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minxia/dis
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release_up
| Author | SHA1 | Date | |
|---|---|---|---|
|
48e3e7b510 |
@@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@@ -46,7 +46,7 @@
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"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
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"This notebook also requires the following packages:\n",
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"* `azureml-contrib-fairness`\n",
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"* `fairlearn==0.4.6` (v0.5.0 will work with minor modifications)\n",
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"* `fairlearn==0.4.6`\n",
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"* `joblib`\n",
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"* `shap`\n",
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"\n",
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@@ -62,20 +62,13 @@
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"# !pip install --upgrade scikit-learn>=0.22.1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a id=\"LoadingData\"></a>\n",
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"## Loading the Data\n",
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"We use the well-known `adult` census dataset, which we will fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
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"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
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]
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},
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{
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@@ -86,16 +79,9 @@
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"source": [
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"from fairlearn.reductions import GridSearch, DemographicParity, ErrorRate\n",
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"from fairlearn.widget import FairlearnDashboard\n",
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"\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.datasets import fetch_openml\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn import svm\n",
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"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.compose import make_column_selector as selector\n",
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"from sklearn.pipeline import Pipeline\n",
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"\n",
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"import pandas as pd"
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]
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},
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@@ -103,7 +89,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We can now load and inspect the data:"
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"We can now load and inspect the data from the `shap` package:"
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]
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},
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{
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@@ -112,13 +98,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from fairness_nb_utils import fetch_openml_with_retries\n",
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"from utilities import fetch_openml_with_retries\n",
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"\n",
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"data = fetch_openml_with_retries(data_id=1590)\n",
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" \n",
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"# Extract the items we want\n",
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"X_raw = data.data\n",
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"y = (data.target == '>50K') * 1\n",
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"Y = (data.target == '>50K') * 1\n",
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"\n",
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"X_raw[\"race\"].value_counts().to_dict()"
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]
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@@ -127,7 +113,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We are going to treat the sex and race of each individual as protected attributes, and in this particular case we are going to remove these attributes from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). Protected attributes are often denoted by 'A' in the literature, and we follow that convention here:"
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"We are going to treat the sex of each individual as a protected attribute (where 0 indicates female and 1 indicates male), and in this particular case we are going separate this attribute out and drop it from the main data (this is not always the best option - see the [Fairlearn website](http://fairlearn.github.io/) for further discussion). We also separate out the Race column, but we will not perform any mitigation based on it. Finally, we perform some standard data preprocessing steps to convert the data into a format suitable for the ML algorithms"
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]
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},
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{
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@@ -137,14 +123,23 @@
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"outputs": [],
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"source": [
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"A = X_raw[['sex','race']]\n",
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"X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
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"X = X_raw.drop(labels=['sex', 'race'],axis = 1)\n",
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"X_dummies = pd.get_dummies(X)\n",
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"\n",
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"sc = StandardScaler()\n",
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"X_scaled = sc.fit_transform(X_dummies)\n",
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"X_scaled = pd.DataFrame(X_scaled, columns=X_dummies.columns)\n",
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"\n",
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"\n",
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"le = LabelEncoder()\n",
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"Y = le.fit_transform(Y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
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"With our data prepared, we can make the conventional split in to 'test' and 'train' subsets:"
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]
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},
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{
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@@ -153,76 +148,21 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
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" X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
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")\n",
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"\n",
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"# Ensure indices are aligned between X, y and A,\n",
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"# after all the slicing and splitting of DataFrames\n",
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"# and Series\n",
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
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" Y, \n",
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" A,\n",
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" test_size = 0.2,\n",
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" random_state=0,\n",
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" stratify=Y)\n",
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"\n",
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"# Work around indexing issue\n",
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"X_train = X_train.reset_index(drop=True)\n",
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"X_test = X_test.reset_index(drop=True)\n",
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"y_train = y_train.reset_index(drop=True)\n",
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"y_test = y_test.reset_index(drop=True)\n",
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"A_train = A_train.reset_index(drop=True)\n",
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"X_test = X_test.reset_index(drop=True)\n",
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"A_test = A_test.reset_index(drop=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
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"\n",
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"For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"numeric_transformer = Pipeline(\n",
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" steps=[\n",
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" (\"impute\", SimpleImputer()),\n",
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" (\"scaler\", StandardScaler()),\n",
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" ]\n",
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")\n",
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"\n",
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"categorical_transformer = Pipeline(\n",
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" [\n",
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" (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
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" (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
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" ]\n",
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")\n",
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"\n",
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"preprocessor = ColumnTransformer(\n",
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" transformers=[\n",
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" (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
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" (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = preprocessor.fit_transform(X_train)\n",
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"X_test = preprocessor.transform(X_test)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -241,7 +181,7 @@
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"source": [
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"unmitigated_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
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"\n",
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"unmitigated_predictor.fit(X_train, y_train)"
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"unmitigated_predictor.fit(X_train, Y_train)"
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]
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},
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{
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@@ -258,7 +198,7 @@
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"outputs": [],
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"source": [
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"FairlearnDashboard(sensitive_features=A_test, sensitive_feature_names=['Sex', 'Race'],\n",
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" y_true=y_test,\n",
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" y_true=Y_test,\n",
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" y_pred={\"unmitigated\": unmitigated_predictor.predict(X_test)})"
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]
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},
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@@ -309,10 +249,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"sweep.fit(X_train, y_train,\n",
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"sweep.fit(X_train, Y_train,\n",
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" sensitive_features=A_train.sex)\n",
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"\n",
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"# For Fairlearn v0.5.0, need sweep.predictors_\n",
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"predictors = sweep._predictors"
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]
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},
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@@ -334,9 +273,9 @@
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" classifier = lambda X: m.predict(X)\n",
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" \n",
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" error = ErrorRate()\n",
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" error.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
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" error.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.sex)\n",
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" disparity = DemographicParity()\n",
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" disparity.load_data(X_train, pd.Series(y_train), sensitive_features=A_train.sex)\n",
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" disparity.load_data(X_train, pd.Series(Y_train), sensitive_features=A_train.sex)\n",
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" \n",
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" errors.append(error.gamma(classifier)[0])\n",
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" disparities.append(disparity.gamma(classifier).max())\n",
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@@ -390,7 +329,7 @@
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"source": [
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"FairlearnDashboard(sensitive_features=A_test, \n",
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" sensitive_feature_names=['Sex', 'Race'],\n",
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" y_true=y_test.tolist(),\n",
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" y_true=Y_test.tolist(),\n",
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" y_pred=predictions_dominant)"
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]
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},
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@@ -398,7 +337,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When using sex as the sensitive feature and accuracy as the metric, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
|
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"When using sex as the sensitive feature, we see a Pareto front forming - the set of predictors which represent optimal tradeoffs between accuracy and disparity in predictions. In the ideal case, we would have a predictor at (1,0) - perfectly accurate and without any unfairness under demographic parity (with respect to the protected attribute \"sex\"). The Pareto front represents the closest we can come to this ideal based on our data and choice of estimator. Note the range of the axes - the disparity axis covers more values than the accuracy, so we can reduce disparity substantially for a small loss in accuracy. Finally, we also see that the unmitigated model is towards the top right of the plot, with high accuracy, but worst disparity.\n",
|
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"\n",
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"By clicking on individual models on the plot, we can inspect their metrics for disparity and accuracy in greater detail. In a real example, we would then pick the model which represented the best trade-off between accuracy and disparity given the relevant business constraints."
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]
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@@ -505,7 +444,7 @@
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"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
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"\n",
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"\n",
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"dash_dict = _create_group_metric_set(y_true=y_test,\n",
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"dash_dict = _create_group_metric_set(y_true=Y_test,\n",
|
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" predictions=predictions_dominant_ids,\n",
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" sensitive_features=sf,\n",
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" prediction_type='binary_classification')"
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@@ -48,7 +48,7 @@
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"Please see the [configuration notebook](../../configuration.ipynb) for information about creating one, if required.\n",
|
||||
"This notebook also requires the following packages:\n",
|
||||
"* `azureml-contrib-fairness`\n",
|
||||
"* `fairlearn==0.4.6` (should also work with v0.5.0)\n",
|
||||
"* `fairlearn==0.4.6`\n",
|
||||
"* `joblib`\n",
|
||||
"* `shap`\n",
|
||||
"\n",
|
||||
@@ -64,20 +64,13 @@
|
||||
"# !pip install --upgrade scikit-learn>=0.22.1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, please ensure that when you downloaded this notebook, you also downloaded the `fairness_nb_utils.py` file from the same location, and placed it in the same directory as this notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<a id=\"LoadingData\"></a>\n",
|
||||
"## Loading the Data\n",
|
||||
"We use the well-known `adult` census dataset, which we fetch from the OpenML website. We start with a fairly unremarkable set of imports:"
|
||||
"We use the well-known `adult` census dataset, which we load using `shap` (for convenience). We start with a fairly unremarkable set of imports:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -87,14 +80,9 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import svm\n",
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"from sklearn.datasets import fetch_openml\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.compose import make_column_selector as selector\n",
|
||||
"from sklearn.pipeline import Pipeline"
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -110,13 +98,13 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fairness_nb_utils import fetch_openml_with_retries\n",
|
||||
"from utilities import fetch_openml_with_retries\n",
|
||||
"\n",
|
||||
"data = fetch_openml_with_retries(data_id=1590)\n",
|
||||
" \n",
|
||||
"# Extract the items we want\n",
|
||||
"X_raw = data.data\n",
|
||||
"y = (data.target == '>50K') * 1"
|
||||
"Y = (data.target == '>50K') * 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -142,7 +130,7 @@
|
||||
"<a id=\"ProcessingData\"></a>\n",
|
||||
"## Processing the Data\n",
|
||||
"\n",
|
||||
"With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and leave the rest of the feature data in `X_raw`:"
|
||||
"With the data loaded, we process it for our needs. First, we extract the sensitive features of interest into `A` (conventionally used in the literature) and put the rest of the feature data into `X`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -152,14 +140,15 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = X_raw[['sex','race']]\n",
|
||||
"X_raw = X_raw.drop(labels=['sex', 'race'],axis = 1)"
|
||||
"X = X_raw.drop(labels=['sex', 'race'],axis = 1)\n",
|
||||
"X_dummies = pd.get_dummies(X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We now preprocess our data. To avoid the problem of data leakage, we split our data into training and test sets before performing any other transformations. Subsequent transformations (such as scalings) will be fit to the training data set, and then applied to the test dataset."
