0.2, random_state=42)\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" df_X, df_y, test_size=0.2, random_state=42\n",
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")\n",
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"\n",
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"stdScaler_many = StandardScaler()\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"source": [
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"optimizers = [optim.SGD(0.01), optim.RMSProp(), optim.AdaGrad()]\n",
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"\n",
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"hedge = DistAggregateRegressor(estimators=[\n",
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" LinearRegression(optimizer=o, intercept_lr=.1)\n",
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" for o in optimizers],\n",
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" learning_rate=0.005\n",
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" )\n",
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"hedge = DistAggregateRegressor(\n",
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" estimators=[LinearRegression(optimizer=o, intercept_lr=0.1) for o in optimizers],\n",
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" learning_rate=0.005,\n",
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")\n",
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"\n",
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"for xi, yi in stream.iter_array(X_train.to_numpy(), y_train.to_numpy(), feature_names=list(X_train.columns)):\n",
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"for xi, yi in stream.iter_array(\n",
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" X_train.to_numpy(), y_train.to_numpy(), feature_names=list(X_train.columns)\n",
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"):\n",
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" xi = np.array([np.fromiter(xi.values(), dtype=float)])\n",
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" yi = np.array([yi])\n",
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" start = time.time()\n",
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" hedge.partial_fit(xi,yi) \n",
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" hedge.partial_fit(xi, yi)\n",
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" # hedge.partial_fit(x, yi)\n",
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"\n",
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"print(\"duration =\", time.time() - start)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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}
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],
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"source": [
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"for i in range(2): \n",
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" for xi, yi in stream.iter_array(X_train.to_numpy(), y_train.to_numpy(), feature_names=list(X_train.columns)):\n",
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"for i in range(2):\n",
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" for xi, yi in stream.iter_array(\n",
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" X_train.to_numpy(), y_train.to_numpy(), feature_names=list(X_train.columns)\n",
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" ):\n",
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" xi = np.array([np.fromiter(xi.values(), dtype=float)])\n",
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" yi = np.array([yi])\n",
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" start = time.time()\n",
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" hedge.partial_fit(xi,yi) \n",
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" hedge.partial_fit(xi, yi)\n",
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" # hedge.partial_fit(x, yi)\n",
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"\n",
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"print(\"duration =\", time.time() - start)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"\n",
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"mse = mean_squared_error(y_test, ypred_hedge)\n",
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"print(\"MSE_hedge: \", mse)\n",
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"print(\"RMSE_hedge: \", mse**(1/2.0))"
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"print(\"RMSE_hedge: \", mse ** (1 / 2.0))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"# plt.plot(x_ax, ypred_many, label=\"pred_many\")\n",
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"# plt.plot(x_ax, ypred_sk, label=\"pred_sk\")\n",
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"plt.title(\"Boston test and predicted data\")\n",
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"plt.xlabel('X-axis')\n",
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"plt.ylabel('Y-axis')\n",
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"plt.legend(loc='best',fancybox=True, shadow=True)\n",
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"plt.xlabel(\"X-axis\")\n",
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"plt.ylabel(\"Y-axis\")\n",
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"plt.legend(loc=\"best\", fancybox=True, shadow=True)\n",
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"plt.grid(True)\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "autosail",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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"version": "3.11.1"
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}
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},
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"nbformat": 4,

‎examples/model_selection/classification_model_selection.py‎

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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import StandardScaler
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from sail.model_selector.holdout_best_model import HoldoutBestModelSelector
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from sail.models.river.linear_model import LogisticRegression
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from sail.models.river.naive_bayes import BernoulliNB
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from autosail.model_selector.holdout_best_model import HoldoutBestModelSelector
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from autosail.models.river.linear_model import LogisticRegression
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from autosail.models.river.naive_bayes import BernoulliNB
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ray.init()
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sgd = LogisticRegression()
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bnb = BernoulliNB()
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offline_model = HoldoutBestModelSelector(
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estimators=[sgd, bnb], metrics=accuracy_score
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)
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offline_model = HoldoutBestModelSelector(estimators=[sgd, bnb], metrics=accuracy_score)
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# Ingestion
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for index in range(2):