|
||||
"Next, we apply a standard set of scalings:"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -168,76 +157,42 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"(X_train, X_test, y_train, y_test, A_train, A_test) = train_test_split(\n",
|
||||
" X_raw, y, A, test_size=0.3, random_state=12345, stratify=y\n",
|
||||
")\n",
|
||||
"sc = StandardScaler()\n",
|
||||
"X_scaled = sc.fit_transform(X_dummies)\n",
|
||||
"X_scaled = pd.DataFrame(X_scaled, columns=X_dummies.columns)\n",
|
||||
"\n",
|
||||
"# Ensure indices are aligned between X, y and A,\n",
|
||||
"# after all the slicing and splitting of DataFrames\n",
|
||||
"# and Series\n",
|
||||
"le = LabelEncoder()\n",
|
||||
"Y = le.fit_transform(Y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we can then split our data into training and test sets, and also make the labels on our test portion of `A` human-readable:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"X_train, X_test, Y_train, Y_test, A_train, A_test = train_test_split(X_scaled, \n",
|
||||
" Y, \n",
|
||||
" A,\n",
|
||||
" test_size = 0.2,\n",
|
||||
" random_state=0,\n",
|
||||
" stratify=Y)\n",
|
||||
"\n",
|
||||
"# Work around indexing issue\n",
|
||||
"X_train = X_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"y_train = y_train.reset_index(drop=True)\n",
|
||||
"y_test = y_test.reset_index(drop=True)\n",
|
||||
"A_train = A_train.reset_index(drop=True)\n",
|
||||
"X_test = X_test.reset_index(drop=True)\n",
|
||||
"A_test = A_test.reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We have two types of column in the dataset - categorical columns which will need to be one-hot encoded, and numeric ones which will need to be rescaled. We also need to take care of missing values. We use a simple approach here, but please bear in mind that this is another way that bias could be introduced (especially if one subgroup tends to have more missing values).\n",
|
||||
"\n",
|
||||
"For this preprocessing, we make use of `Pipeline` objects from `sklearn`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"numeric_transformer = Pipeline(\n",
|
||||
" steps=[\n",
|
||||
" (\"impute\", SimpleImputer()),\n",
|
||||
" (\"scaler\", StandardScaler()),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"categorical_transformer = Pipeline(\n",
|
||||
" [\n",
|
||||
" (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
|
||||
" (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\", sparse=False)),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"preprocessor = ColumnTransformer(\n",
|
||||
" transformers=[\n",
|
||||
" (\"num\", numeric_transformer, selector(dtype_exclude=\"category\")),\n",
|
||||
" (\"cat\", categorical_transformer, selector(dtype_include=\"category\")),\n",
|
||||
" ]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now, the preprocessing pipeline is defined, we can run it on our training data, and apply the generated transform to our test data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = preprocessor.fit_transform(X_train)\n",
|
||||
"X_test = preprocessor.transform(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
@@ -256,7 +211,7 @@
|
||||
"source": [
|
||||
"lr_predictor = LogisticRegression(solver='liblinear', fit_intercept=True)\n",
|
||||
"\n",
|
||||
"lr_predictor.fit(X_train, y_train)"
|
||||
"lr_predictor.fit(X_train, Y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -274,7 +229,7 @@
|
||||
"source": [
|
||||
"svm_predictor = svm.SVC()\n",
|
||||
"\n",
|
||||
"svm_predictor.fit(X_train, y_train)"
|
||||
"svm_predictor.fit(X_train, Y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -393,7 +348,7 @@
|
||||
"\n",
|
||||
"FairlearnDashboard(sensitive_features=A_test, \n",
|
||||
" sensitive_feature_names=['Sex', 'Race'],\n",
|
||||
" y_true=y_test.tolist(),\n",
|
||||
" y_true=Y_test.tolist(),\n",
|
||||
" y_pred=ys_pred)"
|
||||
]
|
||||
},
|
||||
@@ -423,7 +378,7 @@
|
||||
"\n",
|
||||
"from fairlearn.metrics._group_metric_set import _create_group_metric_set\n",
|
||||
"\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=y_test,\n",
|
||||
"dash_dict = _create_group_metric_set(y_true=Y_test,\n",
|
||||
" predictions=ys_pred,\n",
|
||||
" sensitive_features=sf,\n",
|
||||
" prediction_type='binary_classification')"
|
||||
|
||||
@@ -2,7 +2,7 @@ name: azure_automl
|
||||
dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.5.2 and later.
|
||||
- pip==20.2.4
|
||||
- pip<=19.3.1
|
||||
- python>=3.5.2,<3.8
|
||||
- nb_conda
|
||||
- boto3==1.15.18
|
||||
@@ -21,9 +21,8 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.23.0
|
||||
- azureml-widgets~=1.19.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_win32_requirements.txt [--no-deps]
|
||||
- PyJWT < 2.0.0
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_win32_requirements.txt [--no-deps]
|
||||
|
||||
@@ -2,7 +2,7 @@ name: azure_automl
|
||||
dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.5.2 and later.
|
||||
- pip==20.2.4
|
||||
- pip<=19.3.1
|
||||
- python>=3.5.2,<3.8
|
||||
- nb_conda
|
||||
- boto3==1.15.18
|
||||
@@ -21,10 +21,9 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.23.0
|
||||
- azureml-widgets~=1.19.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_linux_requirements.txt [--no-deps]
|
||||
- PyJWT < 2.0.0
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_linux_requirements.txt [--no-deps]
|
||||
|
||||
|
||||
@@ -2,7 +2,7 @@ name: azure_automl
|
||||
dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.5.2 and later.
|
||||
- pip==20.2.4
|
||||
- pip<=19.3.1
|
||||
- nomkl
|
||||
- python>=3.5.2,<3.8
|
||||
- nb_conda
|
||||
@@ -22,9 +22,8 @@ dependencies:
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-widgets~=1.23.0
|
||||
- azureml-widgets~=1.19.0
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.23.0/validated_darwin_requirements.txt [--no-deps]
|
||||
- PyJWT < 2.0.0
|
||||
- -r https://automlcesdkdataresources.blob.core.windows.net/validated-requirements/1.19.0/validated_darwin_requirements.txt [--no-deps]
|
||||
|
||||
@@ -105,7 +105,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -167,7 +167,7 @@
|
||||
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-classification-bank-marketing-all-features
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -93,7 +93,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-classification-credit-card-fraud
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -298,7 +298,7 @@
|
||||
" compute_target=compute_target,\n",
|
||||
" training_data=train_dataset,\n",
|
||||
" label_column_name=target_column_name,\n",
|
||||
" blocked_models = ['LightGBM', 'XGBoostClassifier'],\n",
|
||||
" blocked_models = ['LightGBM'],\n",
|
||||
" **automl_settings\n",
|
||||
" )"
|
||||
]
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-classification-text-dnn
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -81,7 +81,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -143,7 +143,7 @@
|
||||
"You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-continuous-retraining
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -5,13 +5,16 @@ dependencies:
|
||||
- pip<=19.3.1
|
||||
- python>=3.5.2,<3.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy~=1.18.0
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scikit-learn==0.22.1
|
||||
- pandas==0.25.1
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-defaults
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
- PyJWT < 2.0.0
|
||||
- azureml-explain-model
|
||||
|
||||
@@ -6,13 +6,16 @@ dependencies:
|
||||
- nomkl
|
||||
- python>=3.5.2,<3.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy~=1.18.0
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scikit-learn==0.22.1
|
||||
- pandas==0.25.1
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-defaults
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
- PyJWT < 2.0.0
|
||||
- azureml-explain-model
|
||||
|
||||
@@ -67,8 +67,11 @@
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
@@ -90,7 +93,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -113,7 +116,9 @@
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Run History Name'] = experiment_name\n",
|
||||
"output"
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -194,7 +199,6 @@
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
|
||||
"|**scenario**|We need to set this parameter to 'Latest' to enable some experimental features. This parameter should not be set outside of this experimental notebook.|\n",
|
||||
"\n",
|
||||
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
|
||||
]
|
||||
@@ -223,7 +227,6 @@
|
||||
" compute_target = compute_target,\n",
|
||||
" training_data = train_data,\n",
|
||||
" label_column_name = label,\n",
|
||||
" scenario='Latest',\n",
|
||||
" **automl_settings\n",
|
||||
" )"
|
||||
]
|
||||
@@ -271,13 +274,34 @@
|
||||
"## Results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Widget for Monitoring Runs\n",
|
||||
"\n",
|
||||
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
|
||||
"\n",
|
||||
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run.wait_for_completion(show_output=True)"
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(remote_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -342,12 +366,18 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_test = test_data.keep_columns('ERP')\n",
|
||||
"test_data = test_data.drop_columns('ERP')\n",
|
||||
"# preview the first 3 rows of the dataset\n",
|
||||
"\n",
|
||||
"test_data = test_data.to_pandas_dataframe()\n",
|
||||
"y_test = test_data['ERP'].fillna(0)\n",
|
||||
"test_data = test_data.drop('ERP', 1)\n",
|
||||
"test_data = test_data.fillna(0)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"y_train = train_data.keep_columns('ERP')\n",
|
||||
"train_data = train_data.drop_columns('ERP')\n"
|
||||
"train_data = train_data.to_pandas_dataframe()\n",
|
||||
"y_train = train_data['ERP'].fillna(0)\n",
|
||||
"train_data = train_data.drop('ERP', 1)\n",
|
||||
"train_data = train_data.fillna(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -365,16 +395,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.automl.model_proxy import ModelProxy\n",
|
||||
"best_model_proxy = ModelProxy(best_run)\n",
|
||||
"y_pred_train = best_model_proxy.predict(train_data)\n",
|
||||
"y_pred_test = best_model_proxy.predict(test_data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Exploring results"
|
||||
"best_model_proxy = ModelProxy(best_run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -383,15 +404,60 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred_train = y_pred_train.to_pandas_dataframe().values.flatten()\n",
|
||||
"y_train = y_train.to_pandas_dataframe().values.flatten()\n",
|
||||
"y_pred_train = best_model_proxy.predict(train_data).to_pandas_dataframe().values.flatten()\n",
|
||||
"y_residual_train = y_train - y_pred_train\n",
|
||||
"\n",
|
||||
"y_pred_test = y_pred_test.to_pandas_dataframe().values.flatten()\n",
|
||||
"y_test = y_test.to_pandas_dataframe().values.flatten()\n",
|
||||
"y_residual_test = y_test - y_pred_test\n",
|
||||
"print(y_residual_train)\n",
|
||||
"print(y_residual_test)"
|
||||
"y_pred_test = best_model_proxy.predict(test_data).to_pandas_dataframe().values.flatten()\n",
|
||||
"y_residual_test = y_test - y_pred_test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"from sklearn.metrics import mean_squared_error, r2_score\n",
|
||||
"\n",
|
||||
"# Set up a multi-plot chart.\n",
|
||||
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
|
||||
"f.suptitle('Regression Residual Values', fontsize = 18)\n",
|
||||
"f.set_figheight(6)\n",
|
||||
"f.set_figwidth(16)\n",
|
||||
"\n",
|
||||
"# Plot residual values of training set.\n",
|
||||
"a0.axis([0, 360, -100, 100])\n",
|
||||
"a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
|
||||
"a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
|
||||
"a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
|
||||
"a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)),fontsize = 12)\n",
|
||||
"a0.set_xlabel('Training samples', fontsize = 12)\n",
|
||||
"a0.set_ylabel('Residual Values', fontsize = 12)\n",
|
||||
"\n",
|
||||
"# Plot residual values of test set.\n",
|
||||
"a1.axis([0, 90, -100, 100])\n",
|
||||
"a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
|
||||
"a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
|
||||
"a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
|
||||
"a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)),fontsize = 12)\n",
|
||||
"a1.set_xlabel('Test samples', fontsize = 12)\n",
|
||||
"a1.set_yticklabels([])\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"test_pred = plt.scatter(y_test, y_pred_test, color='')\n",
|
||||
"test_test = plt.scatter(y_test, y_test, color='g')\n",
|
||||
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-regression-model-proxy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -113,7 +113,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -218,8 +218,6 @@
|
||||
"\n",
|
||||
"**Time series identifier columns** are identified by values of the columns listed `time_series_id_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
|
||||
"\n",
|
||||
"**Forecast frequency (freq)** This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information.\n",
|
||||
"\n",
|
||||
"This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-forecasting-beer-remote
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -87,7 +87,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -131,7 +131,7 @@
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -205,10 +205,6 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'dataset/bike-no.csv')]).with_timestamp_columns(fine_grain_timestamp=time_column_name) \n",
|
||||
"\n",
|
||||
"# Drop the columns 'casual' and 'registered' as these columns are a breakdown of the total and therefore a leak.\n",
|
||||
"dataset = dataset.drop_columns(columns=['casual', 'registered'])\n",
|
||||
"\n",
|
||||
"dataset.take(5).to_pandas_dataframe().reset_index(drop=True)"
|
||||
]
|
||||
},
|
||||
@@ -255,7 +251,7 @@
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
|
||||
"|**country_or_region_for_holidays**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
|
||||
"|**target_lags**|The target_lags specifies how far back we will construct the lags of the target variable.|\n",
|
||||
"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
|
||||
"|**drop_column_names**|Name(s) of columns to drop prior to modeling|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -318,7 +314,8 @@
|
||||
" time_column_name=time_column_name,\n",
|
||||
" forecast_horizon=forecast_horizon,\n",
|
||||
" country_or_region_for_holidays='US', # set country_or_region will trigger holiday featurizer\n",
|
||||
" target_lags='auto' # use heuristic based lag setting \n",
|
||||
" target_lags='auto', # use heuristic based lag setting \n",
|
||||
" drop_column_names=['casual', 'registered'] # these columns are a breakdown of the total and therefore a leak\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting', \n",
|
||||
@@ -551,9 +548,6 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For more details on what metrics are included and how they are calculated, please refer to [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics). You could also calculate residuals, like described [here](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Since we did a rolling evaluation on the test set, we can analyze the predictions by their forecast horizon relative to the rolling origin. The model was initially trained at a forecast horizon of 14, so each prediction from the model is associated with a horizon value from 1 to 14. The horizon values are in a column named, \"horizon_origin,\" in the prediction set. For example, we can calculate some of the error metrics grouped by the horizon:"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-forecasting-bike-share
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -97,7 +97,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -301,8 +301,7 @@
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
|
||||
"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -498,7 +497,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Evaluate\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE).\n",
|
||||
"\n",
|
||||
"It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
|
||||
]
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-forecasting-energy-demand
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -94,7 +94,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -302,8 +302,7 @@
|
||||
"* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
|
||||
"* Set limitations on the length of experiment run to 15 minutes.\n",
|
||||
"* Finally, we set the task to be forecasting.\n",
|
||||
"* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones.\n",
|
||||
"* [Optional] Forecast frequency parameter (freq) represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
|
||||
"* We apply the lag lead operator to the target value i.e. we use the previous values as a predictor for the future ones."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-forecasting-function
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -82,7 +82,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -126,7 +126,7 @@
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute) for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
|
||||
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
|
||||
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
|
||||
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -169,10 +169,6 @@
|
||||
"source": [
|
||||
"time_column_name = 'WeekStarting'\n",
|
||||
"data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
|
||||
"\n",
|
||||
"# Drop the columns 'logQuantity' as it is a leaky feature.\n",
|
||||
"data.drop('logQuantity', axis=1, inplace=True)\n",
|
||||
"\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
@@ -347,6 +343,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"featurization_config = FeaturizationConfig()\n",
|
||||
"featurization_config.drop_columns = ['logQuantity'] # 'logQuantity' is a leaky feature, so we remove it.\n",
|
||||
"# Force the CPWVOL5 feature to be numeric type.\n",
|
||||
"featurization_config.add_column_purpose('CPWVOL5', 'Numeric')\n",
|
||||
"# Fill missing values in the target column, Quantity, with zeros.\n",
|
||||
@@ -369,8 +366,7 @@
|
||||
"|-|-|\n",
|
||||
"|**time_column_name**|The name of your time column.|\n",
|
||||
"|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n",
|
||||
"|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n",
|
||||
"|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information."
|
||||
"|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -575,7 +571,7 @@
|
||||
"source": [
|
||||
"# Evaluate\n",
|
||||
"\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). For more metrics that can be used for evaluation after training, please see [supported metrics](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#regressionforecasting-metrics), and [how to calculate residuals](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#residuals).\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
|
||||
"\n",
|
||||
"We'll add predictions and actuals into a single dataframe for convenience in calculating the metrics."