‎examples/model_selection/regression_model_selection.py‎

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from sklearn.metrics import r2_score
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from sklearn.model_selection import train_test_split
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from sail.model_selector.holdout_best_model import HoldoutBestModelSelector
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from sail.models.native.ielm import IELM
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from sail.models.river.linear_model import LinearRegression
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from sail.transformers.river.preprocessing import StandardScaler
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from autosail.model_selector.holdout_best_model import HoldoutBestModelSelector
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from autosail.models.native.ielm import IELM
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from autosail.models.river.linear_model import LinearRegression
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from autosail.transformers.river.preprocessing import StandardScaler
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ray.init()
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‎examples/models/auto_ml/prequential_training.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from river import optim\n",
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"from river import metrics\n",
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"from river.drift.binary import EDDM\n",
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"from sail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from sail.models.river.forest import AdaptiveRandomForestRegressor\n",
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"from sail.models.river.linear_model import LinearRegression\n",
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"from sail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from sail.pipeline import SAILPipeline\n",
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"from autosail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from autosail.models.river.forest import AdaptiveRandomForestRegressor\n",
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"from autosail.models.river.linear_model import LinearRegression\n",
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"from autosail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from autosail.pipeline import SAILPipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from sail.transformers.river.preprocessing import StandardScaler"
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"from autosail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from autosail.transformers.river.preprocessing import StandardScaler"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv-sail",
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"display_name": "autosail",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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"version": "3.11.1"
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},
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"orig_nbformat": 4
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},

‎examples/models/auto_ml/sail_auto_pipeline_classification.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from river import optim\n",
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"from river import metrics\n",
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"from river.drift.binary import EDDM\n",
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"from sail.telemetry import TracingClient\n",
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"from sail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from sail.models.river.forest import AdaptiveRandomForestClassifier\n",
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"from sail.models.river.linear_model import LogisticRegression\n",
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"from sail.models.keras import KerasSequentialClassifier\n",
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"from sail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from sail.pipeline import SAILPipeline\n",
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"from autosail.telemetry import TracingClient\n",
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"from autosail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from autosail.models.river.forest import AdaptiveRandomForestClassifier\n",
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"from autosail.models.river.linear_model import LogisticRegression\n",
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"from autosail.models.keras import KerasSequentialClassifier\n",
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"from autosail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from autosail.pipeline import SAILPipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from sail.transformers.river.preprocessing import StandardScaler"
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"from autosail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from autosail.transformers.river.preprocessing import StandardScaler"
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]
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},
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{

‎examples/models/auto_ml/sail_auto_pipeline_regression.ipynb‎

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"cells": [
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{
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"source": [
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"from river import optim\n",
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"from river import metrics\n",
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"from river.drift.binary import EDDM\n",
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"from sail.telemetry import TracingClient\n",
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"from sail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from sail.models.river.forest import AdaptiveRandomForestRegressor\n",
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"from sail.models.river.linear_model import LinearRegression\n",
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"from sail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from sail.pipeline import SAILPipeline\n",
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"from autosail.telemetry import TracingClient\n",
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"from autosail.models.auto_ml.tune import SAILTuneGridSearchCV\n",
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"from autosail.models.river.forest import AdaptiveRandomForestRegressor\n",
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"from autosail.models.river.linear_model import LinearRegression\n",
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"from autosail.models.auto_ml.auto_pipeline import SAILAutoPipeline\n",
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"from autosail.pipeline import SAILPipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sail.models.torch.rnn import RNNRegressor\n",
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"from sail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from sail.transformers.river.preprocessing import StandardScaler"
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"from autosail.models.torch.rnn import RNNRegressor\n",
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"from autosail.drift_detection.drift_detector import SAILDriftDetector\n",
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"from autosail.transformers.river.preprocessing import StandardScaler"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv-sail",
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"display_name": "autosail",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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"version": "3.11.1"
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},
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"orig_nbformat": 4
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},

‎examples/models/keras/run_oslem.py‎

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from matplotlib import pyplot as plt
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from tensorflow import keras
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from sail.models.keras import OSELM
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from autosail.models.keras import OSELM
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model = OSELM(
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loss="mae",

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