|
||||
]
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-forecasting-orange-juice-sales
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-classification-credit-card-fraud-local
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -96,7 +96,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-regression-explanation-featurization
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -66,8 +66,7 @@ engineered_explanations = explainer.explain(['local', 'global'], tag='engineered
|
||||
# Compute the raw explanations
|
||||
raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
|
||||
raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
|
||||
eval_dataset=automl_explainer_setup_obj.X_test_transform,
|
||||
raw_eval_dataset=automl_explainer_setup_obj.X_test_raw)
|
||||
eval_dataset=automl_explainer_setup_obj.X_test_transform)
|
||||
|
||||
print("Engineered and raw explanations computed successfully")
|
||||
|
||||
|
||||
@@ -92,7 +92,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.23.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.19.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
@@ -375,12 +375,18 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_test = test_data.keep_columns('ERP').to_pandas_dataframe()\n",
|
||||
"test_data = test_data.drop_columns('ERP').to_pandas_dataframe()\n",
|
||||
"# preview the first 3 rows of the dataset\n",
|
||||
"\n",
|
||||
"test_data = test_data.to_pandas_dataframe()\n",
|
||||
"y_test = test_data['ERP'].fillna(0)\n",
|
||||
"test_data = test_data.drop('ERP', 1)\n",
|
||||
"test_data = test_data.fillna(0)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"y_train = train_data.keep_columns('ERP').to_pandas_dataframe()\n",
|
||||
"train_data = train_data.drop_columns('ERP').to_pandas_dataframe()\n"
|
||||
"train_data = train_data.to_pandas_dataframe()\n",
|
||||
"y_train = train_data['ERP'].fillna(0)\n",
|
||||
"train_data = train_data.drop('ERP', 1)\n",
|
||||
"train_data = train_data.fillna(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -390,10 +396,10 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred_train = fitted_model.predict(train_data)\n",
|
||||
"y_residual_train = y_train.values - y_pred_train\n",
|
||||
"y_residual_train = y_train - y_pred_train\n",
|
||||
"\n",
|
||||
"y_pred_test = fitted_model.predict(test_data)\n",
|
||||
"y_residual_test = y_test.values - y_pred_test"
|
||||
"y_residual_test = y_test - y_pred_test"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: auto-ml-regression
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -0,0 +1,6 @@
|
||||
name: multi-model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- scikit-learn
|
||||
@@ -0,0 +1,6 @@
|
||||
name: model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- scikit-learn
|
||||
@@ -77,7 +77,7 @@
|
||||
"source": [
|
||||
"## Create trained model\n",
|
||||
"\n",
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset). "
|
||||
"For this example, we will train a small model on scikit-learn's [diabetes dataset](https://scikit-learn.org/stable/datasets/index.html#diabetes-dataset). "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -382,111 +382,13 @@
|
||||
"source": [
|
||||
"## Update Service\n",
|
||||
"\n",
|
||||
"If you want to change your model(s), Conda dependencies or deployment configuration, call `update()` to rebuild the Docker image.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_service.update(models=[model],\n",
|
||||
"If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"local_service.update(models=[SomeOtherModelObject],\n",
|
||||
" inference_config=inference_config,\n",
|
||||
" deployment_config=deployment_config)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy model to AKS cluster based on the LocalWebservice's configuration."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# This is a one time setup for AKS Cluster. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it.\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your AKS cluster\n",
|
||||
"aks_name = 'my-aks-9' \n",
|
||||
"\n",
|
||||
"# Verify the cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" # Use the default configuration (can also provide parameters to customize)\n",
|
||||
" prov_config = AksCompute.provisioning_configuration()\n",
|
||||
"\n",
|
||||
" # Create the cluster\n",
|
||||
" aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = prov_config)\n",
|
||||
"\n",
|
||||
"if aks_target.get_status() != \"Succeeded\":\n",
|
||||
" aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import AksWebservice\n",
|
||||
"# Set the web service configuration (using default here)\n",
|
||||
"aks_config = AksWebservice.deploy_configuration()\n",
|
||||
"\n",
|
||||
"# # Enable token auth and disable (key) auth on the webservice\n",
|
||||
"# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"aks_service_name ='aks-service-1'\n",
|
||||
"\n",
|
||||
"aks_service = local_service.deploy_to_cloud(name=aks_service_name,\n",
|
||||
" deployment_config=aks_config,\n",
|
||||
" deployment_target=aks_target)\n",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output = True)\n",
|
||||
"print(aks_service.state)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test aks service\n",
|
||||
"\n",
|
||||
"sample_input = json.dumps({\n",
|
||||
" 'data': dataset_x[0:2].tolist()\n",
|
||||
"})\n",
|
||||
"\n",
|
||||
"aks_service.run(sample_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Delete the service if not needed.\n",
|
||||
"aks_service.delete()"
|
||||
" deployment_config=local_config)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: deploy-aks-with-controlled-rollout
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -276,17 +276,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AksCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"from azureml.exceptions import ComputeTargetException\n",
|
||||
"\n",
|
||||
"aks_name = \"my-aks-insights\"\n",
|
||||
"aks_name = \"my-aks\"\n",
|
||||
"\n",
|
||||
"creating_compute = False\n",
|
||||
"try:\n",
|
||||
" aks_target = ComputeTarget(ws, aks_name)\n",
|
||||
" print(\"Using existing AKS compute target {}.\".format(aks_name))\n",
|
||||
" print(\"Using existing AKS cluster {}.\".format(aks_name))\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"Creating a new AKS compute target {}.\".format(aks_name))\n",
|
||||
" print(\"Creating a new AKS cluster {}.\".format(aks_name))\n",
|
||||
"\n",
|
||||
" # Use the default configuration (can also provide parameters to customize).\n",
|
||||
" prov_config = AksCompute.provisioning_configuration()\n",
|
||||
@@ -303,7 +302,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"if creating_compute and aks_target.provisioning_state != \"Succeeded\":\n",
|
||||
"if creating_compute:\n",
|
||||
" aks_target.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
@@ -383,7 +382,7 @@
|
||||
" aks_service.wait_for_deployment(show_output=True)\n",
|
||||
" print(aks_service.state)\n",
|
||||
"else:\n",
|
||||
" raise ValueError(\"AKS cluster provisioning failed. Error: \", aks_target.provisioning_errors)"
|
||||
" raise ValueError(\"AKS provisioning failed. Error: \", aks_service.error)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -461,9 +460,7 @@
|
||||
"%%time\n",
|
||||
"aks_service.delete()\n",
|
||||
"aci_service.delete()\n",
|
||||
"model.delete()\n",
|
||||
"if creating_compute:\n",
|
||||
" aks_target.delete()"
|
||||
"model.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: enable-app-insights-in-production-service
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -0,0 +1,8 @@
|
||||
name: onnx-convert-aml-deploy-tinyyolo
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- numpy
|
||||
- git+https://github.com/apple/coremltools@v2.1
|
||||
- onnx<1.7.0
|
||||
- onnxmltools
|
||||
@@ -70,16 +70,16 @@
|
||||
"\n",
|
||||
"import urllib.request\n",
|
||||
"\n",
|
||||
"onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus/model/emotion-ferplus-7.tar.gz?raw=true\"\n",
|
||||
"onnx_model_url = \"https://www.cntk.ai/OnnxModels/emotion_ferplus/opset_7/emotion_ferplus.tar.gz\"\n",
|
||||
"\n",
|
||||
"urllib.request.urlretrieve(onnx_model_url, filename=\"emotion-ferplus-7.tar.gz\")\n",
|
||||
"urllib.request.urlretrieve(onnx_model_url, filename=\"emotion_ferplus.tar.gz\")\n",
|
||||
"\n",
|
||||
"# the ! magic command tells our jupyter notebook kernel to run the following line of \n",
|
||||
"# code from the command line instead of the notebook kernel\n",
|
||||
"\n",
|
||||
"# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
|
||||
"\n",
|
||||
"!tar xvzf emotion-ferplus-7.tar.gz"
|
||||
"!tar xvzf emotion_ferplus.tar.gz"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -570,7 +570,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize = (16, 6))\n",
|
||||
"plt.figure(figsize = (16, 6), frameon=False)\n",
|
||||
"plt.subplot(1, 8, 1)\n",
|
||||
"\n",
|
||||
"plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
name: onnx-inference-facial-expression-recognition-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- numpy
|
||||
- onnx<1.7.0
|
||||
- opencv-python-headless
|
||||
@@ -70,9 +70,9 @@
|
||||
"\n",
|
||||
"import urllib.request\n",
|
||||
"\n",
|
||||
"onnx_model_url = \"https://github.com/onnx/models/blob/master/vision/classification/mnist/model/mnist-7.tar.gz?raw=true\"\n",
|
||||
"onnx_model_url = \"https://www.cntk.ai/OnnxModels/mnist/opset_7/mnist.tar.gz\"\n",
|
||||
"\n",
|
||||
"urllib.request.urlretrieve(onnx_model_url, filename=\"mnist-7.tar.gz\")"
|
||||
"urllib.request.urlretrieve(onnx_model_url, filename=\"mnist.tar.gz\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -86,7 +86,7 @@
|
||||
"\n",
|
||||
"# We use tar and xvcf to unzip the files we just retrieved from the ONNX model zoo\n",
|
||||
"\n",
|
||||
"!tar xvzf mnist-7.tar.gz"
|
||||
"!tar xvzf mnist.tar.gz"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -521,7 +521,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize = (16, 6))\n",
|
||||
"plt.figure(figsize = (16, 6), frameon=False)\n",
|
||||
"plt.subplot(1, 8, 1)\n",
|
||||
"\n",
|
||||
"plt.text(x = 0, y = -30, s = \"True Label: \", fontsize = 13, color = 'black')\n",
|
||||
@@ -684,7 +684,18 @@
|
||||
"\n",
|
||||
"A convolution layer is a set of filters. Each filter is defined by a weight (**W**) matrix, and bias ($b$).\n",
|
||||
"\n",
|
||||
"These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function."
|
||||
"\n",
|
||||
"\n",
|
||||
"These filters are scanned across the image performing the dot product between the weights and corresponding input value ($x$). The bias value is added to the output of the dot product and the resulting sum is optionally mapped through an activation function. This process is illustrated in the following animation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Image(url=\"https://www.cntk.ai/jup/cntk103d_conv2d_final.gif\", width= 200)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -696,6 +707,24 @@
|
||||
"The MNIST model from the ONNX Model Zoo uses maxpooling to update the weights in its convolutions, summarized by the graphic below. You can see the entire workflow of our pre-trained model in the following image, with our input images and our output probabilities of each of our 10 labels. If you're interested in exploring the logic behind creating a Deep Learning model further, please look at the [training tutorial for our ONNX MNIST Convolutional Neural Network](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Max-Pooling for Convolutional Neural Nets\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Pre-Trained Model Architecture\n",
|
||||
"\n",
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
name: onnx-inference-mnist-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- numpy
|
||||
- onnx<1.7.0
|
||||
- opencv-python-headless
|
||||
@@ -0,0 +1,4 @@
|
||||
name: onnx-model-register-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -0,0 +1,4 @@
|
||||
name: onnx-modelzoo-aml-deploy-resnet50
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -0,0 +1,5 @@
|
||||
name: onnx-train-pytorch-aml-deploy-mnist
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,5 @@
|
||||
name: production-deploy-to-aks-gpu
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- tensorflow
|
||||
@@ -226,7 +226,7 @@
|
||||
"# Leaf domain label generates a name using the formula\n",
|
||||
"# \"<leaf-domain-label>######.<azure-region>.cloudapp.azure.net\"\n",
|
||||
"# where \"######\" is a random series of characters\n",
|
||||
"provisioning_config.enable_ssl(leaf_domain_label = \"contoso\", overwrite_existing_domain = True)\n",
|
||||
"provisioning_config.enable_ssl(leaf_domain_label = \"contoso\")\n",
|
||||
"\n",
|
||||
"aks_name = 'my-aks-ssl-1' \n",
|
||||
"# Create the cluster\n",
|
||||
|
||||
@@ -0,0 +1,8 @@
|
||||
name: production-deploy-to-aks-ssl
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- matplotlib
|
||||
- tqdm
|
||||
- scipy
|
||||
- sklearn
|
||||
@@ -0,0 +1,8 @@
|
||||
name: production-deploy-to-aks
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- matplotlib
|
||||
- tqdm
|
||||
- scipy
|
||||
- sklearn
|
||||
@@ -0,0 +1,4 @@
|
||||
name: model-register-and-deploy-spark
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -259,7 +259,7 @@
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
|
||||
|
||||
@@ -0,0 +1,11 @@
|
||||
name: explain-model-on-amlcompute
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- interpret-community[visualization]
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas<2.0.0
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
@@ -57,7 +57,7 @@
|
||||
"Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
|
||||
"\n",
|
||||
"1. Train a SVM classification model using Scikit-learn\n",
|
||||
"2. Run 'explain-model-sample' with AML Run History, which leverages run history service to store and manage the explanation data\n",
|
||||
"2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
|
||||
"---\n",
|
||||
"\n",
|
||||
"Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
|
||||
@@ -475,7 +475,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = 'explain-model-sample'\n",
|
||||
"experiment_name = 'explain_model'\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"run = experiment.start_logging()\n",
|
||||
"client = ExplanationClient.from_run(run)"
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
name: save-retrieve-explanations-run-history
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- interpret-community[visualization]
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- ipywidgets
|
||||
@@ -323,7 +323,7 @@
|
||||
"\n",
|
||||
"# azureml-defaults is required to host the model as a web service.\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
" \n",
|
||||
|
||||
@@ -0,0 +1,10 @@
|
||||
name: train-explain-model-locally-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- interpret-community[visualization]
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas<2.0.0
|
||||
- ipywidgets
|
||||
@@ -267,7 +267,7 @@
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
@@ -431,7 +431,7 @@
|
||||
"\n",
|
||||
"# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
" \n",
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
name: train-explain-model-on-amlcompute-and-deploy
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-interpret
|
||||
- interpret-community[visualization]
|
||||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas<2.0.0
|
||||
- azureml-dataset-runtime
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
@@ -9,7 +9,7 @@ These notebooks below are designed to go in sequence.
|
||||
4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
|
||||
5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
|
||||
6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
|
||||
7. [aml-pipelines-with-commandstep.ipynb](aml-pipelines-with-commandstep.ipynb): This notebook shows how to use the CommandStep.
|
||||
7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
|
||||
8. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
|
||||
9. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
|
||||
10. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore.
|
||||
@@ -19,6 +19,5 @@ These notebooks below are designed to go in sequence.
|
||||
14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
|
||||
15. [aml-pipelines-hot-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
|
||||
16. [aml-pipelines-with-notebook-runner-step.ipynb](https://aka.ms/pl-nbrstep): This notebook shows how you can run another notebook as a step in Azure Machine Learning Pipeline.
|
||||
17. [aml-pipelines-with-commandstep-r.ipynb](aml-pipelines-with-commandstep-r.ipynb): This notebook shows how to use CommandStep to run R scripts.
|
||||
|
||||
|
||||
|
||||
@@ -22,8 +22,6 @@
|
||||
"# Azure Machine Learning Pipeline with DataTransferStep\n",
|
||||
"This notebook is used to demonstrate the use of DataTransferStep in an Azure Machine Learning Pipeline.\n",
|
||||
"\n",
|
||||
"> **Note:** In Azure Machine Learning, you can write output data directly to Azure Blob Storage, Azure Data Lake Storage Gen 1, Azure Data Lake Storage Gen 2, Azure FileShare without going through extra DataTransferStep. Learn how to use [OutputFileDatasetConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig?view=azure-ml-py) to achieve that with sample notebooks [here](https://aka.ms/pipeline-with-dataset).**\n",
|
||||
"\n",
|
||||
"In certain cases, you will need to transfer data from one data location to another. For example, your data may be in Azure SQL Database and you may want to move it to Azure Data Lake storage. Or, your data is in an ADLS account and you want to make it available in the Blob storage. The built-in **DataTransferStep** class helps you transfer data in these situations.\n",
|
||||
"\n",
|
||||
"The below examples show how to move data between different storage types supported in Azure Machine Learning.\n",
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-data-transfer
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-getting-started
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -341,7 +341,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline = Pipeline(workspace=ws, steps=[step])\n",
|
||||
"pipeline_run = Experiment(ws, 'azurebatch_sample').submit(pipeline)"
|
||||
"pipeline_run = Experiment(ws, 'azurebatch_experiment').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -20,15 +20,15 @@
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use CommandStep in Azure ML Pipelines\n",
|
||||
"# How to use EstimatorStep in AML Pipeline\n",
|
||||
"\n",
|
||||
"This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running commands in steps. The example shows running distributed TensorFlow training from within a pipeline.\n",
|
||||
"This notebook shows how to use the EstimatorStep with Azure Machine Learning Pipelines. Estimator is a convenient object in Azure Machine Learning that wraps run configuration information to help simplify the tasks of specifying how a script is executed.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Prerequisite:\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n",
|
||||
" * install the Azure ML SDK\n",
|
||||
" * install the AML SDK\n",
|
||||
" * create a workspace and its configuration file (`config.json`)"
|
||||
]
|
||||
},
|
||||
@@ -100,57 +100,75 @@
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# choose a name for your cluster\n",
|
||||
"cluster_name = \"gpu-cluster\"\n",
|
||||
"cluster_name = \"amlcomp\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" gpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
|
||||
" cpu_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
|
||||
" print('Found existing compute target')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('Creating a new compute target...')\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" gpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
" # can poll for a minimum number of nodes and for a specific timeout. \n",
|
||||
" # if no min node count is provided it uses the scale settings for the cluster\n",
|
||||
" gpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
"\n",
|
||||
"# use get_status() to get a detailed status for the current cluster. \n",
|
||||
"print(gpu_cluster.get_status().serialize())"
|
||||
"print(cpu_cluster.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'gpu-cluster' of type `AmlCompute`."
|
||||
"Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a CommandStep\n",
|
||||
"CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n",
|
||||
"## Use a simple script\n",
|
||||
"We have already created a simple \"hello world\" script. This is the script that we will submit through the estimator pattern. It prints a hello-world message, and if Azure ML SDK is installed, it will also logs an array of values ([Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number))."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build an Estimator object\n",
|
||||
"Estimator by default will attempt to use Docker-based execution. You can also enable Docker and let estimator pick the default CPU image supplied by Azure ML for execution. You can target an AmlCompute cluster (or any other supported compute target types). You can also customize the conda environment by adding conda and/or pip packages.\n",
|
||||
"\n",
|
||||
"- **name:** Name of the step\n",
|
||||
"- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n",
|
||||
"- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
|
||||
"- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n",
|
||||
"- **source_directory:** A folder containing the script and other resources used in the step.\n",
|
||||
"- **compute_target:** Compute target to use \n",
|
||||
"- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
|
||||
"- **version:** Optional version tag to denote a change in functionality for the step\n",
|
||||
"> Note: The arguments to the entry script used in the Estimator object should be specified as *list* using\n",
|
||||
" 'estimator_entry_script_arguments' parameter when instantiating EstimatorStep. Estimator object's parameter\n",
|
||||
" 'script_params' accepts a dictionary. However 'estimator_entry_script_arguments' parameter expects arguments as\n",
|
||||
" a list.\n",
|
||||
"\n",
|
||||
"> Estimator object initialization involves specifying a list of data input and output.\n",
|
||||
" In Pipelines, a step can take another step's output as input. So when creating an EstimatorStep.\n",
|
||||
" \n",
|
||||
"> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"datareference-remarks-sample"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"First define the environment that you want to step to run in. This example users a curated TensorFlow environment, but in practice you can configure any environment you want."
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
|
||||
"\n",
|
||||
"#upload input data to workspaceblobstore\n",
|
||||
"def_blob_store.upload_files(files=['20news.pkl'], target_path='20newsgroups')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -159,20 +177,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"from azureml.core import Dataset\n",
|
||||
"from azureml.data import OutputFileDatasetConfig\n",
|
||||
"\n",
|
||||
"tf_env = Environment.get(ws, name='AzureML-TensorFlow-2.3-GPU')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This example will first create a ScriptRunConfig object that configures the training job. Since we are running a distributed job, specify the `distributed_job_config` parameter. If you are just running a single-node job, omit that parameter.\n",
|
||||
"# create dataset to be used as the input to estimator step\n",
|
||||
"input_data = Dataset.File.from_files(def_blob_store.path('20newsgroups/20news.pkl'))\n",
|
||||
"\n",
|
||||
"> If you have an input dataset you want to use in this step, you can specify that as part of the command. For example, if you have a FileDataset object called `dataset` and a `--data-dir` script argument, you can do the following: `command=['python train.py --epochs 30 --data-dir', dataset.as_mount()]`.\n",
|
||||
"# OutputFileDatasetConfig by default write output to the default workspaceblobstore\n",
|
||||
"output = OutputFileDatasetConfig()\n",
|
||||
"\n",
|
||||
"> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)."
|
||||
"source_directory = 'estimator_train'"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -181,24 +195,28 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"from azureml.core.runconfig import MpiConfiguration\n",
|
||||
"from azureml.train.estimator import Estimator\n",
|
||||
"\n",
|
||||
"src_dir = 'commandstep_train'\n",
|
||||
"distr_config = MpiConfiguration(node_count=2) # you can also specify the process_count_per_node parameter for multi-process-per-node training\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=src_dir,\n",
|
||||
" command=['python train.py --epochs 30'],\n",
|
||||
" compute_target=gpu_cluster,\n",
|
||||
" environment=tf_env,\n",
|
||||
" distributed_job_config=distr_config)"
|
||||
"est = Estimator(source_directory=source_directory, \n",
|
||||
" compute_target=cpu_cluster, \n",
|
||||
" entry_script='dummy_train.py', \n",
|
||||
" conda_packages=['scikit-learn'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter."
|
||||
"## Create an EstimatorStep\n",
|
||||
"[EstimatorStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py) adds a step to run Estimator in a Pipeline.\n",
|
||||
"\n",
|
||||
"- **name:** Name of the step\n",
|
||||
"- **estimator:** Estimator object\n",
|
||||
"- **estimator_entry_script_arguments:** A list of command-line arguments\n",
|
||||
"- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
|
||||
"- **compute_target:** Compute target to use \n",
|
||||
"- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
|
||||
"- **version:** Optional version tag to denote a change in functionality for the step"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -211,16 +229,20 @@
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.steps import CommandStep\n",
|
||||
"from azureml.pipeline.steps import EstimatorStep\n",
|
||||
"\n",
|
||||
"train = CommandStep(name='train-mnist', runconfig=src)"
|
||||
"est_step = EstimatorStep(name=\"Estimator_Train\", \n",
|
||||
" estimator=est, \n",
|
||||
" estimator_entry_script_arguments=[\"--datadir\", input_data.as_mount(), \"--output\", output],\n",
|
||||
" runconfig_pipeline_params=None, \n",
|
||||
" compute_target=cpu_cluster)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build and Submit the Pipeline"
|
||||
"## Build and Submit the Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -231,9 +253,8 @@
|
||||
"source": [
|
||||
"from azureml.pipeline.core import Pipeline\n",
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=[train])\n",
|
||||
"pipeline_run = Experiment(ws, 'train-commandstep-pipeline').submit(pipeline)"
|
||||
"pipeline = Pipeline(workspace=ws, steps=[est_step])\n",
|
||||
"pipeline_run = Experiment(ws, 'Estimator_sample').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -274,7 +295,7 @@
|
||||
"framework": [
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with CommandStep",
|
||||
"friendly_name": "Azure Machine Learning Pipeline with EstimatorStep",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
@@ -290,7 +311,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
"version": "3.6.7"
|
||||
},
|
||||
"order_index": 7,
|
||||
"star_tag": [
|
||||
@@ -299,7 +320,7 @@
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Demonstrates the use of CommandStep"
|
||||
"task": "Demonstrates the use of EstimatorStep"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-how-to-use-estimatorstep
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-how-to-use-modulestep
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -130,7 +130,7 @@
|
||||
"\n",
|
||||
"pipeline_draft = PipelineDraft.create(ws, name=\"TestPipelineDraft\",\n",
|
||||
" description=\"draft description\",\n",
|
||||
" experiment_name=\"pipeline_draft_sample\",\n",
|
||||
" experiment_name=\"helloworld\",\n",
|
||||
" pipeline=pipeline,\n",
|
||||
" continue_on_step_failure=True,\n",
|
||||
" tags={'dev': 'true'},\n",
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-how-to-use-pipeline-drafts
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,9 @@
|
||||
name: aml-pipelines-parameter-tuning-with-hyperdrive
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- numpy
|
||||
- pandas_ml
|
||||
- azureml-dataset-runtime[pandas,fuse]
|
||||
@@ -41,14 +41,14 @@
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Datastore, Experiment, Dataset\n",
|
||||
"from azureml.data import OutputFileDatasetConfig\n",
|
||||
"from azureml.core.compute import AmlCompute\n",
|
||||
"from azureml.core.compute import ComputeTarget\n",
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)\n",
|
||||
"\n",
|
||||
"from azureml.pipeline.core import Pipeline\n",
|
||||
"from azureml.data.data_reference import DataReference\n",
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"from azureml.pipeline.steps import PythonScriptStep\n",
|
||||
"from azureml.pipeline.core.graph import PipelineParameter\n",
|
||||
"\n",
|
||||
@@ -140,9 +140,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define intermediate data using OutputFileDatasetConfig\n",
|
||||
"processed_data1 = OutputFileDatasetConfig(name=\"processed_data1\")\n",
|
||||
"print(\"Output dataset object created\")"
|
||||
"# Define intermediate data using PipelineData\n",
|
||||
"processed_data1 = PipelineData(\"processed_data1\",datastore=def_blob_store)\n",
|
||||
"print(\"PipelineData object created\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -170,7 +170,9 @@
|
||||
"\n",
|
||||
"trainStep = PythonScriptStep(\n",
|
||||
" script_name=\"train.py\", \n",
|
||||
" arguments=[\"--input_data\", blob_input_data.as_mount(), \"--output_train\", processed_data1],\n",
|
||||
" arguments=[\"--input_data\", blob_input_data, \"--output_train\", processed_data1],\n",
|
||||
" inputs=[blob_input_data],\n",
|
||||
" outputs=[processed_data1],\n",
|
||||
" compute_target=aml_compute, \n",
|
||||
" source_directory=source_directory\n",
|
||||
")\n",
|
||||
@@ -193,14 +195,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# extractStep to use the intermediate data produced by trainStep\n",
|
||||
"# extractStep to use the intermediate data produced by step4\n",
|
||||
"# This step also produces an output processed_data2\n",
|
||||
"processed_data2 = OutputFileDatasetConfig(name=\"processed_data2\")\n",
|
||||
"processed_data2 = PipelineData(\"processed_data2\", datastore=def_blob_store)\n",
|
||||
"source_directory = \"publish_run_extract\"\n",
|
||||
"\n",
|
||||
"extractStep = PythonScriptStep(\n",
|
||||
" script_name=\"extract.py\",\n",
|
||||
" arguments=[\"--input_extract\", processed_data1.as_input(), \"--output_extract\", processed_data2],\n",
|
||||
" arguments=[\"--input_extract\", processed_data1, \"--output_extract\", processed_data2],\n",
|
||||
" inputs=[processed_data1],\n",
|
||||
" outputs=[processed_data2],\n",
|
||||
" compute_target=aml_compute, \n",
|
||||
" source_directory=source_directory)\n",
|
||||
"print(\"extractStep created\")"
|
||||
@@ -252,17 +256,15 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Now define compareStep that takes two inputs (both intermediate data), and produce an output\n",
|
||||
"processed_data3 = OutputFileDatasetConfig(name=\"processed_data3\")\n",
|
||||
"\n",
|
||||
"# You can register the output as dataset after job completion\n",
|
||||
"processed_data3 = processed_data3.register_on_complete(\"compare_result\")\n",
|
||||
"\n",
|
||||
"# Now define step6 that takes two inputs (both intermediate data), and produce an output\n",
|
||||
"processed_data3 = PipelineData(\"processed_data3\", datastore=def_blob_store)\n",
|
||||
"source_directory = \"publish_run_compare\"\n",
|
||||
"\n",
|
||||
"compareStep = PythonScriptStep(\n",
|
||||
" script_name=\"compare.py\",\n",
|
||||
" arguments=[\"--compare_data1\", processed_data1.as_input(), \"--compare_data2\", processed_data2.as_input(), \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param], \n",
|
||||
" arguments=[\"--compare_data1\", processed_data1, \"--compare_data2\", processed_data2, \"--output_compare\", processed_data3, \"--pipeline_param\", pipeline_param],\n",
|
||||
" inputs=[processed_data1, processed_data2],\n",
|
||||
" outputs=[processed_data3], \n",
|
||||
" compute_target=aml_compute, \n",
|
||||
" source_directory=source_directory)\n",
|
||||
"print(\"compareStep created\")"
|
||||
@@ -325,7 +327,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# submit a pipeline run\n",
|
||||
"pipeline_run1 = Experiment(ws, 'Pipeline_experiment_sample').submit(pipeline1)\n",
|
||||
"pipeline_run1 = Experiment(ws, 'Pipeline_experiment').submit(pipeline1)\n",
|
||||
"# publish a pipeline from the submitted pipeline run\n",
|
||||
"published_pipeline2 = pipeline_run1.publish_pipeline(name=\"My_New_Pipeline2\", description=\"My Published Pipeline Description\", version=\"0.1\", continue_on_step_failure=True)\n",
|
||||
"published_pipeline2"
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
name: aml-pipelines-publish-and-run-using-rest-endpoint
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -259,7 +259,7 @@
|
||||
"\n",
|
||||
"schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n",
|
||||
" pipeline_id=pub_pipeline_id, \n",
|
||||
" experiment_name='Schedule-run-sample',\n",
|
||||
" experiment_name='Schedule_Run',\n",
|
||||
" recurrence=recurrence,\n",
|
||||
" wait_for_provisioning=True,\n",
|
||||
" description=\"Schedule Run\")\n",
|
||||
@@ -445,7 +445,7 @@
|
||||
"\n",
|
||||
"schedule = Schedule.create(workspace=ws, name=\"My_Schedule\",\n",
|
||||
" pipeline_id=pub_pipeline_id, \n",
|
||||
" experiment_name='Schedule-run-sample',\n",
|
||||
" experiment_name='Schedule_Run',\n",
|
||||
" datastore=datastore,\n",
|
||||
" wait_for_provisioning=True,\n",
|
||||
" description=\"Schedule Run\")\n",
|
||||
@@ -516,7 +516,7 @@
|
||||
"\n",
|
||||
"schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n",
|
||||
" pipeline_endpoint_id=published_pipeline_endpoint_id,\n",
|
||||
" experiment_name='Schedule-run-sample',\n",
|
||||
" experiment_name='Schedule_Run',\n",
|
||||
" recurrence=recurrence, description=\"Schedule_Run\",\n",
|
||||
" wait_for_provisioning=True)\n",
|
||||
"\n",
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-setup-schedule-for-a-published-pipeline
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -553,7 +553,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Experiment\n",
|
||||
"pipeline_run = Experiment(ws, name=\"submit_endpoint_sample\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
|
||||
"pipeline_run = Experiment(ws, name=\"submit_from_endpoint\").submit(pipeline_endpoint_by_name, tags={'endpoint_tag': \"1\"}, pipeline_version=\"0\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
name: aml-pipelines-setup-versioned-pipeline-endpoints
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- requests
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-showcasing-datapath-and-pipelineparameter
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-showcasing-dataset-and-pipelineparameter
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -101,7 +101,7 @@
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create an Azure ML experiment\n",
|
||||
"Let's create an experiment named \"automlstep-sample\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
|
||||
"Let's create an experiment named \"automlstep-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
|
||||
"\n",
|
||||
"The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
|
||||
]
|
||||
@@ -113,7 +113,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Choose a name for the run history container in the workspace.\n",
|
||||
"experiment_name = 'automlstep-sample'\n",
|
||||
"experiment_name = 'automlstep-classification'\n",
|
||||
"project_folder = './project'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
name: aml-pipelines-with-automated-machine-learning-step
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,343 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to use CommandStep in Azure ML Pipelines\n",
|
||||
"\n",
|
||||
"This notebook shows how to use the CommandStep with Azure Machine Learning Pipelines for running R scripts in a pipeline.\n",
|
||||
"\n",
|
||||
"The example shows training a model in R to predict probability of fatality for vehicle crashes.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Prerequisite:\n",
|
||||
"* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
|
||||
"* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://aka.ms/pl-config) to:\n",
|
||||
" * install the Azure ML SDK\n",
|
||||
" * create a workspace and its configuration file (`config.json`)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's get started. First let's import some Python libraries."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"# check core SDK version number\n",
|
||||
"print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Initialize workspace\n",
|
||||
"Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create or Attach existing AmlCompute\n",
|
||||
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, you create `AmlCompute` as your training compute resource."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If we could not find the cluster with the given name, then we will create a new cluster here. We will create an `AmlCompute` cluster of `STANDARD_D2_V2` CPU VMs. This process is broken down into 3 steps:\n",
|
||||
"1. create the configuration (this step is local and only takes a second)\n",
|
||||
"2. create the cluster (this step will take about **20 seconds**)\n",
|
||||
"3. provision the VMs to bring the cluster to the initial size (of 1 in this case). This step will take about **3-5 minutes** and is providing only sparse output in the process. Please make sure to wait until the call returns before moving to the next cell"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# choose a name for your cluster\n",
|
||||
"cluster_name = \"cpu-cluster\"\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
|
||||
" print('Found existing compute target')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('Creating a new compute target...')\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2', max_nodes=4)\n",
|
||||
"\n",
|
||||
" # create the cluster\n",
|
||||
" compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
" # can poll for a minimum number of nodes and for a specific timeout. \n",
|
||||
" # if no min node count is provided it uses the scale settings for the cluster\n",
|
||||
" compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
"\n",
|
||||
"# use get_status() to get a detailed status for the current cluster. \n",
|
||||
"print(compute_target.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that you have created the compute target, let's see what the workspace's `compute_targets` property returns. You should now see one entry named 'cpu-cluster' of type `AmlCompute`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a CommandStep\n",
|
||||
"CommandStep adds a step to run a command in a Pipeline. For the full set of configurable options see the CommandStep [reference docs](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.commandstep?view=azure-ml-py).\n",
|
||||
"\n",
|
||||
"- **name:** Name of the step\n",
|
||||
"- **runconfig:** ScriptRunConfig object. You can configure a ScriptRunConfig object as you would for a standalone non-pipeline run and pass it in to this parameter. If using this option, you do not have to specify the `command`, `source_directory`, `compute_target` parameters of the CommandStep constructor as they are already defined in your ScriptRunConfig.\n",
|
||||
"- **runconfig_pipeline_params:** Override runconfig properties at runtime using key-value pairs each with name of the runconfig property and PipelineParameter for that property\n",
|
||||
"- **command:** The command to run or path of the executable/script relative to `source_directory`. It is required unless the `runconfig` parameter is specified. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.\n",
|
||||
"- **source_directory:** A folder containing the script and other resources used in the step.\n",
|
||||
"- **compute_target:** Compute target to use \n",
|
||||
"- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs. If this is false, a new run will always be generated for this step during pipeline execution.\n",
|
||||
"- **version:** Optional version tag to denote a change in functionality for the step\n",
|
||||
"\n",
|
||||
"> The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure environment\n",
|
||||
"\n",
|
||||
"Configure the environment for the train step. In this example we will create an environment from the Dockerfile we have included.\n",
|
||||
"\n",
|
||||
"> Azure ML currently requires Python as an implicit dependency, so Python must installed in your image even if your training script does not have this dependency."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Environment\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"src_dir = 'commandstep_r'\n",
|
||||
"\n",
|
||||
"env = Environment.from_dockerfile(name='r_env', dockerfile=os.path.join(src_dir, 'Dockerfile'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure input training dataset\n",
|
||||
"\n",
|
||||
"This tutorial uses data from the US National Highway Traffic Safety Administration. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. We have included an Rdata file that includes the accidents data for analysis.\n",
|
||||
"\n",
|
||||
"Here we use the workspace's default datastore to upload the training data file (**accidents.Rd**); in practice you can use any datastore you want."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"datastore = ws.get_default_datastore()\n",
|
||||
"data_ref = datastore.upload_files(files=[os.path.join(src_dir, 'accidents.Rd')], target_path='accidentdata')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now create a FileDataset from the data, which will be used as an input to the train step."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Dataset\n",
|
||||
"dataset = Dataset.File.from_files(datastore.path('accidentdata'))\n",
|
||||
"dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now create a ScriptRunConfig that configures the training run. Note that in the `command` we include the input dataset for the training data.\n",
|
||||
"\n",
|
||||
"> For detailed guidance on how to move data in pipelines for input and output data, see the documentation [Moving data into and between ML pipelines](https://docs.microsoft.com/azure/machine-learning/how-to-move-data-in-out-of-pipelines)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"train_config = ScriptRunConfig(source_directory=src_dir,\n",
|
||||
" command=['Rscript accidents.R --data_folder', dataset.as_mount(), '--output_folder outputs'],\n",
|
||||
" compute_target=compute_target,\n",
|
||||
" environment=env)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now create a CommandStep and pass in the ScriptRunConfig object to the `runconfig` parameter."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.steps import CommandStep\n",
|
||||
"\n",
|
||||
"train = CommandStep(name='train', runconfig=train_config)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Build and Submit the Pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.pipeline.core import Pipeline\n",
|
||||
"from azureml.core import Experiment\n",
|
||||
"\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=[train])\n",
|
||||
"pipeline_run = Experiment(ws, 'r-commandstep-pipeline').submit(pipeline)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## View Run Details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "minxia"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
"compute": [
|
||||
"AML Compute"
|
||||
],
|
||||
"datasets": [
|
||||
"Custom"
|
||||
],
|
||||
"deployment": [
|
||||
"None"
|
||||
],
|
||||
"exclude_from_index": false,
|
||||
"framework": [
|
||||
"Azure ML"
|
||||
],
|
||||
"friendly_name": "Azure Machine Learning Pipeline with CommandStep for R",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
},
|
||||
"order_index": 7,
|
||||
"star_tag": [
|
||||
"None"
|
||||
],
|
||||
"tags": [
|
||||
"None"
|
||||
],
|
||||
"task": "Demonstrates the use of CommandStep for running R scripts"
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -428,7 +428,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipeline_run1 = Experiment(ws, 'Data_dependency_sample').submit(pipeline1)\n",
|
||||
"pipeline_run1 = Experiment(ws, 'Data_dependency').submit(pipeline1)\n",
|
||||
"print(\"Pipeline is submitted for execution\")"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
name: aml-pipelines-with-data-dependency-steps
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
@@ -0,0 +1,6 @@
|
||||
name: aml-pipelines-with-notebook-runner-step
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- azureml-contrib-notebook
|
||||
@@ -1,11 +0,0 @@
|
||||
FROM rocker/tidyverse:4.0.0-ubuntu18.04
|
||||
|
||||
# Install python
|
||||
RUN apt-get update -qq && \
|
||||
apt-get install -y python3
|
||||
|
||||
# Create link for python
|
||||
RUN ln -f /usr/bin/python3 /usr/bin/python
|
||||
|
||||
# Install additional R packages
|
||||
RUN R -e "install.packages(c('optparse'), repos = 'https://cloud.r-project.org/')"
|
||||
@@ -1,34 +0,0 @@
|
||||
#' Copyright(c) Microsoft Corporation.
|
||||
#' Licensed under the MIT license.
|
||||
|
||||
library(optparse)
|
||||
|
||||
options <- list(
|
||||
make_option(c("-d", "--data_folder")),
|
||||
make_option(c("--output_folder"))
|
||||
|
||||
)
|
||||
|
||||
opt_parser <- OptionParser(option_list = options)
|
||||
opt <- parse_args(opt_parser)
|
||||
|
||||
paste(opt$data_folder)
|
||||
|
||||
accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
|
||||
summary(accidents)
|
||||
|
||||
mod <- glm(dead ~ dvcat + seatbelt + frontal + sex + ageOFocc + yearVeh + airbag + occRole, family=binomial, data=accidents)
|
||||
summary(mod)
|
||||
predictions <- factor(ifelse(predict(mod)>0.1, "dead","alive"))
|
||||
accuracy <- mean(predictions == accidents$dead)
|
||||
|
||||
# make directory for output dir
|
||||
output_dir = opt$output_folder
|
||||
if (!dir.exists(output_dir)){
|
||||
dir.create(output_dir)
|
||||
}
|
||||
|
||||
# save model
|
||||
model_path = file.path(output_dir, "model.rds")
|
||||
saveRDS(mod, file = model_path)
|
||||
message("Model saved")
|
||||
Binary file not shown.
@@ -1,8 +0,0 @@
|
||||
channels:
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- python=3.7
|
||||
- pip:
|
||||
- azureml-defaults
|
||||
- tensorflow-gpu==2.3.0
|
||||
- horovod==0.19.5
|
||||
@@ -1,120 +0,0 @@
|
||||
# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
# Script adapted from: https://github.com/horovod/horovod/blob/master/examples/tensorflow2_keras_mnist.py
|
||||
# ==============================================================================
|
||||
|
||||
import tensorflow as tf
|
||||
import horovod.tensorflow.keras as hvd
|
||||
|
||||
import os
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--learning-rate", "-lr", type=float, default=0.001)
|
||||
parser.add_argument("--epochs", type=int, default=24)
|
||||
parser.add_argument("--steps-per-epoch", type=int, default=500)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Horovod: initialize Horovod.
|
||||
hvd.init()
|
||||
|
||||
# Horovod: pin GPU to be used to process local rank (one GPU per process)
|
||||
gpus = tf.config.experimental.list_physical_devices("GPU")
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
if gpus:
|
||||
tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], "GPU")
|
||||
|
||||
(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
|
||||
path="mnist-%d.npz" % hvd.rank()
|
||||
)
|
||||
|
||||
dataset = tf.data.Dataset.from_tensor_slices(
|
||||
(
|
||||
tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),
|
||||
tf.cast(mnist_labels, tf.int64),
|
||||
)
|
||||
)
|
||||
dataset = dataset.repeat().shuffle(10000).batch(128)
|
||||
|
||||
mnist_model = tf.keras.Sequential(
|
||||
[
|
||||
tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
|
||||
tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
|
||||
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
|
||||
tf.keras.layers.Dropout(0.25),
|
||||
tf.keras.layers.Flatten(),
|
||||
tf.keras.layers.Dense(128, activation="relu"),
|
||||
tf.keras.layers.Dropout(0.5),
|
||||
tf.keras.layers.Dense(10, activation="softmax"),
|
||||
]
|
||||
)
|
||||
|
||||
# Horovod: adjust learning rate based on number of GPUs.
|
||||
scaled_lr = args.learning_rate * hvd.size()
|
||||
opt = tf.optimizers.Adam(scaled_lr)
|
||||
|
||||
# Horovod: add Horovod DistributedOptimizer.
|
||||
opt = hvd.DistributedOptimizer(opt)
|
||||
|
||||
# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow
|
||||
# uses hvd.DistributedOptimizer() to compute gradients.
|
||||
mnist_model.compile(
|
||||
loss=tf.losses.SparseCategoricalCrossentropy(),
|
||||
optimizer=opt,
|
||||
metrics=["accuracy"],
|
||||
experimental_run_tf_function=False,
|
||||
)
|
||||
|
||||
callbacks = [
|
||||
# Horovod: broadcast initial variable states from rank 0 to all other processes.
|
||||
# This is necessary to ensure consistent initialization of all workers when
|
||||
# training is started with random weights or restored from a checkpoint.
|
||||
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
|
||||
# Horovod: average metrics among workers at the end of every epoch.
|
||||
#
|
||||
# Note: This callback must be in the list before the ReduceLROnPlateau,
|
||||
# TensorBoard or other metrics-based callbacks.
|
||||
hvd.callbacks.MetricAverageCallback(),
|
||||
# Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
|
||||
# accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
|
||||
# the first three epochs. See https://arxiv.org/abs/1706.02677 for details.
|
||||
hvd.callbacks.LearningRateWarmupCallback(
|
||||
warmup_epochs=3, initial_lr=scaled_lr, verbose=1
|
||||
),
|
||||
]
|
||||
|
||||
# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.
|
||||
if hvd.rank() == 0:
|
||||
output_dir = "./outputs"
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
callbacks.append(
|
||||
tf.keras.callbacks.ModelCheckpoint(
|
||||
os.path.join(output_dir, "checkpoint-{epoch}.h5")
|
||||
)
|
||||
)
|
||||
|
||||
# Horovod: write logs on worker 0.
|
||||
verbose = 1 if hvd.rank() == 0 else 0
|
||||
|
||||
# Train the model.
|
||||
# Horovod: adjust number of steps based on number of GPUs.
|
||||
mnist_model.fit(
|
||||
dataset,
|
||||
steps_per_epoch=args.steps_per_epoch // hvd.size(),
|
||||
callbacks=callbacks,
|
||||
epochs=args.epochs,
|
||||
verbose=verbose,
|
||||
)
|
||||
@@ -0,0 +1,30 @@
|
||||
# Copyright (c) Microsoft Corporation. All rights reserved.
|
||||
# Licensed under the MIT License.
|
||||
import argparse
|
||||
import os
|
||||
|
||||
print("*********************************************************")
|
||||
print("Hello Azure ML!")
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--datadir', type=str, help="data directory")
|
||||
parser.add_argument('--output', type=str, help="output")
|
||||
args = parser.parse_args()
|
||||
|
||||
print("Argument 1: %s" % args.datadir)
|
||||
print("Argument 2: %s" % args.output)
|
||||
|
||||
if not (args.output is None):
|
||||
os.makedirs(args.output, exist_ok=True)
|
||||
print("%s created" % args.output)
|
||||
|
||||
try:
|
||||
from azureml.core import Run
|
||||
run = Run.get_context()
|
||||
print("Log Fibonacci numbers.")
|
||||
run.log_list('Fibonacci numbers', [0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
|
||||
run.complete()
|
||||
except:
|
||||
print("Warning: you need to install Azure ML SDK in order to log metrics.")
|
||||
|
||||
print("*********************************************************")
|
||||
@@ -22,6 +22,3 @@ print("Argument 4: %s" % args.pipeline_param)
|
||||
if not (args.output_compare is None):
|
||||
os.makedirs(args.output_compare, exist_ok=True)
|
||||
print("%s created" % args.output_compare)
|
||||
|
||||
with open(os.path.join(args.output_compare, 'compare.txt'), 'w') as fw:
|
||||
fw.write('here is the compare result')
|
||||
|
||||
@@ -19,8 +19,3 @@ print("Argument 2: %s" % args.output_extract)
|
||||
if not (args.output_extract is None):
|
||||
os.makedirs(args.output_extract, exist_ok=True)
|
||||
print("%s created" % args.output_extract)
|
||||
|
||||
with open(os.path.join(args.input_extract, '20news.pkl'), 'rb') as f:
|
||||
content = f.read()
|
||||
with open(os.path.join(args.output_extract, '20news.pkl'), 'wb') as fw:
|
||||
fw.write(content)
|
||||
|
||||
@@ -20,8 +20,3 @@ print("Argument 2: %s" % args.output_train)
|
||||
if not (args.output_train is None):
|
||||
os.makedirs(args.output_train, exist_ok=True)
|
||||
print("%s created" % args.output_train)
|
||||
|
||||
with open(os.path.join(args.input_data, '20news.pkl'), 'rb') as f:
|
||||
content = f.read()
|
||||
with open(os.path.join(args.output_train, '20news.pkl'), 'wb') as fw:
|
||||
fw.write(content)
|
||||
|
||||
@@ -0,0 +1,10 @@
|
||||
name: nyc-taxi-data-regression-model-building
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-widgets
|
||||
- azureml-opendatasets
|
||||
- azureml-train-automl
|
||||
- matplotlib
|
||||
- pandas
|
||||
- pyarrow
|
||||
@@ -0,0 +1,7 @@
|
||||
name: file-dataset-image-inference-mnist
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
@@ -0,0 +1,7 @@
|
||||
name: tabular-dataset-inference-iris
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-pipeline-steps
|
||||
- azureml-widgets
|
||||
- pandas
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user